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

作　　者：YAO FANG FEI XIA FEIFEI TIAN L EI QU FANG YANG JUAN FANG ZHENHONG HU HAICHAO LIU 

机构地区：[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

出　　处：《BIOCELL》2024年第4期613-621,共9页生物细胞（英文）

基　　金：The study was supported by Yuying Program Incubation Project of General Hospital of Center Theater(ZZYFH202104);Wuhan Young and Middle-Aged Medical Backbone Talent Project 2020(2020-55);Logistics Research Program Project 2019(CLB19J029).

摘　　要：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:Thesefi

关 键 词：Burkholderia pseudomallei Microarray assay Machine learning BIOINFORMATICS FZD4 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]