Differential Proteomic Analysis of the Resistant Soybean Infected by Soybean Cyst Nematode, Heterodera glycines Race 3
Bibliographic record
Abstract
Plant parasitic nematode, Heterodera glycines (Soybean cyst nematode, SCN) is the major pathogen of Glycine max (soybean). This pathogen is widely distributed, seriously disserving, and has a wide approach for spreading. Huipizhi Heidou (ZDD2315), a germplasm resource of soybean originated in China, has an excellent resistance to SCN. In this paper, two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS) were employed to separate the differentially expressed proteins from soybean induced by SCN. The F4 separated populations from the cross between the resistant Huipizhi Heidou and the susceptible Liaodou 15 were used as test materials by bulked segregant analysis. The 2-DE gels analysis revealed 367 protein spots from the resistant samples and 372 protein spots from the sensitive samples. Among those protein spots, 23 protein spots from the resistant samples and 4 protein spots from the sensitive samples were differentially expressed, and then were selected for peptide mass fingerprinting (PMF) and sequencing assay by MS. Of the 27 differentially expressed proteins, 16 protein spots were identified by MALDI-TOF-MS and 11 were not identified due to low scores. Further analysis showed that the majority of these 16 proteins were involved in defense, energy and metabolism, suggesting that they might be related to the soybean resistance to SCN.
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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".