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

Sunjay Jude Fernandes, Hiromasa Morikawa, Ewoud Ewing, Sabrina Ruhrmann, Rubin Narayan Joshi, Vincenzo Lagani, Nestoras Karathanasis, Mohsen Khademi, Nuria Planell, Angelika Schmidt, Ioannis Tsamardinos, Tomas Olsson, Fredrik Piehl, Ingrid Kockum, Maja Jagodic, Jesper Tegner, David Gomez-Cabrero

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.
Original languageEnglish (US)
JournalScientific reports
Issue number1
StatePublished - Aug 19 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank all the patients who have been willing to contribute their blood samples to make this study possible. We acknowledge Peri Noori from the Unit of Computational Medicine for laboratory management. This work was supported by grants from the Swedish Research Council, the Swedish Brain Foundation, the Stockholm County Council (ALF project) and AstraZeneca (AstraZeneca-Science for Life Laboratory collaboration). J.T was supported by funds from King Abdullah University for Science and Technology. IK was supported by Horizon 2020 MultipleMS grant no 733161. VL, NK, and IT were supported by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n. 617393 and STATegra EU FP7 project, No 306000.


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