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Updated: Sep 27, 2026

Investigation of Beige Fat Biology and Metabolism Using the CRISPR SunTag-p65-HSF1 Activation System
Published on: January 6, 2023
Impact of METTL3-mediated m6A regulation on early human beige adipogenesis from hiPSCs
Sanjana Chandran1, Moni Nader2,3, Abdulrahim Sajini4
1Department of Biomedical Engineering and Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
Abstract:
N6-methyladenosine (m6A) is a key post-transcriptional regulator of mammalian mRNA metabolism, yet its molecular role in human adipogenesis remains poorly understood. Using transgene-free human iPSC derived from adult (HDFa) and neonatal (HDFn) fibroblasts, we investigated the role of m6A during early adipocyte specification. We found that transient inhibition of METTL3 during the commitment phase (days 0-4) with STM2457 or UZH2 elicited distinct transcriptional and epitranscriptomic responses between the two human iPSC lines. Specifically, STM2457 reduced global m6A levels and impaired both adipogenic and thermogenic maturation in HDFa-derived iPSCs. Conversely, HDFn-derived iPSCs demonstrated better transcriptional plasticity, partially maintaining beige programs despite sustained m6A depletion. UZH2 yielded more variable outcomes, with more pronounced differentiation-associated alterations in the HDFa-derived line. These findings highlight a strong context-dependency in m6A regulation during adipogenesis. Collectively, our data demonstrate that METTL3-mediated m6A regulation acts as a flexible, time-sensitive coordinator of beige fat development rather than a uniform determinant of cell fate. Furthermore, the divergent responses observed between the two iPSC lines suggest that cellular context influences epitranscriptomic regulation during adipogenesis, reflecting contributions from donor origin, epigenetic state, and clonal variability, and warranting validation across additional independent iPSC lines. This study establishes a framework for investigating m6A biology in human adipogenesis and provides critical insights for modeling metabolic diseases.
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