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Sci Rep. 2019 Aug 19;9(1):11996. doi: 10.1038/s41598-019-48493-7.

Non-parametric combination analysis of multiple data types enables detection of novel regulatory mechanisms in T cells of multiple sclerosis patients.

Author information

1
Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden. sunjay.fernandes@ki.se.
2
Science for Life Laboratory, Solna, Stockholm, Sweden. sunjay.fernandes@ki.se.
3
Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
4
Science for Life Laboratory, Solna, Stockholm, Sweden.
5
Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
6
Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
7
Institute of Chemical Biology, Ilia State University, Tbilisi, Georgia.
8
Gnosis Data Analysis PC, Heraklion, Greece.
9
Computer Science Department, University of Crete, Heraklion, Crete, Greece.
10
Computational Medicine Center, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA, 19107, USA.
11
Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain.
12
Institute for Immunology, Biomedical Center, Ludwig-Maximilians-Universität, München, 82152, Planegg-Martinsried, Germany.
13
Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden. jesper.tegner@kaust.edu.sa.
14
Science for Life Laboratory, Solna, Stockholm, Sweden. jesper.tegner@kaust.edu.sa.
15
Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. jesper.tegner@kaust.edu.sa.
16
Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden. david.gomez.cabrero@navarra.es.
17
Science for Life Laboratory, Solna, Stockholm, Sweden. david.gomez.cabrero@navarra.es.
18
Mucosal and Salivary Biology Division, King's College London Dental Institute, London, SE1 9RT, United Kingdom. david.gomez.cabrero@navarra.es.
19
Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain. david.gomez.cabrero@navarra.es.

Abstract

Multiple Sclerosis (MS) is an autoimmune disease of the central nervous system with prominent neurodegenerative components. The triggering and progression of MS is associated with transcriptional and epigenetic alterations in several tissues, including peripheral blood. The combined influence of transcriptional and epigenetic changes associated with MS has not been assessed in the same individuals. Here we generated paired transcriptomic (RNA-seq) and DNA methylation (Illumina 450 K array) profiles of CD4+ and CD8+ T cells (CD4, CD8), using clinically accessible blood from healthy donors and MS patients in the initial relapsing-remitting and subsequent secondary-progressive stage. By integrating the output of a differential expression test with a permutation-based non-parametric combination methodology, we identified 149 differentially expressed (DE) genes in both CD4 and CD8 cells collected from MS patients. Moreover, by leveraging the methylation-dependent regulation of gene expression, we identified the gene SH3YL1, which displayed significant correlated expression and methylation changes in MS patients. Importantly, silencing of SH3YL1 in primary human CD4 cells demonstrated its influence on T cell activation. Collectively, our strategy based on paired sampling of several cell-types provides a novel approach to increase sensitivity for identifying shared mechanisms altered in CD4 and CD8 cells of relevance in MS in small sized clinical materials.

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