A mechanistic framework for cardiometabolic and coronary artery diseases.

Coronary atherosclerosis results from the delicate interplay of genetic and exogenous risk factors, principally taking place in metabolic organs and the arterial wall. Here we show that 224 gene-regulatory coexpression networks (GRNs) identified by integrating genetic and clinical data from patients with (n = 600) and without (n = 250) coronary artery disease (CAD) with RNA-seq data from seven disease-relevant tissues in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study largely capture this delicate interplay, explaining >54% of CAD heritability. Within 89 cross-tissue GRNs associated with clinical severity of CAD, 374 endocrine factors facilitated inter-organ interactions, primarily along an axis from adipose tissue to the liver (n = 152). This axis was independently replicated in genetically diverse mouse strains and by injection of recombinant forms of adipose endocrine factors (EPDR1, FCN2, FSTL3 and LBP) that markedly altered blood lipid and glucose levels in mice. Altogether, the STARNET database and the associated GRN browser (http://starnet.mssm.edu) provide a multiorgan framework for exploration of the molecular interplay between cardiometabolic disorders and CAD.

Association of Breast Cancer Screening Behaviors With Stage at Breast Cancer Diagnosis and Potential for Additive Multi-Cancer Detection via Liquid Biopsy Screening: A Claims-Based Study.

PURPOSE: To evaluate mammography uptake and subsequent breast cancer diagnoses, as well as the prospect of additive cancer detection via a liquid biopsy multi-cancer early detection (MCED) screening test during a routine preventive care exam (PCE). METHODS: Patients with incident breast cancer were identified from five years of longitudinal Blue Health Intelligence® (BHI®) claims data (2014-19) and their screening mammogram and PCE utilization were characterized. Ordinal logistic regression analyses were performed to identify the association of a biennial screening mammogram with stage at diagnosis. Additional screening opportunities for breast cancer during a PCE within two years before diagnosis were identified, and the method extrapolated to all cancers, including those without recommended screening modalities. RESULTS: Claims for biennial screening mammograms and the time from screening to diagnosis were found to be predictors of breast cancer stage at diagnosis. When compared to women who received a screening mammogram proximal to their breast cancer diagnosis (0-4 months), women who were adherent to guidelines but had a longer time window from their screening mammogram to diagnosis (4-24 months) had a 87% increased odds of a later-stage (stages III or IV) breast cancer diagnosis (p-value <0.001), while women with no biennial screening mammogram had a 155% increased odds of a later-stage breast cancer diagnosis (p-value <0.001). This highlights the importance of screening in the earlier detection of breast cancer. Of incident breast cancer cases, 23% had no evidence of a screening mammogram in the two years before diagnosis. However, 49% of these women had a PCE within that time. Thus, an additional 11% of breast cancer cases could have been screened if a MCED test had been available during a PCE. Additionally, MCED tests have the potential to target up to 58% of the top 5 cancers that are the leading causes of cancer death currently without a USPSTF recommended screening modality (prostate, pancreatic, liver, lymphoma, and ovarian cancer). CONCLUSION: The study used claims data to demonstrate the association of cancer screening with cancer stage at diagnosis and demonstrates the unmet potential for a MCED screening test which could be ordered during a PCE.

Toward a fine-scale population health monitoring system.

Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.

An integrative multiomic network model links lipid metabolism to glucose regulation in coronary artery disease.

Elevated plasma cholesterol and type 2 diabetes (T2D) are associated with coronary artery disease (CAD). Individuals treated with cholesterol-lowering statins have increased T2D risk, while individuals with hypercholesterolemia have reduced T2D risk. We explore the relationship between lipid and glucose control by constructing network models from the STARNET study with sequencing data from seven cardiometabolic tissues obtained from CAD patients during coronary artery by-pass grafting surgery. By integrating gene expression, genotype, metabolomic, and clinical data, we identify a glucose and lipid determining (GLD) regulatory network showing inverse relationships with lipid and glucose traits. Master regulators of the GLD network also impact lipid and glucose levels in inverse directions. Experimental inhibition of one of the GLD network master regulators, lanosterol synthase (LSS), in mice confirms the inverse relationships to glucose and lipid levels as predicted by our model and provides mechanistic insights.

