Genomic and Proteomic Applications in Diagnosis of Platelet Disorders and Classification

ABSTRACT

The transcriptome is the mRNA pool found within a cell. Transcriptomic discovery approaches include microarray-based technologies as well as sequencing-based technologies. Transcriptomic experiments provide dynamic information about gene expression at the tissue level. The proteome is the pool of proteins expressed at a given time and circumstance. The word proteomics summarizes several technologies for visualization, quantitation, and identification of these proteins. Protein separation can be accomplished by two-dimensional electrophoresis, use of protein chips with an affinity matrix, or by a variety of advanced chromatographic methods. Mass spectrometry is used to identify the proteins in conjunction with protein sequence databases. Recent proteomic experiments in resting and activated platelets have identified novel signaling pathways and secreted proteins. Platelet transcriptomic studies in essential thrombocythemia, atherosclerotic disease, sickle cell disease, and an inherited platelet defect are reviewed. Transcript profiling has the potential to distinguish molecular signatures in normal and diseased platelets and to classify prothrombotic patient phenotypes to tailor their therapy.

KEYWORDS

Genomics - transcriptomics - proteomics - microarray - platelets

REFERENCES

• 1 Denis M M, Tolley N D, Bunting M et al.. Escaping the nuclear confines: signal-dependent pre-mRNA splicing in anucleate platelets.  Cell. 2005;  122 379-391
• 2 Gnatenko D V, Perrotta P L, Bahou W F. Proteomic approaches to dissect platelet function: half the story.  Blood. 2006;  108 3983-3991
• 3 Senis Y A, Tomlinson M G, Garcia A et al.. A comprehensive proteomics and genomics analysis reveals novel transmembrane proteins in human platelets and mouse megakaryocytes including G6b-B, a novel immunoreceptor tyrosine-based inhibitory motif protein.  Mol Cell Proteomics. 2007;  6 548-564
• 4 Gnatenko D V, Dunn J J, McCorkle S R, Weissmann D, Perrotta P L, Bahou W F. Transcript profiling of human platelets using microarray and serial analysis of gene expression.  Blood. 2003;  101 2285-2293
• 5 Rox J M, Bugert P, Muller J et al.. Gene expression analysis in platelets from a single donor: evaluation of a PCR-based amplification technique.  Clin Chem. 2004;  50 2271-2278
• 6 McRedmond J P, Park S D, Reilly D F et al.. Integration of proteomics and genomics in platelets: a profile of platelet proteins and platelet-specific genes.  Mol Cell Proteomics. 2004;  3 133-144
• 7 Bugert P, Dugrillon A, Gunaydin A, Eichler H, Kluter H. Messenger RNA profiling of human platelets by microarray hybridization.  Thromb Haemost. 2003;  90 738-748
• 8 Bugert P, Kluter H. Profiling of gene transcripts in human platelets: an update of the platelet transcriptome.  Platelets. 2006;  17 503-504
• 9 Frankhauser P, Grimmer Y, Bugert P, Deuschle M, Schmidt M, Schloss P. Characterization of the neuronal dopamine transporter DAT in human blood platelets.  Neurosci Lett. 2006;  399 197-201
• 10 Rainesalo S, Keranen T, Saransaari P, Honkaniemi J. GABA and glutamate transporters are expressed in human platelets.  Brain Res Mol Brain Res. 2005;  141 161-165
• 11 Macaulay I C, Tijssen M R, Thijssen-Timmer D C et al.. Comparative gene expression profiling of in vitro differentiated megakaryocytes and erythroblasts identifies novel activatory and inhibitory platelet membrane proteins.  Blood. 2007;  109 3260-3269
• 12 Tenedini E, Fagioli M E, Vianelli N et al.. Gene expression profiling of normal and malignant CD34-derived megakaryocytic cells.  Blood. 2004;  104 3126-3135
• 13 Gnatenko D V, Cupit L D, Huang E C, Dhundale A, Perrotta P L, Bahou W F. Platelets express steroidogenic 17beta-hydroxysteroid dehydrogenases. Distinct profiles predict the essential thrombocythemic phenotype.  Thromb Haemost. 2005;  94 412-421
