Medium-chain carboxylates such as n-caproate and n-caprylate are valuable chemicals, which can be produced from renewable feedstock by anaerobic fermentation and lactate-based microbial chain elongation. Acidogenic microbiota involved in lactate-based chain elongation and their interplay with lactic acid bacteria have not been characterized in detail yet. Here, the metabolic and community dynamics were studied in a continuous bioreactor with xylan and lactate as sole carbon sources. Four succession stages were observed during 148 days of operation. After an adaptation period of 36 days, a relatively stable period of 28 days (stage I) was reached with n-butyrate, n-caproate and n-caprylate productivities of 7.2, 8.2 and 1.8 gCOD L^–1 d^–1, respectively. After a transition period, the process changed to another period (stage II), during which 46% more n-butyrate, 51% less n-caproate and 67% less n-caprylate were produced. Co-occurrence networks of species based on 16S rRNA amplicon sequences and correlations with process parameters were analyzed to infer ecological interactions and potential metabolic functions. Diverse functions including hydrolysis of xylan, primary fermentation of xylose to acids (e.g., to acetate by Syntrophococcus, to n-butyrate by Lachnospiraceae, and to lactate by Lactobacillus) and chain-elongation with lactate (by Ruminiclostridium 5 and Pseudoramibacter) were inferred from the metabolic network. In stage I, the sub-network characterized by strongest positive correlations was mainly related to the production of n-caproate and n-caprylate. Lactic acid bacteria of the genus Olsenella co-occurred with potentially chain-elongating bacteria of the genus Pseudoramibacter, and their abundance was positively correlated with n-caproate and n-caprylate concentrations. A new sub-network appeared in stage II, which was mainly related to n-butyrate production and revealed a network of different lactic acid bacteria (Bifidobacterium) and potential n-butyrate producers (Clostridium sensu stricto 12). The synergy effects between lactate-producing and lactate-consuming bacteria constitute a division of labor cooperation of mutual benefit. Besides cooperation, competition between different taxa determined the bacterial community assembly over the four succession stages in this resource-limited system. During long-term reactor operation under constant conditions, chain-elongating bacteria were outcompeted by butyrate-producing bacteria, leading to the increase of n-butyrate yield at the cost of medium-chain carboxylate yields in this closed model system.

The platform chemicals n-caproate and iso-butyrate can be produced by anaerobic fermentation from agro-industrial residues in a process known as microbial chain elongation. Few lactate-consuming chain-elongating species have been isolated and knowledge on their shared genetic features is still limited. Recently we isolated three novel clostridial strains (BL-3, BL-4, and BL-6) that convert lactate to n-caproate and iso-butyrate. Here, we analyzed the genetic background of lactate-based chain elongation in these isolates and other chain-elongating species by comparative genomics. The three strains produced n-caproate, n-butyrate, iso-butyrate, and acetate from lactate, with the highest proportions of n-caproate (18%) for BL-6 and of iso-butyrate (23%) for BL-4 in batch cultivation at pH 5.5. They show high genomic heterogeneity and a relatively small core-genome size. The genomes contain highly conserved genes involved in lactate oxidation, reverse β-oxidation, hydrogen formation and either of two types of energy conservation systems (Rnf and Ech). Including genomes of another eleven experimentally validated chain-elongating strains, we found that the chain elongation-specific core-genome encodes the pathways for reverse β-oxidation, hydrogen formation and energy conservation, while displaying substantial genome heterogeneity. Metabolic features of these isolates are important for biotechnological applications in n-caproate and iso-butyrate production.

Background: The ability to quantitatively predict ecophysiological functions of microbial communities provides an important step to engineer microbiota for desired functions related to specific biochemical conversions. Here, we present the quantitative prediction of medium-chain carboxylate production in two continuous anaerobic bioreactors from 16S rRNA gene dynamics in enriched communities.Results: By progressively shortening the hydraulic retention time (HRT) from 8 to 2 days with different temporal schemes in two bioreactors operated for 211 days, we achieved higher productivities and yields of the target productsn-caproate andn-caprylate. The datasets generated from each bioreactor were applied independently for training and testing machine learning algorithms using 16S rRNA genes to predictn-caproate andn-caprylate productivities. Our dataset consisted of 14 and 40 samples from HRT of 8 and 2 days, respectively. Because of the size and balance of our dataset, we compared linear regression, support vector machine and random forest regression algorithms using the original and balanced datasets generated using synthetic minority oversampling. Further, we performed cross-validation to estimate model stability. The random forest regression was the best algorithm producing more consistent results with median of error rates below 8%. More than 90% accuracy in the prediction ofn-caproate andn-caprylate productivities was achieved. Four inferred bioindicators belonging to the generaOlsenella,Lactobacillus,SyntrophococcusandClostridiumIV suggest their relevance to the higher carboxylate productivity at shorter HRT. The recovery of metagenome-assembled genomes of these bioindicators confirmed their genetic potential to perform key steps of medium-chain carboxylate production.Conclusions: Shortening the hydraulic retention time of the continuous bioreactor systems allows to shape the communities with desired chain elongation functions. Using machine learning, we demonstrated that 16S rRNA amplicon sequencing data can be used to predict bioreactor process performance quantitatively and accurately. Characterizing and harnessing bioindicators holds promise to manage reactor microbiota towards selection of the target processes. Our mathematical framework is transferrable to other ecosystem processes and microbial systems where community dynamics is linked to key functions. The general methodology used here can be adapted to data types of other functional categories such as genes, transcripts, proteins or metabolites.