Pathway cross-talk network analysis identifies critical pathways in neonatal sepsis. (June 2017)

• Record Type: Journal Article
• Authors: Meng, Yu-Xiu; Liu, Quan-Hong; Chen, Deng-Hong; Meng, Ying
• Is Part Of: Computational biology and chemistry. Volume 68(2017)
• Page Start: 101
• Page End: 106
• Publication Date: 2017-06
• Subjects: Neonatal sepsis -- Attractor -- Differential pathway -- Network -- Pathway cross-talk; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.02.007
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Transcriptome data, protein–protein interactions and pathways were integrated. Attract, rank product and impact factor were combined to search critical pathways. The disease pathway cross-talk network had 1249 significant pathways cross-talks. A total of 8 critical pathways were identified. Abstract: Background: Despite advances in neonatal care, sepsis remains a major cause of morbidity and mortality in neonates worldwide. Pathway cross-talk analysis might contribute to the inference of the driving forces in bacterial sepsis and facilitate a better understanding of underlying pathogenesis of neonatal sepsis. Objective: This study aimed to explore the critical pathways associated with the progression of neonatal sepsis by the pathway cross-talk analysis. Methods: By integrating neonatal transcriptome data with known pathway data and protein-protein interaction data, we systematically uncovered the disease pathway cross-talks and constructed a disease pathway cross-talk network for neonatal sepsis. Then, attract method was employed to explore the dysregulated pathways associated with neonatal sepsis. To determine the critical pathways in neonatal sepsis, rank product (RP) algorithm, centrality analysis and impact factor (IF) were introduced sequentially, which synthetically considered the differential expression of genes and pathways, pathways cross-talks and pathway parameters in the network. The dysregulated pathways with the highest IF values as well as RP < 0.01 were defined as critical pathways in neonatal sepsis. Results: By integrating three kinds of data, only 6919 common genes were included to perform the pathway cross-talk analysis. By statistic analysis, a total of 1249 significant pathway cross-talks were selected to construct the pathway cross-talk network. Moreover, 47 dys-regulated pathways were identified via attract method, 20 pathways were identified under RP < 0.01, and the top 10 pathways with the highest IF were also screened from the pathway cross-talk network. Among them, we selected 8 common pathways, i.e. critical pathways. Conclusions: In this study, we systematically tracked 8 critical pathways involved in neonatal sepsis by integrating attract method and pathway cross-talk network. These pathways might be responsible for the host response in infection, and of great value for advancing diagnosis and therapy of neonatal sepsis.