Mapping liver fat female-dependent quantitative trait loci in collaborative cross mice

Mapping novel QTL and fine mapping of previously identified QTL associated with glucose tolerance using the collaborative cross mice

A chronic metabolic illness, type 2 diabetes (T2D) is a polygenic and multifactorial complicated disease. With an estimated 463 million persons aged 20 to 79 having diabetes, the number is expected to rise to 700 million by 2045, creating a significant worldwide health burden. Polygenic variants of diabetes are influenced by environmental variables. T2D is regarded as a silent illness that can advance for years before being diagnosed. Finding genetic markers for T2D and metabolic syndrome in groups with similar environmental exposure is therefore essential to understanding the mechanism of such complex characteristic illnesses. So herein, we demonstrated the exclusive use of the collaborative cross (CC) mouse reference population to identify novel quantitative trait loci (QTL) and, subsequently, suggested genes associated with host glucose tolerance in response to a high-fat diet. In this study, we used 539 mice from 60 different CC lines. The diabetogenic effect in response to high-fat dietary challenge was measured by the three-hour intraperitoneal glucose tolerance test (IPGTT) test after 12 weeks of dietary challenge. Data analysis was performed using a statistical software package IBM SPSS Statistic 23. Afterward, blood glucose concentration at the specific and between different time points during the IPGTT assay and the total area under the curve (AUC0-180) of the glucose clearance was computed and utilized as a marker for the presence and severity of diabetes. The observed AUC0-180 averages for males and females were 51,267.5 and 36,537.5 mg/dL, respectively, representing a 1.4-fold difference in favor of females with lower AUC0-180 indicating adequate glucose clearance. The AUC0-180 mean differences between the sexes within each specific CC line varied widely within the CC population. A total of 46 QTL associated with the different studied phenotypes, designated as T2DSL and its number, for Type 2 Diabetes Specific Locus and its number, were identified during our study, among which 19 QTL were not previously mapped. The genomic interval of the remaining 27 QTL previously reported, were fine mapped in our study. The genomic positions of 40 of the mapped QTL overlapped (clustered) on 11 different peaks or close genomic positions, while the remaining 6 QTL were unique. Further, our study showed a complex pattern of haplotype effects of the founders, with the wild-derived strains (mainly PWK) playing a significant role in the increase of AUC values.

Alleviation of Hepatic Steatosis by Alpha-Defensin Is Associated with Enhanced Lipolysis

Background and Objectives: The neutrophilic peptide, alpha-defensin, is considered an evolving risk factor intimately linked with lipid mobilization. It was previously linked to augmented liver fibrosis. Here, we assess a potential association between alpha-defensin and fatty liver. Materials and Methods: A cohort of transgenic C57BL/6JDef^+/+ male mice that overexpress the human neutrophil-derived alpha-defensin in their polymorphonuclear neutrophils (PMNs) were assessed for liver steatosis and fibrosis development. Wild type (C57BL/6JDef.Wt) and transgenic (C57BL/6JDef^+/+) mice were maintained on a standard rodent chow diet for 8.5 months. At the termination of the experiment, systemic metabolic indices and hepatic immunological cell profiling were assessed. Results: The Def^+/+ transgenic mice exhibited lower body and liver weights, lower serum fasting glucose and cholesterol, and significantly lower liver fat content. These results were associated with impaired liver lymphocytes count and function (lower CD8, NK cells, and killing marker CD107a). The metabolic cage demonstrated dominant fat utilization with a comparable food intake in the Def^+/+ mice. Conclusions: Chronic physiological expression of alpha-defensin induces favorable blood metabolic profile, increased systemic lipolysis, and decreased hepatic fat accumulation. Further studies are needed to characterize the defensin net liver effect.

The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases

Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.

