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

  1. Jack Gisby
  2. Candice L Clarke
  3. Nicholas Medjeral-Thomas
  4. Talat H Malik
  5. Artemis Papadaki
  6. Paige M Mortimer
  7. Norzawani B Buang
  8. Shanice Lewis
  9. Marie Pereira
  10. Frederic Toulza
  11. Ester Fagnano
  12. Marie-Anne Mawhin
  13. Emma E Dutton
  14. Lunnathaya Tapeng
  15. Arianne C Richard
  16. Paul DW Kirk
  17. Jacques Behmoaras
  18. Eleanor Sandhu
  19. Stephen P McAdoo
  20. Maria F Prendecki
  21. Matthew C Pickering
  22. Marina Botto
  23. Michelle Willicombe
  24. David C Thomas
  25. James E Peters  Is a corresponding author
  1. Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, United Kingdom
  2. Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, United Kingdom
  3. Cambridge Institute for Medical Research, University of Cambridge, United Kingdom
  4. CRUK Cambridge Institute, University of Cambridge, United Kingdom
  5. MRC Biostatistics Unit, Forvie Way, University of Cambridge, United Kingdom
  6. Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, United Kingdom
  7. Health Data Research UK, United Kingdom
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Cite this article as: eLife 2021;10:e64827 doi: 10.7554/eLife.64827

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.

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.

Introduction

Coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, displays wide clinical heterogeneity from asymptomatic to fatal disease. Patients with severe disease exhibit marked inflammatory responses and immunopathology. The mechanisms underlying this remain incompletely characterised, and the key molecular mediators are yet to be determined. The first treatment shown to reduce mortality from COVID-19 in randomised trials was dexamethasone (Horby et al., 2020), a corticosteroid that has broad non-specific effects on the immune system. Even with corticosteroid treatment, mortality in severe COVID-19 remains significant. There is a wide armamentarium of existing drugs that target inflammation more selectively, providing potential repurposing opportunities for the treatment of COVID-19. Recently, the REMAP-CAP trial has demonstrated efficacy of anti-IL6 receptor blockade in patients admitted to intensive care units with severe disease (Gordon et al., 2021). In order to select the most promising agents for future trials, we urgently need to better understand the molecular drivers of severe disease. Proteins are the effector molecules of biology and the targets of most drugs. Therefore, proteomic profiling to identify the key mediators of severe disease provides a valuable tool for identifying and prioritising potential drug targets (Suhre et al., 2021).

Risk factors for severe or fatal COVID-19 include age, male sex, non-European ancestry, obesity, diabetes mellitus, cardiovascular disease, and immunosuppression (Williamson et al., 2020). End-stage kidney disease (ESKD) is one of the strongest risk factors for severe COVID-19 (estimated hazard ratio for death 3.69) (Williamson et al., 2020), and ESKD patients hospitalised with COVID-19 have a mortality of approximately 30% (Docherty et al., 2020; Corbett et al., 2020; Ng et al., 2020; Valeri et al., 2020). ESKD patients have a high prevalence of vascular and cardiometabolic disease (e.g. hypertension, ischaemic heart disease, diabetes), either as a result of the underlying cause of their renal disease or as a consequence of renal failure. In addition, ESKD results in both relative immunosuppression and chronic low-grade inflammation, which may impact viral defence and the host inflammatory response.

Here we performed proteomic profiling of serial blood samples of ESKD patients with COVID-19, leveraging the unique opportunity for longitudinal sampling in both the outpatient and inpatient settings afforded by a large multi-ethnic haemodialysis cohort (Figure 1a). These data revealed 221 proteins that are dysregulated in COVID-19 versus matched non-infected ESKD patients. Using linear mixed models, joint models, and machine learning, we identified proteins that are markers of COVID-19 severity and risk of death. Finally, we characterised the temporal dynamics of the blood proteomic response during COVID-19 infection in ESKD patients, uncovering 32 proteins that display altered trajectories in patients with severe versus non-severe disease.

Figure 1 with 1 supplement see all
Study design.

(a) Schematic representing a summary of the patient cohorts, sampling, and the major analyses. Blue and red stick figures represent outpatients and hospitalised patients, respectively. (b) Timing of serial blood sampling in relation to clinical course of COVID-19 (subcohort A). Black asterisks indicate when samples were obtained. Three patients were already in hospital prior to COVID-19 diagnosis (indicated by red bars).

Results

We recruited 55 ESKD patients with COVID-19 (subcohort A; Table 1). All patients were receiving haemodialysis prior to acquiring COVID-19. Blood samples were taken as soon as feasible following COVID-19 diagnosis. At time of initial sample, 30 patients were outpatients attending haemodialysis sessions and 25 were hospitalised inpatients (see Materials and methods, Figure 1). Following the initial blood sample, serial sampling was performed for 51/55 patients. We also recruited 51 non-infected haemodialysis patients as ESKD controls, mirroring the age, sex, and ethnicity distribution of the COVID-19 cases (Figure 1—figure supplement 1a–c). We used the Olink proteomics platform to measure 436 proteins (Supplementary file 1a) in 256 plasma samples from the COVID-19 patients and the 51 control samples. The proteins measured consisted of five multiplex ‘panels’ focussed on proteins relevant to immuno-inflammation, cardiovascular, and cardiometabolic disease. The 436 proteins assayed showed strong enrichment for immune-related proteins (Supplementary file 1b).

Table 1
Characteristics of subcohort A.
COVID-19-positive ESKD patients
(n = 55)
ESKD controls
(n = 51)
OverallPeak severity mild or moderate (n = 28)Peak severity severe or critical (n = 27)
Age
Median
(IQR)

72.2
62.5–77.3

73.4
65.5–76.4

68.5
61.8–78.8

70.1
62.2–75.1
Sex
M
F

39 (70.9%)
16 (29.1%)

18 (64.3%)
10 (35.7%)

21 (77.8%)
6 (22.2%)

36 (70.6%)
15 (29.4%)
Ethnicity
White
Black
South Asian
Asian (other)
Other

16 (29.1%)
8 (14.5%)
18 (32.7%)
4 (7.3%)
9 (16.4%)

5 (17.9%)
5 (17.9%)
10 (35.7%)
1 (3.6%)
7 (25.0%)

11 (40.7%)
3 (11.1%)
8 (29.6%)
3 (11.1%)
2 (7.4%)

13 (25.5%)
8 (15.7%)
20 (39.2%)
3 (5.9%)
7 (13.7%)
Diabetes34 (61.8%)*16 (57.1%)18 (66.7%)24 (47.1%)*
Current smoker1 (1.8%)1 (3.6%)00
ESKD cause
DN
Genetic
GN
HTN/vascular
Other
Unknown

29 (52.7%)
1 (1.8%)
3 (5.5%)
5 (9.1%)
8 (14.5%)
9 (16.4%)

14 (50.0%)
1 (3.6%)
1 (3.6%)
3 (10.7%)
5 (17.9%)
4 (14.3%)

15 (55.6%)
0
2 (7.4%)
2 (7.4%)
3 (11.1%)
5 (18.5%)

20 (39.2%)
1 (2.0%)
9 (17.6%)
7 (13.7%)
4 (7.8%)
10 (19.6%)
Hospitalisation due to COVID-19†33 (60%)6 (21.4%)27 (100%)N/A
Fatal COVID-199 (16.3%)0 (0%)9 (33.3%)N/A
  1. DN = diabetic nephropathy. GN = glomerulonephritis. HTN = hypertension. IQR = inter-quartile range. ‘South Asian’ represents individuals with Indian, Pakistani, or Bangladeshi ancestry. Subsets defined according to peak WHO severity over the course of the illness. N/A = not applicable.

    *One patient had type 1 diabetes, the remainder type 2. 3 patients were hospitalised prior to COVID-19 diagnosis. 8 patients diagnosed with COVID-19 as outpatients subsequently deteriorated were hospitalised.

In addition, we performed the Olink proteomic assays in 52 serum samples from a separate set of 46 COVID-19-positive ESKD patients (subcohort B) and 11 serum samples from ESKD COVID-19-negative controls (a subset of the controls described above). For the large majority of patients in subcohort B, only a single timepoint was available. A higher proportion of these patients (41/46, 89%) were hospitalised and had severe disease (Table 2) than in subcohort A (Figure 1, Table 1).

Table 2
Characteristics of subcohort B.
COVID-19-positive ESKD patients
(n = 46)
COVID-19-negative ESKD controls
(n = 11)*
Age
Median
(IQR)

64.3
60.3–73.0

71.6
(61.7–73.9)
Sex
M
F

32 (69.6%)
14 (30.4%)

8 (72.3%)
3 (27.3%)
Ethnicity
White
Black
South Asian
Asian (other)
Other

11 (23.9%)
8 (17.4%)
12 (26.1%)
7 (15.2%)
8 (17.4%)

3 (27.3%)
3 (27.3%)
3 (27.3%)
0
2 (18.2%)
Diabetes29 (63.0%)6 (54.5%)
Current smoker2 (4.3%)0 (%)
ESKD cause
DN
Genetic
GN
HTN/vascular
Other
Unknown

19 (41.3%)
1 (2.2%)
7 (15.2%)
3 (6.5%)
3 (6.5%)
13 (28.3%)

5 (45.5%)
0
1 (9.1%)
1 (9.1%)
2 (18.2%)
2 (18.2%)
Hospitalisation due to COVID-1941 (89.1%)N/A
Severe or critical COVID-1933 (71.7%)N/A
Fatal COVID-199 (19.6%)N/A
  1. DN = diabetic nephropathy. GN = glomerulonephritis. HTN = hypertension. IQR = inter-quartile range. ‘South Asian’ represents individuals with Indian, Pakistani, or Bangladeshi ancestry. Subsets defined according to peak WHO severity over the course of the illness. N/A = not applicable. *These 11 controls are a subset of the control patients used in subcohort A.

Proteomic differences between COVID-19-positive and -negative ESKD patients

Principal component analysis (PCA) of proteomic data from subcohort A demonstrated differences between samples from COVID-19-positive cases and controls, although the two groups did not separate into discrete clusters (Figure 2a,b). To examine the effects of COVID-19 on the plasma proteome, we performed a differential expression analysis in subcohort A between COVID-19 cases (n = 256 samples passing quality control [QC] from 55 patients) and non-infected ESKD controls (n = 51) using linear mixed models, which account for serial samples from the same individual (see Materials and methods). This revealed 221 proteins associated with COVID-19 (5% false discovery rate, FDR); the vast majority were upregulated, with only 40 downregulated (Figure 3a, Supplementary file 1c). In order to provide a succinct and standardised nomenclature, we report proteins by the symbols of the genes encoding them (see Supplementary file 1a for a mapping of symbols to full protein names). The most strongly upregulated proteins (in terms of fold change) were DDX58, CCL7, IL6, CXCL11, KRT19, and CXCL10, and the most strongly downregulated were SERPINA5, CCL16, FABP2, PON3, ITGA11, and MMP12 (Figure 3—figure supplement 1). Notably, many of the upregulated proteins were chemotaxins.

Figure 2 with 2 supplements see all
Principal component analysis.

PC = principal component. Each point represents a sample. Colouring indicates COVID-19 status. The directions and relative sizes of the six largest PC loadings are plotted as arrows (middle column). (a, b) Subcohort A. Due to serial sampling, there are multiple samples for most patients. The proportion of variance explained in subcohort A by each PC is shown in parentheses on the axis labels. (c, d) Subcohort B. Samples are projected into the PCA coordinates from subcohort A.

Figure 3 with 4 supplements see all
Identification of dysregulated proteins.

