Genetic modifiers interact with Cpe(fat) to affect body weight, adiposity, and hyperglycemia

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.

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.

Obesity, POMC, and POMC-processing Enzymes: Surprising Results From Animal Models

Peptides derived from proopiomelanocortin (POMC) are well-established neuropeptides and peptide hormones that perform multiple functions, including regulation of body weight. In humans and some animals, these peptides include α- and β-melanocyte-stimulating hormone (MSH). In certain rodent species, no β-MSH is produced from POMC because of a change in the cleavage site. Enzymes that convert POMC into MSH include prohormone convertases (PCs), carboxypeptidases (CPs), and peptidyl-α-amidating monooxygenase (PAM). Humans and mice with inactivating mutations in either PC1/3 or carboxypeptidase E (CPE) are obese, which was assumed to result from defective processing of POMC into MSH. However, recent studies have shown that selective loss of either PC1/3 or CPE in POMC-expressing cells does not cause obesity. These findings suggest that defects in POMC processing cannot alone account for the obesity observed in global PC1/3 or CPE mutants. We propose that obesity in animals lacking PC1/3 or CPE activity depends, at least in part, on deficient processing of peptides in non-POMC-expressing cells either in the brain and/or the periphery. Genetic background may also contribute to the manifestation of obesity.

Impact of Obesity on Influenza A Virus Pathogenesis, Immune Response, and Evolution

With the rising prevalence of obesity has come an increasing awareness of its impact on communicable disease. As a consequence of the 2009 H1N1 influenza A virus pandemic, obesity was identified for the first time as a risk factor for increased disease severity and mortality in infected individuals. Over-nutrition that results in obesity causes a chronic state of meta-inflammation with systemic implications for immunity. Obese hosts exhibit delayed and blunted antiviral responses to influenza virus infection, and they experience poor recovery from the disease. Furthermore, the efficacy of antivirals and vaccines is reduced in this population and obesity may also play a role in altering the viral life cycle, thus complementing the already weakened immune response and leading to severe pathogenesis. Case studies and basic research in human cohorts and animal models have highlighted the prolonged viral shed in the obese host, as well as a microenvironment that permits the emergence of virulent minor variants. This review focuses on influenza A virus pathogenesis in the obese host, and on the impact of obesity on the antiviral response, viral shed, and viral evolution. We comprehensively analyze the recent literature on how and why viral pathogenesis is altered in the obese host along with the impact of the altered host and pathogenic state on viral evolutionary dynamics in multiple models. Finally, we summarized the effectiveness of current vaccines and antivirals in this populations and the questions that remain to be answered. If current trends continue, nearly 50% of the worldwide population is projected to be obese by 2050. This population will have a growing impact on both non-communicable and communicable diseases and may affect global evolutionary trends of influenza virus.

Panx3 links body mass index and tumorigenesis in a genetically heterogeneous mouse model of carcinogen-induced cancer

Background

Body mass index (BMI) has been implicated as a primary factor influencing cancer development. However, understanding the relationship between these two complex traits has been confounded by both environmental and genetic heterogeneity.

Methods

In order to gain insight into the genetic factors linking BMI and cancer, we performed chemical carcinogenesis on a genetically heterogeneous cohort of interspecific backcross mice ((Mus Spretus × FVB/N) F1 × FVB/N). Using this cohort, we performed quantitative trait loci (QTL) analysis to identify regions linked to BMI. We then performed an integrated analysis incorporating gene expression, sequence comparison between strains, and gene expression network analysis to identify candidate genes influencing both tumor development and BMI.

Results

Analysis of QTL linked to tumorigenesis and BMI identified several loci associated with both phenotypes. Exploring these loci in greater detail revealed a novel relationship between the Pannexin 3 gene (Panx3) and both BMI and tumorigenesis. Panx3 is positively associated with BMI and is strongly tied to a lipid metabolism gene expression network. Pre-treatment Panx3 gene expression levels in normal skin are associated with tumor susceptibility and inhibition of Panx function strongly influences inflammation.

