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      Increased DNA methylation of SLFN12 in CD4 + and CD8 + T cells from multiple sclerosis patients

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          Abstract

          DNA methylation is an epigenetic mark that is influenced by environmental factors and is associated with changes to gene expression and phenotypes. It may link environmental exposures to disease etiology or indicate important gene pathways involved in disease pathogenesis. We identified genomic regions that are differentially methylated in T cells of patients with relapsing remitting multiple sclerosis (MS) compared to healthy controls. DNA methylation was assessed at 450,000 genomic sites in CD4 + and CD8 + T cells purified from peripheral blood of 94 women with MS and 94 healthy women, and differentially methylated regions were identified using bumphunter. Differential DNA methylation was observed near four loci: MOG/ ZFP57, HLA-DRB1, NINJ2/LOC100049716, and SLFN12. Increased methylation of the first exon of the SLFN12 gene was observed in both T cell subtypes and remained present after restricting analyses to samples from patients who had never been on treatment or had been off treatment for more than 2.5 years. Genes near the regions of differential methylation in T cells were assessed for differential expression in whole blood samples from a separate population of 1,329 women with MS and 97 healthy women. Gene expression of HLA-DRB1, NINJ2, and SLFN12 was observed to be decreased in whole blood in MS patients compared to controls. We conclude that T cells from MS patients display regions of differential DNA methylation compared to controls, and corresponding gene expression differences are observed in whole blood. Two of the genes that showed both methylation and expression differences, NINJ2 and SLFN12, have not previously been implicated in MS. SLFN12 is a particularly compelling target of further research, as this gene is known to be down-regulated during T cell activation and up-regulated by type I interferons (IFNs), which are used to treat MS.

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          Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies.

          During the past 5 years, high-throughput technologies have been successfully used by epidemiology studies, but almost all have focused on sequence variation through genome-wide association studies (GWAS). Today, the study of other genomic events is becoming more common in large-scale epidemiological studies. Many of these, unlike the single-nucleotide polymorphism studied in GWAS, are continuous measures. In this context, the exercise of searching for regions of interest for disease is akin to the problems described in the statistical 'bump hunting' literature. New statistical challenges arise when the measurements are continuous rather than categorical, when they are measured with uncertainty, and when both biological signal, and measurement errors are characterized by spatial correlation along the genome. Perhaps the most challenging complication is that continuous genomic data from large studies are measured throughout long periods, making them susceptible to 'batch effects'. An example that combines all three characteristics is genome-wide DNA methylation measurements. Here, we present a data analysis pipeline that effectively models measurement error, removes batch effects, detects regions of interest and attaches statistical uncertainty to identified regions. We illustrate the usefulness of our approach by detecting genomic regions of DNA methylation associated with a continuous trait in a well-characterized population of newborns. Additionally, we show that addressing unexplained heterogeneity like batch effects reduces the number of false-positive regions. Our framework offers a comprehensive yet flexible approach for identifying genomic regions of biological interest in large epidemiological studies using quantitative high-throughput methods.
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            Identification of polymorphic and off-target probe binding sites on the Illumina Infinium MethylationEPIC BeadChip

            Genome-wide analysis of DNA methylation has now become a relatively inexpensive technique thanks to array-based methylation profiling technologies. The recently developed Illumina Infinium MethylationEPIC BeadChip interrogates methylation at over 850,000 sites across the human genome, covering 99% of RefSeq genes. This array supersedes the widely used Infinium HumanMethylation450 BeadChip, which has permitted insights into the relationship between DNA methylation and a wide range of conditions and traits. Previous research has identified issues with certain probes on both the HumanMethylation450 BeadChip and its predecessor, the Infinium HumanMethylation27 BeadChip, which were predicted to affect array performance. These issues concerned probe-binding specificity and the presence of polymorphisms at target sites. Using in silico methods, we have identified probes on the Infinium MethylationEPIC BeadChip that are predicted to (i) measure methylation at polymorphic sites and (ii) hybridise to multiple genomic regions. We intend these resources to be used for quality control procedures when analysing data derived from this platform.
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              Strategies for aggregating gene expression data: The collapseRows R function

              Background Genomic and other high dimensional analyses often require one to summarize multiple related variables by a single representative. This task is also variously referred to as collapsing, combining, reducing, or aggregating variables. Examples include summarizing several probe measurements corresponding to a single gene, representing the expression profiles of a co-expression module by a single expression profile, and aggregating cell-type marker information to de-convolute expression data. Several standard statistical summary techniques can be used, but network methods also provide useful alternative methods to find representatives. Currently few collapsing functions are developed and widely applied. Results We introduce the R function collapseRows that implements several collapsing methods and evaluate its performance in three applications. First, we study a crucial step of the meta-analysis of microarray data: the merging of independent gene expression data sets, which may have been measured on different platforms. Toward this end, we collapse multiple microarray probes for a single gene and then merge the data by gene identifier. We find that choosing the probe with the highest average expression leads to best between-study consistency. Second, we study methods for summarizing the gene expression profiles of a co-expression module. Several gene co-expression network analysis applications show that the optimal collapsing strategy depends on the analysis goal. Third, we study aggregating the information of cell type marker genes when the aim is to predict the abundance of cell types in a tissue sample based on gene expression data ("expression deconvolution"). We apply different collapsing methods to predict cell type abundances in peripheral human blood and in mixtures of blood cell lines. Interestingly, the most accurate prediction method involves choosing the most highly connected "hub" marker gene. Finally, to facilitate biological interpretation of collapsed gene lists, we introduce the function userListEnrichment, which assesses the enrichment of gene lists for known brain and blood cell type markers, and for other published biological pathways. Conclusions The R function collapseRows implements several standard and network-based collapsing methods. In various genomic applications we provide evidence that both types of methods are robust and biologically relevant tools.
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                Author and article information

