Spatio-Temporal Transcriptional Dynamics of Maize Long Non-Coding RNAs Responsive to Drought Stress
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Long non-coding RNAs (lncRNAs) have emerged as important regulators in plant stress response. Here, we report a genome-wide lncRNA transcriptional analysis in response to drought stress using an expanded series of maize samples collected from three distinct tissues spanning four developmental stages. In total, 3488 high-confidence lncRNAs were identified, among which 1535 were characterized as drought responsive. By characterizing the genomic structure and expression pattern, we found that lncRNA structures were less complex than protein-coding genes, showing shorter transcripts and fewer exons. Moreover, drought-responsive lncRNAs exhibited higher tissue- and development-specificity than protein-coding genes. By exploring the temporal expression patterns of drought-responsive lncRNAs at different developmental stages, we discovered that the reproductive stage R1 was the most sensitive growth stage with more lncRNAs showing altered expression upon drought stress. Furthermore, lncRNA target prediction revealed 653 potential lncRNA-messenger RNA (mRNA) pairs, among which 124 pairs function in cis-acting mode and 529 in trans. Functional enrichment analysis showed that the targets were significantly enriched in molecular functions related to oxidoreductase activity, water binding, and electron carrier activity. Multiple promising targets of drought-responsive lncRNAs were discovered, including the V-ATPase encoding gene, vpp4. These findings extend our knowledge of lncRNAs as important regulators in maize drought response.
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.
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 it