The metabolically connected triad of obesity, diabetes, and cardiovascular diseases is a major public health threat, and is expected to worsen due to the global shift toward energy-rich and sedentary living. Despite decades of intense research, a large part of the molecular pathogenesis behind complex metabolic diseases remains unknown. Recent advances in genetics, epigenomics, transcriptomics, proteomics and metabolomics enable us to obtain large-scale snapshots of the etiological processes in multiple disease-related cells, tissues and organs. These datasets provide us with an opportunity to go beyond conventional reductionist approaches and to pinpoint the specific perturbations in critical biological processes. In this review, we summarize systems biology methodologies such as functional genomics, causality inference, data-driven biological network construction, and higher-level integrative analyses that can produce novel mechanistic insights, identify disease biomarkers, and uncover potential therapeutic targets from a combination of omics datasets. Importantly, we also demonstrate the power of these approaches by application examples in obesity, diabetes, and cardiovascular diseases.

AbstractScopeWe explored the influence of DHA on cardiometabolic and cognitive phenotypes, and multiomic alterations in the brain under two metabolic conditions to understand context-specific nutritional effects.Methods and ResultsRats were randomly assigned to a DHA-rich or a control chow diet while drinking water or high fructose solution, followed by profiling of metabolic and cognitive phenotypes and the transcriptome and DNA methylome of the hypothalamus and hippocampus. DHA reduced serum triglyceride and improved insulin resistance and memory exclusively in the fructose-consuming rats. In hippocampus, DHA affected genes related to synapse functions in the chow group but immune functions in the fructose group; in hypothalamus, DHA altered immune pathways in the chow group but metabolic pathways in the fructose group. Network modeling revealed context-specific regulators of DHA effects, includingKlf4andDusp1for chow condition andLum, Fn1, andCol1a1for fructose condition in hippocampus, as well asCyr61, JunB, Ier2, andPitx2under chow condition andHcar1, Cdh1, andOsr1under fructose condition in hypothalamus.ConclusionDHA exhibits differential influence on epigenetic loci, genes, pathways, and metabolic and cognitive phenotypes under different dietary contexts, supporting population stratification in DHA studies to achieve precision nutrition.

Dietary fructose is a major contributor to the epidemic of diabetes and obesity, and it is an excellent model to study metabolic syndrome. Based on previous studies that Bgn gene occupies a central position in a network of genes in the brain in response to fructose consumption, we assessed the capacity of Bgn to modulate the action of fructose on brain and body. We exposed male biglycan knockout mice (Bgn^0/-) to fructose for 7 weeks, and results showed that Bgn^0/- mice compensated for a decrement in learning and memory performance when exposed to fructose. These results were consistent with an attenuation of the action of fructose on hippocampal CREB levels. Fructose also reduced the levels of CREB and BDNF in primary hippocampal neuronal cultures. Bgn siRNA treatment abolished these effects of fructose on CREB and BDNF levels, in conjunction with a reduction in a fructose-related increase in Bgn protein. In addition, fructose consumption perturbed the systemic metabolism of glucose and lipids, that were also altered in the Bgn^0/ mice. Transcriptomic profiling of hypothalamus, hippocampus, and liver supported the regulatory action of Bgn on key molecular pathways involved in metabolism, immune response, and neuronal plasticity. Overall results underscore the tissue-specific role of the extracellular matrix in the regulation of metabolism and brain function, and support Bgn as a key modulator for the impact of fructose across body and brain.

Genetic mapping of complex diseases to date depends on variations inside or close to the genes that perturb their activities. A strong body of evidence suggests that changes in gene expression play a key role in complex diseases and that numerous loci perturb gene expression in trans. The information in trans variants, however, has largely been ignored in the current analysis paradigm. Here we present a statistical framework for genetic mapping by utilizing collective information in both cis and trans variants. We reason that for a disease-associated gene, any genetic variation that perturbs its expression is also likely to influence the disease risk. Thus, the expression quantitative trait loci (eQTL) of the gene, which constitute a unique "genetic signature," should overlap significantly with the set of loci associated with the disease. We translate this idea into a computational algorithm (named Sherlock) to search for gene-disease associations from GWASs, taking advantage of independent eQTL data. Application of this strategy to Crohn disease and type 2 diabetes predicts a number of genes with possible disease roles, including several predictions supported by solid experimental evidence. Importantly, predicted genes are often implicated by multiple trans eQTL with moderate associations. These genes are far from any GWAS association signals and thus cannot be identified from the GWAS alone. Our approach allows analysis of association data from a new perspective and is applicable to any complex phenotype. It is readily generalizable to molecular traits other than gene expression, such as metabolites, noncoding RNAs, and epigenetic modifications.

