| 1. |
- Fernandes, SJ, et al.
(author)
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Non-parametric combination analysis of multiple data types enables detection of novel regulatory mechanisms in T cells of multiple sclerosis patients
- 2019
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In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 11996-
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Journal article (peer-reviewed)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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| 2. |
- Planell, N, et al.
(author)
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STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline
- 2021
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In: Frontiers in genetics. - : Frontiers Media SA. - 1664-8021. ; 12, s. 620453-
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Journal article (peer-reviewed)abstract
- Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package.1
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| 4. |
- Kular, L, et al.
(author)
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Epigenetic clock indicates accelerated aging in glial cells of progressive multiple sclerosis patients
- 2022
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In: Frontiers in aging neuroscience. - : Frontiers Media SA. - 1663-4365. ; 14, s. 926468-
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Journal article (peer-reviewed)abstract
- Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disease of the central nervous system (CNS) characterized by irreversible disability at later progressive stages. A growing body of evidence suggests that disease progression depends on age and inflammation within the CNS. We aimed to investigate epigenetic aging in bulk brain tissue and sorted nuclei from MS patients using DNA methylation-based epigenetic clocks.MethodsWe applied Horvath’s multi-tissue and Shireby’s brain-specific Cortical clock on bulk brain tissue (n = 46), sorted neuronal (n = 54), and glial nuclei (n = 66) from post-mortem brain tissue of progressive MS patients and controls.ResultsWe found a significant increase in age acceleration residuals, corresponding to 3.6 years, in glial cells of MS patients compared to controls (P = 0.0024) using the Cortical clock, which held after adjustment for covariates (Padj = 0.0263). The 4.8-year age acceleration found in MS neurons (P = 0.0054) did not withstand adjustment for covariates and no significant difference in age acceleration residuals was observed in bulk brain tissue between MS patients and controls.ConclusionWhile the findings warrant replication in larger cohorts, our study suggests that glial cells of progressive MS patients exhibit accelerated biological aging.
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