Genomic analysis of <i>Alternaria alternata</i>, the causal agent of black spot disease in Korla fragrant pears (<i>Pyrus sinkiangensis</i> Yü) in China
Bibliographic record
Abstract
Alternaria alternata is a pathogenic fungus that adversely affects fruit crops and causes substantial economic loss. A specific strain of A. alternata (A923) was successfully isolated and identified from Korla fragrant pears ( Pyrus sinkiangensis Yü). The ability of this strain to induce black spot disease in pears was verified. A combination of Illumina and PacBio Sequel sequencing was used to obtain the genome sequence for understanding the A923 strain. The assembly process revealed that the genome consists of 36 070 468 bp (36.07 Mb). Consequently, the analysis primarily focused on the genes associated with pathogenicity. These findings revealed the presence of 794 CAZymes, including 119 glycosyltransferases, 26 polysaccharide lyases, 153 carbohydrate esterases, 161 auxiliary activities, 19 carbohydrate-binding modules, and 316 glycoside hydrolases. A database of fungal virulence factors identified 1258 genes in strain A923. CUTI_ALTBR, a member of the cutinase family, is responsible for catalyzing the hydrolysis of cutin, a polyester that forms the structure of the plant cuticle. This enzymatic activity allows the pathogenic fungi to breach the cuticular barrier during the initial stages of infection. Cytochrome P450 family analysis successfully annotated 11 458 genes (97.63%). The results obtained in this study provide a useful resource for further investigation of this pathogen and its pathosystem.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".