PXD029376-1

PXD029376 is an original dataset announced via ProteomeXchange.

Title	Large-scale multi-omics analysis identifies novel early markers of COVID-19 severity
Description	Matched samples from individuals before they contracted COVID-19 and after they were diagnosed with it were used for TMT-based relative quantitation of their plasma proteome and glycoproteome to study the effects of this infectious disease. Twenty one COVID-19 patients whose pre-COVID-19 plasma samples were also available were selected for this study. These patients had varied courses of illness and were classified based on WHO guidelines into outpatients and those with severe and critical illness. 21 matched sample pairs were divided into 3 sets for 3 TMTPro 16-plex-based mass spectrometry experiments with 8 (set01), 8 (set02), and 5 (set03) patients each. Plasma-derived tryptic peptides from each sample were TMT-labeled, and each pooled set was used for separate experiments for total proteomics and glycoproteomics. A pooled aliquot from each set was used to enrich glycopeptides by size exclusion chromatography and another aliquot was used to fractionate all peptides by basic pH reversed phase liquid chromatography. Enriched glycopeptides were analyzed by LC-MS/MS and quantified across samples using TMT reporter ion intensities. Fold changes (intensity of protein or glycopeptide from a patient with COVID-19/that from the same patient before they had COVID-19) for each protein and glycopeptide were calculated for all patients to assess changes in the proteome that may be attributable to this illness. We detected 1,520 proteins, of which 472 were detected in all patients. 3,892 glycopeptides were identified at 1% FDR at peptide, glycan and glycopeptides levels and their reporter ion intensities were quantified. 732 glycopeptides from 232 glycoproteins were detected in all patients.
HostingRepository	PRIDE
AnnounceDate	2022-08-12
AnnouncementXML	Submission_2022-08-12_00:46:08.442.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Akhilesh Pandey
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	modifications with monoisotopic mass differences that are nominally equal at 304.199-304.206 Da; monohydroxylated residue; complex glycosylation; iodoacetamide derivatized residue
Instrument	Orbitrap Eclipse

Revision	Datetime	Status	ChangeLog Entry
0	2021-10-27 08:13:07	ID requested
⏵ 1	2022-08-12 00:46:09	announced

Byeon SK, Madugundu AK, Garapati K, Ramarajan MG, Saraswat M, Kumar-M P, Hughes T, Shah R, Patnaik MM, Chia N, Ashrafzadeh-Kian S, Yao JD, Pritt BS, Cattaneo R, Salama ME, Zenka RM, Kipp BR, Grebe SKG, Singh RJ, Sadighi Akha AA, Algeciras-Schimnich A, Dasari S, Olson JE, Walsh JR, Venkatakrishnan AJ, Jenkinson G, O'Horo JC, Badley AD, Pandey A, Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study. Lancet Digit Health, 4(9):e632-e645(2022) [pubmed]

ProteomeXchange project tag: Covid-19

submitter keyword: COVID-19, Glycoproteomics, Multiplexed glycoprotoemics

Akhilesh Pandey
contact affiliation	Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States
contact email	Pandey.Akhilesh@Mayo.edu
lab head
Akhilesh Pandey
contact affiliation	Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905
contact email	pandey.akhilesh@mayo.edu
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2022/08/PXD029376

PRIDE project URI

• PRIDE
  1. PXD029376
     1. Label: PRIDE project
     2. Name: Large-scale multi-omics analysis identifies novel early markers of COVID-19 severity