Control of sucrose accumulation in sugarcane (<i>Saccharum</i> spp. hybrids) involves miRNA‐mediated regulation of genes and transcription factors associated with sugar metabolism
Bibliographic record
Abstract
Abstract Sugarcane is an established industrial crop providing sugar, ethanol and biomass‐derived electricity worldwide. Cane sugar content is an important breeding target, but its improvement remains very slow in many breeding programmes. Biotechnology strategies to improve sucrose accumulation made little progress at the crop level, mainly due to the limited understanding of its regulation. miRNAs regulate many metabolic processes in plants. However, their roles and target genes associated with sugarcane sucrose accumulation remain unknown. Here, we conducted high‐throughput sequencing of transcriptome, small RNAs and degradome of leaves and stem of two early‐maturing sugarcane genotypes with contrasting sucrose content from the early to late stages of sucrose accumulation stages, which provided more insights into miRNA‐associated gene regulation during sucrose accumulation. The stem sucrose content in both genotypes increased steadily with time during sucrose accumulation stage. Transcriptome analysis identified 18,722 differentially expressed genes (DEGs) between both genotypes during sucrose accumulation. The major DEGs identified were involved in starch and sucrose metabolism, and photosynthesis. miRNA sequencing identified 563 known and 281 novel miRNAs from both genotypes during sucrose accumulation. Of these, 311 miRNAs were differentially expressed. A combined transcriptome and miRNA data analysis revealed differentially expressed miRNA‐target mRNA pairs related to sugar metabolism, of which 46 targets were transcription factors (TFs). miR172, miR164, miR396 and miR169 appear to regulate AP2/ERF, NAC, GRF and bZIP TF members associated with sugar metabolism. This is the first report of sugarcane miRNAs associated with sugar accumulation.
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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 | 0.001 |
| 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".