There are adaptive T-cell and antibody autoimmune responses to myelin-derived peptides in multiple sclerosis (MS) and to aquaporin-4 (AQP4) in neuromyelitis optica spectrum disorders (NMOSDs). Strategies aimed at antigen-specific tolerance to these autoantigens are thus indicated for these diseases. One approach involves induction of tolerance with engineered dendritic cells (tolDCs) loaded with specific antigens. We conducted an in-human phase 1b clinical trial testing increasing concentrations of autologous tolDCs loaded with peptides from various myelin proteins and from AQP4. We tested this approach in 12 patients, 8 with MS and 4 with NMOSD. The primary end point was the safety and tolerability, while secondary end points were clinical outcomes (relapses and disability), imaging (MRI and optical coherence tomography), and immunological responses. Therapy with tolDCs was well tolerated, without serious adverse events and with no therapy-related reactions. Patients remained stable clinically in terms of relapse, disability, and in various measurements using imaging. We observed a significant increase in the production of IL-10 levels in PBMCs stimulated with the peptides as well as an increase in the frequency of a regulatory T cell, known as Tr1, by week 12 of follow-up. In this phase 1b trial, we concluded that the i.v. administration of peptide-loaded dendritic cells is safe and feasible. Elicitation of specific IL-10 production by peptide-specific T cells in MS and NMOSD patients indicates that a key element in antigen specific tolerance is activated with this approach. The results warrant further clinical testing in larger trials.

Dysregulation of signaling pathways in multiple sclerosis (MS) can be analyzed by phosphoproteomics in peripheral blood mononuclear cells (PBMCs). We performed in vitro kinetic assays on PBMCs in 195 MS patients and 60 matched controls and quantified the phosphorylation of 17 kinases using xMAP assays. Phosphoprotein levels were tested for association with genetic susceptibility by typing 112 single-nucleotide polymorphisms (SNPs) associated with MS susceptibility. We found increased phosphorylation of MP2K1 in MS patients relative to the controls. Moreover, we identified one SNP located in the PHDGH gene and another on IRF8 gene that were associated with MP2K1 phosphorylation levels, providing a first clue on how this MS risk gene may act. The analyses in patients treated with disease-modifying drugs identified the phosphorylation of each receptor’s downstream kinases. Finally, using flow cytometry, we detected in MS patients increased STAT1, STAT3, TF65, and HSPB1 phosphorylation in CD19+ cells. These findings indicate the activation of cell survival and proliferation (MAPK), and proinflammatory (STAT) pathways in the immune cells of MS patients, primarily in B cells. The changes in the activation of these kinases suggest that these pathways may represent therapeutic targets for modulation by kinase inhibitors.

ObjectiveTo establish cytometry profiles associated with disease stages and immunotherapy in MS.MethodsDemographic/clinical data and peripheral blood samples were collected from 227 patients with MS and 82 sex- and age-matched healthy controls (HCs) enrolled in a cross-sectional study at 4 European MS centers (Spain, Italy, Germany, and Norway). Flow cytometry of isolated peripheral blood mononuclear cells was performed in each center using specifically prepared antibody-cocktail Lyotubes; data analysis was centralized at the Genoa center. Differences in immune cell subsets were assessed between groups of untreated patients with relapsing-remitting or progressive MS (RRMS or PMS) and HCs and between groups of patients with RRMS taking 6 commonly used disease-modifying drugs.ResultsIn untreated patients with MS, significantly higher frequencies of Th17 cells in the RRMS population compared with HC and lower frequencies of B-memory/B-regulatory cells as well as higher percentages of B-mature cells in patients with PMS compared with HCs emerged. Overall, the greatest deviation in immunophenotype in MS was observed by treatment rather than disease course, with the strongest impact found in fingolimod-treated patients. Fingolimod induced a decrease in total CD4+ T cells and in B-mature and B-memory cells and increases in CD4+ and CD8+ T-regulatory and B-regulatory cells.ConclusionsOur highly standardized, multisite cytomics data provide further understanding of treatment impact on MS immunophenotype and could pave the way toward monitoring immune cells to help clinical management of MS individuals.

One sentence summary:A new approach to predict combination therapies based on modeling signaling networks using phosphoproteomics from Multiple Sclerosis patients identifies deregulated pathways and new drug combinations. Significance statementMultiple Sclerosis (MS) is a major health problem, leading to significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood. Further, current treatments only ameliorate the disease and may produce severe side effects.Here, we applied a network-based modeling approach based on phosphoproteomic data upon perturbation with ligands and drugs of healthy donors and MS patients to create donor-specific models. The models uncover the differential activation in signaling wiring between healthy donors, untreated patients and those under different treatments.Further, based in the patient-specific networks, a new approach identifies drug combinations to revert signaling to a healthy-like state.

Background Multiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects. Methods Here, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-β, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a “healthy-like” status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies. Results Analysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-β activated kinase 1 (TAK1) kinase, involved in Transforming growth factor β-1 proprotein (TGF-β), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS. Conclusions Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases.

The development of neuroprotective therapies is a sought-after goal. By screening combinatorial chemical libraries using in vitro assays, we identified the small molecule BN201 that promotes the survival of cultured neural cells when subjected to oxidative stress or when deprived of trophic factors. Moreover, BN201 promotes neuronal differentiation, the differentiation of precursor cells to mature oligodendrocytes in vitro, and the myelination of new axons. BN201 modulates several kinases participating in the insulin growth factor 1 pathway including serum-glucocorticoid kinase and midkine, inducing the phosphorylation of NDRG1 and the translocation of the transcription factor Foxo3 to the cytoplasm. In vivo, BN201 prevents axonal and neuronal loss, and it promotes remyelination in models of multiple sclerosis, chemically induced demyelination, and glaucoma. In summary, we provide a new promising strategy to promote neuroaxonal survival and remyelination, potentially preventing disability in brain diseases.

Background: Methylthioadenosine is a metabolite of the polyamine pathway that modulates methyltransferase activity, thereby influencing DNA and protein methylation. Since methylthioadenosine produces neuroprotection in models of inflammation, ischemia and epilepsy, we set out to evaluate the role of methylthioadenosine in promoting remyelination, a process that will protect axons in demyelinating diseases and that will aid functional recovery.

Background Multiple Sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging, and multimodal biomarkers to define the risk of disease activity. Methods We have analyzed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centers, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Validation was conducted in an independent prospective cohort of 271 MS patients from a single center. Results We found algorithms for predicting confirmed disability accumulation for the different scales, No Evidence of Disease Activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors by using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in the discovery and validation cohorts. Conclusion Combining clinical, imaging, and omics data with machine learning helps to identify MS patients at risk of disability worsening.