Comments made by me at YouTube Channels @ Links about Mouse Models

Do the downloads!! Share!! The diffusion of very important information and knowledge is essential for the world progress always!! Thanks!!

  • – > Mestrado – Dissertation – Tabelas, Figuras e Gráficos – Tables, Figures and Graphics – ´´My´´ Dissertation @ #Innovation #energy #life #health #Countries #Time #Researches #Reference #Graphics #Ages #Age #Mice #People #Person #Mouse #Genetics #PersonalizedMedicine #Diagnosis #Prognosis #Treatment #Disease #UnknownDiseases #Future #VeryEfficientDrugs #VeryEfficientVaccines #VeryEfficientTherapeuticalSubstances #Tests #Laboratories #Investments #Details #HumanLongevity #DNA #Cell #Memory #Physiology #Nanomedicine #Nanotechnology #Biochemistry #NewMedicalDevices #GeneticEngineering #Internet #History #Science #World

Pathol Res Pract. 2012 Jul 15;208(7):377-81. doi: 10.1016/j.prp.2012.04.006. Epub 2012 Jun 8.

The influence of physical activity in the progression of experimental lung cancer in mice

Renato Batista Paceli 1Rodrigo Nunes CalCarlos Henrique Ferreira dos SantosJosé Antonio CordeiroCassiano Merussi NeivaKazuo Kawano NagaminePatrícia Maluf Cury


Impact_Fator-wise_Top100Science_Journals

GRUPO_AF1 – GROUP AFA1 – Aerobic Physical Activity – Atividade Física Aeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO AFAN 1 – GROUP AFAN1 – Anaerobic Physical Activity – Atividade Física Anaeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO_AF2 – GROUP AFA2 – Aerobic Physical Activity – Atividade Física Aeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO AFAN 2 – GROUP AFAN 2 – Anaerobic Physical Activity – Atividade Física Anaeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

Slides – mestrado – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

CARCINÓGENO DMBA EM MODELOS EXPERIMENTAIS

DMBA CARCINOGEN IN EXPERIMENTAL MODELS

Avaliação da influência da atividade física aeróbia e anaeróbia na progressão do câncer de pulmão experimental – Summary – Resumo – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

Abstract

Lung cancer is one of the most incident neoplasms in the world, representing the main cause of mortality for cancer. Many epidemiologic studies have suggested that physical activity may reduce the risk of lung cancer, other works evaluate the effectiveness of the use of the physical activity in the suppression, remission and reduction of the recurrence of tumors. The aim of this study was to evaluate the effects of aerobic and anaerobic physical activity in the development and the progression of lung cancer. Lung tumors were induced with a dose of 3mg of urethane/kg, in 67 male Balb – C type mice, divided in three groups: group 1_24 mice treated with urethane and without physical activity; group 2_25 mice with urethane and subjected to aerobic swimming free exercise; group 3_18 mice with urethane, subjected to anaerobic swimming exercise with gradual loading 5-20% of body weight. All the animals were sacrificed after 20 weeks, and lung lesions were analyzed. The median number of lesions (nodules and hyperplasia) was 3.0 for group 1, 2.0 for group 2 and 1.5-3 (p=0.052). When comparing only the presence or absence of lesion, there was a decrease in the number of lesions in group 3 as compared with group 1 (p=0.03) but not in relation to group 2. There were no metastases or other changes in other organs. The anaerobic physical activity, but not aerobic, diminishes the incidence of experimental lung tumors.

http://www.medsci.org/v15p0403.htm http://www.medsci.org/v15p0403.pdf

Int J Med Sci 2018; 15(4):403-410. doi:10.7150/ijms.23150

Review

Cardiovascular Adaptations Induced by Resistance Training in Animal Models

Corresponding address

S.F.S. Melo1,2, N.D. da Silva Júnior2, V.G. Barauna1, E.M. Oliveira

https://www.taconic.com/pdfs/metabolic-and-cardiovascular-disease-models-and-services.pdf Cardiovascular Disease Models and Services MODELS AND SERVICES DESIGNED TO TAKE YOUR STUDY FURTHER – Diabetes, hypertension, heart disease and obesity have reached epidemic proportions, making research into cardiovascular and metabolic disorders a top priority.

https://www.nature.com/articles/s41598-019-43407-z

Comprehensive Analysis of Animal Models of Cardiovascular Disease using Multiscale X-Ray Phase Contrast Tomography

https://link.springer.com/book/10.1007/978-4-431-55813-2

https://onlinelibrary.wiley.com/doi/full/10.1002/cbf.3173 https://onlinelibrary.wiley.com/doi/pdf/10.1002/cbf.3173

Review Article  Open Access

Large animal models of cardiovascular disease

H. G. TsangN. A. RashdanC. B. A. WhitelawB. M. CorcoranK. M. SummersV. E. MacRaeFirst published: 24 February 2016 https://doi.org/10.1002/cbf.3173 Cited by: 23

https://f1000research.com/articles/7-593

REVIEW

Refinement of a mouse cardiovascular model: Development, application and dissemination

https://pdfs.semanticscholar.org/0d70/014b068e551a311ca4f8619653c785c924aa.pdf A Handbook of Mouse Models of Cardiovascular Disease @ Editor Qinbo Xu St George’s University of London, London, UK

https://academic.oup.com/cardiovascres/advance-article/doi/10.1093/cvr/cvz161/5523845

CORRECTED PROOF

Small animal models of heart failure – European Society of Cardiology – Cardiovascular Research https://watermark.silverchair.com/cvz161.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAoAwggJ8BgkqhkiG9w0BBwagggJtMIICaQIBADCCAmIGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMpE35urUQnxXekPx7AgEQgIICMwXdbkzAD36asN5vUSQ-MIKsOex3bTaDwkHSPktQKgGu8fQJuSAveOGLD3ts0-owa5rGGpTf8YKj6s6H6z1CX02pXBRfwKIZLH4wYOmwy2C5Qdm2sTbblAiQ3BsSmgYSmZWPhjJpCETceKRjCjJLrBv3_x55kRx8QV18lNJ0v-1afOAyzktWSW4e32SG00Hpt8UAaPHCJFefhFR09VcxeEOtMKnYcs8ixQ5ptDl59l_UTfb3NzQ9_6mrz9oyUUJsSp75iFU8oUeIUpRbzs_oX04gWjLFCJrauevUhqKfKggvAaB39omfKrCw0_u8tIKO1AbA_PBLazx1im2k03BtqVItKP41YeLXTtZ3M65mWeEmwLSM1pBClhiZkLBPcqRL_IDKRTn11pNIauHkjDHKkBUBxNwb0qc_vybQcem2H-bpSP22dr28zVe9Y5d-y0OHSgdj0ildDOe_YNdE1qRbDre5M1dcsI0eTAOcB3NJQ74HuDkPEMFvqQFtwQVTzeHf4kiMUAciQzMR43BtR0kNmeEu-4fr1howMre06w4uP45EUKz9rBd6YIrwY2IqEm9TvMbczX3hLJHc4vI81TqQOfgK9HJlWA7rNAp1fnIgPk8UeOnXotzxalHIYqb0vQUfgJxVjsZ0nPSryrtQRTuzhq9yVGDRdBF6HvpdLI2dGK0fKjNF973w1HEdYcmpldRLtbIZDNj-YMi2SEXdzRnt1NOXE9D0mmJEPcyl82Bl07wb4b0d

Christian RiehleJohann BauersachsCardiovascular Research, cvz161, https://doi.org/10.1093/cvr/cvz161Published: 27 June 2019 Article history

https://www.hindawi.com/journals/bmri/2011/497841/ http://downloads.hindawi.com/journals/bmri/2011/497841.pdf

Review Article

Animal Models of Cardiovascular Diseases

https://www.ahajournals.org/doi/full/10.1161/circulationaha.106.682534

Principles of Genetic Murine Models for Cardiac Disease

Katherine E. Yutzey and Jeffrey RobbinsOriginally published13 Feb 2007https://doi.org/10.1161/CIRCULATIONAHA.106.682534Circulation. 2007;115:792–799

Exercise training on cardiovascular diseases: Role of animal models in the elucidation of the mechanisms

https://www.frontiersin.org/articles/10.3389/fcvm.2019.00046/full Mouse Models for Atherosclerosis Research—Which Is My Line?

https://dmm.biologists.org/content/3/3-4/138 https://dmm.biologists.org/content/dmm/3/3-4/138.full.pdf Heart failure and mouse models

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3533445/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3533445/pdf/nihms424172.pdf Genetically Engineered Mouse Models in Cancer Research

https://www.ncbi.nlm.nih.gov/pubmed/21403831 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3042667/ Animal Models of Cardiovascular Diseases https://www.ncbi.nlm.nih.gov/pubmed/21403831 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3042667/

https://www.ahajournals.org/doi/10.1161/CIRCRESAHA.118.313406

Mouse Models of Cardiac Arrhythmias

Dobromir Dobrev and Xander H.T. WehrensOriginally published19 Jul 2018https://doi.org/10.1161/CIRCRESAHA.118.313406Circulation Research. 2018;123:332–334

https://www.researchgate.net/publication/50395974_Animal_Models_of_Cardiovascular_Diseases Animal Models of Cardiovascular Diseases

https://www.jax.org/jax-mice-and-services/solutions-by-therapeutic-area/cardiovascular/models-for-cardiovascular-research COMPARISON OF FEATURED JAX® MICE FOR CARDIOVASCULAR RESEARCH – The Jackson Laboratory

https://www.revespcardiol.org/en-animal-models-cardiovascular-disease-articulo-13131649 https://www.revespcardiol.org/en-pdf-13131649 Animal Models of Cardiovascular Disease – Modelos animales de enfermedad cardiovascular

https://www.revespcardiol.org/en-animal-models-cardiovascular-disease-articulo-13131649 Animal Models of Cardiovascular Disease – Modelos animales de enfermedad cardiovascular

https://www.infrafrontier.eu/precision-mammalian-model-development-mouse-models

https://www.cancer.gov/publications/dictionaries/cancer-terms/def/mouse-model

mouse model listen (… MAH-dul)The use of special strains of mice to study a human disease or condition, and how to prevent and treat it.

https://ki.mit.edu/sbc/escell/models Introduction to Mouse Models

https://www.the-scientist.com/news-opinion/mouse-model-shows-how-parkinsons-disease-begins-in-the-gut-66048 Mouse Model Shows How Parkinson’s Disease Begins in the Gut

https://www.the-scientist.com/news-opinion/new-mouse-model-predicts-two-clinical-trial-failures-in-humans–66223 New Mouse Model Predicts Two Clinical Trial Failures in Humans

https://www.the-scientist.com/techedge/mouse-models-for-disease-research-65931 – Mouse Models for Disease Research

Generating mouse models for biomedical research: technological advancesChannabasavaiah B. Gurumurthy, Kevin C. Kent LloydDisease Models & Mechanisms 2019 12: dmm029462 doi: 10.1242/dmm.029462 Published 8 January 2019 https://dmm.biologists.org/content/12/1/dmm029462 file:///C:/Users/RODRIGO/Desktop/Digicorr/QR%20Codes/Teste/dmm029462.full.pdf

https://dmm.biologists.org/content/12/1/dmm029462

https://www.sciencedirect.com/sdfe/pdf/download/eid/3-s2.0-B9780323033541500900/first-page-pdf THE CONGENITAL MYOPATHIES ●●●● Heinz Jungbluth, Caroline A. Sewry, and Francesco Muntoni

Animal Models in Cancer Drug Discovery 2019, Pages 267-292 https://www.sciencedirect.com/science/article/pii/B9780128147047000118

ttps://www.sciencedirect.com/sdfe/pdf/download/eid/3-s2.0-B9780323033541500900/first-page-pdf

https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/mouse-model

https://www.ncbi.nlm.nih.gov/pubmed/20399958 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3533445/ Genetically engineered mouse models in cancer research.

Search MagazineWHAT IS A MOUSE MODEL?By Dayana Krawchuk, Ph.D.

https://www.jax.org/news-and-insights/2017/january/what-is-a-mouse-model – THE JACKSON LABORATORY

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4875775/

Mouse models of human disease

An evolutionary perspective

PMC

US National Library of Medicine
National Institutes of HealthSearch databasePMCPubMedAll DatabasesAssemblyBiocollectionsBioProjectBioSampleBioSystemsBooksClinVarConserved DomainsdbGaPdbVarGeneGenomeGEO DataSetsGEO ProfilesGTRHomoloGeneIdentical Protein GroupsMedGenMeSHNCBI Web SiteNLM CatalogNucleotideOMIMPMCPopSetProbeProteinProtein ClustersPubChem BioAssayPubChem CompoundPubChem SubstancePubMedSNPSparcleSRAStructureTaxonomyToolKitToolKitAllToolKitBookghSearch term

Clear input

Search

Logo of emph

Evol Med Public Health. 2016; 2016(1): 170–176.Published online 2016 May 21. doi: 10.1093/emph/eow014PMCID: PMC4875775PMID: 27121451

Mouse models of human disease

An evolutionary perspectiveRobert L. Perlman*Author informationArticle notesCopyright and License informationDisclaimerThis article has been cited by other articles in PMC.Go to:

Abstract

The use of mice as model organisms to study human biology is predicated on the genetic and physiological similarities between the species. Nonetheless, mice and humans have evolved in and become adapted to different environments and so, despite their phylogenetic relatedness, they have become very different organisms. Mice often respond to experimental interventions in ways that differ strikingly from humans. Mice are invaluable for studying biological processes that have been conserved during the evolution of the rodent and primate lineages and for investigating the developmental mechanisms by which the conserved mammalian genome gives rise to a variety of different species. Mice are less reliable as models of human disease, however, because the networks linking genes to disease are likely to differ between the two species. The use of mice in biomedical research needs to take account of the evolved differences as well as the similarities between mice and humans.Keywords: allometry, cancer, gene networks, life history, model organisms

If you have cancer and you are a mouse, we can take good care of you. Judah Folkman [1]

Go to:

INTRODUCTION

Because of their phylogenetic relatedness and physiological similarity to humans, the ease of maintaining and breeding them in the laboratory, and the availability of many inbred strains, house mice, Mus musculus, have long served as models of human biology and disease [2]. Genomic studies have highlighted the striking genetic homologies between the two species [34]. These studies, together with the development of methods for the creation of transgenic, knockout, and knockin mice, have provided added impetus and powerful tools for mouse research, and have led to a dramatic increase in the use of mice as model organisms. Studies on mice have contributed immeasurably to our understanding of human biology [5]. All too often, however, mice respond to experimental interventions in ways that differ markedly from humans. Endostatin, the anticancer drug alluded to in the epigraph, is but one of many treatments that cure cancer in mice but have limited effectiveness in humans [6]. Indeed, the majority of oncology drugs that enter clinical trials never reach the marketplace. There are many reasons for the high failure rate of drug development, but the limitations of the animal models used in drug testing are an important factor [7]. Many substances that are carcinogens in mice are not carcinogenic in humans—and vice versa [8]. Moreover, mouse strains that were created to mimic human genetic diseases frequently have phenotypes that differ from their human counterparts [9]. Because of the assumption that mice will serve as reliable models for humans, differences between the two species are often greeted with surprise as well as dismay. But these differences should not elicit surprise; indeed, they should be expected. The lineages leading to modern rodents and primates are thought to have diverged from a common ancestral species that lived some 85 million years ago [10]. Since that time, species in these lineages evolved in and became adapted to very different environments. Our evolved developmental processes produce different kinds of organisms from similar component parts. Differences between mice and humans may be due to selection or drift, acting over the eons of evolutionary time or more recently during the creation and breeding of laboratory mouse strains [11].Go to:

SIZE

The most obvious and perhaps the most fundamental difference between mice and humans is size: humans are roughly 2500 times larger than mice. Size influences many aspects of an organism’s interactions with its environment, including its ability to acquire food, to avoid predators and to attract mating partners, and so has important effects on fitness; in the words of J. B. S. Haldane, organisms must be “the right size” [12]. As the lineages leading to mice and humans evolved, there was presumably selection for organisms that were the right size for their environments. Given its importance, size itself was probably a major target of natural selection [1314]. But a host of traits are correlated with size, and during the course of rodent and primate evolution, these traits evolved together with size. Two prominent sets of traits that are correlated with size are metabolic rate and life history strategy [15].Go to:

METABOLIC RATE

Metabolic rates of placental mammals are closely correlated with size. The relationship between basal metabolic rate (in kcal/day) and body mass (in kg) is usually taken as BMR = 70 × Mass0.75 [16]. Thus, a 30-g mouse has a specific metabolic rate (metabolic rate per gram of tissue) roughly seven times that of a 70-kg human [15]. There is continuing controversy about the reasons for the relationship between size and metabolic rate, and about the value of the allometric exponent [17]. The increased specific metabolic rate of small mammals is presumably related, at least in part, to size-dependent differences in heat loss and in requirements for thermoregulation, and is characterized by increases both in nutrient supply (capillary density) [18] and in nutrient demand (mitochondrial density) [19] in tissues of small animals; since nutrient supply and demand have coevolved and develop together during ontogeny, they are closely matched [20]. Differences in metabolic rate between mice and humans are correlated with many anatomic, physiologic and biochemical differences. Mice have relatively higher amounts of metabolically active tissues, such as liver and kidney, and relatively less inactive tissue, such as bone; in addition, mice have larger deposits of brown fat, which plays a critical role in heat production and thermoregulation. Mouse cells differ from human cells not only in mitochondrial density and metabolic rate, but also in the fatty acid composition of their membrane phospholipids; specifically, membranes in mouse cells have a higher content of the polyunsaturated (and readily oxidizable) fatty acid docosahexaenoic acid [21]. Mice have higher rates of production of reactive oxygen species and suffer higher rates of oxidative damage than do humans. All of these differences presumably evolved in association with selection for differences in size or in association with some other trait that is correlated with body mass, such as life history and rate of aging.Go to:

LIFE HISTORY

Size is also associated with a suite of life history traits, including age at reproductive maturity, length of gestation, litter size, birth interval, fraction of energy devoted to reproduction, and, perhaps most importantly, life expectancy. Female wild mice reach sexual maturity in a matter of 6–8 weeks, have a gestation length of 19–20 days and a litter size of 5–8, and produce multiple litters a year. Many laboratory mouse strains have been selected for increased fertility; they reach sexual maturity earlier and produce larger litters than do wild mice [22]. Mice, like other rodents, invest a much larger proportion of their energy in reproduction than do humans [23]. Both wild and laboratory mice have life spans of about 3–4 years, but wild mice have a much shorter life expectancy (less than a year, depending of course on environmental conditions) than do laboratory strains, which typically live several years [22]. Again, the differences in life history strategies between humans and mice are correlated with, and are probably related to, differences in size.Go to:

DIETS, MICROBIOMES AND PATHOGENS

Evolved differences in murine and human diets are also associated with pervasive differences in the biology of the two species. Although both mice and humans are omnivores, wild mice seem preferentially to consume unprocessed grains and cereals. Mice have large and continuously growing incisors that enable them to eat these foods. Presumably because their ancestors’ diets were low in ascorbic acid, mice have retained the ability to synthesize this essential cofactor; humans, in contrast, have lost this ability and so we now require exogenous vitamin C. And presumably because of their ancestors’ ingestion of different xenobiotics, mice and humans have different complements of cytochrome P450 enzymes and different patterns of xenobiotic metabolism [2425]. At least in part for this reason, toxicology testing in mice has been a poor predictor of human toxicity [26]. More importantly, mice have different microbiomes [27] and have coevolved with different sets of pathogens than have humans. The anatomy of the gastrointestinal track differs between the two species [27]. The ratio of length of the small intestine to that of the colon is greater in mice than in humans, mice have a prominent cecum, and they lack an appendix. In mice, the cecum is an important site for the microbial fermentation of undigested foods. Thus, the two species provide different environments that apparently support the growth of different gastrointestinal microbiota. Moreover, mice have significant amounts of bronchus-associated lymphoid tissue, which has been interpreted to indicate that, because they live close to the ground, they face increased exposure to respiratory pathogens in droplets or particles from the soil [28]. The differences between mice and humans are not only genetic and epigenetic, but also reflect features of their environments, especially their ecological interactions with other species (food sources, microbiota, pathogens, etc.) that are reliably transmitted from generation to generation and affect the course of development.Go to:

DIFFERENCES DUE TO THE DOMESTICATION AND BREEDING OF HOUSE MICE

During the course of murine and human evolution, our ancestors underwent selection for—and so mice and humans now differ in—many other traits, including circadian rhythm (wild mice are nocturnal), sensory systems (mice rely heavily on olfaction, hearing and touch), cognitive development, reproductive behavior and patterns of social organization. Moreover, the domestication and breeding of the laboratory mouse strains that are commonly used in biomedical research have increased the differences between the biology of these strains and that of wild mice, let alone human biology. Many laboratory mouse strains were derived from fancy mice, which had been kept as pets for centuries. These strains were derived largely from the subspecies M. musculus domesticus, which, for unknown reasons, has an exceptionally high rate of robertsonian chromosomal translocations [29]. Initially, domestication entailed selection for such traits as docility and the ability to thrive and reproduce in confinement. Later, as mouse breeding became a commercial enterprise, breeders selected for traits associated with increased reproduction, including early sexual maturity and the production of frequent and large litters [30].

A major impetus for the development of inbred mouse strains was to study the genetic basis of cancer; strains were created that differed in their susceptibility to transplanted tumors or in the incidence of spontaneous neoplasms [31]. These inbred strains have yielded many important insights into cancer biology. Nonetheless, cancer and other diseases in laboratory mice that were selected because they develop (or are resistant to) these diseases may differ from the cognate diseases found in wild mice, as well as from diseases in humans. Common strains of laboratory mice have come to differ from wild mice in a host of traits. Some of these differences, such as increased fertility, can be understood as the result of selection, while the reasons for other differences are not clear [30]. Finally, the genetic homogeneity that makes these strains valuable in the laboratory means, of course, that they lack the genetic variation that characterizes outbred wild populations.

Given the many differences in the biology of mice and humans, it is not surprising that the patterns of disease differ in the two species. The causes of death of feral house mice depend on the environment. Many are killed by predators, and in harsh environments starvation and hypothermia are major causes of death [22]. In the laboratory, mice live longer; there, cancer is a major cause of death, while cardiovascular disease is negligible. The distribution of tumors differs between mice and humans; most murine tumors are of mesenchymal origin, while human tumors arise mainly from epithelial cells. There are many other differences between mouse and human cancers, and many differences between mouse and human cells that appear to contribute to these differences [832–34]. For example, laboratory mouse strains have much longer telomeres than do humans and express telomerase in their somatic cells throughout life. This difference may help to explain why, in vitro, mouse cells undergo spontaneous transformation at much higher rates than do human cells.

Some of the differences between mice and humans are relatively easy to rationalize. As discussed below, differences in the function of the immune system have almost certainly evolved in response to differences in pathogen exposure and in life expectancy [28]. Other differences, such as differences in genomic imprinting, are harder to understand [35]. Additional phylogenetic analyses and functional genomic studies will be necessary to determine which of the differences between mouse and human biology are related to differences in size, either because they are associated with metabolic rate or with life history strategy, which are due to other changes that accompanied the evolutionary divergence of these species, and which have resulted from the selective breeding of laboratory mice.Go to:

IMPLICATIONS OF SPECIES DIFFERENCES FOR MOUSE RESEARCH

The use of model organisms in biological research is based on the concept of unity in biology, a concept expressed most famously in Jacques Monod and François Jacob’s aphorism, “Anything found to be true of E. coli must also be true of elephants” [36]. But biology is characterized by diversity as well as unity; evolution is “descent with modification” [37]. The art of choosing model organisms involves recognizing the properties of these organisms that they are likely to share with organisms of other species—especially, for biomedical research, humans [38]. Monod and Jacob were concerned with genetic regulatory mechanisms and other basic biological processes that must have arisen very early in the evolutionary history of living organisms and so are similar in bacteria and in mammals. Mice have served and will continue to serve as valuable models for the study of basic biological processes that, in Wimsatt’s terms, became developmentally entrenched before the rodent and primate lineages diverged and have been conserved during the separate evolutionary histories of mice and humans [39].

Studies of the immune system highlight both the value of mouse research in elucidating common features of mammalian biology as well as the limitations of translating this research in areas in which humans are likely to differ from mice. Research on mice has contributed greatly to our knowledge of the adaptive immune system; mouse research has led to the discovery of the major histocompatibility complex genes and the T cell receptor, and to our understanding of the regulation of antibody synthesis and many other features of the immune system [40]. But there are many differences between the mouse and human immune systems, such that much research on immunological diseases in mice is not transferable to humans, and many immunologists are now calling for a return to the study of human immunology [2840–42]. From an evolutionary perspective, this is understandable. The adaptive immune system evolved in jawed fish some hundreds of million years before the evolution of mammals. Many features of this ancestral immune system, including antigen recognition, generation of antibody diversity, clonal selection, and immunological tolerance, are critical for survival and have been maintained in most or all of the descendants of these early vertebrates. On the other hand, species differences in the mechanisms for the maintenance of memory T cells must have evolved in response to the evolution of different life spans. Moreover, specific features of the immune system evolve rapidly, as host species coevolve with their pathogens and commensal microbiota [41]. Since humans and mice harbor different sets of pathogens and microbiomes, it is not surprising that host–pathogen and host–microbiome coevolution has led to differences between the human and mouse immune systems.

The fact that the highly conserved mammalian genome can give rise to a wide variety of different species indicates that the relationships between genotype and phenotype differ among mammalian species. Comparisons between mice and humans are invaluable for understanding the developmental mechanisms that lead to such different genotype–phenotype relationships. Some of the genetic differences between mice and humans are differences in coding sequences, which give rise to proteins with different properties. For example, mouse hemoglobin has a lower affinity for O2 than does human hemoglobin, which facilitates the dissociation of O2 from hemoglobin in peripheral tissues and helps to support the higher metabolic rate in mice. Perhaps more importantly, however, are differences in the genetic or epigenetic regulation of gene expression in these species. The expression of potassium channel genes in the heart exemplifies these differences. Mice have a heart rate of ∼600 beats/min, while humans have a resting heart rate of ∼70 beats/min. This difference in heart rate entails that the cardiac action potential be much shorter in mice than in humans. Indeed, the repolarization phase of the cardiac action potential, which is due to outward Kcurrents, is much shorter in mice [43]. This difference is due to different contributions of various Kcurrents, which in turn are presumably due to differences in expression of Kchannel genes in the two species. Evolved differences in the regulation of gene expression are important because they may involve the rewiring of gene (or protein) networks. Gene networks in mice and humans have similar numbers of nodes (genes) but the connectivity of the nodes in these networks, and the relationships between genes and phenotypes, differ between the two species [44–46]. The different network architectures and different genotype–phenotype relationships between mice and humans mean that the relationships between genotype and disease are also likely to differ in these two species. Perturbations of gene and protein networks by environmental manipulation as well as by mutation are likely to have different effects on diseases as well as on other phenotypes in mice than in humans. In short, mice are problematic models for understanding human disease.

There are other good reasons to pursue research on mice. Although house mice are not a major source of human disease, they can transmit lymphocytic choriomeningitis virus and perhaps other pathogens to humans, and other rodent species are important reservoirs for zoonoses. Research on mice may yield information that will help to prevent or ameliorate these diseases. Finally, mice should be studied for their own sake, to understand their biology and to maintain the health of pet mice, laboratory mice, and wild mice.

Unfortunately, despite the many attempts to translate the results of mouse research to humans, we still cannot specify in advance which research in mice is likely to benefit or shed light on human biology and health. For the most part, we have only anecdotal information about studies in mice that translated to humans and those that did not. We need more systematic collection, reporting and analysis of mouse research (and research on other “model organisms”) to figure out what works and what does not. Until we have that information, we need to be more critical in pursuing mouse research and in making claims about the applicability of this research to humans.

In addition to problems resulting from the evolved differences between mice and humans, other aspects of mouse research have compromised the value of this research and have further complicated the extrapolation of mouse research to humans. Thus, e.g., laboratory mice are often housed at temperatures below their thermoneutral zone, and as a result are cold-stressed, sleep deprived, and hypertensive [47]. The biology of laboratory mice may also be affected by their housing in same-sex groups and their lack of opportunities for physical exercise. Although mice are often used as models of diseases of aging, for logistical and financial reasons most mouse research is carried out on young animals. And although mouse cells are more sensitive to oxygen damage than are human cells, cell culture studies are often carried out in 20% oxygen, which is non-physiological and is more damaging to mouse cells than to human cells [48]. Finally, there are no agreed upon standards for the design, analysis, or publication of mouse research (or research with other model organisms). The statistical analysis of studies of mice and other animals is often substandard, and there may be important publication biases because negative results may not get published [4950]. All of these problems need to be addressed before studies on mice can be properly interpreted and extrapolated to humans.Go to:

FINAL COMMENTS

Despite all of the documented differences between mice and humans, and despite the history of “errors in translation” in the application of research on mice to humans, reports of research on mice are frequently accompanied by unwarranted and misleading claims, such as “Furthering our understanding of mouse X should provide novel insights into human Y.” Such claims raise false hopes and are ultimately self-defeating, in that they waste resources and increase public skepticism concerning the value of biomedical research. Indeed, the problems of translating research on mice and other model organisms to humans have led a number of scientists to question the value of this research [51–53]. Furthermore, critical discussions of animal experimentation are routinely distorted by “animal rights” activists to support their belief that this experimentation should be stopped. These intrusions, however unwelcome, should not stifle discussion. For reasons mentioned above, research on mice (and other species) is essential and should be supported. This research should, however, be designed and interpreted with appropriate appreciation of the evolved differences as well as the similarities between M. musculus and H. sapiens.Go to:

ACKNOWLEDGEMENTS

I thank Alan Schechter and Ted Steck for their thoughtful comments and helpful suggestions.

Conflict of interest: None declared.Go to:

REFERENCES

1. Kolata G. Hope in the lab: a special report. A cautious awe greets drugs that eradicate tumors in mice. The New York Times. New York, 3 May 1998. [Google Scholar]2. Morse HCI. Building a better mouse: one hundred years of genetics and biology In: Fox JG, editor. (ed.). The Mouse in Biomedical Research. Amsterdam: Elsevier, 2007, 1–11. [Google Scholar]3. Waterston RH, Lindblad-Toh K, Birney E. et al. Initial sequencing and comparative analysis of the mouse genome. Nature 2002;420:520–62. [PubMed] [Google Scholar]4. Brown SDM, Hancock JM. The mouse genome In: Volff J-N, editor. (ed.). Vertebrate Genomes. Basel: Karger, 2066, 33–45. [Google Scholar]5. Fox JG, Barthold SW, Davisson MT, editors. , et al. (Eds.) The Mouse in Biomedical Research. Amsterdam: Elsevier, 2007. [Google Scholar]6. Kerbel RS. What is the optimal rodent model for anti-tumor drug testing? Cancer Metastasis Rev 1999;17:301–4. [PubMed] [Google Scholar]7. Adams DJ. The Valley of Death in anticancer drug development: a reassessment. Trends Pharmacol Sci 2012;33:173–80. [PMC free article] [PubMed] [Google Scholar]8. Anisimov VN, Ukraintseva SV, Yashin AI. Cancer in rodents: does it tell us about cancer in humans? Nat Rev Cancer 2005;5:807–19. [PubMed] [Google Scholar]9. Elsea SH, Lucas RE. The mousetrap: what we can learn when the mouse model does not mimic the human disease. Ilar J 2002;43:66–79. [PubMed] [Google Scholar]10. Springer MS, Murphy WJ. Mammalian evolution and biomedicine: new views from phylogeny. Biol Rev Camb Philos Soc 2007;82:375–92. [PubMed] [Google Scholar]11. Rader KA. Making Mice: Standardizing Animals for American Biomedical Research, 1900–1955. Princeton: Princeton University Press, 2004. [Google Scholar]12. Haldane JBS. On being the right size Possible Worlds. New York: Harper & Brothers, 1928. [Google Scholar]13. Bonner JT. Why Size Matters. Princeton, NJ: Princeton University Press, 2006. [Google Scholar]14. Stearns SC. The influence of size and phylogeny on patterns of covariation among life-history traits in the mammals. Oikos 1983;41:173–87. [Google Scholar]15. Schmidt-Nielsen K. Scaling: Why is Animal Size So Important? Cambridge: Cambridge University Press, 1984. [Google Scholar]16. Kleiber M. The Fire of Life: An Introduction to Animal Energetics. Huntington, NY: Robert E. Krieger, 1975. [Google Scholar]17. White CR, Seymour RS. Allometric scaling of mammalian metabolism. J Exp Biol 2005;208:1611–9. [PubMed] [Google Scholar]18. West GB, Brown JH, Enquist BJ. A general model for the origin of allometric scaling laws in biology. Science 1997;276:122–6. [PubMed] [Google Scholar]19. Hulbert AJ, Else PL. Membranes and the setting of energy demand. J Exp Biol 2005;208:1593–9. [PubMed] [Google Scholar]20. Suarez RK, Darveau CA. Multi-level regulation and metabolic scaling. J Exp Biol 2005;208:1627–34. [PubMed] [Google Scholar]21. Hulbert AJ. The links between membrane composition, metabolic rate and lifespan. Comp Biochem Physiol A Mol Integr Physiol 2008;150:196–203. [PubMed] [Google Scholar]22. Berry RJ, Bronson FH. Life history and bioeconomy of the house mouse. Biol Rev Camb Philos Soc 1992;67:519–50. [PubMed] [Google Scholar]23. Phelan JP, Rose MR. Why dietary restriction substantially increases longevity in animal models but won’t in humans. Age Res Rev 2005;4:339–50. [PubMed] [Google Scholar]24. Martignoni M, Groothuis GM, de Kanter R. Species differences between mouse, rat, dog, monkey and human CYP-mediated drug metabolism, inhibition and induction. Expert Opin Drug Metab Toxicol 2006;2:875–94. [PubMed] [Google Scholar]25. Anderson S, Luffer-Atlas D, Knadler MP. Predicting circulating human metabolites: how good are we? Chem Res Toxicol 2009;22:243–56. [PubMed] [Google Scholar]26. Olson H, Betton G, Robinson D. et al. Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul Toxicol Pharmacol 2000;32:56–67. [PubMed] [Google Scholar]27. Nguyen TL, Vieira-Silva S, Liston A. et al. How informative is the mouse for human gut microbiota research? Dis Model Mech 2015;8:1–16. [PMC free article] [PubMed] [Google Scholar]28. Mestas J, Hughes CC. Of mice and not men: differences between mouse and human immunology. J Immunol 2004;172:2731–8. [PubMed] [Google Scholar]29. Nachman MW, Searle JB. Why is the house mouse karyotype so variable? Trends Ecol Evol 1995;10:397–402. [PubMed] [Google Scholar]30. Austad SN. A mouse’s tale. Nat Hist 2002;111:64–70. [Google Scholar]31. Paigen K. One hundred years of mouse genetics: an intellectual history. I. The classical period (1902–1980). Genetics 2003;163:1–7. [PMC free article] [PubMed] [Google Scholar]32. Hahn WC, Weinberg RA. Modelling the molecular circuitry of cancer. Nat Rev Cancer 2002;2:331–41. [PubMed] [Google Scholar]33. Rangarajan A, Weinberg RA. Comparative biology of mouse versus human cells: modelling human cancer in mice. Nat Rev Cancer 2003;3:952–9. [PubMed] [Google Scholar]34. Frese KK, Tuveson DA. Maximizing mouse cancer models. Nat Rev Cancer 2007;7:645–58. [PubMed] [Google Scholar]35. Morison IM, Ramsay JP, Spencer HG. A census of mammalian imprinting. Trends Genet 2005;21:457–65. [PubMed] [Google Scholar]36. Monod J, Jacob F. General conclusions: teleonomic mechanisms in cellular metabolism, growth and differentiation. Cold Spring Harb Symp Quant Biol 1961;26:389–401. [PubMed] [Google Scholar]37. Darwin C. On the Origin of Species by Means of Natural Selection. London: John Murray, 1859. [Google Scholar]38. Bolker J. Model organisms: there’s more to life than rats and flies. Nature 2012;491:31–3. [PubMed] [Google Scholar]39. Wimsatt WC. Re-Engineering Philosophy for Limited Beings: Piecewise Approximations to Reality. Cambridge, MA: Harvard University Press, 2007. [Google Scholar]40. Khanna R, Burrows SR. Human immunology: a case for the ascent of non-furry immunology. Immunol Cell Biol 2011;89:330–1. [PubMed] [Google Scholar]41. Bailey M, Christoforidou Z, Lewis MC. The evolutionary basis for differences between the immune systems of man, mouse, pig and ruminants. Vet Immunol Immunopathol 2013;152:13–9. [PubMed] [Google Scholar]42. Davis MM. A prescription for human immunology. Immunity 2008;29:835–8. [PMC free article] [PubMed] [Google Scholar]43. Nerbonne JM, Nichols CG, Schwarz TL. et al. Genetic manipulation of cardiac K(+) channel function in mice: what have we learned, and where do we go from here? Circ Res 2001;89:944–56. [PubMed] [Google Scholar]44. Goh KI, Cusick ME, Valle D. et al. The human disease network. Proc Natl Acad Sci U S A 2007;104:8685–90. [PMC free article] [PubMed] [Google Scholar]45. Kraja AT, Province MA, Huang P. et al. Trends in metabolic syndrome and gene networks in human and rodent models. Endocr Metab Immune Disord Drug Target 2008;8:198–207. [PubMed] [Google Scholar]46. Ravasi T, Suzuki H, Cannistraci CV. et al. An atlas of combinatorial transcriptional regulation in mouse and man. Cell 2010;140:744–52. [PMC free article] [PubMed] [Google Scholar]47. Maloney SK, Fuller A, Mitchell D. et al. Translating animal model research: does it matter that our rodents are cold? Physiology 2014;29:413–20. [PubMed] [Google Scholar]48. Coppe JP, Patil CK, Rodier F. et al. A human-like senescence-associated secretory phenotype is conserved in mouse cells dependent on physiological oxygen. PLoS One 2010;5:e9188.. [PMC free article] [PubMed] [Google Scholar]49. Ioannidis JP. Extrapolating from animals to humans. Sci Transl Med 2012;4:151ps15. [PubMed] [Google Scholar]50. Landis SC, Amara SG, Asadullah K. et al. A call for transparent reporting to optimize the predictive value of preclinical research. Nature 2012;490:187–91. [PMC free article] [PubMed] [Google Scholar]51. Buffenstein R, Nelson OL, Corbit KC. Questioning the preclinical paradigm: natural, extreme biology as an alternative discovery platform. Aging 2014;6:913–20. [PMC free article] [PubMed] [Google Scholar]52. Pound P, Bracken MB. Is animal research sufficiently evidence based to be a cornerstone of biomedical research? BMJ 2014;348:g3387.. [PubMed] [Google Scholar]53. Young NS. Mouse medicine and human biology. Semin Hematol 2013;50:88–91 [PubMed] [Google Scholar]


Articles from Evolution, Medicine, and Public Health are provided here courtesy of Oxford University Press

Formats:

Share

Save items

Similar articles in PubMed

See reviews…See all…

Cited by other articles in PMC

See all…

Links

Recent Activity

ClearTurn Off

See more…

Support CenterSupport Center

Simple NCBI Directory

External link. Please review our privacy policy.NLMNIHDHHSUSA.gov

National Center for Biotechnology InformationU.S. National Library of Medicine8600 Rockville Pike, Bethesda MD, 20894 USAPolicies and Guidelines | Contact

Jackson Laboratory

Search MagazineWHAT IS A MOUSE MODEL?By Dayana Krawchuk, Ph.D.What is a mouse model?The ability to model human disease in the mouse makes it such a valuable experimental system. Genetically and genomically, the human and the mouse are very similar.What is a mouse model?No, no, no — it’s not a mouse walking down the runway at Fashion Week!In architecture, a “model” is a representation of a building.  In mice, a “model” is a representation of a human disease or syndrome.Mice share more than 95% of our DNA — and this means that we’re both affected by disease in surprisingly similar ways.By studying mice that have symptoms of diseases like Alzheimer’s, diabetes, or cancer, we can learn a lot more about how these diseases might be treated in patients.The quest for the best mouse model — or best “representation” — of a disease is always ongoing. The closer we are to accurately modeling genetic diseases in the mouse, the closer we are to discovering cures in the clinic.

Share

Research newsletters

Learn about the latest research breakthroughs in the fight against Alzheimer’s, cancer and other devastating diseases.

SUBSCRIBE

Leading the search forTOMORROW’S CURES

©2019 THE JACKSON LABORATORY

                                 Choose other country or region                                 China 

PubMed

US National Library of MedicineNational Institutes of HealthSearch databasePMCAll DatabasesAssemblyBiocollectionsBioProjectBioSampleBioSystemsBooksClinVarConserved DomainsdbGaPdbVarGeneGenomeGEO DataSetsGEO ProfilesGTRHomoloGeneIdentical Protein GroupsMedGenMeSHNCBI Web SiteNLM CatalogNucleotideOMIMPMCPopSetProbeProteinProtein ClustersPubChem BioAssayPubChem CompoundPubChem SubstancePubMedSNPSparcleSRAStructureTaxonomyToolKitToolKitAllToolKitBookghSearch term

Clear input

Search

Result Filters

Send to

Adv Cancer Res. 2010;106:113-64. doi: 10.1016/S0065-230X(10)06004-5.

Genetically engineered mouse models in cancer research.

Walrath JC1Hawes JJVan Dyke TReilly KM.

Author information

1Mouse Cancer Genetics Program, National Cancer Institute, Frederick, Maryland, USA.

Abstract

Mouse models of human cancer have played a vital role in understanding tumorigenesis and answering experimental questions that other systems cannot address. Advances continue to be made that allow better understanding of the mechanisms of tumor development, and therefore the identification of better therapeutic and diagnostic strategies. We review major advances that have been made in modeling cancer in the mouse and specific areas of research that have been explored with mouse models. For example, although there are differences between mice and humans, new models are able to more accurately model sporadic human cancers by specifically controlling timing and location of mutations, even within single cells. As hypotheses are developed in human and cell culture systems, engineered mice provide the most tractable and accurate test of their validity in vivo. For example, largely through the use of these models, the microenvironment has been established to play a critical role in tumorigenesis, since tumor development and the interaction with surrounding stroma can be studied as both evolve. These mouse models have specifically fueled our understanding of cancer initiation, immune system roles, tumor angiogenesis, invasion, and metastasis, and the relevance of molecular diversity observed among human cancers. Currently, these models are being designed to facilitate in vivo imaging to track both primary and metastatic tumor development from much earlier stages than previously possible. Finally, the approaches developed in this field to achieve basic understanding are emerging as effective tools to guide much needed development of treatment strategies, diagnostic strategies, and patient stratification strategies in clinical research.

Copyright 2010 Elsevier Inc. All rights reserved.PMID: 20399958 PMCID: PMC3533445 DOI: 10.1016/S0065-230X(10)06004-5[Indexed for MEDLINE] Free PMC Article

  • Share on Facebook
  • Share on Twitter
  • Share on Google+

Images from this publication.See all images (8)Free text

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Publication type, MeSH terms, Grant support

LinkOut – more resources

Supplemental Content

Full text links

Icon for Elsevier Science
Icon for PubMed Central

Save items

Add to FavoritesView more options

Similar articles

See reviews…See all…

Cited by 43 PubMed Central articles

See all…

Related information

Recent Activity

ClearTurn Off

See more…You are here: NCBI > Literature > PubMedSupport Center

Simple NCBI Directory

NLMNIHDHHSUSA.gov

National Center for Biotechnology InformationU.S. National Library of Medicine8600 Rockville Pike, Bethesda MD, 20894 USAPolicies and Guidelines | Contact

PMC

US National Library of Medicine
National Institutes of HealthSearch databasePMCPubMedAll DatabasesAssemblyBiocollectionsBioProjectBioSampleBioSystemsBooksClinVarConserved DomainsdbGaPdbVarGeneGenomeGEO DataSetsGEO ProfilesGTRHomoloGeneIdentical Protein GroupsMedGenMeSHNCBI Web SiteNLM CatalogNucleotideOMIMPMCPopSetProbeProteinProtein ClustersPubChem BioAssayPubChem CompoundPubChem SubstancePubMedSNPSparcleSRAStructureTaxonomyToolKitToolKitAllToolKitBookghSearch term

Clear input

Search

Logo of nihpa

Adv Cancer Res. Author manuscript; available in PMC 2012 Dec 31.Published in final edited form as:Adv Cancer Res. 2010; 106: 113–164.doi: 10.1016/S0065-230X(10)06004-5PMCID: PMC3533445NIHMSID: NIHMS424172PMID: 20399958

Genetically Engineered Mouse Models in Cancer Research

Jessica C. WalrathJessica J. HawesTerry Van Dyke, and Karlyne M. ReillyAuthor informationCopyright and License informationDisclaimerThe publisher’s final edited version of this article is available at Adv Cancer ResSee other articles in PMC that cite the published article.Go to:

Abstract

Mouse models of human cancer have played a vital role in understanding tumorigenesis and answering experimental questions that other systems cannot address. Advances continue to be made that allow better understanding of the mechanisms of tumor development, and therefore the identification of better therapeutic and diagnostic strategies. We review major advances that have been made in modeling cancer in the mouse and specific areas of research that have been explored with mouse models. For example, although there are differences between mice and humans, new models are able to more accurately model sporadic human cancers by specifically controlling timing and location of mutations, even within single cells. As hypotheses are developed in human and cell culture systems, engineered mice provide the most tractable and accurate test of their validity in vivo. For example, largely through the use of these models, the microenvironment has been established to play a critical role in tumorigenesis, since tumor development and the interaction with surrounding stroma can be studied as both evolve. These mouse models have specifically fueled our understanding of cancer initiation, immune system roles, tumor angiogenesis, invasion, and metastasis, and the relevance of molecular diversity observed among human cancers. Currently, these models are being designed to facilitate in vivo imaging to track both primary and metastatic tumor development from much earlier stages than previously possible. Finally, the approaches developed in this field to achieve basic understanding are emerging as effective tools to guide much needed development of treatment strategies, diagnostic strategies, and patient stratification strategies in clinical research.Go to:

I. INTRODUCTION

In 2008, for the first time in 10 years of reporting, the incidence and death rates from cancer declined significantly (Jemal et al., 2008). Although encouraging, there were still just under 1.5 million new cases of cancer in the United States, and just over 500,000 deaths per year. The decrease is attributed to a reduction in the most common cancers, lung, prostate, colorectal, and breast, likely due to better screening for early detection of cancer and reduced tobacco use (Jemal et al., 2008). Among the remaining cancers, rates remained stable or increased, highlighting the need for continued research to develop better prevention, detection, and treatment of cancer.

Although improvements in prevention and early detection are leading to a drop in cancer incidence and death, the ability to treat established cancers is still quite limited. Relatively new therapies, such as Gleevec, bevacizumab, and herceptin, are beginning to make a difference to patients with particular molecular subtypes of cancer, demonstrating the power of correctly targeted molecular-based therapy (Normanno et al., 2009Soverini et al., 2008Tan et al., 2008), but also highlighting the limitation in our understanding of the molecular pathways critical to cancer and how they vary between individuals. Cancer is a complex disease in which normal cellular pathways are altered to give rise to the properties leading to cancer, such as inappropriate growth, survival, and invasion. While the study of human tumors has yielded many insights into the molecular changes present in cancers, more rigorous testing of hypotheses through experimental manipulation is necessary to better understand which changes are causative, and therefore targetable, and which are secondary. Mouse model systems provide an experimentally tractable mammalian system to test the hypotheses generated from the observation and study of human tumors, and also provide opportunities to identify novel mechanisms to be confirmed in human tumors. Cross-species comparison has proven to be powerful in improving the understanding of a wide variety of human diseases, including cancer (e.g., see Brown et al., 2008Kim et al., 2005Wang and Paigen, 2005Wang et al., 2005). We describe in this chapter the engineering tools and methods currently available to model cancer in the mouse, and highlight how some key challenges have been and are being addressed using these sophisticated approaches.

Humans are a highly heterogeneous population with variation in genetic background, diet, and environmental exposures. Any of these variables can affect how cancer manifests itself, and thus can become confounding issues in the analysis of human tumors. Studies in mice allow researchers to control these variables to simplify experiments and ask well-structured questions. For example, genetic background can be held constant while testing the effects of diet or carcinogens on cancer, and diet/environment can be held constant while examining the effects of genetic changes on tumorigenesis. As mammals, mice share many anatomical, cellular, and molecular traits with humans that are known to have critical functions in cancer, such as an immune system, maternal effects in utero, imprinting of genes, and alternative splicing. Thus, mice provide an experimentally tractable model system with a wealth of developed research tools to understand basic mechanisms of cancer.

One of the difficulties and frustrations in the development of cancer therapies is that preclinical studies have historically had limited predictive value for the efficacy of drugs in patients. Preclinical studies have been relatively predictive of toxicity in humans, such that many drugs that enter Phase I clinical trials are taken forward to Phase II clinical trials due to tolerable toxicity profiles. In contrast, most drugs (95%) are dropped in Phase II and Phase III clinical trials because of limited efficacy against cancer (e.g., see Sharpless and Depinho, 2006). There are several potential explanations for this. It is clear that some drugs are only effective in a subset of patients (Haas-Kogan et al., 2005Mellinghoff et al., 2005), but because current preclinical testing strategies do not model the relevant molecular and cellular milieu, drug specificity for subtypes can go unappreciated. Indeed, preclinical vetting of drugs is largely performed on xenografts of human tumor cell lines grown subcutaneously in immune-compromised mice. It is now clear, however, that cancer stroma is distinct from normal stroma and provides a supportive microenvironment for tumor growth. Human cancer xenografts lack the appropriate cancer stroma support when grown over relatively short periods of time in immune-compromised mice. As a result, they may be more vulnerable to drug treatment than cancers that have coevolved with their stroma (Olive et al., 2009). Similarly, inflammatory responses play an integral role in cancer development (de Visser and Coussens, 2006Tlsty and Coussens, 2006), but are not effectively modeled in xenografts. Moreover, immune-compromised mice could mount a limited immune attack on engrafted cells (Quintana et al., 2008), making them more susceptible to treatment.

Mice engineered with cell-specific human cancer-associated aberrations can address many of these issues, as tumors arise de novo in the context of a normal immune system and coevolve with surrounding stroma. Furthermore, more than 100 years of genetic research in the mouse has provided powerful strategies for assessing the complex genetics of disease susceptibility, therapeutic responses, and associated toxicities that are so diverse among the human population. Identifying these parameters is key to the development of personalized medicine; while current technologies have accelerated the pace of human epidemiological association studies, there is no doubt that discovery can be accelerated by cross-species comparisons. Finally, several initiatives are under way to use the de novo mouse cancer models in preclinical guidance for human clinical trials. While challenging due to the complexity of the human diseases and the difficulty of modeling them accurately in mice, early studies indicate that this approach offers great promise, opening up a new era of translational research using engineered mouse models (Becher and Holland, 2006Carver and Pandolfi, 2006Frese and Tuveson, 2007Gutmann et al., 2006Huse and Holland, 2009Pritchard et al., 2003Sharpless and Depinho, 2006).

While subcutaneous xenograft models have not been predictive for targeted cancer therapies in humans, they continue to provide a simple testing ground for triage and pharmacodynamic studies prior to analysis in more complex models. However, of significance, xenograft approaches refined to better model the tumor microenvironment may hold significant promise in predicting some human therapeutic outcomes and are currently under study. These methods include the transplantation of human primary tumors into immunocompromised mice at the orthotopic site and into mice that have been “humanized” with transplanted human hematopoietic and other organ systems (reviewed in Bibby, 2004Legrand et al., 2009Richmond and Su, 2008Sharpless and Depinho, 2006). In the end, it is likely that a series of integrated model systems will hold the most power in discovery and prediction of effective human disease management. The uses of xenograft models have been reviewed elsewhere (Lee et al., 2007Pegram and Ngo, 2006Sausville and Burger, 2006Teicher, 2009Troiani et al., 2008), and we focus in this chapter on genetically engineered mouse models of cancer. We describe here the state of the art techniques for modeling cancer in mice both for basic mechanistic research and for preclinical applied studies. We also review the issues in cancer research for which these mouse models are particularly useful due to the preservation of temporal disease development, including coevolution of cancer and stromal compartments.Go to:

II. GENERATION OF GENETICALLY ENGINEERED MOUSE MODELS

With the availability of the complete sequence of the mouse genome, technology to manipulate the mouse genome, and well-defined inbred strains as well as extensive information on the polymorphisms among strains, the ability to engineer mice to test hypotheses of tumorigenesis is impressive. Experiments can now be easily undertaken to assess the outcome when the function of a gene is lost, mutated, underexpressed, or overexpressed in the appropriate cell types in vivo. In addition to individual research efforts, several organized initiatives have been launched using a variety of approaches to systematically target every gene in the genome (Adams et al., 2004Friedel et al., 2007Hansen et al., 2008Schnutgen et al., 2005Skarnes et al., 2004To et al., 2004). Additionally, much can be learned about the natural history of cancer through the use of reporters to image molecular, cellular, and anatomical changes during tumorigenesis. Finally, mutagenesis studies in the mouse have identified new cancer-causing mutations that can then be confirmed in studies of human cancers.

