Techno-industrial advancements the world over had led to the generation of hazardous environmental pollutants. Microbial bioremediation offers the best alternative for the removal of these pollutants. The most recent advancements in microbial bioremediation were catalyzed by the advent of various tools that enable the study microbes at levels of sophisticated detail, including genome analysis tools (genomics), protocols for analyzing expressed proteins and enzymes or proteomes (proteomics), techniques of analyzing ribonucleic acids (RNAs) transcriptomes (transcriptomics), and tools for analyzing metabolic end products/metabolomes (metabolomics). The twenty first century is witnessing an outpour of developments in the application of omics approaches in effective microbial bioremediation, thus, this paper attempts to review some of the most significant insights gained from relatively recent studies over a period of two decades (2000-2020) in the applications of multi-OMICS in microbial bioremediation, including trends and cutting-edge researches. We aim to highlight, particularly, the challenges that need to be overcome before OMICs approaches are successfully enshrined in microbial bioremediation, especially in developing countries. The strategies for overcoming such challenges, and the prospects achieved were also outlined. In the coming years, we envision further researches involving the application of multi-OMICs approach in microbial bioremediation potentially revolutionizing this field, opening up research avenues, and leading to improvements in bioremediation of polluted environment. Keywords: Biodegradation, Bioremediation; Genomics; Multi-OMICs, OMICs techniques.

No abstract

Environmental increase in the spilled diesel creates serious damages to our natural ecosystems. Biodegradation provide the best removal alternative for such diesel contamination. This study was carried out to monitor the bacteria survival response during diesel oil biodegradation. Bacteria isolation was carried out using plating technique and screened at varying diesel concentrations (0 to 10%, v/v) before being identified using the 16S rRNA gene sequencing. Bacteria consortia were formulated using the screened isolates through mathematical permutation approach. Prepared bacteria resting cells of the selected consortium was grown at 2%, v/v diesel oil which was co-contaminated with varying concentrations of Mg++, Mn++, Zn+, Co++ and Fe++ (0 to 4 g/L) in mineral salts medium. Total of 47 different bacteria strains were isolated and the finally screened isolates were identified as Alcaligenes sp., Ochrobactrum sp., Alcaligenes aquatilis and Alcaligenes faecalis UMYU001 (MN519483.1). These bacteria were found to be compatible with one another despite being obtained from different environments. Of the prepared consortia, combination 11 (CST11) was found to produce the highest population of 1.6 x 104 cfu/mL after 48 hours at 2% v/v diesel oil. This CST11 survived optimally at 35°C, 7.0, and 2% v/v of the temperature, pH and diesel oil respectively. Also, the resistance thresholds of the metals for CST11 include Mg++ (3.5 g/L) while Mn++, Zn++, Co++ and Fe++ were only resisted at 1 g/L. This recommends the consortium as good enough in surviving at the sole expense of diesel oil even in the presence of heavy metals co-contaminants.

Many anthropogenic activities produce huge quantities of chemical pollutants that find their ways into the natural environment. Those chemicals can either be of organic or inorganic sources, depending on their originating compounds. Over the years, there had been research findings regarding the application of microorganisms to provide solutions in the environment. This becomes imperative as salient issues in researches on microbial bioremediation will be understood. This review focused more on Kinetics modeling during biodegradation of aromatic hydrocarbons and their nature and effect on the environment coupled with the conventional remediation techniques. Kinetics modeling during bioremediation predicts microbial activities through their mechanism of actions towards the targeted contaminants. This gives better understanding of the rate of chemical degradation through different variable parameters. Modeling the cultivation of degrading organisms can highlight the inhibitory properties of the cells involved. Therefore, specific microbial growth rates can be modeled at various initial concentrations of the involving substrates. Such could be achieved using secondary models of Monod, Teissier, Aiba, Haldane, Yano and Luong. The models can reveal the substrate inhibitory effects to the reduction rate (as in the case of Monod) or inhibitory to the substrate rates like in the other models. Many studies were recently conducted on modeling microbial growth. Hence, utilization of those models are the best evidence that indicate when the substrates are toxic or inhibitory to the microbes. This provides better understanding on the future researches regarding the bioremediation effectiveness on scientific arguments. Keywords: Environment, Pollutants, Microorganisms, Remediation, Modeling