Platelet microRNA-mRNA coexpression profiles correlate with platelet reactivity

Abstract

MicroRNAs (miRNAs) regulate cell physiology by altering protein expression, but the biology of platelet miRNAs is largely unexplored. We tested whether platelet miRNA levels were associated with platelet reactivity by genome-wide profiling using platelet RNA from 19 healthy subjects. We found that human platelets express 284 miRNAs. Unsupervised hierarchical clustering of miRNA profiles resulted in 2 groups of subjects that appeared to cluster by platelet aggregation phenotypes. Seventy-four miRNAs were differentially expressed (DE) between subjects grouped according to platelet aggregation to epinephrine, a subset of which predicted the platelet reactivity response. Using whole genome mRNA expression data on these same subjects, we computationally generated a high-priority list of miRNA-mRNA pairs in which the DE platelet miRNAs had binding sites in 3'-untranslated regions of DE mRNAs, and the levels were negatively correlated. Three miRNA-mRNA pairs (miR-200b:PRKAR2B, miR-495:KLHL5, and miR-107:CLOCK) were selected from this list, and all 3 miRNAs knocked down protein expression from the target mRNA. Reduced activation from platelets lacking PRKAR2B supported these findings. In summary, (1) platelet miRNAs are able to repress expression of platelet proteins, (2) miRNA profiles are associated with and may predict platelet reactivity, and (3) bioinformatic approaches can successfully identify functional miRNAs in platelets.

Figures

Figure 1

Platelet microRNA profiling in 19 healthy subjects. (A) Unsupervised hierarchical clustering of miRNA expression profiles. The dendrogram was generated with 284 miRBase miRNAs that were expressed in the platelets of all 19 subjects included in the study. The dendrogram represents a Euclidean distance dendrogram of the median-centered log ratio values for the 19 samples. Two major clusters are identified, which tend to differentiate between platelets of differing reactivity to epinephrine. (B) Heatmap with miRNAs that were differentially expressed between the 2 platelet reactivity groups using a 2-sample unequal variances t test. Each row in the heatmap indicates the log-ratio intensity data of 1 DE miRNA across the 19 subjects.

Figure 2

Platelet reactivity predicted by differentially expressed miRNAs. Cross-validation analysis using 7 miRNAs (miR-19b, miR-34b, miR-190, miR-320a, miR-320b, miR-320c, and miR-320d) was able to predict the true epinephrine response in hyper-reactive (red) and hyporeactive (blue) platelets (r = 0.71, P = .0006801). See “Biomarker predictions” for criteria used to select the miRNAs.

Figure 3

Rank correlation between platelet and HEL or Meg-01 cells. The miRNA expression levels were ranked for platelets, HEL cells, and Meg-01 cells. Correlations were determined and plotted between platelets and each megakaryocytic cell line.

Figure 4

Expression levels of the mRNA-miRNA pairs. The box plots represent the expression levels of miRNAs and mRNA in hyper-reactive platelets (Hyper) and hyporeactive (Hypo) platelets. (A) The hsa-miR-200b (left) and PRKAR2B mRNA (right) pair. (B) The hsa-miR-495 (left) and KLHL5 mRNA (right) pair. (C) The hsa-miR-107 (left) and CLOCK mRNA (right) pair. In each pair, the directionality of the Hyper versus Hypo group is opposite for the miRNA and the mRNA. Notably, all miRNAs and mRNAs are significantly differentially expressed (P < .05) between the Hyper and Hypo groups.

Figure 5

miRNAs regulate the expression of PRKAR2B, KLHL5, and CLOCK by binding to their 3′-UTR. Cells expressing the protein of interest were transfected by the candidate miRNA and assessed for protein knockdown. The negative miRNA control has a scrambled sequence that does not repress any gene. Each panel shows a Western immunoblot (above) and the mean of at least 3 experiments below. Protein levels were normalized to levels of GAPDH. (A) Pre-miR-200b was transfected into Meg-01 cells (5 × 106 cells), and lysates were harvested at 48 hours. The bar graph represents the mean 21% knockdown of 3 independent experiments. (B) Pre-miR-495 was transfected into Meg-01 cells (5 × 106 cells), and lysates were harvested at 72 hours. The bar graph represents the mean 18% knockdown of 6 independent experiments. The miR-495 knockdown of KLHL5 showed a trend (P = .07). (C) Pre-miR-107 was transfected into HCT-DK cells (0.2 × 106 cells), and lysates were harvested at 48 hours. The bar graph represents the mean 37% knockdown of 4 independent experiments. (D) The 50% decrease in GFP expression when the PRKAR2B 3′-UTR plasmid is cotransfected with pre-miR-200b compared with negative control in HCT-DK cells. Similarly, there was a 23% decrease in GFP when KLHL5 3′-UTR was cotransfected with pre-miR-495 (E) and 50% decrease in GFP with CLOCK 3′-UTR was cotransfected with pre-miR-107 (F). α-Tubulin was used as an internal control to normalize the GFP levels between the test and the control lanes.

Figure 6

Platelets null for PRKAR2B are hyporesponsive. (A) PRKAR2B immunoblot. Lysates of mouse wild-type (wt) brain, wt platelets, and platelets from Prkar2b−/− mice (KO) were subjected to sodium dodecyl sulfate–polyacrylamide gel electrophoresis and immunoblotted with polyclonal antisera raised against recombinant mouse protein as described. Approximately equal loading is shown by immunoblotting for β-actin. (B) Platelet surface expression of the activation marker, P-selectin, was quantified by flow cytometry on platelets from wild-type (WT) mice and mice null for Prkar2b (KO). Platelets were stimulated with a fixed, subthreshold concentration of PAR4AP (0.8mM) and the indicated increasing concentrations of epinephrine.