High throughput identification of genes conferring resistance or sensitivity to toxic effectors delivered by the type VI secretion system
Bibliographic record
Abstract
Abstract The type six secretion system (T6SS) is a prevalent bacterial weapon delivering toxic effector proteins into nearby competitors. In addition to immunity genes that protect against a particular effector, alternate yet crucial nonspecific defences have also recently been identified. To systematically identify genes influencing T6SS susceptibility in numerous species, we designed a Tn-Seq-based competition assay. Combined with follow-up analyses using E. coli and V. cholerae gene knockout collections, we demonstrate that our Tn-Seq competition technique can be used to identify both immunity and non-immunity defences against the T6SS. We also identify E. coli proteins that facilitate T6SS-mediated cell death, including metabolic genes such as cyaA and gltA, where mutant strains were resistant to attack. Our findings act as a proof-of-concept for the technique while also illuminating novel genes of interest. Since Tn-Seq can be applied in numerous species, our method has broad potential for identifying diverse T6SS defence genes across genomes in a high-throughput manner. Importance The type six secretion system (T6SS) is a molecular poison-tipped spear that bacteria use to kill nearby competitors. To prevent self killing, they use antitoxins called immunity genes that specifically neutralize the poisons. Beyond immunity genes, multiple additional defences have recently been discovered but there are likely many more across the genomes of diverse species. To help discover these novel mechanisms, we designed a high-throughput method that can be used in numerous different species to rapidly identify genes involved in sensitivity to T6SS attacks. Using T6SS ‘killers’ delivering individual poisons and two commonly studied ‘prey’ bacteria, we show proof-of-principle that the technique can discover proteins that make the prey cells more resistant or sensitive to particular poisons. This will greatly improve the speed at which genes influencing the T6SS can be identified and selected for further study in follow-up analyses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".