Single-strain behavior predicts responses to environmental pH and osmolality in the gut microbiota
Bibliographic record
Abstract
Abstract Changes to gut environmental factors such as pH and osmolality due to disease or drugs correlate with major shifts in microbiome composition; however, we currently cannot predict which species can tolerate such changes or how the community will be affected. Here, we assessed the growth of 92 representative human gut bacterial strains spanning 28 families across multiple pH values and osmolalities in vitro . The ability to grow in extreme pH or osmolality conditions correlated with the availability of known stress response genes in many cases, but not all, indicating that novel pathways may participate in protecting against acid or osmotic stresses. Machine learning analysis uncovered genes or subsystems that are predictive of differential tolerance in either acid or osmotic stress. For osmotic stress, we corroborated the increased abundance of these genes in vivo during osmotic perturbation. The growth of specific taxa in limiting conditions in isolation in vitro correlated with survival in complex communities in vitro and in an in vivo mouse model of diet-induced intestinal acidification. Our data show that in vitro stress tolerance results are generalizable and that physical parameters may supersede interspecies interactions in determining the relative abundance of community members. Importantly, we provide an extensive resource for predicting shifts in microbial composition and gene abundance in complex perturbations. Furthermore, this work highlights the physical environment as a major driver of bacterial composition and the importance of performing physical measurements in animal and clinical studies to elucidate the drivers of shifts in microbiota abundance. Significance Statement Changes in pH and particle concentration (osmolality) commonly result from gut disease or the ingestion of common drugs, causing changes in bacterial growth and microbiota composition within the intestine. Thus far, the effects of physical parameters on the growth of intestinal bacterial taxa have not been well documented in the context of predicting microbiota community composition. To address this gap, we examined the growth of 92 bacterial species under varying pH and osmolality conditions. We found that physical parameters are key predictors of bacterial abundance in individual-strain cultures and in complex bacterial communities. Moreover, our results identified specific genes and pathways that are predictive of growth in specific environments. Together, these findings can aid in determining the effectiveness of microbiota therapies in gut environments subjected to various perturbations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".