Genetics of murine type 2 diabetes and comorbidities

ABSTRAC

Type 2 diabetes (T2D) is a polygenic and multifactorial complex disease, defined as chronic metabolic disorder. It's a major global health concern with an estimated 463 million adults aged 20-79 years with diabetes and projected to increase up to 700 million by 2045. T2D was reported to be one of the four leading causes of non-communicable disease (NCD) deaths in 2012. Environmental factors play a part in the development of polygenic forms of diabetes. Polygenic forms of diabetes often run-in families. Fortunately, T2D, which accounts for 90-95% of the entire four types of diabetes including, Type 1 diabetes (T1D), T2D, monogenic diabetes syndromes (MGDS), and Gestational diabetes mellitus, can be prevented or delayed through nutrition and lifestyle changes as well as through pharmacologic interventions. Typical symptom of the T2D is high blood glucose levels and comprehensive insulin resistance of the body, producing an impaired glucose tolerance. Impaired glucose tolerance of T2D is accompanied by extensive health complications, including cardiovascular diseases (CVD) that vary in morbidity and mortality among populations. The pathogenesis of T2D varies between populations and/or ethnic groupings and is known to be attributed extremely by genetic components and environmental factors. It is evident that genetic background plays a critical role in determining the host response toward certain environmental conditions, whether or not of developing T2D (susceptibility versus resistant). T2D is considered as a silent disease that can progress for years before its diagnosis. Once T2D is diagnosed, many metabolic malfunctions are observed whether as side effects or as independent comorbidity. Mouse models have been proven to be a powerful tool for mapping genetic factors that underline the susceptibility to T2D development as well its comorbidities. Here, we have conducted a comprehensive search throughout the published data covering the time span from early 1990s till the time of writing this review, for already reported quantitative trait locus (QTL) associated with murine T2D and comorbidities in different mouse models, which contain different genetic backgrounds. Our search has resulted in finding 54 QTLs associated with T2D in addition to 72 QTLs associated with comorbidities associated with the disease. We summarized the genomic locations of these mapped QTLs in graphical formats, so as to show the overlapping positions between of these mapped QTLs, which may suggest that some of these QTLs could be underlined by sharing gene/s. Finally, we reviewed and addressed published reports that show the success of translation of the identified mouse QTLs/genes associated with the disease in humans.

Stratifying individuals into non-alcoholic fatty liver disease risk levels using time series machine learning models

Non-alcoholic fatty liver disease (NAFLD) affects 25% of the population worldwide, and its prevalence is anticipated to increase globally. While most NAFLD patients are asymptomatic, NAFLD may progress to fibrosis, cirrhosis, cardiovascular disease, and diabetes. Research reports, with daunting results, show the challenge that NAFLD's burden causes to global population health. The current process for identifying fibrosis risk levels is inefficient, expensive, does not cover all potential populations, and does not identify the risk in time. Instead of invasive liver biopsies, we implemented a non-invasive fibrosis assessment process calculated from clinical data (accessed via EMRs/EHRs). We stratified patients' risks for fibrosis from 2007 to 2017 by modeling the risk in 5579 individuals. The process involved time-series machine learning models (Hidden Markov Models and Group-Based Trajectory Models) profiled fibrosis risk by modeling patients' latent medical status resulted in three groups. The high-risk group had abnormal lab test values and a higher prevalence of chronic conditions. This study can help overcome the inefficient, traditional process of detecting fibrosis via biopsies (that are also medically unfeasible due to their invasive nature, the medical resources involved, and costs) at early stages. Thus longitudinal risk assessment may be used to make population-specific medical recommendations targeting early detection of high risk patients, to avoid the development of fibrosis disease and its complications as well as decrease healthcare costs.

Studying host genetic background effects on multimorbidity of intestinal cancer development, type 2 diabetes and obesity in response to oral bacterial infection and high-fat diet using the collaborative cross (CC) lines

Background

Multimorbidity of intestinal cancer (IC), type 2 diabetes (T2D) and obesity is a complex set of diseases, affected by environmental and genetic risk factors. High-fat diet (HFD) and oral bacterial infection play important roles in the etiology of these diseases through inflammation and various biological mechanisms.

Methods

To study the complexity of this multimorbidity, we used the collaborative cross (CC) mouse genetics reference population. We aimed to study the multimorbidity of IC, T2D, and obesity using CC lines, measuring their responses to HFD and oral bacterial infection. The study used 63 mice of both sexes generated from two CC lines (IL557 and IL711). For 12 weeks, experimental mice were maintained on specific dietary regimes combined with co-infection with oral bacteria Porphyromonas gingivalis and Fusobacterium nucleatum, while control groups were not infected. Body weight (BW) and results of a intraperitoneal glucose tolerance test (IPGTT) were recorded at the end of 12 weeks, after which length and size of the intestines were assessed for polyp counts.

