Open Access

ARTICLE

Machine learning and bioinformatics to identify biomarkers in response to Burkholderia pseudomallei infection in mice

by YAO FANG^1,2,#, FEI XIA^1,#, FEIFEI TIAN³, LEI QU¹, FANG YANG¹, JUAN FANG^1,2, ZHENHONG HU^1,*, HAICHAO LIU^1,*

1 Department of Respiratory and Critical Care Medicine, General Hospital of Center Theater of PLA, Wuhan, 430070, China
2 School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China
3 School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, 611756, China

* Corresponding Authors: ZHENHONG HU. Email: ; HAICHAO LIU. Email:
# Co-first authors: Yao Fang and Fei Xia contributed equally to this study

(This article belongs to the Special Issue: Bioinformatics Study of Diseases)

BIOCELL 2024, 48(4), 613-621. https://doi.org/10.32604/biocell.2024.031539

Received 30 June 2023; Accepted 17 November 2023; Issue published 09 April 2024

Abstract

Objective: In the realm of Class I pathogens, Burkholderia pseudomallei (BP) stands out for its propensity to induce severe pathogenicity. Investigating the intricate interactions between BP and host cells is imperative for comprehending the dynamics of BP infection and discerning biomarkers indicative of the host cell response process. Methods: mRNA extraction from BP-infected mouse macrophages constituted the initial step of our study. Employing gene expression arrays, the extracted RNA underwent conversion into digital signals. The percentile shift method facilitated data processing, with the identification of genes manifesting significant differences accomplished through the application of the t-test. Subsequently, a comprehensive analysis involving Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway was conducted on the differentially expressed genes (DEGs). Leveraging the ESTIMATE algorithm, gene signatures were utilized to compute risk scores for gene expression data. Support vector machine analysis and gene enrichment scores were instrumental in establishing correlations between biomarkers and macrophages, followed by an evaluation of the predictive power of the identified biomarkers. Results: The functional and pathway associations of the DEGs predominantly centered around G protein-coupled receptors. A noteworthy positive correlation emerged between the blue module, consisting of 416 genes, and the StromaScore. FZD4, identified through support vector machine analysis among intersecting genes, indicated a robust interaction with macrophages, suggesting its potential as a robust biomarker. FZD4 exhibited commendable predictive efficacy, with BP infection inducing its expression in both macrophages and mouse lung tissue. Western blotting in macrophages confirmed a significant upregulation of FZD4 expression from 0.5 to 24 h post-infection. In mouse lung tissue, FZD4 manifested higher expression in the cytoplasm of pulmonary epithelial cells in BP-infected lungs than in the control group. Conclusion: These findings underscore the upregulation of FZD4 expression by BP in both macrophages and lung tissue, pointing to its prospective role as a biomarker in the pathogenesis of BP infection.

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