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Sex-specific and opposite modulatory aspects revealed by PPI network and pathway analysis of ischemic stroke in humans


Autoři: Yan Lv aff001;  XY He aff002;  Dongguo Li aff003;  Tao Liu aff002;  GQ Wen aff002;  Junfa Li aff001
Působiště autorů: Department of Neurobiology, School of Basic Medical Sciences, Capital Medical University, Beijing, China aff001;  Department of Neurology, Hainan General Hospital, Haikou, China aff002;  Department of Bioinformatics and Engineering, School of Basic Medical Sciences, Capital Medical University, Peking, China aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0227481

Souhrn

Background

Ischemic Stroke (IS) is a major disease which greatly threatens human health. Recent studies showed sex-specific outcomes and mechanisms of cerebral ischemic stroke. This study aimed to identify the key changes of gene expression between male and female IS in humans.

Methods

Gene expression dataset GSE22255, including peripheral blood samples, was downloaded from the Gene Expression Omnibus (GEO) dataset. Differentially Expressed Genes (DEGs) with a LogFC>1, and a P-value <0.05 were screened by BioConductor R package and grouped in female, male and overlap DEGs for further bioinformatic analysis. Gene Ontology (GO) functional annotation, Protein-Protein Interaction (PPI) network, “Molecular Complex Detection” (MCODE) modules, CytoNCA (cytoscape network centrality analysis) essential genes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway interrelation analysis were performed.

Results

In a total of 54,665 genes, 185 (73 ups and 112 downs) DEGs in the female dataset, 461 DEGs (297 ups and 164 downs) in the male dataset, within which 118 DEGs overlapped (7 similar changes in female and male, 111 opposite changes in female and male) were obtained from the GSE22255 dataset. Female, male and overlapping DEGs enriched for similar cellular components and molecular function. Male DEGs enriched for divergent biological processes from female and overlapping DEGs. Sex-specific and overlapping DEGs were put into the PPI network. Overlapping genes such as IL6, presented opposite changes and were mainly involved in cytokine-cytokine receptor interactions, the TNF-signalling pathway, etc.

Conclusion

The analysis of sex-specific DEGs from GEO human blood samples showed that not only specific but also opposite DEG alterations in the female and male stroke genome wide dataset. The results provided an overview of sex-specific mechanisms, which might provide insight into stroke and its biomarkers and lead to sex-specific prognosis and treatment strategies in future clinical practice.

Klíčová slova:

stroke – Network analysis – Immune response – Chemokines – Apoptosis – Cell binding – Centrality – Cytokine receptors


Zdroje

1. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, et al. (2016) Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association. Circulation 133: e38–e360. doi: 10.1161/CIR.0000000000000350 26673558

2. Marsh JD, Keyrouz SG (2010) Stroke prevention and treatment. J Am Coll Cardiol 56: 683–691. doi: 10.1016/j.jacc.2009.12.072 20723798

3. Spaander FH, Zinkstok SM, Baharoglu IM, Gensicke H, Polymeris A, et al. (2017) Sex Differences and Functional Outcome After Intravenous Thrombolysis. Stroke 48: 699–703. doi: 10.1161/STROKEAHA.116.014739 28143921

4. Madsen TE, Khoury J, Alwell K, Moomaw CJ, Rademacher E, et al. (2017) Sex-specific stroke incidence over time in the Greater Cincinnati/Northern Kentucky Stroke Study. Neurology 89: 990–996. doi: 10.1212/WNL.0000000000004325 28794254

5. Brait VH, Jackman KA, Walduck AK, Selemidis S, Diep H, et al. (2010) Mechanisms contributing to cerebral infarct size after stroke: gender, reperfusion, T lymphocytes, and Nox2-derived superoxide. J Cereb Blood Flow Metab 30: 1306–1317. doi: 10.1038/jcbfm.2010.14 20145655

6. Ong CT, Wong YS, Sung SF, Wu CS, Hsu YC, et al. (2017) Sex-related differences in the risk factors for in-hospital mortality and outcomes of ischemic stroke patients in rural areas of Taiwan. PLoS One 12: e185361.

