Dysregulated T cells in multiple sclerosis

Chihara, Norio

doi:10.1111/cen3.12438

Cited by 22 publications

(17 citation statements)

References 87 publications

(152 reference statements)

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“…The current understanding of the pathogenesis includes the peripheral activation of myelin-reactive effector CD4 T helper (TH) 1 cells, memory B cells and TH17 cells. [1][2][3] Furthermore, there is emerging evidence for a key role of TH17.1 cells, which share inflammatory features of TH17 and TH1 cells. 4,5 Cladribine (CLAD, MAVENCLAD â ) is an oral drug approved for treatment of active relapsing-remitting MS. 6 This synthetic deoxyadenosine analogue is a prodrug, which selectively depletes immune cells by apoptosis through the caspase system.…”

Section: Introductionmentioning

confidence: 99%

Long‐term peripheral immune cell profiling reveals further targets of oral cladribine in MS

Moser

Schwenker

Seiberl

et al. 2020

Ann Clin Transl Neurol

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Objectives: To expand the knowledge about the immunological consequences of cladribine (CLAD), a pulsed immune reconstitution therapy approved for active multiple sclerosis (MS), beyond the known short-term effects on peripheral immune cell subsets. Methods: In this study, we characterized depletion and restitution kinetics as well as cytokine profiles of peripheral immune cell subsets in 18 patients with MS following treatment with oral CLAD. The methods involved blood collection prior to CLAD and every three months over a period of 24 months, and extensive characterization of various immune cells subsets by multiparametric flow cytometry. Results: We found a selectivity of CLAD towards central memory T cells and memory B cells and detected a hyper-repopulation of maturing B cells. Counts of classical (À65%) and various nonclassical TH17 cells (À84% to À87%) were markedly reduced 24 months after treatment start, and were comparable with depletion rates of classswitched memory B-cell phenotypes (À87% to À95%). The nadir of TH cells was more pronounced in the second treatment year. We observed a proportional surge of CD20 T-cell subsets and an expansion of regulatory T, B and NK cells. Natural killer T cells (NKT) were only depleted in year two and did not recover. Interpretation: Peripheral immune cell profiling revealed more differentiated insights into the immunological effects of CLAD. While some immune cell subsets expanded, we also observed additive depleting effects after the second treatment course. Further studies are required to elucidate whether these changes are paramount for the consistent and prolonged disease-modifying effect of CLAD.

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Section: Introductionmentioning

confidence: 99%

Long‐term peripheral immune cell profiling reveals further targets of oral cladribine in MS

Moser

Schwenker

Seiberl

et al. 2020

Ann Clin Transl Neurol

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“…We identified dSNPs in the SHMT1 gene, a published MS GWAS locus [14], where DeepWAS QTL network analysis pinpoints the TF Ying Yang 1 (YY1), expressed in multiple cell lines including immune related cells, as a potential novel risk factor (S3 Fig). YY1 is a ubiquitously expressed TF shown to be essential for B cell development [42] and serves as master regulator of T cell exhaustion [43]. Such dysregulation of immune related cells has been shown to promote MS progression [44]. Additional SNP-protein association studies (pQTLs) [45] that showed how the dSNP rs2273030 alters YY1 protein abundances could be extended to develop clinical applications in the context of MS.…”

Section: Discussionmentioning

confidence: 99%

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

et al. 2020

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Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a PLOS Computational Biology | https://doi. Data Availability Statement:DeepWAS is an open source collaborative initiative available in the GitHub repository https://github.com/cellmapslab/ DeepWAS. The informed consents given by KORA study participants do not cover data posting in public databases. However, data are available upon request from KORA-gen (https://epi.helmholtzmuenchen.de/). Data requests can be submitted online and are subject to approval by the KORA Board. KKNMS data are available upon approved multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/ cellmapslab/DeepWAS. Author summaryIn the era of steadily increasing amounts of available genetic data, we still lack novel and innovative ideas on how to improve fine-mapping of regulatory variants identified by genome-wide association studies (GWAS), especially in non-coding regions. Current approaches for the identification of functional variants conduct functional annotation after the GWAS analysis either using position-based overlaps of each variant with regulatory elements or deep-learning-based methods predicting regulatory effects per variant on cell-type-specific chromatin features. We here present DeepWAS, which integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Our results provide evidence that DeepWAS results directly identify disease/trait-associated SNPs with a common effect on a specific chromatin feature in a relevant tissue. We can show for multiple sclerosis, major depressive disorder, and body height, that the SNPs identified by DeepWAS are at least nominally significant in classical univariate GWAS analysis of the same cohorts or larger published GWAS. By integ...

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“…YY1 is a ubiquitously expressed TF shown to be essential for B cell development 62 and serves as master regulator of T cell exhaustion 63 . Such dysregulation of immune related cells has been shown to promote MS progression 64,65 . Additional SNP-protein association studies (pQTLs) 66 that show how the deepSNP rs2273030 alter YY1 protein abundances could be extended to develop clinical applications in the context of MS.…”

Section: Discussionmentioning

confidence: 99%

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

Arloth

Eraslan

Andlauer

et al. 2016

Preprint

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Genome-wide association studies (GWAS) identify genetic variants associated with quantitative traits or disease. Thus, GWAS never directly link variants to regulatory mechanisms, which, in turn, are typically inferred during post-hoc analyses. In parallel, a recent deep learning-based method allows for prediction of regulatory effects per variant on currently up to 1,000 cell type-specific chromatin features. We here describe "DeepWAS", a new approach that directly integrates predictions of these regulatory effects of single variants into a multivariate GWAS setting. As a result, single variants associated with a trait or disease are, by design, coupled to their impact on a chromatin feature in a cell type. Up to 40,000 regulatory single-nucleotide polymorphisms (SNPs) were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals) to each identify 43-61 regulatory SNPs, called deepSNPs, which are shown to reach at least nominal significance in large GWAS. MS-and height-specific deepSNPs resided in active chromatin and introns, whereas MDD-specific deepSNPs located mostly to intragenic regions and repressive chromatin states. We found deepSNPs to be enriched in public or cohort-matched expression and methylation quantitative trait loci and demonstrate the potential of the DeepWAS method to directly generate testable functional hypotheses based on genotype data alone. DeepWAS is an innovative GWAS approach with the power to identify individual SNPs in non-coding regions with gene regulatory capacity with a joint contribution to disease risk. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.

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Dysregulated T cells in multiple sclerosis

Cited by 22 publications

References 87 publications

Long‐term peripheral immune cell profiling reveals further targets of oral cladribine in MS

Long‐term peripheral immune cell profiling reveals further targets of oral cladribine in MS

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

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