Hepatic metabolic reprogramming in male mice during short-term caloric restriction involves enhanced glucocorticoid rhythms

Domain: neuroscience
Published: 2025-12-11
Editorial outcome: major_revision

Abstract
Caloric restriction prolongs lifespan and preserves health across species, with feeding times synchronized to day–night cycles further maximizing benefits. However, the mechanisms linking diet, diurnal rhythms, and lifespan remain unclear. In mice, the time point most strongly tied to dietary effects on lifespan coincides with the peak of glucocorticoid secretion (ZT12, lights-off). Caloric restriction raises circulating glucocorticoid hormone levels, implicating these signals as candidate mediators for its benefits. Here we show that in the liver, the glucocorticoid receptor (GR) is required for the metabolic response to caloric restriction. Hepatocyte-specific GR mutant males fail to mount this response, indicating that increased glucocorticoid amplitude is necessary for the adaptation. Using multiomics, we find that nutrient deprivation elicits a nuclear switch from active STAT signaling to increased FOXO1 activity, enabling GR to activate diet-specific gene expression programs. Our results suggest that glucocorticoid rhythms are crucial for caloric restriction-induced metabolic reprogramming.

Introduction
Caloric restriction, a nutritional regimen of reduced energy intake without malnutrition, stands as a potent non-pharmacological approach for preventing a wide range of age-related illnesses including cardiovascular, autoimmune and metabolic diseases, neurodegeneration and cancer1,2. It induces metabolic and hormonal changes, most notably extending life- and healthspan, with underlying mechanisms that are not yet fully understood3,4.

In rodents, a common approach to study caloric restriction is to reduce the food intake by 30-40% of that of an ad libitum diet. Timing meals to coincide with the beginning of the active phase and synchronizing fasting-feeding cycles with internal circadian rhythms, enhances the beneficial anti-aging outcomes of this diet5. In such settings, one of the earliest effects of caloric restriction is the increased amplitude of diurnal glucocorticoid hormone (GC) secretion, coinciding with the beginning of the active/feeding phase6,7. Physiologically, rhythmic glucocorticoid release is known to reciprocally integrate clock signals and to entrain metabolic programs in peripheral tissues8–10. We therefore speculated that glucocorticoids play a fundamental role in the beneficial response to reduced caloric intake11,12. Glucocorticoids directly bind to and activate the Glucocorticoid Receptor (GR), a transcription factor belonging to the nuclear receptor family. Upon ligand binding, the GR enters the nucleus to control the expression of thousands of target genes by direct recognition of regulatory DNA sequences and by crosstalk with co-bound transcription factors13–16.

The aim of this study is to uncover the functional importance of amplified glucocorticoid rhythms and time-point specific mechanisms of GR target gene regulation associated with the favorable effects of caloric restriction in mouse livers. We explore the significance of GR activity in shaping the diurnal hepatic response to a short-term caloric restriction protocol by linking next generation sequencing data to mouse phenotyping and molecular characterization. Our findings suggest that the caloric restriction induced rewiring of hepatic gene expression is achieved through a unique reprogramming of GR chromatin binding, via loss of STAT5 and gain of FOXO1 crosstalk. Consequently, without functional GC signaling, a significant portion of the beneficial outcomes typically associated with caloric restriction is abolished.

In summary, our study underscores the pivotal role of enhanced GR signaling, coupled with energy deprivation, as a crucial determinant eliciting the rhythmic response to caloric restriction.

Results
The GR is required for the transcriptional response to caloric restriction in murine hepatocytes
To characterize the functional impact of glucocorticoids on physiological responses to caloric restriction, we gradually acclimated 12-week-old male C57/BL6J mice to reduced feeding from 100 to 60% of their normal intake over 4 weeks, followed by a 40% restriction for 4 more weeks (short-term caloric restriction, cal. res.). Control mice had ad libitum access to food, except for the final week, during which food was restricted to the 12h dark phase (night-restricted feeding, ctrl.). Importantly, food was given at 6pm (lights off, zeitgeber time 12 - ZT12) to all groups (Fig. 1a). As expected, cal. res. mice showed a consistent body weight reduction (~30%) (Supplementary Fig. 1a). To monitor daily levels of circulating glucose, insulin, and glucocorticoids, we collected blood every 4 hours around the clock. Caloric restriction reduced postprandial glucose and insulin levels, especially at ZT16 (Supplementary Fig. 1b, c). Conversely, the amplitude of diurnal GC secretion was significantly increased by caloric restriction, with a ~3-fold rise of corticosterone levels over controls specifically at ZT12 (Fig. 1b). To a lower extent, these diurnal hormone secretion patterns corresponded to differential nuclear translocation at ZT12 of the GR in the livers of cal. res. and ctrl. mice (Supplementary Fig. 1d, e). To determine whether a milder caloric restriction also amplifies the GC surge, we applied the same feeding protocol using 20% restriction. Although this regimen led to a proportional decrease in body weight, mice subjected to 20% restriction did not exhibit a time-specific increase in circulating glucocorticoids compared to controls (Supplementary Fig. 1f, g). Therefore, all subsequent experiments were performed using the standard, more stringent, protocol.

To reveal gene expression changes during caloric restriction which depend on GR, we performed bulk RNA-seq in livers collected at ZT12 (the end of the fasting phase and the peak of GC levels). In wildtypes, we found a massive reprogramming of the transcriptome upon caloric restriction, with ~6000 deregulated genes (adj-p<0.05) (Supplementary Fig. 1h). Consistent with previous studies, we detected the typical caloric restriction signature, with genes involved in metabolic signaling (Igfr, Irs1, FoxOs, Sirt1), circadian rhythms (Rora, Arntl, Clock, Cry1), lipid metabolism (Acot1, Angptl4, Hmgcr), and cancer (Myc, Cdkn1a, Brca2, Gadd45, Ctnnb1) being strongly regulated (Supplementary Fig. 1i). On the other hand, profiling hepatocyte-specific GR knockouts (GRLKO, Albumin-Cre x GRf/f) on caloric restriction, we detected a total of 1026 down- and 1042 up-regulated genes (p-adj<0.05) in GRLKO mice compared to wild types (WT, GRf/f). Of these, only 417 were also differentially expressed in the control-fed group, meaning that ~80% of GR-dependent genes newly emerged under caloric restriction (Fig. 1c and Supplementary Fig. 1j–l) and highlighting the functional importance of GR in this nutritional setting. Moreover, the inability of GRLKO animals to properly respond to caloric restriction is evident from Fig. 1d: More than half of the down- (713/1026) and up-regulated (654/1042) genes displayed opposite changes in expression relative to the diet-conferred changes in WT (fold change in cal. res. over ctrl. diet in WT) (Fig. 1d and Supplementary Fig. 2a, b). These observations indicate that GR loss significantly counteracts the transcriptional response to caloric restriction for a substantial number of genes. Genes belonging to pathways that are blunted by the diet in WT, such as peroxisomal, fatty acid, and bile acid metabolism, were de-repressed in mutant livers. Conversely, pathways enhanced by caloric restriction in WT mice were dampened in the GRLKO, e.g. FoxO signaling, autophagy, and circadian rhythms (Fig. 1e).

To capture hepatocyte-specific pathways downstream of the GR, single-nucleus RNA-seq was performed in WT and GRLKO mice on cal. res. or ctrl. diets. By using droplet-based approach (10X Genomics Chromium), we analyzed a total of 11,627 high-quality nuclei with an average of 2659 genes per nuclei. Across all samples, after Louvain clustering and data integration, we did not find different clusters depending on the diet or the genotype, as expected (Supplementary Fig. 2c). Globally, neither the diet nor the GR hepatocyte deletion affected the major cell type composition in the liver or cell cycle state in hepatocytes (Supplementary Fig. 2c–e). Uniform manifold approximation and projection (UMAP) of hepatic cells showed cell clustering based on genotypes and dietary treatment (Fig. 1f), displaying cellular heterogeneity and cell-specific gene expression profiles. Hence in hepatocytes, of the 1734 differentially regulated genes by caloric restriction in the wild types, ~80% (580/761 of the induced and 787/973 of the repressed transcripts) were found counter-regulated in cells lacking GR (Fig. 1g, h and Supplementary Fig. 2f). This finding underscores the pivotal role of the GR in regulating genes that respond to caloric restriction. Consistent with bulk data, subsequent pathway enrichment analysis of the counter-regulated genes revealed the de-repression of pathways related to peroxisomes, bile acids, and xenobiotic metabolism. In contrast, pathways such as AMPK/FOXO1 signaling, autophagy, and circadian rhythm were found to be dampened in mutant hepatocytes (Fig. 1i).

Collectively, these data indicate that caloric restriction rewires the hepatic transcriptome through mechanisms dependent on GR.

Caloric restriction rewires oscillatory gene programs by engaging with the GR
In our cohorts, the time of feeding coincided with the beginning of the active phase, thereby enhancing the entrainment of central and peripheral circadian clocks, which was shown to confer additive beneficial health effects in caloric restriction5. Given the role of GC in the synchronization of diurnal oscillations, we hypothesized that the increased amplitude of GC during caloric restriction may play a role in the establishment of novel rhythmic patterns. For example, our GRLKO mice displayed reduced expression of several core clock genes (Arntl, Clock, Per1, Cry1) at ZT12 (Fig. 1c). To explore the Zeitgeber effects of this peak of GC action, we continued by profiling WT and GR mutant livers around the clock by RNA-seq (every 4 hours, for a total of 6 timepoints, ZT 0-4-8-12-16-20), to obtain comprehensive transcriptomes for both light/fasting and dark/feeding phases.

Both unbiased PCA and hierarchical clustering analyses showed that samples from the same time point tended to cluster together under both dietary regimens. However, on caloric restriction, we observed a larger separation between GRLKO and WT at every time point, reaching the maximum distance at ZT12 (Fig. 2a and Supplementary Fig. 3). Differential gene expression analysis (DEseq2, p-adj < 0.05) between wildtypes and mutants further emphasized that the number of deregulated genes was larger on caloric restriction than on control diet at every time point, with a maximum at ZT12 (Fig. 2b).

To quantify diurnal oscillations, we next performed JTK_CYCLE analysis (Amp>0.1; p-adj<0.05). In wildtypes, we detected a total of 3401 and 4376 oscillating transcripts, following ctrl. and cal. res. diets, respectively (Supplementary Fig. 4a). As expected, caloric restriction enhanced the rhythmicity of gene expression by almost 30%. Interestingly, genes whose oscillations were boosted by caloric restriction functionally belonged to processes known to be critical for the adaptation to nutrient deficiency, such as protein catabolism, folding, and translation, DNA repair, aerobic respiration, and cell division (Supplementary Fig. 4b)17,18. These caloric restriction-specific programs were lost in the GRLKO mice, with 1157 out of 1585 genes (73%) losing oscillation in mutant mice being de novo cal. res. rhythmic transcripts. Conversely, GRLKO mice displayed a retention of those rhythms already prevalent on control diet, with over 2000 relevant genes involved in fatty acid, cholesterol, and xenobiotic metabolism (Supplementary Fig. 4c–e). To better categorize drivers of gene oscillations during the dietary intervention, we grouped the rhythmic transcripts into four different clusters: (i) ‘Diet-dependent’, genes that exhibited rhythmicity only under caloric restriction and not on control diet (diet); (ii) ‘Genotype-dependent’, oscillations lost in the GRLKO regardless of the diet (genotype); (iii) ‘Interaction-dependent’, genes being rhythmic only in WT livers on cal. res. (interaction); and (iv) ‘Independent’, genes remaining rhythmic in both diets and genotypes (all) (Fig. 2c–e and Supplementary Fig. 4f, g). Gene ontology analysis of ‘Independent’ genes indicated that macronutrient metabolism (carbohydrates, amino acids and lipids), responses to insulin/diet, and circadian rhythms constituted a solid block of gene oscillations driven by the core clock and synchronized by the feeding schedule. Lipid metabolism genes displayed a certain dependence on intact GR (genotype), whereas proteostasis-related processes were mainly driven by energy depletion (diet) (Fig. 2e). Some of the processes induced by caloric restriction (diet) were also shared by the ‘Interaction’ group. This last cluster included genes that depended on both GR and caloric restriction to sustain their rhythmicity, and that mapped to fasting-specific processes such as translation, chromatin organization, cell division, and aerobic respiration (Fig. 2e and Supplementary Fig. 4g).

Overall, caloric restriction resulted in augmented transcriptional amplitudes at every time point and a phase advance compared to control diet, including core clock genes (Fig. 2f and Supplementary Fig. 5a, b). While some of these core clock genes were differentially expressed in the GRLKO mice at ZT12, we would argue that their oscillatory patterns remained largely intact and therefore may not be sufficient to explain the rhythmic reprogramming induced by caloric restriction (see Supplementary Fig. 5a). In the absence of GR, despite the maintenance of fundamental rhythms, we observed a phase delay as well as a diminished induction of oscillatory behavior and amplitude flattening (Fig. 2c, d and Supplementary Fig. 5c).

In conclusion, we find that glucocorticoid rhythms sustain the caloric restriction-specific rewiring of diurnal gene expression in murine livers.

The enhanced glucocorticoid signaling drives the phenotypic adaptation to caloric restriction
Our transcriptomic profiles suggest that GRLKO mice might differ phenotypically from WT in the adaptation to the reduced energy intake. Consistently, analyses of circulating glucose, free fatty acids and ketone bodies revealed a more robust shift in response to caloric restriction in wild type than in GRLKO mice (Fig. 3a–c).

At the cellular level, the prominent, evolutionarily conserved benefits of caloric restriction include targeted degradation of macromolecules and damaged organelles by autophagy. The conversion of LC3-I to LC3-II by lipidation is a classical marker for autophagosome formation19. Therefore, we quantified the LC3-II/LC3-I ratio in total liver extracts from WT and GRLKO, on cal. res. or ctrl. diet at ZT12. Indeed, the LC3-II/LC3-I ratio indicated increased autophagy in wild type cal. res. livers compared to controls. In GR mutants, however, we observed a decrease in LC3 lipidation, suggesting a resistance to caloric restriction-induced autophagic processes upon GR loss (Fig. 3d). This refractory phenotype manifested itself in a slightly higher liver mass in GR mutants despite the diet (Fig. 3e and Supplementary Fig. 6a, b). This impairment can compromise macromolecule turnover, resulting in an accumulation of glycogen or lipid stores. In our cohorts, caloric restriction led to a similar reduction in triglyceride stores across both genotypes. In contrast, while it caused near-complete depletion of glycogen reserves in WT mice, this effect occurred to a notably lesser degree in the absence of GR (Fig. 3f and Supplementary Fig. 6c).

As hepatocyte size responds to metabolic state during nutrient restriction1,20,21 and ploidy is associated with liver health22,23, we examined how caloric restriction affects nuclear ploidy states using HNF4α as a hepatocyte-specific marker. The cal. res. diet increased the proportion of diploid nuclei (2n: +28%) while substantially decreasing the proportion of tetraploid nuclei (4n: −19%) compared to controls. In line with previous results, GR loss attenuated the caloric restriction response, with a smaller increase in diploid nuclei (2n: +23%), and a less pronounced decrease in tetraploid nuclei (4n: −15%) compared to controls. Of note, no differences in hepatocyte nuclear ploidy between WT and GRLKO mice were observed in mice on control diet (Fig. 3g and Supplementary Fig. 6d, e).

Altogether, we conclude that the hepatocyte-specific GR loss attenuates the homeostatic response to caloric restriction.

The diurnal glucocorticoid spike needs energy depletion to activate protective pathways
Above, we have highlighted the importance of ZT12, the peak of diurnal GC secretion, for the phenotypic and transcriptional changes initiated by caloric restriction. To understand whether these responses may solely be explained by increased GR ligand availability, we mimicked the increased GC amplitude by injecting control-fed mice with either corticosterone or vehicle at ZT12 for seven consecutive days (Fig. 4a). The treated mice were sacrificed either at ZT0 (light on, GC trough) or ZT12 (lights off, GC peak), with their corticosterone maxima being 3-fold higher than vehicle controls (Fig. 4b). Unlike our cal. res. cohort, treatment with corticosterone alone did not alter serum glucose, triglycerides, ketones or free fatty acid levels (Supplementary Fig. 7a–d). However, corticosterone induced lipid accumulation in the liver, which did not occur on caloric restriction (Supplementary Fig. 7e)24.

To test whether increased nuclear GR activity without energy restriction was sufficient to sustain autophagy, we again performed western blots on liver extracts to visualize LC3 lipidation and LC3-II/LC3-I ratios. We found that unlike caloric restriction, corticosterone elevation by itself did not trigger autophagosome formation (Fig. 4c). To further demonstrate that autophagy induction by glucocorticoids was nutrient-dependent, we used a tandem-fluorescent mCherry-EGFP-LC3B reporter in human HepG2 cells. This reporter displays yellow puncta in autophagosomes and red-only puncta in autolysosomes by confocal microscopy. In cells cultured under fed or starvation conditions, we found that dexamethasone (Dex) significantly increased autolysosome formation only in starved cells, but not in fed cells or in those co-treated with the GR antagonist mifepristone (Fig. 4d).

Next, we examined how CORT treatment impacts hepatic gene expression by performing bulk RNA-seq on treated and control animals. In CORT livers, we identified 1299 deregulated genes (DEseq2, p-adj < 0.05) (Fig. 4e and Supplementary Fig. 7f). About half of these genes were found differentially expressed also upon caloric restriction (see Supplementary Fig. 1h, i). Notably, excess corticosterone in the absence of caloric restriction induced genes involved in the acute-phase response (complement and inflammation) while suppressing those involved in energy metabolism and proteostasis (Fig. 4f and Supplementary Fig. 7f, g).

We have previously shown how high-fat diet feeding altered GR chromatin occupancy in a ligand-independent manner, by changing locus-specific interactions with co-occurring transcription factors10. To elucidate the molecular mechanisms underlying GC-driven target gene regulation during caloric restriction, we next performed GR ChIP-seq for all three conditions (cal. res., CORT, ctrl.) in livers collected at ZT12. In both groups with higher GR ligand levels, we found a significant increase in the number of ChIP peaks over controls, with ~6,400 and ~8,000 gained GR binding sites in caloric restriction and corticosterone treated livers, respectively (Fig. 4g). Interestingly, almost all of the sites occupied by GR under control conditions (97%) were also bound in the other samples, indicating an expansion rather than a remodeling of binding sites by higher corticosterone. Accordingly, the signal strength measured across all GR-binding sites was significantly higher in both cal. res. and CORT livers compared to controls, with an even greater increase under corticosterone treatment than with caloric restriction. This suggests that, although the concentration was titrated to mimic caloric glucocorticoid stress, the exogenous compound may exhibit greater stability than endogenous corticosterone (Fig. 4g and Supplementary Fig. 7h, i). Akin the RNA-seq analysis, gene enrichment analysis of the gained sites indicates that responses to increased glucocorticoids converge near genes involved in cholesterol and bile acid metabolism, peroxisomes, signaling axes, and cancer. Notably, only under caloric restriction we find an enrichment of FoxO and circadian rhythm signatures, two prominent pathways that have shown GR dependency in previous transcriptomic analyses25,26.

