Clinical epigenetics · 2020
Genome-wide methylation analysis in Silver-Russell syndrome, Temple syndrome, and Prader-Willi syndrome.
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Hara-Isono Kaori, Matsubara Keiko, Fuke Tomoko, Yamazawa Kazuki, Satou Kazuhito, Murakami Nobuyuki, Saitoh Shinji, Nakabayashi Kazuhiko, Hata Kenichiro, Ogata Tsutomu, Fukami Maki, Kagami Masayo
Аннотация
BACKGROUND: Imprinting disorders (IDs) show overlapping phenotypes, particularly in Silver-Russell syndrome (SRS), Temple syndrome (TS14), and Prader-Willi syndrome (PWS). These three IDs include fetal and postnatal growth failure, feeding difficulty, and muscular hypotonia as major clinical features. However, the mechanism that causes overlapping phenotypes has not been clarified. To investigate the presence or absence of methylation signatures associated with overlapping phenotypes, we performed genome-wide methylation analysis (GWMA).
RESULTS: GWMA was carried out on 36 patients with three IDs (SRS [n = 16], TS14 [n = 7], PWS [n = 13]) and 11 child controls using HumanMethylation450 BeadChip including 475,000 CpG sites across the human genome. To reveal an aberrantly methylated region shared by SRS, TS14, and PWS groups, we compared genome-wide methylation data of the three groups with those of control subjects. All the identified regions were known as SRS-, TS14-, and PWS-related imprinting-associated differentially methylated regions (iDMRs), and there was no hypermethylated or hypomethylated region shared by different ID groups. To examine the methylation pattern shared by SRS, TS14, and PWS groups, we performed clustering analysis based on GWMA data. The result focusing on 620 probes at the 62 known iDMRs (except for SRS-, TS14-, and PWS-related iDMRs) classified patients into two categories: (1) category A, grossly normal methylation patterns mainly consisting of SRS group patients; and (2) category B, broad and mild hypermethylation patterns mainly consisting of TS14 and PWS group patients. However, we found no obvious relationship between these methylation patterns and phenotypes of patients.
CONCLUSIONS: GWMA in three IDs found no methylation signatures shared by SRS, TS14, and PWS groups. Although clustering analysis showed similar mild hypermethylation patterns in TS14 and PWS groups, further study is needed to clarify the effect of methylation patterns on the overlapping phenotypes.
RESULTS: GWMA was carried out on 36 patients with three IDs (SRS [n = 16], TS14 [n = 7], PWS [n = 13]) and 11 child controls using HumanMethylation450 BeadChip including 475,000 CpG sites across the human genome. To reveal an aberrantly methylated region shared by SRS, TS14, and PWS groups, we compared genome-wide methylation data of the three groups with those of control subjects. All the identified regions were known as SRS-, TS14-, and PWS-related imprinting-associated differentially methylated regions (iDMRs), and there was no hypermethylated or hypomethylated region shared by different ID groups. To examine the methylation pattern shared by SRS, TS14, and PWS groups, we performed clustering analysis based on GWMA data. The result focusing on 620 probes at the 62 known iDMRs (except for SRS-, TS14-, and PWS-related iDMRs) classified patients into two categories: (1) category A, grossly normal methylation patterns mainly consisting of SRS group patients; and (2) category B, broad and mild hypermethylation patterns mainly consisting of TS14 and PWS group patients. However, we found no obvious relationship between these methylation patterns and phenotypes of patients.
CONCLUSIONS: GWMA in three IDs found no methylation signatures shared by SRS, TS14, and PWS groups. Although clustering analysis showed similar mild hypermethylation patterns in TS14 and PWS groups, further study is needed to clarify the effect of methylation patterns on the overlapping phenotypes.