Paired plaque and plasma proteomics reveal molecular signatures of symptomatic atherosclerosis

Circulating cell-free RNA signatures associate with plaque calcification in advanced carotid atherosclerosis

Vascular calcification (VC) is a hallmark of advanced atherosclerotic plaque biology. Circulating cell-free RNA (cfRNA) provides a non-invasive window into tissue transcriptional activity and may reflect plaque composition. We examined whether plasma cfRNA profiles are associated with carotid plaque calcification. Plasma cfRNA was profiled by RNA sequencing in 333 patients undergoing carotid endarterectomy, split into discovery ( n  = 216) and internal validation ( n  = 117) cohorts. Plaque calcification was quantified histologically. Differential expression analysis was performed using generalized linear models, followed by pathway enrichment. A cfRNA-derived gene score was constructed, and its incremental value beyond clinical risk factors was evaluated. Cellular deconvolution was applied to explore potential cfRNA origins. In this exploratory analysis, 13 genes showed nominal association with calcification in the discovery cohort ( p  < 0.001). Of these, 11 (84.6%) demonstrated concordant directionality in validation (Spearman ρ  = 0.71, p  = 0.008). Pathway analysis suggested potential involvement of oxidative phosphorylation and calcium signaling. The 11-gene score remained associated with calcification after adjustment for clinical risk factors and improved model performance. Plasma cfRNA was associated with calcification in carotid atherosclerotic plaques and may reflect aspects of underlying plaque biology. These findings are exploratory and require validation in larger, independent populations.

Proteomic insights of peripheral artery disease across the glycemic spectrum: a prospective cohort study.

BACKGROUND Peripheral artery disease (PAD) risk varies substantially across glycemic states, but glycemic status-specific proteomic features of PAD remain poorly characterized. This study aimed to provide comprehensive proteomic insights into PAD risk across the glycemic spectrum. METHODS We included 43,875 UK Biobank participants, categorized into normoglycemia, prediabetes, and type 2 diabetes (T2D). Associations between 2920 plasma proteins and PAD were assessed using Cox regression models. PAD-related proteins underwent pathway enrichment and protein-protein interaction (PPI) analyses, and protein predictors were selected via least absolute shrinkage and selection operator models. The differential expression-sliding window analysis identified proteomic changes across the glycemic continuum. RESULTS We identified 558 proteins associated with PAD risks, and those proteins were predominantly involved in pathways related to immune system regulation, inflammatory processes, and vascular remodeling. Two major PPI networks were identified, centered on tumor necrosis factor in normoglycemic participants and T-cell surface glycoprotein CD4 in those with T2D. The integration of protein predictors or derived protein risk scores into the clinical model significantly improved PAD prediction performance, achieving a maximum C-index of 0.834. Two proteomic peaks were revealed at glycated hemoglobin levels of 37 and 42 mmol/mol (5.5% and 6.0%), at which 11 and 4 proteins, respectively, showed potential causal associations with PAD. CONCLUSION This study revealed glycemic state-specific proteomic features of PAD risk. These findings suggest the involvement of innate immunity in normoglycemia, and adaptive immune dysregulation with chronic inflammation in T2D. Integrating proteomic data also improved PAD risk prediction. Further validation is warranted.

Location-dependent proteomics of the aorta reveal an atherosclerotic disease gradient shaped by hemodynamics

Background and aims Atherosclerotic plaques form preferentially at vascular sites exposed to disturbed blood flow, yet the protein changes underlying this site-specific plaque development remain unclear. Mouse models are widely used to study atherosclerosis but yield only limited amounts of tissue, previously restricting proteomic studies. However, recent advances in mass spectrometry now enable proteomic profiling of very small tissue samples. We aimed to utilise this to uncover site-specific protein changes in aortic regions prone or resistant to plaque formation. Methods Aortic arches from apolipoprotein E-deficient (ApoE−/−) mice fed a Western diet (WD) for 16 weeks were dissected into plaques from the major branches and inner curvature and visibly healthy regions. Proteins were extracted, enzymatically digested, and analysed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Results More than 4000 proteins were identified per sample despite their small size (< 1 mg tissue). Principal component analysis showed clustering by both disease status and anatomical location within the aortic arch, indicating distinct proteomes. Proteins known to drive atherosclerosis – including vascular cell adhesion molecule 1 (Vcam1), apolipoprotein B (Apob), lipoprotein lipase (Lpl), and galectin 3 (Lgals3) – were most abundant in advanced plaques and decreased progressively across anatomical regions, reaching their lowest levels in ‘healthy’ regions furthest from the plaques. Enrichment analysis highlighted pathways related to the extracellular matrix, immune system, hemostasis, and lipoprotein transport as central to disease progression. Conclusions This study demonstrates the feasibility of region-resolved proteomics in individual murine aortas and provide new molecular insights into the site-specific nature of atherosclerotic plaque development.

