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Prepublished online as a Blood First Edition Paper on November 14, 2002; DOI 10.1182/blood-2002-09-2797.
HEMOSTASIS, THROMBOSIS, AND VASCULAR BIOLOGY
From the Department of Medicine, Department of
Pathology, and Program in Genetics, State University of New York, Stony
Brook; Biology Department, Brookhaven National Laboratory, Upton, NY;
and Department of Pathology, Robert Wood Johnson Medical Center, New
Brunswick, NJ.
Human platelets are anucleate blood cells that retain cytoplasmic
mRNA and maintain functionally intact protein translational capabilities. We have adapted complementary techniques of microarray and serial analysis of gene expression (SAGE) for genetic profiling of
highly purified human blood platelets. Microarray analysis using the
Affymetrix HG-U95Av2 approximately 12 600-probe set maximally
identified the expression of 2147 (range, 13%-17%) platelet-expressed transcripts, with approximately 22% collectively involved in
metabolism and receptor/signaling, and an overrepresentation of genes
with unassigned function (32%). In contrast, a modified SAGE protocol using the Type IIS restriction enzyme MmeI (generating
21-base pair [bp] or 22-bp tags) demonstrated that 89% of tags
represented mitochondrial (mt) transcripts (enriched in 16S and 12S
ribosomal RNAs), presumably related to persistent mt-transcription in
the absence of nuclear-derived transcripts. The frequency of non-mt SAGE tags paralleled average difference values (relative expression) for the most "abundant" transcripts as determined by microarray analysis, establishing the concordance of both techniques for platelet
profiling. Quantitative reverse transcription-polymerase chain reaction
(PCR) confirmed the highest frequency of mt-derived transcripts, along
with the mRNAs for neurogranin (NGN, a protein kinase C substrate) and
the complement lysis inhibitor clusterin among the top 5 most abundant
transcripts. For confirmatory characterization, immunoblots and flow
cytometric analyses were performed, establishing abundant cell-surface
expression of clusterin and intracellular expression of NGN. These
observations demonstrate a strong correlation between high transcript
abundance and protein expression, and they establish the validity of
transcript analysis as a tool for identifying novel platelet proteins
that may regulate normal and pathologic platelet (and/or
megakaryocyte) functions.
(Blood. 2003;101:2285-2293) Human blood platelets play critical roles in normal
hemostatic processes and pathologic conditions such as thrombosis,
vascular remodeling, inflammation, and wound repair. Generated as
cytoplasmic buds from precursor bone marrow megakaryocytes, platelets
are anucleate and lack nuclear DNA, although they retain
megakaryocyte-derived mRNAs.1,2 Platelets contain rough
endoplasmic reticulum and polyribosomes, and they retain the ability
for protein biosynthesis from cytoplasmic mRNA.3 Quiescent
platelets generally display minimal translational activity, although
newly formed platelets such as those found in patients with immune
thrombocytopenic purpura (ITP) synthesize various Despite the biologic importance of platelets and their intact protein
synthetic capabilities, remarkably little is known about platelet
mRNAs. Younger platelets contain larger amounts of mRNA with a greater
capacity for protein synthesis, as determined by using fluorescent
nucleic acid dyes such as thiazole orange.10 This assay
has been used as a quantitative determinant of younger or
"reticulated" platelets (RPs). Indeed increased reticulated platelets are typically found in patients with conditions associated with rapid platelet turnover such as ITP; typically RP percentages in
such patients approach 10% to 20% of all platelets, considerably higher than in healthy control subjects.11 Interestingly,
high RPs have been associated with enhanced thrombotic risk when
identified in patients with thrombocytosis,10 suggesting
that quantitatively increased mRNA levels may be associated with the
prothrombotic phenotype. Whether this is related to globally altered
gene expression profiles or to select changes more evident during
situations of rapid platelet turnover remains unknown. Certainly,
technical limitations of this assay limit its utility in defining
prothrombotic genotypes,10-12 and it cannot identify
differentially expressed genes that may be causally implicated in
disordered platelet phenotypes.
