Loss of Lateral suppressor gene is associated with evolution of root nodule symbiosis in Leguminosae
Bibliographic record
Abstract
BACKGROUND: Root nodule symbiosis (RNS) is a fascinating evolutionary event. Given that limited genes conferring the evolution of RNS in Leguminosae have been functionally validated, the genetic basis of the evolution of RNS remains largely unknown. Identifying the genes involved in the evolution of RNS will help to reveal the mystery. RESULTS: Here, we investigate the gene loss event during the evolution of RNS in Leguminosae through phylogenomic and synteny analyses in 48 species including 16 Leguminosae species. We reveal that loss of the Lateral suppressor gene, a member of the GRAS-domain protein family, is associated with the evolution of RNS in Leguminosae. Ectopic expression of the Lateral suppressor (Ls) gene from tomato and its homolog MONOCULM 1 (MOC1) and Os7 from rice in soybean and Medicago truncatula result in almost completely lost nodulation capability. Further investigation shows that Lateral suppressor protein, Ls, MOC1, and Os7 might function through an interaction with NODULATION SIGNALING PATHWAY 2 (NSP2) and CYCLOPS to repress the transcription of NODULE INCEPTION (NIN) to inhibit the nodulation in Leguminosae. Additionally, we find that the cathepsin H (CTSH), a conserved protein, could interact with Lateral suppressor protein, Ls, MOC1, and Os7 and affect the nodulation. CONCLUSIONS: This study sheds light on uncovering the genetic basis of the evolution of RNS in Leguminosae and suggests that gene loss plays an essential role.
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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 | 0.000 | 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) | 0.001 | 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".