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

Patients with end-stage kidney disease (ESKD) are at high risk of severe COVID-19. Here, we performed 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 identified transcriptomic and proteomic signatures of COVID-19 severity, and found distinct temporal molecular profiles in patients with severe disease. Supervised learning revealed that the plasma proteome was a superior indicator of clinical severity than the PBMC transcriptome. We showed that both the levels and trajectory of plasma LRRC15, a proposed co-receptor for SARS-CoV-2, are the strongest predictors of clinical outcome. Strikingly, we observed 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. ### Competing Interest Statement None of the authors have any patents (planned, pending or issued) or competing interests relevant to this work. Other interests unrelated to this work: SPM reports personal fees from Celltrion, Rigel, GSK and Cello; MCP reports consulting honoraria with Alexion, Apellis, Achillion, Novartis and Gyroscope; DCT reports speaker and consultancy fees from Astra-Zeneca and Novartis; JEP has received travel and accommodation expenses and hospitality from Olink to speak at Olink-sponsored academic meetings (none within the past 5 years). None of the other authors have any interests to declare. ### Funding Statement This research was partly funded by Community Jameel and the Imperial President's Excellence Fund and by a UKRI-DHSC COVID-19 Rapid Response Rolling Call (MR/V027638/1) (to JEP), and by funding from UKRI/NIHR through the UK Coronavirus Immunology Consortium (UK-CIC) (to MB). We also acknowledge 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. JEP was supported by UKRI Innovation Fellowship at Health Data Research UK (MR/S004068/2). DCT is supported by a Stage 2 Wellcome-Beit Prize Clinical Research Career Development Fellowship (20661206617/A/17/Z and 206617/A/17/A) and the Sidharth Burman endowment. MCP is a Wellcome Trust Senior Fellow in Clinical Science (212252/Z/18/Z). NM-T and ES are supported by Wellcome Trust and Imperial College London Research Fellowships, and CLC by an Auchi Clinical Research Fellowship. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: 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. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Individual-level data for transcriptomics, proteomics and flow cytometry are available without restriction from Zenodo. Code available from GitHub.