Systems genetics of susceptibility to obesity-induced diabetes in mice

Combination of blockade of endothelin signalling and compensation of IGF1 expression protects the retina from degeneration

Retinitis pigmentosa (RP) and macular dystrophy (MD) cause severe retinal dysfunction, affecting 1 in 4000 people worldwide. This disease is currently assumed to be intractable, because effective therapeutic methods have not been established, regardless of genetic or sporadic traits. Here, we examined a RP mouse model in which the Prominin-1 (Prom1) gene was deficient and investigated the molecular events occurring at the outset of retinal dysfunction. We extracted the Prom1-deficient retina subjected to light exposure for a short time, conducted single-cell expression profiling, and compared the gene expression with and without stimuli. We identified the cells and genes whose expression levels change directly in response to light stimuli. Among the genes altered by light stimulation, Igf1 was decreased in rod photoreceptor cells and astrocytes under the light-stimulated condition. Consistently, the insulin-like growth factor (IGF) signal was weakened in light-stimulated photoreceptor cells. The recovery of Igf1 expression with the adeno-associated virus (AAV) prevented photoreceptor cell death, and its treatment in combination with the endothelin receptor antagonist led to the blockade of abnormal glial activation and the promotion of glycolysis, thereby resulting in the improvement of retinal functions, as assayed by electroretinography. We additionally demonstrated that the attenuation of mammalian/mechanistic target of rapamycin (mTOR), which mediates IGF signalling, leads to complications in maintaining retinal homeostasis. Together, we propose that combinatorial manipulation of distinct mechanisms is useful for the maintenance of the retinal condition.

A Novel Calibration Step in Gene Co-Expression Network Construction

High-throughput technologies such as DNA microarrays and RNA-sequencing are used to measure the expression levels of large numbers of genes simultaneously. To support the extraction of biological knowledge, individual gene expression levels are transformed to Gene Co-expression Networks (GCNs). In a GCN, nodes correspond to genes, and the weight of the connection between two nodes is a measure of similarity in the expression behavior of the two genes. In general, GCN construction and analysis includes three steps; 1) calculating a similarity value for each pair of genes 2) using these similarity values to construct a fully connected weighted network 3) finding clusters of genes in the network, commonly called modules. The specific implementation of these three steps can significantly impact the final output and the downstream biological analysis. GCN construction is a well-studied topic. Existing algorithms rely on relatively simple statistical and mathematical tools to implement these steps. Currently, software package WGCNA appears to be the most widely accepted standard. We hypothesize that the raw features provided by sequencing data can be leveraged to extract modules of higher quality. A novel preprocessing step of the gene expression data set is introduced that in effect calibrates the expression levels of individual genes, before computing pairwise similarities. Further, the similarity is computed as an inner-product of positive vectors. In experiments, this provides a significant improvement over WGCNA, as measured by aggregate p-values of the gene ontology term enrichment of the computed modules.

Identification of Novel Potential Type 2 Diabetes Genes Mediating β-Cell Loss and Hyperglycemia Using Positional Cloning

Type 2 diabetes (T2D) is a complex metabolic disease regulated by an interaction of genetic predisposition and environmental factors. To understand the genetic contribution in the development of diabetes, mice varying in their disease susceptibility were crossed with the obese and diabetes-prone New Zealand obese (NZO) mouse. Subsequent whole-genome sequence scans revealed one major quantitative trait loci (QTL), Nidd/DBA on chromosome 4, linked to elevated blood glucose and reduced plasma insulin and low levels of pancreatic insulin. Phenotypical characterization of congenic mice carrying 13.6 Mbp of the critical fragment of DBA mice displayed severe hyperglycemia and impaired glucose clearance at week 10, decreased glucose response in week 13, and loss of β-cells and pancreatic insulin in week 16. To identify the responsible gene variant(s), further congenic mice were generated and phenotyped, which resulted in a fragment of 3.3 Mbp that was sufficient to induce hyperglycemia. By combining transcriptome analysis and haplotype mapping, the number of putative responsible variant(s) was narrowed from initial 284 to 18 genes, including gene models and non-coding RNAs. Consideration of haplotype blocks reduced the number of candidate genes to four (Kti12, Osbpl9, Ttc39a, and Calr4) as potential T2D candidates as they display a differential expression in pancreatic islets and/or sequence variation. In conclusion, the integration of comparative analysis of multiple inbred populations such as haplotype mapping, transcriptomics, and sequence data substantially improved the mapping resolution of the diabetes QTL Nidd/DBA. Future studies are necessary to understand the exact role of the different candidates in β-cell function and their contribution in maintaining glycemic control.

