Abstract
Modeling human disease has proven to be a challenge for the scientific community. For years, generating an animal model was complicated and restricted to very few species. With the rise of CRISPR/Cas9, it is now possible to generate more or less any animal model. In this review, we will show how this technology is and will change our way to obtain relevant disease animal models and how it should impact human health.
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Introduction
Genome editing and especially the easy and accessible CRISPR/Cas9 technology have open new opportunities in modeling human diseases. Genome-Wide Association Studies (GWAS) with specific point mutations, mutation in coding and non-coding genes, copy number variants (CNVs), and regulatory mutations are now feasible in more or less any genetic background and any species. In this review, we will focus on the latest advancements in the development of disease model in rodents. Because of their phylogenetic relatedness and physiological similarity to humans, their maintenance facility, and easy breeding in the laboratory, mice and rats are the most widely used organisms in research. Genetically engineered rodents allowed major discoveries but sometimes failed to translate to human.
CRISPR/Cas9 to develop better animal models of human disease
Even if animal models like mouse and rat allowed major breakthrough in biomedical research, a striking issue in modern biology is certainly some failure of mice and other model organism studies to be replicated or translated to humans (Buffenstein et al. 2014; Collins and Tabak 2014; Justice and Dhillon 2016; Pound and Bracken 2014; Young 2013). The predominant utilization of mouse—which represent 61% of the animal used in research in Europe (European Commission 2013)—may be one explanation as mice may respond to experimental interventions in ways that differ strikingly from humans (Perlman 2016). Improper data analysis is also a key factor that limits reproducibility and validity of preclinical mouse research (Kafkafi et al. 2017). The extensive use of few strains like C57BL/6 and 129 substrains (129 mice are a complex collection of various backgrounds (Simpson et al. 1997)) has certainly contributed to this failure too. There is plenty of literature showing that the inbred genetic background has an effect on the phenotype (Bilovocky et al. 2003; No 1997), this clearly demonstrates that a unique mouse inbred strain cannot mimic the outbred diversity of human beings. More recently, Sittig et al. beautifully illustrated that genetic background limits generalizability of genotype–phenotype relationships (Sittig et al. 2016). In the MGI database, C57BL/6 lines (congenic or coisogenic) represent 68% of the >28,000 lines available (MGI extract, February 2017). Mainly due to the fact that only few ES cells lines from specific genetic background (mostly C57BL/6N and 129) are germline competent, most of the phenotyping analyses were done in one of these genetic contexts, mixed backgrounds or 129 models were backcrossed to C57BL/6. Indeed, 129 substrains provide an unfavorable genetic background for some experiments, as most substrains are characterized by poor reproductive performance, neuroanatomical and behavioral abnormalities (Eisener-Dorman et al. 2009).
The use of CRISPR/Cas9 is opening completely new opportunities as it is now possible to generate mutant in almost any genetic background and in various species. In rodents, the only limitation to CRISPR is the knowledge of assisted reproductive techniques (ART) in a given species. The capacity to recover fertilized eggs, to perform the microinjection (in cytoplasm or pronucleus) and to implant them in pseudo-pregnant females is indeed needed to perform CRISPR/Cas9 editing. With a minimal set of ART, It is thus now possible to obtain specific mutation in a scientifically selected genetic background in order to obtain better model. For example, Li et al. achieved high rate Fah gene targeting in NOD-SCID-IL2RgammaC-null (NSG) mutant (Li et al. 2014) combining CRISPR and in vitro fertilization. These mice are critical for efficient engraftment of human cells or tissues. Of course, some backgrounds or species are still reluctant to these manipulations. For instance, in Arvicanthis ansorgei, a diurnal rodent widely used for the study of circadian rhythms (Hubbard et al. 2015) it is still not possible to assess when fertilization occurs and when fertilized eggs can be recovered for microinjection (personal communication). Another limitation is, of course, the availability of the genomic sequences as a good CRISPR strategy can only be developed when the whole-genome sequence is known as the specificity of a sgRNA has to be assessed.
Creating a single-nucleotide polymorphism (SNP) animal model of human disease by CRISPR/Cas9 genome editing is now routine in rodent. These models lead to functional insights into the human genetics and allow development of potential new therapies. For example, a human GWAS identified a potential pathological SNP (rs1039084 A > G) in the STXBP5 gene, regulator of platelet secretion in humans. This mutation was then reproduced by CRISPR in the mouse with the nearly same thrombosis phenotype allowing to confirm the causality of this SNP in human (Zhu et al. 2017). Likewise, whole-genome sequencing was used to perform a GWAS in a population-based biobank from Estonia. A number of potential causal variants and underlying mechanisms were identified. One of them is a regulatory element that is necessary for basophil production, it acts specifically during this process to regulate expression of the transcription factor CEBPA. This enhancer was perturbed by CRISPR/Cas9 in hematopoietic stem and progenitor cells demonstrating that it specifically regulates CEBPA expression during basophil differentiation (Guo et al. 2016).
CRISPR/Cas9 can also specifically reduce the expression of protein in vivo when heterozygous SNPs are involved in dominant inherited conditions. This has been shown in a humanized model of Meesmann’s epithelial corneal dystrophy (MECD), where a mutation within KRT12 leads to the occurrence of a novel protospacer adjacent motif (PAM). Injection into the corneal stroma of a specific sgRNA (new PAM) with Cas9 resulted in frame-shift deletions within the mutant KRT12 allele a resulted to a reduced expression of mutant KRT12 mRNA and protein (Courtney et al. 2015).
With more than 84.7 million different SNP (Huddleston and Eichler 2016) found by sequencing 2500 human genomes, understanding which one are pathologic, neutral, or protective is more than a challenge. We are only at the beginning of understanding SNP function in human and CRISPR/Cas9 genome editing will provide great help to confirm the function of human GWAS-selected SNPs. Indeed, it is now possible to easily introduce specific SNPs in inbred (uniform) and outbreed (heterogeneous) with the help of CRISPR/Cas9.
