Text-Mining to Identify Gene Sets Involved in Biocorrosion by Sulfate-Reducing Bacteria: A Semi-Automated Workflow

• Published in: Microorganisms (Basel) 2023-01, Vol.11 (1), p.119
• Main Authors: Thakur, Payal, Alaba, Mathew O, Rauniyar, Shailabh, Singh, Ram Nageena, Saxena, Priya, Bomgni, Alain, Gnimpieba, Etienne Z, Lushbough, Carol, Goh, Kian Mau, Sani, Rajesh Kumar
• Format: Article
• Language: English
• Subjects: Amino acid sequence; Amino acids; Artificial neural networks; Automation; Bacteria; Bacterial corrosion; Binding; biocorrosion; Biofilms; Cell cycle; Corrosion; Corrosion potential; Corrosion rate; Cytochromes; Data mining; Electron transfer; Electron transport; Gene families; Genes; Genomes; Hydrogen; Hydrogenase; Metabolism; Metabolites; metal ion; Metal ions; Metal surfaces; Metals; Microbial corrosion; Network analysis; Neural networks; Proteins; Sulfate reduction; Sulfate-reducing bacteria; Sulfates; Sulfur; sulfur metabolism; text mining; Unstructured data; Workflow
• ISSN: 2076-2607; 2076-2607
• DOI: 10.3390/microorganisms11010119

A significant amount of literature is available on biocorrosion, which makes manual extraction of crucial information such as genes and proteins a laborious task. Despite the fast growth of biology related corrosion studies, there is a limited number of gene collections relating to the corrosion process (biocorrosion). Text mining offers a potential solution by automatically extracting the essential information from unstructured text. We present a text mining workflow that extracts biocorrosion associated genes/proteins in sulfate-reducing bacteria (SRB) from literature databases (e.g., PubMed and PMC). This semi-automatic workflow is built with the Named Entity Recognition (NER) method and Convolutional Neural Network (CNN) model. With PubMed and PMCID as inputs, the workflow identified 227 genes belonging to several species. To validate their functions, Gene Ontology (GO) enrichment and biological network analysis was performed using UniprotKB and STRING-DB, respectively. The GO analysis showed that metal ion binding, sulfur binding, and electron transport were among the principal molecular functions. Furthermore, the biological network analysis generated three interlinked clusters containing genes involved in metal ion binding, cellular respiration, and electron transfer, which suggests the involvement of the extracted gene set in biocorrosion. Finally, the dataset was validated through manual curation, yielding a similar set of genes as our workflow; among these, and , and and were identified as the metal ion binding and sulfur metabolism genes, respectively. The identified genes were mapped with the pangenome of 63 SRB genomes that yielded the distribution of these genes across 63 SRB based on the amino acid sequence similarity and were further categorized as core and accessory gene families. SRB's role in biocorrosion involves the transfer of electrons from the metal surface via a hydrogen medium to the sulfate reduction pathway. Therefore, genes encoding hydrogenases and cytochromes might be participating in removing hydrogen from the metals through electron transfer. Moreover, the production of corrosive sulfide from the sulfur metabolism indirectly contributes to the localized pitting of the metals. After the corroboration of text mining results with SRB biocorrosion mechanisms, we suggest that the text mining framework could be utilized for genes/proteins extraction and significantly reduce the manual curation time.