Biofilm, is a special-complex organization of bacterial cells with multiple layers, formed in certain environmental conditions. In the complex biofilm different bacterial cells perform different functions and contribute to overall biofilm activity, in different physiological states of growth, stress and signaling. Sulphate reducing bacteria (SRB) contributes to huge economic loss ($4B) causing microbial induced corrosion. To effectively combat the challenges posed by SRB, it is essential to understand their molecular mechanisms of biofilm formation and biocorrosion. Regulation of all key pathways and mechanisms involved in growth, stress management and biofilm formation performed by non-coding genomes. Here, in this study we have identified genome wide distribution of non-coding genome (ncRNAs) in a biofilm forming and corrosive SRB Oleidesulfovibrio alaskensis (earlier Desulfovibrio alaskensis) strain G20 (OA-G20). It is understood that regulatory small RNAs play key role in therefore objective of this study was to identify the noncoding RNAs in OA-G20 genome and unveil their role in biofilm formation and corrosive nature. This is the first study to identify ncRNAs in SRB. Here, we are presenting the results using computational methods to identify noncoding genome of OA-G20 and potential role in biofilm formation.

Biofilm, is a special-complex organization of bacterial cells with multiple layers, formed in certain environmental conditions. In the complex biofilm different bacterial (single) cells perform different functions and contribute to overall biofilm activity, where single bacterial cells are in different physiological states of growth, stress and signaling. Sulphate reducing bacteria (SRB) contributes to huge economic loss (${\$}$4B) causing microbial induced corrosion. To effectively combat the challenges posed by SRB, it is essential to understand their molecular mechanisms of biofilm formation and biocorrosion. Single Cell Genomics (single cell transcriptomics) is a technique which investigates the gene expression at the level of a single biological cell and could not be attainable in bulk analysis (RNASeq) could decipher the uniqueness of each cell in complex group. We are developing a NGS (oxford Nanopore)-workflow for single cell transcriptomics of biofilms using Oleidesulfovibrio alaskensis (earlier Desulfovibrio alaskensis) strain G20 (OA-G20) as a model. We encountered several challenges to design and develop the workflow such 1) absence of ploy(A) tail in mRNAs, 2) amount of total RNA, 3) half-lives of mRNAs, 4) perforation of cell membrane, 5) low copy number of mRNAs, 6) single cell suspension and cell sorting, 7) quantification of bacterial cells, 8) fixation of cells, 9) designing of probes, 10) hybridization of probes and 11) amplification of mRNAs. Here, we are presenting how we resolved some of these challenges to design and develop a single cell genomics (transcriptomics) workflow for SRB biofilms.

Essential genes are crucial for the survival and growth of any organism, and therefore alteration of such genes could result in unexpected behavioral change. Identification of essential genes and their role in functioning of organisms is a basic knowledge requirement for any research, which could be manipulated to understand the mechanisms of survival and growth [1]. Several decades have witnessed the virtues and iniquities of the gram-negative facultative anaerobes, sulfate reducing bacteria (SRB) in both ecological and commercial arena. Despite of relentless increase in the number of published articles that belong to diverse research areas-from industrial biotechnology (removal of heavy metals and waste valorization) to molecular biology (genetic architecture of the genes in biocorrosion and biofilm formation on metals), not much information about the essential genes of SRB community is known yet [2]. The Desulfovibrio alaskensis G20 (DA-G20) is a well-known SRB; its genes have been annotated but have large numbers that encode for hypothetical proteins. Till date no categorization is available for the genes of DA-G20 with reference to essentiality [3]. The in-vitro prediction of essential genes relies highly on the exhaustive multi-omics strategies. In the era of big-data and artificial intelligence research, demand of abstraction and interpretation of complex relationships of biological importance using text mining has increased. Therefore, to propose an alternative and economic method, text mining is a comparable method for the prediction of the essential genes. In this study, we reported the essential genes of DA-G20 using text mining and biological network analysis. Moreover, the present work provides a foundation for the expansion of genome wide investigation and identification of essential genes in prokaryotes using machine learning and data science approaches.

Background: The central repository of scientific models from the literature; however, the manual knowledge is research publications which also plays a selection of pertinent information from databases like crucial role in communication within the scientific PubMed, PMC, Dimension, Google scholar, and community. The public repositories like PubMed, Semantic scholar can be tedious; therefore, a robust PMC, Dimension, Google scholar, and Semantic approach like text mining can be used for this process. scholar act as a storehouse of biological systems data. Text mining can be defined as a practical approach to A substantial amount of information can be recovered extracting biologically relevant information from the in a semi-structured form in the literature. The main growing amount of published literature. It comprises obstacle to large-scale analysis of this kind of data is three main tasks: information retrieval from relevant their highly unstructured and heterogenous format, documents, extraction of information of interest, and making it even harder to extract information contained data mining, which allows identifying new within the literature. Nonetheless, this information is associations among the extracted set of information. inherently helpful in a variety of genomics and Here, we show that a text mining approach can exploit systems biology contexts. For example, it is a standard large literature databases like PubMed and PMC to practice in the genomics community to manually extract genes/proteins related to biocorrosion by curate and extract literature-derived protein-protein Sulfate-reducing bacteria(SRB). The corrosion of metal due to microbial activity is known as biocorrosion or MIC(Microbial Induced corrosion). The primary class of bacteria associated with corrosion of metals in aquatic and terrestrial habitats is Sulfur Reducing Bacteria(SRB). Biocorrosion results from collaborative interactions between the metal surface, corrosion products, and bacterial cells and their metabolites. SRB are nonpathogenic and anaerobic bacteria, but SRB can act as a catalyst in the reduction reaction of sulfate to sulfide. It means they can make severe corrosion of metals in a water system by producing enzymes, which can accelerate the reduction of sulphate compounds to hydrogen sulfide. MIC is also known as metabolite corrosion or chemical microbially influenced corrosion (CMIC) owing to the generation of corrosive metabolite (hydrogen sulfide).In contrast, corrosion through direct withdrawal of electrons is called electrical microbial influenced corrosion. The presence of biofilm affects microbial corrosion; It is recognized that under the biofilm at the metal/biofilm interface, the concentrations of acidic metabolites are much greater, and their impact is amplified, leading to higher metal corrosion. It is also becoming apparent that one predominant mechanism of biocorrosion does not exist, and experimentally validating each of these theories can be laborious. Therefore, there is a need for an advanced technique for identifying genes and proteins of SRB involved in biocorrosion; this can help construct other biological processes, related pathways, and other processes associated with these genes.

Bacterial biofilms are complex surface attached communities of bacteria glued together by extracellular polymeric substance (EPS) matrix, secreted proteins, and extracellular DNAs [1]. Biofilm show reduced growth rates and metabolism. Biofilm formation is a survival mechanism that provides with better options compared to their planktonic counterparts. It impart bacterial communities stronger ability to grow in oligotrophic environments, greater access to nutritional resources, and enhanced syntropic interactions as well as greater tolerance towards environmental stress [2]. Biofilm play a detrimental role in many areas such as healthcare, food industry, water distribution systems, oil and gas industry etc. The composition of biofilm microbial community is varies depending on the environment in which it is formed. Biofilms are stratified formations where deeper layers maintain anoxic conditions. These anoxic niches promote the growth of certain specific groups, including sulfate reducing bacteria (SRB), that use the surface (usually metal) as resources for their survival.