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Published in: Translational Stroke Research 1/2019

01-02-2019 | Original Article

Shifts in Leukocyte Counts Drive the Differential Expression of Transcriptional Stroke Biomarkers in Whole Blood

Authors: Grant C. O’Connell, Madison B. Treadway, Connie S. Tennant, Noelle Lucke-Wold, Paul D. Chantler, Taura L. Barr

Published in: Translational Stroke Research | Issue 1/2019

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Abstract

Our group recently identified a panel of ten genes whose RNA expression levels in whole blood have utility for detection of stroke. The purpose of this study was to determine the mechanisms by which these genes become differentially expressed during stroke pathology. First, we assessed the transcriptional distribution of the ten genes across the peripheral immune system by measuring their expression levels on isolated neutrophils, monocytes, B-lymphocytes, CD-4+ T-lymphocytes, CD-8+ T-lymphocytes, and NK-cells generated from the blood of healthy donors (n = 3). Then, we examined the relationship between the whole-blood expression levels of the ten genes and white blood cell counts in a cohort of acute ischemic stroke patients (n = 36) and acute stroke mimics (n = 15) recruited at emergency department admission. All ten genes displayed strong patterns of lineage-specific expression in our analysis of isolated leukocytes, and their whole-blood expression levels were correlated with white blood cell differential across the total patient population, suggesting that many of them are likely differentially expressed in whole blood during stroke as an artifact of stroke-induced shifts in leukocyte counts. Specifically, factor analysis inferred that over 50% of the collective variance in their whole-blood expression levels across the patient population was driven by underlying variance in white blood cell counts alone. However, the cumulative expression levels of the ten genes displayed a superior ability to discriminate between stroke patients and stroke mimics relative to white blood cell differential, suggesting that additional less prominent factors influence their expression levels which add to their diagnostic utility. These findings not only provide insight regarding this particular panel of ten genes, but also into the results of prior stroke transcriptomics studies performed in whole blood.
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Literature
1.
go back to reference Tang Y, Xu H, Du X, Lit L, Walker W, Lu A, 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 Off J Int Soc Cereb Blood Flow Metab. 2006;26:1089–102.CrossRef Tang Y, Xu H, Du X, Lit L, Walker W, Lu A, 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 Off J Int Soc Cereb Blood Flow Metab. 2006;26:1089–102.CrossRef
2.
go back to reference Stamova B, Xu H, Jickling G, Bushnell C, Tian Y, Ander BP, et al. Gene expression profiling of blood for the prediction of ischemic stroke. Stroke J Cereb Circ. 2010;41:2171–7. Stamova B, Xu H, Jickling G, Bushnell C, Tian Y, Ander BP, et al. Gene expression profiling of blood for the prediction of ischemic stroke. Stroke J Cereb Circ. 2010;41:2171–7.
3.
go back to reference O’Connell GC, Petrone AB, Treadway MB, Tennant CS, Lucke-Wold N, Chantler PD, et al. Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke. Npj Genomic Med. 2016;1:16038–8. O’Connell GC, Petrone AB, Treadway MB, Tennant CS, Lucke-Wold N, Chantler PD, et al. Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke. Npj Genomic Med. 2016;1:16038–8.
4.
go back to reference Raman K, O’Donnell MJ, Czlonkowska A, Duarte YC, Lopez-Jaramillo P, Peñaherrera E, et al. Peripheral blood MCEMP1 gene expression as a biomarker for stroke prognosis. Stroke J Cereb Circ. 2016;47:652–8.CrossRef Raman K, O’Donnell MJ, Czlonkowska A, Duarte YC, Lopez-Jaramillo P, Peñaherrera E, et al. Peripheral blood MCEMP1 gene expression as a biomarker for stroke prognosis. Stroke J Cereb Circ. 2016;47:652–8.CrossRef
