Quantitative trait loci associated with blood pressure of metabolic syndrome in the progeny of NZO/HILtJxC3H/HeJ intercrosses

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Functional promoter polymorphisms direct the expression of cystathionine gamma-lyase gene in mouse models of essential hypertension

Despite the well-known role of cystathionine γ-lyase (Cth) in cardiovascular pathophysiology, transcriptional regulation of Cth remains incompletely understood. Sequencing of the Cth promoter region in mouse models of genetic/essential hypertension (viz. Blood Pressure High [BPH], Blood Pressure Low [BPL] and Blood Pressure Normal [BPN] mice) identified several genetic variations. Transient transfections of BPH/BPL-Cth promoter-reporter plasmids into various cell types revealed higher promoter activity of BPL-Cth than that of BPH-Cth. Corroboratively, endogenous Cth mRNA levels in kidney and liver tissues were also elevated in BPL mice. Computational analysis of the polymorphic Cth promoter region predicted differential binding affinity of c-Rel, HOXA3 and IRF1 with BPL/BPH-Cth promoter domains. Over-expression of c-Rel/HOXA3/IRF1 modulated BPL/BPH-Cth promoter activities in a consistent manner. Gel shift assays using BPH/BPL-Cth-promoter oligonucleotides with/without binding sites for c-Rel/HOXA3/IRF1 displayed formation of specific complexes with c-Rel/HOXA3/IRF1; addition of antibodies to reaction mixtures resulted in supershifts/inhibition of Cth promoter-transcription factor complexes. Furthermore, chromatin immunoprecipitation (ChIP) assays proved differential binding of c-Rel, HOXA3 and IRF1 with the polymorphic promoter region of BPL/BPH-Cth. Tumor necrosis factor-α (TNF-α) reduced the activities of BPL/BPH-Cth promoters to different extents that were further declined by ectopic expression of IRF1; on the other hand, siRNA-mediated down-regulation of IRF1 rescued the TNF-α-mediated suppression of the BPL/BPH-Cth promoter activities. In corroboration, ChIP analysis revealed enhanced binding of IRF1 with BPH/BPL-Cth promoter following TNF-α treatment. BPL/BPH-Cth promoter activity was diminished upon exposure of hepatocytes and cardiomyoblasts to ischemia-like pathological condition due to reduced binding of c-Rel with BPL/BPH-Cth-promoter. Taken together, this study reveals the molecular basis for the differential expression of Cth in mouse models of essential hypertension under basal and pathophysiological conditions.

Architecture of energy balance traits in emerging lines of the Collaborative Cross

The potential utility of the Collaborative Cross (CC) mouse resource was evaluated to better understand complex traits related to energy balance. A primary focus was to examine if genetic diversity in emerging CC lines (pre-CC) would translate into equivalent phenotypic diversity. Second, we mapped quantitative trait loci (QTL) for 15 metabolism- and exercise-related phenotypes in this population. We evaluated metabolic and voluntary exercise traits in 176 pre-CC lines, revealing phenotypic variation often exceeding that seen across the eight founder strains from which the pre-CC was derived. Many phenotypic correlations existing within the founder strains were no longer significant in the pre-CC population, potentially representing reduced linkage disequilibrium (LD) of regions harboring multiple genes with effects on energy balance or disruption of genetic structure of extant inbred strains with substantial shared ancestry. QTL mapping revealed five significant and eight suggestive QTL for body weight (Chr 4, 7.54 Mb; CI 3.32-10.34 Mb; Bwq14), body composition, wheel running (Chr 16, 33.2 Mb; CI 32.5-38.3 Mb), body weight change in response to exercise (1: Chr 6, 77.7Mb; CI 72.2-83.4 Mb and 2: Chr 6, 42.8 Mb; CI 39.4-48.1 Mb), and food intake during exercise (Chr 12, 85.1 Mb; CI 82.9-89.0 Mb). Some QTL overlapped with previously mapped QTL for similar traits, whereas other QTL appear to represent novel loci. These results suggest that the CC will be a powerful, high-precision tool for examining the genetic architecture of complex traits such as those involved in regulation of energy balance.

