Genome-Wide Placental Gene Methylations in Gestational Diabetes Mellitus, Fetal Growth and Metabolic Health Biomarkers in Cord Blood
Gestational diabetes mellitus (GDM) “program” an elevated risk of metabolic syndrome in the offspring. Epigenetic alterations are a suspected mechanism. GDM has been associated with placental DNA methylation changes in some epigenome-wide association studies. It remains unclear which genes or pathwa...
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2022.875180/full |
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| author | Wen-Juan Wang Wen-Juan Wang Wen-Juan Wang Rong Huang Tao Zheng Qinwen Du Qinwen Du Meng-Nan Yang Ya-Jie Xu Xin Liu Min-Yi Tao Hua He Fang Fang Fei Li Jian-Gao Fan Jun Zhang Laurent Briollais Fengxiu Ouyang Zhong-Cheng Luo Zhong-Cheng Luo |
| author_facet | Wen-Juan Wang Wen-Juan Wang Wen-Juan Wang Rong Huang Tao Zheng Qinwen Du Qinwen Du Meng-Nan Yang Ya-Jie Xu Xin Liu Min-Yi Tao Hua He Fang Fang Fei Li Jian-Gao Fan Jun Zhang Laurent Briollais Fengxiu Ouyang Zhong-Cheng Luo Zhong-Cheng Luo |
| author_sort | Wen-Juan Wang |
| collection | DOAJ |
| description | Gestational diabetes mellitus (GDM) “program” an elevated risk of metabolic syndrome in the offspring. Epigenetic alterations are a suspected mechanism. GDM has been associated with placental DNA methylation changes in some epigenome-wide association studies. It remains unclear which genes or pathways are affected, and whether any placental differential gene methylations are correlated to fetal growth or circulating metabolic health biomarkers. In an epigenome-wide association study using the Infinium MethylationEPIC Beadchip, we sought to identify genome-wide placental differentially methylated genes and enriched pathways in GDM, and to assess the correlations with fetal growth and metabolic health biomarkers in cord blood. The study samples were 30 pairs of term placentas in GDM vs. euglycemic pregnancies (controls) matched by infant sex and gestational age at delivery in the Shanghai Birth Cohort. Cord blood metabolic health biomarkers included insulin, C-peptide, proinsulin, IGF-I, IGF-II, leptin and adiponectin. Adjusting for maternal age, pre-pregnancy BMI, parity, mode of delivery and placental cell type heterogeneity, 256 differentially methylated positions (DMPs,130 hypermethylated and 126 hypomethylated) were detected between GDM and control groups accounting for multiple tests with false discovery rate <0.05 and beta-value difference >0.05. WSCD2 was identified as a differentially methylated gene in both site- and region-level analyses. We validated 7 hypermethylated (CYP1A2, GFRA1, HDAC4, LIMS2, NAV3, PAX6, UPK1B) and 10 hypomethylated (DPP10, CPLX1, CSMD2, GPR133, NRXN1, PCSK9, PENK, PRDM16, PTPRN2, TNXB) genes reported in previous epigenome-wide association studies. We did not find any enriched pathway accounting for multiple tests. DMPs in 11 genes (CYP2D7P1, PCDHB15, ERG, SIRPB1, DKK2, RAPGEF5, CACNA2D4, PCSK9, TSNARE1, CADM2, KCNAB2) were correlated with birth weight (z score) accounting for multiple tests. There were no significant correlations between placental gene methylations and cord blood biomarkers. In conclusions, GDM was associated with DNA methylation changes in a number of placental genes, but these placental gene methylations were uncorrelated to the observed metabolic health biomarkers (fetal growth factors, leptin and adiponectin) in cord blood. We validated 17 differentially methylated placental genes in GDM, and identified 11 differentially methylated genes relevant to fetal growth. |
| first_indexed | 2024-04-13T21:06:04Z |
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| issn | 1664-2392 |
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| last_indexed | 2024-04-13T21:06:04Z |
| publishDate | 2022-05-01 |
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| series | Frontiers in Endocrinology |
| spelling | doaj.art-021ae3c29813479999714e90aba5a9f12022-12-22T02:29:57ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922022-05-011310.3389/fendo.2022.875180875180Genome-Wide Placental Gene Methylations in Gestational Diabetes Mellitus, Fetal Growth and Metabolic Health Biomarkers in Cord BloodWen-Juan Wang0Wen-Juan Wang1Wen-Juan Wang2Rong Huang3Tao Zheng4Qinwen Du5Qinwen Du6Meng-Nan Yang7Ya-Jie Xu8Xin Liu9Min-Yi Tao10Hua He11Fang Fang12Fei Li13Jian-Gao Fan14Jun Zhang15Laurent Briollais16Fengxiu Ouyang17Zhong-Cheng Luo18Zhong-Cheng Luo19Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, ChinaLunenfeld-Tanenbaum Research Institute, Prosserman Centre for Population Health Research, Department of Obstetrics and Gynecology, Mount Sinai Hospital, Faculty of Medicine, University of Toronto, Toronto, ON, CanadaClinical Skills Center, School of Clinical Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, ChinaLunenfeld-Tanenbaum Research Institute, Prosserman Centre for Population Health Research, Department of Obstetrics and Gynecology, Mount Sinai Hospital, Faculty of Medicine, University of Toronto, Toronto, ON, CanadaDepartment of Obstetrics and Gynecology, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, ChinaMinistry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, ChinaDepartment