With the goal of identifying discriminatory features and gaining molecular insight into the pathways underlying the reprogramming by caloric restriction, we performed bioinformatic motif searches of our ChIP-seq data. In general, all GR cistromes featured Glucocorticoid Response Elements (GREs) and the expected hepatocyte-specific motifs C/EBP, HNF4α, FOX, and PPAR (Supplementary Fig. 8a–c). To characterize specific signatures among cal. res.- versus CORT-induced binding sites, we performed an over representation analysis restricted to the ~1594 (cal. res.) and the ~3157 (CORT) gained sites against each other (Fig. 4i): Indeed, consensus motifs for liver nuclear receptors (HNF4, PPAR, GR), Bmal1 (E-Box, bHLH), and forkhead factors (FOX) were selectively enriched among cal. res.-specific GR binding sites. Conversely, GR ChIP-sequences bound only in CORT-treated livers featured motifs for STAT and immune-related factors (Fig. 4i). These signatures support the notion that GR shapes the hepatic response to caloric restriction via the coordinated interaction with metabolic and clock transcription factors, as opposed to corticosterone treatment, which favors co-occupancy with STATs and inflammatory mediators.

Altogether, our experiments unveil that, during caloric restriction, enhanced GR signaling combines with energy deprivation to create essential conditions eliciting the beneficial outcomes of this dietary intervention.

A molecular switch between Stat and FoxO1 signaling licenses caloric restriction specific GR binding events
Given that GR ChIP-seq motif analyses pointed towards transcriptional crosstalk between GR and FOXO transcription factors upon caloric restriction, as opposed to co-occupancy with STATs in control and CORT-treated livers (Supplementary Fig. 8d, e), we next probed for the activity of these nuclear proteins. We hypothesized that low insulin signaling would result in lower phosphorylation and consequently higher nuclear localization of FOXO1, while lower growth hormone secretion would potentially lead to lower STAT5 phosphorylation and activation. Indeed, compared to the control diet, caloric restriction diminished the phosphorylation of STAT5 and FOXO1, as evidenced by immunoblotting for pY694-STAT5 and pS256-FOXO1 in liver extracts. Interestingly, GRLKO mice again exhibited an attenuated response to caloric restriction, as indicated by genotype-specific differences in STAT5 and FOXO1 phosphorylation (Fig. 5a and Supplementary Fig. 9a).

To determine whether control and caloric restriction diets differentially influence the formation of GR transcriptional complexes with STAT5 or FOXO1, we conducted a ChIP-MS (GR ChIP coupled with mass spectrometry proteomics) experiment. We detected 829 and 1039 proteins significantly enriched in GR over IgG immunoprecipitates in the livers of ctrl. and cal. res. mice, respectively (Fig. 5b and Supplementary Fig. 9b, c). To better discriminate the effects of diet on the GR interactome, we plotted the log2 fold change (log2FC) of GR over IgG in both ctrl. and cal. res. mice. We then classified proteins as enriched in one group or the other if their GR log2FC exceeded 20% of the average fold change between both conditions. (Fig. 5c and Supplementary Fig. 9d). Equally enriched proteins included chromatin remodelers, RNA-binding factors, coregulators, and transcription factors, such as CEBPα/β, HNF4α, RXRα, and NCOA5 (Fig. 5c and Supplementary Fig. 9e). Among the 259 proteins enriched in the control group, we identified several STATs, including STAT5a and STAT5b, along with known coregulators of the STAT signaling pathway. Additionally, proteins enriched in the control group included factors that regulate the expression of genes involved in bile acid synthesis, cholesterol homeostasis, and triglyceride synthesis, such as LXRβ, FXR, ChREB, and LRH-1. While FOXO1 was not directly detected in the GR interactome (495 proteins), the dataset was enriched for components of energy-sensing and insulin/growth factor signaling pathways - including AMPK, SIRT3, PRKAα2, PRKAγ1, DEPTOR, and YWHAG - which are upstream regulators of FOXO1 activation (Fig. 5d and Supplementary Fig. 9f). This suggested a potential direct link between GR and FOXO1 under conditions of caloric restriction. To test this, we performed co-immunoprecipitation experiments in isolated liver nuclei and observed a marked increase in GR–FOXO1 complex formation during caloric restriction, supporting a protein–protein interaction that is enhanced under energy-deprived states (Fig. 5e and Supplementary Fig. 9g).

To complement our in vivo findings, we performed experiments in primary hepatocytes cultured under defined nutritional and ligand conditions. We found that the nuclear localization of FOXO1, which is induced in starvation medium, is markedly enhanced by Dex treatment, whereas ligand treatment alone in fed hepatocytes has minimal effect. These findings provide cell-autonomous evidence that energy deficit and GR activation synergistically promote nuclear stabilization of FOXO1 (Fig. 5f).

Last, we interrogated our multiome data set to see if this diet-specific signaling affects chromatin accessibility in hepatocytes. We found 6403 differentially accessible regions (p-val<0.05) and ranked them by statistical significance (Supplementary Fig. 10a). In accordance with our proteomics observations, the top 500 most significant sites with higher accessibility in caloric restriction corresponded to genes related to insulin and FOXO1 signaling. While sites with lower accessibility were linked to genes important for lipid metabolism and inflammation (Supplementary Fig. 10b). In line with our functional annotation, motif analyses revealed a significant enrichment for GR and FOX elements in regions with increased accessibility (Supplementary Fig. 10c, d).

Taken together, by combining genomic, proteomic, and imaging approaches, we revealed gene expression programs regulated by the GC in response to dietary treatments, in coordination with hormone-activated transcription factors STAT5 and FOXO1, which interact and crosstalk with the GR in the hepatocytes.

The enhanced GR-FOXO1 interaction upholds GR-dependent gene programs in response to caloric restriction
The FOXO1 protein has been linked to many of the positive effects seen with caloric restriction25–27. To get a deeper insight into GR-FOXO1 crosstalk, we first determined if our cal. res. transcripts were targets of FOXO1, by integrating the transcriptomic profiles at ZT12 with the published ‘hepatic FoxO1 regulome’, which combines FOXO1 ChIP-seq and RNA-seq data from FoxO1 loss-of-function livers in response to short-term fasting28. As expected, wildtype livers displayed an upregulation of this FoxO1 signature after caloric restriction. Interestingly, this upregulation did not occur in GRLKO mice under caloric restriction, suggesting that the resistance to nutrient deprivation in the absence of GR can arise from its interaction with FOXO1 (Fig. 6a, b). We hypothesized that the cal. res.-specific rewiring of transcriptional regulation by GR could be mediated by FOXO1 co-occupancy at diet-responsive enhancers. Therefore, we performed ChIP-seq for FOXO1 in our ZT12 livers with caloric restriction. Indeed, of the 7732 identified FOXO1 genomic binding sites, 5723 were co-bound by GR in cal. res. livers (74% of total) (Fig. 6c and Supplementary Fig. 10e, f). Importantly, the loci bound by both GR and FOXO1 had significantly higher ChIP-seq signal intensities than those bound by GR without detectable FOXO1, indicating a stronger GR chromatin binding at sites co-occupied by FOXO1 (Supplementary Fig. 10g). To understand if these co-bound loci were also diet-responsive, we compared signal intensities of our GR cistromes in cal. res. and ctrl. diet. Notably, we found that the fold change in cal. res. over ctrl. diet was significantly higher in the GR+FOXO1 compared to GR alone sites, suggesting that the co-bound loci are more sensitive to caloric restriction (Fig. 6d). Functional characterization of the co-bound/diet-responsive peaks revealed an enrichment of pathways shaped by the diet with half of the annotated genes being up- (30.2%) or down-regulated (17.3%) in cal. res. livers compared to controls. Strikingly, 2436 (80.9%) of the diet-induced genes and 1940 (64.3%) of the diet-repressed genes, harbored FOXO1 and/or GR occupied cis-regulatory elements. It is worth noting that GR+FOXO1 peaks compared to GR-only peaks were predominantly associated with cal. res.-induced transcripts, arguing for direct gene regulation. These genes again corresponded to pathways such as longevity, circadian rhythms, autophagy, cancer, and FOXO1 signaling (Fig. 6e and Supplementary Fig. 10h).

To further test whether increased crosstalk with FOXO1 was enabling GR to fully regulate its caloric restriction specific target genes, we cloned some of these co-bound loci into luciferase reporter vectors and found that significant activation of these reporters by GR required co-transfection of constitutively active FOXO1 expression constructs. The synergistic activation of these cis-regulatory elements was eliminated when the predicted FOXO1 binding site was mutated (Fig. 6f). These data suggest that GR acts in concert with active nuclear FOXO1 to bind to and regulate its caloric restriction-specific target genes, in a diet-responsive and insulin-dependent manner.

Collectively, our experiments reveal a multi-layered mechanism by which caloric restriction promotes synergistic coordination between FOXO1 and GR. This involves enhanced physical interaction, increased nuclear co-localization, co-occupancy at cal. res.-responsive genomic loci, and functional cooperation in the regulation of target genes.

Methods
Animal experiments
Food intake regimen. From 12 weeks of age, mice were single-housed, and their ad libitum food consumption was measured for seven days. Caloric restriction was implemented gradually: The 20% group received a 10% reduction for one week followed by 20% restriction for six weeks, while the 40% group underwent weekly incremental reductions (10%, 20%, 30%, and finally 40%) over four weeks, then maintained at 40% for three more weeks. During the restriction, food was provided daily at ZT12 (6 p.m.). To minimize food-related zeitgeber effects, starting one week before sacrifice, the control group was placed on a night restricted feeding regimen, with food access limited to ZT12 (6 p.m.) - ZT0 (6 a.m.). Meal timing was inferred by indirect calorimetry (TSE PhenoMaster). In caloric-restricted mice, oxygen consumption and CO₂ production peaked at ZT14–ZT16 (8–10 p.m.), consistent with immediate food consumption upon availability (Supplementary Fig. 6h).

Corticosterone treatment. 12-week-old male C57BL/6J mice were acclimatized and adapted to handling for two weeks to reduce basal stress levels. Corticosterone (Sigma-Aldrich, #27840) was diluted in a solution containing 10% EtOH/90% Miglyol-812 oil (Sigma-Aldrich, #3274) to a final concentration of 1mg/ml. Mice were administered 5mg/kg corticosterone intraperitoneally at ZT11 every day for 7 days. Vehicle-treated mice received proportional doses of 10% EtOH/90% Miglyol-812 oil intraperitoneally. Mice were subjected to night restricted feeding during the 7 days treatment protocol.

Samples collection. Mice were sacrificed at the end of the eight-week protocol. To avoid stress-induced alterations in glucocorticoid hormone levels, mice were euthanized via rapid cervical dislocation. When available, serum was obtained by blood centrifugation at 2000g for 10 min at 4°C. Liver and serum were snap frozen in liquid nitrogen and stored at −80°C.

Serum chemistry
Blood glucose levels were measured immediately after euthanasia from the tail using a glucometer (AccuCheck Aviva, Roche). Serum triglyceride, ketone, and non-esterified fatty acid levels were quantified using a Serum Analyzer (Beckman Coulter AU480). Triglycerides were measured with the General Chemistry Reagent Triglycerides kit (Beckman Coulter; #OSR601), ketones with the Autokit total ketone bodies assay (Fujifilm Wako Chemicals, #413-73601 and #415-73301), and non-esterified fatty acids with the NEFA-HR kit R1 set (Fujifilm Wako Chemicals, #434-91795) and NEFA-HR kit R2 set (Fujifilm Wako Chemicals, #436-91995), following manufacturer guidelines. Corticosterone was quantified using enzyme immunoassay kits from Enzo Life Sciences (#ADI-900-097) (Fig. 1b) and Arbor assays (#K014-H1) (Fig. 4b and Supplementary Fig. 1g), while circulating insulin was measured using an enzyme hypersensitive mouse insulin ELISA kit (Fujifilm Wako Chemicals; #290-63701), using 2ul and 5ul of unhemolyzed serum, respectively.

Liver tissue assays
Hepatic triglycerides were extracted from 25-50mg of liver tissue, initially digested in 6 volumes of EtOH/30% KOH (2:1, v/v) under shaking at 60°C overnight. After digestion, samples were mixed with 1M MgCl2 at a ratio of 1.08:1 (vol.) and incubated on ice for 10 minutes, followed by centrifugation for 30 minutes at maximal speed at 4°C. The hepatic supernatants were measured by colorimetric assay following the company’s instructions (Fujifilm Wako Chemicals, #632-50991). Glycogen was extracted from ~10mg of liver tissue, post-digestion in 200µl of hydrolysis buffer. The hepatic supernatants were also measured using a colorimetric assay kit (Abcam, #ab169558), adhering to the manufacturer’s instructions.

Ploidy measurement
Paraffin-embedded liver samples were sectioned at 3µm thickness. Sections were co-stained with β-catenin (1:20, goat polyclonal anti-β-catenin, R&D Systems, #AF1392; 1:100, AlexaFluor™ 550-conjugated donkey anti-goat IgG (H+L) cross-adsorbed secondary antibody, Invitrogen, #A21432) and HNF4α (1:50, rabbit anti-HNF4α mAb, Cell Signaling, #3113; 1:100, AlexaFluor™ 647-conjugated donkey anti-rabbit IgG (H+L) highly cross-adsorbed secondary antibody, Invitrogen, #A31573). Nuclei were labeled with Hoechst 33342 (H1399, Thermo Fisher Scientific). Stained sections were scanned using an AxioScan 7 digital slide scanner (Carl Zeiss, Jena, Germany) equipped with a 20× objective.

Scanned sections were visualized and analyzed using Visiopharm software (Version 2023.09.014530 Visiopharm, Hørsholm, Denmark). The entire liver section was selected for analysis. The software was trained to identify all nuclei based on Hoechst signal, with hepatocyte nuclei specifically identified by HNF4α-positive staining.

For nuclear ploidy classification, size thresholds were established based on the nuclear diameter distribution in wild-type mice on control diet (Supplementary Fig. 6d). The distribution revealed distinct peaks corresponding to different ploidy levels, allowing classification of nuclei into three ploidy categories: Diploid (2n, diameter <7.75µm), tetraploid (4n, diameter 7.75–10.25µm), and octaploid (8n, diameter >10.25µm). These threshold criteria were then applied consistently to analyze nuclear ploidy distributions across all experimental conditions (WT ctrl. n=4, WT cal. res. n=4, GRLKO ctrl. n=3, GRLKO cal. res. n=3), as demonstrated in Fig. 3g and Supplementary Fig. 6e.

Parallel analysis was performed using Hoechst staining to assess whole-liver ploidy distribution independent of hepatocyte-specific markers. The same nuclear diameter thresholds were applied to all Hoechst-positive nuclei across experimental groups with diameter distributions and ploidy quantification shown in Supplementary Fig. 6f, g. This complementary approach distinguished hepatocyte-specific ploidy patterns from whole-liver ploidy distribution, revealing that hepatocyte-specific analysis is essential for detecting diet-induced ploidy changes that are otherwise masked by the predominantly diploid non-parenchymal cells in whole-liver assessments.

Liver protein extraction and detection
Total lysate. Total protein was extracted from 30mg of liver tissue using cold RIPA buffer (50mM Tris HCl pH 8.0, 150mM NaCl, 1% Triton-X 100, 0.1% SDS, and 0.5% sodium deoxycholate), supplemented with complete mini protease (Roche Applied Science, #11836170001) and phosphatase inhibitors (Thermo Fisher Scientific, #A32957). A 5mm stainless bead (QIAGEN, #69975) was added to each sample for lysis, followed by homogenization in the Mixer Mill (Retsch #MM400). After incubation on ice for 30 minutes and centrifugation at 4°C (15 minutes, 19,000g), the supernatant was collected, and protein concentration was determined using Bradford reagent (BioRad, #5000205).

Nuclear protein extracts. Nuclear fraction was isolated from 50mg of frozen liver tissue using cold cell lysis buffer (10mM Hepes-KOH pH 7.9, 1.5mM MgCl2, 10mM KCl, 0.5mM DTT, and 0.15% NP-40), supplemented with complete mini protease and phosphatase inhibitors. After centrifugation at 4°C (20 minutes at 2,700g), the nuclear pellet was washed in cold PBS before lysis in 100µL of nuclear lysis buffer (420mM NaCl, 20mM Hepes-KOH pH 7.9, 2mM EDTA, 0.5mM DTT, 0.1% NP40, and 20% glycerol) following 5-10 passages through an insulin syringe. After rotation and centrifugation at 4°C, nuclear protein fractions were obtained and quantified using Bradford reagent according to the manufacturer’s instructions.

Co-immunoprecipitation. Nuclei were isolated from 160mg of frozen liver tissue using a protocol adapted from Korenfeld et al44. Liver tissue was homogenized in a Dounce homogenizer with cold homogenization buffer (10.9% sucrose, 10mM Tris-HCl pH 7.5, 0.1% NP-40, 3mM MgCl₂, 10mM KCl, 0.2mM DTT, 0.5mM spermidine) containing protease inhibitors. The homogenate was filtered through a 40 μm strainer and centrifuged (500g, 10 minutes, 4°C). The nuclear pellet was washed with cold PBS and lysed in 200μL nuclear lysis buffer (420mM NaCl, 20mM HEPES-KOH pH 7.9, 2mM MgCl₂, 0.4mM EDTA, 23.3% glycerol, 0.1% NP-40) with protease inhibitors, passed through a 24G syringe, and incubated for 1 hour at 4°C with rotation. Lysate was cleared by centrifugation (9600g, 30 minutes, 4°C).

For immunoprecipitation, 200μg of nuclear lysate was used per reaction with 2% reserved as input. The lysate was incubated overnight at 4°C with 3μg of GR antibody (ProteinTech, #24050-1-AP) or IgG control (Cell Signaling, #2729) in 300μL wash buffer (150mM NaCl, 20mM HEPES-KOH pH 7.9, 2mM MgCl₂, 0.4mM EDTA, 23.3% glycerol, 0.1% NP-40, containing protease inhibitors). Pre-washed Dynabeads (30μL) were added and incubated for 2 hours at 4°C. After collecting flow-through and washing beads four times, immunoprecipitated proteins were eluted in 2x Lämmli buffer with 135mM DTT by boiling at 95°C for 5 minutes and analyzed by western blot alongside input samples. Co-immunoprecipitation were performed on two biological replicates per group in independent experiments.

