Integrative Genomic Analysis Reveals Four Protein Biomarkers for Platelet Traits

Abstract

Rationale: Mean platelet volume (MPV) and platelet count (PLT) are platelet measures that have been linked to cardiovascular disease (CVD) and mortality risk. Identifying protein biomarkers for these measures may yield insights into CVD mechanisms.

Objective: We aimed to identify causal protein biomarkers for MPV and PLT among 71 CVD-related plasma proteins measured in FHS (Framingham Heart Study) participants.

Methods and results: We conducted integrative analyses of genetic variants associated with PLT/MPV with protein quantitative trait locus variants associated with plasma proteins followed by Mendelian randomization to infer causal relations of proteins for PLT/MPV. We also tested protein-PLT/MPV association in FHS participants. Using induced pluripotent stem cell-derived megakaryocyte clones that produce functional platelets, we conducted RNA-sequencing and analyzed expression differences between low- and high-platelet producing clones. We then performed small interfering RNA gene knockdown experiments targeting genes encoding proteins with putatively causal platelet effects in megakaryocyte clones to examine effects on platelet production. In protein-trait association analyses, ten proteins were associated with MPV and 31 with PLT. Mendelian randomization identified 4 putatively causal proteins for MPV and 4 for PLT. GP-5 (Glycoprotein V), GRN (granulin), and MCAM (melanoma cell adhesion molecule) were associated with PLT, while MPO (myeloperoxidase) showed significant association with MPV in both analyses. RNA-sequencing analysis results were directionally concordant with observed and Mendelian randomization-inferred associations for GP-5, GRN, and MCAM. In siRNA gene knockdown experiments, silencing GP-5, GRN, and MPO decreased PLTs. Genome-wide association study results suggest several of these may be linked to CVD risk.

Conclusions: We identified 4 proteins that are causally linked to PLTs. These proteins may also have roles in the pathogenesis of CVD via a platelet/blood coagulation-based mechanism.

Keywords: biomarkers; cardiovascular diseases; genetics; megakaryocytes; proteomics.

Figure 1.

Flowchart of Study Design

Figure 2.. Comparison of the Effect Estimates of MR and FHS Protein-Trait Association Analyses

MPO and NTProBNP were associated with MPV (top), while ADM, CNTN1, CRP, GP5, GRN, and MCAM were associated with PLT (bottom). Proteins with P < 0.05 in both protein-trait and MR analyses were included in the plot. Units of effect estimates: per standard deviation increment of rank-based inverse normal transformed protein level. ggplot2 package in R version 3.6.2 was used to create the plot.

Figure 2.. Comparison of the Effect Estimates of MR and FHS Protein-Trait Association Analyses

MPO and NTProBNP were associated with MPV (top), while ADM, CNTN1, CRP, GP5, GRN, and MCAM were associated with PLT (bottom). Proteins with P < 0.05 in both protein-trait and MR analyses were included in the plot. Units of effect estimates: per standard deviation increment of rank-based inverse normal transformed protein level. ggplot2 package in R version 3.6.2 was used to create the plot.

Figure 3.. iPSC Megakaryocyte gene knockdown experiment results

Silencing GRN, GP5 and MPO significantly decreased platelet count, but silencing MCAM did not result in a significant change in platelet count. Two-sample t-test was used to compare the control (GFP) with GP5 and MCAM groups. Wilcoxon Rank-sum test was used to compare the control with GRN and MPO groups because GRN and MPO group data were not normally distributed (Wilcoxon rank-sum test p-values are marked with an asterisk). Bonferroni-corrected p-value threshold for significance is 0.05/7 = 0.0071 because GFP group value was compared with 7 other groups (LUC, PRMT7, HDAC7, GRN, GP5, MPO, MCAM). All statistical tests were performed using R version 3.6.2. ggplot2 package in R version 3.6.2 was used to create the plot.