5.
go back to reference Barr TL, Conley Y, Ding J, Dillman a, Warach S, Singleton a, et al. Genomic biomarkers and cellular pathways of ischemic stroke by RNA gene expression profiling. Neurology 2010;75:1009–1014. Barr TL, Conley Y, Ding J, Dillman a, Warach S, Singleton a, et al. Genomic biomarkers and cellular pathways of ischemic stroke by RNA gene expression profiling. Neurology 2010;75:1009–1014.
6.
go back to reference O’Connell GC, Chantler PD, Barr TL. Stroke-associated pattern of gene expression previously identified by machine-learning is diagnostically robust in an independent patient population. Genomics Data. 2017;14:47–52.CrossRef O’Connell GC, Chantler PD, Barr TL. Stroke-associated pattern of gene expression previously identified by machine-learning is diagnostically robust in an independent patient population. Genomics Data. 2017;14:47–52.CrossRef
7.
go back to reference Chamorro Á, Meisel A, Planas AM, Urra X, van de Beek D, Veltkamp R. The immunology of acute stroke. Nat Rev Neurol. 2012;8:401–10.CrossRef Chamorro Á, Meisel A, Planas AM, Urra X, van de Beek D, Veltkamp R. The immunology of acute stroke. Nat Rev Neurol. 2012;8:401–10.CrossRef
8.
go back to reference Iadecola C, Anrather J. The immunology of stroke: from mechanisms to translation. Nat Med. 2011;17:796–808.CrossRef Iadecola C, Anrather J. The immunology of stroke: from mechanisms to translation. Nat Med. 2011;17:796–808.CrossRef
9.
go back to reference Macrez R, Ali C, Toutirais O, Le Mauff B, Defer G, Dirnagl U, et al. Stroke and the immune system: from pathophysiology to new therapeutic strategies. Lancet Neurol. 2011;10:471–80.CrossRef Macrez R, Ali C, Toutirais O, Le Mauff B, Defer G, Dirnagl U, et al. Stroke and the immune system: from pathophysiology to new therapeutic strategies. Lancet Neurol. 2011;10:471–80.CrossRef
10.
go back to reference Urra X, Cervera Á, Obach V, Climent N, Planas AM, Chamorro A. Monocytes are major players in the prognosis and risk of infection after acute stroke. Stroke. 2009;40:1262–8.CrossRef Urra X, Cervera Á, Obach V, Climent N, Planas AM, Chamorro A. Monocytes are major players in the prognosis and risk of infection after acute stroke. Stroke. 2009;40:1262–8.CrossRef
11.
go back to reference Vogelgesang A, Grunwald U, Langner S, Jack R, Bröker BM, Kessler C, et al. Analysis of lymphocyte subsets in patients with stroke and their influence on infection after stroke. Stroke. 2008;39:237–41.CrossRef Vogelgesang A, Grunwald U, Langner S, Jack R, Bröker BM, Kessler C, et al. Analysis of lymphocyte subsets in patients with stroke and their influence on infection after stroke. Stroke. 2008;39:237–41.CrossRef
12.
go back to reference Guo Z, Yu S, Xiao L, Chen X, Ye R, Zheng P, et al. Dynamic change of neutrophil to lymphocyte ratio and hemorrhagic transformation after thrombolysis in stroke. J Neuroinflammation. 2016;13:199–9. Guo Z, Yu S, Xiao L, Chen X, Ye R, Zheng P, et al. Dynamic change of neutrophil to lymphocyte ratio and hemorrhagic transformation after thrombolysis in stroke. J Neuroinflammation. 2016;13:199–9.
13.
go back to reference Rosell A, Cuadrado E, Ortega-Aznar A, Hernández-Guillamon M, Lo EH, Montaner J. MMP-9-positive neutrophil infiltration is associated to blood-brain barrier breakdown and basal lamina type IV collagen degradation during hemorrhagic transformation after human ischemic stroke. Stroke. 2008;39:1121–6.CrossRef Rosell A, Cuadrado E, Ortega-Aznar A, Hernández-Guillamon M, Lo EH, Montaner J. MMP-9-positive neutrophil infiltration is associated to blood-brain barrier breakdown and basal lamina type IV collagen degradation during hemorrhagic transformation after human ischemic stroke. Stroke. 2008;39:1121–6.CrossRef
14.
