Combined small RNA and degradome sequencing reveals novel miRNAs and their targets in response to low nitrate availability in maize
Bibliographic record
Abstract
BACKGROUND AND AIMS: MicroRNAs (miRNAs) play an important role in the responses and adaptation of plants to many stresses including low nitrogen (LN). Characterizing relevant miRNAs will improve our understanding of nitrogen (N) use efficiency and LN tolerance and thus contribute to sustainable maize production. The objective of this study was to identify novel and known miRNAs and their targets involved in the response and adaptation of maize (Zea mays) to LN stress. METHODS: MiRNAs and their targets were identified by combined analysis of deep sequencing of small RNA and degradome libraries. The identity of target genes was confirmed by gene-specific RNA ligase-mediated rapid amplification of 5' cDNA ends (RLM-RACE) and by quantitative expression analysis. KEY RESULTS: Over 150 million raw reads of small RNA and degradome sequence data were generated. A total of 46 unique mature miRNA sequences belonging to 23 maize miRNA families were sequenced. Eighty-five potentially new miRNAs were identified, with corresponding miRNA* also identified for 65 of them. Twenty-five new miRNAs showed >2-fold relative change in response to LN. In addition to known miR169 species, two novel putative miR169 species were identified. Deep sequencing of miRNAs and the degradome, and RLM-RACE and quantitative polymerase chain reaction (PCR) analyses of their targets showed that miRC10- and miRC68-mediated target cleavage may play a major role among miR169 families in the adaptation to LN by maize seedlings. CONCLUSIONS: Small RNA and degradome sequencing combined with quantitative reverse transcription-PCR and RLM-RACE verification enabled the efficient identification of miRNAs and their target genes. The generated data sets and the two novel miR169 species that were identified will contribute to our understanding of the physiological basis of adaptation to LN stress in maize plants.
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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 | 0.002 | 0.001 |
| 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".