(a) Proteins upregulated (red) or downregulated (blue) in COVID-19-positive patients versus COVID-19-negative ESKD patients n = 256 plasma samples from 55 COVID-19-positive patients, versus n = 51 ESKD controls (one sample per control patient). (b) Proteins associated with disease severity associations of protein levels against WHO severity score at the time of sampling. Linear gradient indicates the effect size. A positive effect size (red) indicates that an increase in protein level is associated with increasing disease severity and a negative gradient (blue) the opposite. n = 256 plasma samples from 55 COVID-19-positive patients. For (a, b), p-values from linear mixed models after Benjamini–Hochberg adjustment; significance threshold = 5% FDR; dark-grey = non-significant. (c) Heatmap showing protein levels for selected proteins with strong associations with severity. Each column represents a sample (n = 256 COVID-19 samples and 51 non-infected samples). Each row represents a protein. Proteins are annotated using the symbol of their encoding gene. For the purposes of legibility, not all significantly associated proteins are shown; the heatmap is limited to the 17% most up- or downregulated proteins (by effect size) of those with a significant association. Proteins are ordered by hierarchical clustering. Samples are ordered by WHO severity at the time of blood sample (‘Severity’). ‘Overall course’ indicates the peak WHO severity over the course of the illness.

We observed that a high proportion of the measured proteins were associated with COVID-19. Given the highly targeted nature of the Olink panels that we used (enriched for immune and inflammation-related proteins), this was not surprising. Nevertheless, to ensure that the Benjamini–Hochberg adjustment of p-values was controlling the FDR at the 5% level, we performed two additional analyses (see Materials and methods). First, we estimated the FDR using an alternative method (the plug-in procedure ; Hastie et al., 2001); this confirmed appropriate FDR control. Second, we used permutation to estimate the distribution of the number of proteins expected to be declared significant under the null hypothesis of no association between any proteins and COVID-19. This showed that the probability of observing the number of differentially abundant proteins we identified was highly unlikely under the null (empirical p<1×10−5; Figure 3—figure supplement 2).

Although our COVID-19-negative controls were well matched in terms of age, sex, and ethnicity (Figure 1—figure supplement 1a–c), perfect matching of comorbidities was not feasible in the context of the healthcare emergency at the time of patient recruitment. There was a higher prevalence of diabetes in the COVID-19 cases compared to the controls (61.8% versus 47.1%, respectively; Table 1). To evaluate whether differing rates of diabetes had impacted the proteins identified as differentially abundant between cases and controls, we performed a sensitivity analysis adding diabetes as an additional covariate in the linear mixed model. This did not materially affect our findings; estimated effect sizes and –log10 p-values from models with and without the inclusion of diabetes were highly correlated (Pearson r > 0.99, and r = 0.95, respectively; Figure 3—figure supplement 3a,b). Full results from both models are shown in Supplementary file 1c. Similarly, there were also differences in the underlying cause of ESKD in cases compared to controls (Table 1). We therefore performed a further sensitivity analysis adjusting for underlying cause of renal failure. This did not make any meaningful difference to our results (Figure 3—figure supplement 3c,d, Supplementary file 1c).

We also considered the possibility that timing of haemodialysis might affect the plasma proteome. To minimise the impact of this, all samples were taken prior to haemodialysis. For the large majority (86.6%) of samples, the most recent haemodialysis was between 48 and 72 hr prior to blood draw. This consistency in timing of blood sampling reduces the potential for impact of this issue. Nevertheless, to evaluate whether timing of haemodialysis might have impacted our results, we performed a sensitivity analysis including time from last haemodialysis as a covariate. Our results were not materially affected by this, with −log10 p-values and estimated effect sizes very highly correlated with those obtained without inclusion of this covariate (Pearson r > 0.99 for effect size estimates and for −log10 p-values; Figure 3—figure supplement 4a,b, Supplementary file 1c).

We used the smaller subcohort B (n = 52 serum samples from 46 patients with COVID-19; see Materials and methods) for validation. We first projected the data from subcohort B into the PCA space of subcohort A to examine how well the separation of cases and controls in the PCA space replicated (see Materials and methods). This revealed clearer separation of infected and non-infected patients than in subcohort A (Figure 2c,d), perhaps reflecting the higher proportion of hospitalised patients (41 of 46 patients) in subcohort B (Table 2). We next performed differential abundance analysis in subcohort B and found 201 proteins that were dysregulated in cases versus controls (5% FDR) (Supplementary file 1c). Of the 221 differentially abundant proteins from subcohort A, 150 (69.7%) were also identified in subcohort B at 5% FDR (Figure 4a). Effect sizes in each dataset showed a strong correlation (r = 0.80, Figure 4b). This demonstrates that our findings are highly reproducible despite differences in sample sizes and blood materials (plasma versus serum in subcohorts A and B, respectively).

Figure 4 with 1 supplement see all
Validation.

(a) Overlap between the significant associations in the differential abundance analysis between ESKD patients with and without COVID-19 in subcohorts A and B. 5% FDR was used as the significance threshold in both analyses. (b) Comparison of estimated effect sizes for all 436 proteins in the differential abundance analyses (COVID-19 positive versus negative) in subcohort A and B. Each point represents a protein. Pearson’s r is shown. Differential abundance analyses were performed using linear mixed models. Subcohort A analysis (plasma samples): 256 samples from 55 COVID-19 patients versus 51 non-infected patient samples (single time-point). Subcohort B (serum samples): 52 samples from 55 COVID-19 patients and 11 non-infected patient samples (single timepoint).

Proteins associated with COVID-19 severity

Examination of the principal components plot labelling samples by clinical severity at the time of sampling (defined by WHO severity scores, graded as mild, moderate, severe, or critical) demonstrated a gradient of COVID-19 severity, best captured by principal components 1 and 3 (Figure 2—figure supplement 1a). To determine the proteomic effects of COVID-19 severity, we tested for associations between proteins and WHO severity score at the time of blood sampling, using linear mixed models with severity encoded as an ordinal predictor (see Materials and methods). This analysis revealed 203 proteins associated with severity (Figure 3b, Supplementary file 1d). The majority of these were upregulated in more severe disease, with only 42 downregulated. A sensitivity analysis adjusting for time since last haemodialysis made no significant impact on our results (Figure 3—figure supplement 4c,d, Supplementary file 1d). Consistent with previous reports, we found that severe COVID-19 was characterised by elevated IL6. In addition, we observed a signature of upregulated monocyte chemokines (e.g. CCL2, CCL7, CXCL10), neutrophil activation and degranulation (e.g. PRTN3, MPO), and epithelial injury (e.g. KRT19, AREG, PSIP1, GRN). (Figures 3b,c and 5). SERPINA5 and leptin showed the greatest downregulation as COVID-19 severity increased (Figure 3b,c).

Selected proteins strongly associated with COVID-19 severity.

Violin plots showing distribution of plasma protein levels according to COVID-19 status at the time of blood draw. Boxplots indicate median and inter-quartile range. n = 256 samples from 55 COVID-19 patients and 51 samples from non-infected patients. WHO severity indicates the clinical severity score of the patient at the time the sample was taken. Mild n = 135 samples; moderate n = 77 samples; severe n = 29 samples; critical n = 15 samples. Upper: monocyte chemokines. Middle: markers of epithelial injury. Lower: two neutrophil proteases and IL6.

We next asked how does the COVID-19 severity protein signature relate to the proteins that are differentially abundant between cases and controls? The majority (140/203; 69%) of severity-associated proteins were also identified as differentially abundant in the COVID-19-positive versus -negative analysis (Figure 6a). Log fold changes for proteins in COVID-19 versus non-infected patients were correlated with effect sizes in the severity analysis, such that the proteins most upregulated in cases versus controls also tended to show the greatest upregulation in severe disease (Figure 6b). However, there were some notable exceptions (e.g. CCL20, IL17C, OSM) that were strongly associated with severity, but not differentially expressed in infected versus non-infected patients (Figure 6c).

Comparison of proteins differentially expressed in COVID-19 with those associated with clinical severity.

(a) Overlap between the proteins significantly differentially expressed in COVID-19 (n = 256 COVID-19 samples and 51 non-infected samples) versus those associated with severity (within-case analysis, n = 256 samples) (subcohort A). 5% FDR was used as the significant cut-off in both analyses. (b) Comparison of effect sizes for each protein in the COVID-19-positive versus -negative analysis (x-axis) and severity analysis (y-axis). Each point represents a protein. Pearson’s r is shown. (c) Examples of proteins specifically associated with severity, but not significantly differentially abundant in the comparison of all cases versus controls. Violin plots showing distribution of plasma protein levels according to COVID-19 status at the time of blood draw. Boxplots indicate median and inter-quartile range. n = 256 samples from 55 COVID-19 patients and 51 samples from non-infected patients. WHO severity indicates the clinical severity score of the patient at the time the sample was taken. Mild n = 135 samples; moderate n = 77 samples; severe n = 29 samples; critical n = 15 samples.

Supervised learning to predict COVID-19 severity

PCA revealed that some samples from patients who had mild or moderate disease at the time of sampling clustered with samples from patients with severe disease (Figure 2—figure supplement 1a). Examination of the same PCA plot labelling samples according to the patient’s overall clinical course (measured by peak WHO severity score over the duration of the illness) (Figure 2—figure supplement 1b) revealed that these samples came from individuals who subsequently developed severe or critical disease. This suggested that molecular changes may predate clinical deterioration. To evaluate this further, we used supervised learning approaches to test whether the proteomic signature of the first blood sample for each patient in our dataset could identify whether the patient either had clinically severe COVID-19 at the time of sampling or would develop severe disease in the future. Whereas differential expression analyses consider each protein marker separately, machine-learning techniques allow examination of all proteins concurrently, thus capturing non-linear relationships in the dataset. Using Random Forests, we trained a classifier on the first sample for each COVID-19 patient to predict the overall clinical course, defined by peak WHO severity. For the purposes of this analysis, we binarised clinical course into either WHO mild/moderate or severe/critical.

The Random Forests method achieved 71% accuracy in predicting peak severity. By contrast, using only clinically available predictors (demographics, comorbidities, and clinical laboratory results), the Random Forests method achieved 66% accuracy in predicting peak severity. Combining clinical parameters plus proteins did not improve accuracy (71%) compared to using proteomic predictors alone, suggesting that the information contained in the clinical predictors is captured at the proteomic level. While we do not believe that proteomic profiling is likely to enter clinical practice for risk stratification during this pandemic, the features selected by the classifier can highlight proteins of biological importance. We therefore interrogated the model to identify key proteins by calculating feature importance metrics (see Materials and methods, Supplementary file 1e). The most important proteins for indicating the presence of current or future severe disease were IL18BP, CTSD (Cathepsin D), GDF15, KRT19, TNFSF11, and IL1RL1 (ST2) (Figure 7a). It is notable that through this distinct analytical approach, KRT19 again emerged as a key biomarker of severe disease.

Figure 7 with 1 supplement see all
Prediction of severe COVID-19 and death.

(a) The 12 most important proteins for predicting overall clinical course (defined by peak COVID-19 WHO severity) using Random Forests supervised learning. If a variable is important for prediction, it is likely to appear in many decision trees (number of trees) and be close to the root node (i.e. have a low minimal depth). The mean minimal depth across all trees (white box) was used as the primary feature selection metric. (b) Proteins that are significant predictors of death (Benjamini–Hochberg adjusted p<0.05). n = 256 samples from 55 COVID-19-positive patients, of whom nine died. Risk coefficient estimates are from a joint model. Bars indicate 95% confidence intervals. For proteins with a positive risk coefficient, a higher concentration corresponds to a high risk of death, and vice versa for proteins with negative coefficients.

