<i>Paenibacillus terrae</i> NK3-4 regulates the transcription of growth-related and stress resistance-related genes in rice
Bibliographic record
Abstract
NK3-4 is a plant growth-promoting rhizobacterium. In this study, the effects of NK3-4 on rice growth and gene transcription were determined. The results indicated that a seed soaking treatment and a pre-germination seed treatment using NK3-4 promoted rice growth, especially spraying rice seedlings with NK3-4 increased the root number and root length by 34.2% and 34.1%, respectively. Moreover, NK3-4 induced the differential transcription of genes annotated with gene ontology (GO) terms; the number of up-regulated genes was 4.38-times higher than the number of down-regulated genes. The NK3-4 treatment induced the differential transcription of genes in 1794 GO functional groups, with 1531 functional groups containing up-regulated genes. Specific growth-related genes up-regulated by NK3-4 are involved in biological processes, including responses to auxin, hormone biosynthesis, cellular component biogenesis, root system development, and other functions. Furthermore, stress resistance-related genes were up-regulated, some of which encode WRKYs, NPK1-related protein kinase, NPR1-like 4, CaM-like proteins, MYBs, ERFs, TIFYs, NACs, EL5s, PR1s, PR2, PR8, PODs, and PAD4. Considered together, these findings imply that NK3-4 may promote plant growth and enhance stress resistance by regulating gene expression, making it a potentially useful microbe for regulating rice growth and stress resistance.
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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".