Strain-level variation controls nutrient niche occupancy by health-associated <i>Anaerostipes hadrus</i>
Bibliographic record
Abstract
Abstract Nutrient niche access by the gut microbiota impacts community assembly and dynamics, the production of host-benefiting short-chain fatty acids (SCFAs), and pathogen inhibition through colonization resistance. Furthermore, deciphering if and how niche access varies on a strain level will be important as individual strains of gut microbes are selected for inclusion in new live biotherapeutic products. Despite this, for many gut anaerobes, nutrient niche occupancy and impacts of strain variation remain unknown. Here, we examined nutrient niches of Anaerostipes hadrus (AH), a butyrate-producing member of the Lachnospiraceae family. We found that AH isolates encode a carbohydrate metabolism gene repertoire that is distinct from other Lachnospiraceae. Furthermore, tested AH isolates show variation in carbohydrate-related genes between strains and large numbers of genes associated with horizontal gene transfer events. Functionally, we demonstrate that AH isolates exhibit strain-specific patterns of nutrient niche access that can be associated with the gain, loss, and disruption of gene clusters enabling specific carbohydrate metabolism. This strain-specific carbohydrate use drives variable SCFA production. Unexpectedly, strains exhibit differential preferences for carbohydrates, which alter SCFA profiles in environments with multiple possible nutrient niches available. Furthermore, when strains of AH interact in an environment with multiple nutrient niches available, strain–strain interactions result in varying SCFA profiles that extend beyond the additive effects of individual strain behavior. Altogether, these results demonstrate the importance of evaluating strain-level variation in the design of future live biotherapeutic products.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".