Expression of small heat shock proteins and heat tolerance in potato (Solanum tuberosum L.)
Bibliographic record
Abstract
We have examined the correlation between heat tolerance and small heat shock protein (sHSP) expression under heat stress conditions in potato (Solanum tuberosum L.). The relative heat tolerance of nine potato cultivars grown under greenhouse conditions was determined using the electrolyte leakage assay (ELA), a standard quantitative assay for heat tolerance. Three cultivars differing in heat tolerance were selected and designated as heat-tolerant (?Laura?), moderately sensitive (?Liseta?) and heat-sensitive (?Agria?) genotypes. The expression of cytosolic HSP18 and chloroplast HSP21 was analyzed at the protein level in the leaves of selected cultivars, both ex vitro- and in vitro-grown, after heat stress or control treatment. Immunoblot analysis revealed heat-induced HSP18 and HSP21 expression in all examined genotypes. A similar pattern of examined sHSP expression was observed ex vitro and in vitro: heat-tolerant ?Laura? accumulated higher levels of both HSP18 and HSP21 compared to heat-sensitive ?Liseta? and ?Agria?. Our results indicate that ELA combined with immunoblot analysis of sHSP accumulation under HS conditions, might be considered as a reliable procedure in screening potato genotypes for heat tolerance. To our knowledge, this is the first study where sHSP expression between ex vitro- and in vitro-grown potato plants was compared.
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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) | 0.000 | 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 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".