• 14 Gnatenko D V, Zhu W, Dhundale A, Bahou W F. Class prediction models of thrombocytosis using gene profiling.  , [abstract] Blood. 2007;  110 38a
• 15 Golub T R, Slonim D K, Tamayo P et al.. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.  Science. 1999;  286 531-537
• 16 Sun L, Gorospe J R, Hoffman E P, Rao A K. Decreased platelet expression of myosin regulatory light chain polypeptide (MYL9) and other genes with platelet dysfunction and CBFA2/RUNX1 mutation: insights from platelet expression profiling.  J Thromb Haemost. 2007;  5 146-154
• 17 Raghavachari N, Xu X, Harris A et al.. Amplified expression profiling of platelet transcriptome reveals changes in arginine metabolic pathways in patients with sickle cell disease.  Circulation. 2007;  115 1551-1562
• 18 Healy A M, Pickard M D, Pradhan A D et al.. Platelet expression profiling and clinical validation of myeloid-related protein-14 as a novel determinant of cardiovascular events.  Circulation. 2006;  113 2278-2284
• 19 Krishnan U, Goodall A H, Bugert P. Letter by Krishnan et al regarding article, “Platelet expression profiling and clinical validation of myeloid-related protein-14 as a novel determinant of cardiovascular events.  Circulation. 2007;  115 e186
• 20 Morrow D A, Wang Y, Croce K et al.. Myeloid-related protein 8/14 and the risk of cardiovascular death or myocardial infarction after an acute coronary syndrome in the Pravastatin or Atorvastatin Evaluation and Infection Therapy: Thrombolysis in Myocardial Infarction (PROVE IT-TIMI 22) trial.  Am Heart J. 2008;  155 49-55
• 21 Peck D, Crawford E D, Ross K N, Stegmaier K, Golub T R, Lamb J. A method for high-throughput gene expression signature analysis.  Genome Biol. 2006;  7 R61
• 22 Eisenstein M. Microarrays: quality control.  Nature. 2006;  442 1067-1070
• 23 Strauss E. Arrays of hope.  Cell. 2006;  127 657-659
• 24 Maguire P B, Foy M, Fitzgerald D J. Using proteomics to identify potential therapeutic targets in platelets.  Biochem Soc Trans. 2005;  33 409-412
• 25 de Hoog C L, Mann M. Proteomics.  Annu Rev Genomics Hum Genet. 2004;  5 267-293
• 26 Delahunty C M, Yates III J R. MudPIT: multidimensional protein identification technology.  Biotechniques. 2007;  43 563, 565, 567
• 27 Kocher T, Superti-Furga G. Mass spectrometry-based functional proteomics: from molecular machines to protein networks.  Nat Methods. 2007;  4 807-815
• 28 Garcia A. Proteome analysis of signaling cascades in human platelets.  Blood Cells Mol Dis. 2006;  36 152-156
• 29 Macaulay I C, Carr P, Gusnanto A, Ouwehand W H, Fitzgerald D, Watkins N A. Platelet genomics and proteomics in human health and disease.  J Clin Invest. 2005;  115 3370-3377
• 30 Dittrich M, Birschmann I, Stuhlfelder C et al.. Understanding platelets. Lessons from proteomics, genomics and promises from network analysis.  Thromb Haemost. 2005;  94 916-925
• 31 Maynard D M, Heijnen H F, Horne M K, White J G, Gahl W A. Proteomic analysis of platelet alpha-granules using mass spectrometry.  J Thromb Haemost. 2007;  5 1945-1955
• 32 Hernandez-Ruiz L, Valverde F, Jimenez-Nunez M D et al.. Organellar proteomics of human platelet dense granules reveals that 14–3-3zeta is a granule protein related to atherosclerosis.  J Proteome Res. 2007;  6 4449-4457
• 33 Greening D W, Glenister K M, Kapp E A et al.. Comparison of human platelet membrane-cytoskeletal proteins with the plasma proteome: Towards understanding the platelet-plasma nexus.  Proteomics Clin Appl. 2008;  2 63-77
• 34 Moebius J, Zahedi R P, Lewandrowski U, Berger C, Walter U, Sickmann A. The human platelet membrane proteome reveals several new potential membrane proteins.  Mol Cell Proteomics. 2005;  4 1754-1761
• 35 Smalley D M, Root K E, Cho H, Ross M M, Ley K. Proteomic discovery of 21 proteins expressed in human plasma-derived but not platelet-derived microparticles.  Thromb Haemost. 2007;  97 67-80
• 36 Garcia B A, Smalley D M, Cho H, Shabanowitz J, Ley K, Hunt D F. The platelet microparticle proteome.  J Proteome Res. 2005;  4 1516-1521
