Multi-omics identify falling LRRC15 as a COVID-19 severity marker and persistent pro-thrombotic signals in convalescence.

Patients with end-stage kidney disease (ESKD) are at high risk of severe COVID-19. Here, we perform longitudinal blood sampling of ESKD haemodialysis patients with COVID-19, collecting samples pre-infection, serially during infection, and after clinical recovery. Using plasma proteomics, and RNA-sequencing and flow cytometry of immune cells, we identify transcriptomic and proteomic signatures of COVID-19 severity, and find distinct temporal molecular profiles in patients with severe disease. Supervised learning reveals that the plasma proteome is a superior indicator of clinical severity than the PBMC transcriptome. We show that a decreasing trajectory of plasma LRRC15, a proposed co-receptor for SARS-CoV-2, is associated with a more severe clinical course. We observe that two months after the acute infection, patients still display dysregulated gene expression related to vascular, platelet and coagulation pathways, including PF4 (platelet factor 4), which may explain the prolonged thrombotic risk following COVID-19.

Comparison of immunogenicity and clinical effectiveness between BNT162b2 and ChAdOx1 SARS-CoV-2 vaccines in people with end-stage kidney disease receiving haemodialysis: A prospective, observational cohort study.

Background: People with end-stage kidney disease, including people on haemodialysis, are susceptible to greater COVID-19 related morbidity and mortality. This study compares the immunogenicity and clinical effectiveness of BNT162B2 versus ChAdOx1 in haemodialysis patients. Methods: In this observational cohort study, 1021 patients were followed-up from time of vaccination until December 2021. All patients underwent weekly RT-PCR screening. Patients were assessed for nucleocapsid(anti-NP) and spike(anti-S) antibodies at timepoints after second(V2) and third(V3) vaccinations. 191 patients were investigated for T-cell responses. Vaccine effectiveness (VE) for prevention of infection, hospitalisation and mortality was evaluated using the formula VE=(1-adjustedHR)x100. Findings: 45.7% (467/1021) had evidence of prior infection. There was no difference in the proportion of infection-naïve patients who seroconverted by vaccine type, but median anti-S antibody titres were higher post-BNT162b2 compared with ChAdOx1; 462(152-1171) and 78(20-213) BAU/ml respectively, p<0.001.  Concomitant immunosuppressant use was a risk factor for non-response, OR 0.12[95% CI 0.05-0.25] p<0.001.  Post-V3 (all BNT162b2), median anti-S antibody titres remained higher in those receiving BNT162b2 versus ChAdOx1 as primary doses; 2756(187-1246) and 1250(439-2635) BAU/ml respectively, p=0.003.Anti-S antibodies waned over time. Hierarchical levels of anti-S post-V2 predicted risk of infection; patients with no/low anti-S being at highest risk. VE for preventing infection, hospitalisation and death was 53% (95% CI 6-75), 77% (95% CI 30-92) and 93% (95% CI 59-99) respectively, with no difference seen by vaccine type. Interpretation: Serum anti-S concentrations predict risk of breakthrough infection. Anti-S responses vary dependent upon clinical features, infection history and vaccine type. Monitoring of serological responses may enable individualised approaches to vaccine boosters in at risk populations. Funding: National Institute for Health Research (NIHR) Biomedical Research Centre based at Imperial College Healthcare NHS Trust and Imperial College London.

Humoral and T-cell responses to SARS-CoV-2 vaccination in patients receiving immunosuppression.

OBJECTIVE: There is an urgent need to assess the impact of immunosuppressive therapies on the immunogenicity and efficacy of SARS-CoV-2 vaccination. METHODS: Serological and T-cell ELISpot assays were used to assess the response to first-dose and second-dose SARS-CoV-2 vaccine (with either BNT162b2 mRNA or ChAdOx1 nCoV-19 vaccines) in 140 participants receiving immunosuppression for autoimmune rheumatic and glomerular diseases. RESULTS: Following first-dose vaccine, 28.6% (34/119) of infection-naïve participants seroconverted and 26.0% (13/50) had detectable T-cell responses to SARS-CoV-2. Immune responses were augmented by second-dose vaccine, increasing seroconversion and T-cell response rates to 59.3% (54/91) and 82.6% (38/46), respectively. B-cell depletion at the time of vaccination was associated with failure to seroconvert, and tacrolimus therapy was associated with diminished T-cell responses. Reassuringly, only 8.7% of infection-naïve patients had neither antibody nor T-cell responses detected following second-dose vaccine. In patients with evidence of prior SARS-CoV-2 infection (19/140), all mounted high-titre antibody responses after first-dose vaccine, regardless of immunosuppressive therapy. CONCLUSION: SARS-CoV-2 vaccines are immunogenic in patients receiving immunosuppression, when assessed by a combination of serology and cell-based assays, although the response is impaired compared with healthy individuals. B-cell depletion following rituximab impairs serological responses, but T-cell responses are preserved in this group. We suggest that repeat vaccine doses for serological non-responders should be investigated as means to induce more robust immunological response.

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

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.

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.