Towards Improved Detection and Identification of Rust Fungal Pathogens in Environmental Samples Using a Metabarcoding Approach
Bibliographic record
Abstract
The dispersion of fungal inocula such as the airborne spores of rust fungi (Pucciniales) can be monitored through metabarcoding of the internal transcribed spacer 2 (ITS2) of the rRNA gene in environmental DNAs. This method is largely dependent on a high-quality reference database (refDB) and primers with proper taxonomic coverage and specificity. For this study, a curated ITS2 reference database (named CR-ITS2-refDB) comprising representatives of the major cereal rust fungi and phylogenetically related species was compiled. Interspecific and intraspecific variation analyses suggested that the ITS2 region had reasonable discriminating power for the majority of the Puccinia species or species complexes in the database. In silico evaluation of nine forward and seven reverse ITS2 primers, including three newly designed, revealed marked variation in DNA amplification efficiency for the rusts. We validated the theoretical assessment of rust-enhanced (Rust2inv/ITS4var_H) and universal fungal (ITS9F/ITS4) ITS2 primer pairs by profiling the airborne rust fungal communities from environmental samples via a metabarcoding approach. Species- or subspecies-level identification of the rusts was improved by use of CR-ITS2-refDB and the Automated Oligonucleotide Design Pipeline (AODP), which identified all mutations distinguishing highly conserved DNA markers between close relatives. A generic bioinformatics pipeline was developed, including all steps used in this study from in silico evaluation of primers to accurate identification of short metabarcodes at the level of interest for defining phytopathogens. The results highlight the importance of primer selection, refDBs that are resolved to reflect phylogenetic relationships, and the use of AODP for improving the reliability of metabarcoding in phytopathogen biosurveillance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".