Host–pathogen interplay and the evolution of bacterial effectors
Bibliographic record
Abstract
Many bacterial pathogens require a type III secretion system (T3SS) and suite of type III secreted effectors (T3SEs) to successfully colonize their hosts, extract nutrients and consequently cause disease. T3SEs, in particular, are key components of the bacterial arsenal, as they function directly inside the host to disrupt or suppress critical components of the defence network. The development of host defence and surveillance systems imposes intense selective pressures on these bacterial virulence factors, resulting in a host-pathogen co-evolutionary arms race. This arms race leaves its genetic signature in the pattern and structure of natural genetic variation found in T3SEs, thereby permitting us to infer the specific evolutionary processes and pressures driving these interactions. In this review, we summarize our current knowledge of T3SS-mediated host-pathogen co-evolution. We examine the evolution of the T3SS and the T3SEs that traverse it, in both plant and animal pathosystems, and discuss the processes that maintain these important pathogenicity determinants within pathogen populations. We go on to examine the possible origins of T3SEs, the mechanisms that give rise to new T3SEs and the processes that underlie their evolution.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".