"Type A" response regulators are involved in the plant-microbe interaction.
Bibliographic record
Abstract
Plant-microbe interaction can be established as either symbiotic or pathogenic association. Regardless of any type of interaction interplayed, common strategy can be seen in the partners. Recent advances in bioinformatics and availability of abundant transcriptomics data have provided new tools for comparative analysis and achieving significant insights into underlying regulatory background of symbiosis and pathogenesis. In the present study, to find the genes which are involved in both pathogenic and symbiotic interactions, we used the microarray data pertaining bacterial interactions in M. truncatula. Those data which were publically available in NCBI analyzed using Expression Console and FlexyArray. In order to interpret gene expression patterns and investigate the relationship between co-over expressed genes, a literature survey was performed using the tools provided by Pathway Studio v9 (Elsevier). Our data analysis identified type-A response regulators (RRs) as genes that potentially respond to pathogenic and symbiotic interactions. Type-A RRs act as negative regulators of cytokinins. This study speculates that plants do not recognize bacterial pathogens from symbionts at early stage of plant-microbe interactions. Pathway analysis revealed the involvement of WUSCHEL (WUS) and SIAMES (SIM) in cross-talk between pathogen and type-A of RRs. Considering the central role of WUS and SIM in cell division, we suggest that the reduction of growth by repression of cell division is one of the adaptive responses in plants to bacterial interaction. In this way, plants try to preserve the limited energy of the mother cell and to avoid heritable damage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".