A Noncoding Variant Near PPP1R3B Promotes Liver Glycogen Storage and MetS, but Protects Against Myocardial Infarction.

CONTEXT: Glycogen storage diseases are rare. Increased glycogen in the liver results in increased attenuation. OBJECTIVE: Investigate the association and function of a noncoding region associated with liver attenuation but not histologic nonalcoholic fatty liver disease. DESIGN: Genetics of Obesity-associated Liver Disease Consortium. SETTING: Population-based. MAIN OUTCOME: Computed tomography measured liver attenuation. RESULTS: Carriers of rs4841132-A (frequency 2%-19%) do not show increased hepatic steatosis; they have increased liver attenuation indicative of increased glycogen deposition. rs4841132 falls in a noncoding RNA LOC157273 ~190 kb upstream of PPP1R3B. We demonstrate that rs4841132-A increases PPP1R3B through a cis genetic effect. Using CRISPR/Cas9 we engineered a 105-bp deletion including rs4841132-A in human hepatocarcinoma cells that increases PPP1R3B, decreases LOC157273, and increases glycogen perfectly mirroring the human disease. Overexpression of PPP1R3B or knockdown of LOC157273 increased glycogen but did not result in decreased LOC157273 or increased PPP1R3B, respectively, suggesting that the effects may not all occur via affecting RNA levels. Based on electronic health record (EHR) data, rs4841132-A associates with all components of the metabolic syndrome (MetS). However, rs4841132-A associated with decreased low-density lipoprotein (LDL) cholesterol and risk for myocardial infarction (MI). A metabolic signature for rs4841132-A includes increased glycine, lactate, triglycerides, and decreased acetoacetate and beta-hydroxybutyrate. CONCLUSIONS: These results show that rs4841132-A promotes a hepatic glycogen storage disease by increasing PPP1R3B and decreasing LOC157273. rs4841132-A promotes glycogen accumulation and development of MetS but lowers LDL cholesterol and risk for MI. These results suggest that elevated hepatic glycogen is one cause of MetS that does not invariably promote MI.

Multiscale causal networks identify VGF as a key regulator of Alzheimer's disease.

Though discovered over 100 years ago, the molecular foundation of sporadic Alzheimer's disease (AD) remains elusive. To better characterize the complex nature of AD, we constructed multiscale causal networks on a large human AD multi-omics dataset, integrating clinical features of AD, DNA variation, and gene- and protein-expression. These probabilistic causal models enabled detection, prioritization and replication of high-confidence master regulators of AD-associated networks, including the top predicted regulator, VGF. Overexpression of neuropeptide precursor VGF in 5xFAD mice partially rescued beta-amyloid-mediated memory impairment and neuropathology. Molecular validation of network predictions downstream of VGF was also achieved in this AD model, with significant enrichment for homologous genes identified as differentially expressed in 5xFAD brains overexpressing VGF. Our findings support a causal role for VGF in protecting against AD pathogenesis and progression.

Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention.

Cancer treatments are often more successful when the disease is detected early. We evaluated the feasibility and safety of multicancer blood testing coupled with positron emission tomography-computed tomography (PET-CT) imaging to detect cancer in a prospective, interventional study of 10,006 women not previously known to have cancer. Positive blood tests were independently confirmed by a diagnostic PET-CT, which also localized the cancer. Twenty-six cancers were detected by blood testing. Of these, 15 underwent PET-CT imaging and nine (60%) were surgically excised. Twenty-four additional cancers were detected by standard-of-care screening and 46 by neither approach. One percent of participants underwent PET-CT imaging based on false-positive blood tests, and 0.22% underwent a futile invasive diagnostic procedure. These data demonstrate that multicancer blood testing combined with PET-CT can be safely incorporated into routine clinical care, in some cases leading to surgery with intent to cure.

