Cold storage reveals distinct metabolic perturbations in processing and non-processing cultivars of potato
Bibliographic record
Abstract
Abstract Cold-induced sweetening (CIS) causes a great loss to the potato ( Solanum tuberosum L.) processing industry wherein selection of potato genotypes using biochemical information through marker-trait associations has found to be advantageous. In the present study, we have performed nuclear magnetic resonance (NMR) spectroscopy-based metabolite profiling on tubers from five potato cultivars (Atlantic, Frito Lay-1533, Kufri Jyoti, Kufri Pukhraj, and PU1) differing in their CIS ability and processing characteristics at harvest and after one month of cold storage at 4°C. A total of 39 water-soluble metabolites were detected using 1 H NMR. Multivariate statistical analysis indicated significant differences in metabolite profiles between processing and non-processing potato cultivars. Further analysis revealed distinct metabolite perturbations as induced by cold storage in both types of cultivars wherein significantly affected metabolites were categorized mainly as sugars, sugar alcohols, amino acids, and organic acids. Significant metabolic perturbations were used to carry out metabolic pathway analysis that in turn tracked 130 genes encoding enzymes (involved directly and/or indirectly) involved in CIS pathway using potato genome sequence survey data. Based on the metabolite perturbations, the possible relevant metabolite biomarkers, significantly affected metabolic pathways, and key candidate genes responsible for the observed metabolite variation were identified. Overall, studies provided new insights in further manipulation of specific metabolites playing a crucial role in determining the cold-induced ability and processing quality of potato cultivars for improved quality traits. Highlight Metabolomic profiling using 1D 1 H-NMR and bioinformatics analysis of potato cultivars for the identification of metabolites and genes controlling biochemical pathways in cold-stored potato tubers
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".