DNA Methylation variability among individuals is related to CpGs cluster density and evolutionary signatures

Palumbo, Domenico; Affinito, Ornella; Monticelli, Antonella; Cocozza, Sérgio

doi:10.1186/s12864-018-4618-9

Cited by 22 publications

(19 citation statements)

References 41 publications

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“…Interestingly, the so-called CCAAT box is highly conserved in all the promoters of the H1 loci and it is followed by a CA dinucleotide. www.nature.com/scientificreports www.nature.com/scientificreports/ A correlation between the local methylation profiles and CpGs density is known [27][28][29] . CpGs proximal to a TSS show less variation (more stability) than distal CpGs 6 .…”

Section: Discussionmentioning

confidence: 99%

DNA sequence context as a marker of CpG methylation instability in normal and cancer tissues

Scala

Federico

Palumbo

et al. 2020

Sci Rep

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DnA methylation alterations are related to multiple molecular mechanisms. the DnA context of cpG sites plays a crucial role in the maintenance and stability of methylation patterns. the quantitative relationship between DnA composition and DnA methylation has been studied in normal as well as pathological conditions, showing that DNA methylation status is highly dependent on the local sequence context. In this work, we describe this relationship by analyzing the DNA sequence context associated to methylation profiles in both physiological and pathological conditions. In particular, we used DnA motifs to describe methylation stability patterns in normal tissues and aberrant methylation events in cancer lesions. In this manuscript, we show how different groups of DNA sequences can be related to specific epigenetic events, across normal and cancer tissues, and provide a thorough structural and functional characterization of these sequences.

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Section: Discussionmentioning

confidence: 99%

DNA sequence context as a marker of CpG methylation instability in normal and cancer tissues

Scala

Federico

Palumbo

et al. 2020

Sci Rep

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“…To evaluate the effects of pre-processing on the between-sample variability and on the identification of Differentially Methylated Positions (DMPs) in case-control studies, we analyse four different collections of datasets. The datasets have been selected based on their sample size and on the assumption that there is no global DNA methylation variation among samples (see Introduction and [5]). This assumption is required to apply methods that are based on quantile normalization [4,6].…”

Section: Methodsmentioning

confidence: 99%

“…The pre-processing pipeline designed to obtain methylation profiles from raw experimental data (.idat files) in the form of β-values (β = M/(M + U + α), where typically α = 100 [3]) is not standardized, even within the same technological platform, as illustrated in Table 2. Indeed, different pre-processing methods make different assumptions on the data regarding their distribution, their correlation structure and the extent (localized or global) of the DNA methylation variability on the genome [4], since this is still a matter of debate [5]. of this author is articulated in the 'author contributions' section.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of pre-processing on the meta-analysis of DNA methylation data from the Illumina HumanMethylation450 BeadChip platform

et al. 2020

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Introduction Meta-analysis is a powerful means for leveraging the hundreds of experiments being run worldwide into more statistically powerful analyses. This is also true for the analysis of omic data, including genome-wide DNA methylation. In particular, thousands of DNA methylation profiles generated using the Illumina 450k are stored in the publicly accessible Gene Expression Omnibus (GEO) repository. Often, however, the intensity values produced by the BeadChip (raw data) are not deposited, therefore only pre-processed values-obtained after computational manipulation-are available. Pre-processing is possibly different among studies and may then affect meta-analysis by introducing non-biological sources of variability. Material and methods To systematically investigate the effect of pre-processing on meta-analysis, we analysed four different collections of DNA methylation samples (datasets), each composed of two subsets, for which raw data from controls (i.e. healthy subjects) and cases (i.e. patients) are available. We pre-processed the data from each dataset with nine among the most common pipelines found in literature. Moreover, we evaluated the performance of regRCPqn, a modification of the RCP algorithm that aims to improve data consistency. For each combination of pre-processing (9 × 9), we first evaluated the between-sample variability among control subjects and, then, we identified genomic positions that are differentially methylated between cases and controls (differential analysis).

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“…This variability may contribute to the finding that there is no overlap in the CpGs described in the manuscripts reviewed (Additional file 2: Table S2). This variability may reflect the importance of immune system pathways, which may show greater DNAm volatility, in the response to air pollution exposure, and this may contribute to disease susceptibility differences between individuals over time [65].…”

Section: Effects Of Air Pollutants On Dna Methylation Across the Lifementioning

confidence: 99%

Air pollution and DNA methylation: effects of exposure in humans

2019

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Air pollution exposure is estimated to contribute to approximately seven million early deaths every year worldwide and more than 3% of disability-adjusted life years lost. Air pollution has numerous harmful effects on health and contributes to the development and morbidity of cardiovascular disease, metabolic disorders, and a number of lung pathologies, including asthma and chronic obstructive pulmonary disease (COPD). Emerging data indicate that air pollution exposure modulates the epigenetic mark, DNA methylation (DNAm), and that these changes might in turn influence inflammation, disease development, and exacerbation risk. Several traffic-related air pollution (TRAP) components, including particulate matter (PM), black carbon (BC), ozone (O 3 ), nitrogen oxides (NO x ), and polyaromatic hydrocarbons (PAHs), have been associated with changes in DNAm; typically lowering DNAm after exposure. Effects of air pollution on DNAm have been observed across the human lifespan, but it is not yet clear whether early life developmental sensitivity or the accumulation of exposures have the most significant effects on health. Air pollution exposure-associated DNAm patterns are often correlated with long-term negative respiratory health outcomes, including the development of lung diseases, a focus in this review. Recently, interventions such as exercise and B vitamins have been proposed to reduce the impact of air pollution on DNAm and health. Ultimately, improved knowledge of how exposure-induced change in DNAm impacts health, both acutely and chronically, may enable preventative and remedial strategies to reduce morbidity in polluted environments. Electronic supplementary material The online version of this article (10.1186/s13148-019-0713-2) contains supplementary material, which is available to authorized users.

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DNA Methylation variability among individuals is related to CpGs cluster density and evolutionary signatures

Cited by 22 publications

References 41 publications

DNA sequence context as a marker of CpG methylation instability in normal and cancer tissues

DNA sequence context as a marker of CpG methylation instability in normal and cancer tissues

Evaluation of pre-processing on the meta-analysis of DNA methylation data from the Illumina HumanMethylation450 BeadChip platform

Air pollution and DNA methylation: effects of exposure in humans

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