Dietary fructose is a major contributor to the epidemic of diabetes and obesity, and it is an excellent model to study metabolic syndrome. Based on previous studies that Bgn gene occupies a central position in a network of genes in the brain in response to fructose consumption, we assessed the capacity of Bgn to modulate the action of fructose on brain and body. We exposed male biglycan knockout mice (Bgn^0/-) to fructose for 7 weeks, and results showed that Bgn^0/- mice compensated for a decrement in learning and memory performance when exposed to fructose. These results were consistent with an attenuation of the action of fructose on hippocampal CREB levels. Fructose also reduced the levels of CREB and BDNF in primary hippocampal neuronal cultures. Bgn siRNA treatment abolished these effects of fructose on CREB and BDNF levels, in conjunction with a reduction in a fructose-related increase in Bgn protein. In addition, fructose consumption perturbed the systemic metabolism of glucose and lipids, that were also altered in the Bgn^0/ mice. Transcriptomic profiling of hypothalamus, hippocampus, and liver supported the regulatory action of Bgn on key molecular pathways involved in metabolism, immune response, and neuronal plasticity. Overall results underscore the tissue-specific role of the extracellular matrix in the regulation of metabolism and brain function, and support Bgn as a key modulator for the impact of fructose across body and brain.

Scope

The influence of docosahexaenoic acid (DHA) on cardiometabolic and cognitive phenotypes, and multi-omic alterations in the brain under two metabolic conditions is explored to understand context-specific nutritional effects.

Methods and results

Rats are randomly assigned to a DHA-rich or a control chow diet while drinking water or high fructose solution, followed by profiling of metabolic and cognitive phenotypes and the transcriptome and DNA methylome of the hypothalamus and hippocampus. DHA reduces serum triglyceride and improves insulin resistance and memory exclusively in the fructose-consuming rats. In hippocampus, DHA affects genes related to synapse functions in the chow group but immune functions in the fructose group; in hypothalamus, DHA alters immune pathways in the chow group but metabolic pathways in the fructose group. Network modeling reveals context-specific regulators of DHA effects, including Klf4 and Dusp1 for chow condition and Lum, Fn1, and Col1a1 for fructose condition in hippocampus, as well as Cyr61, JunB, Ier2, and Pitx2 under chow condition and Hcar1, Cdh1, and Osr1 under fructose condition in hypothalamus.

Conclusion

DHA exhibits differential influence on epigenetic loci, genes, pathways, and metabolic and cognitive phenotypes under different dietary contexts, supporting population stratification in DHA studies to achieve precision nutrition.

Objective

Recent genome-wide association studies of coronary artery disease (CAD) have revealed 58 genome-wide significant and 148 suggestive genetic loci. However, the molecular mechanisms through which they contribute to CAD and the clinical implications of these findings remain largely unknown. We aim to retrieve gene subnetworks of the 206 CAD loci and identify and prioritize candidate regulators to better understand the biological mechanisms underlying the genetic associations.

Approach and results

We devised a new integrative genomics approach that incorporated (1) candidate genes from the top CAD loci, (2) the complete genetic association results from the 1000 genomes-based CAD genome-wide association studies from the Coronary Artery Disease Genome Wide Replication and Meta-Analysis Plus the Coronary Artery Disease consortium, (3) tissue-specific gene regulatory networks that depict the potential relationship and interactions between genes, and (4) tissue-specific gene expression patterns between CAD patients and controls. The networks and top-ranked regulators according to these data-driven criteria were further queried against literature, experimental evidence, and drug information to evaluate their disease relevance and potential as drug targets. Our analysis uncovered several potential novel regulators of CAD such as LUM and STAT3, which possess properties suitable as drug targets. We also revealed molecular relations and potential mechanisms through which the top CAD loci operate. Furthermore, we found that multiple CAD-relevant biological processes such as extracellular matrix, inflammatory and immune pathways, complement and coagulation cascades, and lipid metabolism interact in the CAD networks.