Western blot. 10 to 15µg of nuclear or total extracts were loaded onto 4%-12% Bis-Tris SDS-PAGE gels (Life Technologies, #NP0336 and #NP0321) and transferred to a 0.45µm PVDF membrane (Immobilon, #05317) using standard methods. Amido Black staining assessed even loading. Antibodies used included Rabbit anti GR (Cell Signaling, #12041) (1:1000), Rabbit anti α-LC3A/B (Cell Signaling, #12741) (1:1000), Rabbit anti FOXO1 (Ser256) (Cell Signaling, #9461) (1:1000), Rabbit anti α,β-STAT5 (pY694, pY699) (Cell Signaling, #9351) (1:1000), Mouse anti α-GR (Santa Cruz, #sc-393232) (1:5000), Mouse anti FOXO1 (Cell Signaling, #97635) (1:1000), Goat anti Mouse IgG/HRP Conjugated (BioRad, #1706516) (1:5000), Goat anti Rabbit IgG/HRP Conjugated (Dako, #P044801-2) (1:5000), Rabbit anti IgG and HRP-linked Antibody (Cell Signaling, #7074) (1:5000). For experiments requiring more than one gel due to the number of biological replicates, the gels were run and processed in parallel with identical antibody incubations, and all replicates were quantified together to ensure direct comparability across conditions. Band intensity was quantified by densitometry using ImageJ Software and normalized to the total protein control.

Cell culture studies
Tandem mCherry-EGFP-LC3B assay (Autophagy flux assay). HepG2 cells were obtained from (ATCC #HB-8065) and cultured in low glucose Dulbecco’s Eagle’s Medium (DMEM) (Sigma Aldrich, #D6046) supplemented with 10% fetal bovine serum (FBS) (Sigma Aldrich, #F7524), 1× non-essential amino acids (NEAAs) (Thermo Fisher Scientific, #11140035), and 1% penicillin/streptomycin (P/S) (Sigma Aldrich, #P4333), referred to as LG-DMEM, at 37°C with 5% CO2.

Cells were sub-cultured in 8-well chambers (ibidi, Munich, Germany, #80826) at a density of 10,000-12,000 cells per well and maintained in LG-DMEM medium. 24h after seeding, cells reached 50–60% confluency and were transiently transfected with 80ng of mCherry-EGFP-LC3B plasmid construct (kind gift from Dr. C. Mammuccari) using Lipofectamine® 3000 (Thermo Fisher Scientific, #L3000001) according to the manufacturer’s instructions45. Cells were used for microscopy within 24h after transfection.

Three hours before imaging, cells were subjected to either fed medium (LG-DMEM) or starvation medium (Earle’s balanced salt solution (EBSS)) (Thermo Fisher Scientific, #24010043) supplemented with 1% P/S. Prior to starvation, cells were washed with Hank’s balanced salt solution (Thermo Fisher Scientific, #14025050) to remove nutrient residues. Cells under both nutritional conditions were treated with: (1) vehicle control (ethanol, veh), (2) 100nM dexamethasone (Dex, Sigma Aldrich, #D4902) as a GR agonist, or (3) 100nM dexamethasone plus 10μM mifepristone (Dex + Mif, where mifepristone acts as a GR antagonist, Sigma Aldrich, #M8046).

Confocal images were acquired using an Olympus FV1200 confocal laser scanning microscope system with a 60× oil immersion objective (NA 1.4) and FV10-ASW software. The system was equipped with conventional photomultiplier tube detectors (Hamamatsu R7862) and a DM405/488/559/635 dichroic mirror. EGFP was excited with a 488nm laser and detected through a “500- 545” nm emission filter (SDM560 dichroic). mCherry was excited with a 559nm laser and detected through a “575-675” nm bandpass filter (BA570-625, SDM640 dichroic). Images were acquired at 1024×1024 pixel resolution at 12-bit depth with a pixel dwell time of 10.0 µs. Identical acquisition settings were maintained across all experimental groups. Images are displayed using a linear lookup table (green for EGFP and red for mCherry) without post-acquisition adjustment. The mCherry-EGFP-LC3B protein is distributed within the autophagosome, where both the green and the red fluorescent signals are expressed, resulting in yellow puncta in the merged channel. In autolysosomes, the low pH quenches the EGFP signal while the red signal remains unaffected, leading to a reduction in the number of yellow puncta and the presence of only red puncta.

Olympus Fluoview version 4.2b software was used to view the images, and the number of yellow and only red puncta were manually counted. The data were presented as the ratio of only red to yellow puncta per cell and displayed as box-and-whiskers plots depicting the 10th, 25th, 50th, 75th, and 90th percentiles and the arithmetic mean (+). Between 19–50 cells per condition were analyzed across n=3 independent experiments. Statistical significance was assessed by robust two-way ANOVA followed by pairwise Wilcoxon test with adjustment for multiple comparisons.

Immunofluorescence in primary hepatocytes. Primary hepatocytes were isolated via collagenase perfusion from 8–12-week-old WT male mice46. The cells were cultured in a collagen sandwich configuration within 8-well μ-slides (ibidi, Munich, Germany, #80826) at a density of 8×10⁴ cells per well. Cultures were maintained in William’s Medium E (Pan Biotech, #P04-29050) supplemented with 10% FBS (Sigma Aldrich, #F7524), 1% penicillin-streptomycin (Sigma Aldrich, #P4333). The cells were incubated at 37°C with 5% CO2. Fasting and refeeding media were prepared using William’s Medium E without glucose, phenol red, and glutamine as base medium (Pan Biotech, #P04-29050S4), supplemented as follows: Fasting with 5mM glucose (Sigma-Aldrich, #G8769), 0.5% charcoal-stripped FBS, 2mM glutamine (Gibco, #25030024), and 10nM glucagon (Sigma-Aldrich, #G2044-1MG). Refeeding with 11mM glucose (Sigma-Aldrich, #G8769), 10% charcoal stripped FBS, 2mM glutamine (Gibco, #25030024), and 10nM insulin (Sigma-Aldrich, #I5500). Prior to treatment, cells were incubated in fasting medium for 16h, followed by exposure to the indicated treatment conditions: 2h in fasting medium for starvation, or 2h in refeeding medium for fed condition, with or without 1μM Dex.

For immunofluorescence, cells were fixed with 4% paraformaldehyde at room temperature for 30 min and washed twice with PBS. Cells were permeabilized with 0.1% Triton X-100 for 1h, washed, and then blocked with 10% horse serum for 2h. Primary antibodies against FOXO1 (1:150, rabbit polyclonal, ProteinTech, #18592-1-AP) were incubated overnight at 4°C. Following primary antibody incubation, cells were washed extensively for 4h in wash buffer (300mM NaCl, 0.1% Tween-20, 10mM Tris-HCl) with multiple buffer exchanges. Secondary antibodies (2µg/ml, Alexa Fluor 568 goat-anti-rabbit, Invitrogen #A11011, for FOXO1 detection) and Alexa Fluor 488 phalloidin (1:400, Invitrogen #A12379, as a marker for the cytoskeleton) were applied and incubated overnight at 4°C in a humidified chamber. After extensive washing at room temperature, mounting medium containing DAPI (Sigma, #DUO82040) was added to each well. Samples were imaged after 30 min of incubation.

Confocal images were acquired using an Olympus FV1200 confocal laser scanning microscope system with a 60× oil immersion objective (NA 1.4) and FV10-ASW software at 1024×1024 pixel resolution. The system was equipped with conventional photomultiplier tube detectors (Hamamatsu R7862) and a DM405/488/559/635 dichroic mirror. DAPI was excited using a 405nm laser and detected through a 425-475nm emission filter (SDM490 dichroic), Alexa Fluor 488-phalloidin was excited with a 488nm laser and detected through a 500-545nm emission filter (SDM560 dichroic), and FOXO1 (Alexa Fluor 568-conjugated secondary antibody) was excited with a 559nm laser and detected a 559nm laser and detected through a 575-675nm or 575-620nm bandpass filter (BA575-675 or BA575-620, SDM640 dichroic). Single optical sections were acquired at 12-bit depth with a scan speed of 20 µs/pixel. Identical acquisition settings were maintained across all experimental groups. Images are displayed using a linear lookup table without post-acquisition adjustment (blue for DAPI, green for phalloidin, red for FOXO1). Quantitative analysis of FOXO1 subcellular localization was performed using ImageJ software (NIH, version 1.53c). The nuclear and cytoplasmic FOXO1 signal intensities (Alexa Fluor 568) were measured separately for each cell. Nuclear and cytoplasmic regions were manually defined based on DAPI staining to identify nuclei and Alexa Fluor 488 Phalloidin to visualize cell boundaries. For each experimental condition, 86-126 cells were analyzed across multiple fields of view from n=2 independent experiments. Data are presented as the nuclear-to-cytoplasmic (nuc/cyt) fluorescence intensity ratio. Statistical significance was assessed by robust two-way ANOVA followed by pairwise Wilcoxon test with adjustment for multiple comparisons.

Luciferase reporter assays. Luciferase reporters were generated by cloning cis-regulatory elements into pGL4.23 (Promega, #E8411). Mutagenesis of the putative FOXO1 binding sites was performed using the Q5 site-directed mutagenesis kit (NEB, #E0554S). Expression vector for murine GR was generated by cloning mGR CDS from pCR.BluntII Topo (IMAGE 40111802) into pCDNA3.1 (#V79020). Expression vector for constitutively active murine FOXO1 was generated by cloning FLAG-FOXO1ADA mutant (3 point mutations of Akt phosphorylation sites, T24A/S253D/S316A) from pCMV5 (Addgene, #12149) into pCDNA3.147. Primers are listed in Supplementary Table 1. Luciferase reporter assays were performed as previously described48,49.

CV-1 fibroblast cells obtained from Salk institute used for luciferase reporter assays, were cultured in high glucose DMEM supplemented with 10% FBS and 1% Pen/Strep at 37°C with 5% CO2. Prior to transfection, cells were incubated in phenol red-free DMEM supplemented with 10% charcoal-stripped FBS. Cells were transfected with luciferase reporters along with expression vectors for mouse GR and constitutively active FOXO1. pRL-CMV (Promega, #E2261) Renilla was used for normalization. On the next day, cells were treated for 24h with 1µM dexamethasone.

Dual-Glo Luciferase Assay System was used according to the manufacturer’s protocol. Each experiment included 3 technical replicates. Experiments were performed as 3 independent biological replicates for WT reporters (n=3) and 2 independent biological replicates for mutant reporters (n=2), with WT included as an internal control in experiments containing mutants. Relative luciferase activity is represented as fold change compared to the WT luciferase reporters with control vector.

RNA isolation, sequencing, and qPCR analyses
RNA isolation. 50mg of frozen liver tissue was homogenized in 1mL of QIAZOL reagent (QIAGEN, #79306) using Mixer Mill and stainless beads. Total RNA extraction was carried out following the manufacturer’s instructions (QIAGEN, #79306), with isolated RNA being further treated with DNase (Invitrogen, #AM1906) to remove contaminating DNA. Concentration and integrity were assessed using the Qubit RNA High Sensitivity Kit (Thermo Fisher, #Q32855) and the Agilent RNA 6000 Nano Kit (Agilent Technologies, #5067-1511) on a 2100 Bioanalyzer (Agilent, #G2939BA).

Library preparation and sequencing. 1μg RNA from at least 3 biological replicates underwent poly(A) selection, fragmentation and reverse transcription using the Elute, Prime, Fragment Mix (Illumina, #15026782). End repair, A-tailing, adaptor ligation and library enrichment were performed as described in Illumina Stranded mRNA Prep (Illumina) and TruSeq RNA Library Prep (Illumina) guides. The quality and quantity of RNA libraries were assessed using the 2100 BioAnalyzer and the Quant-iT PicoGreen dsDNA Assay Kit (Life Technologies, #P7589). Sequencing was performed as 100bp paired-end runs on both an Illumina NovaSeq 6000 platform and HiSeq 2500. On average, approximately 3.5Gb of sequence data were generated per sample.

Quantitative real-time PCR. The reverse transcription was initiated from 1µg of mRNA following the manufacturer’s instructions provided in the QuantiTect reverse transcription kit (QIAGEN, #205311). The qPCR analysis was performed using the Viia 6/7 Real-time PCR system with the Power SYBR Green PCR Master Mix (Life Technologies, #4367659). Detailed information regarding the primer sequences is available in Supplementary Table 1. To ensure precise analysis, the expression levels were normalized using the housekeeping gene Rplp0.

Single-nucleus analyses
Nuclei isolation. Nuclei were isolated as described50,51. Small pieces of frozen liver (roughly 30 mm3) from 2 biological replicates were minced in 1mL of homogenization buffer (HB) (250mM sucrose, 25mM KCl, 5mM MgCl2, 10mM Tris buffer, 1μM DTT), supplemented with 1× protease inhibitors Complete (Sigma Aldrich, #11873580001), 0.4U/μl RNase In (Takara Bio, #2313B), 0.2U/μl SUPERase-IN (Thermo Fisher Scientific; #AM2694), 0.1% Triton X-100 (v/v) and 10μg/ml Hoechst (Thermo Fisher Scientific; #33342) in RNase-free water. Homogenization was performed by using a pre-chilled 2mL Dounce homogenizer (Lab Logistics, #9651632) with ice cold HB to a final volume of 2mL. The liver homogenate was passed through a 50μm sterile filter (CellTrics, Sysmex, #04-004-2327) into pre-chilled 1.5mL tubes and centrifuged at 1000g for 8min at 4°C. Following centrifugation, the supernatant was aspirated, and the nuclei pellet was resuspended in 250μl of cold HB. For obtaining high quality of single nuclei, a 2-layer (29% and 25%) density gradient centrifugation clean-up step was performed using Iodixanol gradient solution (Sigma Aldrich, Optiprep, #D1556) at 12,000g for 20min at 4°C. The clean nuclei pellet was washed once with 1mL of PBS + 2% BSA + Protector RNAse inhibitor (Sigma Aldrich, #3335402001) before proceeding directly to nuclei permeabilization (Step 1.2_10x Genomics protocol CG000375• Rev C). The nuclei suspension quality was evaluated under a microscope, using trypan blue and directly continuing to the single cell Multiome protocol according with the kit provider (10X Genomics_. User Guide CG000338 Rev F).

Single-nuclei RNA-seq and ATAC-seq library preparation. The single-nuclei libraries were constructed by following the 10x Genomics Chromium Next GEM Single Cell Multiome ATAC + Gene Expression user guide (CG000338 Rev F). In brief, the nuclei suspensions were incubated with the kit transposase, which fragmented the DNA in the open regions of the chromatin and added the adapter sequences. The transposed nuclei suspension was loaded onto the Chip J (PN-1000234, 10x Genomics) with partitioning oil and barcoded single-cell gel beads. All samples were loaded with nuclei recovery aim of 5000. The final products were ATAC and gene expression libraries prepared separately. The quality and traces of the libraries were controlled using the Agilent High Sensitive DNA Kit (#5067-4626) with a Bioanalyzer 2100 (Agilent Technologies). Each sample libraries concentrations were accurately determined by qPCR before pooling for sequencing, using the Collibri Library Quantification kit (ThermoFisher Scientific, #A38524500) on a QuantStudio 6 (ThermoFisher Scientific). Samples were sequenced in a NovaSeq 6000 using S1 100 flow cells (Illumina, Inc.).

Chromatin immunoprecipitation (ChIP) and sequencing
ChIP was performed in 2 biological replicates as previously described48 and the following antibodies were used: rabbit anti GR (ProteinTech, #24050-1-AP), rabbit anti Foxo1 (ProteinTech, 18592-1-AP) and rabbit anti Foxo1 (Abcam, ab39670).

Library preparation and sequencing. ChIP libraries were prepared with the KAPA Hyperprep Kit (Kapa Biosystems, KK8504) as previously described10,52. Illumina compatible adapters were synthesized by IDT (Integrated DNA Technologies). Adapter-ligated libraries were size-selected (360-610bp) in a Pippin Gel station (Sage Science) using 2% dye free gels (Sage Science). Library concentrations were estimated by RT-PCR with the KAPA Library Quantification Kit (Kapa Biosystems). Quality of the libraries was evaluated with the Agilent High Sensitivity DNA Kit in a 2100 Bioanalyzer (Agilent). Sequencing was performed as 100bp paired-end runs on Illumina NovaSeq 6000 platform. On average, approximately 3.5Gb of sequence data were generated per sample.

Chromatin immunoprecipitation (ChIP) and mass-spectrometry
ChIP was performed as previously described52 using four biological replicates in control and caloric restricted livers, using 6µg of rabbit anti GR (ProteinTech, #24050-1-AP) or rabbit anti IgG (Cell Signaling, #2729) antibodies. Beads conjugated to the immunoprecipitate were submitted to the Proteomics Research Infrastructure (PRI) at the University of Copenhagen for mass spectrometry-based proteomics and bioinformatic analyses. Washed beads were incubated for 30min with elution buffer 1 (2M Urea, 50mM Tris-HCl pH 7.5, 2mM DTT, 20μg/mL trypsin) followed by a second elution for 5min with elution buffer 2 (2M Urea, 50mM Tris-HCl pH 7.5, 10mM Chloroacetamide). Both eluates were combined and further incubated at room temperature over-night. Tryptic peptide mixtures were acidified to 1% TFA and loaded on Evotips (Evosep). Peptides were separated on a 15cm, 150μM ID column packed with C18 beads (1.9 μm) (Pepsep) on an Evosep ONE HPLC applying the 30SPD method and injected via a CaptiveSpray source and 10 μm emitter into a timsTOF pro2 mass spectrometer (Bruker) operated in PASEF mode.

MS data were acquired over an m/z range of 100–1700 and a TIMS mobility range of 0.6–1.6 1/K₀. Ion mobility was calibrated using Agilent ESI-L Tuning Mix ions (m/z 622.0289, 922.0097, and 1221.9906). TIMS ramp and accumulation times were both set to 100ms, with 10 PASEF ramps acquired per cycle, resulting in a total cycle time of 1.17s. The MS/MS target intensity and intensity threshold were set to 20,000 and 2,500, respectively. Dynamic exclusion was applied for 0.4min for precursors within 0.015 m/z and 0.015 V·cm⁻².

Statistical analyses
Animal experiments were randomized into control (ctrl.) and caloric restriction (cal. res.) groups, and individual data points are visualized in box and whiskers plots.

Statistical analyses were performed using GraphPad Prism (version 10) and the stats and WRS2 R packages (version 4.3.1). Prior to hypothesis testing, datasets were screened for outliers (ROUT method, Q=10%), normality (Shapiro–Wilk test, p>0.05), and homogeneity of variances (Brown–Forsythe test, p>0.05) to determine the appropriate statistical approach.

Single-factor experimental designs: For experiments involving a single factor or when analyzing specific group comparisons, one-way ANOVA followed by Tukey’s post-hoc test was used when data met the assumptions of normality and equal variances. Welch’s ANOVA with Dunnett’s T3 post-hoc test was applied when variance homogeneity was violated. Kruskal–Wallis test followed by Dunn’s multiple comparisons test was used as a non-parametrical alternative when normality was not satisfied.