of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaMinistry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, ChinaMinistry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, ChinaMinistry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, ChinaMinistry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, ChinaMinistry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, ChinaMinistry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, ChinaMinistry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, ChinaCenter for Fatty Liver, Shanghai Key Lab of Pediatric Gastroenterology and Nutrition, Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaMinistry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, ChinaLunenfeld-Tanenbaum Research Institute, Prosserman Centre for Population Health Research, Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, CanadaMinistry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, ChinaMinistry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, ChinaLunenfeld-Tanenbaum Research Institute, Prosserman Centre for Population Health Research, Department of Obstetrics and Gynecology, Mount Sinai Hospital, Faculty of Medicine, University of Toronto, Toronto, ON, CanadaGestational diabetes mellitus (GDM) “program” an elevated risk of metabolic syndrome in the offspring. Epigenetic alterations are a suspected mechanism. GDM has been associated with placental DNA methylation changes in some epigenome-wide association studies. It remains unclear which genes or pathways are affected, and whether any placental differential gene methylations are correlated to fetal growth or circulating metabolic health biomarkers. In an epigenome-wide association study using the Infinium MethylationEPIC Beadchip, we sought to identify genome-wide placental differentially methylated genes and enriched pathways in GDM, and to assess the correlations with fetal growth and metabolic health biomarkers in cord blood. The study samples were 30 pairs of term placentas in GDM vs. euglycemic pregnancies (controls) matched by infant sex and gestational age at delivery in the Shanghai Birth Cohort. Cord blood metabolic health biomarkers included insulin, C-peptide, proinsulin, IGF-I, IGF-II, leptin and adiponectin. Adjusting for maternal age, pre-pregnancy BMI, parity, mode of delivery and placental cell type heterogeneity, 256 differentially methylated positions (DMPs,130 hypermethylated and 126 hypomethylated) were detected between GDM and control groups accounting for multiple tests with false discovery rate <0.05 and beta-value difference >0.05. WSCD2 was identified as a differentially methylated gene in both site- and region-level analyses. We validated 7 hypermethylated (CYP1A2, GFRA1, HDAC4, LIMS2, NAV3, PAX6, UPK1B) and 10 hypomethylated (DPP10, CPLX1, CSMD2, GPR133, NRXN1, PCSK9, PENK, PRDM16, PTPRN2, TNXB) genes reported in previous epigenome-wide association studies. We did not find any enriched pathway accounting for multiple tests. DMPs in 11 genes (CYP2D7P1, PCDHB15, ERG, SIRPB1, DKK2, RAPGEF5, CACNA2D4, PCSK9, TSNARE1, CADM2, KCNAB2) were correlated with birth weight (z score) accounting for multiple tests. There were no significant correlations between placental gene methylations and cord blood biomarkers. In conclusions, GDM was associated with DNA methylation changes in a number of placental genes, but these placental gene methylations were uncorrelated to the observed metabolic health biomarkers (fetal growth factors, leptin and adiponectin) in cord blood. We validated 17 differentially methylated placental genes in GDM, and identified 11 differentially methylated genes relevant to fetal growth.https://www.frontiersin.org/articles/10.3389/fendo.2022.875180/fullGDMfetal growthplacental DNA methylationscord blood biomarkersbirth weight |
| spellingShingle | Wen-Juan Wang Wen-Juan Wang Wen-Juan Wang Rong Huang Tao Zheng Qinwen Du Qinwen Du Meng-Nan Yang Ya-Jie Xu Xin Liu Min-Yi Tao Hua He Fang Fang Fei Li Jian-Gao Fan Jun Zhang Laurent Briollais Fengxiu Ouyang Zhong-Cheng Luo Zhong-Cheng Luo Genome-Wide Placental Gene Methylations in Gestational Diabetes Mellitus, Fetal Growth and Metabolic Health Biomarkers in Cord Blood Frontiers in Endocrinology GDM fetal growth placental DNA methylations cord blood biomarkers birth weight |
| title | Genome-Wide Placental Gene Methylations in Gestational Diabetes Mellitus, Fetal Growth and Metabolic Health Biomarkers in Cord Blood |
| title_full | Genome-Wide Placental Gene Methylations in Gestational Diabetes Mellitus, Fetal Growth and Metabolic Health Biomarkers in Cord Blood |
| title_fullStr | Genome-Wide Placental Gene Methylations in Gestational Diabetes Mellitus, Fetal Growth and Metabolic Health Biomarkers in Cord Blood |
| title_full_unstemmed | Genome-Wide Placental Gene Methylations in Gestational Diabetes Mellitus, Fetal Growth and Metabolic Health Biomarkers in Cord Blood |
| title_short | Genome-Wide Placental Gene Methylations in Gestational Diabetes Mellitus, Fetal Growth and Metabolic Health Biomarkers in Cord Blood |
| title_sort | genome wide placental gene methylations in gestational diabetes mellitus fetal growth and metabolic health biomarkers in cord blood |
| topic | GDM fetal growth placental DNA methylations cord blood biomarkers birth weight |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2022.875180/full |
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