Transcriptomics Curation of SARS-CoV-2 Related Host Genes in Mice With COVID-19 Comorbidity: A Pilot Study

The pandemic of coronavirus disease 2019 (COVID-19), a respiratory disease caused by a novel severe acute respiratory syndrome coronavirus-2, is causing substantial morbidity and mortality. Along with the respiratory symptoms, underlying diseases in senior patients, such as diabetes, hypertension, and coronary heart disease, are the most common comorbidities, which cause more severe outcomes and even death. During cellular attachment and entry of severe acute respiratory syndrome coronavirus-2, the key protein involved is the angiotensin I converting enzyme 2 (ACE2), which is located on the membrane of host cells. Here, we aim to curate an expression profile of Ace2 and other COVID-19 related genes across the available diabetes murine strains. Based on strictly manual curation and bioinformatics analysis of the publicly deposited expression datasets, Ace2 and other potentially involved genes such as Furin, Tmprss2, Ang, and Ang2 were examined. We found that Ace2 expression is rather ubiquitous in three selected diabetes prone strains (db/db, ob/ob and diet-induced obese). With the most abundant datasets present, the liver shows a medium Ace2 expression level compared with the lungs, pancreatic islets, brain and even T cells. Age is a more critical factor for Ace2 expression in db/db compared with the other two strains. Besides Ace2, the other four host genes showed varied levels of correlation to each other. To accelerate research on the interaction between COVID-19 and underlying diseases, the Murine4Covid transcriptomics database (www.geneureka.org/Murine4Covid) will facilitate the design of research on COVID-19 and comorbidities.

Conserved properties of genetic architecture of renal and fat transcriptomes in rat models of insulin resistance

To define renal molecular mechanisms that are affected by permanent hyperglycaemia and might promote phenotypes relevant to diabetic nephropathy, we carried out linkage analysis of genome-wide gene transcription in the kidneys of F2 offspring from the Goto-Kakizaki (GK) rat model of type 2 diabetes and normoglycaemic Brown Norway (BN) rats. We mapped 2526 statistically significant expression quantitative trait loci (eQTLs) in the cross. More than 40% of eQTLs mapped in the close vicinity of the linked transcripts, underlying possible cis-regulatory mechanisms of gene expression. We identified eQTL hotspots on chromosomes 5 and 9 regulating the expression of 80-165 genes, sex or cross direction effects, and enriched metabolic and immunological processes by segregating GK alleles. Comparative analysis with adipose tissue eQTLs in the same cross showed that 496 eQTLs, in addition to the top enriched biological pathways, are conserved in the two tissues. Extensive similarities in eQTLs mapped in the GK rat and in the spontaneously hypertensive rat (SHR) suggest a common aetiology of disease phenotypes common to the two strains, including insulin resistance, which is a prominent pathophysiological feature in both GK rats and SHRs. Our data shed light on shared and tissue-specific molecular mechanisms that might underlie aetiological aspects of insulin resistance in the context of spontaneously occurring hyperglycaemia and hypertension.

Summary: Kidney and fat expression QTL mapping in rat models of spontaneously occurring insulin resistance associated with either diabetes or hypertension reveals conserved gene expression regulation, suggesting shared aetiology of disease phenotypes.

Genetic Drivers of Pancreatic Islet Function

The majority of gene loci that have been associated with type 2 diabetes play a role in pancreatic islet function. To evaluate the role of islet gene expression in the etiology of diabetes, we sensitized a genetically diverse mouse population with a Western diet high in fat (45% kcal) and sucrose (34%) and carried out genome-wide association mapping of diabetes-related phenotypes. We quantified mRNA abundance in the islets and identified 18,820 expression QTL. We applied mediation analysis to identify candidate causal driver genes at loci that affect the abundance of numerous transcripts. These include two genes previously associated with monogenic diabetes (PDX1 and HNF4A), as well as three genes with nominal association with diabetes-related traits in humans (FAM83E, IL6ST, and SAT2). We grouped transcripts into gene modules and mapped regulatory loci for modules enriched with transcripts specific for α-cells, and another specific for δ-cells. However, no single module enriched for β-cell-specific transcripts, suggesting heterogeneity of gene expression patterns within the β-cell population. A module enriched in transcripts associated with branched-chain amino acid metabolism was the most strongly correlated with physiological traits that reflect insulin resistance. Although the mice in this study were not overtly diabetic, the analysis of pancreatic islet gene expression under dietary-induced stress enabled us to identify correlated variation in groups of genes that are functionally linked to diabetes-associated physiological traits. Our analysis suggests an expected degree of concordance between diabetes-associated loci in the mouse and those found in human populations, and demonstrates how the mouse can provide evidence to support nominal associations found in human genome-wide association mapping.

Microbial Regulation of Glucose Metabolism and Insulin Resistance

Type 2 diabetes is a combined disease, resulting from a hyperglycemia and peripheral and hepatic insulin resistance. Recent data suggest that the gut microbiota is involved in diabetes development, altering metabolic processes including glucose and fatty acid metabolism. Thus, type 2 diabetes patients show a microbial dysbiosis, with reduced butyrate-producing bacteria and elevated potential pathogens compared to metabolically healthy individuals. Furthermore, probiotics are a known tool to modulate the microbiota, having a therapeutic potential. Current literature will be discussed to elucidate the complex interaction of gut microbiota, intestinal permeability and inflammation leading to peripheral and hepatic insulin resistance. Therefore, this review aims to generate a deeper understanding of the underlying mechanism of potential microbial strains, which can be used as probiotics.