Proteins associated with risk of death

Nine of 55 patients in subcohort A died. We therefore sought to identify proteins associated with risk of death. To leverage the dynamic nature of repeated protein measurements for prediction of death, we utilised joint models, which combine linear mixed models and Cox proportional hazards models (Ibrahim et al., 2010; Rizopoulos, 2010) (see Materials and methods). This analysis identified 44 proteins for which increased concentration was associated with increased risk of death (Figure 7b, Supplementary file 1f), including CST3, IL22RA1, AZU1, CCL28, and SPON1, and 25 proteins for which increased concentration was associated with reduced risk of death, including CD84, TNFSF12, TANK, PRKCQ, and ADM.

Associations with clinical laboratory tests

A number of routine clinical laboratory tests have well-characterised associations with COVID-19 (e.g. elevated inflammatory markers, d-dimer, and reduced lymphocyte count) (Guan et al., 2020). We therefore compared our proteomic data from COVID-19 patients at each timepoint to contemporaneous clinical laboratory measurements using linear mixed models (see Materials and methods). We found associations between plasma proteins and all clinical laboratory measurements except troponin (Figure 8, Supplementary file 1g). Many of these proteins were also markers of severity (e.g. IL6, KRT19, IFN-gamma, and CXCL10 were strongly associated with raised CRP and ferritin and reduced lymphocyte counts). Of note, CCL7, a monocyte chemokine that was also identified as an important marker of severity by the Random Forests classifier, was associated with lower monocyte count and raised inflammatory markers. Elevated neutrophil count was associated with Oncostatin-M, which regulates IL6, GCSF, and GMCSF production, and with the proteases MMP9 and defensin.

Associations of clinical laboratory markers with plasma proteins.

Proteins that are positively (red) or negatively (blue) associated with clinical laboratory parameters (5% FDR). p-values from differential abundance analysis using linear mixed models after Benjamini–Hochberg adjustment. Dark-grey = non-significant. Two associations were found for d-dimer (not shown – see Supplementary file 1g).

Longitudinal analysis reveals proteins with distinct temporal profiles according to severity

The immune response to infection is dynamic, and therefore snapshot measurements provide only partial insights. Leveraging the dense serial sampling in our dataset (Figure 1), we modelled the temporal trajectory of each protein and asked whether or not any protein trajectories differed in patients with a severe/critical versus mild/moderate overall clinical course. This was achieved using linear mixed models that included a term for time from first symptoms and a time × severity interaction term (see Materials and methods).

One hundred and seventy-eight proteins displayed a significant association with time from first symptoms (5% FDR), demonstrating the temporal variability in plasma proteins across the disease course (Supplementary file 1h). Moreover, we identified 32 proteins for which there was significant interaction between time and severity, that is, proteins displaying differential temporal trajectories between mild/moderate and severe/critical infections (Supplementary file 1h, Figure 9). Among the proteins with the strongest temporal differences according to clinical course were the integrins ITGA11 and ITGB6, the adhesion molecule ICAM1, TNFRSF10B (a receptor for TRAIL), and PLAUR (the receptor for urokinase plasminogen activator). Most of these proteins exhibited rising profiles in the more severe patients but flat profiles in milder cases. ACE2, the receptor for SARS-CoV-2, also displayed this pattern (Figure 9). In contrast, abundance of ITGA11, which was also identified as reduced in the analysis of infected versus non-infected patients, fell over time in the severe group.

Figure 9 with 2 supplements see all
Modelling of temporal protein trajectories.

The top 18 proteins displaying the most significantly (5% FDR) different longitudinal trajectories between patients with a mild or moderate (n = 28) versus severe or critical (n = 27) overall clinical course (defined by peak WHO severity). Means and 95% confidence intervals for each group, predicted using linear mixed models (see Materials and methods), are plotted. The remainder of significant proteins are shown in Figure 9—figure supplement 1. Individual data points are shown in Figure 9—figure supplement 2.

Testing for proteins associated with ethnicity

In the UK, individuals from ethnic minorities are at higher risk of severe disease and death from COVID-19 (Williamson et al., 2020). We therefore examined whether any of the proteins we measured exhibited differences across ethnicities, analysing COVID-19-positive cases and controls separately (see Materials and Methods). In COVID-19-negative ESKD patients, no proteins were significantly associated with ethnicity in a multivariable model adjusting for age and sex. In COVID-19-positive ESKD patients, there is the potential for protein associations with ethnicity to be confounded by disease severity. To account for this, we included severity as well as age and sex as covariates. A single protein, LY75, was associated with ethnicity in this multivariable model (nominal p-value 0.0001, Benjamini–Hochberg adjusted p-value 0.04, with higher levels in white patients). Using the same within-case analysis strategy in subcohort B, we found no proteins were significantly associated with ethnicity after multiple testing correction, although the nominal p-value for LY75 was 0.025. While these analyses failed to identify substantial ethnicity-related variation in the proteins we measured, an important caveat is that there were relatively modest numbers of individuals from each ethnic group, and so statistical power was limited. Larger multi-ethnic studies are needed to adequately address this question.

Comparisons to other proteomic studies in COVID-19

Other studies have used a variety of proteomic platforms to investigate COVID-19. We compared our findings to those of three published studies (Shen et al., 2020; Lucas et al., 2020; Arunachalam et al., 2020) and a preprint by Filbin et al., 2020. Of the 221 proteins that were differentially abundant in our analysis of COVID-19-positive versus -negative ESKD patients, 116 associations had been previously reported (Supplementary file 1i). Of the 203 proteins associated with severity, 165 had previously been reported (Supplementary file 1j).

We focussed in more detail on the study by Filbin et al., 2020 because of the large sample size and the breadth of proteomic assay used. This study comprised 384 patients with acute respiratory distress (306 COVID-19 positive and 79 COVID-19 negative) and measured 1472 proteins using the Olink Explore platform. Four hundred and seventeen of these were also measured in our study. Of the 221 proteins differentially abundant in our case/control analysis, 210 were measured in their study. Of these, 100 (47.6%) were significant in their analysis of COVID-19-positive versus COVID-19-negative respiratory distress. In addition, we observed strong correlation (r = 0.69) between the estimated log fold changes in our and their studies (Figure 4—figure supplement 1). Of the 203 proteins associated with severity in our study, 192 were measured in their study. One hundred and fifty-seven of these were significantly associated with severity, giving a concordance of 81.8%. Thus, despite the differences in study design and clinical populations, we observed notable similarities in our results and those reported by Filbin et al., 2020.

Discussion

In this study, we performed plasma proteomic profiling of haemodialysis patients with COVID-19. A strength of our study was that we were able to perform serial blood sampling in both the outpatient and inpatient settings, including longitudinal samples from the same individual before and after hospitalisation. This was possible because haemodialysis patients are unable to fully isolate as they must continue to attend for regular dialysis sessions. Moreover, haemodialysis patients represent an important group since ESKD is one of the strongest risk factors for death from COVID-19 (Williamson et al., 2020; Corbett et al., 2020; Ng et al., 2020; Valeri et al., 2020). Data from the UK Renal Registry shows that 7 and 14 day mortality for COVID-19-infected in-centre haemodialysis patients was 11% and 19%, respectively (COVID-19 Data, 2020). Data from the Scottish Renal Registry estimates 30 day mortality following a positive COVID-19 test as 22%, and as of 31 May 2020, 28.2% of renal replacement therapy patients who had a positive COVID-19 test had died (Scottish Renal Registry COVID-19 report, 2020). In our local population of 1352 in-centre haemodialysis patients, 315 patients had tested positive for COVID-19 by the end of our study period (31 May 2020), of whom 53% required hospitalisation and 85 (27%) died. The OpenSAFELY study (Williamson et al., 2020) examined ~17 million UK primary care records and linked these to the UK COVID-19 mortality register. Patients with estimated glomerular filtration rate (eGFR) < 30 ml/min/1.73 m2 had a hazard ratio (HR) for death of 3.56 after adjustment for age and sex.

In part, the high mortality from COVID-19 in ESKD patients likely reflects the fact that these patients are enriched for cardiometabolic traits that predispose to severe COVID-19. However, in multivariable analyses adjusting for these factors, impaired renal function remains an independent risk factor for severe COVID-19 (Williamson et al., 2020). Moreover, there is an inverse relationship between renal function and risk of death from COVID-19 across the spectrum of chronic kidney disease. These observations support the notion that the state of ESKD per se is an important determinant of outcome in COVID-19. ESKD is well-recognised as an immunosuppressed state (Eiselt et al., 2016; Girndt et al., 1999; Sarnak and Jaber, 2000), with defects in both innate and adaptive immunity (Alexiewicz et al., 1991; Massry and Smogorzewski, 2001; Girndt et al., 2001; Meier et al., 2002). Accordingly, ESKD confers increased vulnerability to viral infections including influenza and respiratory syncytial virus (Betjes, 2013; Boattini et al., 2020; Prasad et al., 2020; Usvyat et al., 2012). In addition, ESKD results in a chronic low-grade inflammatory state (Carrero and Stenvinkel, 2010). This tendency to a pro-inflammatory state, combined with reduced ability to respond to viruses, may contribute to the abnormal host response to SARS-CoV-2 infection, producing the immunopathology that leads to severe COVID-19.

Our comparison of COVID-19-positive and -negative haemodialysis patient plasma samples revealed 221 proteins that were differentially abundant in COVID-19. The majority of these were upregulated, with strong representation of viral response proteins (e.g. DDX58, IFNG), cytokines/chemokines (e.g. IL6, CCL7, CXCL10, and CXCL11), and epithelial proteins (e.g. KRT19, PSIP1) (Figure 3a). The COVID-19-negative controls in this analysis were carefully matched to cases in terms of age, sex, and ethnicity. However, complete matching of clinical characteristics was not feasible; there were differences in the prevalence of diabetes and the underlying causes of ESKD between COVID-19-positive cases and controls (Table 1). Sensitivity analyses adjusting for these covariates gave highly consistent results, indicating that our findings are robust. In addition, we validated our findings when we analysed serum samples from a separate subcohort of COVID-19-positive ESKD patients.

ESKD is itself likely to significantly impact the plasma proteome. Previous cross-sectional studies have shown that the levels of many circulating proteins have an inverse relationship with eGFR (Naseeb et al., 2015; Christensson et al., 2018). A longitudinal study using an Olink proteomics panel (although not one used in our study) found that for 74% of the 84 proteins measured, protein levels rose as eGFR fell (Lind et al., 2019). For many proteins, it is unclear whether this inverse relationship with renal function reflects cause or effect. Some proteins may be increased in chronic kidney disease due to reduced renal clearance, some may be elevated secondary to tissue injury or chronic inflammation, and others may be drivers of renal injury. Regardless, this observation of widespread changes in the blood proteome of kidney disease patients emphasises the importance of using COVID-19 ESKD patients rather than healthy individuals as our control group.

Analysis within COVID-19 cases revealed 203 proteins associated with disease severity, the strongest of which was IL6 (Figure 3b). Association of IL6 with severe disease is well-established and has already received considerable attention (Wu et al., 2020; Li et al., 2020). Despite promising initial case reports of IL6R receptor blockade in COVID-19, convincing efficacy was not demonstrated in early randomised trials (Furlow, 2020). More recently, the REMAP-CAP trial has shown the benefit of anti-IL6R therapy when given to critically ill patients on admission to intensive care units (Gordon et al., 2021), indicating that IL6 does contribute to critical illness from COVID-19. Our finding that IL6 was most strongly upregulated in severe disease demonstrates the value of plasma proteomic profiling in identifying putative drug targets.

