Background: DNA methylation at specific CpG sites within gene promoter regions is known to regulate transcriptional activity in vitro. In human adipose tissue, basal transcription of the aromatase (CYP19A1) gene is driven primarily by the I.4 promoter however the role of DNA methylation in regulating expression in ex vivo mature adipocytes is unknown. This observational study reports the correlation of DNA methylation within the I.4 promoter region of human mature subcutaneous and omental adipocytes with aromatase expression and body composition measures.
Methods: Omental and subcutaneous adipose tissue were collected from 25 obese subjects undergoing bariatric surgery and the mature adipocyte fraction purified. DNA methylation status of 5 CpG sites within a 550 base pair region encompassing the transcription start site (TSS) of promoter I.4 was determined using pyrosequencing. Relative aromatase and I.4 promoter specific mRNA expression was determined by qRT-PCR and whole body DXA performed in 25 participants.
Results: Site-specific DNA methylation varied from 21 ± 10% to 81 ± 11%. In omental adipocytes percentage methylation at the I.4.1 and I.4.2 CpG sites, but not other nearby sites, was negatively correlated with relative aromatase mRNA expression (R = - 0.52, P = 0.017 and R = - 0.52, P = 0.015). In contrast subcutaneous adipocytes percentage DNA methylation at the I.4.3 and I.4.5 sites were positively correlated with relative aromatase mRNA expression (R = 0.47, P = 0.022 and R = 0.55, P = 0.004). In a small subset of patients DNA methylation at the I.4.5 site was also positively correlated with whole body lean mass, bone mineral content and density.
Conclusions: In conclusion in mature adipocytes, the primary source of estradiol after menopause, increasing DNA methylation was correlated with aromatase mRNA expression and thus estradiol biosynthesis. These findings support a tissue-specific epigenetic regulation of the basal promoter activity in mature adipocytes; the mechanisms influencing this regulation and its physiological role remain to be elucidated.
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http://dx.doi.org/10.1186/1471-2350-14-87 | DOI Listing |
Acta Neuropathol Commun
January 2025
Institute of Cancer Research, London, UK.
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January 2025
Department of Maternal‑Fetal Biology, National Center for Child Health and Development, Tokyo, 157‑8535, Japan.
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View Article and Find Full Text PDFSci Rep
January 2025
Department of Biological Sciences, Virginia Tech, Blacksburg, VA, 24061-0910, USA.
Sepsis is a leading cause of death worldwide, with most patient mortality stemming from lingering immunosuppression in sepsis survivors. This is due in part to immune dysfunction resulting from monocyte exhaustion, a phenotype of reduced antigen presentation, altered CD14/CD16 inflammatory subtypes, and disrupted cytokine production. Whereas previous research demonstrated improved sepsis survival in Ticam2 mice, the contribution of TICAM2 to long-term exhaustion memory remained unknown.
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January 2025
Department of Botany and Evolutionary Ecology, University of Warmia and Mazury in Olsztyn, Plac Łódzki 1, Olsztyn, 10-719, Poland.
Riccia sorocarpa Bisch., commonly known as common crystalwort, is a plant belonging to the Marchantiales order with a cosmopolitan distribution among a wide range of habitats: fields, gardens, waste ground, on paths, cliff tops, and thin soil over rocks or by water bodies. However, research into the genetic aspects of this species is limited.
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January 2025
The Medical Image and Health Informatics Lab, the School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Despite vast data support in DNA methylation (DNAm) biomarker discovery to facilitate health-care research, this field faces huge resource barriers due to preliminary unreliable candidates and the consequent compensations using expensive experiments. The underlying challenges lie in the confounding factors, especially measurement noise and individual characteristics. To achieve reliable identification of a candidate pool for DNAm biomarker discovery, we propose a Causality-driven Deep Regularization framework to reinforce correlations that are suggestive of causality with disease.
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