Conclusions

These studies have identified several genetic loci that influence both BMI and carcinogenesis and implicate Panx3 as a candidate gene that links these phenotypes through its effects on inflammation and lipid metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0334-8) contains supplementary material, which is available to authorized users.

Genetic dissection of susceptibility genes for diabetes and related phenotypes on mouse chromosome 14 by means of congenic strains

Background

A susceptibility locus, Nidd2n, for type 2 diabetes has been mapped to mouse chromosome 14 (Chr 14) and confirmed using the consomic strain (C3H-Chr 14^NSY) of the Nagoya-Shibata-Yasuda (NSY) mouse, an animal model of spontaneous type 2 diabetes. The aim of this study was to localize and characterize Nidd2n.

Results

We constructed two novel congenic strains homozygous for different segments of NSY-Chr 14 on the control C3H/HeNcrj (C3H) background: R1 (C3H.NSY-(D14Mit206-D14Mit5)) possesses the proximal and middle segment, and R2 (C3H.NSY-(D14Mit206-D14Mit186)) possesses the most proximal segment of NSY-Chr 14. Diabetes-related phenotypes were studied in comparison with those of consomic C3H-Chr 14^NSY (R0) and parental NSY and C3H strains. Congenic R1 and R2 showed significantly higher post-challenge glucose than that in C3H mice. Fasting glucose, in contrast, was significantly lower in R1 and R2 than in C3H mice. Insulin sensitivity was significantly impaired in R1 and R2 compared to C3H mice. R2 showed significantly higher body weight and fat-pad weight than those in C3H and R1. Leptin level was significantly higher in R0, R1 and R2 than in C3H mice, with R2 showing the highest level, similar to that in NSY mice. Serum adiponectin level was significantly lower in R0, R1 and R2 than in C3H mice, while it was significantly higher in NSY than in C3H mice.

Conclusions

These data indicate that Chr 14 harbors multiple genes for diabetes-related phenotypes. The original Nidd2n, which is located in the middle region of Chr 14, was divided into two segments; Nidd2.1n in proximal Chr 14 and Nidd2.2n in distal Chr 14. Nidd2.1n contributes to post-challenge hyperglycemia, insulin resistance and adiposity. Nidd2.2n contributes to fasting as well as post-challenge hyperglycemia and insulin resistance. Adp1n, which contributes to decreased adiposity and increased insulin sensitivity, rather than a diabetogenic gene, was mapped in the middle segment.

Selecting the "right" mouse model for metabolic syndrome and type 2 diabetes research

This is not a "Methods" chapter in the traditional sense. Rather, it is an essay designed to help address one of the most frequently asked questions by investigators about to embark on a study requiring an animal model of diabetes - what is the "right" model for the reader's specific research application. Because genetic heterogeneity and the requirement for complex gene-environment interaction characterize the various mouse models of Type 2 diabetes as well as the human disease manifestations, the readers may come to share the author's conclusion that more than one model is required if the investigator is interested in knowing how broadly effective a given compound with putative therapeutic efficacy might be.

Quantitative trait loci for impaired glucose tolerance in nondiabetic SM/J and A/J mice

The SMXA-5 recombinant inbred strain, which was established from nondiabetic parental SM/J and A/J mice, develops diabetic phenotypes such as impaired glucose tolerance. The combination of diabetogenic genes in the SM/J and A/J genomes impairs glucose tolerance in SMXA-5 mice. Using (SM/J x SMXA-5)F2 mice fed a high-fat diet, we previously detected a diabetogenic locus, T2dm2sa, on chromosome (Chr) 2. The A/J allele at this locus is diabetogenic. The SM.A-T2dm2sa congenic mouse, in which the Chr 2 region of A/J including T2dm2sa was introgressed into SM/J, showed obviously impaired glucose tolerance. These results indicate that SM.A-T2dm2sa mice develop diabetogenic traits due to T2dm2sa with the A/J allele and unknown diabetogenic loci with the SM/J allele. The aim of this study was to dissect these unknown loci, using quantitative trait locus (QTL) analysis in the (A/J x SM.A-T2dm2sa) F2 intercross fed a high-fat diet. The results revealed a highly significant QTL, T2dm4sa, for glucose tolerance on Chr 6 and a significant QTL, T2dm5sa, for glucose tolerance on Chr 11. These loci with the SM/J allele were diabetogenic. The diabetogenic effect of T2dm4sa or T2dm5sa was verified by the impairment of glucose tolerance in the A/J-6(SM) or A/J-11(SM) consomic strain, in which Chr 6 or Chr 11 of SM/J is introgressed into A/J, respectively. These results demonstrate that diabetogenic loci exist in the genomes of nondiabetic A/J and SM/J mice and suggest that T2dm2sa with the A/J allele and T2dm4sa and/or T2dm5sa with the SM/J allele elicit impaired glucose tolerance in SM.A-T2dm2sa mice.

