Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes
journal contributionposted on 17.06.2022, 14:34 authored by Denis Seyres, Alessandra Cabassi, John J Lambourne, Frances Burden, Samantha Farrow, Harriet McKinney, Joana Batista, Carly Kempster, Maik Pietzner, Oliver Slingsby, Huy Cao Thong, Paulene A Quinn, Luca Stefanucci, Matthew C Sims, Karola Rehnstrom, Claire L Adams, Amy Frary, Bekir Erguener, Roman Kreuzhuber, Gabriele Mocciaro, Simona D'Amore, Albert Koulman, Luigi Grassi, Julian L Griffin, Leong Loke Ng, Adrian Park, David B Savage, Claudia Langenberg, Christoph Bock, Kate Downes, Nicholas J Wareham, Michael Allison, Michele Vacca, Paul DW Kirk, Mattia Frontini
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.
Published inCLINICAL EPIGENETICS
Science & TechnologyLife Sciences & BiomedicineOncologyGenetics & HeredityEpigeneticsMetabolitesLipidsMulti-omicsObesityLipodystrophyBariatric surgeryClassificationInnate immune cellsCardiometabolic syndromeNEUTROPHIL EXTRACELLULAR TRAPSCARDIOVASCULAR RISKINSULIN-RESISTANCEGENE-EXPRESSIONREAD ALIGNMENTWEB SERVERINFLAMMATIONOBESITYWEIGHTBLOOD