Resistance in chickpea to Ascochyta rabiei is conferred by genes encoding pathogen recognition and other genes in basic defense pathways
Bibliographic record
Abstract
This study examines the molecular basis of resistance to Ascochyta rabiei in chickpea. An inter-specific population of RIL derived from C. arietinum ICC4958 x C. reticulatum PI489777 was used to develop a high-density linkage map consisting of 1328 SNP and SSR markers. Progenies were inoculated separately with two isolates from Canada and one from Syria using a detached leaf-assay. This allowed an accurate record of lesion development over time that was used to calculate the area under the disease progress curve (AUDPC). The parental lines interacted differentially with the isolates as AUDPC were 20.6, 9.3 and 4.4 in ICC4958 compared to 2.0, 3.2 and 9.3 in PI489777. Ten quantitative resistance loci (QRL) explained 5 to 26% of the phenotypic variation. The two parents contributed to resistance at different loci. Each QRL interacted with a single isolate, except one that interacted with two isolates. Since a portion of the SNP markers were designed in exonic sequences a search of GenBank and Medicago data bases revealed annotated gene function. A major discovery was a match of SNP markers at six QRL with genes encoding serine/threonine kinase proteins involved in pathogen recognition, innate immune response, programmed cell death and signal transduction. Genes at the remaining QRL were associated with well known basic defense pathways such as thaumatin, chalcone-stilbene, peroxidase and ethylene. Evidence suggest that recognition of A. rabiei results in qualitative disease phenotypes seen in some studies, while the quantitative nature of resistance in other studies might be a result of activation of additional networks of basic defense pathways.
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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) | 0.001 | 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".