The Indian Journal of Pediatrics

, Volume 84, Issue 6, pp 430–436 | Cite as

Detection of Significant Pneumococcal Meningitis Biomarkers by Ego Network

Original Article
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Abstract

Objective

To identify significant biomarkers for detection of pneumococcal meningitis based on ego network.

Methods

Based on the gene expression data of pneumococcal meningitis and global protein-protein interactions (PPIs) data recruited from open access databases, the authors constructed a differential co-expression network (DCN) to identify pneumococcal meningitis biomarkers in a network view. Here EgoNet algorithm was employed to screen the significant ego networks that could accurately distinguish pneumococcal meningitis from healthy controls, by sequentially seeking ego genes, searching candidate ego networks, refinement of candidate ego networks and significance analysis to identify ego networks. Finally, the functional inference of the ego networks was performed to identify significant pathways for pneumococcal meningitis.

Results

By differential co-expression analysis, the authors constructed the DCN that covered 1809 genes and 3689 interactions. From the DCN, a total of 90 ego genes were identified. Starting from these ego genes, three significant ego networks (Module 19, Module 70 and Module 71) that could predict clinical outcomes for pneumococcal meningitis were identified by EgoNet algorithm, and the corresponding ego genes were GMNN, MAD2L1 and TPX2, respectively. Pathway analysis showed that these three ego networks were related to CDT1 association with the CDC6:ORC:origin complex, inactivation of APC/C via direct inhibition of the APC/C complex pathway, and DNA strand elongation, respectively.

Conclusions

The authors successfully screened three significant ego modules which could accurately predict the clinical outcomes for pneumococcal meningitis and might play important roles in host response to pathogen infection in pneumococcal meningitis.

Keywords

Pneumococcal meningitis EgoNet algorithm Ego gene Pathway Differential co-expression network 

