Proteomics

Dataset Information

0

Large-scale multi-omics analysis identifies novel early markers of COVID-19 severity


ABSTRACT: 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.

INSTRUMENT(S): Orbitrap Eclipse

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

DISEASE(S): Covid-19

SUBMITTER: Akhilesh Pandey  

LAB HEAD: Akhilesh Pandey

PROVIDER: PXD029376 | Pride | 2022-08-12

REPOSITORIES: Pride

Dataset's files

Source:
Action DRS
MS_Pre_Post_1st_Set_TP.xlsx Xlsx
MS_Pre_Post_2nd_Set_SEC_NGP_B1-B2.raw Raw
MS_Pre_Post_2nd_Set_SEC_NGP_B11-B12.raw Raw
MS_Pre_Post_2nd_Set_SEC_NGP_B3-B4.raw Raw
MS_Pre_Post_2nd_Set_SEC_NGP_B5-B6.raw Raw
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Publications

Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study.

Byeon Seul Kee SK   Madugundu Anil K AK   Garapati Kishore K   Ramarajan Madan Gopal MG   Saraswat Mayank M   Kumar-M Praveen P   Hughes Travis T   Shah Rameen R   Patnaik Mrinal M MM   Chia Nicholas N   Ashrafzadeh-Kian Susan S   Yao Joseph D JD   Pritt Bobbi S BS   Cattaneo Roberto R   Salama Mohamed E ME   Zenka Roman M RM   Kipp Benjamin R BR   Grebe Stefan K G SKG   Singh Ravinder J RJ   Sadighi Akha Amir A AA   Algeciras-Schimnich Alicia A   Dasari Surendra S   Olson Janet E JE   Walsh Jesse R JR   Venkatakrishnan A J AJ   Jenkinson Garrett G   O'Horo John C JC   Badley Andrew D AD   Pandey Akhilesh A  

The Lancet. Digital health 20220711 9


<h4>Background</h4>COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications.<h4>Methods</h4>In this retrospective cohort study, we analysed samples fr  ...[more]

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