Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death

eLife

Arianne C Richard¹  Talat H Malik²  Artemis Papadaki²  Jack Gisby²  Emma E Dutton²  Shanice Lewis²  Lunnathaya Tapeng²  Jacques Behmoaras²  Marie-Anne Mawhin²  Ester Fagnano²  Matthew C Pickering²  Norzawani B Buang²  Paige M Mortimer²  Marina Botto²  Marie Pereira²  Frederic Toulza²  James E Peters³  Eleanor Sandhu⁴  Candice L Clarke⁴  Maria F Prendecki⁴  David C Thomas⁴  Nicholas Medjeral-Thomas⁴  Stephen P McAdoo⁴  Michelle Willicombe⁴  Paul DW Kirk⁵ 

[1] Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom;CRUK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom;Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom;Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom;Health Data Research UK, London, United Kingdom;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;MRC Biostatistics Unit, Forvie Way, University of Cambridge, Cambridge, United Kingdom;Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, United Kingdom;

关键词: COVID-19;    proteomics;    longitudinal;    biomarkers;    cytokines;    end-stage kidney disease;    Human;   

DOI  :  10.7554/eLife.64827

来源: eLife Sciences Publications, Ltd

【 摘 要 】

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.

【 授权许可】

CC BY