Dual transcriptomic and epigenomic study of reaction severity in peanut-allergic children.

BACKGROUND: Unexpected allergic reactions to peanut are the most common cause of fatal food-related anaphylaxis. Mechanisms underlying the variable severity of peanut-allergic reactions remain unclear. OBJECTIVES: We sought to expand mechanistic understanding of reaction severity in peanut allergy. METHODS: We performed an integrated transcriptomic and epigenomic study of peanut-allergic children as they reacted in vivo during double-blind, placebo-controlled peanut challenges. We integrated whole-blood transcriptome and CD4+ T-cell epigenome profiles to identify molecular signatures of reaction severity (ie, how severely a peanut-allergic child reacts when exposed to peanut). A threshold-weighted reaction severity score was calculated for each subject based on symptoms experienced during peanut challenge and the eliciting dose. Through linear mixed effects modeling, network construction, and causal mediation analysis, we identified genes, CpGs, and their interactions that mediate reaction severity. Findings were replicated in an independent cohort. RESULTS: We identified 318 genes with changes in expression during the course of reaction associated with reaction severity, and 203 CpG sites with differential DNA methylation associated with reaction severity. After replicating these findings in an independent cohort, we constructed interaction networks with the identified peanut severity genes and CpGs. These analyses and leukocyte deconvolution highlighted neutrophil-mediated immunity. We identified NFKBIA and ARG1 as hubs in the networks and 3 groups of interacting key node CpGs and peanut severity genes encompassing immune response, chemotaxis, and regulation of macroautophagy. In addition, we found that gene expression of PHACTR1 and ZNF121 causally mediates the association between methylation at corresponding CpGs and reaction severity, suggesting that methylation may serve as an anchor upon which gene expression modulates reaction severity. CONCLUSIONS: Our findings enhance current mechanistic understanding of the genetic and epigenetic architecture of reaction severity in peanut allergy.

Differential activity of transcribed enhancers in the prefrontal cortex of 537 cases with schizophrenia and controls.

Transcription at enhancers is a widespread phenomenon which produces so-called enhancer RNA (eRNA) and occurs in an activity-dependent manner. However, the role of eRNA and its utility in exploring disease-associated changes in enhancer function, and the downstream coding transcripts that they regulate, is not well established. We used transcriptomic and epigenomic data to interrogate the relationship of eRNA transcription to disease status and how genetic variants alter enhancer transcriptional activity in the human brain. We combined RNA-seq data from 537 postmortem brain samples from the CommonMind Consortium with cap analysis of gene expression and enhancer identification, using the assay for transposase-accessible chromatin followed by sequencing (ATACseq). We find 118 differentially transcribed eRNAs in schizophrenia and identify schizophrenia-associated gene/eRNA co-expression modules. Perturbations of a key module are associated with the polygenic risk scores. Furthermore, we identify genetic variants affecting expression of 927 enhancers, which we refer to as enhancer expression quantitative loci or eeQTLs. Enhancer expression patterns are consistent across studies, including differentially expressed eRNAs and eeQTLs. Combining eeQTLs with a genome-wide association study of schizophrenia identifies a genetic variant that alters enhancer function and expression of its target gene, GOLPH3L. Our novel approach to analyzing enhancer transcription is adaptable to other large-scale, non-poly-A depleted, RNA-seq studies.

Carbonyl reductase 1 catalyzes 20ß-reduction of glucocorticoids, modulating receptor activation and metabolic complications of obesity.

