Experimental evaluation of ecological principles to understand and modulate the outcome of bacterial strain competition in gut microbiomes
Bibliographic record
Abstract
Abstract It is unclear if coexistence theory can be applied to gut microbiomes to understand their characteristics and modulate their composition. Through strictly controlled colonization experiments in mice, we demonstrated that strains of Akkermansia muciniphila and Bacteroides vulgatus could only be established if microbiomes were devoid of exactly these species. Strains of A. muciniphila showed strict competitive exclusion, while B. vulgates strains coexistedbut populations were still influenced by competitive interactions. Priority effects were detected for both species as strains’ competitive fitness increased when colonizing first. Based on these observations, we devised a subtractive strategy for A. muciniphila using antibiotics and demonstrated that a strain from an assembled community can be stably replaced by another strain. Altogether, these results suggest that aspects of coexistence theory, e.g., niche partitioning and the impact of priority effects on fitness differences, can be applied to explain ecological characteristics of gut microbiomes and modulate their composition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".