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Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data

BACKGROUND: Abnormal DNA methylation is thought to contribute to the onset and progression of systemic sclerosis. Currently, the most comprehensive assay for profiling DNA methylation is whole-genome bisulfite sequencing (WGBS), but its precision depends on read depth and it may be subject to sequen...

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Autores principales: Yu, Jeffrey C. Y., Zeng, Yixiao, Zhao, Kaiqiong, Lu, Tianyuan, Oros Klein, Kathleen, Colmegna, Inés, Lora, Maximilien, Bhatnagar, Sahir R., Leask, Andrew, Greenwood, Celia M. T., Hudson, Marie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239181/
https://www.ncbi.nlm.nih.gov/pubmed/37270501
http://dx.doi.org/10.1186/s13148-023-01513-w
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author Yu, Jeffrey C. Y.
Zeng, Yixiao
Zhao, Kaiqiong
Lu, Tianyuan
Oros Klein, Kathleen
Colmegna, Inés
Lora, Maximilien
Bhatnagar, Sahir R.
Leask, Andrew
Greenwood, Celia M. T.
Hudson, Marie
author_facet Yu, Jeffrey C. Y.
Zeng, Yixiao
Zhao, Kaiqiong
Lu, Tianyuan
Oros Klein, Kathleen
Colmegna, Inés
Lora, Maximilien
Bhatnagar, Sahir R.
Leask, Andrew
Greenwood, Celia M. T.
Hudson, Marie
author_sort Yu, Jeffrey C. Y.
collection PubMed
description BACKGROUND: Abnormal DNA methylation is thought to contribute to the onset and progression of systemic sclerosis. Currently, the most comprehensive assay for profiling DNA methylation is whole-genome bisulfite sequencing (WGBS), but its precision depends on read depth and it may be subject to sequencing errors. SOMNiBUS, a method for regional analysis, attempts to overcome some of these limitations. Using SOMNiBUS, we re-analyzed WGBS data previously analyzed using bumphunter, an approach that initially fits single CpG associations, to contrast DNA methylation estimates by both methods. METHODS: Purified CD4+ T lymphocytes of 9 SSc and 4 control females were sequenced using WGBS. We separated the resulting sequencing data into regions with dense CpG data, and differentially methylated regions (DMRs) were inferred with the SOMNiBUS region-level test, adjusted for age. Pathway enrichment analysis was performed with ingenuity pathway analysis (IPA). We compared the results obtained by SOMNiBUS and bumphunter. RESULTS: Of 8268 CpG regions of ≥ 60 CpGs eligible for analysis with SOMNiBUS, we identified 131 DMRs and 125 differentially methylated genes (DMGs; p-values less than Bonferroni-corrected threshold of 6.05–06 controlling family-wise error rate at 0.05; 1.6% of the regions). In comparison, bumphunter identified 821,929 CpG regions, 599 DMRs (of which none had ≥ 60 CpGs) and 340 DMGs (q-value of 0.05; 0.04% of all regions). The top ranked gene identified by SOMNiBUS was FLT4, a lymphangiogenic orchestrator, and the top ranked gene on chromosome X was CHST7, known to catalyze the sulfation of glycosaminoglycans in the extracellular matrix. The top networks identified by IPA included connective tissue disorders. CONCLUSIONS: SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13148-023-01513-w.
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spelling pubmed-102391812023-06-04 Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data Yu, Jeffrey C. Y. Zeng, Yixiao Zhao, Kaiqiong Lu, Tianyuan Oros Klein, Kathleen Colmegna, Inés Lora, Maximilien Bhatnagar, Sahir R. Leask, Andrew Greenwood, Celia M. T. Hudson, Marie Clin Epigenetics Research BACKGROUND: Abnormal DNA methylation is thought to contribute to the onset and progression of systemic sclerosis. Currently, the most comprehensive assay for profiling DNA methylation is whole-genome bisulfite sequencing (WGBS), but its precision depends on read depth and it may be subject to sequencing errors. SOMNiBUS, a method for regional analysis, attempts to overcome some of these limitations. Using SOMNiBUS, we re-analyzed WGBS data previously analyzed using bumphunter, an approach that initially fits single CpG associations, to contrast DNA methylation estimates by both methods. METHODS: Purified CD4+ T lymphocytes of 9 SSc and 4 control females were sequenced using WGBS. We separated the resulting sequencing data into regions with dense CpG data, and differentially methylated regions (DMRs) were inferred with the SOMNiBUS region-level test, adjusted for age. Pathway enrichment analysis was performed with ingenuity pathway analysis (IPA). We compared the results obtained by SOMNiBUS and bumphunter. RESULTS: Of 8268 CpG regions of ≥ 60 CpGs eligible for analysis with SOMNiBUS, we identified 131 DMRs and 125 differentially methylated genes (DMGs; p-values less than Bonferroni-corrected threshold of 6.05–06 controlling family-wise error rate at 0.05; 1.6% of the regions). In comparison, bumphunter identified 821,929 CpG regions, 599 DMRs (of which none had ≥ 60 CpGs) and 340 DMGs (q-value of 0.05; 0.04% of all regions). The top ranked gene identified by SOMNiBUS was FLT4, a lymphangiogenic orchestrator, and the top ranked gene on chromosome X was CHST7, known to catalyze the sulfation of glycosaminoglycans in the extracellular matrix. The top networks identified by IPA included connective tissue disorders. CONCLUSIONS: SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13148-023-01513-w. BioMed Central 2023-06-03 /pmc/articles/PMC10239181/ /pubmed/37270501 http://dx.doi.org/10.1186/s13148-023-01513-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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.
spellingShingle Research
Yu, Jeffrey C. Y.
Zeng, Yixiao
Zhao, Kaiqiong
Lu, Tianyuan
Oros Klein, Kathleen
Colmegna, Inés
Lora, Maximilien
Bhatnagar, Sahir R.
Leask, Andrew
Greenwood, Celia M. T.
Hudson, Marie
Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data
title Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data
title_full Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data
title_fullStr Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data
title_full_unstemmed Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data
title_short Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data
title_sort novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239181/
https://www.ncbi.nlm.nih.gov/pubmed/37270501
http://dx.doi.org/10.1186/s13148-023-01513-w
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