Genetic analysis of intestinal polyp development in Collaborative Cross mice carrying the Apc (Min/+) mutation

Lowering fasting blood glucose with non-dialyzable material of cranberry extract is dependent on host genetic background, sex and diet

BACKGROUND

Type 2 diabetes (T2D) is a polygenic metabolic disease, characterized by high fasting blood glucose (FBG). The ability of cranberry (CRN) fruit to regulate glycemia in T2D patients is well known. Here, a cohort of 13 lines of the genetically diverse Collaborative Cross (CC) mouse model was assessed for the effect of non-dialyzable material (NDM) of cranberry extract in lowering fasting blood glucose.

METHODS

Eight-week-old mice were maintained on either a standard chow diet (control group) or a high-fat diet (HFD) for 12 weeks, followed by injections of intraperitoneal (IP) NDM (50 mg/kg) per mouse, three times a week for the next 6 weeks. Absolute FBG (mg/dl) was measured bi-weekly and percentage changes in FBG (%FBG) between weeks 0 and 12 were calculated.

RESULTS

Statistical analysis showed a significant decrease in FBG between weeks 0 and 12 in male and female mice maintained on CHD. However, a non-significant increase in FBG values was observed in male and female mice maintained on HFD during the same period. Following administration of NDM during the following 6 weeks, the results show a variation in significant levels of FBG lowering between lines, male and female mice and under the different diets.

CONCLUSION

The results suggest that the efficacy of NDM treatment in lowering FGB depends on host genetic background (pharmacogenetics), sex of the mouse (pharmacosex), and diet (pharmacodiet). All these results support the need for follow-up research to better understand and implement a personalized medicine approach/utilization of NDM for reducing FBG.

Genetic mapping of novel modifiers for ApcMin induced intestinal polyps' development using the genetic architecture power of the collaborative cross mice

BACKGROUND

Familial adenomatous polyposis is an inherited genetic disease, characterized by colorectal polyps. It is caused by inactivating mutations in the Adenomatous polyposis coli (Apc) gene. Mice carrying a nonsense mutation in the Apc gene at R850, which is designated ApcMin/+ (Multiple intestinal neoplasia), develop intestinal adenomas. Several genetic modifier loci of Min (Mom) were previously mapped, but so far, most of the underlying genes have not been identified. To identify novel modifier loci associated with ApcMin/+, we performed quantitative trait loci (QTL) analysis for polyp development using 49 F1 crosses between different Collaborative Cross (CC) lines and C57BL/6 J-ApcMin/+mice. The CC population is a genetic reference panel of recombinant inbred lines, each line independently descended from eight genetically diverse founder strains. C57BL/6 J-ApcMin/+ males were mated with females from 49 CC lines. F1 offspring were terminated at 23 weeks and polyp counts from three sub-regions (SB1-3) of small intestinal and colon were recorded.

RESULTS

The number of polyps in all these sub-regions and colon varied significantly between the different CC lines. At 95% genome-wide significance, we mapped nine novel QTL for variation in polyp number, with distinct QTL associated with each intestinal sub-region. QTL confidence intervals varied in width between 2.63-17.79 Mb. We extracted all genes in the mapped QTL at 90 and 95% CI levels using the BioInfoMiner online platform to extract, significantly enriched pathways and key linker genes, that act as regulatory and orchestrators of the phenotypic landscape associated with the ApcMin/+ mutation.

CONCLUSIONS

Genomic structure of the CC lines has allowed us to identify novel modifiers and confirmed some of the previously mapped modifiers. Key genes involved mainly in metabolic and immunological processes were identified. Future steps in this analysis will be to identify regulatory elements - and possible epistatic effects - located in the mapped QTL.

