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MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data

DNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated c...

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Autores principales: De Riso, Giulia, Sarnataro, Antonella, Scala, Giovanni, Cuomo, Mariella, Della Monica, Rosa, Amente, Stefano, Chiariotti, Lorenzo, Miele, Gennaro, Cocozza, Sergio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9803872/
https://www.ncbi.nlm.nih.gov/pubmed/36601577
http://dx.doi.org/10.1093/nargab/lqac096
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author De Riso, Giulia
Sarnataro, Antonella
Scala, Giovanni
Cuomo, Mariella
Della Monica, Rosa
Amente, Stefano
Chiariotti, Lorenzo
Miele, Gennaro
Cocozza, Sergio
author_facet De Riso, Giulia
Sarnataro, Antonella
Scala, Giovanni
Cuomo, Mariella
Della Monica, Rosa
Amente, Stefano
Chiariotti, Lorenzo
Miele, Gennaro
Cocozza, Sergio
author_sort De Riso, Giulia
collection PubMed
description DNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated cytosines are distributed in each single DNA molecule. Here, we propose Methylation Class (MC) profiling as a genome-wide approach to the study of DNA methylation heterogeneity from bulk bisulfite sequencing experiments. The proposed approach is built on the concept of MCs, groups of DNA molecules sharing the same number of methylated cytosines. The relative abundances of MCs from sequencing reads incorporates the information on the average methylation, and directly informs on the methylation level of each molecule. By applying our approach to publicly available bisulfite-sequencing datasets, we individuated cell-to-cell differences as the prevalent contributor to methylation heterogeneity. Moreover, we individuated signatures of loci undergoing imprinting and X-inactivation, and highlighted differences between the two processes. When applying MC profiling to compare different conditions, we identified methylation changes occurring in regions with almost constant average methylation. Altogether, our results indicate that MC profiling can provide useful insights on the epigenetic status and its evolution at multiple genomic regions.
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spelling pubmed-98038722023-01-03 MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data De Riso, Giulia Sarnataro, Antonella Scala, Giovanni Cuomo, Mariella Della Monica, Rosa Amente, Stefano Chiariotti, Lorenzo Miele, Gennaro Cocozza, Sergio NAR Genom Bioinform High Throughput Sequencing Methods DNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated cytosines are distributed in each single DNA molecule. Here, we propose Methylation Class (MC) profiling as a genome-wide approach to the study of DNA methylation heterogeneity from bulk bisulfite sequencing experiments. The proposed approach is built on the concept of MCs, groups of DNA molecules sharing the same number of methylated cytosines. The relative abundances of MCs from sequencing reads incorporates the information on the average methylation, and directly informs on the methylation level of each molecule. By applying our approach to publicly available bisulfite-sequencing datasets, we individuated cell-to-cell differences as the prevalent contributor to methylation heterogeneity. Moreover, we individuated signatures of loci undergoing imprinting and X-inactivation, and highlighted differences between the two processes. When applying MC profiling to compare different conditions, we identified methylation changes occurring in regions with almost constant average methylation. Altogether, our results indicate that MC profiling can provide useful insights on the epigenetic status and its evolution at multiple genomic regions. Oxford University Press 2022-12-31 /pmc/articles/PMC9803872/ /pubmed/36601577 http://dx.doi.org/10.1093/nargab/lqac096 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle High Throughput Sequencing Methods
De Riso, Giulia
Sarnataro, Antonella
Scala, Giovanni
Cuomo, Mariella
Della Monica, Rosa
Amente, Stefano
Chiariotti, Lorenzo
Miele, Gennaro
Cocozza, Sergio
MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data
title MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data
title_full MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data
title_fullStr MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data
title_full_unstemmed MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data
title_short MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data
title_sort mc profiling: a novel approach to analyze dna methylation heterogeneity in genome-wide bisulfite sequencing data
topic High Throughput Sequencing Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9803872/
https://www.ncbi.nlm.nih.gov/pubmed/36601577
http://dx.doi.org/10.1093/nargab/lqac096
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