Quantitative proteomic analysis for identification of phosphorous acid-responsive proteins
Bibliographic record
Abstract
Late blight is one of the most devastating diseases of potatoes (Solanum tuberosum L.). Phosphorous acid (PA; Confineᵀᴹ) was recently registered in Canada for late blight suppression in leaves and tubers. Field trials were conducted in Prince Edward Island between 2007 and 2009 to evaluate the efficacy of PA in foliar late blight control. Phosphorous acid is a relatively harmless chemical and the mechanism of increased resistance in plants treated with PA remains enigmatic. Evaluation of the infection rate revealed that plants treated with PA suppressed late blight infection. Proteomics is a powerful tool for investigating complex biological processes at the molecular level. To understand the plant response to the PA treatment, proteomic profiles of leaves from treated and untreated plants were collected. Since the cell wall and the cytosol are the battlefields for plants against the pathogen, identification of proteins in these subcellular locations is essential. Our earlier studies have confirmed that more than 60% of identical proteins could be obtained in replicated samples. In this study, 591 reproducible proteins in cytosolic fraction and 589 proteins in cell wall fraction were identified and their abundance was statistically calculated. Results showed that 38 proteins from the cytosolic fraction and 33 proteins from the cell wall fraction were up-regulated (mean fold change > 1.4). Biological processes for up-regulated proteins by PA are classified into response to stress, proteolysis, cell wall degradation, oxidative stress and signalling. Eighteen and 13 proteins in cytosolic and wall fractions, respectively, were down-regulated (mean fold change < 0.75). Approximately 60% of down-regulated proteins play roles in metabolic processes. These preliminary results provide us with some insight into the functions of PA for late blight resistance in potatoes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".