RNA-Seq Dissects Incomplete Activation of Phytoalexin Biosynthesis by the Soybean Transcription Factors GmMYB29A2 and GmNAC42-1
Bibliographic record
Abstract
Glyceollins, isoflavonoid-derived antimicrobial metabolites, are the major phytoalexins in soybean (Glycine max). They play essential roles in providing resistance to the soil-borne pathogen Phytophthora sojae and have unconventional anticancer and neuroprotective activities that render them desirable for pharmaceutical development. Our previous studies revealed that the transcription factors GmMYB29A2 and GmNAC42-1 have essential roles in activating glyceollin biosynthesis, yet each cannot activate the transcription of all biosynthesis genes in the absence of a pathogen elicitor treatment. Here, we report that co-overexpressing both transcription factors is also insufficient to activate glyceollin biosynthesis. To understand this insufficiency, we compared the transcriptome profiles of hairy roots overexpressing each transcription factor with glyceollin-synthesizing roots treated with wall glucan elicitor (WGE) from P. sojae. GmMYB29A2 upregulated most of the WGE-regulated genes that encode enzymatic steps spanning from primary metabolism to the last step of glyceollin biosynthesis. By contrast, GmNAC42-1 upregulated glyceollin biosynthesis genes only when overexpressed in the presence of WGE treatment. This is consistent with our recent discovery that, in the absence of WGE, GmNAC42-1 is bound by GmJAZ1 proteins that inhibit its transactivation activity. WGE, and not GmMYB29A2 or GmNAC42-1, upregulated the heat shock family gene GmHSF6-1, the homolog of Arabidopsis HSFB2a that directly activated the transcription of several glyceollin biosynthesis genes. Our results provide important insights into what biosynthesis genes will need to be upregulated to activate the entire glyceollin biosynthetic pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".