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

Autor: Maria Prendecki, Lunnathaya Tapeng, Michelle Willicombe, Marie-Anne Mawhin, Emma E Dutton, Arianne C. Richard, Candice Clarke, Talat H. Malik, Norzawani Buang, Frederic Toulza, Shanice Lewis, Paul D. W. Kirk, James E. Peters, David C. Thomas, Marie Pereira, Marina Botto, Jacques Behmoaras, Stephen P. McAdoo, Eleanor Sandhu, Matthew C. Pickering, Jack Gisby, Nicholas R. Medjeral-Thomas, Artemis Papadaki, Paige M Mortimer, Ester Fagnano
Přispěvatelé: Medical Research Council (MRC), Medical Research Council, Community Jameel Imperial College COVID-19 Excellence Fund, Gisby, Jack [0000-0003-0511-8123], Pereira, Marie [0000-0003-0711-3385], Richard, Arianne C [0000-0002-8708-9997], Prendecki, Maria F [0000-0001-7048-7457], Botto, Marina [0000-0002-1458-3791], Peters, James E [0000-0002-9415-3440], Apollo - University of Cambridge Repository
Jazyk: angličtina
Rok vydání: 2021
Předmět:
0301 basic medicine
Male
medicine
0601 Biochemistry and Cell Biology
01 natural sciences
Severity of Illness Index
immunology
010104 statistics & probability
Immunology and Inflammation
end-stage kidney disease
Longitudinal Studies
Biology (General)
Kidney
General Neuroscience
General Medicine
Middle Aged
Prognosis
Blood proteins
3. Good health
Hospitalization
medicine.anatomical_structure
Female
medicine.symptom
Research Article
medicine.medical_specialty
Coronavirus disease 2019 (COVID-19)
longitudinal
QH301-705.5
Science
Inflammation
Dialysis patients
General Biochemistry
Genetics and Molecular Biology
03 medical and health sciences
Immune system
proteomics
Renal Dialysis
Internal medicine
Severity of illness
Humans
human
0101 mathematics
Aged
General Immunology and Microbiology
Proteomic Profiling
business.industry
SARS-CoV-2
COVID-19
biomarkers
cytokines
030104 developmental biology
inflammation
Kidney Failure
Chronic
business
Forecasting
Zdroj: eLife
eLife, Vol 10 (2021)
Popis: 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.
eLife digest COVID-19 varies from a mild illness in some people to fatal disease in others. Patients with severe disease tend to be older and have underlying medical problems. People with kidney failure have a particularly high risk of developing severe or fatal COVID-19. Patients with severe COVID-19 have high levels of inflammation, causing damage to tissues around the body. Many drugs that target inflammation have already been developed for other diseases. Therefore, to repurpose existing drugs or design new treatments, it is important to determine which proteins drive inflammation in COVID-19. Here, Gisby, Clarke, Medjeral-Thomas et al. measured 436 proteins in the blood of patients with kidney failure and compared the levels between patients who had COVID-19 to those who did not. This revealed that patients with COVID-19 had increased levels of hundreds of proteins involved in inflammation and tissue injury. Using a combination of statistical and machine learning analyses, Gisby et al. probed the data for proteins that might predict a more severe disease progression. In total, over 200 proteins were linked to disease severity, and 69 with increased risk of death. Tracking how levels of blood proteins changed over time revealed further differences between mild and severe disease. Comparing this data with a similar study of COVID-19 in people without kidney failure showed many similarities. This suggests that the findings may apply to COVID-19 patients more generally. Identifying the proteins that are a cause of severe COVID-19 – rather than just correlated with it – is an important next step that could help to select new drugs for severe COVID-19.
Databáze: OpenAIRE
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