Genetically distinct pathways guide effector export through the type <scp>VI</scp> secretion system
Bibliographic record
Abstract
Bacterial secretion systems often employ molecular chaperones to recognize and facilitate export of their substrates. Recent work demonstrated that a secreted component of the type VI secretion system (T6SS), haemolysin co-regulated protein (Hcp), binds directly to effectors, enhancing their stability in the bacterial cytoplasm. Herein, we describe a quantitative cellular proteomics screen for T6S substrates that exploits this chaperone-like quality of Hcp. Application of this approach to the Hcp secretion island I-encoded T6SS (H1-T6SS) of Pseudomonas aeruginosa led to the identification of a novel effector protein, termed Tse4 (type VI secretion exported 4), subsequently shown to act as a potent intra-specific H1-T6SS-delivered antibacterial toxin. Interestingly, our screen failed to identify two predicted H1-T6SS effectors, Tse5 and Tse6, which differ from Hcp-stabilized substrates by the presence of toxin-associated PAAR-repeat motifs and genetic linkage to members of the valine-glycine repeat protein G (vgrG) genes. Genetic studies further distinguished these two groups of effectors: Hcp-stabilized effectors were found to display redundancy in interbacterial competition with respect to the requirement for the two H1-T6SS-exported VgrG proteins, whereas Tse5 and Tse6 delivery strictly required a cognate VgrG. Together, we propose that interaction with either VgrG or Hcp defines distinct pathways for T6S effector export.
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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 | 0.001 |
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".