A. Studying Loss of Gene Function in Mouse Models

1. MOUSE GENE KNOCKOUTS

Studying the loss of function of genes provides insight into understanding the biological functions for which the protein product is required. Loss-of-function studies most commonly use “knockout” strategies to remove the gene of interest by engineering constitutive or conditional deletions in the gene. For genes that span large genomic regions, deletion of the first few exons encoding the start codon is often sufficient to block transcription or translation into a functional protein product. However, sometimes an alternative start codon or alternative splicing can lead to a truncated protein product with partial function that can mask the significance of the gene of interest in biological loss-of-function studies. Thus, careful molecular characterization of genetically engineered alleles is important to verify that the function of the gene is truly lost and also that additional inadvertent gene rearrangements or deletions are not present.

In translational cancer research, loss-of-function studies provide a powerful approach to assessing the potential validity of targeted therapies, since the target can be specifically inactivated in the context of a developing or developed tumor. In this approach, accurate interpretation requires understanding of any functional redundancy differences between mouse and human. For example, if one species can utilize alternative gene products in a cancer pathway, but the other cannot, results will not be concordant. Fortunately, careful study using existing technologies, along with the available extensive genomic information, allows for this assessment and provides additional information on which to base further therapeutic design.

In basic research studies, the use of knockout strategies have been critical in understanding cause and effect relationships in cancer development, and can be applied to the assessment of many gene classes, including oncogenes, tumor suppressor genes, and metabolic (“housekeeping”) genes. The classification of a gene as a tumor suppressor depends on the demonstration that impaired function can facilitate tumor development, and that can only be achieved in the context of a developing tumor within the host. Germline loss-of-function (homozygous deletion) studies often lead to embryonic lethality precluding assessment for adult diseases, making conclusive determination of tumor suppressor genes more difficult. In many cancer models, however, animals heterozygous for a tumor suppressor knockout allele are susceptible to tumor formation either due to haploinsufficiency (impaired function from insufficient levels) or by somatic loss of the wild type allele (Cichowski et al., 1999Kost-Alimova and Imreh, 2007Macleod and Jacks, 1999Reilly et al., 2000Zheng and Lee, 2002). Nonetheless, a more valid and versatile approach is to conditionally induce a somatic mutation, as discussed below. Use of loss-of-function mouse models to study cancer has been extensively reviewed elsewhere (Maddison and Clarke, 2005Van Dyke and Jacks, 2002). Knockout gene targeting strategies and techniques have also been reviewed extensively (Capecchi, 2005LePage and Conlon, 2006Mikkola and Orkin, 2005Porret et al., 2006) and will not be discussed in detail here.

2. MOUSE CONDITIONAL GENE MUTATIONS

With conventional knockouts, loss of a vital gene can often lead to embryonic lethality, severe developmental abnormalities, or adult sterility, making it impossible to study the gene in the desired disease context. For example, one cannot study the role of genes such as BRCA1 and BRCA2 in breast cancer if the animals die before birth or adulthood (Evers and Jonkers, 2006). In addition, ablation of the gene of interest in the entire body does not mimic spontaneous tumorigenesis in humans, where tumors evolve in a wild-type environment, and the timing of gene loss may be a critical factor in disease development.

To circumvent conventional knockout limitations, sophisticated conditional genetic engineering technology has been developed to create systems where genetic events can be tightly controlled spatially and temporally. Bacterial Cre and yeast FLP enzymes are site-specific recombinases that catalyze specific recombination between defined 34 bp loxP and FRT sites, respectively (Branda and Dymecki, 2004). Therefore, in the presence of Cre or FLP protein expression, homologous recombination is induced between loxP or FRT sites that flank the gene of interest and are oriented in the same direction, thus recombining out the flanked genetic sequence and deleting the gene of interest. By temporally and spatially controlling expression of the recombinase, it is then possible to temporally and spatially control deletion of the gene of interest, overcoming interference from developmental abnormalities and lethality (Branda and Dymecki, 2004). Mice carrying the Cre or FLP recombinase under control of a tissue-specific promoter are crossed with mice carrying the gene of interest flanked by loxP or FRT sites to conditionally knockout the gene in a specific tissue or cell type, such as progenitor cells, or at specific times during development.

Multiple types of Cre delivery systems have been developed to temporally and spatially control Cre expression. These include promoter-driven cell or tissue-specific (discussed above), viral, somatically introduced, and temporally inducible systems. Viral Cre, such as adenoviral or lentiviral Cre, in which the Cre gene is packaged into viral particles, can be locally delivered topically or by injection to infect cells and create a regional or clonal knockout of cells within a given area (Jackson et al., 2001Marumoto et al., 2009). Both adeno- and lentiviral vectors have advantages and disadvantages that should be considered in the context of the experiment (DuPage et al., 2009). For example, adenoviral vectors typically induce a significant inflammatory response that can confound interpretations of causation. However, adenoviral infection is transient and does not result in viral genome insertion. Lentiviruses are suitable for infecting nondividing somatic cells while adenovirus requires dividing cells. Lentiviral genome integration can also present a confounding variable due to insertion site mutation, although there is evidence that this may be less of an issue with lentivirus than with other insertional viruses (Gonzalez-Murillo et al., 2008Montini et al., 2009). Nonetheless, the ability to mark a clone by a specific insertion site or long-term expression of a viral gene product also offers a strategy to track tumorigenesis (De Palma et al., 2003Hosoda et al., 2009).

Conditional Cre systems that temporally control Cre expression (e.g., using the tet promoter or ER fusion systems described below) are continuing to be developed and offer the greatest amount of control over gene removal. Two of the most commonly used systems are the tetracycline-inducible system (Gossen and Bujard, 1992) and the tamoxifen-inducible system (Metzger and Chambon, 2001) (see also Bockamp et al., 2008, and references therein). The tetracycline-repressor-based system is composed of a transactivator and an effector. The DNA-binding domain of the Escherichia coli tetR gene fused to the transactivation domain of the herpes simplex virion protein 16 (VP16) gene (tetR/VP16) makes up the tetracycline-controlled transactivator (tTA) that can then be driven by tissue-specific promoters (Baron et al., 1997). The tTA binds to the tetracycline operator (tetO) that controls the activity of the human cytomegalovirus promoter driving conditional gene expression, including Cre to generate conditional knockouts. In this Tet-Off system, tTA is bound by tetracycline, or its more stable analogue doxycycline, inhibiting association with the tetO and blocking gene transcription. In the Tet-On system, the tetracycline-repressor has been mutated (rtTA) such that it is only in the proper conformation for association with tetO when it is bound to tetracycline or doxycycline, thus inducing expression of the gene in the presence of drug. The expression of tTA or rtTA can lead to cytotoxicity, likely due to overexpression of the VP16 transactivator domain and titration of transcription factor machinery (Berger et al., 1990), which has spurred the development of next-generation tTAs (Bockamp et al., 2008). Success using the tetracycline-inducible systems depends on screening transgenic mouse lines for tight repression, good induction, and appropriate levels of repressor expression to avoid secondary toxicity issues.

The tamoxifen-inducible system depends on fusion of the Cre recombinase gene to a mutated ligand-binding domain of the human estrogen receptor (Cre-ER(T)) that is specifically activated by tamoxifen. In the absence of tamoxifen, the ER fusion protein is excluded from the nucleus, but is transported to the nucleus upon binding to tamoxifen where Cre can then recombine DNA. Using these techniques, temporal expression of Cre can be manually controlled by simply delivering or withholding tamoxifen. Other, less commonly used inducible systems include the insect steroid molting hormone ecdysone (No et al., 1996), the progesterone antagonist mifepristone (Ngan et al., 2002), the Lac operator–repressor (Cronin et al., 2001), and the GAL4/UAS system (Wang et al., 1999). While all of these inducible systems have been shown to be functional in the mouse, they differ in their efficiency and leakiness that affects tight control of gene expression. Because recombinase-mediated excision of the gene is irreversible, leaky Cre expression can have substantial and permanent effects that could compromise experimental outcomes.

3. MOUSE MODELS OF RNA INTERFERENCE

Alternatively, loss-of-function studies can use RNA interference (RNAi) to specifically knockdown the expression of target genes posttranscriptionally before the mRNA can be translated into protein (Meister and Tuschl, 2004). Target sequence-specific small interfering RNAs (siRNAs) are short antisense peptides 21–28 nucleotides long. The RNA-induced silencing complex (RISC) recognizes the double-stranded siRNA fragments and cleaves the endogenous complementary messenger RNA (mRNA) that is then rapidly degraded. Though siRNA has been widely used to knockdown expression of target genes, it is limited to transient transfection in vitro. Short hairpin RNA (shRNA) can be used to cause long-term gene knockdown, both in vitro and in vivo. The shRNAs are much longer, generally between 50 and 70 nucleotides in length, allowing them to be transcribed as stable messages in vivo. They are made as a single-strand molecule that then forms a short hairpin tertiary structure, folding in on itself to form a stem–loop structure in vivo. After transcription and folding, the enzyme Dicer cleaves off the loop leaving behind a double-stranded siRNA molecule that can then be recognized by RISC. The shRNA sequences can be cloned into viral vectors to be stably incorporated into the genome for sustained knockdown of target genes.

Several groups have used shRNA-expressing constructs to successfully create RNAi transgenic knockdown animals (Sandy et al., 2005). The shRNA expressing mice can be generated by inserting the promoter-shRNA construct into the mouse genome using normal knockout and transgenic techniques, including embryonic stem cell electroporation as well as lentiviral shRNA transgene injection into the pronucleus of fertilized eggs or the perivitelline space of single-cell mouse embryos. Knockin shRNA animals can also be made using homologous recombination to target the insertion of the shRNA transgene to a specific site, such as the ubiquitously expressed Rosa26 locus. Since shRNAs are promoter-driven, they can be constructed using inducible, reversible, or tissue-specific promoters (Dickins et al., 2007), or with lox-STOP-lox (LSL)(described in Section II.B.3), or other Cre-lox-regulated control elements (Tiscornia et al., 2004Ventura et al., 2004). Recently, Cre-regulated RNAi was achieved by combining a Cre-regulated shRNA transgene within an FLP expression cassette such that the RNAi transgene and a green fluorescent protein (GFP) reporter are inverted upon recombination to induce expression (Stern et al., 2008). Although these methods have shown promise, they do have limitations, especially regarding the incomplete penetrance of the silencing, possibly due to varying expression of shRNAs from retroviral constructs (reviewed in Westbrook et al., 2005).

As the details of RNAi in mammalian cells became better understood, new shRNAs have been developed that take advantage of natural miRNA biogenesis (Silva et al., 2005Westbrook et al., 2005). Artificial vectors are designed that contain the shRNA sequence within miR30 (Silva et al., 2005), a naturally occurring, well-characterized microRNA transcript (Cullen, 2004Zeng et al., 2002). The expression of mature shRNA and subsequent knockdown of the target gene are greater when expressed from the endogenous miR30 promoter than from exogenous promoters, due to more efficient microRNA biogenesis (Westbrook et al., 2005). Current studies are focusing on utilizing lentiviral vectors to deliver shRNA-miRs and generate in vivo mRNA knockdown (Singer and Verma, 2008Stegmeier et al., 2005).

Since RNAi results in a knockdown of gene expression rather than complete inhibition, RNAi transgenic animals are likely to be hypomorphs as opposed to null phenocopies. RNAi is also susceptible to nonspecific effects and off-target repression of other genes. Nevertheless, RNAi animal models are a powerful approach to studying gene knockdown, because targeting constructs can be rapidly designed and tested, genes that give a lethal phenotype when knocked out can be studied without needing the complex genetic crosses involved in conditional knockouts, and model organisms can be studied for which knockout technology has not been developed, such as the worm and rat.

4. MOUSE SINGLE-CELL KNOCKOUTS

Human cancers are spontaneous diseases caused by accumulation of somatic mutations that arise from single mutant cells. Sporadic tumors developing in a wild-type or heterozygous microenvironment can develop differently compared to those developing in a homogeneous genetically mutant environment. Therefore, conventional knockout, knockin, and transgenic GEM tumor models, even tissue-specific or cell-specific models, in which the deletion or mutation occurs in a whole organ or animal, are limited in that they cannot reproduce the clonal nature of human cancer (Fig. 1). Tumors from heterozygous mouse models, such as p53+/− mice, arise from clonal expansion of cells that have sporadically lost the wild-type allele of the tumor suppressor. While these loss of heterozygosity-dependent models more closely recapitulate the clonal nature of human tumors, the incidence and spectrum of spontaneous tumor development is dependent on variables that affect the stochastic nature of the loss of heterozygosity event (Attardi and Donehower, 2005). Single-cell knockout approaches can be used to control tumor-initiating events by driving genetic events in individual cells, generating homozygous mutant cells on a heterozygous mutant background at very low frequency to create in vivo mosaics (reviewed in Lozano and Behringer, 2007). The clonal nature of single-cell knockouts can be used to more clearly elucidate the mechanisms of cellular processes such as cell cycle control and tumor formation in the context of a more normal microenvironment, potentially redefining the roles of genes previously studied in other GEM models.Fig. 1

Genetically engineered mouse models of cancer. Technology is now available to control the overexpression and loss of gene expression, shown in gray, (A) throughout the mouse, as is often the case with classic knockouts, constitutive transgenics, or in some cases knockins; (B) in a specific organ (e.g., the heart) or at a specific time in development, using conditional knockouts or conditional overexpression transgenics or knockins; and (C) in single-cell knockouts through sporadic loss events.

Sporadic single-cell knockout mouse models have been created using genetic techniques derived from Drosophila systems. Recombinase enzymes can be used under tissue specific and temporal controls to induce rare G2 mitotic interchromosomal recombination events between FRT or loxP sites residing on homologous mouse chromosomes, yielding a wild-type and a mutant daughter cell amidst a population of heterozygous cells. During the G2 phase of the cell cycle, homologous chromosomes align in preparation for mitosis. While aligned, the Cre or FLP recombinase can recombine the homologues such that regions of the chromosome are homozygosed in the daughter cells. For example, Wang et al. (2007) used a ubiquitously expressed recombinase to induce rare interchromosomal recombination events between FRT or loxP sites centromeric to p53 and generate single cell p53−/− clones (Wang et al., 2007) within a p53+/− environment. The sporadic p53 knockout phenotype more closely resembles human Li–Fraumeni syndrome, in which the human p53 gene is mutated, than previous models. This system can be used to target loss of heterozygosity events to particular cells or tissues using different Cre or FLP recombinase mouse lines.

The mosaic analysis with double markers (MADM) approach described by Muzumdar et al.(2007) uses a tissue-specific Cre/loxP system to induce sporadic mitotic interchromosomal recombination and knockout target genes while simultaneously labeling resultant daughter cells (Muzumdar et al., 2007). Homozygous knockout and wild-type daughter cells are identified with red and green markers and further distinguished from neighboring yellow heterozygous cells. Thus, MADM allows progeny from single loss of heterozygosity events to be tracked and distinguished from normal cells within the microenvironment.

B. Studying Gain of Gene Function in Mouse Models

1. MOUSE CONSTITUTIVE TRANSGENIC MODELS

Gain-of-function studies are often used to study oncogenes in mouse models. Transgenic or knockin animals constitutively overexpressing an oncogene can be used to study how the oncogene drives tumorigenesis in vivo. Transgenic animals have been very useful in studying many oncogenes and for creating Cre-expressing mice and other inducible systems. Transgenic animals are created by the pronuclear injection of transgenes directly into fertilized oocytes, followed by implantation into pseudopregnant females (Macleod and Jacks, 1999Porret et al., 2006). The transgene is randomly incorporated into the genome and thus can incorporate into a gene necessary for development or fertility, causing deleterious effects and limiting the usefulness of the transgenic model. Furthermore, the epigenetic regulation of gene expression in the region surrounding the transgene integration can affect transgene expression levels and often result in silencing. Therefore, multiple founders must be screened to confirm adequate and specific expression of the transgene.

In addition to insertion site effects, transgenes often do not show the expression patterns of the endogenous gene promoter, due to the lack of regulatory sequences that can be located several kilobases from the coding region of the gene of interest. Bacterial artificial chromosomes (BAC) contain large fragments of genomic DNA (150–300 kb), and therefore can accommodate entire genes, including the cis-regulatory sequences, allowing for increased fidelity of gene expression (Shizuya et al., 1992). Under control of the gene’s regulatory elements, the gene of interest will likely show the endogenous expression pattern (Swing and Sharan, 2004). Due to their large size, BACs are more likely to insert at a lower copy number, increasing the chances of single copy transgenics, which will be expressed at endogenous levels due to the regulatory sequences. Although the effects of additional genes on the BAC must be considered, molecular analysis of the BAC can be used to eliminate the possibility of other known genes being present, as well as confirm the presence of the entire coding sequence of the gene of interest (Swing and Sharan, 2004). With the development of recombineering technology (Sharan et al., 2009), large BACs can be rapidly modified and used to generate transgenic mice to compare the phenotypic effects of precise changes in a gene (Chang et al., 2009) or regulatory sequence.

2. MOUSE GENE KNOCKIN MODELS

To circumvent transgenic limitations associated with random insertion, knockin mice are created by inserting a gene of interest into a specific region of the genome using homologous recombination techniques, much like those used when creating knockouts. The Rosa26 locus is commonly used as an insertion site for knockin animals because it is devoid of essential genes and allows for good expression of the transgene (Friedrich and Soriano, 1991). While transgenics have the potential for multiple insertions of the transgene, knockin animals carry only one copy of the transgene. Knockin approaches can also be used to replace a normal gene copy with a mutated version of the gene to examine specific mutational events in the context of normal control of the gene (e.g., Johnson et al., 2001Lang et al., 2004).

3. MOUSE CONDITIONAL OVEREXPRESSION MODELS

Transgenic and knockin expression of deleterious genes may lead to lethality, sterility, and developmental defects that impede study of the gene of interest in cancer, as seen with many conventional knockouts. Therefore, spatial and temporal control of transgene and knockin expression may be necessary to circumvent these limitations. Conditional transgenics and conditional knockins can be created using tissue-specific promoters to constitutively drive expression or created by inserting a strong translational and transcriptional termination (STOP) sequence flanked by loxP or FRT sites in between the promoter sequence and the gene of interest (Lakso et al., 1992). Examples of commonly used STOP cassettes are the lox-STOP-lox (LSL) in which multiple STOP sequences are arrayed between loxP sites (Jackson et al., 2001) and the NEO-STOP cassette in which the neomycin resistance gene and a STOP sequence is inserted between loxP sites (Dragatsis and Zeitlin, 2001). The presence of the STOP sequence blocks transcription of the gene of interest. However, in the presence of Cre or FLP recombinase, the STOP cassette is removed, allowing expression. Since gene expression is dependent on excision of the STOP cassette and recombinase expression, gene expression can be spatially, temporally, and inducibly controlled with the myriad of Cre systems. STOP cassettes can be leaky and thus must be assessed empirically. STOP technology has greatly expanded the opportunities for controlling gene expression in gain-of-function studies. For example, initial K-rasG12D transgenic models of lung carcinoma showed a large variance in tumor penetrance, as well as death due to respiratory failure prior to tumor progression (Johnson et al., 2001). Treatment of the conditional LSL-K-ras G12D strain with virally-infected allowed control of the timing, location, and number of tumors, therefore allowing the study of initiation and early-stage pulmonary adenocarcinoma (Jackson et al., 2001).

C. Modeling Chromosomal Translocations in Mice

1. TRANSLOCATOR MICE

Human cancers, such as leukemia, often involve chromosomal translocations that lead to fusion proteins with new functions that play a role in tumorigenesis. Mouse models have been developed to study de novo protein functions as a result of chromosomal translocations. Basic approaches such as transgenic or knockin insertion of fusion genes resulting from human chromosomal translocations can lead to embryonic lethality or nonauthentic phenotypes (Prosser and Bradley, 2003). While this can be circumvented with conditional transgenic or conditional knockin approaches, these approaches still suffer from the limitation that they produce a population of cells expressing the fusion gene within the specified tissue compartment and do not recapitulate the clonal onset of the disease in humans. Translocation- derived cancers develop from a single cell that undergoes chromosomal translocation and then clonally expands in an environment that is initially composed of normal cells. Therefore, a more physiological approach to mimic chromosomal translocations in human disease is through induction of the chromosomal translocation event in mice (Forster et al., 20032005aProsser and Bradley, 2003). This can be accomplished by inserting loxP sites in trans at chromosomal breakpoints observed in human cancers, resulting in Cre-dependent interchromosomal reciprocal translocation (Forster et al., 2003van der Weyden et al., 2009) (Fig. 2). This is similar to the approach used for single-cell knockouts, described above, except that the loxP sites are on different chromosomes, rather than homologous chromosomes. As with single-cell knockouts, the interchromosomal recombination is a rare event and as such more closely mimics the situation seen in human cancers. If the chromosomal translocation results in a fusion protein with de novo function animals may develop cancers similar to those in humans. For example in a mouse model of the MLL-ENL translocation fusion protein, the animals can develop a leukemia very similar to humans, with high penetrance and most likely from a clonal origin (Forster et al., 2003). Since not all mouse models using this approach successfully develop cancer (Buchholz et al., 2000Collins et al., 2000), it is very likely that cancer development resulting fromCre-driven interchromosomal reciprocal translocations are dependent on the expression level and distribution of the Cre transgene expression (Forster et al., 2003). This could reflect the importance of the translocations occurring in the proper cell type, compartment and/or fusion gene sequence for cancer development. The success of this approach also depends on the compatibility of the transcriptional orientation of the two mouse chromosome genes.Fig. 2

Modeling chromosomal rearrangements in mouse models of cancer. (A) Interchromosomal rearrangements are generated in translocator mice by inserting single flox recombination sites on different chromosomes. Recombination between the sites exchanges pieces of the two chromosomes. (B) Intrachromosomal rearrangements are generated by inserting two flox recombination sites on the same chromosome oriented in opposite directions. By using flox sites with two different mutations, the resulting flox sites on the inverted chromosome cannot recombine and the inversion becomes stable in the presence of Cre enzyme.

2. INVERTOR MICE

Since not all fusion genes arising from chromosomal translocations in human cancers are compatible with the Cre-driven interchromosomal translocation approach, a conditional invertor approach has been used to study the de novo function of fusion genes in mice (Forster et al., 2005bLobato et al., 2008). Chromosomal inversions occur when two breaks occur within the same chromosome and the genetic material in between the breaks is inverted. This is achieved using loxP sites orientated in opposite directions at the breakpoint sites resulting in inversion of the flanked DNA sequence containing the second half of the fusion protein (Fig. 2). Thus, after inversion, the sequence for the entire fusion protein is in frame allowing for expression of the de novo protein (Forster et al., 2005b).Fig. 3

Measuring mutagenesis levels using the Big Blue® mouse. Big Blue® mice have been engineered to carry many copies (30–40) of the bacterial lacI gene, encoding the LacI repressor protein. When Big Blue® mice are subjected to mutagens or crossed to mice with a mutator phenotype, the lacI gene is mutated. Genomic DNA is isolated from the mice and infected into lacI bacteria via lambda phage. Bacteria carrying unmutated lacI continue to repress the lacZ gene, whereas bacteria carrying mutant lacI expressed lacZ and form blue colonies on X-gal indicator plates. The ratio of blue to clear bacterial colonies is a measure of mutagenesis.

Since recombination products between inverted and wild-type chromosomes do not produce viable gametes, inverted regions suppress recombination. Therefore, inversions can also be used as genetic tools to maintain the linkage of mutant loci on chromosomes. Inverted regions carrying phenotypic markers, such as the dominant K14 Agouti coat-color gene, are known as balancer chromosomes and can be used for large scale mutagenesis screens, similar to experiments in Drosophila (van der Weyden et al., 2009).

D. Studying the Mechanisms and Timing of Tumorigenesis

1. MOLECULAR-GENETIC IMAGING

Molecular-genetic imaging utilizes reporter transgenes to visualize proteomic, metabolic, cellular, or genetic events in vivo. The three major methods of animal imaging are optical, magnetic resonance imaging, and nuclear medicine (for review see Kang and Chung, 2008Lyons, 2005). Although conventional magnetic resonance imaging, computed tomography, ultra-sound, and radiography imaging modalities have the advantage of not being dependent on genetically engineered reporter mice, they are limited to visualization of anatomic morphological changes. Optical imaging using genetically engineered reporters can be used to detect rapid molecular and genetic changes, including those that occur in response to drug treatment or during early stages of carcinogenesis before the onset of anatomical change. Furthermore, fluorescent and bioluminescent optical imaging modalities are sensitive, less expensive, less time consuming, and more convenient and user friendly than other imaging techniques.

Reporter transgenes can be made to express reporter genes under the control of specific promoter/enhancer elements to assess promoter activation in vivo during tumorigenesis or drug treatment. For example, transgenic reporter mice expressing luciferase under control of the estrogen receptor enhancer have been used to study the dynamics of estrogen receptor activity in response to various ligands and drug treatments (Maggi et al., 2004). Furthermore, transgenic reporter mice can be crossed onto mouse models of human cancer to visualize tumor growth in vivo. For example, transgenic mice expressing firefly luciferase under control of the human E2F1 promoter, which is active during proliferation, can be crossed with mouse cancer models to characterize tumor cell proliferation in vivo (Momota and Holland, 2005).

Firefly luciferase is the most commonly used reporter gene for bioluminescence in mice, although renilla luciferase has some advantages in that it does not require ATP, and so may be less dependent on changes in the metabolic state of cells. Bioluminescence can be detected within 10 min of intraperitoneal injection of the luciferase substrate luciferin and multiple animals can be imaged together leading to a relatively quick turn-around time. Although bioluminescence has the advantage of extremely low background signal and high sensitivity, photon emissions are attenuated by hair color and the amount of tissue between the tumor and the detector, making it difficult to accurately compare tumors at varying depths and in different coat color backgrounds.

Green fluorescent protein (GFP) and red fluorescent protein (RFP) are commonly used as reporters for in vivo imaging (Hoffman, 2009). Since there are five different fluorescent proteins with unique emission wavelengths between 450 and 650 nm, simultaneous imaging of multiple fluorescent reporters in one animal is possible. Since fluorescence requires excitation, tissue scattering and absorption by endogenous fluorochromes can limit the sensitivity of in vivo fluorescence imaging.

2. RECOMBINATION REPORTERS AND LINEAGE TRACING

Cre- and FLP-dependent reporters are useful not only for determining where site-specific recombination of conditional knockout and transgenes occur (Soriano, 1999) but also for lineage tracing studies (Chai et al., 2000Jiang et al., 2000). A ubiquitous promoter drives a conditional reporter transgene, such as a lacZ or GFP reporter, and is interrupted by the presence of a STOP cassette flanked by loxP or FRT sites. When conditional reporter transgenic mice are crossed with site-specific Cre or FLP mice driven by cell-type specific promoters, the STOP cassette of the conditional transgene is removed only in Cre- or FLP-expressing cells and the reporter continues to be expressed in the progeny of the recombined cells. For example, by using mice carrying a transgene for Cre or FLP expression in progenitor cells, the reporter expression is turned on in the progenitor cells and inherited by all subsequent progeny.

Yamamoto et al. (2009) have recently described a sophisticated dual reporter R26NZG mouse line for Cre- and FLP-dependent lineage analysis. The R26NZG reporter mice were constructed by knocking the NZG reporter cassette into the Rosa26 locus. The NZG reporter cassette consists of the CAG promoter followed by the neo-STOP cassette, the lacZ reporter gene flanked by FRT sites, and finally the enhanced GFP (EGFP) gene. In the absence of Cre and FLP recombinases, neither reporter is expressed. In the presence of Cre recombinase, the PGK-neo cassette is removed and lacZ expression is turned on; however, EGFP expression is still turned off due to a STOP codon in the preceding lacZ sequence. In the subsequent presence of FLP recombinase, the lacZ sequence is removed and EGFP is expressed. Thus, lacZ expression is turned on following Cre-expression and EGFP expression is only turned on after expression of both Cre and FLP. Although there is an increasing breadth of Cre-expressing lines becoming available, the advantages of the dual reporter technology are currently limited by the smaller availability and lower efficiency of FLP-expressing lines. Nevertheless, the R26NZG mouse line in conjunction with mouse cancer models is likely to be a powerful tool in future lineage-tracing studies of tumor initiating cells.

3. GENOMIC INSTABILITY

Human cancers often show abnormal karyotypes that include, most prevalently, aneuploidy and multiple chromosomal translocations. Genomic instability is believed to contribute to human cancer by increasing the number and rate of genetic changes seen in a tumor, thus accelerating cancer development. However, the identification of the “driver” mutations necessary for tumorigenesis is confounded by the presence of many “passenger” alterations that do not contribute to tumorigenesis. Because mouse models of cancer are predisposed to tumorigenesis by engineered mutations, they can result in tumors with far fewer chromosomal aberrations, giving a means to separate driver and passenger mutations through cross-species comparisons (Peeper and Berns, 2006). For example, by comparing copy number aberrations found in mouse and human melanomas and liver tumors, new oncogenes associated with chromosomal aberrations were identified (Kim et al., 2006Zender et al., 2006). Between mouse models, differences in genome stability can correlate to differences in tumor subtype, as shown in mammary tumor models with either p53 loss or p53 and Brca1 loss (Liu et al., 2007). Both tumor models develop mammary tumors; however, the p53;Brca1 null tumors show greatly increased genomic instability and more closely resemble human BRCA1 mutant tumors molecularly, whereas the p53 null tumors show very few chromosomal alterations and more closely resemble human estrogen receptor mutant breast cancer.

While there are advantages to the reduced mutation number seen in many mouse tumors, the mutations driving mouse tumors serve to limit the range of mutations that develop. In this way, specific mouse models may filter out human “driver” mutations that are important in a different genetic context, in addition to filtering out the “passenger” mutations in cross-species comparisons. To ensure a more unbiased approach, mice with increased genomic instability may be better models for comparative oncogenomics (Maser et al., 2007). Multiple approaches have been developed to drive chromosomal instability in mouse models and better mimic the structural abnormalities seen in human tumors.

In humans, loss of telomeres as a part of aging is believed to contribute to cancer incidence due to genomic changes and chromosomal fusions. Mouse chromosomes have much longer telomeres than humans and sustained telomerase expression. This may explain the reduced number of chromosomal abnormalities in mouse tumors, as well as the difference in tumor types seen, as epithelial cancers are believed to require more aberrations than the mesenchymal tumors that are more commonly seen in mice. In order to examine the importance of telomeres in cancer development, telomerase knockout mice were generated (Lee et al., 1998). Early generations of telomerase-deficient mice show initial resistance to tumorigenesis, due to apoptosis or cell-cycle arrest (Chin et al., 1999Karlseder et al., 1999). As mice are bred over successive generations, the long mouse telomeres erode in the absence of telomerase, and in late generations tumorigenesis occurs through increased chromosome instability. Interestingly, mice with both telomere deficiency and loss of p53 show both an increased rate of tumorigenesis and a shift in tumor spectrum toward the epithelial cancers seen in humans (Artandi et al., 2000). Additionally, the chromosomal changes seen in these mouse epithelial cancers mimic those often seen in human tumors, suggesting that increasing genomic instability contributes toward a more accurate model of human tumor development (O’Hagan et al., 2002).

Another system to investigate genomic instability is the mouse model of Bloom syndrome, a genetic disorder involving mutation of the BLM helicase gene. Bloom syndrome is associated with both genomic instability and increased risk of a wide variety of malignancies (German, 1993). A conditional mouse model of Blm results in mice with an increased rate of mitotic recombination, leading to an increased loss of heterozygosity (Luo et al., 2000) resulting in increased tumorigenicity (Goss et al., 2002Luo et al., 2000). The increased genomic instability of the Blm mutant mouse and resulting loss of heterozygosity is being used to identify novel tumor suppressor genes in mice (discussed below) (Suzuki et al., 2006). In addition to the mouse model of Bloom syndrome, many models have been developed targeting specific steps in the DNA damage response and DNA repair, allowing researchers to study how DNA instability/damage alters tumor phenotypes (e.g., see Wei et al. (2002) for models of DNA mismatch repair genes; see Hande (2004) for DNA damage signaling genes; see Barlow et al. (1996) and Westphal et al. (1997) for Atm mutant models).

In order to assay in vivo for genetic instability caused by toxins and carcinogens, many groups make use of the commercially available Big Blue® mouse and Muta™ Mouse animal models (Stratagene, La Jolla, CA). Both models harbor multiple copies of a mutational target within a recombinant phage that can be extracted from the genomic DNA of mouse tissues. For a review of animal mutation models, see Nohmi et al. (2000). In the Big Blue® Transgenic Mouse Mutagenesis Assay System, 30–40 copies of the lacI repressor gene have been stably integrated in tandem into the mouse genome (Kohler et al., 1991). Big Blue® mice are either treated with mutagenic compounds or crossed with genetically engineered mouse models and used to determine changes in chromosomal stability by measuring mutagenesis of the lacI gene (Fig. 3). Mutation of lacI disables its ability to repress lacZ expression when the extracted phage is infected into host bacteria. LacZ is measured in bacteria by growing on substrate that turns blue in the presence of enzymatic activity of the product of the LacZ gene. The ratio of blue to colorless plaques is used as a measure of mutagenicity. The Muta™Mouse uses a similar system, but relies on negative selection in measuring mutagenesis of lacZ, rather than lacI (Gossen et al., 1989). While these systems rely on colorometric differentiation to identify mutations, addition of cII selection measures cell survival using bacteriophage lambda lytic and lysogenic multiplication cycles. Using the cII system in conjunction with the Big Blue® or Muta™Mouse can lead to better quantification of mutation frequencies, reduction of false positives, and reduced cost.

E. Discovery of Novel Tumor Genes

1. INSERTIONAL MUTAGENESIS

Forward genetic screens can identify cancer-causing genes by unbiased, whole genome mutation screening in mouse tumors. Chemical mutagenesis has been used to produce a high frequency of mutations in the mouse. Phenotypic screens of these mutant mice identify cancer-related phenotypes, but gene identification is very difficult. Alternatively, insertional mutagenesis allows for identification of mutation sites using the integrant as a molecular tag that marks the region of the genome important for tumorigenesis.

Retroviruses have been used as insertional mutagens to identify cancer-causing genes since it was determined that certain strains of mice develop leukemia while other strains develop mammary tumors due to ecotropic retroviral infection (Gross, 1978van Lohuizen and Berns, 1990). The cancers in these mouse strains are not due to acute transforming retroviruses that express viral oncogenes such as v-abl or v-myb (reviewed in Lipsick and Wang, 1999Shore et al., 2002). Rather, these slow-transforming retroviruses, such as murine leukemia virus (MuLV) and mouse mammary tumor virus (MMTV), lead to transformation by acting as insertional mutagens that integrate into the genome and disrupt the normal expression of oncogenes and tumor suppressor genes.

Retroviral integration is regulated by the long terminal repeats (LTRs) that contain enhancer and promoter regions, as well as start and stop sites for transcription of retroviral genes. Retroviruses disrupt gene expression by four basic mechanisms, dependent on the viral location and orientation relative to the gene (Jonkers and Berns, 1996). Viral integrations can cause gene mutations by promoter insertion, disruption of RNA or protein destabilization motifs, viral enhancement of transcription, or premature termination of genes (Jonkers and Berns, 1996, reviewed in Uren et al., 2005) (Fig. 4). For example, proviral insertion in the gene promoter region (Fig. 4A) places the gene under the control of the LTR promoter region and has been found to upregulate the oncogenes LCK, N-Ras, and E2a in MuLV-induced leukemia, as well the novel candidate oncogene Evi-1 (Morishita et al., 1988, reviewed in Hirai et al., 2001). Disruption of 3′ UTR sequences due to proviral integration causes activation of N-myc in MuLV-induced leukemia (van Lohuizen et al., 1989) (Fig. 4B). Wnt1 and Fgf3 are commonly overexpressed in MMTV-induced mammary tumors, due to activation by enhancer sequences of the integrated MMTV provirus, often at a distance up to 25 kb 5′ or 3′ of the gene (Fig. 4C). Although tumor suppressor inactivation is less common, mice infected with Friend leukemia virus have inactivating viral integrations within the p53 gene (Ben David et al., 1988Mowat et al., 1985), and Nf1 is often inactivated in MuLV leukemia (Cho et al., 1995Largaespada et al., 1995) (Fig. 4D). By utilizing the virus as a molecular marker, many groups have cloned sites of integration to identify genes involved in specific types of cancer. High-throughput methods of cloning using PCR-based methods have allowed the identification of a large number of sites, compiled in the Retroviral Tagged Cancer Gene Database (http://rtcgd.ncifcrf.gov/) (Akagi et al., 2004). Common sites of integration, sites that are disrupted in multiple independently derived tumors, are more likely to play a causal role in tumor progression pathways.Open in a separate windowFig. 4

Mechanisms of retroviral mutagenesis. Because of the presence of enhancer and promoter sequences in the LTR regions of the retrovirus, there are four basic mechanisms by which retroviruses disrupt gene function. (A) Retroviruses can insert into gene promoters, providing a stronger promoter signal from the LTR and increasing the level of gene expression. (B) Retroviruses can insert into the 3′UTR of a gene, changing mRNA stability thus altering the amount of protein translated. (C) Retroviruses can act as enhancers of gene transcription when inserted upstream, or downstream, of the gene. (D) Retroviruses can mutate genes by inserting into an exon and causing premature truncation of the protein.

One drawback of retroviral insertion is that the majority of tumors are caused by gene activation of proto-oncogenes, suggesting that this model is inefficient at identifying tumor suppressor genes. However, as technologies improve and greater numbers of insertion sites are rapidly and completely identified (reviewed in Kool and Berns, 2009), the increased number of insertions sites per tumor will improve the frequency of identifying tumor suppressor genes (Uren et al., 2008), especially those with haploinsufficient tumor suppressor functions. Additionally, in order to increase loss of function by viral insertion, screening can be combined with the Blm mutant mouse (Suzuki et al., 2006) that has a higher rate of mitotic recombination and increased rate of loss of heterozygosity (discussed above) (Luo et al., 2000). When the Blm mutation was crossed onto mouse strains prone to retroviral insertion-mediated B-cell lymphomas, cancer latency was reduced, and loss of heterozygosity was found in multiple known and novel tumor suppressor genes (Suzuki et al., 2006). The increased efficiency of retroviral oncogene activation compared to tumor suppressor gene inactivation may be due to the preference of retroviruses to integrate in the 5′ end of genes (Johnson et al., 2001). This promoter region preference may also indicate that only specific regions of the genome are accessible to viral integration, suggesting that whole genome coverage by viral insertional mutagenesis may be impossible. Although retroviral integration has been useful in identifying candidate cancer genes, these models generally only develop hematopoietic and mammary tumors.

2. MUTAGENESIS BY TRANSPOSONS

In an attempt to utilize somatic mutagenesis beyond hematopoietic and mammary tumorigenesis, and perhaps to more fully cover the genome with mutagenic events, DNA transposons are now being used as a mobile genetic tag in the mouse. Transposable elements have been used in multiple organisms for years, but the first active transposon in the mouse is Sleeping Beauty, a member of the Tc1/mariner family, which was reconstructed by directed mutagenesis from a dormant element (Ivics et al., 1997). More recently, piggyBac has been demonstrated to be an efficient transposable element in mammalian cells in vitro and in vivo (Ding et al., 2005Wilson et al., 2007). Both systems consist of two elements: the transposon (the mobile DNA sequence) and transposase (the enzyme that mobilizes it) (Fig. 5). The T2/onc (Collier et al., 2005) and T2/onc2 (Collier et al., 2005Dupuy et al., 2005Sleeping Beauty transposons are currently the most commonly used for somatic mutagenesis in cancer gene identification experiments. These transposons contain splice acceptors followed by polyadenylation sequences in both orientations in order to generate loss-of-function mutations, as well as retroviral LTR enhancer/promoter elements to drive overexpression of nearby genes. Two groups have shown the ability of Sleeping Beauty to mobilize in somatic cells, leading to tumor formation (Collier et al., 2005Dupuy et al., 2005). Although the transposons are similar, the transgenic lines vary greatly in copy number. The lines contain multicopy arrays of the transposons at the initial site of insertion; T2/onc lines contain approximately 25 copies (Collier et al., 2005), whereas T2/onc2 transgenic lines contain 150–350 copies of the transposon (Dupuy et al., 2005). The differences in copy number are believed to contribute to the phenotype seen in the mice (Collier et al., 2005Dupuy et al., 2005). The T2/onc2 lines cause tumorigenesis in an otherwise wild-type background; however, these mice develop aggressive hematopoietic tumors that kill the mice prior to the development of any solid tumors (Dupuy et al., 2005). Although the T2/onc line does not cause tumorigenesis in wild-type mice, a wide variety of tumors form when crossed onto a p19Arf deficient background, including osteosarcomas, soft tissue sarcomas, lymphomas, malignant meningiomas, myeloid leukemia, and pulmonary adenocarcinoma (Collier et al., 2005), similar to the tumor spectrum seen in p19Arf−/− mice on the C57BL/6 genetic background (Kamijo et al., 1999). Crossing different transposon and transposase transgenic lines allowed the system to be refined to generate high-penetrance hematopoeitic tumors without secondary effects of embryonic lethality (Collier et al., 2009). Furthermore, by combining a Cre-inducible Sleeping Beauty transposase with a third generation Sleeping Beauty transposon line (T2/Onc3), Dupuy et al. (2009) were able to successfully control the extent of hematopoeitic tumors to generate a wide variety of mouse solid tumors by insertional mutagenesis. Because Sleeping Beauty shows preferences for local integration near the original transposon site, the system may have some limitations for saturating the genome with mutations. However, the differences in accessibility of the genome to mutation by DNA transposons and mouse retroviruses may allow saturating mutagenesis through a combination of the two techniques, particularly as mouse panels are developed with DNA transposon arrays integrated systematically onto the different mouse chromosomes.Open in a separate windowFig. 5

Transposon insertional mutagenesis with Sleeping Beauty. Mice genetically engineered to carry arrays of transposon insertions are crossed to mice genetically engineered to express the transposase. (A) The Sleeping Beauty T2/Onc2 transposon structure is shown (Dupuy et al., 2005) in which the transposon carries two splice acceptors (SA) in opposite directions, a bidirectional poly(A) tail (pA), and the MCSV LTR (MCSV) with a splice donor (SD) that can act as a promoter/enhancer of gene expression. These sequences allow for both gain-of-function and loss-of-function mutations when inserted into a gene locus. The transposon is carried by transgenic mice in arrays of 150–350 transposon copies. Transposon-carrying mice are crossed to mice expressing the Sleeping Beauty (SB) transposase from a variety of different promoters to induce mutagenesis in different tissues. (B) The progeny of transposon and transposase mice express the tranposase and cause both intrachromosomal and interchromosomal hopping of the transposon to mutate the genome.

In order to more accurately model specific tumor types, a conditional Sleeping Beauty system has been developed by knocking a lox-STOP-lox SB11 transposase allele into the Rosa26 locus (Dupuy et al., 2009Keng et al., 2009). Expression of the Sleeping Beauty transposase can therefore be activated using a tissue-specific Cre to drive transposition in the tissue and developmental stage of interest. A model of hepatocellular carcinomas was developed by crossing the Sleeping Beauty line to a hepatocyte-specific albumin-Cre (Keng et al., 2009), and a colorectal cancer mouse model was developed by crossing to a Villin-Cre transgenic mouse line that expresses Cre only in the epithelial cells of the gastrointestinal tract (Starr et al., 2009). Analysis of the insertion sites in both models identified multiple common insertion sites near genes known to be mutated in hepatocellular carcinoma and colorectal cancer, as well as genes not previously associated with these cancers (Keng et al., 2009Starr et al., 2009). These models demonstrate the ability of transposons to identify relevant cancer-causing genes, in a tissue-specific manner in solid tumors.

3. GENETIC MODIFIER SCREENS

Many of the genetic mutations that have been identified as cancer causing are rare mutations that have highly penetrant effects on tumor susceptibility. Studies have shown that more common variants with subtle effects, such as polymorphisms seen in different mouse strains, also contribute to tumorigenesis. Different inbred strains show large differences in susceptibility to different tumor types, confirming the ability of low-penetrance polymorphisms to modify cancer risk (reviewed in Dragani, 2003). Similarly, the analysis of multiple human studies has shown that the majority of cancers occur in individuals with a genetic predisposition (reviewed in Demant, 2003). These studies suggest that the combined contributions of low-penetrance genes have a larger effect on cancer morbidity and mortality than the rare mutations that have been identified (reviewed in Demant, 2003). Modifier genes have been seen to affect a variety of tumorigenic phenotypes including overall resistance, metastatic ability, angiogenic potential, multiplicity, size, and survival.

As with quantitative trait loci (QTL) screens in mouse models of many human diseases (Hunter and Crawford, 2008Peters et al., 2007), modifier screens identify cancer susceptibility loci in the mouse by taking advantage of the known phenotypic differences between strains. The sequencing of the genome and the identification of genetic markers, such as simple sequence length polymorphisms, microsatellite markers, and single nucleotide polymorphisms (SNPs), have increased the ability to identify these low-penetrance modifier genes. Generally, modifier screening is based on crossing two parental strains, one resistant and one highly susceptible to a specific tumor, then backcrossing or intercrossing the F1 progeny to identify those genetic regions which affect the tumorigenic phenotype in a statistically significant way (Fig. 6A).Open in a separate windowFig. 6

Genomic tools for mapping cancer modifiers in mouse. (A) Classic mapping approaches have used two generations of breeding to generate recombinants in the genome, either through F2 intercrosses or backcrosses. Individual mice are then genotyped at polymorphic loci between the resistant and susceptible strain to correlate the genotype to the modifier phenotype. (B) Reference strain panels, such as chromosome substitution strains, recombinant inbred lines, and the Collaborative Cross, provide stable, genotyped recombinants and can be compared for their cancer phenotype to directly map modifiers, or can be crossed to genetically engineered mouse models of cancer to map modifiers in the first generation.

Even with the increase of genetic markers, many modifier loci have been found but few specific genes have been identified due to the difficulty of narrowing down a genetic locus to a single gene via positional cloning. Recent advances are being utilized to increase the ability to identify the specific genes that underlie QTL, including cancer modifiers. These include both genomic and animal resources, as well as new techniques and analytical tools (reviewed in Flint et al., 2005). Some current studies have taken advantage of recombinant inbred strains (Fig. 6B), panels generated from an initial intercross of two inbred strains, followed by repeated brother–sister matings, which result in each recombinant inbred strain containing unique combinations of the progenitor genomes. These strains have been a useful resource for mapping by contrasting phenotypes and genotypes between individual recombinant inbred strains, but their use in establishing linkage to low penetrance genes is limited by both the small set size of available recombinant inbred panels, and the small number of different recombinant inbred panels that have been generated (Demant, 2003Flint et al., 2005). A more recent development has been the use of chromosome substitution strains (Fig. 6B), in which one chromosome comes from a different strain background than the other 20. This contributes to increased QTL detection by reducing potential epistatic interactions between the modifier of interest and modifiers on other chromosomes, and allowing initial studies of modifier function (Flint et al., 2005Nadeau et al., 2000). Additionally, backcrossing the chromosome substitution strains can further increase resolution of modifier mapping (Hunter and Crawford, 2008).

A current proposal to further ease the difficulty in cloning modifier genes is an expansion of the idea of the recombinant inbred strain. This Collaborative Cross resource, suggested by the Complex Trait Consortium (Churchill et al., 2004) is currently being generated through the collaboration of multiple groups (Chesler et al., 2008Iraqi et al., 2008Morahan et al., 2008). The Collaborative Cross is a panel of an estimated 1000 recombinant inbred lines being generated, following a specific breeding scheme, from eight divergent founder strains A/J, C57BL/6J, 129S1/SvImJ, NOD/LtJ, NZO/HiLtJ, CAST/Ei, PWK/PhJ, and WSB/EiJ (Chesler et al., 2008Iraqi et al., 2008Morahan et al., 2008). Each new recombinant inbred line will be genotyped initially, resulting in a large number of genetically defined strains to be used in comparative studies (Fig. 6B) (Churchill et al., 2004). The Collaborative Cross, when fully inbred, will have a QTL mapping resolution of approximately 1 MB (Broman, 2005Valdar et al., 2006), much higher than is currently possible. Because many inbred strains are closely related and only differ in a subset of their genome, comparison of any two inbred strains (as is the case with backcrossing or intercrossing (Fig. 6A), and chromosome substitution strains or recombinant inbred strains (Fig. 6B)) leads to “blind spots” in the genome where the strains are identical by descent. The Collaborative Cross takes advantage of more diverse strains, increasing the amount of genetic diversity between the strains, and thus the extent of the genome available to be screened (Churchill et al., 2004). In conjunction with the Collaborative Cross, the Jackson Laboratory is generating the Diversity Outbred Mouse Population to further improve resources for gene mapping. The diversity outcross is a heterogeneous stock population that will be maintained as an outbred stock, approximating the heterogeneous human population. Diversity Outbred mice will be generated from 160 breeding lines of the Collaborative Cross, allowing the early recombination events to be maintained while avoiding inbreeding (Lambert, 2009).

As mentioned above, although many modifier loci have been mapped, few genes have been cloned (reviewed in Flint et al., 2005), with the number involved in cancer being even smaller. The first locus to be mapped as a cancer modifier in the mouse was the Mom1 locus on chromosome 4 (Dietrich et al., 1993), found to modify the Apc gene mutation, Min (Apcmin), an inducer of intestinal cancer (Moser et al., 1992). Additional studies showed that Mom1 modified not only intestinal tumor number, the phenotype for which it was identified, but also tumor size (Gould et al., 1996). Further analysis identified secretory phospholipase A2 (Plag2g2a) as a candidate gene for the Mom1 locus (MacPhee et al., 1995); Plag2g2a was later shown to functionally modify the Apc mutation (Cormier et al., 1997). Interestingly, studies in humans suggested that PLA2G2A does not play a major modifying role in human colorectal cancers, due to a lack of functional polymorphic differences in the human gene (Riggins et al., 1995Tomlinson et al., 1996). Although this highlights the differences between mouse and human, additional Modifier of Min (Mom) genes have been mapped, and the genes are currently being identified and tested for their role in human cancer.

In contrast, the gene Sipa1, originally identified as the gene underlying the metastasis modifier loci Mtes1, is associated with breast cancer tumorigenesis in both mouse and humans. The MMTV-polyoma middle T (MMTV-PyMT) transgenic mouse model develops mammary tumors as well as extensive metastases, with metastatic growth being influenced by strain background (Lifsted et al., 1998). QTL mapping identified Mtes1 as a metastasis modifier locus on mouse chromosome 19 (Hunter et al., 2001), and the region was further narrowed down by taking advantage of haplotype analysis (Park et al., 2003b). A combination of sequence and functional analysis originally identified Sipa1 as a polymorphic candidate for the Mtes1 locus (Park et al., 2005). SNP analysis of human breast tumors found that germline polymorphisms of the Sipa1 gene are associated with metastatic potential and poor prognosis (Crawford et al., 2006), demonstrating that genes identified by modifier screens in the mouse can play a role in human cancer.