Systems genetics analysis of oral squamous cell carcinoma susceptibility using the mouse model: current position and new perspective

Oral squamous cell carcinoma (OSCC) is one of the most common human malignancies with complex etiology and poor prognosis. Although environmental carcinogens and carcinogenic viruses are still considered the main etiologic factors for OSCC development, genetic factors obviously play a key role in the initiation and progression of this neoplasm, given that not all individuals exposed to carcinogens develop the same severity of the disease, if any. Identifying genetic loci modulating OSCC risk may have several important clinical implications, including early detection, prevention and developing new treatment strategies. Due to limitations in controlled and standardized genetic studies in humans, genetic components underlying susceptibility of OSCC development remain largely unknown. A combination of quantitative trait loci mapping in mice, with complementary association studies in humans, has the potential to discover novel cancer risk loci. As of today, a limited number of genetic analyses were applied on rodent models to locate novel genetic loci associated with human OSCC. Here, we discuss the current status of the mouse models use for dissecting the genetic basis of OSCC and highlight how systems genetics analysis using mouse models, may increase our understanding of human OSCC susceptibility.

Studying the pharmacogenomic effect of cranberry extract on reducing body weight using collaborative cross mice

The non-dialyzable material (NDM) of polyphenol-rich cranberry extract (CRE) powder (NDM-CRE) was studied for its effect of inducing body weight (BW) loss in 13 different mouse lines with well-defined genetically diverse backgrounds, named the collaborative cross (CC). From the age of 8 weeks, the mice were maintained on a high-fat diet (HFD) for 18 weeks, to induce obesity, and BW was measured biweekly. From week 12, CRE was injected intraperitoneally (IP) (50 mg kg^-1) 3 times a week per mouse for a 6 week period. Statistical analysis results have shown a significant increase in body weight between week 0 and week 12; the increase in BW of 13 lines of mice on HFD was in the range of 10.41% to 68.65% for males and 9.78% to 64.74% for females. After injecting NDM-CRE extract, our analysis has shown an induced change in BW between week 12 and week 18. In males, NDM-CRE caused a significant decrease in BW of 5 out of the 13 lines in the range of -5.68% to -16.69% and a significant increase of 8.31% in BW of one male line, whereas in seven lines there was no significant decrease (-2.14% to -4.09%). In females, NDM-CRE caused a significant decrease in BW of 5 out of the 13 lines in the range of -3.90% to -11.83%, whereas in eight lines there were no significant changes in BW and it ranged between -1.50% and 4.90%. The broad-sense heritability (H²) and genetic coefficient of variation (CV_g) were estimated and found to be between 0.71 and 0.81 for H², and 0.18 and 0.24 for CV_g of females and males, respectively, with respect to the efficacy of NDM-CRE on body weight reduction. Our results have shown that hosts with different genetic backgrounds respond differently to body weight increase, as well as to NDM-CRE treatment for body weight reduction. These results provide a platform for assessing more CC lines and mapping genes underlying the efficacy of the NDM-CRE treatment as a way of understanding pharmacogenomics.

Designing a QTL Mapping Study for Implementation in the Realized Collaborative Cross Genetic Reference Population

The Collaborative Cross (CC) mouse resource is a next-generation mouse genetic reference population (GRP) designed for high-resolution mapping of quantitative trait loci (QTL) of large effect affecting complex traits during health and disease. The CC resource consists of a set of 72 recombinant inbred lines (RILs) generated by reciprocal crossing of five classical and three wild-derived mouse founder strains. Complex traits are controlled by variations within multiple genes and environmental factors, and their mutual interactions. These traits are observed at multiple levels of the animals' systems, including metabolism, body weight, immune profile, and susceptibility or resistance to the development and progress of infectious or chronic diseases. Herein, we present general guidelines for design of QTL mapping experiments using the CC resource-along with full step-by-step protocols and methods that were implemented in our lab for the phenotypic and genotypic characterization of the different CC lines-for mapping the genes underlying host response to infectious and chronic diseases. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: CC lines for whole body mass index (BMI) Basic Protocol 2: A detailed assessment of the power to detect effect sizes based on the number of lines used, and the number of replicates per line Basic Protocol 3: Obtaining power for QTL with given target effect by interpolating in Table 1 of Keele et al. (2019).

