Shifts in Leukocyte Counts Drive the Differential Expression of Transcriptional Stroke Biomarkers in Whole Blood
- 86 Downloads
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.
KeywordsTriage Microarray RNA sequencing CBC Complete blood count PCR NLR Neutrophil-lymphocyte ratio MLR Monocyte-lymphocyte ratio
The authors would foremost like to thank the subjects and their families, as this work was truly made possible by their selfless contribution. In addition, we would like to thank the stroke team at Ruby Memorial Hospital for supporting this research effort.
Work was funded via a Robert Wood Johnson Foundation Nurse Faculty Scholar award to TLB (70319), a National Institutes of Health CoBRE sub-award to TLB (P20 GM109098), and Case Western Reserve University FPB School of Nursing start-up funds issued to GCO.
Compliance with Ethical Standards
Procedures were approved by the institutional review boards of West Virginia University and Ruby Memorial Hospital (IRB protocol 1410450461R001), and written informed consent was obtained from all subjects or their authorized representatives prior to study procedures.
Conflict of Interest
GCO and TLB have a patent pending re: genomic patterns of expression for stroke diagnosis. TLB serves as chief scientific officer for Valtari Bio Incorporated. Work by GCO is part of a pending licensing agreement with Valtari Bio Incorporated. GCO has received consulting fees from Valtari Bio Incorporated. The remaining authors report no potential conflicts of interest.
- 2.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.Google Scholar
- 3.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.Google Scholar
- 5.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.Google Scholar
- 12.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.Google Scholar
- 13.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.CrossRefGoogle Scholar
- 14.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.Google Scholar
- 17.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.Google Scholar
- 21.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.Google Scholar
- 22.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.CrossRefGoogle Scholar
- 23.Ross I, Robert G, Ihaka R, Gentleman R. R: a language for data analysis and graphics. J Comput Graph Stat. 1996;5:299–314.Google Scholar
- 27.Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. 1995:289–300.Google Scholar
- 29.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.Google Scholar
- 30.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.Google Scholar
- 34.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.Google Scholar