Dataset Information

Functional and epigenetic phenotypes of humans and mice with DNMT3A Overgrowth Syndrome.

ABSTRACT: Germline pathogenic variants in DNMT3A were recently described in patients with overgrowth, obesity, behavioral, and learning difficulties (DNMT3A Overgrowth Syndrome/DOS). Somatic mutations in the DNMT3A gene are also the most common cause of clonal hematopoiesis, and can initiate acute myeloid leukemia (AML). Using whole genome bisulfite sequencing, we studied DNA methylation in peripheral blood cells of 11 DOS patients and found a focal, canonical hypomethylation phenotype, which is most severe with the dominant negative DNMT3A^R882H mutation. A germline mouse model expressing the homologous Dnmt3a^R878H mutation phenocopies most aspects of the human DOS syndrome, including the methylation phenotype and an increased incidence of spontaneous hematopoietic malignancies, suggesting that all aspects of this syndrome are caused by this mutation.

SUBMITTER: Smith AM

PROVIDER: S-EPMC8316576 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Functional and epigenetic phenotypes of humans and mice with DNMT3A Overgrowth Syndrome.

Smith Amanda M AM LaValle Taylor A TA Shinawi Marwan M Ramakrishnan Sai M SM Abel Haley J HJ Hill Cheryl A CA Kirkland Nicole M NM Rettig Michael P MP Helton Nichole M NM Heath Sharon E SE Ferraro Francesca F Chen David Y DY Adak Sangeeta S Semenkovich Clay F CF Christian Diana L DL Martin Jenna R JR Gabel Harrison W HW Miller Christopher A CA Ley Timothy J TJ

Nature communications 20210727 1

Germline pathogenic variants in DNMT3A were recently described in patients with overgrowth, obesity, behavioral, and learning difficulties (DNMT3A Overgrowth Syndrome/DOS). Somatic mutations in the DNMT3A gene are also the most common cause of clonal hematopoiesis, and can initiate acute myeloid leukemia (AML). Using whole genome bisulfite sequencing, we studied DNA methylation in peripheral blood cells of 11 DOS patients and found a focal, canonical hypomethylation phenotype, which is most seve ...[more]

PMID: 34315901

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Project description:BackgroundPredicting cis-regulatory modules (CRMs) in a genome and their functional states in various cell/tissue types of the organism are two related challenging computational tasks. Most current methods attempt to simultaneously achieve both using data of multiple epigenetic marks in a cell/tissue type. Though conceptually attractive, they suffer high false discovery rates and limited applications. To fill the gaps, we proposed a two-step strategy to first predict a map of CRMs in the genome, and then predict functional states of all the CRMs in various cell/tissue types of the organism. We have recently developed an algorithm for the first step that was able to more accurately and completely predict CRMs in a genome than existing methods by integrating numerous transcription factor ChIP-seq datasets in the organism. Here, we presented machine-learning methods for the second step.ResultsWe showed that functional states in a cell/tissue type of all the CRMs in the genome could be accurately predicted using data of only 1~4 epigenetic marks by a variety of machine-learning classifiers. Our predictions are substantially more accurate than the best achieved so far. Interestingly, a model trained on a cell/tissue type in humans can accurately predict functional states of CRMs in different cell/tissue types of humans as well as of mice, and vice versa. Therefore, epigenetic code that defines functional states of CRMs in various cell/tissue types is universal at least in humans and mice. Moreover, we found that from tens to hundreds of thousands of CRMs were active in a human and mouse cell/tissue type, and up to 99.98% of them were reutilized in different cell/tissue types, while as small as 0.02% of them were unique to a cell/tissue type that might define the cell/tissue type.ConclusionsOur two-step approach can accurately predict functional states in any cell/tissue type of all the CRMs in the genome using data of only 1~4 epigenetic marks. Our approach is also more cost-effective than existing methods that typically use data of more epigenetic marks. Our results suggest common epigenetic rules for defining functional states of CRMs in various cell/tissue types in humans and mice.

Dataset Information

Functional and epigenetic phenotypes of humans and mice with DNMT3A Overgrowth Syndrome.

Publications

Functional and epigenetic phenotypes of humans and mice with DNMT3A Overgrowth Syndrome.

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