Members of the CCL and CXCL chemokine families (e.g. CCL2, CCL7, CCL20, and CXCL10) were strongly associated with severity. Likewise, higher levels of CCL2, CCL7, CCL20, and CXCL10 were associated with lower blood lymphocyte count and higher inflammatory markers (CRP and ferritin) (Supplementary file 1g), which are clinical markers of severe disease and poorer outcome in COVID-19 (Gupta et al., 2021). Of note, CCL20 is a chemoattractant for lymphocytes (Schutyser et al., 2003), and its negative association with lymphocyte count may reflect a direct effect on migration of lymphocytes from the blood into the tissues rather than simply marking severe disease. CCL2 (also known as MCP-1) and CCL7 (MCP-3) are both chemokines for monocytes, and CXCL10 has pleiotropic immunological effects including chemotaxis. These chemokines were also negatively correlated with blood monocyte count, suggesting recruitment of these innate immune cells into damaged tissues.

The neutrophil proteases PRTN3 (proteinase-3) and MPO (myeloperoxidase) (Figure 5) and the neutrophil-derived protein AZU1 were associated with severe disease (Supplementary file 1d), indicating that neutrophil activation and degranulation are features of severe COVID-19. Degranulation of neutrophils releasing PRTN3 and MPO could potentially contribute to oxidative damage in the lungs and thus more severe disease.

A striking finding of our study was the association of disease severity with upregulation of epithelial proteins (e.g. KRT19) and epithelial tissue repair pathways (e.g. PSIP1, AREG, GRN [progranulin]), most likely reflecting lung and vascular damage. KRT19 was notably prominent in our analyses, as well as the study by Filbin et al., 2020Supplementary file 1j. KRT19 is an intermediate filament protein, important for the structural integrity of epithelial cells (Saha et al., 2017). These data suggest that severe COVID-19 is characterised by destruction of the lung epithelium and vascular endothelium. Vascular injury might thus explain the high level of vascular thrombosis seen in patients in severe disease. In summary, our data reveal that severe COVID-19 is characterised proteomically by a signature of innate immune activation and epithelial injury.

Sixty-nine percent of proteins associated with severity were also differentially abundant in the case versus control analysis (Figure 6a), and for the large majority of proteins the within-case severity analysis, effect size was proportional to the fold change between cases and controls (Figure 6b). This suggests that, in general, the distinction in the plasma proteome between severe and mild COVID-19 is a quantitative difference in the COVID-19 signature, rather than there being an orthogonal signature involving a different set of proteins. Consistent with this concept, examination of PCA plots coloured by severity revealed that while there was a gradient of COVID-19 severity, the samples from severe or critical patients did not form a discrete cluster distinct from those from patients with milder disease (Figure 2—figure supplement 1). However, there were a few exceptions where proteins that were associated with severity were not upregulated in the case–control analysis. These included OSM, IL17C, and CCL20 (Figure 6c). These proteins therefore reflect biological processes specifically of severe disease and may represent therapeutic targets.

Survival analysis identified 44 proteins associated with increased risk of death (Figure 7b). As expected, many of these were also associated with disease severity, high CRP, and lower lymphocyte count (Figure 7—figure supplement 1). In contrast, 25 proteins were associated with reduced risk of death (Figure 7b). One such protein is the multi-functional cytokine TNFSF12 (TWEAK). Although TWEAK can exert pro-inflammatory effects, it also can inhibit the innate immune response (Maecker et al., 2005) and promote tissue repair and endothelial cell proliferation and survival (Burkly et al., 2007), which may be beneficial responses in COVID-19. This illustrates that although proteins associated with inflammation are often thought to be destructive, the inflammatory response also induces programmes for limiting injury and initiating tissue repair. Insufficient activation of such homeostatic mechanisms may contribute to why some individuals get severe COVID-19.

The host immune response to COVID-19 is a dynamic process, and clinical deterioration typically occurs 7–10 days after first symptoms. Temporal information may therefore be important in determining optimum timing of therapeutic intervention (e.g. blockade of a particular cytokine). By taking serial samples and examining their patterns within individuals over time, we were able to model protein trajectories and found that many proteins display temporal variability during COVID-19. Longitudinal measurements also allow molecular comparison of severe versus mild disease trajectories. By modelling the interaction term between time from first symptoms and overall disease course, we found 32 proteins that displayed distinct temporal profiles in severe versus mild disease. These results point to enhanced leucocyte–endothelial cell interactions indicated by upregulation of cell adhesion molecules (e.g. ITGB6, ICAM1) in severe disease. This endothelial activation may contribute to COVID-19-associated thrombosis discussed above. Management of thrombosis in COVID-19 currently consists of anticoagulation. Our results suggest that disrupting leucocyte–endothelial interactions may be a complementary therapeutic strategy.

Several proteins associated with either risk of death or clinical severity lie in pathways targeted by existing drugs. PARP1 was identified as an important marker of current or future severe COVID-19 and also was associated with risk of death. PARP1 is associated with inflammatory and vascular disease (Henning et al., 2018). PARP1 inhibitors are in use for cancer (Rouleau et al., 2010), and our data suggest that re-purposing of PARP1 inhibition in COVID-19 should be explored further. IL33 was associated with both risk of death and clinical severity, and its receptor IL1RL1 (ST2) was associated with clinical severity and identified as an important predictor of severe clinical course. Monoclonal antibodies against IL33 and its receptor are in late-stage development for asthma (Corren, 2019) and could also be explored in COVID-19. As discussed above, MPO was associated with clinical severity. MPO inhibitors (Galijasevic, 2019) might have a role in reducing neutrophil-mediated tissue injury in COVID-19. Finally, inhibitors of monocyte chemokines (e.g. CCL2) and their receptors have been developed (Vergunst et al., 2008; Haringman et al., 2006), although drugging these pathways is made more challenging by molecular cross-talk. 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 prioritising drug targets.

Other groups have studied the plasma or serum proteome in COVID-19 (Shen et al., 2020; Lucas et al., 2020; Arunachalam et al., 2020; Filbin et al., 2020; Rodriguez et al., 2020), using either mass spectrometry or immunoassays including the Olink platform. Mass spectrometry is less sensitive than immunoassays and so it is 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 of differentially abundant proteins. 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., 2020, who used a different Olink proteomics platform. Moreover, we observed consistent effect sizes (Figure 4—figure supplement 1). These similarities are striking given the difference in clinical populations and control groups; in Filbin et al.’s report, the controls included patients with non-COVID-19 respiratory infections, whereas our control group did not have active infection. The concordance in proteins associated with COVID-19 severity within cases was even higher (81.8%). The similarities suggest a similar plasma proteomic signature of COVID-19 across different clinical populations, particularly the signature associated with severity.

In summary, this study reveals proteins associated with COVID-19 infection and severity and demonstrates altered dynamic profiles between patients with severe disease and those with a more indolent course. Our results emphasise the importance of studying and targeting mechanisms that reduce the lung epithelial and endothelial damage to both alleviate the severity of the infection and reduce the chance of long-lasting complications. These data provide a valuable resource for therapeutic target prioritisation.

Materials and methods

Subjects and samples

Ethical approval

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All participants (patients and controls) were recruited from the Imperial College Renal and Transplant Centre and its satellite dialysis units, London, United Kingdom, and provided written informed consent prior to participation. Study ethics were reviewed by the UK National Health Service (NHS) Health Research Authority (HRA) and Health and Care Research Wales (HCRW) Research Ethics Committee (reference 20/WA/0123: The impact of COVID-19 on patients with renal disease and immunosuppressed patients). Ethical approval was given.

Subcohort A

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We recruited 55 COVID-19-positive haemodialysis patients, either as outpatients or as inpatients (Table 1). All patients were receiving in-centre outpatient haemodialysis prior to COVID-19 diagnosis. COVID-19 was confirmed in all cases with positive nasal PCR for the SARS-CoV-2 virus. Patients were recruited during the first UK national lockdown, with recruitment from 8 April to 30 May 2020. Blood was collected in EDTA tubes and centrifuged to obtain plasma and stored at –80°C. Sample processing was performed within 4 hours of venepuncture. The initial sample was taken as an outpatient for 30 patients and as an inpatient for 25 patients. Where feasible, serial blood samples were taken. In total, 259 samples were taken (three subsequently failed QC – see below). The median number of serial samples was 5 (range 1–10) (Figure 1—figure supplement 1d). Eight patients who were recruited as outpatients were subsequently admitted to hospital with COVID-19 over the course of the study. Twenty-seven of 55 (49.1%) patients had severe or critical disease (defined by peak WHO severity). Nine (16.4%) patients died.

In addition, we recruited 51 COVID-19-negative haemodialysis controls. COVID-19-negative haemodialysis controls were selected to mirror the cases in terms of demographic features (age, sex, ethnicity) (Figure 1—figure supplement 1a–c). These control patients had no clinical features of any other infection.

Subcohort B

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We also recruited a separate set of 46 COVID-19-positive haemodialysis patients (Table 2). These patients were recruited from the same centre, but slightly earlier than subcohort A (recruitment commenced on 30 March 2020). For these patients, blood was collected in serum tubes and centrifuged to obtain serum. At this time, we had very limited access to laboratory facilities, and so plasma was not collected from these patients. Five were outpatients and 41 were inpatients, reflecting the fact that UK policy was weighted towards inpatient testing at the time these patients were recruited. Thirty-three of 46 patients (71.7%) had severe or critical disease (by peak WHO severity), and nine (19.6%) patients died. For 40 patients, only one sample from a single timepoint was collected, and for six patients, two samples were collected. To provide controls for subcohort B, we used serum samples from 11 non-infected haemodialysis patients (collected at the same time as plasma from a subset of the ESKD control group described above).

Clinical severity scores

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Severity scoring was performed based on WHO classifications (WHO clinical management of COVID-19: Interim guidance 27 May 2020) adapted for clinical data available from electronic medical records. ‘Mild’ was defined as COVID-19 symptoms but no evidence of pneumonia and no hypoxia. ‘Moderate’ was defined as symptoms of pneumonia or hypoxia with oxygen saturation (SaO2) greater than 92% on air, or an oxygen requirement no greater than 4 L/min. ‘Severe’ was defined as SaO2 less than 92% on air, or respiratory rate more than 30 per minute, or oxygen requirement more than 4 L/min. ‘Critical’ was defined as organ dysfunction or shock or need for high dependency or intensive care support (i.e. the need for non-invasive ventilation or intubation). Severity scores were charted throughout a patient’s illness. We defined the overall severity/clinical course for each patient as the peak severity score that occurred during the patient’s illness.

Proteomic assays

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Plasma and serum proteomic measurements were performed using Olink proximity extension immunoassays (https://www.olink.com/products/). Five 92-protein multiplex Olink panels were run (‘inflammation’, ‘immune response’, ‘cardiometabolic’, ‘cardiovascular 2’, and ‘cardiovascular 3’), resulting in 460 measurements per sample. Since a small number of proteins were measured on more than one panel, we measured a total of 436 unique proteins. The Olink assays were run using 88 samples/plate. All plates were run in a single batch. Plate layouts was carefully designed to avoid confounding of potential plate effects with biological or clinical variables of interest. To achieve this, we used an experimental design that combined ensuring case/control balance across plates with random selection of samples from each category and random ordering of allocation to wells. This is outlined in more detail as follows. We ensured that each plate contained a mixture of control and case samples. Specifically, a fixed proportion of each plate was designated for control samples. The allocation of specific control samples to each plate was performed using randomisation. For the case samples, we again used randomisation for plate assignment, with the constraint that once one sample from a given patient was allocated to a plate, all other longitudinal samples from that patient were assigned to same plate. Finally, once all the samples had been allocated to plates, the layout of samples within each plate was determined through a further randomisation step for well allocation.

Protein annotation

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We used the Human Protein Atlas version 20.0 (Uhlén et al., 2015) for protein annotation (Figure 1—figure supplement 1). We performed enrichment analysis of the 436 proteins that we measured using string-db (Szklarczyk et al., 2019).