Another example of the complexities of modifier interactions in cancer is the nerve sheath tumor resistance locus (Nstr1), a modifier locus on mouse chromosome 19 found to affect incidence of peripheral nerve sheath tumors in an Nf1−/+;p53−/+ cis mouse model of neurofibromatosis type 1 (Reilly et al., 2006). Interestingly, the Nstr1-modifying effect is dependent on epigenetic factors on other chromosomes, with susceptibility differences only being seen if the Nf1;p53 mutant chromosome 11 was passed on to the progeny from the father. More recent studies used a chromosome substitution strain with a C57BL/6J background and A/J chromosome 19 to show that a modifier on chromosome 19 affects both the incidence and latency of peripheral nerve sheath tumors, as well as astrocytoma, another tumor type seen in the Nf1−/+ p53−/+ cis mouse model but not previously believed to be modified by a locus on chromosome 19. Additionally, the effects of the A/J chromosome 19 vary based not only on inheritance of the Nf1;p53 mutant chromosome as seen previously but also on the sex of the progeny (Walrath et al., 2009). These findings demonstrate not only the complex interactions between genetic background and tumorigenesis but also stress the importance of interpreting the role of genes in tumorigenesis in the context of the genetic background being studied.Go to:

III. CANCER PARADIGMS AND LESSONS FROM THE MOUSE

Although much has been learned over the past three decades about the cellular and genetic changes that occur within the cancer cell, there is a growing appreciation that the environmental context in which the cancer cell develops is critical for how cancer forms. This highlights the importance of performing cancer cell experiments in more physiologically relevant contexts to recapitulate the environments experienced by the cancer cell in the patient. Mouse model systems can provide experimentally tractable systems to study the role of development and environment in cancer. We describe here ongoing research into the distinction between different cell types within a tumor, with a focus on the cancer stem cell (CSC), the role of the microenvironment, with special emphasis on the vasculature and immune system, and metastasis, in which cancer cells leave their current environment and adapt to survive at distant sites from the tumor. We discuss how mouse models can help to better understand these non-cell-autonomous aspects of cancer.

A. The Cancer Stem Cell and Initiating Cell

In order to eradicate tumors and prevent recurrence, it is important to understand tumor growth dynamics and the cellular subpopulations driving tumor growth. A comparison of the current hypotheses of tumor growth mechanisms is reviewed by Adams and Strasser (2008). According to the CSC model (Fig. 7), tumor growth is driven by a rare population of CSCs that maintains the ability to self-renew and gives rise to more differentiated cell types (reviewed in Tan et al., 2006). This theory is based on evidence from mouse and human studies showing that malignant tumors, such as acute myeloid leukemia, contain a subpopulation of cells that have stem cell-like self-renewal and differentiation characteristics, express stem cell markers, and can engraft into immune-compromised NOD-SCID mice. The clonal evolution model postulates that tumor growth can be maintained by a substantial portion of the tumor cells that are proliferating rapidly and that may be at various stages of the differentiation pathway rather than a specific CSC. A third mixed model provides that a clone with CSC properties arises late as a dominant subclone after selection within the growing tumor. Given the broad diversity of cancer, it may be impossible to generalize across all tumor types and it is likely that multiple models accurately describe tumors from different origins (Gupta et al., 2009). Therefore, the predominant growth model for each tumor type must be determined experimentally.Fig. 7

The cancer stem cell model. In this model, tumors contain rare populations of cells with the ability to repopulate the tumor (cancer stem cells; CSC). These CSCs divide asymmetrically to give rise to more differentiated cells (light gray) and additional CSCs (dark gray). Only the CSCs can give rise to tumors. More differentiated cells can proliferate, but are not sufficient to repopulate the tumor.

In contrast to the CSCs that can maintain tumor growth within established tumors, the tumor-initiating cell is the cell that first undergoes mutation in the progression to cancer. There is much ongoing debate as to whether tumors must initiate in a normal stem cell compartment, in a cell with ongoing proliferative capacity, or whether normal, differentiated cells arrested in the G0 phase of the cell cycle can give rise to tumors. Identification of the tumor-initiating cell will provide insight into the process of tumorigenesis that may lead to new ideas in cancer prevention and treatment. While the idea that CSCs are present in many tumors is widely accepted, how and where they come from is still under debate. One possibility is that CSCs arise from stem/progenitor cells that have lost the ability to senesce or differentiate properly, as has been shown for acute myeloid leukemia and promyelocytic leukemia. Another possibility is that more differentiated cells have acquired stem cell-like characteristics (Hambardzumyan et al., 2008), as has been suggested by work on astroglia (Moon et al., 2008Steindler and Laywell, 2003), oligodendroglia (Grinspan et al., 1996), myocytes (Odelberg, 2002), adult mouse fibroblasts, and human somatic cells (Takahashi and Yamanaka, 2006). Whether the tumor-initiating cell that gives rise to the CSC comes from a stem/progenitor cell gone awry or a more differentiated cell with acquired stemness, the identity of the tumor-initiating cell is likely to be a major determinant of tumor characteristics.

Conditional knockout mice with tissue-specific Cre expression have been used to identify stem/progenitor cell types that can serve as tumor-initiating cells. Conditional loss of Nf1 in GFAP+ progenitor cells of the subventricular zone is sufficient to drive astrocytoma formation in mice, suggesting that stem/progenitor cells can be tumor-initiating cells for astrocytoma (Zhu et al., 2005). Conditional loss of Nf1 in fetal neural crest stem/progenitor cells of the Schwann cell lineage leads to development of abnormal differentiated Schwann cells, which then serve as neurofibroma tumor-initiating cells (Zheng et al., 2008). This suggests that the neural crest stem/progenitor cell is not the direct tumor-initiating cell, but that deregulation at the stem/progenitor level can develop into an abnormal cell type that initiates tumors. Although conditional genetically engineered mouse models facilitate elegant studies that indicate which cell types are sufficient to initiate tumorigenesis, induction of genetic changes in specific cell types does not test whether tumorigenesis must initiate in the tested cell population, or whether alternate pathways to tumorigenesis are possible. Thus, the true identity of the tumor-initiating cell in spontaneous tumors is still unclear for most tumor types. The development of additional mouse models to identify tumor-initiating cells in spontaneous tumors would help address this question. Unfortunately, the identification of tumor-initiating cells has been largely dependent on the definition of stem/progenitor cell lineages using markers of cell differentiation. These markers help define the cells being studied and are used to specifically drive Cre expression in cell populations to test hypotheses of tumor initiation. Whereas cell lineage and characteristic markers of the different stages of differentiation are well defined in the hematopoietic system, other organ systems are not as clearly understood, making it difficult to study in which cells tumors arise. Furthermore, as more stem/progenitor cell markers are identified and more differentiation steps are defined, the developmental steps from a stem cell to more committed progenitor cells and ultimately differentiated cell types may expand. Therefore, the cell identities capable of initiating tumors are likely to be refined as more stem/progenitor cell types along differentiation pathways are identified.

The progression of stem cells from totipotent to pluripotent to developmentally restricted progenitors is controlled by changes in expression of transcription factors through chromatin remodeling and modification, generally referred to as epigenetic changes (Niwa, 2007; see reviews by Zardo et al. (2008) and Jones and Baylin (2007) for more information). Alterations in epigenetic control, such as changes in chromatin regulators and chromatin modifications, may alter the expression of lineage-specific genes in stem cells during development, thereby inhibiting proper differentiation into more committed progenitors or differentiated cells and leading to expansion of cells with CSC potential. For example, overexpression of the chromatin regulator, Bmi1, in hematopoietic stem cells of transgenic mice results in increased stem cell self-renewal and leukemia (van Lohuizen et al., 1991), while loss of Bmi1 blocks leukemic proliferation and transplantation (Lessard and Sauvageau, 2003Park et al., 2003a).

Mouse models have been used to study the importance of regulation of epigenetic determinants, such as DNA methylation. The control of gene transcription and epigenetics is tightly regulated by DNA methyltransferases (DNMTs). Changes in DNA methylation profiles reflect changes in epigenetic states and cell fate decisions, including stemness (Bibikova et al., 2006) and tumorigenesis (Lee et al., 2008Martinez et al., 2009), as well as playing a critical role in genome stability (Eden et al., 2003Gaudet et al., 2003). Studies that compare the role of de novo DNMTs, Dnmt3a and Dnmt3b (Okano et al., 1999), to the maintenance DNMT, Dmnt1 (Hirasawa et al., 2008Lei et al., 1996), have helped to delineate the role of DNMTs in development and cell differentiation. Embryonic stem cells lacking DNMTs are severely hypomethylated and fail to differentiate. In stem cells with reduced levels of methylation, Dnmt1 is critical for stem cell differentiation, whereas embryonic stem cells in which the de novo DNMTs are mutated can still differentiate, despite similar levels of DNA methylation in the two situations. These data emphasize that the regulation of stem cell epigenetics and differentiation capacity is dependent not on the methylation levels per se, but on the mechanism by which methylation is maintained (Jackson et al., 2004). Given that tumors are often globally hypomethylated and locally hypermethylated and that upregulation of DNMT1 correlates with poorer prognosis (Kanai, 2008), it will be important to fully understand the relationship between normal epigenetic control of stem cells and the role of epigenetic changes in cancer, both in terms of gene expression changes and genome stability. Genetically engineered mice with altered expression and/or function of epigenetic regulators, such as Bmi1 and Dnmt, are powerful tools to study the epigenetics of stem cells and how deregulation of stem cell epigenetics can lead to cancer.

B. The Tumor Microenvironment

Cancer research has long focused on the mutated neoplastic cells and as a result early mouse models were designed to identify the effects of cell-autonomous mutations on tumorigenesis. Over the past decade, tumors have been found to consist of many nonneoplastic cell types, including fibroblasts, endothelial cells that form the vasculature and lymphatic system, and innate and adaptive immune cells, all of which interact to influence tumorigenesis (reviewed in Weinberg, 2008). There is some debate over whether stromal cells evolve and mutate to make them more capable of supporting tumor growth. Recent studies indicate that large DNA alterations are not present in fibroblasts, epithelial cells, or other neighboring stromal cells of human breast and ovarian cancers (Allinen et al., 2004Qiu et al., 2008, reviewed in Weinberg, 2008), although expression changes occur suggesting that epigenetic changes may play a role in evolving tumor stroma. In contrast, studies in mouse models support the hypothesis that mutations in the microenvironment are important for tumorigenesis (Hill et al., 2005Zhu et al., 2002). Additional experiments are needed to determine the role of mutations outside the neoplastic cells, as well as to determine how epigenetic changes in stroma contribute to tumorigenesis. Meanwhile, research suggests that the interaction between a tumor and the microenvironment causes a disruption in normal tissue homeostasis, resulting in an environment more conducive to tumorigenesis (Fig. 8).Fig. 8

The complexity of the tumor microenvironment. This cartoon shows an example of the tumor cell types making up the tumor microenvironment, based on the current understanding of brain tumors. In addition to differentiated tumor cells that make up the bulk of the tumor, tumor stem cells reside in a specific niche along blood vessels. Normal cells, such as reactive astrocytes, can react to the changes in the local microenvironment to become altered in morphology or gene expression. Inflammatory cells, such as lymphocytes and microglia, invade the region of the tumor and can further change the local microenvironment.

1. INFLAMMATION

Chronic inflammation is one common mode of microenvironment disruption that contributes to a tumorigenic phenotype and has been found to affect tumor development, both in the clinic and in mouse models of tumorigenesis (reviewed in de Visser and Coussens, 2006Tlsty and Coussens, 2006). Two different models of colon cancer, the T-cell-receptor-β/Trp53 double knockout and the Rag2/TGFβ-deficient double knockout, do not develop tumors when maintained in germ-free housing, demonstrating a requirement for inflammation in tumorigenesis (Engle et al., 2002Kado et al., 2001). Inflammation has also been implicated in a transgenic model of epithelial cancer in which the early genes of human papillomavirus type 16 are expressed under control of the human keratin 14 promoter (K14-HPV16) (Arbeit et al., 1994Coussens et al., 1996, reviewed in de Visser and Coussens, 2006de Visser et al., 2006). In this model, the premalignant skin is infiltrated by innate immune cells, primarily mast cells and granulocytes (Coussens et al., 1999de Visser et al., 2005). In a mast cell-deficient background, the K14-HPV16 mice show attenuated tumor development, confirming the functional contribution of the immune cells to cancer development (Coussens et al., 1999). Interestingly, a similar model in which HPV16 infection occurs in the cervical epithelium also shows an influx of immune cells, but in contrast to the skin model the immune cells in the cervix are infiltrating macrophages (Giraudo et al., 2004) rather than mast cells. This demonstrates the specificity of changes in the microenvironment in different tumor types, even when initiated by the same oncogene. The importance of mast cells in tumor initiation has also been illustrated in a study of neurofibroma development. Although Schwann cells are the tumor-initiating cells in an Nf1 conditional knockout, Nf1+/− mast cells are required for the development of neurofibromas, and infiltrate the area around the peripheral nerve prior to tumorigenesis (Yang et al., 2008Zhu et al., 2002).

Inflammation has been found to affect not only premalignant progression and tumor initiation but also tumor growth and metastasis. In the polyoma-middle-T-antigen (PyMT) transgenic mouse model of mammary tumors, PyMT mice lacking the cytokine CSF-1 had reduced macrophage recruitment to neoplastic tissue, but early neoplastic development was unaffected (Lin et al., 2001). However, lack of CSF-1 significantly delayed the development of invasive carcinoma, and pulmonary metastases were reduced. This metastatic ability was restored with transgenic CSF-1 expression in the mammary epithelium (Lin et al., 2001).

Inflammatory cells modulate tumor development by both direct and indirect effects on neoplastic cells. Cytokines and chemokines, as well as other inflammatory cell signaling molecules, promote tumor progression by several mechanisms (reviewed in Kundu and Surh, 2008). Two independent mouse models show that the signaling molecule nuclear factor κB (NF-κB), a proinflammatory transcription factor, may provide a link between inflammatory cells and signal pathway activation within neoplastic cells (reviewed in Balkwill and Coussens, 2004). In the Mdr2-knockout mouse model of inflammation-associated hepatocellular carcinoma, Pikarsky et al. (2004) demonstrate that hepatocyte NF-κB activation is controlled by inflammatory cell upregulation of tumor-necrosis factor α (TNF-α). In a colitis-associated colorectal cancer, IKKβ, an upstream positive regulator of NF-κB, has been shown to play a role in both the tumor cell and the inflammatory cell. When IKKβ is deleted in colonic enterocytes, the putative tumor-initiating cell, tumor incidence is decreased without affecting inflammation pathways. Myeloid-specific IKKβ deletion decreases tumor size through decreased production of cancer-promoting factors and a reduction in enterocyte proliferation (Greten et al., 2004). In combination, these studies suggest that inflammatory cells directly impact neoplastic cells by promoting proliferation through secretion of growth factors into the tumor microenvironment. Immune cells are also known to have an indirect role on tumor growth by impacting the cellular microenvironment through tissue remodeling and angiogenesis. Mouse models of inflammation and immunomodulation are reviewed by de Visser et al. (de Visser and Coussens, 2006de Visser et al., 2006).

It is clear that inflammation can have a tumor-promoting effect through the stimulating effect of cytokines on tumor cells and the ability of inflammatory cells to remodel the microenvironment. However, it should be noted that certain types of innate immune cells, particularly type 1 natural killer T cells, have an antitumorigenic response, as do some cytokines (reviewed in Fujii, 2008Terabe and Berzofsky, 20072008). Genetically engineered mouse models lacking certain cell types of the immune system, or certain cytokines, have been critical in understanding the role of immune cells on tumor immunity. For example, the mice mutant for Vα14Jα18 (Jα18 mice) lack type 1 natural killer T-cells, whereas CD1d mutant mice lack both type 1 and type 2 natural killer cells. Comparison of tumor growth in these two mouse models has led to the understanding that type 1 natural killer T cells promote tumor immunesurveillance, whereas type 2 natural killer T cells suppress this immunosurveillance (reviewed in Terabe and Berzofsky, 2007). This suggests that inflammation can have both a positive and negative effect on tumor growth, stressing the importance of understanding the balance between normal and abnormal regulation of the immune response.

2. ANGIOGENESIS

It was originally proposed by Folkman (1972) that cancers need to be vascularized to grow beyond a certain size and become malignant, and this theory has been supported over the years. The induction of vasculature growth to provide sufficient nutrients to the tumor has been termed the angiogenic switch, and occurs at varying stages of tumor progression, dependent on tumor type and environment. It was originally believed that tumor cells themselves were driving tumor angiogenesis, but recent data suggests that inflammatory cells play an important role by impacting tissue remodeling and activation of angiogenesis. The angiogenic switch is regulated by the balance between pro- and antiangiogenic factors. Normal angiogenesis is tightly regulated, whereas tumors lose appropriate control, such as failure of endothelial cells to become quiescent, allowing for constant growth of tumor blood vessels. Two classic mouse models of tumor angiogenesis are the K14-HPV16 mouse, described above, and the RIPTag mouse model of pancreatic islet carcinoma (Hager and Hanahan, 1999Parangi et al., 1996). Neovascularization occurs early in dysplasia in these models, and is required for tumor formation. The tumors develop in temporally and histologically distinct stages that can be characterized by angiogenic status: normal cells, hyperplasia, angiogenic dysplasia, and last, highly vascularized invasive tumors.

The angiogenic factor vascular endothelial growth factor A (VEGF-A) has been confirmed to play a role in tumor development in multiple mouse models, including the RIPTag model mentioned above (reviewed in Crawford and Ferrara, 2009). In the APCmin mouse discussed above, studies suggested that VEGF was upregulated in adenoma epithelial and stromal cells. Treatment with the VEGF inhibitor, Mab G6-31, was found to arrest tumor growth but not prevent incidence, supporting the hypothesis that upregulated VEGF was contributing to tumor progression, but may not play a role in initiation (Korsisaari et al., 2007). Interestingly, the effectiveness of VEGF inhibition was found to be time dependent, such that when mice are treated earlier, the tumor number is reduced (Goodlad et al., 2006), whereas at later times only reduction in tumor size is seen (Goodlad et al., 2006Korsisaari et al., 2007). In the MEN1 mouse model of multiple endocrine neoplasia (Crabtree et al., 2001), anti-VEGF treatment has been found to reduce vascular density and tumor size in both pituitary and pancreatic tumors, suggesting blocking of VEGF may be effective in treating multiple tumors within the same individual (Korsisaari et al., 2008). In the RIP-Tag2 model of pancreatic islet carcinogenesis, angiogenesis is involved in the progression of tumorigenesis. Although VEGF-A expression is not significantly upregulated in tumors, VEGF deletion in the B cells of the Rip-Tag mice reduces angiogenesis and attenuates tumor growth, supporting the necessity for VEGF in tumor growth. Matrix metalloproteinase 9(MMP-9) promotes angiogenesis in the RIP-Tag model and it also increases the bioavailability of VEGF, providing a potential explanation for VEGF dependence without mRNA upregulation (Bergers et al., 2000). Certain antiangiogenic therapies, such as the therapeutic antibody bevaciumab, specifically inhibit only human VEGF (Yu et al., 2008), making it difficult to test the effects of these drugs in mouse models. A knockin mouse has been developed that replaces 10 of the 19 differing amino acids between mouse and human VEGF, under the control of the endogenous murine promoter, minimizing developmental abnormalities. The biological activity of “humanized” VEGF is similar to human VEGF, as is its interaction with VEGF-blocking antibodies (Gerber et al., 2007) (reviewed in Crawford and Ferrara, 2009).

The many cell types and cellular interactions in the microenvironment have made it difficult to determine which changes within the tumor are truly causal. Mouse models with activation and deactivation of specific oncogenic mutations in vivo have been created to follow the chain of tumorigenic events that occurs following oncogene mutation. In a Myc model of pancreatic cancer, MycERTAM, a Myc fusion protein with a modified estrogen receptor-binding domain is activated in the presence of 4-hydroxytamoxifen (Lawlor et al., 2006). Removing 4-hydroxytamoxifen stops Myc activity and allows analysis of both the Myc requirement and the timeline of events to tumorigenesis. Activation of Myc in islet cells triggers apoptosis, leading to islet involution. However, when apoptosis is blocked by coexpression of Bcl-xL, Myc activation triggers pancreatic β cell expansion, leading to highly vascularized carcinoma (Pelengaris et al., 2002). Myc overexpression in the islet cell compartments leads to the activation of angiogenic factors, such as VEGFA, that recruit endothelial cells and drive angiogenesis. Additional studies with this model suggest that VEGFA is released from the extracellular matrix via MMPs, such as MMP-9 (Shchors et al., 2006) (reviewed in Shchors and Evan, 2007), and that secretion of interleukin-1β (IL-1β) from the β islet cells (Maedler et al., 2002) is also important for the release of VEGFA and tumor angiogenesis.

There are exceptions to the angiogenic requirement for tumor growth. For example, astrocytomas are initially able to co-opt the blood supply from normal brain blood vessels without initiation of angiogenesis. Typically, astrocytomas develop along blood vessels without a tumor capsule, allowing growth and causing invasion into the brain. At higher grades, astrocytomas and glioblastomas become hypoxic and necrotic due to increased proliferation, tumor size, and ANG2-activated vessel regression (D’Angelo et al., 2000). Hypoxia then induces VEGF, promoting vascular remodeling and vessel sprouting, defining the progression from grade III astrocytoma to grade IV glioblastoma (Bergers and Benjamin, 2003Theurillat et al., 1999). In this case where tumor cells are highly invasive from the early stages of tumorigenesis, angiogenesis is only required at late stages of progression when the tumor bulk overwhelms the normal vasculature system.

Different tumors respond varyingly to antiangiogenic therapy, making treatment less reliable than originally hoped (Folkman, 1972). The efficacy of antiangiogenic treatments is dependent on tumor stage. The angiogenesis inhibitor endostatin, as well as MMP and VEGF inhibitors, have been most successful in treating early-stage disease in the RIP-Tag mouse model, both by blocking growth and preventing the angiogenic switch. Other inhibitors are able to block proliferation and migration in end-stage tumors, but have no effect on early-stage disease prevention, suggesting that there may be qualitative differences in angiogenic vasculature or its regulation at different stages (reviewed in Bergers and Benjamin, 2003).

3. METASTASIS

Metastasis occurs when tumor cells leave the primary tumor and colonize another organ. For metastasis to occur, tumor cells disassociate from the primary tumor, invade and break down the extracellular matrix to enter the blood or lymphatic system, and disseminate to a new site, requiring survival in order to proliferate and colonize the new tissue (Chambers et al., 2000Husemann and Klein, 2009Weinberg, 2007). However, many questions remain, including what factors trigger metastatic spread, the genetic and epigenetic changes required, and the importance of selection or adaptation at the new site (Husemann and Klein, 2009).

Modeling metastasis has been difficult in the mouse, although mouse models have played an important role in studying the role of genetic background in metastasis, as discussed above (Section II.E.3). Spontaneous metastasis is rare in the mouse, and those tumors that do metastasize have long latency periods. Even for genetically engineered mouse models of metastasis, penetrance is variable, often much lower than primary tumor incidence, and the primary tumor may need to be surgically removed to allow the study of metastases. In the majority of models, latency is still over 3 months (summarized in Khanna and Hunter, 2005). However, mouse models of metastasis will be able to address specific questions that cannot be answered in cell culture or in human studies, such as what are the specific interactions that occur between the tumor and microenvironment that facilitate specific mechanisms of metastasis.

Recently, mouse models have provided important information regarding the metastatic potential of tumors, and the timing of acquiring this potential. It had been thought that metastatic capability develops as a late step in tumor progression, due to continuing accumulation of somatic mutations in the primary tumor and selection for metastatic ability in a small subset of cells (Fearon and Vogelstein, 1990Fidler and Kripke, 1977). However, recent data suggests that tumors capable of metastasis actually show a specific and predictive gene expression fingerprint very early in tumorigenesis (Ramaswamy et al., 2003van ’t Veer et al., 2002). Similar findings have also been made in the mouse, as shown by Qiu et al. (2004). They demonstrate that a gene expression signature can distinguish between MMTV-PyMT mammary tumors in a highly metastatic strain and tumors in a low metastasis strain. Importantly, when looking specifically at orthologs of the 17 differentially expressed genes in the human fingerprint (Ramaswamy et al., 2003), 16 of the 17 genes show the same directional expression differences in the mouse correlating to a calculated metastatic potential (Qiu et al., 2004). These findings support the idea that a small number of metastasis-associated genes may be predictive in both humans and mouse. Interestingly, because these arrays were done comparing the signatures of primary tumors, these findings suggest that the metastatic predictive signature is not a rare phenomenon seen only in a small group of cells. Similarly, as the tumors in this experiment were all generated by the same oncogenic event, this suggests that the differences are most likely due to genetic background rather than oncogenic mutations. Therefore, qPCR was performed on normal mammary tissue from strains with high and low metastatic potential, using 10 of the 16 metastatic predictive genes, and it was found that nine of these signature genes show differential expression in normal tissues as well (Yang et al., 2005). These data suggest that gene expression patterns that predict metastatic ability may be influenced by hereditary polymorphisms, rather than just genetic mutations. It also suggests that certain subgroups of humans may be more susceptible to metastases than others, and that nontumor tissue can be used to predict risk (Qiu et al., 2004).

Other studies in the mouse have also supported the idea that metastasis is not a late step in tumor development. Another study of mammary tumors in MMTV rat HER-2/neu transgenic BALB/c mice found that HER-2+ mammary cells were detected in the bone marrow as early as 4–9 weeks, during a period of atypical ductal hyperplasia, but prior to carcinoma development (Husemann et al., 2008). Similarly, MMTV-PyMT mice had tumor cells present in the bone marrow at 4–6 weeks, with lung micrometastases found beginning at 14 weeks (Husemann et al., 2008). Electron microscopy confirmed that individual cells could be seen breaking through the basement membrane at early time points (Husemann et al., 2008).

As mentioned previously, genetically engineered mouse models are useful for imaging, as techniques for early detection become more accurate, and tumor cells can be labeled to follow cell growth. The ability to detect metastatic cell clusters and fully formed metastases by imaging can be used to improve the understanding of how metastases target specific organs. Recent studies have been done following the fate of single cells in mouse cancer models, and have given insight into interactions with the microenvironment, as well as the efficiency of cell survival in ectopic tissues (Chambers et al., 2000Khanna et al., 2004).

4. PRECLINICAL TESTING OF THERAPEUTICS IN MOUSE MODELS

Though our understanding of cancer has expanded greatly in the past 40 years, our ability to translate that knowledge into patient treatment and therapy has been much more limited. Although genetically engineered mouse models have now been widely used to study and understand the basic science and molecular mechanisms of cancer, applying these models to preclinical studies of candidate therapeutics has been slower to develop. Most preclinical testing has been done in cell culture systems in vitro and xenografts in vivo due to the ability to control timing of tumorigenesis and generate large numbers of synchronized test subjects. Unfortunately, while these model systems have been successful at predicting toxicity issues in humans, they have been less successful at predicting efficacy of antitumor compounds. Genetically engineered mouse models are more difficult and costly to use for preclinical testing due to the number of animals that need to be bred to generate a test cohort; however, these models have certain advantages in modeling human disease that may make them more predictive of efficacy in humans. Because of the expense of clinical trials and the limited population of patients to be entered into clinical trials, the increased cost of using genetically engineered models may be offset by the increased ability to predict responses in humans, reducing the number of human trials that need to be performed. Genetically engineered mouse models of cancer have the advantages that the initiating mutation is known, which is particularly important for the testing of molecularly targeted therapies; that tumors develop spontaneously in the normal tissue for that tumor type, with a coevolving microenvironment; and that the immune system is intact (Becher and Holland, 2006). The advances in mouse models discussed in this chapter may provide the tools to better predict drug efficacy prior to Phase II/III drug trials.

Improvements in drug discovery and screening will require numerous steps to which improved mouse models may be able to contribute. These include the identification of cancer targets, including the context in which they are required for tumor maintenance, the determinations of most effective and least toxic compounds on these targets, and the identification of biomarkers (reviewed in Gutmann et al., 2006Sharpless and Depinho, 2006). Genetically engineered mouse models can play an important role in target validation, because specific genetic events in human cancer are used to guide the development of the mouse model. Many genetically engineered tumor models have been generated to mimic the genetic, molecular, and phenotypic traits of the specific human cancer (Becher and Holland, 2006).

In order to make genetically engineered models effective for preclinical drug screenings, models with short tumor latency and high penetrance will help to keep maintenance manageable and costs reduced. However, an increase in mutations in order to decrease latency may skew tumor development, as well as reduce the requirement for gain of secondary or epigenetic mutations. A K-ras model of pancreatic cancer shows that in an Ink4a/Arf heterozygous background, the primary tumors have increased genomic complexity than those on an Ink4a/Arf−/− background (Bardeesy et al., 2006), suggesting that a less biased model leads to greater genetic instability, as seen in human cancers, and is therefore a more accurate representation of the human tumor. Additionally, fast-growing tumors may not accurately reflect the interaction between the tumor cells and the microenvironment.

Genetically engineered mouse models can be used for systematic screening of compounds, and have been used to test drugs for both therapeutic and cancer prevention properties, as reviewed in Carver and Pandolfi (2006)Gutmann et al. (2006), and Sharpless and Depinho (2006). Potential therapies can be screened and compared when tested on a well-defined model. Effective drug therapy can also be stopped to determine if the drug cures the cancer, or if recurrence or drug resistance occurs. Multiple drug combinations can also be tested for synergistic effects in the mouse. Because recent molecularly targeted therapies are showing very specific efficacy in a subset of human patients (e.g., see Haas-Kogan et al., 2005Mellinghoff et al., 2005), compounds can be tested on modeled tumors with different engineered mutations or on different strain backgrounds to determine whether specific efficacy can be predicted in advance to allow better design of clinical trials.Go to:

IV. SUMMARY

As reviewed here, advances in mouse modeling techniques continue to allow the further understanding of the biology of tumor development and growth. Mouse models have shown some of the complexities of tumor cell interactions, including tumor interaction with nonneoplastic cells in the microenvironment and the effect of normal genetic polymorphisms on tumor susceptibility. Identifying cancer-causing genes, and most specifically targets that are responsive to therapeutics, continues to be a challenge. New modalities of genetic engineering have given us the ability to more accurately model human cancer in an effort to identify new targets and markers that will allow us better predictive and treatment abilities. Future therapies will utilize the advances that have been made in these mice to further understand and treat cancer.Go to:

ACKNOWLEDGMENTS

The authors thank Shyam Sharan and Bernard Ramsahoye for helpful comments on the text.Go to:

