Clinical EpigeneticsGenomicsPeer-reviewed journal article
Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes.
Denis SeyresiD, Alessandra CabassiiD, John J. LambourneiD, Frances BurdeniD, Samantha Farrow, Harriet McKinney, Joana BatistaiD, Carly KempsteriD, Maik PietzneriD, Oliver C. SlingsbyiD, Thong Huy CaoiD, Paulene A. Quinn, Luca StefanucciiD, Matthew C. SimsiD, Karola Rehnström, Claire L. Adams, Amy J. FraryiD, Bekir ErgüneriD, Roman KreuzhuberiD, Gabriele MocciaroiD, Simona D’AmoreiD, Albert KoulmaniD, Luigi GrassiiD, Julian Leether GriffiniD, Leong L. NgiD, , Mattia FrontiniiD
Abstract
Improving the understanding of cardiometabolic syndrome pathophysiology and its relationship with thrombosis are ongoing healthcare challenges. Using plasma biomarkers analysis coupled with the transcriptional and epigenetic characterisation of cell types involved in thrombosis, obtained from two extreme phenotype groups (obese and lipodystrophy) and comparing these to lean individuals and blood donors, the present study identifies the molecular mechanisms at play, highlighting patterns of abnormal activation in innate immune phagocytic cells and shows that extreme phenotype groups could be distinguished from lean individuals, and from each other, across all data layers. The characterisation of the same obese group, six months after bariatric surgery shows the loss of the patterns of abnormal activation of innate immune cells previously observed. However, rather than reverting to the gene expression landscape of lean individuals, this occurs via the establishment of novel gene expression landscapes. Netosis and its control mechanisms emerge amongst the pathways that show an improvement after surgical intervention. Taken together, by integrating across data layers, the observed molecular and metabolic differences form a disease signature that is able to discriminate, amongst the blood donors, those individuals with a higher likelihood of having cardiometabolic syndrome, even when not presenting with the classic features.
- organism: human
- assay: bulk RNA-seq
- assay: DNA methylation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- approach: benchmark study
- Citations
- 13
- FWCI
- 2.17
- Field percentile
- top 13%
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