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Updated: Jul 26, 2025

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae
Published on: April 25, 2015
Prediction of target genes in community-acquired pneumonia based on the bioinformatics method
Yangsong Zuo1, Wenyi Shen1, Guiming Chen2
1Department of Aspiration Medicine, Lianshui County People's Hospital, Huai'an, China.
Background:
To screen the related genes of community-acquired pneumonia (CAP) by bioinformatics technology, and to analyze the clinical value of key genes.
Methods:
Gene chip data sets containing CAP patients and normal controls were screened from the Gene Expression Omnibus (GEO) database. The downregulated differentially expressed genes (DEGs) were screened using a gene expression analysis tool (GEO2R). Simultaneously, gene set enrichment analysis (GSEA) was used to explore the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and core genes related to CAP. The candidate genes were then intersected with the genes reported in Online Mendelian Inheritance in Man (OMIM), and the clinical value of these candidate genes was examined based on a literature search. Finally, the clinical data of the CAP patients were retrospectively analyzed. Detect the type of pathogenic bacteria in bronchial-alveolar lavage fluid (BALF) using metagenomics next-generation sequencing (mNGS) high throughput sequencing technology, and detect the expression of key genes through liquid based cell immunohistochemistry to analyze the correlation between pathogenic bacteria and key genes.
Results:
Through the intersection of Venn diagrams, 175 co-expressed downregulated DEGs related to CAP were identified. A total of 4 candidate genes, including ICOS, IL7R, ITK, and ZAP70, were obtained by constructing the protein mutual aid network and conducting a module analysis of the common differentially expressed genes. The core genes in the GSEA enrichment pathways were intersected with the CAP-related genes reported in the relevant literature retrieved from the OMIM database. In the Venn diagram, two genes that coexist with OMIM include IL7R and PIK3R1. After considering our findings and the relevant literature, we determined that the key gene related to the occurrence and development of CAP was IL7R. The mNGS detected 13 kinds of bacteria, 4 kinds of fungi, and 2 kinds of viruses. Based on immunohistochemical results, it was found that there were relatively more bacteria detected in the IL7R high expression group.
Conclusions:
The identification of the key gene IL7R and the related signaling pathways extend understanding of the pathogenesis of CAP and provide a theoretical basis for clinical targeted therapy research.
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