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Helminths are parasites that cause disease at considerable cost to public health and present a risk for emergence as novel human infections. Although recent research has elucidated characteristics conferring a propensity to emergence in other parasite groups (e.g. viruses), the understanding of factors associated with zoonotic potential in helminths remains poor. We applied an investigator-directed learning algorithm to a global dataset of mammal helminth traits to identify factors contributing to spillover of helminths from wild animal hosts into humans. We characterized parasite traits that distinguish between zoonotic and non-zoonotic species with 91% accuracy. Results suggest that helminth traits relating to transmission (e.g. definitive and intermediate hosts) and geography (e.g. distribution) are more important to discriminating zoonotic from non-zoonotic species than morphological or epidemiological traits. Whether or not a helminth causes infection in companion animals (cats and dogs) is the most important predictor of propensity to cause human infection. Finally, we identified helminth species with high modelled propensity to cause zoonosis (over 70%) that have not previously been considered to be of risk. This work highlights the importance of prioritizing studies on the transmission of helminths that infect pets and points to the risks incurred by close associations with these animals. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.
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Needle in a Haystack: A Droplet Digital Polymerase Chain Reaction Assay to Detect Rare Helminth Parasites Infecting Natural Host Populations
ABSTRACT Helminths infect humans, livestock, and wildlife, yet remain understudied despite their significant impact on public health and agriculture. Because many of the most prevalent helminth‐borne diseases are zoonotic, understanding helminth transmission among wildlife could improve predictions and management of infection risks across species. A key challenge to understanding helminth transmission dynamics in wildlife is accurately and quantitatively tracking parasite load across hosts and environments. Traditional methods, such as visual parasite identification from environmental samples or infected hosts, are time‐consuming, while standard molecular techniques (e.g., PCR and qPCR) often lack the sensitivity to reliably detect lower parasite burdens. These limitations can underestimate the prevalence and severity of infection, hindering efforts to manage infectious diseases. Here, we developed a multiplexed droplet digital PCR (ddPCR) assay to quantify helminth loads in aquatic habitats using 18S rRNA target genes. UsingSchistocephalus solidusand their copepod hosts as a case study, we demonstrate ddPCR's sensitivity and precision. The assay is highly reproducible, reliably detecting target genes at concentrations as low as 1 pg of DNA in lab standards and field samples (multi‐species and eDNA). Thus, we provide a toolkit for quantifying parasite load in intermediate hosts and monitoring infection dynamics across spatio‐temporal scales in multiple helminth systems of concern for public health, agriculture, and conservation biology.
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- Award ID(s):
- 2243076
- PAR ID:
- 10600207
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Molecular Ecology Resources
- Volume:
- 25
- Issue:
- 7
- ISSN:
- 1755-098X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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