Novel genes on rat chromosome 10 are linked to body fat mass, preadipocyte number and adipocyte size

BACKGROUND

The genetic architecture of obesity is multifactorial. We have previously identified a quantitative trait locus (QTL) on rat chromosome 10 in a F2 cross of Wistar Ottawa Karlsburg (WOKW) and Dark Agouti (DA) rats responsible for obesity-related traits. The QTL was confirmed in congenic DA.WOKW10 rats. To pinpoint the region carrying causal genes, we established two new subcongenic lines, L1 and L2, with smaller refined segments of chromosome 10 to identify novel candidate genes.

METHODS

All lines were extensively characterized under different diet conditions. We employed transcriptome analysis in visceral adipose tissue (VAT) by RNA-Seq technology to identify potential underlying genes in the segregating regions. Three candidate genes were measured in human paired samples of VAT and subcutaneous (SC) AT (SAT) (N=304) individuals with a wide range of body weight and glucose homeostasis parameters.

RESULTS

DA.WOKW and L1 subcongenic lines were protected against body fat gain under high-fat diet (HFD), whereas L2 and DA had significantly more body fat after high-fat feeding. Interestingly, adipocyte size distribution in SAT and epigonadal AT of L1 subcongenic rats did not undergo typical ballooning under HFD and the number of preadipocytes in AT was significantly elevated in L2 compared with L1 and parental rats. Transcriptome analysis identified three candidate genes in VAT on rat chromosome 10. In humans, these candidate genes were differentially expressed between SAT and VAT. Moreover, HID1 mRNA significantly correlates with parameters of obesity and glucose metabolism.

CONCLUSIONS

Our data suggest novel candidate genes for obesity that map on rat chromosome 10 in an interval 102.2-104.7 Mb and are strongly associated with body fat mass regulation, preadipocyte number and adipocyte size in rats. Among those genes, AT head involution defective (HID1) mRNA expression may be relevant for human fat distribution and glucose homeostasis.

ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis

Meta-analysis has become a popular tool for genetic association studies to combine different genetic studies. A key challenge in meta-analysis is heterogeneity, or the differences in effect sizes between studies. Heterogeneity complicates the interpretation of meta-analyses. In this paper, we describe ForestPMPlot, a flexible visualization tool for analyzing studies included in a meta-analysis. The main feature of the tool is visualizing the differences in the effect sizes of the studies to understand why the studies exhibit heterogeneity for a particular phenotype and locus pair under different conditions. We show the application of this tool to interpret a meta-analysis of 17 mouse studies, and to interpret a multi-tissue eQTL study.

An integrative systems genetics approach reveals potential causal genes and pathways related to obesity

Background

Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach.

Methods

Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data.

Results

Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate < 0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B.

Conclusions

To our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-015-0229-0) contains supplementary material, which is available to authorized users.

The diabetes gene Zfp69 modulates hepatic insulin sensitivity in mice

AIMS/HYPOTHESIS

Zfp69 was previously identified by positional cloning as a candidate gene for obesity-associated diabetes. C57BL/6J and New Zealand obese (NZO) mice carry a loss-of-function mutation due to the integration of a retrotransposon. On the NZO background, the Zfp69 locus caused severe hyperglycaemia and loss of beta cells. To provide direct evidence for a causal role of Zfp69, we investigated the effects of its overexpression on both a lean [B6-Tg(Zfp69)] and an obese [NZO/B6-Tg(Zfp69)] background.

METHODS

Zfp69 transgenic mice were generated by integrating the cDNA into the ROSA locus of the C57BL/6 genome and characterised.

RESULTS

B6-Tg(Zfp69) mice were normoglycaemic, developed hyperinsulinaemia, and exhibited increased expression of G6pc and Pck1 and slightly reduced phospho-Akt levels in the liver. During OGTTs, glucose clearance was normal but insulin levels were significantly higher in the B6-Tg(Zfp69) than in control mice. The liver fat content and plasma triacylglycerol levels were significantly increased in B6-Tg(Zfp69) and NZO/B6-Tg(Zfp69) mice on a high-fat diet compared with controls. Liver transcriptome analysis of B6-Tg(Zfp69) mice revealed a downregulation of genes involved in glucose and lipid metabolism. Specifically, expression of Nampt, Lpin2, Map2k6, Gys2, Bnip3, Fitm2, Slc2a2, Ppargc1α and Insr was significantly decreased in the liver of B6-Tg(Zfp69) mice compared with wild-type animals. However, overexpression of Zfp69 did not induce overt diabetes with hyperglycaemia and beta cell loss.

CONCLUSIONS/INTERPRETATION

Zfp69 mediates hyperlipidaemia, liver fat accumulation and mild insulin resistance. However, it does not induce type 2 diabetes, suggesting that the diabetogenic effect of the Zfp69 locus requires synergy with other as yet unidentified genes.

Interactions between Gut Microbiota, Host Genetics and Diet Modulate the Predisposition to Obesity and Metabolic Syndrome

Obesity, diabetes, and metabolic syndrome result from complex interactions between genetic and environmental factors, including the gut microbiota. To dissect these interactions, we utilized three commonly used inbred strains of mice-obesity/diabetes-prone C57Bl/6J mice, obesity/diabetes-resistant 129S1/SvImJ from Jackson Laboratory, and obesity-prone but diabetes-resistant 129S6/SvEvTac from Taconic-plus three derivative lines generated by breeding these strains in a new, common environment. Analysis of metabolic parameters and gut microbiota in all strains and their environmentally normalized derivatives revealed strong interactions between microbiota, diet, breeding site, and metabolic phenotype. Strain-dependent and strain-independent correlations were found between specific microbiota and phenotypes, some of which could be transferred to germ-free recipient animals by fecal transplantation. Environmental reprogramming of microbiota resulted in 129S6/SvEvTac becoming obesity resistant. Thus, development of obesity/metabolic syndrome is the result of interactions between gut microbiota, host genetics, and diet. In permissive genetic backgrounds, environmental reprograming of microbiota can ameliorate development of metabolic syndrome.

