Cerebrospinal fluid extracellular vesicle enrichment for protein biomarker discovery in neurological disease; multiple sclerosis

The discovery of disease biomarkers, along with the use of “liquid biopsies” as a minimally invasive source of biomarkers, continues to be of great interest. In inflammatory diseases of the central nervous system (CNS), cerebrospinal fluid (CSF) is the most obvious biofluid source. Extracellular vesicles (EVs) are also present in CSF and are thought to be potential “biomarker treasure chests”. However, isolating these CSF-derived EVs remains challenging. This small-scale pilot study developed and tested a protocol to enrich for CSF-EVs, both in relapsing remitting multiple sclerosis (RRMS) CSF and controls. These were subsequently compared, using an aptamer based proteomics array, SOMAscan™. EVs were enriched from RRMS patient (n = 4) and non-demyelinating control (idiopathic intracranial hypertension (IIH) (n = 3)) CSF using precipitation and mini size-exclusion chromatography (SEC). EV-enriched fractions were selected using pre-defined EV characteristics, including increased levels of tetraspanins. EVs and paired CSF were analysed by SOMAscan™, providing relative abundance data for 1128 proteins. CSF-EVs were characterised, revealing exosome-like features: rich in tetraspanins CD9 and CD81, size ~100 nm, and exosome-like morphology by TEM. Sufficient quantities of, SOMAscan™ compatible, EV material was obtained from 5 ml CSF for proteomics analysis. Overall, 348 and 580 proteins were identified in CSF-EVs and CSF, respectively, of which 50 were found to be significantly (t-test) and exclusively enriched in RRMS CSF-EVs. Selected proteins, Plasma kallikrein and Apolipoprotein-E4, were further validated by western blot and appeared increased in CSF-EVs compared to CSF. Functional enrichment analysis of the 50 enriched proteins revealed strong associations with biological processes relating to MS pathology and also extracellular regions, consistent with EV enrichment. This pilot study demonstrates practicality for EV enrichment in CSF derived from patients with MS and controls, allowing detailed analysis of protein profiles that may offer opportunities to identify novel biomarkers and therapeutic approaches in CNS inflammatory disease

Plasma complement biomarkers distinguish multiple sclerosis and neuromyelitis optica spectrum disorder

Background: Multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) are autoimmune demyelinating diseases distinguished clinically by selective involvement in NMOSD of optic nerves and spinal cord. Early clinical manifestations are similar, complicating clinical management. Aquaporin-4 autoantibody measurement aids diagnosis of NMOSD but is frequently negative, creating unmet need for alternative biomarkers. Objective: We investigated whether plasma complement proteins are altered in MS and NMOSD and whether these provide biomarkers that reliably distinguish the diseases. Methods: Plasma from 53 NMOSD, 49 MS and 69 control donors was tested in multiplex assays measuring complement activation products and proteins. Logistic regression was used to test whether combinations of complement analyte measurements distinguish NMOSD from controls and MS. Results: All activation products were significantly elevated in NMOSD compared to either control or MS. Four complement proteins (C1inh, C1s, C5, FH) were significantly higher in NMOSD compared to MS or controls. A model comprising C1 inhibitor and TCC distinguished NMOSD from MS (area under curve (AUC) 0.98), while C1 inhibitor and C5 distinguished NMOSD from controls (AUC 0.94). Conclusions: NMOSD is distinguished from MS by plasma complement activation

A blood-based metabolomics test to distinguish relapsing–remitting and secondary progressive multiple sclerosis: addressing practical considerations for clinical application

