Faculty Opinions recommendation of An interbacterial toxin inhibits target cell growth by synthesizing (p)ppApp.
Bibliographic record
Abstract
Bacteria have evolved sophisticated mechanisms to inhibit the growth of competitors1. One such mechanism involves type VI secretion systems, which bacteria can use to inject antibacterial toxins directly into neighbouring cells. Many of these toxins target the integrity of the cell envelope, but the full range of growth inhibitory mechanisms remains unknown2. Here we identify a type VI secretion effector, Tas1, in the opportunistic pathogen Pseudomonas aeruginosa. The crystal structure of Tas1 shows that it is similar to enzymes that synthesize (p)ppGpp, a broadly conserved signalling molecule in bacteria that modulates cell growth rate, particularly in response to nutritional stress3. However, Tas1 does not synthesize (p)ppGpp; instead, it pyrophosphorylates adenosine nucleotides to produce (p)ppApp at rates of nearly 180,000 molecules per minute. Consequently, the delivery of Tas1 into competitor cells drives rapid accumulation of (p)ppApp, depletion of ATP, and widespread dysregulation of essential metabolic pathways, thereby resulting in target cell death. Our findings reveal a previously undescribed mechanism for interbacterial antagonism and demonstrate a physiological role for the metabolite (p)ppApp in bacteria. PMID: 31695193 Funding information This work was supported by: NIH HHS, United States Grant ID: S10 OD021527 NIGMS NIH HHS, United States Grant ID: P30 GM124165 NIAID NIH HHS, United States Grant ID: HHSN272201700060C NIAID NIH HHS, United States Grant ID: HHSN272201200026C NIGMS NIH HHS, United States Grant ID: R01 GM082899
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.699 | 0.464 |
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".