Identification and characterization of NBS–LRR class resistance gene analogs in faba bean (Vicia faba L.) and chickpea (Cicer arietinum L.)
Bibliographic record
Abstract
A PCR approach with degenerate primers designed from conserved NBS-LRR (nucleotide binding site-leucine-rich repeat) regions of known disease-resistance (R) genes was used to amplify and clone homologous sequences from 5 faba bean (Vicia faba) lines and 2 chickpea (Cicer arietinum) accessions. Sixty-nine sequenced clones showed homologies to various R genes deposited in the GenBank database. The presence of internal kinase-2 and kinase-3a motifs in all the sequences isolated confirm that these clones correspond to NBS-containing genes. Using an amino-acid sequence identity of 70% as a threshold value, the clones were grouped into 10 classes of resistance-gene analogs (RGA01 to RGA10). The number of clones per class varied from 1 to 30. RGA classes 1, 6, 8, and 9 were comprised solely of clones isolated from faba bean, whereas classes 2, 3, 4, 5, and 7 included only chickpea clones. RGA10, showing a within-class identity of 99%, was the only class consisting of both faba bean and chickpea clones. A phylogenetic tree, based on the deduced amino-acid sequences of 12 representative clones from the 10 RGA classes and the NBS domains of 6 known R genes (I2 and Prf from tomato, RPP13 from Arabidopsis, Gro1-4 from potato, N from tobacco, L6 from flax), clearly indicated the separation between TIR (Toll/interleukin-1 receptor homology: Gro1-4, L6, N, RGA05 to RGA10)- and non-TIR (I2, Prf, RPP13, RGA01 to RGA04)-type NBS-LRR sequences. The development of suitable polymorphic markers based on cloned RGA sequences to be used in genetic mapping will facilitate the assessment of their potential linkage relationships with disease-resistance genes in faba bean and chickpea. This work is the first to report on faba bean RGAs.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
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 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".