The transition from relapsing–remitting multiple sclerosis (RRMS) to secondary progressive MS (SPMS) represents a huge clinical challenge. We previously demonstrated that serum metabolomics could distinguish RRMS from SPMS with high diagnostic accuracy. As differing sample-handling protocols can affect the blood metabolite profile, it is vital to understand which factors may influence the accuracy of this metabolomics-based test in a clinical setting. Herein, we aim to further validate the high accuracy of this metabolomics test and to determine if this is maintained in a ‘real-life’ clinical environment. Blood from 31 RRMS and 28 SPMS patients was subjected to different sample-handling protocols representing variations encountered in clinics. The effect of freeze–thaw cycles (0 or 1) and time to erythrocyte removal (30, 120, or 240 min) on the accuracy of the test was investigated. For test development, samples from the optimised protocol (30 min standing time, 0 freeze–thaw) were used, resulting in high diagnostic accuracy (mean ± SD, 91.0 ± 3.0%). This test remained able to discriminate RRMS and SPMS samples that had experienced additional freeze–thaw, and increased standing times of 120 and 240 min with accuracies ranging from 85.5 to 88.0%, because the top discriminatory metabolite biomarkers from the optimised protocol remained discriminatory between RRMS and SPMS despite these sample-handling variations. In conclusion, while strict sample-handling is essential for the development of metabolomics-based blood tests, the results confirmed that the RRMS vs. SPMS test is resistant to sample-handling variations and can distinguish these two MS stages in the clinics

CSF-resident CD4+ T-cells display a distinct gene expression profile with relevance to immune surveillance and multiple sclerosis

The CNS has traditionally been considered an immune privileged site, but is now understood to have a system of immune surveillance, predominantly involving CD4+ T-cells. Identifying functional differences between CNS and blood CD4+ T-cells, therefore, have relevance to CNS immune surveillance as well as to neurological conditions, such as multiple sclerosis, in which CD4+ T-cells play a central role. Here, CD4+ T-cells were purified from CSF and blood from 21 patients with newly diagnosed treatment-naïve multiple sclerosis and 20 individuals with non-inflammatory disorders using fluorescence-activated cell sorting, and their transcriptomes were profiled by RNA sequencing. Paired comparisons between CD4+ T-cells from CSF and blood identified 5156 differentially expressed genes in controls and 4263 differentially expressed in multiple sclerosis patients at false discovery rate <5%. Differential expression analysis of CD4+ T-cells collected from the CSF highlighted genes involved in migration, activation, cholesterol biosynthesis and signalling, including those with known relevance to multiple sclerosis pathogenesis and treatment. Expression of markers of CD4+ T-cell subtypes suggested an increased proportion of Th1 and Th17 cells in CSF. Gene ontology terms significant only in multiple sclerosis were predominantly those involved in cellular proliferation. A two-way comparison of CSF versus blood CD4+ T-cells in multiple sclerosis compared with non-inflammatory disorder controls identified four significant genes at false discovery rate <5% (CYP51A1, LRRD1, YES1 and PASK), further implicating cholesterol biosynthesis and migration mechanisms. Analysis of CSF CD4+ T-cells in an extended cohort of multiple sclerosis cases (total N = 41) compared with non-inflammatory disorder controls (total N = 38) identified 140 differentially expressed genes at false discovery rate < 5%, many of which have known relevance to multiple sclerosis, including XBP1, BHLHE40, CD40LG, DPP4 and ITGB1. This study provides the largest transcriptomic analysis of purified cell subpopulations in CSF to date and has relevance for the understanding of CNS immune surveillance, as well as multiple sclerosis pathogenesis and treatment discovery

Cerebrospinal fluid complement system biomarkers in demyelinating disease

Background: Multiple sclerosis (MS) can be difficult to differentiate from other demyelinating diseases, notably neuromyelitis optica spectrum disorder (NMOSD). We previously showed that NMOSD is distinguished from MS by plasma complement biomarkers. Objective: Here, we measure cerebrospinal fluid (CSF) complement proteins in MS, NMOSD and clinically isolated syndrome (CIS), a neurological episode that may presage MS, to test whether these distinguish NMOSD from MS and CIS. Materials and methods: CSF (53 MS, 17 CIS, 11 NMOSD, 35 controls) was obtained; complement proteins (C4, C3, C5, C9, C1, C1q, Factor B (FB)), regulators (Factor I (FI), Factor H (FH), FH-Related Proteins 1, 2 and 5 (FHR125), C1 Inhibitor (C1INH), Properdin) and activation products (terminal complement complex (TCC), iC3b) were quantified by ELISA and results expressed relative to CSF total protein (μg/mg). Results: Compared to control CSF, (1) levels of C4, C1INH and Properdin were elevated in MS; (2) TCC, iC3b, FI and FHR125 were increased in CIS; and (3) all complement biomarkers except TCC, FHR125, Properdin and C5 were higher in NMOSD CSF. A statistical model comprising six analytes (C3, C9, FB, C1q, FI, Properdin) plus age/gender optimally differentiated MS from NMOSD

