Analysis of Phaseolus vulgaris Response to Its Association with Trichoderma harzianum (ALL-42) in the Presence or Absence of the Phytopathogenic Fungi Rhizoctonia solani and Fusarium solani
Bibliographic record
Abstract
The present study was carried out to evaluate the ability of Trichoderma harzianum (ALL 42-isolated from Brazilian Cerrado soil) to promote common bean growth and to modulate its metabolism and defense response in the presence or absence of the phytopathogenic fungi Rhizoctonia solani and Fusarium solani using a proteomic approach. T. harzianum was able to promote common bean plants growth as shown by the increase in root/foliar areas and by size in comparison to plants grown in its absence. The interaction was shown to modulate the expression of defense-related genes (Glu1, pod3 and lox1) in roots of P. vulgaris. Proteomic maps constructed using roots and leaves of plants challenged or unchallenged by T. harzianum and phytopathogenic fungi showed differences. Reference gels presented differences in spot distribution (absence/presence) and relative volumes of common spots (up or down-regulation). Differential spots were identified by peptide fingerprinting MALDI-TOF mass spectrometry. A total of 48 identified spots (19 for leaves and 29 for roots) were grouped into protein functional classes. For leaves, 33%, 22% and 11% of the identified proteins were categorized as pertaining to the groups: metabolism, defense response and oxidative stress response, respectively. For roots, 17.2%, 24.1% and 10.3% of the identified proteins were categorized as pertaining to the groups: metabolism, defense response and oxidative stress response, respectively.
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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".