Evolution of the type III secretion system and its effectors in plant–microbe interactions
Bibliographic record
Abstract
Summary Many bacterial plant pathogens require the type III secretion system (T3SS) and its effector proteins (T3SEs) to invade and extract nutrients from their hosts successfully. While the molecular function of this system is being studied intensively, we know comparatively little about the evolutionary and ecological pressures governing its fate over time, and even less about the detailed mechanisms underlying and driving complex T3SS‐mediated coevolutionary dynamics. In this review we summarize our current understanding of how host–pathogen interactions evolve, with a particular focus on the T3SS of bacterial plant pathogens. We explore the evolutionary origins of the T3SS relative to the closely related flagellar system, and investigate the evolutionary pressures on this secretion and translocation apparatus. We examine the evolutionary forces acting on T3SEs, and compare the support for vertical descent with modification of these virulence‐associated systems (pathoadaptation) vs horizontal gene transfer. We address the evolutionary origins of T3SEs from the perspective of both the evolutionary mechanisms that generate new effectors, and the mobile elements that may be the source of novel genetic material. Finally, we propose a number of questions raised by these studies, which may serve to guide our thinking about these complex processes. Contents Summary 33 I. Introduction 34 II. The type III secretion system and the plant immune system 34 III. The evolution of the type III secretion apparatus 36 IV. The evolution of type III secreted effectors 39 V. The origin of type III secreted effectors 41 VI. Conclusion and future directions 43 Acknowledgements 44 References 44
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".