Identification of divergent type VI secretion effectors using a conserved chaperone domain
Bibliographic record
Abstract
The type VI secretion system (T6SS) is a lethal weapon used by many bacteria to kill eukaryotic predators or prokaryotic competitors. Killing by the T6SS results from repetitive delivery of toxic effectors. Despite their importance in dictating bacterial fitness, systematic prediction of T6SS effectors remains challenging due to high effector diversity and the absence of a conserved signature sequence. Here, we report a class of T6SS effector chaperone (TEC) proteins that are required for effector delivery through binding to VgrG and effector proteins. The TEC proteins share a highly conserved domain (DUF4123) and are genetically encoded upstream of their cognate effector genes. Using the conserved TEC domain sequence, we identified a large family of TEC genes coupled to putative T6SS effectors in Gram-negative bacteria. We validated this approach by verifying a predicted effector TseC in Aeromonas hydrophila. We show that TseC is a T6SS-secreted antibacterial effector and that the downstream gene tsiC encodes the cognate immunity protein. Further, we demonstrate that TseC secretion requires its cognate TEC protein and an associated VgrG protein. Distinct from previous effector-dependent bioinformatic analyses, our approach using the conserved TEC domain will facilitate the discovery and functional characterization of new T6SS effectors in Gram-negative bacteria.
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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) | 0.000 | 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".