7. Koellhoffer EC, McCullough LD (2013) The effects of estrogen in ischemic stroke. Transl Stroke Res 4: 390–401. doi: 10.1007/s12975-012-0230-5 24323337

8. Zaninovich OA, Ramey WL, Walter CM, Dumont TM (2017) Completion of the Circle of Willis Varies by Gender, Age, and Indication for Computed Tomography Angiography. World Neurosurg 106: 953–963. doi: 10.1016/j.wneu.2017.07.084 28736349

9. Etherton MR, Wu O, Cougo P, Giese AK, Cloonan L, et al. (2017) Structural Integrity of Normal Appearing White Matter and Sex-Specific Outcomes After Acute Ischemic Stroke. Stroke 48: 3387–3389. doi: 10.1161/STROKEAHA.117.019258 29127272

10. Chauhan A, Moser H, McCullough LD (2017) Sex differences in ischaemic stroke: potential cellular mechanisms. Clin Sci (Lond) 131: 533–552.

11. Colbert JF, Traystman RJ, Poisson SN, Herson PS, Ginde AA (2016) Sex-Related Differences in the Risk of Hospital-Acquired Sepsis and Pneumonia Post Acute Ischemic Stroke. J Stroke Cerebrovasc Dis 25: 2399–2404. doi: 10.1016/j.jstrokecerebrovasdis.2016.06.008 27363622

12. McCullough LD, Mirza MA, Xu Y, Bentivegna K, Steffens EB, et al. (2016) Stroke sensitivity in the aged: sex chromosome complement vs. gonadal hormones. Aging (Albany NY) 8: 1432–1441.

13. Asdaghi N, Romano JG, Wang K, Ciliberti-Vargas MA, Koch S, et al. (2016) Sex Disparities in Ischemic Stroke Care: FL-PR CReSD Study (Florida-Puerto Rico Collaboration to Reduce Stroke Disparities). Stroke 47: 2618–2626. doi: 10.1161/STROKEAHA.116.013059 27553032

14. Gibson CL, Attwood L (2016) The impact of gender on stroke pathology and treatment. Neurosci Biobehav Rev 67: 119–124. doi: 10.1016/j.neubiorev.2015.08.020 26657813

15. Di Carlo A, Lamassa M, Baldereschi M, Pracucci G, Basile AM, et al. (2003) Sex differences in the clinical presentation, resource use, and 3-month outcome of acute stroke in Europe: data from a multicenter multinational hospital-based registry. Stroke 34: 1114–1119. doi: 10.1161/01.STR.0000068410.07397.D7 12690218

16. Morrison HW, Filosa JA (2016) Sex differences in astrocyte and microglia responses immediately following middle cerebral artery occlusion in adult mice. Neuroscience 339: 85–99. doi: 10.1016/j.neuroscience.2016.09.047 27717807

17. Nguyen TV, Frye JB, Zbesko JC, Stepanovic K, Hayes M, et al. (2016) Multiplex immunoassay characterization and species comparison of inflammation in acute and non-acute ischemic infarcts in human and mouse brain tissue. Acta Neuropathol Commun 4: 100. doi: 10.1186/s40478-016-0371-y 27600707

18. Tatusova TA, Karsch-Mizrachi I, Ostell JA (1999) Complete genomes in WWW Entrez: data representation and analysis. Bioinformatics 15: 536–543. doi: 10.1093/bioinformatics/15.7.536 10487861

19. Krug T, Gabriel JP, Taipa R, Fonseca BV, Domingues-Montanari S, et al. (2012) TTC7B emerges as a novel risk factor for ischemic stroke through the convergence of several genome-wide approaches. J Cereb Blood Flow Metab 32: 1061–1072. doi: 10.1038/jcbfm.2012.24 22453632

20. Tatusova T, DiCuccio M, Badretdin A, Chetvernin V, Nawrocki EP, et al. (2016) NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res 44: 6614–6624. doi: 10.1093/nar/gkw569 27342282

21. Carvalho BS, Irizarry RA (2010) A framework for oligonucleotide microarray preprocessing. Bioinformatics 26: 2363–2367. doi: 10.1093/bioinformatics/btq431 20688976

22. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, et al. (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43: e47. doi: 10.1093/nar/gkv007 25605792

23. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25: 25–29. doi: 10.1038/75556 10802651

24. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, et al. (2017) The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 45: D362–D368. doi: 10.1093/nar/gkw937 27924014

25. Rivals I, Personnaz L, Taing L, Potier MC (2007) Enrichment or depletion of a GO category within a class of genes: which test? Bioinformatics 23: 401–407. doi: 10.1093/bioinformatics/btl633 17182697

26. Glickman ME, Rao SR, Schultz MR (2014) False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol 67: 850–857. doi: 10.1016/j.jclinepi.2014.03.012 24831050

27. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, et al. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13: 2498–2504. doi: 10.1101/gr.1239303 14597658

28. Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4: 2. doi: 10.1186/1471-2105-4-2 12525261

29. Tang Y, Li M, Wang J, Pan Y, Wu FX (2015) CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems 127: 67–72. doi: 10.1016/j.biosystems.2014.11.005 25451770

30. Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4: 44–57. doi: 10.1038/nprot.2008.211 19131956

31. Black M, Wang W, Wang W (2015) Ischemic Stroke: From Next Generation Sequencing and GWAS to Community Genomics? OMICS 19: 451–460. doi: 10.1089/omi.2015.0083 26230531

32. Ye Z, Zhang H, Sun L, Cai H, Hao Y, et al. (2018) GWAS-Supported CRP Gene Polymorphisms and Functional Outcome of Large Artery Atherosclerotic Stroke in Han Chinese. Neuromolecular Med.

33. Tan X, Zhang X, Pan L, Tian X, Dong P (2017) Identification of Key Pathways and Genes in Advanced Coronary Atherosclerosis Using Bioinformatics Analysis. Biomed Res Int 2017: 4323496. doi: 10.1155/2017/4323496 29226137

34. Fahmi RM, Elsaid AF (2016) Infarction Size, Interleukin-6, and Their Interaction Are Predictors of Short-Term Stroke Outcome in Young Egyptian Adults. J Stroke Cerebrovasc Dis 25: 2475–2481. doi: 10.1016/j.jstrokecerebrovasdis.2016.06.021 27402591

35. Srivastava MV, Bhasin A, Chaudhry R, Sharma S, Subbaiah V, et al. (2014) Novel inflammatory biomarkers and their correlation to Chlamydia pneumoniae titres in acute ischemic stroke. J Stroke Cerebrovasc Dis 23: 2391–2396. doi: 10.1016/j.jstrokecerebrovasdis.2014.05.016 25263435

36. Jin G (2017) The relationship between serum CXCL16 level and carotid vulnerable plaque in patients with ischemic stroke. Eur Rev Med Pharmacol Sci 21: 3911–3915. 28975971

37. Feng J, Liu YH, Yang QD, Zhu ZH, Xia K, et al. (2013) TNFSF4 gene polymorphism rs3861950 but not rs3850641 is associated with the risk of cerebral infarction in a Chinese population. J Thromb Thrombolysis 36: 307–313. doi: 10.1007/s11239-012-0849-9 23184501

38. Li WZ, Gao CY, He WL, Zhang HM (2016) Association of the interleukin-10 gene -1082A/G genetic polymorphism with risk of ischemic stroke in a Chinese population. Genet Mol Res 15.

39. Banerjee A, Wang J, Bodhankar S, Vandenbark AA, Murphy SJ, et al. (2013) Phenotypic changes in immune cell subsets reflect increased infarct volume in male vs. female mice. Transl Stroke Res 4: 554–563. doi: 10.1007/s12975-013-0268-z 24187596

40. Bodhankar S, Lapato A, Chen Y, Vandenbark AA, Saugstad JA, et al. (2015) Role for microglia in sex differences after ischemic stroke: importance of M2. Metab Brain Dis 30: 1515–1529. doi: 10.1007/s11011-015-9714-9 26246072

41. Li Y, Suo L, Liu Y, Li H, Xue W (2017) Protective effects of ginsenoside Rg1 against oxygen-glucose-deprivation-induced apoptosis in neural stem cells. J Neurol Sci 373: 107–112. doi: 10.1016/j.jns.2016.12.036 28131165

42. Yang JL, Mukda S, Chen SD (2018) Diverse roles of mitochondria in ischemic stroke. Redox Biol 16: 263–275. doi: 10.1016/j.redox.2018.03.002 29549824


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