Comparative Metabolic Modeling of Multiple Sulfate-reducing Prokaryotes
Reveals Versatile Energy Conservation Mechanisms
Abstract
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 is conducted to
compare the genomes of SRPs, from which a list of orthologous genes
related to central and energy metabolism is obtained. 24 SRP metabolic
models via the inference of pan-genome analysis are constructed
efficiently. The reference model of the well-studied model SRP
Desulfovibrio vulgaris Hildenborough (DvH) is validated via Flux balance
analysis (FBA). The DvH model predictions match reported experimental
growth and energy yields, which demonstrates that the core metabolic
model works successfully. Further, steady-state simulation of SRP
metabolic models under different growth conditions shows how the use of
different electron transfer pathways leads to energy generation. Three
energy conservation mechanisms are identified, including
menaquinone-based redox loop, hydrogen cycling, and proton pumping.
Flavin-based electron bifurcation (FBEB) is 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.