Identification of common regulators of genes in co-expression networks affecting muscle and meat properties

Understanding the genetic contributions behind skeletal muscle composition and metabolism is of great interest in medicine and agriculture. Attempts to dissect these complex traits combine genome-wide genotyping, expression data analyses and network analyses. Weighted gene co-expression network analysis (WGCNA) groups genes into modules based on patterns of co-expression, which can be linked to phenotypes by correlation analysis of trait values and the module eigengenes, i.e. the first principal component of a given module. Network hub genes and regulators of the genes in the modules are likely to play an important role in the emergence of respective traits. In order to detect common regulators of genes in modules showing association with meat quality traits, we identified eQTL for each of these genes, including the highly connected hub genes. Additionally, the module eigengene values were used for association analyses in order to derive a joint eQTL for the respective module. Thereby major sites of orchestrated regulation of genes within trait-associated modules were detected as hotspots of eQTL of many genes of a module and of its eigengene. These sites harbor likely common regulators of genes in the modules. We exemplarily showed the consistent impact of candidate common regulators on the expression of members of respective modules by RNAi knockdown experiments. In fact, Cxcr7 was identified and validated as a regulator of genes in a module, which is involved in the function of defense response in muscle cells. Zfp36l2 was confirmed as a regulator of genes of a module related to cell death or apoptosis pathways. The integration of eQTL in module networks enabled to interpret the differentially-regulated genes from a systems perspective. By integrating genome-wide genomic and transcriptomic data, employing co-expression and eQTL analyses, the study revealed likely regulators that are involved in the fine-tuning and synchronization of genes with trait-associated expression.

Obesity genetics in mouse and human: back and forth, and back again

Obesity is a major public health concern. This condition results from a constant and complex interplay between predisposing genes and environmental stimuli. Current attempts to manage obesity have been moderately effective and a better understanding of the etiology of obesity is required for the development of more successful and personalized prevention and treatment options. To that effect, mouse models have been an essential tool in expanding our understanding of obesity, due to the availability of their complete genome sequence, genetically identified and defined strains, various tools for genetic manipulation and the accessibility of target tissues for obesity that are not easily attainable from humans. Our knowledge of monogenic obesity in humans greatly benefited from the mouse obesity genetics field. Genes underlying highly penetrant forms of monogenic obesity are part of the leptin-melanocortin pathway in the hypothalamus. Recently, hypothesis-generating genome-wide association studies for polygenic obesity traits in humans have led to the identification of 119 common gene variants with modest effect, most of them having an unknown function. These discoveries have led to novel animal models and have illuminated new biologic pathways. Integrated mouse-human genetic approaches have firmly established new obesity candidate genes. Innovative strategies recently developed by scientists are described in this review to accelerate the identification of causal genes and deepen our understanding of obesity etiology. An exhaustive dissection of the molecular roots of obesity may ultimately help to tackle the growing obesity epidemic worldwide.

Dual Linkage of a Locus to Left Ventricular Mass and a Cardiac Gene Co-Expression Network Driven by a Chromosome Domain

We have previously reported Lvm1 as a quantitative trait locus (QTL) on chromosome 13 that links to cardiac left ventricular mass (LVM) in a panel of AxB/BxA mouse recombinant inbred strains (RIS). When performing a gene expression QTL (eQTL) analysis, we detected 33 cis-eQTLs that correlated with LVM. Among the latter, a group of eight cis-eQTLs clustered in a genomic region smaller than 6 Mb and surrounding the Lvm1 peak on chr13. Co-variant analysis indicated that all eight genes correlated with the phenotype in a causal rather than a reactive fashion, a finding that (despite its functional interest) did not provide grounds to prioritize any of these candidate genes. As a complementary approach, we performed weighted gene co-expression network analysis, which allowed us to detect 49 modules of highly connected genes. The module that correlated best with LVM: (1) showed linkage to a module QTL whose boundaries matched closely those of the phenotypic Lvm1 QTL on chr13; (2) harbored a disproportionately high proportion of genes originating from a small genomic region on chromosome 13 (including the 8 previously detected cis-eQTL genes); (3) contained genes that, beyond their individual level of expression, correlated with LVM as a function of their inter-connectivity; and (4) showed increased abundance of polymorphic insertion–deletion elements in the same region. Taken together, these data suggest that a domain on chromosome 13 constitutes the biologic principle responsible for the organization and linkage of the gene co-expression module, and indicate a mechanism whereby genetic variants within chromosome domains may associate to phenotypic changes via coordinate changes in the expression of several genes. One other possible implication of these findings is that candidate genes to consider as contributors to a particular phenotype should extend further than those that are closest to the QTL peak.

