Decipher syntrophies within C2-C4 organic acids-degrading anaerobic microbiomes: A multi-omic exploration

Gabriele Ghiotto, Anna Detman-Ignatowska,Aleksandra Chojnacka,Esteban Orellana,Nicola de Bernardini, Sofia Fraulini,Laura Treu,Anna Sikora,Stefano Campanaro

Chemical Engineering Journal(2024)

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摘要
In methanogenic ecosystems, carboxylic acid degradation is a crucial process facilitated by specialized bacteria working in syntrophy with methanogens. This study explores the transcriptomic responses in 178 metagenome-assembled genomes upon changes in feeding composition, specifically lactate, butyrate, propionate, and acetate, in four lab-scale bioreactors. Methanothrix soehngenii and Methanoculleus sp. emerged as key biomethanation contributors. Through metatranscriptomics, machine learning-based functional annotation, and flux balance analysis the underlying microbial interactions and dynamics were deciphered. Syntrophomonadaceae and Mesotoga species maintained mutualistic metabolite exchanges with hydrogenotrophic methanogens, degrading primarily butyrate and lactate, respectively. Acetate was mainly consumed by Smithellaceae sp., in competition with M. soehngenii in all reactors. Furthermore, the acetoclastic archaeon exhibited a previously undocumented capability to metabolize lactate, thereby confirming the prevalence of acetotrophic pathway. Transcriptomic profiles revealed an additional layer of complexity, where propionate dismutation and beta-oxidation pathways are interconnected by the exchanges of butyrate between putative syntrophs, including Syntrophomonadaceae and Smithellaceae species. Integrating multi-omics data with genome-scale metabolic modeling enabled the accurate reconstruction of dynamics within the controlled ecosystem. This composite novel approach was applied to the AD system to unveil the intricate relationships operating within the microbiome to promote thriving. Results elucidated the still poorly explored organization of the so-called microbial dark matter.
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关键词
Anaerobic digestion,Flux balance analysis,Genome scale metabolic modeling,Machine learning functional prediction,Metatranscriptomics
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