Synthetic methanogenic communities reveal differential impact of ecological perturbations on aceto- and hydrogeno-trophic methanogens
Bibliographic record
Abstract
ABSTRACT Synthetic microbial communities provide reduced microbial ecologies that can be studied under defined conditions. Here, we use this approach to study the interactions underpinning anaerobic digestion communities and involving the key microbial populations of a sulfate reducer ( Desulfovibrio vulgaris ), and aceto-( Methanosarcina barkeri ) and hydrogenotrophic ( Methanococcus maripaludis ) methanogens. We create all possible mixed culture combinations of these species and analyse the stability and productivity of each system over multiple sub-culturings and under different sulfate levels, mimicking ecological perturbation in the form of strong electron acceptor availability. We find that all three species can co-exist in the absence of sulfate, and that system productivity (in form of methane production from lactate) increases by almost two-fold compared to co-cultures. With increasing sulfate availability, co-existence is perturbed and both methanogenic populations display a diminishing trend. Interestingly, we find that, despite the continued presence of acetate in the system, the acetotrophic methanogens are more readily disrupted by sulfate perturbation. We show that this is due to a shift in M. barkeri metabolism towards increased co-utilisation of hydrogen with acetate, which we verified through experiments on mono cultures and mass balance calculations in co-cultures. We conclude that hydrogen is a key factor for both hydrogeno- and aceto-trophic methanogenesis and can influence these populations differentially under the common ecological perturbation of strong electron acceptor availability. These findings will help engineering of larger synthetic communities for specific applications in biodegradation and understanding complex anaerobic digestion communities found in animal guts, sediments, and bioreactors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".