Normalisation and quality assessment and control

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The data was normalised using standard Olink workflows to produce relative protein abundance on a log2 scale (‘NPX’). Quality assessment was performed by (1) examination of Olink internal controls and (2) inspection of boxplots, relative log expression plots (Gandolfo and Speed, 2018), and PCA. Following these steps, three poor-quality samples were removed. In addition, five samples failed QC on a single proteomic panel only, with the remaining panels passing QC. For these samples, proteins on the panel that failed QC were set to missing, and the data for the remaining proteins was retained.

PCA revealed no substantial impact of plate effects (Figure 2—figure supplement 2). Thirteen proteins were assayed more than once due to their inclusion in multiple Olink panels. For plasma, the median correlation between the assays was 0.986 with an inter-quartile range (IQR) of 0.974–0.993 and a range of 0.925–0.998. For serum, the median correlation between the assays was 0.991 with an IQR of 0.952–0.995 and a range of 0.737–0.999. We removed duplicate assays at random prior to subsequent analyses.

For 11 ESKD controls, we had contemporaneous plasma and serum samples. To assess the comparability of these two matrices, we calculated the Pearson’s correlation coefficient between the assays for each protein (Supplementary file 1k). Three hundred and forty-four of 436 (78.9%) proteins had a Pearson’s r > 0.5. We also report the variance of each protein in plasma and serum since low correlation may reflect low variance. The proteins with the lowest estimated Pearson correlation coefficient were AZU1, STK4, and TANK. We highlight that this comparison had small sample size (only 11 samples) and that the samples were from control patients without infection. Caution should be made in extrapolating these findings to the context of active infection where protein dynamic ranges may be different.

Missing values

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Following QC, 0.22% data points were missing for the plasma dataset and 0.35% for the serum dataset. For analyses that required no missing values (PCA and supervised learning), we imputed missing values as follows. The dataset was first scaled and centred, and missing values imputed using caret’s k-nearest neighbours method (Kuhn, 2008). The five closest samples (by Euclidean distance) were used to estimate each missing value.

Principal component analysis

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Singular value decomposition was used to perform PCA on the proteomic data from subcohort A (plasma samples). We then used the loadings from subcohort A together with the proteomic data from subcohort B to calculate principal component scores. This enabled projection of subcohort B data into the PCA space of subcohort A.

Differential protein abundance analysis: COVID-19 positive versus negative

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Differential protein abundance analyses between COVID-19 positive and negative samples were performed using linear mixed models, to account for the use of serial samples from the same individuals (R lme4 package Bates et al., 2015). This analysis compared 256 samples from 55 COVID-19 patients with 51 non-infected patients (one sample per non-infected patient). Age, sex, and ethnicity were included as covariates. We used a random intercept term to estimate the variability between individuals in the study and account for repeated measures. The regression model in R notation was:

NPX covidstatus+sex+age+ethnicity+(1|individual)

where NPX represents the protein abundance and covid_status was a categorical variable (infected/non-infected). Sex and ethnicity were also categorical variables. Age was a quantitative variable. We calculated P-values using a type 3 F-test in conjunction with Satterthwaite’s method for estimating the degrees of freedom for fixed effects (Kuznetsova et al., 2017). The regression model was fitted for each of the 436 proteins individually. Multiple testing correction was performed using the Benjamini–Hochberg method and a 5% FDR used for the significance threshold.

The same approach was used for subcohort B. This analysis comprised 52 serum samples from 46 COVID-19-positive patients versus 11 samples from non-infected patient samples (one sample per non-infected patient).

As sensitivity analyses, we repeated the differential abundance analyses between case and controls for the subcohort A adjusting for additional covariates and comparing this to the basic model (i.e. using age, sex, and ethnicity alone). This was performed for each of the following parameters: diabetes status, cause of ESKD, and time to last haemodialysis.

Testing for associations between proteins and clinical severity

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For testing the association of plasma proteins with the four-level WHO severity rating (mild, moderate, severe, and critical) within COVID-19-positive cases from subcohort A (n = 256 samples from 55 patients), we used a similar linear mixed modelling approach to the COVID-19-positive versus -negative differential abundance analysis; for this analysis, the covid_status term was replaced by a severity variable encoded using orthogonal polynomial contrasts to account for ordinal nature of severity levels. As before, age, sex, and ethnicity were included as covariates. As a sensitivity analysis, we repeated the analysis with time to last haemodialysis as an additional covariate.

Testing for associations between proteins and clinical laboratory tests

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The linear mixed modelling strategy was also employed for testing association of temporal clinical laboratory variables and protein levels, with the value of the clinical variable (as a quantitative trait) used in place of covid_status. Only COVID-19-positive patients were included in this analysis. Contemporaneous lab measurements were not available for all samples. This varied according to the clinical lab parameter. Some (e.g. troponin, d-dimer) were measured less frequently than full blood count and CRP. Details of the proportion of missing values for each lab parameter are included in Supplementary file 1g. We also calculated correlations between clinical laboratory variables and protein levels using the R package rmcorr, which determines the overall within-individual relationship among paired measures that have been taken on two or more occasion (Bakdash and Marusich, 2017).

Testing for associations between proteins and ethnicity

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We performed testing of protein levels and ethnicity separately in COVID-19-negative ESKD patients and COVID-19-positive ESKD patients. These analyses were limited to individuals who were White, South Asian (Indian, Pakistani, or Bangladeshi ancestry), or Black as there were too few individuals from other ethnic groups for meaningful interpretation. For COVID-19-negative patients (one sample per patient), we performed linear regression for each protein with ethnicity as the predictor variable and age and sex as covariates. For COVID-19-positive patients, we used a linear mixed model to account for serial samples from the same individual, again with age, and sex as covariates.

Multiple testing correction

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We used the Benjamini–Hochberg method to control the FDR at 5% for all statistical analyses.

Alternative estimation of the FDR using the plug-in method

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To provide additional support that the Benjamini–Hochberg procedure was providing adequate control of the FDR, we also used the plug-in procedure (Hastie et al., 2001) as an alternative method to estimate the FDR, as described below.

  1. We defined R as the number of associations declared significant in the real data.

  2. We defined C as the test statistic used as the significance threshold used in the real data (i.e. that corresponding to an adjusted p-value of 0.05).

  3. The expected number of proteins that we would find significant under the null hypothesis that no proteins are differentially abundant between COVID-19-positive versus -negative patients (i.e. false positives) was estimated using a permutation strategy. We randomly permuted each individual’s COVID-19 status label 100,000 times and, in each case, repeated the differential abundance analysis on the permuted data. The estimated the number of false positives (V^) was then estimated by the number of associations with test statistic > C in 100,000 permutations of the data, divided by the number of permutations.

  4. The estimated FDR was then calculated as V^/R.

We implemented a similar approach for the testing the association of proteins with severity scores within cases.

Using this method, the estimated FDR for the case versus control analysis was 0.062 and for the severity analysis 0.057, indicating that we had appropriately controlled the FDR.

Empirical p-value calculation

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As a complementary analysis, based on the approach of Filbin et al., 2020, we estimated the empirical p-value for the likelihood of observing as many significant proteins as we identified in the real data if the null hypothesis of no differentially abundant proteins in cases versus controls were true. We again used 100,000 permutations of the case–control labels to estimate the null distribution. We performed Benjamini–Hochberg adjustment on the nominal p-values of each permutation and counted the number of proteins that were significant (adjusted p-value<0.05) in each permutation.

The distribution of the number of proteins declared significant is shown in Figure 3—figure supplement 2a; on no occasion in 100,000 permutations did we observe more proteins declared significant than in the real data. We can thus state that the empirical p-value (the fraction of permutation runs where we observed ≥ the number of associations in the real data) is less than 1/100,000 = 1×10−5.

We also applied this method to the association testing of proteins with severity scores within cases (Figure 3—figure supplement 2b). Again, on no occasion in 100,000 permutations did we observe more proteins declared significant than in the real data (empirical p-value<1×10−5).

Supervised learning

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Random forest models were fitted using R’s randomForest and caret packages (Kuhn, 2008; Leo, 2001). Data was centred, scaled, and imputed as in Missing values with the caveat that, during cross-validation, the pre-processing procedure was first applied on the resampled (training) data before the same method was applied without re-calculation to the holdout (test) set. To estimate model accuracy, we used fourfold cross-validation. The cross-validation procedure was repeated 100 times. The model’s parameters were kept constant at 500 trees and an mtry value (number of proteins randomly sampled as candidates at each node) calculated as the square root of the number of features. After parameter estimation, we fitted a final model trained using the entirety of the dataset. This model was used for subsequent feature extraction. Random forest feature extraction was carried out using the R randomForestExplainer package. We made use of the following importance measures: accuracy decrease (the average decrease in prediction accuracy upon swapping out a feature), number of trees (the number of trees with a node corresponding to a feature), and mean minimal depth (the average depth at which a node corresponding to a feature occurs). Three models were generated with different input features: (1) proteomic data alone; (2) clinical parameters alone; and (3) proteomic data and clinical parameters. Clinical parameters included sex, age, ethnicity, cause of ESKD, comorbidities, smoking status, radiological evidence of pulmonary infiltrates, and clinical laboratory tests.

Survival analysis using joint modelling

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Following scaling and centring, we fitted linear mixed models for each protein to capture the temporal trajectories of each individual. A polynomial spline of degree two was used to model protein concentration with respect to time (from symptom onset, measured in days); the spline was fitted for samples that were taken between 1 and 28 days from first symptoms, inclusive. Proteomic data after that point was censored. We estimated both random intercepts and random slopes for each individual, as per the following R formula notation:

NPXtime+(time|individual)

These were joined to a Cox regression model using the jointModel package (Rizopoulos, 2010) in order to estimate the association of each protein with risk for death. P-values were calculated using a Wald test for the association between the linear mixed model and Cox regression. Benjamini–Hochberg adjustment was applied, with an adjusted p-value of 0.05 used as the significance threshold.

Longitudinal analysis

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We also used linear mixed models to estimate the temporal profile of each protein. For this longitudinal analysis, we explicitly modelled the time from first symptoms. We set up the model to test for each protein (1) whether the protein significantly change over time and (2) whether the protein changes over time differently in individuals with a mild versus severe disease course. The latter was performed statistically by testing for an interaction effect between time and clinical course. For the purposes of this analysis, we binarised patients into severe or non-severe clinical course according to the peak WHO severity disease of their illness. Patients with a peak WHO score of mild or moderate were considered non-severe and those with a peak score of severe or critical were considered severe.

We then used R’s bs function to fit a polynomial spline of degree two to model protein concentration with respect time (from symptom onset, measured in days) (Perperoglou et al., 2019). The spline was fitted for samples that were taken between 1 and 21 days from symptom onset, inclusive. We estimated random slopes with respect to time, in addition to random intercepts, to account for each individual’s unique disease course. For each protein, we fitted the following model (R notation):

NPXtimeseverity+sex+age+ethnicity+(time|individual)

To identify proteins that changed significantly over time, we examined the p-values for the main effect of time. To identify proteins with distinct temporal profiles between severe and non-severe cases, we examined the p-values for the time × severity interaction term. For each of these two research questions, p-values were adjusted for the multiple proteins tested using the Benjamini–Hochberg method and 5% FDR used as the significance threshold.

Code availability

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Code is available in the following GitHub repository: https://github.com/jackgisby/longitudinal_olink_proteomics; Gisby, 2021; copy archived at swh:1:rev:32f08137859d44707ec4f086eed9af9b9ee91a87.

