Identification of four functionally important microRNA families with contrasting differential expression profiles between drought-tolerant and susceptible rice leaf at vegetative stage
Bibliographic record
Abstract
BACKGROUND: Developing drought-tolerant rice varieties with higher yield under water stressed conditions provides a viable solution to serious yield-reduction impact of drought. Understanding the molecular regulation of this polygenic trait is crucial for the eventual success of rice molecular breeding programmes. microRNAs have received tremendous attention recently due to its importance in negative regulation. In plants, apart from regulating developmental and physiological processes, microRNAs have also been associated with different biotic and abiotic stresses. Hence here we chose to analyze the differential expression profiles of microRNAs in three drought treated rice varieties: Vandana (drought-tolerant), Aday Sel (drought-tolerant) and IR64 (drought-susceptible) in greenhouse conditions via high-throughput sequencing. RESULTS: Twenty-six novel microRNA candidates involved in the regulation of diverse biological processes were identified based on the detection of miRNA*. Out of their 110 predicted targets, we confirmed 16 targets from 5 novel microRNA candidates. In the differential expression analysis, mature microRNA members from 49 families of known Oryza sativa microRNA were differentially expressed in leaf and stem respectively with over 28 families having at least a similar mature microRNA member commonly found to be differentially expressed between both tissues. Via the sequence profiling data of leaf samples, we identified osa-miR397a/b, osa-miR398b, osa-miR408-5p and osa-miR528-5p as being down-regulated in two drought-tolerant rice varieties and up-regulated in the drought-susceptible variety. These microRNAs are known to be involved in regulating starch metabolism, antioxidant defence, respiration and photosynthesis. A wide range of biological processes were found to be regulated by the target genes of all the identified differentially expressed microRNAs between both tissues, namely root development (5.3-5.7 %), cell transport (13.2-18.4 %), response to stress (10.5-11.3 %), lignin catabolic process (3.8-5.3 %), metabolic processes (32.1-39.5 %), oxidation-reduction process (9.4-13.2 %) and DNA replication (5.7-7.9 %). The predicted target genes of osa-miR166e-3p, osa-miR166h-5p*, osa-miR169r-3p* and osa-miR397a/b were found to be annotated to several of the aforementioned biological processes. CONCLUSIONS: The experimental design of this study, which features rice varieties with different drought tolerance and tissue specificity (leaf and stem), has provided new microRNA profiling information. The potentially regulatory importance of the microRNA genes mentioned above and their target genes would require further functional analyses.
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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".