bioRxivMicrobiology
Multi-omic analysis of medium-chain fatty acid synthesis by Candidatus Weimerbacter bifidus, gen. nov., sp. nov., and Candidatus Pseudoramibacter fermentans, sp. nov.
Abstract
Chain elongation is emerging as a bioprocess to produce valuable medium-chain fatty acids (MCFA; 6 to 8 carbons in length) from organic waste streams by harnessing the metabolism of anaerobic microbiomes. Although our understanding of chain elongation physiology is still evolving, the reverse {beta}-oxidation pathway has been identified as a key metabolic function to elongate the intermediate products of fermentation to MCFA. Here, we describe two chain-elongating microorganisms that were enriched in an anaerobic microbiome transforming the residues from a lignocellulosic biorefining process to short- and medium-chain fatty acids. Based on a multi-omic analysis of this microbiome, we predict that Candidatus Weimerbacter bifidus, gen. nov., sp. nov. used xylose to produce MCFA, whereas Candidatus Pseudoramibacter fermentans, sp. nov., used glycerol and lactate as substrates for chain elongation. Both organisms are predicted to use an energy conserving hydrogenase to improve the overall bioenergetics of MCFA production.\n\nIMPORTANCEMicrobiomes are vital to human health, agriculture, environmental processes, and are receiving attention as biological catalysts for production of renewable industrial chemicals. Chain elongation by MCFA-producing microbiomes offer an opportunity to produce valuable chemicals from organic streams that otherwise would be considered waste. However, the physiology and energetics of chain elongation is only beginning to be studied, and we are analyzing MCFA production by self-assembled communities to complement the knowledge that has been acquired from pure culture studies. Through a multi-omic analysis of an MCFA-producing microbiome, we characterized metabolic functions of two chain elongating bacteria and predict previously unreported features of this process.
- organism: bacteria (other)
- assay: metagenomics
- research focus: cell type annotation
- approach: statistical model
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