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Project description:BackgroundThe plasticity of monocytes enables them to exert multiple roles during an immune response, including promoting immune tolerance. How monocytes alter their functions to convey immune tolerance in the context of lower respiratory tract infections in humans is not well understood. Here, we sought to identify epigenetic and transcriptomic features of cytokine production capacity in circulating monocytes during community-acquired pneumonia (CAP).MethodsCirculating CD14+ monocytes were obtained from the blood of CAP patients included in a longitudinal, observational cohort study, on hospitalization (acute stage, n=75), and from the same patients after a 1-month follow-up (recovery stage, n=56). Age and sex-matched non-infectious participants were included as controls (n=41). Ex vivo cytokine production after lipopolysaccharide (LPS) exposure was assessed by multiplex assay. Transcriptomes of circulating monocytes were generated by RNA-sequencing, and DNA methylation levels in the same monocytes were measured by reduced representation bisulfite sequencing. Data were integrated by fitting projection-to-latent-structure models, and signatures derived by partial least squares discrimination.ResultsMonocytes captured during the acute stage exhibited impaired TNF, IL-1β, IL-6, and IL-10 production after ex vivo stimulation with LPS, relative to controls. IL-6 production was not resolved in recovery monocytes. Multivariate analysis of RNA-sequencing data identified 2938 significantly altered RNA transcripts in acute-stage monocytes (fold expression ≤-1.5 or ≥1.5; adjusted p ≤ 0.01), relative to controls. Comparing DNA methylation levels in circulating monocytes of CAP patients to controls revealed minimal differences, specifically in DNAse hypersensitive sites (HS) of acute-stage monocytes. Data integration identified a cholesterol biosynthesis gene signature and DNAse HS axis of IL-1β and IL-10 production (R2 =0.51).ConclusionsCirculating monocytes obtained from CAP patients during the acute stage exhibited impaired cytokine production capacities, indicative of reprogramming to a state of immune tolerance, which was not fully resolved after 1 month. Our split-sample study showed that 51% of the immune tolerance phenotype can be explained, at least in part, by coordinated shifts in cholesterol biosynthesis gene expression and DNAse HS methylation levels. A multi-scale model identified an epigenetic and transcriptomic signature of immune tolerance in monocytes, with implications for future interventions in immunosuppression.Trial registrationNCT number NCT02928367.
| S-EPMC8365568 | biostudies-literature
Project description:An epigenetic and transcriptomic signature of immune tolerance in human monocytes by multi-omic integration
| PRJNA672960 | ENA
Project description:MotivationCells respond to environments by regulating gene expression to exploit resources optimally. Recent advances in technologies allow for measuring the abundances of RNA, proteins, lipids and metabolites. These highly complex datasets reflect the states of the different layers in a biological system. Multi-omics is the integration of these disparate methods and data to gain a clearer picture of the biological state. Multi-omic studies of the proteome and metabolome are becoming more common as mass spectrometry technology continues to be democratized. However, knowledge extraction through the integration of these data remains challenging.ResultsConnections between molecules in different omic layers were discovered through a combination of machine learning and model interpretation. Discovered connections reflected protein control (ProC) over metabolites. Proteins discovered to control citrate were mapped onto known genetic and metabolic networks, revealing that these protein regulators are novel. Further, clustering the magnitudes of ProC over all metabolites enabled the prediction of five gene functions, each of which was validated experimentally. Two uncharacterized genes, YJR120W and YDL157C, were accurately predicted to modulate mitochondrial translation. Functions for three incompletely characterized genes were also predicted and validated, including SDH9, ISC1 and FMP52. A website enables results exploration and also MIMaL analysis of user-supplied multi-omic data.Availability and implementationThe website for MIMaL is at https://mimal.app. Code for the website is at https://github.com/qdickinson/mimal-website. Code to implement MIMaL is at https://github.com/jessegmeyerlab/MIMaL.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9801967 | biostudies-literature
Project description:Biological aging is accompanied by increasing morbidity, mortality, and healthcare costs; however, its molecular mechanisms are poorly understood. Here, we use multi-omic methods to integrate genomic, transcriptomic, and metabolomic data and identify biological associations with four measures of epigenetic age acceleration and a human longevity phenotype comprising healthspan, lifespan, and exceptional longevity (multivariate longevity). Using transcriptomic imputation, fine-mapping, and conditional analysis, we identify 22 high confidence associations with epigenetic age acceleration and seven with multivariate longevity. FLOT1, KPNA4, and TMX2 are novel, high confidence genes associated with epigenetic age acceleration. In parallel, cis-instrument Mendelian randomization of the druggable genome associates TPMT and NHLRC1 with epigenetic aging, supporting transcriptomic imputation findings. Metabolomics Mendelian randomization identifies a negative effect of non-high-density lipoprotein cholesterol and associated lipoproteins on multivariate longevity, but not epigenetic age acceleration. Finally, cell-type enrichment analysis implicates immune cells and precursors in epigenetic age acceleration and, more modestly, multivariate longevity. Follow-up Mendelian randomization of immune cell traits suggests lymphocyte subpopulations and lymphocytic surface molecules affect multivariate longevity and epigenetic age acceleration. Our results highlight druggable targets and biological pathways involved in aging and facilitate multi-omic comparisons of epigenetic clocks and human longevity.