Factorial experimental designs: For experiments involving two factors (e.g. genotype and diet), two-way ANOVA was used to simultaneously assess main effects and interactions. When normality and variance assumptions were met, standard two-way ANOVA was applied followed by Tukey’s for post-hoc test comparisons. When these assumptions were violated, a robust two-way ANOVA53 was used to assess main effects, followed by pairwise Wilcoxon tests with Benjamini-Hochberg correction for multiple comparisons. Post-hoc analyses were performed as follows: i) When significant interactions were detected: simple effects analysis or interaction contrasts using Tukey’s test; ii) When no significant interaction was found but main effects were significant: pairwise comparisons across relevant groups using Tukey’s test; iii) When neither interactions nor main effects were significant: no post-hoc comparisons were performed.

Pairwise comparisons: Student’s t-test or Mann-Whitney U test (when normality assumptions were violated) were used for direct pairwise comparisons between two groups.

Tests with p-values<0.05 were deemed statistically significant and presented as: *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Bioinformatics
RNA-sequencing. The quality of the reads was checked in FastQC ver. 0.11.954. Reads were aligned using Salmon ver. 1.8.055. Afterwards, to visualize the output of FastQC, we used MultiQC ver. 1.1256. Count matrix was read in RStudio ver. 4.0.1. using tximport package 1.18.057. Transformation of count matrix using variance stabilizing transformation (VST) and differential expression analysis were performed by DESeq2 package ver. 1.3058. Unsupervised hierarchical clustering and principal component analysis (PCA) of the normalized samples was used to evaluate distances among the different experimental groups. PCA calculation was done by using stat R package, while PCA plot was generated using ggplot2 and ggfortify R packages59. For the unsupervised hierarchical clustering, Pearson correlation coefficient was calculated and heatmaps were produced by applying similarities measure as a scale and complete linkage as a clustering method. Meanwhile, we drew ellipsoids in the PCA to enclose all replicates within the same group (time point + genotype). The ellipses were generated using geom_mark_ellipse and the Kachiyan algorithm, which estimates the enclosing ellipses60. Heatmap was produced using pheatmap R package. We used the VST-transformed count matrices to perform JTK cycle analysis for differential rhythmicity determination by using Metacycle package in R61. Rhythmicity was observed within a period of 24h filtered by using an adjusted p-value < 0.05 and amplitude > 0.1. Heatmap visualization of cycling genes was generated using pheatmap R package62. To visualize the temporal expression patterns of individual genes, a cosinor regression model was fitted to the data using GraphPad Prism (Version 10). Volcanoplot was generated using ggplot2 and ggfortify R packages59. Outputs from Deseq2 and JTK cycle analyses are enclosed in Supplementary Data 1.

Single nuclei multiome. Data preprocessing and clustering: The sequences were aligned to the mm10 genome using the Cell Ranger ARC 2.0.2 software from 10X Genomics with the –include introns flag. The output was loaded into R version 4.3.263. Afterwards, we used Seurat version 5.1.064–67 and Signac version 1.13.068 to analyze the samples. For quality control, we implemented threshold for RNA modality: nCount RNA>500-1000 and <15,000-50,000, nFeature RNA>500-700 and <4000-7500, percentage of mitochondria <1.5-20%; while for the ATAC modality: nCount ATAC>150-500 and <15,000-42,000, nFeature ATAC>60-250 and <6000-16,000, nucleosome signal>0.1-0.25 and <0.75-1.0, and TSS enrichment >2-3 and <10-12. The cut-offs were made based on the violin plot profile of each parameter per sample.

For the peaks used in further analysis, they are called using MACS2 version 2.2.669. Then the samples are merged, and RNA modality was normalized and scaled using SCTransform V2. Afterwards, the ATAC modality was integrated with the canonical correlation analysis (CCA) method or Harmony70. Clustering was performed with K-nearest neighbor and Louvain clustering method, with resolution=0.8. Samples are annotated with manual annotation on the RNA modality, with marker genes adapted from Richter et al.71. The variation in the number of cells profiled per donor was attributed in part to sample viability, technical differences in cell capture rates in each single-cell RNA-sequencing (scRNA-seq) run, and sequencing depth. Next, the ATAC modality was normalized and scaled using the term frequency-inverse document frequency (TF-IDF) method, followed by feature selection and dimension reduction by singular value decomposition (SVD)72. Then, the ATAC modality was integrated with the CCA method. Lastly, both modalities were integrated using weighted nearest neighbor (WNN) method from Seurat. Cells were assigned to cell cycle phases (G1, S, G2M) with cyclone function from Scran package73. Visualization of gene expression was performed on normalized RNA count of the dataset.

For differential analysis the dataset was subset into hepatocytes only. Prior to differential analysis, RNA modality of the data was normalized by regression out mitochondrial genes percentage and sequencing depth to account for batch effect. Differential expression analysis was performed with ‘MAST’ parameter in Seurat74. Meanwhile, differential accessible region (DAR) analysis for ATAC modality was performed with ‘LR’ parameter in Signac. We use the parameter min.pct = 0.05. For both analyses, genes and peaks are considered significant when p-adjusted < 0.05. Outputs from MAST and DAR analyses are included in Supplementary Data 2.

ChIP-sequencing. The quality of the reads was checked in FastQC ver. 0.11.9. Reads were aligned using BWA ver. 0.7.17 to mouse genome mm10, using the MEM algorithm75. Afterwards, the BAM files were sorted and indexed using Picard ver. 2.27.176. Duplicates and multi-mappers were removed using Picard and Bamtools ver. 2.5.277, with the parameter of mapQuality >28. Filtered BAM files were scored for flag status in Samtools ver. 1.15.175. For the FOXO1 sample, we merged the filtered BAM files into one. Peaks were called using MACS2 ver. 2.2.7.1 with parameter q-value=0.1 and peak called for paired-end dataset, except for merged FOXO1 sample which used cutoff q-value=10-569. Peaks were filtered for blacklisted regions with bedtools 2.30.078. Peak universe table with unique genomic locations from control, caloric restricted, and CORT groups were created for GR data sets by using bedtools Multiple Intersect in Galaxy ver. 2.30.078,79. This table consists of a unified peak list with unique ranges across the genome and containing overlapping ctrl., cal. res., and CORT peak information, including the tag count for each biological replicate. We assumed as called peak in each group, if both replicates had a MACS2 peak and/or ranges with read count > 50. The read count was generated by using featureCounts package, version 2.0.680. Bigwig tracks were generated from filtered BAM file using ‘bamtobigwig’ command from Deeptools ver. 3.5.181. We ran peak annotation in Homer ver. 4.1182. We used mm10 as the reference for assigning peak features to our dataset. For the FOXO1 dataset, we used the peaks called from the merged filtered BAM and overlapped it with our GR peak universe.

Heatmap and tag density plot was produced by using deeptools81. Two biological replicates from GR and FOXO1 were used to generate the respective merged bigWig files. The BED file included the GR unique, FOXO1 unique and GR and FOXO1 common binding sites78.

To generate scatter plots, read counts are generated from featureCounts package80 for all peaks in peak universe. Then, the read counts for each condition are plotted using ggplot259. The ChIP-seq peak lists and overlaps are presented in Supplementary Data 3.

Motif analyses. Motif prediction analysis was performed using HOMER. Motif analysis with default background was performed by motif enrichment and discovery of the provided BED file with randomly selected sequence from HOMER that went through GC normalization and autonormalization before used for the analysis. For custom background analysis, we provided a custom BED file (e.g., CR-unique peaks as the background for CORT-unique peaks and vice versa) to the –bg parameter, which was also GC-normalized and autonormalized. This allowed us to directly compare motif profiles between conditions82. For motif analysis from the ATAC modality, the top 500 peaks from differential analysis result were taken and saved as BED file.

ChIP-MS. Raw mass spectrometry data were analyzed with MaxQuant (v1.6.15.0). Peak lists were searched against mouse Uniprot FASTA databases (UP000000589_10090 Release 2023_05 consensus and additional sequences) combined with 262 common contaminants by the integrated Andromeda search engine. False discovery rate was set to 1% for both peptides (minimum length of 7 amino acids) and proteins. “Match between runs” (MBR) was enabled with a Match time window of 0.7, and a Match ion mobility window of 0.05 min. Relative protein amounts were determined by the MaxLFQ algorithm with a minimum ratio count of two. All statistical analysis was performed using in-house developed python code, based on the automated analysis pipeline of the Clinical Knowledge Graph83. Protein entries referring to potential contaminants, proteins identified by matches to the decoy reversed database, and proteins identified only by modified sites, were removed. LFQ intensity values were normalized by log2 transformation and proteins with less than 70% of valid values in at least one group were filtered out. The remaining missing values were imputed using the MinProb approach (random draws from a Gaussian distribution; width = 0.2 and downshift = 1.8)84. Differentially enriched proteins in each group comparison were identified by SAMR multiclass test with permutation-based FDR correction for multiple hypothesis (FDR<0.01, s0=1, permutations=250), followed by post hoc pairwise comparison unpaired t-tests using the same parameters and permutation-based FDR correction. The interactome was defined by significantly up-regulated proteins in the comparisons. Protein clusters were identified based on their enrichment patterns by comparing the log2 fold change (log2FC) between cal. res. and ctrl. conditions. Proteins showing an absolute log2FC difference of more than 20% were classified according to the positive (cal. res.) or negative (ctrl.) sign of this difference. Proteins with an absolute log2FC difference of less than 20% were considered to have equal enrichment in both conditions. Volcanoplots were generated using the ggplot2 package in R59 while violin plots were created using GraphPad Prism. To generate the scatter plot with ggplot2 and heatmap with pheatmap, fold change (FC) values for non-enriched proteins were set to 0, and log2 of pseudo counts (log2(FC+1)) were calculated to avoid undefined values. Pathway and gene ontology enrichment was done using Metascape and dot plots were generated by using ggplot2 in R. Refer to Supplementary Data 4 for GR-enriched proteins from ChIP-MS in both control and caloric restriction. Protein interaction networks were generated using the STRING database, with proteins from each cluster submitted independently. GR and FOXO1 were manually added to identify proteins linked to FOXO and STAT pathways. The process included isolating primary and secondary interactions with GR and the contributors of the aforementioned pathways85.

