iTRAQ-Based Quantitative Proteomics Reveals <i>ChAcb1</i> as a Novel Virulence Factor in <i>Colletotrichum higginsianum</i>
Bibliographic record
Abstract
Colletotrichum higginsianum is an important hemibiotrophic fungal pathogen that causes anthracnose disease on various cruciferous plants. Discovery of new virulence factors could lead to strategies for effectively controlling anthracnose. Acyl-CoA binding proteins (ACBPs) are mainly involved in binding and trafficking acyl-CoA esters in eukaryotic cells. However, the functions of this important class of proteins in plant fungal pathogens remain unclear. In this study, we performed an isobaric tags for relative and absolute quantification (iTRAQ)-based quantitative proteomic analysis to identify differentially expressed proteins (DEPs) between a nonpathogenic mutant ΔCh-MEL1 and the wild type. Based on iTRAQ data, DEPs in the ΔCh-MEL1 mutant were mainly associated with melanin biosynthesis, carbohydrate and energy metabolism, lipid metabolism, redox processes, and amino acid metabolism. Proteomic analysis revealed that many DEPs might be involved in growth and pathogenesis of C. higginsianum. Among them, an acyl-CoA binding protein, ChAcb1, was selected for further functional studies. Deletion of ChAcb1 caused defects in vegetative growth and conidiation. ChAcb1 is also required for response to hyperosmotic and oxidative stresses, and maintenance of cell wall integrity. Importantly, the ΔChAcb1 mutant exhibited reduced virulence, and microscopic examination revealed that it was defective in appressorial penetration and infectious growth. Furthermore, the ΔChAcb1 mutant was impaired in fatty acid and lipid metabolism. Taken together, ChAcb1 was identified as a new virulence gene in this plant pathogenic fungus.
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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) | 0.001 | 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".