Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90-95% of proteomes to their correct genome and for 95-99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (  60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.

BACKGROUND: Chronic psychological distress is associated with increased risk of cardiovascular disease (CVD) and investigators have posited inflammatory factors may be centrally involved in these relationships. However, mechanistic evidence and molecular underpinnings of these processes remain unclear, and data are particularly sparse among women. This study examined if a metabolite profile linked with distress was associated with increased CVD risk and inflammation-related risk factors.

METHODS: A plasma metabolite-based distress score (MDS) of twenty chronic psychological distress-related metabolites was developed in cross-sectional, 1:1 matched case-control data comprised of 558 women from the Nurses' Health Study (NHS; 279 women with distress, 279 controls). This MDS was then evaluated in two other cohorts: the Women's Health Initiative Observational Cohort (WHI-OS) and the Prevención con Dieta Mediterránea (PREDIMED) trial. We tested the MDS's association with risk of future CVD in each sample and with levels of C-reactive protein (CRP) in the WHI-OS. The WHI-OS subsample included 944 postmenopausal women (472 CHD cases; mean time to event = 5.8 years); the PREDIMED subsample included 980 men and women (224 CVD cases, mean time to event = 3.1 years).

RESULTS: In the WHI-OS, a 1-SD increase in the plasma MDS was associated with a 20% increased incident CHD risk (odds ratio [OR] = 1.20, 95% CI: 1.04 - 1.38), adjusting for known CVD risk factors excluding total and HDL cholesterol. This association was attenuated after including total and HDL cholesterol. CRP mediated an average 12.9% (95% CI: 4.9% - 28%, p < 10-15) of the total effect of MDS on CHD risk when adjusting for matching factors. This effect was attenuated after adjusting for known CVD risk factors. Of the metabolites in the MDS, tryptophan and threonine were inversely associated with incident CHD risk in univariate models. In PREDIMED, each one SD increase in the MDS was associated with an OR of 1.19 (95% CI: 1.00 - 1.41) for incident CVD risk, after adjusting all risk factors. Similar associations were observed in men and women. Four metabolites in the MDS were associated with incident CVD risk in PREDIMED in univariate models. Biliverdin and C36:5 phosphatidylcholine (PC) plasmalogen had inverse associations; C16:0 ceramide and C18:0 lysophosphatidylethanolamine(LPE) each had positive associations with CVD risk.

CONCLUSIONS: Our study points to molecular alterations that may underlie the association between chronic distress and subsequent risk of cardiovascular disease in adults.

Partial reprogramming by cyclic short-term expression of Yamanaka factors holds promise for shifting cells to younger states and consequently delaying the onset of many diseases of aging. However, the delivery of transgenes and potential risk of teratoma formation present challenges for in vivo applications. Recent advances include the use of cocktails of compounds to reprogram somatic cells, but the characteristics and mechanisms of partial cellular reprogramming by chemicals remain unclear. Here, we report a multi-omics characterization of partial chemical reprogramming in fibroblasts from young and aged mice. We measured the effects of partial chemical reprogramming on the epigenome, transcriptome, proteome, phosphoproteome, and metabolome. At the transcriptome, proteome, and phosphoproteome levels, we saw widescale changes induced by this treatment, with the most notable signature being an upregulation of mitochondrial oxidative phosphorylation. Furthermore, at the metabolome level, we observed a reduction in the accumulation of aging-related metabolites. Using both transcriptomic and epigenetic clock-based analyses, we show that partial chemical reprogramming reduces the biological age of mouse fibroblasts. We demonstrate that these changes have functional impacts, as evidenced by changes in cellular respiration and mitochondrial membrane potential. Taken together, these results illuminate the potential for chemical reprogramming reagents to rejuvenate aged biological systems, and warrant further investigation into adapting these approaches for in vivo age reversal.

Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.

Dendritic cells (DCs) have a role in the development and activation of self-reactive pathogenic T cells1,2. Genetic variants that are associated with the function of DCs have been linked to autoimmune disorders3,4, and DCs are therefore attractive therapeutic targets for such diseases. However, developing DC-targeted therapies for autoimmunity requires identification of the mechanisms that regulate DC function. Here, using single-cell and bulk transcriptional and metabolic analyses in combination with cell-specific gene perturbation studies, we identify a regulatory loop of negative feedback that operates in DCs to limit immunopathology. Specifically, we find that lactate, produced by activated DCs and other immune cells, boosts the expression of NDUFA4L2 through a mechanism mediated by hypoxia-inducible factor 1α (HIF-1α). NDUFA4L2 limits the production of mitochondrial reactive oxygen species that activate XBP1-driven transcriptional modules in DCs that are involved in the control of pathogenic autoimmune T cells. We also engineer a probiotic that produces lactate and suppresses T cell autoimmunity through the activation of HIF-1α-NDUFA4L2 signalling in DCs. In summary, we identify an immunometabolic pathway that regulates DC function, and develop a synthetic probiotic for its therapeutic activation.

