9 research outputs found

    Evidence, detailed characterization and clinical context of complement activation in acute multisystem inflammatory syndrome in children

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    Multisystem inflammatory syndrome in children (MIS-C) is a rare, life-threatening complication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. MIS-C develops with high fever, marked inflammation and shock-like picture several weeks after exposure to, or mild infection with SARS-CoV-2. Deep immune profiling identified activated macrophages, neutrophils, B-plasmablasts and CD8 + T cells as key determinants of pathogenesis together with multiple inflammatory markers. The disease rapidly responds to intravenous immunoglobulin (IVIG) treatment with clear changes of immune features. Here we present the results of a comprehensive analysis of the complement system in the context of MIS-C activity and describe characteristic changes during IVIG treatment. We show that activation markers of the classical, alternative and terminal pathways are highly elevated, that the activation is largely independent of anti-SARS-CoV-2 humoral immune response, but is strongly associated with markers of macrophage activation. Decrease of complement activation is closely associated with rapid improvement of MIS-C after IVIG treatment

    Combination protein biomarkers predict multiple sclerosis diagnosis and outcomes

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    Establishing biomarkers to predict multiple sclerosis diagnosis and prognosis has been challenging using a single biomarker approach. We hypothesised that a combination of biomarkers would increase the accuracy of prediction models to differentiate multiple sclerosis from other neurological disorders and enhance prognostication for people with multiple sclerosis. We measured 24 fluid biomarkers in the blood and cerebrospinal fluid of 77 people with multiple sclerosis and 80 people with other neurological disorders, using ELISA or Single Molecule Array assays. Primary outcomes were multiple sclerosis versus any other diagnosis, time to first relapse, and time to disability milestone (Expanded Disability Status Scale 6), adjusted for age and sex. Multivariate prediction models were calculated using the area under the curve value for diagnostic prediction, and concordance statistics (the percentage of each pair of events that are correctly ordered in time for each of the Cox regression models) for prognostic predictions. Predictions using combinations of biomarkers were considerably better than single biomarker predictions. The combination of cerebrospinal fluid [chitinase-3-like-1 + TNF-receptor-1 + CD27] and serum [osteopontin + MCP-1] had an area under the curve of 0.97 for diagnosis of multiple sclerosis, compared to the best discriminative single marker in blood (osteopontin: area under the curve 0.84) and in cerebrospinal fluid (chitinase-3-like-1 area under the curve 0.84). Prediction for time to next relapse was optimal with a combination of cerebrospinal fluid[vitamin D binding protein + Factor I + C1inhibitor] + serum[Factor B + Interleukin-4 + C1inhibitor] (concordance 0.80), and time to Expanded Disability Status Scale 6 with cerebrospinal fluid [C9 + Neurofilament-light] + serum[chitinase-3-like-1 + CCL27 + vitamin D binding protein + C1inhibitor] (concordance 0.98). A combination of fluid biomarkers has a higher accuracy to differentiate multiple sclerosis from other neurological disorders and significantly improved the prediction of the development of sustained disability in multiple sclerosis. Serum models rivalled those of cerebrospinal fluid, holding promise for a non-invasive approach. The utility of our biomarker models can only be established by robust validation in different and varied cohorts

    Complement lectin pathway activation is associated with COVID-19 disease severity, independent of MBL2 genotype subgroups

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    IntroductionWhile complement is a contributor to disease severity in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, all three complement pathways might be activated by the virus. Lectin pathway activation occurs through different pattern recognition molecules, including mannan binding lectin (MBL), a protein shown to interact with SARS-CoV-2 proteins. However, the exact role of lectin pathway activation and its key pattern recognition molecule MBL in COVID-19 is still not fully understood.MethodsWe therefore investigated activation of the lectin pathway in two independent cohorts of SARS-CoV-2 infected patients, while also analysing MBL protein levels and potential effects of the six major single nucleotide polymorphisms (SNPs) found in the MBL2 gene on COVID-19 severity and outcome.ResultsWe show that the lectin pathway is activated in acute COVID-19, indicated by the correlation between complement activation product levels of the MASP-1/C1-INH complex (p=0.0011) and C4d (p<0.0001) and COVID-19 severity. Despite this, genetic variations in MBL2 are not associated with susceptibility to SARS-CoV-2 infection or disease outcomes such as mortality and the development of Long COVID.ConclusionIn conclusion, activation of the MBL-LP only plays a minor role in COVID-19 pathogenesis, since no clinically meaningful, consistent associations with disease outcomes were noted

