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

eLife

Eleanor Sandhu¹  Maria F Prendecki²  Artemis Papadaki³  Michelle Willicombe³  Nicholas Medjeral-Thomas³  James E Peters³  Jacques Behmoaras⁴  Shanice Lewis⁵  Ester Fagnano⁵  Candice L Clarke⁵  Paul DW Kirk⁵  Arianne C Richard⁵  Paige M Mortimer⁵  Frederic Toulza⁵  Norzawani B Buang⁵  Lunnathaya Tapeng⁵  Jack Gisby⁵  Emma E Dutton⁵  Marina Botto⁵  Talat H Malik⁵  David C Thomas⁵  Marie-Anne Mawhin⁵  Matthew C Pickering⁵  Marie Pereira⁵  Stephen P McAdoo⁶ 

[1] CRUK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom;Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, United Kingdom;Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom;Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom;Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom;MRC Biostatistics Unit, Forvie Way, University of Cambridge, Cambridge, United Kingdom;

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

DOI  :  10.7554/eLife.64827

来源: DOAJ

【 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】

Unknown