| S-EPMC10115892 | biostudies-literature
Project description:MOTIVATION:Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients. RESULTS:We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data imputation. In extensive testing on ten cancer datasets spanning 3168 patients, NEMO achieved results comparable to the best of nine state-of-the-art multi-omics clustering algorithms on full data and showed an improvement on partial data. On some of the partial data tests, PVC, a multi-view algorithm, performed better, but it is limited to two omics and to positive partial data. Finally, we demonstrate the advantage of NEMO in detailed analysis of partial data of AML patients. NEMO is fast and much simpler than existing multi-omics clustering algorithms, and avoids iterative optimization. AVAILABILITY AND IMPLEMENTATION:Code for NEMO and for reproducing all NEMO results in this paper is in github: https://github.com/Shamir-Lab/NEMO. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC6748715 | biostudies-literature
Project description:The recent advancement of omic technologies provides researchers with the possibility to search for disease-associated biomarkers at the system level. The integrative analysis of data from a large number of molecules involved at various layers of the biological system offers a great opportunity to rank disease biomarker candidates. In this paper, we propose MOTA, a network-based method that uses data acquired at multiple layers to rank candidate disease biomarkers. The networks constructed by MOTA allow users to investigate the biological significance of the top-ranked biomarker candidates. We evaluated the performance of MOTA in ranking disease-associated molecules from three sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and controls with liver cirrhosis. The results demonstrate that MOTA allows the identification of more top-ranked metabolite biomarker candidates that are shared by two different cohorts compared to traditional statistical methods. Moreover, the mRNA candidates top-ranked by MOTA comprise more cancer driver genes compared to those ranked by traditional differential expression methods.
| S-EPMC7241240 | biostudies-literature
Project description:Rapid growth in size and complexity of biological data sets has led to the 'Big Data to Knowledge' challenge. We develop advanced data integration methods for multi-level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. First, we show that pairwise integration of primary omics data reveals regularities that tie cellular processes together in Escherichia coli: the number of protein molecules made per mRNA transcript and the number of ribosomes required per translated protein molecule. Second, we show that genome-scale models, based on genomic and bibliomic data, enable quantitative synchronization of disparate data types. Integrating omics data with models enabled the discovery of two novel regularities: condition invariant in vivo turnover rates of enzymes and the correlation of protein structural motifs and translational pausing. These regularities can be formally represented in a computable format allowing for coherent interpretation and prediction of fitness and selection that underlies cellular physiology.
| S-EPMC5095171 | biostudies-literature
Project description:BackgroundVaccination remains one of the most effective means of reducing the burden of infectious diseases globally. Improving our understanding of the molecular basis for effective vaccine response is of paramount importance if we are to ensure the success of future vaccine development efforts.MethodsWe applied cutting edge multi-omics approaches to extensively characterize temporal molecular responses following vaccination with hepatitis B virus (HBV) vaccine. Data were integrated across cellular, epigenomic, transcriptomic, proteomic, and fecal microbiome profiles, and correlated to final HBV antibody titres.ResultsUsing both an unsupervised molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Biological associations were unravelled, with signalling pathways such as JAK-STAT and interleukin signalling, Toll-like receptor cascades, interferon signalling, and Th17 cell differentiation emerging as important pre-vaccination modulators of response.ConclusionThis study provides further evidence that baseline cellular and molecular characteristics of an individual's immune system influence vaccine responses, and highlights the utility of integrating information across many parallel molecular datasets.
| S-EPMC7734088 | biostudies-literature
Project description:Vaccination remains one of the most effective means of reducing the burden of infectious diseases globally. Climate change is projected to lead to an increase in the burden of vector- and water-borne, as well as zoonotic infectious diseases including COVID-19. Improving our understanding of the molecular basis for effective vaccine response is of paramount importance if we are to ensure the success of future vaccine development efforts. We applied cutting edge multi-omics approaches to characterize temporal molecular responses following vaccination with hepatitis B virus (HBV) vaccine. Data were integrated across cellular, epigenomic, transcriptomic, proteomic, and fecal microbiome profiles, and correlated to final hepatitis B antibody titres. Using both a molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we overcame the p>>>n problem (predictors much greater than observations) and uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Correlations were identified with signalling pathways such as JAK-STAT and interleukin signalling, Toll-Like Receptor Cascades, interferon signalling and Th17 cell differentiation. This study provides further evidence that baseline cellular and molecular characteristics of an individual’s immune system influence vaccine responses, and highlights the utility of integrating information across many parallel molecular datasets.
2020-12-21 | GSE161020 | GEO
Project description:Identification of cancer driver mutations is critical for advancing cancer research and personalized medicine. Due to inter-tumor genetic heterogeneity, many driver mutations occur at low frequencies, which make it challenging to distinguish them from passenger mutations. Here, we show that a novel Bayesian hierarchical modeling approach, named rDriver can achieve enhanced prediction accuracy by identifying mutations that not only have high functional impact scores but also are associated with systemic variation in gene expression levels. In examining 3,080 tumor samples from 8 cancer types in The Cancer Genome Atlas, rDriver predicted 1,389 driver mutations. Compared with existing tools, rDriver identified more low frequency mutations associated with lineage specific functional properties, timing of occurrence and patient survival. Evaluation of rDriver predictions using engineered cell-line models resulted in a positive predictive value of 0.94 in PIK3CA genes. Our study highlights the importance of integrating multi-omic data in predicting cancer driver mutations and provides a statistically rigorous solution for cancer target discovery and development.
| S-EPMC5940219 | biostudies-literature