go back to reference Maestrini I, Strbian D, Gautier S, Haapaniemi E, Moulin S, Sairanen T, et al. Higher neutrophil counts before thrombolysis for cerebral ischemia predict worse outcomes. Neurology. 2015;85(16):1408–16. Maestrini I, Strbian D, Gautier S, Haapaniemi E, Moulin S, Sairanen T, et al. Higher neutrophil counts before thrombolysis for cerebral ischemia predict worse outcomes. Neurology. 2015;85(16):1408–16.
15.
go back to reference Urra X, Cervera Á, Villamor N, Planas a M, Chamorro Á. Harms and benefits of lymphocyte subpopulations in patients with acute stroke. Neuroscience. 2009;158:1174–83.CrossRef Urra X, Cervera Á, Villamor N, Planas a M, Chamorro Á. Harms and benefits of lymphocyte subpopulations in patients with acute stroke. Neuroscience. 2009;158:1174–83.CrossRef
16.
go back to reference Abbas a R, Baldwin D, Ma Y, Ouyang W, Gurney a MF, et al. Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data. Genes Immun. 2005;6:319–31.CrossRef Abbas a R, Baldwin D, Ma Y, Ouyang W, Gurney a MF, et al. Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data. Genes Immun. 2005;6:319–31.CrossRef
17.
go back to reference Whitney AR, Diehn M, Popper SJ, Alizadeh AA, Boldrick JC, Relman DA, et al. Individuality and variation in gene expression patterns in human blood. Proc Natl Acad Sci U A. 2003;100:1896–901. Whitney AR, Diehn M, Popper SJ, Alizadeh AA, Boldrick JC, Relman DA, et al. Individuality and variation in gene expression patterns in human blood. Proc Natl Acad Sci U A. 2003;100:1896–901.
18.
go back to reference Kidwell CS, Warach S. Acute ischemic cerebrovascular syndrome: diagnostic criteria. Stroke. 2003;34:2995–8.CrossRef Kidwell CS, Warach S. Acute ischemic cerebrovascular syndrome: diagnostic criteria. Stroke. 2003;34:2995–8.CrossRef
19.
go back to reference Bamford J, Sandercock P, Dennis M, Burn J, Warlow C. Classification and natural history of clinically identifiable subtypes of cerebral infarction. Lancet Lond Engl. 1991;337:1521–6.CrossRef Bamford J, Sandercock P, Dennis M, Burn J, Warlow C. Classification and natural history of clinically identifiable subtypes of cerebral infarction. Lancet Lond Engl. 1991;337:1521–6.CrossRef
20.
go back to reference Lee LJ, Kidwell CS, Alger J, Starkman S, Saver JL. Impact on stroke subtype diagnosis of early diffusion-weighted magnetic resonance imaging and magnetic resonance angiography. Stroke. 2000;31:1081–9.CrossRef Lee LJ, Kidwell CS, Alger J, Starkman S, Saver JL. Impact on stroke subtype diagnosis of early diffusion-weighted magnetic resonance imaging and magnetic resonance angiography. Stroke. 2000;31:1081–9.CrossRef
21.
go back to reference Heckmann L-H, Sørensen PB, Krogh PH, Sørensen JG. NORMA-gene: a simple and robust method for qPCR normalization based on target gene data. BMC Bioinformatics. 2011;12:250–0. Heckmann L-H, Sørensen PB, Krogh PH, Sørensen JG. NORMA-gene: a simple and robust method for qPCR normalization based on target gene data. BMC Bioinformatics. 2011;12:250–0.
22.
go back to reference O’Connell GC, Treadway MB, Petrone AB, Tennant CS, Lucke-Wold N, Chantler PD, et al. Leukocyte dynamics influence reference gene stability in whole blood: data-driven qRT-PCR normalization is a robust alternative for measurement of transcriptional biomarkers. Lab Med. 2017;48:346–56.CrossRef O’Connell GC, Treadway MB, Petrone AB, Tennant CS, Lucke-Wold N, Chantler PD, et al. Leukocyte dynamics influence reference gene stability in whole blood: data-driven qRT-PCR normalization is a robust alternative for measurement of transcriptional biomarkers. Lab Med. 2017;48:346–56.CrossRef
23.
go back to reference Ross I, Robert G, Ihaka R, Gentleman R. R: a language for data analysis and graphics. J Comput Graph Stat. 1996;5:299–314. Ross I, Robert G, Ihaka R, Gentleman R. R: a language for data analysis and graphics. J Comput Graph Stat. 1996;5:299–314.
24.
go back to reference Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:1–8.CrossRef Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:1–8.CrossRef
26.
