Molecular Diagnostic Tools for Detection and Differentiation of Phytoplasmas Based on Chaperonin-60 Reveal Differences in Host Plant Infection Patterns
Bibliographic record
Abstract
Phytoplasmas ('Candidatus Phytoplasma' spp.) are insect-vectored bacteria that infect a wide variety of plants, including many agriculturally important species. The infections can cause devastating yield losses by inducing morphological changes that dramatically alter inflorescence development. Detection of phytoplasma infection typically utilizes sequences located within the 16S-23S rRNA-encoding locus, and these sequences are necessary for strain identification by currently accepted standards for phytoplasma classification. However, these methods can generate PCR products >1400 bp that are less divergent in sequence than protein-encoding genes, limiting strain resolution in certain cases. We describe a method for accessing the chaperonin-60 (cpn60) gene sequence from a diverse array of 'Ca.Phytoplasma' spp. Two degenerate primer sets were designed based on the known sequence diversity of cpn60 from 'Ca.Phytoplasma' spp. and used to amplify cpn60 gene fragments from various reference samples and infected plant tissues. Forty three cpn60 sequences were thereby determined. The cpn60 PCR-gel electrophoresis method was highly sensitive compared to 16S-23S-targeted PCR-gel electrophoresis. The topology of a phylogenetic tree generated using cpn60 sequences was congruent with that reported for 16S rRNA-encoding genes. The cpn60 sequences were used to design a hybridization array using oligonucleotide-coupled fluorescent microspheres, providing rapid diagnosis and typing of phytoplasma infections. The oligonucleotide-coupled fluorescent microsphere assay revealed samples that were infected simultaneously with two subtypes of phytoplasma. These tools were applied to show that two host plants, Brassica napus and Camelina sativa, displayed different phytoplasma infection patterns.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".