An interbacterial toxin inhibits target cell growth by synthesizing (p)ppApp
Bibliographic record
Abstract
Bacteria have evolved sophisticated mechanisms to inhibit the growth of competitors. One such mechanism involves type VI secretion systems, which can be used by bacteria to directly inject antibacterial toxins into neighbouring cells. Many of these toxins target cell envelope integrity, but the full range of growth inhibitory mechanisms remains to be determined. Here, we discover a novel type VI secretion effector, Tas1, in the opportunistic pathogen Pseudomonas aeruginosa . A crystal structure of Tas1 reveals similarity to enzymes that synthesize (p)ppGpp, a broadly conserved signaling molecule in bacteria that modulates cell growth rate, particularly in response to nutritional stress. Strikingly, however, we find that Tas1 does not synthesize (p)ppGpp, and instead pyrophosphorylates adenosine nucleotides to produce (p)ppApp at rates of nearly 180,000 per minute. Consequently, delivery of Tas1 into competitor cells drives the rapid accumulation of (p)ppApp, depletion of ATP, and widespread dysregulation of essential metabolic pathways, resulting in target cell death. Collectively, our findings reveal a new mechanism for interbacterial antagonism and demonstrate, for the first time, a physiological role for the metabolite (p)ppApp in bacteria. Support or Funding Information Canadian Institutes of Health Research, David Braley Centre for Antibiotic Discovery, National Institutes of Health, Howard Hughes Medical Institute Structure of the (p)ppApp synthetase toxin Tas1 in complex with its cognate immunity protein, Tis1. Figure 1 Tas1 is a pyrophosphokinase that is specific for canonical 5′‐adenosine nucleotides. Figure 2
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".