Toward the goal of defining the molecular anatomy of the platelet
genome, we have adapted complementary techniques of microarray and
serial analysis of gene expression (SAGE) for genetic profiling of
highly purified human blood platelets. Microarray technology represents
a "closed" profiling strategy limited by the target genes imprinted
onto gene chips. In contrast, SAGE is an "open" architectural
system that can be used to identify novel genes and to quantify
differentially expressed mRNAs.13-15 The sequence of each
tag along with its positional location uniquely identifies the gene
from which it is derived, and differentially expressed genes can be
identified in a quantitative manner because the tag frequency reflects
the mRNA level at the time of cellular harvest and analysis. By using
both technologies, we have identified a number of previously
uncharacterized genes that appear to be expressed in human platelets,
while simultaneously establishing the dominant frequency of
mitochondrial-expressed genomes comprising the platelet mRNA pool.
These observations provide a panoramic overview of the platelet
transcriptome, while additionally providing insights into the molecular
pathways regulating platelet (and/or megakaryocyte) function in normal
and pathologic conditions.
Reagents and supplies
Platelet isolation, purification, and immunodetection
The efficiency of platelet purification was documented at each step by flow cytometry.17 Briefly, aliquots containing 2 × 106 platelets were incubated with saturating concentrations of FITC-conjugated anti-CD41, PE-conjugated antiglycophorin, and PERCP-conjugated anti-CD45 for 15 minutes in the dark at 25°C, washed with phosphate-buffered saline (PBS), and fixed in PBS/1% formalin. Samples were analyzed using a FACScan (fluorescence-activated cell sorter scan) flow cytometer (Becton Dickinson) using CELLQuest software designed to quantify the number of CD45+ and glycophorin-positive events in the sample (expressed as the number of events per 100 000 CD41+ events). For some experiments, fixed platelets were permeabilized with 0.1% Triton-X/PBS for 30 minutes at 25°C prior to the addition of primary antibodies, all as previously described.17 Platelet protein detection was completed by sodium dodecyl sulfate (SDS)-polyacrylamide gel electrophoresis (PAGE) and immunoblot analysis as previously described, using the species-specific horseradish peroxidase-conjugated secondary antibody and enhanced chemiluminescence.16 Antibodies included the anticlusterin monoclonal antibody (Quidel, Santa Clara, CA; 1:1000 primary and 1:10 000 secondary) and the antineurogranin rabbit polyclonal antibody (Chemicon International, Temecula, CA; 1:1000 primary and 1:10 000 secondary). Molecular analyses and microarray profiling Purified, individual cell fractions were resuspended in 10 mL Trizol reagent (Invitrogen), transferred into diethylpyrocarbonate (DEPC)-treated Corex (Springfield, MA) tubes, and serially purified and precipitated by using isopropanol essentially as previously described.16 Total cellular RNA was harvested by centrifugation at 12 500g for 20 minutes at 4°C, washed 2 times with 75% ethanol (10 mL/tube), and resuspended in 100 µL DEPC-treated water. Platelet mRNA quantitation was performed by using fluorescence-based real-time PCR (polymerase chain reaction) technology (TaqMan Real-Time PCR; Applied Biosystems, Foster City, CA). Oligonucleotide primer pairs were generated by using Primer3 software (www-genome.wi.mit.edu), designed to generate approximately 200-base pair (bp) PCR products at the same annealing temperature, and are outlined in Table 1. Purified platelet mRNA (4 µg) was used for first-strand cDNA synthesis using oligo(dT) and SuperScript II reverse transcriptase (Invitrogen). For real-time reverse transcription (RT)-PCR analysis, the RT reaction was equally divided among primer pairs and used in a 40-cycle PCR reaction for each target gene by using the following cycle: 94°C for 30 seconds, 55°C for 30 seconds, 72°C for 1 minute, and 71°C for 10 seconds (40 cycles total). mRNA levels were quantified by monitoring real-time fluorometric intensity of SYBR green I. Relative mRNA abundance was determined from triplicate assays performed in parallel for each primer pair and was calculated by using the comparative threshold cycle number ( -Ct method) as previously