Results

Polyp counts ranged between 2 and 10 per CC line. The combination of HFD and infection significantly reduced (P < .01) the colon polyp size of IL557 females to 2.5 cm2, compared to the other groups. Comparing BW gain, IL557 males on HFD gained 18 g, while the females gained 10 g under the same conditions and showed the highest area under curve (AUC) values of 40 000-45 000 (min mg/dL) in the IPGTT.

Conclusion

The results show that mice from different genetic backgrounds respond differently to a high fat diet and oral infection in terms of polyp development and glucose tolerance, and this effect is gender related.

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).

Hepatic gene expression variations in response to high-fat diet-induced impaired glucose tolerance using RNAseq analysis in collaborative cross mouse population

Hepatic gene expression is known to differ between healthy and type 2 diabetes conditions. Identifying these variations will provide better knowledge to the development of gene-targeted therapies. The aim of this study is to assess diet-induced hepatic gene expression of susceptible versus resistant CC lines to T2D development. Next-generation RNA-sequencing was performed for 84 livers of diabetic and non-diabetic mice of 41 different CC lines (both sexes) following 12 weeks on high-fat diet (42% fat). Data analysis revealed significant variations of hepatic gene expression in diabetic versus non-diabetic mice with significant sex effect, where 601 genes were differentially expressed (DE) in overall population (males and females), 718 genes in female mice, and 599 genes in male mice. Top prioritized DE candidate genes were Lepr, Ins2, Mb, Ckm, Mrap2, and Ckmt2 for the overall population; for females-only group were Hdc, Serpina12, Socs1, Socs2, and Mb, while for males-only group were Serpine1, Mb, Ren1, Slc4a1, and Atp2a1. Data analysis for sex differences revealed 193 DE genes in health (Top: Lepr, Cav1, Socs2, Abcg2, and Col5a3), and 389 genes DE between diabetic females versus males (Top: Lepr, Clps, Ins2, Cav1, and Mrap2). Furthermore, integrating gene expression results with previously published QTL, we identified significant variants mapped at chromosomes at positions 36-49 Mb, 62-71 Mb, and 79-99 Mb, on chromosomes 9, 11, and 12, respectively. Our findings emphasize the complexity of T2D development and that significantly controlled by host complex genetic factors. As well, we demonstrate the significant sex differences between males and females during health and increasing to extent levels during disease/diabetes. Altogether, opening the venue for further studies targets the discovery of effective sex-specific and personalized preventions and therapies.

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.

Mapping novel genetic loci associated with female liver weight variations using Collaborative Cross mice

BACKGROUND

Liver weight is a complex trait, controlled by polygenic factors and differs within populations. Dissecting the genetic architecture underlying these variations will facilitate the search for key role candidate genes involved directly in the hepatomegaly process and indirectly involved in related diseases etiology.

METHODS

Liver weight of 506 mice generated from 39 different Collaborative Cross (CC) lines with both sexes at age 20 weeks old was determined using an electronic balance. Genomic DNA of the CC lines was genotyped with high-density single nucleotide polymorphic markers.

RESULTS

Statistical analysis revealed a significant (P < 0.05) variation of liver weight between the CC lines, with broad sense heritability (H 2) of 0.32 and genetic coefficient of variation (CVG) of 0.28. Subsequently, quantitative trait locus (QTL) mapping was performed, and results showed a significant QTL only for females on chromosome 8 at genomic interval 88.61-93.38 Mb (4.77 Mb). Three suggestive QTL were mapped at chromosomes 4, 12 and 13. The four QTL were designated as LWL1-LWL4 referring to liver weight loci 1-4 on chromosomes 8, 4, 12 and 13, respectively.

CONCLUSION

To our knowledge, this report presents, for the first time, the utilization of the CC for mapping QTL associated with baseline liver weight in mice. Our findings demonstrate that liver weight is a complex trait controlled by multiple genetic factors that differ significantly between sexes.