REFERENCES

  • Adams JM, Strasser A. Is tumor growth sustained by rare cancer stem cells or dominant clones? Cancer Res. 2008;68:4018–4021. [PubMed] [Google Scholar]
  • Adams DJ, et al. Mutagenic insertion and chromosome engineering resource (MICER) Nat. Genet. 2004;36:867–871. [PubMed] [Google Scholar]
  • Akagi K, et al. RTCGD: Retroviral tagged cancer gene database. Nucleic Acids Res. 2004;32:D523–D527. [PMC free article] [PubMed] [Google Scholar]
  • Allinen M, et al. Molecular characterization of the tumor microenvironment in breast cancer. Cancer Cell. 2004;6:17–32. [PubMed] [Google Scholar]
  • Arbeit JM, et al. Progressive squamous epithelial neoplasia in K14-human papillomavirus type 16 transgenic mice. J. Virol. 1994;68:4358–4368. [PMC free article] [PubMed] [Google Scholar]
  • Artandi SE, et al. Telomere dysfunction promotes non-reciprocal translocations and epithelial cancers in mice. Nature. 2000;406:641–645. [PubMed] [Google Scholar]
  • Attardi LD, Donehower LA. Probing p53 biological functions through the use of genetically engineered mouse models. Mutat. Res. 2005;576:4–21. [PubMed] [Google Scholar]
  • Balkwill F, Coussens LM. Cancer: An inflammatory link. Nature. 2004;431:405–406. [PubMed] [Google Scholar]
  • Bardeesy N, et al. Both p16(Ink4a) and the p19(Arf)-p53 pathway constrain progression of pancreatic adenocarcinoma in the mouse. Proc. Natl. Acad. Sci. USA. 2006;103:5947–5952. [PMC free article] [PubMed] [Google Scholar]
  • Barlow C, et al. Atm-deficient mice: A paradigm of ataxia telangiectasia. Cell. 1996;86:159–171. [PubMed] [Google Scholar]
  • Baron U, et al. Tetracycline-controlled transcription in eukaryotes: Novel transactivators with graded transactivation potential. Nucleic Acids Res. 1997;25:2723–2729. [PMC free article] [PubMed] [Google Scholar]
  • Becher OJ, Holland EC. Genetically engineered models have advantages over xenografts for preclinical studies. Cancer Res. 2006;66:3355–3358. discussion 3358-3359. [PubMed] [Google Scholar]
  • Ben David Y, et al. Inactivation of the p53 oncogene by internal deletion or retroviral integration in erythroleukemic cell lines induced by Friend leukemia virus. Oncogene. 1988;3:179–185. [PubMed] [Google Scholar]
  • Berger SL, et al. Selective inhibition of activated but not basal transcription by the acidic activation domain of VP16: Evidence for transcriptional adaptors. Cell. 1990;61:1199–1208. [PubMed] [Google Scholar]
  • Bergers G, Benjamin LE. Tumorigenesis and the angiogenic switch. Nat. Rev. Cancer. 2003;3:401–410. [PubMed] [Google Scholar]
  • Bergers G, et al. Matrix metalloproteinase-9 triggers the angiogenic switch during carcinogenesis. Nat. Cell Biol. 2000;2:737–744. [PMC free article] [PubMed] [Google Scholar]
  • Bibby MC. Orthotopic models of cancer for preclinical drug evaluation: Advantages and disadvantages. EurJCancer. 2004;40:852–857. [PubMed] [Google Scholar]
  • Bibikova M, et al. Human embryonic stem cells have a unique epigenetic signature. Genome Res. 2006;16:1075–1083. [PMC free article] [PubMed] [Google Scholar]
  • Bockamp E, et al. Conditional transgenic mouse models: From the basics to genomewide sets of knockouts and current studies of tissue regeneration. Regen. Med. 2008;3:217–235. [PubMed] [Google Scholar]
  • Branda CS, Dymecki SM. Talking about a revolution: The impact of sitespecific recombinases on genetic analyses in mice. Dev. Cell. 2004;6:7–28. [PubMed] [Google Scholar]
  • Broman KW. The genomes of recombinant inbred lines. Genetics. 2005;169:1133–1146. [PMC free article] [PubMed] [Google Scholar]
  • Brown SD, et al. Quiet as a mouse: Dissecting the molecular and genetic basis of hearing. Nat. Rev. Genet. 2008;9:277–290. [PubMed] [Google Scholar]
  • Buchholz F, et al. Inducible chromosomal translocation of AML1 and ETO genes through Cre/loxP-mediated recombination in the mouse. EMBO Rep. 2000;1:133–139. [PMC free article] [PubMed] [Google Scholar]
  • Capecchi MR. Gene targeting in mice: Functional analysis of the mammalian genome for the twenty-first century. Nat. Rev. Genet. 2005;6:507–512. [PubMed] [Google Scholar]
  • Carver BS, Pandolfi PP. Mouse modeling in oncologic preclinical and translational research. Clin. Cancer Res. 2006;12:5305–5311. [PubMed] [Google Scholar]
  • Chai Y, et al. Fate of the mammalian cranial neural crest during tooth and mandibular morphogenesis. Development. 2000;127:1671–1679. [PubMed] [Google Scholar]
  • Chambers AF, et al. Molecular biology of breast cancer metastasis. Clinical implications of experimental studies on metastatic inefficiency. Breast Cancer Res. 2000;2:400–407. [PMC free article] [PubMed] [Google Scholar]
  • Chang S, et al. Expression of human BRCA1 variants in mouse ES cells allows functional analysis of BRCA1 mutations. J. Clin. Invest. 2009;119:3160–3171. [PMC free article] [PubMed] [Google Scholar]
  • Chesler EJ, et al. The Collaborative Cross at Oak Ridge National Laboratory: Developing a powerful resource for systems genetics. Mamm. Genome. 2008;19:382–389. [PMC free article] [PubMed] [Google Scholar]
  • Chin L, et al. p53 deficiency rescues the adverse effects of telomere loss and cooperates with telomere dysfunction to accelerate carcinogenesis. Cell. 1999;97:527–538. [PubMed] [Google Scholar]
  • Cho BC, et al. Frequent disruption of the Nf1 gene by a novel murine AIDS virusrelated provirus in BXH-2 murine myeloid lymphomas. J. Virol. 1995;69:7138–7146. [PMC free article] [PubMed] [Google Scholar]
  • Churchill GA, et al. The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nat. Genet. 2004;36:1133–1137. [PubMed] [Google Scholar]
  • Cichowski K, et al. Mouse models of tumor development in neurofibromatosis type 1. Science. 1999;286:2172–2176. [PubMed] [Google Scholar]
  • Collier LS, et al. Cancer gene discovery in solid tumours using transposon-based somatic mutagenesis in the mouse. Nature. 2005;436:272–276. [PubMed] [Google Scholar]
  • Collier LS, et al. Whole-body sleeping beauty mutagenesis can cause penetrant leukemia/lymphoma and rare high-grade glioma without associated embryonic lethality. Cancer Res. 2009;69:8429–8437. [PMC free article] [PubMed] [Google Scholar]
  • Collins EC, et al. Inter-chromosomal recombination of Mll and Af9 genes mediated by cre-loxP in mouse development. EMBO Rep. 2000;1:127–132. [PMC free article] [PubMed] [Google Scholar]
  • Cormier RT, et al. Secretory phospholipase Pla2g2a confers resistance to intestinal tumorigenesis. Nat. Genet. 1997;17:88–91. [PubMed] [Google Scholar]
  • Coussens LM, et al. Genetic predisposition and parameters of malignant progression in K14-HPV16 transgenic mice. Am. J. Pathol. 1996;149:1899–1917. [PMC free article] [PubMed] [Google Scholar]
  • Coussens LM, et al. Inflammatory mast cells up-regulate angiogenesis during squamous epithelial carcinogenesis. Genes Dev. 1999;13:1382–1397. [PMC free article] [PubMed] [Google Scholar]
  • Crabtree JS, et al. A mouse model of multiple endocrine neoplasia, type 1, develops multiple endocrine tumors. Proc. Natl. Acad. Sci. USA. 2001;98:1118–1123. [PMC free article] [PubMed] [Google Scholar]
  • Crawford NP, et al. Germline polymorphisms in SIPA1 are associated with metastasis and other indicators of poor prognosis in breast cancer. Breast Cancer Res. 2006;8:R16. [PMC free article] [PubMed] [Google Scholar]
  • Crawford Y, Ferrara N. VEGF inhibition: Insights from preclinical and clinical studies. Cell Tissue Res. 2009;335:261–269. [PubMed] [Google Scholar]
  • Cronin CA, et al. The lac operator–repressor system is functional in the mouse. Genes Dev. 2001;15:1506–1517. [PMC free article] [PubMed] [Google Scholar]
  • Cullen BR. Transcription and processing of human microRNA precursors. Mol. Cell. 2004;16:861–865. [PubMed] [Google Scholar]
  • D’Angelo M, et al. Angiogenesis in transgenic models of multistep carcinogenesis. J. Neurooncol. 2000;50:89–98. [PubMed] [Google Scholar]
  • Demant P. Cancer susceptibility in the mouse: Genetics, biology and implications for human cancer. Nat. Rev. Genet. 2003;4:721–734. [PubMed] [Google Scholar]
  • De Palma M, et al. In vivo targeting of tumor endothelial cells by systemic delivery of lentiviral vectors. Hum. Gene Ther. 2003;14:1193–1206. [PubMed] [Google Scholar]
  • de Visser KE, Coussens LM. The inflammatory tumor microenvironment and its impact on cancer development. Contrib. Microbiol. 2006;13:118–137. [PubMed] [Google Scholar]
  • de Visser KE, et al. De novo carcinogenesis promoted by chronic inflammation is B lymphocyte dependent. Cancer Cell. 2005;7:411–423. [PubMed] [Google Scholar]
  • de Visser KE, et al. Paradoxical roles of the immune system during cancer development. Nat. Rev. Cancer. 2006;6:24–37. [PubMed] [Google Scholar]
  • Dickins RA, et al. Tissue-specific and reversible RNA interference in transgenic mice. Nat. Genet. 2007;39:914–921. [PMC free article] [PubMed] [Google Scholar]
  • Dietrich WF, et al. Genetic identification of Mom-1, a major modifier locus affecting Min-induced intestinal neoplasia in the mouse. Cell. 1993;75:631–639. [PubMed] [Google Scholar]
  • Ding S, et al. Efficient transposition of the piggyBac (PB) transposon in mammalian cells and mice. Cell. 2005;122:473–483. [PubMed] [Google Scholar]
  • Dragani TA. 10 years of mouse cancer modifier loci: Human relevance. Cancer Res. 2003;63:3011–3018. [PubMed] [Google Scholar]
  • Dragatsis I, Zeitlin S. A method for the generation of conditional gene repair mutations in mice. Nucleic Acids Res. 2001;29:E10. [PMC free article] [PubMed] [Google Scholar]
  • DuPage M, et al. Conditional mouse lung cancer models using adenoviral or lentiviral delivery of Cre recombinase. Nat. Protoc. 2009;4:1064–1072. [PMC free article] [PubMed] [Google Scholar]
  • Dupuy AJ, et al. Mammalian mutagenesis using a highly mobile somatic Sleeping Beauty transposon system. Nature. 2005;436:221–226. [PubMed] [Google Scholar]
  • Dupuy AJ, et al. A modified sleeping beauty transposon system that can be used to model a wide variety of human cancers in mice. Cancer Res. 2009;69:8150–8156. [PMC free article] [PubMed] [Google Scholar]
  • Eden A, et al. Chromosomal instability and tumors promoted by DNA hypomethylation. Science. 2003;300:455. [PubMed] [Google Scholar]
  • Engle SJ, et al. Elimination of colon cancer in germ-free transforming growth factor beta 1-deficient mice. Cancer Res. 2002;62:6362–6366. [PubMed] [Google Scholar]
  • Evers B, Jonkers J. Mouse models of BRCA1 and BRCA2 deficiency: Past lessons, current understanding and future prospects. Oncogene. 2006;25:5885–5897. [PubMed] [Google Scholar]
  • Fearon E, Vogelstein B. A genetic model for colorectal tumorigenesis. Cell. 1990;61:759–767. [PubMed] [Google Scholar]
  • Fidler IJ, Kripke ML. Metastasis results from preexisting variant cells within a malignant tumor. Science. 1977;197:893–895. [PubMed] [Google Scholar]
  • Flint J, et al. Strategies for mapping and cloning quantitative trait genes in rodents. Nat. Rev. Genet. 2005;6:271–286. [PubMed] [Google Scholar]
  • Folkman J. Anti-angiogenesis: New concept for therapy of solid tumors. Ann. Surg. 1972;175:409–416. [PMC free article] [PubMed] [Google Scholar]
  • Forster A, et al. Engineering de novo reciprocal chromosomal translocations associated with Mll to replicate primary events of human cancer. Cancer Cell. 2003;3:449–458. [PubMed] [Google Scholar]
  • Forster A, et al. Chromosomal translocation engineering to recapitulate primary events of human cancer. Cold Spring Harb. Symp. Quant. Biol. 2005a;70:275–282. [PubMed] [Google Scholar]
  • Forster A, et al. The invertor knock-in conditional chromosomal translocation mimic. Nat. Methods. 2005b;2:27–30. [PubMed] [Google Scholar]
  • Frese KK, Tuveson DA. Maximizing mouse cancer models. Nat. Rev. Cancer. 2007;7:645–658. [PubMed] [Google Scholar]
  • Friedel RH, et al. EUCOMM—The European conditional mouse mutagenesis program. Brief Funct. Genomic Proteomic. 2007;6:180–185. [PubMed] [Google Scholar]
  • Friedrich G, Soriano P. Promoter traps in embryonic stem cells: A genetic screen to identify and mutate developmental genes in mice. Genes Dev. 1991;5:1513–1523. [PubMed] [Google Scholar]
  • Fujii S. Exploiting dendritic cells and natural killer T cells in immunotherapy against malignancies. Trends Immunol. 2008;29:242–249. [PubMed] [Google Scholar]
  • Gaudet F, et al. Induction of tumors in mice by genomic hypomethylation. Science. 2003;300:489–492. [PubMed] [Google Scholar]
  • Gerber HP, et al. Mice expressing a humanized form of VEGF-A may provide insights into the safety and efficacy of anti-VEGF antibodies. Proc. Natl. Acad. Sci. USA. 2007;104:3478–3483. [PMC free article] [PubMed] [Google Scholar]
  • German J. Bloom syndrome: A mendelian prototype of somatic mutational disease. Medicine (Baltimore) 1993;72:393–406. [PubMed] [Google Scholar]
  • Giraudo E, et al. An amino-bisphosphonate targets MMP-9-expressing macrophages and angiogenesis to impair cervical carcinogenesis. J. Clin. Invest. 2004;114:623–633. [PMC free article] [PubMed] [Google Scholar]
  • Gonzalez-Murillo A, et al. Unaltered repopulation properties of mouse hematopoietic stem cells transduced with lentiviral vectors. Blood. 2008;112:3138–3147. [PMC free article] [PubMed] [Google Scholar]
  • Goodlad RA, et al. Inhibiting vascular endothelial growth factor receptor-2 signaling reduces tumor burden in the ApcMin/+ mouse model of early intestinal cancer. Carcinogenesis. 2006;27:2133–2139. [PubMed] [Google Scholar]
  • Goss KH, et al. Enhanced tumor formation in mice heterozygous for Blm mutation. Science. 2002;297:2051–2053. [PubMed] [Google Scholar]
  • Gossen M, Bujard H. Tight control of gene expression in mammalian cells by tetracycline-responsive promoters. Proc. Natl. Acad. Sci. USA. 1992;89:5547–5551. [PMC free article] [PubMed] [Google Scholar]
  • Gossen JA, et al. Efficient rescue of integrated shuttle vectors from transgenic mice: A model for studying mutations in vivo. Proc. Natl. Acad. Sci. USA. 1989;86:7971–7975. [PMC free article] [PubMed] [Google Scholar]
  • Gould KA, et al. Mom1 is a semi-dominant modifier of intestinal adenoma size and multiplicity in Min/+ mice. Genetics. 1996;144:1769–1776. [PMC free article] [PubMed] [Google Scholar]
  • Greten FR, et al. IKKbeta links inflammation and tumorigenesis in a mouse model of colitis-associated cancer. Cell. 2004;118:285–296. [PubMed] [Google Scholar]
  • Grinspan JB, et al. Re-entry into the cell cycle is required for bFGF-induced oligodendroglial dedifferentiation and survival. J. Neurosci. Res. 1996;46:456–464. [PubMed] [Google Scholar]
  • Gross L. Viral etiology of cancer and leukemia: A look into the past, present and future—G.H.A. Clowes Memorial Lecture. Cancer Res. 1978;38:485–493. [PubMed] [Google Scholar]
  • Gupta PB, et al. Cancer stem cells: Mirage or reality? Nat. Med. 2009;15:1010–1012. [PubMed] [Google Scholar]
  • Gutmann DH, et al. Harnessing preclinical mouse models to inform human clinical cancer trials. J. Clin. Invest. 2006;116:847–852. [PMC free article] [PubMed] [Google Scholar]
  • Haas-Kogan DA, et al. Epidermal growth factor receptor, protein kinase B/Akt, and glioma response to erlotinib. J. Natl. Cancer Inst. 2005;97:880–887. [PubMed] [Google Scholar]
  • Hager JH, Hanahan D. Tumor cells utilize multiple pathways to down-modulate apoptosis. Lessons from a mouse model of islet cell carcinogenesis. Ann. NYAcad. Sci. 1999;887:150–163. [PubMed] [Google Scholar]
  • Hambardzumyan D, et al. Glioma formation, cancer stem cells, and akt signaling. Stem Cell Rev. 2008;4:203–210. [PubMed] [Google Scholar]
  • Hande MP. DNA repair factors and telomere-chromosome integrity in mammalian cells. Cytogenet. Genome Res. 2004;104:116–122. [PubMed] [Google Scholar]
  • Hansen GM, et al. Large-scale gene trapping in C57BL/6N mouse embryonic stem cells. Genome Res. 2008;18:1670–1679. [PMC free article] [PubMed] [Google Scholar]
  • Hill R, et al. Selective evolution of stromal mesenchyme with p53 loss in response to epithelial tumorigenesis. Cell. 2005;123:1001–1011. [PubMed] [Google Scholar]
  • Hirai H, et al. Oncogenic mechanisms of Evi-1 protein. Cancer Chemother. Pharmacol. 2001;48(Suppl 1):S35–S40. [PubMed] [Google Scholar]
  • Hirasawa R, et al. Maternal and zygotic Dnmt1 are necessary and sufficient for the maintenance of DNA methylation imprints during preimplantation development. Genes Dev. 2008;22:1607–1616. [PMC free article] [PubMed] [Google Scholar]
  • Hoffman RM. Imaging cancer dynamics in vivo at the tumor and cellular level with fluorescent proteins. Clin. Exp. Metastasis. 2009;26:345–355. [PubMed] [Google Scholar]
  • Hosoda T, et al. Clonality of mouse and human cardiomyogenesis in vivo. Proc. Natl. Acad. Sci. USA. 2009;106:17169–17174. [PMC free article] [PubMed] [Google Scholar]
  • Hunter KW, Crawford NP. The future of mouse QTL mapping to diagnose disease in mice in the age of whole-genome association studies. Annu. Rev. Genet. 2008;42:131–141. [PubMed] [Google Scholar]
  • Hunter KW, et al. Predisposition to efficient mammary tumor metastatic progression is linked to the breast cancer metastasis suppressor gene Brms1. Cancer Res. 2001;61:8866–8872. [PubMed] [Google Scholar]
  • Huse JT, Holland EC. Genetically engineered mouse models of brain cancer and the promise of preclinical testing. Brain Pathol. 2009;19:132–143. [PMC free article] [PubMed] [Google Scholar]
  • Husemann Y, Klein CA. The analysis of metastasis in transgenic mouse models. Transgenic Res. 2009;18:1–5. [PubMed] [Google Scholar]
  • Husemann Y, et al. Systemic spread is an early step in breast cancer. Cancer Cell. 2008;13:58–68. [PubMed] [Google Scholar]
  • Iraqi FA, et al. The Collaborative Cross, developing a resource for mammalian systems genetics: A status report of theWellcome Trust cohort. Mamm. Genome. 2008;19:379–381. [PubMed] [Google Scholar]
  • Ivics Z, et al. Molecular reconstruction of Sleeping Beauty, a Tc1-like transposon from fish, and its transposition in human cells. Cell. 1997;91:501–510. [PubMed] [Google Scholar]
  • Jackson EL, et al. Analysis of lung tumor initiation and progression using conditional expression of oncogenic K-ras. Genes Dev. 2001;15:3243–3248. [PMC free article] [PubMed] [Google Scholar]
  • Jackson M, et al. Severe global DNA hypomethylation blocks differentiation and induces histone hyperacetylation in embryonic stem cells. Mol. Cell. Biol. 2004;24:8862–8871. [PMC free article] [PubMed] [Google Scholar]
  • Jemal A, et al. Annual report to the nation on the status of cancer, 1975-2005, featuring trends in lung cancer, tobacco use, tobacco control. J. Natl. Cancer Inst. 2008;100:1672–1694. [PMC free article] [PubMed] [Google Scholar]
  • Jiang X, et al. Fate of themammalian cardiac neural crest. Development. 2000;127:1607–1616. [PubMed] [Google Scholar]
  • Johnson L, et al. Somatic activation of the K-ras oncogene causes early onset lung cancer in mice. Nature. 2001;410:1111–1116. [PubMed] [Google Scholar]
  • Jones PA, Baylin SB. The epigenomics of cancer. Cell. 2007;128:683–692. [PMC free article] [PubMed] [Google Scholar]
  • Jonkers J, Berns A. Retroviral insertional mutagenesis as a strategy to identify cancer genes. Biochim. Biophys. Acta. 1996;1287:29–57. [PubMed] [Google Scholar]
  • Kado S, et al. Intestinal microflora are necessary for development of spontaneous adenocarcinoma of the large intestine in T-cell receptor beta chain and p53 double-knockout mice. Cancer Res. 2001;61:2395–2398. [PubMed] [Google Scholar]
  • Kamijo T, et al. Tumor spectrum in ARF-deficient mice. Cancer Res. 1999;59:2217–2222. [PubMed] [Google Scholar]
  • Kanai Y. Alterations of DNA methylation and clinicopathological diversity of human cancers. Pathol. Int. 2008;58:544–558. [PubMed] [Google Scholar]
  • Kang JH, Chung JK. Molecular-genetic imaging based on reporter gene expression. J. Nucl. Med. 2008;49(Suppl 2):164S–179S. [PubMed] [Google Scholar]
  • Karlseder J, et al. p53- and ATM-dependent apoptosis induced by telomeres lacking TRF2. Science. 1999;283:1321–1325. [PubMed] [Google Scholar]
  • Keng VW, et al. A conditional transposon-based insertional mutagenesis screen for genes associated with mouse hepatocellular carcinoma. Nat. Biotechnol. 2009;27:264–274. [PMC free article] [PubMed] [Google Scholar]
  • Khanna C, Hunter K. Modeling metastasis in vivo. Carcinogenesis. 2005;26:513–523. [PubMed] [Google Scholar]
  • Khanna C, et al. The membrane-cytoskeleton linker ezrin is necessary for osteosarcoma metastasis. Nat. Med. 2004;10:182–186. [PubMed] [Google Scholar]
  • Kim CF, et al. Mouse models of human non-small-cell lung cancer: Raising the bar. Cold Spring Harb. Symp. Quant. Biol. 2005;70:241–250. [PubMed] [Google Scholar]
  • Kim M, et al. Comparative oncogenomics identifies NEDD9 as a melanoma metastasis gene. Cell. 2006;125:1269–1281. [PubMed] [Google Scholar]
  • Kohler SW, et al. Analysis of spontaneous and induced mutations in transgenic mice using a lambda ZAP/lacI shuttle vector. Environ. Mol. Mutagen. 1991;18:316–321. [PubMed] [Google Scholar]
  • Kool J, Berns A. High-throughput insertional mutagenesis screens in mice to identify oncogenic networks. Nat. Rev. Cancer. 2009;9:389–399. [PubMed] [Google Scholar]
  • Korsisaari N, et al. Inhibition of VEGF-A prevents the angiogenic switch and results in increased survival of Apc+/min mice. Proc. Natl. Acad. Sci. USA. 2007;104:10625–10630. [PMC free article] [PubMed] [Google Scholar]
  • Korsisaari N, et al. Blocking vascular endothelial growth factor-A inhibits the growth of pituitary adenomas and lowers serum prolactin level in a mouse model of multiple endocrine neoplasia type 1. Clin. Cancer Res. 2008;14:249–258. [PubMed] [Google Scholar]
  • Kost-Alimova M, Imreh S. Modeling non-random deletions in cancer. Semin. Cancer Biol. 2007;17:19–30. [PubMed] [Google Scholar]
  • Kundu JK, Surh YJ. Inflammation: Gearing the journey to cancer. Mutat. Res. 2008;659:15–30. [PubMed] [Google Scholar]
  • Lakso M, et al. Targeted oncogene activation by site-specific recombination in transgenic mice. Proc. Natl. Acad. Sci. USA. 1992;89:6232–6236. [PMC free article] [PubMed] [Google Scholar]
  • Lambert R. Jax Notes. Vol. 514. Bar Harbor, ME: The Jackson Laboratory; 2009. Diversity Outbred and Collaborative Cross mice to offer maximum allelic variation; p. 2. [Google Scholar]
  • Lang GA, et al. Gain of function of a p53 hot spot mutation in a mouse model of Li-Fraumeni syndrome. Cell. 2004;119:861–872. [PubMed] [Google Scholar]
  • Largaespada DA, et al. Retroviral insertion at the Evi-2 locus in BXH-2 myeloid leukemia cell lines disrupts Nf1 expression without changes in steady state ras-GTP levels. J. Virol. 1995;69:5095–5102. [PMC free article] [PubMed] [Google Scholar]
  • Lawlor ER, et al. Reversible kinetic analysis of Myc targets in vivo provides novel insights into Myc-mediated tumorigenesis. Cancer Res. 2006;66:4591–4601. [PubMed] [Google Scholar]
  • Lee HW, et al. Essential role of mouse telomerase in highly proliferative organs. Nature. 1998;392:569–574. [PubMed] [Google Scholar]
  • Lee EM, et al. Xenograft models for the preclinical evaluation of new therapies in acute leukemia. Leuk. Lymphoma. 2007;48:659–668. [PubMed] [Google Scholar]
  • Lee J, et al. Epigenetic-mediated dysfunction of the bone morphogenetic protein pathway inhibits differentiation of glioblastoma-initiating cells. Cancer Cell. 2008;13:69–80. [PMC free article] [PubMed] [Google Scholar]
  • Legrand N, et al. Humanized mice for modeling human infectious disease: Challenges, progress, and outlook. Cell Host Microbe. 2009;6:5–9. [PubMed] [Google Scholar]
  • Lei H, et al. De novo DNA cytosine methyltransferase activities in mouse embryonic stem cells. Development. 1996;122:3195–3205. [PubMed] [Google Scholar]
  • LePage DF, Conlon RA. Animal models for disease: Knockout, knock-in, and conditional mutant mice. Methods Mol. Med. 2006;129:41–67. [PubMed] [Google Scholar]
  • Lessard J, Sauvageau G. Bmi-1 determines the proliferative capacity of normal and leukaemic stem cells. Nature. 2003;423:255–260. [PubMed] [Google Scholar]
  • Lifsted T, et al. Identification of inbred mouse strains harboring genetic modifiers of mammary tumor age of onset and metastatic progression. IntJCancer. 1998;77:640–644. [PubMed] [Google Scholar]
  • Lin EY, et al. Colony-stimulating factor 1 promotes progression of mammary tumors to malignancy. J. Exp. Med. 2001;193:727–740. [PMC free article] [PubMed] [Google Scholar]
  • Lipsick JS, Wang DM. Transformation by v-Myb. Oncogene. 1999;18:3047–3055. [PubMed] [Google Scholar]
  • Liu X, et al. Somatic loss of BRCA1 and p53 in mice induces mammary tumors with features of human BRCA1-mutated basal-like breast cancer. Proc. Natl. Acad. Sci. USA. 2007;104:12111–12116. [PMC free article] [PubMed] [Google Scholar]
  • Lobato MN, et al. Modeling chromosomal translocations using conditional alleles to recapitulate initiating events in human leukemias. J. Natl. Cancer Inst. Monogr. 2008;39:58–63. [PubMed] [Google Scholar]
  • Lozano G, Behringer RR. New mouse models of cancer: Single-cell knockouts. Proc. Natl. Acad. Sci. USA. 2007;104:4245–4246. [PMC free article] [PubMed] [Google Scholar]
  • Luo G, et al. Cancer predisposition caused by elevated mitotic recombination in Bloom mice. Nat. Genet. 2000;26:424–429. [PubMed] [Google Scholar]
  • Lyons SK. Advances in imaging mouse tumour models in vivo. J. Pathol. 2005;205:194–205. [PubMed] [Google Scholar]
  • Macleod KF, Jacks T. Insights into cancer from transgenic mouse models. J. Pathol. 1999;187:43–60. [PubMed] [Google Scholar]
  • MacPhee M, et al. The secretory phospholipase A2 gene is a candidate for the Mom1 locus, a major modifier of ApcMin-induced intestinal neoplasia. Cell. 1995;81:957–966. [PubMed] [Google Scholar]
  • Maddison K, Clarke AR. New approaches for modelling cancer mechanisms in the mouse. J. Pathol. 2005;205:181–193. [PubMed] [Google Scholar]
  • Maedler K, et al. Glucose-induced beta cell production of IL-1beta contributes to glucotoxicity in human pancreatic islets. J. Clin. Invest. 2002;110:851–860. [PMC free article] [PubMed] [Google Scholar]
  • Maggi A, et al. Techniques: Reporter mice—A new way to look at drug action. Trends Pharmacol. Sci. 2004;25:337–342. [PubMed] [Google Scholar]
  • Martinez R, et al. A microarray-based DNA methylation study of glioblastoma multiforme. Epigenetics. 2009;4:255–264. [PubMed] [Google Scholar]
  • Marumoto T, et al. Development of a novel mouse glioma model using lentiviral vectors. Nat. Med. 2009;15:110–116. [PMC free article] [PubMed] [Google Scholar]
  • Maser RS, et al. Chromosomally unstable mouse tumours have genomic alterations similar to diverse human cancers. Nature. 2007;447:966–971. [PMC free article] [PubMed] [Google Scholar]
  • Meister G, Tuschl T. Mechanisms of gene silencing by double-stranded RNA. Nature. 2004;431:343–349. [PubMed] [Google Scholar]
  • Mellinghoff IK, et al. Molecular determinants of the response of glioblastomas to EGFR kinase inhibitors. N. Engl. J. Med. 2005;353:2012–2024. [PubMed] [Google Scholar]
  • Metzger D, Chambon P. Site- and time-specific gene targeting in the mouse. Methods. 2001;24:71–80. [PubMed] [Google Scholar]
  • Mikkola HK, Orkin SH. Gene targeting and transgenic strategies for the analysis of hematopoietic development in the mouse. Methods Mol. Med. 2005;105:3–22. [PubMed] [Google Scholar]
  • Momota H, Holland EC. Bioluminescence technology for imaging cell proliferation. Curr. Opin. Biotechnol. 2005;16:681–686. [PubMed] [Google Scholar]
  • Montini E, et al. The genotoxic potential of retroviral vectors is strongly modulated by vector design and integration site selection in a mouse model of HSC gene therapy. J. Clin. Invest. 2009;119:964–975. [PMC free article] [PubMed] [Google Scholar]
  • Moon JH, et al. Induction of neural stem cell-like cells (NSCLCs) from mouse astrocytes by Bmi1. Biochem. Biophys. Res. Commun. 2008;371:267–272. [PubMed] [Google Scholar]
  • Morahan G, et al. Establishment of “The Gene Mine”: A resource for rapid identification of complex trait genes. Mamm. Genome. 2008;19:390–393. [PubMed] [Google Scholar]
  • Morishita K, et al. Retroviral activation of a novel gene encoding a zinc finger protein in IL-3-dependent myeloid leukemia cell lines. Cell. 1988;54:831–840. [PubMed] [Google Scholar]
  • Moser AR, et al. The Min (multiple intestinal neoplasia) mutation: Its effect on gut epithelial cell differentiation and interaction with a modifier system. J. Cell Biol. 1992;116:1517–1526. [PMC free article] [PubMed] [Google Scholar]
  • Mowat M, et al. Rearrangements of the cellular p53 gene in erythroleukaemic cells transformed by Friend virus. Nature. 1985;314:633–636. [PubMed] [Google Scholar]
  • Muzumdar MD, et al. Modeling sporadic loss of heterozygosity in mice by using mosaic analysis with double markers (MADM) Proc. Natl. Acad. Sci. USA. 2007;104:4495–4500. [PMC free article] [PubMed] [Google Scholar]
  • Nadeau JH, et al. Analysing complex genetic traits with chromosome substitution strains. Nat. Genet. 2000;24:221–225. [PubMed] [Google Scholar]
  • Ngan ES, et al. The mifepristone-inducible gene regulatory system in mouse models of disease and gene therapy. Semin. Cell Dev. Biol. 2002;13:143–149. [PubMed] [Google Scholar]
  • Niwa H. How is pluripotency determined and maintained? Development. 2007;134:635–646. [PubMed] [Google Scholar]
  • No D, et al. Ecdysone-inducible gene expression in mammalian cells and transgenic mice. Proc. Natl. Acad. Sci. USA. 1996;93:3346–3351. [PMC free article] [PubMed] [Google Scholar]
  • Nohmi T, et al. Recent advances in the protocols of transgenic mouse mutation assays. Mutat. Res. 2000;455:191–215. [PubMed] [Google Scholar]
  • Normanno N, et al. Target-based therapies in breast cancer: Current status and future perspectives. Endocr. Relat. Cancer. 2009;16:675–702. [PubMed] [Google Scholar]
  • Odelberg SJ. Inducing cellular dedifferentiation: A potential method for enhancing endogenous regeneration in mammals. Semin. Cell Dev. Biol. 2002;13:335–343. [PubMed] [Google Scholar]
  • O’Hagan RC, et al. Telomere dysfunction provokes regional amplification and deletion in cancer genomes. Cancer Cell. 2002;2:149–155. [PubMed] [Google Scholar]
  • Okano M, et al. DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell. 1999;99:247–257. [PubMed] [Google Scholar]
  • Olive KP, et al. Inhibition of Hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer. Science. 2009;324:1457–1461. [PMC free article] [PubMed] [Google Scholar]
  • Parangi S, et al. Antiangiogenic therapy of transgenic mice impairs de novo tumor growth. Proc. Natl. Acad. Sci. USA. 1996;93:2002–2007. [PMC free article] [PubMed] [Google Scholar]
  • Park IK, et al. Bmi-1 is required for maintenance of adult self-renewing haematopoietic stem cells. Nature. 2003a;423:302–305. [PubMed] [Google Scholar]
  • Park YG, et al. Multiple cross and inbred strain haplotype mapping of complex-trait candidate genes. Genome Res. 2003b;13:118–121. [PMC free article] [PubMed] [Google Scholar]
  • Park YG, et al. Sipa1 is a candidate for underlying the metastasis efficiency modifier locus Mtes1. Nat. Genet. 2005;37:1055–1062. [PMC free article] [PubMed] [Google Scholar]
  • Peeper D, Berns A. Cross-species oncogenomics in cancer gene identification. Cell. 2006;125:1230–1233. [PubMed] [Google Scholar]
  • Pegram M, Ngo D. Application and potential limitations of animal models utilized in the development of trastuzumab (Herceptin): A case study. Adv. Drug Deliv. Rev. 2006;58:723–734. [PubMed] [Google Scholar]
  • Pelengaris S, et al. Suppression of Myc-induced apoptosis in beta cells exposes multiple oncogenic properties of Myc and triggers carcinogenic progression. Cell. 2002;109:321–334. [PubMed] [Google Scholar]
  • Peters LL, et al. The mouse as a model for human biology: A resource guide for complex trait analysis. Nat. Rev. Genet. 2007;8:58–69. [PubMed] [Google Scholar]
  • Pikarsky E, et al. NF-kappaB functions as a tumour promoter in inflammation-associated cancer. Nature. 2004;431:461–466. [PubMed] [Google Scholar]
  • Porret A, et al. Tissue-specific transgenic and knockout mice. Methods Mol. Biol. 2006;337:185–205. [PubMed] [Google Scholar]
  • Pritchard JB, et al. The role of transgenic mouse models in carcinogen identification. Environ. Health Perspect. 2003;111:444–454. [PMC free article] [PubMed] [Google Scholar]
  • Prosser H, Bradley A. Transgenics at breaking-point. Cancer Cell. 2003;3:411–413. [PubMed] [Google Scholar]
  • Qiu TH, et al. Global expression profiling identifies signatures of tumor virulence in MMTV-PyMT-transgenic mice: Correlation to human disease. Cancer Res. 2004;64:5973–5981. [PubMed] [Google Scholar]
  • Qiu W, et al. No evidence of clonal somatic genetic alterations in cancer-associated fibroblasts from human breast and ovarian carcinomas. Nat. Genet. 2008;40:650–655. [PMC free article] [PubMed] [Google Scholar]
  • Quintana E, et al. Efficient tumour formation by single human melanoma cells. Nature. 2008;456:593–598. [PMC free article] [PubMed] [Google Scholar]
  • Ramaswamy S, et al. A molecular signature of metastasis in primary solid tumors. Nat. Genet. 2003;33:49–54. [PubMed] [Google Scholar]
  • Reilly KM, et al. Nf1;Trp53 mutant mice develop glioblastoma with evidence of strain-specific effects. Nat. Genet. 2000;26:109–113. [PubMed] [Google Scholar]
  • Reilly KM, et al. An imprinted locus epistatically influences Nstr1 and Nstr2 to control resistance to nerve sheath tumors in a neurofibromatosis type 1 mouse model. Cancer Res. 2006;66:62–68. [PMC free article] [PubMed] [Google Scholar]
  • Richmond A, Su Y. Mouse xenograft models vs GEM models for human cancer therapeutics. Dis. Model Mech. 2008;1:78–82. [PMC free article] [PubMed] [Google Scholar]
  • Riggins GJ, et al. Absence of secretory phospholipase A2 gene alterations in human colorectal cancer. Cancer Res. 1995;55:5184–5186. [PubMed] [Google Scholar]
  • Sandy P, et al. Mammalian RNAi: A practical guide. Biotechniques. 2005;39:215–224. [PubMed] [Google Scholar]
  • Sausville EA, Burger AM. Contributions of human tumor xenografts to anticancer drug development. Cancer Res. 2006;66:3351–3354. discussion 3354. [PubMed] [Google Scholar]
  • Schnutgen F, et al. Genomewide production of multipurpose alleles for the functional analysis of the mouse genome. Proc. Natl. Acad. Sci. USA. 2005;102:7221–7226. [PMC free article] [PubMed] [Google Scholar]
  • Sharan SK, et al. Recombineering: A homologous recombination-based method of genetic engineering. Nat. Protoc. 2009;4:206–223. [PMC free article] [PubMed] [Google Scholar]
  • Sharpless NE, Depinho RA. The mighty mouse: Genetically engineered mouse models in cancer drug development. Nat. Rev. Drug Discov. 2006;5:741–754. [PubMed] [Google Scholar]
  • Shchors K, Evan G. Tumor angiogenesis: Cause or consequence of cancer? Cancer Res. 2007;67:7059–7061. [PubMed] [Google Scholar]
  • Shchors K, et al. The Myc-dependent angiogenic switch in tumors is mediated by interleukin 1beta. Genes Dev. 2006;20:2527–2538. [PMC free article] [PubMed] [Google Scholar]
  • Shizuya H, et al. Cloning and stable maintenance of 300-kilobase-pair fragments of human DNA in Escherichia coli using an F-factor-based vector. Proc. Natl. Acad. Sci. USA. 1992;89:8794–8797. [PMC free article] [PubMed] [Google Scholar]
  • Shore SK, et al. Transforming pathways activated by the v-Abl tyrosine kinase. Oncogene. 2002;21:8568–8576. [PubMed] [Google Scholar]
  • Silva JM, et al. Second-generation shRNA libraries covering the mouse and human genomes. Nat. Genet. 2005;37:1281–1288. [PubMed] [Google Scholar]
  • Singer O, Verma IM. Applications of lentiviral vectors for shRNA delivery and transgenesis. Curr. Gene Ther. 2008;8:483–488. [PMC free article] [PubMed] [Google Scholar]
  • Skarnes WC, et al. A public gene trap resource for mouse functional genomics. Nat. Genet. 2004;36:543–544. [PMC free article] [PubMed] [Google Scholar]
  • Soriano P. Generalized lacZ expression with the ROSA26 Cre reporter strain. Nat. Genet. 1999;21:70–71. [PubMed] [Google Scholar]
  • Soverini S, et al. Imatinib mesylate for the treatment of chronic myeloid leukemia. Expert Rev. Anticancer Ther. 2008;8:853–864. [PubMed] [Google Scholar]
  • Starr TK, et al. A transposon-based genetic screen in mice identifies genes altered in colorectal cancer. Science. 2009;323:1747–1750. [PMC free article] [PubMed] [Google Scholar]
  • Stegmeier F, et al. A lentiviral microRNA-based system for single-copy polymerase II-regulated RNA interference in mammalian cells. Proc. Natl. Acad. Sci. USA. 2005;102:13212–13217. [PMC free article] [PubMed] [Google Scholar]
  • Steindler DA, Laywell ED. Astrocytes as stem cells: Nomenclature, phenotype, and translation. Glia. 2003;43:62–69. [PubMed] [Google Scholar]
  • Stern P, et al. A system for Cre-regulated RNA interference in vivo. Proc. Natl. Acad. Sci. USA. 2008;105:13895–13900. [PMC free article] [PubMed] [Google Scholar]
  • Suzuki T, et al. Tumor suppressor gene identification using retroviral insertional mutagenesis in Blm-deficient mice. EMBO J. 2006;25:3422–3431. [PMC free article] [PubMed] [Google Scholar]
  • Swing DA, Sharan SK. BAC rescue: A tool for functional analysis of the mouse genome. Methods Mol. Biol. 2004;256:183–198. [PubMed] [Google Scholar]
  • Takahashi K, Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell. 2006;126:663–676. [PubMed] [Google Scholar]
  • Tan BT, et al. The cancer stem cell hypothesis: A work in progress. Lab. Invest. 2006;86:1203–1207. [PubMed] [Google Scholar]
  • Tan SH, et al. Pharmacogenetics in breast cancer therapy. Clin. Cancer Res. 2008;14:8027–8041. [PubMed] [Google Scholar]
  • Teicher BA. In vivo/ex vivo and in situ assays used in cancer research: A brief review. Toxicol. Pathol. 2009;37:114–122. [PubMed] [Google Scholar]
  • Terabe M, Berzofsky JA. NKT cells in immunoregulation of tumor immunity: A new immunoregulatory axis. Trends Immunol. 2007;28:491–496. [PubMed] [Google Scholar]
  • Terabe M, Berzofsky JA. The role of NKT cells in tumor immunity. Adv. Cancer Res. 2008;101:277–348. [PMC free article] [PubMed] [Google Scholar]
  • Theurillat JP, et al. Early induction of angiogenetic signals in gliomas of GFAP-v-src transgenic mice. Am. J. Pathol. 1999;154:581–590. [PMC free article] [PubMed] [Google Scholar]
  • Tiscornia G, et al. CRE recombinase-inducible RNA interference mediated by lentiviral vectors. Proc. Natl. Acad. Sci. USA. 2004;101:7347–7351. [PMC free article] [PubMed] [Google Scholar]
  • Tlsty TD, Coussens LM. Tumor stroma and regulation of cancer development. Annu. Rev. Pathol. 2006;1:119–150. [PubMed] [Google Scholar]
  • To C, et al. The Centre for Modeling Human Disease Gene Trap resource. Nucleic Acids Res. 2004;32:D557–D559. [PMC free article] [PubMed] [Google Scholar]
  • Tomlinson IP, et al. Variants at the secretory phospholipase A2 (PLA2G2A) locus: Analysis of associations with familial adenomatous polyposis and sporadic colorectal tumours. Ann. Hum. Genet. 1996;60:369–376. [PubMed] [Google Scholar]
  • Troiani T, et al. The use of xenograft models for the selection of cancer treatments with the EGFR as an example. Crit. Rev. Oncol. Hematol. 2008;65:200–211. [PubMed] [Google Scholar]
  • Uren AG, et al. Retroviral insertional mutagenesis: Past, present and future. Oncogene. 2005;24:7656–7672. [PubMed] [Google Scholar]
  • Uren AG, et al. Large-scale mutagenesis in p19(ARF)- and p53-deficient mice identifies cancer genes and their collaborative networks. Cell. 2008;133:727–741. [PMC free article] [PubMed] [Google Scholar]
  • Valdar W, et al. Simulating the collaborative cross: Power of quantitative trait loci detection and mapping resolution in large sets of recombinant inbred strains of mice. Genetics. 2006;172:1783–1797. [PMC free article] [PubMed] [Google Scholar]
  • van derWeyden L, et al. Chromosome engineering in ES cells. Methods Mol. Biol. 2009;530:49–77. [PubMed] [Google Scholar]
  • Van Dyke T, Jacks T. Cancer modeling in the modern era: Progress and challenges. Cell. 2002;108:135–144. [PubMed] [Google Scholar]
  • van Lohuizen M, Berns A. Tumorigenesis by slow-transforming retroviruses—An update. Biochim. Biophys. Acta. 1990;1032:213–235. [PubMed] [Google Scholar]
  • van Lohuizen M, et al. N-myc is frequently activated by proviral insertion in MuLVinduced T cell lymphomas. EMBO J. 1989;8:133–136. [PMC free article] [PubMed] [Google Scholar]
  • van Lohuizen M, et al. Identification of cooperating oncogenes in E mu-myc transgenic mice by provirus tagging. Cell. 1991;65:737–752. [PubMed] [Google Scholar]
  • van ’t Veer LJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–536. [PubMed] [Google Scholar]
  • Ventura A, et al. Cre-lox-regulated conditional RNA interference from transgenes. Proc. Natl. Acad. Sci. USA. 2004;101:10380–10385. [PMC free article] [PubMed] [Google Scholar]
  • Walrath JC, et al. Chr 19(A/J) modifies tumor resistance in a sex- and parent-of-origin- specific manner. Mamm. Genome. 2009;20:214–223. [PMC free article] [PubMed] [Google Scholar]
  • Wang X, Paigen B. Genetics of variation in HDL cholesterol in humans and mice. Circ. Res. 2005;96:27–42. [PubMed] [Google Scholar]
  • Wang XJ, et al. Development of gene-switch transgenic mice that inducibly express transforming growth factor beta1 in the epidermis. Proc. Natl. Acad. Sci. USA. 1999;96:8483–8488. [PMC free article] [PubMed] [Google Scholar]
  • Wang X, et al. Identifying novel genes for atherosclerosis through mouse-human comparative genetics. Am. J. Hum. Genet. 2005;77:1–15. [PMC free article] [PubMed] [Google Scholar]
  • Wang W, et al. Induced mitotic recombination of p53 in vivo. Proc. Natl. Acad. Sci. USA. 2007;104:4501–4505. [PMC free article] [PubMed] [Google Scholar]
  • Wei K, et al. Mouse models for human DNA mismatch-repair gene defects. Trends Mol. Med. 2002;8:346–353. [PubMed] [Google Scholar]
  • Weinberg RA. Is metastasis predetermined? Mol. Oncol. 2007;1:263–264. author reply 265–266. [PubMed] [Google Scholar]
  • Weinberg RA. Coevolution in the tumor microenvironment. Nat. Genet. 2008;40:494–495. [PubMed] [Google Scholar]
  • Westbrook TF, et al. Dissecting cancer pathways and vulnerabilities with RNAi. Cold Spring Harb. Symp. Quant. Biol. 2005;70:435–444. [PubMed] [Google Scholar]
  • Westphal CH, et al. atm and p53 cooperate in apoptosis and suppression of tumorigenesis, but not in resistance to acute radiation toxicity. Nat. Genet. 1997;16:397–401. [PubMed] [Google Scholar]
  • Wilson MH, et al. PiggyBac transposon-mediated gene transfer in human cells. Mol. Ther. 2007;15:139–145. [PubMed] [Google Scholar]
  • Yamamoto M, et al. A multifunctional reporter mouse line for Cre- and FLP-dependent lineage analysis. Genesis. 2009;47:107–114. [PubMed] [Google Scholar]
  • Yang H, et al. Metastasis predictive signature profiles pre-exist in normal tissues. Clin. Exp. Metastasis. 2005;22:593–603. [PMC free article] [PubMed] [Google Scholar]
  • Yang FC, et al. Nf1-dependent tumors require a microenvironment containing Nf1 +/− and c-kit-dependent bone marrow. Cell. 2008;135:437–448. [PMC free article] [PubMed] [Google Scholar]
  • Yu L, et al. Interaction between bevacizumab and murine VEGF-A: A reassessment. Invest. Ophthalmol. Vis. Sci. 2008;49:522–527. [PubMed] [Google Scholar]
  • Zardo G, et al. Epigenetic plasticity of chromatin in embryonic and hematopoietic stem/progenitor cells: Therapeutic potential of cell reprogramming. Leukemia. 2008;22:1503–1518. [PubMed] [Google Scholar]
  • Zender L, et al. Identification and validation of oncogenes in liver cancer using an integrative oncogenomic approach. Cell. 2006;125:1253–1267. [PMC free article] [PubMed] [Google Scholar]
  • Zeng Y, et al. Both natural and designed micro RNAs can inhibit the expression of cognate mRNAs when expressed in human cells. Mol. Cell. 2002;9:1327–1333. [PubMed] [Google Scholar]
  • Zheng L, Lee WH. Retinoblastoma tumor suppressor and genome stability. Adv. Cancer Res. 2002;85:13–50. [PubMed] [Google Scholar]
  • Zheng H, et al. Induction of abnormal proliferation by nonmyelinating Schwann cells triggers neurofibroma formation. Cancer Cell. 2008;13:117–128. [PubMed] [Google Scholar]
  • Zhu Y, et al. Neurofibromas in NF1: Schwann cell origin and role of tumor environment. Science. 2002;296:920–922. [PMC free article] [PubMed] [Google Scholar]
  • Zhu Y, et al. Early inactivation of p53 tumor suppressor gene cooperating with NF1 loss induces malignant astrocytoma. Cancer Cell. 2005;8:119–130. [PMC free article] [PubMed] [Google Scholar]

Formats:

Share

Save items

Add to FavoritesView more options

Similar articles in PubMed

See reviews…See all…

Cited by other articles in PMC

See all…

Links

Recent Activity

ClearTurn Off

See more…

See more …

See more …

See more …

See more …

See more …

See more …

See more …

Support CenterSupport Center

Simple NCBI Directory

External link. Please review our privacy policy.NLMNIHDHHSUSA.gov

National Center for Biotechnology InformationU.S. National Library of Medicine8600 Rockville Pike, Bethesda MD, 20894 USAPolicies and Guidelines | Contact

                  

Create accountSign in

Mouse Model

A mouse model is currently being generated to investigate whether the upregulation of cardiac actin may alleviate the myopathy caused by ACTA1 mutations.163

From: Neurology and Clinical Neuroscience, 2007

Related terms:

View all TopicsDownload as PDFSet alertAbout this page

Learn more about Mouse Model

Mouse Models

Siân E. Piret, Rajesh V. Thakker, in Genetics of Bone Biology and Skeletal Disease, 2013

II Methods for Generating Mouse Models

Non-Targeted Strategies

Spontaneous mutations in mice may result in benign phenotypes such as variable coat colors, or in disorders that have similarities to diseases in humans, e.g. the hypophosphatamia (Hyp) mouse, which is representative of X-linked hypophosphatemia in humans.52 Such spontaneous mutations occur at very low frequencies, thus several techniques that increase the rate of mutation induction in the mouse genome by either non-targeted (random) or targeted strategies have been developed (see Tables 13.1 and 13.2). An early example of non-targeted mutagenesis is provided by irradiation, which generated the Gy mouse, a second model for X-linked hypophosphatemia.52 More recently, chemical mutagens have been used in large-scale mutagenesis programs. Successful agents include isopropyl methane sulfonate (iPMS) used to generate the Nuf mouse model with an activating calcium-sensing receptor (CaSR) mutation, and N-ethyl-N-nitrosourea (ENU) used to generate a mouse model for osteogenesis imperfecta with a collagen 1 alpha 1 (COL1A1) mutation. ENU, which is an alkylating agent that primarily introduces point mutations via transfer of the ENU alkyl group to the DNA base followed by mispairing and subsequent basepair substitution during the next round of DNA replication (Figure 13.1A), is the most potent mutagen in mice.14 Intraperitoneal injections of ENU to male mice generate approximately one mutation per 1–1.5 Mbp of sperm DNA,14 which allows the mutations to be inherited (Figure 13.1B). ENU mutagenesis programs utilize two complementary approaches, which are phenotype-driven and genotype-driven screens. In phenotype-driven screens, the offspring of mutagenized mice are assessed for phenotypic variances, using a panel of morphological, biochemical, or behavioral tests, in a “hypothesis-generating” strategy, which aims to elucidate new genes, pathways and mechanisms for a disease phenotype14 (Figure 13.1B). By establishing appropriate matings, phenotype-driven screens can be used to identify dominant or recessive phenotypes. Genotype-driven screens, in which mutations in a gene of interest are sought, are “hypothesis-driven” and are feasible by using available parallel archives of DNA and sperm samples from mutagenized male mice (Figure 13.1B). Archived DNA samples from the mutagenized male mice are used to search for mutations in the gene of interest, and once mutations are identified in the mouse DNA, then the corresponding sperm sample for the male mouse harboring the mutation is used to establish progeny carrying the mutation by in vitro fertilization.14 It is estimated that the probability of finding three or more mutant alleles in an archive of >5000 DNA samples is >90%.53 Thus, the gene-driven approach can be used to generate an “allelic series” of mutations within one gene, which may yield insights into genotype–phenotype correlations in the gene and disease of interest.54

ENU mutations most frequently result in missense mutations (>80%) that may generate hypo- and hypermorphs, although occasionally nonsense or frame-shift mutations (<10%) generating knockout models may be obtained.55 However, a more recent and reliable method for generating non-targeted knockout models on a large scale is by the use of insertional mutagenesis, utilizing gene-trap strategies.56,57 Gene-trap vectors usually consist of a reporter gene, either with or without a promoter, and a strong splice acceptor site, which causes any upstream exons to splice directly to the gene-trap15 (see Figure 13.1C). The vector is either electroporated or retrovirally infected into embryonic stem (ES) cells, after which it randomly inserts into the genome. Mutagenized ES cells are then re-introduced into developing blastocysts to generate chimeric mice, from which germline mutant mice can be bred (Figure 13.2). A recent refinement of the gene-trap strategy is targeted trapping, in which the vector also contains regions homologous to the targeted gene, thereby facilitating the deletion of a specific gene.16,56

Targeted Strategies

A specific loss of function (i.e. knockout) of a gene of interest in the germline can be generated to yield conventional targeted knockout models, as follows. A targeting construct is assembled, which contains two “arms” of sequence homologous to the gene of interest and that flank a positive selection cassette such as the E. coli neomycin phosphotransferase (NeoR) gene (Figure 13.3A). Integration of the NeoR gene (and therefore the targeting construct) into the ES cell genome allows these ES cells to survive normally toxic amounts of antibiotic treatment, thereby allowing selection of ES cells that have been successfully targeted by homologous recombination. Furthermore, replacement of an exon (or exons) by the NeoR cassette results in gene disruption, i.e. “knockout”17 (Figure 13.3A). To facilitate further the selection of ES cells that have undergone successful targeting by homologous recombination, a negative selection cassette, such as the Herpes simplex virus thymidine kinase (TK) gene, may also be used. The TK gene cassette is inserted at one end of the homologous region of the targeting construct, such that the TK cassette is lost if homologous recombination has occurred (Figure 13.3A), but retained if non-homologous recombination has occurred. In the presence of a thymidine analog in the growth medium, ES cells containing the TK cassette (i.e. following non-homologous recombination) will not undergo cell division, as the thymidine analog will undergo phosphorylation and will be incorporated into the DNA by the TK, and thereby disrupt cell division, hence selecting out these ES cells. In contrast, those ES cells that do not have the TK cassette (i.e. following homologous recombination) will not have disrupted cell division due to incorporation of the thymidine analog and, as a result, will proliferate.17 Correctly targeted ES cells are used to generate chimeric mice (see Figure 13.2), which are then bred with wild-type mice to yield mice with germline transmission of the disrupted allele, i.e. “knockout” mice, that have one copy of the disrupted allele in all of their cells. Cross-breeding of these heterozygous knockout mice can then yield homozygous knockout mice, which will have a disruption of both alleles of the gene in all of their cells. These “conventional” knockout models have proved to be very useful in studies of human diseases, although their use may be limited if the disruption of the gene in a critical organ results in early death, e.g. at any embryonic stage. To overcome such limitations, it may be useful to generate tissue-specific (i.e. conditional knockout) or time-specific (i.e. inducible knockout) models. This can be achieved by refining the gene-trap and “conventional” knockout strategies by the addition of either LoxP or flippase (FLP) recombinase target (FRT) sites in the targeting vector (Figure 13.3B). LoxP and FRT sites are short DNA sequences which are recognized and acted upon by the enzymes Cre (causes recombination) recombinase or FLP recombinase enzymes, respectively and, when inserted to flank the genomic region of interest, will result in either excision or inversion of the DNA flanked by the LoxP or FRT sequences, depending on whether the two sequences are in the same orientation (Figure 13.3B), or opposite orientations, respectively. Thus, insertion of the LoxP and FRT sequences allows several variations on the knockout mouse, including either tissue-specific (conditional) or time-specific (inducible) knockouts (see Table 13.2). Thus, if mice containing alleles in which the exon containing the start codon is flanked by LoxP sites (“floxed”) or FRT sites (“flirted”), are crossed with transgenic mice expressing Cre or FLP under the control of tissue-specific promoters (e.g. the PTH gene promoter for parathyroid-specific expression), the gene of interest can be knocked out in a specific tissue (Figure 13.3B). The inducible models utilize a fusion protein, such as a modified ligand-binding domain of the estrogen receptor fused to the Cre (CreER) or FLP gene which, on administration of an estrogen receptor antagonist (tamoxifen), translocates to the nucleus to excise the floxed allele(s), thereby allowing the gene to be permanently knocked out at the desired time, which may be either during embryonic or neonatal development, or in adult life.18 These conditional and inducible knockout mouse models have proved particularly useful to overcome embryonic or early postnatal lethality, for example in studies of Blomstrand’s chondrodysplasia due to PTHR1 loss of function (see Table 13.4), or to understand the role of a protein in one particular tissue.

Knockout mice have been very valuable for the study of physiological functions of proteins and the elucidation of disease mechanisms. However, knockout models are not always the most appropriate, particularly when the human disease being studied is not due to a loss of function or null allele for the gene. Indeed, the majority of human diseases are unlikely to be due to null alleles, but are instead associated with point mutations, which may result in a constitutively active protein, or a toxic gain of function, as illustrated by PTHR1 mutations in Jansen’s disease (see Table 13.4), or dominant negative effects. Thus, to generate appropriate murine models for these diseases, the specific mutation needs to be introduced into the mouse genome, and this may be achieved by targeted knock-in or transgenic approaches (see Tables 13.1 and 13.2). The generation of targeted knock-in models utilizes a similar approach to that described above for targeted knockout models, except that a targeting vector which carries the desired mutation must be specifically generated (see Figure 13.3C). In addition, the positive selection cassette is normally placed in an intron and floxed so that it can be excised and cause minimal effects on gene expression.19 The generation of transgenic models utilizes a targeting construct which usually contains the cDNA carrying the mutation, together with an appropriate promoter and poly(A) sequence, which is injected into the pronucleus of fertilizd mouse eggs.20,21 The transgene undergoes random insertion into the genome, and several copies are often inserted together, which therefore generates an overexpression model. As reviewed below, these different strategies for generating mouse models of human diseases have greatly facilitated studies of inherited bone and mineral disorders that have investigated mechanisms and treatments, which would not be easily feasible in patients.

Read full chapterPurchase book

Mouse models of gastrointestinal cancers in drug development and research

Ishfaq Ahmed, … Ashiq Masood, in Animal Models in Cancer Drug Discovery, 2019

5 Conclusion

Mouse models are an essential tool to assess the efficacy of novel drugs. There have been significant advances in developing novel therapeutics for cancer treatment. Through several years of research on drug efficacy in mouse models, we have learned that for a successful transition to clinical trials, innovative and more sophisticated models are needed for the purpose of drug testing. Though mouse models allow us to have a greater understanding of the biology of disease; all suffer limitations for various applications. The recent advances in the implementation of PDOs, organoid xenografts, and patient-derived xenografts for preclinical assessment along with the study of novel immunotherapies in Genetically engineered mouse models offer hope for the translation of novel therapeutic strategies for drug development.

Read full chapterPurchase book

Mouse Models of The Nuclear Envelopathies and Related Diseases

Henning F. Horn, in Current Topics in Developmental Biology, 2014

9 Conclusions

Mouse models provide a valuable tool for studying human diseases. This has certainly been true for mouse models of LINC complex proteins and their associated diseases. The various Nesprin-1 mouse models have augmented our understanding of the underlying biology of muscular dystrophyautosomal recessive arthrogryposis, and autosomal recessive cerebellar ataxia. The mouse models for the Nesprin-4-associated hearing loss were critical in elucidating the cell biology and provided key insights into this entirely novel class of human hearing loss. And mouse models of SUN1 and KASH5 have allowed us a greater appreciation for the importance of chromosomal movement in the development of gametes. Indeed, our mouse model-generated understanding of LINC complex functions may even prove to be predictive for human diseases. For example, a novel human disorder was recently described that has features of mandibular acral dysplasia but also includes deafness and male hypogonadism as prominent associated features (Shastry et al., 2010). Several candidate genes were examined, but no mutations were found to cause this genetic condition. However, given our knowledge of the roles of SUN1, and the phenotypes of the SUN1 mouse models (hearing loss and hypogonadism), it would be interesting to check the function of SUN1 in these patients.

While a number of mouse models now exist for LINC complex proteins, the field is still relatively young. Indeed, we are still discovering novel LINC complex functions and novel variants of LINC complexes. We therefore expect that future mouse models will continue to augment our understanding of the LINC complex in normal as well as pathophysiological roles.

Read full chapterPurchase book

Alzheimer’s Disease: Transgenic Mouse Models

K.H. Ashe, in Encyclopedia of Neuroscience, 2009

Transgenic mouse models of Alzheimer’s disease (AD) have been created to study the structural and functional consequences of the accumulation of the amyloid-β and tau proteins in the brain. They have also been used to test experimental therapeutic interventions for AD. No transgenic mouse model perfectly represents all stages and facets of AD; transgenic mouse models cannot supplant the need for studying the disease in humans and human clinical trials. However, studies in transgenic mouse models allow researchers to understand aspects of the pathophysiology of AD and coordinate efforts to diagnose and treat the illness in humans.

Read full chapterPurchase book

In Vitro and In Vivo Animal Models

Azka Khan, … Alvina Gul, in Omics Technologies and Bio-Engineering, 2018

18.2.13 Transgenic Mouse of PD

Mouse models have been a vital tool for research in neurodegenerative diseases. They have been proved as an effective model organism for PD. Both in vitro and in vivo mouse models have been extensively used. Many transgenic mouse models have been generated to study PD; α-synuclein protein has very important role in the pathology of this disease. KO mice and some transgenic mice with the ability to overexpress α-synuclein possess familial A53T or A30P mutations. α-Synuclein KO mice are viable and fertile, and they support a significant role of α-synuclein in regulation of dopaminergic neurotransmission, synaptic plasticity, and presynaptic vesicular release and recycling (Janus and Welzl, 2010).

Read full chapterPurchase book

Biomarkers for Assessing Risk of Cancer

Xifeng Wu, Jian Gu, in The Molecular Basis of Cancer (Fourth Edition), 2015

Mouse Models for Cancer Susceptibility Study

Mouse models that cross tumor-resistant with tumor-susceptible strains have been instrumental in mapping several candidate cancer susceptibility loci and expression quantitative trait loci (eQTLs)125-131 before the wide application of GWAS in human cancers. Although hundreds of cancer susceptibility loci have been identified through GWAS, the majority of the heritable risk of cancer cannot be explained by the main effects of common alleles. Gene-gene and gene-environment interaction clearly play important roles in cancer development, which is challenging in human studies because of the heterogeneity of human cancers. Mouse models have a defined genetic background that does not possess the genetic heterogeneity characteristic of human cancers. Crossing genetically distinct mouse strains can allow the analysis of the combinatorial effects of host genetic background and somatic events at different stages of cancer development. A recent study applied a network analysis in a mouse model of skin cancer that produces both benign tumors and malignant carcinomas and identified a genetic architecture affecting inflammation and tumor susceptibility.132 Gene–environment interactions can also be investigated using mouse models to identify how genetic modifiers of tumor initiation interact with specific environmental effects identified through epidemiological studies. Mouse models will also be a major tool for mechanistic studies of cancer susceptibility loci.

Read full chapterPurchase book

Molecular Basis of Lung Cancer

Mitsuo Sato, … John D. Minna, in The Molecular Basis of Cancer (Third Edition), 2008

New Transgenic Mouse Models of Lung Cancer

Mouse models that recapitulate the carcinogenic process of human lung cancer are powerful tools to improve our understanding of lung cancer pathogenesis, develop targeted therapeutics, and evaluate their in vivo efficacies. Several different types of transgenic mouse models for studying lung cancer have been developed with innovative strategies. Bitransgenic models using Cre/LoxP recombination or tetracycline-inducible gene expression system have enabled regulating the expression of a gene in mice in a timely and spatially controlled manner. Two groups engineered mouse strains harboring conditional mutant K-ras alleles that are expressed only after Cre/LoxP-mediated recombination occurs. Both groups showed that oncogenic K-ras activation induces lung adenocarcinoma, demonstrating the contributions of oncogenic K-ras to lung cancer pathogenesis (46). Moreover, Meuwissen et al. developed a mouse model of SCLC by inactivating both Rb and p53 using Cre/LoxP recombination system (46). Using a tetracycline-inducible gene expression system, mice harboring EGFR tyrosine kinase domain mutations were engineered. These mice developed adenocarcinomas very similar to human adenocarcinomas with EGFR mutations. Although there can be significant differences in lung tumor development between humans and mice, mouse models have a complete physiologic environment and allow analyzing host tumor interaction and angiogenesis, which cannot be studied in tissue culture. Finally, no mouse model of squamous cell carcinoma of the lung has been developed.

Read full chapterPurchase book

Huntington Disease

Laura A. Wagner, … Blair R. Leavitt, in Animal and Translational Models for CNS Drug Discovery, 2008

Validity of Animal Models of HD

Mouse models of HD are important to the discovery and validation of drug targets for HD as well as central to proving drug efficacy preceding human therapeutic trials. The development and validation of an effective mouse model of disease is no trivial matter and requires extensive characterization and rigorous validation (see Table 6.2). The ideal mouse model for HD agrees in etiology, pathophysiologysymptomatology, and response to therapeutics when compared to the human condition. Originally, chemical models of HD were investigated based on their similar striatal neurodegenerative pattern as seen in human HD patients. These chemical models met the very basic symptomatology criterion alone. Since the discovery of the HD gene, however, more accurate gene models of HD have been developed as transgenic mice representing HD etiology, pathophysiology, and symptomatology. Although species differences complicate the exact phenotype comparisons that can be made, genetic HD mice overall recapitulate cognitive failure, motor dysfunction, and striatal neurodegeneration as seen in human HD patients.

Table 6.2. Validation of animal models of diseasea

Face validity- a superficial resemblance between the mouse model and human disease. A similarity seen in symptoms is a common justification in this case (e.g., chemical models of HD).
Predictive validity- the ability of a model to predict the performance of the condition being modeled. One example is a model’s capacity to predict compound efficacy in therapeutic human trials.
Construct validity- a theoretical clarification of what a model is supposed to represent. This validation accounts for the inherent difference that may occur in a process when looking across species.
Etiological validity- in this case the model and the human condition must undergo identical etiologies. The simplest disease to model in this situation is that of a simple inheritance disease.

aVan Dam and De Deyn. (2006). Drug discovery in dementia: The role of rodent models. Nat Rev: Drug Discov 5:956–970.

Three basic design strategies have been applied in developing HD gene mouse models giving rise to three broad model categories including: (i) fragment models containing N-terminal fragments of the human mutant Htt protein in addition to both alleles of murine Hdh, (ii) full-length models containing the full-length human HD gene with an expanded polyglutamine tract in addition to both alleles of murine Hdh, and (iii) knock-in models of HD with pathogenic CAG expansions in murine Hdh. Individually these gene models are believed to represent certain aspects of HD based on their design and phenotype. These characteristics help define the strength of the model and its subsequent use in the field of HD research. Together these different gene models provide confirmatory proof of the dysfunction and disease caused by a Htt CAG expansion in mice. As a result, HD gene mouse models provide a powerful analysis for target validation and drug discovery preceding clinical trials. To date, the fourth criterion of an ideal HD mouse model, its predictive power in identifying effective drugs for HD awaits verification by emerging and ongoing human clinical trials.

Read full chapterPurchase book

Application of Mouse Genetics to Human Disease

Teresa M. Gunn, Brenda Canine, in Rosenberg’s Molecular and Genetic Basis of Neurological and Psychiatric Disease (Fifth Edition), 2015

Summary

Mouse models have led – and are certain to continue to lead – to significant breakthroughs in identifying genes, mechanisms, and pathways that underlie human neurologic diseases. Mice are also ideal for testing therapeutic approaches, something we are likely to see more of in the coming years. New methodologies have increased the speed and accuracy with which new mouse models can be generated, and technological advances have led to improved tools to analyze them. Models of multigenic disorders remain scarce. This is primarily because it is difficult to identify the variants that cause these traits, and most mouse models are presently generated using gene targeting, which requires the causative loci be known. Random mutagenesis and thorough phenotypic analysis (including behavioral studies) of existing mutants may reveal subtle and/or unexpected traits, and will complement other, ongoing projects aimed at discovering disease-associated variants in human populations. There is much excitement over the ability to reprogram fibroblasts or other patient-derived cells into induced pluripotent stem cells (iPSC), and the ability to differentiate those iPSC into neuronal stem cells allows for the analysis of those cells in culture. Injecting these cells into the mouse brain will create a new class of mouse models that will provide insight into the in vivo behavior of patient-derived cells in the mammalian nervous system. Combining these models with existing genetic models and reporter mice will create a powerful system for analyzing the pathogenesis of neurological disorders.

Read full chapterPurchase book

Gsα, Pseudohypoparathyroidism, Fibrous Dysplasia, and McCune–Albright Syndrome

Lee S. Weinstein, Michael T. Collins, in Genetics of Bone Biology and Skeletal Disease (Second Edition), 2018

3.4 Animal Models

Mouse models leading to constitutively activation of cAMP formation have been created by transgenic overexpression of Gsα, by expression of R201H or Q227L mutant forms of Gsα, or by expression of the cholera toxin A1 subunit, which covalently modifies R201 (Fig. 35.1). Transgenic expression of the cholera toxin A1 subunit in somatotrophs leads to pituitary hyperplasia and gigantism, whereas expression in thyroid cells leads to thyroid hyperplasia and hyperthyroidism.109 Gsα overexpression in the heart leads to cardiomyopathy,110 and expression of constitutively-activated forms of Gsα in the forebrain disrupts associative and spatial learning.111 A model of FD was created by transplanting Gsα-mutated skeletal progenitor cells into immunocompromised mice.112 A mouse model with germline expression of the R201C mutation survived, and with aging developed a skeletal dysplasia radiographically and histologically similar to FD.113

Read full chapterPurchase book

We use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies.

Copyright © 2019 Elsevier B.V. or its licensors or contributors. ScienceDirect ® is a registered trademark of Elsevier B.V.

          omuscular Diseases: Muscle 1168 CHAPTER 86 THE CONGENITAL MYOPATHIES ●●●● Heinz Jungbluth, Caroline A. Sewry, and Francesco Muntoni The congenital myopathies are a clinically and genetically heterogeneous group of congenital muscle disorders with characteristic structural abnormalities evident on muscle biopsy, visible after preparation with specific histochemical stains and/or on electron microscopy. Central core disease (CCD),1,2 nemaline myopathy,3 myotubular (centronuclear) myopathy,4 and minicore myopathy (or multi-minicore disease [MmD])5 are the major disease entities. Other conditions with more unusual structural abnormalities are very rare, and it is not clear whether all are genetic entities.6 In the congenital myopathies, structural abnormalities of the central nervous system or the peripheral nerves are not evident, and intelligence is usually normal. Although generally nonprogressive, respiratory involvement may be disproportionate to overall muscle weakness and is the main prognostic factor. The term congenital myopathy applies only to conditions with defined structural abnormalities, not to other neuromuscular disorders with congenital onset such as congenital muscular dystrophies and mitochondrial and other metabolic myopathies. Whereas most of these conditions manifest at birth or in early childhood, milder variants manifesting in adulthood have been reported. It is currently unclear whether those are part of a clinical and genetic spectrum or are separate entities with similar histopathological features. Autosomal dominant, autosomal recessive, and X-linked inheritance are all recognized in this group of disorders, and some conditions such as nemaline myopathy, CCD, and myotubular myopathy may have more than one mode of inheritance. Genetic advances have implicated several genes encoding sarcomeric and sarcotubular proteins (Table 86–1). The boundaries between these conditions, originally defined according to histopathological and clinical criteria, are often indistinct and do not necessarily reflect underlying molecular mechanisms: Mutations in the same gene can indeed give rise to diverse clinical and histopathological phenotypes, and, conversely, a similar histopathological and clinical phenotype may arise from mutations in a variety of genes. Although clinical management is currently the main form of treatment of the congenital myopathies, further advances in the understanding of the precise molecular mechanisms underlying each disorder may result in more rational therapeutic options in the future. This chapter summarizes the epidemiology, clinical features, investigations, and management of the congenital myopathies as a group and outlines specific features, genetics, and pathogenesis of the major disease entities in more detail. The authors have intentionally adhered to a clinical and pathological categorization rather than a molecular one, because the assessment of clinical and histopathological features directs the molecular analysis, but the molecular assessment is not the starting point. The histopathological features used to classify each disorder are not specific. Molecular analyses have highlighted the overlap of histopathological features within genetically defined disorders and are helping clarify the spectrum of features associated with each defective gene. Several cases in the literature classified according to histopathological criteria were reported before molecular analysis became available, and it is not clear whether these historical cases are part of a spectrum relating to a single disorder or whether they are, in fact, heterogeneous. EPIDEMIOLOGY Epidemiological data on the congenital myopathies are few, and larger geographical surveys are limited. The overall incidence of the congenital myopathies is estimated at 6 per 100,000 live births, representing approximately 10% of all neuromuscular disorders.7 Studies in northern Ireland8 and western Sweden9 suggest that the prevalence of the congenital myopathies in a pediatric population is between 3.5 and 5.0 per 100,000. The relative frequency of individual conditions is unknown, but CCD and conditions associated with mutations in the skeletal muscle ryanodine receptor gene (RYR1) appear to be more common in the patient population (see later discussion) than are nemaline myopathy and the much rarer centronuclear myopathies. Also, the prevalence of specific congenital myopathies may have been previously underestimated, inasmuch as not all muscle biopsy specimens from individuals carrying disease-causing mutations exhibit the characteristic structural abnormalities.10 CLINICAL FEATURES Most of the clinical features are nonspecific, despite some variations in overall severity, distribution of weakness, and associated features. The diagnosis of a specific congenital myopathy can therefore be made only tentatively on clinical grounds

Advertisement

supporting biologists
inspiring biology

Search for this keywordAdvanced search

RSS
Twitter
Facebook
YouTube

AT A GLANCEGenerating mouse models for biomedical research: technological advancesChannabasavaiah B. Gurumurthy, Kevin C. Kent LloydDisease Models & Mechanisms 2019 12: dmm029462 doi: 10.1242/dmm.029462 Published 8 January 2019

ABSTRACT

Over the past decade, new methods and procedures have been developed to generate genetically engineered mouse models of human disease. This At a Glance article highlights several recent technical advances in mouse genome manipulation that have transformed our ability to manipulate and study gene expression in the mouse. We discuss how conventional gene targeting by homologous recombination in embryonic stem cells has given way to more refined methods that enable allele-specific manipulation in zygotes. We also highlight advances in the use of programmable endonucleases that have greatly increased the feasibility and ease of editing the mouse genome. Together, these and other technologies provide researchers with the molecular tools to functionally annotate the mouse genome with greater fidelity and specificity, as well as to generate new mouse models using faster, simpler and less costly techniques.

