The large repertoire of conifer NLR resistance genes includes drought responsive and highly diversified RNLs
Bibliographic record
Abstract
The NLRs or NBS-LRRs (nucleotide-binding, leucine-rich-repeat) form the largest resistance gene family in plants, with lineage-specific contingents of TNL, CNL and RNL subfamilies and a central role in resilience to stress. The origin, evolution and distribution of NLR sequences has been unclear owing in part to the variable size and diversity of the RNL subfamily and a lack of data in Gymnosperms. We developed, searched and annotated transcriptomes assemblies of seven conifers and identified a resource of 3816 expressed NLR sequences. Our analyses encompassed sequences data spanning the major groups of land plants and determinations of NLR transcripts levels in response to drought in white spruce. We showed that conifers have among the most diverse and numerous RNLs in tested land plants. We report an evolutionary swap in the formation of RNLs, which emerged from the fusion of an RPW8 domain to a NB-ARC domain of CNL. We uncovered a quantitative relationship between RNLs and TNLs across all land plants investigated, with an average ratio of 1:10. The conifer RNL repertoire harbours four distinct groups, with two that differ from Angiosperms, one of which contained several upregulated sequences in response to drought while the majority of responsive NLRs are downregulated.
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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 | 0.001 |
| 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".