Quantitative trait loci that control body weight and obesity in an F2 intercross between C57BL/6J and DDD.Cg-Ay mice

I have developed a congenic mouse strain for the A(y) allele at the agouti locus in an inbred DDD/Sgn strain, DDD.Cg-A(y). DDD.Cg-A(y) females are extremely obese and significantly heavier than B6.Cg-A(y) females. The objectives of this study were to determine the genetic basis of obesity in DDD.Cg-A(y) mice, and to determine whether or not their high body weight was due to the presence of DDD background-specific modifiers. I performed quantitative trait locus (QTL) analyses for body weight and body mass index in two types of F(2) mice [F2 A(y) (F(2) mice carrying the A(y) allele) and F(2) non-A(y) (F2 mice without the A(y) allele)] produced by crossing C57BL/6J females and DDD.Cg-A(y) males. The results of the QTL analysis of F(2) A(y) mice were very similar to those obtained for F(2) non-A(y) mice. It was unlikely that the high body weight of DDD.Cg-A(y) mice was due to the presence of specific modifiers. When both F(2) datasets were merged and analyzed, four significant body weight QTLs were identified on chromosomes 6, 9, and 17 (2 loci) and four significant obesity QTLs were identified on chromosomes 1, 6, 9, and 17. Although the presence of DDD background-specific modifiers was not confirmed, a multifactorial basis of obesity in DDD.Cg-A(y) females was thus revealed.

Behavioral and genetic investigations of low exploratory behavior in Il18r1(-/-) mice: we can't always blame it on the targeted gene

The development of gene-targeting technologies has enabled research with immune system-related knockout mouse strains to advance our understanding of how cytokines and their receptors interact and influence a number of body systems, including the central nervous system (CNS). A critical issue when we are interpreting phenotypic data from these knockout strains is the potential role of genes other than the targeted one. Although many of the knockout strains have been made congenic on a C57BL/6 (B6) genetic background, there remains a certain amount of genetic material from the129 substrain that was used in the development of these strains. This genetic material could result in phenotypes incorrectly attributed to the targeted gene. We recently reported low-activity behavior in Il10(-/-) mice that was linked to this genetic material rather than the targeted gene itself. In the current study we confirm the generalizability of those earlier findings, by assessing behavior in Il18(-/-) and Il18r1(-/-) knockout mice. We identified low activity and high anxiety-like behaviors in Il18r1(-/-) mice, whereas Il18(-/-) mice displayed little anxiety-like behavior. Although Il18r1(-/-) mice are considered a congenic strain, we have identified substantial regions of 129P2-derived genetic material not only flanking the ablated Il18r1 on Chromosome 1, but also on Chromosomes 4, 5, 8, 10, and 14. Our studies suggest that residual 129-derived gene(s), rather than the targeted Il18r1 gene, is/are responsible for the low level of activity seen in the Il18r1(-/-) mice. Mapping studies are necessary to identify the gene or genes contributing to the low-activity phenotype.

Metabolic syndrome components in murine models

Animal models have enriched understanding of the physiological basis of metabolic disorders and advanced identification of genetic risk factors underlying the metabolic syndrome (MetS). Murine models are especially appropriate for this type of research, and are an excellent resource not only for identifying candidate genomic regions, but also for illuminating the possible molecular mechanisms or pathways affected in individual components of MetS. In this review, we briefly discuss findings from mouse models of metabolic disorders, particularly in light of issues raised by the recent flood of human genome-wide association studies (GWAS) results. We describe how mouse models are revealing that genotype interacts with environment in important ways, indicating that the underlying genetics of MetS is highly context dependant. Further we show that epistasis, imprinting and maternal effects each contribute to the genetic architecture underlying variation in metabolic traits, and mouse models provide an opportunity to dissect these aspects of the genetic architecture that are difficult if not impossible to ascertain in humans. Finally we discuss how knowledge gained from mouse models can be used in conjunction with comparative genomic methods and bioinformatic resources to inform human MetS research.