Introduction

Researchers are entering a new era of human disease modeling in animals. For many years now, the laboratory mouse (Mus musculus) has remained the quintessential research animal of choice for studying human biology, pathology and disease processes (Rosenthal and Brown, 2007Lloyd et al., 2016). The mouse possesses numerous biological characteristics that make it the most commonly used animal in biomedical research for modeling human disease mechanisms; these characteristics include its short life cycle, gestation period and lifespan, as well as its high fecundity and breeding efficiency (Silver, 2001). Another key advantage is its high degree of conservation with humans, as reflected in its anatomy, physiology and genetics (Justice and Dhillon, 2016).

The highly conserved genetic homology that exists between mice and humans has justified the development of technologies to manipulate the mouse genome to create mouse models to reveal the genetic components of disease. It is important to note that, as technologies for genetic engineering and phenotypic analysis have advanced, some studies using mouse models have struggled to accurately predict human disease pathogenesis and clinical response to drug therapy (Perrin, 2014). For these reasons, it is essential to apply scientific principles of rigor and reproducibility (Kilkenny et al., 2010Karp et al., 2015) when designing and conducting experiments to associate mouse genes with human phenotypes at a systems level (Perlman, 2016).

Early mouse genetics research relied on mice having visible physical defects and readily measurable phenotypes, such as those caused by random spontaneous or induced mutations (Russell et al., 1979Justice, 1999). This ‘forward genetics’ approach depends on the presence of a phenotype to guide the search for the underlying genetic mutation. With the advent of techniques that enabled molecular cloning and the use of recombinant DNA to efficiently manipulate mouse genomes, researchers no longer needed to search for a relevant phenotype. Instead, they could engineer a pre-determined specific mutation into the mouse genome in real time in pluripotent mouse embryonic stem (ES) cells (Gordon and Ruddle, 1981Gordon et al., 1980Palmiter et al., 1982Thomas and Capecchi, 19861987). This ‘reverse genetics’ approach enabled scientists to study the phenotypic consequences of a known specific genetic mutation. This approach can generate ‘knockout’ mice (see Box 1 for a glossary of terms) by genetically manipulating the genome of ES cells, and then injecting the targeted cells into morulae or blastocysts (Box 1), which are then implanted into pseudopregnant female mice (Box 1). The resulting chimeric embryos develop into offspring that bear the desired gene deletion. After backcrossing to test for germline transmission of the knockout allele and subsequent intercrossing to achieve homozygosity, the phenotypic consequences of the mutation can be assessed. Phenotypes can also be assessed in transgenic mice (Box 1), which are generated by introducing an exogenous gene via microinjection into the one-cell-stage zygote. When successful, these genetic manipulations can also undergo germline transmission to the next generation (Palmiter et al., 1982Brinster et al., 1989).

With the sequencing of the mouse and human genomes (Venter et al., 2001Mouse Genome Sequencing Consortium, 2002), attention soon turned to determining the function of protein-coding genes (Nadeau et al., 2001). A growing number (∼6000) of inherited disease syndromes (https://www.omim.org/statistics/geneMap) further motivated efforts to functionally annotate every human gene and to determine the genetic basis of rare, simple and common complex human diseases using mouse models. Mouse models are thus vitally important for elucidating gene function. Those that express the pathophysiology of human disease are an essential resource for understanding disease mechanisms, improving diagnostic strategies and for testing therapeutic interventions (Rosenthal and Brown, 2007Bradley et al., 2012Justice and Dhillon, 2016Meehan et al., 2017). Even mouse models that only partially recapitulate the human phenotype, such as mutations in individual paralogs, can still provide important insights into disease mechanisms.

In this At a Glance article, we review recent technological advances for generating new and improved mouse models for biomedical research. This article aims to update a previous poster published in this journal several years ago (Justice et al., 2011). This earlier article discussed the role of natural variation, random transgenesis, reverse genetics via ES-cell-derived knockouts, forward genetics via ethylnitrosurea (ENU)-induced chemical mutagenesis, and genetic manipulation using transposons in the generation of mouse models. Many technological advances have since emerged, leading to refinements and improvements in the generation of more precise mouse models. These new technologies overcome some of the limitations of earlier mouse models by adding specificity, reproducibility and efficiency to the generation of alleles that can expand our knowledge of disease pathogenesis. For example, the ability to generate mouse models that recapitulate the single-nucleotide variants (SNVs) found in humans will enable us to differentiate between disease-causing and disease-associated mechanisms (Hara and Takada, 2018).

In the poster accompanying this article, we feature four areas of advancement:

(1) conditional mutagenesis strategies in mouse ES cells;

(2) gene function knockdown using RNA interference (RNAi);

(3) targeted transgenesis in zygotes (Piedrahita et al., 1999Shen et al., 2007) via homologous recombination (Box 1) in ES cells; and

(4) the use of programmable endonucleases (Box 1) in zygotes, to edit and manipulate the mouse genome in ways not previously possible.

These technologies represent a new paradigm in our ability to manipulate the mouse genome. However, as we discuss, these approaches are not without limitations. For example, the success of conditional mutagenesis can be hampered by poor gene-targeting efficiency in ES cells and by the limited production of germline-competent chimeras (Box 1) that can transmit the mutant allele to subsequent generations in their germline. Furthermore, protein expression can be highly variable following mRNA knockdown by RNAi, which can make experimental reproducibility a challenge. The major limitations of programmable endonucleases, the latest genome-editing tools, is mosaicism and their potential, albeit addressable, problem of inducing off-target mutations. Nonetheless, such pitfalls do not detract from the versatility that these newer technologies afford for manipulating the mouse genome.

Box 1. Glossary

Blastocyst: an early-stage (3.5 days post-fertilization) multicellular mouse embryo, which contains an inner mass of cells, a fluid-filled central cavity and an outer trophoblast cell layer.

Chimera: a founder mouse that contains a mix of gene-targeted, embryonic stem (ES)-cell-derived cells and host blastocyst-derived cells, typically identified by the contribution of the two different genetic backgrounds of somatic cells to its coat color.

Conditional alleles: an engineered allele that can be turned off (or on) in an exogenously controlled manner; for example, by recombinase-mediated deletion of genomic sequences.

Cre/loxP: a molecular recombination system that consists of a bacteriophage-derived recombinase protein (Cre) that binds to specific, non-mammalian, 34-nucleotide target sequences (loxP).

Footprint-free point mutations: an induced mutation that is created without changes being made to untargeted sequences and without leaving exogenous DNA in place.

Gene targeting: the methods used to make sequence changes to a specific gene rather than making random sequence changes; for example, gene targeting can be used to inactivate a gene.

Homologous recombination: a natural DNA recombination process that occurs, for example, during meiosis and DNA repair, in which similar or identical DNA sequences are exchanged between two adjacent strands of DNA.

Homology-directed repair (HDR): a DNA repair process involving the use of a single-stranded donor DNA template with short regions of homology (typically 30-60 bases long) as a donor template to fuse the cut ends of double-stranded DNA breaks created by programmable nucleases.

Knock-down mouse: a genetically altered mouse in which gene expression is lowered or silenced by using RNAi to degrade the mRNA of that gene.

Knock-in mouse: a genetically altered mouse in which a new mutation is introduced into an endogenous gene or an exogenous gene is introduced using genetic-engineering technologies.

Knockout mouse: a genetically altered mouse in which an endogenous gene is deleted and/or inactivated using genetic-engineering technologies.

loxP-stop-loxP: a commonly used DNA cassette, containing a stop codon flanked by loxP sites, included between the promoter and the coding sequences, to prevent expression of the coding sequence until the stop codon is excised by Cre-mediated recombination.

Morula: an early-stage (2.5 days post-fertilization) pre-implantation mouse embryo, typically consisting of 4-8 blastomeres.

Non-homologous end joining (NHEJ): a DNA repair mechanism that joins two DNA ends following a double-stranded break. Because the two ends are generally not homologous to each other, the process is named non-homologous end joining.

Programmable endonuclease: an enzyme that, when coupled with molecular targeting elements (e.g. a guide RNA), creates site-specific double-stranded DNA breaks.

Pronuclei: the structure in a one-cell-stage mouse embryo that contains the nucleus of the sperm and egg before these nuclei fuse.

Pseudopregnant female: the state of ‘false’ pregnancy, created when a female in estrus is mated with a vasectomized male to induce the hormonal changes that simulate pregnancy in the absence of fertilized embryos.

Recombinase-mediated cassette exchange (RMCE): a DNA integration strategy that uses site-specific recombinases, such as Cre or Flp, to exchange a DNA segment from one DNA molecule to another. Both the donor and target sequence are flanked by site-specific recombination sites, such as loxP or FRT. Double reciprocal recombination between these sites brings about DNA exchange.

Safe-harbor sites: a genomic locus that, when genetically manipulated, neither interferes with the expression of an integrated transgene nor disrupts endogenous gene activity.

Short hairpin (sh)RNA: a short or small RNA molecule with a hairpin loop used to silence gene expression by causing the degradation of the target mRNA.

Small interfering (si)RNA: a short or small linear RNA molecule used to interfere with, or to silence, gene expression by causing the degradation of the target mRNA.

Transgenic mouse: a genetically engineered mouse created by the pronuclear injection of recombinant DNA (transgene), which typically inserts at a random location in the genome.

Conditional mutagenesis strategies in mouse ES cells

The most common form of mouse genetic manipulation is the creation of gene knockout models. Gene-targeting in mouse ES cells was pioneered in the late 1980s and was first used to generate ubiquitous knockout models, in which the gene is deleted in every cell (Thomas and Capecchi, 1987Thompson et al., 1989). We refer readers to the previous At a Glance article on modeling human disease in mice (Justice et al., 2011) for details on how to use gene targeting (Box 1) to generate simple deletion and/or conditional alleles (Box 1) in ES cells to generate whole-body and tissue-specific knockout mice, respectively. In this article, we focus on the generation of more-complex alleles in ES cells (Poster panel 1) that retain wild-type expression and are amenable to conditional, tissue-specific and/or time-dependent deletion. This approach is particularly necessary for manipulating the approximately 30% of genes that affect the viability of homozygous mutants when deleted (Dickinson et al., 2016). For example, embryonic lethality caused by the deletion of the coding regions of Mixl1 (Pulina et al., 2014), Erbb4 (Gassmann et al., 1995) or Brca1 (Xu et al., 1999) can be rescued by conditional mutagenesis. This generates models that can be used to investigate specific gene-dependent processes during mammalian embryogenesis (Pulina et al., 2014), neurodevelopment (Golub et al., 2004) and breast cancer (Shakya et al., 2008) when combined with an appropriate Cre-expressing line that enables tissue- or developmental-stage-specific gene deletion (Dubois et al., 2006).

The versatility of naturally occurring recombinase-enzyme–target-sequence systems, such as Cre/loxP (Box 1) and Flp/FRT, which derive from bacteria and yeast, respectively, have been adapted to create tools for manipulating mammalian genomes (Gu et al., 1994Rajewsky et al., 1996Dymecki, 1996). These tools have dramatically expanded the types and varieties of alleles that can be designed to study gene function in vivo (Dymecki, 1996Nagy, 2000Nern et al., 2011). A fundamental principle of conditional mutagenesis is the ability to efficiently and reliably convert a functional allele into a mutant one in a specific cell type (called tissue-specific conditional mutagenesis) and/or at a specific time point during development (called time-specific or ‘inducible’ conditional mutagenesis).

Numerous strategies using recombinase-enzyme–target-sequence systems have been developed for conditional mutagenesis (Marth, 1996). Common to all these strategies is the use of short palindromic recombinase target sequences to flank a specific region of a gene (e.g. a critical coding exon common to all transcripts). Such sequences include the Creassociated loxP sequence (to generate a ‘floxed’ allele) or the Flp-associated FRT sequence (to generate an ‘FRT’-flanked allele) (Bouabe and Okkenhaug, 2013). In the absence of the associated recombinase enzyme, these flanking sequences have no effect on normal transcription nor on the expression of the endogenous gene. However, when exposed to the recombinase, the flanking recombinase target sequences recombine with each other to excise or invert the critical coding exon, depending on their orientation and positioning (McLellan et al., 2017) (Poster panel 2A). In its simplest use, if two flanking recombinase target sequences are placed in an asymmetrical head-to-tail orientation, they will recombine to delete the intervening genetic sequence upon exposure to recombinase. Alternatively, if pairs of target sequences are positioned symmetrically in a head-to-head orientation, their recombination will invert the intervening sequence. If target sequences are located on different chromosomes, recombination results in a chromosomal translocation.

There are different ways to elicit recombination. For example, as shown in Poster panel 2B, when a mouse that expresses a floxed allele is mated with a transgenic mouse that expresses the recombinase gene, its progeny will express the recombined allele (Gu et al., 1994). The tissue(s) in which the allele is recombined will depend on the expression pattern of the recombinase, i.e. where the promoter is activated to drive tissue-specific expression of the recombinase. Recombination can also be induced by the in vitro treatment of embryos or tissues with cell-permeable recombinase protein, or via the delivery of viral vectors that express the recombinase (Chambers et al., 2007Lewandoski et al., 1997Su et al., 2002). Recombinase activity can also be targeted to particular tissues by driving the expression of a recombinase from a cell-specific promoter. Recombinase expression can also be induced by expressing the recombinase from an inducible (e.g. drug-responsive) promoter (Sauer, 1998).

The simplest example of the recombinase-enzyme–target-sequence system is shown in Poster panel 2C. This panel shows a molecular targeting construct in which the critical coding exon is flanked by loxP sites. The construct also contains a contiguous endogenous coding sequence of between 3 and 8 kb that is homologous to the wild-type allele. This construct is then introduced into ES cells, for example by electroporation, where it then replaces, via homologous recombination, the endogenous wild-type allele (Hadjantonakis et al., 2008). The conditional allele can then undergo recombination upon exposure to the recombinase to delete the intervening critical coding exon, thereby inhibiting gene expression (null allele).

Another strategy, termed ‘knockout-first’, uses a variation of gene targeting to create a highly versatile allele that combines both gene trap (Friedel and Soriano, 2010) and conditional gene targeting (Jovicić et al., 1990) to generate a lacZ-tagged knockout allele (Testa et al., 2004) (Poster panel 2D). The ‘knockout-first’ allele is generated by inserting an FRT-flanked gene-trap vector, which contains a splice-acceptor sequence upstream of a lacZ reporter gene and a strong polyadenylation stop sequence, into an upstream intron. This creates an in-frame fusion transcript that will disrupt the expression of the targeted allele. Additionally, an adjacent exon coding sequence is flanked with loxP sites (Rosen et al., 2015). This allele can then be converted into a null allele by Cre to abrogate gene expression or into a conditional allele by Flp, which can subsequently be converted by Cre into a null allele (Testa et al., 2004Skarnes et al., 2011). The knockout-first strategy is versatile because it uses a single targeting vector to monitor gene expression using lacZ and tissue-specific gene function using Cre, thereby avoiding embryonic lethality. This strategy has been used effectively to enable the rapid and high-throughput production of thousands of gene knockouts in mouse ES cells in large-scale, genome-wide targeted mutagenesis programs, such as the International Knockout Mouse Consortium (IKMC) (Bradley et al., 2012). Hundreds of mutant mouse models of human genetic diseases have been generated using the knockout-first strategy, including models of skin abnormalities (Liakath-Ali et al., 2014), bone and cartilage disease (Freudenthal et al., 2016), and age-related hearing loss (Kane et al., 2012).

Lastly, an elegant technique termed ‘conditionals by inversion’ (COIN) employs an inverted COIN module that contains a reporter gene (e.g. lacZ) flanked by mutant recombinase target sites (lox66 and lox71) positioned in a head-to-head orientation to enable inversion by Cre recombinase (Albert et al., 1995) inserted into the anti-sense strand of a target gene (Economides et al., 2013) (Poster panel 2E). Cre ‘flips’ the COIN module into the sense strand, interfering with and inhibiting target-gene transcription while activating the reporter. The COIN approach is particularly applicable to single-exon genes and to genes in which the exon–intron structure is not clearly defined. This approach has been used to model an angiogenesis defect in delta-like 4 (Dll4) knockout mice (Billiard et al., 2012) and to generate immunological phenotypes in interleukin 2 receptor, gamma chain (Il2rg) knockout mice (Economides et al., 2013).

Gene expression knockdown using RNAi

About two decades ago, researchers observed that the introduction of double-stranded RNA (dsRNA) that was homologous to a specific gene resulted in its posttranscriptional silencing (Fire et al., 1998). This dsRNA-induced gene silencing was termed RNA interference (RNAi), and it occurs via two main steps (Poster panel 3A). First, Dicer, an enzyme of the RNase III family of nucleases, processes the dsRNA into small double-stranded fragments termed siRNAs (small interfering RNAs; Box 1). Then, the siRNAs are incorporated into a nuclease complex called RISC (for RNA-induced silencing complex), which unwinds the siRNA and finds homologous target mRNAs using the siRNA sequence as a guide; this complex then cleaves the target mRNAs. In the early 2000s, some groups explored whether RNAi could be used to reduce (or ‘knock down’) gene expression in mice by creating transgenic mice that express siRNA (Poster panel 3B). The first proof-of-principle for gene knockdown was demonstrated by delivering lentivirus particles expressing siRNA into green fluorescent protein (GFP) transgenic mice to knock down GFP (Tiscornia et al., 2003). Subsequently, knockdown mice were generated using standard pronuclear injection of constructs that express short-hairpin RNAs (shRNA; Box 1) (Chang et al., 2004Peng et al., 2006Seibler et al., 2007Dickins et al., 2007). Some examples of transgenic knockdown disease models include: an Abca1-deficient mouse line that mimics Tangier disease (Chang et al., 2004); insulin receptor (Insr)-knockdown mice that develop severe hyperglycemia within 7 days (Seibler et al., 2007); and the reversible knockdown of Trp53 as a model useful for tumor regression studies (Dickins et al., 2007).

The advantage of the RNAi knockdown strategy over traditional methods for generating knockout mice is that it provides a rapid and inexpensive approach by which to selectively and, in some cases, reversibly block the translation of a transcript. Although knockdown models can be generated more quickly and cheaply than gene-targeted knockout models (Liu, 2013), a key disadvantage of a knockdown is that transcript inhibition can be variable and transient, and therefore less reliable and reproducible than a knockout. The effects of random insertion, together with varying levels of RNAi in different cells within a tissue, were among the most common pitfalls associated with using RNAi technology to modify mouse gene expression (Peng et al., 2006Yamamoto-Hino and Goto, 2013).

Because of such challenges, and due to the lack of success in generating reliable transgenic RNAi models, this approach did not gain the expected popularity. Alternative strategies were developed to overcome the effect of randomly inserted RNAi constructs by targeting the knockdown cassettes to safe-harbor sites (Box 1), such as the Gt(ROSA)26Sor locus (Kleinhammer et al., 2010) or the Cola1 locus (Premsrirut et al., 2011). These strategies also include making the system modular by incorporating features such as: (i) the Flp-FRT recombinase-mediated cassette exchange (RMCE; Box 1), which facilitates the insertion of a single-copy expression cassette; (ii) a fluorescence reporter that enables gene expression analysis; (iii) microRNA (miRNA) architectures, such as miR30 with reduced general toxicity (McBride et al., 2008); and (iv) tetracycline-inducible elements to enable the expression of the RNAi cassettes upon doxycycline administration (Chang et al., 2004Seibler et al., 2007). A few models that are useful for cancer research have been generated using these approaches, such as Pax5 and eIF4F knockdown models for leukemia (Lin et al., 2012Liu et al., 2014). However, interest in generating knockdown models, as well as in using ES-cell-based gene targeting, began to wane with the development of programmable nuclease technologies (as discussed later).

More recently, an elegant approach that combines the use of the RNA-guided Cas9 nuclease system with RNAi technology has been developed to generate knockdown mouse models by inserting the knockdown cassettes into the intronic sites of endogenous genes (Miura et al., 2015). With this method, a single-copy artificial miRNA against the Otx2 gene was inserted into intron 6 of the Eef2 gene to knock down Otx2 in mid-gestation mouse embryos. This strategy was also used to conditionally activate knockdown cassettes using unidirectional recombinase-mediated inversion of the shRNA cassette. The Miura et al. method offers a feasible and simple strategy to generate gene knockdown models because: (i) it uses an endogenous promoter, unlike other knockdown approaches that require an exogenous promoter to drive the RNAi cassette; (ii) the knockdown cassette is inserted as a single copy at a known site in the genome, unlike approaches that randomly insert the cassette with no control over the number of copies inserted or the number of genomic insertion sites; and (iii) the transgene is not susceptible to silencing, in contrast to other transgenes that are often silenced following random genomic integration.

Pronuclear injection-based transgenesis

Traditional transgenic methods developed over three decades ago involve the injection of linearized DNA expression cassettes into fertilized zygotes (Gordon et al., 1980Palmiter et al., 1982) (Poster panel 4A). Some of the most commonly used transgenic DNA expression cassettes include: (i) cDNA encoding the wild-type or mutant allele; (ii) inducible reporter cassettes, such as the loxP-stop-loxP reporter (Box 1), that incorporate markers such as lacZ or the fluorescent reporters GFP, red fluorescent protein (RFP) or tdTomato; (iii) recombinases, such as Cre (Gu et al., 1994), tamoxifen-inducible Cre (CreERT2) (Feil et al., 1996) and Flp (Dymecki, 1996); and (iv) transcriptional inducers, such as tetracycline transactivators (tTA) or reverse tetracycline transactivators (rtTA) (Gossen and Bujard, 1992).

To produce transgenic mice, a DNA construct is microinjected into the pronuclei (Box 1) of one-cell-stage zygotes (Bockamp et al., 2008). All or part of the injected DNA then inserts randomly at one or more genomic loci as either a single or as multiple (e.g. tandem-repeat) copies. The suitability of this approach for generating animal models is limited by the uncertainty of obtaining a desired level of gene expression due to the random nature of transgene insertion and copy number (Chiang et al., 2012). As a result, ES-cell-based methods were developed to target expression cassettes (such as those encoding Cre) into a specific locus in the genome; for example, the Gt(ROSA)26Sor locus, which enables the ubiquitous expression of an inserted transgene (Soriano, 1999). Depending on the construct and insertion site, transgene expression could be driven by a target gene’s endogenous promoter and/or by other regulatory elements (Rickert et al., 1997). In this way, an intact, single-copy transgene becomes integrated into a predetermined genomic location in ES cells via homologous recombination, thereby optimizing transgene expression (Rickert et al., 1997Soriano, 1999). The targeted ES cells are then introduced into morulae or blastocysts, as previously explained, before being implanted into pseudopregnant females. Although this approach overcomes some of the constraints inherent to random transgenesis (such as high variability of gene expression, and difficulty in obtaining the desired transgene expression patterns and levels), homologous recombination has technical hurdles of its own that make it expensive, labor intensive and time consuming. In addition, germline transmission of the exogenous allele can fail, creating a frustrating struggle for researchers who need to reliably and regularly manipulate the mouse genome (Ohtsuka et al., 2012a). Another disadvantage of the ES cell targeting approach is that ES cell genomes do not always remain stable in culture, and can undergo changes before and after gene targeting (Liang et al., 2008).

The recently developed targeted transgenic technologies enable the integration of single-copy transgenes at specific loci in the genome, directly via pronuclear injection. In pioneering work, Masato Ohtsuka and co-workers developed a method called pronuclear injection-based targeted transgenesis (PITT) (Ohtsuka et al., 2010), which allows a single copy of a complete transgene to be precisely inserted at a desired genomic locus in the zygote (Poster panel 4B). The PITT method involves two steps. First, a landing pad (for example, a cassette containing a combination of mutant loxP sites) is inserted at a defined locus in ES cells to generate a ‘seed’ mouse strain. Second, the PITT components – a donor plasmid containing the DNA of interest (DOI) and a Cre source (either plasmid or mRNA) – are injected into fertilized eggs collected from the seed strain mice. The DOI inserts at the landing pad via recombination-mediated cassette exchange (RMCE). The landing pad and the donor DNA contain compatible sequence elements that enable the donor DNA to insert precisely into the target locus. In the first report (Ohtsuka et al., 2010), the authors employed a well-established CreloxP system (as the components of the landing pad and the donor plasmid elements) to achieve RMCE. Soon after the first description of the PITT technology, another group reported a similar approach using the PhiC31 integrase and attP/B system, which correspond to the landing pad components and donor plasmid elements (Tasic et al., 2011). This modified method to achieve targeted transgenesis was named Targatt™ (Chen-Tsai et al., 2014). The main advantages of the various targeted transgenesis methods that use either CreloxP recombination or PhiC31attP/B integration, are that: (i) they overcome the problems associated with random transgene insertion, such as fragmented insertion of the transgenes, multicopy insertions, transgene silencing or interference in the expression of the endogenously disrupted gene; and (ii) they resolve the time and cost limitations associated with ES-cell-based approaches by targeting DNA cassettes to specific sites in the genome.

In initial reports of the PITT method, the Cre recombinase was encoded by a plasmid, and the plasmid DNA was injected into the pronuclei of zygotes together with the donor DNA. This method has since been improved by: (i) the use of Cre mRNA instead of plasmid DNA, which was done because plasmid DNA needs to be transcribed, which delays the expression of Cre, by which time the donor DNA might have degraded (Ohtsuka et al., 2012b); (ii) the development of new PITT-compatible donor vectors (Ohtsuka et al., 2012b); and (iii) the development of a seed mouse strain that contains both CreloxP and PhiC31-attP/B cassette insertion systems, providing researchers with the flexibility to use either (Ohtsuka et al., 2015). In this format, multiple different PITT donor plasmids can be included in the microinjection mix: any one of these donors can be inserted at the landing pad in separate founder mice, resulting in independent transgenic mouse lines generated in a single session of microinjection. These latest technical tools, dubbed ‘improved PITT’ (i-PITT), allow up to three transgenic mouse lines to be generated simultaneously, such that each line has a different DOI after a single microinjection session (Ohtsuka et al., 2015). The PITT technology is reviewed in detail in Ohtsuka et al., 2012a and a comprehensive list of available PITT tools was recently described (Schilit et al., 2016). The PITT/i-PITT approaches have been used to generate many reliable single-copy transgenic reporter mouse lines that are useful for disease research, including in neuroscience (Madisen et al., 2015) and nephrology (Tsuchida et al., 2016). For example, Tsuchida et al. (2016) reported generating a nephrin-promoter-driven EGFP transgenic mouse model; they further showed that cultured glomeruli from this model serve as tools to screen for compounds that enhance nephrin-promoter activity. Although PITT strategies have overcome the limitations of random transgenesis, a major pitfall of this approach is that custom PITT seed mouse strains need to be generated for a given locus and maintained as breeder colonies as zygote donors for targeted transgenesis.

Despite the technical advances in genetic engineering over the past four decades, one recent and remarkable technical breakthrough is rapidly superseding nearly all of these advances: programmable endonucleases.

Programmable endonucleases for genome editing

Programmable endonucleases bypass the classical ES-cell-based gene-targeting steps to engineer a precise and heritable mutation at a specific site in the genome. Injection directly into one- or two-cell-stage embryos enables the germline modification of a specific genetic locus without the need for the three complex steps above.

Programmable endonucleases can introduce genetic mutations in one of two ways (Joung and Sander, 2012Gaj et al., 2013Sander and Joung, 2014Cox et al., 2015). They can cause: (i) imprecise, error-prone DNA repair as a result of non-homologous end joining (NHEJ; Box 1) of the cleaved DNA ends; or (ii) the precise repair of cleaved DNA ends by homology-directed repair (HDR; Box 1) via the co-injection of a DNA repair template. Nonetheless, the imprecise insertion of the donor DNA can still occur in HDR-mediated repair. The development of programmable endonucleases for genome editing has opened up a whole new set of technical possibilities to create animal models for biomedical research using virtually any suitable species.

There are four major platforms that employ programmable endonucleases, which were initially discovered in microbiology research applications (Chevalier and Stoddard, 2001Li et al., 1992Mojica and Garrett, 2013Mojica et al., 1993Römer et al., 2007) and have since been repurposed for editing the genomes of higher animals, including mice. They are, in the order they were developed: homing endonucleases (HEs); zinc-finger nucleases (ZFNs); transcription activator-like effector nucleases (TALENs); and the clustered regularly interspaced short palindromic repeats/CRISPR-associated 9 (CRISPR/Cas9) system (Poster panel 5). Common to all four programmable endonuclease platforms is their sequence-specific nuclease activity, which allows researchers to cleave DNA at a specific target site for genome editing (Joung and Sander, 2012Gaj et al., 2013Sander and Joung, 2014Cox et al., 2015).

The HEs were among the first of the endonucleases (Rouet et al., 1994) to be used for genome manipulation. Although HEs were shown to increase gene-targeting efficiency in ES cells (Smih et al., 1995), there is little evidence to suggest that they have been used successfully to genetically engineer mutant mice. This is probably because of the numerous steps required to design and construct HEs to target specific genomic sites, and because only a small number of genomic sites could be targeted. The ZFNs, unlike HEs, offered greater flexibility as they are easier to engineer and can target more genomic locations than can HEs (Poster panel 5). From 2002 onwards, ZFNs became more widely used than HEs, especially as a research tool in various organisms, including flies, fish and plants (Urnov et al., 2010Carroll, 2011). The first ZFN-modified mutant mouse models were described in 2010 by Carbery and co-workers via the direct injection of ZFNs that target and inactivate Mdr1aJag1 and Notch3 (Carbery et al., 2010). Nevertheless, the technical complexity of building ZFNs, and intellectual property restrictions, limited their widespread adaptability. TALENs, the next set of programmable nucleases, were developed in 2010 and overcame many of the limitations of HEs and ZFNs. TALENs were simpler, easier to build and could be used to target a greater number of genomic sites than could HEs or ZFNs, and thus were immediately adopted by hundreds of labs as research tools. The first mutant mouse models using TALENs were developed by Sung and co-workers in 2013 via the direct injection of TALENs that targeted Pibf1 and Sepw1 to inactivate them (Sung et al., 2013).

At the time when ZFNs and TALENs were being developed, each platform proved to be quite versatile and superior to the previously available genetic engineering tools. Then came the development of the CRISPR/Cas9 genome editing tool in late 2012 and early 2013 (Jinek et al., 2012Cong et al., 2013Mali et al., 2013) (Poster panel 5). A series of papers from multiple groups, published within a few months of each other, demonstrated that dsDNA breaks at specific sites in the genome could be generated with very high efficiency in mammalian cells by using guide RNAs complementary to the target site and the Cas9 nuclease (Jinek et al., 20122013Mali et al., 2013Cong et al., 2013Cho et al., 2013). Within just a few months, some groups demonstrated that the RNA-guided Cas9 nuclease system could be used to rapidly generate mutant mouse models (Shen et al., 2013Wang et al., 2013). Since then, the RNA-guided Cas9 nuclease system has almost completely superseded all other technologies for genome editing. A direct comparison of the RNA-guided Cas9 nuclease system with the previous nuclease-based platforms (HEs, ZFNs and TALENs) clearly shows that it has several advantages (Sander and Joung, 2014Porteus, 2015Woolf et al., 2017). These include its simplicity of use, lower cost and higher efficiency. The RNA-guided Cas9 nuclease system is constantly being improved to make it increasingly efficient and versatile, including optimizing and improving the efficiency of existing Cas nucleases (Kleinstiver et al., 2016Slaymaker et al., 2016), and the development of novel Cas nucleases (Shmakov et al., 2015Zetsche et al., 2015). The RNA-guided Cas9 nuclease system is considered a ‘disruptive’ technology because it is quickly making previously well-established and fully developed technologies outdated. In recent years, researchers have come to prefer this approach over ES-cell-based gene-targeting methods (Burgio, 2018Skarnes, 2015) because RNA-guided Cas9 nuclease approaches are relatively quicker, less expensive and less cumbersome.

The versatility of the RNA-guided Cas9 nuclease system allows researchers to engineer and edit the genome in ways that were previously not possible using non-nuclease-based approaches (Poster panel 5). This includes the ease and speed with which researchers can induce a footprint-free point mutation (Box 1) (Inui et al., 2014Gurumurthy et al., 2016a). Many human disease conditions are caused by subtle genetic changes, such as point mutations, or by the addition or deletion of a few nucleotides (Gonzaga-Jauregui et al., 2012). Developing animal models of such subtle genetic changes, by using ES-cell-based targeting approaches, inevitably requires the addition of other genetic elements near the vicinity of the genetic change [such as a drug selection marker (neomycin or puromycin) and recombinase elements (such as loxP or FRT sites)]. By contrast, the RNA-guided Cas9 nuclease system can generate animal models with subtle genetic changes with high precision, rapidly, efficiently and without leaving any residual genetic alterations. Compared to previous methods, this capability represents a significant advance in murine genome editing for human disease modeling. The RNA-guided Cas9 nuclease tool has also facilitated the generation of multiple mutant mouse models in a single experiment by inducing dsDNA breaks at multiple target sites, resulting in several different gene disruption models (Wang et al., 2013). The RNA-guided Cas9 nuclease system also enables the generation of mutant mouse models on genetic backgrounds that were not amenable to being genetically manipulated with earlier approaches, such as the immunodeficient NOD/Scid-ILgamma (NSG) strain (Li et al., 2014). The RNA-guided Cas9 nuclease system has also become a powerful tool for both forward and reverse genetics (Gurumurthy et al., 2016c), generating models that are relevant for many diseases, including cancer (Platt et al., 2014). Several recent review articles discuss the Cas9-nuclease-generated mouse models for different disease types, including for cancer (Mou et al., 2015Roper et al., 2017), cardiovascular diseases (Miano et al., 2016), neurodegenerative diseases (Yang et al., 2016) and kidney diseases (Higashijima et al., 2017). In addition, several reviews on Cas9-nuclease-generated models have been recently published that discuss their human disease relevance (Dow, 2015Tschaharganeh et al., 2016Cai et al., 2016Yang et al., 2016Birling et al., 2017).

Despite its advantages, the RNA-guided Cas9 nuclease system poses challenges, such as mosaicism (Yen et al., 2014) and off-target effects. If one of the two haploid genomes in the one-cell-stage zygote is not cleaved before the zygote divides, or if Cas9 activity persists at the two-cell or later stages, additional mutant alleles can be generated, resulting in more than three mutant alleles in the developing offspring. Consequently, as many as six or more types of alleles were detected in one founder (G0) mouse (Li et al., 2013). It is therefore essential to genotype F1 offspring to identify a desired mutant allele. This mosaicism can also be considered an advantage because multiple different alleles can be segregated and used as separate mutant models. For example, the same founder mouse could contain a complete insertion deletion (indel) allele and the foreign cassette knock-in allele; each can be used for different research applications. Because the Cas9 target sequence is only 23 nucleotides long, including the protospacer adjacent motif, it is likely that imperfect target-matching sequences are present elsewhere in the genome that contain one or a few mismatches. Cas9 can potentially bind to such imperfect target sites and thus generate dsDNA breaks and indels at those sites. Indel mutations in off-target sites can have confounding effects in mouse phenotyping experiments. However, off-target effects are not considered a major concern because they: (i) are generally negligible in mice (Iyer et al., 2015); and (ii) can be segregated during mouse breeding. Another recent study, now retracted, reported the presence of high rates of off-target effects in Cas9 engineered mice (Schaefer et al., 2017); however, this report’s experimental design and interpretations have been questioned by the scientific community (Kim et al., 2018Lescarbeau et al., 2018Nutter et al., 2018Wilson et al., 2018).

A current challenge to the broader use of RNA-guided Cas9 nuclease is the inability to use it to insert large fragments of DNA reliably and efficiently. Because most genetic-engineering approaches in mice involve the insertion of engineered DNA cassettes, efforts are underway to improve the ‘knock-in’ capabilities of this system. While a few RNA-guided Cas9 nuclease strategies have been modified to support the insertion of new cassettes (Aida et al., 2015Maruyama et al., 2015Sakuma et al., 2016), including a strategy that combines PITT and RNA-guided Cas9 nuclease approaches (Quadros et al., 2015), none has yet been successfully adapted for the routine engineering of the mouse genome. A report from Ohtsuka’s group, which used long single-stranded DNA (lssDNA) donors (generated via in vitro transcription and reverse transcription), demonstrated that lssDNAs could serve as efficient donors for insertion at the Cas9 cleavage sites (Miura et al., 2015). Another report, which used lssDNAs purified from nicked plasmids to create rat knock-in models, also demonstrated that the lssDNA donor strategy could be a reliable approach for creating insertion alleles (Yoshimi et al., 2016). More recent reports show that co-injecting lssDNA donors with commercially available CRISPR ribonucleoprotein complexes (instead of the previous formats of Cas9 mRNA and sgRNAs), offers a highly robust and efficient strategy for insertion alleles in a method termed Easi-CRISPR (efficient additions with ssDNA inserts-CRISPR) (Quadros et al., 2017Miura et al., 2017).

RNA-guided Cas9 nuclease reagents have also been delivered into zygotes via electroporation of RNA and/or of ribonucleoproteins (Chen et al., 2016Hashimoto and Takemoto, 2015Qin et al., 2015). The ability to deliver RNA-guided Cas9-nuclease gene-editing reagents into several zygotes at once overcomes the need to inject each individual zygote, one at a time, and greatly simplifies the process of generating mouse models. Furthermore, electroporation is less damaging to embryos than microinjection (Chen et al., 2016Hashimoto and Takemoto, 2015Qin et al., 2015). Another advance in delivering the RNA-guided Cas9 nuclease system is a method called GONAD (genome editing via oviductal nucleic acids delivery). This procedure delivers Cas9 reagents to embryos in the oviduct using electroporation (Takahashi et al., 2015Gurumurthy et al., 2016bSato et al., 2016Ohtsuka et al., 2018). Unlike standard approaches, this method does not require any of the three major steps of animal transgenesis: zygote isolation from a female donor; ex vivo handling of zygotes (involving either microinjection or electroporation); and the transfer of zygotes to a pseudopregnant female mouse. This approach requires surgical skills that are equivalent to performing the oviductal transfer of embryos. The GONAD method can be used to generate knockout mice (Takahashi et al., 2015), and, by using the so-called improved-GONAD (i-GONAD), more complex animal models, such as knock-ins and large-deletion models, can be generated at an efficiency similar to the microinjection-based methods (Ohtsuka et al., 2018). The i-GONAD method also uses only a third of the mice used in standard microinjection or in ex vivo zygote electroporation methods (Ohtsuka et al., 2018). These methods need not be limited to centralized facilities, sophisticated equipment or highly skilled technical personnel. It is thought that the technical advances such as Easi-CRISPR and i-GONAD have the potential to entirely reshape the traditional route of generating modified alleles in mice if the techniques are widely adopted by many research groups and by transgenic core facilities (Burgio, 2018).

Concluding remarks and future perspectives

Recent technological breakthroughs have enabled very rapid changes in the way we generate genetically altered mouse models. Most notably, the RNA-guided Cas9 nuclease system is assuming a key role in shaping this new technological landscape. While the use of the RNA-guided Cas9 nuclease system has transformed and eclipsed traditional transgenic technologies in many ways, challenges remain, including the inability to insert large DNA constructs to generate a knock-in mouse (Box 1) with reporter, conditional or humanized alleles, or to engineer chromosomal rearrangements and other complex alleles easily, routinely and efficiently.

Genetic manipulation also underpins the ongoing efforts to elucidate the functional roles of every gene in the mouse genome, as a first step to understanding the role of ‘disease alleles’ identified by the exome and genome sequencing of human patients. Genomic and precision medicine depends on our ability to differentiate benign from pathogenic variant alleles, and disease-causing alleles from the longer list of disease-associated ones. Genetic manipulation of the mouse genome is thus essential for understanding gene function and for uncovering the genetic and molecular basis of human disease, leading to improved diagnostic accuracy, development of targeted therapeutics and the implementation of effective prevention strategies.

