A Rapid, Simple, Laboratory and Field-Adaptable DNA Extraction and Diagnostic Method Suitable for Insect-Transmitted Plant Pathogen and Insect Identification
Bibliographic record
Abstract
Watch a presentation about this research by first author Karolina Pusz-Bochenska. Surveillance for insect-transmitted pathogens of plants involves sampling insects in the field, followed by transport of the samples to the laboratory for DNA extraction and molecular analysis. Sample transport and DNA extraction are time consuming and can delay the implementation of measures to mitigate the effects of insect-transmitted plant pathogens. Looking for a fast and reliable method to extract DNA in the field where insects were collected, we used Flinders Technology Associates PlantSaver cards, which are designed for plant DNA extraction. Insect DNA extraction can be achieved in the field, and extracted DNA can be amplified using the field-adaptable method, loop-mediated isothermal amplification (LAMP), in less than 1 h. Additionally, we demonstrate the feasibility and accuracy of the paper extraction method for molecular identification to the species level using mitochondrial cytochrome oxidase 1 amplification and sequencing on 11 genera of insects including beetles, leafhoppers, flies, psyllids, and aphids. The method was suitable for insects collected using three common methods: live-trapped and frozen, stored in ethanol, or trapped on sticky cards. Moreover, by testing leafhoppers collected in 2018 in the field, we demonstrated that the LAMP assay using the chaperonin-60 target detects a higher proportion of samples positive for ‘Candidatus Phytoplasma asteris’ than conventional PCR targeting 16S rRNA. Lastly, the paper extraction method was used to determine the prevalence of leafhoppers carrying the plant pathogenic bacterium ‘Ca. P. asteris’, which causes aster yellows from a laboratory-reared colony using PCR-based tests (conventional PCR, qPCR, and droplet digital PCR) and the non-PCR-based LAMP assay.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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".