Data availability

All data generated during this study are included in the manuscript and supporting files. Underlying source data for all analyses (individual-level proteomic and clinical phenotyping data) are available without restriction as Source Data Files 1-4. In addition, these data have been deposited in the Dryad Digital Repository (https://doi.org/10.5061/dryad.6t1g1jwxj). Code is available in the following GitHub repository: https://github.com/jackgisby/longitudinal_olink_proteomics copy archived at https://archive.softwareheritage.org/swh:1:rev:32f08137859d44707ec4f086eed9af9b9ee91a87/.

The following data sets were generated

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Decision letter

  1. Evangelos J Giamarellos-Bourboulis
    Reviewing Editor; National and Kapodistrian University of Athens, Medical School, Greece
  2. Matthias Barton
    Senior Editor; University of Zurich, Switzerland
  3. Evangelos J Giamarellos-Bourboulis
    Reviewer; National and Kapodistrian University of Athens, Medical School, Greece

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The authors analyzed blood proteomic profiles from COVID-19 patients with end-stage renal disease (ESRD) and report changes compatible with an aberrant innate immune response. They also identified several novel molecular predictors of death, as well as markers of severe versus non-severe clinical development in high-risk patients with ESRD infected with SARS-CoV2. Overall, the findings are novel and important as they are the first to characterize potential molecular markers that may help to predict the severity of the clinical course of COVID-19 in this high-risk group of patients.

Decision letter after peer review:

Thank you for submitting your article "Longitudinal proteomic profiling of high-risk COVID-19 patients reveals markers of severity and predictors of death" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Evangelos J Giamarellos-Bourboulis as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Senior Editor.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

1) The authors are trying to do generalizations for all COVID-19 patients. They need to focus on ESRD which is their main aim. This should be reflected on the title that needs to be appropriately revised.

2) The authors claim that ESRD is a major predisposing factor for COVID-19. This is not yet supported by the existing literature whereas many clinicians feel that patients with ESRD have a milder course than the rest of patients with COVID-19. The authors need to provide support of this (i.e. the selected patients come from how many infected and how was their course compared to the rest of the available cohort) and they need to provide evidence if this is an underlying confounding factor which does not allow the clustering of proteins clearly separating ESRD patients with COVID-19 from ESRD without COVID-19.

3a) The authors discuss about longitudinal sampling whereas they analyze all samples together.

3b) I suggest that they provide analysis of the samples on the day of peak of symptoms that defined the severity classification and compare with samples collected before and after that day.

4) The authors need to provide analysis how the haemodialysis per se has impacted their findings. This can be provided through adjustment with the time since last dialysis.

5) The non-ESRD patients with COVID-19 do not seem appropriate since they have lower frequency of diabetes as co-morbidity.

6) The validation cohort needs to be described in more detail. Are these just more samples from the same study?

7) Figure 5: Individual measured data points should be added to these time-series to show how well the data is modeled.

8) Supplementary Figure 4: As this is supposed to be a validation, please add lead loadings from the primary cohort (shown in Figure 2—figure supplement 1). How well do these replicate?

9) Supplementary Figure 8: X-axis units are "Relative Log2 Abundance" – are these identical to NPX values?

10) The technical replication using just two samples is not relevant (too small). Better refer to technical replications done by Olink or in the literature with larger sample sizes. Also, the reported Pearson correlation of 0.999 looks overly optimistic and is likely a result from correlating data over a vast range of concentrations. What is the relative error (e.g. sum over the differences between the NPX values for each protein divided by their sums)?

11) It appears that some of the controls have been measured twice, in serum and plasma. These data should be compared and substantial differences reported. This information may be important if proteins are taken forward for follow-up and to interpret differences between future studies.

12) All predictions and models should be compared to models that use relevant clinical parameters alone as a reference and then proteins + clinical parameters to test how much information is added by the protein(s).

13) Some information about the role of OLINK measured proteins in ESKD should be provided (maybe from the literature).

14) Ethnicity appears to play a role in Covid-19 severity. The authors included ethnicity into the models. It would be important to highlight and discuss proteins that associate with ethnicity in light of selecting the most suitable and unconfounded biomarkers in future studies.

15) The authors do not describe the matching procedure. Moreover, as shown in Supplementary Table 1 controls differ significantly in terms of comorbidities.

16) Second, the authors should describe better the validation cohort used. Was from the same site? Was in parallel recruited and if so, why is it independent.

17) The Discussion section is confusing. It should be fully re-written in more hierarchical way outscoring the main important findings. Emphasis should be given in ESRD and not in differences with all other papers of proteomics in COVID-19.

18) The authors cite a preprint by Huang (medRxiv, 5 Oct 2020) which has a similar design. In the meantime, a more complete evaluation of that same dataset has been posted by Filbin et al. (Filbin et al., 2020). It may be worthwhile to take some of their results into account in the Discussion.

https://doi.org/10.7554/eLife.64827.sa1

Author response

1) The authors are trying to do generalizations for all COVID-19 patients. They need to focus on ESRD which is their main aim. This should be reflected on the title that needs to be appropriately revised.

We have revised the title to “Longitudinal proteomic profiling of dialysis patients with COVID-19 patients reveals markers of severity and predictors of death".

2) The authors claim that ESRD is a major predisposing factor for COVID-19. This is not yet supported by the existing literature whereas many clinicians feel that patients with ESRD have a milder course than the rest of patients with COVID-19. The authors need to provide support of this (i.e. the selected patients come from how many infected and how was their course compared to the rest of the available cohort) and they need to provide evidence if this is an underlying confounding factor which does not allow the clustering of proteins clearly separating ESRD patients with COVID-19 from ESRD without COVID-19.

The reviewer’s comments that end-stage kidney disease is not an established risk factor for severe or fatal COVID-19, and that ESKD patients have a milder disease course, are not correct. Multiple major epidemiological and registry studies provide unequivocal evidence that chronic kidney disease (CKD), particularly ESKD, is a major risk factor for severe or fatal COVID-19.

i) Data from the UK Renal Registry shows that 7- and 14-day mortality for COVID-19 infected in-centre haemodialysis (HD) patients was 11% and 19%, respectively.

ii) Data from the Scottish Renal Registry estimates 30-day mortality for HD patients following a positive COVID-19 test as 22% (https://beta.isdscotland.org/find-publications-and-data/population-health/covid-19/scottish-renal-registry-covid-19-report/ ). As of 31 May 2020, 28.2% of renal replacement therapy patients who had had a positive COVID-19 test had died.

iii) The OpenSAFELY study (Williamson et al., 2020) examined ~17 million UK primary care records and linked these to the UK national COVID-19 mortality register. This enabled identification of risk factors for fatal COVID-19. Impaired renal function was a strong risk factor for death from COVID-19, and the risk was proportional to the degree of renal impairment. Patients with estimated glomerular filtration rate (eGFR) <30ml/min/1.73m2 had a hazard ratio (HR) for death of 3.56 after adjustment for age and sex (dialysis patients typically have eGFR <15). The association of impaired renal function with risk of death from COVID-19 persisted after adjustment for all other demographic and clinical risk factors in multivariable analysis (eGFR <30 conferred HR for death of 2.52 in the fully adjusted model). This suggests that ESKD per se is a risk factor for COVID19 rather than simply a marker of multi-morbidity. ESKD was one of the strongest risk factors for death from COVID-19, with similar risk to organ transplant or haematological malignancy.

iv) The ISARIC study (Docherty et al. BMJ 2020;369:m1985) examined 20,133 hospitalised patient records and identified CKD as the comorbidity with the strongest association with death (estimated HR 1.28 in multivariable analysis). This study analysed CKD as one group, so the HR for ESKD patients specifically is not available.

v) In our local cohort of 1,352 in-centre haemodialysis patients, by the end of our study recruitment period (30 May 2020), 315 patients had tested positive for COVID-19 (of which 11 acquired COVID-19 in hospital). Excluding the 11 patients who acquired COVID-19 hospital, 160 (53%) required hospitalisation. Of the 315 patients, 85 died (76 in hospital, 9 without admission), leading to a 27% mortality rate. This figure is in line with the Scottish registry data.

Clearly, these multiple sources of data show that mortality rate for ESKD patients far exceeds that of the general population, and that the mortality rate of the patient sample on whom our proteomic study was performed is in line with that observed elsewhere.

We understand the second part of the reviewers’ comment to be asking whether any potential selection bias for severe cases has influenced the differential expression analysis of COVID-19 positive vs. negative ESKD patients. As can be seen from the figures above, the subset of patients we sampled is not overrepresented for mortality compared to either our local haemodialysis population or the UK more generally. Moreover, the striking concordance between our results and that the study of Filbin et al. of COVID-19 positive vs. negative patients in a non-renal population (see our response to reviewer comment 18 and new Figure 4—figure supplement 1) provides reassurance that our findings of proteins differentially expressed in COVID-19 are robust.

We have expanded the Discussion to reflect the above points.

3a) The authors discuss about longitudinal sampling whereas they analyze all samples together.

To clarify, we performed 5 distinct major analyses. Some, but not all of these, use all available samples. This is explained below:

1) Differential protein abundance analysis between all case samples and control samples (i.e. COVID-19 negative haemodialysis patients).

As the reviewer implies, for this analysis we leveraged the fact that for our cases we had serial samples which enables greater statistical power. To appropriately account for the non-independence of serial samples, we used linear mixed models.

2) Analysis of severity within cases.

For this analysis, we analysed associations between protein levels and clinical severity at the time of blood sampling. This analysis is greatly enhanced by the fact that we have serial protein measurements, and sometimes changing clinical severity levels, for a given individual. Again, to appropriately model non-independence of samples from the same individual we used a linear mixed modelling strategy. By leveraging all the data, including intra-individual changes in proteome and severity, we gain power.

3) Supervised learning to predict severe disease.

This analysis was performed using only the first sample for each patient to identify a model that could predict whether a patient either had severe disease or would develop it in the future.

4) Identifying death associated proteins through modelling with joint models.

This analysis uses repeated measurements to identify predictors of death from serial data. A joint model combines a linear mixed model and a Cox proportional hazard model and is the appropriate analytical method when the predictor (i.e. the protein) is itself a dynamic entity that may be impacted by the clinical state.

5) Identifying proteins with distinct longitudinal profiles.

This analysis is the “longitudinal” model and explicitly models the time from first symptoms. This model tests a) whether proteins significantly change over time and b) examines whether there were proteins that change over time differently in individuals with a mild versus severe disease course. The latter was performed statistically by testing for an interaction effect between time and clinical course. We have realised that we omitted to show the results from a) in the previous manuscript and these are now included (see “Changes to the manuscript in response to 3a) and b)” below).

3b) I suggest that they provide analysis of the samples on the day of peak of symptoms that defined the severity classification and compare with samples collected before and after that day.

It is our view that the analysis we performed to be preferable to the approach suggested by the reviewer – although we acknowledge that this may not have been clear from our original description of our analyses. Linear mixed modelling appropriately accounts for the relationship between serial samples from the same individuals, and is a widely used, well-established, and principled approach for longitudinal data analysis (see, for example, Laird, N.M. and Ware, J.H., 1982. Random-effects models for longitudinal data. Biometrics, pp.963-974; Verbeke, G., 1997. Linear mixed models for longitudinal data. In Linear mixed models in practice (pp. 63-153). Springer, New York, NY).

In contrast, the reviewer’s suggested approach would limit longitudinal analysis to just 3 samples (the sample from the day of peak severity, and the sample immediately preceding and following this). Apart from the practical difficulties that this would present (e.g. for patients for whom the WHO severity score is mild throughout, which samples should be selected?), we do not believe that a reliable longitudinal proteomic trajectory can be constructed through analysis of just 3 data points.

Moreover, we note that the very high concordance (81.8%) in the proteins we identified as severity-associated and those reported in the study by Filbin et al. (see reviewer comment 18 and new Figure 4—figure supplement 1), providing independent support that our approach is robust.