Footnotes

  • © 2019. Published by The Company of Biologists Ltd

http://creativecommons.org/licenses/by/4.0

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

References

    1. Aida, T., 
    2. Chiyo, K., 
    3. Usami, T., 
    4. Ishikubo, H., 
    5. Imahashi, R., 
    6. Wada, Y., 
    7. Tanaka, K. F., 
    8. Sakuma, T., 
    9. Yamamoto, T. and 
    10. Tanaka, K.
     (2015). Cloning-free CRISPR/Cas system facilitates functional cassette knock-in in mice. Genome Biol. 16, 87. doi:10.1186/s13059-015-0653-xCrossRefPubMedGoogle Scholar
    1. Albert, H., 
    2. Dale, E. C., 
    3. Lee, E. and 
    4. Ow, D. W.
     (1995). Site-specific integration of DNA into wild-type and mutant lox sites placed in the plant genome. Plant J. Cell Mol. Biol. 7, 649-659. doi:10.1046/j.1365-313X.1995.7040649.xCrossRefPubMedWeb of ScienceGoogle Scholar
    1. Billiard, F., 
    2. Lobry, C., 
    3. Darrasse-Jèze, G., 
    4. Waite, J., 
    5. Liu, X., 
    6. Mouquet, H., 
    7. DaNave, A ., 
    8. Tait, M., 
    9. Idoyaga, J., 
    10. Leboeuf, M. et al.
     (2012). Dll4–Notch signaling in Flt3-independent dendritic cell development and autoimmunity in mice. J. Exp. Med. 209, 1011-1028. doi:10.1084/jem.20111615Abstract/FREE Full TextGoogle Scholar
    1. Birling, M.-C., 
    2. Herault, Y. and 
    3. Pavlovic, G.
     (2017). Modeling human disease in rodents by CRISPR/Cas9 genome editing. Mamm. Genome 28, 291-301. doi:10.1007/s00335-017-9703-xCrossRefGoogle Scholar
    1. Bockamp, E., 
    2. Sprengel, R., 
    3. Eshkind, L., 
    4. Lehmann, T., 
    5. Braun, J. M., 
    6. Emmrich, F. and 
    7. Hengstler, J. G.
     (2008). Conditional transgenic mouse models: from the basics to genome-wide sets of knockouts and current studies of tissue regeneration. Regen. Med. 3, 217-235. doi:10.2217/17460751.3.2.217CrossRefPubMedGoogle Scholar
    1. Bouabe, H. and 
    2. Okkenhaug, K.
     (2013). Gene targeting in mice: a review. Methods Mol. Biol. 1064, 315-336. doi:10.1007/978-1-62703-601-6_23CrossRefPubMedGoogle Scholar
    1. Bradley, A., 
    2. Anastassiadis, K., 
    3. Ayadi, A., 
    4. Battey, J. F., 
    5. Bell, C., 
    6. Birling, M.-C., 
    7. Bottomley, J., 
    8. Brown, S. D., 
    9. Bürger, A., 
    10. Bult, C. J. et al.
     (2012). The mammalian gene function resource: the international knockout mouse consortium. Mamm. Genome 23, 580-586. doi:10.1007/s00335-012-9422-2CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Brinster, R. L., 
    2. Sandgren, E. P., 
    3. Behringer, R. R. and 
    4. Palmiter, R. D.
     (1989). No simple solution for making transgenic mice. Cell 59, 239-241. doi:10.1016/0092-8674(89)90282-1CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Burgio, G.
     (2018). Redefining mouse transgenesis with CRISPR/Cas9 genome editing technology. Genome Biol. 19, 27. doi:10.1186/s13059-018-1409-1CrossRefGoogle Scholar
    1. Cai, L., 
    2. Fisher, A. L., 
    3. Huang, H. and 
    4. Xie, Z.
     (2016). CRISPR-mediated genome editing and human diseases. Genes Dis. 3, 244-251. doi:10.1016/j.gendis.2016.07.003CrossRefGoogle Scholar
    1. Carbery, I. D., 
    2. Ji, D., 
    3. Harrington, A., 
    4. Brown, V., 
    5. Weinstein, E. J., 
    6. Liaw, L. and 
    7. Cui, X.
     (2010). Targeted genome modification in mice using zinc-finger nucleases. Genetics 186, 451-459. doi:10.1534/genetics.110.117002Abstract/FREE Full TextGoogle Scholar
    1. Carroll, D.
     (2011). Genome engineering with zinc-finger nucleases. Genetics 188, 773-782. doi:10.1534/genetics.111.131433Abstract/FREE Full TextGoogle Scholar
    1. Chambers, I., 
    2. Silva, J., 
    3. Colby, D., 
    4. Nichols, J., 
    5. Nijmeijer, B., 
    6. Robertson, M., 
    7. Vrana, J., 
    8. Jones, K., 
    9. Grotewold, L. and 
    10. Smith, A.
     (2007). Nanog safeguards pluripotency and mediates germline development. Nature 450, 1230-1234. doi:10.1038/nature06403CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Chang, H.-S., 
    2. Lin, C.-H., 
    3. Chen, Y.-C. and 
    4. Yu, W. C. Y.
     (2004). Using siRNA technique to generate transgenic animals with spatiotemporal and conditional gene knockdown. Am. J. Pathol. 165, 1535-1541. doi:10.1016/S0002-9440(10)63411-6CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Chen, S., 
    2. Lee, B., 
    3. Lee, A. Y.-F., 
    4. Modzelewski, A. J. and 
    5. He, L.
     (2016). Highly efficient mouse genome editing by CRISPR ribonucleoprotein electroporation of zygotes. J. Biol. Chem. 291, 14457-14467. doi:10.1074/jbc.M116.733154Abstract/FREE Full TextGoogle Scholar
    1. Chen-Tsai, R. Y., 
    2. Jiang, R., 
    3. Zhuang, L., 
    4. Wu, J., 
    5. Li, L. and 
    6. Wu, J.
     (2014). Genome editing and animal models. Chin. Sci. Bull. 59, 1-6. doi:10.1007/s11434-013-0032-5CrossRefGoogle Scholar
    1. Chevalier, B. S. and 
    2. Stoddard, B. L.
     (2001). Homing endonucleases: structural and functional insight into the catalysts of intron/intein mobility. Nucleic Acids Res. 29, 3757-3774. doi:10.1093/nar/29.18.3757CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Chiang, C., 
    2. Jacobsen, J. C., 
    3. Ernst, C., 
    4. Hanscom, C., 
    5. Heilbut, A., 
    6. Blumenthal, I., 
    7. Mills, R. E., 
    8. Kirby, A., 
    9. Lindgren, A. M., 
    10. Rudiger, S. R. et al.
     (2012). Complex reorganization and predominant non-homologous repair following chromosomal breakage in karyotypically balanced germline rearrangements and transgenic integration. Nat. Genet. 44, 390-397, S1. doi:10.1038/ng.2202CrossRefPubMedGoogle Scholar
    1. Cho, S. W., 
    2. Kim, S., 
    3. Kim, J. M. and 
    4. Kim, J.-S.
     (2013). Targeted genome engineering in human cells with the Cas9 RNA-guided endonuclease. Nat. Biotechnol. 31, 230-232. doi:10.1038/nbt.2507CrossRefPubMedGoogle Scholar
    1. Cong, L., 
    2. Ran, F. A., 
    3. Cox, D., 
    4. Lin, S., 
    5. Barretto, R., 
    6. Habib, N., 
    7. Hsu, P. D., 
    8. Wu, X., 
    9. Jiang, W., 
    10. Marraffini, L. A.
     et al. (2013). Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819-823. doi:10.1126/science.1231143Abstract/FREE Full TextGoogle Scholar
    1. Cox, D. B. T., 
    2. Platt, R. J. and 
    3. Zhang, F.
     (2015). Therapeutic genome editing: prospects and challenges. Nat. Med. 21, 121-131. doi:10.1038/nm.3793CrossRefPubMedGoogle Scholar
    1. Dickins, R. A., 
    2. McJunkin, K., 
    3. Hernando, E., 
    4. Premsrirut, P. K., 
    5. Krizhanovsky, V., 
    6. Burgess, D. J., 
    7. Kim, S. Y., 
    8. Cordon-Cardo, C., 
    9. Zender, L., 
    10. Hannon, G. J. et al.
     (2007). Tissue-specific and reversible RNA interference in transgenic mice. Nat. Genet. 39, 914-921. doi:10.1038/ng2045CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Dickinson, M. E., 
    2. Flenniken, A. M., 
    3. Ji, X., 
    4. Teboul, L., 
    5. Wong, M. D., 
    6. White, J. K., 
    7. Meehan, T. F., 
    8. Weninger, W. J., 
    9. Westerberg, H., 
    10. Adissu, H. et al.
     (2016). High-throughput discovery of novel developmental phenotypes. Nature 537, 508-514. doi:10.1038/nature19356CrossRefPubMedGoogle Scholar
    1. Dow, L. E.
     (2015). Modeling disease in vivo with CRISPR/Cas9. Trends Mol. Med. 21, 609-621. doi:10.1016/j.molmed.2015.07.006CrossRefPubMedGoogle Scholar
    1. Dubois, N. C., 
    2. Hofmann, D., 
    3. Kaloulis, K., 
    4. Bishop, J. M. and 
    5. Trumpp, A.
     (2006). Nestin-Cre transgenic mouse line Nes-Cre1 mediates highly efficient Cre/loxP mediated recombination in the nervous system, kidney, and somite-derived tissues. Genesis 44, 355-360. doi:10.1002/dvg.20226CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Dymecki, S. M.
     (1996). Flp recombinase promotes site-specific DNA recombination in embryonic stem cells and transgenic mice. Proc. Natl. Acad. Sci. USA 93, 6191-6196. doi:10.1073/pnas.93.12.6191Abstract/FREE Full TextGoogle Scholar
    1. Economides, A. N., 
    2. Frendewey, D., 
    3. Yang, P., 
    4. Dominguez, M. G., 
    5. Dore, A. T., 
    6. Lobov, I. B., 
    7. Persaud, T., 
    8. Rojas, J., 
    9. McClain, J., 
    10. Lengyel, P. et al.
     (2013). Conditionals by inversion provide a universal method for the generation of conditional alleles. Proc. Natl. Acad. Sci. USA 110, E3179-E3188. doi:10.1073/pnas.1217812110Abstract/FREE Full TextGoogle Scholar
    1. Feil, R., 
    2. Brocard, J., 
    3. Mascrez, B., 
    4. LeMeur, M., 
    5. Metzger, D. and 
    6. Chambon, P.
     (1996). Ligand-activated site-specific recombination in mice. Proc. Natl. Acad. Sci. USA 93, 10887-10890. doi:10.1073/pnas.93.20.10887Abstract/FREE Full TextGoogle Scholar
    1. Fire, A., 
    2. Xu, S. Q., 
    3. Montgomery, M. K., 
    4. Kostas, S. A., 
    5. Driver, S. E. and 
    6. Mello, C. C.
     (1998). Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 391, 806-811. doi:10.1038/35888CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Freudenthal, B., 
    2. Logan, J., Sanger Institute Mouse Pipelines, 
    3. Croucher, P. I., 
    4. Williams, G. R. and 
    5. Bassett, J. H. D.
     (2016). Rapid phenotyping of knockout mice to identify genetic determinants of bone strength. J. Endocrinol. 231, R31-R46. doi:10.1530/JOE-16-0258Abstract/FREE Full TextGoogle Scholar
    1. Friedel, R. H. and 
    2. Soriano, P.
     (2010). Gene trap mutagenesis in the mouse. Methods Enzymol. 477, 243-269. doi:10.1016/S0076-6879(10)77013-0CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Gaj, T., 
    2. Gersbach, C. A. and 
    3. Barbas, C. F.
     (2013). ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering. Trends Biotechnol. 31, 397-405. doi:10.1016/j.tibtech.2013.04.004CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Gassmann, M., 
    2. Casagranda, F., 
    3. Orioli, D., 
    4. Simon, H., 
    5. Lai, C., 
    6. Klein, R. and 
    7. Lemke, G.
     (1995). Aberrant neural and cardiac development in mice lacking the ErbB4 neuregulin receptor. Nature 378, 390-394. doi:10.1038/378390a0CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Golub, M. S., 
    2. Germann, S. L. and 
    3. Lloyd, K. C. K.
     (2004). Behavioral characteristics of a nervous system-specific erbB4 knock-out mouse. Behav. Brain Res. 153, 159-170. doi:10.1016/j.bbr.2003.11.010CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Gonzaga-Jauregui, C., 
    2. Lupski, J. R. and 
    3. Gibbs, R. A.
     (2012). Human genome sequencing in health and disease. Annu. Rev. Med. 63, 35-61. doi:10.1146/annurev-med-051010-162644CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Gordon, J. W. and 
    2. Ruddle, F. H.
     (1981). Integration and stable germ line transmission of genes injected into mouse pronuclei. Science 214, 1244-1246. doi:10.1126/science.6272397Abstract/FREE Full TextGoogle Scholar
    1. Gordon, J. W., 
    2. Scangos, G. A., 
    3. Plotkin, D. J., 
    4. Barbosa, J. A. and 
    5. Ruddle, F. H.
     (1980). Genetic transformation of mouse embryos by microinjection of purified DNA. Proc. Natl. Acad. Sci. USA 77, 7380-7384. doi:10.1073/pnas.77.12.7380Abstract/FREE Full TextGoogle Scholar
    1. Gossen, M. and 
    2. Bujard, H.
     (1992). Tight control of gene expression in mammalian cells by tetracycline-responsive promoters. Proc. Natl. Acad. Sci. USA 89, 5547-5551. doi:10.1073/pnas.89.12.5547Abstract/FREE Full TextGoogle Scholar
    1. Gu, H., 
    2. Marth, J. D., 
    3. Orban, P. C., 
    4. Mossmann, H. and 
    5. Rajewsky, K.
     (1994). Deletion of a DNA polymerase beta gene segment in T cells using cell type-specific gene targeting. Science 265, 103-106. doi:10.1126/science.8016642Abstract/FREE Full TextGoogle Scholar
    1. Gurumurthy, C. B., 
    2. Quadros, R. M., 
    3. Sato, M., 
    4. Mashimo, T., 
    5. Lloyd, K. C. K. and 
    6. Ohtsuka, M.
     (2016a). CRISPR/Cas9 and the paradigm shift in mouse genome manipulation technologies. In Genome Editing (ed. K. Turksen), pp. 65-77. Cham: Springer International Publishing.Google Scholar
    1. Gurumurthy, C. B., 
    2. Takahashi, G., 
    3. Wada, K., 
    4. Miura, H., 
    5. Sato, M. and 
    6. Ohtsuka, M.
     (2016b). GONAD: a novel CRISPR/Cas9 genome editing method that does not require ex vivo handling of embryos. In Current Protocols in Human Genetics (ed. J. L. Haines, B. R. Korf, C. C. Morton, C. E. Seidman, J. G. Seidman and D. R. Smith), pp. 15.8.1-15.8.12. Hoboken, NJ, USA: John Wiley & Sons, Inc.Google Scholar
    1. Gurumurthy, C. B., 
    2. Grati, M., 
    3. Ohtsuka, M., 
    4. Schilit, S. L. P., 
    5. Quadros, R. M. and 
    6. Liu, X. Z.
     (2016c). CRISPR: a versatile tool for both forward and reverse genetics research. Hum. Genet. 135, 971-976. doi:10.1007/s00439-016-1704-4CrossRefGoogle Scholar
    1. Hadjantonakis, A.-K., 
    2. Pirity, M. and 
    3. Nagy, A.
     (2008). Cre recombinase mediated alterations of the mouse genome using embryonic stem cells. Methods Mol. Biol. 461, 111-132. doi:10.1007/978-1-60327-483-8_8CrossRefPubMedGoogle Scholar
    1. Hara, S. and 
    2. Takada, S.
     (2018). Genome editing for the reproduction and remedy of human diseases in mice. J. Hum. Genet. 63, 107-113. doi:10.1038/s10038-017-0360-4CrossRefGoogle Scholar
    1. Hashimoto, M. and 
    2. Takemoto, T.
     (2015). Electroporation enables the efficient mRNA delivery into the mouse zygotes and facilitates CRISPR/Cas9-based genome editing. Sci. Rep. 5, 11315. doi:10.1038/srep11315CrossRefPubMedGoogle Scholar
    1. Higashijima, Y., 
    2. Hirano, S., 
    3. Nangaku, M. and 
    4. Nureki, O.
     (2017). Applications of the CRISPR-Cas9 system in kidney research. Kidney Int. 92, 324-335. doi:10.1016/j.kint.2017.01.037CrossRefGoogle Scholar
    1. Inui, M., 
    2. Miyado, M., 
    3. Igarashi, M., 
    4. Tamano, M., 
    5. Kubo, A., 
    6. Yamashita, S., 
    7. Asahara, H., 
    8. Fukami, M. and 
    9. Takada, S.
     (2014). Rapid generation of mouse models with defined point mutations by the CRISPR/Cas9 system. Sci. Rep. 4, 5396. doi:10.1038/srep05396CrossRefPubMedGoogle Scholar
    1. Iyer, V., 
    2. Shen, B., 
    3. Zhang, W., 
    4. Hodgkins, A., 
    5. Keane, T., 
    6. Huang, X. and 
    7. Skarnes, W. C.
     (2015). Off-target mutations are rare in Cas9-modified mice. Nat. Methods 12, 479-479. doi:10.1038/nmeth.3408CrossRefPubMedGoogle Scholar
    1. Jinek, M., 
    2. Chylinski, K., 
    3. Fonfara, I., 
    4. Hauer, M., 
    5. Doudna, J. A. and 
    6. Charpentier, E.
     (2012). A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816-821. doi:10.1126/science.1225829Abstract/FREE Full TextGoogle Scholar
    1. Jinek, M., 
    2. East, A., 
    3. Cheng, A., 
    4. Lin, S., 
    5. Ma, E. and 
    6. Doudna, J.
     (2013). RNA-programmed genome editing in human cells. eLife 2, e00471. doi:10.7554/eLife.00471CrossRefPubMedGoogle Scholar
    1. Joung, J. K. and 
    2. Sander, J. D.
     (2012). TALENs: a widely applicable technology for targeted genome editing. Nat. Rev. Mol. Cell Biol. 14, 49-55. doi:10.1038/nrm3486CrossRefPubMedGoogle Scholar
    1. Jovicić, A., 
    2. Ivanisević, V. and 
    3. Magdić, B.
     (1990). [Treatment of epilepsy in adults]. Vojnosanit. Pregl. 47, 112-117.PubMedGoogle Scholar
    1. Justice, M. J.
     (1999). Mouse ENU Mutagenesis. Hum. Mol. Genet. 8, 1955-1963. doi:10.1093/hmg/8.10.1955CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Justice, M. J. and 
    2. Dhillon, P.
     (2016). Using the mouse to model human disease: increasing validity and reproducibility. Dis. Model. Mech. 9, 101-103. doi:10.1242/dmm.024547Abstract/FREE Full TextGoogle Scholar
    1. Justice, M. J., 
    2. Siracusa, L. D. and 
    3. Stewart, A. F.
     (2011). Technical approaches for mouse models of human disease. Dis. Model. Mech. 4, 305-310. doi:10.1242/dmm.000901Abstract/FREE Full TextGoogle Scholar
    1. Kane, K. L., 
    2. Longo-Guess, C. M., 
    3. Gagnon, L. H., 
    4. Ding, D., 
    5. Salvi, R. J. and 
    6. Johnson, K. R.
     (2012). Genetic background effects on age-related hearing loss associated with Cdh23 variants in mice. Hear. Res. 283, 80-88. doi:10.1016/j.heares.2011.11.007CrossRefPubMedGoogle Scholar
    1. Karp, N. A., 
    2. Meehan, T. F., 
    3. Morgan, H., 
    4. Mason, J. C., 
    5. Blake, A., 
    6. Kurbatova, N., 
    7. Smedley, D., 
    8. Jacobsen, J., 
    9. Mott, R. F., 
    10. Iyer, V. et al.
     (2015). Applying the ARRIVE guidelines to an in vivo database. PLoS Biol. 13, e1002151. doi:10.1371/journal.pbio.1002151CrossRefPubMedGoogle Scholar
    1. Kilkenny, C., 
    2. Browne, W. J., 
    3. Cuthill, I. C., 
    4. Emerson, M. and 
    5. Altman, D. G.
     (2010). Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS Biol. 8, e1000412. doi:10.1371/journal.pbio.1000412CrossRefPubMedGoogle Scholar
    1. Kim, S.-T., 
    2. Park, J., 
    3. Kim, D., 
    4. Kim, K., 
    5. Bae, S., 
    6. Schlesner, M. and 
    7. Kim, J.-S.
     (2018). Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo”. Nat. Methods 15, 239. doi:10.1038/nmeth.4554CrossRefPubMedGoogle Scholar
    1. Kleinhammer, A., 
    2. Wurst, W. and 
    3. Kühn, R.
     (2010). Gene knockdown in the mouse through RNAi. In Methods in Enzymology (eds P. M. Wassarman and P. M. Soriano) pp. 387-414. Elsevier.Google Scholar
    1. Kleinstiver, B. P., 
    2. Pattanayak, V., 
    3. Prew, M. S., 
    4. Tsai, S. Q., 
    5. Nguyen, N. T., 
    6. Zheng, Z. and 
    7. Joung, J. K.
     (2016). High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature 529, 490-495. doi:10.1038/nature16526CrossRefPubMedGoogle Scholar
    1. Lescarbeau, R. M., 
    2. Murray, B., 
    3. Barnes, T. M. and 
    4. Bermingham, N.
     (2018). Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo”. Nat. Methods 15, 237. doi:10.1038/nmeth.4553CrossRefPubMedGoogle Scholar
    1. Lewandoski, M., 
    2. Wassarman, K. M. and 
    3. Martin, G. R.
     (1997). Zp3-cre, a transgenic mouse line for the activation or inactivation of loxP-flanked target genes specifically in the female germ line. Curr. Biol. 7, 148-151. doi:10.1016/S0960-9822(06)00059-5CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Li, L., 
    2. Wu, L. P. and 
    3. Chandrasegaran, S.
     (1992). Functional domains in Fok I restriction endonuclease. Proc. Natl. Acad. Sci. USA 89, 4275-4279. doi:10.1073/pnas.89.10.4275Abstract/FREE Full TextGoogle Scholar
    1. Li, D., 
    2. Qiu, Z., 
    3. Shao, Y., 
    4. Chen, Y., 
    5. Guan, Y., 
    6. Liu, M., 
    7. Li, Y., 
    8. Gao, N., 
    9. Wang, L., 
    10. Lu, X. et al.
     (2013). Heritable gene targeting in the mouse and rat using a CRISPR-Cas system. Nat. Biotechnol. 31, 681-683. doi:10.1038/nbt.2661CrossRefPubMedGoogle Scholar
    1. Li, F., 
    2. Cowley, D. O., 
    3. Banner, D., 
    4. Holle, E., 
    5. Zhang, L. and 
    6. Su, L.
     (2014). Efficient genetic manipulation of the NOD-Rag1-/-IL2RgammaC-null mouse by combining in vitro fertilization and CRISPR/Cas9 technology. Sci. Rep. 4, 5290. doi:10.1038/srep05290CrossRefPubMedGoogle Scholar
    1. Liakath-Ali, K., 
    2. Vancollie, V. E., 
    3. Heath, E., 
    4. Smedley, D. P., 
    5. Estabel, J., 
    6. Sunter, D., 
    7. Ditommaso, T., 
    8. White, J. K., 
    9. Ramirez-Solis, R., 
    10. Smyth, I. et al.
     (2014). Novel skin phenotypes revealed by a genome-wide mouse reverse genetic screen. Nat. Commun. 5, 3540. doi:10.1038/ncomms4540CrossRefPubMedGoogle Scholar
    1. Liang, Q., 
    2. Conte, N., 
    3. Skarnes, W. C. and 
    4. Bradley, A.
     (2008). Extensive genomic copy number variation in embryonic stem cells. Proc. Natl. Acad. Sci. USA 105, 17453-17456. doi:10.1073/pnas.0805638105Abstract/FREE Full TextGoogle Scholar
    1. Lin, C.-J., 
    2. Nasr, Z., 
    3. Premsrirut, P. K., 
    4. Porco, J. A., 
    5. Hippo, Y., 
    6. Lowe, S. W. and 
    7. Pelletier, J.
     (2012). Targeting synthetic lethal interactions between Myc and the eIF4F complex impedes tumorigenesis. Cell Rep. 1, 325-333. doi:10.1016/j.celrep.2012.02.010CrossRefPubMedGoogle Scholar
    1. Liu, C.
     (2013). Strategies for designing transgenic DNA constructs. In Lipoproteins and Cardiovascular Disease (ed. L. A. Freeman), pp. 183-201. Totowa, NJ: Humana Press.Google Scholar
    1. Liu, G. J., 
    2. Cimmino, L., 
    3. Jude, J. G., 
    4. Hu, Y., 
    5. Witkowski, M. T., 
    6. McKenzie, M. D., 
    7. Kartal-Kaess, M., 
    8. Best, S. A., 
    9. Tuohey, L., 
    10. Liao, Y. et al.
     (2014). Pax5 loss imposes a reversible differentiation block in B-progenitor acute lymphoblastic leukemia. Genes Dev. 28, 1337-1350. doi:10.1101/gad.240416.114Abstract/FREE Full TextGoogle Scholar
    1. Lloyd, K. C. K., 
    2. Robinson, P. N. and 
    3. MacRae, C. A.
     (2016). Animal-based studies will be essential for precision medicine. Sci. Transl. Med. 8, 352ed12. doi:10.1126/scitranslmed.aaf5474FREE Full TextGoogle Scholar
    1. Madisen, L., 
    2. Garner, A. R., 
    3. Shimaoka, D., 
    4. Chuong, A. S., 
    5. Klapoetke, N. C., 
    6. Li, L., 
    7. van der Bourg, A., 
    8. Niino, Y., 
    9. Egolf, L., 
    10. Monetti, C. et al.
     (2015). Transgenic mice for intersectional targeting of neural sensors and effectors with high specificity and performance. Neuron 85, 942-958. doi:10.1016/j.neuron.2015.02.022CrossRefPubMedGoogle Scholar
    1. Mali, P., 
    2. Yang, L., 
    3. Esvelt, K. M., 
    4. Aach, J., 
    5. Guell, M., 
    6. DiCarlo, J. E., 
    7. Norville, J. E. and 
    8. Church, G. M.
     (2013). RNA-guided human genome engineering via Cas9. Science 339, 823-826. doi:10.1126/science.1232033Abstract/FREE Full TextGoogle Scholar
    1. Marth, J. D.
     (1996). Recent advances in gene mutagenesis by site-directed recombination. J. Clin. Invest. 97, 1999-2002. doi:10.1172/JCI118634CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Maruyama, T., 
    2. Dougan, S. K., 
    3. Truttmann, M. C., 
    4. Bilate, A. M., 
    5. Ingram, J. R. and 
    6. Ploegh, H. L.
     (2015). Increasing the efficiency of precise genome editing with CRISPR-Cas9 by inhibition of nonhomologous end joining. Nat. Biotechnol. 33, 538-542. doi:10.1038/nbt.3190CrossRefPubMedGoogle Scholar
    1. McBride, J. L., 
    2. Boudreau, R. L., 
    3. Harper, S. Q., 
    4. Staber, P. D., 
    5. Monteys, A. M., 
    6. Martins, I., 
    7. Gilmore, B. L., 
    8. Burstein, H., 
    9. Peluso, R. W., 
    10. Polisky, B. et al.
     (2008). Artificial miRNAs mitigate shRNA-mediated toxicity in the brain: implications for the therapeutic development of RNAi. Proc. Natl. Acad. Sci. USA 105, 5868-5873. doi:10.1073/pnas.0801775105Abstract/FREE Full TextGoogle Scholar
    1. McLellan, M. A., 
    2. Rosenthal, N. A. and 
    3. Pinto, A. R.
     (2017). Cre-loxP-mediated recombination: general principles and experimental considerations. Curr. Protoc. Mouse Biol. 7, 1-12. doi:10.1002/cpmo.22CrossRefGoogle Scholar
    1. Meehan, T. F., 
    2. Conte, N., 
    3. West, D. B., 
    4. Jacobsen, J. O., 
    5. Mason, J., 
    6. Warren, J., 
    7. Chen, C.-K., 
    8. Tudose, I., 
    9. Relac, M., 
    10. Matthews, P. et al.
     (2017). Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium. Nat. Genet. 49, 1231-1238. doi:10.1038/ng.3901CrossRefGoogle Scholar
    1. Miano, J. M., 
    2. Zhu, Q. M. and 
    3. Lowenstein, C. J.
     (2016). A CRISPR path to engineering new genetic mouse models for cardiovascular research. Arterioscler. Thromb. Vasc. Biol. 36, 1058-1075. doi:10.1161/ATVBAHA.116.304790Abstract/FREE Full TextGoogle Scholar
    1. Miura, H., 
    2. Gurumurthy, C. B., 
    3. Sato, T., 
    4. Sato, M. and 
    5. Ohtsuka, M.
     (2015). CRISPR/Cas9-based generation of knockdown mice by intronic insertion of artificial microRNA using longer single-stranded DNA. Sci. Rep. 5, 12799. doi:10.1038/srep12799CrossRefPubMedGoogle Scholar
    1. Miura, H., 
    2. Quadros, R. M., 
    3. Gurumurthy, C. B. and 
    4. Ohtsuka, M.
     (2017). Easi-CRISPR for creating knock-in and conditional knockout mouse models using long ssDNA donors. Nat. Protoc. 13, 195-215. doi:10.1038/nprot.2017.153CrossRefGoogle Scholar
    1. Mojica, F. J. M. and 
    2. Garrett, R. A.
     (2013). Discovery and seminal developments in the CRISPR Field. In CRISPR-Cas Systems (ed. R. Barrangou and J. van der Oost), pp. 1-31. Berlin, Heidelberg: Springer Berlin Heidelberg.Google Scholar
    1. Mojica, F. J. M., 
    2. Juez, G. and 
    3. Rodriguez-Valera, F.
     (1993). Transcription at different salinities of Haloferax mediterranei sequences adjacent to partially modified PstI sites. Mol. Microbiol. 9, 613-621. doi:10.1111/j.1365-2958.1993.tb01721.xCrossRefPubMedGoogle Scholar
    1. Mou, H., 
    2. Kennedy, Z., 
    3. Anderson, D. G., 
    4. Yin, H. and 
    5. Xue, W.
     (2015). Precision cancer mouse models through genome editing with CRISPR-Cas9. Genome Med. 7, 53. doi:10.1186/s13073-015-0178-7CrossRefPubMedGoogle Scholar
  1. Mouse Genome Sequencing Consortium (2002). Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520-562. doi:10.1038/nature01262CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Nadeau, J. H., 
    2. Balling, R., 
    3. Barsh, G., 
    4. Beier, D., 
    5. Brown, S. D., 
    6. Bucan, M., 
    7. Camper, S., 
    8. Carlson, G., 
    9. Copeland, N., 
    10. Eppig, J. et al.
     (2001). Sequence interpretation. Functional annotation of mouse genome sequences. Science 291, 1251-1255. doi:10.1126/science.1058244Abstract/FREE Full TextGoogle Scholar
    1. Nagy, A.
     (2000). Cre recombinase: the universal reagent for genome tailoring. Genesis 26, 99-109. doi:10.1002/(SICI)1526-968X(200002)26:2<99::AID-GENE1>3.0.CO;2-BCrossRefPubMedWeb of ScienceGoogle Scholar
    1. Nern, A., 
    2. Pfeiffer, B. D., 
    3. Svoboda, K. and 
    4. Rubin, G. M.
     (2011). Multiple new site-specific recombinases for use in manipulating animal genomes. Proc. Natl. Acad. Sci. USA 108, 14198-14203. doi:10.1073/pnas.1111704108Abstract/FREE Full TextGoogle Scholar
    1. Nutter, L. M. J., 
    2. Heaney, J. D., 
    3. Lloyd, K. C. K., 
    4. Murray, S. A., 
    5. Seavitt, J. R., 
    6. Skarnes, W. C., 
    7. Teboul, L., 
    8. Brown, S. D. M. and 
    9. Moore, M.
     (2018). Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo”. Nat. Methods 15, 235-236. doi:10.1038/nmeth.4559CrossRefPubMedGoogle Scholar
    1. Ohtsuka, M., 
    2. Ogiwara, S., 
    3. Miura, H., 
    4. Mizutani, A., 
    5. Warita, T., 
    6. Sato, M., 
    7. Imai, K., 
    8. Hozumi, K., 
    9. Sato, T., 
    10. Tanaka, M. et al.
     (2010). Pronuclear injection-based mouse targeted transgenesis for reproducible and highly efficient transgene expression. Nucleic Acids Res. 38, e198. doi:10.1093/nar/gkq860CrossRefPubMedGoogle Scholar
    1. Ohtsuka, M., 
    2. Miura, H., 
    3. Sato, M., 
    4. Kimura, M., 
    5. Inoko, H. and 
    6. Gurumurthy, C. B.
     (2012a). PITT: pronuclear injection-based targeted transgenesis, a reliable transgene expression method in mice. Exp. Anim. Jpn. Assoc. Lab. Anim. Sci. 61, 489-502. doi:10.1538/expanim.61.489CrossRefGoogle Scholar
    1. Ohtsuka, M., 
    2. Miura, H., 
    3. Nakaoka, H., 
    4. Kimura, M., 
    5. Sato, M. and 
    6. Inoko, H.
     (2012b). Targeted transgenesis through pronuclear injection of improved vectors into in vitro fertilized eggs. Transgenic Res. 21, 225-226. doi:10.1007/s11248-011-9505-yCrossRefPubMedGoogle Scholar
    1. Ohtsuka, M., 
    2. Miura, H., 
    3. Mochida, K., 
    4. Hirose, M., 
    5. Hasegawa, A., 
    6. Ogura, A., 
    7. Mizutani, R., 
    8. Kimura, M., 
    9. Isotani, A., 
    10. Ikawa, M. et al.
     (2015). One-step generation of multiple transgenic mouse lines using an improved Pronuclear Injection-based Targeted Transgenesis (i-PITT). BMC Genomics 16, 274. doi:10.1186/s12864-015-1432-5CrossRefGoogle Scholar
    1. Ohtsuka, M., 
    2. Sato, M., 
    3. Miura, H., 
    4. Takabayashi, S., 
    5. Matsuyama, M., 
    6. Koyano, T., 
    7. Arifin, N., 
    8. Nakamura, S., 
    9. Wada, K. and 
    10. Gurumurthy, C. B.
     (2018). i-GONAD: a robust method for in situ germline genome engineering using CRISPR nucleases. Genome Biol. 19, 25. doi:10.1186/s13059-018-1400-xCrossRefGoogle Scholar
    1. Palmiter, R. D., 
    2. Brinster, R. L., 
    3. Hammer, R. E., 
    4. Trumbauer, M. E., 
    5. Rosenfeld, M. G., 
    6. Birnberg, N. C. and 
    7. Evans, R. M.
     (1982a). Dramatic growth of mice that develop from eggs microinjected with metallothionein-growth hormone fusion genes. Nature 300, 611-615. doi:10.1038/300611a0CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Peng, S., 
    2. York, J. P. and 
    3. Zhang, P.
     (2006). A transgenic approach for RNA interference-based genetic screening in mice. Proc. Natl. Acad. Sci. USA 103, 2252-2256. doi:10.1073/pnas.0511034103Abstract/FREE Full TextGoogle Scholar
    1. Perlman, R. L.
     (2016). Mouse models of human disease: an evolutionary perspective. Evol. Med. Public Health 2016, 170-176. doi:10.1093/emph/eow014CrossRefPubMedGoogle Scholar
    1. Perrin, S.
     (2014). Preclinical research: make mouse studies work. Nature 507, 423-425. doi:10.1038/507423aCrossRefPubMedWeb of ScienceGoogle Scholar
    1. Piedrahita, J. A., 
    2. Dunne, P., 
    3. Lee, C.-K., 
    4. Moore, K., 
    5. Rucker, E. and 
    6. Vazquez, J. C.
     (1999). Use of embryonic and somatic cells for production of transgenic domestic animals. Cloning 1, 73-87. doi:10.1089/15204559950019960CrossRefPubMedGoogle Scholar
    1. Platt, R. J., 
    2. Chen, S., 
    3. Zhou, Y., 
    4. Yim, M. J., 
    5. Swiech, L., 
    6. Kempton, H. R., 
    7. Dahlman, J. E., 
    8. Parnas, O., 
    9. Eisenhaure, T. M., 
    10. Jovanovic, M. et al.
     (2014). CRISPR-Cas9 knockin mice for genome editing and cancer modeling. Cell 159, 440-455. doi:10.1016/j.cell.2014.09.014CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Porteus, M. H.
     (2015). Towards a new era in medicine: therapeutic genome editing. Genome Biol. 16, 286. doi:10.1186/s13059-015-0859-yCrossRefPubMedGoogle Scholar
    1. Premsrirut, P. K., 
    2. Dow, L. E., 
    3. Kim, S. Y., 
    4. Camiolo, M., 
    5. Malone, C. D., 
    6. Miething, C., 
    7. Scuoppo, C., 
    8. Zuber, J., 
    9. Dickins, R. A., 
    10. Kogan, S. C. et al.
     (2011). A rapid and scalable system for studying gene function in mice using conditional RNA interference. Cell 145, 145-158. doi:10.1016/j.cell.2011.03.012CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Pulina, M. V., 
    2. Sahr, K. E., 
    3. Nowotschin, S., 
    4. Baron, M. H. and 
    5. Hadjantonakis, A.-K.
     (2014). A conditional mutant allele for analysis of Mixl1 function in the mouse. Genesis 52, 417-423. doi:10.1002/dvg.22768CrossRefGoogle Scholar
    1. Qin, W., 
    2. Dion, S. L., 
    3. Kutny, P. M., 
    4. Zhang, Y., 
    5. Cheng, A. W., 
    6. Jillette, N. L., 
    7. Malhotra, A., 
    8. Geurts, A. M., 
    9. Chen, Y.-G. and 
    10. Wang, H.
     (2015). Efficient CRISPR/Cas9-mediated genome editing in mice by zygote electroporation of nuclease. Genetics 200, 423-430. doi:10.1534/genetics.115.176594Abstract/FREE Full TextGoogle Scholar
    1. Quadros, R. M., 
    2. Harms, D. W., 
    3. Ohtsuka, M. and 
    4. Gurumurthy, C. B.
     (2015). Insertion of sequences at the original provirus integration site of mouse ROSA26 locus using the CRISPR/Cas9 system. FEBS Open Biol. 5, 191-197. doi:10.1016/j.fob.2015.03.003CrossRefGoogle Scholar
    1. Quadros, R. M., 
    2. Miura, H., 
    3. Harms, D. W., 
    4. Akatsuka, H., 
    5. Sato, T., 
    6. Aida, T., 
    7. Redder, R., 
    8. Richardson, G. P., 
    9. Inagaki, Y., 
    10. Sakai, D. et al.
     (2017). Easi-CRISPR: a robust method for one-step generation of mice carrying conditional and insertion alleles using long ssDNA donors and CRISPR ribonucleoproteins. Genome Biol. 18, 92. doi:10.1186/s13059-017-1220-4CrossRefGoogle Scholar
    1. Rajewsky, K., 
    2. Gu, H., 
    3. Kühn, R., 
    4. Betz, U. A., 
    5. Müller, W., 
    6. Roes, J. and 
    7. Schwenk, F.
     (1996). Conditional gene targeting. J. Clin. Invest. 98, 600-603. doi:10.1172/JCI118828CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Rickert, R. C., 
    2. Roes, J. and 
    3. Rajewsky, K.
     (1997). B lymphocyte-specific, Cre-mediated mutagenesis in mice. Nucleic Acids Res. 25, 1317-1318. doi:10.1093/nar/25.6.1317CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Römer, P., 
    2. Hahn, S., 
    3. Jordan, T., 
    4. Strauss, T., 
    5. Bonas, U. and 
    6. Lahaye, T.
     (2007). Plant pathogen recognition mediated by promoter activation of the pepper Bs3 resistance gene. Science 318, 645-648. doi:10.1126/science.1144958Abstract/FREE Full TextGoogle Scholar
    1. Roper, J., 
    2. Tammela, T., 
    3. Cetinbas, N. M., 
    4. Akkad, A., 
    5. Roghanian, A., 
    6. Rickelt, S., 
    7. Almeqdadi, M., 
    8. Wu, K., 
    9. Oberli, M. A., 
    10. Sánchez-Rivera, F. et al.
     (2017). In vivo genome editing and organoid transplantation models of colorectal cancer and metastasis. Nat. Biotechnol. 35, 569-576. doi:10.1038/nbt.3836CrossRefGoogle Scholar
    1. Rosen, B., 
    2. Schick, J. and 
    3. Wurst, W.
     (2015). Beyond knockouts: the International Knockout Mouse Consortium delivers modular and evolving tools for investigating mammalian genes. Mamm. Genome Off. J. Int. Mamm. Genome Soc. 26, 456-466. doi:10.1007/s00335-015-9598-3CrossRefGoogle Scholar
    1. Rosenthal, N. and 
    2. Brown, S.
     (2007). The mouse ascending: perspectives for human-disease models. Nat. Cell Biol. 9, 993-999. doi:10.1038/ncb437CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Rouet, P., 
    2. Smih, F. and 
    3. Jasin, M.
     (1994). Expression of a site-specific endonuclease stimulates homologous recombination in mammalian cells. Proc. Natl. Acad. Sci. USA 91, 6064-6068. doi:10.1073/pnas.91.13.6064Abstract/FREE Full TextGoogle Scholar
    1. Russell, W. L., 
    2. Kelly, E. M., 
    3. Hunsicker, P. R., 
    4. Bangham, J. W., 
    5. Maddux, S. C. and 
    6. Phipps, E. L.
     (1979). Specific-locus test shows ethylnitrosourea to be the most potent mutagen in the mouse. Proc. Natl. Acad. Sci. USA 76, 5818-5819. doi:10.1073/pnas.76.11.5818Abstract/FREE Full TextGoogle Scholar
    1. Sakuma, T., 
    2. Nakade, S., 
    3. Sakane, Y., 
    4. Suzuki, K.-I. T. and 
    5. Yamamoto, T.
     (2016). MMEJ-assisted gene knock-in using TALENs and CRISPR-Cas9 with the PITCh systems. Nat. Protoc. 11, 118-133. doi:10.1038/nprot.2015.140CrossRefPubMedGoogle Scholar
    1. Sander, J. D. and 
    2. Joung, J. K.
     (2014). CRISPR-Cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 32, 347-355. doi:10.1038/nbt.2842CrossRefPubMedGoogle Scholar
    1. Sato, M., 
    2. Ohtsuka, M., 
    3. Watanabe, S. and 
    4. Gurumurthy, C. B.
     (2016). Nucleic acids delivery methods for genome editing in zygotes and embryos: the old, the new, and the old-new. Biol. Direct 11, 16. doi:10.1186/s13062-016-0115-8CrossRefGoogle Scholar
    1. Sauer, B.
     (1998). Inducible gene targeting in mice using the Cre/lox system. Methods 14, 381-392. doi:10.1006/meth.1998.0593CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Schaefer, K. A., 
    2. Wu, W.-H., 
    3. Colgan, D. F., 
    4. Tsang, S. H., 
    5. Bassuk, A. G. and 
    6. Mahajan, V. B.
     (2017). Unexpected mutations after CRISPR–Cas9 editing in vivo. Nat. Methods 14, 547-548. doi:10.1038/nmeth.4293CrossRefGoogle Scholar
    1. Schilit, S. L. P., 
    2. Ohtsuka, M., 
    3. Quadros, R. M. and 
    4. Gurumurthy, C. B.
     (2016). Pronuclear injection-based targeted transgenesis: pronuclear injection-based targeted transgenesis. In Current Protocols in Human Genetics (ed. J. L. Haines, B. R. Korf, C. C. Morton, C. E., Seidman, J. G. Seidman and D. R. Smith), p. 15.10.1-15.10.28. Hoboken, NJ, USA: John Wiley & Sons, Inc.Google Scholar
    1. Seibler, J., 
    2. Kleinridders, A., 
    3. Küter-Luks, B., 
    4. Niehaves, S., 
    5. Brüning, J. C. and 
    6. Schwenk, F.
     (2007). Reversible gene knockdown in mice using a tight, inducible shRNA expression system. Nucleic Acids Res. 35, e54. doi:10.1093/nar/gkm122CrossRefPubMedGoogle Scholar
    1. Shakya, R., 
    2. Szabolcs, M., 
    3. McCarthy, E., 
    4. Ospina, E., 
    5. Basso, K., 
    6. Nandula, S., 
    7. Murty, V., 
    8. Baer, R. and 
    9. Ludwig, T.
     (2008). The basal-like mammary carcinomas induced by Brca1 or Bard1 inactivation implicate the BRCA1/BARD1 heterodimer in tumor suppression. Proc. Natl. Acad. Sci. USA 105, 7040-7045. doi:10.1073/pnas.0711032105Abstract/FREE Full TextGoogle Scholar
    1. Shen, W., 
    2. Lan, G., 
    3. Yang, X., 
    4. Li, L., 
    5. Min, L., 
    6. Yang, Z., 
    7. Tian, L., 
    8. Wu, X., 
    9. Sun, Y., 
    10. Chen, H. et al.
     (2007). Targeting the exogenous htPAm gene on goat somatic cell beta-casein locus for transgenic goat production. Mol. Reprod. Dev. 74, 428-434. doi:10.1002/mrd.20595CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Shen, B., 
    2. Zhang, J., 
    3. Wu, H., 
    4. Wang, J., 
    5. Ma, K., 
    6. Li, Z., 
    7. Zhang, X., 
    8. Zhang, P. and 
    9. Huang, X.
     (2013). Generation of gene-modified mice via Cas9/RNA-mediated gene targeting. Cell Res. 23, 720-723. doi:10.1038/cr.2013.46CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Shmakov, S., 
    2. Abudayyeh, O. O., 
    3. Makarova, K. S., 
    4. Wolf, Y. I., 
    5. Gootenberg, J. S., 
    6. Semenova, E., 
    7. Minakhin, L., 
    8. Joung, J., 
    9. Konermann, S., 
    10. Severinov, K. et al.
     (2015). Discovery and functional characterization of diverse class 2 CRISPR-Cas systems. Mol. Cell 60, 385-397. doi:10.1016/j.molcel.2015.10.008CrossRefPubMedGoogle Scholar
    1. Silver, L. M.
     (2001). Mice as experimental organisms. In eLS (ed. John Wiley & Sons, Ltd), pp. 1-5. Chichester: John Wiley & Sons, Ltd. doi:10.1038/npg.els.0002029CrossRefGoogle Scholar
    1. Skarnes, W. C.
     (2015). Is mouse embryonic stem cell technology obsolete? Genome Biol. 16, 109. doi:10.1186/s13059-015-0673-6CrossRefGoogle Scholar
    1. Skarnes, W. C., 
    2. Rosen, B., 
    3. West, A. P., 
    4. Koutsourakis, M., 
    5. Bushell, W., 
    6. Iyer, V., 
    7. Mujica, A. O., 
    8. Thomas, M., 
    9. Harrow, J., 
    10. Cox, T. et al.
     (2011). A conditional knockout resource for the genome-wide study of mouse gene function. Nature 474, 337-342. doi:10.1038/nature10163CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Slaymaker, I. M., 
    2. Gao, L., 
    3. Zetsche, B., 
    4. Scott, D. A., 
    5. Yan, W. X. and 
    6. Zhang, F.
     (2016). Rationally engineered Cas9 nucleases with improved specificity. Science 351, 84-88. doi:10.1126/science.aad5227Abstract/FREE Full TextGoogle Scholar
    1. Smih, F., 
    2. Rouet, P., 
    3. Romanienko, P. J. and 
    4. Jasin, M.
     (1995). Double-strand breaks at the target locus stimulate gene targeting in embryonic stem cells. Nucleic Acids Res. 23, 5012-5019. doi:10.1093/nar/23.24.5012CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Soriano, P.
     (1999). Generalized lacZ expression with the ROSA26 Cre reporter strain. Nat. Genet. 21, 70-71. doi:10.1038/5007CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Su, H., 
    2. Mills, A. A., 
    3. Wang, X. and 
    4. Bradley, A.
     (2002). A targeted X-linked CMV-Cre line. Genesis 32, 187-188. doi:10.1002/gene.10043CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Sung, Y. H., 
    2. Baek, I.-J., 
    3. Kim, D. H., 
    4. Jeon, J., 
    5. Lee, J., 
    6. Lee, K., 
    7. Jeong, D., 
    8. Kim, J.-S. and 
    9. Lee, H.-W.
     (2013). Knockout mice created by TALEN-mediated gene targeting. Nat. Biotechnol. 31, 23-24. doi:10.1038/nbt.2477CrossRefPubMedGoogle Scholar
    1. Takahashi, G., 
    2. Gurumurthy, C. B., 
    3. Wada, K., 
    4. Miura, H., 
    5. Sato, M. and 
    6. Ohtsuka, M.
     (2015). GONAD: genome-editing via Oviductal Nucleic Acids Delivery system: a novel microinjection independent genome engineering method in mice. Sci. Rep. 5, 11406. doi:10.1038/srep11406CrossRefPubMedGoogle Scholar
    1. Tasic, B., 
    2. Hippenmeyer, S., 
    3. Wang, C., 
    4. Gamboa, M., 
    5. Zong, H., 
    6. Chen-Tsai, Y. and 
    7. Luo, L.
     (2011). Site-specific integrase-mediated transgenesis in mice via pronuclear injection. Proc. Natl. Acad. Sci. USA 108, 7902-7907. doi:10.1073/pnas.1019507108Abstract/FREE Full TextGoogle Scholar
    1. Testa, G., 
    2. Schaft, J., 
    3. van der Hoeven, F., 
    4. Glaser, S., 
    5. Anastassiadis, K., 
    6. Zhang, Y., 
    7. Hermann, T., 
    8. Stremmel, W. and 
    9. Stewart, A. F.
     (2004). A reliable lacZ expression reporter cassette for multipurpose, knockout-first alleles. Genesis 38, 151-158. doi:10.1002/gene.20012CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Thomas, K. R. and 
    2. Capecchi, M. R.
     (1986). Targeting of genes to specific sites in the mammalian genome. Cold Spring Harb. Symp. Quant. Biol. 51, 1101-1113. doi:10.1101/SQB.1986.051.01.128Abstract/FREE Full TextGoogle Scholar
    1. Thomas, K. R. and 
    2. Capecchi, M. R.
     (1987). Site-directed mutagenesis by gene targeting in mouse embryo-derived stem cells. Cell 51, 503-512. doi:10.1016/0092-8674(87)90646-5CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Thompson, S., 
    2. Clarke, A. R., 
    3. Pow, A. M., 
    4. Hooper, M. L. and 
    5. Melton, D. W.
     (1989). Germ line transmission and expression of a corrected HPRT gene produced by gene targeting in embryonic stem cells. Cell 56, 313-321. doi:10.1016/0092-8674(89)90905-7CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Tiscornia, G., 
    2. Singer, O., 
    3. Ikawa, M. and 
    4. Verma, I. M.
     (2003). A general method for gene knockdown in mice by using lentiviral vectors expressing small interfering RNA. Proc. Natl. Acad. Sci. USA 100, 1844-1848. doi:10.1073/pnas.0437912100Abstract/FREE Full TextGoogle Scholar
    1. Tschaharganeh, D. F., 
    2. Lowe, S. W., 
    3. Garippa, R. J. and 
    4. Livshits, G.
     (2016). Using CRISPR/Cas to study gene function and model disease in vivoFEBS J. 283, 3194-3203. doi:10.1111/febs.13750CrossRefPubMedGoogle Scholar
    1. Tsuchida, J., 
    2. Matsusaka, T., 
    3. Ohtsuka, M., 
    4. Miura, H., 
    5. Okuno, Y., 
    6. Asanuma, K., 
    7. Nakagawa, T., 
    8. Yanagita, M. and 
    9. Mori, K.
     (2016). Establishment of Nephrin reporter mice and use for chemical screening. PLoS ONE 11, e0157497. doi:10.1371/journal.pone.0157497CrossRefGoogle Scholar
    1. Urnov, F. D., 
    2. Rebar, E. J., 
    3. Holmes, M. C., 
    4. Zhang, H. S. and 
    5. Gregory, P. D.
     (2010). Genome editing with engineered zinc finger nucleases. Nat. Rev. Genet. 11, 636-646. doi:10.1038/nrg2842CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Venter, J. C., 
    2. Adams, M. D., 
    3. Myers, E. W., 
    4. Li, P. W., 
    5. Mural, R. J., 
    6. Sutton, G. G., 
    7. Smith, H. O., 
    8. Yandell, M., 
    9. Evans, C. A., 
    10. Holt, R. A. et al.
     (2001). The sequence of the human genome. Science 291, 1304-1351. doi:10.1126/science.1058040Abstract/FREE Full TextGoogle Scholar
    1. Wang, H., 
    2. Yang, H., 
    3. Shivalila, C. S., 
    4. Dawlaty, M. M., 
    5. Cheng, A. W., 
    6. Zhang, F. and 
    7. Jaenisch, R.
     (2013). One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153, 910-918. doi:10.1016/j.cell.2013.04.025CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Wilson, C. J., 
    2. Fennell, T., 
    3. Bothmer, A., 
    4. Maeder, M. L., 
    5. Reyon, D., 
    6. Cotta-Ramusino, C., 
    7. Fernandez, C. A., 
    8. Marco, E., 
    9. Barrera, L. A., 
    10. Jayaram, H. et al.
     (2018). Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo”. Nat. Methods 15, 236-237. doi:10.1038/nmeth.4552CrossRefPubMedGoogle Scholar
    1. Woolf, T. M., 
    2. Gurumurthy, C. B., 
    3. Boyce, F. and 
    4. Kmiec, E. B.
     (2017). To cleave or not to cleave: therapeutic gene editing with and without programmable nucleases. Nat. Rev. Drug Discov. 16, 296. doi:10.1038/nrd.2017.42CrossRefGoogle Scholar
    1. Xu, X., 
    2. Wagner, K.-U., 
    3. Larson, D., 
    4. Weaver, Z., 
    5. Li, C., 
    6. Ried, T., 
    7. Hennighausen, L., 
    8. Wynshaw-Boris, A. and 
    9. Deng, C.-X.
     (1999). Conditional mutation of Brca1 in mammary epithelial cells results in blunted ductal morphogenesis and tumour formation. Nat. Genet. 22, 37-43. doi:10.1038/8743CrossRefPubMedWeb of ScienceGoogle Scholar
    1. Yamamoto-Hino, M. and 
    2. Goto, S.
     (2013). In vivo RNAi-based screens: studies in model organisms. Genes 4, 646-665. doi:10.3390/genes4040646CrossRefGoogle Scholar
    1. Yang, W., 
    2. Tu, Z., 
    3. Sun, Q. and 
    4. Li, X.-J.
     (2016). CRISPR/Cas9: implications for modeling and therapy of neurodegenerative diseases. Front. Mol. Neurosci. 9, 30. doi:10.3389/fnmol.2016.00030CrossRefGoogle Scholar
    1. Yen, S.-T., 
    2. Zhang, M., 
    3. Deng, J. M., 
    4. Usman, S. J., 
    5. Smith, C. N., 
    6. Parker-Thornburg, J., 
    7. Swinton, P. G., 
    8. Martin, J. F. and 
    9. Behringer, R. R.
     (2014). Somatic mosaicism and allele complexity induced by CRISPR/Cas9 RNA injections in mouse zygotes. Dev. Biol. 393, 3-9. doi:10.1016/j.ydbio.2014.06.017CrossRefPubMedGoogle Scholar
    1. Yoshimi, K., 
    2. Kunihiro, Y., 
    3. Kaneko, T., 
    4. Nagahora, H., 
    5. Voigt, B. and 
    6. Mashimo, T.
     (2016). ssODN-mediated knock-in with CRISPR-Cas for large genomic regions in zygotes. Nat. Commun. 7, 10431. doi:10.1038/ncomms10431CrossRefPubMedGoogle Scholar
    1. Zetsche, B., 
    2. Gootenberg, J. S., 
    3. Abudayyeh, O. O., 
    4. Slaymaker, I. M., 
    5. Makarova, K. S., 
    6. Essletzbichler, P., 
    7. Volz, S. E., 
    8. Joung, J., 
    9. van der Oost, J., 
    10. Regev, A. et al.
     (2015). Cpf1 is a single RNA-guided endonuclease of a Class 2 CRISPR-Cas system. Cell 163, 759-771. doi:10.1016/j.cell.2015.09.038CrossRefPubMedGoogle Scholar

View Abstract

Recommended for you by TrendMD

  1. Generation of primary tumors with Flp recombinase in FRT-flanked p53 mice.Chang-Lung Lee et al., Dis Model Mech, 2011
  2. The generation and characterization of novel Col1a1FRT-Cre-ER-T2-FRT and Col1a1FRT-STOP-FRT-Cre-ER-T2 mice for sequential mutagenesis.Minsi Zhang et al., Dis Model Mech, 2015
  3. New approaches for modelling sporadic genetic disease in the mouse.Elizabeth M C Fisher et al., Dis Model Mech, 2009
  4. Generation of a multipurpose Prdm16 mouse allele by targeted gene trappingAlexander Strassman et al., Dis Model Mech, 2017
  5. Dre recombinase, like Cre, is a highly efficient site-specific recombinase in E. coli, mammalian cells and miceKonstantinos Anastassiadis et al., Dis Model Mech, 2009
  1. Translational Recoding Signals: Expanding the Synthetic Biology ToolboxJonathan D. Dinman, Journal of Biological Chemistry, 2019
  2. N6-Methyladenine DNA Modification in Glioblastoma – VideoMinesh P Mehta MD, FASTRO, PracticeUpdate(US), 2019
  3. PerkinElmer Posts Flat Q4 RevenuesGenomeWeb, 2010
  4. Pseudomonas aeruginosa Requires the DNA-Specific Endonuclease EndA To Degrade Extracellular Genomic DNA To Disperse from the BiofilmKathryn E. Cherny, J Bacteriol, 2019
  5. Febre familiar do MediterrâneoErkan Demirkaya et al., BMJ Best Practice, 2018

Powered by Previous ArticleNext Article  Back to top Previous ArticleNext Article 

This Issue

RSS
RSS

Keywords

 Download PDFEmailShareCitation ToolsAlerts

Article navigation

 Related articles

 Cited by…

More in this TOC section

Similar articles

Subject collections

Other journals from The Company of Biologists

Development

Journal of Cell Science

Journal of Experimental Biology

Biology OpenAdvertisement

Editor’s Choice – Development of mouse models of angiosarcoma driven by p53

Valerie Brunton and colleagues report that deletion of p53 in endothelial cells leads to reliable angiosarcoma generation, which, along with establishment of a transplantation model, provides a novel approach for testing potential new therapeutics.


At a Glance – Disease modelling in human organoids

organoids disease modelling

Organoids are self-organizing tissues derived from stem cells that provide a unique system to examine organ development, homeostasis and disease. This At a Glance by Madeline Lancaster and Meritxell Huch summarises the current organoid models of several human diseases, and discusses future prospects for these technologies.


Featured Article – Active receptor tyrosine kinases, but not Brachyury, are sufficient to trigger chordoma in zebrafish

zebrafish chordoma

An injection-based chordoma model in zebrafish shows that the hypothesized chordoma oncogene brachyury is insufficient, whereas EGFR and VEGFR2 are sufficient, to trigger notochord hyperplasia, find Alexa Burger and colleagues.


First Person interviews

Have you seen our interviews with the early-career first authors of our papers? Recently, we caught up with Marie Rodinova and Christiaan Veerman.  


DMM partners with Publons for peer reviewer recognition

Disease Models & Mechanisms is pleased to announce a new partnership with Publons, part of the Web of Science Group (a Clarivate Analytics company). Publons gives reviewers formal recognition of their peer review contributions using the Reviewer Recognition Service.


preLights – Injury stimulates stem cells to resist radiation-induced apoptosis

Maya Emmons-Bell highlights a preprint showing that wounding a planarian shortly after radiation protects stem cells from death, which is important for regenerative growth. This work provides important insight into the complex reactions that occur upon tissue injury.

Catch up with recent preLights selected for the translational biology community.

