Relative quantitative proteomic analysis reveals wound response proteins correlated with after‐cooking darkening
Bibliographic record
Abstract
Many common potato tuber defects are difficult to elucidate because of the degree of genetic complexity involved, making systems biology approaches necessary. Interaction between chlorogenic acid and iron is responsible for the darkening of potato tuber tissues upon heating--termed after-cooking darkening (ACD). To explore mechanisms of darkening severity in tuber tissues, we have employed relative quantitative proteomics to discover differentially expressed proteins involved in ACD. Tuber tissue samples were collected from a family of diploid clones which possess a highly segregated degree of the darkening. Exploiting this segregation, as well as the observation that darkening is more prevalent in the stem end of the tuber than the apical end, three sample groups were formed: (i) stem ends of three high-ACD clones, (ii) stem ends of three low-ACD clones, and (iii) apical ends of three low-ACD clones. Protein samples were digested and differentially labeled using isotopic reductive methylation, allowing for an orthogonal two-way comparison of protein profiles of the sample groups using 2-D-LC-MS/MS. Using a cutoff fold change of 2 between the high- and the low-ACD sample groups, 30 proteins showed a correlation with tissue darkening. Overall, we observed changes in relative protein abundance that showed an enhanced wound-response program in high-ACD tissues. Among these proteins, five proteins were further validated at the transcript level using qRT-PCR. These proteins may be incorporated into design strategies to create potato cultivars with low levels of ACD.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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".