Changes to the manuscript in response to 3a) and b:)

We appreciate that we did not articulate clearly enough the multiple types of analyses we performed. We thus have made the following changes in response to the reviewer’s comments:

i) We have revised the text and introduced new subheadings in the Results section to make the distinction between these analyses clearer. We have also revised the Discussion so that the main findings are discussed in a more structured manner (see also response to reviewer comment 17). We have also edited the Materials and methods to improve clarity.

ii) We have made a new figure (Figure 7—figure supplement 1) illustrating how the death-associated proteins detected in Analysis 4 above relate to the associations with clinical markers of disease severity (e.g. CRP, lymphocyte count).

iii) We have added results showing proteins that significantly change over time (new Supplementary file 1H), in addition to proteins that have different temporal trajectories between severe and non-severe cases. We have amended the text of the Results, Discussion and Materials and methods accordingly.

iv) We have made plots (new Figure 9 and Figure 9—figure supplement 1) showing the temporal trajectories for all proteins that display a significantly different longitudinal profile (5% FDR) between patients with a mild versus severe clinical course, since the shape of trajectories was not evident from the p-values alone.

4) The authors need to provide analysis how the haemodialysis per se has impacted their findings. This can be provided through adjustment with the time since last dialysis.

We agree with the reviewer that it is possible that haemodialysis might impact the plasma proteome. To reduce the potential confounding effects of haemodialysis, we designed the study such that all samples were taken immediately before haemodialysis. For the large majority (86.6%) of samples, last haemodialysis was between 48 and 72 hours prior to blood draw. This consistency in timing of blood sampling reduces the potential for impact of this issue.

To evaluate whether time from last haemodialysis might have nevertheless impacted our results, we performed the analysis suggested by the reviewer, including time from last haemodialysis as a covariate along with age, sex and ethnicity in the regression models. Our results were not substantially changed by this. In the differential abundance analysis of COVID-19 positive vs. negative patients, the correlation between log fold changes estimated with and without inclusion of time from last dialysis as a covariate was >0.99 (Figure 3—figure supplement 4A). Minus log10 p-values were also very highly correlated (r >0.99) (Figure 3—figure supplement 4B). Similarly, in the analysis of severity, the correlation of estimated effect sizes and of -log10 p-values were both > 0.99 (Figure 3—figure supplement 4C-D).

We have amended Supplementary file 1C (COVID-19 + vs. – analysis) and Supplementary file 1D (severity analysis) to show for all proteins the results from a) both the model using only age, sex and ethnicity as covariates, and b) the extended model that also includes time for last dialysis (see under the column header “sensitivity analyses with additional covariates”).

We have added this description of the analysis and the results in the Results and Materials and methods.

5) The non-ESRD patients with COVID-19 do not seem appropriate since they have lower frequency of diabetes as co-morbidity.

16) Second, the authors should describe better the validation cohort used. Was from the same site? Was in parallel recruited and if so, why is it independent.

We address reviewers’ points 5 and 16 together due to their similarity.

We matched COVID-19 positive and negative ESKD patients as closely as possible in terms of age, sex and ethnicity. We believe that this close demographic matching and the fact that our controls were also haemodialysis patients represents an advance on previous studies. Unfortunately, exact matching of all comorbidities was not feasible, and this issue also applies to other published COVID19 proteomic studies (where hospitalised COVID-19 patients with a high prevalence of comorbidity have often been compared to healthy controls who, by definition, do not have comorbidities). However, we agree with the reviewers that it is important to check whether the lower prevalence of diabetes in our controls might be a potential confounder.

To address this, we have repeated the differential abundance analysis between COVID-19 positive and COVID-19 negative patients adjusting for diabetes status (i.e. adding diabetes as a covariate along with age, sex and ethnicity). Comparison of effect size estimates and -log10 p-values revealed highly consistent results with the model that used only age, sex and ethnicity (Figure 3—figure supplement 3A-B). We now show the results from the extended model under the column header “sensitivity analyses with additional covariates” in Supplementary file 1C.

In addition to the differences in diabetes prevalence, there were also other differences in the underlying cause of ESKD in cases compared controls. We therefore performed a further sensitivity analysis adjusting for underlying cause of renal failure. This did not make any meaningful difference to our results (Figure 3—figure supplement 3C-D). ESKD is characterised pathologically by small shrunken kidneys, loss of glomeruli and scarring, irrespective of the original cause of the renal injury. Thus, even though there maybe disparate causes, ESKD is a more clinically, biochemically and pathologically homogeneous entity than the renal diseases that lead to it. This may explain the lack of impact of this covariate on the plasma proteome.

We have made changes to the text detailing these sensitivity analyses in the Results, Discussion and Materials and methods.

6) The validation cohort needs to be described in more detail. Are these just more samples from the same study?

15) The authors do not describe the matching procedure. Moreover, as shown in Supplementary Table 1 controls differ significantly in terms of comorbidities.

Given the similarities in comments 6 and 15 we have responded to these points together.

The two groups of COVID-19 patients were collected from the same site but there was a small temporal difference in collection and a difference in sample types (plasma versus serum).

To put the collections into context, patients were recruited during a highly challenging period for both clinical care and research during the first UK national lockdown. We initiated patient recruitment on 30th March 2020. At this time, we had very limited access to laboratory facilities and were only able to process serum. Once we had the relevant approvals to open our research laboratory facilities, we switched to collecting plasma (from 8th April 2020). The reason for the switch was that a) although the Olink platform can be used for both serum and plasma, it is optimised for the latter, and b) given the high incidence of micro- and macrovascular thrombosis, we hypothesised that it might be important to measure coagulation factors (which can only be measured in plasma).

The initial sample collection (serum) was considerably smaller than the subsequent collection (plasma). In addition, for the majority of patients in the initial collection, we only had a single timepoint, thus precluding longitudinal analyses. Therefore, we used the larger subcohort as the discovery cohort, and the smaller cohort to provide validation for the case-control analysis.

In terms of clinical characteristics, the validation cohort had a higher proportion of hospitalised and severely ill patients. This was because PCR COVID-19 testing in the UK at that time was largely limited to hospitalised patients. By the time we recruited the discovery cohort, the capacity to perform COVID PCR testing had been expanded and was available for patients attending outpatient haemodialysis. Consequently, we were able to identify and recruit patients with milder disease as well as hospitalised patients. Therefore, the discovery cohort included a broader spread of illness severity.

We used the term “independent” to indicate that the validation cohort was a set of non-overlapping patients from the discovery cohort. However, we can now appreciate that this might be misleading and lead a reader to interpret this as patients from a different centre.

Actions:

– We have removed the term “independent cohort” from the revised manuscript.

– To improve clarity, we now refer to the two patient groups as subcohort A (the larger collection that we previously called the discovery cohort) and subcohort B (what was previously called the validation cohort).

– We have added a new table (Table 2) with characteristics of subcohort B.

7) Figure 5: Individual measured data points should be added to these time-series to show how well the data is modeled.

We now provide these in Figure 9—figure supplement 2. Since the large number of data points makes the modelled trajectories difficult to visualise, we show the modelled trajectories without individual data points in the main display item (Figure 9).

8) Supplementary Figure 4: As this is supposed to be a validation, please add lead loadings from the primary cohort (shown in Figure 2—figure supplement 1). How well do these replicate?

We thank the reviewer for this excellent suggestion. To best demonstrate how the patterns in the PCA for subcohort A compare to those in subcohort B, we have done the following:

We present the PCA on the primary cohort and then project the data from subcohort B onto the same PCA coordinates (so that the loadings are orientated identically) (new Figure 2). This reveals that the axis along which cases and controls are separated in subcohort A clearly divides cases and controls in subcohort B (despite the differences in blood materials used, i.e. plasma vs. serum).

We have amended the text as follows:

Results:

“We used the smaller subcohort B (n=52 serum samples from 46 patients with COVID-19; Materials and methods) for validation. […] This revealed clearer separation of infected and non-infected patients than in subcohort A (Figure 2C-D), perhaps reflecting the higher proportion of hospitalised patients (41 of 46 patients) in subcohort B (Table 2).”

Materials and methods:

“Principal Components Analysis

Singular value decomposition was used to perform PCA on the proteomic data from subcohort A (plasma samples). […] This enabled projection of subcohort B data into the PCA space of subcohort A.”

In addition, we have revised the presentation of the PCA analysis to improve clarity as described below:

i) We now show plots for both PC1 vs. PC2 and PC1 vs. PC3 for each subcohort (new Figure 2).

ii) In Figure 2 we show the samples coloured by case control status, and in Figure 2—figure supplement 1 we show samples coloured by severity.

9) Supplementary Figure 8: X-axis units are "Relative Log2 Abundance" – are these identical to NPX values?

Yes these are NPX values. We were trying to make the plot more accessible to readers not familiar with the Olink platform and used the term “relative” to indicate that the measurements were not in absolute units (e.g. ng/L). However, we can see that this may cause confusion.

We have now changed to the axis units to “NPX (log2 protein abundance)”, and added the definition of NPX in the legend. We have also made this change for all relevant figures (new Figure 5, Figure 6C, Figure 9, Figure 9—figure supplement 1 and Figure 9—figure supplement 2).

10) The technical replication using just two samples is not relevant (too small). Better refer to technical replications done by Olink or in the literature with larger sample sizes. Also, the reported Pearson correlation of 0.999 looks overly optimistic and is likely a result from correlating data over a vast range of concentrations. What is the relative error (e.g. sum over the differences between the NPX values for each protein divided by their sums)?

We accept the reviewers’ point that technical replication using just two samples is too small for reliable inference and we have removed this from the manuscript.

11) It appears that some of the controls have been measured twice, in serum and plasma. These data should be compared and substantial differences reported. This information may be important if proteins are taken forward for follow-up and to interpret differences between future studies.

For the 11 control samples from which we had both serum and plasma, we now present the correlation between plasma and serum levels for each protein in Supplementary file 1K.

We have made the following changes to the text to reflect this:

“For 11 ESKD controls, we had contemporaneous plasma and serum samples. To assess the comparability of these two matrices, we calculated the Pearson’s correlation coefficient between the assays for each protein (Supplementary file 1K). […] Caution should be made in extrapolating these findings to the context of active infection where protein dynamic ranges may be different.”

12) All predictions and models should be compared to models that use relevant clinical parameters alone as a reference and then proteins + clinical parameters to test how much information is added by the protein(s).

We have performed the analysis suggested by the reviewer. We also made a minor modification to our method for estimating model accuracy: we now use 4-fold cross-validation and increased the number of iterations to 100 to ensure stability of the accuracy estimate.

Using the Random Forests supervised learning approach, the accuracy of the models are as follows:

– Using only the clinical data (demographics, comorbidities and clinical laboratory tests): 66%.

– Using only the protein data: 71%.

– Using clinical + protein data 71%.

If our primary goal was to develop a clinically useful test based on Olink plasma proteomics to predict severe disease, then developing a predictive model that combined standard clinical parameters and proteins would be most logical. However, we do not believe there is any realistic prospect of Olink proteomics being applied outside the research context in this pandemic. Our goal in performing the supervised learning was not to develop a predictive tool per se, but to use the predictive model to better understand the biology of severe COVID-19 by identifying proteins that the algorithm selected as important predictors. Interrogation of the model to this end is best done for the analysis that used only the proteomic data.

We have made changes to the text to reflect these new analyses (Results).

13) Some information about the role of OLINK measured proteins in ESKD should be provided (maybe from the literature).

We have added this to the Discussion:

“ESKD is itself likely to significantly impact the plasma proteome. Previous cross-sectional studies have shown that the levels of many circulating proteins have an inverse relationship with eGFR [Naseeb et al., 2015; Christensson et al., 2018]. […] Regardless, this observation of widespread changes in the blood proteome of kidney disease patients emphasises the importance of using COVID-19 ESKD patients rather than healthy individuals as our control group.”

