Prioritizing Candidates of Post–Myocardial Infarction Heart Failure Using Plasma Proteomics and Single-Cell Transcriptomics

Abstract

Background: Heart failure (HF) is the most common long-term complication of acute myocardial infarction (MI). Understanding plasma proteins associated with post-MI HF and their gene expression may identify new candidates for biomarker and drug target discovery. Methods: We employed aptamer-based affinity-capture plasma proteomics to measure 1305 plasma proteins at one month post-MI in a New Zealand cohort (CDCS) including 181 post-MI patients who were subsequently hospitalized for HF compared with 250 post-MI patients who remained event-free over a median follow-up of 4.9 years. We then correlated plasma proteins with left ventricular ejection fraction measured at 4 months post-MI and identified proteins potentially co-regulated in post-MI HF using Weighted Gene Co-expression Network Analysis (WCGNA). A Singapore cohort (IMMACULATE) of 223 post-MI patients, of which 33 patients were hospitalized for HF (median follow-up 2.0 years), was used for further candidate enrichment of plasma proteins using Fisher meta-analysis, resampling-based statistical testing and machine learning. We then cross-referenced differentially-expressed proteins with their differentially-expressed genes from single-cell transcriptomes of non-myocyte cardiac cells isolated from a murine MI model, and single-cell and single-nuclei transcriptomes of cardiac myocytes from murine HF models and human HF patients. Results: In the CDCS cohort, 212 differentially-expressed plasma proteins were significantly associated with subsequent HF events. Of these, 96 correlated with left ventricular ejection fraction measured at 4 months post-MI. WCGNA prioritised 63 of the 212 proteins that demonstrated significantly higher correlations among patients who developed post-MI HF compared with event-free controls (dataset 1). Cross-cohort meta-analysis of the IMMACULATE cohort identified 36 plasma proteins associated with post-MI HF (dataset 2) while single-cell transcriptomes identified 15 gene-protein candidates (dataset 3). The majority of prioritized proteins were of matricellular origin. The 6 most highly-enriched proteins that were common to all 3 datasets included well-established biomarkers of post-MI HF - N-terminal B-type natriuretic peptide and troponin T - as well as newly-emergent biomarkers - angiopoietin-2, thrombospondin-2, latent transforming growth factor-β binding protein-4 and follistatin-related protein-3. Conclusions: Large-scale human plasma proteomics, cross-referenced to unbiased cardiac cell transcriptomics at single-cell resolution, prioritized protein candidates associated with post-MI HF for further mechanistic and clinical validation.

In-Depth Analysis of the Plasma Proteome in ME/CFS Exposes Disrupted Ephrin-Eph and Immune System Signaling

“…The ephrin receptor signaling pathway will be discussed at greater length below. It is, however, noteworthy that EFNA4, EFNA5 as well as TNFRSF1α were among the upregulated proteins of a correlation network uniquely associated with the regulation of actin filament process, ephrin receptor signaling, and regulation of muscle system processes in a heart failure study [ 43 ]. Furthermore, almost all the other proteins of that network were upregulated in our ME/CFS patient cohort ( Supplementary File 1 ).…”

Role of Transportome in the Gills of Chinese Mitten Crabs in Response to Salinity Change: A Meta-Analysis of RNA-Seq Datasets

“…The output of the DE analysis from all datasets was further employed for the meta-analysis, which was carried out by using the metaRNASeq [ 39 ] package. metaRNASeq uses Fisher’s combined probability test [ 40 ] for meta-analyses and has been used in several studies [ 41 , 42 , 43 ]. As the name suggests, this method combines the p -values for each gene from individual studies using the following formula [ 39 , 40 ]: …”

Advances in Aptamer-Based Biomarker Discovery

“…In addition, 3 proteins, EGFR, GDF11/8, and hemojuvelin were associated with lower risk ( Nayor et al, 2020 ). Chan et al (2020) found that 4 newly emergent biomarkers (AGN-2, THBS-2, LTBP-4, and FSTL3) were consistently associated with the development of post–MI HF.…”