Insights into obesity and diabetes at the intersection of mouse and human genetics

Many of our insights into obesity and diabetes come from studies in mice carrying natural or induced mutations. In parallel, genome-wide association studies (GWAS) in humans have identified numerous genes that are causally associated with obesity and diabetes, but discovering the underlying mechanisms required in-depth studies in mice. We discuss the advantages of studying natural variation in mice and summarize several examples where the combination of human and mouse genetics opened windows into fundamental physiological pathways. A noteworthy example is the melanocortin-4 receptor (MC4R) and its role in energy balance. The pathway was delineated by discovering the gene responsible for the Agouti mutation in mice. With more targeted phenotyping, we predict that additional pathways relevant to human pathophysiology will be discovered.

The role of genetics in estrogen responses: a critical piece of an intricate puzzle

The estrogens are female sex hormones that are involved in a variety of physiological processes, including reproductive development and function, wound healing, and bone growth. They are mainly known for their roles in reproductive tissues--specifically, 17β-estradiol (E2), the primary estrogen, which is secreted by the ovaries and induces cellular proliferation and growth of the uterus and mammary glands. In addition to the role of estrogens in promoting tissue growth and development during normal physiological states, they have a well-established role in determining susceptibility to disease, particularly cancer, in reproductive tissues. The responsiveness of various tissues to estrogen is genetically controlled, with marked quantitative variation observed across multiple species, including humans. This variation presents both researchers and clinicians with a veritable physiological puzzle, the pieces of which--many of them unknown--are complex and difficult to fit together. Although genetics is known to play a major role in determining sensitivity to estrogens, there are other factors, including parent of origin and the maternal environment, that are intimately linked to heritable phenotypes but do not represent genotype, per se. The objectives of this review article were to summarize the current knowledge of the role of genotype, and uterine and neonatal environments, in phenotypic variation in the response to estrogens; to discuss recent findings and the potential mechanisms involved; and to highlight exciting research opportunities for the future.

Integrated genome-wide association, coexpression network, and expression single nucleotide polymorphism analysis identifies novel pathway in allergic rhinitis

BACKGROUND

Allergic rhinitis is a common disease whose genetic basis is incompletely explained. We report an integrated genomic analysis of allergic rhinitis.

METHODS

We performed genome wide association studies (GWAS) of allergic rhinitis in 5633 ethnically diverse North American subjects. Next, we profiled gene expression in disease-relevant tissue (peripheral blood CD4+ lymphocytes) collected from subjects who had been genotyped. We then integrated the GWAS and gene expression data using expression single nucleotide (eSNP), coexpression network, and pathway approaches to identify the biologic relevance of our GWAS.

RESULTS

GWAS revealed ethnicity-specific findings, with 4 genome-wide significant loci among Latinos and 1 genome-wide significant locus in the GWAS meta-analysis across ethnic groups. To identify biologic context for these results, we constructed a coexpression network to define modules of genes with similar patterns of CD4+ gene expression (coexpression modules) that could serve as constructs of broader gene expression. 6 of the 22 GWAS loci with P-value ≤ 1x10-6 tagged one particular coexpression module (4.0-fold enrichment, P-value 0.0029), and this module also had the greatest enrichment (3.4-fold enrichment, P-value 2.6 × 10-24) for allergic rhinitis-associated eSNPs (genetic variants associated with both gene expression and allergic rhinitis). The integrated GWAS, coexpression network, and eSNP results therefore supported this coexpression module as an allergic rhinitis module. Pathway analysis revealed that the module was enriched for mitochondrial pathways (8.6-fold enrichment, P-value 4.5 × 10-72).

CONCLUSIONS

Our results highlight mitochondrial pathways as a target for further investigation of allergic rhinitis mechanism and treatment. Our integrated approach can be applied to provide biologic context for GWAS of other diseases.

Allele-specific expression and eQTL analysis in mouse adipose tissue

Background

The simplest definition of cis-eQTLs versus trans, refers to genetic variants that affect expression in an allele specific manner, with implications on underlying mechanism. Yet, due to technical limitations of expression microarrays, the vast majority of eQTL studies performed in the last decade used a genomic distance based definition as a surrogate for cis, therefore exploring local rather than cis-eQTLs.

Results

In this study we use RNAseq to explore allele specific expression (ASE) in adipose tissue of male and female F1 mice, produced from reciprocal crosses of C57BL/6J and DBA/2J strains. Comparison of the identified cis-eQTLs, to local-eQTLs, that were obtained from adipose tissue expression in two previous population based studies in our laboratory, yields poor overlap between the two mapping approaches, while both local-eQTL studies show highly concordant results. Specifically, local-eQTL studies show ~60% overlap between themselves, while only 15-20% of local-eQTLs are identified as cis by ASE, and less than 50% of ASE genes are recovered in local-eQTL studies. Utilizing recently published ENCODE data, we also find that ASE genes show significant bias for SNPs prevalence in DNase I hypersensitive sites that is ASE direction specific.

Conclusions

We suggest a new approach to analysis of allele specific expression that is more sensitive and accurate than the commonly used fisher or chi-square statistics. Our analysis indicates that technical differences between the cis and local-eQTL approaches, such as differences in genomic background or sex specificity, account for relatively small fraction of the discrepancy. Therefore, we suggest that the differences between two eQTL mapping approaches may facilitate sorting of SNP-eQTL interactions into true cis and trans, and that a considerable portion of local-eQTL may actually represent trans interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-471) contains supplementary material, which is available to authorized users.