A meta-analysis of QTL for diabetes-related traits in rodents

Crossbreeding studies in rodents have identified numerous quantitative trait loci (QTL) that are linked to diabetes-related component traits. To identify genetic consensus regions implicated in insulin action and glucose homeostasis, we have performed a meta-analysis of genomewide linkage scans for diabetes-related traits. From a total of 43 published genomewide scans we assembled a nonredundant collection of 153 QTL for glucose levels, insulin levels, and glucose tolerance. Collectively, these studies include data from 48 different parental strains and >11,000 individual animals. The results of the studies were analyzed by the truncated product method (TPM). The analysis revealed significant evidence for linkage of glucose levels, insulin levels, and glucose tolerance to 27 different segments of the mouse genome. The most prominent consensus regions [localized to chromosomes 2, 4, 7, 9, 11, 13, and 19; logarithm of odds (LOD) scores 10.5-17.4] cover approximately 11% of the mouse genome and collectively contain the peak markers for 47 QTL. Approximately half of these genomic segments also show significant linkage to body weight and adiposity, indicating the presence of multiple obesity-dependent and -independent consensus regions for diabetes-related traits. At least 84 human genetic markers from genomewide scans and >80 candidate genes from human and rodent studies map into the mouse consensus regions for diabetes-related traits, indicating a substantial overlap between the species. Our results provide guidance for the identification of novel candidate genes and demonstrate the presence of numerous distinct consensus QTL regions with highly significant LOD scores that control glucose homeostasis. An interactive physical map of the QTL is available online at http://www.diabesitygenes.org.

Genetic loci affecting body weight and fatness in a C57BL/6J x PWK/PhJ mouse intercross

To determine the genetic variation that contributes to body composition in the mouse, we interbred a wild-derived strain (PWK/PhJ; PWK) with a common laboratory strain (C57BL/6J; B6). The parental, F(1), and F(2) mice were phenotyped at 18 weeks old for body weight and composition using dual-energy X-ray absorptiometry (DEXA). A total of 479 (244 male and 235 female) F(2) mice were genotyped for 117 polymorphic markers spanning the autosomes. Twenty-eight suggestive or significant linkages for four traits (body weight, adjusted lean and fat weight, and percent fat) were detected. Of these, three QTLs were novel: one on the proximal portion of Chr 5 for body weight (Bwq8; LOD = 4.7), one on Chr 3 for lean weight (Bwtq13; LOD = 3.6), and one on Chr 11 for percent fat (Adip19; LOD = 5.8). The remaining QTLs overlapped previously identified linkages, e.g., Adip5 on Chr 9. One QTL was sex-specific (present in males only) and seven were sex-biased (more prominent in one sex than the other). Most alleles that increased body weight were contributed by the B6 strain, and most alleles that increased percent fat were contributed by the PWK strain. Eight pairs of interacting loci were identified, none of which exactly overlapped the main-effect QTLs. Many of the QTLs found in the B6 x PWK cross map to the location of previously reported linkages, suggesting that some QTLs are common to many strains (consensus QTLs), but three new QTLs appear to be particular to the PWK strain. The location and type of QTLs detected in this new cross will assist in future efforts to identify the genetic variation that determines the ratio of lean to fat weight as well as body size in mice.

