Investigation of orthologous pathogen recognition gene-rich regions in solanaceous species
Bibliographic record
Abstract
Pathogen receptor proteins such as receptor-like protein (RLP), receptor-like kinase (RLK), and nucleotide-binding leucine-rich repeat (NLR) play a leading role in plant immunity activation. The genome architecture of such genes has been extensively investigated in several plant species. However, we still know little about their elaborate reorganization that arose during the plant speciation process. Using recently released pepper and eggplant genome sequences, we were able to identify 1097 pathogen recognition genes (PRGs) in the cultivated pepper Zunla-1 and 775 in the eggplant line Nakate-Shinkuro. The retrieved genes were analysed for their tendency to cluster, using different methods to infer the means of grouping. Orthologous relationships among clustering loci were found, and interesting reshuffling within given loci was observed for each analysed species. The information obtained was integrated into a comparative map to highlight the evolutionary dynamics in which the PRG loci were involved. Diversification of 14 selected PRG-rich regions was also explored using a DNA target-enrichment approach. A large number of gene variants were found as well as rearrangements of sequences encoding single protein domain and changes in chromosome gene order among species. Gene duplication and transposition activity have clearly influenced plant genome R-gene architecture and diversification. Our findings contribute to addressing several biological questions concerning the parallel evolution that occurred between genomes of the family Solanaceae. Moreover, the integration of different methods proved a powerful approach to reconstruct the evolutionary history in plant families and to transfer important biology findings among plant genomes.
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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 | 0.002 | 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".