The genetic basis of obesity-associated type 2 diabetes (diabesity) in polygenic mouse models

Obesity-associated diabetes (“diabesity”) in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells. This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse. In lean strains such as C57BLKS/J, BTBR T+tf/J or DBA/2 J carrying diabetes susceptibility genes (“diabetes susceptible” background), it can be induced by introgression of the obesity-causing mutations Lep<ob> (ob) or Lepr<db> (db). Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1,Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation). Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism. Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease.

Network analysis: a new approach to study endocrine disorders

Systems biology is the study of the interactions that occur between the components of individual cells - including genes, proteins, transcription factors, small molecules, and metabolites, and their relationships to complex physiological and pathological processes. The application of systems biology to medicine promises rapid advances in both our understanding of disease and the development of novel treatment options. Network biology has emerged as the primary tool for studying systems biology as it utilises the mathematical analysis of the relationships between connected objects in a biological system and allows the integration of varied 'omic' datasets (including genomics, metabolomics, proteomics, etc.). Analysis of network biology generates interactome models to infer and assess function; to understand mechanisms, and to prioritise candidates for further investigation. This review provides an overview of network methods used to support this research and an insight into current applications of network analysis applied to endocrinology. A wide spectrum of endocrine disorders are included ranging from congenital hyperinsulinism in infancy, through childhood developmental and growth disorders, to the development of metabolic diseases in early and late adulthood, such as obesity and obesity-related pathologies. In addition to providing a deeper understanding of diseases processes, network biology is also central to the development of personalised treatment strategies which will integrate pharmacogenomics with systems biology of the individual.

Network statistics of genetically-driven gene co-expression modules in mouse crosses

In biology, networks are used in different contexts as ways to represent relationships between entities, such as for instance interactions between genes, proteins or metabolites. Despite progress in the analysis of such networks and their potential to better understand the collective impact of genes on complex traits, one remaining challenge is to establish the biologic validity of gene co-expression networks and to determine what governs their organization. We used WGCNA to construct and analyze seven gene expression datasets from several tissues of mouse recombinant inbred strains (RIS). For six out of the 7 networks, we found that linkage to “module QTLs” (mQTLs) could be established for 29.3% of gene co-expression modules detected in the several mouse RIS. For about 74.6% of such genetically-linked modules, the mQTL was on the same chromosome as the one contributing most genes to the module, with genes originating from that chromosome showing higher connectivity than other genes in the modules. Such modules (that we considered as “genetically-driven”) had network statistic properties (density and centralization) that set them apart from other modules in the network. Altogether, a sizeable portion of gene co-expression modules detected in mouse RIS panels had genetic determinants as their main organizing principle. In addition to providing a biologic interpretation validation for these modules, these genetic determinants imparted on them particular properties that set them apart from other modules in the network, to the point that they can be predicted to a large extent on the basis of their network statistics.

Meta-analysis identifies gene-by-environment interactions as demonstrated in a study of 4,965 mice

Identifying environmentally-specific genetic effects is a key challenge in understanding the structure of complex traits. Model organisms play a crucial role in the identification of such gene-by-environment interactions, as a result of the unique ability to observe genetically similar individuals across multiple distinct environments. Many model organism studies examine the same traits but under varying environmental conditions. For example, knock-out or diet-controlled studies are often used to examine cholesterol in mice. These studies, when examined in aggregate, provide an opportunity to identify genomic loci exhibiting environmentally-dependent effects. However, the straightforward application of traditional methodologies to aggregate separate studies suffers from several problems. First, environmental conditions are often variable and do not fit the standard univariate model for interactions. Additionally, applying a multivariate model results in increased degrees of freedom and low statistical power. In this paper, we jointly analyze multiple studies with varying environmental conditions using a meta-analytic approach based on a random effects model to identify loci involved in gene-by-environment interactions. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional uni- or multi-variate approaches for discovery of gene-by-environment interactions. We apply our new method to combine 17 mouse studies containing in aggregate 4,965 distinct animals. We identify 26 significant loci involved in High-density lipoprotein (HDL) cholesterol, many of which are consistent with previous findings. Several of these loci show significant evidence of involvement in gene-by-environment interactions. An additional advantage of our meta-analysis approach is that our combined study has significantly higher power and improved resolution compared to any single study thus explaining the large number of loci discovered in the combined study.

Identifying gene-by-environment interactions is important for understand the architecture of a complex trait. Discovering gene-by-environment interaction requires the observation of the same phenotype in individuals under different environments. Model organism studies are often conducted under different environments. These studies provide an unprecedented opportunity for researchers to identify the gene-by-environment interactions. A difference in the effect size of a genetic variant between two studies conducted in different environments may suggest the presence of a gene-by-environment interaction. In this paper, we propose to employ a random-effect-based meta-analysis approach to identify gene-by-environment interaction, which assumes different or heterogeneous effect sizes between studies. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional approaches for discovery of gene-by-environment interactions, which treats the gene-by-environment interactions as covariates in the analysis. We provide a intuitive way to visualize the results of the meta-analysis at a locus which allows us to obtain the biological insights of gene-by-environment interactions. We demonstrate our method by searching for gene-by-environment interactions by combining 17 mouse genetic studies totaling 4,965 distinct animals.