Humanization of whole genomic fragments is becoming easier. Addition of human sequences as well as replacement of rat sequences by human sequence by straight injection into fertilized eggs was one of the achievements of Yoshimi and collaborators (Yoshimi et al. 2016) (see Fig. 1a, b). A knock-in of a 200-kb human BAC containing the human SIRPA locus, concomitantly knocking out a rat gene was obtained by combining CRISPR/Cas9 (two sgRNAS) with single-stranded oligodeoxynucleotides (Yoshimi et al. 2016). A gene replacement approach using 3 sgRNAs was also achieved. The success rate seems however to be low (1 positive pup for 130 embryos injected, 1 out of 23 offspring) and one of the disadvantage we might see is that the BAC backbone remains present in the rat genome. In our lab, we developed a hybrid approach to humanize a large genomic fragment by using a dual-sgRNAs approach combined to homologous recombination of a targeting construct baring the human sequence with 5-kb mouse homologous arms and two selection markers in ES cells (see Fig. 1c). The murine region is deleted by the two double-strand breaks (DSBs) and some clones undergo homologous recombination repair with the supplied circular vector leading to the humanization of the locus. The frequency of these humanization events is increased by the addition of selection cassettes at both extremities of the human sequences. We have been able to humanize this way a 40-kb region in ES cells (Fig. 1c) and confirm germline transmission for the first model. Without CRISPR, the frequency of these HR events would have been very low (if not null). With CRISPR, 30 (out 186 screened clones; 16.1%) were humanized.
Understanding the human structural variations that leads to disease
Structural variants (SVs) are large genomic alteration that involves segments of DNA greater than one kilobase (Freeman et al. 2006). Copy number variants (CNVs) are a subfamily of SV that corresponds only to deletion or duplication and do not include inversion or translocation (Freeman et al. 2006). SVs and particularly CNVs are known to be associated with neuropsychiatric diseases (Cook and Scherer 2008; Girirajan et al. 2011). Some CNVs may be larger than 100 kb, although CNVs of this size typically occur at low frequency in the population. Multiple genes and regulatory regions can be affected in CNVs of all sizes (Dasouki et al. 2011; Itsara et al. 2009; Torres et al. 2016). SVs likely play a major role in various diseases not only restricted to neuronal disorders (Conrad et al. 2010; Fanciulli et al. 2007; McCarroll and Altshuler 2007; Wu and Hurst 2016). To date, more than >60,000 SVs were discovered in human (Huddleston and Eichler 2016). Out of more than 60,000 SVs, the ones that are pathological SVs are mostly not known. Likewise, the underlying mechanisms of SVs diseases, even for neuropsychiatric diseases, remain poorly understood. Most of our actual knowledge is based on molecular data from human genome sequencing lacking functional validation.
Chromosome engineering led to a series of mouse models that precisely mimicked the genetic architecture of human patients. In many cases, the approach enabled the etiology of the disease to be linked to individual or small groups of genes. Modeling SV has been achieved so far by using the Cre-lox technology in combination with ES cells or alternative strategies (Adams et al. 2004; Ruf et al. 2011; Zheng et al. 1999) only in mouse. This complex and time-consuming strategy requires the generation of two mouse lines with loxP site followed by 3-step breeding to introduce a Cre driver expressed in the germline (Hérault et al. 1998; Ramírez-Solis et al. 1995; Spitz et al. 2005). Moreover, this approach was not adaptable to rats which are relevant and highly complementary behavior models mainly due to the absence of stable germline-competent ES cells. In mouse, most of the models were analyzed in the C57BL/6 genetic context.
With recent advances in genomic engineering via the use of CRISPR technology, it is now feasible to dissect SV diseases and identify individual genes contributing to their phenotypes, especially for neuropsychiatric disorder SVs. Indeed, with the genomic data connecting SVs with a multitude of human neuropsychiatric disease, our current technical ability to model such chromosomal anomalies in mouse and rat (Birling et al. 2017) and the existence of precise behavioral measures of endophenotypes argue that the time is ripe for systematic dissection of the genetic mechanisms underlying such disease.
CRISPR/Cas9 and in vitro SVs models
CRISPR/Cas9 genome editing can also be used to achieve interesting in vitro SVs models. One example is the study of recurrent SVs in human-induced pluripotent stem cells (iPSCs). Recurrent SVs arise because of the presence of repetitive sequences within the genome, known as low copy repeats (LCRs). LCRs are stretches of DNA that are typically 10–500 kb (though their size can vary), with greater than 90% sequence identity to another place in the genome (Bailey et al. 2001). The presence of LCRs puts the genome at risk for chromosomal rearrangements that can cause CNVs (Stankiewicz and Lupski 2002). These rearrangements occur because LCRs serve as substrates for non-allelic homologous recombination (NAHR) (Sasaki et al. 2010), which is a recombination between two highly similar DNA sequences that are not alleles (Shaw 2004). NAHR event may result in deletion, duplication, or inversion of large DNA fragment (Turner et al. 2008). Recent in vitro manipulations in human iPSCs reported the use of CRISPR to model the recurrent CNVs 16p11.2 and 15q13.3 (Tai et al. 2016). Reciprocal CNV of a small segment of chromosome 16p11.2 (OMIM 611913) is a common recurrent microdeletion and microduplication syndromes (rMDS) that have been associated with intellectual disability, autism spectrum disorder, schizophrenia, and other neuropsychiatric disorders, as well as anthropometric traits, including obesity (Maillard et al. 2015). 16p11.2 rMDS involves gain or loss of a unique genic segment and one copy equivalent of the segmental duplication. The unique genic segment of the 16p11.2 CNV spans 593 kb (Weiss et al. 2008) containing 47 genes, of which 28 are annotated as protein coding (based on Ensembl GRCh37). It is flanked by parallel and highly homologous (>99% identity) segmental duplications, each spanning 147 kb (Weiss et al. 2008) and containing 6 duplicated genes (Weiss et al. 2008). Tai et al. (2016) obtained rMDS in iPSCs by using two sgRNAs to delete a 575-kb region (one sgRNA targeting both extremities) and an unique sgRNA to target the segmental duplications promoting this way a model of non-allelic homologous recombination (NAHR)-mediated CNV (740 kb) mimicking the consequences of NAHR. This approach will enable modeling of rMDS in multiple tissue types and, with further development and optimization, could provide a tractable route to in vitro correction of these common genomic imbalances.