described.18
Gene expression profiles were completed by using the approximately 12 600-probe set HG-U95Av2 gene chip (Affymetrix, Santa Clara, CA). Total cellular RNA (5 µg) was used for cDNA synthesis by using SuperScript Choice system (Life Technologies, Rockville, MD) and an oligo(dT) primer containing the T7 polymerase recognition sequence (Primer S1; Table 1), followed by cDNA purification using GFX spin columns. In vitro transcription was completed in the presence of biotinylated ribonucleotides by using a BioArray HighYield RNA Transcript Labeling Kit (Enzo Diagnostics, Farmingdale, NY), and, after metal-induced fragmentation, 15 µg biotinylated cRNA was hybridized to the HG-U95Av2 oligonucleotide probe array for 16 hours at 45°C. After washing, the cRNA was detected with streptavidin-phycoerythrin (Molecular Probes, Eugene, OR) and analysis was completed by using a Hewlett-Packard Gene Array Scanner (Affymetrix). The average difference value (AD) for each probe set was quantified using MAS 4.01 software (Affymetrix), calculated as an average of fluorescence differences for perfectly matched versus single-nucleotide mismatched 25-mer oligonucleotides (16 to 20 oligonucleotide pairs per probe set). The software is designed to exclude "positive calls" in the presence of high average differences with associated high mismatch intensities. SAGE profiles Platelet SAGE libraries were generated essentially as previously described,13 modified as outlined in Figure 1 for the use of MmeI as the tagging enzyme.19 This type IIS restriction enzyme cleaves 20 of 18 bp past its nonpalindromic (TCCRAC) recognition sequence, thereby generating longer tags (21- or 22-mer) than those obtained using BsmFI as the standard tagging enzyme (13-14 bp tags). These longer MmeI-generated tags potentially provide for more definitive "tag-to-gene" identification and are particularly useful in characterizing expression patterns in the absence of complete genomic sequence data (comprehensive methods detailed in Dunn et al19). Briefly, poly(A) mRNA was isolated from 10 µg total platelet RNA using the oligo-(dT) S1 primer conjugated to magnetic beads (Dynal Biotech, Lake Success, NY), followed by cDNA synthesis using SuperScript II reverse transcriptase (Invitrogen). The cDNA was then digested with the restriction enzyme NlaIII (anchoring enzyme), ligated to cassette A using T4 DNA ligase, and, after the beads were extensively washed, the cDNA was digested with MmeI to release the tags from the beads. After purification, tags were ligated to degenerate cassette B linkers (specifically designed to anneal to the nonuniform MmeI overhangs), and PCR-amplified using biotinylated primers S2 and S3 for 30 cycles (95°C for 30 seconds; 58°C for 30 seconds; 72°C for 30 seconds) using Platinum Taq DNA polymerase (Gibco BRL). A fraction (20%) of the pooled PCR products were then subjected to one round of linear amplification using primer pair S2/S3, followed by a second round of 25 amplifications using primer S2 alone (95°C for 30 seconds, 58°C for 30 seconds, 72°C for 30 seconds). Primer S3 was subsequently added for one cycle (95°C for 2.5 minutes, 58°C for 30 seconds, 72°C for 5 minutes); the latter steps were collectively adapted to exclude heteroduplex formation.18 Unincorporated primers were removed by incubation with 200 U Escherichia coli exonuclease I for 60 minutes at 37°C. PCR products were then pooled and digested with NlaIII to release tags, and biotinylated linker arms were cleared using streptavidin-coated immunoaffinity magnetic beads (Dynal Biotech). Tags were concatamerized using 5 U/µL T4 DNA ligase, and products more than 100 bp were isolated by size-fractionation in low-melting agarose gels. The DNA was purified by GFX spin columns, and the concatamers were cloned into the SphI site of pZero (Invitrogen). After transformation into E coli TOP10 cells, recombinant clones were isolated and sequenced in 96-well microtiter plates using an ABI 377 sequencer and ABI Prism BigDye terminator chemistry (Perkin-Elmer Applied Biosystems, Branchburg, NJ).
