Plasma proteomic signature predicts who will get persistent symptoms following SARS-CoV-2 infection.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36182629", "kind": "science-record", "revision": 4, "content_hash": "sha256:81f1e243c37b366daf4b5283a67b0836b5c5a44cb4e26f0277c6386c45e8c025", "created_at": "2026-11-01T18:15:28Z", "updated_at": "2026-11-01T22:07:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Plasma proteomic signature predicts who will get persistent symptoms following SARS-CoV-2 infection.", "doi": "10.1016/j.ebiom.2022.104293", "pmid": "36182629", "authors": ["Gabriella Captur", "James C Moon", "Constantin-Cristian Topriceanu", "George Joy", "Leo Swadling", "Jenny Hallqvist", "Ivan Doykov", "Nina Patel", "Justyna Spiewak", "Tomas Baldwin", "Matt Hamblin", "Katia Menacho", "Marianna Fontana", "Thomas A Treibel", "Charlotte Manisty", "Ben O'Brien", "Joseph M Gibbons", "Corrina Pade", "Tim Brooks", "Daniel M Altmann", "Rosemary J Boyton", "Áine McKnight", "Mala K Maini", "Mahdad Noursadeghi", "Kevin Mills", "Wendy E Heywood"], "journal": "EBioMedicine", "year": 2022, "abstract": "BACKGROUND: The majority of those infected by ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) during the UK first wave (starting March 2020) did not require hospitalisation. Most had a short-lived mild or asymptomatic infection, while others had symptoms that persisted for weeks or months. We hypothesized that the plasma proteome at the time of first infection would reflect differences in the inflammatory response that linked to symptom severity and duration.\nMETHODS: We performed a nested longitudinal case-control study and targeted analysis of the plasma proteome of 156 healthcare workers (HCW) with and without lab confirmed SARS-CoV-2 infection. Targeted proteomic multiple-reaction monitoring analysis of 91 pre-selected proteins was undertaken in uninfected healthcare workers at baseline, and in infected healthcare workers serially, from 1 week prior to 6 weeks after their first confirmed SARS-CoV-2 infection. Symptom severity and antibody responses were also tracked. Questionnaires at 6 and 12 months collected data on persistent symptoms.\nFINDINGS: Within this cohort (median age 39 years, interquartile range 30-47 years), 54 healthcare workers (44% male) had PCR or antibody confirmed infection, with the remaining 102 (38% male) serving as uninfected controls. Following the first confirmed SARS-CoV-2 infection, perturbation of the plasma proteome persisted for up to 6 weeks, tracking symptom severity and antibody responses. Differentially abundant proteins were mostly coordinated around lipid, atherosclerosis and cholesterol metabolism pathways, complement and coagulation cascades, autophagy, and lysosomal function. The proteomic profile at the time of seroconversion associated with persistent symptoms out to 12 months. Data are available via ProteomeXchange with identifier PXD036590.\nINTERPRETATION: Our findings show that non-severe SARS-CoV-2 infection perturbs the plasma proteome for at least 6 weeks. The plasma proteomic signature at the time of seroconversion has the potential to identify which individuals are more likely to suffer from persistent symptoms related to SARS-CoV-2 infection.\nFUNDING INFORMATION: The COVIDsortium is supported by funding donated by individuals, charitable Trusts, and corporations including Goldman Sachs, Citadel and Citadel Securities, The Guy Foundation, GW Pharmaceuticals, Kusuma Trust, and Jagclif Charitable Trust, and enabled by Barts Charity with support from University College London Hospitals (UCLH) Charity. This work was additionally supported by the Translational Mass Spectrometry Research Group and the Biomedical Research Center (BRC) at Great Ormond Street Hospital.", "claims": [{"id": "claim-method", "statement": "We applied random forest-based modelling using the Breiman and Cutler algorithm in R package ‘randomForest’, as previously described,21 to rank the 91 plasma proteomics biomarkers according to their discriminative power to predict persistent symptoms at 12 months. In the absence of a separate test dataset to validate the performance of the random forest multi-marker panel in predicting persistent symptoms, we undertook linear discriminant analysis (LDA) using R package ‘MASS’ in the same cohort of seropositive HCW to estimate the misclassification rate.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36182629", "locator": "sections[8] \"General statistical methods\" paragraph 3", "quote": "We applied random forest-based modelling using the Breiman and Cutler algorithm in R package ‘randomForest’, as previously described,21 to rank the 91 plasma proteomics biomarkers according to their discriminative power to predict persistent symptoms at 12 months."}, {"asset": "source:pmid:36182629", "locator": "sections[8] \"General statistical methods\" paragraph 3", "quote": "In the absence of a separate test dataset to validate the performance of the random forest multi-marker panel in predicting persistent symptoms, we undertook linear discriminant analysis (LDA) using R package ‘MASS’ in the same cohort of seropositive HCW to estimate the misclassification rate."}], "state": "complete"}, {"id": "claim-data-access", "statement": "With publication all deidentified MS proteomics will be openly available via the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) and the PRIDE partner repository18 with identifier PXD036590. All COVIDsortium HCW clinicodemographic data, including study protocols and templates of informed consent forms used for the study, are freely available following a data access request through the COVIDsortium data access portal. Source code and test data are available via GitHub (https://github.com/gcaptur/COVID-Proteomics).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36182629", "locator": "sections[22] \"Data sharing statement\" paragraph 2", "quote": "With publication all deidentified MS proteomics will be openly available via the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) and the PRIDE partner repository18 with identifier PXD036590. All COVIDsortium HCW clinicodemographic data, including study protocols and templates of informed consent forms used for the study, are freely available following a data access request through the COVIDsortium data access portal. 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