Systematic analysis of microRNA fingerprints in thrombocythemic platelets using integrated platforms

Abstract

Posttranscriptional and translational controls mediated by microRNAs (miRNA) regulate diverse biologic processes. We dissected regulatory effects of miRNAs relevant to megakaryocytopoiesis and platelet biology by analyzing expression patterns in 79 subjects with thrombocytosis and controls, and integrated data with transcriptomic and proteomic platforms. We validated a unique 21-miRNA genetic fingerprint associated with thrombocytosis, and demonstrated that a 3-member subset defines essential thrombocythemia (ET). The genetic signature includes functional guide and passenger strands of the previously uncharacterized miR 490 (5p and 3p), which displayed restricted, low-level expression in megakaryocytes/platelets (compared with leukocytes), and aberrant expression during thrombocytosis, most profound in ET. Overexpression of miR 490 in a bilineage differentiation model of megakaryocyte/erythroid progenitor formation was insufficient for hematopoietic colony differentiation and/or lineage specification. Integration of transcriptomic and mass spectrometric datasets with functional reporter assays identified dishevelled associated activator of morphogenesis 1 (DAAM1) as a miR 490 5p protein target demonstrating decreased expression in ET platelets, putatively by translational control (and not by mRNA target degradation). Our data define a dysregulated miRNA fingerprint in thrombocytosis and support a developmentally restricted function of miR 490 (and its putative DAAM1 target) to conditions associated with exaggerated megakaryocytopoiesis and/or proplatelet formation.

Figure 1

General miRNA expression patterns across normal and thrombocytotic cohorts. (A) Aggregate expression patterns are displayed for platelet miRNAs displaying > 70% present calls by cohort, in order of increasing fluorescence intensities. Expression data for each cohort represent normalized means from healthy controls (n = 30), ET (n = 27), or RT (n = 22). (B-D) Unsupervised hierarchical clustering was completed based on correlation proximity; miRNA expression data are standardized to mean = 0, SD = 1 before clustering, and displayed as a continuous data range from −4 to +4. ET cohorts are substratified by the presence (ET⁺) or absence (ET⁻) of JAK2^V617F. (B) The clustering dendrograms using the aggregate 392-member miRNAs, which demonstrates relative segregation of ET from RT and NO cohorts, whereas (C) ET and NO phenotypes alone and (D) all 3 phenotypes delineate the phenotypic segregation using the 21-member miRNA list. Note the relative continuum of phenotypes in panel D as defined by the 21-member miRNAs, exclusive of a small genetically heterogeneous subset (hatched box).

Figure 2

Cross-platform validation and class prediction models. (A-B) Normalized miRNA expression data using paired microarray (A) or quantitative RT-PCR (B) are displayed for a randomly selected subset of ET (N = 10) and control (N = 10) platelets (each group included 5 males and 5 females). For both microarray and quantitative RT-PCR, boxes represent the within-group interquartile range encompassing 50% of the values, whereas the 95% confidence intervals and outliers are depicted; the horizontal bar within each box represents the group median. (C) Scatter plots demonstrate between-platform concordance for individual miRNAs by phenotype; the x- and y-axes represent the microarray and quantitative RT-PCR data, respectively, using a unified scale standardized to mean = 0 and SD = 1; 19 of 20 miRNAs (except for miR 1274a) exhibit concordant group medians, validating the expression data using independent platforms. *Statistically significant Spearman correlation coefficients (r) with P < .05. (D-E) Linear discriminant analysis plots display the posterior classification probability of each subject using a 3-biomarker subset (miR 10a, miR 148a, and miR 490 5p) based on microarray (D; N = 57) or quantitative RT-PCR (E; N = 20). Triangles represent samples containing the JAK2V⁶¹⁷F mutation (either homozygous or heterozygous).

Figure 3

Cellular and cohort-specific miRNA expression. (A) Quantitative RT-PCR was used to quantify miRNA expression patterns in leukocytes (WBC), normal platelets, or thrombocytosis (ET or RT), displayed by group mean (N = 5 samples/group). The threshold sensitivity of miR 490 3p and 5p assays is delineated by boundaries of the stippled box. (B-C) Normalized aggregate expression data for miR 490 3p and miR 490 5p are displayed for microarray (B; N = 79) or quantitative RT-PCR data (C; N = 10/group). Data are presented as the mean ± SEM.

Figure 4

Genetic and functional characterization of miR 490. (A) Endogenous miR 490 expression patterns were monitored by quantitative RT-PCR using CD34⁺ cells differentiated in vitro over 21 days using a bilineage culture system; miR 150 expression is shown for comparison. (B-E) CD34⁺ cells transduced with lentiviruses LV 000 or LV490 were puromycin-selected (or not for mock-treated controls) and used for monitoring miR 490 expression patterns over a 21-day period (B); expression values for normal (NO) and ET platelets are shown for comparison. Hematopoietic progenitor assays were completed at day 14 after lentivirus infection (C), megakaryocyte colony assays on day 10 after infection (D), and megakaryocyte/erythroid lineage specification was analyzed at day 7 after infection by flow cytometry using anti-CD41 (Mk marker) or anti-glycophorin A (erythroid) antibodies for quantification (displayed in boxes; E). All results are from a single representative experiment repeated on one occasion. (A-B) P values were calculated by paired t test comparing aggregate miR 490 3p and miR 490 5p time series data.

Figure 5

Identification and characterization of miR 490 targets. (A) Schema delineating the workflow model for target identification delineates 2 distinct pathways based on mRNA degradation or translational control. Supplemental Tables 5 and 6 detail miRNA/mRNA pairs for either pathway. (B) Schema of DAAM1 mRNA (RefSeq accession no. NM_014992) with open-reading frame (ORF) and miR 490 binding sites is shown (top); topology of the DAAM1/miR 490 binding and calculated minimum free energy (MFE) are displayed as predicted using RNA hybrid (http://bibiserv.techfak.uni-bielefeld.de/rnahybrid). (C) Luciferase reporter assay quantification was completed 24 hours after transfection of HEK 293 cells using the 975-bp DAAM1 3′-UTR inserted downstream of Renilla luciferase (DAAM1) or cotransfected with a fixed concentration (10nM) of miR 490 3p or miR 490 5p chemical mimetics (or nontargeting Caenorhabditis elegans miR 239b stem-loop primer as control). Functional luciferase data from individual wells are expressed as the normalized mean ± SEM relative to DAAM1 expression (N = 6 wells/group representing aggregate data from 2 independent experiments). P values are calculated by paired t test.

Figure 6

Characterization of disheveled-associated activator of morphogenesis 1 in platelets. (A) DAAM1 quantitative RT-PCR was completed using ET or normal platelets (N = 5/group). (B) Normalized DAAM1-specific spectral counts from MudPit analyses are expressed as the mean ± SEM from 3 normal and 3 ET subjects. (C) DAAM1 peptide abundance was quantified between cohorts for each of 2 peptides and expressed as the mean ± SEM (N = 3/cohort). (D) Immunoblot analysis was completed using 4%-15% SDS-PAGE for each of 6 platelet lysates. (E) Quantitative multiple reaction monitoring from one normal and one ET platelet sample is displayed as the area under the curve (in parentheses) of the m/z 1215.8 transition to its doubly charged Y₁₉ ion. (F) MS/MS spectra of the DAAM1 peptide IQPDEFFGIFDQFLQAVSEAK specifies the fragmentation and Y₁₉ ion(s) used for abundance quantification.