Bioinformatic analyses Functional grouping of genes determined to be present by Affymetrix MAS 4.01 software was performed using a dChip program linked to the National Center for Biotechnology LocusLink, which is an annotated reference database for genes and their postulated functions.20 Of the approximately 12 600-probe sets represented on the Affymetrix HG-U95Av2 Gene chip, functional annotations exist for approximately 8100 with the remainder categorized as unknown. Microarray data were visualized and analyzed using BRB-ArrayTools software (Version 2.1), kindly developed and provided by Dr Richard Simon and Amy Peng (linus.nci.nih.gov/BRB-ArrayTools.html). A logarithmic (base 2) transformation was applied to the average difference values for individual data sets for determination of microarray concordancies. Discordancy was defined as a 2-log difference in the maximum log intensities between individual experiments.SAGE tags were extracted by using in-house SAGE software uniquely modified to identify MmeI tags. The software ensures that only unambiguous 21- to 22-bp tag sequences are extracted for transcript profiling. Tags with ambiguities (Ns), lengths other than 21 or 22 bp, or with ambiguous orientations were extracted to separate files for manual editing or further examination. Finalized data were exported to a relational database for tag quantification and genetic identification.20
Platelet purification To ensure that the RNA profiles accurately represented those of circulating blood platelets, a number of complementary methods were implemented to remove contaminating nucleated leukocytes. Purification methods incorporating gel filtration, a 5-µm leukocyte reduction filter, and magnetic CD45 immunodepletion allowed for the cumulative enrichment of highly purified platelets. The efficacy of this purification method was initially established by using peripheral blood platelet-rich plasma as the starting material. The final product contained no more than 3 to 5 leukocytes per 1×105 platelets as determined by parallel flow cytometric analysis, representing an approximate 450-fold reduction of nucleated leukocytes. These results correlated well with molecular evidence for leukocyte depletion as determined by RT-PCR using both CD45 and T-cell receptor -chain (TCR) primers (see
Figure 2). Because the total RNA yield from peripheral blood
platelets was insufficient for microarray studies, we adapted the
protocol to platelet apheresis donors with nearly identical final
purity (Figure 2). The platelet
recovery was nearly 65% of the starting material, yielding
approximately 2.3 × 1011 platelets from an
initial apheresis pack containing approximately
3.6 × 1011 platelets. The bulk of the losses
occurred during the initial centrifugation and filtration steps. The
purification protocol was less effective at removing erythrocytes,
although there were less than 50 glycophorin-positive cells per
1 × 105 platelets after the final purification step.
Nonetheless, these cells represent unlikely sources for contaminating
cellular RNA (see "Cellular microarray analysis"
below).
Cellular microarray analysis The purified platelet RNA was sufficient for microarray studies and was used for cRNA generation and hybridization to the Affymetrix HG-U95Av2 GeneChip. The anatomic profile of platelet RNAs from 3 healthy male donors was determined by using Affymetrix software. Of the 12 599 probe sets imprinted onto the chip, a maximum of 2147 (17%) transcripts were computationally identified as "present" by the Affymetrix software, 152 (1.2%) were equivocal, and nearly 82% were absent. As a fraction of the total genes present on the chip, the percentage of platelet-expressed genes (15%-17%) was generally lower than that obtained from other human cell types in which 30% to 50% of genes are present as determined by Affymetrix software (J. Schwedes, personal communication, May 2002). The "limited number" of platelet-expressed transcripts presumably reflects the lack of ongoing gene transcription in the anucleate platelet. Because less than 1% of circulating red blood cells contain residual RNA, it is unlikely that any of these transcripts are erythrocyte derived, although this was formally addressed by isolating total cellular RNA from 20 mL of whole blood (corresponding to an ~3-log fold excess of erythrocytes than that identified in our final sample). The total cellular yield of RNA from this starting material was approximately 250 ng, suggesting that less than 1 ng erythrocyte-derived RNA was present in the purified platelet preparations. Despite this, however, both -
and -globin transcripts along with both the ferritin heavy and
light chainswere identified as abundant transcripts (Table
2). Although the most parsimonious
explanation would be residual contaminating reticulocytes, this is not
supported by our erythrocyte contamination estimates, and their
significance remains unresolved.
