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      Association of peripheral blood DNA methylation level with Alzheimer’s disease progression

      research-article
      1 , , 2 , 2 , 2 , 3 , 2 , 3 , for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
      Clinical Epigenetics
      BioMed Central
      Epigenetics, EWAS, DMP, DMR, Alzheimer’s disease

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          Abstract

          Background

          Identifying biomarkers associated with Alzheimer’s disease (AD) progression may enable patient enrichment and improve clinical trial designs. Epigenome-wide association studies have revealed correlations between DNA methylation at cytosine-phosphate-guanine (CpG) sites and AD pathology and diagnosis. Here, we report relationships between peripheral blood DNA methylation profiles measured using Infinium® MethylationEPIC BeadChip and AD progression in participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort.

          Results

          The rate of cognitive decline from initial DNA sampling visit to subsequent visits was estimated by the slopes of the modified Preclinical Alzheimer Cognitive Composite (mPACC; mPACC digit and mPACC trailsB) and Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) plots using robust linear regression in cognitively normal (CN) participants and patients with mild cognitive impairment (MCI), respectively. In addition, diagnosis conversion status was assessed using a dichotomized endpoint. Two CpG sites were significantly associated with the slope of mPACC in CN participants ( P < 5.79 × 10 −8 [Bonferroni correction threshold]); cg00386386 was associated with the slope of mPACC digit, and cg09422696 annotated to RP11-661A12.5 was associated with the slope of CDR-SB. No significant CpG sites associated with diagnosis conversion status were identified. Genes involved in cognition and learning were enriched. A total of 19, 13, and 5 differentially methylated regions (DMRs) associated with the slopes of mPACC trailsB, mPACC digit, and CDR-SB, respectively, were identified by both comb-p and DMRcate algorithms; these included DMRs annotated to HOXA4. Furthermore, 5 and 19 DMRs were associated with conversion status in CN and MCI participants, respectively. The most significant DMR was annotated to the AD-associated gene PM20D1 (chr1: 205,818,956 to 205,820,014 [13 probes], Sidak-corrected P = 7.74 × 10 −24), which was associated with both the slope of CDR-SB and the MCI conversion status.

          Conclusion

          Candidate CpG sites and regions in peripheral blood were identified as associated with the rate of cognitive decline in participants in the ADNI cohort. While we did not identify a single CpG site with sufficient clinical utility to be used by itself due to the observed effect size, a biosignature composed of DNA methylation changes may have utility as a prognostic biomarker for AD progression.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13148-021-01179-2.

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          Most cited references75

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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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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              Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities.

              Genome-scale studies have revealed extensive, cell type-specific colocalization of transcription factors, but the mechanisms underlying this phenomenon remain poorly understood. Here, we demonstrate in macrophages and B cells that collaborative interactions of the common factor PU.1 with small sets of macrophage- or B cell lineage-determining transcription factors establish cell-specific binding sites that are associated with the majority of promoter-distal H3K4me1-marked genomic regions. PU.1 binding initiates nucleosome remodeling, followed by H3K4 monomethylation at large numbers of genomic regions associated with both broadly and specifically expressed genes. These locations serve as beacons for additional factors, exemplified by liver X receptors, which drive both cell-specific gene expression and signal-dependent responses. Together with analyses of transcription factor binding and H3K4me1 patterns in other cell types, these studies suggest that simple combinations of lineage-determining transcription factors can specify the genomic sites ultimately responsible for both cell identity and cell type-specific responses to diverse signaling inputs. Copyright 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                qli2@its.jnj.com
                Journal
                Clin Epigenetics
                Clin Epigenetics
                Clinical Epigenetics
                BioMed Central (London )
                1868-7075
                1868-7083
                15 October 2021
                15 October 2021
                2021
                : 13
                : 191
                Affiliations
                [1 ]GRID grid.497530.c, ISNI 0000 0004 0389 4927, Neuroscience, , Janssen Research and Development, LLC, ; 1125 Trenton-Harbourton Road, Titusville, NJ 08560 USA
                [2 ]GRID grid.431072.3, ISNI 0000 0004 0572 4227, Genomics Research Center, AbbVie, ; North Chicago, IL USA
                [3 ]GRID grid.257413.6, ISNI 0000 0001 2287 3919, Indiana Alzheimer’s Disease Research Center, Department of Radiology and Imaging Sciences, , Indiana University School of Medicine, ; Indianapolis, IN USA
                Author information
                http://orcid.org/0000-0003-4182-4535
                Article
                1179
                10.1186/s13148-021-01179-2
                8518178
                34654479
                84f9c66e-b3ae-449e-928f-4ff5068b64da
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 5 April 2021
                : 29 September 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100007333, Alzheimer's Disease Neuroimaging Initiative;
                Award ID: U01 AG024904
                Funded by: DoD Alzheimer's Disease Neuroimaging Initiative (US)
                Award ID: W81XWH-12-2-0012
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Genetics
                epigenetics,ewas,dmp,dmr,alzheimer’s disease
                Genetics
                epigenetics, ewas, dmp, dmr, alzheimer’s disease

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