Conclusions

Our data-driven integrative genomics framework unraveled tissue-specific relations among the candidate genes of the CAD genome-wide association studies loci and prioritized novel network regulatory genes orchestrating biological processes relevant to CAD.

The complexity of the traumatic brain injury (TBI) pathology, particularly concussive injury, is a serious obstacle for diagnosis, treatment, and long-term prognosis. Here we utilize modern systems biology in a rodent model of concussive injury to gain a thorough view of the impact of TBI on fundamental aspects of gene regulation, which have the potential to drive or alter the course of the TBI pathology. TBI perturbed epigenomic programming, transcriptional activities (expression level and alternative splicing), and the organization of genes in networks centered around genes such as Anax2, Ogn, and Fmod. Transcriptomic signatures in the hippocampus are involved in neuronal signaling, metabolism, inflammation, and blood function, and they overlap with those in leukocytes from peripheral blood. The homology between genomic signatures from blood and brain elicited by TBI provides proof of concept information for development of biomarkers of TBI based on composite genomic patterns. By intersecting with human genome-wide association studies, many TBI signature genes and network regulators identified in our rodent model were causally associated with brain disorders with relevant link to TBI. The overall results show that concussive brain injury reprograms genes which could lead to predisposition to neurological and psychiatric disorders, and that genomic information from peripheral leukocytes has the potential to predict TBI pathogenesis in the brain.

Background

The pig, which shares greater similarities with human than with mouse, is important for agriculture and for studying human diseases. However, similarities in the genetic architecture and molecular regulations underlying phenotypic variations in humans and swine have not been systematically assessed.

Results

We systematically surveyed ~500 F2 pigs genetically and phenotypically. By comparing candidates for anemia traits identified in swine genome-wide SNP association and human genome-wide association studies (GWAS), we showed that both sets of candidates are related to the biological process "cellular lipid metabolism" in liver. Human height is a complex heritable trait; by integrating genome-wide SNP data and human adipose Bayesian causal network, which closely represents bone transcriptional regulations, we identified PLAG1 as a causal gene for limb bone length. This finding is consistent with GWAS findings for human height and supports the common genetic architecture between swine and humans. By leveraging a human protein-protein interaction network, we identified two putative candidate causal genes TGFB3 and DAB2IP and the known regulators MESP1 and MESP2 as responsible for the variation in rib number and identified the potential underlying molecular mechanisms. In mice, knockout of Tgfb3 and Tgfb2 together decreases rib number.

Conclusion

Our findings show that integrative network analyses reveal causal regulators underlying the genetic association of complex traits in swine and that these causal regulators have similar effects in humans. Thus, swine are a potentially good animal model for studying some complex human traits that are not under intense selection.

Background

Although cardiovascular disease (CVD) and type 2 diabetes mellitus (T2D) share many common risk factors, potential molecular mechanisms that may also be shared for these 2 disorders remain unknown.

Methods and results

Using an integrative pathway and network analysis, we performed genome-wide association studies in 8155 blacks, 3494 Hispanic American, and 3697 Caucasian American women who participated in the national Women's Health Initiative single-nucleotide polymorphism (SNP) Health Association Resource and the Genomics and Randomized Trials Network. Eight top pathways and gene networks related to cardiomyopathy, calcium signaling, axon guidance, cell adhesion, and extracellular matrix seemed to be commonly shared between CVD and T2D across all 3 ethnic groups. We also identified ethnicity-specific pathways, such as cell cycle (specific for Hispanic American and Caucasian American) and tight junction (CVD and combined CVD and T2D in Hispanic American). In network analysis of gene-gene or protein-protein interactions, we identified key drivers that included COL1A1, COL3A1, and ELN in the shared pathways for both CVD and T2D. These key driver genes were cross-validated in multiple mouse models of diabetes mellitus and atherosclerosis.

Conclusions

Our integrative analysis of American women of 3 ethnicities identified multiple shared biological pathways and key regulatory genes for the development of CVD and T2D. These prospective findings also support the notion that ethnicity-specific susceptibility genes and process are involved in the pathogenesis of CVD and T2D.