As a means of better dissecting the molecular anatomy of the platelet, expressed genes were grouped on the basis of assigned gene annotations, and this analysis was used to provide a panoramic definition of the platelet transcriptome. Of the genes that could be cataloged within assigned "clusters," those involved in metabolism (11%) and receptor/signaling (11%) represented the largest groups. Also evident in these analyses is the relatively large percentage of genes involved in functions unrelated to these key groups (ie, miscellaneous, 25%), and the overrepresentation of genes with unknown function (32%) as annotated by Affymetrix and RefSeq databases.21 These results identify a vast array (nearly one half) of platelet genes (and gene products) that presumably have important, but poorly characterized functions, in platelet and/or megakaryocyte biology. Although microarray analysis is not truly quantitative, rank-ordering
using the mean log-intensities from 3 independent microarray analyses
allowed for the categorization of the top platelet transcripts (Table
2). Computational analyses demonstrated that only 10 of the top 100 genes were discordant among the 3 platelet microarrays, although 71 of
100 genes were discordant between platelet and leukocyte arrays. An
inventory of the top 50 platelet genes is listed in Table 2, which also
delineates those found to be highly expressed in peripheral blood
leukocytes by parallel microarray experiments with this purified
cellular fraction (data not shown). Further analysis of these cell
subsets demonstrated that approximately 25% (n = 547) of
the total platelet transcripts were platelet restricted. Furthermore,
only 10 of the 50 most highly expressed genes were found to overlap,
confirming the distinct cellular profiles of each transcriptome. Of the
12 overlap genes, 3 corresponded to globin or ferritin chains (again
suggesting the presence of contaminating reticulocytes in both purified
fractions), and another 4 were involved in actin cytoskeletal
reorganization and human leukocyte antigen (HLA) expression, gene
products that regulate critical functions in both cell types. Given the
importance of cytoskeletal reorganization in downstream platelet
activation events, it is not unexpected that components of the actin
machinery system would demonstrate prominent transcript expression.
Previous estimates suggest that 20% to 30% of the total platelet
proteome is comprised of actin with other components such as
actin-binding protein, mysosin, and talin accounting for an additional
2% to 5% of the total protein.1,22 The mRNAs encoding
the actin-related machinery are overrepresented in our microarray
analysis, with 8 such transcripts found among the 50 highest
platelet-expressed genes. Interestingly thymosin Platelet SAGE analyses Although these initial studies identified the distribution and relative expression patterns of the genes within the Affymetrix data set, they do not allow for analyses of genes that are unrepresented by these oligonucleotide chips. Unlike closed microarray profiling strategies, SAGE is an open architectural system that is ideally suited for novel gene and pathway identification. Accordingly, the platelet RNA used for microarray studies was used for platelet SAGE. A total of 2033 tags were initially cataloged, of which 1800 (89%) corresponded to mitochondrial-derived genes. These results were quite different from those obtained by microarray analyses, but the discrepancy can be resolved by the nonrepresentation of the mitochondrial genome on the gene chip. The mitochondrial genome is a compact approximately 16.6-kilobase (kb) sequence encoding 13 genes and 2 ribosomal subunits.24 Primary mitochondrial transcripts are polycistronic and typically contain premature termination or unpredictable splice sites, resulting in multiple polyadenylated transcripts from individual genes.24,25 Indeed, the overall distribution of platelet-derived mitochondrial SAGE tags is quite similar to that found in muscle.25 All 13 genes containing NlaIII sites were detected, whereas neither of the non-NlaIII-containing genes were identified (nicotinamide adenine dinucleotide [NADH] dehydrogenase subunit 4L and adenosine triphosphatase [ATPase] 8). Most of the tags were from the 16S and 12S ribosomal RNAswhich collectively accounted for
68% of the total mitochondrial tagswith the fewest tags represented
by NADH dehydrogenase subunits 3, 5, 6, and cytochrome c oxidase 1 (Figure 3). The NADH dehydrogenase subunit 6 RNA is the only mRNA encoded by the light (L) strand of
mitochondrial DNA and was the least abundantly detected
transcript.
