Distinct lactate utilization strategies drive niche differentiation between two co-existing <i>Megasphaera</i> species in the rumen microbiome
Bibliographic record
Abstract
Lactate utilization mitigates rumen acidosis and is associated with decreased methane production in the rumen. While several lactate utilization pathways exist across different microbial species in the rumen, how they are metabolically differentiated remains unclear. Here, we show that the key lactate-utilizing species Megasphaera hexanoica and Megasphaera elsdenii display distinct growth strategies based on their fermentative end products. This allows them to co-exist and play distinct metabolic roles, which appear particularly relevant in the early stages of rumen development, as both species are highly enriched in the calf. Specifically, M. hexanoica is more strongly associated with rumen microbiome states that involve increased lactate utilization and preferentially runs reverse beta-oxidation (termed chain elongation) to produce butyrate and medium-chain fatty acids from lactate. As M. elsdenii instead utilizes lactate via the acrylate pathway to produce propionate, we leverage Enzyme Cost Minimization to predict how this pathway relates to a distinct growth strategy. We find that M. elsdenii maximizes growth rate when lactate transiently accumulates, which contrasts M. hexanoica's invariably high-yield strategy. This trade-off, which is supported by the analysis of growth kinetics, metabolic flux, and bioreactors simulating the rumen microbiome, ultimately contributes to co-existence on lactate and may have driven niche differentiation. Lastly, we demonstrate how lactate utilization in the Megasphaera is threatened by toxins widespread in feed, which points to dietary interventions to support calf health.
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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 | 0.001 | 0.001 |
| 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".