Abstract
To identify disrupted pathways associated with neonatal sepsis, we performed a research based on the combination of protein-protein interactions (PPIs) and pathway data. Firstly, a total of 23,292 genes, 787,896 PPIs and 1,675 human pathways were obtained, respectively. Then, under the threshold value of false discovery rate (FDR)<0.05 and a delta cut-off value >4.36, a total of 986 differentially expressed genes (DEGs) were identified. In the following, by degree centrality for the objective PPI network, 20 hub genes were obtained. Finally, pathway enrichment analysis and randomization tests indicated that pathways of gene expression, immune system and innate immune system were with remarkable significance in neonatal sepsis. Therefore, in the present study, we presented a novel pathway method, and we successfully identified several pathways in neonatal sepsis, which might be underlying indicators in the detection and treatment of neonatal sepsis.
Similar content being viewed by others
References
Samsygina, G.A. et al. Sepsis in the newborn. Arkh. Patol. Suppl, 1–48 (2004).
Liu, L. et al. Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. Lancet 379, 2151–2161 (2012).
Vergnano, S. et al. Neonatal sepsis: an international perspective. Arch. Dis. Child. Fetal Neonatal Ed. 90, F220–224 (2005).
Lupu, F., Keshari, R.S., Lambris, J.D. & Coggeshall, K.M. Crosstalk between the coagulation and complement systems in sepsis. Thrombosis Research 133, S28–S31 (2014).
Streimish, I. et al. Neutrophil CD64 with hematologic criteria for diagnosis of neonatal sepsis. Am. J. Perinatol. 31, 21–30 (2014).
Abdollahi, A., Shoar, S., Nayyeri, F. & Shariat, M. Diagnostic Value of Simultaneous Measurement of Procalcitonin, Interleukin-6 and hs-CRP in Prediction of Early-Onset Neonatal Sepsis. Mediterr. J. Hematol. Infect. Dis. 4, e2012028 (2012).
Dickinson, P. et al. Whole blood gene expression profiling of neonates with confirmed bacterial sepsis. Genomics Data 3, 41–48 (2015).
Smith, C.L. et al. Identification of a human neonatal immune-metabolic network associated with bacterial infection. Nat. Commun. 5, 4649 (2014).
Stelzl, U. et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell 122, 957–968 (2005).
Dreze, M. et al. High-quality binary interactome mapping. Methods Enzymol. 470, 281–315 (2010).
Szklarczyk, D. et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39, D561–568 (2011).
Ideker, T. & Sharan, R. Protein networks in disease. Genome Res. 18, 644–652 (2008).
Venkatesan, K. et al. An empirical framework for binary interactome mapping. Nat. Methods 6, 83–90 (2009).
Yu, H. et al. High-quality binary protein interaction map of the yeast interactome network. Science 322, 104–110 (2008).
Xu, J. & Li, Y. Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics 22, 2800–2805 (2006).
Cary, M.P., Bader, G.D. & Sander, C. Pathway information for systems biology. FEBS Lett. 579, 1815–1820 (2005).
Huang da, W., Sherman, B.T. & Lempicki, R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).
Khatri, P., Sirota, M. & Butte, A.J. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput. Biol. 8, e1002375 (2012).
Gu, Z. et al. Centrality-based pathway enrichment: a systematic approach for finding significant pathways dominated by key genes. BMC Syst. Biol. 6, 56 (2012).
Stoll, B.J. et al. Early onset neonatal sepsis: the burden of group B Streptococcal and E. coli disease continues. Pediatrics 127, 817–826 (2011).
Hornik, C.P. et al. Early and late onset sepsis in verylow-birth-weight infants from a large group of neonatal intensive care units. Early Hum. Dev. 88 Suppl 2, S69–74 (2012).
Sharma, A.A., Jen, R., Butler, A. & Lavoie, P.M. The developing human preterm neonatal immune system: a case for more research in this area. Clin. Immunol. 145, 61–68 (2012).
Cuenca, A.G., Wynn, J.L., Moldawer, L.L. & Levy, O. Role of innate immunity in neonatal infection. Am. J. Perinatol. 30, 105–112 (2013).
McDonagh, S. et al. Viral and bacterial pathogens at the maternal-fetal interface. J. Infect. Dis. 190, 826–834 (2004).
Makhseed, M. et al. Th1 and Th2 cytokine profiles in recurrent aborters with successful pregnancy and with subsequent abortions. Hum. Reprod. 16, 2219–2226 (2001).
Marchini, G. et al. Erythema toxicum neonatorum is an innate immune response to commensal microbes penetrated into the skin of the newborn infant. Pediatr Res. 58, 613–616 (2005).
Janeway, C.A., Jr. & Medzhitov, R. Innate immune recognition. Annu. Rev. Immunol. 20, 197–216 (2002).
Lyu, J. et al. Sepsis-induced brain mitochondrial dysfunction is associated with altered mitochondrial Src and PTP1B levels. Brain Res. 1620, 130–138 (2015).
Liu, G., Wong, L. & Chua, H.N. Complex discovery from weighted PPI networks. Bioinformatics 25, 1891–1897 (2009).
Croft, D. et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 39, D691–697 (2011).
Zhao, J., Yang, T.-H., Huang, Y. & Holme, P. Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach. PloS One 6, e24306 (2011).
Tusher, V.G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA. 98, 5116–5121 (2001).
Reiner, A., Yekutieli, D. & Benjamini, Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19, 368–375 (2003).
Junker, B.H. & Schreiber, F. Analysis of biological networks. John Wiley & Sons. 2 (2011).
Brandes, U. & Erlebach, T. Network analysis: methodological foundations. Springer Science & Business Media. (2005).
Koschützki, D. & Schreiber, F. Centrality analysis methods for biological networks and their application to gene regulatory networks. Gene Regul. Syst. Bio. 2, 193 (2008).
He, X. & Zhang, J. Why do hubs tend to be essential in protein networks? PLoS Genet. 2, e88 (2006).
Allen, H.L. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).
Edgington, E. & Onghena, P. Randomization tests. CRC Press. (2007).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Qiao, X., Zhu, S., Zhang, S. et al. Disrupted pathways associated with neonatal sepsis: Combination of protein-protein interactions and pathway data. BioChip J 11, 1–7 (2017). https://doi.org/10.1007/s13206-016-1101-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13206-016-1101-z