Human Host Cellular Response to HCoV-229E Infection Multi-Omics (ACS-JM-DP2)

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Created on updated on 2024-10-01T21:34:23+00:00 by LN Anderson; Last updated 2024-11-13T15:01:44+00:00

Human Host Cellular Response to HCoV-229E Infection Proteomics (ACS-JM-DP2)

The purpose of this experiment was to evaluate the human host cellular response to wild-type Human coronavirus strain 229E (HCoV-229E) infection. Sample data was obtained for mock and infected (MOI 3) immortalized human lung epithelial cell (A549) nuclear extracts, immortalized human lung fibroblasts cells (MRC5) nuclear extracts, and primary human airway epithelial (HAE) cells from lung tissue and processed for proteome and transcriptome expression analysis.

Accessible Digital Data Downloads (pending)*

Processed datasets are openly accessible from the download button and contain secondary processed proteomic and transcriptomic results files and supporting metadata materials. Experimental proteomics samples were prepared using Limited Proteolysis (LiP) methods for Label-free quantification (LFQ) and global proteomic evaluation. Sample data was acquired using a Q-Exactive HF-X mass spectrometer and was processed and compiled using MaxQuant software (v.1.6.17.0). Processed proteomic data downloads include a sample naming key, processed MaxQuant results/parameters, and protein annotated relative abundance files. Experimental transcriptomics samples were collected in parallel with proteomics samples and processed for RNA sequencing (RNA-Seq). Sample data was acquired using a Illumina HiSeq 4000 sequencer system and further processed for RNA-Seq expression analysis. Processed RNA-Seq data downloads include a sample naming key, infection titer metadata, normalized counts, and relevant computational source code information. See corresponding primary data accessions below and LiP post-processing source code supporting data transparency and reuse.

*Corresponding primary publication pending.

Linked Primary Data

Primary liquid chromatography-mass spectrometry (LC-MS) raw measurement data are openly accessible for download at the Mass Spectrometry Interactive Virtual Environment (MassIVE) community repository under the accession MSV000096268 and can be formally cited using the MassIVE registered digital object identifier at https://doi.org/10.25345/C5XP6VF83. Primary RNA-Seq raw measurement data are openly accessible for download at the Gene Expression Omnibus (GEO) community repository under the accession GSE279463.

 

Funding Acknowledgments

The research data described here was funded in whole or in part by the Predictive Phenomics Initiative (PPI) at Pacific Northwest National Laboratory (PNNL). This work was conducted under the Laboratory Directed Research and Development Program at PNNL. A portion of this research was performed in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the U.S. Department of Energy (DOE) Office of Science located at PNNL. PNNL is a multiprogram national laboratory operated by Battelle for the DOE under Contract No. DE-AC05-76RL01830.

Citation Policy

In efforts to enable discovery, reproducibility, and reuse of PPI-funded project dataset citations in accordance with best practices (as outlined by the FORCE11 Data Citation Principles), we ask that all reuse of project data and metadata download materials acknowledge all primary and secondary dataset citations and corresponding journal articles where applicable.

Data Licensing

CC BY 4.0 (dataset DOI downloads), CC0 1.0 (PNNL DataHub policy default)

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Amy Sims, PhD is a Biomedical Scientist in the Chemical and Biological Signatures Division of the National Security Directorate at the Pacific Northwest National Laboratory (PNNL) in Richland, WA. She earned her Ph.D. from Vanderbilt University Medical Center and worked with Ralph Baric, PhD at the...

John is an accomplished lipid biochemist and structural biologist with an interest in understanding molecular pathology of disease. He earned his Ph.D. from Wake Forest School of Medicine where he received training in lipid biochemistry under the late Dr. Lawrence Rudel studying the role of low...

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...

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