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Updated: Jun 7, 2025

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform
Published on: January 13, 2016
Identification of molecular and cellular infection response biomarkers associated with anthrax infection through
Swati Rani1, Varsha Ramesh1, Mehnaj Khatoon1
1ICAR-National Institute of Veterinary Epidemiology and Disease Informatics, Bengaluru, Karnataka, 560064, India.
Abstract:
Bacillus anthracis, a gram-positive bacillus capable of forming spores, causes anthrax in mammals, including humans, and is recognized as a potential biological weapon agent. The diagnosis of anthrax is challenging due to variable symptoms resulting from exposure and infection severity. Despite the availability of a licensed vaccines, their limited long-term efficacy underscores the inadequacy of current human anthrax vaccines, highlighting the urgent need for next-generation alternatives. Our study aimed to identify molecular biomarkers and essential biological pathways for the early detection and accurate diagnosis of human anthrax infection. Using a comparative analysis of Bacillus anthracis gene expression data from the Gene Expression Omnibus (GEO) database, this cost-effective approach enables the identification of shared differentially expressed genes (DEGs) across separate microarray datasets without additional hybridization. Three microarray datasets (GSE34407, GSE14390, and GSE12131) of B. anthracis-infected human cell lines were analyzed via the GEO2R tool to identify shared DEGs. We identified 241 common DEGs (70 upregulated and 171 downregulated) from cell lines treated similarly to lethal toxins. Additionally, 10 common DEGs (5 upregulated and 5 downregulated) were identified across different treatments (lethal toxins and spores) and cell lines. Network meta-analysis identified JUN and GATAD2A as the top hub genes for overexpression, and NEDD4L and GULP1 for underexpression. Furthermore, prognostic analysis and SNP detection of the two identified upregulated hub genes were carried out in conjunction with machine learning classification models, with SVM yielding the best classification accuracy of 87.5 %. Our comparative analysis of Bacillus anthracis infection revealed striking similarities in gene expression 241 profiles across diverse datasets, despite variations in treatments and cell lines. These findings underscore how anthrax infection activates shared genes across different cell types, emphasizing this approach in the discovery of novel gene markers. These markers offer insights into pathogenesis and may lead to more effective therapeutic strategies. By identifying these genetic indicators, we can advance the development of precise immunotherapies, potentially enhancing vaccine efficacy and treatment outcomes.

