Genetic network identifies novel pathways contributing to atherosclerosis susceptibility in the innominate artery

Abstract Background Atherosclerosis, the underlying cause of cardiovascular disease, results from both genetic and environmental factors. Methods In the current study we take a systems-based approach using weighted gene co-expression analysis to identify a candidate pathway of genes related to atherosclerosis. Bioinformatic analyses are performed to identify candidate genes and interactions and several novel genes are characterized using in-vitro studies. Results We identify 1 coexpression module associated with innominate artery atherosclerosis that is also enriched for inflammatory and macrophage gene signatures. Using a series of bioinformatics analysis, we further prioritize the genes in this pathway and identify Cd44 as a critical mediator of the atherosclerosis. We validate our predictions generated by the network analysis using Cd44 knockout mice. Conclusion These results indicate that alterations in Cd44 expression mediate inflammation through a complex transcriptional network involving a number of previously uncharacterized genes

Responsiveness of cardiometabolic-related microbiota to diet is influenced by host genetics

Intestinal microbial community structure is driven by host genetics in addition to environmental factors such as diet. In comparison with environmental influences, the effect of host genetics on intestinal microbiota, and how host-driven differences alter host metabolism is unclear. Additionally, the interaction between host genetics and diet, and the impact on the intestinal microbiome and possible down-stream effect on host metabolism is not fully understood, but represents another aspects of inter-individual variation in disease risk. The objectives of this study were to investigate how diet and genetic background shape microbial communities, and how these diet- and genetic-driven microbial differences relate to cardiometabolic phenotypes. To determine these effects, we used the 8 progenitor strains of the collaborative cross/diversity outbred mapping panels (C57BL/6J, A/J, NOD/ShiLtJ, NZO/HILtJ, WSB/EiJ, CAST/EiJ, PWK/PhJ, and 129S1/SvImJ). 16s rRNA profiling of enteric microbial communities in addition to the assessment of phenotypes central to cardiometabolic health was conducted under baseline nutritional conditions and in response to diets varying in atherogenic nutrient (fat, cholesterol, cholic acid) composition. These studies revealed strain-driven differences in enteric microbial communities which were retained with dietary intervention. Diet–strain interactions were seen for a core group of cardiometabolic-related microbial taxa. In conclusion, these studies highlight diet and genetically regulated cardiometabolic-related microbial taxa. Furthermore, we demonstrate the progenitor model is useful for nutrigenomic-based studies and screens seeking to investigate the interaction between genetic background and the phenotypic and microbial response to diet.Electronic supplementary materialThe online version of this article (doi:10.1007/s00335-014-9540-0) contains supplementary material, which is available to authorized users

Vegetation Cover Analysis of Hazardous Waste Sites in Utah and Arizona Using Hyperspectral Remote Sensing

This study investigated the usability of hyperspectral remote sensing for characterizing vegetation at hazardous waste sites. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using three different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. HyMap airborne data (126 bands at 2.3 x 2.3 m spatial resolution), collected over the U. S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona, were used. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. Regression trees resulted in the best calibration performance of LAI estimation (R-2 > 0.80. The use of REPs failed to accurately predict LAI (R-2 < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (<1 m) found on the sites.open111

Genetic network identifies novel pathways contributing to atherosclerosis susceptibility in the innominate artery

Abstract Background Atherosclerosis, the underlying cause of cardiovascular disease, results from both genetic and environmental factors. Methods In the current study we take a systems-based approach using weighted gene co-expression analysis to identify a candidate pathway of genes related to atherosclerosis. Bioinformatic analyses are performed to identify candidate genes and interactions and several novel genes are characterized using in-vitro studies. Results We identify 1 coexpression module associated with innominate artery atherosclerosis that is also enriched for inflammatory and macrophage gene signatures. Using a series of bioinformatics analysis, we further prioritize the genes in this pathway and identify Cd44 as a critical mediator of the atherosclerosis. We validate our predictions generated by the network analysis using Cd44 knockout mice. Conclusion These results indicate that alterations in Cd44 expression mediate inflammation through a complex transcriptional network involving a number of previously uncharacterized genes

Obesogenic and diabetic effects of CD44 in mice are sexually dimorphic and dependent on genetic background

IntroductionCD44 is a candidate gene for obesity and diabetes development and may be a critical mediator of a systemic inflammation associated with obesity and diabetes.MethodsWe investigated the relationship of CD44 with obesity in CD44-deficient mice challenged with a high-fat diet.ResultsIn mice fed a diet high in fat, cholesterol, and sucrose for 12&nbsp;weeks fat mass accumulation was reduced in CD44-deficient mice bred onto both a C57BL/6J and the naturally TLR deficient C3H/HeJ background. Reduced fat mass could not be attributed to lower food intake or an increase in energy expenditure as measured by indirect calorimetry. However, we observed a 40-60% lower mRNA expression of the inflammation markers, F4/80, CD11b, TNF-α, and CD14, in adipose tissue of CD44-deficient mice on the C57BL/6J background but not the C3H/HeJ background, perhaps indicating that alternative factors may be affecting adiposity in this model. Measures of hepatic steatosis and insulin sensitivity were improved in CD44-deficient mice on a C57BL/6J but not in the C3H/HeJ mice. These results were highly sexually dimorphic as there were no detectable effects of CD44 inactivation in female mice on a C57BL/6&nbsp;J or C3H/HeJ background.ConclusionCD44 was associated with adiposity, liver fat, and glucose in male mice. However, the effects of CD44 on obesity may be independent of TLR4 signaling