Carbonyl Reductase 1 (CBR1) is a ubiquitously expressed cytosolic enzyme important in exogenous drug metabolism but the physiological function of which is unknown. Here, we describe a role for CBR1 in metabolism of glucocorticoids. CBR1 catalyzes the NADPH- dependent production of 20ß-dihydrocortisol (20ß-DHF) from cortisol. CBR1 provides the major route of cortisol metabolism in horses and is up-regulated in adipose tissue in obesity in horses, humans and mice. We demonstrate that 20ß-DHF is a weak endogenous agonist of the human glucocorticoid receptor (GR). Pharmacological inhibition of CBR1 in diet-induced obesity in mice results in more marked glucose intolerance with evidence for enhanced hepatic GR signaling. These findings suggest that CBR1 generating 20ß-dihydrocortisol is a novel pathway modulating GR activation and providing enzymatic protection against excessive GR activation in obesity.

Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach.

BACKGROUND: The link between DNA methylation, obesity, and adiposity-related diseases in the general population remains uncertain. METHODS AND FINDINGS: We conducted an association study of body mass index (BMI) and differential methylation for over 400,000 CpGs assayed by microarray in whole-blood-derived DNA from 3,743 participants in the Framingham Heart Study and the Lothian Birth Cohorts, with independent replication in three external cohorts of 4,055 participants. We examined variations in whole blood gene expression and conducted Mendelian randomization analyses to investigate the functional and clinical relevance of the findings. We identified novel and previously reported BMI-related differential methylation at 83 CpGs that replicated across cohorts; BMI-related differential methylation was associated with concurrent changes in the expression of genes in lipid metabolism pathways. Genetic instrumental variable analysis of alterations in methylation at one of the 83 replicated CpGs, cg11024682 (intronic to sterol regulatory element binding transcription factor 1 [SREBF1]), demonstrated links to BMI, adiposity-related traits, and coronary artery disease. Independent genetic instruments for expression of SREBF1 supported the findings linking methylation to adiposity and cardiometabolic disease. Methylation at a substantial proportion (16 of 83) of the identified loci was found to be secondary to differences in BMI. However, the cross-sectional nature of the data limits definitive causal determination. CONCLUSIONS: We present robust associations of BMI with differential DNA methylation at numerous loci in blood cells. BMI-related DNA methylation and gene expression provide mechanistic insights into the relationship between DNA methylation, obesity, and adiposity-related diseases.

EXPLORING THE REPRODUCIBILITY OF PROBABILISTIC CAUSAL MOLECULAR NETWORK MODELS.

Network reconstruction algorithms are increasingly being employed in biomedical and life sciences research to integrate large-scale, high-dimensional data informing on living systems. One particular class of probabilistic causal networks being applied to model the complexity and causal structure of biological data is Bayesian networks (BNs). BNs provide an elegant mathematical framework for not only inferring causal relationships among many different molecular and higher order phenotypes, but also for incorporating highly diverse priors that provide an efficient path for incorporating existing knowledge. While significant methodological developments have broadly enabled the application of BNs to generate and validate meaningful biological hypotheses, the reproducibility of BNs in this context has not been systematically explored. In this study, we aim to determine the criteria for generating reproducible BNs in the context of transcription-based regulatory networks. We utilize two unique tissues from independent datasets, whole blood from the GTEx Consortium and liver from the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Team (STARNET) study. We evaluated the reproducibility of the BNs by creating networks on data subsampled at different levels from each cohort and comparing these networks to the BNs constructed using the complete data. To help validate our results, we used simulated networks at varying sample sizes. Our study indicates that reproducibility of BNs in biological research is an issue worthy of further consideration, especially in light of the many publications that now employ findings from such constructs without appropriate attention paid to reproducibility. We find that while edge-to-edge reproducibility is strongly dependent on sample size, identification of more highly connected key driver nodes in BNs can be carried out with high confidence across a range of sample sizes.

Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases.

