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

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.

Genome-Wide Computational Prediction and Analysis of Noncoding RNAs in Oleidesulfovibrio alaskensis G20

“…tRNA-Sec regulates the synthesis of selenocysteine, which plays crucial role in the oxygen resilience (reducing reactive oxygen species) of OA G20. In our other study, using a text-mining and proteinprotein network approach, we found a gene SelA (synthesizes Sec from Ser-tRNA) in the OA G20 genome to be essential [42]. Researchers have identified a selenocysteine-containing enzyme (formate dehydrogenase H) in E. coli [93].…”

“…OA G20 is a well-studied model SRB for MIC due to biofilm formation [21,42,43]. The OA G20 genome has 3257 CDSs and 66 tRNA genes (Table 2).…”

Convergence research and training in computational bioengineering: a case study on AI/ML driven biofilm-material interaction discovery

“…The outcome exceeded expectations: some projects earned slots for presentation at the international IEEE Bioinformatics conference in 2022 [5][6][7][8], yielding three Machine Learning models published in a journal [9][10][11][12][13][14][15][16][17][18]. Feedback from participants a rmed the success of this experience, underlining the potential for integrating convergence research into the curriculum through problem-based learning.…”

Recent advances in the characterization of essential genes and development of a database of essential genes

“…For example, by employing manual curation and a predictive model, Saxena et al have successfully identified key essential gene sets of Oleidesulfovibrio alaskensis G20 using the sulfate‐reducing bacterium model and classified these genes through pan‐genome analysis. The results indicated that most essential genes belonged to the core gene category, whereas essential genes in other categories may be environment‐specific [ 195 ]. Furthermore, Wu et al proposed a strategy to reduce the genome size of B. subtilis while simultaneously ensuring the retention of core and essential genes [ 196 ].…”