CRISPR/Cas9 and in vivo SVs models
In the past 2 years, CRISPR/Cas9 was used to manipulate large genomic region in the mouse genome. The Wu lab (Li et al. 2015) demonstrated that DNA elements of up to 29 kb can be manipulated directly in mice. Zheng and Li’s lab showed that deletion of up to 95 kb can be generated in mice (Wang et al. 2015; Zhang et al. 2015). Similarly, gene clusters of up to 800 kb have been deleted, duplicated, and inversed in mouse ES cell (Kraft et al. 2015). CRISPR/Cas9 was used for the generation of up to 1 Mb structural deletion and inversion around the Tyrosinase locus in mouse zygotes but duplications appeared less frequently and did not pass the germline (Boroviak et al. 2016).
Very recently, we have demonstrated that CRISPR/Cas9 genome editing allows to generate easily and quickly any type of SV mutations in mouse and also in rat (Birling et al. 2017). The variety of SVs which can be obtained is depicted in Fig. 2. Our data suggest that the timing of CRISPR/Cas9-mediated DSB during the growth phase G1 or G2 of the cell division cycle is certainly changing the outcomes. DSB occurring during the G2 phase will lead to an impressive potential variety of alleles (see Fig. 2). We have obtained up to six different alleles for one single founder. Indeed, animal models generated via zygotic injection of CRISPR reagents are often genetic mosaics (Yen et al. 2014), com pers & data not shown). These animals are thus composed of cells (somatic but also germinal) that may carry different mutations in the target allele. Of course, it is impossible to define if these arose from the mitosis of one cell or are the results of the action of CRISPRs at the 2-cell stage. We (Birling et al. 2017) have been able to generate Down Syndrome models by duplicating the syntenic region in both rat and mouse. The largest region we managed to duplicate is 24 Mb in the rat corresponding to the main human chromosome 21 syntenic region located on chromosome 11 in the rat. Established mouse and rat lines have been obtained in which regions as large as 4.9 Mb are duplicated (resulting of three copies of the region) or deleted (resulting of one copy of the region). Inversions also occurred at a good frequency. The only limit for obtaining a specific SV is the viability of the mutant cells and of the organism. For example, the deletion of the 24-Mb Lipi-Zfp295 genomic region does not seem viable in mouse. We were able to observe this deletion in newborn rats but they died quickly. Of course, chromosomal translocations can be made by designing CRISPRs to appropriate breakpoints on distinct chromosomes. Finally, we have optimized our CRISPR protocol and shown that four CRISPRs (two pairs at the desired breakpoints) are even more efficient to generate SVs. With our protocol, the efficiency for generating SV mutations seems as good as what we observed for the generation of a critical exon knock-out by CRISPR injection in eggs (on eight projects, we obtained 5–80% of the founders with the awaited deletion, inversion or duplication)(Birling et al. 2017). The use of ES cells can be easily avoided for the generation of SVs.
CRISPR/Cas9 and SVs cancer models
Somatically acquired SVs can induce alterations in genes that directly contribute to cellular transformation (Feuk et al. 2006). Again CRISPR/Cas9 genome editing changes the game to study such diseases. An in vivo model of somatically acquired SV human lung cancer has been generated by Maddalo et al. by viral delivery of CRISPR/Cas9 to somatic cells of adult mice to achieve an 11-Mb inversion. Two sgRNAs were used to generate the 11-Mb inversion on the short arm of chromosome 2: inv(2)(p21p23) leading to the expression of the echinoderm microtubule-associated protein like 4-anaplastic lymphoma kinase (EML4–ALK) oncogene in a small subset of human cells. The resulting cells invariably harbor the Eml4–Alk inversion, express the Eml4–Alk fusion gene, display histopathological and molecular features typical of ALK1 human non-small cell lung cancers (NSCLCs), and respond to treatment with ALK inhibitors (Maddalo et al. 2014). In the case of this experiment, low efficiency of viral delivery and CRISPR-mediated inversion events are an advantage allowing that only a subset of somatic cells are modified and so recapitulating the stochastic nature of tumor formation in humans.
CRISPR/Cas9 therapeutic applications
For the past few years, CRISPR/Cas9 gene editing technology has become essential for the generation of animal model of human diseases. CRISPR genome editing is now also under evaluation for therapeutic applications like cancer immunotherapy, tissue regeneration, gene therapy, HIV and viral disease, and obesity and metabolism.
