Abstract Body (Do not enter title and authors here): Background/Objective:
Type 2 diabetes (T2DM) remains a major risk factor for atherosclerotic cardiovascular disease (ASCVD) despite modern lipid, glycemic and hypertension control therapies, indicating additional unknown pathogenic factors. Previous work from our group identified a pathogenic foaming gene signature in circulating monocytes that was predictive of ASCVD incidence; however, the mechanism by which T2DM primes monocytes toward atherogenic functions is unknown. The goal of this study is to identify T2DM-induced transcriptomic signatures that contribute to ASCVD pathogenesis.

Methods:
To investigate how T2DM induces monocyte actions contributing to ASCVD risk, we performed single-cell RNA-sequencing (scRNA-seq) analysis of peripheral blood mononuclear cells (PBMC) obtained from patient samples from the UConn Health Biorepository (n=40; 21 males, 19 females). We analyzed samples from patients with both T2DM and ASCVD (n=11), T2DM alone (n=9), ASCVD alone (n=11), and controls (no T2DM or ASCVD, n=9). We identified monocyte clusters following dimension reduction and clustering, then performed sub-clustering analysis to identify monocyte sub-populations. In addition to characterization of disease-associated monocyte sub-clusters using conventional scRNA-seq approaches, we performed functional analysis of monocyte polarization, maturation, and lipid handling using our AtheroSpectrum bioinformatics tool.

Results:
From our analysis of monocyte sub-clusters, we identified T2DM-associated shifts in monocyte sub-population proportions. AtheroSpectrum analysis identified a population of monocytes enriched in T2DM-ASCVD patients with a high Macrophage Polarization Index (MPI) score, indicating a more inflammatory state. This population was enriched for inflammatory pathways including Toll-like receptor signaling and neutrophil degranulation. Further analysis of these monocytes identified a transcriptomic signature network characterized by enriched Type I interferon signaling genes and metabolic regulators.

Conclusions:
By integrating conventional high-resolution single-cell transcriptomic analysis with immune function-guided bioinformatics tools, we identified a T2DM-specific transcriptomic signature network associated with ASCVD. Drivers of this network can serve as targets for mechanistic and functional investigations of T2DM-specific ASCVD pathogenic factors.