Functional annotation. Pathway enrichment and gene ontology annotation were performed by using the gProfiler and Metascape websites, respectively86,87.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript by Makris et al reports an interesting functional link between elevated GC levels seen during calory restriction and gene regulation controlled by calory restriction in the liver (focus on hepatocytes). They specifically observe a gene expression signature in bulk liver (assessed by bulk liver RNAseq) and hepatocytes (assessed by snRNAseq) controlled by calory restriction in a GR-dependent manner (assessed by a hepatocyte-specific GR KO mouse model). And GR needs a metabolic input to regulate a specific part of this gene program (elevated GCs are not sufficient). Also, they observe that some systemic metabolic signatures of calory restriction and calory-restricted induced liver autophagy are compromised by loss of GR expression in hepatocytes. GR cistromic analysis suggests cal res induce some reprogramming of GR occupancy to the genome in the liver and using additional genomic and proteomic approaches they link this to GR cooperation with FOXO1.\nThe findings are certainly very interesting and argues for an important role of hepatic GR for some beneficial effects of calory restriction. The data is well presented and described in the manuscript, yet some parts could be improved to strengthen the data and conclusions. Moreover, the link to GR-FOXO1 cooperation is interesting but less convincing and could be improved by additional experimental data, see comments below.\nSpecific comments to the manuscript, experimental data and data analysis.\nThe abstract states that GR is important for the effects of calory restriction. However, it should be mentioned that the study primarily focuses on effects in liver which is not obvious from the abstract alone. Thus, the sentence “We show that the glucocorticoid receptor (GR) is critical for the effects of caloric restriction” should be modified to include liver/hepatocyte.\nLines 82-85 and fig 1b. First, the cort measurements shown in figure 1b should be shown as non-normalized values (e.g. ng/ml). In this way it is possible to compare values with other studies. Second, the authors state that the difference in GC levels to a lower extend corresponds to differential nuclear correlation. Would be more precise to state that it does not correspond to differential nuclear correlation (extended fig 1d shows no difference). Is overall GR occupancy of chromatin changed? ChIPseq data is presented in figure 4 and a simple boxplot of reads in all GR peaks (ctrl vs cal res) could address this.\nLine 113-114. The authors state that GR disruption does not change cell composition of the liver. However, extended figure 2b suggests higher relative proportion of hepatocytes and lower proportion of other cell types in GR KO, especially under cal res condition. Is this a sampling issue or biological relevant?\nFigure 3 shows data from metabolic characterization of the mice used in the study. The authors show additional data in extended figure 6 including liver TG levels which are elevated in GRKO (this is not sufficiently commented on in the manuscript) and may thus be a source of energy in the liver under cal res. The TG measurements should be commented/described. Can this help explain the seemingly lower effect on liver glycogen levels by cal res in hepatocyte GR KO mice? And this also helps to explain why glucose are higher in cal res in GR KO compared to control.\nFig 3f. Shows data from glycogen measurements. A total of four samples is included in each group, however for other livers measurements (and serum measurements) data from at least 6 biological replicates were shown. Has any data been excluded from the glycogen measurements? Since GR is involved gluconeogenesis one would expect glycogen levels being lower, at least in the ad libitum fed animals.\nLine 209-210, fig3g. Nuclei ploidy is estimated by nuclei size. This is an indirect way to measure ploidy levels. Would be better/accurate to use FACS on isolated nuclei stained with e.g. DAPI. Can be done on nuclei isolated from frozen tissue and thus on the livers samples isolated from the mouse cohort used in the study. This would help verify the indirect measurements. Also, the analysis presented in figure 3g does not indicate any potential variation between biological replication. Biological replicates should be included.\nLine 242-245, fig 4e. The GR ChIPseq interestingly suggests that a significant fraction of GR occupancy of chromatin in cal res is not alone connected to increased GC levels but to additional metabolic effects. First, it would be informative if the authors show 3-5 genome browser examples of peaks near genes which expression is also not only assigned to cort effects but also metabolic effects. Second, would be informative to show the GR ChIPseq data from cal res and cort conditions as a read count scatterplot so the reader can evaluate the difference in GR peak read count density between the two conditions (ctrl and cal res). And plot the GR ChIPseq read count data (all three conditions) from the four groups as boxplots to evaluate the overall peak read counts from all three conditions (ctrl, cal res and cort).\nIn the relation to differential GR occupancy in cort versus cal res conditions presented in figure 3. It would be informative to also perform RNAseq to evaluate any potential differences in gene expression that can be assigned to cort alone and cort in combination with metabolism.\nLine 258-260, fig 4g. Unclear how the relative motif enrichment presented in figure 4g was performed. Not really described in the methods. If the hypothesis is that cort specific GR binding is partially driven by STAT and cal res specific GR binding is driven by FOXO, then one would expect that the logodds motif score (can be calculated by the HOMER pipeline) is different among these two GR peak sets. Is the STAT motif score higher in cort specific GR peaks compared to cal res specific GR peaks. And vice versa for FOXO motifs? And is the GR motif score the same or different among the different GR peaks sets (shared, cal res specific and cort specific)?\nLine 262-263, fig 4h. It is unclear what fig4h shows. What does the dots represent? Also, it is hard to evaluate the difference when data from the individual motifs are not compared directly across conditions. For example, what are the log p-values for STAT motif in the cal res condition and vice versa for FOXO. This comparison should be performed for all shown motifs.\nLine 278-280, fig 5a. Cal res is reported to affect the level of STAT5 and FOXO1 phosphorylation, with strong effect size for STAT5 and somewhat less for FOXO1. Does cal res effect occupancy of FOXO1 to the genome? And does cal res change FOXO1 nuclear localization?\nFig 5c. Y and X axis needs to be better annotated. Unclear what axis shows what condition.\nThe GR ChIP-MS data presented in figure 5 suggests condition dependent GR interaction with FOXO1 (indirect evidence) and STAT5. This observed condition dependent interaction should be validated by co-IP experiments. Could be done on the cross-linked material.\nFig5. Figure legend for panel g and h is not correctly described. Does seem to match what is presented in the figure.\nLine 309-311, fig 5h. The analysis presented is not well explained. What does the dots represent? And each motif should be compared across conditions. As pointed out above a logodds motif score analysis of STAT5 and FOXO1 motifs would be informative – comparing ATAC regions differential accessible in ctrl and cal res. Also, it would be informative with analysis of ATAC signal from GR peaks in the different conditions. One would expect that cal res would increase ATAC signal in GR peaks unique for cal res compared to ctrl.\nIn fig6 the authors present data on FOXO1 occupancy of chromatin using ChIPseq and show data for cal res condition. It is critical that FOXO1 ChIPseq is also performed in the ctrl to analyze if cal res indeed changes FOXO1 occupancy as suggested by the model presented in fig 6h. Similar experiments ChIPseq experiments could be performed for STAT5.\nFig 6g. GR and FOXO1 cooperativity is tested in a reporter assay where FOXO1 binding sites are muted. CREs investigated are shown in extended fig 10. Is the grey bar not placed at a wrong place for Cry1 CRE? Also, for Ppargc1c CRE why can GR activate alone and not activate when FOXO1 is present?\nLine 346-351, fig 6f. A hepatocyte specific FOXO1 knockout model was used to experimentally test the importance of FOXO1 for GR binding to chromatin. Here GR ChIP-qPCR was performed on specific sites. And all analyzed sites show reduced GR binding. But GR sites supposedly not affected by FOXO1 occupancy (GR sites not occupied by FOXO1) were not analyzed as control. This should be done to check potential global effects on GR occupancy. Even better a GR ChIPseq should be performed on the FOXO1 KO background. Also, GR expression levels and serum GC levels should be analyzed to exclude that differential GR occupancy is not explained level of GR expression and its ligand availability.", "reviewer_2": "This study shows that, in a context of energy deprivation with meals timing aligned with the beginning of the mouse active phase, rhythmic/time-dependent glucocorticoid receptor signaling is critical for short-term caloric restriction-induced metabolic reprogramming in young male C57BL/6J mice.\nThis work is unique at the interface of glucocorticoid rhythms and short-term caloric restriction, providing a potential mechanism (needs further validation through in vivo experiments in a comparable timescale) for the greater beneficial effects of circadian alignment of caloric restriction on health outcomes previously published. In general, the authors discuss both consistent and conflicting results with previous literature.\nThe manuscript is written in a clear and well-organized manner. The in vivo and in vitro experiments designed, techniques used, and general methodology are sound, and enable to test the authors’ hypothesis. In general, the conclusions are supported by the data provided. Nevertheless, there are several aspects that need to be clarified or revised in order to make this article suitable for publication. These issues are detailed below.\nTitle section:\n1. The expression “the outcomes of caloric restriction” is too broad for the scope of this article. Please rephrase the title in order to better reflect what was done in this study (i.e., circumscribe it to metabolic reprogramming induced by short-term caloric restriction). For example, “metabolic reprogramming induced by short-term caloric restriction is driven by enhanced glucocorticoid rhythms”.\nResults section:\n2. The different outcomes measured have different sample size. This difference in sample size is understandable between variables such us body weight (n=6) and single-nuclei RNAseq (n=1) due of resources constrains (Ext. Data Fig. 1 and Fig. 1g, respectively). However, it is not clear why sample size is different between corticosterone, insulin and glucose measurements, to mention one example (Fig. 1b and Ext. Data Fig. 1b-c, respectively). Did the authors select samples of specific animals? Were some animals excluded? Were some samples lost? Please revise this aspect throughout the manuscript and clearly state the rationale behind these differences in sample size for all outcomes measured.\n3. Can the authors provide the food consumption trajectories of the different experimental groups (WT under control diet, GR under control diet, etc.)? Did the authors check at which ZT the cal. res. animals finished their meals? If available, please provide this information in methods section.\n4. In lines 83-85 the authors state: “To a lower extent, these diurnal hormone secretion patterns corresponded to differential nuclear translocation of the GR in the livers of cal.res. and control mice (Extended Data Fig. 1d).”. At which ZT do the authors detect this differential nuclear translocation of the GR? Is this difference statistically significant? If significant, indicate the corresponding p-value in the figure. If not, clearly state it in the manuscript.\n5. Regarding Ext. Data Fig. 3. Please provide the scale corresponding to the dendrograms. Also, indicate which unsupervised hierarchical clustering method was used.\n6. In lines 139-140 the authors state “Unbiased PCA and hierarchical clustering analyses showed that samples from the same time point clustered together under both dietary regimens.”. Please add “in general” before “clustered together under both…”, as in the way the sentence is written now it does not hold true for some time points/genotypes, specifically in control diet mice. In Ext. Data Fig. 3a, note how WT mice of ZT0 and ZT20 are more similar between each other than GR mice of ZT0 and ZT20 (i.e., this is a case in which samples from the same time point do not cluster together. This indicates that, at these time points, the differences promoted by the genotype are more pronounced than the differences induced by the ZT). Otherwise, indicate which is the cutoff distance considered to establish that the same time points cluster together.\n7. How do the authors relate the results regarding autophagy shown in Fig. 3d (lines 188-193) with those in Fig. 2e and Ext. Data Fig. 4g?\n8. In the legend of Ext. Data Fig.7, please indicate the diet to which CORT animals were subjected (in the main manuscript it is indicated that these animals were subjected to control diet, but this information is missing in the figure legend). See also the previous comment regarding the rationale for the differences in sample size according to the outcome.\n9. In Fig. 1b, even when the peak at ZT12 in the cal. res. group seems numerically distinguishable from the other group-mean values, it should be noted that with a sample size of 3, a two-way ANOVA for multiple comparisons is way underpowered (this is a 2x6 two-way ANOVA, where 2 corresponds to the levels of the factor “diet” and 6 to the levels of the factor “time points”). I recommend that all group-mean values are plotted for reference (as they are now), but the differences between diets are tested at a specific ZT only and/or the differences between specific ZT are tested within a given diet (i.e., specify which contrasts you are interested in testing, instead of performing multiple comparisons, and limit the scope of the results to that). Also, although the time-dependent pattern is easier to recognize by plotting lines connecting the measured time points, this can be misleading as these are not repeated measures of the same animals. I recommend removing the lines connecting the group-mean values.\n10. In the legend of Fig. 2a, please indicate what the ellipsoids and triangles in the PCAs mean.\n11. I recommend that amplitudes distributions in Fig. 2f are shown for each ZT instead of combining ZT0-ZT20, as in the way they are plotted now can be misleading.\n12. In the legend of Fig. 3a-c, please specify the sample size. See also the previous comment regarding the rationale for the differences in sample size according to the outcome.\n13. In the legend of Fig. 6a, what is the sample size of n=3 referring to? This sample size is not consistent with the dots observable in the boxplot. It is expected that the sample size to be stated is the one that corresponds to analysis. Please amend and/or clarify this inconsistency. Also, do the dots in the boxplot show all observations?\n14. Fig. 6e and Ext. Data Fig. 10e, please indicate what the colors of the pie charts indicate.\n15. Ext. Data Fig. 1a, as in previous comments, a two-way ANOVA for multiple comparisons (2x9, where 2 corresponds the levels of the factor “diet” and 9 to the levels of the factor “week”) is underpowered if the sample size was n=6. Comparisons should be limited to the time points of interest. Moreover, are these repeated measures of the same animals? If so, ANOVA with repeated measures should be performed to properly account for the lack of independence between measures of the same animal. Also, please indicate what type of error bars are shown and use upper and lower error bars as in Ext. Data Fig. 1b-c. Please revise this last aspect throughout all figures.\n16. In the legend of Ext. Data Fig. 2a-d and Ext. Data Fig. 4a, please indicate sample size.\n17. Ext. Data Fig. 4g, please use the same scale in the x-axis of all four barplots, for ease of comparison between the groups (diet, genotype, interaction and all).\n18. Fig. 1i and Ext. Data Fig. 6d-e, these figures are not referenced in the main text. Are they needed?\n19. Fig. 4b-d, Fig. 5a, and Fig. 6a and f, please clearly indicate which type of ANOVA was used in each case.\n20. Fig. 4d (quantification), please indicate what the “+” symbol in the box of the boxplots mean.\nMethods section:\n21. Lines 554-555, the panels of Figs. 1 and 3 are indicated with uppercase but panels are referenced with lowercase in the rest of manuscript.\nPotential limitations of the study:\n22. Results regarding characterization and modulation of rhythms by the different factors (knock out models, dietary intervention, etc.) should be taken with caution. The sampling period encompasses less than 24 hours (ZT0 to ZT20), when ideally a reliable outcome requires sampling every few hours at least during a 24-h period, which represents a complete cycle (or, even better, 48-h or 72-h to assess consistency).\n23. Throughout the manuscript the authors discuss the relevance of their results in the context of aging, but their experiments were performed in 3-month-old mice subjected to short-term (less than 2 months) caloric restriction. Considering that the authors demonstrate that the energetic context is critical in the role of the Glucocorticoid Receptor (GR), what can they discuss regarding this role of GR and the changes in the energetic context with aging?\n24. Sample size in single-nuclei RNAseq is of 1, please highlight this limitation in the manuscript and/or indicate that results need to be further validated.", "reviewer_3": "This is an interesting study, aiming to elucidate how nutrition reroutes hormonal responses and how GR cistromic reprogramming could support anti-aging programs. The authors propose that the GR plays a key role in promoting the healthy metabolic effects of energy deprivation by reshaping gene expression rhythms in response to caloric restriction.\n1. The study elucidates mechanisms of transcriptional reprogramming of hepatic energy metabolism during caloric restriction by glucocorticoids. The results show that cal.res. rewires the hepatic transcriptome through mechanisms dependent on GR.\nThe results are impressive, but not too surprising since glucocorticoids are crucial for the homeostasis of the organism and adaptation to the external environment.\n2. Massive responses are shown, but it is still unclear which of these that are driving the phenotypic changes. Is it possible to better identify the most important downstream responses?\n3. ChIP experiments in mouse livers indicate that low insulin signalling leads to increased nuclear localization and activity of FOXO1, which enables GR to bind to and regulate target genes only in caloric restriction. Which of these genes are most important?\n4. The experimental protocol (30% Cal restriction) leads to massive (30%) weight loss (studies with such weight loss are not accepted by the ethical committees in all countries!). Are these mechanisms seen after more modest Cal restriction?"}, "author_response": "NCOMMS-24-46730-T\nHepatic metabolic reprogramming during short-term caloric restriction is driven by enhanced\nglucocorticoid rhythms.\nby Makris K. et al.\nWe sincerely thank the reviewers for their thorough and constructive evaluation of our manuscript.\nTogether with their time and efforts, we also appreciate their recognition of the significance of our\nfindings. In response, we have now carefully addressed all the comments, clarified uncertainties, and\nperformed most of the proposed experiments as suggested, which we believe have significantly\nstrengthened our conclusions.\nSpecifically, we can provide additional lines of evidence supporting the functional cooperation between\nGR and FOXO1 under caloric restriction, including nuclear co-localization, physical interaction, and\nspecificity of FOX motif enrichment in cal. res.-specific GR cistromes. In contrast, by deepening our\nanalyses of the CORT treatment, we revealed a distinct set of GR targets, aligning with the known\ndetrimental effects of exogenous glucocorticoids. Our findings underscore the context-specific nature\nof GR action in the liver.\nWe trust these revisions have substantially improved the manuscript and have addressed the list of\nconcerns. All changes in the manuscript are marked in red.\nPlease find our specific responses to each of your major and minor points below:\nPoint by Point Response\nReviewer #1 (Remarks to the Author)\nThe manuscript by Makris et al reports an interesting functional link between elevated GC levels seen\nduring calory restriction and gene regulation controlled by calory restriction in the liver (focus on\nhepatocytes). They specifically observe a gene expression signature in bulk liver (assessed by bulk liver\nRNAseq) and hepatocytes (assessed by snRNAseq) controlled by calory restriction in a GR-dependent\nmanner (assessed by a hepatocyte-specific GR KO mouse model). And GR needs a metabolic input to\nregulate a specific part of this gene program (elevated GCs are not sufficient). Also, they observe that\nsome systemic metabolic signatures of calory restriction and calory-restricted induced liver autophagy\nare compromised by loss of GR expression in hepatocytes. GR cistromic analysis suggests cal res induce\nsome reprogramming of GR occupancy to the genome in the liver and using additional genomic and\nproteomic approaches they link this to GR cooperation with FOXO1.\nThe findings are certainly very interesting and argues for an important role of hepatic GR for some\nbeneficial effects of calory restriction. The data is well presented and described in the manuscript, yet\nsome parts could be improved to strengthen the data and conclusions. Moreover, the link to GR-FOXO1\ncooperation is interesting but less convincing and could be improved by additional experimental data,\nsee comments below.\nSpecific comments to the manuscript, experimental data and data analysis.\n1) The abstract states that GR is important for the effects of calory restriction. However, it should be\nmentioned that the study primarily focuses on effects in liver which is not obvious from the abstract\nalone. Thus, the sentence “We show that the glucocorticoid receptor (GR) is critical for the effects of\ncaloric restriction” should be modified to include liver/hepatocyte.\nPoint well taken, the abstract and the title have been modified to include the liver-specific focus.\n2) Lines 82-85 and fig 1b. First, the cort measurements shown in figure 1b should be shown as non-\nnormalized values (e.g. ng/ml). In this way it is possible to compare values with other studies. Second,\nthe authors state that the difference in GC levels to a lower extend corresponds to differential nuclear\ncorrelation. Would be more precise to state that it does not correspond to differential nuclear correlation\n(extended fig 1d shows no difference). Is overall GR occupancy of chromatin changed? ChIPseq data is\npresented in figure 4 and a simple boxplot of reads in all GR peaks (ctrl vs cal res) could address this.\nRegarding the corticosterone measurements: We fully agree about the importance of showing non-\nnormalized values. We have now included absolute corticosterone concentrations (ng/ml) in Ext. Data\nFig. 1g, derived from a new cohort of mice sacrificed at the hormone peak. Our initial measurements\nhad yielded a standard curve with lower absolute values than typically observed in our studies and in\nthe literature, which we attribute to some technical constraints. Nevertheless, the relative differences\nbetween conditions and time points remain consistent.