• 37 Coppinger J A, Cagney G, Toomey S et al.. Characterization of the proteins released from activated platelets leads to localization of novel platelet proteins in human atherosclerotic lesions.  Blood. 2004;  103 2096-2104
• 38 Martens L, Van D P, Van D J et al.. The human platelet proteome mapped by peptide-centric proteomics: a functional protein profile.  Proteomics. 2005;  5 3193-3204
• 39 Nanda N, Bao M, Lin H et al.. Platelet endothelial aggregation receptor 1 (PEAR1), a novel epidermal growth factor repeat-containing transmembrane receptor, participates in platelet contact-induced activation.  J Biol Chem. 2005;  280 24680-24689
• 40 Garcia A, Prabhakar S, Hughan S et al.. Differential proteome analysis of TRAP-activated platelets: involvement of DOK-2 and phosphorylation of RGS proteins.  Blood. 2004;  103 2088-2095
• 41 Marcus K, Immler D, Sternberger J, Meyer H E. Identification of platelet proteins separated by two-dimensional gel electrophoresis and analyzed by matrix assisted laser desorption/ionization-time of flight-mass spectrometry and detection of tyrosine-phosphorylated proteins.  Electrophoresis. 2000;  21 2622-2636
• 42 O'Neill E E, Brock C J, von Kriegsheim A F et al.. Towards complete analysis of the platelet proteome.  Proteomics. 2002;  2 288-305
• 43 Bugert P, Ficht M, Kluter H. Towards the identification of novel platelet receptors: Comparing RNA and proteome approaches.  Transfus Med Hemother. 2006;  33 236-243
• 44 Hillmann A G, Harmon S, Park S D, O'brien J, Shields D C, Kenny D. Comparative RNA expression analyses from small-scale, single-donor platelet samples.  J Thromb Haemost. 2006;  4 349-356
• 45 Guerrier L, Claverol S, Fortis F et al.. Exploring the platelet proteome via combinatorial, hexapeptide ligand libraries.  J Proteome Res. 2007;  6 4290-4303
• 46 Yee D L, Sun C W, Bergeron A L et al.. Aggregometry detects platelet hyperreactivity in healthy individuals.  Blood. 2005;  106 2723-2729
• 47 Tang Y, Xu H, Du X et al.. Gene expression in blood changes rapidly in neutrophils and monocytes after ischemic stroke in humans: a microarray study.  J Cereb Blood Flow Metab. 2006;  26 1089-1102
• 48 Moore D F, Li H, Jeffries N et al.. Using peripheral blood mononuclear cells to determine a gene expression profile of acute ischemic stroke: a pilot investigation.  Circulation. 2005;  111 212-221
• 49 Ma J, Liew C C. Gene profiling identifies secreted protein transcripts from peripheral blood cells in coronary artery disease.  J Mol Cell Cardiol. 2003;  35 993-998
• 50 Tabibiazar R, Wagner R A, Ashley E A et al.. Signature patterns of gene expression in mouse atherosclerosis and their correlation to human coronary disease.  Physiol Genomics. 2005;  22 213-226
• 51 Seo D, Wang T, Dressman H et al.. Gene expression phenotypes of atherosclerosis.  Arterioscler Thromb Vasc Biol. 2004;  24 1922-1927
• 52 Karra R, Vemullapalli S, Dong C et al.. Molecular evidence for arterial repair in atherosclerosis.  Proc Natl Acad Sci U S A. 2005;  102 16789-16794
• 53 Potti A, Bild A, Dressman H K, Lewis D A, Nevins J R, Ortel T L. Gene-expression patterns predict phenotypes of immune-mediated thrombosis.  Blood. 2006;  107 1391-1396
• 54 Pellagatti A, Vetrie D, Langford C F et al.. Gene expression profiling in polycythemia vera using cDNA microarray technology.  Cancer Res. 2003;  63 3940-3944
• 55 Goerttler P S, Kreutz C, Donauer J et al.. Gene expression profiling in polycythaemia vera: overexpression of transcription factor NF-E2.  Br J Haematol. 2005;  129 138-150
• 56 Kralovics R, Teo S S, Buser A S et al.. Altered gene expression in myeloproliferative disorders correlates with activation of signaling by the V617F mutation of Jak2.  Blood. 2005;  106 3374-3376
• 57 Hyman T, Huizing M, Blumberg P M, Falik-Zaccai T C, Anikster Y, Gahl W A. Use of a cDNA microarray to determine molecular mechanisms involved in grey platelet syndrome.  Br J Haematol. 2003;  122 142-149