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      Persistent complement dysregulation with signs of thromboinflammation in active Long Covid

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          Abstract

          Long Covid is a debilitating condition of unknown etiology. We performed multimodal proteomics analyses of blood serum from COVID-19 patients followed up to 12 months after confirmed severe acute respiratory syndrome coronavirus 2 infection. Analysis of >6500 proteins in 268 longitudinal samples revealed dysregulated activation of the complement system, an innate immune protection and homeostasis mechanism, in individuals experiencing Long Covid. Thus, active Long Covid was characterized by terminal complement system dysregulation and ongoing activation of the alternative and classical complement pathways, the latter associated with increased antibody titers against several herpesviruses possibly stimulating this pathway. Moreover, markers of hemolysis, tissue injury, platelet activation, and monocyte–platelet aggregates were increased in Long Covid. Machine learning confirmed complement and thromboinflammatory proteins as top biomarkers, warranting diagnostic and therapeutic interrogation of these systems.

          Editor’s summary

          Some individuals can endure persistent, debilitating symptoms for many months after an initial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, the factors underpinning these health issues, called Long Covid, are poorly understood. Comparing the blood of patients with confirmed SARS-CoV-2 infection with that of uninfected controls, Cervia-Hasler et al . found that patients experiencing Long COVID exhibited changes to blood serum proteins indicating activation of the immune system’s complement cascade, altered coagulation, and tissue injury (see the Perspective by Ruf). At the cellular level, Long Covid was linked to aggregates comprising monocytes and platelets. These findings provide a resource of potential biomarkers for diagnosis and may inform directions for treatments. —Sarah H. Ross

          Abstract

          Analysis of blood revealed that Long COVID is characterized by changes to complement proteins and platelet activation.

          Abstract

          INTRODUCTION

          Acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes various clinical phenotypes, ranging from asymptomatic to life-threatening COVID-19. About 5% of all infected individuals do not recover from acute disease but develop long-term complications, called Long Covid. Current hypotheses on factors contributing to Long Covid include tissue damage, viral reservoirs, autoimmunity, and persistent inflammation. There are currently no diagnostic tests or therapeutic solutions for affected patients.

          RATIONALE

          We followed 39 healthy controls and 113 COVID-19 patients for up to 1 year after initial confirmation of acute SARS-CoV-2 infection to identify biomarkers associated with Long Covid. At 6-month follow-up, 40 patients had Long Covid symptoms. Repeated clinical assessments were paired with blood draws, resulting in a total of 268 longitudinal blood samples. We measured >6500 proteins in serum by proteomics. Top candidate biomarkers were identified using computational tools and further evaluated experimentally.

          RESULTS

          Long Covid patients exhibited increased complement activation during acute disease, which also persisted at 6-month follow-up. The complement system is part of the innate immune system and contributes to immunity and homeostasis by targeting pathogens and damaged cells, among other functions. Interestingly, blood complement levels normalized in Long Covid patients recovering before their 6-month follow-up. The complement system can be activated by various triggers, resulting in formation of the terminal complement complex (TCC), made of the complement components C5b-9. These complexes can integrate into cell membranes and induce cell activation or lysis. Long Covid patients showed imbalanced TCC formation, marked by increased soluble C5bC6 complexes and decreased levels of C7-containing TCC formations that can incorporate into cell membranes. This suggested increased membrane insertion of TCCs in Long Covid patients, contributing to tissue damage. Accordingly, Long Covid patients showed elevated tissue injury markers in blood and a thromboinflammatory signature, characterized by markers of endothelial activation, such as von Willebrand factor (vWF), and red blood cell lysis. Low antithrombin III levels in Long Covid patients were accompanied by signs of increased cleavage by thrombin, a driver of TCC formation. Furthermore, Long Covid patients had elevated platelet activation markers and monocyte–platelet aggregates at 6-month follow-up, particularly in cases where Long Covid persisted for 12 months or more. These patients also showed signs of antibody-mediated activation of the classical complement pathway, which was associated with increased anti-CMV (cytomegalovirus, also known as human herpesvirus 5) and anti-EBV (Epstein-Barr virus) immunoglobulin G (IgG) antibody levels.

          CONCLUSION

          Our data suggest that active Long Covid is accompanied by a blood protein signature marked by increased complement activation and thromboinflammation, including activated platelets and markers of red blood cell lysis. Tissue injury may also be complement-mediated and, in turn, activate the complement system. Moreover, complement activation may be driven by antigen–antibody complexes, involving autoantibodies and antibodies against herpesviruses, as well as cross-talk with a dysregulated coagulation system. In addition to offering a basis for new diagnostic solutions, our work provides support for clinical research on complement modulators for patients suffering from Long Covid.

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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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              Fast gapped-read alignment with Bowtie 2.

              As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                January 19 2024
                January 19 2024
                : 383
                : 6680
                Affiliations
                [1 ]Department of Immunology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland.
                [2 ]Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.
                [3 ]Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
                [4 ]Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
                [5 ]Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
                [6 ]Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
                [7 ]Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research, University of Zurich, 7265 Davos, Switzerland.
                [8 ]Institute of Neuropathology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland.
                [9 ]Epidemiology, Biostatistics and Prevention Institute, University of Zurich, 8001 Zurich, Switzerland.
                [10 ]Unit for Clinical Pediatrics, Department of Women’s and Children’s Health, Karolinska Institute, 17165 Solna, Sweden.
                [11 ]Department of Immunology and Inflammation, Imperial College London, London W12 0NN, UK.
                [12 ]Division of Data Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
                [13 ]Faculty of Medicine and Faculty of Science, University of Zurich, 8006 Zurich, Switzerland.
                Article
                10.1126/science.adg7942
                38236961
                dedc25ab-257a-47b1-b982-7e46d1c0c87a
                © 2024

                Free to read

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