Genome-wide association studies (GWAS) have identified hundreds of cardiometabolic disease (CMD) risk loci. However, they contribute little to genetic variance, and most downstream gene-regulatory mechanisms are unknown. We genotyped and RNA-sequenced vascular and metabolic tissues from 600 coronary artery disease patients in the Stockholm-Tartu Atherosclerosis Reverse Networks Engineering Task study (STARNET). Gene expression traits associated with CMD risk single-nucleotide polymorphism (SNPs) identified by GWAS were more extensively found in STARNET than in tissue- and disease-unspecific gene-tissue expression studies, indicating sharing of downstream cis-/trans-gene regulation across tissues and CMDs. In contrast, the regulatory effects of other GWAS risk SNPs were tissue-specific; abdominal fat emerged as an important gene-regulatory site for blood lipids, such as for the low-density lipoprotein cholesterol and coronary artery disease risk gene PCSK9 STARNET provides insights into gene-regulatory mechanisms for CMD risk loci, facilitating their translation into opportunities for diagnosis, therapy, and prevention.

Integrative functional genomics identifies regulatory mechanisms at coronary artery disease loci.

Coronary artery disease (CAD) is the leading cause of mortality and morbidity, driven by both genetic and environmental risk factors. Meta-analyses of genome-wide association studies have identified >150 loci associated with CAD and myocardial infarction susceptibility in humans. A majority of these variants reside in non-coding regions and are co-inherited with hundreds of candidate regulatory variants, presenting a challenge to elucidate their functions. Herein, we use integrative genomic, epigenomic and transcriptomic profiling of perturbed human coronary artery smooth muscle cells and tissues to begin to identify causal regulatory variation and mechanisms responsible for CAD associations. Using these genome-wide maps, we prioritize 64 candidate variants and perform allele-specific binding and expression analyses at seven top candidate loci: 9p21.3, SMAD3, PDGFD, IL6R, BMP1, CCDC97/TGFB1 and LMOD1. We validate our findings in expression quantitative trait loci cohorts, which together reveal new links between CAD associations and regulatory function in the appropriate disease context.

Endothelial to mesenchymal transition is common in atherosclerotic lesions and is associated with plaque instability.

Endothelial to mesenchymal transition (EndMT) plays a major role during development, and also contributes to several adult cardiovascular diseases. Importantly, mesenchymal cells including fibroblasts are prominent in atherosclerosis, with key functions including regulation of: inflammation, matrix and collagen production, and plaque structural integrity. However, little is known about the origins of atherosclerosis-associated fibroblasts. Here we show using endothelial-specific lineage-tracking that EndMT-derived fibroblast-like cells are common in atherosclerotic lesions, with EndMT-derived cells expressing a range of fibroblast-specific markers. In vitro modelling confirms that EndMT is driven by TGF-ß signalling, oxidative stress and hypoxia; all hallmarks of atherosclerosis. 'Transitioning' cells are readily detected in human plaques co-expressing endothelial and fibroblast/mesenchymal proteins, indicative of EndMT. The extent of EndMT correlates with an unstable plaque phenotype, which appears driven by altered collagen-MMP production in EndMT-derived cells. We conclude that EndMT contributes to atherosclerotic patho-biology and is associated with complex plaques that may be related to clinical events.

Assembly and diploid architecture of an individual human genome via single-molecule technologies.

We present the first comprehensive analysis of a diploid human genome that combines single-molecule sequencing with single-molecule genome maps. Our hybrid assembly markedly improves upon the contiguity observed from traditional shotgun sequencing approaches, with scaffold N50 values approaching 30 Mb, and we identified complex structural variants (SVs) missed by other high-throughput approaches. Furthermore, by combining Illumina short-read data with long reads, we phased both single-nucleotide variants and SVs, generating haplotypes with over 99% consistency with previous trio-based studies. Our work shows that it is now possible to integrate single-molecule and high-throughput sequence data to generate de novo assembled genomes that approach reference quality.

Integrated genome-wide association, coexpression network, and expression single nucleotide polymorphism analysis identifies novel pathway in allergic rhinitis.

