Cloning and characterization of miRNAs from maize seedling roots under low phosphorus stress
Bibliographic record
Abstract
MicroRNAs (miRNAs) are a class of small, non-coding regulatory RNAs that regulate gene expression by guiding target mRNA cleavage or translational inhibition in plants and animals. In this study, a small RNA library was constructed to identify conserved miRNAs as well as novel miRNAs in maize seedling roots under low level phosphorus stress. Twelve miRNAs were identified by high throughput sequencing of the library and subsequent analysis, two belong to conserved miRNA families (miRNA399b and miRNA156), and the remaining ten are novel and one of latter is conserved in gramineous species. Based on sequence homology, we predicted 125 potential target genes of these miRNAs and then expression patterns of 7 miRNAs were validated by semi-RT-PCR analysis. MiRNA399b, Zma-miR3, and their target genes (Zmpt1 and Zmpt2) were analyzed by real-time PCR. It is shown that both miRNA399b and Zma-miR3 are induced by low phosphorus stress and regulated by their target genes (Zmpt1 and Zmpt2). Moreover, Zma-miR3, regulated by two maize inorganic phosphate transporters as a newly identified miRNAs, would likely be directly involved in phosphate homeostasis, so was miRNA399b in Arabidopsis and rice. These results indicate that both conserved and maize-specific miRNAs play important roles in stress responses and other physiological processes correlated with phosphate starvation, regulated by their target genes. Identification of these differentially expressed miRNAs will facilitate us to uncover the molecular mechanisms underlying the progression of maize seedling roots development under low level phosphorus stress.
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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 | 0.001 |
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".