Altered macronutrient composition and genetics influence the complex transcriptional network associated with adiposity in the Collaborative Cross

BackgroundObesity is a serious disease with a complex etiology characterized by overaccumulation of adiposity resulting in detrimental health outcomes. Given the liver's critical role in the biological processes that attenuate adiposity accumulation, elucidating the influence of genetics and dietary patterns on hepatic gene expression is fundamental for improving methods of obesity prevention and treatment. To determine how genetics and diet impact obesity development, mice from 22 strains of the genetically diverse recombinant inbred Collaborative Cross (CC) mouse panel were challenged to either a high-protein or high-fat high-sucrose diet, followed by extensive phenotyping and analysis of hepatic gene expression.ResultsOver 1000 genes differentially expressed by perturbed dietary macronutrient composition were enriched for biological processes related to metabolic pathways. Additionally, over 9000 genes were differentially expressed by strain and enriched for biological process involved in cell adhesion and signaling. Weighted gene co-expression network analysis identified multiple gene clusters (modules) associated with body fat % whose average expression levels were influenced by both dietary macronutrient composition and genetics. Each module was enriched for distinct types of biological functions.ConclusionsGenetic background affected hepatic gene expression in the CC overall, but diet macronutrient differences also altered expression of a specific subset of genes. Changes in macronutrient composition altered gene expression related to metabolic processes, while genetic background heavily influenced a broad range of cellular functions and processes irrespective of adiposity. Understanding the individual role of macronutrient composition, genetics, and their interaction is critical to developing therapeutic strategies and policy recommendations for precision nutrition

Genetic Background Shapes Phenotypic Response to Diet for Adiposity in the Collaborative Cross

Defined as chronic excessive accumulation of adiposity, obesity results from long-term imbalance between energy intake and expenditure. The mechanisms behind how caloric imbalance occurs are complex and influenced by numerous biological and environmental factors, especially genetics, and diet. Population-based diet recommendations have had limited success partly due to the wide variation in physiological responses across individuals when they consume the same diet. Thus, it is necessary to broaden our understanding of how individual genetics and diet interact relative to the development of obesity for improving weight loss treatment. To determine how consumption of diets with different macronutrient composition alter adiposity and other obesity-related traits in a genetically diverse population, we analyzed body composition, metabolic rate, clinical blood chemistries, and circulating metabolites in 22 strains of mice from the Collaborative Cross (CC), a highly diverse recombinant inbred mouse population, before and after 8 weeks of feeding either a high protein or high fat high sucrose diet. At both baseline and post-diet, adiposity and other obesity-related traits exhibited a broad range of phenotypic variation based on CC strain; diet-induced changes in adiposity and other traits also depended largely on CC strain. In addition to estimating heritability at baseline, we also quantified the effect size of diet for each trait, which varied by trait and experimental diet. Our findings identified CC strains prone to developing obesity, demonstrate the genotypic and phenotypic diversity of the CC for studying complex traits, and highlight the importance of accounting for genetic differences when making dietary recommendations

Genetic architecture modulates diet-induced hepatic mRNA and miRNA expression profiles in Diversity Outbred mice

Genetic approaches in model organisms have consistently demonstrated that molecular traits such as gene expression are under genetic regulation, similar to clinical traits. The resulting expression quantitative trait loci (eQTL) have revolutionized our understanding of genetic regulation and identified numerous candidate genes for clinically relevant traits. More recently, these analyses have been extended to other molecular traits such as protein abundance, metabolite levels, and miRNA expression. Here, we performed global hepatic eQTL and microRNA expression quantitative trait loci (mirQTL) analysis in a population of Diversity Outbred mice fed two different diets. We identified several key features of eQTL and mirQTL, namely differences in the mode of genetic regulation (cis or trans) between mRNA and miRNA. Approximately 50% of mirQTL are regulated by a trans-acting factor, compared to ∼25% of eQTL. We note differences in the heritability of mRNA and miRNA expression and variance explained by each eQTL or mirQTL. In general, cis-acting variants affecting mRNA or miRNA expression explain more phenotypic variance than trans-acting variants. Finally, we investigated the effect of diet on the genetic architecture of eQTL and mirQTL, highlighting the critical effects of environment on both eQTL and mirQTL. Overall, these data underscore the complex genetic regulation of two well-characterized RNA classes (mRNA and miRNA) that have critical roles in the regulation of clinical traits and disease susceptibility