Isolation of a family of resistance gene analogue sequences of the nucleotide binding site (NBS) type from<i>Lens</i>species
Bibliographic record
Abstract
Most known plant disease-resistance genes (R genes) include in their encoded products domains such as a nucleotide-binding site (NBS) or leucine-rich repeats (LRRs). Sequences with unknown function, but encoding these conserved domains, have been defined as resistance gene analogues (RGAs). The conserved motifs within plant NBS domains make it possible to use degenerate primers and PCR to isolate RGAs. We used degenerate primers deduced from conserved motifs in the NBS domain of NBS-LRR resistance proteins to amplify genomic sequences from Lens species. Fragments from approximately 500-850 bp were obtained. The nucleotide sequence analysis of these fragments revealed 32 different RGA sequences in Lens species with a high similarity (up to 91%) to RGAs from other plants. The predicted amino acid sequences showed that lentil sequences contain all the conserved motifs (P-loop, kinase-2, kinase-3a, GLPL, and MHD) present in the majority of other known plant NBS-LRR resistance genes. Phylogenetic analyses grouped the Lens NBS sequences with the Toll and interleukin-1 receptor (TIR) subclass of NBS-LRR genes, as well as with RGA sequences isolated from other legume species. Using inverse PCR on one putative RGA of lentil, we were able to amplify the flanking regions of this sequence, which contained features found in R proteins.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".