Characterization of the porcine carboxypeptidase E cDNA: comparative analysis and investigation of a non-synonymous SNP

Carboxypeptidase E (CPE) is an important enzyme responsible for the proteolytic processing of prohormone intermediates. A naturally occurring point mutation, leading to an accumulation of many neuroendocrine peptides has been characterized within exon 5 of the CPE gene in mice. In the present study the sequence of the cDNA for the porcine CPE gene including all the coding region and the 3'-UTR region was generated. Comparisons with bovine, human, mouse, and rat CPE cDNA sequences showed that the coding regions of the gene are highly conserved both at the nucleotide and at the amino acid level. A very low nonsynonymous/synonymous substitution ratios between the proteins was found indicating that purifying selection is acting on the CPE gene. A nonsynonymous SNP identified at position 1272 in the transcript resulting in a codon change from TCA (Serine) to TTA (Leucine) was genotyped in the Danish pig populations. However, the mutated allele was only identified in one particular family. Exon 5 was sequenced in 45 unrelated pigs. No evidence of variation was found in this region. In accordance with previous results and in accordance with comparative mapping information the gene was accurately mapped to porcine chromosome 8 using the ImpRH panel.

The genetic landscape of type 2 diabetes in mice

Inbred mouse strains provide genetic diversity comparable to that of the human population. Like humans, mice have a wide range of diabetes-related phenotypes. The inbred mouse strains differ in the response of their critical physiological functions, such as insulin sensitivity, insulin secretion, beta-cell proliferation and survival, and fuel partitioning, to diet and obesity. Most of the critical genes underlying these differences have not been identified, although many loci have been mapped. The dramatic improvements in genomic and bioinformatics resources are accelerating the pace of gene discovery. This review describes how mouse genetics can be used to discover diabetes-related genes, summarizes how the mouse strains differ in their diabetes-related phenotypes, and describes several examples of how loci identified in the mouse may directly relate to human diabetes.

A strategy to search for common obesity and type 2 diabetes genes

Worldwide, the incidence of type 2 diabetes is rising rapidly, mainly because of the increase in the incidence of obesity, which is an important risk factor for this condition. Both obesity and type 2 diabetes are complex genetic traits but they also share some nongenetic risk factors. Hence, it is tempting to speculate that the susceptibility to type 2 diabetes and obesity might also partly be due to shared genes. By comparing all of the published genome scans for type 2 diabetes and obesity, five overlapping chromosomal regions for both diseases (encompassing 612 candidate genes) have been identified. By analysing these five susceptibility loci for type 2 diabetes and obesity, using six freely available bioinformatics tools for disease gene identification, 27 functional candidate genes have been pinpointed that are involved in eating behaviour, metabolism and inflammation. These genes might reveal a molecular link between the two disorders.

The genetics of health

Recent experience with several high-profile drugs demonstrates the great challenges in developing effective and safe therapeutics. A complementary approach to the popular paradigm of disease genetics is based on inherited factors that reduce the incidence and severity of disease among individuals who are genetically predisposed to disease. We propose testing specifically for modifier genes and protective alleles among at-risk individuals and studying the efficacy of therapeutics based on the genetics of health.

The human obesity gene map: the 2005 update

This paper presents the 12th update of the human obesity gene map, which incorporates published results up to the end of October 2005. Evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, transgenic and knockout murine models relevant to obesity, quantitative trait loci (QTL) from animal cross-breeding experiments, association studies with candidate genes, and linkages from genome scans is reviewed. As of October 2005, 176 human obesity cases due to single-gene mutations in 11 different genes have been reported, 50 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. There are 244 genes that, when mutated or expressed as transgenes in the mouse, result in phenotypes that affect body weight and adiposity. The number of QTLs reported from animal models currently reaches 408. The number of human obesity QTLs derived from genome scans continues to grow, and we now have 253 QTLs for obesity-related phenotypes from 61 genome-wide scans. A total of 52 genomic regions harbor QTLs supported by two or more studies. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has also increased considerably, with 426 findings of positive associations with 127 candidate genes. A promising observation is that 22 genes are each supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. The electronic version of the map with links to useful publications and relevant sites can be found at http://obesitygene.pbrc.edu.