Using deep amplicon sequencing as a molecular xenomonitoring approach for detecting filarial nematodes in biting arthropod vectors
Bibliographic record
Abstract
Abstract Filarial nematodes are an important group of parasites that impact public, veterinary, and wildlife health globally. In order to understand these impacts and minimize their effects, scientists use molecular xenomonitoring techniques to understand their distribution and track elimination efforts. However, these molecular techniques can have narrow diagnostic capacity due to their species-specific approach which limits our understanding of important co-endemic filarial nematodes. Next-generation sequencing offers the ability to detect multiple species of coinfecting filarial nematodes and thus improve are ability to monitor, treat, and eliminate these pathogens. In this paper, we have developed a deep amplicon sequencing approach using filarial nematode primers targeting the cytochrome oxidase c subunit 1 ( coxI ) gene. To replicate molecular xenomonitoring conditions, third stage larvae (L3) of three species of filarioid nematodes ( Brugia malayi , Brugia pahangi , Dirofilaria immitis ) were spiked in different proportions to pools comprising various amounts of female Aedes aegypti mosquitoes (0, 10, 50, 100). Each pool was subjected to DNA extraction and Oxford Nanopore Technologies (ONT) deep amplicon sequencing protocols. Two sets of demultiplexing pipelines were utilized to optimize this novel approach, each reaching, 92.71% and 97.92% accuracy in identification of species composition across mock pools. However, in heterogenous pools, filarial species D. immitis exhibited an overrepresentation of reads and B. pahangi an underrepresentation of reads. We discuss reasons for recount biases and how this new molecular xenomonitoring tool could be implemented to serve public health, veterinary medicine, and scientific advancement. Note: Supplementary data associated with this article Author summary Human and animal diseases caused by filarial nematodes affect millions of people worldwide, particularly in low-income countries. These parasites are transmitted by blood-feeding arthropod vectors, such as mosquitoes and black flies. Thus, a major sector of public health research focuses on how to monitor, treat, and eliminate these harmful pathogens. An effective way is to capture these arthropod vectors and molecularly test these for filarial DNA in large sample pools. However, these pools can comprise multiple filarial species which targeted genetic analysis can miss. This proof-of-concept study seeks to circumvent these issues by using new next-generation sequencing approaches to capture the wider filarial diversity that may be contained in a single vector pool. We believe this tool could largely be beneficial to governments and organizations seeking to eliminate these filarial nematodes and become certified as a region free of certain devastating filarial species. Furthermore, we know very little about filarial diversity and this could be an integral tool to define their geographic distribution and future emerging threats to both human and animal health.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".