Heat-stress-induced sprouting and differential gene expression in growing potato tubers: Comparative transcriptomics with that induced by postharvest sprouting
Bibliographic record
Abstract
Crops face increased risk from heat stress due to climate change. Potato (Solanum tuberosum L.) tubers grown in hot summers often have defects including pre-harvest sprouting ("heat sprouts"). We have used 18 potato cultivars to investigate whether heat stress (HS) conditions alone could cause heat sprouting and dormancy changes in tubers. We also examined transcriptomic responses of potato to HS and whether these responses are like those induced by postharvest sprouting. We demonstrated that HS alone caused heat sprouts and shortened postharvest dormancy period, heat-sprouted tubers became dormant after harvest, and cultivars varied substantially for producing heat spouts but there was no clear association with cultivar maturity earliness. Cultivar Innovator did not show any heat sprouts and still had long dormancy. Dormancy-associated genes (DOG1 and SLP) were downregulated in HS tubers like in postharvest sprouting tubers. We have identified 1201 differentially expressed genes, 14 enriched GO terms and 12 enriched KEGG pathways in response to HS in growing tubers of 'Russet Burbank'. Transcriptomic response of 'Russet Burbank' to HS showed significant similarities to that of postharvest non-HS sprouted tubers. Gibberellin biosynthesis pathway was enriched in heat-stressed tubers and was likely involved in heat sprouting and dormancy release. Heat sprouting and postharvest sprouting shared common candidate genes and had significant similarity in gene expression. Our study has significance for selecting potato cultivars for farming, planning storage and utilization of heat-stressed tubers, identifying sprouting-related genes, understanding heat-stress biology, and breeding heat-tolerant potato cultivars, especially for sustainable potato production under climate change.
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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".