The most striking example is the exon skipping approach developed on Duchenne Muscular Dystrophy (DMD) mice. DMD is one of the most prevalent fatal genetic diseases, with no successful, long-term treatments currently available. It is caused by any of a large spectrum of mutations in the Dystrophin gene that lead to loss of functional protein making it a prime candidate for editing by CRISPR/Cas9. Although many genetic therapeutic approaches for DMD have been attempted over the years, success has been very limited, in part by the large size of Dystrophin and the difficulty of achieving long-term rescue. Since 62% of DMD patients have mutations in exons 45–55 of Dystrophin, targeting this non-essential region to restore the open reading frame (ORF) by exon skipping has been a compelling strategy (Ousterout et al. 2015). Following proof-of-principle studies, several groups recently reported Cas9-mediated gene editing in vivo using the mdx mouse model of DMD, which contains a natural mutation in exon 23 of Dystrophin. Using adeno-associated virus (AAV) delivery, all three groups targeted Cas9 to the exon 23 splice junctions in Dystrophin, taking advantage of repair by non-homologous end joining (NHEJ) to delete the mutated exon and restore the ORF. The clinical impact of this technology is that genome editing can permanently correct disease-causing mutations and circumvent the hurdles of traditional gene- and cell-based therapies (Long et al. 2016; Nelson et al. 2016; Tabebordbar et al. 2016a). In all three reports, Dystrophin expression was restored to therapeutic levels in the affected muscles and the dystrophic phenotype was improved. Following muscle-tropic AAV9 delivery of Cas9 components in the mdx mouse, a small percentage of muscle satellite cells displayed evidence of gene editing (Tabebordbar et al. 2016a), and numbers of dystrophin-positive myofibers increased over time (Long et al. 2016), consistent with editing of satellite cells. These mouse models are good proof of concept for potential gene therapy in human but of course, the development of Cas9-based technologies into a therapeutic approach for DMD will require advances on several fronts, including tissue delivery, increased efficiency of genome editing/modification and technical improvements in the stability, specificity, and delivery of Cas9 components.
Using CRISPR/Cas9 to generate thousands mouse models improving knowledge on protein-coding genes
The International Mouse Phenotyping Consortium (IMPC) (http://www.mousephenotype.org/) builds on the efforts of research institutions worldwide to produce knock-outs of protein-coding genes and carry out high-throughput phenotyping of these mutants. The ultimate goal is to determine the function of every gene in the mouse genome (Brown and Moore 2012). These mice are preserved in repositories and made available to the scientific community representing a valuable resource for basic scientific research as well as generating new models for human diseases. Nearly 6000 mouse lines have already been generated in pure C57BL/6 N background through homologous recombination in ES cells and most of them have a ‘knockout first, conditional ready’ allele. This clever allele allows the generation of a lacZ-tagged null allele or a conditional allele, respectively (Bradley et al. 2012), by a simple breeding with Cre or Flp delete lines. The phenotyping information for more than 3000 protein-coding genes has been made freely available online to the scientific community. This initial work has confirmed that about one-third of these genes are essential for life, and has provided phenotyping information for many genes with unknown function, helping to piece together gaps in our understanding of the genome (Dickinson et al. 2016; de Angelis et al. 2015). The database allows scientists to research a gene of interest and provides crucial insight into the underlying causes for rare and common diseases. To date, more than 4000 human diseases are associated with IMPC mouse models (IMPC website, March 2017). Each mutant mouse line are tested through a broad-based primary phenotyping pipeline in all the major adult organ systems and most areas of major human diseases. Phenotyping tests are standardized and cross validated between centers of the consortium (Simon et al. 2013) for decreasing the percentage of non-replicable discoveries. It is important to point out here that heterozygous KO mouse lines are phenotyped when homozygous KO animals are lethal or subviable. The IMPC web site is really user friendly and allows to search by gene, phenotype, embryonic phenotyping, gene interaction but also by human diseases which renders this site particularly interesting for human geneticists (http://www.mousephenotype.org/). A search is possible by OMIM reference. The known genes implicated in a specific disease are registered and all potential mouse models with phenotypic similarities are registered by similarity scores (Rosen et al. 2015).
Since 2014, limited by the availability and quality of targeted ES cells and also because of its ease of use and cost, the IMPC members have decided to switch to CRISPR to generate knock-out (KO) alleles. The choice was to obtain straight KO of all genes as CRISPR has been shown to be very efficient whatever the genomic complexity and the generation of such line will be at lower cost. More than a 1000 lines have already been generated by this approach. Most of the KO alleles are the deletion of one (or more) critical exon(s). The only limit is the founder’s viability if sgRNAs are too efficient (high number of homozygous KO cells or haplo-insufficient genes). Indeed, we have shown that approximately 30% of full-gene KOs die during embryogenesis or at early post-natal days (Dickinson et al. 2016; de Angelis et al. 2015). A paper describing in more detail this work is currently under redaction.
Non-coding genetic and regulatory elements
Despite the overwhelming number of human non-coding RNAs reported so far, little is known about their physiological functions for the majority of them (Table 1). On their website, the ENCODE project, an international consortium that aims to build a comprehensive parts list of functional elements in the mammalian genomes, estimates that the human and mouse genome contains 23,025 and 16,592 non-coding genes, respectively. In the Mouse Genome Informatics resource (MGI, http://www.informatics.jax.org/), the most exhaustive database for the laboratory mouse, only 161 (1.0%) non-coding genes knock-out mouse models have been generated and 146 (0.9%) of them have a phenotype described. No more data exist on the other regulatory elements. For example, it is estimated that the human genome contains >500,000 putative enhancers, a staggering number that poses a major challenge for the identification of functional regulatory elements (Korkmaz et al. 2016).
Lack of knowledge is obvious and very few examples of CRISPR/Cas9 gene editing of non-coding genes or other regulatory elements can be found. The long non-coding RNA AK023948 is an example of how CRISPR/Cas9 can be used to decipher non-coding gene function (Koirala et al. 2017). AK023948 knock-out in human MCF-7 cells suppresses the AKT activity, a critical pathway involved in cell survival, growth, proliferation, angiogenesis, metabolism, and cell migration. The potential impact of AK023948 in the promotion of tumorigenesis is clearly highlighted (Koirala et al. 2017).
