Potential functions of microRNAs in starch metabolism and development revealed by miRNA transcriptome profiling of cassava cultivars and their wild progenitor
Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRNAs) are small (approximately 21 nucleotide) non-coding RNAs that are key post-transcriptional gene regulators in eukaryotic organisms. More than 100 cassava miRNAs have been identified in a conservation analysis and a repertoire of cassava miRNAs have also been characterised by next-generation sequencing (NGS) in recent studies. Here, using NGS, we profiled small non-coding RNAs and mRNA genes in two cassava cultivars and their wild progenitor to identify and characterise miRNAs that are potentially involved in plant growth and starch biosynthesis. RESULTS: Six small RNA and six mRNA libraries from leaves and roots of the two cultivars, KU50 and Arg7, and their wild progenitor, W14, were subjected to NGS. Analysis of the sequencing data revealed 29 conserved miRNA families and 33 new miRNA families. Together, these miRNAs potentially targeted a total of 360 putative target genes. Whereas 16 miRNA families were highly expressed in cultivar leaves, another 13 miRNA families were highly expressed in storage roots of cultivars. Co-expression analysis revealed that the expression level of some targets had negative relationship with their corresponding miRNAs in storage roots and leaves; these targets included MYB33, ARF10, GRF1, RD19, APL2, NF-YA3 and SPL2, which are known to be involved in plant development, starch biosynthesis and response to environmental stimuli. CONCLUSION: The identified miRNAs, target mRNAs and target gene ontology annotation all shed light on the possible functions of miRNAs in Manihot species. The differential expression of miRNAs between cultivars and their wild progenitor, together with our analysis of GO annotation and confirmation of miRNA: target pairs, might provide insight into know the differences between wild progenitor and cultivated cassava.
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.
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 | 0.000 | 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 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".