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Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data
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....
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Published in: | Clinical epigenetics 2023-06, Vol.15 (1), p.96-13, Article 96 |
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creator | 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 |
description | 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.
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.
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.
SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis. |
doi_str_mv | 10.1186/s13148-023-01513-w |
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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.
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.
SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis.</description><identifier>ISSN: 1868-7083</identifier><identifier>ISSN: 1868-7075</identifier><identifier>EISSN: 1868-7083</identifier><identifier>EISSN: 1868-7075</identifier><identifier>DOI: 10.1186/s13148-023-01513-w</identifier><identifier>PMID: 37270501</identifier><language>eng</language><publisher>Germany: BioMed Central Ltd</publisher><subject>Agreements ; Binomial distribution ; Bisulfite ; CD4 antigen ; Chromosomes ; Connective tissues ; CpG Islands ; Differentially methylated regions ; Disease ; DNA fingerprinting ; DNA Methylation ; DNA sequencing ; Epigenetics ; Extracellular matrix ; Female ; Genes ; Genetic research ; Genomes ; Genomics ; Glycosaminoglycans ; Health maintenance organizations ; Humans ; Lymphocytes T ; Methods ; Methylation ; Pathogenesis ; Rankings ; Regions ; Scleroderma ; Scleroderma (Disease) ; Scleroderma, Systemic - genetics ; Smoothing ; Sulfites ; Systemic scleroderma ; Systemic sclerosis ; T cells ; Whole genome bisulfite sequencing ; Whole Genome Sequencing - methods</subject><ispartof>Clinical epigenetics, 2023-06, Vol.15 (1), p.96-13, Article 96</ispartof><rights>2023. The Author(s).</rights><rights>COPYRIGHT 2023 BioMed Central Ltd.</rights><rights>2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c564t-3243346f2eecc0faf59ff65131d82415a077a752cb543d09472569385f5160e33</citedby><cites>FETCH-LOGICAL-c564t-3243346f2eecc0faf59ff65131d82415a077a752cb543d09472569385f5160e33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239181/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2827113605?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37270501$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yu, Jeffrey C Y</creatorcontrib><creatorcontrib>Zeng, Yixiao</creatorcontrib><creatorcontrib>Zhao, Kaiqiong</creatorcontrib><creatorcontrib>Lu, Tianyuan</creatorcontrib><creatorcontrib>Oros Klein, Kathleen</creatorcontrib><creatorcontrib>Colmegna, Inés</creatorcontrib><creatorcontrib>Lora, Maximilien</creatorcontrib><creatorcontrib>Bhatnagar, Sahir R</creatorcontrib><creatorcontrib>Leask, Andrew</creatorcontrib><creatorcontrib>Greenwood, Celia M T</creatorcontrib><creatorcontrib>Hudson, Marie</creatorcontrib><title>Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data</title><title>Clinical epigenetics</title><addtitle>Clin Epigenetics</addtitle><description>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.
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.
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.
SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis.</description><subject>Agreements</subject><subject>Binomial distribution</subject><subject>Bisulfite</subject><subject>CD4 antigen</subject><subject>Chromosomes</subject><subject>Connective tissues</subject><subject>CpG Islands</subject><subject>Differentially methylated regions</subject><subject>Disease</subject><subject>DNA fingerprinting</subject><subject>DNA Methylation</subject><subject>DNA sequencing</subject><subject>Epigenetics</subject><subject>Extracellular matrix</subject><subject>Female</subject><subject>Genes</subject><subject>Genetic research</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Glycosaminoglycans</subject><subject>Health maintenance organizations</subject><subject>Humans</subject><subject>Lymphocytes T</subject><subject>Methods</subject><subject>Methylation</subject><subject>Pathogenesis</subject><subject>Rankings</subject><subject>Regions</subject><subject>Scleroderma</subject><subject>Scleroderma (Disease)</subject><subject>Scleroderma, Systemic - genetics</subject><subject>Smoothing</subject><subject>Sulfites</subject><subject>Systemic scleroderma</subject><subject>Systemic sclerosis</subject><subject>T cells</subject><subject>Whole genome bisulfite sequencing</subject><subject>Whole Genome Sequencing - methods</subject><issn>1868-7083</issn><issn>1868-7075</issn><issn>1868-7083</issn><issn>1868-7075</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptUk1v1DAUtBCIVkv_AAcUiQuXFH_EiXNCVVXaShVc4Gw5znPWqyReYmdXy4W_ztvdUroI--Bne2aseR5C3jJ6yZgqP0YmWKFyykVOmWQi374g53ih8ooq8fJZfUYuYlxRHKKua0ZfkzNR8YpKys7Jry9hA33mx-i7ZYpYpJDFXUwweJtF28MUoo_ZHP3YZSaLgMjkN5DZMKznZJIPo-mzAdIytBmSDW53PyHbLkMPeQdjGCBrfJx75xOgwI8ZRrtXa00yb8grZ_oIF4_rgnz_fPPt-i5_-Hp7f331kFtZFikXvBCiKB0HsJY642TtXIm2Wat4waShVWUqyW0jC9HSuqi4LGuhpJOspCDEgtwfddtgVno9-cFMOx2M14eDMHXaTMmjX60Mo6ql1rVGFaWlNaesUaVoFBOqAYpan45a67kZoLUwpsn0J6KnN6Nf6i5sNMPfqhnKLMiHR4UpYDti0oOPFvrejBDmqLniXJRVrRRC3_8DXYV5wh4fUBVjoqTyL6oz6MCPLuDDdi-qrypsQS0pOliQy_-gcLb73w4jOI_nJwR-JFhMQZzAPZlkVO9jqI8x1GhMH2Kot0h697w9T5Q_oRO_AfyN2LQ</recordid><startdate>20230603</startdate><enddate>20230603</enddate><creator>Yu, Jeffrey C Y</creator><creator>Zeng, Yixiao</creator><creator>Zhao, Kaiqiong</creator><creator>Lu, Tianyuan</creator><creator>Oros Klein, Kathleen</creator><creator>Colmegna, Inés</creator><creator>Lora, Maximilien</creator><creator>Bhatnagar, Sahir R</creator><creator>Leask, Andrew</creator><creator>Greenwood, Celia M T</creator><creator>Hudson, Marie</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20230603</creationdate><title>Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data</title><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></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c564t-3243346f2eecc0faf59ff65131d82415a077a752cb543d09472569385f5160e33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Agreements</topic><topic>Binomial distribution</topic><topic>Bisulfite</topic><topic>CD4 antigen</topic><topic>Chromosomes</topic><topic>Connective tissues</topic><topic>CpG Islands</topic><topic>Differentially methylated regions</topic><topic>Disease</topic><topic>DNA fingerprinting</topic><topic>DNA Methylation</topic><topic>DNA sequencing</topic><topic>Epigenetics</topic><topic>Extracellular matrix</topic><topic>Female</topic><topic>Genes</topic><topic>Genetic research</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Glycosaminoglycans</topic><topic>Health maintenance organizations</topic><topic>Humans</topic><topic>Lymphocytes T</topic><topic>Methods</topic><topic>Methylation</topic><topic>Pathogenesis</topic><topic>Rankings</topic><topic>Regions</topic><topic>Scleroderma</topic><topic>Scleroderma (Disease)</topic><topic>Scleroderma, Systemic - genetics</topic><topic>Smoothing</topic><topic>Sulfites</topic><topic>Systemic scleroderma</topic><topic>Systemic sclerosis</topic><topic>T cells</topic><topic>Whole genome bisulfite sequencing</topic><topic>Whole Genome Sequencing - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yu, Jeffrey C Y</creatorcontrib><creatorcontrib>Zeng, Yixiao</creatorcontrib><creatorcontrib>Zhao, Kaiqiong</creatorcontrib><creatorcontrib>Lu, Tianyuan</creatorcontrib><creatorcontrib>Oros Klein, Kathleen</creatorcontrib><creatorcontrib>Colmegna, Inés</creatorcontrib><creatorcontrib>Lora, Maximilien</creatorcontrib><creatorcontrib>Bhatnagar, Sahir R</creatorcontrib><creatorcontrib>Leask, Andrew</creatorcontrib><creatorcontrib>Greenwood, Celia M T</creatorcontrib><creatorcontrib>Hudson, Marie</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>Clinical epigenetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yu, Jeffrey C Y</au><au>Zeng, Yixiao</au><au>Zhao, Kaiqiong</au><au>Lu, Tianyuan</au><au>Oros Klein, Kathleen</au><au>Colmegna, Inés</au><au>Lora, Maximilien</au><au>Bhatnagar, Sahir R</au><au>Leask, Andrew</au><au>Greenwood, Celia M T</au><au>Hudson, Marie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data</atitle><jtitle>Clinical epigenetics</jtitle><addtitle>Clin Epigenetics</addtitle><date>2023-06-03</date><risdate>2023</risdate><volume>15</volume><issue>1</issue><spage>96</spage><epage>13</epage><pages>96-13</pages><artnum>96</artnum><issn>1868-7083</issn><issn>1868-7075</issn><eissn>1868-7083</eissn><eissn>1868-7075</eissn><abstract>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.
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.
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.
SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis.</abstract><cop>Germany</cop><pub>BioMed Central Ltd</pub><pmid>37270501</pmid><doi>10.1186/s13148-023-01513-w</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Agreements Binomial distribution Bisulfite CD4 antigen Chromosomes Connective tissues CpG Islands Differentially methylated regions Disease DNA fingerprinting DNA Methylation DNA sequencing Epigenetics Extracellular matrix Female Genes Genetic research Genomes Genomics Glycosaminoglycans Health maintenance organizations Humans Lymphocytes T Methods Methylation Pathogenesis Rankings Regions Scleroderma Scleroderma (Disease) Scleroderma, Systemic - genetics Smoothing Sulfites Systemic scleroderma Systemic sclerosis T cells Whole genome bisulfite sequencing Whole Genome Sequencing - methods |
title | Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data |
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