Dataset Information

Strategy for the analysis of tissue-specific methylation changes without physical isolation.

ABSTRACT: One common experimental hurdle that arises when explore patterns of cytosine methylation is the generation of data derived from a single specific tissue, often arduous to isolate from a heterogeneous biospecimen. Here we show a new strategy for exploring environment- or mutation-caused changes in cell type- or tissue-specific methylation landscapes, which requires neither transgenic reporter cell lines nor physical separation. This approach takes advantage of a known distinct methylation signature existing in only one of the tissues within an organ under a particular condition. From the information on such compared published methylomes, one can design a set of PCR primers that specifically amplify bisulfite-converted DNA of two nearby genomic regions of interest, thus allowing for tissue-specific DNA methylation data. To validate the performance of the approach, we designed primers able to amplify a portion of a gene in the context of root biology: the Arabidopsis homeotic gene Glabra-2 (Gl2), expressed only in epidermis during cell differentiation. We found that the extent of methylated cytosines appears remarkably different when root epidermis-specific primers were used vs. non-specific ones under three genetic backgrounds involving mutations in genes also associated with the establishment of cell identity. Although the genetic or environmental perturbations to be studied might modify methylation in the primer-annealing zone, leading to a possible misinterpretation of the data, the strategy presented here can become a useful first round screening tool to detect differences in tissue-specific epigenetic status under new conditions.

SUBMITTER: Beyrne CC

PROVIDER: S-EPMC6380432 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Strategy for the analysis of tissue-specific methylation changes without physical isolation.

Beyrne Cecilia C CC González Rodrigo M RM Iusem Norberto D ND

Epigenetics 20190121 1

One common experimental hurdle that arises when explore patterns of cytosine methylation is the generation of data derived from a single specific tissue, often arduous to isolate from a heterogeneous biospecimen. Here we show a new strategy for exploring environment- or mutation-caused changes in cell type- or tissue-specific methylation landscapes, which requires neither transgenic reporter cell lines nor physical separation. This approach takes advantage of a known distinct methylation signatu ...[more]

PMID: 30632887

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Project description:BACKGROUND: The computational prediction of DNA methylation has become an important topic in the recent years due to its role in the epigenetic control of normal and cancer-related processes. While previous prediction approaches focused merely on differences between methylated and unmethylated DNA sequences, recent experimental results have shown the presence of much more complex patterns of methylation across tissues and time in the human genome. These patterns are only partially described by a binary model of DNA methylation. In this work we propose a novel approach, based on profile analysis of tissue-specific methylation that uncovers significant differences in the sequences of CpG islands (CGIs) that predispose them to a tissue- specific methylation pattern. RESULTS: We defined CGI methylation profiles that separate not only between constitutively methylated and unmethylated CGIs, but also identify CGIs showing a differential degree of methylation across tissues and cell-types or a lack of methylation exclusively in sperm. These profiles are clearly distinguished by a number of CGI attributes including their evolutionary conservation, their significance, as well as the evolutionary evidence of prior methylation. Additionally, we assess profile functionality with respect to the different compartments of protein coding genes and their possible use in the prediction of DNA methylation. CONCLUSION: Our approach provides new insights into the biological features that determine if a CGI has a functional role in the epigenetic control of gene expression and the features associated with CGI methylation susceptibility. Moreover, we show that the ability to predict CGI methylation is based primarily on the quality of the biological information used and the relationships uncovered between different sources of knowledge. The strategy presented here is able to predict, besides the constitutively methylated and unmethylated classes, two more tissue specific methylation classes conserving the accuracy provided by leading binary methylation classification methods.

Dataset Information

Strategy for the analysis of tissue-specific methylation changes without physical isolation.

Publications

Strategy for the analysis of tissue-specific methylation changes without physical isolation.

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