Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes

Author:

Seyres DenisORCID,Cabassi Alessandra,Lambourne John J.,Burden Frances,Farrow Samantha,McKinney Harriet,Batista Joana,Kempster Carly,Pietzner Maik,Slingsby Oliver,Cao Thong Huy,Quinn Paulene A.,Stefanucci Luca,Sims Matthew C.,Rehnstrom Karola,Adams Claire L.,Frary Amy,Ergüener Bekir,Kreuzhuber Roman,Mocciaro Gabriele,D’Amore Simona,Koulman Albert,Grassi Luigi,Griffin Julian L.,Ng Leong Loke,Park Adrian,Savage David B.,Langenberg Claudia,Bock Christoph,Downes Kate,Wareham Nicholas J.,Allison Michael,Vacca Michele,Kirk Paul D. W.,Frontini Mattia

Abstract

Abstract Background This work is aimed at improving the understanding of cardiometabolic syndrome pathophysiology and its relationship with thrombosis by generating a multi-omic disease signature. Methods/results We combined classic plasma biochemistry and plasma biomarkers with the transcriptional and epigenetic characterisation of cell types involved in thrombosis, obtained from two extreme phenotype groups (morbidly obese and lipodystrophy) and lean individuals to identify the molecular mechanisms at play, highlighting patterns of abnormal activation in innate immune phagocytic cells. Our analyses showed 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, 6 months after bariatric surgery, revealed the loss of the abnormal activation of innate immune cells previously observed. However, rather than reverting to the gene expression landscape of lean individuals, this occurred via the establishment of novel gene expression landscapes. NETosis and its control mechanisms emerge amongst the pathways that show an improvement after surgical intervention. Conclusions We showed that the morbidly obese and lipodystrophy groups, despite some differences, shared a common cardiometabolic syndrome signature. We also showed that this could be used to discriminate, amongst the normal population, those individuals with a higher likelihood of presenting with the disease, even when not displaying the classic features.

Funder

British Heart Foundation Cambridge Centre of Excellence

MRC Clinical Research Training Fellowships

Wellcome Trust

MRC Metabolic Disease Unit

The National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre and NIHR Rare Disease Translational Research Collaboration

NHS Health Education England

Medical Research Council

British Heart Foundation

NIHR Cambridge Biomedical Research Centre

Isaac Newton fellowship

NIHR Leicester Biomedical Research Centre and the John and Lucille Van Geest Foundation

Publisher

Springer Science and Business Media LLC

Subject

Genetics (clinical),Developmental Biology,Genetics,Molecular Biology

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