In Planta Effector Competition Assays Detect<i>Hyaloperonospora arabidopsidis</i>Effectors That Contribute to Virulence and Localize to Different Plant Subcellular Compartments
Bibliographic record
Abstract
The genome of the pathogenic oomycete Hyaloperonospora arabidopsidis is predicted to encode at least 134 high-confidence effectors (HaRxL) carrying the RxLR motif implicated in their translocation into plant cells. However, only four avirulence genes (ATR1, ATR13, ATR5, and ATR39) have been isolated. This indicates that identification of HaRxL effectors based on avirulence is low throughput. We aimed at rapidly identifying H. arabidopsidis effectors that contribute to virulence by developing methods to detect and quantify multiple candidates in bacterial mixed infections using either Illumina sequencing or capillary electrophoresis. In these assays, referred to here as in planta effector competition assays, we estimate the contribution to virulence of individual effectors by calculating the abundance of each HaRxL in the bacterial population recovered from leaves 3 days after inoculation relative to abundance in the initial mixed inoculum. We identified HaRxL that enhance Pseudomonas syringae pv. tomato DC3000 growth in some but not all Arabidopsis accessions. Further analysis showed that HaRxLL464, HaRxL75, HaRxL22, HaRxLL441, and HaRxL89 suppress pathogen-associated molecular pattern-triggered immunity (PTI) and localize to different subcellular compartments in Nicotiana benthamiana, providing evidence for a multilayered suppression of PTI by pathogenic oomycetes and molecular probes for the dissection of PTI.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".