Genome analysis of poplar LRR-RLP gene clusters reveals RISP, a defense-related gene coding a candidate endogenous peptide elicitor
Bibliographic record
Abstract
In plants, cell-surface receptors control immunity and development through the recognition of extracellular ligands. Leucine-rich repeat receptor-like proteins (LRR-RLPs) constitute a large multigene family of cell-surface receptors. Although this family has been intensively studied, a limited number of ligands has been identified so far, mostly because methods used for their identification and characterization are complex and fastidious. In this study, we combined genome and transcriptome analyses to describe the LRR-RLP gene family in the model tree poplar (Populus trichocarpa). In total, 82 LRR-RLP genes have been identified in P. trichocarpa genome, among which 66 are organized in clusters of up to seven members. In these clusters, LRR-RLP genes are interspersed by orphan, poplar-specific genes encoding small proteins of unknown function (SPUFs). In particular, the nine largest clusters of LRR-RLP genes (47 LRR-RLPs) include 71 SPUF genes that account for 59% of the non-LRR-RLP gene content within these clusters. Forty-four LRR-RLP and 55 SPUF genes are expressed in poplar leaves, mostly at low levels, except for members of some clusters that show higher and sometimes coordinated expression levels. Notably, wounding of poplar leaves strongly induced the expression of a defense SPUF gene named Rust-Induced Secreted protein (RISP) that has been previously reported as a marker of poplar defense responses. Interestingly, we show that the RISP-associated LRR-RLP gene is highly expressed in poplar leaves and slightly induced by wounding. Both gene promoters share a highly conserved region of ~300 nucleotides. This led us to hypothesize that the corresponding pair of proteins could be involved in poplar immunity, possibly as a ligand/receptor couple. In conclusion, we speculate that some poplar SPUFs, such as RISP, represent candidate endogenous peptide ligands of the associated LRR-RLPs and we discuss how to investigate further this hypothesis.
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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 | 0.000 |
| 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) | 0.000 | 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".