CRISPR/Cas9, polygenic disorders, and personalized medicine
Genetic disorders that are caused by the combined action of more than one gene are another layer of the genome complexity. Common human diseases or traits—such as size, diabetes, heart disease, schizophrenia, and autism—are typically polygenic (Gandal et al. 2016; Marouli et al. 2017; Roberts et al. 2013; Sharma and Vella 2017). A variety of genetic resource are available in mouse such as recombinant inbred lines (Carneiro et al. 2009; Williams et al. 2001), consomics (Gregorová et al. 2008), heterogeneous stocks (Valdar et al. 2006), and the Collaborative Cross (Churchill et al. 2004). These resources are powerful tools to study polygenic diseases but have been underutilized because genetic modifications in these strains were very complex. Whereas obtaining germline transmission from a fully validated ES cells can sometimes be difficult, strictly due to the intrinsic germline competency of the ES cells (and not to the engineered allele), this is not anymore an issue when CRISPR is injected straight into the eggs. Fertilized eggs of almost any genetic background can be recovered, microinjected (alternatively an electroporation) with CRISPRs and implanted. Specific mutations can be introduced in any genetic background. For example, in NOD mouse, a model of spontaneous type 1 diabetes (T1D), a R619W mutation in the protein tyrosine phosphatase non-receptor type 22 (Ptpn22) gene was introduced by CRISPR/Cas9 and homology-directed repair (Lin et al. 2016). This mutation corresponds to the human allelic variant of PTPN22 (R620W), an allele strongly associated with type 1 diabetes (T1D) which increases the risk of T1D by two- to fourfold. The resulting Ptpn22 (R619W) mice showed increased insulin autoantibodies, earlier onset and higher penetrance of T1D. This is the first report demonstrating enhanced T1D in a mouse modeling human PTPN22 (R620W), it shows the utility of CRISPR/Cas9 for direct genetic alternation of NO D mice. It is now possible to study polygenic disorders in various genetic contexts and this gives new opportunities for personalized medicine in human. By this ability to study a gene in various genetic contexts, CRISPR may also be highly valuable to study incomplete penetrance and variable expressivity as these traits probably result from a combination of genetic and environmental factors.
Will CRISPR genome editing allow to decrypt the human genome?
Sixteen years have passed since the initial sequencing and analysis of the human genome was published by International Human Genome Sequencing Consortium (Lander et al. 2001; Venter 2001). It immediately brings high expectations for improvements in the treatment of common disorders and strategies for the prevention of disease. However, our genome (and the genome of the other mammals) remains poorly decrypted and the effort of high-throughput programs like the IMPC to discover and ascribe biological function to each gene is more than ever required.
First, the function of half of the protein-coding genes is not known (Table 1). Pleiotropy is poorly assessed for most of the coding genes but however seems to be the rule. The analysis of 449 knock-out mouse mutant models by the EUMODIC consortium show that 65% of the corresponding genes are pleiotropic (de Angelis et al. 2015). Likewise, very little is known about the number and function of non-coding genes (Table 1). For example, the GENCODE project estimates that the human genome contains many thousands of long non-coding RNAs (Derrien et al. 2012). Again, the function of the vast majority of these potential genes remains to be decrypted (Table 1). Finally, the “remaining” part of the mammalian genome (pseudogenes, repeated sequences, desert islands …) may not be accurately considered as junk DNA. For example, there is emerging evidence that many of the pseudogenes could be biologically active (Frankish and Harrow 2014). In a few cases what was named a pseudogene was indeed having a concrete function (Barau et al. 2016). Similarly, the ENCODE project assigned potential biochemical functions for 80% of the human genome (Dunham et al. 2012) raising two questions:
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1)
Which of these active genomic elements code for a real biological function or is it just noise?
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Do some of these sequences have evolutionary functions?
Today, most of our knowledge on the mammalian genomes is based on bioinformatics analyses of large set of molecular data. In a world where the sequencing of genomes is becoming cheap and fast, how can we decrypt the function of the human genome and its genomic variations? We believe that CRISPR/Cas genome editing is the Swiss knife for functional studies. By using more wisely animal models, CRISPR/Cas9 will be a tool to link human genome to diseases and phenotypes. Now, the main limit is not to obtain an animal model: the choice of the relevant background and species to mimic a human pathology is the challenge.
Finally, as the ultimate goal of most animal models is humanization and as the CRISPR technology gives now the tools to perform more or less any genomic modification, one might step back and ask which humanization strategy is the best. For example, introduction of an orthologous (causal) mutation can lead to a ‘better’ model than a whole gene humanization. Indeed, genes have evolved from a common ancestral gene by speciation and we know that orthologs generally retain the same function in the course of evolution. The introduction of a human disease causative mutation in an orthologous rodent gene very often leads to the similar phenotype. We cannot anticipate how a human gene will behave in another species, regulatory sequences can be located in introns and 5′ and 3′ sequences. Do we have to keep the human regulatory sequences or is it better to keep to host species regulatory elements. It is really important to keep the human introns or is it better to use a cDNA for a faithful expression of a gene? How will a humanized protein interact with its rodent counterpart(s)? How will it interact in complexes or pathways? A case by case study is certainly the answer. Now that the genetic tools are available, any relevant animal model of human disease seems possible.
