Phytophthora sojae Avirulence Effector Avr3b is a Secreted NADH and ADP-ribose Pyrophosphorylase that Modulates Plant Immunity
Bibliographic record
Abstract
Plants have evolved pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI) to protect themselves from infection by diverse pathogens. Avirulence (Avr) effectors that trigger plant ETI as a result of recognition by plant resistance (R) gene products have been identified in many plant pathogenic oomycetes and fungi. However, the virulence functions of oomycete and fungal Avr effectors remain largely unknown. Here, we combined bioinformatics and genetics to identify Avr3b, a new Avr gene from Phytophthora sojae, an oomycete pathogen that causes soybean root rot. Avr3b encodes a secreted protein with the RXLR host-targeting motif and C-terminal W and Nudix hydrolase motifs. Some isolates of P. sojae evade perception by the soybean R gene Rps3b through sequence mutation in Avr3b and lowered transcript accumulation. Transient expression of Avr3b in Nicotiana benthamiana increased susceptibility to P. capsici and P. parasitica, with significantly reduced accumulation of reactive oxygen species (ROS) around invasion sites. Biochemical assays confirmed that Avr3b is an ADP-ribose/NADH pyrophosphorylase, as predicted from the Nudix motif. Deletion of the Nudix motif of Avr3b abolished enzyme activity. Mutation of key residues in Nudix motif significantly impaired Avr3b virulence function but not the avirulence activity. Some Nudix hydrolases act as negative regulators of plant immunity, and thus Avr3b might be delivered into host cells as a Nudix hydrolase to impair host immunity. Avr3b homologues are present in several sequenced Phytophthora genomes, suggesting that Phytophthora pathogens might share similar strategies to suppress plant immunity.
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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 | 0.000 | 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) | 0.000 | 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".