The unusually high preponderance of mitochondrial-derived genes is not inconsistent with the known enrichment of these genomes in human platelets,1,24 and presumably reflects persistent transcription from the mitochondrial (mt) genome in the absence of nuclear-derived transcripts. This overrepresentation of mtDNA in platelets is considerably greater than that of its closest cell type (skeletal muscle), in which mt genomes represent approximately 20% to 25% of all SAGE tags.25 Interestingly, the energy metabolism of platelets is not dissimilar from that of skeletal muscle, both cell types actively using glycolysis and large amounts of glycogen for ATP generation.26 Like muscle, platelets are metabolically adapted to rapidly expend large amounts of energy required for aggregation, granule release, and clot retraction. Similar to the situation in all eukaryotic cells, platelet mitochondria represent the primary source of ATP, which is generated from oxidative phosphorylation reactions occurring within these organelles. Mitochondria are also responsible for most of the toxic reactive oxygen species generated as by-products of oxidative phosphorylation and are central regulators of the apoptotic process in other cellular types. The mtDNA encodes polypeptides found within 4 of the 5 multifunctional complexes that regulate oxidative phosphorylation within the platelet mitochondria.27 Whether the continued generation of these polypeptides has a role in platelet energy metabolism and/or the apoptotic mechanisms regulating platelet survival remains speculative, although not inconsistent with our observations. Comparative analysis of SAGE and microarray transcript abundance Complete SAGE libraries require the sequencing of up to 30 000 tags for an exhaustive cataloging of individual mRNAs, especially those with limited copy numbers.13,28 Given the preponderance of mt-derived transcripts, comparable sampling would have required sequence analysis of nearly 300 000 SAGE tags, an inordinate number for comprehensive analysis of the platelet transcriptome. For platelets, alternative methodologies incorporating subtractive SAGE will be required for more comprehensive transcript profiling.29 Our initial sampling of nonmitochondrial genes remains informative, however, and entirely consistent with the results of platelet microarray studies. As shown in Table 3, SAGE tags for the genes encoding thymosin4, 2-microglobulin, neurogranin, and the platelet glycoprotein Ib polypeptide were among the most frequently
identified platelet genes, similar to the rank-ordered results
determined by microarray analysis. To formally confirm the results
independently obtained by SAGE and microarray analysis, quantitative
RT-PCR was completed by using oligonucleotide primers specific for 2 abundant mitochondrial transcripts, 16S rRNA and NADH2 thymosin 4
(high-abundance by microarray and SAGE), 2 incompletely characterized high-abundance transcripts (neurogranin and clusterin; see "Protein immunoanalysis of platelet clusterin and neurogranin"), a
low-abundant transcript (T-cell receptor -polypeptide), and the
genes encoding proteins with well-established quantitative
determinations (ie, glycoprotein IIB3
[~50 000 receptors/platelet]; protease-activated receptor-1 (PAR1)
[~1800 receptors/platelet]).1 As shown in Figure
4, these analyses reveal excellent
concordance between SAGE and microarray studies, demonstrating the
predominant frequency of the mitochondrial-derived 16S rRNA/NADH2
transcripts, with incrementally lower expression of other transcripts
as initially demonstrated by microarray (16S > NADH2 > thymosin 4 > neurogranin > clusterin > IIB3 > PAR1 > TCR).
