Transcriptome-wide marker gene expression analysis of stress-responsive sulfate-reducing bacteria

• Published in: Scientific reports Vol. 13; no. 1; pp. 16181 - 18
• Main Authors: Jawaharraj, Kalimuthu, Peta, Vincent, Dhiman, Saurabh Sudha, Gnimpieba, Etienne Z., Gadhamshetty, Venkataramana
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27-09-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/1647; 631/326; 631/337; 631/61; Bacteria; Biogeochemical cycles; Built environment; Carbon cycle; Deep learning; Desulfovibrio; Environmental stress; Food Chain; Food chains; Gene Expression Profiling; Gene mapping; Gene regulation; Genes; Genomic analysis; Heavy metals; Humanities and Social Sciences; Humans; multidisciplinary; Natural environment; Science; Science (multidisciplinary); Stress analysis; Sulfate reduction; Sulfate-reducing bacteria; Sulfates; Sulfur; Transcription; Transcriptome; Transcriptomes; Urban environments
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-43089-8

Sulfate-reducing bacteria (SRB) are terminal members of any anaerobic food chain. For example, they critically influence the biogeochemical cycling of carbon, nitrogen, sulfur, and metals (natural environment) as well as the corrosion of civil infrastructure (built environment). The United States alone spends nearly $4 billion to address the biocorrosion challenges of SRB. It is important to analyze the genetic mechanisms of these organisms under environmental stresses. The current study uses complementary methodologies, viz
.,
transcriptome-wide marker gene panel mapping and gene clustering analysis to decipher the stress mechanisms in four SRB. Here, the accessible RNA-sequencing data from the public domains were mined to identify the key transcriptional signatures. Crucial transcriptional candidate genes of
Desulfovibrio
spp. were accomplished and validated the gene cluster prediction. In addition, the unique transcriptional signatures of
Oleidesulfovibrio alaskensis
(OA-G20) at graphene and copper interfaces were discussed using in-house RNA-sequencing data. Furthermore, the comparative genomic analysis revealed 12,821 genes with translation, among which 10,178 genes were in homolog families and 2643 genes were in singleton families were observed among the 4 genomes studied. The current study paves a path for developing predictive deep learning tools for interpretable and mechanistic learning analysis of the SRB gene regulation.