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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Microbial Immunology
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1450782
This article is part of the Research Topic Epigenetics in the microbiome-host crosstalk: from mechanisms to Therapeutics View all 5 articles

Identification and validation of diagnostic biomarkers and immune cell abundance characteristics in Staphylococcus aureus bloodstream infection by integrative bioinformatics analysis

Provisionally accepted
JunHong Shi JunHong Shi 1Li Shen Li Shen 1Yanghua Xiao Yanghua Xiao 1Cailing Wan Cailing Wan 1Wang Bingjie Wang Bingjie 1Peiyao Zhou Peiyao Zhou 1Jiao Zhang Jiao Zhang 1Weihua Han Weihua Han 1Rongrong Hu Rongrong Hu 2Fangyou Yu Fangyou Yu 1*Hongxiu Wang Hongxiu Wang 1
  • 1 Department of Clinical Laboratory, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, Shanghai Municipality, China
  • 2 Shanghai Institute of Immunology and Infection, Chinese Academy of Sciences (CAS), Shanghai, Shanghai Municipality, China

The final, formatted version of the article will be published soon.

    Staphylococcus aureus (S. aureus) is an opportunistic pathogen that could cause life-threatening bloodstream infections. The objective of this study was to identify potential diagnostic biomarkers of S. aureus bloodstream infection. Gene expression dataset GSE33341 was optimized as the discovery dataset, which contained samples from human and mice. GSE65088 dataset was utilized as a validation dataset. First, after overlapping the differentially expressed genes (DEGs) in S. aureus infection samples from GSE33341-human and GSE33341-mice samples, we detected 63 overlapping genes. Subsequently, the hub genes including DRAM1, PSTPIP2, and UPP1 were identified via three machine-learning algorithms: random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator. Additionally, the receiver operating characteristic curve was leveraged to verify the efficacy of the hub genes. DRAM1 (AUC=1), PSTPIP2 (AUC=1), and UPP1 (AUC=1) were investigated and demonstrated significant expression differences (all P < 0.05) and diagnostic efficacy in the training and validation datasets. Furthermore, the relationship between the diagnostic markers and the abundance of immune cells was assessed using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). These three diagnostic indicators also correlated with multiple immune cells to varying degrees. The expression of DRAM1 was significantly positively correlated with B cell naive and mast cell activation, and negatively correlated with NK cells and CD4/CD8+ T cells. The expression of PSTPIP2 was significantly positively correlated with macrophage M0, macrophage M1, B cell naive, and dendritic cell activation, while the expression of PSTPIP2 was negatively correlated with NK cells and CD4/CD8 + T cells. Significant negative correlations between UPP1 expression and T cell CD4 memory rest and neutrophils were also observed. Finally, we established a mouse model of S. aureus bloodstream infection and collected the blood samples for RNA-Seq analysis and RT-qPCR experiments. The analysis results in RNA-Seq and RT-qPCR experiments further confirmed the significant expression differences (all P < 0.05) of these three genes. Overall, three candidate hub genes (DRAM1, PSTPIP2, and UPP1) were identified initially for S. aureus bloodstream infection diagnosis. Our study could provide potential diagnostic biomarkers for S. aureus bloodstream infection patients.

    Keywords: Staphylococcus aureus, Bloodstream infection, machine-learning, biomarkers, Immune cell abundance

    Received: 18 Jun 2024; Accepted: 01 Aug 2024.

    Copyright: © 2024 Shi, Shen, Xiao, Wan, Bingjie, Zhou, Zhang, Han, Hu, Yu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Fangyou Yu, Department of Clinical Laboratory, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, Shanghai Municipality, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.