Disaggregation as an interaction mechanism among intestinal bacteria
Bibliographic record
Abstract
Abstract The gut microbiome contains hundreds of interacting species that together influence host health and development. The mechanisms by which intestinal microbes can interact, however, remain poorly mapped and are often modeled as spatially unstructured competitions for chemical resources. Recent imaging studies examining the zebrafish gut have shown that patterns of aggregation are central to bacterial population dynamics. In this study, we focus on bacterial species of genera Aeromonas and Enterobacter . Two zebrafish gut derived isolates, Aeromonas ZOR0001 (AE) and Enterobacter ZOR0014 (EN), when mono-associated with the host, are highly aggregated and located primarily in the intestinal midgut. An Aeromonas isolate derived from the commensal strain, Aeromonas-MB4 (AE-MB4), differs from the parental strain in that it is composed mostly of planktonic cells localized to the anterior gut. When challenged by AE-MB4, clusters of EN rapidly fragment into non-motile, slow-growing, dispersed individual cells with overall abundance two orders of magnitude lower than the mono-association value. In the presence of a certain set of additional gut bacterial species, these effects on EN are dampened. In particular, if AE-MB4 invades an already established multi-species community, EN persists in the form of large aggregates. These observations reveal an unanticipated competition mechanism based on manipulation of bacterial spatial organization, namely dissolution of aggregates, and provide evidence that multi-species communities may facilitate stable intestinal co-existence. Significance The diverse microbial communities inhabiting vertebrate intestines influence many aspects of their hosts’ health and development. The rules governing community assembly and even the types of interactions that are possible among gut microbes remain poorly understood, however, with the role of spatial aggregation in interspecies dynamics being especially unclear. To address this, we performed live imaging studies of the larval zebrafish gut, focusing on two spatially distinct bacterial species that exhibit strong competition, discovering that the latter can induce a striking, rapid disintegration of aggregates of the former. This competition is attenuated in the presence of a particular set of additional bacterial species. Our findings reveal an unanticipated interaction mechanism and highlight that diverse communities may stabilize the gut microbiome.
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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 | 0.000 |
| 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".