Terminal Reassortment Drives the Quantum Evolution of Type III Effectors in Bacterial Pathogens
Bibliographic record
Abstract
Many bacterial pathogens employ a type III secretion system to deliver type III secreted effectors (T3SEs) into host cells, where they interact directly with host substrates to modulate defense pathways and promote disease. This interaction creates intense selective pressures on these secreted effectors, necessitating rapid evolution to overcome host surveillance systems and defenses. Using computational and evolutionary approaches, we have identified numerous mosaic and truncated T3SEs among animal and plant pathogens. We propose that these secreted virulence genes have evolved through a shuffling process we have called "terminal reassortment." In terminal reassortment, existing T3SE termini are mobilized within the genome, creating random genetic fusions that result in chimeric genes. Up to 32% of T3SE families in species with relatively large and well-characterized T3SE repertoires show evidence of terminal reassortment, as compared to only 7% of non-T3SE families. Terminal reassortment may permit the near instantaneous evolution of new T3SEs and appears responsible for major modifications to effector activity and function. Because this process plays a more significant role in the evolution of T3SEs than non-effectors, it provides insight into the evolutionary origins of T3SEs and may also help explain the rapid emergence of new infectious agents.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".