Cell motility empowers bacterial contact weapons
Bibliographic record
Abstract
Many bacteria kill competitors using short-range weapons, such as the Type VI secretion system and contact dependent inhibition (CDI). Although these weapons can deliver powerful toxins, they rely on direct contact between attacker and target cells. We hypothesized that movement enables attackers to contact more targets and thus greatly empower their weapons. To explore this, we developed individual-based and continuum models of contact-dependent combat which show that motility greatly improves toxin delivery through two underlying processes. First, genotypic mixing increases the inter-strain contact probability of attacker and sensitive cells. Second, target switching ensures attackers constantly attack new cells, instead of repeatedly hitting the same cell. We test our predictions with the pathogen Pseudomonas aeruginosa, using genetically engineered strains to study the interaction between CDI and twitching motility. As predicted, we find that motility works synergistically with CDI, in some cases increasing weapon efficacy up to 10,000-fold compared with non-motile scenarios. Moreover, we demonstrate that both mixing processes occur using timelapse single-cell microscopy and quantify their relative importance by combining experimental data with our model. Our work shows how bacteria can combine cell movement with contact-based weapons to launch powerful attacks on their competitors.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".