Human symbionts inject and neutralize antibacterial toxins to persist in the gut
Bibliographic record
Abstract
The human gut microbiome is a dynamic and densely populated microbial community that can provide important benefits to its host. Cooperation and competition for nutrients among its constituents only partially explain community composition and interpersonal variation. Notably, certain human-associated Bacteroidetes--one of two major phyla in the gut--also encode machinery for contact-dependent interbacterial antagonism, but its impact within gut microbial communities remains unknown. Here we report that prominent human gut symbionts persist in the gut through continuous attack on their immediate neighbors. Our analysis of just one of the hundreds of species in these communities reveals 12 candidate antibacterial effector loci that can exist in 32 combinations. Through the use of secretome studies, in vitro bacterial interaction assays and multiple mouse models, we uncover strain-specific effector/immunity repertoires that can predict interbacterial interactions in vitro and in vivo, and find that some of these strains avoid contact-dependent killing by accumulating immunity genes to effectors that they do not encode. Effector transmission rates in live animals can exceed 1 billion events per minute per gram of colonic contents, and multiphylum communities of human gut commensals can partially protect sensitive strains from these attacks. Together, these results suggest that gut microbes can determine their interactions through direct contact. An understanding of the strategies human gut symbionts have evolved to target other members of this community may provide new approaches for microbiome manipulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".