Efficient protocols and methods for high-throughput utilization of the Collaborative Cross mouse model for dissecting the genetic basis of complex traits

The Collaborative Cross (CC) mouse model is a next-generation mouse genetic reference population (GRP) designated for a high-resolution quantitative trait loci (QTL) mapping of complex traits during health and disease. The CC lines were generated from reciprocal crosses of eight divergent mouse founder strains composed of five classical and three wild-derived strains. Complex traits are defined to be controlled by variations within multiple genes and the gene/environment interactions. In this article, we introduce and present variety of protocols and results of studying the host response to infectious and chronic diseases, including type 2 diabetes and metabolic diseases, body composition, immune response, colorectal cancer, susceptibility to Aspergillus fumigatus, Klebsiella pneumoniae, Pseudomonas aeruginosa, sepsis, and mixed infections of Porphyromonas gingivalis and Fusobacterium nucleatum, which were conducted at our laboratory using the CC mouse population. These traits are observed at multiple levels of the body systems, including metabolism, body weight, immune profile, susceptibility or resistance to the development and progress of infectious or chronic diseases. Herein, we present full protocols and step-by-step methods, implemented in our laboratory for the phenotypic and genotypic characterization of the different CC lines, mapping the gene underlying the host response to these infections and chronic diseases. The CC mouse model is a unique and powerful GRP for dissecting the host genetic architectures underlying complex traits, including chronic and infectious diseases.

Whole Genome Sequencing and Progress Toward Full Inbreeding of the Mouse Collaborative Cross Population

Two key features of recombinant inbred panels are well-characterized genomes and reproducibility. Here we report on the sequenced genomes of six additional Collaborative Cross (CC) strains and on inbreeding progress of 72 CC strains. We have previously reported on the sequences of 69 CC strains that were publicly available, bringing the total of CC strains with whole genome sequence up to 75. The sequencing of these six CC strains updates the efforts toward inbreeding undertaken by the UNC Systems Genetics Core. The timing reflects our competing mandates to release to the public as many CC strains as possible while achieving an acceptable level of inbreeding. The new six strains have a higher than average founder contribution from non-domesticus strains than the previously released CC strains. Five of the six strains also have high residual heterozygosity (>14%), which may be related to non-domesticus founder contributions. Finally, we report on updated estimates on residual heterozygosity across the entire CC population using a novel, simple and cost effective genotyping platform on three mice from each strain. We observe a reduction in residual heterozygosity across all previously released CC strains. We discuss the optimal use of different genetic resources available for the CC population.

Modeling the quantitative nature of neurodevelopmental disorders using Collaborative Cross mice

Background

Animal models for neurodevelopmental disorders (NDD) generally rely on a single genetic mutation on a fixed genetic background. Recent human genetic studies however indicate that a clinical diagnosis with ASDAutism Spectrum Disorder (ASD) is almost always associated with multiple genetic fore- and background changes. The translational value of animal model studies would be greatly enhanced if genetic insults could be studied in a more quantitative framework across genetic backgrounds.

Methods

We used the Collaborative Cross (CC), a novel mouse genetic reference population, to investigate the quantitative genetic architecture of mouse behavioral phenotypes commonly used in animal models for NDD.

Results

Classical tests of social recognition and grooming phenotypes appeared insufficient for quantitative studies due to genetic dilution and limited heritability. In contrast, digging, locomotor activity, and stereotyped exploratory patterns were characterized by continuous distribution across our CC sample and also mapped to quantitative trait loci containing genes associated with corresponding phenotypes in human populations.

Conclusions

These findings show that the CC can move animal model studies beyond comparative single gene-single background designs, and point out which type of behavioral phenotypes are most suitable to quantify the effect of developmental etiologies across multiple genetic backgrounds.

gQTL: A Web Application for QTL Analysis Using the Collaborative Cross Mouse Genetic Reference Population

Multi-parental recombinant inbred populations, such as the Collaborative Cross (CC) mouse genetic reference population, are increasingly being used for analysis of quantitative trait loci (QTL). However specialized analytic software for these complex populations is typically built in R that works only on command-line, which limits the utility of these powerful resources for many users. To overcome analytic limitations, we developed gQTL, a web accessible, simple graphical user interface application based on the DOQTL platform in R to perform QTL mapping using data from CC mice.

