bioRxivPhysiologyPreprint
Hepatocyte FGF21 is not required for fasting-induced metabolic responses but guides protein appetite after energy depletion
Justine BruseiD, Clothilde Marbach, Arnaud PolizziiD, Tatiana Landré, Juliette SalviiD, Valérie Alquier‐BacquieiD, Chen Shiou-Ping, Marine HuilletiD, Clémence Rives, Céline M.P. Martin, Prunelle PerrieriD, Fadila BenhamediD, Marion RégnieriD, Stefan WegeriD, Claire Naylies, Yannick LippiiD, Caroline Sommer, Mikaël Albin, Frédéric Lasserre, Thierry LevadeiD, Michael SchuppiD, Laurence Gamet‐PayrastreiD, Léon KautziD, Nicolas LoiseauiD, Walter WahliiD, , Anne FougeratiD
Abstract
Fasting initiates a coordinated metabolic response to preserve energy balance. As glycogen stores are depleted, the body transitions to mobilizing fatty acids from adipose tissue and generating ketone bodies in the liver to sustain the function of vital organs. A network of hormonal signals and transcriptional programs coordinate these adaptations. Among these, the hepatokine fibroblast growth factor 21 (FGF21) is strongly upregulated during fasting and has been proposed as a key mediator of the fasting response. To investigate the physiological functions of FGF21, we studied mice with hepatocyte-specific deletion of Fgf21. Although the liver is the primary source of circulating FGF21 during fasting, its absence in hepatocytes did not alter typical fasting-induced gene expression or key metabolic pathways such as hepatic gluconeogenesis, adipose tissue lipolysis, or ketone production. Instead, we uncovered a distinct role for FGF21 in promoting protein appetite following a fast. These findings challenge the conventional view of hepatocyte-produced FGF21 as a fasting-acting hormone and reveal a more specialized function in guiding nutrient selection after energy depletion.
- organism: mouse
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: metabolism & bioenergetics
- approach: experimental assay
- approach: genetic perturbation / CRISPR
- approach: statistical model
- Citations
- 0
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