13C metabolic flux analysis of three divergent extremely thermophilic bacteria: Geobacillus sp. LC300, Thermus thermophilus HB8, and Rhodothermus marinus DSM 4252

Abstract

Thermophilic organisms are being increasingly investigated and applied in metabolic engineering and biotechnology. The distinct metabolic and physiological characteristics of thermophiles, including broad substrate range and high uptake rates, coupled with recent advances in genetic tool development, present unique opportunities for strain engineering. However, poor understanding of the cellular physiology and metabolism of thermophiles has limited the application of systems biology and metabolic engineering tools to these organisms. To address this concern, we applied high resolution 13C metabolic flux analysis to quantify fluxes for three divergent extremely thermophilic bacteria from separate phyla: Geobacillus sp. LC300, Thermus thermophilus HB8, and Rhodothermus marinus DSM 4252. We performed 18 parallel labeling experiments, using all singly labeled glucose tracers for each strain, reconstructed and validated metabolic network models, measured biomass composition, and quantified precise metabolic fluxes for each organism. In the process, we resolved many uncertainties regarding gaps in pathway reconstructions and elucidated how these organisms maintain redox balance and generate energy. Overall, we found that the metabolisms of the three thermophiles were highly distinct, suggesting that adaptation to growth at high temperatures did not favor any particular set of metabolic pathways. All three strains relied heavily on glycolysis and TCA cycle to generate key cellular precursors and cofactors. None of the investigated organisms utilized the Entner-Doudoroff pathway and only one strain had an active oxidative pentose phosphate pathway. Taken together, the results from this study provide a solid foundation for future model building and engineering efforts with these and related thermophiles.

Correlating enzyme annotations with a large set of microbial growth temperatures reveals metabolic adaptations to growth at diverse temperatures

“…Of the eight KEGG pathways -with significant over-representation of enzymes changing with temperature -four were previously identified as over-represented in metagenomic studies of high temperature habitats (Badhai, Ghosh, and Das 2015;Saxena et al 2016) : carbon fixation pathway in prokaryotes, pyruvate metabolism, methane metabolism and purine metabolism. The TCA cycle, another enriched KEGG pathway, is known to be extensively utilized in three thermophilic bacteria (Cordova et al 2017) . In my approach, by identifying pathways with enzymes whose occurrence strongly correlates with growth temperatures, I strengthen the argument that these pathways are under evolutionary pressure in temperature adaptation.…”

“…Flux analysis of three extremely thermophilic bacteria indicated that the metabolisms of the studied thermophiles were highly distinct, with differences in such pathways as amino acid or NADPH metabolism (Cordova et al 2017) . However, a communality is that all three strains relied heavily on glycolysis and the TCA cycle.…”

Model metabolic strategy for heterotrophic bacteria in the cold ocean based on Colwellia psychrerythraea 34H

“…Advances in sequencing techniques, genomic analyses, and metabolic analyses have aided species characterization by providing the functional output of microorganisms and revealing their genotype-tophenotype regulations. For some environments, the functional metabolisms of representative species (mainly thermophiles and mesophiles) have been characterized in detail (2)(3)(4)(5). For cold environments, few studies have addressed the metabolic pathways of cold-adapted microorganisms (psychrophiles) directly, the emphasis being on genomic and diversity analyses instead (6)(7)(8).…”

Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

“…FBA and TFA analysis were performed (64 tests for E. coli and 32 for T. thermophilus ), by assuming maximisation of biomass yield as the objective function. Results were tested against available experimental data ( 13 C-MFA (38, 39) and metabolomics (38)) by calculating the Pearson correlation coefficient, and Kendall’s W to determine the agreement between criteria (only for E. coli ). In addition, a MDF approach was tested against experimental metabolomics data to assess its predictive capabilities in comparison with mod-matTFA.…”

“…An approach improving the resolution consisting in also measuring MIDs from intracellular intermediates has been suggested (32), but it is not commonly performed (11). Hence, 13 C-MFA data for E. coli and T. thermophilus (38, 39) was assumed as the gold standard in this study, as stated above (15).…”