Large-Scale Profiling on lncRNAs in Human Platelets: Correlation with Platelet Reactivity.
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ABSTRACT:
SUBMITTER: Sun Y
PROVIDER: S-EPMC9319970 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Within the healthy population, there is substantial, heritable, and interindividual variability in the platelet response. We explored whether a proportion of this variability could be accounted for by interindividual variation in gene expression. Through a correlative analysis of genome-wide platelet RNA expression data from 37 subjects representing the normal range of platelet responsiveness within a cohort of 500 subjects, we identified 63 genes in which transcript levels correlated with variation in the platelet response to adenosine diphosphate and/or the collagen-mimetic peptide, cross-linked collagen-related peptide. Many of these encode proteins with no reported function in platelets. An association study of 6 of the 63 genes in 4235 cases and 6379 controls showed a putative association with myocardial infarction for COMMD7 (COMM domain-containing protein 7) and a major deviation from the null hypo thesis for LRRFIP1 [leucine-rich repeat (in FLII) interacting protein 1]. Morpholino-based silencing in Danio rerio identified a modest role for commd7 and a significant effect for lrrfip1 as positive regulators of thrombus formation. Proteomic analysis of human platelet LRRFIP1-interacting proteins indicated that LRRFIP1 functions as a component of the platelet cytoskeleton, where it interacts with the actin-remodeling proteins Flightless-1 and Drebrin. Taken together, these data reveal novel proteins regulating the platelet response.
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Project description:Platelet hyperreactivity associates with cardiovascular events in humans. Studies in mice and humans suggest that prostaglandin E2 (PGE2) regulates platelet activation. In mice, activation of the PGE2 receptor subtype 3 (EP3) promotes thrombosis, but the significance of EP3 in humans is less well understood.To characterize the regulation of thromboxane-dependent human platelet activation by PGE2.Platelets collected from nineteen healthy adults were studied using an agonist of the thromboxane receptor (U46,619), PGE2, and selective agonists and/or antagonists of the EP receptor subtypes. Platelet activation was assayed by (1) optical aggregometry, (2) measurement of dense granule release, and (3) single-platelet counting.Healthy volunteers demonstrated significant interindividual variation in platelet response to PGE2. PGE2 completely inhibited U46,619-induced platelet aggregation and ATP release in 26% of subjects; the remaining 74% had partial or no response to PGE2. Antagonism of EP4 abolished the inhibitory effect of PGE2. In all volunteers, a selective EP2 agonist inhibited U46,619-induced aggregation. Furthermore, the selective EP3 antagonist DG-041 converted all PGE2 nonresponders to full responders.There is significant interindividual variation of platelet response to PGE2 in humans. The balance between EP2, EP3, and EP4 activation determines its net effect. PGE2 can prevent thromboxane-induced platelet aggregation in an EP4-dependent manner. EP3 antagonism converts platelets of nonresponders to a PGE2-responsive phenotype. These data suggest that therapeutic targeting of EP pathways may have cardiovascular benefit by decreasing platelet reactivity.
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Project description: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'UTRs 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. Total RNA from the platelets of 19 donors was harvested and labeled with Hy3. Reference RNA (a pool of all samples) was labeled with Hy5. This submission represents the miRNA expression component of the study.
2011-09-26 | E-GEOD-27917 | biostudies-arrayexpress
Project description: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'UTRs 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.
2011-09-27 | GSE27917 | GEO
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Project description:BackgroundThe biological regulatory system is highly dynamic. Correlations between functionally related genes change over different biological conditions, which are often unobserved in the data. At the gene level, the dynamic correlations result in three-way gene interactions involving a pair of genes that change correlation, and a third gene that reflects the underlying cellular conditions. This type of ternary relation can be quantified by the Liquid Association statistic. Studying these three-way interactions at the gene triplet level have revealed important regulatory mechanisms in the biological system. Currently, due to the extremely large amount of possible combinations of triplets within a high-throughput gene expression dataset, no method is available to examine the ternary relationship at the biological system level and formally address the false discovery issue.ResultsHere we propose a new method, Hypergraph for Dynamic Correlation (HDC), to construct module-level three-way interaction networks. The method is able to present integrative uniform hypergraphs to reflect the global dynamic correlation pattern in the biological system, providing guidance to down-stream gene triplet-level analyses. To validate the method's ability, we conducted two real data experiments using a melanoma RNA-seq dataset from The Cancer Genome Atlas (TCGA) and a yeast cell cycle dataset. The resulting hypergraphs are clearly biologically plausible, and suggest novel relations relevant to the biological conditions in the data.ConclusionsWe believe the new approach provides a valuable alternative method to analyze omics data that can extract higher order structures. The software is at https://github.com/yunchuankong/HypergraphDynamicCorrelation .
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