Novel Lesional Transcriptional Signature Separates Atherosclerosis with and without Diabetes in Yorkshire Swine and Humans

Stefan Haemmig, Ali Hashemi Gheinani, Marina Zaromytidou, Gerasimos Siasos, Ahmet Umit Coskun, Michelle A. Cormier, David A. Gross, A. K.M.Khyrul Wara, Antonios P. Antoniadis, Xinghui Sun, Galina K. Sukhova, Fred G.P. Welt, Ioannis Andreou, Carl Whatling, Li Ming Gan, Johannes Wikström, Elazer R. Edelman, Peter Libby, Peter H. Stone, Mark W. Feinberg

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: Accelerated atherosclerosis in diabetes constitutes an ongoing challenge despite optimal medical therapies. This study aimed to identify evolutionarily conserved lesion-based regulatory signaling networks in diabetic versus nondiabetic conditions during the development of atherosclerosis in an initial translational effort to provide insights for targets. Approach and Results: Serial 3-mm coronary artery segments of hypercholesterolemic Yorkshire swine and diabetic-hypercholesterolemic swine were characterized as mild, moderate, or severe phenotypic manifestations of coronary atherosclerosis based on histopathologic examination. Lesional RNA sequencing was performed (n=3-8 lesions per group) corresponding to increasing phenotypic severity. Differentially expressed genes, transcription factors, upstream regulators, and hubs were validated using the NanoString technology and a human atherosclerotic specimen cohort. Despite similar stage histopathologic characterization of lesions, genome-wide transcriptomics revealed gene sets and nodal signaling pathways uniquely expressed in diabetic lesions including signaling pathways for Th17, IL (interleukin)-17F, TWEAK (TNF [tumor necrosis factor]-related weak inducer of apoptosis), CD27, and PI3K/Akt. In contrast, pathways of nondiabetic lesions involved TREM-1 and Th1 and Th2 responses during the initiation stage, whereas networks for mitochondrial dysfunction, oxidative phosphorylation, and lipid metabolism emerged with progression. RNA sequencing data were validated in a human atherosclerosis specimen cohort using machine learning algorithms. F8, MAPKAPK3, and ITGB1 emerged as powerful genes for clustering diabetic versus nondiabetic lesions and for separating different degrees of atherosclerosis progression. Conclusions: This study identifies evolutionarily conserved gene signatures and signaling pathways in a stage-specific manner that successfully distinguishes diabetes-and non-diabetes-Associated atherosclerosis. These findings establish new molecular insights and therapeutic opportunities to address accelerated atherosclerotic lesion formation in diabetes.

Original languageEnglish (US)
Pages (from-to)1487-1503
Number of pages17
JournalArteriosclerosis, Thrombosis, and Vascular Biology
DOIs
StateAccepted/In press - 2021

Keywords

  • algorithms
  • atherosclerosis
  • coronary vessels
  • mitochondria
  • technology

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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