go back to reference DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.CrossRef DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.CrossRef
27.
go back to reference Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. 1995:289–300. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. 1995:289–300.
28.
go back to reference Droste A, Sorg C, Högger P. Shedding of CD163, a novel regulatory mechanism for a member of the scavenger receptor cysteine-rich family. Biochem Biophys Res Commun. 1999;256:110–3.CrossRef Droste A, Sorg C, Högger P. Shedding of CD163, a novel regulatory mechanism for a member of the scavenger receptor cysteine-rich family. Biochem Biophys Res Commun. 1999;256:110–3.CrossRef
29.
go back to reference O’Connell GC, Tennant CS, Lucke-Wold N, Kabbani Y, Tarabishy AR, Chantler PD, et al. Monocyte-lymphocyte cross-communication via soluble CD163 directly links innate immune system activation and adaptive immune system suppression following ischemic stroke. Sci Rep. 2017;7:12940–0. O’Connell GC, Tennant CS, Lucke-Wold N, Kabbani Y, Tarabishy AR, Chantler PD, et al. Monocyte-lymphocyte cross-communication via soluble CD163 directly links innate immune system activation and adaptive immune system suppression following ischemic stroke. Sci Rep. 2017;7:12940–0.
30.
go back to reference Leclerc JL, Lampert AS, Loyola Amador C, Schlakman B, Vasilopoulos T, Svendsen P, et al. The absence of the CD163 receptor has distinct temporal influences on intracerebral hemorrhage outcomes. J Cereb Blood Flow Metab. 2017;0271678X1770145-0271678X1770145. Leclerc JL, Lampert AS, Loyola Amador C, Schlakman B, Vasilopoulos T, Svendsen P, et al. The absence of the CD163 receptor has distinct temporal influences on intracerebral hemorrhage outcomes. J Cereb Blood Flow Metab. 2017;0271678X1770145-0271678X1770145.
31.
go back to reference Liu R, Cao S, Hua Y, Keep RF, Huang Y, Xi G. CD163 expression in neurons after experimental intracerebral hemorrhage. Stroke. 2017;48:1369–75.CrossRef Liu R, Cao S, Hua Y, Keep RF, Huang Y, Xi G. CD163 expression in neurons after experimental intracerebral hemorrhage. Stroke. 2017;48:1369–75.CrossRef
32.
go back to reference Petrone AB, O’Connell GC, Regier MD, Chantler PD, Simpkins JW, Barr TL. The role of arginase 1 in post-stroke immunosuppression and ischemic stroke severity. Transl Stroke Res. 2016;7:103–10.CrossRef Petrone AB, O’Connell GC, Regier MD, Chantler PD, Simpkins JW, Barr TL. The role of arginase 1 in post-stroke immunosuppression and ischemic stroke severity. Transl Stroke Res. 2016;7:103–10.CrossRef
33.
go back to reference Jickling GC, Ander BP, Zhan X, Noblett D, Stamova B, Liu D. MicroRNA expression in peripheral blood cells following acute ischemic stroke and their predicted gene targets. PLoS One. 2014;9:e99283.CrossRef Jickling GC, Ander BP, Zhan X, Noblett D, Stamova B, Liu D. MicroRNA expression in peripheral blood cells following acute ischemic stroke and their predicted gene targets. PLoS One. 2014;9:e99283.CrossRef
34.
go back to reference Dykstra-Aiello C, Jickling GC, Ander BP, Shroff N, Zhan X, Liu D, et al. Altered expression of long noncoding RNAs in blood after ischemic stroke and proximity to putative stroke risk loci. 2016. Dykstra-Aiello C, Jickling GC, Ander BP, Shroff N, Zhan X, Liu D, et al. Altered expression of long noncoding RNAs in blood after ischemic stroke and proximity to putative stroke risk loci. 2016.
Metadata
Title
Shifts in Leukocyte Counts Drive the Differential Expression of Transcriptional Stroke Biomarkers in Whole Blood
Authors
Grant C. O’Connell
Madison B. Treadway
Connie S. Tennant
Noelle Lucke-Wold
Paul D. Chantler
Taura L. Barr
Publication date
01-02-2019
Publisher
Springer US
Published in
Translational Stroke Research / Issue 1/2019
Print ISSN: 1868-4483
Electronic ISSN: 1868-601X
DOI
https://doi.org/10.1007/s12975-018-0623-1

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