ZmbZIP60 mRNA is spliced in maize in response to ER stress
Bibliographic record
Abstract
BACKGROUND: Adverse environmental conditions produce ER stress and elicit the unfolded protein response (UPR) in plants. Plants are reported to have two "arms" of the ER stress signaling pathway-one arm involving membrane-bound transcription factors and the other involving a membrane-associated RNA splicing factor, IRE1. IRE1 in yeast to mammals recognizes a conserved twin loop structure in the target RNA. RESULTS: A segment of the mRNA encoding ZmbZIP60 in maize can be folded into a twin loop structure, and in response to ER stress this mRNA is spliced, excising a 20b intron. Splicing converts the predicted protein from a membrane-associated transcription factor to one that is targeted to the nucleus. Splicing of ZmbZIP60 can be elicited in maize seedlings by ER stress agents such as dithiothreitol (DTT) or tunicamycin (TM) or by heat treatment. Younger, rather than older seedlings display a more robust splicing response as do younger parts of leaf, along a developmental gradient in a leaf. The molecular signature of an ER stress response in plants includes the upregulation of Binding Protein (BIP) genes. Maize has numerous BIP-like genes, and ER stress was found to upregulate one of these, ZmBIPb. CONCLUSIONS: The splicing of ZmbZIP60 mRNA is an indicator of ER stress in maize seedlings resulting from adverse environmental conditions such as heat stress. ZmbZIP60 mRNA splicing in maize leads predictively to the formation of active bZIP transcription factor targeted to the nucleus to upregulate stress response genes. Among the genes upregulated by ER stress in maize is one of 22 BIP-like genes, ZmBIPb.
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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".