References
Adams DJ, Biggs PJ, Cox T, Davies R, van der Weyden L, Jonkers J, Smith J, Plumb B, Taylor R, Nishijima I et al (2004) Mutagenic insertion and chromosome engineering resource (MICER). Nat Genet 36:867–871
Bailey JA, Yavor AM, Massa HF, Trask BJ, Eichler EE (2001) Segmental duplications: organization and impact within the current human genome project assembly. Genome Res 11:1005–1017
Barau J, Teissandier A, Zamudio N, Roy S, Nalesso V, Hérault Y, Guillou F, Bourc’his D (2016) The DNA methyltransferase DNMT3C protects male germ cells from transposon activity. Science 354:909–912
Bilovocky NA, Romito-DiGiacomo RR, Murcia CL, Maricich SM, Herrup K (2003) Factors in the genetic background suppress the engrailed-1 cerebellar phenotype. J Neurosci Off J Soc Neurosci 23:5105–5112
Birling M-C, Schaeffer L, André P, Lindner L, Maréchal D, Ayadi A, Sorg T, Pavlovic G, Hérault Y (2017) Efficient and rapid generation of large genomic variants in rats and mice using CRISMERE. Sci Rep 7:43331
Boroviak K, Doe B, Banerjee R, Yang F, Bradley A (2016) Chromosome engineering in zygotes with CRISPR/Cas9: chromosome engineering in zygotes with CRISPR/Cas9. Genesis 54:78–85
Bradley A, Anastassiadis, K., Ayadi A, Battey JF, Bell C, Birling M-C, Bottomley J, Brown SD, Bürger A, Bult CJ et al (2012) The mammalian gene function resource: the international knockout mouse consortium. Mamm Genome 23: 580–586
Brown SDM, Moore MW (2012) The international mouse phenotyping consortium: past and future perspectives on mouse phenotyping. Mamm Genome 23:632–640
Buffenstein R, Nelson OL, Corbit KC (2014) Questioning the preclinical paradigm: natural, extreme biology as an alternative discovery platform. Aging 6:913–920
Carneiro AMD, Airey DC, Thompson B, Zhu C-B, Lu L, Chesler EJ, Erikson KM, Blakely RD (2009) Functional coding variation in recombinant inbred mouse lines reveals multiple serotonin transporter-associated phenotypes. Proc Natl Acad Sci USA 106:2047–2052
Churchill GA, Airey DC, Allayee H, Angel JM, Attie AD, Beatty J, Beavis WD, Belknap JK, Bennett B, Berrettini W et al (2004) The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nat Genet 36:1133–1137
Collins FS, Tabak LA (2014) Policy: NIH plans to enhance reproducibility. Nature 505:612–613
Conrad DF, Pinto D, Redon R, Feuk L, Gokcumen O, Zhang Y, Aerts J, Andrews TD, Barnes C, Campbell P et al (2010) Origins and functional impact of copy number variation in the human genome. Nature 464:704–712
Cook EH, Scherer SW (2008) Copy-number variations associated with neuropsychiatric conditions. Nature 455:919–923
Courtney DG, Moore JE, Atkinson SD, Maurizi E, Allen EHA, Pedrioli DML, McLean WHI, Nesbit MA, Moore CBT (2015) CRISPR/Cas9 DNA cleavage at SNP-derived PAM enables both in vitro and in vivo KRT12 mutation-specific targeting. Gene Ther 23:108–112
Dasouki MJ, Lushington GH, Hovanes K, Casey J, Gorre M (2011) The 3q29 microdeletion syndrome: report of three new unrelated patients and in silico “RNA binding” analysis of the 3q29 region. Am J Med Genet A 155 A, 1654–1660.
de Angelis HM, Nicholson G, Selloum M, White JK, Morgan H, Ramirez-Solis R, Sorg T, Wells S, Fuchs H, Fray M et al (2015a) Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics. Nat Genet 47:969–978
de Angelis MH, Nicholson G, Selloum M, White JK, Morgan H, Ramirez-Solis R, Sorg T, Wells S, Fuchs H, Fray M et al (2015b) Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics. Nat Genet 47:969–978
Derrien T, Johnson R, Bussotti G, Tanzer A, Djebali S, Tilgner H, Guernec G, Martin D, Merkel A, Knowles DG et al (2012) The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res 22:1775–1789
Dickinson ME, Flenniken AM, Ji X, Teboul L, Wong MD, White JK, Meehan TF, Weninger WJ, Westerberg H, Adissu H et al (2016) High-throughput discovery of novel developmental phenotypes. Nature 537:508–514
Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F, Epstein CB, Frietze S, Harrow J, Kaul R et al (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74
Eisener-Dorman AF, Lawrence DA, Bolivar VJ (2009. Cautionary insights on knockout mouse studies: the gene or not the gene? Brain Behav Immun 23:318–324
European Commission (2013) Seventh Report on the Statistics on the Number of Animals used for Experimental and other Scientific Purposes in the Member States of the European Union (European Commission)
Fanciulli M, Norsworthy PJ, Petretto E, Dong R, Harper L, Kamesh L, Heward JM, Gough SCL, de Smith A, Blakemore AIF et al (2007) FCGR3B copy number variation is associated with susceptibility to systemic, but not organ-specific, autoimmunity. Nat Genet 39:721–723
Feuk L, Carson AR, Scherer SW (2006) Structural variation in the human genome. Nat Rev Genet 7:85–97
Frankish A, Harrow J (2014) GENCODE pseudogenes. Methods Mol Biol Clifton NJ 1167:129–155
Freeman JL, Perry GH, Feuk L, Redon R, McCarroll SA, Altshuler DM, Aburatani H, Jones KW, Tyler-Smith C, Hurles ME et al (2006) Copy number variation: new insights in genome diversity. Genome Res 16:949–961
