Metagenomic detection of protozoan parasites on leafy greens aided by a rapid and efficient DNA extraction protocol
Bibliographic record
Abstract
Introduction Infections with protozoan parasites associated with the consumption of fresh produce is an on-going issue in developed countries but mitigating the risk is hampered by the lack of adequate methods for their detection and identification. Materials and methods We developed a metagenomic next-generation sequencing (mNGS) assay using a MinION sequencer for the identification of parasites in intentionally contaminated lettuce to achieve a more accurate and rapid method than the traditional molecular and microscopy methods commonly used for regulatory purposes. Lettuce (25 g) was spiked with varying numbers of Cryptosporidium parvum oocysts, and microbes washed from the surface of the lettuce were lysed using the OmniLyse device. DNA was then extracted by acetate precipitation, followed by whole genome amplification. The amplified DNA was sequenced by nanopore technology and validated with the Ion Gene Studio S5, and the generated fastq files raw reads were uploaded to the CosmosID webserver for the bioinformatic identification of microbes in the metagenome. To demonstrate the ability of the procedure to distinguish other common food and waterborne protozoan parasites, lettuce was also spiked with C. hominis, C. muris, Giardia duodenalis and Toxoplasma gondii individually or together. Results The efficient lysis of oocysts and cysts was a prerequisite for the sensitive detection of parasite DNA and was rapidly achieved within 3 min. Amplification of extracted DNA led to the generation of 0.16–8.25 μg of DNA (median = 4.10 μg), sufficient to perform mNGS. Nanopore sequencing followed by bioinformatic analysis led to the consistent identification of as few as 100 oocysts of C. parvum in 25 g of fresh lettuce. Similar results were obtained using the Ion S5 sequencing platform. The assay proved useful for the simultaneous detection of C. parvum, C. hominis, C. muris, G. duodenalis and T. gondii. Discussion Our metagenomic procedure led to the identification of C. parvum present on lettuce at low numbers and successfully identified and differentiated other protozoa either of the same genus or of different genera. This novel mNGS assay has the potential for application as a single universal test for the detection of foodborne parasites, and the subtyping of parasites for foodborne outbreak investigations and surveillance studies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".