Host whole genome sequence data represent an untapped resource for characterising affiliated parasite diversity

Area of Science:

• Parasitology
• Genomics
• Disease Ecology

Background:

• Parasites are ecologically and evolutionarily significant, but their characterization is often challenging and expensive.
• High-throughput sequencing (HTS) generates abundant whole genome sequence (WGS) data, often available in public repositories.
• Existing WGS data represents an underutilized resource for identifying host-associated parasites.

Purpose of the Study:

• To investigate the utility of mining existing WGS data for endogenous eukaryotic parasites.
• To compare parasite detection in WGS data with 18S metabarcoding, microscopy, and PCR.
• To establish a framework for leveraging WGS data in host-parasite interaction studies.

Main Methods:

• Analysis of WGS data from the silvereye (Zosterops lateralis) to detect inadvertently captured parasite DNA.
• Comparison of WGS parasite detection with 18S metabarcoding.
• Validation using traditional microscopy of blood smears and targeted multiplex Polymerase Chain Reaction (PCR) for haemosporidian parasites.

Main Results:

• Mining WGS data identified the broadest range of parasite genera.
• Detection of haemosporidian parasites was consistent across microscopy, multiplex PCR, and WGS data.
• 18S metabarcoding failed to detect haemosporidian parasites.

Conclusions:

• Existing WGS datasets are a valuable, cost-effective resource for estimating endoparasite diversity.
• WGS data mining provides greater insights into parasite diversity compared to metabarcoding.
• Repurposing WGS data offers a powerful tool for studying host-parasite interactions and disease ecology.