bioRxivSystems BiologyPreprint
Female-specific m6A remodeling in the liver correlates with post-transcriptional metabolic adaptation to high fat diet
Sofia V. KrylovaiD, Maxwell Horton, G Bucciarelli, Li LiuiD, Jacob L. BerriganiD, Ronald CutleriD, Kartik ChandraniD, Nathaniel W. SnyderiD, Toma TebaldiiD, Simone SidoliiD, Kamini SinghiD, Jeffrey E. PessiniD
Abstract
Sex differences strongly influence susceptibility to metabolic dysfunction-associated steatotic liver disease (MASLD), yet the regulatory mechanisms underlying these differences remain incompletely understood. To examine sex-specific hepatic adaptation to a high-fat (HF) diet mouse model of MASLD, we integrated proteomics, transcriptomics, and Oxford Nanopore direct RNA sequencing for transcriptome-wide m6A profiling in male and female mouse livers. Female mice were relatively protected from HF diet-induced hepatic steatosis and exhibited distinct proteome remodeling enriched for peroxisomal pathways. In contrast, transcriptomic responses in females were dominated by inflammatory signatures and did not recapitulate the metabolic adaptations observed at the protein level, revealing extensive RNA-protein discordance and post-transcriptional remodeling. Integrated RNA-protein analyses identified female-specific amplification of peroxisomal proteins despite modest transcript-level changes. HF diet also induced sex-specific remodeling of m6A RNA methylation and altered regulation of the m6A methylation system. Notably, reduced 3' UTR m6A methylation of peroxisomal transcripts inversely correlated with increased protein abundance relative to RNA expression in female mice. Together, these findings implicate m6A-associated post-transcriptional regulation in sex-specific hepatic adaptation to HF diet exposure and the basis for discordance between many of the mRNAs and proteins in the liver.
- organism: mouse
- assay: proteomics
- assay: bulk RNA-seq
- assay: long-read sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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