Given the small number of nonmitochondrial SAGE tags available for analysis (n = 233), limited conclusions can be drawn using traditional (nonsubtraction) platelet SAGE libraries as presented here. Overall, a total of 126 unique tags were identified, the majority of which (94) were represented only once. Of the total unique tags, nearly one half represented novel genes not present on the Affymetrix U95Av2 GeneChip. Of the genes with unique tags identified more than once, there was excellent concordance with microarray expression analysis, with nearly all of the SAGE tags in Table 3 corresponding to platelet top 75 microarray transcripts. The platelet factor (PF) 4 variant represents a single aberration because this was rank-ordered approximately 350 by microarray, although its SAGE tag frequency was identical to that of the predominant PF4 transcript. The lack of extensive nonmitochondrial SAGE sampling precludes any further extrapolations from this apparent aberration. Of note, a subset of these tags had long poly(A) tracts, although they all corresponded to genes identified in the RefSeq database.21 We cannot exclude the possibility of a SAGE artifact for this small subset of tags (~2%, representing 46 of 2033 tags), although the authenticity of the vast majority of tags (~98%) clearly validates the methodology. These tags are most likely explained by the unique biology of the platelet (ie, mRNA decay in the absence of de novo transcription) or to mRNA degradation occurring during the extensive purification methods. In summary, even with a remarkably limited sampling, the power of this approach in gene identification of relatively abundant and less abundant transcripts is evident. It is clear, however, given the unique molecular anatomy of the platelet (ie, abundance of mitochondrial transcripts), that SAGE adaptations will be required for more comprehensive genetic profiling.29 Protein immunoanalysis of platelet clusterin and neurogranin Although most of the "most abundant" transcripts would conform to a priori predictions for platelet-expressed mRNAs, a number of transcripts were identified that had been poorly characterized in human platelets. To further establish the authenticity of highly expressed transcripts such as clusterin and neurogranin, confirmatory protein analyses were completed. As shown in Figure 5, both proteins were clearly detected in purified platelet lysates; furthermore, their cellular platelet distributions conformed to those predicted based on previously proposed functions. Note for example that clusterinfunctionally characterized as a complement lysis inhibitor able to block the terminal complement reactionis primarily expressed on the extracellular platelet membrane.30 Given the
importance of complement activation in platelet destruction, the
prominent expression of cell-surface clusterin might suggest a role for this protein in normal and pathologic events regulating platelet survival. Interestingly, a clusterin-deficient knockout mouse has been
generated that demonstrates enhanced cardiac dysfunction in a model of
autoimmune myocarditis.31 Although these mice apparently
have normal baseline hemograms (B. Aronow, personal communication,
October 2002), it remains unestablished if they would be predisposed to
immune-type thrombocytopenia in systemic models of autoimmunity.
Similarly, the gene encoding an intracellular effector protein that may have key roles in downstream platelet activation events has now been demonstrated to have abundant transcript expression in human platelets. Neurogranin is a highly expressed platelet transcript with its gene product demonstrating a primarily intracellular pattern of distribution. Neurogranin is generally described as a brain-specific, Ca2+-sensitive calmodulin-binding phosphoprotein that is preferentially expressed in neuronal cell bodies and dendrites.32,33 It is a specific protein kinase C (PKC) substrate that can also be modified by nitric oxide and other oxidants to form intramolecular disulfide bonds. Both its phosphorylation and oxidation state attenuate its binding affinity for calmodulin.33 In stimulated platelets, PKC generation is linked to various activation pathways such as calcium-regulated kinases, mitogen-activated protein (MAP) kinases, and receptor tyrosine kinases.1 Thus, these observations suggest that platelet neurogranin may function as a previously unidentified component of a PKC-dependent activation pathway coupled to one (or more) of these effector proteins.