BACKGROUND: Allergic rhinitis is a common disease whose genetic basis is incompletely explained. We report an integrated genomic analysis of allergic rhinitis. METHODS: We performed genome wide association studies (GWAS) of allergic rhinitis in 5633 ethnically diverse North American subjects. Next, we profiled gene expression in disease-relevant tissue (peripheral blood CD4+ lymphocytes) collected from subjects who had been genotyped. We then integrated the GWAS and gene expression data using expression single nucleotide (eSNP), coexpression network, and pathway approaches to identify the biologic relevance of our GWAS. RESULTS: GWAS revealed ethnicity-specific findings, with 4 genome-wide significant loci among Latinos and 1 genome-wide significant locus in the GWAS meta-analysis across ethnic groups. To identify biologic context for these results, we constructed a coexpression network to define modules of genes with similar patterns of CD4+ gene expression (coexpression modules) that could serve as constructs of broader gene expression. 6 of the 22 GWAS loci with P-value ≤ 1x10-6 tagged one particular coexpression module (4.0-fold enrichment, P-value 0.0029), and this module also had the greatest enrichment (3.4-fold enrichment, P-value 2.6 × 10-24) for allergic rhinitis-associated eSNPs (genetic variants associated with both gene expression and allergic rhinitis). The integrated GWAS, coexpression network, and eSNP results therefore supported this coexpression module as an allergic rhinitis module. Pathway analysis revealed that the module was enriched for mitochondrial pathways (8.6-fold enrichment, P-value 4.5 × 10-72). CONCLUSIONS: Our results highlight mitochondrial pathways as a target for further investigation of allergic rhinitis mechanism and treatment. Our integrated approach can be applied to provide biologic context for GWAS of other diseases.

Dissection of immune gene networks in primary melanoma tumors critical for antitumor surveillance of patients with stage II-III resectable disease.

Patients with resected stage II-III cutaneous melanomas remain at high risk for metastasis and death. Biomarker development has been limited by the challenge of isolating high-quality RNA for transcriptome-wide profiling from formalin-fixed and paraffin-embedded (FFPE) primary tumor specimens. Using NanoString technology, RNA from 40 stage II-III FFPE primary melanomas was analyzed and a 53-immune-gene panel predictive of non-progression (area under the curve (AUC)=0.920) was defined. The signature predicted disease-specific survival (DSS P<0.001) and recurrence-free survival (RFS P<0.001). CD2, the most differentially expressed gene in the training set, also predicted non-progression (P<0.001). Using publicly available microarray data from 46 primary human melanomas (GSE15605), a coexpression module enriched for the 53-gene panel was then identified using unbiased methods. A Bayesian network of signaling pathways based on this data identified driver genes. Finally, the proposed 53-gene panel was confirmed in an independent test population of 48 patients (AUC=0.787). The gene signature was an independent predictor of non-progression (P<0.001), RFS (P<0.001), and DSS (P=0.024) in the test population. The identified driver genes are potential therapeutic targets, and the 53-gene panel should be tested for clinical application using a larger data set annotated on the basis of prospectively gathered data.

Decoding dendritic cell function through module and network analysis.

Systems biology approaches that utilize large genomic data sets hold great potential for deciphering complex immunological process. In this paper, we propose such an approach to derive informative modules and networks from large gene expression data sets. Our approach starts with the clustering of such data sets to derive groups of tightly co-expressed genes, also known as co-expression modules. These modules are then converted into co-expression networks, and combined with transcriptional regulatory and protein interaction data to generate integrated networks that can help decipher the regulatory structure of these modules. We use this approach to derive the first set of modules and networks focused on dendritic cells (DCs). These cells are responsible for sampling the local environment to inform the adaptive immune system about peripheral stimuli, thus leading to the induction of an immune response. Using the ImmGen gene expression data set, we derive co-expression modules and integrated networks for the pDC, cDC and CD8+ DC subsets. In addition to recapitulating genes known to regulate the functions of these subsets, these networks reveal several novel genes and interactions that might have important roles in DC biology.