Biomarkers for combat-related PTSD: focus on molecular networks from high-dimensional data

Posttraumatic stress disorder (PTSD) and other deployment-related outcomes originate from a complex interplay between constellations of changes in DNA, environmental traumatic exposures, and other biological risk factors. These factors affect not only individual genes or bio-molecules but also the entire biological networks that in turn increase or decrease the risk of illness or affect illness severity. This review focuses on recent developments in the field of systems biology which use multidimensional data to discover biological networks affected by combat exposure and post-deployment disease states. By integrating large-scale, high-dimensional molecular, physiological, clinical, and behavioral data, the molecular networks that directly respond to perturbations that can lead to PTSD can be identified and causally associated with PTSD, providing a path to identify key drivers. Reprogrammed neural progenitor cells from fibroblasts from PTSD patients could be established as an in vitro assay for high throughput screening of approved drugs to determine which drugs reverse the abnormal expression of the pathogenic biomarkers or neuronal properties.

Analysis of allele-specific expression in mouse liver by RNA-Seq: a comparison with Cis-eQTL identified using genetic linkage

We report an analysis of allele-specific expression (ASE) and parent-of-origin expression in adult mouse liver using next generation sequencing (RNA-Seq) of reciprocal crosses of heterozygous F1 mice from the parental strains C57BL/6J and DBA/2J. We found a 60% overlap between genes exhibiting ASE and putative cis-acting expression quantitative trait loci (cis-eQTL) identified in an intercross between the same strains. We discuss the various biological and technical factors that contribute to the differences. We also identify genes exhibiting parental imprinting and complex expression patterns. Our study demonstrates the importance of biological replicates to limit the number of false positives with RNA-Seq data.

A novel mechanism regulating insulin secretion involving Herpud1 in mice

AIMS/HYPOTHESIS

Type 2 diabetes results from beta cell dysfunction after prolonged physiological stress, which causes oversecretion of insulin. We recently found that insulin hypersecretion is mediated by at least two genes. Among mouse models of type 2 diabetes, the DBA/2 mouse strain is more susceptible to diabetes than is the C57BL/6J (B6J) strain. One distinctive feature of the DBA/2 mouse is that it hypersecretes insulin, independent of changes in insulin sensitivity; we identified Nnt as a gene responsible for this trait.

METHODS

To identify the other gene(s) affecting insulin hypersecretion, we tested a panel of recombinant inbred BXD strains, which have different combinations of B6 and DBA/2 alleles.

RESULTS

We found that 25% of the BXD strains hypersecreted insulin in response to glucose. Microarray profiling of islets from high- and low-secretor strains showed that at least four genes were differentially expressed. One gene was consistently underexpressed in islets from both DBA/2 and the high-secretor BXD strains. This gene (Herpud1 or Herp) encodes the 54 kDa endoplasmic reticulum stress-inducible protein (HERP) that resides in the integral endoplasmic reticulum membrane. To test directly whether Herpud1 can interact with Nnt, Herpud1 was either knocked down or overexpressed in MIN6 cells. These results showed that when Herpud1 was suppressed, Nnt expression was reduced, while overexpression of Herpud1 led to increased Nnt expression. Furthermore, Herpud1 suppression resulted in significantly decreased glucose-stimulated insulin secretion in the DBA/2 islets but not B6J islets.

CONCLUSIONS/INTERPRETATION

We conclude that Herpud1 regulates insulin secretion via control of Nnt expression.

Genetic determinants of voluntary exercise

Variation in voluntary exercise behavior is an important determinant of long-term human health. Increased physical activity is used as a preventative measure or therapeutic intervention for disease, and a sedentary lifestyle has generally been viewed as unhealthy. Predisposition to engage in voluntary activity is heritable and induces protective metabolic changes, but its complex genetic/genomic architecture has only recently begun to emerge. We first present a brief historical perspective and summary of the known benefits of voluntary exercise. Second, we describe human and mouse model studies using genomic and transcriptomic approaches to reveal the genetic architecture of exercise. Third, we discuss the merging of genomic information and physiological observations, revealing systems and networks that lead to a more complete mechanistic understanding of how exercise protects against disease pathogenesis. Finally, we explore potential regulation of physical activity through epigenetic mechanisms, including those that persist across multiple generations.

The systems genetics resource: a web application to mine global data for complex disease traits

The Systems Genetics Resource (SGR) (http://systems.genetics.ucla.edu) is a new open-access web application and database that contains genotypes and clinical and intermediate phenotypes from both human and mouse studies. The mouse data include studies using crosses between specific inbred strains and studies using the Hybrid Mouse Diversity Panel. SGR is designed to assist researchers studying genes and pathways contributing to complex disease traits, including obesity, diabetes, atherosclerosis, heart failure, osteoporosis, and lipoprotein metabolism. Over the next few years, we hope to add data relevant to deafness, addiction, hepatic steatosis, toxin responses, and vascular injury. The intermediate phenotypes include expression array data for a variety of tissues and cultured cells, metabolite levels, and protein levels. Pre-computed tables of genetic loci controlling intermediate and clinical phenotypes, as well as phenotype correlations, are accessed via a user-friendly web interface. The web site includes detailed protocols for all of the studies. Data from published studies are freely available; unpublished studies have restricted access during their embargo period.