Gandal MJ, Leppa V, Won H, Parikshak NN, Geschwind DH (2016) The road to precision psychiatry: translating genetics into disease mechanisms. Nat Neurosci 19:1397–1407
Girirajan S, Brkanac Z, Coe BP, Baker C, Vives L, Vu TH, Shafer N, Bernier R, Ferrero GB, Silengo M et al (2011) Relative burden of large CNVs on a range of neurodevelopmental phenotypes. PLoS Genet 7:e1002334
Gregorová S, Divina P, Storchova R, Trachtulec Z, Fotopulosova V, Svenson KL, Donahue LR, Paigen B, Forejt J (2008) Mouse consomic strains: exploiting genetic divergence between Mus m. musculus and Mus m. domesticus subspecies. Genome Res 18:509–515
Guo MH, Nandakumar SK, Ulirsch JC, Zekavat SM, Buenrostro JD, Natarajan P, Salem RM, Chiarle R, Mitt M, Kals M et al (2016) Comprehensive population-based genome sequencing provides insight into hematopoietic regulatory mechanisms. Proc Natl Acad Sci 114:E327–E336
Hérault Y, Rassoulzadegan M, Cuzin F, Duboule D (1998) Engineering chromosomes in mice through targeted meiotic recombination (TAMERE). Nat Genet 20:381–384
Hubbard J, Ruppert E, Calvel L, Robin-Choteau L, Gropp C-M, Allemann C, Reibel S, Sage-Ciocca D, Bourgin P (2015) Arvicanthis ansorgei, a novel model for the study of sleep and waking in diurnal rodents. Sleep. doi: 10.5665/sleep.4754
Huddleston J, Eichler EE (2016) An Incomplete Understanding of Human Genetic Variation. Genetics 202:1251–1254
Itsara A, Cooper GM, Baker C, Girirajan S, Li J, Absher D, Krauss RM, Myers RM, Ridker PM, Chasman DI et al (2009) Population analysis of large copy number variants and hotspots of human genetic disease. Am J Hum Genet 84:148–161
Justice MJ, Dhillon P (2016) Using the mouse to model human disease: increasing validity and reproducibility. Dis Model Mech 9:101–103
Kafkafi N, Golani I, Jaljuli I, Morgan H, Sarig T, Würbel H, Yaacoby S, Benjamini Y (2017) Addressing reproducibility in single-laboratory phenotyping experiments. Nat Methods 14:462–464
Koirala P, Huang J, Ho T-T, Wu F, Ding X, Mo Y-Y (2017) LncRNA AK023948 is a positive regulator of AKT. Nat Commun 8: 14422
Korkmaz G, Lopes R, Ugalde AP, Nevedomskaya E, Han R, Myacheva K, Zwart W, Elkon R, Agami R (2016) Functional genetic screens for enhancer elements in the human genome using CRISPR-Cas9. Nat Biotechnol 34:192–198
Kraft K, Geuer S, Will AJ, Chan WL, Paliou C, Borschiwer M, Harabula I, Wittler L, Franke M, Ibrahim DM et al (2015) Deletions, inversions, duplications: engineering of structural variants using CRISPR/Cas in mice. Cell Rep 10:833–839
Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921
Li F, Cowley DO, Banner D, Holle E, Zhang L, 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
Li J, Shou J, Guo Y, Tang Y, Wu Y, Jia Z, Zhai Y, Chen Z, Xu Q, Wu Q (2015) Efficient inversions and duplications of mammalian regulatory DNA elements and gene clusters by CRISPR/Cas9. J Mol Cell Biol 7:284–298
Lin X, Pelletier S, Gingras S, Rigaud S, Maine CJ, Marquardt K, Dai YD, Sauer K, Rodriguez AR, Martin G et al (2016) CRISPR-Cas9-mediated modification of the NOD mouse genome with Ptpn22 R619W mutation increases autoimmune diabetes. Diabetes 65:2134–2138
Long C, Amoasii L, Mireault AA, McAnally JR, Li H, Sanchez-Ortiz E, Bhattacharyya S, Shelton JM, Bassel-Duby R, Olson EN (2016) Postnatal genome editing partially restores dystrophin expression in a mouse model of muscular dystrophy. Science 351:400–403
Maddalo D, Manchado E, Concepcion CP, Bonetti C, Vidigal JA, Han Y-C, Ogrodowski P, Crippa A, Rekhtman N, de Stanchina E et al (2014) In vivo engineering of oncogenic chromosomal rearrangements with the CRISPR/Cas9 system. Nature 516:423–427
Maillard AM, Ruef A, Pizzagalli F, Migliavacca E, Hippolyte L, Adaszewski S, Dukart J, Ferrari C, Conus P, Männik K et al (2015) The 16p11.2 locus modulates brain structures common to autism, schizophrenia and obesity. Mol Psychiatry 20:140–147
Marouli E, Graff M, Medina-Gomez C, Lo KS, Wood AR, Kjaer TR, Fine RS, Lu Y, Schurmann C, Highland HM et al (2017) Rare and low-frequency coding variants alter human adult height. Nature 542:186–190
McCarroll SA, Altshuler DM (2007) Copy-number variation and association studies of human disease. Nat Genet 39:S37–S42
Nelson CE, Hakim CH, Ousterout DG, Thakore PI, Moreb EA, Rivera RMC, Madhavan S, Pan X, Ran FA, Yan WX, et al (2016) In vivo genome editing improves muscle function in a mouse model of Duchenne muscular dystrophy. Science 351:403–407
No authors (1997) Mutant mice and neuroscience: recommendations concerning genetic background. Banbury Conference on genetic background in mice. Neuron 19:755–759
Ousterout DG, Kabadi AM, Thakore PI, Majoros WH, Reddy TE, Gersbach CA (2015) Multiplex CRISPR/Cas9-based genome editing for correction of dystrophin mutations that cause Duchenne muscular dystrophy. Nat Commun 6:6244
Perlman RL (2016) Mouse models of human disease: an evolutionary perspective. Evol Med Public Health 16:170–176
Pound P, Bracken MB (2014) Is animal research sufficiently evidence based to be a cornerstone of biomedical research?. BMJ 348:g3387
Ramírez-Solis R, Liu P, Bradley A (1995) Chromosome engineering in mice. Nature 378:720–724