References

  1. 1.
    O'Brien KL, Wolfson LJ, Watt JP, et al. Burden of disease caused by Streptococcus pneumoniae in children younger than 5 years: global estimates. Lancet. 2009;374:893–902.CrossRefPubMedGoogle Scholar
  2. 2.
    Htar MTT, Madhava H, Balmer P, Christopoulou D, Menegas D, Bonnet E. A review of the impact of pneumococcal polysaccharide conjugate vaccine (7-valent) on pneumococcal meningitis. Adv Ther. 2013;30:748–62.CrossRefGoogle Scholar
  3. 3.
    Conklin LM, Bigogo G, Jagero G, et al. High Streptococcus pneumoniae colonization prevalence among HIV-infected Kenyan parents in the year before pneumococcal conjugate vaccine introduction. BMC Infect Dis. 2015;16:1–10.CrossRefGoogle Scholar
  4. 4.
    Dancey JE, Bedard PL, Onetto N, Hudson TJ. The genetic basis for cancer treatment decisions. Cell. 2012;148:409–20.CrossRefPubMedGoogle Scholar
  5. 5.
    Wang X, Gulbahce N, Yu H. Network-based methods for human disease gene prediction. Brief Funct Genomics. 2011;10:280–93.CrossRefPubMedGoogle Scholar
  6. 6.
    Dutkowski J, Ideker T. Protein networks as logic functions in development and cancer. PLoS Comput Biol. 2011;7:e1002180.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Zhu Y, Shen X, Pan W. Network-based support vector machine for classification of microarray samples. BMC Bioinform. 2009;10:S21.CrossRefGoogle Scholar
  8. 8.
    Yang R, Bai Y, Qin Z, Yu T. EgoNet: identification of human disease ego-network modules. BMC Genomics. 2014;15:314.Google Scholar
  9. 9.
    Irwin AD, Marriage F, Mankhambo LA, et al. Novel biomarker combination improves the diagnosis of serious bacterial infections in Malawian children. BMC Med Genomics. 2012;5:13.Google Scholar
  10. 10.
    Carrol ED, Mankhambo LA, Jeffers G, et al. The diagnostic and prognostic accuracy of five markers of serious bacterial infection in Malawian children with signs of severe infection. PLoS One. 2009;4:e6621.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Ma L, Robinson LN, Towle HC. ChREBP* mlx is the principal mediator of glucose-induced gene expression in the liver. J Biol Chem. 2006;281:28721–30.CrossRefPubMedGoogle Scholar
  12. 12.
    Rifai N, Ridker PM. Proposed cardiovascular risk assessment algorithm using high-sensitivity C-reactive protein and lipid screening. Clin Chem. 2001;47:28–30.PubMedGoogle Scholar
  13. 13.
    Pepper SD, Saunders EK, Edwards LE, Wilson CL, Miller CJ. The utility of MAS5 expression summary and detection call algorithms. BMC Bioinform. 2007;8:273.CrossRefGoogle Scholar
  14. 14.
    Benesty J, Chen J, Huang Y, Cohen I. Pearson correlation coefficient. In: Cohen I, Huang Y, Chen J, Benesty J, editors. Noise reduction in speech processing. Berlin: Springer; 2009. p. 1–4.Google Scholar
  15. 15.
    Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.CrossRefPubMedGoogle Scholar
  16. 16.
    Chen L, Xuan J, Riggins RB, Wang Y, Clarke R. Identifying protein interaction subnetworks by a bagging Markov random field-based method. Nucleic Acids Res. 2013;41:e42.CrossRefPubMedGoogle Scholar
  17. 17.
    Goodman LA. Snowball sampling. Ann Math Stat. 1961;32:148–70.CrossRefGoogle Scholar
  18. 18.
    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc Series B (Methodological). 1995;57:289–300.Google Scholar
  19. 19.
    Ahn T, Lee E, Huh N, Park T. Personalized identification of altered pathways in cancer using accumulated normal tissue data. Bioinformatics. 2014;30:i422–9.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Mook-Kanamori BB, Geldhoff M, van der Poll T, van de Beek D. Pathogenesis and pathophysiology of pneumococcal meningitis. Clin Microbiol Rev. 2011;24:557–91.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Sanders M, van Well G, Ouburg S, Morre S, van Furth AM. Genetic variation of innate immune response genes in invasive pneumococcal and meningococcal disease applied to the pathogenesis of meningitis. Genes Immun. 2011;12:321–34.CrossRefPubMedGoogle Scholar
  22. 22.
    Pinheiro DML, Fontes FL, de Oliveira AHS, et al. Polymorphisms in DNA repair gene XRCC1 (Arg194Trp) and (Arg399Gln) and their role in the susceptibility of bacterial meningitis. J Meningitis. 2016;1:105.Google Scholar
  23. 23.
    Ogunniyi AD, Giammarinaro P, Paton JC. The genes encoding virulence-associated proteins and the capsule of Streptococcus pneumoniae are upregulated and differentially expressed in vivo. Microbiology. 2002;148:2045–53.CrossRefPubMedGoogle Scholar
  24. 24.
    Wells DB, Tighe PJ, Wooldridge KG, Robinson K, Ala’ Aldeen DA. Differential gene expression during meningeal-meningococcal interaction: evidence for self-defense and early release of cytokines and chemokines. Infect Immun. 2001;69:2718–22.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010;38:D355–60.CrossRefPubMedGoogle Scholar
  26. 26.
    McGarry TJ, Kirschner MW. Geminin, an inhibitor of DNA replication, is degraded during mitosis. Cell. 1998;93:1043–53.CrossRefPubMedGoogle Scholar
  27. 27.
    Wohlschlegel JA, Dwyer BT, Dhar SK, Cvetic C, Walter JC, Dutta A. Inhibition of eukaryotic DNA replication by geminin binding to Cdt1. Science. 2000;290:2309–12.CrossRefPubMedGoogle Scholar
  28. 28.
    Gonzalez MA, Tachibana KE, Adams DJ, et al. Geminin is essential to prevent endoreduplication and to form pluripotent cells during mammalian development. Genes Dev. 2006;20:1880–4.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Luo L, Yang X, Takihara Y, Knoetgen H, Kessel M. The cell-cycle regulator geminin inhibits Hox function through direct and polycomb-mediated interactions. Nature. 2004;427:749–53.CrossRefPubMedGoogle Scholar
  30. 30.
    Lei M, Tye BK. Initiating DNA synthesis: from recruiting to activating the MCM complex. J Cell Sci. 2001;114:1447–54.PubMedGoogle Scholar
  31. 31.
    Blow JJ, Tada S. Cell cycle. A new check on issuing the licence. Nature. 2000;404:560–1.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Chan GK, Yen TJ. The mitotic checkpoint: a signaling pathway that allows a single unattached kinetochore to inhibit mitotic exit. Prog Cell Cycle Res. 2003;5:431–9.PubMedGoogle Scholar
  33. 33.
    Sudakin V, Chan GK, Yen TJ. Checkpoint inhibition of the APC/C in HeLa cells is mediated by a complex of BUBR1, BUB3, CDC20, and MAD2. J Cell Biol. 2001;154:925–36.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Oosthuysen WF, Mueller T, Dittrich MT, Schubert-Unkmeir A. Neisseria meningitidis causes cell cycle arrest of human brain microvascular endothelial cells at S phase via p21 and cyclin G2. Cell Microbiol. 2016;18:46–65.CrossRefPubMedGoogle Scholar
  35. 35.
    Joyce EA, Popper SJ, Falkow S. Streptococcus Pneumoniae nasopharyngeal colonization induces type I interferons and interferon-induced gene expression. BMC Genomics. 2009;10:404.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Kufer TA, Sillje HH, Korner R, Gruss OJ, Meraldi P, Nigg EA. Human TPX2 is required for targeting aurora-a kinase to the spindle. J Cell Biol. 2002;158:617–23.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Gruss OJ, Wittmann M, Yokoyama H, et al. Chromosome-induced microtubule assembly mediated by TPX2 is required for spindle formation in HeLa cells. Nat Cell Biol. 2002;4:871–9.CrossRefPubMedGoogle Scholar
  38. 38.
    Stewart S, Fang G. Anaphase-promoting complex/cyclosome controls the stability of TPX2 during mitotic exit. Mol Cell Biol. 2005;25:10516–27.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Schang LM. The cell cycle, cyclin-dependent kinases, and viral infections: new horizons and unexpected connections. Prog Cell Cycle Res. 2003;5:103–24.PubMedGoogle Scholar
  40. 40.
    Nougayrede JP, Taieb F, De Rycke J, Oswald E. Cyclomodulins: bacterial effectors that modulate the eukaryotic cell cycle. Trends Microbiol. 2005;13:103–10.CrossRefPubMedGoogle Scholar
  41. 41.
    Oswald E, Nougayrede JP, Taieb F, Sugai M. Bacterial toxins that modulate host cell-cycle progression. Curr Opin Microbiol. 2005;8:83–91.CrossRefPubMedGoogle Scholar

Copyright information

© Dr. K C Chaudhuri Foundation 2017

Authors and Affiliations

  • Qian Wang
    • 1
  • Zhifeng Lou
    • 1
  • Liansuo Zhai
    • 2
  • Haibin Zhao
    • 3
  1. 1.Department of PediatricsJiyang Public HospitalJinanChina
  2. 2.Department of OrthopedicsJiyang Public HospitalJinanChina
  3. 3.Department of NeurologyJiyang Public HospitalJinanChina

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