Nod factors potentiate auxin signaling for transcriptional regulation and lateral root formation in<i>Medicago truncatula</i>
Bibliographic record
Abstract
Nodulation (Nod) factors (NFs) are symbiotic molecules produced by rhizobia that are essential for establishment of the rhizobium-legume endosymbiosis. Purified NFs can stimulate lateral root formation (LRF) in Medicago truncatula, but little is known about the molecular mechanisms involved. Using a combination of reporter constructs, pharmacological and genetic approaches, we show that NFs act on early steps of LRF in M. truncatula, independently of the ethylene signaling pathway and of the cytokinin receptor MtCRE1, but in interaction with auxin. We conducted a whole-genome transcriptomic study upon NF and/or auxin treatments, using a lateral root inducible system adapted for M. truncatula. This revealed a large overlap between NF and auxin signaling and, more interestingly, synergistic interactions between these molecules. Three groups showing interaction effects were defined: group 1 contained more than 1500 genes responding specifically to the combinatorial treatment of NFs and auxin; group 2 comprised auxin-regulated genes whose expression was enhanced or antagonized by NFs; and in group 3 the expression of NF regulated genes was antagonized by auxin. Groups 1 and 2 were enriched in signaling and metabolic functions, which highlights important crosstalk between NF and auxin signaling for both developmental and symbiotic processes.
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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".