Dual-Omics Profiling of Carotid Plaques Reveals Stage-Dependent Host–Microbiome Interaction Dynamics from Formation to Rupture

Background: Carotid plaque rupture is a critical event in ischemic stroke, yet the potential involvement of the intraplaque microbiota across disease stages remains unclear. Methods: We performed dual-omics profiling by analyzing host transcriptomes and PathSeq-derived microbiomes from 48 human carotid RNA-seq specimens spanning early lesions (intimal thickening; n = 10), stable plaques (n = 20), and unstable plaques (n = 18). Host transcriptomes were profiled alongside intraplaque microbiomes extracted via the GATK PathSeq pipeline with rigorous in silico decontamination. We integrated differential expression analysis, microbial diversity metrics, and functional inference. Furthermore, an integrated machine learning approach (incorporating Boruta feature selection) was employed to identify exploratory cross-kingdom diagnostic biomarkers. Results: Microbial beta diversity diverged significantly across disease stages, accompanied by the progressive upregulation of 54 host genes critical for extracellular matrix remodeling and immune chemotaxis. Strikingly, despite the inherent noise and artifacts associated with low-biomass sequencing, we computationally detected the distinct enrichment of 21 bacterial taxa in unstable plaques, predominantly oral and gut mucosal pathobionts. Computationally inferred functional profiling revealed that these unstable plaque-associated microbiota were significantly linked to predicted cell death, IL-17, and HIF-1 signaling pathways and exhibited strong positive correlations with host matrix-degrading transcripts. Statistical modeling suggested associative links among specific microbial enrichment, host transcriptomic dysregulation, and plaque instability, highlighting concurrent biological cross-talk. Importantly, our integrated machine learning pipeline established a 14-feature cross-kingdom biomarker panel (10 host genes and 4 bacteria) that discriminated stable from unstable plaques (cross-validated AUC = 0.869). Conclusions: Intraplaque microbiome dynamics computationally associate with host transcriptomic alterations during carotid plaque evolution. This synergistic host–microbiome association provides a hypothesis-generating framework linking microbial dysbiosis to plaque destabilization, offering novel mechanistic insights and highlighting the exploratory cross-kingdom biomarker panel as a highly promising foundation for future experimental validation and stage-tailored clinical diagnostics.

Plasma proteomic profiling deciphers molecular dynamics linking insulin resistance to multiple chronic diseases and mortality.

BACKGROUND Insulin resistance (IR) correlates with a wide spectrum of diseases and death. However, the specific proteomic signatures of IR and their associations with health outcomes remain incompletely understood. METHODS Leveraging data of 2,920 plasma proteins from 19,556 individuals in UK biobank study, we employed the elastic net model to dissect IR-related proteins and construct proteomic signature scores. Cox proportional hazards model was fitted to examine the longitudinal associations of distinct IR indicators and their proteomic signatures with multiple chronic disease and mortality. Mediation analyses were conducted to explore the role of individual proteins and proteomic signatures in IR-disease associations. RESULTS Over a mean follow-up of over 12 years, higher levels of IR surrogate makers were linked to cardiovascular-kidney-metabolic events and mortality, with eGDR outperforming other metrics in predictive discrimination. Furthermore, most IR proteomic signatures were prospectively associated with the incidence of type 2 diabetes mellitus, ischemic heart diseases, stroke, chronic kidney diseases, and mortality. Proteins associated with IR were primarily enriched in inflammation, immune response, and lipid metabolism, with immune-related proteins holding crucial roles. Multiple IR-disease associations were significantly mediated by proteomic signatures and specific proteins, like HAVCR1, CXCL17, and GDF15. CONCLUSION This study probed into the plasma proteomic profiles in IR settings and the associations of relevant protein signatures with multiple chronic diseases and mortality, providing additional guidelines on targeted intervention strategies for cardiovascular-kidney-metabolic outcomes.

Circulating Protein Biomarkers for Primary Cardiomyopathy: Evidence from Mendelian Randomization and Clinical Validation.