eLife (Mar 2021)
Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death
- Jack Gisby,
- Candice L Clarke,
- Nicholas Medjeral-Thomas,
- Talat H Malik,
- Artemis Papadaki,
- Paige M Mortimer,
- Norzawani B Buang,
- Shanice Lewis,
- Marie Pereira,
- Frederic Toulza,
- Ester Fagnano,
- Marie-Anne Mawhin,
- Emma E Dutton,
- Lunnathaya Tapeng,
- Arianne C Richard,
- Paul DW Kirk,
- Jacques Behmoaras,
- Eleanor Sandhu,
- Stephen P McAdoo,
- Maria F Prendecki,
- Matthew C Pickering,
- Marina Botto,
- Michelle Willicombe,
- David C Thomas,
- James E Peters
Affiliations
- Jack Gisby
- ORCiD
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Candice L Clarke
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom; Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- Nicholas Medjeral-Thomas
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom; Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- Talat H Malik
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Artemis Papadaki
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Paige M Mortimer
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Norzawani B Buang
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Shanice Lewis
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Marie Pereira
- ORCiD
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Frederic Toulza
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Ester Fagnano
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Marie-Anne Mawhin
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Emma E Dutton
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Lunnathaya Tapeng
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Arianne C Richard
- ORCiD
- Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom; CRUK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Paul DW Kirk
- MRC Biostatistics Unit, Forvie Way, University of Cambridge, Cambridge, United Kingdom; Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, United Kingdom
- Jacques Behmoaras
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Eleanor Sandhu
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom; Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- Stephen P McAdoo
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom; Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- Maria F Prendecki
- ORCiD
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom; Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- Matthew C Pickering
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Marina Botto
- ORCiD
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
- Michelle Willicombe
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom; Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- David C Thomas
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom; Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- James E Peters
- ORCiD
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom; Health Data Research UK, London, United Kingdom
- DOI
- https://doi.org/10.7554/eLife.64827
- Journal volume & issue
-
Vol. 10
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. Two hundred and three 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.
Keywords
