Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death
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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.
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SciScore for 10.1101/2020.11.05.20223289: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:An important caveat is that we cannot determine whether the associations we observed are drivers of pathology in COVID-19 or simply reflect the downstream consequences of inflammation and tissue injury. Future studies using Mendelian randomisation analysis will provide a useful tool for assessing causality and prioritizing drug targets. Other groups have studied the plasma or serum proteome in COVID-19 [13–16,48], using either mass …
SciScore for 10.1101/2020.11.05.20223289: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:An important caveat is that we cannot determine whether the associations we observed are drivers of pathology in COVID-19 or simply reflect the downstream consequences of inflammation and tissue injury. Future studies using Mendelian randomisation analysis will provide a useful tool for assessing causality and prioritizing drug targets. Other groups have studied the plasma or serum proteome in COVID-19 [13–16,48], using either mass spectrometry or immunoassays including the Olink platform. Mass spectrometry is less sensitive than immunoassays and so it likely to be unable to detect many of the cytokines measured here. Conversely, it can provide complementary information by measuring many proteins that our immunoassays did not target. A limitation of our study was that we used Olink panels that measured specific proteins selected on their relevance to inflammation, immunity, cardiovascular and metabolic disease. This bias precluded formal pathway enrichment analysis. In general, our results had greater similarities to studies that used immunoassays over mass spectrometry (Supplementary File 1i-j). 47.6% of proteins differentially expressed in COVID-19 positive versus negative ESKD patients in our study were differentially expressed in COVID-19 positive versus negative acute respiratory distress syndrome patients in the study of Filbin et al [16], who used a different Olink proteomics platform. Moreover, we observed consistent effect sizes (Figure 4 figure supplement 1). These ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
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