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce multi-set correlation and factor analysis (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets.

BACKGROUND: Identifying novel epigenetic signatures associated with serum immunoglobulin E (IgE) may improve our understanding of molecular mechanisms underlying asthma and IgE-mediated diseases.

METHODS: We performed an epigenome-wide association study using whole blood from Framingham Heart Study (FHS; n = 3,471, 46% females) participants and validated results using the Childhood Asthma Management Program (CAMP; n = 674, 39% females) and the Genetic Epidemiology of Asthma in Costa Rica Study (CRA; n = 787, 41% females). Using the closest gene to each IgE-associated CpG, we highlighted biologically plausible pathways underlying IgE regulation and analyzed the transcription patterns linked to IgE-associated CpGs (expression quantitative trait methylation loci; eQTMs). Using prior UK Biobank summary data from genome-wide association studies of asthma and allergy, we performed Mendelian randomization (MR) for causal inference testing using the IgE-associated CpGs from FHS with methylation quantitative trait loci (mQTLs) as instrumental variables.

FINDINGS: We identified 490 statistically significant differentially methylated CpGs associated with IgE in FHS, of which 193 (39.3%) replicated in CAMP and CRA (FDR < 0.05). Gene ontology analysis revealed enrichment in pathways related to transcription factor binding, asthma, and other immunological processes. eQTM analysis identified 124 cis-eQTMs for 106 expressed genes (FDR < 0.05). MR in combination with drug-target analysis revealed CTSB and USP20 as putatively causal regulators of IgE levels (Bonferroni adjusted P < 7.94E-04) that can be explored as potential therapeutic targets.

INTERPRETATION: By integrating eQTM and MR analyses in general and clinical asthma populations, our findings provide a deeper understanding of the multidimensional inter-relations of DNA methylation, gene expression, and IgE levels.

FUNDING: US NIH/NHLBI grants: P01HL132825, K99HL159234. N01-HC-25195 and HHSN268201500001I.

Submaximal exercise capacity is an indicator of cardiorespiratory fitness with clinical and public health implications. Submaximal exercise capacity and its response to exercise programs are characterized by heritability levels of about 40%. Using physical working capacity (power output) at a heart rate of 150 beats/min (PWC150) as an indicator of submaximal exercise capacity in subjects of the HERITAGE Family Study, we have undertaken multi-omics and in silico explorations of the underlying biology of PWC150 and its response to 20 wk of endurance training. Our goal was to illuminate the biological processes and identify panels of genes associated with human variability in intrinsic PWC150 (iPWC150) and its trainability (dPWC150). Our bioinformatics approach was based on a combination of genome-wide association, skeletal muscle gene expression, and plasma proteomics and metabolomics experiments. Genes, proteins, and metabolites showing significant associations with iPWC150 or dPWC150 were further queried for the enrichment of biological pathways. We compared genotype-phenotype associations of emerging candidate genes with reported functional consequences of gene knockouts in mouse models. We investigated the associations between DNA variants and multiple muscle and cardiovascular phenotypes measured in HERITAGE subjects. Two panels of prioritized genes of biological relevance to iPWC150 (13 genes) and dPWC150 (6 genes) were identified, supporting the hypothesis that genes and pathways associated with iPWC150 are different from those underlying dPWC150. Finally, the functions of these genes and pathways suggested that human variation in submaximal exercise capacity is mainly driven by skeletal muscle morphology and metabolism and red blood cell oxygen-carrying capacity.NEW & NOTEWORTHY Multi-omics and in silico explorations of the genes and underlying biology of submaximal exercise capacity and its response to 20 wk of endurance training were undertaken. Prioritized genes were identified: 13 genes for variation in submaximal exercise capacity in the sedentary state and 5 genes for the response level to endurance training, with no overlap between them. Genes and pathways associated with submaximal exercise capacity in the sedentary state are different from those underlying trainability.

In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites. We identified metabolites associated with higher or lower risk of HF incidence, the associations that were not confounded by the other metabolites, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. We revealed the underlying relationships of the findings. For example, asparagine directly influenced glycine, and both were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids which are not synthesized in the human body and come directly from the diet. Metabolites may play a critical role in linking genetic background and lifestyle factors to HF progression. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates a mechanistic understanding of HF progression.