\nRegarding the GR nuclear localization and the subsequent chromatin occupancy: In our hands, when\nusing Western Blots, to capture GR at time points with lower GC concentrations, the bands at ZT12\ntend\nto saturate, making accurate quantification of the differences very difficult. To address this, we have\nincluded an immunoblot representing only the nuclear localization of GR at the peak of the hormone\n(ZT12). This shows that, our statement is correct and validated by experimental evidence (Ext. Data Fig.\n1e). Under caloric restriction, the GR exhibits greater nuclear stability, which translates into greater\nchromatin occupancy, as shown by the number of peaks detected in comparison to control diet.\nFollowing your suggestion, we further corroborated this observation by including a violin plot showing\nnormalized read counts of our GR cistromes (Fig. 4g) and scatter plots that correlate signal intensities\nwithin the peak universe (Ext. Data Fig 7h).\n3) Line 113-114. The authors state that GR disruption does not change cell composition of the liver.\nHowever, extended figure 2b suggests higher relative proportion of hepatocytes and lower proportion of\nother cell types in GR KO, especially under cal res condition. Is this a sampling issue or biological\nrelevant?\nRegarding the changes in the number of nuclei isolated per condition in Extended Data Fig. 2d, in our\nexperimental experience, the total number of nuclei captured in droplet-based approaches (i.e. 10x\nChromium, 10x Genomics) may vary between samples. However, we did not observe major changes in\nthe percentage of hepatocytes in the GRLKO. To clarify, we have generated new plots in which the\nproportions of cell types have been calculated per sample and per condition. The percentage of\nhepatocytes in wildtype and GRLKO mice is ~80% in both cases (Supplementary Table 2).\nExtra Figure for Reviewer #1: Bar plots showing percentages of cell types in individual samples and per condition\nfrom WT and GRLKO mouse livers under ctrl. and cal. res. diets.\n4) Figure 3 shows data from metabolic characterization of the mice used in the study. The authors show\nadditional data in extended figure 6 including liver TG levels which are elevated in GRKO (this is not\nsufficiently commented on in the manuscript) and may thus be a source of energy in the liver under cal\nres. The TG measurements should be commented/described. Can this help explain the seemingly lower\neffect on liver glycogen levels by cal res in hepatocyte GR KO mice? And this also helps to explain why\nglucose are higher in cal res in GR KO compared to control.\nFig 3f. Shows data from glycogen measurements. A total of four samples is included in each group,\nhowever for other livers measurements (and serum measurements) data from at least 6 biological\nreplicates were shown. Has any data been excluded from the glycogen measurements? Since GR is\ninvolved gluconeogenesis one would expect glycogen levels being lower, at least in the ad libitum fed\nanimals.\nWe concur and we have now introduced a comment regarding TG levels in our cohort in the main text\n(lines 210-211). We and others (Quagliarini F, Mol. Cell. 2019; Correia CM; Endocrinology. 2023) had\ndocumented the accumulation of liver triglycerides in GRLKO mice in control and high-fat fed conditions.\nThis observation is seemingly reproduced in Ext. Data Fig. 6c in our control mice (at the end of a 12 h\ndaylight-fasting period). Under caloric restriction, the liver dramatically reduces its production of new\nlipids by suppressing de novo lipogenesis, primarily driven by lower insulin levels and heightened\nAMPK activity. In such an environment, the differences in TG accumulation between GRLKO and\nwildtype mice become less pronounced, highlighting that the regulatory role of glucocorticoids in liver\nTG metabolism is most evident when the animal is not experiencing a negative energy balance.\nRegarding the data you observe—namely, that in calorically restricted GRLKO mice liver glycogen levels\nappear less reduced and blood glucose levels are higher compared to wildtypes—this could be\ninterpreted in the context of the established role of hepatic GR in metabolic adaptation. Conceivably,\nGR plays a “permissive” role—enhancing the responsiveness of hepatocytes to other hormones like\nglucagon, catecholamines, and insulin—which may help the liver to adjust rapidly to shifts in energy\nstates. In the context of severe energy deficit, the permissive effect that normally allows a rapid hepatic\nresponse to other circulating hormones, is dampened. As a result, even under caloric restriction, when\ncirculating insulin levels fall dramatically, the liver in GRLKO mice may not properly mobilize glycogen\nstores or stimulate gluconeogenesis. Accordingly, we demonstrated (Fig. 1) how GR absence could lead\nto an inappropriate gene expression profile during caloric restriction, where the liver fails to fully engage\nfasting pathways.\nFinally, concerning the glycogen measurement sample size: We used n=4 for glycogen measurements\nversus n≥6 for other parameters due to technical considerations. The colorimetric glycogen assay\nshowed low variability in our hands, enabling detection of statistically significant differences with fewer\nsamples. No data points were excluded from the analysis.\n5) Line 209-210, fig3g. Nuclei ploidy is estimated by nuclei size. This is an indirect way to measure\nploidy levels. Would be better/accurate to use FACS on isolated nuclei stained with e.g. DAPI. Can be\ndone on nuclei isolated from frozen tissue and thus on the livers samples isolated from the mouse cohort\nused in the study. This would help verify the indirect measurements. Also, the analysis presented in\nfigure 3g does not indicate any potential variation between biological replication. Biological replicates\nshould be included.\nWe appreciate the reviewer’s suggestions to improve our nuclear ploidy assessment. We acknowledge\nthat FACS analysis of DAPI-stained nuclei provides a more direct measurement of ploidy than our initial\nsize-based approach. Following this recommendation, we conducted comprehensive analysis using\nmultiple complementary approaches.\nWe performed FACS analysis on DAPI-stained nuclei isolated from flash-frozen liver samples. This\nrevealed no significant differences in the proportion of diploid (2n) nuclei between control and caloric\nrestriction conditions in WT mice (NRF: 73.13% ± 7.14%; CR: 67.2% ± 3.72%; n=5 biological\nreplicates per group) (Panel A).\nHowever, an important distinction emerged: DAPI staining detects all cell nuclei present in the liver\n(both parenchymal and non-parenchymal), whereas HNF4α staining, which we used in our initial\nanalysis, specifically labels hepatocyte nuclei. This difference proved critical for interpreting the results.\nTo provide a more comparable approach with flow cytometry, we reanalyzed our IHC images using\nVisio Pharm, an AI-based program for image analysis. This allowed us to expand our analysis to whole\nliver sections, substantially increasing the number of analyzed nuclei to be comparable with FACS.\nWe re-analyzed the IHC images using both DAPI (staining all nuclei, for comparison with FACS) and\nHNF4α (to assess hepatocyte-specific nuclei, replicating our initial analysis but with more nuclei).\nThe IHC re-analysis revealed consistent results across methodologies:\n1. DAPI-based histological analysis showed higher percentages of diploid nuclei (70.6%) in\ncontrol livers, closely matching our flow cytometry results (73.1%) (Panel B).\n2. HNF4α-based analysis confirmed our previous data showing ~50% diploid hepatocytes in\ncontrol livers, consistent with our original findings (Panel C).\nThese results demonstrate that our original nuclear size-based approach using HNF4α accurately\nidentified hepatocyte-specific nuclear ploidy distribution, whereas DAPI staining captures all liver cell\ntypes, including the predominantly diploid non-parenchymal cells.\nAs suggested by the reviewer, we have replaced the original analysis shown in Figure 3g (presented here\nas Panel D for reference) with a more comprehensive analysis that includes individual biological\nreplicates (Panel E). This new analysis incorporates a substantially larger number of nuclei and clearly\ndisplays the variation between biological samples. Additionally, we have included the complementary\nDAPI-based analysis in the supplementary materials (Ext. Data Fig. 6d,e) to provide further validation\nof our methodology\nExtra Figure for Reviewer #1: Multi-method analysis of hepatic nuclear ploidy; comparison of FACS data (A)\nversus immunostainings in murine livers, DAPI-only (B) and DAPI+HNF4 staining (C). For reference, original\n(D) and new analysis (E) are also shown. Violin plots representing individual measurements from different\nbiological replicates, in WT control and caloric restriction samples.\n6) Line 242-245, fig 4e. The GR ChIPseq interestingly suggests that a significant fraction of GR\noccupancy of chromatin in cal res is not alone connected to increased GC levels but to additional\nmetabolic effects. First, it would be informative if the authors show 3-5 genome browser examples of\npeaks near genes which expression is also not only assigned to cort effects but also metabolic effects.\nSecond, would be informative to show the GR ChIPseq data from cal res and cort conditions as a read\ncount scatterplot so the reader can evaluate the difference in GR peak read count density between the\ntwo conditions (ctrl and cal res). And plot the GR ChIPseq read count data (all three conditions) from\nthe four groups as boxplots to evaluate the overall peak read counts from all three conditions (ctrl,\ncal res and cort).\nIn order to provide a more robust quantification of chromatin bound GR, we have now included\nrepresentative genome tracks in Ext. Data Fig. 7i showing GR-occupied sites near genes that respond to\ncaloric restriction and/or corticosterone treatment. As requested, we have included a violin plot showing\nnormalized read counts of our GR cistromes (Fig. 4g), along with scatter plots correlating signal\nintensities within the peak universe (Ext. Data Fig 7h). Our analysis indicates that GR binding intensities\ngenerally correlate with circulating corticosterone levels, with both cal. res. and CORT groups showing\nhigher binding strength than controls (Fig. 4g).\n7) In the relation to differential GR occupancy in cort versus cal res conditions presented in figure 3. It\nwould be informative to also perform RNAseq to evaluate any potential differences in gene expression\nthat can be assigned to cort alone and cort in combination with metabolism.\nWe followed the reviewer’s suggestion to further explore the differential outcomes of GC elevation\nunder distinct physiological contexts. We hence carried out RNA-seq on corticosterone treated and\ncontrol livers to assess transcriptional changes induced by CORT exposure.\nDifferential gene expression analysis using DEseq2 identified a set of deregulated genes, which we then\ncompared with those differentially expressed in caloric restricted livers (Ext. Data 1h,i). About 50% of\nthe up- and down-regulated genes by CORT treatment were in common with the caloric restriction\ntranscriptome, reflecting the strong transcriptional response to high glucocorticoids and the shared GR\ngained binding to the chromatin (Fig. 4g,h).\nImportantly, we identified distinct sets of genes uniquely regulated upon corticosterone injections (Fig.\n4g,h and Ext. Data Fig. 7f,g). This divergence is manifested by the strikingly different phenotypic\nsignatures induced by the two treatments (Ext. Data Fig 7a-e). It is well documented that exogenous\nglucocorticoids promote lipogenesis and insulin resistance, leading to lipotoxicity and oxidative stress.\nConsequently, it is not surprising to see pro-inflammatory & pro-fibrotic pathways exclusively enriched\nin the CORT group. Moreover, our data supports the notion these pathways may be directly activated by\nGR binding to liver chromatin, because CORT-specific ChIP-seq peaks revealed an enrichment of\ntranscription factors motifs associated with inflammation (STAT, IRF) and fibrosis (SMAD).\n8) Line 258-260, fig 4g. Unclear how the relative motif enrichment presented in figure 4g was\nperformed. Not really described in the methods. If the hypothesis is that cort specific GR binding is\npartially driven by STAT and cal res specific GR binding is driven by FOXO, then one would expect\nthat the logodds motif score (can be calculated by the HOMER pipeline) is different among these two\nGR peak sets. Is the STAT motif score higher in cort specific GR peaks compared to cal res specific GR\npeaks. And vice versa for FOXO motifs? And is the GR motif score the same or different among the\ndifferent GR peaks sets (shared, cal res specific and cort specific)?\nLine 262-263, fig 4h. It is unclear what fig4h shows. What does the dots represent? Also, it is hard to\nevaluate the difference when data from the individual motifs are not compared directly across conditions.\nFor example, what are the log p-values for STAT motif in the cal res condition and vice versa for FOXO.\nThis comparison should be performed for all shown motifs.\nApologies for the confusion. We have added more details in the method section for the motifs discovery\n(lines 947-950). Briefly, we ran HOMER motif analysis with customized background instead of using\nHOMER default; specifically:\n1. For ‘cal. res. Unique’: all the peaks in the GR peak universe (21,870) minus the unique peaks\nin caloric restriction (1,594).\n2. For ‘CORT unique’: all the peaks in the GR peak universe (21,870) minus the unique peaks in\nCORT treatment (3,157).\nWe recognize that this comparison of p-values between groups is rather indirect, and we followed your\nsuggestion to evaluate the log-odds score for FoxO1, Stat5, and GR in GR peaks across all conditions;\ncontrol (12,280), cal. res.-unique (1,594), and CORT-unique (3,157). As shown in Panel A, we did not\nobserve a clear difference in scores distribution for any of the scanned motifs, except for a modest trend\ntoward lower ranks for Stat5 in the cal. res. specific peaks.\nWe agree with the reviewer that log-odds scores capture motif strength across peaks by reflecting the\nscores of individual sequences and can indicate affinity shifts between conditions, however they do not\ndirectly assess enrichment significance (whether a motif is significantly more enriched in one condition\nthan another).\nTherefore, we employed a more direct comparative approach by performing differential motif\nenrichment analyses based on a ‘Reciprocal Background Analysis’, which allows for a direct statistical\ncomparison between experimental conditions by:\n1. Using ‘cal. res. Unique’ peaks as background for the CORT analysis.\n2. Using ‘CORT unique’ peaks as background for the cal. res. analysis.\nThis reciprocal approach is particularly powerful for detecting true differential enrichment as it directly\ncompares the two biological conditions against each other rather than against a more general\nbackground.\nUsing this approach, we found significant over-representation of liver nuclear receptors (HNF4, PPAR,\nGR), as well as clock and forkhead factors (FOX), in the cal. res. group. In contrast, STATs and immune-\nrelated factors were enriched in the CORT-specific peaks (previous Fig. 4h now 4i).\nSince it is difficult to discriminate motif signatures among the several STAT and FOX family members,\nwe compiled the p-values of all motifs assigned to each member of the family in the respective groups.\nAs shown in Panel B, we found that Fox and STAT motifs are significantly more enriched in the cal.\nres. and CORT condition, respectively (also in Ext. Data Fig. 8d,e).\nRegarding GR binding motifs, we detected an under-representation in the CORT peaks. This finding is\nconsistent with our previous observation that higher GR stabilization in the nucleus, resulting from\nexogenous treatment, can lead to binding at sites with less conserved GREs (glucocorticoid responsive\nelements). This allows for more widespread genomic binding during pharmacological activation\ncompared to the more selective binding under physiological caloric restriction.\nExtra Figure for Reviewer #1: A. Log odd scores distribution, as calculated by HOMER in each peak within the\nrespective groups (ctrl #12,280, cal. res. #1,594, and CORT #3,157 peaks), were plotted for FoxO1, STAT5 and\nGR motifs. B. Log-transformed p-value distributions, calculated using reciprocal background analysis for cal.\nres.- and CORT-specific peaks, are shown. The plotted data represents motif clusters belonging to the Fox,\nSTAT, and GR transcription factor families.\n9) Line 278-280, fig 5a. Cal res is reported to affect the level of STAT5 and FOXO1 phosphorylation,\nwith strong effect size for STAT5 and somewhat less for FOXO1. Does cal res effect occupancy of\nFOXO1 to the genome? And does cal res change FOXO1 nuclear localization?\nYes, we hypothesized that the observed decrease in insulin levels and the consequent reduction in\nFOXO1 phosphorylation (Extended Data Fig. 1b and Fig. 5a) indicate an increase in nuclear FOXO1.\nWe have now confirmed this by immunoblotting FOXO1 in nuclear extracts from livers of mice fed\ncontrol or caloric restriction diet (Extended Data Fig. 9a). Unfortunately, due to limitation of\ncommercially available resources, we were not able to successfully ChIP FOXO1 in control livers. As a\nresult, we can only infer an expansion of chromatin binding under caloric restriction relative to the\ncontrol condition based on indirect evidence.\n10) Fig 5c. Y and X axis needs to be better annotated. Unclear what axis shows what condition.\nWe have now revised the axis labels; the x-axis represents log2(FC+1(GR vs IgG)) in caloric restriction\nconditions, while the y-axis shows in log2(FC+1(GR vs IgG)) in control conditions.\n11) The GR ChIP-MS data presented in figure 5 suggests condition dependent GR interaction with\nFOXO1 (indirect evidence) and STAT5. This observed condition dependent interaction should be\nvalidated by co-IP experiments. Could be done on the cross-linked material.\nWe appreciate the reviewer's suggestion and fully agree with this point. Indeed, we repeatedly detected\nthe STAT5-GR interaction in our proteomics experiments, including the current study and in Quagliarini\net al., Mol. Cell. 2019. We have now also validated GR’S enhanced physical interaction with FOXO1\nunder caloric restriction by co-IP in isolated mouse liver nuclei (in two independent biological\nreplicates). Our evidence for the nutritional regulation of GR's protein interaction network has been\nincorporated into the revised manuscript as Fig. 5e and Extended Data Fig. 9g.\n12) Fig5. Figure legend for panel g and h is not correctly described. Does seem to match what is\npresented in the figure.\nThe legend is now corrected to match the content shown, now in Ext. Data Fig. 10.\n13) Line 309-311, fig 5h. The analysis presented is not well explained. What does the dots represent?\nAnd each motif should be compared across conditions. As pointed out above a logodds motif score\nanalysis of STAT5 and FOXO1 motifs would be informative – comparing ATAC regions differential\naccessible in ctrl and cal res. Also, it would be informative with analysis of ATAC signal from GR peaks\nin the different conditions. One would expect that cal res would increase ATAC signal in GR peaks\nunique for cal res compared to ctrl.\nWe have taken the Reviewer’s comment into careful consideration and here is a clarification for: In the\ninitial version of the manuscript, we used GIGGLE to overlap our top 500 up- and down-regulated\naccessible regions with published transcription factor (TF) cistromes, accessed through the CistromeDB\nToolkit portal (http://dbtoolkit.cistrome.org/) (Original_Fig. 5h, now replaced).\nFollowing the reviewer's suggestion, we analyzed log-odds scores for FoxO1, Stat5, and GRE motifs in\nthe top 500 accessible sites gained or lost under caloric restriction (Panel A). Similar to our findings\nwith GR peaks, detailed above, this analysis showed no apparent differences in distribution between\nconditions.\nDespite the limitations of log-odds analyses in detecting differences, the motif enrichment analysis using\nthe reciprocal background approach revealed clear patterns that align with our overall findings about\ndifferential transcription factor activity (Panel B).\nIn the revised version, after consideration, we have now replaced the GIGGLE analysis with HOMER\nmotif searches using customized backgrounds, as shown in the Ext. Data Fig. 10c,d.\nExtra Figure for Reviewer #1: A. Log odd scores distribution, as calculated by HOMER in each peak within the respective\ngroups (open: top 500 regions with increased accessibility in cal. res. compared to ctrl.; closed: top 500 regions with decreased\naccessibility in cal. res. compared to ctrl.), were plotted for FoxO1, STAT5 and GR motifs. B. Log-transformed p-value\ndistributions, calculated using reciprocal background analysis for open and closed regions, are shown. The plotted data\nrepresents motif clusters belonging to the Fox, STAT, and GR transcription factor families.\n14) In fig6 the authors present data on FOXO1 occupancy of chromatin using ChIPseq and show data\nfor cal res condition. It is critical that FOXO1 ChIPseq is also performed in the ctrl to analyze if cal res\nindeed changes FOXO1 occupancy as suggested by the model presented in fig 6h. Similar experiments\nChIPseq experiments could be performed for STAT5.\nWe agree on the need to rigorously determine whether caloric restriction increases FOXO1 chromatin\nbinding. Unfortunately, generating comparable FoxO1 ChIP-seq data in control samples has been\ntechnically challenging due to the difficulty of detecting endogenous FOXO1 in vivo (see our response\nin 9). The low efficiency of available antibodies for FOXO1 immunoprecipitation has significantly\nhindered efforts to map its genomic binding sites (see Kitamoto T, Proc Natl Acad Sci U S A. 2021).\nTo date, this limitation prevented us to perform a direct comparison between dietary conditions by ChIP\nor immunofluorescence in liver sections. However, our caloric restriction FOXO1 ChIPseq dataset\nreveals that those FOXO1 binding sites that overlap with GR display ‘diet-sensitive’ behavior, as\nevidenced by significantly increased GR ChIP signal under caloric restriction compared to control diet\n(Fig. 6d). This supports the notion that the transcription factor binding responds dynamically to energetic\nshifts, and aligns with our earlier findings on STAT5, where high-fat diet (HFD) feeding enhanced GR–\nSTAT5 crosstalk, pointing that transcription factors cooperation is context-dependent (high vs low\nenergy) (Quagliarini F, Mol. Cell. 2019).\nDespite the current limitations, we validate the GR-FOXO1 caloric restriction shift by localizing\nendogenous FOXO1 in the nucleus of ctrl. and cal. res. livers by western blot of both nuclear extracts\nand GR-immunoprecipitates, and in vitro by immunofluorescence in starved primary hepatocytes (Fig.\n5e,f).\n15) Fig 6g. GR and FOXO1 cooperativity is tested in a reporter assay where FOXO1 binding sites are\nmuted. CREs investigated are shown in extended fig 10. Is the grey bar not placed at a wrong place for\nCry1 CRE? Also, for Ppargc1c CRE why can GR activate alone and not activate when FOXO1 is\npresent?\nGreatly appreciated, there was indeed a labeling error. The GR-FOXO1 co-bound Cry1 CRE is now\ncorrectly highlighted in Fig. 6f.