Decrease of Obesity by Allantoin via Imidazoline I 1 -Receptor Activation in High Fat Diet-Fed Mice

The activation of the imidazoline I₁-receptor (I₁R) is known to regulate appetite. Allantoin, an active ingredient in the yam, has been reported to improve lipid metabolism in high fat diet- (HFD-)fed mice. However, the effect of allantoin on obesity remains unclear. In the present study, we investigated the effects of allantoin on HFD-induced obesity. The chronic administration of allantoin to HFD-fed mice for 8 weeks significantly decreased their body weight, and this effect was reversed by efaroxan at a dose sufficient to block I₁R. The epididymal white adipose tissue (eWAT) cell size and weight in HFD-fed mice were also decreased by allantoin via the activation of I₁R. In addition, allantoin significantly decreased the energy intake of HFD-fed mice, and this reduction was associated with a decrease in the NPY levels in the brain. However, no inhibitory effect of allantoin on energy intake was observed in db/db mice. Moreover, allantoin lowered HFD-induced hyperleptinemia, and this activity was abolished by I₁R blockade with efaroxan. Taken together, these data suggest that allantoin can ameliorate energy intake and eWAT accumulation by activating I₁R to improve HFD-induced obesity.

Genome-wide association mapping of blood cell traits in mice

Genetic variations in blood cell parameters can impact clinical traits. We report here the mapping of blood cell traits in a panel of 100 inbred strains of mice of the Hybrid Mouse Diversity Panel (HMDP) using genome-wide association (GWA). We replicated a locus previously identified in using linkage analysis in several genetic crosses for mean corpuscular volume (MCV) and a number of other red blood cell traits on distal chromosome 7. Our peak for SNP association to MCV occurred in a linkage disequilibrium (LD) block spanning from 109.38 to 111.75 Mb that includes Hbb-b1, the likely causal gene. Altogether, we identified five loci controlling red blood cell traits (on chromosomes 1, 7, 11, 12, and 16), and four of these correspond to loci for red blood cell traits reported in a recent human GWA study. For white blood cells, including granulocytes, monocytes, and lymphocytes, a total of six significant loci were identified on chromosomes 1, 6, 8, 11, 12, and 15. An average of ten candidate genes were found at each locus and those were prioritized by examining functional variants in the HMDP such as missense and expression variants. These results provide intermediate phenotypes and candidate loci for genetic studies of atherosclerosis and cancer as well as inflammatory and immune disorders in mice.

Systems genetic analysis of osteoblast-lineage cells

The osteoblast-lineage consists of cells at various stages of maturation that are essential for skeletal development, growth, and maintenance. Over the past decade, many of the signaling cascades that regulate this lineage have been elucidated; however, little is known of the networks that coordinate, modulate, and transmit these signals. Here, we identify a gene network specific to the osteoblast-lineage through the reconstruction of a bone co-expression network using microarray profiles collected on 96 Hybrid Mouse Diversity Panel (HMDP) inbred strains. Of the 21 modules that comprised the bone network, module 9 (M9) contained genes that were highly correlated with prototypical osteoblast maker genes and were more highly expressed in osteoblasts relative to other bone cells. In addition, the M9 contained many of the key genes that define the osteoblast-lineage, which together suggested that it was specific to this lineage. To use the M9 to identify novel osteoblast genes and highlight its biological relevance, we knocked-down the expression of its two most connected “hub” genes, Maged1 and Pard6g. Their perturbation altered both osteoblast proliferation and differentiation. Furthermore, we demonstrated the mice deficient in Maged1 had decreased bone mineral density (BMD). It was also discovered that a local expression quantitative trait locus (eQTL) regulating the Wnt signaling antagonist Sfrp1 was a key driver of the M9. We also show that the M9 is associated with BMD in the HMDP and is enriched for genes implicated in the regulation of human BMD through genome-wide association studies. In conclusion, we have identified a physiologically relevant gene network and used it to discover novel genes and regulatory mechanisms involved in the function of osteoblast-lineage cells. Our results highlight the power of harnessing natural genetic variation to generate co-expression networks that can be used to gain insight into the function of specific cell-types.

The osteoblast-lineage consists of a range of cells from osteogenic precursors that mature into bone-forming osteoblasts to osteocytes that are entombed in bone. Each cell in the lineage serves a number of distinct and critical roles in the growth and maintenance of the skeleton, as well as many extra-skeletal functions. Over the last decade, many of the major regulatory pathways governing the differentiation and activity of these cells have been discovered. In contrast, little is known regarding the composition or function of gene networks within the lineage. The goal of this study was to increase our understanding of how genes are organized into networks in osteoblasts. Towards this goal, we used microarray gene expression profiles from bone to identify a group of genes that formed a network specific to the osteoblast-lineage. We used the knowledge of this network to identify novel genes that are important for regulating various aspects of osteoblast function. These data improve our understanding of the gene networks operative in cells of the osteoblast-lineage.