Genome-Scale Metabolic Model as a Virtual Platform to Reveal the Environmental Contribution of Methanogens

Background: Methanogens utilize low carbon molecules in anaerobic environments and produce CH4 that serves as a key component in the global carbon cycle in the atmosphere. Genome-scale metabolic modeling is a proficient computational tool for integrative analysis of their cellular and metabolic processes. Thus, genome-scale models of methanogens have gained great importance in environmental and biotechnological applications.

Methods: The study explored literature databases in-depth for peer-reviewed research papers related to the methanogens and their ecological importance, particularly enteric methane emission. Metabolic information for these organisms was collected from MetaCyc database. Genome-scale metabolic modeling information was collected from Systems Biology Research Group and qualitative content was deduced from corresponding literature. This paper used standard tools to assess the validity of retrieved papers for highlighting the overall hypothesis. The conceptual framework was designed by using a deductive qualitative content analysis of screened papers.

Results: In this review, 100 peer reviewed research papers were included out of which 60 papers were related to the methanogens. The present review describes the significance of genomic and metabolic features of genome-scale metabolic models and some potential rumen methanogens to reveal their environmental contributions in assorted ecosystem. Six genome-scale metabolic models (iMM518, iMG746, iMB745, iVS941, iAF692, iMAC868) have been constructed with detailed biochemical information for the methanogens. These models were further refined by comparing predicted growth yield with experimental growth data to validate the model’s consistency. Metabolic flux balance analysis was used to assess their biological impact on the carbon balance of methanogenic communities. The effect of the present ecosystem on global CH4 cycling, particularly reverse methanogenesis has been studied with genome- scale models of methanogens.

Conclusion: Genome-scale models related to the experimental growth data would improve the prediction accuracy of metabolic flux coefficients and specific growth rate of diverse methanogens under the nutrient-deprivation conditions in anaerobic environment. Using genome-scale models, chemogenomic and vaccine targets for CH4 mitigation have also been identified and evaluated.