Articles

About us

For Authors

Journal Info

Contact

Twitter
YouTube
LinkedIn

© 2019   The Company of Biologists Ltd   Registered Charity 277992

AT A GLANCE Generating mouse models for biomedical research: technological advances Channabasavaiah B. Gurumurthy1,2 and Kevin C. Kent Lloyd3,4,* ABSTRACT Over the past decade, new methods and procedures have been developed to generate genetically engineered mouse models of human disease. This At a Glance article highlights several recent technical advances in mouse genome manipulation that have transformed our ability to manipulate and study gene expression in the mouse. We discuss how conventional gene targeting by homologous recombination in embryonic stem cells has given way to more refined methods that enable allele-specific manipulation in zygotes. We also highlight advances in the use of programmable endonucleases that have greatly increased the feasibility and ease of editing the mouse genome. Together, these and other technologies provide researchers with the molecular tools to functionally annotate the mouse genome with greater fidelity and specificity, as well as to generate new mouse models using faster, simpler and less costly techniques. KEY WORDS: CRISPR, Genome editing, Mouse, Mutagenesis Introduction Researchers are entering a new era of human disease modeling in animals. For many years now, the laboratory mouse (Mus musculus) has remained the quintessential research animal of choice for studying human biology, pathology and disease processes (Rosenthal and Brown, 2007; Lloyd et al., 2016). The mouse possesses numerous biological characteristics that make it the most commonly used animal in biomedical research for modeling human disease mechanisms; these characteristics include its short life 1 Developmental Neuroscience, Munroe Meyer Institute for Genetics and Rehabilitation, University of Nebraska Medical Center, Omaha, NE 68106-5915, USA. 2 Mouse Genome Engineering Core Facility, Vice Chancellor for Research Office, University of Nebraska Medical Center, Omaha, NE 68106-5915, USA. 3Department of Surgery, School of Medicine, University of California, Davis, CA 95618, USA. 4Mouse Biology Program, University of California, Davis, CA 95618, USA. *Author for correspondence (kclloyd@ucdavis.edu) C.B.G., 0000-0002-8022-4033; K.C.K.L., 0000-0002-5318-4144 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. 1 © 2019. Published by The Company of Biologists Ltd | Disease Models & Mechanisms (2019) 12, dmm029462. doi:10.1242/dmm.029462 Disease Models & Mechanisms cycle, gestation period and lifespan, as well as its high fecundity and breeding efficiency (Silver, 2001). Another key advantage is its high degree of conservation with humans, as reflected in its anatomy, physiology and genetics (Justice and Dhillon, 2016). The highly conserved genetic homology that exists between mice and humans has justified the development of technologies to manipulate the mouse genome to create mouse models to reveal the genetic components of disease. It is important to note that, as technologies for genetic engineering and phenotypic analysis have advanced, some studies using mouse models have struggled to accurately predict human disease pathogenesis and clinical response to drug therapy (Perrin, 2014). For these reasons, it is essential to apply scientific principles of rigor and reproducibility (Kilkenny et al., 2010; Karp et al., 2015) when designing and conducting experiments to associate mouse genes with human phenotypes at a systems level (Perlman, 2016). Early mouse genetics research relied on mice having visible physical defects and readily measurable phenotypes, such as those caused by random spontaneous or induced mutations (Russell et al., 1979; Justice, 1999). This ‘forward genetics’ approach depends on the presence of a phenotype to guide the search for the underlying genetic mutation. With the advent of techniques that enabled molecular cloning and the use of recombinant DNA to efficiently manipulate mouse genomes, researchers no longer needed to search for a relevant phenotype. Instead, they could engineer a predetermined specific mutation into the mouse genome in real time in pluripotent mouse embryonic stem (ES) cells (Gordon and Ruddle, 1981; Gordon et al., 1980; Palmiter et al., 1982; Thomas and Capecchi, 1986, 1987). This ‘reverse genetics’ approach enabled scientists to study the phenotypic consequences of a known specific genetic mutation. This approach can generate ‘knockout’ mice (see Box 1 for a glossary of terms) by genetically manipulating the genome of ES cells, and then injecting the targeted cells into morulae or blastocysts (Box 1), which are then implanted into pseudopregnant female mice (Box 1). The resulting chimeric embryos develop into offspring that bear the desired gene deletion. After backcrossing to test for germline transmission of the knockout allele and subsequent intercrossing to achieve homozygosity, the phenotypic consequences of the mutation can be assessed. Phenotypes can also be assessed in transgenic mice (Box 1), which are generated by introducing an exogenous gene via microinjection into the one-cell-stage zygote. When successful, these genetic manipulations can also undergo germline transmission to the next generation (Palmiter et al., 1982; Brinster et al., 1989). With the sequencing of the mouse and human genomes (Venter et al., 2001; Mouse Genome Sequencing Consortium, 2002), attention soon turned to determining the function of protein-coding genes (Nadeau et al., 2001). A growing number (∼6000) of inherited disease syndromes (https://www.omim.org/statistics/geneMap) further motivated efforts to functionally annotate every human gene and to determine the genetic basis of rare, simple and common complex human diseases using mouse models. Mouse models are thus vitally important for elucidating gene function. Those that express the pathophysiology of human disease are an essential resource for understanding disease mechanisms, improving diagnostic strategies and for testing therapeutic interventions (Rosenthal and Brown, 2007; Bradley et al., 2012; Justice and Dhillon, 2016; Meehan et al., 2017). Even mouse models that only partially recapitulate the human phenotype, such as mutations in individual paralogs, can still provide important insights into disease mechanisms. In this At a Glance article, we review recent technological advances for generating new and improved mouse models for biomedical research. This article aims to update a previous poster published in this journal several years ago (Justice et al., 2011). This earlier article discussed the role of natural variation, random transgenesis, reverse genetics via ES-cell-derived knockouts, forward genetics via ethylnitrosurea (ENU)-induced chemical mutagenesis, and genetic manipulation using transposons in the generation of mouse models. Many technological advances have since emerged, leading to refinements and improvements in the generation of more precise mouse models. These new technologies overcome some of the limitations of earlier mouse models by adding specificity, reproducibility and efficiency to the generation of alleles that can expand our knowledge of disease pathogenesis. For example, the ability to generate mouse models that recapitulate the single-nucleotide variants (SNVs) found in humans will enable us to differentiate between disease-causing and disease-associated mechanisms (Hara and Takada, 2018). In the poster accompanying this article, we feature four areas of advancement: (1) conditional mutagenesis strategies in mouse ES cells; (2) gene function knockdown using RNA interference (RNAi); (3) targeted transgenesis in zygotes (Piedrahita et al., 1999; Shen et al., 2007) via homologous recombination (Box 1) in ES cells; and (4) the use of programmable endonucleases (Box 1) in zygotes, to edit and manipulate the mouse genome in ways not previously possible. These technologies represent a new paradigm in our ability to manipulate the mouse genome. However, as we discuss, these approaches are not without limitations. For example, the success of conditional mutagenesis can be hampered by poor gene-targeting efficiency in ES cells and by the limited production of germlinecompetent chimeras (Box 1) that can transmit the mutant allele to subsequent generations in their germline. Furthermore, protein expression can be highly variable following mRNA knockdown by RNAi, which can make experimental reproducibility a challenge. The major limitations of programmable endonucleases, the latest genome-editing tools, is mosaicism and their potential, albeit addressable, problem of inducing off-target mutations. Nonetheless, such pitfalls do not detract from the versatility that these newer technologies afford for manipulating the mouse genome. Conditional mutagenesis strategies in mouse ES cells The most common form of mouse genetic manipulation is the creation of gene knockout models. Gene-targeting in mouse ES cells was pioneered in the late 1980s and was first used to generate ubiquitous knockout models, in which the gene is deleted in every cell (Thomas and Capecchi, 1987; Thompson et al., 1989). We refer readers to the previous At a Glance article on modeling human disease in mice (Justice et al., 2011) for details on how to use gene targeting (Box 1) to generate simple deletion and/or conditional alleles (Box 1) in ES cells to generate whole-body and tissuespecific knockout mice, respectively. In this article, we focus on the generation of more-complex alleles in ES cells (Poster panel 1) that retain wild-type expression and are amenable to conditional, tissuespecific and/or time-dependent deletion. This approach is particularly necessary for manipulating the approximately 30% of genes that affect the viability of homozygous mutants when deleted (Dickinson et al., 2016). For example, embryonic lethality caused by the deletion of the coding regions of Mixl1 (Pulina et al., 2014), Erbb4 (Gassmann et al., 1995) or Brca1 (Xu et al., 1999) can be rescued by conditional mutagenesis. This generates models that can be used to investigate specific gene-dependent processes during mammalian embryogenesis (Pulina et al., 2014), neurodevelopment 2 AT A GLANCE Disease Models & Mechanisms (2019) 12, dmm029462. doi:10.1242/dmm.029462 Disease Models & Mechanisms (Golub et al., 2004) and breast cancer (Shakya et al., 2008) when combined with an appropriate Cre-expressing line that enables tissue- or developmental-stage-specific gene deletion (Dubois et al., 2006). The versatility of naturally occurring recombinase-enzyme– target-sequence systems, such as Cre/loxP (Box 1) and Flp/FRT, which derive from bacteria and yeast, respectively, have been adapted to create tools for manipulating mammalian genomes (Gu et al., 1994; Rajewsky et al., 1996; Dymecki, 1996). These tools have dramatically expanded the types and varieties of alleles that can be designed to study gene function in vivo (Dymecki, 1996; Nagy, 2000; Nern et al., 2011). A fundamental principle of conditional mutagenesis is the ability to efficiently and reliably convert a functional allele into a mutant one in a specific cell type (called tissue-specific conditional mutagenesis) and/or at a specific time point during development (called time-specific or ‘inducible’ conditional mutagenesis). Numerous strategies using recombinase-enzyme–target-sequence systems have been developed for conditional mutagenesis (Marth, 1996). Common to all these strategies is the use of short palindromic recombinase target sequences to flank a specific region of a gene (e.g. a critical coding exon common to all transcripts). Such sequences include the Cre-associated loxP sequence (to generate a ‘floxed’ allele) or the Flp-associated FRT sequence (to generate an ‘FRT’-flanked allele) (Bouabe and Okkenhaug, 2013). In the absence of the associated recombinase enzyme, these flanking sequences have no effect on normal transcription nor on the expression of the endogenous gene. However, when exposed to the recombinase, the flanking recombinase target sequences recombine with each other to excise or invert the critical coding exon, depending on their orientation and positioning (McLellan et al., 2017) (Poster panel 2A). In its simplest use, if two flanking recombinase target sequences are placed in an asymmetrical head-totail orientation, they will recombine to delete the intervening genetic sequence upon exposure to recombinase. Alternatively, if pairs of target sequences are positioned symmetrically in a head-to-head orientation, their recombination will invert the intervening sequence. If target sequences are located on different chromosomes, recombination results in a chromosomal translocation. There are different ways to elicit recombination. For example, as shown in Poster panel 2B, when a mouse that expresses a floxed allele is mated with a transgenic mouse that expresses the recombinase gene, its progeny will express the recombined allele (Gu et al., 1994). The tissue(s) in which the allele is recombined will depend on the expression pattern of the recombinase, i.e. where the promoter is activated to drive tissue-specific expression of the recombinase. Recombination can also be induced by the in vitro treatment of embryos or tissues with cell-permeable recombinase protein, or via the delivery of viral vectors that express the recombinase (Chambers et al., 2007; Lewandoski et al., 1997; Su et al., 2002). Recombinase activity can also be targeted to particular tissues by driving the expression of a recombinase from a cellspecific promoter. Recombinase expression can also be induced by expressing the recombinase from an inducible (e.g. drug-responsive) promoter (Sauer, 1998). The simplest example of the recombinase-enzyme–targetsequence system is shown in Poster panel 2C. This panel shows a molecular targeting construct in which the critical coding exon is flanked by loxP sites. The construct also contains a contiguous endogenous coding sequence of between 3 and 8 kb that is homologous to the wild-type allele. This construct is then introduced into ES cells, for example by electroporation, where it Box 1. Glossary Blastocyst: an early-stage (3.5 days post-fertilization) multicellular mouse embryo, which contains an inner mass of cells, a fluid-filled central cavity and an outer trophoblast cell layer. Chimera: a founder mouse that contains a mix of gene-targeted, embryonic stem (ES)-cell-derived cells and host blastocyst-derived cells, typically identified by the contribution of the two different genetic backgrounds of somatic cells to its coat color. Conditional alleles: an engineered allele that can be turned off (or on) in an exogenously controlled manner; for example, by recombinasemediated deletion of genomic sequences. Cre/loxP: a molecular recombination system that consists of a bacteriophage-derived recombinase protein (Cre) that binds to specific, non-mammalian, 34-nucleotide target sequences (loxP). Footprint-free point mutations: an induced mutation that is created without changes being made to untargeted sequences and without leaving exogenous DNA in place. Gene targeting: the methods used to make sequence changes to a specific gene rather than making random sequence changes; for example, gene targeting can be used to inactivate a gene. Homologous recombination: a natural DNA recombination process that occurs, for example, during meiosis and DNA repair, in which similar or identical DNA sequences are exchanged between two adjacent strands of DNA. Homology-directed repair (HDR): a DNA repair process involving the use of a single-stranded donor DNA template with short regions of homology (typically 30-60 bases long) as a donor template to fuse the cut ends of double-stranded DNA breaks created by programmable nucleases. Knock-down mouse: a genetically altered mouse in which gene expression is lowered or silenced by using RNAi to degrade the mRNA of that gene. Knock-in mouse: a genetically altered mouse in which a new mutation is introduced into an endogenous gene or an exogenous gene is introduced using genetic-engineering technologies. Knockout mouse: a genetically altered mouse in which an endogenous gene is deleted and/or inactivated using genetic-engineering technologies. loxP-stop-loxP: a commonly used DNA cassette, containing a stop codon flanked by loxP sites, included between the promoter and the coding sequences, to prevent expression of the coding sequence until the stop codon is excised by Cre-mediated recombination. Morula: an early-stage (2.5 days post-fertilization) pre-implantation mouse embryo, typically consisting of 4-8 blastomeres. Non-homologous end joining (NHEJ): a DNA repair mechanism that joins two DNA ends following a double-stranded break. Because the two ends are generally not homologous to each other, the process is named non-homologous end joining. Programmable endonuclease: an enzyme that, when coupled with molecular targeting elements (e.g. a guide RNA), creates site-specific double-stranded DNA breaks. Pronuclei: the structure in a one-cell-stage mouse embryo that contains the nucleus of the sperm and egg before these nuclei fuse. Pseudopregnant female: the state of ‘false’ pregnancy, created when a female in estrus is mated with a vasectomized male to induce the hormonal changes that simulate pregnancy in the absence of fertilized embryos. Recombinase-mediated cassette exchange (RMCE): a DNA integration strategy that uses site-specific recombinases, such as Cre or Flp, to exchange a DNA segment from one DNA molecule to another. Both the donor and target sequence are flanked by site-specific recombination sites, such as loxP or FRT. Double reciprocal recombination between these sites brings about DNA exchange. Safe-harbor sites: a genomic locus that, when genetically manipulated, neither interferes with the expression of an integrated transgene nor disrupts endogenous gene activity. Short hairpin (sh)RNA: a short or small RNA molecule with a hairpin loop used to silence gene expression by causing the degradation of the target mRNA. Small interfering (si)RNA: a short or small linear RNA molecule used to interfere with, or to silence, gene expression by causing the degradation of the target mRNA. Transgenic mouse: a genetically engineered mouse created by the pronuclear injection of recombinant DNA (transgene), which typically inserts at a random location in the genome. 3 AT A GLANCE Disease Models & Mechanisms (2019) 12, dmm029462. doi:10.1242/dmm.029462 Disease Models & Mechanisms then replaces, via homologous recombination, the endogenous wild-type allele (Hadjantonakis et al., 2008). The conditional allele can then undergo recombination upon exposure to the recombinase to delete the intervening critical coding exon, thereby inhibiting gene expression (null allele). Another strategy, termed ‘knockout-first’, uses a variation of gene targeting to create a highly versatile allele that combines both gene trap (Friedel and Soriano, 2010) and conditional gene targeting (Jovicićet al., 1990) to generate a lacZ-tagged knockout allele (Testa et al., 2004) (Poster panel 2D). The ‘knockout-first’ allele is generated by inserting an FRT-flanked gene-trap vector, which contains a splice-acceptor sequence upstream of a lacZ reporter gene and a strong polyadenylation stop sequence, into an upstream intron. This creates an in-frame fusion transcript that will disrupt the expression of the targeted allele. Additionally, an adjacent exon coding sequence is flanked with loxP sites (Rosen et al., 2015). This allele can then be converted into a null allele by Cre to abrogate gene expression or into a conditional allele by Flp, which can subsequently be converted by Cre into a null allele (Testa et al., 2004; Skarnes et al., 2011). The knockout-first strategy is versatile because it uses a single targeting vector to monitor gene expression using lacZ and tissue-specific gene function using Cre, thereby avoiding embryonic lethality. This strategy has been used effectively to enable the rapid and high-throughput production of thousands of gene knockouts in mouse ES cells in large-scale, genome-wide targeted mutagenesis programs, such as the International Knockout Mouse Consortium (IKMC) (Bradley et al., 2012). Hundreds of mutant mouse models of human genetic diseases have been generated using the knockout-first strategy, including models of skin abnormalities (Liakath-Ali et al., 2014), bone and cartilage disease (Freudenthal et al., 2016), and age-related hearing loss (Kane et al., 2012). Lastly, an elegant technique termed ‘conditionals by inversion’ (COIN) employs an inverted COIN module that contains a reporter gene (e.g. lacZ) flanked by mutant recombinase target sites (lox66 and lox71) positioned in a head-to-head orientation to enable inversion by Cre recombinase (Albert et al., 1995) inserted into the anti-sense strand of a target gene (Economides et al., 2013) (Poster panel 2E). Cre ‘flips’ the COIN module into the sense strand, interfering with and inhibiting target-gene transcription while activating the reporter. The COIN approach is particularly applicable to single-exon genes and to genes in which the exon– intron structure is not clearly defined. This approach has been used to model an angiogenesis defect in delta-like 4 (Dll4) knockout mice (Billiard et al., 2012) and to generate immunological phenotypes in interleukin 2 receptor, gamma chain (Il2rg) knockout mice (Economides et al., 2013). Gene expression knockdown using RNAi About two decades ago, researchers observed that the introduction of double-stranded RNA (dsRNA) that was homologous to a specific gene resulted in its posttranscriptional silencing (Fire et al., 1998). This dsRNA-induced gene silencing was termed RNA interference (RNAi), and it occurs via two main steps (Poster panel 3A). First, Dicer, an enzyme of the RNase III family of nucleases, processes the dsRNA into small double-stranded fragments termed siRNAs (small interfering RNAs; Box 1). Then, the siRNAs are incorporated into a nuclease complex called RISC (for RNAinduced silencing complex), which unwinds the siRNA and finds homologous target mRNAs using the siRNA sequence as a guide; this complex then cleaves the target mRNAs. In the early 2000s, some groups explored whether RNAi could be used to reduce (or ‘knock down’) gene expression in mice by creating transgenic mice that express siRNA (Poster panel 3B). The first proof-of-principle for gene knockdown was demonstrated by delivering lentivirus particles expressing siRNA into green fluorescent protein (GFP) transgenic mice to knock down GFP (Tiscornia et al., 2003). Subsequently, knockdown mice were generated using standard pronuclear injection of constructs that express short-hairpin RNAs (shRNA; Box 1) (Chang et al., 2004; Peng et al., 2006; Seibler et al., 2007; Dickins et al., 2007). Some examples of transgenic knockdown disease models include: an Abca1-deficient mouse line that mimics Tangier disease (Chang et al., 2004); insulin receptor (Insr)-knockdown mice that develop severe hyperglycemia within 7 days (Seibler et al., 2007); and the reversible knockdown of Trp53 as a model useful for tumor regression studies (Dickins et al., 2007). The advantage of the RNAi knockdown strategy over traditional methods for generating knockout mice is that it provides a rapid and inexpensive approach by which to selectively and, in some cases, reversibly block the translation of a transcript. Although knockdown models can be generated more quickly and cheaply than genetargeted knockout models (Liu, 2013), a key disadvantage of a knockdown is that transcript inhibition can be variable and transient, and therefore less reliable and reproducible than a knockout. The effects of random insertion, together with varying levels of RNAi in different cells within a tissue, were among the most common pitfalls associated with using RNAi technology to modify mouse gene expression (Peng et al., 2006; Yamamoto-Hino and Goto, 2013). Because of such challenges, and due to the lack of success in generating reliable transgenic RNAi models, this approach did not gain the expected popularity. Alternative strategies were developed to overcome the effect of randomly inserted RNAi constructs by targeting the knockdown cassettes to safe-harbor sites (Box 1), such as the Gt(ROSA)26Sor locus (Kleinhammer et al., 2010) or the Cola1 locus (Premsrirut et al., 2011). These strategies also include making the system modular by incorporating features such as: (i) the Flp-FRT recombinase-mediated cassette exchange (RMCE; Box 1), which facilitates the insertion of a single-copy expression cassette; (ii) a fluorescence reporter that enables gene expression analysis; (iii) microRNA (miRNA) architectures, such as miR30 with reduced general toxicity (McBride et al., 2008); and (iv) tetracycline-inducible elements to enable the expression of the RNAi cassettes upon doxycycline administration (Chang et al., 2004; Seibler et al., 2007). A few models that are useful for cancer research have been generated using these approaches, such as Pax5 and eIF4F knockdown models for leukemia (Lin et al., 2012; Liu et al., 2014). However, interest in generating knockdown models, as well as in using ES-cell-based gene targeting, began to wane with the development of programmable nuclease technologies (as discussed later). More recently, an elegant approach that combines the use of the RNA-guided Cas9 nuclease system with RNAi technology has been developed to generate knockdown mouse models by inserting the knockdown cassettes into the intronic sites of endogenous genes (Miura et al., 2015). With this method, a single-copy artificial miRNA against the Otx2 gene was inserted into intron 6 of the Eef2 gene to knock down Otx2 in mid-gestation mouse embryos. This strategy was also used to conditionally activate knockdown cassettes using unidirectional recombinase-mediated inversion of the shRNA cassette. The Miura et al. method offers a feasible and simple strategy to generate gene knockdown models because: (i) it uses an endogenous promoter, unlike other knockdown approaches that require an exogenous promoter to drive the RNAi cassette; (ii) the knockdown cassette is inserted as a single copy at a known 4 AT A GLANCE Disease Models & Mechanisms (2019) 12, dmm029462. doi:10.1242/dmm.029462 Disease Models & Mechanisms site in the genome, unlike approaches that randomly insert the cassette with no control over the number of copies inserted or the number of genomic insertion sites; and (iii) the transgene is not susceptible to silencing, in contrast to other transgenes that are often silenced following random genomic integration. Pronuclear injection-based transgenesis Traditional transgenic methods developed over three decades ago involve the injection of linearized DNA expression cassettes into fertilized zygotes (Gordon et al., 1980; Palmiter et al., 1982) (Poster panel 4A). Some of the most commonly used transgenic DNA expression cassettes include: (i) cDNA encoding the wild-type or mutant allele; (ii) inducible reporter cassettes, such as the loxP-stoploxP reporter (Box 1), that incorporate markers such as lacZ or the fluorescent reporters GFP, red fluorescent protein (RFP) or tdTomato; (iii) recombinases, such as Cre (Gu et al., 1994), tamoxifen-inducible Cre (CreERT2) (Feil et al., 1996) and Flp (Dymecki, 1996); and (iv) transcriptional inducers, such as tetracycline transactivators (tTA) or reverse tetracycline transactivators (rtTA) (Gossen and Bujard, 1992). To produce transgenic mice, a DNA construct is microinjected into the pronuclei (Box 1) of one-cell-stage zygotes (Bockamp et al., 2008). All or part of the injected DNA then inserts randomly at one or more genomic loci as either a single or as multiple (e.g. tandem-repeat) copies. The suitability of this approach for generating animal models is limited by the uncertainty of obtaining a desired level of gene expression due to the random nature of transgene insertion and copy number (Chiang et al., 2012). As a result, ES-cell-based methods were developed to target expression cassettes (such as those encoding Cre) into a specific locus in the genome; for example, the Gt(ROSA)26Sor locus, which enables the ubiquitous expression of an inserted transgene (Soriano, 1999). Depending on the construct and insertion site, transgene expression could be driven by a target gene’s endogenous promoter and/or by other regulatory elements (Rickert et al., 1997). In this way, an intact, single-copy transgene becomes integrated into a predetermined genomic location in ES cells via homologous recombination, thereby optimizing transgene expression (Rickert et al., 1997; Soriano, 1999). The targeted ES cells are then introduced into morulae or blastocysts, as previously explained, before being implanted into pseudopregnant females. Although this approach overcomes some of the constraints inherent to random transgenesis (such as high variability of gene expression, and difficulty in obtaining the desired transgene expression patterns and levels), homologous recombination has technical hurdles of its own that make it expensive, labor intensive and time consuming. In addition, germline transmission of the exogenous allele can fail, creating a frustrating struggle for researchers who need to reliably and regularly manipulate the mouse genome (Ohtsuka et al., 2012a). Another disadvantage of the ES cell targeting approach is that ES cell genomes do not always remain stable in culture, and can undergo changes before and after gene targeting (Liang et al., 2008). The recently developed targeted transgenic technologies enable the integration of single-copy transgenes at specific loci in the genome, directly via pronuclear injection. In pioneering work, Masato Ohtsuka and co-workers developed a method called pronuclear injection-based targeted transgenesis (PITT) (Ohtsuka et al., 2010), which allows a single copy of a complete transgene to be precisely inserted at a desired genomic locus in the zygote (Poster panel 4B). The PITT method involves two steps. First, a landing pad (for example, a cassette containing a combination of mutant loxP sites) is inserted at a defined locus in ES cells to generate a ‘seed’ mouse strain. Second, the PITT components – a donor plasmid containing the DNA of interest (DOI) and a Cre source (either plasmid or mRNA) – are injected into fertilized eggs collected from the seed strain mice. The DOI inserts at the landing pad via recombination-mediated cassette exchange (RMCE). The landing pad and the donor DNA contain compatible sequence elements that enable the donor DNA to insert precisely into the target locus. In the first report (Ohtsuka et al., 2010), the authors employed a wellestablished Cre-loxP system (as the components of the landing pad and the donor plasmid elements) to achieve RMCE. Soon after the first description of the PITT technology, another group reported a similar approach using the PhiC31 integrase and attP/B system, which correspond to the landing pad components and donor plasmid elements (Tasic et al., 2011). This modified method to achieve targeted transgenesis was named Targatt™ (Chen-Tsai et al., 2014). The main advantages of the various targeted transgenesis methods that use either Cre-loxP recombination or PhiC31-attP/B integration, are that: (i) they overcome the problems associated with random transgene insertion, such as fragmented insertion of the transgenes, multicopy insertions, transgene silencing or interference in the expression of the endogenously disrupted gene; and (ii) they resolve the time and cost limitations associated with ES-cell-based approaches by targeting DNA cassettes to specific sites in the genome. In initial reports of the PITT method, the Cre recombinase was encoded by a plasmid, and the plasmid DNA was injected into the pronuclei of zygotes together with the donor DNA. This method has since been improved by: (i) the use of Cre mRNA instead of plasmid DNA, which was done because plasmid DNA needs to be transcribed, which delays the expression of Cre, by which time the donor DNA might have degraded (Ohtsuka et al., 2012b); (ii) the development of new PITT-compatible donor vectors (Ohtsuka et al., 2012b); and (iii) the development of a seed mouse strain that contains both Cre-loxP and PhiC31-attP/B cassette insertion systems, providing researchers with the flexibility to use either (Ohtsuka et al., 2015). In this format, multiple different PITT donor plasmids can be included in the microinjection mix: any one of these donors can be inserted at the landing pad in separate founder mice, resulting in independent transgenic mouse lines generated in a single session of microinjection. These latest technical tools, dubbed ‘improved PITT’ (i-PITT), allow up to three transgenic mouse lines to be generated simultaneously, such that each line has a different DOI after a single microinjection session (Ohtsuka et al., 2015). The PITT technology is reviewed in detail in Ohtsuka et al., 2012a and a comprehensive list of available PITT tools was recently described (Schilit et al., 2016). The PITT/i-PITT approaches have been used to generate many reliable single-copy transgenic reporter mouse lines that are useful for disease research, including in neuroscience (Madisen et al., 2015) and nephrology (Tsuchida et al., 2016). For example, Tsuchida et al. (2016) reported generating a nephrin-promoter-driven EGFP transgenic mouse model; they further showed that cultured glomeruli from this model serve as tools to screen for compounds that enhance nephrinpromoter activity. Although PITT strategies have overcome the limitations of random transgenesis, a major pitfall of this approach is that custom PITT seed mouse strains need to be generated for a given locus and maintained as breeder colonies as zygote donors for targeted transgenesis. Despite the technical advances in genetic engineering over the past four decades, one recent and remarkable technical breakthrough is rapidly superseding nearly all of these advances: programmable endonucleases. 5 AT A GLANCE Disease Models & Mechanisms (2019) 12, dmm029462. doi:10.1242/dmm.029462 Disease Models & Mechanisms Programmable endonucleases for genome editing Programmable endonucleases bypass the classical ES-cell-based gene-targeting steps to engineer a precise and heritable mutation at a specific site in the genome. Injection directly into one- or two-cellstage embryos enables the germline modification of a specific genetic locus without the need for the three complex steps above. Programmable endonucleases can introduce genetic mutations in one of two ways (Joung and Sander, 2012; Gaj et al., 2013; Sander and Joung, 2014; Cox et al., 2015). They can cause: (i) imprecise, error-prone DNA repair as a result of non-homologous end joining (NHEJ; Box 1) of the cleaved DNA ends; or (ii) the precise repair of cleaved DNA ends by homology-directed repair (HDR; Box 1) via the co-injection of a DNA repair template. Nonetheless, the imprecise insertion of the donor DNA can still occur in HDRmediated repair. The development of programmable endonucleases for genome editing has opened up a whole new set of technical possibilities to create animal models for biomedical research using virtually any suitable species. There are four major platforms that employ programmable endonucleases, which were initially discovered in microbiology research applications (Chevalier and Stoddard, 2001; Li et al., 1992; Mojica and Garrett, 2013; Mojica et al., 1993; Römer et al., 2007) and have since been repurposed for editing the genomes of higher animals, including mice. They are, in the order they were developed: homing endonucleases (HEs); zinc-finger nucleases (ZFNs); transcription activator-like effector nucleases (TALENs); and the clustered regularly interspaced short palindromic repeats/CRISPRassociated 9 (CRISPR/Cas9) system (Poster panel 5). Common to all four programmable endonuclease platforms is their sequencespecific nuclease activity, which allows researchers to cleave DNA at a specific target site for genome editing (Joung and Sander, 2012; Gaj et al., 2013; Sander and Joung, 2014; Cox et al., 2015). The HEs were among the first of the endonucleases (Rouet et al., 1994) to be used for genome manipulation. Although HEs were shown to increase gene-targeting efficiency in ES cells (Smih et al., 1995), there is little evidence to suggest that they have been used successfully to genetically engineer mutant mice. This is probably because of the numerous steps required to design and construct HEs to target specific genomic sites, and because only a small number of genomic sites could be targeted. The ZFNs, unlike HEs, offered greater flexibility as they are easier to engineer and can target more genomic locations than can HEs (Poster panel 5). From 2002 onwards, ZFNs became more widely used than HEs, especially as a research tool in various organisms, including flies, fish and plants (Urnov et al., 2010; Carroll, 2011). The first ZFN-modified mutant mouse models were described in 2010 by Carbery and co-workers via the direct injection of ZFNs that target and inactivate Mdr1a, Jag1 and Notch3 (Carbery et al., 2010). Nevertheless, the technical complexity of building ZFNs, and intellectual property restrictions, limited their widespread adaptability. TALENs, the next set of programmable nucleases, were developed in 2010 and overcame many of the limitations of HEs and ZFNs. TALENs were simpler, easier to build and could be used to target a greater number of genomic sites than could HEs or ZFNs, and thus were immediately adopted by hundreds of labs as research tools. The first mutant mouse models using TALENs were developed by Sung and co-workers in 2013 via the direct injection of TALENs that targeted Pibf1 and Sepw1 to inactivate them (Sung et al., 2013). At the time when ZFNs and TALENs were being developed, each platform proved to be quite versatile and superior to the previously available genetic engineering tools. Then came the development of the CRISPR/Cas9 genome editing tool in late 2012 and early 2013 (Jinek et al., 2012; Cong et al., 2013; Mali et al., 2013) (Poster panel 5). A series of papers from multiple groups, published within a few months of each other, demonstrated that dsDNA breaks at specific sites in the genome could be generated with very high efficiency in mammalian cells by using guide RNAs complementary to the target site and the Cas9 nuclease (Jinek et al., 2012, 2013; Mali et al., 2013; Cong et al., 2013; Cho et al., 2013). Within just a few months, some groups demonstrated that the RNA-guided Cas9 nuclease system could be used to rapidly generate mutant mouse models (Shen et al., 2013; Wang et al., 2013). Since then, the RNA-guided Cas9 nuclease system has almost completely superseded all other technologies for genome editing. A direct comparison of the RNA-guided Cas9 nuclease system with the previous nuclease-based platforms (HEs, ZFNs and TALENs) clearly shows that it has several advantages (Sander and Joung, 2014; Porteus, 2015; Woolf et al., 2017). These include its simplicity of use, lower cost and higher efficiency. The RNAguided Cas9 nuclease system is constantly being improved to make it increasingly efficient and versatile, including optimizing and improving the efficiency of existing Cas nucleases (Kleinstiver et al., 2016; Slaymaker et al., 2016), and the development of novel Cas nucleases (Shmakov et al., 2015; Zetsche et al., 2015). The RNA-guided Cas9 nuclease system is considered a ‘disruptive’ technology because it is quickly making previously wellestablished and fully developed technologies outdated. In recent years, researchers have come to prefer this approach over ES-cellbased gene-targeting methods (Burgio, 2018; Skarnes, 2015) because RNA-guided Cas9 nuclease approaches are relatively quicker, less expensive and less cumbersome. The versatility of the RNA-guided Cas9 nuclease system allows researchers to engineer and edit the genome in ways that were previously not possible using non-nuclease-based approaches (Poster panel 5). This includes the ease and speed with which researchers can induce a footprint-free point mutation (Box 1) (Inui et al., 2014; Gurumurthy et al., 2016a). Many human disease conditions are caused by subtle genetic changes, such as point mutations, or by the addition or deletion of a few nucleotides (Gonzaga-Jauregui et al., 2012). Developing animal models of such subtle genetic changes, by using ES-cell-based targeting approaches, inevitably requires the addition of other genetic elements near the vicinity of the genetic change [such as a drug selection marker (neomycin or puromycin) and recombinase elements (such as loxP or FRT sites)]. By contrast, the RNA-guided Cas9 nuclease system can generate animal models with subtle genetic changes with high precision, rapidly, efficiently and without leaving any residual genetic alterations. Compared to previous methods, this capability represents a significant advance in murine genome editing for human disease modeling. The RNAguided Cas9 nuclease tool has also facilitated the generation of multiple mutant mouse models in a single experiment by inducing dsDNA breaks at multiple target sites, resulting in several different gene disruption models (Wang et al., 2013). The RNA-guided Cas9 nuclease system also enables the generation of mutant mouse models on genetic backgrounds that were not amenable to being genetically manipulated with earlier approaches, such as the immunodeficient NOD/Scid-ILgamma (NSG) strain (Li et al., 2014). The RNAguided Cas9 nuclease system has also become a powerful tool for both forward and reverse genetics (Gurumurthy et al., 2016c), generating models that are relevant for many diseases, including cancer (Platt et al., 2014). Several recent review articles discuss the Cas9-nuclease-generated mouse models for different disease types, including for cancer (Mou et al., 2015; Roper et al., 2017), cardiovascular diseases (Miano et al., 2016), neurodegenerative 6 AT A GLANCE Disease Models & Mechanisms (2019) 12, dmm029462. doi:10.1242/dmm.029462 Disease Models & Mechanisms diseases (Yang et al., 2016) and kidney diseases (Higashijima et al., 2017). In addition, several reviews on Cas9-nuclease-generated models have been recently published that discuss their human disease relevance (Dow, 2015; Tschaharganeh et al., 2016; Cai et al., 2016; Yang et al., 2016; Birling et al., 2017). Despite its advantages, the RNA-guided Cas9 nuclease system poses challenges, such as mosaicism (Yen et al., 2014) and offtarget effects. If one of the two haploid genomes in the one-cellstage zygote is not cleaved before the zygote divides, or if Cas9 activity persists at the two-cell or later stages, additional mutant alleles can be generated, resulting in more than three mutant alleles in the developing offspring. Consequently, as many as six or more types of alleles were detected in one founder (G0) mouse (Li et al., 2013). It is therefore essential to genotype F1 offspring to identify a desired mutant allele. This mosaicism can also be considered an advantage because multiple different alleles can be segregated and used as separate mutant models. For example, the same founder mouse could contain a complete insertion deletion (indel) allele and the foreign cassette knock-in allele; each can be used for different research applications. Because the Cas9 target sequence is only 23 nucleotides long, including the protospacer adjacent motif, it is likely that imperfect target-matching sequences are present elsewhere in the genome that contain one or a few mismatches. Cas9 can potentially bind to such imperfect target sites and thus generate dsDNA breaks and indels at those sites. Indel mutations in off-target sites can have confounding effects in mouse phenotyping experiments. However, off-target effects are not considered a major concern because they: (i) are generally negligible in mice (Iyer et al., 2015); and (ii) can be segregated during mouse breeding. Another recent study, now retracted, reported the presence of high rates of off-target effects in Cas9 engineered mice (Schaefer et al., 2017); however, this report’s experimental design and interpretations have been questioned by the scientific community (Kim et al., 2018; Lescarbeau et al., 2018; Nutter et al., 2018; Wilson et al., 2018). A current challenge to the broader use of RNA-guided Cas9 nuclease is the inability to use it to insert large fragments of DNA reliably and efficiently. Because most genetic-engineering approaches in mice involve the insertion of engineered DNA cassettes, efforts are underway to improve the ‘knock-in’ capabilities of this system. While a few RNA-guided Cas9 nuclease strategies have been modified to support the insertion of new cassettes (Aida et al., 2015; Maruyama et al., 2015; Sakuma et al., 2016), including a strategy that combines PITT and RNA-guided Cas9 nuclease approaches (Quadros et al., 2015), none has yet been successfully adapted for the routine engineering of the mouse genome. A report from Ohtsuka’s group, which used long single-stranded DNA (lssDNA) donors (generated via in vitro transcription and reverse transcription), demonstrated that lssDNAs could serve as efficient donors for insertion at the Cas9 cleavage sites (Miura et al., 2015). Another report, which used lssDNAs purified from nicked plasmids to create rat knock-in models, also demonstrated that the lssDNA donor strategy could be a reliable approach for creating insertion alleles (Yoshimi et al., 2016). More recent reports show that coinjecting lssDNA donors with commercially available CRISPR ribonucleoprotein complexes (instead of the previous formats of Cas9 mRNA and sgRNAs), offers a highly robust and efficient strategy for insertion alleles in a method termed Easi-CRISPR (efficient additions with ssDNA inserts-CRISPR) (Quadros et al., 2017; Miura et al., 2017). RNA-guided Cas9 nuclease reagents have also been delivered into zygotes via electroporation of RNA and/or of ribonucleoproteins (Chen et al., 2016; Hashimoto and Takemoto, 2015; Qin et al., 2015). The ability to deliver RNA-guided Cas9-nuclease gene-editing reagents into several zygotes at once overcomes the need to inject each individual zygote, one at a time, and greatly simplifies the process of generating mouse models. Furthermore, electroporation is less damaging to embryos than microinjection (Chen et al., 2016; Hashimoto and Takemoto, 2015; Qin et al., 2015). Another advance in delivering the RNA-guided Cas9 nuclease system is a method called GONAD (genome editing via oviductal nucleic acids delivery). This procedure delivers Cas9 reagents to embryos in the oviduct using electroporation (Takahashi et al., 2015; Gurumurthy et al., 2016b; Sato et al., 2016; Ohtsuka et al., 2018). Unlike standard approaches, this method does not require any of the three major steps of animal transgenesis: zygote isolation from a female donor; ex vivo handling of zygotes (involving either microinjection or electroporation); and the transfer of zygotes to a pseudopregnant female mouse. This approach requires surgical skills that are equivalent to performing the oviductal transfer of embryos. The GONAD method can be used to generate knockout mice (Takahashi et al., 2015), and, by using the so-called improved-GONAD (i-GONAD), more complex animal models, such as knock-ins and large-deletion models, can be generated at an efficiency similar to the microinjection-based methods (Ohtsuka et al., 2018). The i-GONAD method also uses only a third of the mice used in standard microinjection or in ex vivo zygote electroporation methods (Ohtsuka et al., 2018). These methods need not be limited to centralized facilities, sophisticated equipment or highly skilled technical personnel. It is thought that the technical advances such as Easi-CRISPR and i-GONAD have the potential to entirely reshape the traditional route of generating modified alleles in mice if the techniques are widely adopted by many research groups and by transgenic core facilities (Burgio, 2018). Concluding remarks and future perspectives Recent technological breakthroughs have enabled very rapid changes in the way we generate genetically altered mouse models. Most notably, the RNA-guided Cas9 nuclease system is assuming a key role in shaping this new technological landscape. While the use of the RNA-guided Cas9 nuclease system has transformed and eclipsed traditional transgenic technologies in many ways, challenges remain, including the inability to insert large DNA constructs to generate a knock-in mouse (Box 1) with reporter, conditional or humanized alleles, or to engineer chromosomal rearrangements and other complex alleles easily, routinely and efficiently. Genetic manipulation also underpins the ongoing efforts to elucidate the functional roles of every gene in the mouse genome, as a first step to understanding the role of ‘disease alleles’ identified by the exome and genome sequencing of human patients. Genomic and precision medicine depends on our ability to differentiate benign from pathogenic variant alleles, and disease-causing alleles from the longer list of disease-associated ones. Genetic manipulation of the mouse genome is thus essential for understanding gene function and for uncovering the genetic and molecular basis of human disease, leading to improved diagnostic accuracy, development of targeted therapeutics and the implementation of effective prevention strategies. At a glance A high-resolution version of the poster is available for downloading in the online version of this article at http://dmm.biologists.org/content/12/1/dmm029462/F1. poster.jpg. References Aida, T., Chiyo, K., Usami, T., Ishikubo, H., Imahashi, R., Wada, Y., Tanaka, K. F., Sakuma, T., Yamamoto, T. and Tanaka, K. (2015). Cloning-free CRISPR/Cas system facilitates functional cassette knock-in in mice. Genome Biol. 16, 87. 7 AT A GLANCE Disease Models & Mechanisms (2019) 12, dmm029462. doi:10.1242/dmm.029462 Disease Models & Mechanisms Albert, H., Dale, E. C., Lee, E. and Ow, D. W. (1995). Site-specific integration of DNA into wild-type and mutant lox sites placed in the plant genome. Plant J. Cell Mol. Biol. 7, 649-659. Billiard, F., Lobry, C., Darrasse-Jeze, G., Waite, J., Liu, X., Mouquet, H., DaNave, ̀ A., Tait, M., Idoyaga, J., Leboeuf, M. et al. (2012). Dll4–Notch signaling in Flt3- independent dendritic cell development and autoimmunity in mice. J. Exp. Med. 209, 1011-1028. Birling, M.-C., Herault, Y. and Pavlovic, G. (2017). Modeling human disease in rodents by CRISPR/Cas9 genome editing. Mamm. Genome 28, 291-301. Bockamp, E., Sprengel, R., Eshkind, L., Lehmann, T., Braun, J. M., Emmrich, F. and Hengstler, J. G. (2008). Conditional transgenic mouse models: from the basics to genome-wide sets of knockouts and current studies of tissue regeneration. Regen. Med. 3, 217-235. Bouabe, H. and Okkenhaug, K. (2013). Gene targeting in mice: a review. Methods Mol. Biol. 1064, 315-336. Bradley, A., Anastassiadis, K., Ayadi, A., Battey, J. F., Bell, C., Birling, M.-C., Bottomley, J., Brown, S. D., Bürger, A., Bult, C. J. et al. (2012). The mammalian gene function resource: the international knockout mouse consortium. Mamm. Genome 23, 580-586. Brinster, R. L., Sandgren, E. P., Behringer, R. R. and Palmiter, R. D. (1989). No simple solution for making transgenic mice. Cell 59, 239-241. Burgio, G. (2018). Redefining mouse transgenesis with CRISPR/Cas9 genome editing technology. Genome Biol. 19, 27. Cai, L., Fisher, A. L., Huang, H. and Xie, Z. (2016). CRISPR-mediated genome editing and human diseases. Genes Dis. 3, 244-251. Carbery, I. D., Ji, D., Harrington, A., Brown, V., Weinstein, E. J., Liaw, L. and Cui, X. (2010). Targeted genome modification in mice using zinc-finger nucleases. Genetics 186, 451-459. Carroll, D. (2011). Genome engineering with zinc-finger nucleases. Genetics 188, 773-782. Chambers, I., Silva, J., Colby, D., Nichols, J., Nijmeijer, B., Robertson, M., Vrana, J., Jones, K., Grotewold, L. and Smith, A. (2007). Nanog safeguards pluripotency and mediates germline development. Nature 450, 1230-1234. Chang, H.-S., Lin, C.-H., Chen, Y.-C. and Yu, W. C. Y. (2004). Using siRNA technique to generate transgenic animals with spatiotemporal and conditional gene knockdown. Am. J. Pathol. 165, 1535-1541. Chen, S., Lee, B., Lee, A. Y.-F., Modzelewski, A. J. and He, L. (2016). Highly efficient mouse genome editing by CRISPR ribonucleoprotein electroporation of zygotes. J. Biol. Chem. 291, 14457-14467. Chen-Tsai, R. Y., Jiang, R., Zhuang, L., Wu, J., Li, L. and Wu, J. (2014). Genome editing and animal models. Chin. Sci. Bull. 59, 1-6. Chevalier, B. S. and Stoddard, B. L. (2001). Homing endonucleases: structural and functional insight into the catalysts of intron/intein mobility. Nucleic Acids Res. 29, 3757-3774. Chiang, C., Jacobsen, J. C., Ernst, C., Hanscom, C., Heilbut, A., Blumenthal, I., Mills, R. E., Kirby, A., Lindgren, A. M., Rudiger, S. R. et al. (2012). Complex reorganization and predominant non-homologous repair following chromosomal breakage in karyotypically balanced germline rearrangements and transgenic integration. Nat. Genet. 44, 390-397, S1. Cho, S. W., Kim, S., Kim, J. M. and Kim, J.-S. (2013). Targeted genome engineering in human cells with the Cas9 RNA-guided endonuclease. Nat. Biotechnol. 31, 230-232. Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A. et al. (2013). Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819-823. Cox, D. B. T., Platt, R. J. and Zhang, F. (2015). Therapeutic genome editing: prospects and challenges. Nat. Med. 21, 121-131. Dickins, R. A., McJunkin, K., Hernando, E., Premsrirut, P. K., Krizhanovsky, V., Burgess, D. J., Kim, S. Y., Cordon-Cardo, C., Zender, L., Hannon, G. J. et al. (2007). Tissue-specific and reversible RNA interference in transgenic mice. Nat. Genet. 39, 914-921. Dickinson, M. E., Flenniken, A. M., Ji, X., Teboul, L., Wong, M. D., White, J. K., Meehan, T. F., Weninger, W. J., Westerberg, H., Adissu, H. et al. (2016). Highthroughput discovery of novel developmental phenotypes. Nature 537, 508-514. Dow, L. E. (2015). Modeling disease in vivo with CRISPR/Cas9. Trends Mol. Med. 21, 609-621. Dubois, N. C., Hofmann, D., Kaloulis, K., Bishop, J. M. and Trumpp, A. (2006). Nestin-Cre transgenic mouse line Nes-Cre1 mediates highly efficient Cre/loxP mediated recombination in the nervous system, kidney, and somite-derived tissues. Genesis 44, 355-360. Dymecki, S. M. (1996). Flp recombinase promotes site-specific DNA recombination in embryonic stem cells and transgenic mice. Proc. Natl. Acad. Sci. USA 93, 6191-6196. Economides, A. N., Frendewey, D., Yang, P., Dominguez, M. G., Dore, A. T., Lobov, I. B., Persaud, T., Rojas, J., McClain, J., Lengyel, P. et al. (2013). Conditionals by inversion provide a universal method for the generation of conditional alleles. Proc. Natl. Acad. Sci. USA 110, E3179-E3188. Feil, R., Brocard, J., Mascrez, B., LeMeur, M., Metzger, D. and Chambon, P. (1996). Ligand-activated site-specific recombination in mice. Proc. Natl. Acad. Sci. USA 93, 10887-10890. Fire, A., Xu, S. Q., Montgomery, M. K., Kostas, S. A., Driver, S. E. and Mello, C. C. (1998). Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 391, 806-811. Freudenthal, B., Logan, J., Sanger Institute Mouse Pipelines, Croucher, P. I., Williams, G. R. and Bassett, J. H. D. (2016). Rapid phenotyping of knockout mice to identify genetic determinants of bone strength. J. Endocrinol. 231, R31-R46. Friedel, R. H. and Soriano, P. (2010). Gene trap mutagenesis in the mouse. Methods Enzymol. 477, 243-269. Gaj, T., Gersbach, C. A. and Barbas, C. F. (2013). ZFN, TALEN, and CRISPR/ Cas-based methods for genome engineering. Trends Biotechnol. 31, 397-405. Gassmann, M., Casagranda, F., Orioli, D., Simon, H., Lai, C., Klein, R. and Lemke, G. (1995). Aberrant neural and cardiac development in mice lacking the ErbB4 neuregulin receptor. Nature 378, 390-394. Golub, M. S., Germann, S. L. and Lloyd, K. C. K. (2004). Behavioral characteristics of a nervous system-specific erbB4 knock-out mouse. Behav. Brain Res. 153, 159-170. Gonzaga-Jauregui, C., Lupski, J. R. and Gibbs, R. A. (2012). Human genome sequencing in health and disease. Annu. Rev. Med. 63, 35-61. Gordon, J. W. and Ruddle, F. H. (1981). Integration and stable germ line transmission of genes injected into mouse pronuclei. Science 214, 1244-1246. Gordon, J. W., Scangos, G. A., Plotkin, D. J., Barbosa, J. A. and Ruddle, F. H. (1980). Genetic transformation of mouse embryos by microinjection of purified DNA. Proc. Natl. Acad. Sci. USA 77, 7380-7384. Gossen, M. and Bujard, H. (1992). Tight control of gene expression in mammalian cells by tetracycline-responsive promoters. Proc. Natl. Acad. Sci. USA 89, 5547-5551. Gu, H., Marth, J. D., Orban, P. C., Mossmann, H. and Rajewsky, K. (1994). Deletion of a DNA polymerase beta gene segment in T cells using cell typespecific gene targeting. Science 265, 103-106. Gurumurthy, C. B., Quadros, R. M., Sato, M., Mashimo, T., Lloyd, K. C. K. and Ohtsuka, M. (2016a). CRISPR/Cas9 and the paradigm shift in mouse genome manipulation technologies. In Genome Editing (ed. K. Turksen), pp. 65-77. Cham: Springer International Publishing. Gurumurthy, C. B., Takahashi, G., Wada, K., Miura, H., Sato, M. and Ohtsuka, M. (2016b). GONAD: a novel CRISPR/Cas9 genome editing method that does not require ex vivo handling of embryos. In Current Protocols in Human Genetics (ed. J. L. Haines, B. R. Korf, C. C. Morton, C. E. Seidman, J. G. Seidman and D. R. Smith), pp. 15.8.1-15.8.12. Hoboken, NJ, USA: John Wiley & Sons, Inc. Gurumurthy, C. B., Grati, M., Ohtsuka, M., Schilit, S. L. P., Quadros, R. M. and Liu, X. Z. (2016c). CRISPR: a versatile tool for both forward and reverse genetics research. Hum. Genet. 135, 971-976. Hadjantonakis, A.-K., Pirity, M. and Nagy, A. (2008). Cre recombinase mediated alterations of the mouse genome using embryonic stem cells. Methods Mol. Biol. 461, 111-132. Hara, S. and Takada, S. (2018). Genome editing for the reproduction and remedy of human diseases in mice. J. Hum. Genet. 63, 107-113. Hashimoto, M. and Takemoto, T. (2015). Electroporation enables the efficient mRNA delivery into the mouse zygotes and facilitates CRISPR/Cas9-based genome editing. Sci. Rep. 5, 11315. Higashijima, Y., Hirano, S., Nangaku, M. and Nureki, O. (2017). Applications of the CRISPR-Cas9 system in kidney research. Kidney Int. 92, 324-335. Inui, M., Miyado, M., Igarashi, M., Tamano, M., Kubo, A., Yamashita, S., Asahara, H., Fukami, M. and Takada, S. (2014). Rapid generation of mouse models with defined point mutations by the CRISPR/Cas9 system. Sci. Rep. 4, 5396. Iyer, V., Shen, B., Zhang, W., Hodgkins, A., Keane, T., Huang, X. and Skarnes, W. C. (2015). Off-target mutations are rare in Cas9-modified mice. Nat. Methods 12, 479-479. Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A. and Charpentier, E. (2012). A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816-821. Jinek, M., East, A., Cheng, A., Lin, S., Ma, E. and Doudna, J. (2013). RNAprogrammed genome editing in human cells. eLife 2, e00471. Joung, J. K. and Sander, J. D. (2012). TALENs: a widely applicable technology for targeted genome editing. Nat. Rev. Mol. Cell Biol. 14, 49-55. Jovicić, A., Ivanisević, V. and Magdić, B. (1990). [Treatment of epilepsy in adults]. Vojnosanit. Pregl. 47, 112-117. Justice, M. J. (1999). Mouse ENU Mutagenesis. Hum. Mol. Genet. 8, 1955-1963. Justice, M. J. and Dhillon, P. (2016). Using the mouse to model human disease: increasing validity and reproducibility. Dis. Model. Mech. 9, 101-103. Justice, M. J., Siracusa, L. D. and Stewart, A. F. (2011). Technical approaches for mouse models of human disease. Dis. Model. Mech. 4, 305-310. Kane, K. L., Longo-Guess, C. M., Gagnon, L. H., Ding, D., Salvi, R. J. and Johnson, K. R. (2012). Genetic background effects on age-related hearing loss associated with Cdh23 variants in mice. Hear. Res. 283, 80-88. Karp, N. A., Meehan, T. F., Morgan, H., Mason, J. C., Blake, A., Kurbatova, N., Smedley, D., Jacobsen, J., Mott, R. F., Iyer, V. et al. (2015). Applying the ARRIVE guidelines to an in vivo database. PLoS Biol. 13, e1002151. 8 AT A GLANCE Disease Models & Mechanisms (2019) 12, dmm029462. doi:10.1242/dmm.029462 Disease Models & Mechanisms Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M. and Altman, D. G. (2010). Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS Biol. 8, e1000412. Kim, S.-T., Park, J., Kim, D., Kim, K., Bae, S., Schlesner, M. and Kim, J.-S. (2018). Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo”. Nat. Methods 15, 239. Kleinhammer, A., Wurst, W. and Kühn, R. (2010). Gene knockdown in the mouse through RNAi. In Methods in Enzymology (eds P. M. Wassarman and P. M. Soriano) pp. 387-414. Elsevier. Kleinstiver, B. P., Pattanayak, V., Prew, M. S., Tsai, S. Q., Nguyen, N. T., Zheng, Z. and Joung, J. K. (2016). High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature 529, 490-495. Lescarbeau, R. M., Murray, B., Barnes, T. M. and Bermingham, N. (2018). Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo”. Nat. Methods 15, 237. Lewandoski, M., Wassarman, K. M. and Martin, G. R. (1997). Zp3-cre, a transgenic mouse line for the activation or inactivation of loxP-flanked target genes specifically in the female germ line. Curr. Biol. 7, 148-151. Li, L., Wu, L. P. and Chandrasegaran, S. (1992). Functional domains in Fok I restriction endonuclease. Proc. Natl. Acad. Sci. USA 89, 4275-4279. Li, D., Qiu, Z., Shao, Y., Chen, Y., Guan, Y., Liu, M., Li, Y., Gao, N., Wang, L., Lu, X. et al. (2013). Heritable gene targeting in the mouse and rat using a CRISPRCas system. Nat. Biotechnol. 31, 681-683. Li, F., Cowley, D. O., Banner, D., Holle, E., Zhang, L. and Su, L. (2014). Efficient genetic manipulation of the NOD-Rag1-/-IL2RgammaC-null mouse by combining in vitro fertilization and CRISPR/Cas9 technology. Sci. Rep. 4, 5290. Liakath-Ali, K., Vancollie, V. E., Heath, E., Smedley, D. P., Estabel, J., Sunter, D., Ditommaso, T., White, J. K., Ramirez-Solis, R., Smyth, I. et al. (2014). Novel skin phenotypes revealed by a genome-wide mouse reverse genetic screen. Nat. Commun. 5, 3540. Liang, Q., Conte, N., Skarnes, W. C. and Bradley, A. (2008). Extensive genomic copy number variation in embryonic stem cells. Proc. Natl. Acad. Sci. USA 105, 17453-17456. Lin, C.-J., Nasr, Z., Premsrirut, P. K., Porco, J. A., Hippo, Y., Lowe, S. W. and Pelletier, J. (2012). Targeting synthetic lethal interactions between Myc and the eIF4F complex impedes tumorigenesis. Cell Rep. 1, 325-333. Liu, C. (2013). Strategies for designing transgenic DNA constructs. In Lipoproteins and Cardiovascular Disease (ed. L. A. Freeman), pp. 183-201. Totowa, NJ: Humana Press. Liu, G. J., Cimmino, L., Jude, J. G., Hu, Y., Witkowski, M. T., McKenzie, M. D., Kartal-Kaess, M., Best, S. A., Tuohey, L., Liao, Y. et al. (2014). Pax5 loss imposes a reversible differentiation block in B-progenitor acute lymphoblastic leukemia. Genes Dev. 28, 1337-1350. Lloyd, K. C. K., Robinson, P. N. and MacRae, C. A. (2016). Animal-based studies will be essential for precision medicine. Sci. Transl. Med. 8, 352ed12. Madisen, L., Garner, A. R., Shimaoka, D., Chuong, A. S., Klapoetke, N. C., Li, L., van der Bourg, A., Niino, Y., Egolf, L., Monetti, C. et al. (2015). Transgenic mice for intersectional targeting of neural sensors and effectors with high specificity and performance. Neuron 85, 942-958. Mali, P., Yang, L., Esvelt, K. M., Aach, J., Guell, M., DiCarlo, J. E., Norville, J. E. and Church, G. M. (2013). RNA-guided human genome engineering via Cas9. Science 339, 823-826. Marth, J. D. (1996). Recent advances in gene mutagenesis by site-directed recombination. J. Clin. Invest. 97, 1999-2002. Maruyama, T., Dougan, S. K., Truttmann, M. C., Bilate, A. M., Ingram, J. R. and Ploegh, H. L. (2015). Increasing the efficiency of precise genome editing with CRISPR-Cas9 by inhibition of nonhomologous end joining. Nat. Biotechnol. 33, 538-542. McBride, J. L., Boudreau, R. L., Harper, S. Q., Staber, P. D., Monteys, A. M., Martins, I., Gilmore, B. L., Burstein, H., Peluso, R. W., Polisky, B. et al. (2008). Artificial miRNAs mitigate shRNA-mediated toxicity in the brain: implications for the therapeutic development of RNAi. Proc. Natl. Acad. Sci. USA 105, 5868-5873. McLellan, M. A., Rosenthal, N. A. and Pinto, A. R. (2017). Cre-loxP-mediated recombination: general principles and experimental considerations. Curr. Protoc. Mouse Biol. 7, 1-12. Meehan, T. F., Conte, N., West, D. B., Jacobsen, J. O., Mason, J., Warren, J., Chen, C.-K., Tudose, I., Relac, M., Matthews, P. et al. (2017). Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium. Nat. Genet. 49, 1231-1238. Miano, J. M., Zhu, Q. M. and Lowenstein, C. J. (2016). A CRISPR path to engineering new genetic mouse models for cardiovascular research. Arterioscler. Thromb. Vasc. Biol. 36, 1058-1075. Miura, H., Gurumurthy, C. B., Sato, T., Sato, M. and Ohtsuka, M. (2015). CRISPR/ Cas9-based generation of knockdown mice by intronic insertion of artificial microRNA using longer single-stranded DNA. Sci. Rep. 5, 12799. Miura, H., Quadros, R. M., Gurumurthy, C. B. and Ohtsuka, M. (2017). EasiCRISPR for creating knock-in and conditional knockout mouse models using long ssDNA donors. Nat. Protoc. 13, 195-215. Mojica, F. J. M. and Garrett, R. A. (2013). Discovery and seminal developments in the CRISPR Field. In CRISPR-Cas Systems (ed. R. Barrangou and J. van der Oost), pp. 1-31. Berlin, Heidelberg: Springer Berlin Heidelberg. Mojica, F. J. M., Juez, G. and Rodriguez-Valera, F. (1993). Transcription at different salinities of Haloferax mediterranei sequences adjacent to partially modified PstI sites. Mol. Microbiol. 9, 613-621. Mou, H., Kennedy, Z., Anderson, D. G., Yin, H. and Xue, W. (2015). Precision cancer mouse models through genome editing with CRISPR-Cas9. Genome Med. 7, 53. Mouse Genome Sequencing Consortium (2002). Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520-562. Nadeau, J. H., Balling, R., Barsh, G., Beier, D., Brown, S. D., Bucan, M., Camper, S., Carlson, G., Copeland, N., Eppig, J. et al. (2001). Sequence interpretation. Functional annotation of mouse genome sequences. Science 291, 1251-1255. Nagy, A. (2000). Cre recombinase: the universal reagent for genome tailoring. Genesis 26, 99-109. Nern, A., Pfeiffer, B. D., Svoboda, K. and Rubin, G. M. (2011). Multiple new sitespecific recombinases for use in manipulating animal genomes. Proc. Natl. Acad. Sci. USA 108, 14198-14203. Nutter, L. M. J., Heaney, J. D., Lloyd, K. C. K., Murray, S. A., Seavitt, J. R., Skarnes, W. C., Teboul, L., Brown, S. D. M. and Moore, M. (2018). Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo”. Nat. Methods 15, 235-236. Ohtsuka, M., Ogiwara, S., Miura, H., Mizutani, A., Warita, T., Sato, M., Imai, K., Hozumi, K., Sato, T., Tanaka, M. et al. (2010). Pronuclear injection-based mouse targeted transgenesis for reproducible and highly efficient transgene expression. Nucleic Acids Res. 38, e198. Ohtsuka, M., Miura, H., Sato, M., Kimura, M., Inoko, H. and Gurumurthy, C. B. (2012a). PITT: pronuclear injection-based targeted transgenesis, a reliable transgene expression method in mice. Exp. Anim. Jpn. Assoc. Lab. Anim. Sci. 61, 489-502. Ohtsuka, M., Miura, H., Nakaoka, H., Kimura, M., Sato, M. and Inoko, H. (2012b). Targeted transgenesis through pronuclear injection of improved vectors into in vitro fertilized eggs. Transgenic Res. 21, 225-226. Ohtsuka, M., Miura, H., Mochida, K., Hirose, M., Hasegawa, A., Ogura, A., Mizutani, R., Kimura, M., Isotani, A., Ikawa, M. et al. (2015). One-step generation of multiple transgenic mouse lines using an improved Pronuclear Injection-based Targeted Transgenesis (i-PITT). BMC Genomics 16, 274. Ohtsuka, M., Sato, M., Miura, H., Takabayashi, S., Matsuyama, M., Koyano, T., Arifin, N., Nakamura, S., Wada, K. and Gurumurthy, C. B. (2018). i-GONAD: a robust method for in situ germline genome engineering using CRISPR nucleases. Genome Biol. 19, 25. Palmiter, R. D., Brinster, R. L., Hammer, R. E., Trumbauer, M. E., Rosenfeld, M. G., Birnberg, N. C. and Evans, R. M. (1982a). Dramatic growth of mice that develop from eggs microinjected with metallothionein-growth hormone fusion genes. Nature 300, 611-615. Peng, S., York, J. P. and Zhang, P. (2006). A transgenic approach for RNA interference-based genetic screening in mice. Proc. Natl. Acad. Sci. USA 103, 2252-2256. Perlman, R. L. (2016). Mouse models of human disease: an evolutionary perspective. Evol. Med. Public Health 2016, 170-176. Perrin, S. (2014). Preclinical research: make mouse studies work. Nature 507, 423-425. Piedrahita, J. A., Dunne, P., Lee, C.-K., Moore, K., Rucker, E. and Vazquez, J. C. (1999). Use of embryonic and somatic cells for production of transgenic domestic animals. Cloning 1, 73-87. Platt, R. J., Chen, S., Zhou, Y., Yim, M. J., Swiech, L., Kempton, H. R., Dahlman, J. E., Parnas, O., Eisenhaure, T. M., Jovanovic, M. et al. (2014). CRISPR-Cas9 knockin mice for genome editing and cancer modeling. Cell 159, 440-455. Porteus, M. H. (2015). Towards a new era in medicine: therapeutic genome editing. Genome Biol. 16, 286. Premsrirut, P. K., Dow, L. E., Kim, S. Y., Camiolo, M., Malone, C. D., Miething, C., Scuoppo, C., Zuber, J., Dickins, R. A., Kogan, S. C. et al. (2011). A rapid and scalable system for studying gene function in mice using conditional RNA interference. Cell 145, 145-158. Pulina, M. V., Sahr, K. E., Nowotschin, S., Baron, M. H. and Hadjantonakis, A.-K. (2014). A conditional mutant allele for analysis of Mixl1 function in the mouse. Genesis 52, 417-423. Qin, W., Dion, S. L., Kutny, P. M., Zhang, Y., Cheng, A. W., Jillette, N. L., Malhotra, A., Geurts, A. M., Chen, Y.-G. and Wang, H. (2015). Efficient CRISPR/Cas9-mediated genome editing in mice by zygote electroporation of nuclease. Genetics 200, 423-430. Quadros, R. M., Harms, D. W., Ohtsuka, M. and Gurumurthy, C. B. (2015). Insertion of sequences at the original provirus integration site of mouse ROSA26 locus using the CRISPR/Cas9 system. FEBS Open Biol. 5, 191-197. Quadros, R. M., Miura, H., Harms, D. W., Akatsuka, H., Sato, T., Aida, T., Redder, R., Richardson, G. P., Inagaki, Y., Sakai, D. et al. (2017). Easi-CRISPR: a robust method for one-step generation of mice carrying conditional and insertion alleles using long ssDNA donors and CRISPR ribonucleoproteins. Genome Biol. 18, 92. 9 AT A GLANCE Disease Models & Mechanisms (2019) 12, dmm029462. doi:10.1242/dmm.029462 Disease Models & Mechanisms Rajewsky, K., Gu, H., Kühn, R., Betz, U. A., Müller, W., Roes, J. and Schwenk, F. (1996). Conditional gene targeting. J. Clin. Invest. 98, 600-603. Rickert, R. C., Roes, J. and Rajewsky, K. (1997). B lymphocyte-specific, Cremediated mutagenesis in mice. Nucleic Acids Res. 25, 1317-1318. Römer, P., Hahn, S., Jordan, T., Strauss, T., Bonas, U. and Lahaye, T. (2007). Plant pathogen recognition mediated by promoter activation of the pepper Bs3 resistance gene. Science 318, 645-648. Roper, J., Tammela, T., Cetinbas, N. M., Akkad, A., Roghanian, A., Rickelt, S., Almeqdadi, M., Wu, K., Oberli, M. A., Sánchez-Rivera, F. et al. (2017). In vivo genome editing and organoid transplantation models of colorectal cancer and metastasis. Nat. Biotechnol. 35, 569-576. Rosen, B., Schick, J. and Wurst, W. (2015). Beyond knockouts: the International Knockout Mouse Consortium delivers modular and evolving tools for investigating mammalian genes. Mamm. Genome Off. J. Int. Mamm. Genome Soc. 26, 456-466. Rosenthal, N. and Brown, S. (2007). The mouse ascending: perspectives for human-disease models. Nat. Cell Biol. 9, 993-999. Rouet, P., Smih, F. and Jasin, M. (1994). Expression of a site-specific endonuclease stimulates homologous recombination in mammalian cells. Proc. Natl. Acad. Sci. USA 91, 6064-6068. Russell, W. L., Kelly, E. M., Hunsicker, P. R., Bangham, J. W., Maddux, S. C. and Phipps, E. L. (1979). Specific-locus test shows ethylnitrosourea to be the most potent mutagen in the mouse. Proc. Natl. Acad. Sci. USA 76, 5818-5819. Sakuma, T., Nakade, S., Sakane, Y., Suzuki, K.-I. T. and Yamamoto, T. (2016). MMEJ-assisted gene knock-in using TALENs and CRISPR-Cas9 with the PITCh systems. Nat. Protoc. 11, 118-133. Sander, J. D. and Joung, J. K. (2014). CRISPR-Cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 32, 347-355. Sato, M., Ohtsuka, M., Watanabe, S. and Gurumurthy, C. B. (2016). Nucleic acids delivery methods for genome editing in zygotes and embryos: the old, the new, and the old-new. Biol. Direct 11, 16. Sauer, B. (1998). Inducible gene targeting in mice using the Cre/lox system. Methods 14, 381-392. Schaefer, K. A., Wu, W.-H., Colgan, D. F., Tsang, S. H., Bassuk, A. G. and Mahajan, V. B. (2017). Unexpected mutations after CRISPR–Cas9 editing in vivo. Nat. Methods 14, 547-548. Schilit, S. L. P., Ohtsuka, M., Quadros, R. M. and Gurumurthy, C. B. (2016). Pronuclear injection-based targeted transgenesis: pronuclear injection-based targeted transgenesis. In Current Protocols in Human Genetics (ed. J. L. Haines, B. R. Korf, C. C. Morton, C. E., Seidman, J. G. Seidman and D. R. Smith), p. 15.10.1-15.10.28. Hoboken, NJ, USA: John Wiley & Sons, Inc. Seibler, J., Kleinridders, A., Küter-Luks, B., Niehaves, S., Brüning, J. C. and Schwenk, F. (2007). Reversible gene knockdown in mice using a tight, inducible shRNA expression system. Nucleic Acids Res. 35, e54. Shakya, R., Szabolcs, M., McCarthy, E., Ospina, E., Basso, K., Nandula, S., Murty, V., Baer, R. and Ludwig, T. (2008). The basal-like mammary carcinomas induced by Brca1 or Bard1 inactivation implicate the BRCA1/BARD1 heterodimer in tumor suppression. Proc. Natl. Acad. Sci. USA 105, 7040-7045. Shen, W., Lan, G., Yang, X., Li, L., Min, L., Yang, Z., Tian, L., Wu, X., Sun, Y., Chen, H. et al. (2007). Targeting the exogenous htPAm gene on goat somatic cell beta-casein locus for transgenic goat production. Mol. Reprod. Dev. 74, 428-434. Shen, B., Zhang, J., Wu, H., Wang, J., Ma, K., Li, Z., Zhang, X., Zhang, P. and Huang, X. (2013). Generation of gene-modified mice via Cas9/RNA-mediated gene targeting. Cell Res. 23, 720-723. Shmakov, S., Abudayyeh, O. O., Makarova, K. S., Wolf, Y. I., Gootenberg, J. S., Semenova, E., Minakhin, L., Joung, J., Konermann, S., Severinov, K. et al. (2015). Discovery and functional characterization of diverse class 2 CRISPR-Cas systems. Mol. Cell 60, 385-397. Silver, L. M. (2001). Mice as experimental organisms. In eLS (ed. John Wiley & Sons, Ltd), pp. 1-5. Chichester: John Wiley & Sons, Ltd. Skarnes, W. C. (2015). Is mouse embryonic stem cell technology obsolete? Genome Biol. 16, 109. Skarnes, W. C., Rosen, B., West, A. P., Koutsourakis, M., Bushell, W., Iyer, V., Mujica, A. O., Thomas, M., Harrow, J., Cox, T. et al. (2011). A conditional knockout resource for the genome-wide study of mouse gene function. Nature 474, 337-342. Slaymaker, I. M., Gao, L., Zetsche, B., Scott, D. A., Yan, W. X. and Zhang, F. (2016). Rationally engineered Cas9 nucleases with improved specificity. Science 351, 84-88. Smih, F., Rouet, P., Romanienko, P. J. and Jasin, M. (1995). Double-strand breaks at the target locus stimulate gene targeting in embryonic stem cells. Nucleic Acids Res. 23, 5012-5019. Soriano, P. (1999). Generalized lacZ expression with the ROSA26 Cre reporter strain. Nat. Genet. 21, 70-71. Su, H., Mills, A. A., Wang, X. and Bradley, A. (2002). A targeted X-linked CMV-Cre line. Genesis 32, 187-188. Sung, Y. H., Baek, I.-J., Kim, D. H., Jeon, J., Lee, J., Lee, K., Jeong, D., Kim, J.-S. and Lee, H.-W. (2013). Knockout mice created by TALEN-mediated gene targeting. Nat. Biotechnol. 31, 23-24. Takahashi, G., Gurumurthy, C. B., Wada, K., Miura, H., Sato, M. and Ohtsuka, M. (2015). GONAD: genome-editing via Oviductal Nucleic Acids Delivery system: a novel microinjection independent genome engineering method in mice. Sci. Rep. 5, 11406. Tasic, B., Hippenmeyer, S., Wang, C., Gamboa, M., Zong, H., Chen-Tsai, Y. and Luo, L. (2011). Site-specific integrase-mediated transgenesis in mice via pronuclear injection. Proc. Natl. Acad. Sci. USA 108, 7902-7907. Testa, G., Schaft, J., van der Hoeven, F., Glaser, S., Anastassiadis, K., Zhang, Y., Hermann, T., Stremmel, W. and Stewart, A. F. (2004). A reliable lacZ expression reporter cassette for multipurpose, knockout-first alleles. Genesis 38, 151-158. Thomas, K. R. and Capecchi, M. R. (1986). Targeting of genes to specific sites in the mammalian genome. Cold Spring Harb. Symp. Quant. Biol. 51, 1101-1113. Thomas, K. R. and Capecchi, M. R. (1987). Site-directed mutagenesis by gene targeting in mouse embryo-derived stem cells. Cell 51, 503-512. Thompson, S., Clarke, A. R., Pow, A. M., Hooper, M. L. and Melton, D. W. (1989). Germ line transmission and expression of a corrected HPRT gene produced by gene targeting in embryonic stem cells. Cell 56, 313-321. Tiscornia, G., Singer, O., Ikawa, M. and Verma, I. M. (2003). A general method for gene knockdown in mice by using lentiviral vectors expressing small interfering RNA. Proc. Natl. Acad. Sci. USA 100, 1844-1848. Tschaharganeh, D. F., Lowe, S. W., Garippa, R. J. and Livshits, G. (2016). Using CRISPR/Cas to study gene function and model disease in vivo. FEBS J. 283, 3194-3203. Tsuchida, J., Matsusaka, T., Ohtsuka, M., Miura, H., Okuno, Y., Asanuma, K., Nakagawa, T., Yanagita, M. and Mori, K. (2016). Establishment of Nephrin reporter mice and use for chemical screening. PLoS ONE 11, e0157497. Urnov, F. D., Rebar, E. J., Holmes, M. C., Zhang, H. S. and Gregory, P. D. (2010). Genome editing with engineered zinc finger nucleases. Nat. Rev. Genet. 11, 636-646. Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A. et al. (2001). The sequence of the human genome. Science 291, 1304-1351. Wang, H., Yang, H., Shivalila, C. S., Dawlaty, M. M., Cheng, A. W., Zhang, F. and Jaenisch, R. (2013). One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153, 910-918. Wilson, C. J., Fennell, T., Bothmer, A., Maeder, M. L., Reyon, D., CottaRamusino, C., Fernandez, C. A., Marco, E., Barrera, L. A., Jayaram, H. et al. (2018). Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo”. Nat. Methods 15, 236-237. Woolf, T. M., Gurumurthy, C. B., Boyce, F. and Kmiec, E. B. (2017). To cleave or not to cleave: therapeutic gene editing with and without programmable nucleases. Nat. Rev. Drug Discov. 16, 296. Xu, X., Wagner, K.-U., Larson, D., Weaver, Z., Li, C., Ried, T., Hennighausen, L., Wynshaw-Boris, A. and Deng, C.-X. (1999). Conditional mutation of Brca1 in mammary epithelial cells results in blunted ductal morphogenesis and tumour formation. Nat. Genet. 22, 37-43. Yamamoto-Hino, M. and Goto, S. (2013). In vivo RNAi-based screens: studies in model organisms. Genes 4, 646-665. Yang, W., Tu, Z., Sun, Q. and Li, X.-J. (2016). CRISPR/Cas9: implications for modeling and therapy of neurodegenerative diseases. Front. Mol. Neurosci. 9, 30. Yen, S.-T., Zhang, M., Deng, J. M., Usman, S. J., Smith, C. N., Parker-Thornburg, J., Swinton, P. G., Martin, J. F. and Behringer, R. R. (2014). Somatic mosaicism and allele complexity induced by CRISPR/Cas9 RNA injections in mouse zygotes. Dev. Biol. 393, 3-9. Yoshimi, K., Kunihiro, Y., Kaneko, T., Nagahora, H., Voigt, B. and Mashimo, T. (2016). ssODN-mediated knock-in with CRISPR-Cas for large genomic regions in zygotes. Nat. Commun. 7, 10431. Zetsche, B., Gootenberg, J. S., Abudayyeh, O. O., Slaymaker, I. M., Makarova, K. S., Essletzbichler, P., Volz, S. E., Joung, J., van der Oost, J., Regev, A. et al. (2015). Cpf1 is a single RNA-guided endonuclease of a Class 2 CRISPRCas system. Cell 163, 759-771. 1

