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      Epigenome-wide DNA methylation association study of circulating IgE levels identifies novel targets for asthma

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          Summary

          Background

          Identifying novel epigenetic signatures associated with serum immunoglobulin E (IgE) may improve our understanding of molecular mechanisms underlying asthma and IgE-mediated diseases.

          Methods

          We performed an epigenome-wide association study using whole blood from Framingham Heart Study (FHS; n = 3,471, 46% females) participants and validated results using the Childhood Asthma Management Program (CAMP; n = 674, 39% females) and the Genetic Epidemiology of Asthma in Costa Rica Study (CRA; n = 787, 41% females). Using the closest gene to each IgE-associated CpG, we highlighted biologically plausible pathways underlying IgE regulation and analyzed the transcription patterns linked to IgE-associated CpGs (expression quantitative trait methylation loci; eQTMs). Using prior UK Biobank summary data from genome-wide association studies of asthma and allergy, we performed Mendelian randomization (MR) for causal inference testing using the IgE-associated CpGs from FHS with methylation quantitative trait loci (mQTLs) as instrumental variables.

          Findings

          We identified 490 statistically significant differentially methylated CpGs associated with IgE in FHS, of which 193 (39.3%) replicated in CAMP and CRA (FDR < 0.05). Gene ontology analysis revealed enrichment in pathways related to transcription factor binding, asthma, and other immunological processes. eQTM analysis identified 124 cis-eQTMs for 106 expressed genes (FDR < 0.05). MR in combination with drug-target analysis revealed CTSB and USP20 as putatively causal regulators of IgE levels (Bonferroni adjusted P < 7.94E-04) that can be explored as potential therapeutic targets.

          Interpretation

          By integrating eQTM and MR analyses in general and clinical asthma populations, our findings provide a deeper understanding of the multidimensional inter-relations of DNA methylation, gene expression, and IgE levels.

          Funding

          US doi 10.13039/100000002, NIH; / doi 10.13039/100000050, NHLBI; grants: P01HL132825, K99HL159234. N01-HC-25195 and HHSN268201500001I.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                Journal
                eBioMedicine
                EBioMedicine
                eBioMedicine
                Elsevier
                2352-3964
                18 August 2023
                September 2023
                18 August 2023
                : 95
                : 104758
                Affiliations
                [a ]The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
                [b ]The Framingham Heart Study, Framingham, MA 01702, USA
                [c ]Brigham and Women’s Hospital, Channing Division of Network Medicine, Boston, MA 02115, USA
                [d ]University of Southern California Methylation Characterization Center, University of Southern California, Los Angeles, CA 90033, USA
                [e ]Boston University School of Medicine, Pulmonary Center, Boston, MA 02118, USA
                Author notes
                []Corresponding author. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, 181 Longwood Avenue, Boston, MA 02115, USA. dawn.demeo@ 123456channing.harvard.edu
                [∗∗ ]Corresponding author. Framingham Heart Study, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA 01702, USA. levyd@ 123456nhlbi.nih.gov
                [f]

                These authors have contributed equally to this work and share first authorship.

                [g]

                These authors have contributed equally to this work and share last authorship.

                Article
                S2352-3964(23)00323-7 104758
                10.1016/j.ebiom.2023.104758
                10462855
                37598461
                782b426b-ab89-490e-a463-7ada99d2460e
                © 2023 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 14 March 2023
                : 1 August 2023
                : 2 August 2023
                Categories
                Articles

                ewas,dna methylation,ige,asthma,rna-sequencing,mendelian randomization,lung,eqtm,drug targets

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