Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death
- Imperial College London, United Kingdom
- University of Cambridge, United Kingdom
Abstract
End-stage kidney disease (ESKD) patients are at high risk of severe COVID-19. We measured 436 circulating proteins in serial blood samples from hospitalised and non-hospitalised ESKD patients with COVID-19 (n=256 samples from 55 patients). Comparison to 51 non-infected patients revealed 221 differentially expressed proteins, with consistent results in a separate subcohort of 46 COVID-19 patients. 203 proteins were associated with clinical severity, including IL6, markers of monocyte recruitment (e.g. CCL2, CCL7), neutrophil activation (e.g. proteinase-3) and epithelial injury (e.g. KRT19). Machine learning identified predictors of severity including IL18BP, CTSD, GDF15, and KRT19. Survival analysis with joint models revealed 69 predictors of death. Longitudinal modelling with linear mixed models uncovered 32 proteins displaying different temporal profiles in severe versus non-severe disease, including integrins and adhesion molecules. These data implicate epithelial damage, innate immune activation, and leucocyte-endothelial interactions in the pathology of severe COVID-19 and provide a resource for identifying drug targets.
Data availability
All data generated during this study are included in the manuscript and supporting files. Underlying source data for all analyses (individual-level proteomic and clinical phenotyping data) are available without restriction as Source Data Files 1-4. In addition, these data have been deposited in the Dryad Digital Repository (doi:10.5061/dryad.6t1g1jwxj). Code is available in the following GitHub repository: https://github.com/jackgisby/longitudinal_olink_proteomics
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Longitudinal proteomic profiling of high-risk patients with COVID-19 reveals markers of severity and predictors of fatal diseaseDryad Digital Repository, 10.5061/dryad.6t1g1jwxj.
Article and author information
Author details
James Edward Peters
Funding
UK Research and Innovation (MR/V027638/1)
- James Edward Peters
Imperial College London (Community Jameel and the Imperial President's Excellence Fund)
- James Edward Peters
UK Research and Innovation (UKRI Innovation Fellowship at Health Data Research UK (MR/S004068/2))
- James Edward Peters
Wellcome Trust (Wellcome-Beit Prize Clinical Research Career Development Fellowship (206617/A/17/A))
- David C Thomas
Wellcome Trust (Wellcome Trust Senior Fellow in Clinical Science (212252/Z/18/Z))
- Matthew C Pickering
Wellcome Trust and Imperial College London (Wellcome Trust and Imperial College London Research Fellowship)
- Nicholas Medjeral-Thomas
Wellcome Trust and Imperial College London (Wellcome Trust and Imperial College London Research Fellowship)
- Eleanor Sandhu
Auchi Renal Research Fund (Auchi Clinical Research Fellowship)
- Candice L Clarke
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Evangelos J Giamarellos-Bourboulis, National and Kapodistrian University of Athens, Medical School, Greece
Ethics
Human subjects: All participants (patients and controls) were recruited from the Imperial College Renal and Transplant Centre and its satellite dialysis units, London, and provided written informed consent prior to participation. Study ethics were reviewed by the UK National Health Service (NHS) Health Research Authority (HRA) and Health and Care Research Wales (HCRW) Research Ethics Committee (reference 20/WA/0123: The impact of COVID-19 on patients with renal disease and immunosuppressed patients). Ethical approval was given.
Version history
- Received: November 12, 2020
- Accepted: March 10, 2021
- Accepted Manuscript published: March 11, 2021 (version 1)
- Version of Record published: April 23, 2021 (version 2)
- Version of Record updated: April 28, 2021 (version 3)
Copyright
© 2021, Gisby et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death
eLife 10:e64827.
https://doi.org/10.7554/eLife.64827
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