Copper (Cu) is an essential micronutrient required as a co-factor in the catalytic center of many enzymes. However, excess Cu can generate pleiotropic effects in the microbial cell. In addition, leaching of Cu from pipelines results in elevated Cu concentration in the environment, which is of public health concern. Sulfate-reducing bacteria (SRB) have been demonstrated to grow in toxic levels of Cu. However, reports on Cu toxicity towards SRB have primarily focused on the degree of toxicity and subsequent elimination. Here, Cu(II) stress-related effects on a model SRB, Desulfovibrio alaskensis G20, is reported. Cu(II) stress effects were assessed as alterations in the transcriptome through RNA-Seq at varying Cu(II) concentrations (5 µM and 15 µM). In the pairwise comparison of control vs. 5 µM Cu(II), 61.43% of genes were downregulated, and 38.57% were upregulated. In control vs. 15 µM Cu(II), 49.51% of genes were downregulated, and 50.5% were upregulated. The results indicated that the expression of inorganic ion transporters and translation machinery was massively modulated. Moreover, changes in the expression of critical biological processes such as DNA transcription and signal transduction were observed at high Cu(II) concentrations. These results will help us better understand the Cu(II) stress-response mechanism and provide avenues for future research.

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 Desulfovibrio 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, hysB and hydA, and sat and dsrB 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.

The mimicking of evolution on a laboratory timescale to enhance biocatalyst specificity, substrate utilization activity, and/or product formation, is an effective and well-established approach that does not involve genetic engineering or regulatory details of the microorganism. The present work employed an evolutionary adaptive approach to improve the lignocellulose deconstruction capabilities of the strain by inducing the expression of laccase, a multicopper oxidase, in Geobacillus sp. strain WSUCF1. This bacterium is highly efficient in depolymerizing unprocessed lignocellulose, needing no preprocessing/pretreatment of the biomasses. However, it natively produces low levels of laccase. After 15 rounds of serially adapting this thermophilic strain in the presence of unprocessed corn stover as the selective pressure, we recorded a 20-fold increase in catalytic laccase activity, at 9.23 ± 0.6 U/mL, in an adapted yet stable strain of Geobacillus sp. WSUCF1, compared with the initial laccase production (0.46 ± 0.04 U/mL) obtained with the unadapted strain grown on unprocessed corn stover before optimization. Chemical composition analysis demonstrated that lignin removal by the adapted strain was 22 wt.% compared with 6 wt.% removal by the unadapted strain. These results signify a favorable prospect for fast, cost competitive bulk production of this thermostable enzyme. Also, this work has practical importance, as this fast adaptation of the Geobacillus sp. strain WSUCF1 suggests the possibility of growing industrial quantities of Geobacillus sp. strain WSUCF1 cells as biocatalysts on reasonably inexpensive carbon sources for commercial use. This work is the first application of the adaptive laboratory evolution approach for developing the desired phenotype of enhanced ligninolytic capability in any microbial strain.

Particulate methane monooxygenase (pMMO), a membrane-bound enzyme having three subunits (α, β, and γ) and copper-containing centers, is found in most of the methanotrophs that selectively catalyze the oxidation of methane into methanol. Active sites in the pMMO of Methylosinus trichosporium OB3b were determined by docking the modeled structure with ethylbenzene, toluene, 1,3-dibutadiene, and trichloroethylene. The docking energy between the modeled pMMO structure and ethylbenzene, toluene, 1,3-dibutadiene, and trichloroethylene was −5.2, −5.7, −4.2, and −3.8 kcal/mol, respectively, suggesting the existence of more than one active site within the monomeric subunits due to the presence of multiple binding sites within the pMMO monomer. The evaluation of tunnels and cavities of the active sites and the docking results showed that each active site is specific to the radius of the substrate. To increase the catalysis rates of methane in the pMMO of M. trichosporium OB3b, selected amino acid residues interacting at the binding site of ethylbenzene, toluene, 1,3-dibutadiene, and trichloroethylene were mutated. Based on screening the strain energy, docking energy, and physiochemical properties, five mutants were downselected, B:Leu31Ser, B:Phe96Gly, B:Phe92Thr, B:Trp106Ala, and B:Tyr110Phe, which showed the docking energy of −6.3, −6.7, −6.3, −6.5, and −6.5 kcal/mol, respectively, as compared to the wild type (−5.2 kcal/mol) with ethylbenzene. These results suggest that these five mutants would likely increase methane oxidation rates compared to wild-type pMMO.

Copper is known to have toxic effects on bacterial growth. This study aimed to determine the influence of copper ions on Oleidesulfovibrio alaskensis G20 biofilm formation in a lactate-C medium supplemented with variable copper ion concentrations. OA G20, when grown in media supplemented with high copper ion concentrations of 5, 15, and 30 µM, exhibited inhibited growth in its planktonic state. Conversely, under similar copper concentrations, OA G20 demonstrated enhanced biofilm formation on glass coupons. Microscopic studies revealed that biofilms exposed to copper stress demonstrated a change in cellular morphology and more accumulation of carbohydrates and proteins than controls. Consistent with these findings, sulfur (dsrA, dsrB, sat, aprA) and electron transport (NiFeSe, NiFe, ldh, cyt3) genes, polysaccharide synthesis (poI), and genes involved in stress response (sodB) were significantly upregulated in copper-induced biofilms, while genes (ftsZ, ftsA, ftsQ) related to cellular division were negatively regulated compared to controls. These results indicate that the presence of copper ions triggers alterations in cellular morphology and gene expression levels in OA G20, impacting cell attachment and EPS production. This adaptation, characterized by increased biofilm formation, represents a crucial strategy employed by OA G20 to resist metal ion stress.