Omics-Lethal Human Viruses, West Nile Experiment WCB001

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Last updated on 2023-01-30T00:09:57+00:00 by LN Anderson

West Nile Virus Experiment WCB001

The purpose of this experiment was to evaluate the host response to wild-type West Nile virus (WNV-NY99 382) and mutant WNV-E218A (WNV-NY99 382 E218A 2 nt) virus infection. Sample data was obtained from mouse cerebellum tissue and processed for mRNA, miRNA, proteomics, metabolomics, and lipidomics expression analysis. Tissues from this experiment were matched to tissues from GSE77193 (WCT001), using 1/4 of the cortex and 1/2 of cerebellum from the same mice.

Secondary host-associated viral dataset downloads contain one or more statistically processed (normalization data transformation) quantitative dataset collections resulting in qualitative expression analyses of primary host-pathogen experimental study designs. Leveraging unique high-resolution Omics capabilities for proteomics, metabolomics, lipidomics, and transcriptomics dataset downloads each have a direct relationship to a primary sample submission corresponding to a specific West Nile virus infection.

Accessible Digital Data Downloads


  1. Expression profiling by array (mRNA) 
  2. Non-coding RNA profiling by array (miRNA)

Proteomics, Metabolomics, Lipidomics

  1. Protein quantification by liquid chromatography mass spectrometry (LC-MS)
  2. Metabolite quantification by gas chromatography mass spectrometry (GC-MS)
  3. Lipid quantification by liquid chromatography mass spectrometry (LC-MS)

Reference Citation

Anderson, Lindsey N, Eisfeld, Amie J, Waters, Katrina M, and Modeling Host Responses to Understand Severe Human Virus Infections Program Project. Omics-Lethal Human Viruses, West Nile Experiment WCB001. United States. 2021. PNNL DataHub (Web). DOI: 10.25584/LHVWCB001/1661948

Linked Primary Data Accessions

NCBI BioProject(s): PRJNA309683 (mRNA), PRJNA309681 (miRNA)

GEO Series:  GSE77192 (mRNA transcriptome response), GSE77160 (miRNA transcriptome response)

MassIVE Accession(s):  MSV000080360 (proteome response), MSV000080020 (metabolome response), MSV000081052 (lipidome response)


Acknowledgment of Federal Funding

The data described here was funded in whole or in part by the National Institute of Allergy and Infectious Diseases, of the National Institutes of Health under award number U19AI106772 and is a contribution of the "Modeling Host Responses to Understand Severe Human Virus Infections" Project at Pacific Northwest National Laboratory. Data generated by the Omics-LHV Core for proteomics, metabolomics, and lipidomics analyses for were performed at Pacific Northwest National Laboratory in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the Department of Energy’s (DOE) Office, operating under the Battelle Memorial Institute for the DOE under contract number DE-AC05-76RLO1830. 

Citation Policy

In efforts to enable discovery, reproducibility, and reuse of NIH-funded project dataset citations, we ask that all reuse of project data and metadata download materials acknowledge all primary and secondary dataset citations where applicable and direct corresponding journal articles (Grant U19AI106772) where allowable in accordance with best practices outlined by the FORCE11 Joint Declaration of Data Citation Principles in alignment with NIH acknowledgement requirements.

Data Licensing

CC BY-SA 4.0 (secondary dataset download DOIs), CC0 1.0 (project metadata and PNNL DataHub policy default)

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Lindsey Anderson’s research has been dedicated to the identification and characterization of novel, targeted and non-targeted, functional metabolic interactions using a high-throughput systems biology and computational biology approach. Her expertise in functional metabolism and multidisciplinary...

Dr. Katrina Waters is the division director for Biological Sciences at the Pacific Northwest National Laboratory. Waters has a Ph.D. in biochemistry and more than 15 years of experience in microarray and proteomics data analysis. Her research interests are focused on the integration of genomics...

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