These data provide documentation for a unique platelet mRNA profile that may provide a tool for analyzing platelet molecular networks. Nonetheless, the molecular analysis of the platelet transcriptome may be confounded by the constant decay of mRNAs in the absence of new gene transcription, a situation that may, for example, limit the identification of low-abundance transcripts. Similarly, because the circulating platelet pool contains a mixed population of variably aged platelets, a "static" mRNA profile represents an average of this heterogeneous blood pool. Despite these potential limitations, the combination of genomic and proteomic technologies are likely to provide powerful tools for the global analysis of platelet function. Current strategies for cataloging "whole cellular proteomes" are generally accomplished by using 2 developing methodologies: (1) high resolution 2-dimensional polyacrylamide gel electrophoresis (2-DE) with mass spectrometric sequence identification,34 and (2) microcapillary liquid chromatography with tandem mass spectrometry (µLC-MC/MC).35 Further modifications of both procedures have been devised for direct comparative studies between 2 cellular proteomes. The introduction of 2-DE differential gel electrophoresis has now made it possible to detect and quantify differences between experimental sample pairs resolved on the same 2-dimensional gel.36 Likewise, the application of isotope-coded affinity tags to µLC-MC/MC represent a novel means of quantitative analyses between cellular proteomes.37 The success of both approaches relies on the availability of comprehensive genomic databases and mathematical algorithms for optimal protein identification. Indeed, mathematical modeling studies have demonstrated the need to delineate both protein and mRNA expression levels for optimal definition of intracellular networks.38 Our data present an initial framework for delineating platelet function by defining the molecular anatomy of human platelets, information that is likely to provide important clues into the dynamic protein interactions regulating normal and pathologic platelet functions. Furthermore, because the platelet transcriptome mirrors the mRNAs derived from precursor megakaryocytes, these analyses may provide insights into the biochemical and molecular events regulating megakaryocytopoiesis and/or proplatelet formation.
We thank Dr Maureen Krause, Jean Wainer, and Lesley Scudder for assistance with some of the experiments; John Schwedes (University DNA microarray facility) with the microarray analysis; and Ms Shirley Murray for manuscript preparation.
Submitted September 16, 2002; accepted November 3, 2002.
Prepublished online as Blood First Edition Paper, November 14, 2002; DOI 10.1182/blood-2002-09-2797.
Supported by grants HL49141 and HL53665, by a Veteran's Administration REAP award (W.F.B.), and by National Institutes of Health Center grant MO1 10710-5 to the University Hospital General Clinical Research Center. W.B. is an Established Investigator of the American Heart Association. Studies at Brookhaven National Laboratory were supported by a Laboratory Directed Research and Development award (J.J.D.) and by the Offices of Biological and Environmental Research, and of Basic Energy Sciences (Division of Energy Biosciences) of the US Department of Energy.
The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked "advertisement" in accordance with 18 U.S.C. section 1734.
Reprints: Wadie F. Bahou, Division of Hematology, HSCT15-040, State University of New York at Stony Brook, Stony Brook, NY 11794-8151; e-mail: wbahou{at}notes.cc.sunysb.edu.
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M. L. Jison, P. J. Munson, J. J. Barb, A. F. Suffredini, S. Talwar, C. Logun, N. Raghavachari, J. H. Beigel, J. H. Shelhamer, R. L. Danner, et al. Blood mononuclear cell gene expression profiles characterize the oxidant, hemolytic, and inflammatory stress of sickle cell disease Blood, July 1, 2004; 104(1): 270 - 280. [Abstract] [Full Text] [PDF]
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J. A. Coppinger, G. Cagney, S. Toomey, T. Kislinger, O. Belton, J. P. McRedmond, D. J. Cahill, A. Emili, D. J. Fitzgerald, and P. B. Maguire Characterization of the proteins released from activated platelets leads to localization of novel platelet proteins in human atherosclerotic lesions Blood, March 15, 2004; 103(6): 2096 - 2104. [Abstract] [Full Text] [PDF]
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J. P. McRedmond, S. D. Park, D. F. Reilly, J. A. Coppinger, P. B. Maguire, D. C. Shields, and D. J. Fitzgerald Integration of Proteomics and Genomics in Platelets: A PROFILE OF PLATELET PROTEINS AND PLATELET-SPECIFIC GENES Mol. Cell. Proteomics, February 1, 2004; 3(2): 133 - 144. [Abstract] [Full Text] [PDF]
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A. S. Weyrich and G. A. Zimmerman Evaluating the relevance of the platelet transcriptome Blood, August 15, 2003; 102(4): 1550 - 1551. [Full Text] [PDF]
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Top
Abstract
Introduction
X174 DNA are shown. (B) Real-time
RT-PCR was completed by using 1.8 µg total RNA and TCR
) represents
IIb, (
) represents IIIa, (
) represents PAR1, (
) represents 16S
rRNA, (
) represents NADH2, (
) represents thymosin, (
)
represents clusterin, (
) represents neurogranin, and (
)
represents TCR