bioRxivPhysiologyPreprint
A high-resolution, sex-stratified atlas of transcriptional and alternative splicing dynamics in the fasting mouse liver
Jessica TreebyiD, Shin-Yu KungiD, Ben SaeriD, Judith Prillo, Louise HunteriD, Jean‐Michel FustiniD
Abstract
The livers response to fasting is a fundamental adaptive mechanism, yet its temporal complexity, circadian dimension and sexual dimorphism remain incompletely understood. Here we present a high-resolution transcriptomic atlas of the fasting mouse liver, profiling both male and female C57BL/6 mice every 4 hours across a 36-hour fast initiated at a defined circadian time. Using an integrated framework combining high-resolution time-series analysis with stringent statistical filtering, we identify 2,995 genes organised into eight kinetically distinct clusters that collectively delineate a phased transcriptional transition from acute metabolic remodelling to prolonged nutrient deprivation. Contrary to prior reports, fasting amplifies rather than diminishes circadian transcription, increasing the number of rhythmically expressed genes from 727 to 1,233--indicative of a clock re-gearing rather than clock disruption. Extending our analysis to exon-level quantification reveals that differential exon usage constitutes an independent regulatory layer, targeting processes not captured by transcript abundance alone, including SAM metabolism, ferroptosis and mRNA processing. Circadian rhythmicity in exon usage is, to our knowledge, demonstrated here for the first time. Sexual dimorphism is pervasive but primarily quantitative, reflecting differences in the magnitude and temporal precision of a conserved programme rather than divergent regulatory logic. This open-access, multi-layered dataset provides a comprehensive resource for the study of nutritional and circadian regulation of liver metabolism.
- organism: mouse
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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