Comparative metabolic modeling of multiple sulfate-reducing prokaryotes reveals versatile energy conservation mechanisms.

Sulfate-reducing prokaryotes (SRPs) are crucial participants in the cycling of sulfur, carbon, and various metals in the natural environment and in engineered systems. Despite recent advances in genetics and molecular biology bringing a huge amount of information about the energy metabolism of SRPs, little effort has been made to link this important information with their biotechnological studies. This study aims to construct multiple metabolic models of SRPs that systematically compile genomic, genetic, biochemical, and molecular information about SRPs to study their energy metabolism. Pan-genome analysis was conducted to compare the genomes of SRPs, from which a list of orthologous genes related to central and energy metabolism was obtained. Twenty-four SRP metabolic models via the inference of pan-genome analysis were efficiently constructed. The metabolic model of the well-studied model SRP Desulfovibrio vulgaris Hildenborough (DvH) was validated via flux balance analysis (FBA). The DvH model predictions matched reported experimental growth and energy yields, which demonstrated that the core metabolic model worked successfully. Further, steady-state simulation of SRP metabolic models under different growth conditions showed how the use of different electron transfer pathways leads to energy generation. Three energy conservation mechanisms were identified, including menaquinone-based redox loop, hydrogen cycling, and proton pumping. Flavin-based electron bifurcation (FBEB) was also demonstrated to be an essential mechanism for supporting energy conservation. The developed models can be easily extended to other species of SRPs not examined in this study. More importantly, the present work develops an accurate and efficient approach for constructing metabolic models of multiple organisms, which can be applied to other critical microbes in environmental and industrial systems, thereby enabling the quantitative prediction of their metabolic behaviors to benefit relevant applications.