Formate-induced CO tolerance and innovative methanogenesis inhibition in co-fermentation of syngas and plant biomass for carboxylate production
Baleeiro, F. C. F.; Varchmin, L.; Kleinsteuber, S.; Sträuber, H.; Neumann, A.
Show abstract
Production of monocarboxylates using microbial communities is highly dependent on local and degradable biomass feedstocks. Syngas or different mixtures of H2, CO, and CO2 can be co-fed to a fermenter to alleviate this dependence. To understand the effects of adding these gases during anaerobic fermentation of plant biomass, a series of batch experiments was carried out with different syngas compositions and corn silage (pH 6.0, 32{degrees}C). Co-fermentation of syngas with corn silage increased the overall carboxylate yield per gram of volatile solids (VS) by up to 44% (0.36 {+/-} 0.07 g gVS-1; in comparison to 0.23 {+/-} 0.04 g gVS-1 with a N2/CO2 headspace), despite slowing down biomass degradation. Ethylene and CO exerted a synergistic effect in preventing methanogenesis, leading to net carbon fixation. Less than 12% of the electrons were misrouted to CH4 when either 15 kPa CO or 5 kPa CO + 1.5 kPa ethylene was used. CO increased the selectivity to acetate and propionate, which accounted for 86% (electron equivalents) of all products at 49 kPa CO, by favoring lactic acid bacteria and actinobacteria over n-butyrate and n-caproate producers. This happened even when an inoculum pre-acclimatized to syngas and lactate was used. Intriguingly, the effect of CO on n-butyrate and n-caproate production was reversed when formate was present in the broth. The concept of co-fermenting syngas and plant biomass shows promise in two aspects: by making anaerobic fermentation a carbon-fixing process and by increasing the production of propionate and acetate. Testing the concept in a continuous process could improve selectivity to n-butyrate and n-caproate by enriching chain-elongating bacteria adapted to CO and complex biomass.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Inter-kingdom microbial interactions revealed by a comparative machine-learning guided multi-omics analysis of industrial-scale biogas plants 97%
- Genomic and kinetic analysis of novel Nitrospinae enriched by cell sorting 95%
- Protozoa populations are ecosystem engineers that shape prokaryotic community structure and function of the rumen microbial ecosystem 95%
Similar papers in this journal
- Physiological characterization of nitrate ammonifying bacteria isolated from rice paddy soils via a newly developed high-throughput screening method 94%
- Harnessing Escherichia coli for bio-based production of formate under pressurized H2 and CO2 gases. 94%
- Enhancement of nitrous oxide emissions in soil microbial consortia via copper competition between proteobacterial methanotrophs and denitrifiers 94%
Similar papers in this journal
- Escherichia coli metabolism under short-term repetitive substrate dynamics: Adaptation and trade-offs 96%
- Genome-scale metabolic modelling enables deciphering ethanol metabolism via the acrylate pathway in the propionate-producer Anaerotignum neopropionicum 96%
- Population dynamics analysis of Saccharomyces cerevisiae deletion library during fed-batch cultivation using Bar-seq 95%
Similar papers in this journal
Similar papers in this journal
- Thiocyanate and organic carbon inputs drive convergent selection for specific autotrophic Afipia and Thiobacillus strains within complex microbiomes 96%
- An open-source multiple-bioreactor system for replicable gas-fermentation experiments: Nitrate feed results in stochastic inhibition events, but improves ethanol production of Clostridium ljungdahlii with CO2 and H2 95%
- The isolate Caproiciproducens sp. 7D4C2 produces n-caproate at mildly acidic conditions from hexoses: genome and rBOX comparison with related strains and chain-elongating bacteria 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.