Evidence map›Paper›PMID 41194238›Full record

ArticleParasites & vectors2025

Simultaneous population genomics of hosts and their parasites with selective whole genome amplification.

Vincenzo A Ellis, Angela Theodosopoulos, Ishika Sharma, Amélie Bardil, Martin Stjernman, Olof Hellgren

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Article in Parasites & vectors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Vincenzo A EllisDepartment of Entomology and Wildlife Ecology, University of Delaware, Newark, DE, USA. vaellis@udel.edu.
Angela TheodosopoulosDepartment of Biology, Lund University, Lund, Sweden.
Ishika SharmaDepartment of Entomology and Wildlife Ecology, University of Delaware, Newark, DE, USA.
Amélie BardilCEFE, CNRS, EPHE, University Montpellier, Montpellier, IRD, France.
Martin StjernmanDepartment of Biology, Lund University, Lund, Sweden.
Olof HellgrenDepartment of Biology, Lund University, Lund, Sweden. olof.hellgren@biol.lu.se.

Funding

Predictive Modeling & Optimal Control Framework for Model-Based Epidemic Response in DelawareP20GM103446 · NIGMS · UNIVERSITY OF DELAWARE · PI MELINDA K DUNCAN · 2012 to 2026
$67.2M
Data Management and Storage System for Shared Resource FacilitiesS10OD028725 · OD · UNIVERSITY OF DELAWARE · PI POLSON, SHAWN W · 2021 to 2021
$600k
NIGMS NIH HHS P20 GM103446NIH HHS NIH/NIGMS P20 GM103446, S10 OD028725NIH HHS S10 OD028725Swedish Research Council VR 2021-03663USDA Hatch DEL00854, NE1943, NE2443
6 · The paper itself

Abstract

backgroundGenerating parasite genomes is challenging when little of the DNA in infected host tissue is from the parasite. We used selective whole genome amplification (SWGA) to generate genomic data from wildlife samples of the avian haemosporidian Haemoproteus majoris (lineage PARUS1) and its host, the blue tit (Cyanistes caeruleus).

methodsWe used SWGA to amplify the parasite DNA in nine avian blood samples collected between 1996 and 2021, and subsequently performed short-read sequencing and bioinformatically separated the host and parasite reads in each sample.

resultsSWGA increased the percentage of parasite reads significantly. Sequencing to a depth of about 56 million reads (forward and reverse) per sample resulted on average (± standard error [SE]) in 11.3X ± 1.85 for the host genome and 1.17X ± 0.446 mean depth of coverage for the host and parasite, respectively, after SWGA. Furthermore, about 74% of the host genome (genome size approx. 1.2 Gb) and 33% of the parasite genome (approx. 23.9 Mb) had at least 1X coverage on average; two samples had 1X coverage of approximately 60% of the parasite genome. Parasite sequencing success was positively correlated with parasitemia. When comparing the parasite sequences in the four best samples, we identified 9895 sites (minimum 5X coverage) that varied among the infections. When filtering the full dataset to at least six samples per variant, we identified 14,512,339 and 7068 sites that varied among samples in the host and parasite populations, respectively, revealing variation among samples and years.

conclusionsSWGA facilitates dual host-parasite population genomics in this system and will greatly expand our understanding of host-parasite interactions over space and time.

Indexed as

Bird DiseasesGenome, ProtozoanHaemosporidaHost-Parasite InteractionsPasseriformesProtozoan Infections, AnimalAnimalsDNA, ProtozoanGenomicsMetagenomicsDNA, ProtozoanAvian malariaCoevolutionHost genomicsParasite evolutionPathogen genomics

Identifiers

PMID41194238
PMCPMC12587599

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.