Wounding, insect chewing and phloem sap feeding differentially alter the leaf proteome of potato, Solanum tuberosum L.
Bibliographic record
Abstract
BACKGROUND: Various factors shape the response of plants to herbivorous insects, including wounding patterns, specific chemical effectors and feeding habits of the attacking herbivore. Here we performed a comparative proteomic analysis of the plant's response to wounding and herbivory, using as a model potato plants (Solanum tuberosum L.) subjected to mechanical wounding, defoliation by the Colorado potato beetle Leptinotarsa decemlineata Say, or phloem sap feeding by the potato aphid Macrosiphum euphorbiae Thomas. RESULTS: Out of ~500 leaf proteins monitored by two-dimensional gel electrophoresis (2-DE), 31 were up- or downregulated by at least one stress treatment compared to healthy control plants. Of these proteins, 29 were regulated by beetle chewing, 8 by wounding and 8 by aphid feeding. Some proteins were up- or downregulated by two different treatments, while others showed diverging expression patterns in response to different treatments. A number of modulated proteins identified by mass spectrometry were typical defense proteins, including wound-inducible protease inhibitors and pathogenesis-related proteins. Proteins involved in photosynthesis were also modulated, notably by potato beetle feeding inducing a strong decrease of some photosystem I proteins. Quantitative RT PCR assays were performed with nucleotide primers for photosynthesis-related proteins to assess the impact of wounding and herbivory at the gene level. Whereas different, sometimes divergent, responses were observed at the proteome level in response to wounding and potato beetle feeding, downregulating effects were systematically observed for both treatments at the transcriptional level. CONCLUSIONS: These observations illustrate the differential impacts of wounding and insect herbivory on defense- and photosynthesis-related components of the potato leaf proteome, likely associated with the perception of distinct physical and chemical cues in planta.
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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".