Abstract Description

Bioremediation involving microbial metabolic process that easily gets affected by various factors, demands insights into the biochemical pathway requires deciphering. Knowledge of metabolic inducer, inhibitors of gene expression and protein activities together with bioavailability of their co-factors can provide intel on bacteria to enrich for the targeted contaminant. Bioinformatics as an approach to predict efficiency of synergistic pathways from different bacterial groups relives one from conducting multiple experiments with complex biochemical processes to optimize for a bioremediation system to be successful. The current study underscores the synergy of applying metagenomics, metabolomics and microbiological applications for different bacterial groups cultured to perform different steps of a stepwise complete denitrification in a closed system. Wastewater samples with high (over 100 mg/L) NO³⁻ concentrations were collected from mining sites and subjected to chemical and molecular analysis. Microbial diversity data was analyzed using R-studio with different packages for metabolic assortments. Based on genes detected, indigenous bacteria known to possess those genes were enriched as consortia. The consortia were evaluated on effectiveness of complete denitrification with supplementing of different NO3- concentrations, metal-cofactors for targeted functional genes and assessments of metabolic products. From the data analyzed using FAPROTAX and KEGG, it was found that Fe²⁺ and Cu²⁺ play crucial roles in inducing gene transcription and protein activities of the five functional proteins involved in denitrification which could be present in different bacterial groups. As theses heavy metals can be toxic to bacteria at excess concentrations, MIC experiments revealed that inorganic Cu²⁺ supplemented at 50 mg/L promote reduction of N₂O to N₂ which is a determining step to prove efficacy of microbial denitrification. Application of this concentration in benchtop bioreactor seeded with enriched defined denitrifying consortia uncovered the synergistic effects bacteria had in performing complete denitrification of 1000 mg/L producing more N₂ than N₂O when Cu²⁺ was supplemented at 50 mg/L for over 90 days. The successful incorporation of bioinformatics in environmental biotechnology was portrayed in this study with benchmarked approach to industrial wastewater bioremediation using indigenous bacteria. This study further provides insight on metal co-factors that promote metabolic processes that are in a natural state, challenging to perform by a group of microorganisms. Furthermore, the findings support that efficiency and efficacy of bioremediation systems can be predicted by mapping the metabolic pathways of bacteria involved in bioremediation.