    Additional file 1 of Combination protein biomarkers predict multiple sclerosis diagnosis and outcomes

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    Additional file 1: Table S1. An overview of the assays used for this study. BDNF: brain-derived neurotrophic factor; CCL27: CC motif chemokine ligand 27; CRP: C-reactive protein; CXCL: C-X-C motif chemokine ligand; GFAP: glial fibrillary acidic protein; IL: interleukin; LIF: leukaemia inhibitory factor; MCP1: monocyte chemoattractant protein 1; TGF-β: transforming growth factor beta; TNFα: tumour necrosis factor alpha; TNFR1: tumour necrosis factor receptor 1; TCC: terminal complement complex, VDBP: vitamin D binding protein. Table S2. Rates of missing or imputed data in assay results. Table S3. Characteristics of 4 test/ train cohorts. Table S4. Comparison of CSF biomarkers between multiple sclerosis and non-multiple sclerosis groups. Mann–Whitney comparisons, corrected for multiple comparisons using Benjamini–Hochberg procedure. Table S5. Comparison of serum biomarkers between multiple sclerosis and non-multiple sclerosis groups Mann–Whitney comparisons corrected for multiple comparisons using Benjamini–Hochberg procedure. Table S6. A progression from single through combinations of multiple biomarkers to predict multiple sclerosis versus non-multiple sclerosis status. Table S7. Breakdown of the Train / Test results for the combined CSF & serum modelling of MS versus non-MS status. Mean AUC values were ordered from lowest to highest, and the optimum model was selected when addition of a further analyte resulted in an AUC increase < 0.01. Table S8. Sensitivity analysis: all biomarker concentrations were corrected for age and sex according to a linear model generated in control samples. A progression from single through combinations of multiple biomarkers to predict multiple sclerosis versus non-multiple sclerosis status. Table S9. Concordance of biomarkers in predicting time to next relapse in univariate analysis (adjusted for sex and age). Table S10. A progression from single through combinations of multiple biomarkers to predict time to relapse and time to disability, adjusted for age and sex. Table S11. Concordance of biomarkers in predicting time to EDSS 6 in univariate analysis (adjusted for sex, age and disease modifying therapy). Fig. S1. The plots show the 3 models below, run on 1000 random selection of (approx 75%) Train and (approx. 25%) Test data. The left hand plot is the range of AUC’s from the Train data when used also as Test and the right-hand plot shows the range of AUC’s when Test data are used as test

    Complement Levels at Admission Reflecting Progression to Severe Acute Kidney Injury (AKI) in Coronavirus Disease 2019 (COVID-19): A Multicenter Prospective Cohort Study

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    Background: Dysregulation of complement system is thought to be a major player in development of multi-organ damage and adverse outcomes in patients with coronavirus disease 2019 (COVID-19). This study aimed to examine associations between complement system activity and development of severe acute kidney injury (AKI) among hospitalized COVID-19 patients. Materials and methods: In this multicenter, international study, complement as well as inflammatory and thrombotic parameters were analyzed in COVID-19 patients requiring hospitalization at one US and two Hungarian centers. The primary endpoint was development of severe AKI defined by KDIGO stage 2+3 criteria, while the secondary endpoint was need for renal replacement therapy (RRT). Complement markers with significant associations with endpoints were then correlated with a panel of inflammatory and thrombotic biomarkers and assessed for independent association with outcome measures using logistic regression. Results: A total of 131 hospitalized COVID-19 patients (median age 66 [IQR, 54-75] years; 54.2% males) were enrolled, 33 from the US, and 98 from Hungary. There was a greater prevalence of complement over-activation and consumption in those who developed severe AKI and need for RRT during hospitalization. C3a/C3 ratio was increased in groups developing severe AKI (3.29 vs. 1.71; p &lt; 0.001) and requiring RRT (3.42 vs. 1.79; p &lt; 0.001) in each cohort. Decrease in alternative and classical pathway activity, and consumption of C4 below reference range, as well as elevation of complement activation marker C3a above the normal was more common in patients progressing to severe AKI. In the Hungarian cohort, each standard deviation increase in C3a (SD = 210.1) was independently associated with 89.7% increased odds of developing severe AKI (95% CI, 7.6-234.5%). Complement was extensively correlated with an array of inflammatory biomarkers and a prothrombotic state. Conclusion: Consumption and dysregulation of complement system is associated with development of severe AKI in COVID-19 patients and could represent a promising therapeutic target for reducing thrombotic microangiopathy in SARS-CoV-2 infection
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