14) Ethnicity appears to play a role in Covid-19 severity. The authors included ethnicity into the models. It would be important to highlight and discuss proteins that associate with ethnicity in light of selecting the most suitable and unconfounded biomarkers in future studies.

We have added an analysis of protein associations with ethnicity.

Our view is that this is best done separately for COVID-19 negative and positive ESKD patients, as in the latter proteins associated with severity may be confounded with ethnicity.

We therefore adopted the following approach:

i) In COVID-19 negative ESKD patients (where there was only 1 sample per patient) we fit the following linear regression model (R notation):

NPX ~ ethnicity + age + sex

This revealed no proteins associated with ethnicity at 5% FDR.

ii) In COVID-19 positive ESKD patients (where there were serial samples from the same patient) we included fit the following linear mixed model (again R notation), with a term to adjust for the potential confounding effect of severity:

NPX ~ ethnicity + age + sex + severity + (1 | individual)

This revealed a single protein (LY75) associated with ethnicity at 5% FDR in subcohort A. In subcohort B no proteins were significant at 5% FDR, although LY75 did have nominal P <0.05. In summary, we did not find evidence for substantial ethnicity effects on the proteins we measured. However, we think these results should be interpreted cautiously as the number of individuals in any one ethnic group were modest and consequently power was limited. We do not believe we can make any strong conclusions on this matter from our data and that larger multi-ethnic proteomic studies are needed to properly address this important question.

We have revised the text to reflect the above (Results and Materials and methods).

17) The Discussion section is confusing. It should be fully re-written in more hierarchical way outscoring the main important findings. Emphasis should be given in ESRD and not in differences with all other papers of proteomics in COVID-19.

We thank the reviewer for this helpful suggestion. We have fully re-written the Discussion with a structure corresponding to the major analyses.

We believe that some comparison with other COVID-19 proteomic studies is important to understand whether our findings are applicable only in the context of ESKD or have more general relevance, but we agree with the reviewer’s suggestion to reduce the emphasis on this. We have revised the text accordingly and we now focus on the paper by Filbin et al. as suggested in reviewer comment 18 below. The interested reader can still find comparisons to other studies in Supplementary file 1I (comparison with other COVID-19 positive vs. negative analyses) and in Supplementary file 1J (comparison with other analyses testing association with severity).

18) The authors cite a preprint by Huang (medRxiv, 5 Oct 2020) which has a similar design. In the meantime, a more complete evaluation of that same dataset has been posted by Filbin et al. (Filbin et al., 2020). It may be worthwhile to take some of their results into account in the Discussion.

We now present a comparison of our data with the analysis of Filbin et al. Despite the clinical differences in the populations studied and the differences in Olink proteomics platform used, we observed strikingly similar results. We compared effect sizes from our differential abundance analysis of COVID-19 +ve and COVID-19 -ve ESKD patients to those of Filbin et al. (COVID-19 +ve respiratory failure vs. COVID-19 -ve respiratory failure). This revealed a strong correlation, r = 0.688, (new figure 4—figure supplement 1). Moreover, we observed very high concordance (81.8%) in proteins associated with clinical severity between the two studies. This indicates that similarities in the COVID-19 plasma proteomic signature across clinical contexts and that our findings have relevance beyond the narrow context of COVID-19 in ESKD patients

We have restructured the Results so there is a subsection on comparisons with previous studies, focussing on the report of Filbin et al. (subsection “Comparisons to other proteomic studies in COVID-19”).

We have also revised the Discussion to reflect this.

https://doi.org/10.7554/eLife.64827.sa2

Article and author information

Author details

  1. Jack Gisby

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft
    Contributed equally with
    Candice L Clarke and Nicholas Medjeral-Thomas
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0511-8123
  2. Candice L Clarke

    1. Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    2. Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
    Contribution
    Data curation, Investigation, Patient recruitment and sample collection, Clinical phenotyping
    Contributed equally with
    Jack Gisby and Nicholas Medjeral-Thomas
    Competing interests
    No competing interests declared
  3. Nicholas Medjeral-Thomas

    1. Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    2. Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
    Contribution
    Data curation, Investigation, Patient recruitment and sample collection, Clinical phenotyping
    Contributed equally with
    Jack Gisby and Candice L Clarke
    Competing interests
    No competing interests declared
  4. Talat H Malik

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Investigation, Sample processing
    Competing interests
    No competing interests declared
  5. Artemis Papadaki

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared
  6. Paige M Mortimer

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Investigation, Sample processing
    Competing interests
    No competing interests declared
  7. Norzawani B Buang

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Investigation, Sample processing
    Competing interests
    No competing interests declared
  8. Shanice Lewis

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Data curation, Investigation, Sample processing, Clinical phenotyping
    Competing interests
    No competing interests declared
  9. Marie Pereira

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Investigation, Sample processing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0711-3385
  10. Frederic Toulza

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Investigation, Sample processing
    Competing interests
    No competing interests declared
  11. Ester Fagnano

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Investigation, Sample processing
    Competing interests
    No competing interests declared
  12. Marie-Anne Mawhin

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Investigation, Sample processing
    Competing interests
    No competing interests declared
  13. Emma E Dutton

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Investigation, Sample processing
    Competing interests
    No competing interests declared
  14. Lunnathaya Tapeng

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Investigation, Sample processing
    Competing interests
    No competing interests declared
  15. Arianne C Richard

    1. Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
    2. CRUK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
    Contribution
    Visualization, Methodology, Writing - review and editing, Results interpretation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8708-9997
  16. Paul DW Kirk

    1. MRC Biostatistics Unit, Forvie Way, University of Cambridge, Cambridge, United Kingdom
    2. Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, United Kingdom
    Contribution
    Methodology, Statistical methodology
    Competing interests
    No competing interests declared
  17. Jacques Behmoaras

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Writing - review and editing, Results interpretation
    Competing interests
    No competing interests declared
  18. Eleanor Sandhu

    1. Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    2. Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
    Contribution
    Investigation, Patient recruitment and sample collection, Clinical phenotyping
    Competing interests
    No competing interests declared
  19. Stephen P McAdoo

    1. Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    2. Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
    Contribution
    Investigation, Patient recruitment and sample collection
    Competing interests
    reports personal fees from Celltrion, Rigel, GSK and Cello, outside the submitted work.
  20. Maria F Prendecki

    1. Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    2. Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
    Contribution
    Data curation, Investigation, Patient recruitment and sample collection, Clinical phenotyping
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7048-7457
  21. Matthew C Pickering

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Writing - review and editing, Conceived and designed the study, Clinical phenotyping, Results interpretation, Funding acquisition
    Competing interests
    No competing interests declared
  22. Marina Botto

    Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    Contribution
    Conceptualization, Resources, Funding acquisition, Investigation, Project administration, Writing - review and editing, Conceived and designed the study, Sample processing, Results interpretation, Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1458-3791
  23. Michelle Willicombe

    1. Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    2. Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
    Contribution
    Funding acquisition, Investigation, Project administration, Conceived and designed the study, Patient recruitment and sample collection, Led and coordinated patient recruitment.
    Contributed equally with
    David C Thomas and James E Peters
    Competing interests
    No competing interests declared
  24. David C Thomas

    1. Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    2. Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
    Contribution
    Conceptualization, Funding acquisition, Investigation, Project administration, Writing - review and editing, Conceived and designed the study. Patient recruitment and sample collection. Results interpretation
    Contributed equally with
    Michelle Willicombe and James E Peters
    Competing interests
    No competing interests declared
  25. James E Peters

    1. Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
    2. Health Data Research UK, London, United Kingdom
    Contribution
    Conceptualization, Data curation, Supervision, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Conceived and designed the study, Devised the analysis plan and supervised the analysis, Curated clinical phenotyping data, Funding acquisition
    Contributed equally with
    Michelle Willicombe and David C Thomas
    For correspondence
    j.peters@imperial.ac.uk
    Competing interests
    has received travel and accommodation expenses and hospitality from Olink to speak at Olink-sponsored academic meetings.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9415-3440

Funding

UK Research and Innovation (COVID-19 Rapid Response Rolling Call (MR/V027638/1))

  • James Edward Peters

Imperial College London (Community Jameel and the Imperial President's Excellence Fund)

  • James Edward Peters

UK Research and Innovation (UKRI Innovation Fellowship at Health Data Research UK (MR/S004068/2))

  • James Edward Peters

Wellcome Trust (Wellcome-Beit Prize Clinical Research Career Development Fellowship (206617/A/17/A))

  • David C Thomas

Wellcome Trust (Wellcome Trust Senior Fellow in Clinical Science (212252/Z/18/Z))

  • Matthew C Pickering

Wellcome Trust (Wellcome Trust and Imperial College London Research Fellowship)

  • Nicholas Medjeral-Thomas
  • Eleanor Sandhu

Auchi Renal Research Fund (Auchi Clinical Research Fellowship)

  • Candice L Clarke

Medical Research Council (MC_UU_00002/13)

  • Paul DW Kirk

The Sidharth Burman endowment

  • David C Thomas

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors thank the patients who volunteered for this study and the staff at Imperial College Healthcare NHS Trust (the Imperial College Healthcare NHS Trust renal COVID-19 group and dialysis staff): Appelbe M, Ashby DR, Brown EA, Cairns T, Charif R, Condon M, Corbett RW, Duncan N, Edwards C, Frankel A, Griffith M, Harris S, Hill P, Kousios A, Levy JB, Loucaidou M, Lightstone L, Liu L, Lucisano G, Lynch K, Mclean A, Moabi D, Muthusamy A, Nevin M, Palmer A, Parsons D, Prout V, Salisbury E, Smith C, Tam F, Tanna A, Tansey K, Tomlinson J, Webster P.

We also acknowledge the efforts of renal specialist doctors in training for assistance with recruiting patients to this study.

We acknowledge support from UKRI/NIHR through the UK Coronavirus Immunology Consortium (UK-CIC) and the National Institute for Health Research (NIHR) Biomedical Research Centre based at Imperial College Healthcare NHS Trust and Imperial College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

We thank:

Hari and Rachna Murgai and Milan and Rishi Khosla for generous support with sample transport.

Dr Kerry Rostron for exceptional support with laboratory facilities in challenging circumstances and the Department administrators for their help.

Dr Brian Tom and Dr Jessica Barrett (MRC Biostatistics Unit, University of Cambridge) for statistical advice.

Prof Sir John Savill (Melbourne Academic Centre for Health) for comments on the manuscript.

Ethics

Human subjects: All participants (patients and controls) were recruited from the Imperial College Renal and Transplant Centre and its satellite dialysis units, London, and provided written informed consent prior to participation. Study ethics were reviewed by the UK National Health Service (NHS) Health Research Authority (HRA) and Health and Care Research Wales (HCRW) Research Ethics Committee (reference 20/WA/0123: The impact of COVID-19 on patients with renal disease and immunosuppressed patients). Ethical approval was given.

Senior Editor

  1. Matthias Barton, University of Zurich, Switzerland

Reviewing Editor

  1. Evangelos J Giamarellos-Bourboulis, National and Kapodistrian University of Athens, Medical School, Greece

Reviewer

  1. Evangelos J Giamarellos-Bourboulis, National and Kapodistrian University of Athens, Medical School, Greece

Publication history

  1. Received: November 12, 2020
  2. Accepted: March 10, 2021
  3. Accepted Manuscript published: March 11, 2021 (version 1)
  4. Version of Record published: April 23, 2021 (version 2)
  5. Version of Record updated: April 28, 2021 (version 3)

Copyright

© 2021, Gisby et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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