Proteomic profiling of two maize inbreds during early gibberella ear rot infection
Bibliographic record
Abstract
Fusarium graminearum is the causal agent of gibberella ear rot in maize ears, resulting in yield losses due to mouldy and mycotoxin-contaminated grain. This study represents a global proteomic approach to document the early infection by F. graminearum of two maize inbreds, B73 and CO441, which differ in disease susceptibility. Mock- and F. graminearum-treated developing kernels were sampled 48 h post-inoculation over three field seasons. Infected B73 kernels consistently contained higher concentrations of the mycotoxin deoxynivalenol than the kernels of the more tolerant inbred CO441. A total of 2067 maize proteins were identified in the iTRAQ analysis of extracted kernel proteins at a 99% confidence level. A subset of 878 proteins was identified in at least two biological replicates and exhibited statistically significantly altered expression between treatments and/or the two inbred lines of which 96 proteins exhibited changes in abundance >1.5-fold in at least one of the treatments. Many proteins associated with the defense response were more abundant after infection, including PR-10 (PR, pathogenesis-related), chitinases, xylanase inhibitors, proteinase inhibitors, and a class III peroxidase. Kernels of the tolerant inbred CO441 contained higher levels of these defense-related proteins than B73 kernels even after mock treatment, suggesting that these proteins may provide a basal defense against Fusarium infection in CO441.
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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".