The Collaborative Cross mouse model for dissecting genetic susceptibility to infectious diseases

Infectious diseases, also known as communicable diseases, refer to a full range of maladies caused by pathogen invasion to the host body. Host response towards an infectious pathogen varies between individuals, and can be defined by responses from asymptomatic to lethal. Host response to infectious pathogens is considered as a complex trait controlled by gene-gene (host-pathogen) and gene-environment interactions, leading to the extensive phenotypic variations between individuals. With the advancement of the human genome mapping approaches and tools, various genome-wide association studies (GWAS) were performed, aimed at mapping the genetic basis underlying host susceptibility towards infectious pathogens. In parallel, immense efforts were invested in enhancing the genetic mapping resolution and gene-cloning efficacy, using advanced mouse models including advanced intercross lines; outbred populations; consomic, congenic; and recombinant inbred lines. Notwithstanding the evident advances achieved using these mouse models, the genetic diversity was low and quantitative trait loci (QTL) mapping resolution was inadequate. Consequently, the Collaborative Cross (CC) mouse model was established by full-reciprocal mating of eight divergent founder strains of mice (A/J, C57BL/6J, 129S1/SvImJ, NOD/LtJ, NZO/HiLtJ, CAST/Ei, PWK/PhJ, and WSB/EiJ) generating a next-generation mouse genetic reference population (CC lines). Presently, the CC mouse model population comprises a set of about 200 recombinant inbred CC lines exhibiting a unique high genetic diversity and which are accessible for multidisciplinary studies. The CC mouse model efficacy was validated by various studies in our lab and others, accomplishing high-resolution (< 1 MB) QTL genomic mapping for a variety of complex traits, using about 50 CC lines (3-4 mice per line). Herein, we present a number of studies demonstrating the power of the CC mouse model, which has been utilized in our lab for mapping the genetic basis of host susceptibility to various infectious pathogens. These include Aspergillus fumigatus, Klebsiella pneumoniae, Porphyromonas gingivalis and Fusobacterium nucleatum (causing oral mixed infection), Pseudomonas aeruginosa, and the bacterial toxins Lipopolysaccharide and Lipoteichoic acid.

Genetic determinants of cholangiopathies: Molecular and systems genetics

Familial cholangiopathies are rare but potentially severe diseases. Their spectrum ranges from fairly benign conditions as, for example, benign recurrent intrahepatic cholestasis to low-phospholipid associated cholelithiasis and progressive familial intrahepatic cholestasis (PFIC). Many cholangiopathies such as primary biliary cholangitis (PBC) or primary sclerosing cholangitis (PSC) affect first the bile ducts ("ascending pathophysiology") but others, such as PFIC, start upstream in hepatocytes and cause progressive damage "descending" down the biliary tree and leading to end-stage liver disease. In recent years our understanding of cholestatic diseases has improved, since we have been able to pinpoint numerous disease-causing mutations that cause familial cholangiopathies. Accordingly, six PFIC subtypes (PFIC type 1-6) have now been defined. Given the availability of genotyping resources, these findings can be introduced in the diagnostic work-up of patients with peculiar cholestasis. In addition, functional studies have defined the pathophysiological consequences of some of the detected variants. Furthermore, ABCB4 variants do not only cause PFIC type 3 but confer an increased risk for chronic liver disease in general. In the near future these findings will serve to develop new therapeutic strategies for patients with liver diseases. Here we present the latest data on the genetic background of familial cholangiopathies and discuss their application in clinical practice for the differential diagnosis of cholestasis of unknown aetiology. As look in the future we present "system genetics" as a novel experimental tool for the study of cholangiopathies and disease-modifying genes. This article is part of a Special Issue entitled: Cholangiocytes in Health and Disease edited by Jesus Banales, Marco Marzioni, Nicholas LaRusso and Peter Jansen.

How mice are indispensable for understanding obesity and diabetes genetics

PURPOSE OF REVIEW

The task of cataloging human genetic variation and its relation to disease is rapidly approaching completion. The new challenge is to discover the function of disease-associated genes and to understand the pathways that lead to human disease. We propose that achieving this new level of understanding will increasingly rely on the use of model organisms. We discuss the advantages of the mouse as a model organism to our understanding of human disease.

RECENT FINDINGS

The collection of available mouse strains represents as much genetic and phenotypic variation as is found in the human population. However, unlike humans, mice can be subjected to experimental breeding protocols and the availability of tissues allows for a far greater and deeper level of phenotyping. New methods for gene editing make it relatively easy to create mouse models of known human mutations. The distinction between genetic and epigenetic inheritance can be studied in great detail. Various experimental protocols enable the exploration of the role of the microbiome in physiology and disease.

SUMMARY

We propose that there will be an interdependence between human and model organism research. Technological advances and new genetic screening platforms in the mouse have greatly improved the path to gene discovery and mechanistic studies of gene function.

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.