Complement system biomarkers in epilepsy

Purpose To explore whether complement dysregulation occurs in a routinely recruited clinical cohort of epilepsy patients, and whether complement biomarkers have potential to be used as markers of disease severity and seizure control. Methods Plasma samples from 157 epilepsy cases (106 with focal seizures, 46 generalised seizures, 5 unclassified) and 54 controls were analysed. Concentrations of 10 complement analytes (C1q, C3, C4, factor B [FB], terminal complement complex [TCC], iC3b, factor H [FH], Clusterin [Clu], Properdin, C1 Inhibitor [C1Inh] plus C-reactive protein [CRP]) were measured using enzyme linked immunosorbent assay (ELISA). Univariate and multivariate statistical analysis were used to test whether combinations of complement analytes were predictive of epilepsy diagnoses and seizure occurrence. Correlation between number and type of anti-epileptic drugs (AED) and complement analytes was also performed. Results We found: 1) significant differences between all epilepsy patients and controls for TCC (p < 0.01) and FH (p < 0.01) after performing univariate analysis. 2) multivariate analysis combining six analytes (C3, C4, Properdin, FH, C1Inh, Clu) to give a predictive value (area under the curve) of 0.80 for differentiating epilepsy from controls. 3) significant differences in complement levels between patients with controlled seizures (n = 65) in comparison with uncontrolled seizures (n = 87). Levels of iC3b, Properdin and Clu were decreased and levels of C4 were increased in patients with uncontrolled seizures. 4) no correlation was found between the level of complement biomarkers and the number of AEDs taken, but an association between some analyte levels and drug therapy was seen in patients taking sodium valproate, clobazam, and perampanel. Conclusion This study adds to evidence implicating complement in pathogenesis of epilepsy and may allow the development of better therapeutics and prognostic markers in the future. Replication in a larger sample set is needed to validate the findings of the study

Systemic complement profiling in multiple sclerosis as a biomarker of disease state

Background: There is increasing evidence of significant and dynamic systemic activation and upregulation of complement in multiple sclerosis (MS), which may contribute to disease pathogenesis. Objective: We aimed to investigate the pathological role of complement in MS and the potential role for complement profiling as a biomarker of MS disease state. Methods: Key components of the classical, alternative and terminal pathways of complement were measured in plasma and cerebrospinal fluid (CSF) of patients with MS in different clinical phases of disease and in matched controls. Results: Increased plasma levels of C3 (p<0.003), C4 (p<0.001), C4a (p<0.001), C1 inhibitor (p<0.001), and factor H (p<0.001), and reduced levels of C9 (p<0.001) were observed in MS patients compared with controls. Combined profiling of these analytes produced a statistical model with a predictive value of 97% for MS and 73% for clinical relapse when combined with selected demographic data. CSF-plasma correlations suggested that source of synthesis of these components was both systemic and central. Conclusion: These data provide further evidence of alterations in both local and systemic expression and activation of complement in MS and suggest that complement profiling may be informative as a biomarker of MS disease, although further work is needed to determine its use in distinguishing MS from its differential

Longitudinal characterization of autoantibodies to the thyrotropin receptor (TRAb) during alemtuzumab therapy; evidence that TRAb may precede thyroid dysfunction by many years.

