Environmental DNA metabarcoding to detect pathogenic Leptospira and associated organisms in leptospirosis-endemic areas of Japan
Bibliographic record
Abstract
Leptospires, which cause the zoonotic disease leptospirosis, persist in soil and aqueous environments. Several factors, including rainfall, the presence of reservoir animals, and various abiotic and biotic components interact to influence leptospiral survival, persistence, and pathogenicity in the environment. However, how these factors modulate the risk of infection is poorly understood. Here we developed an approach using environmental DNA (eDNA) metabarcoding for detecting the microbiome, vertebrates, and pathogenic Leptospira in aquatic samples. Specifically, we combined 4 sets of primers to generate PCR products for high-throughput sequencing of multiple amplicons through next-generation sequencing. Using our method to analyze the eDNA of leptospirosis-endemic areas in northern Okinawa, Japan, we found that the microbiota in each river shifted over time. Operating taxonomic units corresponding to pathogenic L. alstonii, L. kmetyi, and L. interrogans were detected in association with 12 nonpathogenic bacterial species. In addition, the frequencies of 11 of these species correlated with the amount of rainfall. Furthermore, 10 vertebrate species, including Sus scrofa, Pteropus dasymallus, and Cynops ensicauda, showed high correlation with leptospiral eDNA detection. Our eDNA metabarcoding method is a powerful tool for understanding the environmental phase of Leptospira and predicting human infection risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".