Roberts R, Marian AJ, Dandona S, Stewart AFR (2013) Genomics in cardiovascular disease. J Am Coll Cardiol 61:2029–2037
Rosen B, Schick J, Wurst W (2015) Beyond knockouts: the International Knockout Mouse Consortium delivers modular and evolving tools for investigating mammalian genes. Mamm Genome 26:456–466
Ruf S, Symmons O, Uslu VV, Dolle D, Hot C, Ettwiller L, Spitz F (2011) Large-scale analysis of the regulatory architecture of the mouse genome with a transposon-associated sensor. Nat Genet 43:379–386
Sasaki M, Lange J, Keeney S (2010) Genome destabilization by homologous recombination in the germ line. Nat Rev Mol Cell Biol 11:182–195
Sharma A, Vella A (2017) Obstacles to translating genotype-phenotype correlates in metabolic disease. Physiology. 32:42–50
Shaw CJ (2004) Implications of human genome architecture for rearrangement-based disorders: the genomic basis of disease. Hum Mol Genet 13:R57–R64
Simon MM, Greenaway S, White JK, Fuchs H, Gailus-Durner V, Wells S, Sorg T, Wong K, Bedu E, Cartwright EJ et al (2013) A comparative phenotypic and genomic analysis of C57BL/6 J and C57BL/6 N mouse strains. Genome Biol 14:R82
Simpson EM, Linder CC, Sargent EE, Davisson MT, Mobraaten LE, Sharp JJ (1997) Genetic variation among 129 substrains and its importance for targeted mutagenesis in mice. Nat Genet 16:19–27
Sittig LJ, Carbonetto P, Engel KA, Krauss KS, Barrios-Camacho CM, Palmer AA (2016) Genetic background limits generalizability of genotype-phenotype relationships. Neuron 91:1253–1259
Spitz F, Herkenne C, Morris MA, Duboule D (2005) Inversion-induced disruption of the Hoxd cluster leads to the partition of regulatory landscapes. Nat Genet 37:889–893
Stankiewicz P, Lupski JR (2002) Genome architecture, rearrangements and genomic disorders. TRENDS Genet 18:74–82
Tabebordbar M, Zhu K, Cheng JKW, Chew WL, Widrick JJ, Yan WX, Maesner C, Wu EY, Xiao R, Ran FA, et al (2016a) In vivo gene editing in dystrophic mouse muscle and muscle stem cells. Science 351:407–411
Tabebordbar M, Zhu K, Cheng, J.K.W., Chew WL, Widrick JJ, Yan WX, Maesner C, Wu EY, Xiao R, Ran FA et al (2016b) In vivo gene editing in dystrophic mouse muscle and muscle stem cells. Science 351:407–411
Tai, D.J.C., Ragavendran A, Manavalan P, Stortchevoi A, Seabra CM, Erdin S, Collins RL, Blumenthal I, Chen X, Shen Y et al (2016) Engineering microdeletions and microduplications by targeting segmental duplications with CRISPR. Nat Neurosci 19:517–522
Torres F, Barbosa M, Maciel P (2016) Recurrent copy number variations as risk factors for neurodevelopmental disorders: critical overview and analysis of clinical implications. J Med Genet 53:73–90
Turner DJ, Miretti M, Rajan D, Fiegler H, Carter NP, Blayney ML, Beck S, Hurles ME (2008) Germline rates of de novo meiotic deletions and duplications causing several genomic disorders. Nat Genet 40:90–95
Valdar W, Solberg LC, Gauguier D, Burnett S, Klenerman P, Cookson WO, Taylor MS, Rawlins, J.N.P., Mott R, Flint J (2006) Genome-wide genetic association of complex traits in heterogeneous stock mice. Nat Genet 38:879–887
Venter JC (2001) The sequence of the human genome. Science 291:1304–1351
Wang L, Shao Y, Guan Y, Li L, Wu L, Chen F, Liu M, Chen H, Ma Y, Ma X et al (2015) Large genomic fragment deletion and functional gene cassette knock-in via Cas9 protein mediated genome editing in one-cell rodent embryos. Sci Rep 5:17517
Weiss LA, Shen Y, Korn JM, Arking DE, Miller DT, Fossdal R, Saemundsen E, Stefansson H, Ferreira MAR, Green T et al (2008) Association between microdeletion and microduplication at 16p11.2 and autism. N Engl J Med 358:667–675
Williams RW, Gu, J., Qi, S., Lu, L. (2001) The genetic structure of recombinant inbred mice: high-resolution consensus maps for complex trait analysis. Genome Biol. doi: 10.1186/gb-2001-2-11-research0046</bib>
Wu X, Hurst LD (2016) Determinants of the usage of splice-associated cis-motifs predict the distribution of human pathogenic SNPs. Mol Biol Evol 33:518–529
Yen S-T, Zhang M, Deng JM, Usman SJ, Smith CN, Parker-Thornburg J, Swinton PG, Martin JF, Behringer RR (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
Young NS (2013) Mouse medicine and human biology. Semin Hematol 50:88–91
Zhang L, Jia R, Palange NJ, Satheka AC, Togo J, An Y, Humphrey M, Ban L, Ji Y, Jin H et al (2015) Large genomic fragment deletions and insertions in mouse using CRISPR/Cas9. PLoS ONE 10:e0120396
Zheng B, Mills AA, Bradley A (1999) A system for rapid generation of coat color-tagged knockouts and defined chromosomal rearrangements in mice. Nucleic Acids Res 27:2354–2360
Zhu QM, Ko KA, Ture S, Mastrangelo MA, Chen M-H, Johnson AD, O’Donnell CJ, Morrell CN, Miano JM, Lowenstein CJ (2017) Novel Thrombotic Function of a human SNP in STXBP5 revealed by CRISPR/Cas9 gene editing in mice. Arterioscler Thromb Vasc Biol 37:264–270
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Birling, MC., Herault, Y. & Pavlovic, G. Modeling human disease in rodents by CRISPR/Cas9 genome editing. Mamm Genome 28, 291–301 (2017). https://doi.org/10.1007/s00335-017-9703-x
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DOI: https://doi.org/10.1007/s00335-017-9703-x