A High-Throughput, Seedling Screen for Plant Immunity
Bibliographic record
Abstract
An understanding of how biological diversity affects plant–microbe interactions is becoming increasingly important, particularly with respect to components of the pathogen effector arsenal and the plant immune system. Although technological improvements have greatly advanced our ability to examine molecular sequences and interactions, relatively few advances have been made that facilitate high-throughput, in vivo pathology screens. Here, we present a high-throughput, microplate-based, nondestructive seedling pathology assay, and apply it to identify Arabidopsis thaliana effector-triggered immunity (ETI) responses against Pseudomonas syringae type III secreted effectors. The assay was carried out in a 48-well microplate format with spray inoculation, and disease symptoms were quantitatively recorded in a semiautomated manner, thereby greatly reducing both time and costs. The assay requires only slight modifications of common labware and uses no proprietary software. We validated the assay by recapitulating known ETI responses induced by P. syringae in Arabidopsis. We also demonstrated that we can quantitatively differentiate responses from a diversity of plant genotypes grown in the same microplate. Finally, we showed that the results obtained from our assay can be used to perform genome-wide association studies to identify host immunity genes, recapitulating results that have been independently obtained with mature plants.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".