Host–pathogen interplay and the evolution of bacterial effectors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it