\nRegarding the Ppargc1a CRE activation by GR: the data may suggest that when FOXO1 is unable to\nbind DNA, it can act dominant-negative manner through protein-protein interactions, potentially\ninterfering with GR and preventing its functional engagement with chromatin. In the mutant, GR can\nstill bind and activate the reporter but the synergistic activation of GR-FOXO1 co-transfection seen in\nthe wildtype is abolished.\n16) Line 346-351, fig 6f. A hepatocyte specific FOXO1 knockout model was used to experimentally test\nthe importance of FOXO1 for GR binding to chromatin. Here GR ChIP-qPCR was performed on specific\nsites. And all analyzed sites show reduced GR binding. But GR sites supposedly not affected by FOXO1\noccupancy (GR sites not occupied by FOXO1) were not analyzed as control. This should be done to\ncheck potential global effects on GR occupancy. Even better a GR ChIPseq should be performed on the\nFOXO1 KO background. Also, GR expression levels and serum GC levels should be analyzed to exclude\nthat differential GR occupancy is not explained level of GR expression and its ligand availability.\nPoint well taken. We didn’t detect any mRNA expression changes of the Nr3c1 gene encoding GR,\nunder\nour experimental conditions in hepatocyte-specific FoxO1 knockouts (FoxO1LKO) and wildtype\nlittermates (WT), consistent with previous reports (Kitamoto T, Proc Natl Acad Sci U S A. 2021) (Panel\nA). As suggested, we further assayed corticosterone concentrations and validated that GC response to\ncaloric restriction remains intact in mutant mice (Panel B). However, we understand the reviewer’s\nconcerns and we now decided to remove this figure panel from the revised version of the manuscript.\nNevertheless, we remain confident that the GR–FOXO1 axis is a key mediator of the caloric restriction\nresponse at the molecular level. This is supported by multiple independent lines of evidence: (1) Motif\nenrichment analysis of GR cistromes and ATAC-seq profiles reveals increased FOX and GR responsive\nelements in cal. res.-specific sites (Fig. 4i and Ext. Data Fig. 8d,e and 10c); (2) GR interactome profiling\n(ChIP-MS) and co-immunoprecipitation assays demonstrate enhanced GR–FOXO1 interactions during\ncaloric restriction (Fig. 5c-e and Ext. Data Fig. 9f,g); (3) Context-specific GR–FOXO1 coordination is\nindirectly evidenced by immunofluorescence in primary hepatocytes, where FOXO1 remains\ncytoplasmic in fed conditions but shows nuclear enrichment during starvation, which is further enhanced\nby GR ligand treatment (Fig. 5f); (4) FoxO1 target genes are significantly downregulated in GR\nknockout mice under caloric restriction, providing a more integrated and functionally relevant readout\nof GR–FOXO1 crosstalk and suggesting a GR-dependent reinforcement of FOXO1 signaling (Fig. 6a);\n(5) ChIP-seq co-occupancy analyses reveal that GR and FOXO1 co-bind to genomic regions showing\nenhanced GR binding during caloric restriction, indicating synergistic recruitment at functionally\nrelevant loci (Fig. 6c,d); (6) Luciferase reporter assays show that mutation of FOX motifs abolishes GR-\nmediated transcriptional activity at selected cal. res.-responsive loci (Fig. 6f).\nWe believe that together, these findings underscore the functional relevance of the GR–FOXO1 axis in\norchestrating transcriptional responses to caloric restriction and motivate further exploration of the\nmechanistic basis behind this interaction.\nExtra Figure for Reviewer #1: Analyses of WT and Foxo1LKO livers, on ctrl. or cal. res. at ZT12 A. RT-qPCR of FoxO1 and\nNr3c1 targets, mRNA expression was normalized to Rplp0. B. Circulating corticosterone levels measured by ELISA.\nReviewer #2 (Remarks to the Author)\nThis study shows that, in a context of energy deprivation with meals timing aligned with the beginning\nof the mouse active phase, rhythmic/time-dependent glucocorticoid receptor signaling is critical for\nshort-term caloric restriction-induced metabolic reprogramming in young male C57BL/6J mice.\nThis work is unique at the interface of glucocorticoid rhythms and short-term caloric restriction,\nproviding a potential mechanism (needs further validation through in vivo experiments in a comparable\ntimescale) for the greater beneficial effects of circadian alignment of caloric restriction on health\noutcomes previously published. In general, the authors discuss both consistent and conflicting results\nwith previous literature.\nThe manuscript is written in a clear and well-organized manner. The in vivo and in vitro experiments\ndesigned, techniques used, and general methodology are sound, and enable to test the authors’\nhypothesis. In general, the conclusions are supported by the data provided. Nevertheless, there are\nseveral aspects that need to be clarified or revised in order to make this article suitable for publication.\nThese issues are detailed below.\nTitle section:\n1. The expression “the outcomes of caloric restriction” is too broad for the scope of this article. Please\nrephrase the title in order to better reflect what was done in this study (i.e., circumscribe it to metabolic\nreprogramming induced by short-term caloric restriction). For example, “metabolic reprogramming\ninduced by short-term caloric restriction is driven by enhanced glucocorticoid rhythms”.\nWe agree on your point and have changed the title to: ‘Hepatic metabolic reprogramming during short-\nterm caloric restriction is driven ba enhanced glucocorticoid rhythms.’\nResults section:\n2. The different outcomes measured have different sample size. This difference in sample size is\nunderstandable between variables such us body weight (n=6) and single-nuclei RNAseq (n=1) due of\nresources constrains (Ext. Data Fig. 1 and Fig. 1g, respectively). However, it is not clear why sample\nsize is different between corticosterone, insulin and glucose measurements, to mention one example\n(Fig. 1b and Ext. Data Fig. 1b-c, respectively). Did the authors select samples of specific animals? Were\nsome animals excluded? Were some samples lost? Please revise this aspect throughout the manuscript\nand clearly state the rationale behind these differences in sample size for all outcomes measured.\nWe apologize for any confusion regarding the differences in sample sizes across outcomes. We\nemphasize that no samples were excluded to misrepresent the data or distort distribution patterns. Rather,\nthese differences reflect several practical and methodological factors inherent to the experimental\nprocedures:\n Sample collection limitation: While glucose measurements require only a small drop of blood from\nthe tail immediately after euthanasia and can be easily performed on all the animals, serum\ncollection for assays such as ELISA or those performed using serum analyzers is more challenging\ndue to limitations imposed by our harvest method. Specifically, to minimize stress and the\nconsequent corticosterone increase, the mice are sacrificed by quick cervical dislocation, then blood\nis collected rapidly from the vena cava after a small incision. This procedure yields limited and\noccasionally hemolyzed serum, rendering it unsuitable for sensitive assays.\n Assay requirements and experimental timing: More precise methods, such as ELISA or glycogen\nassays, tend to offer lower variability and therefore need fewer replicates. These kits are also less\ncost‐effective compared to glucometer strips or the triglycerides assay. Moreover, as this project\ntook several years, the different tests were not performed in all cohorts.\n Occasionally, statistical outlier exclusion further affected final sample sizes.\nWe have ensured that all sample sizes are clearly reported in the legends and additional justification has\nbeen provided in the Methods of the revised manuscript.\n3. Can the authors provide the food consumption trajectories of the different experimental groups (WT\nunder control diet, GR under control diet, etc.)? Did the authors check at which ZT the cal. res.\nLKO\nanimals finished their meals? If available, please provide this information in methods section.\nWhile we cannot precisely determine the timing of meal consumption from the data collected, we can\ninfer it using parameters from indirect calorimetry, such as VO₂, CO₂, and the respiratory exchange ratio\n(RER). These values typically increase after a meal as metabolic activity rises to process ingested food.\nIn cal. res. mice, both oxygen consumption and carbon dioxide production show a sharp peak ZT14 and\nZT16 (8-10 p.m.), reflecting enhanced metabolic activity following a meal. These patterns are in line\nwith what reported in the literature, showing that in caloric restriction protocols mice consume their\nfood as soon it becomes available and resulting in temporal restriction of food intake with long (ca. 22\nh) fasting times (Acosta-Rodríguez VA, Cell Metab. 2017; Pak HH, Nat Metab. 2021; Acosta-Rodríguez\nVA, Science. 2022). No genotype-dependent variations were observed in our experimental groups. A\nnote was added to the Food intake section of the Methods.\nExtra Figure for Reviewer #2: Indirect calorimetry measures (96 h) of VO2, VCO2, RER means during the light and dark\nphase are shown. WT and hepatocyte-specific GR knockouts (GRLKO) mice were subjected to control (right) and\ncaloric restriction (left) feeding regimen. Each line represents the mean of each respective group (n = 7-8/group) measured\nevery 15 min.\n4. In lines 83-85 the authors state: “To a lower extent, these diurnal hormone secretion patterns\ncorresponded to differential nuclear translocation of the GR in the livers of cal.res. and control mice\n(Extended Data Fig. 1d).”. At which ZT do the authors detect this differential nuclear translocation of\nthe GR? Is this difference statistically significant? If significant, indicate the corresponding p-value in\nthe figure. If not, clearly state it in the manuscript.\nWe acknowledge that our western blot analysis was unable to effectively demonstrate differences in GR\nnuclear localization across all circadian time points. The diurnal shuttling of the GR protein poses\nchallenges for accurate quantification due to band saturation at ZT12. To address this limitation, we have\nincluded an immunoblot specifically focusing on the nuclear localization of GR at ZT12, the time point\ncorresponding to the peak of the hormone. Analysis of the new blot reveals a statistically significant\nincrease in nuclear GR levels at ZT12 (see now Ext. Data Fig. 1e).\n5. Regarding Ext. Data Fig. 3. Please provide the scale corresponding to the dendrograms. Also, indicate\nwhich unsupervised hierarchical clustering method was used.\nWe have now included this part in the Methods section. For unsupervised hierarchical clustering,\nwe first calculated the Pearson correlation coefficients between different replicates in the transcriptome\ndataset. Using this correlation matrix, we applied a correlation-based similarity measure defined as:\nsimilarity = correlation. The clustering was performed using the pheatmap function in R, with complete\nlinkage as the clustering method.\n6. In lines 139-140 the authors state “Unbiased PCA and hierarchical clustering analyses showed that\nsamples from the same time point clustered together under both dietary regimens.”. Please add “in\ngeneral” before “clustered together under both…”, as in the way the sentence is written now it does not\nhold true for some time points/genotypes, specifically in control diet mice. In Ext. Data Fig. 3a, note\nhow WT mice of ZT0 and ZT20 are more similar between each other than GR mice of ZT0 and ZT20\nLKO\n(i.e., this is a case in which samples from the same time point do not cluster together. This indicates that,\nat these time points, the differences promoted by the genotype are more pronounced than the differences\ninduced by the ZT). Otherwise, indicate which is the cutoff distance considered to establish that the\nsame time points cluster together.\nWe agree with the reviewer’s assessment and have revised the statement in the manuscript accordingly\n(lines 149-150).\n7. How do the authors relate the results regarding autophagy shown in Fig. 3d (lines 188-193) with those\nin Fig. 2e and Ext. Data Fig. 4g?\nAutophagy is a fundamental homeostatic process involved in various physiological contexts, including\ncell survival, metabolism, and stress response. It comprises multiple steps, including a) signaling and\nregulatory mechanisms, b) cargo selection and selective autophagy, c) initiation and core complex\nformation, and d) lysosomal degradation.\nIt is not surprising that our oscillating clusters feature autophagy-related processes, as they all represent\nsome level of diurnal metabolic adaptation. However, considering that caloric restriction is a potent\ninducer of autophagy, and that we observed its dampening in GR knockout livers, one might expect a\nprevalence of autophagy-related genes within both diet (genes rhythmic only under caloric restriction\nand not on control diet) and interaction (genes that depend on both GR and caloric restriction to sustain\ntheir rhythmicity) clusters.\nTo explore this further, we conducted a more detailed analysis to identify specific autophagic signatures\nwithin these clusters. The 129 autophagic genes that were found in our oscillatory dataset where\nmanually curated and 123 of those were classified to one of the four functional categories (a-d, see\nabove). Interestingly, 13 out of 24 genes belonging to the ‘core autophagosome formation’ are enriched\nin the diet cluster. Meanwhile, 15 out of 31 genes involved in ‘vesicle trafficking and lysosomal\ndegradation’ are predominant in the interaction cluster. This aligns with our experimental data in liver\ntissue (Fig. 3d, Fig. 4c) and in HepG2 cells (Fig. 4d), where we observed that glucocorticoids increased\nboth autophagosome formation and autolysosome induction, respectively.\nTogether, these findings support the idea that the coordinated action of energy restriction and\nglucocorticoid signaling regulates complementary aspects of the autophagy pathway, thus ensuring its\ncomplete and efficient execution.\nExtra Figure for Reviewer #2: Functional classification of autophagy-related genes across experimental clusters. Bar plot\nshowing the distribution of autophagy-related genes assigned to four major steps of the autophagic pathway. Each bar is\nsubdivided to indicate the contribution from genes assigned to the clusters: ‘All’ (pink), ‘Genotype’ (blue), ‘Diet’ (gray-blue),\nand “Interaction” (green). Notably, genes involved in vesicle trafficking and lysosomal degradation are enriched in the\n‘interaction’ cluster, whereas core autophagosome components are prominent in the diet cluster—reflecting a coordinated\nregulation of autophagy under combined metabolic and hormonal cues.\n8. In the legend of Ext. Data Fig.7, please indicate the diet to which CORT animals were subjected (in\nthe main manuscript it is indicated that these animals were subjected to control diet, but this information\nis missing in the figure legend). See also the previous comment regarding the rationale for the differences\nin sample size according to the outcome.\nThe legend has been changed to add more clarity on the experimental groups.\n9. In Fig. 1b, even when the peak at ZT12 in the cal. res. group seems numerically distinguishable from\nthe other group-mean values, it should be noted that with a sample size of 3, a two-way ANOVA for\nmultiple comparisons is way underpowered (this is a 2x6 two-way ANOVA, where 2 corresponds to the\nlevels of the factor “diet” and 6 to the levels of the factor “time points”). I recommend that all group-\nmean values are plotted for reference (as they are now), but the differences between diets are tested at a\nspecific ZT only and/or the differences between specific ZT are tested within a given diet (i.e., specify\nwhich contrasts you are interested in testing, instead of performing multiple comparisons, and limit the\nscope of the results to that). Also, although the time-dependent pattern is easier to recognize by plotting\nlines connecting the measured time points, this can be misleading as these are not repeated measures of\nthe same animals. I recommend removing the lines connecting the group-mean values.\nWe agree on the limitation of two-way anova with our sample size and have revised our statistical\nanalyses accordingly (see Statistical analyses in Methods). Specifically for this comparisons, we have\nnow carried out independent t-tests on the time points of interest as required fewer replicates.\nRegarding the connecting lines, while we recognize that these are not repeated measures from the same\nanimal, it is common practice to use them when depicting circadian or diurnal patterns, even in cases\nwhere samples are collected from different individuals (Mezhina V, Proc Natl Acad Sci U S A. 2022;\nAcosta-Rodríguez VA, Cell Rep. 2024). This graphical representation is supposed to aid in visualizing\nrhythmic patterns. In the cases of not ‘repeated measures`, we opted to use dashed instead of solid lines,\nand we hope you’ll agree.\n10. In the legend of Fig. 2a, please indicate what the ellipsoids and triangles in the PCAs mean.\nWe have now updated the figure legend, and the Methods section to improve visual clarity as requested.\nWe drew ellipsoids to enclose all replicates within the same group (time point + genotype). The ellipses\nwere generated using geom_mark_ellipse and the Kachiyan algorithm, which estimates the enclosing\nellipses (source: https://ggforce.data-imaginist.com/reference/geom_mark_ellipse.html).\nThe triangles represent the mean of all replicates within the same group, calculated by averaging the\nPC1 and PC2 values of each replicate.\n11. I recommend that amplitudes distributions in Fig. 2f are shown for each ZT instead of combining\nZT0-ZT20, as in the way they are plotted now can be misleading.\nFigure 2f illustrates the amplitudes of caloric restriction-responsive genes distributed among the clusters\ndisplayed in Fig. 2c. We did not observe any significant association or trend with specific time points\nand found that making further subdivision by phase was unnecessary (Panel A). Our findings indicate\nthat caloric restriction enhances the amplitudes of oscillating genes compared to control diet, with this\namplification being partially sustained by GR. Furthermore, when analyzing all circadian genes across\nthe three groups (JTK; p-adj > 0.05), we observe that the amplitude effect of the diet is consistent and\nevenly distributed among the phases (Panel B).\nA sentence has been modified to make this detail clear to the reader (line 184).\nExtra Figure for Reviewer #2: A. Box plots display the distribution of oscillation amplitudes for rhythmic transcripts under\ncaloric restriction (JTK: amplitude >0.1, p-adj <0.05). The plots illustrate the median, quartiles, whiskers (5th-95th\npercentiles), and individual extreme values (points). Rhythmic genes were categorized based on their oscillation phase into six\ntime points: ZT0, ZT4, ZT8, ZT12, ZT16, and ZT20, allowing visualization of phase-specific amplitude distributions. B. Phase\nvs. amplitude distribution of all rhythmic genes across all 3 experimental conditions. The polar plot illustrates the phase (in\nhours) and amplitude of gene expression oscillations across three experimental groups: wild type mice on control diet (ctrl.\nWT), wild type mice on caloric restriction (cal. res. WT), and GRLKO mice on caloric restriction (cal. res. GRLKO). Each point\nrepresents an individual gene, with its radial position indicating amplitude and angular position corresponding to the phase.\nGenes plotted are those deemed rhythmic by JTK-cycle (p-adj <0.05) within each condition independently. Phase and amplitude\nare calculated through a nonlinear least-squares regression of a cosine-wave model. Phase and amplitude were extracted from\nthe best-fit parameters. The clustering near the center suggests a prevalence of low-amplitude rhythmic genes, while more\nwidely distributed points indicate genes with stronger oscillatory behavior.\n12. In the legend of Fig. 3a-c, please specify the sample size. See also the previous comment regarding\nthe rationale for the differences in sample size according to the outcome.\nWe have now added sample size information to the legends for Fig. 3a-c. The variation in sample sizes\nacross different measurements reflects both technical considerations and biological variability, as\ndetailed above.\n13. In the legend of Fig. 6a, what is the sample size of n=3 referring to? This sample size is not consistent\nwith the dots observable in the boxplot. It is expected that the sample size to be stated is the one that\ncorresponds to analysis. Please amend and/or clarify this inconsistency. Also, do the dots in the boxplot\nshow all observations?\nThe box plot we provide in Fig. 6a presents the distribution of mRNA expression values for FOXO1\ntarget genes in wildtype (WT) and hepatocyte-specific GR knockouts (GRLKO) mice subjected to control\n(ctrl.) and caloric restriction (cal. res.) diets at ZT12. The plots display median, quartiles, and the 5th-\n95th percentiles (whiskers).\nThe n = 3 mentioned in the legend refers to the number of independent biological replicates (livers) per\ncondition used for our RNA-seq analysis.\nWe defined a set of 208 FoxO1-dependent genes based on a published liver RNA-seq dataset (Kitamoto\nT, Proc Natl Acad Sci U S A. 2021). These genes were identified as being down-regulated in FoxO1-\ndeficient conditions. We then assessed the expression levels of these 208 genes in our RNA-seq data, z-\nscore normalized values from 3 independent biological replicates per group, and plotted the distribution\nacross the three experimental groups. To avoid confusion, we now deleted the points (representing the\nextremes) in the new version (revised Fig. 6a).\n14. Fig. 6e and Ext. Data Fig. 10e, please indicate what the colors of the pie charts indicate.\nIn both Figures, the pie chart colors indicate the proportion of peaks—GR + FOXO1 (Fig. 6e, Top),\nGR\nonly (Fig. 6e, Bottom), or FOXO1 only (Ext. Data Fig. 10h)—that are annotated near genes either up-\nregulated (red) or down-regulated (blue) by caloric restriction. The background colors have now been\nremoved to prevent misinterpretation.\n15. Ext. Data Fig. 1a, as in previous comments, a two-way ANOVA for multiple comparisons (2x9,\nwhere 2 corresponds the levels of the factor “diet” and 9 to the levels of the factor “week”) is\nunderpowered if the sample size was n=6. Comparisons should be limited to the time points of interest.\nMoreover, are these repeated measures of the same animals? If so, ANOVA with repeated measures\nshould be performed to properly account for the lack of independence between measures of the same\nanimal. Also, please indicate what type of error bars are shown and use upper and lower error bars as in\nExt. Data Fig. 1b-c. Please revise this last aspect throughout all figures.\nFor body weight measurements in Ext. Data Fig. 1a, we followed the body weight in 12 animals\n(6/groups) weekly for the duration of the dietary intervention. Since the values were normally\ndistributed, we applied a two-way ANOVA with Repeated Measures. The p-values shown refer to\npairwise comparisons with corrections for multiple testing (Šídák).\nFollowing your suggestion, we have now edited the error bars throughout the figures (when required)\nand specified the type of error bars in each figure legend accordingly.\n16. In the legend of Ext. Data Fig. 2a-d and Ext. Data Fig. 4a, please indicate sample size.\nWe have now added the sample sizes to the legends of Extended Data Fig. 2a-d and Extended Data Fig.\n4a.\n17. Ext. Data Fig. 4g, please use the same scale in the x-axis of all four barplots, for ease of comparison\nbetween the groups (diet, genotype, interaction and all).\nFollowing your suggestion, x-axes have been scaled equally among categories.