NIAID Modeling Host Responses to Understand Severe Human Virus Infections Research Program Processed Data

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Description

Omics-Lethal Human Virus Computational Modeling & Omics Core Processed Data Collection

The National Institute of Allergy and Infectious Diseases (NIAID) "Modeling Host Responses to Understand Severe Human Virus Infections" (U19AI106772) program project was a comprehensive and interactive systems core, previously funded under the NIH from 2013-06-01 to 2018-05-31. Project data outcomes aim to enhance predictive modeling of infectious disease for identifying critical regulators of severe human virus pathogenicity that may be exploited for the development of therapeutic interventions.

Core sub-projects from the Pacific Northwest National Laboratory included the Proteomics, Metabolomics, and Lipidomics Acquisition and Computational Modeling Core (CMC) research activities applied world-class mass spectrometry (MS)-based platforms and unique capabilities to collection and analyze experimental transcriptomic (mRNA & miRNA), proteomic, metabolomic and lipidomic data from multiple host tissue systems over the time course of infection in both human and animal model systems. Identifying genes, pathways, and the regulatory networks driving the host response, this project both developed and validated predictive models of infectious disease initiation, progression and outcomes contributing to virus pathogenicity.

Using a highly integrated systems biology approach, secondary processed dataset download collections and molecular metadata profiles provided here, serve to enable new mechanistic insights into host-pathogen interactions and aid the future of biohazard data preparedness efforts to combat global health concerns attributed to lethal human viral infections. 

Available Normalized Multi-Omic Data (DOI Catalog Collections)

PML and CMC Core dataset downloads contain one or more statistically normalized integrated secondary dataset generated from a combination of “omics” technologies comprised of various infection studies from human subjects, including primary host cells, or in vivo experiments from animal models relevant to the human host response to infectious disease. Processed time course data files related to a transcriptomic, proteomic, metabolomic, and/or lipidomic experimental study associated with an Ebola virus (EBOV), Influenza A virus (lAV), West Nile virus (WNV), Middle Eastern Respiratory Syndrome coronavirus (MERS-CoV), and/or host interferon (IFN) response to viral infection. 

 

Reusable FAIRsharing Standard Selections

Primary Data Archive

Mass Spectrometry (proteomics, metabolomics, lipidomics)

Sequencing (transcriptomics, microarray)

Secondary Data Archive

Integrated Omics (transcriptomics, proteomics, metabolomics, lipidomics + experimental metadata)

Software Citation Archive 

 

Linked Open Data

Primary Sequencing Quantification Data

Primary transcriptome experimental data files (mRNA, miRNA) and associated metadata for both Agilent and Affymetrix microarray experiments are available at the NCBI Gene Expression Omnibus (GEO) under the BioProject Umbrella PRJNA274402 (37 Homo sapiens, 23 Mus musculus) corresponding the GEO dataset SuperSeries collection GSE65575 (2,480 samples).

Primary Mass Spectrometry Quantification Data

Experimental mass spectrometry quantification of ~70 data files and associated parameter files (.raw and .mzXML) including those used for accurate mass and time (AMT) tag database generation have been uploaded to the and MassIVE data repository corresponding to aggregated collections provided a the PRIDE database.

 

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. 

Cite as:

  1. Anderson, Lindsey N, McDermott, Jason E, Waters, Katrina M, & Eisfeld, Amie. 2021. "PNNL DataHub Project Omics-LHV Profiling of Host Response to Ebola Infection Post-Processed Data Package DOIs". United States. doi: 10.25584/LHVEBOV/1784282. url: https://www.osti.gov/biblio/1784282.
  2. Anderson, Lindsey N, McDermott, Jason E, Waters, Katrina M, & Eisfeld, Amie. 2021. "PNNL DataHub Project Omics-LHV Profiling of Host Response to Influenza Infection Post-Processed Data Package DOIs". United States. doi: 10.25584/LHVFLU/1773428. url: https://www.osti.gov/biblio/1773428.
  3. Anderson, Lindsey N, McDermott, Jason E, Waters, Katrina M, & Eisfeld, Amie. 2021. "PNNL DataHub Project Omics-LHV Profiling of Host Response to West Nile Infection Post-Processed Data Package DOIs". United States. doi: 10.25584/LHVWNV/1784305. url: https://www.osti.gov/biblio/1784305.
  4. Anderson, Lindsey N, McDermott, Jason E, Waters, Katrina M, & Eisfeld, Amie. 2021. "PNNL DataHub Project Omics-LHV Profiling of Host Interferon-Stimulated Response to Virus Infection Post-Processed Data Package DOIs". United States. doi: 10.25584/LHVIFN/1786979. url: https://data.pnnl.gov/group/nodes/project/13060.
  5. Anderson, Lindsey N, McDermott, Jason E, Waters, Katrina M, & Eisfeld, Amie. 2021. "PNNL DataHub Project Omics-LHV Profiling of Host Response to Middle Eastern Respiratory Syndrome coronavirus Infection Post-Processed Data Package DOIs". United States. doi: 10.25584/LHVMERS/1813911. url: https://data.pnnl.gov/group/nodes/project/13061.
  6. Kelly G. Stratton & Lisa M. Bramer. (2018). pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data (0.10.0). Zenodo. doi: 10.5281/zenodo.6108668. url: https://github.com/pmartR/pmartR/tree/v1.0.0
  7. Matthew Monroe, Cameron Casey, Grant Fujimoto, Christopher Wilkins, Joon-Yong Lee, Cameron Giberson, & Michael Degan. (2022). LIQUID: an-open source software for identifying lipids in LC-MS/MS-based lipidomics data. doi: 10.5281/zenodo.6459463. url: https://github.com/PNNL-Comp-Mass-Spec/LIQUID

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English
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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...

Tom Metz is a Principal Investigator within the Integrative Omics group at PNNL and the Metabolomics Team Lead for a group of scientists that focuses on development and applications of high throughput metabolomics and lipidomics methods to various biological questions. He has worked to develop state...

Dr. Jason McDermott, senior research scientist, has extensive research experience in molecular and structural virology and data resource design, data integration and prediction of biological networks, bridging experimental and computational biology. Currently, his research interests include data...

Data Sources (4)
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Software (1)