The expression of <i>LjROP4</i> is required for rhizobial infection in <i>Lotus japonicus</i>
Bibliographic record
Abstract
Increasing evidence suggests that Rho of plant (ROP) GTPases play important roles in the rhizobium–legume symbiotic nodulation, but the molecular mechanisms of their regulation in symbiosis remain poorly understood. In this study, we showed that ROP4 in Lotus japonicus (LjROP4) is involved in the symbiotic interaction between L. japonicus and Mesorhizobium loti. Tissue expression analysis showed that LjROP4 expressed highly in the root. Histochemical staining analysis showed that after rhizobia inoculation, GUS reporter activity increased in the root vascular bundle, root tip, and lateral root primordia. During nodule development, GUS activity was detected in the cortex of nodule primordia and young nodules. In the mature nodules, GUS activity was detected only in the vascular bundle. Compared with the control, the overexpression of ROP4 and ROP4-CA produced much more pronounced root hair swelling and curling induced by M. loti. The infection event and nodule number noticeably increased, which was consistent with this promotion of root hair deformation. Moreover, RNA interference of LjROP4 produced opposite phenotypes. These data suggest that LjROP4 is required for root hair deformation during rhizobial infection. Thus, our study provides important information about root hair deformation responses induced by nod factors in the early stages of symbiotic interaction.
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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".