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Article Open Access

Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation

  • Authors:
    • Zhenliang Li
    • Ying Zhang
    • Yaling Liu
    • Yanchun Liu
    • Youyi Li
  • View Affiliations / Copyright

    Affiliations: Intensive Care Unit, Pinggu Hospital Affiliated to Capital Medical University, Beijing 101200, P.R. China, Department of Infection Diseases, Beijing Pinggu Hospital of Traditional Chinese Medicine, Beijing 101200, P.R. China
    Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 3133-3146
    |
    Published online on: July 15, 2017
       https://doi.org/10.3892/mmr.2017.7013
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Abstract

Sepsis is an inflammatory response to pathogens (such as Gram‑positive and Gram‑negative bacteria), which has high morbidity and mortality in critically ill patients. The present study aimed to identify the key genes in Gram‑positive and Gram‑negative sepsis. GSE6535 was downloaded from Gene Expression Omnibus, containing 17 control samples, 18 Gram‑positive samples and 25 Gram‑negative samples. Subsequently, the limma package in R was used to screen the differentially expressed genes (DEGs). Hierarchical clustering was conducted for the specific DEGs in Gram‑negative and Gram‑negative samples using cluster software and the TreeView software. To analyze the correlation of samples at the gene level, a similarity network was constructed using Cytoscape software. Functional and pathway enrichment analyses were conducted for the DEGs using DAVID. Finally, stochastic perturbation was used to determine the significantly differential functions between Gram‑positive and Gram‑negative samples. A total of 340 and 485 DEGs were obtained in Gram‑positive and Gram‑negative samples, respectively. Hierarchical clustering revealed that there were significant differences between control and sepsis samples. In Gram‑positive and Gram‑negative samples, myeloid cell leukemia sequence 1 was associated with apoptosis and programmed cell death. Additionally, NADH:ubiquinone oxidoreductase subunit S4 was associated with mitochondrial respiratory chain complex I assembly. Stochastic perturbation analysis revealed that NADH:ubiquinone oxidoreductase subunit B2 (NDUFB2), NDUFB8 and ubiquinol‑cytochrome c reductase hinge protein (UQCRH) were associated with cellular respiration in Gram‑negative samples, whereas large tumor suppressor kinase 2 (LATS2) was associated with G1/S transition of the mitotic cell cycle in Gram‑positive samples. NDUFB2, NDUFB8 and UQCRH may be biomarkers for Gram‑negative sepsis, whereas LATS2 may be a biomarker for Gram‑positive sepsis. These findings may promote the therapies of sepsis caused by Gram‑positive and Gram‑negative bacteria.
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Copy and paste a formatted citation
Spandidos Publications style
Li Z, Zhang Y, Liu Y, Liu Y and Li Y: Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation. Mol Med Rep 16: 3133-3146, 2017.
APA
Li, Z., Zhang, Y., Liu, Y., Liu, Y., & Li, Y. (2017). Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation. Molecular Medicine Reports, 16, 3133-3146. https://doi.org/10.3892/mmr.2017.7013
MLA
Li, Z., Zhang, Y., Liu, Y., Liu, Y., Li, Y."Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation". Molecular Medicine Reports 16.3 (2017): 3133-3146.
Chicago
Li, Z., Zhang, Y., Liu, Y., Liu, Y., Li, Y."Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation". Molecular Medicine Reports 16, no. 3 (2017): 3133-3146. https://doi.org/10.3892/mmr.2017.7013
Copy and paste a formatted citation
x
Spandidos Publications style
Li Z, Zhang Y, Liu Y, Liu Y and Li Y: Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation. Mol Med Rep 16: 3133-3146, 2017.
APA
Li, Z., Zhang, Y., Liu, Y., Liu, Y., & Li, Y. (2017). Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation. Molecular Medicine Reports, 16, 3133-3146. https://doi.org/10.3892/mmr.2017.7013
MLA
Li, Z., Zhang, Y., Liu, Y., Liu, Y., Li, Y."Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation". Molecular Medicine Reports 16.3 (2017): 3133-3146.
Chicago
Li, Z., Zhang, Y., Liu, Y., Liu, Y., Li, Y."Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation". Molecular Medicine Reports 16, no. 3 (2017): 3133-3146. https://doi.org/10.3892/mmr.2017.7013
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