Analysis of NBS-LRR Gene Family in Adzuki Bean Evolution and Disease Resistance Potential
Bibliographic record
Abstract
Adzuki beans ( Vigna angularis ), as one of the important legume crops in China, are widely used in food, health care and agricultural production. However, they are often threatened by various diseases during their growth process. The NS-LRR (nucleotide-binding site-leucine-rich repeat) gene family is a core gene population in the study of plant disease resistance and plays an important role in pathogen recognition and signal transduction. To systematically analyze the structural characteristics, evolutionary relationships and disease resistance potential of the NBS-LRR gene in adzuki beans, this study conducted the identification and analysis of the NBS-LRR gene family based on whole-genome data. In this study, several NS-LRR genes in the adzuki bean genome were identified and classified into subtypes such as TNL type and CNL type according to domain characteristics. Through phylogenetic tree construction, gene structure analysis and conserved motif comparison, the diversity and evolutionary dynamics of the NB-LRR gene family of adzuki beans were revealed. The expression patterns at different tissues and growth stages were further analyzed. Combined with RNA-Seq and qRT-PCR data, it was found that multiple genes were induced to express in disease-resistant strains and had potential disease-resistant functions. Meanwhile, some key genes also demonstrated correlations with hormone signaling pathways, such as salicylic acid (SA) and jasmonic acid (JA) responses. This research not only enriches the understanding of the disease-resistant genes related to adzuki beans, but also provides a theoretical basis and candidate gene resources for subsequent functional verification and molecular breeding.
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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".