A host–pathogen interactome uncovers phytopathogenic strategies to manipulate plant <scp>ABA</scp> responses
Bibliographic record
Abstract
Summary The phytopathogen Pseudomonas syringae delivers into host cells type III secreted effectors (T3SEs) that promote virulence. One virulence mechanism employed by T3SEs is to target hormone signaling pathways to perturb hormone homeostasis. The phytohormone abscisic acid (ABA) influences interactions between various phytopathogens and their plant hosts, and has been shown to be a target of P. syringae T3SEs. In order to provide insight into how T3SEs manipulate ABA responses, we generated an ABA‐T3SE interactome network (ATIN) between P. syringae T3SEs and Arabidopsis proteins encoded by ABA‐regulated genes. ATIN consists of 476 yeast‐two‐hybrid interactions between 97 Arabidopsis ABA‐regulated proteins and 56 T3SEs from four pathovars of P. syringae. We demonstrate that T3SE interacting proteins are significantly enriched for proteins associated with transcription. In particular, the ETHYLENE RESPONSIVE FACTOR (ERF) family of transcription factors is highly represented. We show that ERF105 and ERF8 displayed a role in defense against P. syringae, supporting our overall observation that T3SEs of ATIN converge on proteins that influence plant immunity. In addition, we demonstrate that T3SEs that interact with a large number of ABA‐regulated proteins can influence ABA responses. One of these T3SEs, HopF3Pph6, inhibits the function of ERF8, which influences both ABA‐responses and plant immunity. These results provide a potential mechanism for how HopF3Pph6 manipulates ABA‐responses to promote P. syringae virulence, and also demonstrate the utility of ATIN as a resource to study the ABA‐T3SE interface.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".