The blood proteome in disease: Understanding the function of human blood serum proteins in disease has been limited by difficulties in monitoring their production, accumulation, and distribution. Emilsson et al. investigated human serum proteins of more than 5000 Icelanders over the age of 65. The composition of blood serum includes a complex regulatory network of proteins that are globally coordinated across most or all tissues. The authors identified modules and functional groups associated with disease and health outcomes and were able to link genetic variants to complex diseases. Science , this issue p. 769

Recent studies implicate a strong association between elevated plasma branched-chain amino acids (BCAAs) and insulin resistance (IR). However, a causal relationship and whether interrupted BCAA homeostasis can serve as a therapeutic target for diabetes remain to be established experimentally. In this study, unbiased integrative pathway analyses identified a unique genetic link between obesity-associated IR and BCAA catabolic gene expression at the pathway level in human and mouse populations. In genetically obese (ob/ob) mice, rate-limiting branched-chain α-keto acid (BCKA) dehydrogenase deficiency (i.e., BCAA and BCKA accumulation), a metabolic feature, accompanied the systemic suppression of BCAA catabolic genes. Restoring BCAA catabolic flux with a pharmacological inhibitor of BCKA dehydrogenase kinase (BCKDK) ( a suppressor of BCKA dehydrogenase) reduced the abundance of BCAA and BCKA and markedly attenuated IR in ob/ob mice. Similar outcomes were achieved by reducing protein (and thus BCAA) intake, whereas increasing BCAA intake did the opposite; this corroborates the pathogenic roles of BCAAs and BCKAs in IR in ob/ob mice. Like BCAAs, BCKAs also suppressed insulin signaling via activation of mammalian target of rapamycin complex 1. Finally, the small-molecule BCKDK inhibitor significantly attenuated IR in high-fat diet–induced obese mice. Collectively, these data demonstrate a pivotal causal role of a BCAA catabolic defect and elevated abundance of BCAAs and BCKAs in obesity-associated IR and provide proof-of-concept evidence for the therapeutic validity of manipulating BCAA metabolism for treating diabetes.

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions. Sudden death due to heart attack ranks among the top causes of death in the world, and family studies have shown that genetics has a substantial effect on heart disease risk. Recent studies suggest that multiple genetic factors each with modest effects are necessary for the development of CAD, but the genes and molecular processes involved remain poorly understood. We conducted an integrative genomics study where we used the information of gene-gene interactions to capture groups of genes that are most likely to increase heart disease risk. We not only confirmed the importance of several known CAD risk processes such as the metabolism and transport of cholesterol, immune response, and blood coagulation, but also revealed many novel processes such as neuroprotection, cell cycle, and proteolysis that were not previously implicated in CAD. In particular, we highlight several genes such as GLO1 with key regulatory roles within these processes not detected by the first wave of genetic analyses. These results highlight the value of integrating population genetic data with diverse resources that functionally annotate the human genome. Such integration facilitates the identification of novel molecular processes involved in the pathogenesis of CAD as well as potential novel targets for the development of efficacious therapeutic interventions.

The complex neuropathology of traumatic brain injury (TBI) is difficult to dissect, given the convoluted cytoarchitecture of affected brain regions such as the hippocampus. Hippocampal dysfunction during TBI results in cognitive decline that may escalate to other neurological disorders, the molecular basis of which is hidden in the genomic programs of individual cells. Using the unbiased single cell sequencing method Drop-seq, we report that concussive TBI affects previously undefined cell populations, in addition to classical hippocampal cell types. TBI also impacts cell type-specific genes and pathways and alters gene co-expression across cell types, suggesting hidden pathogenic mechanisms and therapeutic target pathways. Modulating the thyroid hormone pathway as informed by the T4 transporter transthyretin Ttr mitigates TBI-associated genomic and behavioral abnormalities. Thus, single cell genomics provides unique information about how TBI impacts diverse hippocampal cell types, adding new insights into the pathogenic pathways amenable to therapeutics in TBI and related disorders.

Objective—: Genetic approaches have identified numerous loci associated with coronary heart disease (CHD). The molecular mechanisms underlying CHD gene–disease associations, however, remain unclear. We hypothesized that genetic variants with both strong and subtle effects drive gene subnetworks that in turn affect CHD. Approach and Results—: We surveyed CHD-associated molecular interactions by constructing coexpression networks using whole blood gene expression profiles from 188 CHD cases and 188 age- and sex-matched controls. Twenty-four coexpression modules were identified, including 1 case-specific and 1 control-specific differential module (DM). The DMs were enriched for genes involved in B-cell activation, immune response, and ion transport. By integrating the DMs with gene expression–associated single-nucleotide polymorphisms and with results of genome-wide association studies of CHD and its risk factors, the control-specific DM was implicated as CHD causal based on its significant enrichment for both CHD and lipid expression–associated single-nucleotide polymorphisms. This causal DM was further integrated with tissue-specific Bayesian networks and protein–protein interaction networks to identify regulatory key driver genes. Multitissue key drivers ( SPIB and TNFRSF13C ) and tissue-specific key drivers (eg, EBF1 ) were identified. Conclusions—: Our network-driven integrative analysis not only identified CHD-related genes, but also defined network structure that sheds light on the molecular interactions of genes associated with CHD risk.