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Epigenomics and transcriptomics of systemic sclerosis CD4+ T cells reveal long-range dysregulation of key inflammatory pathways mediated by disease-associated susceptibility loci.

ABSTRACT: BACKGROUND:Systemic sclerosis (SSc) is a genetically complex autoimmune disease mediated by the interplay between genetic and epigenetic factors in a multitude of immune cells, with CD4+ T lymphocytes as one of the principle drivers of pathogenesis. METHODS:DNA samples exacted from CD4+ T cells of 48 SSc patients and 16 healthy controls were hybridized on MethylationEPIC BeadChip array. In parallel, gene expression was interrogated by hybridizing total RNA on Clariom™ S array. Downstream bioinformatics analyses were performed to identify correlating differentially methylated CpG positions (DMPs) and differentially expressed genes (DEGs), which were then confirmed utilizing previously published promoter capture Hi-C (PCHi-C) data. RESULTS:We identified 9112 and 3929 DMPs and DEGs, respectively. These DMPs and DEGs are enriched in functional categories related to inflammation and T cell biology. Furthermore, correlation analysis identified 17,500 possible DMP-DEG interaction pairs within a window of 5?Mb, and utilizing PCHi-C data, we observed that 212 CD4+ T cell-specific pairs of DMP-DEG also formed part of three-dimensional promoter-enhancer networks, potentially involving CTCF. Finally, combining PCHi-C data with SSc GWAS data, we identified four important SSc-associated susceptibility loci, TNIP1 (rs3792783), GSDMB (rs9303277), IL12RB1 (rs2305743), and CSK (rs1378942), that could potentially interact with DMP-DEG pairs cg17239269-ANXA6, cg19458020-CCR7, cg10808810-JUND, and cg11062629-ULK3, respectively. CONCLUSION:Our study unveils a potential link between genetic, epigenetic, and transcriptional deregulation in CD4+ T cells of SSc patients, providing a novel integrated view of molecular components driving SSc pathogenesis.

SUBMITTER: Li T

PROVIDER: S-EPMC7519528 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Epigenomics and transcriptomics of systemic sclerosis CD4+ T cells reveal long-range dysregulation of key inflammatory pathways mediated by disease-associated susceptibility loci.

Li Tianlu T Ortiz-Fernández Lourdes L Andrés-León Eduardo E Ciudad Laura L Javierre Biola M BM López-Isac Elena E Guillén-Del-Castillo Alfredo A Simeón-Aznar Carmen Pilar CP Ballestar Esteban E Martin Javier J

Genome medicine 20200925 1

<h4>Background</h4>Systemic sclerosis (SSc) is a genetically complex autoimmune disease mediated by the interplay between genetic and epigenetic factors in a multitude of immune cells, with CD4+ T lymphocytes as one of the principle drivers of pathogenesis.<h4>Methods</h4>DNA samples exacted from CD4+ T cells of 48 SSc patients and 16 healthy controls were hybridized on MethylationEPIC BeadChip array. In parallel, gene expression was interrogated by hybridizing total RNA on Clariom™ S array. Dow ...[more]

PMID: 32977850

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Project description:BackgroundThe metabolic characteristics of premature ovarian insufficiency (POI), a reproductive endocrine disease characterized by abnormal sex hormone metabolism and follicle depletion, remain unclear. Metabolomics is a powerful tool for exploring disease phenotypes and biomarkers. This study aims to identify metabolic markers and construct diagnostic models, and elucidate the underlying pathological mechanisms for POI.MethodsNon-targeted metabolomics was utilized to characterize the plasma metabolic profile of 40 patients. The metabolic markers were identified through bioinformatics and machine learning, and constructed an optimal diagnostic model by classified multi-model analysis. Enzyme-linked immunosorbent assay (ELISA) was used to verify antioxidant indexes, mitochondrial enzyme complexes, and ATP levels. Finally, integrated transcriptomics and metabolomics were used to reveal the dysregulated pathways and molecular regulatory mechanisms of POI.ResultsThe study identified eight metabolic markers significantly correlated with ovarian reserve function. The XGBoost diagnostic model was developed based on six machine learning models, demonstrating its robust diagnostic performance and clinical applicability through the evaluation of receiver operating characteristic (ROC) curve, decision curve analysis (DCA), calibration curve, and precise recall (PR) curve. Multi-omics analysis showed that mitochondrial respiratory chain electron carrier (CoQ10) and enzyme complex subunits were down-regulated in POI. ELISA validation revealed an elevation in oxidative stress markers and a reduction in the activities of antioxidant enzymes, CoQ10, and mitochondrial enzyme complexes in POI.ConclusionOur findings highlight that mitochondrial dysfunction and energy metabolism disorders are closely related to the pathogenesis of POI. The identification of metabolic markers and predictive models holds significant implications for the diagnosis, treatment, and monitoring of POI.

Dataset Information

Epigenomics and transcriptomics of systemic sclerosis CD4+ T cells reveal long-range dysregulation of key inflammatory pathways mediated by disease-associated susceptibility loci.

Publications

Epigenomics and transcriptomics of systemic sclerosis CD4+ T cells reveal long-range dysregulation of key inflammatory pathways mediated by disease-associated susceptibility loci.

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