\n18. Fig. 1i and Ext. Data Fig. 6d-e, these figures are not referenced in the main text. Are they needed?\nWe have now introduced the missing references in the text (lines 135 and 220).\n19. Fig. 4b-d, Fig. 5a, and Fig. 6a and f, please clearly indicate which type of ANOVA was used in each\ncase.\nWe have now included details of the statistical tests used in each figure legend.\n20. Fig. 4d (quantification), please indicate what the “+” symbol in the box of the boxplots mean.\nThe '+' symbol in the boxplots represents the mean value of each experimental group, while the\nhorizontal line within each box indicates the median. We have now clarified this in the legend for Fig.\n4d.\nMethods section:\n21. Lines 554-555, the panels of Figs. 1 and 3 are indicated with uppercase but panels are referenced\nwith lowercase in the rest of manuscript.\nWe've corrected the panel references to match the lowercase style used throughout the manuscript.\nPotential limitations of the study:\n22. Results regarding characterization and modulation of rhythms by the different factors (knock out\nmodels, dietary intervention, etc.) should be taken with caution. The sampling period encompasses less\nthan 24 hours (ZT0 to ZT20), when ideally a reliable outcome requires sampling every few hours at\nleast during a 24-h period, which represents a complete cycle (or, even better, 48-h or 72-h to assess\nconsistency).\nWe acknowledge that our sampling period (ZT0 to ZT20) does not cover a full 24-hour cycle and\ntherefore limits the ability to fully characterize diurnal rhythmicity. We agree that more frequent\nsampling over a full 24–48 hours would strengthen rhythmic analyses. Our design aimed to capture key\ntime points with maximal biological variation within practical and ethical constraints.\n23. Throughout the manuscript the authors discuss the relevance of their results in the context of aging,\nbut their experiments were performed in 3-month-old mice subjected to short-term (less than 2 months)\ncaloric restriction. Considering that the authors demonstrate that the energetic context is critical in the\nrole of the Glucocorticoid Receptor (GR), what can they discuss regarding this role of GR and the\nchanges in the energetic context with aging?\nWe agree with the reviewer that our experimental design does not allow us to draw definitive conclusions\nabout the role of glucocorticoid surges during caloric restriction in old mice and their implications in\nlongevity mechanisms. As such, we limited our speculations to the Discussion section.\nCaloric restriction profoundly remodels hepatic metabolism, promoting lipid oxidation, ketogenesis, and\nautophagy while repressing anabolic processes. With this work, we evidenced that the glucocorticoid\nreceptor (GR) is a key mediator of many of these adaptations. Loss of GR in hepatocytes blunted caloric\nrestriction-induced transcriptional rhythms and impaired the activation of genes central to energy\nbalance, including AMPK, FOXO, PPARα signaling, proteostasis, and DNA repair)—many of which\nare known to decline with age, contributing to hepatic dysfunction. It is worth noticing how caloric\nrestriction in aged mice is able to prevent the age-associated remodeling and to rescue diurnal timed\nmetabolism (Sato S, Cell. 2017; Acosta-Rodríguez VA, Science. 2022; Tyshkovskiy A, Cell. 2023).\nIn our view, these effects appear to be linked to rhythmic GC signaling, as our caloric restriction protocol\nwas aligned with the natural GC peak. In aging, GC rhythmicity is dampened, likely disrupting the\ntemporal coordination of catabolic and anabolic transitions and impairing processes such as autophagy,\nand amino acid and fatty acid metabolism (Gupta D, Compr Physiol. 2014; Valbuena Perez JV, Aging\nCell. 2020). Taken together, our findings highlight the glucocorticoid receptor as a central node in the\nintegration of circadian, nutritional, and stress signals and suggest that preserving GR signaling during\naging may be crucial for maintaining metabolic resilience and maximizing the health benefits of dietary\ninterventions.\nWe believe that our findings require further validation in independent aging studies, meanwhile we have\nemphasized this limitation in the manuscript (lines 410-413).\n24. Sample size in single-nuclei RNAseq is of 1, please highlight this limitation in the manuscript and/or\nindicate that results need to be further validated.\nWe would like to clarify that our single-nuclei multiome analysis (ATAC-seq + RNA-seq) was\nperformed with n = 2 biological replicates per group, which enables robust bioinformatic analyses.\nWhile we acknowledge that larger sample sizes would strengthen statistical power, the use of two\nbiological replicates is consistent with current practices in the field, particularly given the high cost and\ncomplexity of multiome experiments. The number of replicates is stated in Fig. 1f and Ext. Data Fig. 2c\nas well in the Methods.\nReviewer #3 (Remarks to the Author):\nThis is an interesting study, aiming to elucidate how nutrition reroutes hormonal responses and how GR\ncistromic reprogramming could support anti-aging programs. The authors propose that the GR plays a\nkey role in promoting the healthy metabolic effects of energy deprivation by reshaping gene expression\nrhythms in response to caloric restriction.\n1. The study elucidates mechanisms of transcriptional reprogramming of hepatic energy metabolism\nduring caloric restriction by glucocorticoids. The results show that cal.res. rewires the hepatic\ntranscriptome through mechanisms dependent on GR. The results are impressive, but not too surprising\nsince glucocorticoids are crucial for the homeostasis of the organism and adaptation to the external\nenvironment.\nIndeed, the role of glucocorticoids in maintaining homeostasis and mediating adaptive responses to\nstress is well established. Our study extends this knowledge by uncovering specific GR-dependent\ntranscriptional programs that are engaged only during caloric restriction, providing a more detailed\nunderstanding of how systemic hormonal signals interface with hepatic gene regulatory networks under\nmetabolic stress. To date, the extensive body of research linking elevated glucocorticoid levels—\nwhether due to chronic stress or pharmacological interventions—to detrimental effects has not been\nmatched by investigations into the potential hormetic, beneficial effects of physiological glucocorticoid\nsignaling under caloric restriction. Although the phenomenon of ‘caloric stress’ was described decades\nago, only sparse and observational studies have been carried out. We believe our work provides new\ninsight into this overlooked area by identifying GR as a key mediator of transcriptional reprogramming\nduring metabolic adaptation to caloric restriction, addressing a significant gap in the field.\n2. Massive responses are shown, but it is still unclear which of these that are driving the phenotypic\nchanges. Is it possible to better identify the most important downstream responses?\n3. ChIP experiments in mouse livers indicate that low insulin signaling leads to increased nuclear\nlocalization and activity of FOXO1, which enables GR to bind to and regulate target genes only in\ncaloric restriction. Which of these genes are most important?\nWe agree on the importance of the functional/phenotypical relevance of the broad transcriptional\nresponses observed under caloric restriction (Points 2 and 3). Indeed, our data show that the combination\nof diminished insulin signaling and the elevated glucocorticoid action is a key hormonal signature of\ncaloric restriction, driving massive gene expression reprogramming.\nTo address the reviewer's question about identifying important downstream responses driving the\nphenotypic changes, we can highlight a subset of genes that meet three criteria: (i) being co-bound by\nGR and FOXO1, (ii) being significantly deregulated in GRLKO livers under caloric restriction, and (iii)\nbeing functionally linked to the observed metabolic phenotypes. This group enlist multiple core\nmetabolic regulators (eg: Ppargc1a, Nnmt, Cry1, Ahcy, Angptl4, Igfbp1, Foxo1, and Foxo3), as wells as\nprominent enzymes involved in energy metabolism (eg: Pck1, Acacb, Mat1a, Cat, Mthfr). While these\ngenes represent strong candidates for mediating GR/FOXO1-dependent phenotypic effects, we\nacknowledge that establishing causality relationships would require additional functional validation\nbeyond the scope of this study.\nMoreover, we recognize the complexity in distinguishing transcriptional outputs driven by GR-FOXO1\ndirect chromatin binding from those arising via indirect crosstalk mechanisms, changes in metabolite\nlevels and cofactor availability, or involvement of other transcription factors.\nOur findings underscore the established ‘permissive’ role of hepatic GR in enabling transcriptional\nresponses to metabolic stress. This is most evident in the GRLKO diurnal transcriptome, which fails to\nappropriately induce genes required for caloric restriction response and instead resembles the\ntranscriptional profile of ad libitum-fed controls. (Fig. 1 and Ext. Data Fig. 4).\n4. The experimental protocol (30% Cal restriction) leads to massive (30%) weight loss (studies with\nsuch weight loss are not accepted by the ethical committees in all countries!). Are these mechanisms\nseen after more modest Cal restriction?\nThank you for bringing this to our attention. Indeed, our feeding regimen is severe and leads to a\ndramatic weight loss in our cohorts (20-30%). To investigate if a milder protocol could similarly increase\ncirculating GC at ZT12 (‘caloric stress’), we conducted an independent experiment comparing the severe\n(30-40%) to a milder (10-20%) caloric restriction. As in our previous protocol, control animals were fed\nad libitum during the dark phase. These experiments were approved by the Bavarian authorities\naccording to the rules and regulations at Helmholtz Munich (see Methods)\nAs expected, body weight loss at the time of sacrifice was proportional to the degree of caloric restriction\n(Ext. Data Fig. 1f, Panel A). Importantly, the milder group did not display the time point specific\nincrease of glucocorticoids (ZT12). While a few individual animals appeared stressed at the time of\nsacrifice, the overall trend in glucocorticoid levels closely resembled that of the control group (Ext. Data\nFig. 1g, Panel B). Similarly, glucose levels were comparable to the unrestricted mice (Panel C).\nTherefore, we consider our adopted protocol the optimal feeding regimen to study the effects of\nglucocorticoids in response to caloric restriction. Moreover, despite the severity, this level of restriction\nis widely regarded as the gold standard for studying the beneficial effects of caloric restriction on\nmetabolic health and lifespan in mice (Di Francesco A, Nature 2024; Mitchell SE, The Journals of\nGerontology: Series A 2023).\nExtra Figure for Reviewer #3: A-C. Body weight (A), serum corticosterone (B), and blood glucose (C) of mice subjected to\ntwo caloric restriction regimens for 8 weeks: 40% and 20%. Mice fed ad libitum during the dark fed are used as controls (ctrl.)\n(n=6-8). Biological replicates were collected at ZT12 (6pm). Data are shown as box plots, with minimum, 25% quartile,\nmedian, 75% quartile, and maximum. Due to variance heterogeneity Welch’s ANOVA test was used."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed the comments from the initial review of the manuscript. The new supportive data is included in the revised manuscripts. This reviewer has no additional comments and supports the publication of the study.", "reviewer_2": "I thank the authors for carefully considering all my previous recommendations. The answers provided to most of my queries are satisfactory, and I consider that the changes made in the text, analysis and figures have substantially improved the quality of the manuscript. However, there are still some important issues that need to be revised or clarified in order to make this article suitable for publication.\nIn response to my previous request, now the statistical tests used for the different analyses have been provided. They reflect that in several instances the authors have tested the effects of two factors with two or more levels each one of them by means of one-way ANOVA and/or equivalent non-parametric one-way tests (followed by post-hoc multiple comparisons), which is not correct. In an experimental design like this, a two-way ANOVA approach (or non-parametric equivalent, as needed) should be used to allow assessment of the single main effect of each factor and their interaction simultaneously, instead of performing multiple one-way ANOVA tests, which inflates the Type I error as it increases the probability of false positives across tests. In addition, a factorial ANOVA enables one to more transparently assess if an effect is independent or not from the other factor (e.g., does caloric restriction promote an effect in both genotypes or only in the WT mice?). In case the null hypothesis of this test is rejected, then the corresponding post-hoc comparison should be performed. Alternatively, multiple one-way ANOVA tests with a priori defined contrasts (comparisons of interest are defined before performing the statistical test and no comparison outside those a priori defined are explored) could be also used with this experimental design, but this approach does not seem to have been described in methods section. Otherwise, interpretation of results is hindered, and it might be confusing to which comparisons the p-values refer to or even what the asterisks in the figures are indicating. For example, in lines 196-199, the authors state “Our transcriptomic profiles suggest that GRLKO mice might differ phenotypically from WT in the response to the reduced energy intake. Indeed, circulating serum glucose was higher (Fig. 3a), and the levels of free fatty acids and ketone bodies, which are increased by fasting, were lower in GRLKO compared to WT mice (Fig. 3b,c).”, but Figure 3a seems to only show that circulating serum glucose is lower in mice subjected to caloric restriction in both genotypes (i.e., there is a group of asterisks between control and cal. res. in the wild type mice, and another group of asterisks between control and cal. res. in the GRLKO mice), which seems inconsistent with the text. If not, to which pair-wise comparison are the asterisks of the figure associated? Is the comparison WT-cal.res. versus GRLKO-cal.res. statistically significant? Another example, in lines 299-301 the authors state “Interestingly, GRLKO mice again showed an attenuated response to caloric restriction, with higher signals remaining for phosphorylated FOXO1 and STAT5 proteins (Fig. 5a and Extended Data Fig. 9a).”, but for the quantification of p-STAT5 in Figure 5a, there is only a group of asterisks between control and cal. res. wild type mice, which also seems inconsistent/incomplete with the regards to the text. Even when statistical significance, estimates of effect size and/or confidence intervals result similar to the ones obtained now, the data presented in Fig. 1h, Fig. 3a-g, Fig. 4b and 4d, Fig. 5a and 5f, and Fig. 6a should be analyzed through a two-way ANOVA approach (or non-parametric equivalent, as needed). I strongly recommend including the results of the ANOVA tables and post-hoc comparison in the supplementary material.", "reviewer_3": "The responses are comprehensive, specific, and address each point raised with appropriate detail and supporting data. The authors acknowledge limitations where relevant, provide additional experimental evidence where requested, and clearly justify their methodological choices. They also transparently discuss the translational and ethical considerations of their model. Overall, the responses are adequate"}, "author_response": "NCOMMS-24-46730-B\nHepatic metabolic reprogramming during short-term caloric restriction is driven by\nenhanced glucocorticoid rhythms.\nby Makris K. et al.\nReviewer #1 (Remarks to the Author):\nThe authors have addressed the comments from the initial review of the manuscript. The new\nsupportive data is included in the revised manuscripts. This reviewer has no additional\ncomments and supports the publication of the study.\nWe sincerely appreciate the reviewer’s positive feedback and we are pleased to know that the\nrevisions have satisfactorily addressed the concerns raised. We thank the reviewer for\nsupporting the publication of our work.\nReviewer #2 (Remarks to the Author):\nI thank the authors for carefully considering all my previous recommendations. The answers\nprovided to most of my queries are satisfactory, and I consider that the changes made in the\ntext, analysis and figures have substantially improved the quality of the manuscript. However,\nthere are still some important issues that need to be revised or clarified in order to make this\narticle suitable for publication.\nIn response to my previous request, now the statistical tests used for the different analyses have\nbeen provided. They reflect that in several instances the authors have tested the effects of two\nfactors with two or more levels each one of them by means of one-way ANOVA and/or\nequivalent non-parametric one-way tests (followed by post-hoc multiple comparisons), which\nis not correct. In an experimental design like this, a two-way ANOVA approach (or non-\nparametric equivalent, as needed) should be used to allow assessment of the single main effect\nof each factor and their interaction simultaneously, instead of performing multiple one-way\nANOVA tests, which inflates the Type I error as it increases the probability of false positives\nacross tests. In addition, a factorial ANOVA enables one to more transparently assess if an effect\nis independent or not from the other factor (e.g., does caloric restriction promote an effect in both\ngenotypes or only in the WT mice?). In case the null hypothesis of this test is rejected, then the\ncorresponding post-hoc comparison should be performed. Alternatively, multiple one-\nway ANOVA tests with a priori defined contrasts (comparisons of interest are defined before\nperforming the statistical test and no comparison outside those a priori defined are explored)\ncould be also used with this experimental design, but this approach does not seem to have been\ndescribed in methods section. Otherwise, interpretation of results is hindered, and it might be\nconfusing to which comparisons the p-values refer to or even what the asterisks in the figures\nare indicating. For example, in lines 196-199, the authors state “Our transcriptomic profiles\nsuggest that GRLKO mice might differ phenotypically from WT in the response to the reduced\nenergy intake. Indeed, circulating serum glucose was higher (Fig. 3a), and the levels of free\nfatty acids and ketone bodies, which are increased by fasting, were lower in GRLKO compared\nto WT mice (Fig. 3b,c).”, but Figure 3a seems to only show that circulating serum glucose is\nlower in mice subjected to caloric restriction in both genotypes (i.e., there is a group of asterisks\nbetween control and cal. res. in the wild type mice, and another group of asterisks between\ncontrol and cal. res. in the GRLKO mice), which seems inconsistent with the text. If not, to\nwhich pair-wise comparison are the asterisks of the figure associated? Is the comparison WT-\ncal.res. versus GRLKO-cal.res. statistically significant? Another example, in lines 299-301 the\nauthors state “Interestingly, GRLKO mice again showed an attenuated response to caloric\nrestriction, with higher signals remaining for phosphorylated FOXO1 and STAT5 proteins (Fig.\n5a and Extended Data Fig. 9a).”, but for the quantification of p-STAT5 in Figure 5a, there is\nonly a group of asterisks between control and cal. res. wild type mice, which also seems\ninconsistent/incomplete with the regards to the text. Even when statistical significance,\nestimates of effect size and/or confidence intervals result similar to the ones obtained now, the\ndata presented in Fig. 1h, Fig. 3a-g, Fig. 4b and 4d, Fig. 5a and 5f, and Fig. 6a should be\nanalyzed through a two-way ANOVA approach (or non-parametric equivalent, as needed). I\nstrongly recommend including the results of the ANOVA tables and post-hoc comparison in\nthe supplementary material.\nWe appreciate the reviewer’s positive evaluation of our revised manuscript and for\nacknowledging the improvements made in response to the previous round of comments. We\nalso appreciate the reviewer’s constructive suggestions for further refinement. Below, we\naddress the remaining concerns in detail, with additional analyses and textual revisions as\nrecommended.\nIn the original version, we used one-way ANOVA or equivalent non-parametric tests to assess\ndifferences within specific groups. This was intended to maintain consistency across analyses\nand apply a conservative approach that minimized assumptions. However, we acknowledge\nthat this strategy limited the ability to detect and interpret potential interactions (e.g. between\ngenotype and diet). Therefore, we have now reanalyzed the relevant datasets using two-way\nANOVA as recommended (or non-parametric factorial alternatives where assumptions are not\nmet). This allows for a more rigorous and transparent evaluation of main effects and\ninteractions.\nFollowing two-way ANOVA analysis, we applied the following decision framework:\n When significant interactions were detected, simple effects analysis or interaction\ncontrasts were performed, with Tukey's correction for multiple comparisons.\n When no significant interaction was found but main effects were significant, post-hoc\npairwise comparisons were conducted using Tukey's HSD test.\n When neither interactions nor main effects were significant, no post-hoc comparisons\nwere performed.\nThese revisions apply to the main Figures: Fig. 3a–g; Fig. 4b and d; Fig. 5a and f; Fig. 6f, as\nwell as the corresponding Extended Data Figures: Fig. 6a–c.\nWe excluded the violin plots in Fig. 1h and Extended Data Fig. 2f from the two-way ANOVA\nreanalysis described above. These panels display individual genes that were already identified\nas differentially expressed through Seurat's MAST framework, which incorporates appropriate\nmultiple comparison corrections across the entire transcriptome. The violin plots serve to\ndemonstrate expression patterns of selected representative genes under our experimental\nconditions, rather than as hypothesis-testing comparisons, therefore re-analyzing them with\ntwo-way ANOVA represents a different statistical approach that may not be directly\ncomparable to the original analysis.\nFollowing re-evaluation of the revised analyses, the overall conclusions of our study remain\nunchanged. However, we have clarified the text in specific sections of the Results, particularly\nregarding glycemia (lines 196–199) and P-STAT5 blot (lines 300–301), where the previous\nstatistical approach created ambiguity about the nature of the effects. Our factorial analysis\nnow clearly demonstrates a significant genotype × diet interaction: While WT and GRLKO mice\nshow similar responses under control diet conditions, GRLKO mice exhibit an attenuated\nresponse to caloric restriction compared to WT mice, indicating genotype-dependent metabolic\nadaptation to energy deficits.\nWe have now included the outputs of all statistical analyses in the updated Data Source file\n(highlighted in grey). The revised figures and legends now clearly indicate the interaction terms\nand the basis for the statistical annotations (e.g., asterisks). Finally, the Methods section has\nbeen updated to reflect our revised analytical strategy.\nWe believe these changes improve the rigor and interpretability of our findings, and we are\ngrateful to the reviewer for their insightful comments that helped enhance the quality of the\nmanuscript."}]

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