Evidence map›Paper›PMID 41271835›Full record

ArticleScientific data2025

Adaptive immune response to West Nile virus infection in the Collaborative Cross mouse model: A database of cellular phenotypes and Quantitative Trait Loci.

Steven Chamberlin, Jessica Graham, Sophia Jeng, Jessica Swarts, Martin Ferris, Mark Heise, Ralph S Baric, Shannon McWeeney, Jennifer Lund, Michael Mooney

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Steven ChamberlinDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, 97239, USA. chambest@ohsu.edu.
Jessica GrahamVaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Sophia JengKnight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239, USA.
Jessica SwartsVaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Martin FerrisDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Mark HeiseDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID 0000-0001-5829-8477
Ralph S BaricDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7435, USA.ORCID 0000-0001-6827-8701
Shannon McWeeneyDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, 97239, USA.ORCID 0000-0001-8333-6607
Jennifer LundVaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.ORCID 0000-0003-3284-979X
Michael MooneyDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, 97239, USA.

Funding

Unlocking Zika Virus Immune Control and Pathogenesis with the Collaborative CrossU19AI100625 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI PARDO-MANUEL DE VILLENA, FERNANDO · 2012 to 2021
$36.6M
Network models to capture multiple outcomes resulting from multi-component, salutogenic interventionsK08AT012477 · NCCIH · OREGON HEALTH & SCIENCE UNIVERSITY · PI Steven Chamberlin · 2023 to 2026
$621k
NCCIH NIH HHS K08 AT012477NIAID NIH HHS U19 AI100625U.S. Department of Health & Human Services | National Institutes of Health (NIH) U19AI100625
6 · The paper itself

Abstract

West Nile virus (WNV) infection can lead to a wide range of clinical outcomes, from asymptomatic to self-limiting febrile and serious neuro-invasive disease. Knowledge of the genetic factors contributing to the heterogeneity of this disease can inform pathogenic mechanisms, help guidethe development of cell based therapeutics, vaccines and inform lifestyle choices. Yet this knowledge is incomplete in humans. Here we present data from a large-scale experiment aimed at identifying quantitative trait loci (QTL) associated with adaptive immune cellular phenotypes observed in response to infection with WNV. This data was generated using the Collaborative Cross (CC) mouse model, previously demonstrated as a representative model for human WNV infection and homeostatic immune states. In addition, due to challenges of QTL mapping with the large number of intermediate, highly coordinated immune and virologic phenotypes, we also provide a computational pipeline for the prioritization of gene candidates designed to leverage those characteristics for downstream mechanistic studies.

Indexed as

Adaptive ImmunityQuantitative Trait LociWest Nile FeverAnimalsCollaborative Cross MiceDisease Models, AnimalMicePhenotypeWest Nile virus

Identifiers

PMID41271835
PMCPMC12749056

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.