BACKGROUND Thyroid autoimmunity, especially Graves’ disease or hypothyroidism with positive autoantibodies (TRAb) to the thyrotropin receptor (TSHR), occurs in 30-40% of patients with relapsing multiple sclerosis (MS) following treatment with alemtuzumab (ALTZ). ALTZ therapy therefore provides a unique opportunity to study the evolution of TRAb prior to clinical presentation. TRAb can stimulate (TSAb), block (TBAb) or not affect (“neutral”: TNAb) the TSHR function, causing hyperthyroidism, hypothyroidism or euthyroidism, respectively. METHODS We conducted a longitudinal retrospective analysis of TRAb bioactivity over a period of 9 years in 45 MS patients receiving ALTZ using available stored serum; 31 developed thyroid dysfunction (TD) and 14 remained euthyroid despite being followed for a minimum of 5 years (NO-TD). The presence of TRAb was evaluated at standardized time points: A) pre-ALTZ, B) latest time available post-ALTZ and before TD onset, C) post-ALTZ during/after TD onset. Serum TRAb were detected by published in-house assays (ihTRAb): flow cytometry (FC) detecting any TSHR-binding TRAb and luciferase bioassays (LB) detecting TSAb/TBAb bioactivity. Purified IgGs were used to verify TSAb/TBAb in selected hypothyroid cases. Standard clinical automated measurements of TRAb (autTRAb), anti-thyroid peroxidase autoantibodies (TPOAb), thyroid stimulating hormone, free-thyroxine and free-triiodothyronine were also collected. RESULTS Pre-ALTZ, combined ihTRAb (positive with FC and/or LB), but not autTRAb, were present in 5/16 (31.2%) TD versus 0/14 (0%) NO-TD (p=0.017). Detectable ihTRAb preceded TD development in 9/28 (32.1%) and by a median of 1.2 years (range 28 days – 7.3 years). Combination testing of ihTRAb and TPOAb at baseline predicted 20% of subsequent cases of hyperthyroidism and 83% of hypothyroidism. CONCLUSIONS We present evidence that TRAb measured with custom-made assays can be detected prior to any change in thyroid function in up to a third of cases of ALTZ-related TD. Furthermore, The presence of ihTRAb prior to ALTZ treatment was strongly predictive of subsequent TD. Our findings suggest that a period of affinity maturation of TRAb may precede clinical disease onset in some cases. Combined testing of TPOAb and ihTRAb may increase our ability to predict those who will develop thyroid dysfunction post ALTZ

COVID‐19 Vaccine Response in People with Multiple Sclerosis

ObjectiveThe purpose of this study was to investigate the effect of disease modifying therapies on immune response to severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) vaccines in people with multiple sclerosis (MS).MethodsFour hundred seventy-three people with MS provided one or more dried blood spot samples. Information about coronavirus disease 2019 (COVID-19) and vaccine history, medical, and drug history were extracted from questionnaires and medical records. Dried blood spots were eluted and tested for antibodies to SARS-CoV-2. Antibody titers were partitioned into tertiles with people on no disease modifying therapy as a reference. We calculated the odds ratio of seroconversion (univariate logistic regression) and compared quantitative vaccine response (Kruskal Wallis) following the SARS-CoV-2 vaccine according to disease modifying therapy. We used regression modeling to explore the effect of vaccine timing, treatment duration, age, vaccine type, and lymphocyte count on vaccine response.ResultsCompared to no disease modifying therapy, the use of anti-CD20 monoclonal antibodies (odds ratio = 0.03, 95% confidence interval [CI] = 0.01–0.06, p [less than] 0.001) and fingolimod (odds ratio = 0.04; 95% CI = 0.01–0.12) were associated with lower seroconversion following the SARS-CoV-2 vaccine. All other drugs did not differ significantly from the untreated cohort. Both time since last anti-CD20 treatment and total time on treatment were significantly associated with the response to the vaccination. The vaccine type significantly predicted seroconversion, but not in those on anti-CD20 medications. Preliminary data on cellular T-cell immunity showed 40% of seronegative subjects had measurable anti-SARS-CoV-2 T cell responses.InterpretationSome disease modifying therapies convey risk of attenuated serological response to SARS-CoV-2 vaccination in people with MS. We provide recommendations for the practical management of this patient group. ANN NEUROL 202