                Contributors
                Role: Formal analysisRole: MethodologyRole: VisualizationRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: InvestigationRole: ResourcesRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Formal analysisRole: Methodology
                Role: Data curationRole: Investigation
                Role: Data curationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: ResourcesRole: Writing – review & editing
                Role: Formal analysisRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: ValidationRole: VisualizationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                31 October 2018
                2018
                : 13
                : 10
                : e0206511
                Affiliations
                [1 ] Computational Biology Graduate Group, University of California, Berkeley, Berkeley, CA, United States of America
                [2 ] Genetic Epidemiology and Genomics Laboratory, Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, CA, United States of America
                [3 ] Institute of Clinical Medicine, University of Oslo, Oslo, Norway
                [4 ] Department of Neurology, Oslo University Hospital, Oslo, Norway
                [5 ] Department for Mechanical, Electronics and Chemical Engineering, Oslo Metropolitan University, Oslo, Norway
                [6 ] MS-Centre Hakadal, Hakadal, Norway
                [7 ] Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
                [8 ] Translational & Integrative Analytics, Biogen, Inc., Cambridge, MA, United States of America
                [9 ] Statistical Genetics & Genetic Epidemiology, Biogen, Inc., Cambridge, MA, United States of America
                [10 ] Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
                [11 ] Centre for Information Based Medicine, Hunter Medical Research Institute, Newcastle, Australia
                [12 ] School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, Australia
                [13 ] School of Medicine and Public Health, University of Newcastle, Newcastle, Australia
                [14 ] Molecular Genetics, Pathology North, John Hunter Hospital, Newcastle, Australia
                [15 ] Department of Neurology, John Hunter Hospital, Newcastle, Australia
                Northwestern University Feinberg School of Medicine, UNITED STATES
                Author notes

                Competing Interests: Dipen P. Sangurdekar and Paola G. Bronson are employees at Biogen, Inc. Stine Marit Moen reports an unrestricted travel grant from Biogen Idec and speaker's fees from Biogen Idec and Novartis. Hanne F. Harbo reports personal fees from Biogen Norway, Merck Norway, and Genzyme Norway, and grants and personal fees from Novartis Norway. Pål Berg-Hansen reports grants and personal fees from Novartis and personal fees from Biogen Idec and Teva. Jeannette Lechner-Scott reports grants and personal fees from Biogen, Novartis, and TEVA, and personal fees from Sanofi Genzyme, Roche, and Merck. Tone Berge reports unrestricted grants from Biogen Idec and Sanofi Genzyme. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                ‡ These authors also contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-1839-4883
                http://orcid.org/0000-0001-6691-9413
                http://orcid.org/0000-0002-9127-6488
                http://orcid.org/0000-0001-9474-0808
                http://orcid.org/0000-0003-2149-3556
                http://orcid.org/0000-0002-3785-4742
                http://orcid.org/0000-0001-7724-3404
                http://orcid.org/0000-0002-6047-0122
                http://orcid.org/0000-0002-2975-7520
                Article
                PONE-D-18-16452
                10.1371/journal.pone.0206511
                6209300
                30379917
                4c847ada-db02-4546-aebd-ac029228d51f
                © 2018 Rhead et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 6 June 2018
                : 15 October 2018
                Page count
                Figures: 4, Tables: 4, Pages: 17
                Funding
                This work is supported by grant number F31NS096885 from the National Institute of Neurological Disorders and Stroke ( https://www.ninds.nih.gov/), R01ES017080 from the National Institute of Environmental Health Sciences ( https://www.niehs.nih.gov/), 240102 from the Norwegian Research Council ( https://www.forskningsradet.no/), and 39961 from South-Eastern Norway Regional Health Authority ( https://www.helse-sorost.no/south-eastern-norway-regional-health-authority), as well as unrestricted research grants from “Legatet til Henrik Homans Minde” ( https://www.unifor.no/Fund.aspx?fund=59) and Sanofi Genzyme ( https://www.sanofigenzyme.com/en/), a travel grant from “Odd Fellow Medisinske Fond” ( https://www.oddfellow.no/), and fellowships from the Canadian Institutes of Health Research ( http://www.cihr-irsc.gc.ca/) and Multiple Sclerosis Research Australia ( https://msra.org.au/). The funder provided support in the form of salaries for authors D.P.S. and P.G.B., but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.
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                The DNA methylation data have not been deposited in a publicly accessible location because their use is restricted to MS research only. Data will be made available upon request, subject to approval by the Norwegian Regional Committee for Medical and Health Research Ethics. Requests for the Norwegian data can be initiated by contacting Peder Utne at the Oslo University Hospital Department for Research Administration & Biobanking at grants@ 123456ous-hf.no . Requests for the Australian data can be initiated by contacting the manager of the Hunter Medical Research Institute Biobanking Facility at HCRA-Biospecimens@ 123456newcastle.edu.au . The expression data for the four genes included in the validation study is proprietary but will be made available to researchers upon request by contacting the Director of Medical Genetics at Biogen, Dr. Heiko Runz, at heiko.runz@ 123456biogen.com .

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