NEWS & OPINIONMAGAZINESUBJECTSMULTIMEDIACAREERSSUBSCRIBE

  1. Home
  2. TechEdge

Mouse Models for Disease Research

Mouse Models for Disease Research

Genetically modified mice have revolutionized the biological sciences, helping to uncover countless mechanisms of physiological and pathological function, as well as being instrumental for testing potential intervention possibilities. Understanding how mouse models work goes a long way in helping each scientist find a model that can help them answer their own research questions.

May 24, 2019
THE SCIENTIST CREATIVE SERVICES TEAM

19

The laboratory mouse (Mus musculus) has emerged as a vital research tool in life science laboratories around the world, contributing to key findings in research for cancer, cardiovascular disease, metabolic disorders, and aging. The mouse’s utility as a model of biological function and disease can be attributed to several factors. First, despite vast phenotypic disparities, the mouse shares a remarkable similarity with humans at a genetic level, possessing roughly 85% homology on average in coding regions. Second, the mouse’s quick estrus cycle, short gestational duration, considerable litter size, and rapid post-partum maturation makes it logistically feasible to generate a large colony of genetically similar (if not identical) experimental specimens within a limited timespan. Finally, the mouse embryo is generally easier to manipulate than that of other species, facilitating the generation of custom transgenic models possessing desired genetic alterations of interest.

Already know the basics?
Watch our disease research mouse model review videos.

WHAT TYPES OF GENETICALLY ENGINEERED/MUTANT MICE CAN BE CREATED?

Genome manipulation in mice can be performed in a variety of ways. Arguably, the most popular type of transgenic mouse is the knockout model, where genes of interest are inactivated by replacing or disrupting their coding sequences with exogenous DNA. Knockdown models are similar to knockout models, except that the expression of the gene of interest is significantly reduced rather than completely abrogated. 

The functional opposite to knockdown and knockout models are gain-of-function models (a.k.a. overexpression or knock-in models), where the introduction of genetic material either results in de novo protein synthesis or increases the amount of protein synthesis to beyond exogenous levels. Gain-of-function models offer the added convenience of being able to insert a tag during the process of manipulating the gene of interest, allowing researchers to both detect the protein of interest and distinguish protein generated as a result of gene alterations from protein produced owing to endogenous responses. 

A combination of gain-of-function and loss-of-function models is very useful for delineating the specific role of a given gene and its complementary protein within a homeostatic mechanism, as well as identifying how departures from homeostatic expression levels affect the rest of the mechanism and potentially lead to disease states.

WHAT DO I HAVE TO CONSIDER WHEN DEVELOPING A GENETICALLY ENGINEERED/MUTANT MOUSE MODEL?

Beyond the standard issues that are associated with any process involving transfection and/or transduction, such as gene integration, expression, and stability, researchers also have to take into account the possibility of unintended phenotypic alterations when developing a genetically engineered mouse. Given that gene expression is highly fluid throughout an organism’s lifespan, knocking out a gene can result in unintended consequences, such as embryonic lethality (where the genetically modified embryo/fetus fails to survive gestation), stunted growth post-partum, and sterility. 

Workarounds have been devised for such situations, with knockdown models, in particular, proving useful in situations where completely knocking out a gene would result in embryonic lethality. Alternatively, scientists can generate “inducible” models using mechanisms such as the Cre-loxP recombination or the tetracycline-controlled transactivator systems. 

These systems can be used for both loss- and gain-of-function. For example, the Cre-loxP system confers loss-of-function by flanking the gene of interest with loxP sites (floxing), typically resulting in sequence deletion upon Cre induction. Alternatively, placing a floxed stop codon ahead of a gene of interest means that Cre induction restores transcription. Finally, by integrating them with specific promoters, induction systems can be used to create cell/tissue-specific genetically engineered mice – something that is very useful for ensuring that a model mimics its associated human condition as closely as possible.

GENETIC MODIFICATION ASIDE, WHAT OTHER PARAMETERS DO I HAVE TO BE AWARE OF?

When using genetically modified mice, it’s important to remember that these are living multicellular organisms, susceptible to not only inherent heterogeneity across individuals and generations, but also capable of undergoing genetic drift, whether naturally or due to improper breeding practices. While genetic drift cannot be completely eliminated, it can be mitigated by detailed record-keeping, careful observation for phenotypic changes, avoiding selection pressure by selecting breeders at random, and “refreshing” your colony by backcrossing breeders with wildtype mice every few generations. 

Additionally, the identification and use of proper controls is paramount to delineating a novel phenomenon from variation and noise. It’s clear just from looking at them that there are many different strains of mice, and as such, the wildtype controls for an experiment should be, at minimum, of the same strain as the genetically engineered mice used in that experiment. Ideally, researchers should strive to obtain and use wildtype littermates as controls, with the caveat that a breeding regimen involving heterozygote x heterozygote crosses is not always logistically feasible. If wildtype mice are unavailable, heterozygote littermates may be viable control specimens, provided that no phenotypic deviation from the wildtype is present.

Nowadays, a mouse model exists for just about every known situation, but researchers are continuously pushing the envelope when it comes to asking new questions and requiring novel models. Understanding how mouse models work and the considerations that go into their creation goes a long way in helping each scientist find a model that can help them answer their own research questions.LET US HELP YOU ON THIS JOURNEY WITH OUR MODEL REVIEW VIDEOS BELOW.

The below sponsored product videos are sponsored by their respective manufacturers
and were produced by
 The Scientist’s TechEdge Team in conjuction with them.

MuScreenTM

DEVELOPED BY: CROWN BIOSCIENCE

The rise of immune checkpoint inhibitors has revolutionized the cancer therapy landscape. A thorough preclinical investigation of drug mechanism of action and efficacy is key to selecting the right candidate and treatment strategy for clinical applications. MuScreenTM is the first large-scale in vivo screening platform, using syngeneic and tumor homograft models to fast-track immunooncology drug development.

The above video was sponsored by and produced in conjunction with Crown Bioscience. 

Your information will only be shared with the listed sponsor(s) to whom you have submitted a quote request.  The sponsoring company will only contact you to provide information related to this product. You can unsubscribe from this communication at any time. To contact sponsors directly regarding data privacy issues, please refer to the below information:  

Crown Bioscience: 16550 West Bernardo Drive, Building 5, Suite 525, San Diego, CA, USA 92127.
Bill Collins, Digital Marketing Manager. Email: bill.collins@crownbio.comRelated Articles

Mouse Model Shows How Parkinson’s Disease Begins in the Gut

Mouse Model Shows How Parkinson’s Disease Begins in the Gut

<img src="https://cdn.the-scientist.com/assets/articleNo/66340/aImg/33258/microscope-thumb-t.png" alt="Genetics Models Move Beyond <em>Drosophila

Genetics Models Move Beyond Drosophila and the Humble Lab Mouse

New Mouse Model Predicts Two Clinical Trial Failures in Humans

New Mouse Model Predicts Two Clinical Trial Failures in Humans

Researchers Develop New Method for Sexing Sperm

Researchers Develop New Method for Sexing SpermTrending

GM Mosquito Progeny Not Dying in Brazil: Study

GM Mosquito Progeny Not Dying in Brazil: Study

Neanderthal DNA in Modern Human Genomes Is Not Silent

Neanderthal DNA in Modern Human Genomes Is Not Silent

Biogen, Eisai End Two Late-Stage Trials for Alzheimer’s Treatment

Biogen, Eisai End Two Late-Stage Trials for Alzheimer’s Treatment

Trump Administration Overturns Clean Water Regulation

Trump Administration Overturns Clean Water Regulation

The Scientist's Current Issue's Magazine Cover

SEPTEMBER 2019

Our Inner Neanderthal

Ancient secrets in the human genomeSUBSCRIBE TODAY

The Race to Nab Cheating Athletes

The Race to Nab Cheating AthletesAnti-doping organizations are constantly developing new tests to catch athletes trying to boost their performance in increasingly sophisticated ways.

Neanderthal DNA in Modern Human Genomes Is Not Silent

Neanderthal DNA in Modern Human Genomes Is Not SilentFrom skin color to immunity, human biology is linked to our archaic ancestry.

Recent Trials for Fragile X Syndrome Offer Hope

Recent Trials for Fragile X Syndrome Offer HopeDespite a solid understanding of the biological basis of fragile X syndrome, researchers have struggled to develop effective treatments.Sponsored ContentLabQuizzesWebinarsVideosInfographicseBooksTechEdge

Seeing More Matters in Scientific Research: Ultra High Resolution Ultrasound

Seeing More Matters in Scientific Research: Ultra High Resolution UltrasoundDownload this poster from FUJIFILM VisualSonics to learn more about ultra high resolution ultrasound, a silent hero in research!

Mouse Models for Disease Research

Mouse Models for Disease ResearchGenetically modified mice have revolutionized the biological sciences, helping to uncover countless mechanisms of physiological and pathological function, as well as being instrumental for testing potential intervention possibilities. Understanding how mouse models work goes a long way in helping each scientist find a model that can help them answer their own research questions.Gene-Modulation Technologies in the Development of Cell-Based TherapiesSartorius invites you to join them for an educational webinar.

Advances in Immune Cell Profiling

Advances in Immune Cell ProfilingLearn more about the role of the immune system in cancer, multiplexing immune cell profiling, compiling immune cell phenotypes, and high-throughput cell profiling!MarketplaceSponsored Product Updates

Evaluating the Functional Potency of Immunotherapies Targeting Tumors of B Cell Origin

Evaluating the Functional Potency of Immunotherapies Targeting Tumors of B Cell OriginDownload this application note to learn how the ACEA xCELLigence system can evaluate the potency of an immunotherapy against a broad spectrum of liquid tumors and monitor the destruction kinetics of liquid cancers at physiologically relevant effector:target cell ratios.

Kinetics of Tumor Cell Killing by Tumor-Infiltrating Leukocytes

Kinetics of Tumor Cell Killing by Tumor-Infiltrating LeukocytesWatch this webinar to learn how Dr. Cara Haymaker and her team are assessing the reactivity of tumor-infiltrating lymphocytes and modulating the mechanisms of resistance to therapy and exploring biomarkers involved in the response.

Atlas Antibodies Presents QPrEST Standards for Absolute Quantification of Proteins using Mass Spectrometry

Atlas Antibodies Presents QPrEST Standards for Absolute Quantification of Proteins using Mass SpectrometryAtlas Antibodies AB, a leading supplier of advanced research reagents, announced today the introduction of pre-quantified QPrEST™ Protein Standards for absolute quantification of proteins in biological samples such as cell lysate and plasma using liquid chromatography (LC)–mass spectrometry (MS).

BIA Separations introduces Cornerstone AAV Process Development Service to accelerate gene therapy production

BIA Separations introduces Cornerstone AAV Process Development Service to accelerate gene therapy productionCORNERSTONE program integrates process development expertise and novel technology to remove development bottlenecks in the manufacture of Gene Therapy Medicinal Products (GTMPs). Portfolio includes novel CIMasphere™ technology for higher yielding processes and safer products in AAV-based programsStay Connected with

E-NEWSLETTER SIGN-UP

Subscribe to receive The Scientist Daily E-Newsletter in your inbox!

FACEBOOK PAGES

THE SCIENTISTTHE SCIENTIST CAREERSTHE GENOME SCIENTISTTHE ENVIROSCIENTISTTHE CELL SCIENTISTTHE MICRO SCIENTISTTHE CANCER SCIENTISTTHE NEUROSCIENTISTABOUT & CONTACTPRIVACY POLICYJOB LISTINGSSUBSCRIBEADVERTISENOW PART OF THE LABX MEDIA GROUP:LAB MANAGER MAGAZINE|LABX|LABWRENCH

© 1986–2019 THE SCIENTIST. ALL RIGHTS RESERVED.

New Mouse Model Predicts Two Clinical Trial Failures in Humans
  1. Home
  2. News & Opinion

New Mouse Model Predicts Two Clinical Trial Failures in Humans

The lab animals had more natural microbiomes seeded by wild mice, unlike conventional models that are kept in sterile conditions.

Aug 1, 2019
EMMA YASINSKI

1.4K

ABOVE: Mice trapped in the wild served as gestational surrogates for lab mice with the goal of seeding the offspring with a more natural microbiome.
PIXABAY, BEN FREWIN

Anew mouse model designed with a microbiome similar to that of wild mice may be a better predictor of human responses to some drugs than commonly used lab mice, according to a study published today in Science. The authors repeated two preclinical studies that had demonstrated positive results in mice only to fail when they reached human testing. With the new models, the team saw results that resembled the drugs’ effects in people rather than the previously misleading mouse results.

Mice can be a valuable model for early biomedical research, but many of the drugs that demonstrate promising results in these animals still fail in human trials. Characteristics such as genetics and physiology often take the blame, but researchers are beginning to realize that environmental factors such as the microbiome may also have an effect.

The methods the researchers used show “great potential to unveil mechanisms that have challenged translational research for years,” says Allison Weis, a postdoc studying immunology and the microbiome at the University of Utah School of Medicine who was not involved in the work. “The findings in this study illustrate the power of the bacterial, fungal, and viral microbiota.”

In most preclinical work, researchers use inbred mice that express certain genes and are raised in sterile environments. According to Stephan Rosshart, who conducted the experiments at the National Institute of Diabetes and Digestive and Kidney Diseases, this would be comparable to a human kept in completely sterile conditions from birth to adulthood, without ever getting an infection, “not even a common cold in his or her whole lifetime.” Such a non–real world experience would be likely to distort their immune response.

There is a silver lining that, now, a better mouse model is available to confirm or refute promising results in traditional lab mice.—Franck Carbonero, Washington State University

After attending a lecture about six years ago, where he first heard the word “microbiome,” Rosshart, a gastroenterologist now based at the University Medical Center Frieberg, became curious about whether a mouse with a more “wild” microbiome—one that represented the co-evolution of the mouse and the bacteria, viruses, and fungi that live in and on it—might provide a more accurate model of humans and their own microbiomes.

So he went from performing endoscopies to wrangling mice outdoors. It was difficult at first, but he says he’s now “an expert at trapping wild mice” and has collected more than 1,000 from peanut-butter laced traps placed in horse stables around Maryland and Washington, DC.

There’s a reason researchers use the “cleaner” inbred mouse strains. With sequenced genomes and limited variability, they’re easier to study. Rosshart and his team took advantage of that reliability and transplanted embryos of C57BL/6 mice—the most commonly used strain of lab mouse—into female wild mice, which then gave birth to what the group referred to as “wildlings.” Transferring an embryo like this is a common practice in research, but this is the first time embryos from the lab have been transferred into wild animals. The goal was to ensure that the wildlings were exposed to the surrogate mothers’ microbes in the birth canal, just like a newborn mouse would be in the wild. 

“In essence, you have the genetics of a known laboratory strain with sort of the microbiota of wild mice,” says Joseph Petrosino, a microbiologist at Baylor College of Medicine who has worked with some of the researchers before but was not involved in this study.

To find out how the wildlings would fare as preclinical models, Rosshart’s group chose two immune system–targeting treatments that had extremely promising results in mouse trials but had either catastrophic or no effects when they were tested in humans.

The first experiment tested a monoclonal antibody, CD28SA, to target regulatory T cells. In the original mouse study published in 1999, the animals that received the treatment showed a proliferation of the T cells, which dampened inflammation. Researchers thought it could be used in patients with autoimmune disorders or who were undergoing transplants. Unfortunately, the first human patients who received the drug experienced the opposite effect—dangerously increased inflammation—and the trial was halted.

When Rosshart and his team repeated the study, the C57BL/6 mice showed the same positive response they had in the original work, but the wildlings reacted with increased inflammation, like the humans had. This model might have predicted the human outcome and prevented the trial from taking place, says Rosshart.

The group replicated a second study of an antibody designed to treat sepsis and saw similar results: the C57BL/6 mice demonstrated misleadingly positive results, while the wildlings responded to the treatment in a way similar to the humans.

“These observations represent yet another warning to translational medical researchers that lab mice are very imperfect predictors of results’ translation to humans. But there is a silver lining that, now, a better mouse model is available to confirm or refute promising results in traditional lab mice,” says Franck Carbonero, who studies gut microbiology at Washington State University.

“I don’t want to create the impression . . . that laboratory mice are horrible for research and we shouldn’t do anything with them,” says Rosshart, emphasizing that the wildlings may be best suited for certain types of research such as immunology. “But you can make laboratory mice better, and that’s what we are trying to do.”

S. Rosshart et al., “Laboratory mice born to wild mice have natural microbiota and model human immune responses,” Sciencedoi:10.1126/eaaw4361, 2019.

Emma Yasinski is a Florida-based freelance reporter. Follow her on Twitter @EmmaYas24.

Keywords:

clinical researchclinical trialsenvironmentimmunologylab animalsmicrobiologymicrobiomemouse modelsNewspreclinicalRelated Articles

Personalized Cancer Vaccines in Clinical Trials

Personalized Cancer Vaccines in Clinical Trials

Human Cortical Organoids Model Neuronal Networks

Human Cortical Organoids Model Neuronal Networks

Mouse Models for Disease Research

Mouse Models for Disease Research

Gut Microbes Boost Flu Vaccine’s Success: Clinical Trial

Gut Microbes Boost Flu Vaccine’s Success: Clinical TrialTrending

GM Mosquito Progeny Not Dying in Brazil: Study

GM Mosquito Progeny Not Dying in Brazil: Study

Neanderthal DNA in Modern Human Genomes Is Not Silent

Neanderthal DNA in Modern Human Genomes Is Not Silent

Biogen, Eisai End Two Late-Stage Trials for Alzheimer’s Treatment

Biogen, Eisai End Two Late-Stage Trials for Alzheimer’s Treatment

Trump Administration Overturns Clean Water Regulation

Trump Administration Overturns Clean Water Regulation

The Scientist's Current Issue's Magazine Cover

SEPTEMBER 2019

Our Inner Neanderthal

Ancient secrets in the human genomeSUBSCRIBE TODAY

The Race to Nab Cheating Athletes

The Race to Nab Cheating AthletesAnti-doping organizations are constantly developing new tests to catch athletes trying to boost their performance in increasingly sophisticated ways.

Neanderthal DNA in Modern Human Genomes Is Not Silent

Neanderthal DNA in Modern Human Genomes Is Not SilentFrom skin color to immunity, human biology is linked to our archaic ancestry.

Recent Trials for Fragile X Syndrome Offer Hope

Recent Trials for Fragile X Syndrome Offer HopeDespite a solid understanding of the biological basis of fragile X syndrome, researchers have struggled to develop effective treatments.Sponsored ContentLabQuizzesWebinarsVideosInfographicseBooksTechEdge

Seeing More Matters in Scientific Research: Ultra High Resolution Ultrasound

Seeing More Matters in Scientific Research: Ultra High Resolution UltrasoundDownload this poster from FUJIFILM VisualSonics to learn more about ultra high resolution ultrasound, a silent hero in research!

Mouse Models for Disease Research

Mouse Models for Disease ResearchGenetically modified mice have revolutionized the biological sciences, helping to uncover countless mechanisms of physiological and pathological function, as well as being instrumental for testing potential intervention possibilities. Understanding how mouse models work goes a long way in helping each scientist find a model that can help them answer their own research questions.Gene-Modulation Technologies in the Development of Cell-Based TherapiesSartorius invites you to join them for an educational webinar.

Advances in Immune Cell Profiling

Advances in Immune Cell ProfilingLearn more about the role of the immune system in cancer, multiplexing immune cell profiling, compiling immune cell phenotypes, and high-throughput cell profiling!MarketplaceSponsored Product Updates

Evaluating the Functional Potency of Immunotherapies Targeting Tumors of B Cell Origin

Evaluating the Functional Potency of Immunotherapies Targeting Tumors of B Cell OriginDownload this application note to learn how the ACEA xCELLigence system can evaluate the potency of an immunotherapy against a broad spectrum of liquid tumors and monitor the destruction kinetics of liquid cancers at physiologically relevant effector:target cell ratios.

Kinetics of Tumor Cell Killing by Tumor-Infiltrating Leukocytes

Kinetics of Tumor Cell Killing by Tumor-Infiltrating LeukocytesWatch this webinar to learn how Dr. Cara Haymaker and her team are assessing the reactivity of tumor-infiltrating lymphocytes and modulating the mechanisms of resistance to therapy and exploring biomarkers involved in the response.

Atlas Antibodies Presents QPrEST Standards for Absolute Quantification of Proteins using Mass Spectrometry

Atlas Antibodies Presents QPrEST Standards for Absolute Quantification of Proteins using Mass SpectrometryAtlas Antibodies AB, a leading supplier of advanced research reagents, announced today the introduction of pre-quantified QPrEST™ Protein Standards for absolute quantification of proteins in biological samples such as cell lysate and plasma using liquid chromatography (LC)–mass spectrometry (MS).

BIA Separations introduces Cornerstone AAV Process Development Service to accelerate gene therapy production

BIA Separations introduces Cornerstone AAV Process Development Service to accelerate gene therapy productionCORNERSTONE program integrates process development expertise and novel technology to remove development bottlenecks in the manufacture of Gene Therapy Medicinal Products (GTMPs). Portfolio includes novel CIMasphere™ technology for higher yielding processes and safer products in AAV-based programsStay Connected with

E-NEWSLETTER SIGN-UP

Subscribe to receive The Scientist Daily E-Newsletter in your inbox!

FACEBOOK PAGES

THE SCIENTISTTHE SCIENTIST CAREERSTHE GENOME SCIENTISTTHE ENVIROSCIENTISTTHE CELL SCIENTISTTHE MICRO SCIENTISTTHE CANCER SCIENTISTTHE NEUROSCIENTISTABOUT & CONTACTPRIVACY POLICYJOB LISTINGSSUBSCRIBEADVERTISENOW PART OF THE LABX MEDIA GROUP:LAB MANAGER MAGAZINE|LABX|LABWRENCH

© 1986–2019 THE SCIENTIST. ALL RIGHTS RESERVED.

Mouse Model Shows How Parkinson’s Disease Begins in the Gut
  1. Home
  2. News & Opinion

Mouse Model Shows How Parkinson’s Disease Begins in the Gut

Johns Hopkins’s Ted Dawson discusses his lab’s demonstration that misfolded α-synuclein can move from the stomach to the brain and cause physical and cognitive symptoms.

Jun 26, 2019
EMMA YASINSKI

11.5K

ABOVE: Scans of the brains of mice show a reduction in dopamine (colored areas) in the striatum of the Parkinson’s disease model that was injected with pathogenic α-synuclein (right; control mouse on left).
TED DAWSON ET AL. / NEURON, 2019

In 2003, Heiko Braak, then a neuroanatomist at the University of Frankfurt, suggested that Parkinson’s disease pathology may start in the gut and travel from there to the brain long before a patient shows symptoms. The idea, based on postmortem analyses of samples from parkinson’s patients, has been hotly debated ever since. 

In a study published today (June 26) in NeuronTed Dawson, a neurologist at Johns Hopkins School of Medicine, and his team created an animal model of the disease by injecting particular proteins into the stomachs of mice. About a month later, the animals showed symptoms of Parkinson’s disease. The model not only demonstrates how the disease protein can travel up from the gut to the brain, but also presents nonmotor symptoms rarely seen in other animal models.

The Scientist spoke with Dawson about the work.

Ted DawsonJOHNS HOPKINS MEDICINE

The Scientist: Why did you develop this model?

Ted Dawson: Well, there is this idea that was building that was started by Dr. Braak that Parkinson’s disease could start in the gastrointestinal tract, and there was good human data that suggested that possibility. What was lacking was an animal model that could validate that hypothesis. [The model] validates [the hypothesis] by providing evidence that it’s possible that Parkinson’s disease could start in the gut.

There’s been this very detailed study by Dr. Braak and later by other pathologists that the pathologic alpha-synuclein seemed to progress from the gut up the neuroaxis into the brain, and what our work shows is that this is possible.

See “Can the Flu and Other Viruses Cause Neurodegeneration?

TS: How did you do it?

TD: We took pathologic alpha-synuclein, . . . and then we injected those preformed fibrils into the pylorus [an area of the stomach close to the small intestine] into one of the mice near where the vagal nerve innervates those regions. Over time, the animals developed Parkinson’s disease.

[To clarify], we’re injecting exogenous pathologic alpha-synuclein. And that is causing the endogenous synuclein to misfold and transmit up the vagal nerve. We know that because when we injected the exogenous alpha-synuclein in the alpha-synuclein knock-out, nothing happened. So the exogenous alpha-synuclein is not the synuclein that’s transmitting. It’s causing the endogenous synuclein to misfold and transmit.

TS: What is the importance of the vagus nerve in the pathologic process?

TD: It’s essential for the pathologic alpha-synuclein to get up to the brain. In a subgroup of animals, we performed vagotomies and the pathologic alpha-synuclein did not ascend into the brain. And as a consequence of that, the animals did not develop Parkinson’s disease.

TS: What else makes this model unique?

TD: One of the other things that we think is really exciting about this model is that not only do the mice have the motor features of Parkinson’s disease, they also have the nonmotor features. They’ve got cognitive dysfunction, anxiety, depression, problems with smell. And so we now have an animal model to study those problems. We hope that it opens up a whole new set of investigations using those animal models.

This model shows that it’s possible for synuclein to ascend from the stomach via the vagal nerve to the brain. What we don’t know in humans with Parkinson’s disease is how that process starts. That would be the next step, to figure out how it actually starts in humans.

S. Kim et al., “Transneuronal propagation of pathologic α-synuclein from the gut to the brain models Parkinson’s disease,” Neuron, doi:10.1016/j.neuron.2019.05.035, 2019.

Editor’s note: The interview was edited for brevity.

Emma Yasinski is a Florida-based freelance reporter. Follow her on Twitter @EmmaYas24.

Keywords:

alpha-synucleinanimal modelsdisease & medicinegut-brain axisneurodegenerationneuroscienceNewsParkinson’sQ&AtechniquesRelated Articles

Mouse Models for Disease Research

Mouse Models for Disease Research

New Mouse Model Predicts Two Clinical Trial Failures in Humans

New Mouse Model Predicts Two Clinical Trial Failures in Humans

<img src="https://cdn.the-scientist.com/assets/articleNo/66340/aImg/33258/microscope-thumb-t.png" alt="Genetics Models Move Beyond <em>Drosophila

Genetics Models Move Beyond Drosophila and the Humble Lab Mouse

Reprogrammed Glia Improve Symptoms in a Mouse Model of Parkinson’s

Reprogrammed Glia Improve Symptoms in a Mouse Model of Parkinson’sTrending

GM Mosquito Progeny Not Dying in Brazil: Study

GM Mosquito Progeny Not Dying in Brazil: Study

Neanderthal DNA in Modern Human Genomes Is Not Silent

Neanderthal DNA in Modern Human Genomes Is Not Silent

Biogen, Eisai End Two Late-Stage Trials for Alzheimer’s Treatment

Biogen, Eisai End Two Late-Stage Trials for Alzheimer’s Treatment

Trump Administration Overturns Clean Water Regulation

Trump Administration Overturns Clean Water Regulation

The Scientist's Current Issue's Magazine Cover

SEPTEMBER 2019

Our Inner Neanderthal

Ancient secrets in the human genomeSUBSCRIBE TODAY

The Race to Nab Cheating Athletes

The Race to Nab Cheating AthletesAnti-doping organizations are constantly developing new tests to catch athletes trying to boost their performance in increasingly sophisticated ways.

Neanderthal DNA in Modern Human Genomes Is Not Silent

Neanderthal DNA in Modern Human Genomes Is Not SilentFrom skin color to immunity, human biology is linked to our archaic ancestry.

Recent Trials for Fragile X Syndrome Offer Hope

Recent Trials for Fragile X Syndrome Offer HopeDespite a solid understanding of the biological basis of fragile X syndrome, researchers have struggled to develop effective treatments.Sponsored ContentLabQuizzesWebinarsVideosInfographicseBooksTechEdgeSponsored Interactive Crossword Puzzle

Mouse Models for Disease Research

Mouse Models for Disease ResearchGenetically modified mice have revolutionized the biological sciences, helping to uncover countless mechanisms of physiological and pathological function, as well as being instrumental for testing potential intervention possibilities. Understanding how mouse models work goes a long way in helping each scientist find a model that can help them answer their own research questions.

Advances in Immune Cell Profiling

Advances in Immune Cell ProfilingLearn more about the role of the immune system in cancer, multiplexing immune cell profiling, compiling immune cell phenotypes, and high-throughput cell profiling!Gene-Modulation Technologies in the Development of Cell-Based TherapiesSartorius invites you to join them for an educational webinar.MarketplaceSponsored Product Updates

Evaluating the Functional Potency of Immunotherapies Targeting Tumors of B Cell Origin

Evaluating the Functional Potency of Immunotherapies Targeting Tumors of B Cell OriginDownload this application note to learn how the ACEA xCELLigence system can evaluate the potency of an immunotherapy against a broad spectrum of liquid tumors and monitor the destruction kinetics of liquid cancers at physiologically relevant effector:target cell ratios.

Kinetics of Tumor Cell Killing by Tumor-Infiltrating Leukocytes

Kinetics of Tumor Cell Killing by Tumor-Infiltrating LeukocytesWatch this webinar to learn how Dr. Cara Haymaker and her team are assessing the reactivity of tumor-infiltrating lymphocytes and modulating the mechanisms of resistance to therapy and exploring biomarkers involved in the response.

Atlas Antibodies Presents QPrEST Standards for Absolute Quantification of Proteins using Mass Spectrometry

Atlas Antibodies Presents QPrEST Standards for Absolute Quantification of Proteins using Mass SpectrometryAtlas Antibodies AB, a leading supplier of advanced research reagents, announced today the introduction of pre-quantified QPrEST™ Protein Standards for absolute quantification of proteins in biological samples such as cell lysate and plasma using liquid chromatography (LC)–mass spectrometry (MS).

BIA Separations introduces Cornerstone AAV Process Development Service to accelerate gene therapy production

BIA Separations introduces Cornerstone AAV Process Development Service to accelerate gene therapy productionCORNERSTONE program integrates process development expertise and novel technology to remove development bottlenecks in the manufacture of Gene Therapy Medicinal Products (GTMPs). Portfolio includes novel CIMasphere™ technology for higher yielding processes and safer products in AAV-based programsStay Connected with

E-NEWSLETTER SIGN-UP

Subscribe to receive The Scientist Daily E-Newsletter in your inbox!

FACEBOOK PAGES

THE SCIENTISTTHE SCIENTIST CAREERSTHE GENOME SCIENTISTTHE ENVIROSCIENTISTTHE CELL SCIENTISTTHE MICRO SCIENTISTTHE CANCER SCIENTISTTHE NEUROSCIENTISTABOUT & CONTACTPRIVACY POLICYJOB LISTINGS