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. 2022 Feb 9:12:801232.
doi: 10.3389/fcimb.2022.801232. eCollection 2022.

Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis

Qingliang Wang  1 , Xiaojie Li  2 , Wenting Tang  3 , Xiaoling Guan  2 , Zhiyong Xiong  1 , Yong Zhu  4 , Jiao Gong  2 , Bo Hu  2
Affiliations

Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis

Qingliang Wang et al. Front Cell Infect Microbiol. .

Abstract

Background: The host response to bacterial sepsis is reported to be nonspecific regardless of the causative pathogen. However, newer paradigms indicated that the host response of Gram-negative sepsis may be different from Gram-positive sepsis, and the difference has not been clearly clarified. The current study aimed to explore the difference by identifying the differential gene sets using the genome-wide technique.

Methods: The training dataset GSE6535 and the validation dataset GSE13015 were used for bioinformatics analysis. The distinct gene sets of sepsis with different infections were screened using gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). The intersection gene sets based on the two algorithms were confirmed through Venn analysis. Finally, the common gene sets between GSE6535 and GSE13015 were determined by GSEA.

Results: Two immunological gene sets in GSE6535 were identified based on GSVA, which could be used to discriminate sepsis caused by Gram-positive, Gram-negative, or mixed infection. A total of 19 gene sets were obtained in GSE6535 through Venn analysis based on GSVA and GSEA, which revealed the heterogeneity of Gram-negative and Gram-positive sepsis at the molecular level. The result was also verified by analysis of the validation set GSE13015, and 40 common differential gene sets were identified between dataset GSE13015 and dataset GSE6535 by GSEA.

Conclusions: The identified differential gene sets indicated that host response may differ dramatically depending on the inciting organism. The findings offer new insight to investigate the pathophysiology of bacterial sepsis.

Keywords: Gram-negative; Gram-positive; gene sets; microarray analysis; sepsis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Analysis workflow of this study.
Figure 2
Figure 2
Heatmap of enrichment score of (A) Hallmark gene sets, (B) C2 gene sets, and (C) C7 gene sets in patients with Gram−positive sepsis, Gram-negative sepsis, mixed sepsis, and normal control. The rows in the heatmap indicate the expression values of each gene set, and the columns indicate the 72 samples examined in dataset GSE6535.
Figure 3
Figure 3
Heatmap of differential gene sets between (A) Gram-positive and Gram-negative sepsis, (B) mixed sepsis versus Gram-negative sepsis, and (C) mixed sepsis versus Gram-positive sepsis. Venn diagram of (D) differential gene sets across various infection types and (E) the identified two distinct gene sets.
Figure 4
Figure 4
Protein–protein interaction network of the two distinct gene sets, namely, (A) the top 5 hub genes and (B–D) the top 3 clusters.
Figure 5
Figure 5
Gene set enrichment analysis for dataset GSE6535. (A) Representative images of annotated gene sets with p value. (B) Venn diagram of the common differential gene sets between Gram-negative and Gram-positive sepsis.
Figure 6
Figure 6
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the genes involved in the intersection gene sets. (A) Molecular function, (B) cellular component, and (C) biological process for GO analysis. (D) The top 10 of KEGG pathway enrichment.
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