Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria

Priya Saxena; Shailabh Rauniyar; Payal Thakur; Ram Nageena Singh; Alain Bomgni; Mathew O Alaba; Abhilash Kumar Tripathi; Etienne Z Gnimpieba; Carol Lushbough; Rajesh Kumar Sani

doi:10.3389/fmicb.2023.1086021

Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria

Front Microbiol. 2023 Apr 13:14:1086021. doi: 10.3389/fmicb.2023.1086021. eCollection 2023.

Authors

Priya Saxena^{1

2}, Shailabh Rauniyar^{1

3}, Payal Thakur^{1

2}, Ram Nageena Singh^{1

3}, Alain Bomgni⁴, Mathew O Alaba⁴, Abhilash Kumar Tripathi^{1

3}, Etienne Z Gnimpieba⁴, Carol Lushbough⁴, Rajesh Kumar Sani^{1

2

3

5}

Affiliations

¹ Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD, United States.
² Data Driven Material Discovery Center for Bioengineering Innovation, South Dakota School of Mines and Technology, Rapid City, SD, United States.
³ 2-Dimensional Materials for Biofilm Engineering, Science and Technology, South Dakota School of Mines and Technology, Rapid City, SD, United States.
⁴ Department of Biomedical Engineering, University of South Dakota, Sioux Falls, SD, United States.
⁵ BuG ReMeDEE Consortium, South Dakota School of Mines and Technology, Rapid City, SD, United States.

Abstract

The growth and survival of an organism in a particular environment is highly depends on the certain indispensable genes, termed as essential genes. Sulfate-reducing bacteria (SRB) are obligate anaerobes which thrives on sulfate reduction for its energy requirements. The present study used Oleidesulfovibrio alaskensis G20 (OA G20) as a model SRB to categorize the essential genes based on their key metabolic pathways. Herein, we reported a feedback loop framework for gene of interest discovery, from bio-problem to gene set of interest, leveraging expert annotation with computational prediction. Defined bio-problem was applied to retrieve the genes of SRB from literature databases (PubMed, and PubMed Central) and annotated them to the genome of OA G20. Retrieved gene list was further used to enrich protein-protein interaction and was corroborated to the pangenome analysis, to categorize the enriched gene sets and the respective pathways under essential and non-essential. Interestingly, the sat gene (dde_2265) from the sulfur metabolism was the bridging gene between all the enriched pathways. Gene clusters involved in essential pathways were linked with the genes from seleno-compound metabolism, amino acid metabolism, secondary metabolite synthesis, and cofactor biosynthesis. Furthermore, pangenome analysis demonstrated the gene distribution, where 69.83% of the 116 enriched genes were mapped under "persistent," inferring the essentiality of these genes. Likewise, 21.55% of the enriched genes, which involves specially the formate dehydrogenases and metallic hydrogenases, appeared under "shell." Our methodology suggested that semi-automated text mining and network analysis may play a crucial role in deciphering the previously unexplored genes and key mechanisms which can help to generate a baseline prior to perform any experimental studies.

Keywords: Oleidesulfovibrio alaskensis G20; SRB; essential genes; pathways; semi-automated model; text mining.

Abstract

Grants and funding