Genetic Network Complexity Shapes Background-Dependent Phenotypic Expression

The phenotypic consequences of a given mutation can vary across individuals. This so-called background effect is widely observed, from mutant fitness of loss-of-function variants in model organisms to variable disease penetrance and expressivity in humans; however, the underlying genetic basis often remains unclear. Taking insights gained from recent large-scale surveys of genetic interaction and suppression analyses in yeast, we propose that the genetic network context for a given mutation may shape its propensity of exhibiting background-dependent phenotypes. We argue that further efforts in systematically mapping the genetic interaction networks beyond yeast will provide not only key insights into the functional properties of genes, but also a better understanding of the background effects and the (un)predictability of traits in a broader context.

The background puzzle: how identical mutations in the same gene lead to different disease symptoms

Identical disease-causing mutations can lead to different symptoms in different people. The reason for this has been a puzzling problem for geneticists. Differential penetrance and expressivity of mutations has been observed within individuals with different and similar genetic backgrounds. Attempts have been made to uncover the underlying mechanisms that determine differential phenotypic effects of identical mutations through studies of model organisms. From these studies evidence is accumulating that to understand disease mechanism or predict disease prevalence, an understanding of the influence of genetic background is as important as the putative disease-causing mutations of relatively large effect. This review highlights current insights into phenotypic variation due to gene interactions, epigenetics and stochasticity in model organisms, and discusses their importance for understanding the mutational effect on disease symptoms.

Glucose tolerance female-specific QTL mapped in collaborative cross mice

Type-2 diabetes (T2D) is a complex metabolic disease characterized by impaired glucose tolerance. Despite environmental high risk factors, host genetic background is a strong component of T2D development. Herein, novel highly genetically diverse strains of collaborative cross (CC) lines from mice were assessed to map quantitative trait loci (QTL) associated with variations of glucose-tolerance response. In total, 501 mice of 58 CC lines were maintained on high-fat (42 % fat) diet for 12 weeks. Thereafter, an intraperitoneal glucose tolerance test (IPGTT) was performed for 180 min. Subsequently, the values of Area under curve for the glucose at zero and 180 min (AUC_0-180), were measured, and used for QTL mapping. Heritability and coefficient of variations in glucose tolerance (CVg) were calculated. One-way analysis of variation was significant (P < 0.001) for AUC_0-180 between the CC lines as well between both sexes. Despite Significant variations for both sexes, QTL analysis was significant, only for females, reporting a significant female-sex-dependent QTL (~2.5 Mbp) associated with IPGTT AUC_0-180 trait, located on Chromosome 8 (32-34.5 Mbp, containing 51 genes). Gene browse revealed QTL for body weight/size, genes involved in immune system, and two main protein-coding genes involved in the Glucose homeostasis, Mboat4 and Leprotl1. Heritability and coefficient of genetic variance (CVg) were 0.49 and 0.31 for females, while for males, these values 0.34 and 0.22, respectively. Our findings demonstrate the roles of genetic factors controlling glucose tolerance, which significantly differ between sexes requiring independent studies for females and males toward T2D prevention and therapy.

Genetic Background Limits Generalizability of Genotype-Phenotype Relationships

Genome-wide association studies (GWASs) have identified numerous loci that influence risk for psychiatric diseases. Genetically engineered mice are often used to characterize genes implicated by GWASs. These studies are based on the assumption that observed genotype-phenotype relationships will generalize to humans, implying that the results would at least generalize to other inbred mouse strains. Given current concerns about reproducibility, we sought to directly test this assumption. We produced F1 crosses between male C57BL/6J mice heterozygous for null alleles of Cacna1c and Tcf7l2 and wild-type females from 30 inbred laboratory strains. We found extremely strong interactions with genetic background that sometimes supported diametrically opposing conclusions. These results do not negate the invaluable contributions of mouse genetics to biomedical science, but they do show that genotype-phenotype relationships cannot be reliably inferred by studying a single genetic background, and thus constitute a major challenge to the status quo. VIDEO ABSTRACT.