Human Host Cellular Response to HCoV-229E Infection Proteomics (ACS-JM-DP2) (in EN)
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 immortalized human lung epithelial cells (A549) (MOI 5) nuclear extracts, immortalized human lung fibroblasts cells (MRC5) (MOI5) nuclear extracts, and primary human airway epithelial (HAE) (MOI 3) cells from lung tissue and processed for proteome analysis. Processed datasets are openly accessible from the download button and contain secondary processed proteomic 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. See corresponding primary data accessions below and Viral Experiment LiP Analysis source code supporting data transparency and reuse. Experimental transcriptomics samples were collected in parallel and processed for RNA sequencing (RNA-Seq) as summarized under ACS-DP1 (https://data.pnnl.gov/group/nodes/dataset/34069).
- Research Organization:
- Pacific Northwest National Laboratory 2; PNNL
- Sponsoring Organization:
- SC
- Contributing Organization:
- Predictive Phenomics Initiative, Pacific Northwest National Laboratory
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 2475744
- Report Number(s):
- PNNL-SA-204366; 34073
- Availability:
- rc-support@pnnl.gov
- Country of Publication:
- United States
- Language:
- EN
Predictive Phenomics Initiative Project Dataset Catalog Collection
|
dataset | January 2024 |
| MassIVE MSV000096268 - Human coronavirus-229E infection alters the structure of host RNA processing complexes | dataset | January 2024 |
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