Genetic analysis of blood pressure in 8 mouse intercross populations

The genetic basis of hypertension is well established, yet very few genes that cause common forms of hypertension are known. Quantitative trait locus (QTL) analyses in rodent models can guide the search for human hypertension genes, but the excellent genetic resources for mice have been underused in this regard. To address this issue, we surveyed blood pressure variation in mice from 37 inbred strains and generated 2577 mice in 8 intercross populations to perform QTL analyses of blood pressure. We identified 14 blood pressure QTL in these populations, including > or =7 regions of the mouse genome not linked previously to blood pressure. Many QTL were detected in multiple crosses, either within our study or in studies published previously, which facilitates the use of bioinformatics methods to narrow the QTL and focus the search for candidate genes. The regions of the human genome that correspond to all but 1 of the 14 blood pressure QTL in mice are linked to blood pressure in humans, suggesting that these regions contain causal genes with a conserved role in blood pressure control. These results greatly expand our knowledge of the genomic regions underlying blood pressure regulation in mice and support future studies to identify the causal genes within these QTL intervals.

Identification of multiple quantitative trait loci affecting the size and shape of the mandible in mice

A quantitative trait locus (QTL) analysis was performed on the size and shape of the mandible in F(2) mice between KK-A ( y ) and C57BL/6 J strains and the effect of the A ( y ) allele on the morphology of the mandible was analyzed. A total of 13 measurements were taken on each right mandible. By means of discriminant and canonical discriminant analyses, KK-A ( y ) males and KK males were exactly discriminated from each other. In contrast to its known effects on body weight, the A ( y ) allele reduced the overall size of the mandible. QTL analysis of the 13 measurements and on three principal components extracted from these measurements identified multiple QTLs. When F(2) a/a and F(2) A ( y )/a were analyzed separately, 11 significant main-effect QTLs were identified in F(2) a/a, whereas only two QTLs were identified in F(2) A ( y )/a. Although four significant interactions were identified, all were in F(2) a/a. The A ( y ) allele thus made the difference in the size and shape of the mandible between strains obscure. Among mandible QTLs, those on Chrs 5 (Mssq6 and Mssq7) and 15 (Mssq14) were important. Mssq6 had an effect on the height of the posterior mandible. Mssq7 had an effect on mandible length. Mssq14 had an effect on the height of the anterior and posterior mandible. Mssq7 and Mssq14 also had an effect on the overall size. Thus, mandible QTLs have distinct and characteristic sites of action. Therefore, mandible morphology will be determined largely by the combination of these QTLs.

Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies

BACKGROUND

Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers provide opportunities to detect epistatic SNPs associated with quantitative traits and to detect the exact mode of an epistasis effect. Computational difficulty is the main bottleneck for epistasis testing in large scale GWAS.

RESULTS

The EPISNPmpi and EPISNP computer programs were developed for testing single-locus and epistatic SNP effects on quantitative traits in GWAS, including tests of three single-locus effects for each SNP (SNP genotypic effect, additive and dominance effects) and five epistasis effects for each pair of SNPs (two-locus interaction, additive x additive, additive x dominance, dominance x additive, and dominance x dominance) based on the extended Kempthorne model. EPISNPmpi is the parallel computing program for epistasis testing in large scale GWAS and achieved excellent scalability for large scale analysis and portability for various parallel computing platforms. EPISNP is the serial computing program based on the EPISNPmpi code for epistasis testing in small scale GWAS using commonly available operating systems and computer hardware. Three serial computing utility programs were developed for graphical viewing of test results and epistasis networks, and for estimating CPU time and disk space requirements.

CONCLUSION

The EPISNPmpi parallel computing program provides an effective computing tool for epistasis testing in large scale GWAS, and the epiSNP serial computing programs are convenient tools for epistasis analysis in small scale GWAS using commonly available computer hardware.

Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling

Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptin^ob/ob and the diabetes-susceptible BTBR leptin^ob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.

Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identifying individual genes and their potential roles in molecular pathways leading to disease remains a challenge. In this study, we include transcriptional and metabolic profiling in genomic analyses to address this limitation. We investigated an F2 intercross between the diabetes-resistant C57BL/6 leptin^ob/ob and the diabetes-susceptible BTBR leptin^ob/ob mouse strains that segregates for genotype and diabetes-related physiological traits; blood glucose, plasma insulin and body weight. Our study shows that liver metabolites (comprised of amino acids, organic acids, and acyl-carnitines) map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal, testable networks for control of specific metabolic processes in liver. We apply an in vitro study to confirm the validity of this integrative method, and thus provide a novel approach to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.