Circadian clocks and periodic anticipated fasting prevent fasting-associated hepatic steatosis in calorie restriction

Oghogho P Ebeigbe; Volha Mezhnina; Artem Astafev; Nikkhil Velingkaar; Jillian Kodger; Allan Poe; Jonathan Fritz; Kadaia Z Williams; Evelina Trokhimenko; Josefa-Marie B Rom; Yana Sandlers; Roman V Kondratov

doi:10.1016/j.celrep.2025.116141

. Author manuscript; available in PMC: 2025 Sep 24.

Published in final edited form as: Cell Rep. 2025 Aug 12;44(8):116141. doi: 10.1016/j.celrep.2025.116141

Circadian clocks and periodic anticipated fasting prevent fasting-associated hepatic steatosis in calorie restriction

Oghogho P Ebeigbe ^1,², Volha Mezhnina ^1,², Artem Astafev ^1,², Nikkhil Velingkaar ^1,², Jillian Kodger ³, Allan Poe ^1,², Jonathan Fritz ^1,², Kadaia Z Williams ^1,², Evelina Trokhimenko ^1,², Josefa-Marie B Rom ^1,², Yana Sandlers ³, Roman V Kondratov ^1,^2,^4,^*

PMCID: PMC12456247 NIHMSID: NIHMS2107289 PMID: 40802509

SUMMARY

Calorie restriction (CR) improves health and longevity. CR induces a periodic fasting cycle in mammals; our study compares CR with unanticipated fasting (F), when the food is unexpectedly withheld. F induces hepatic steatosis, whereas CR reduces it; surprisingly, the difference is not due to hepatic β-oxidation. Liver transcriptome analysis identifies fatty acid transporters (Slc27a1 and Slc27a2), triglyceride (TAG) synthesis (Gpat4), and lipid storage (Plin2 and Cidec) genes to be upregulated only in F, in agreement with hepatic steatosis. The circadian clock and anticipated fasting contribute to preventing fasting-associated hepatic steatosis in CR. Mechanistically, the Slc27a1, Plin2, and Cidec genes are upregulated, and liver TAGs accumulate in circadian clock mutant mice on CR or if wild-type CR mice miss their anticipated meal. The results highlight the similarities and differences between F and CR, suggesting that circadian clock–dependent gating of transcriptional response to fasting controls lipid homeostasis and prevents hepatic steatosis.

In brief

Ebeigbe et al. show that caloric restriction and fasting differentially reprogram gene expression and fat metabolism in the liver. Caloric restriction reduces the accumulation of lipid droplets through mechanisms dependent on the circadian clock and entrained anticipation of fasting.

Graphical Abstract

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INTRODUCTION

Liver lipid homeostasis is maintained by the balance of fatty acid synthesis, uptake, storage, and oxidation. The disruption of lipid homeostasis leads to hepatic steatosis when triglycerides (TAGs) accumulate in the liver.¹ Hepatic steatosis is one of the hallmarks of liver diseases such as alcoholic and nonalcoholic fatty liver diseases.^2,3 Chronic overnutrition is one of the main causes of hepatic TAG accumulation.⁴ Contrary to this, diets with reduced caloric intake such as calorie restriction (CR), intermittent fasting, and low-calorie Mediterranean diets have positive effects on preventing hepatic fat accumulation in both animal models and humans.^5–10

CR is a robust dietary intervention known to improve health and increase longevity.^11–14 The mechanisms of CR are still not well known. Improved glucose homeostasis, increased insulin sensitivity, decreased mechanistic target of rapamycin (mTOR) and insulin-like growth factor-1 (IGF-1) signaling, and enhanced circadian rhythms are among the mechanisms contributing to the health benefits of the CR diet.^15,16 CR is commonly implemented by providing mice with a single meal at the same time each day. Such pattern of feeding induces a self-implemented feeding/fasting cycle, when animals consume all their daily food within a few hours and fast for the rest of the day.^17,18 There is also evidence that this CR-associated fasting contributes to the metabolic and health benefits of CR.^17,19–21 The most common form of experimental fasting is when the food is unexpectedly removed and the animals do not anticipate the fast. Fasting induces multiple metabolic changes such as reduction of blood glucose and induction of serum nonesterified fatty acids (NEFAs) and ketone bodies.^22,23 Several recent reports highlight some similarities^24–26 and also significant differences between F and CR in glucose metabolism and metabolic signaling^17,27; however, lipid metabolism was not directly compared between F and CR diets.

During fasting, fatty acids are released from TAGs stored in the adipose tissue and utilized as the predominant source of fuel, where they undergo β-oxidation to produce energy in the liver and other metabolic tissues.²³ Some fatty acids are re-esterified in the liver to form TAGs and accumulate in lipid droplets (LDs) in the fasted liver, thereby causing hepatic steatosis^28–30 through poorly understood mechanisms. It is proposed that the imbalance between fatty acid uptake by the liver and the rate of liver β-oxidation promotes hepatic lipid accumulation.³¹ In agreement with that, hepatic lipid accumulation is reduced in mice with adipose tissue–specific knockout of triglyceride lipase (Atgl), which suppresses fatty acid release from the adipose tissue.³² Defective hepatic β-oxidation through the knockout of either Pparα or Cpt2 aggravates fasting hepatic steatosis.^33–35 Enhanced hepatic β-oxidation ameliorates fasting-induced hepatic steatosis.^28,36 Additional control of hepatic steatosis occurs through the regulation of hepatic LD formation and catabolism.^37,38 Surprisingly, although CR has a fasting component, the effect of the diet on liver lipid accumulation is opposite to that of unanticipated fasting: CR reduces hepatic steatosis.^6,39 However, the difference in experimental setup between F and CR prevents a direct comparison of the above-cited reports and the mechanisms that account for the differences between CR and F in regulating hepatic steatosis are not well understood.

CR is intertwined with the circadian clock, a network of biological timekeepers that generate circadian rhythms in behavior and physiology.⁴⁰ Light entrains the master circadian clock in the suprachiasmatic nucleus (SCN) of the hypothalamus. SCN synchronizes the peripheral circadian clocks in other tissues with the periodic environment.^41–44 The molecular circadian clock operates as transcriptional-translational feedback loops that drive 24-h rhythms in gene expression.^41–44 Disruption of circadian rhythms is associated with the development of metabolic diseases.^42,45 CR reprograms circadian rhythms in behavior, gene expression, and metabolism, and the circadian clock contributes to the health and metabolic benefits of CR.^{21,25,40,46–50} Circadian clocks are implicated in regulating hepatic steatosis^51,52 and may play a role in regulating liver lipid homeostasis and steatosis in CR mice.

To find the molecular mechanisms driving the difference in hepatic lipid accumulation between F and CR, we directly compared the kinetics of metabolic changes induced by CR and F. Both F and CR significantly decreased blood glucose and induced serum NEFAs and blood ketone bodies, whereas the accumulation of TAGs in the form of LDs was observed only in F but not in CR liver. Importantly, the metabolic changes induced by the diets were paralleled with differential reprogramming of the hepatic transcriptome. Both F and CR induced the expression of β-oxidation genes, although the kinetics was different. At the same time, the expression of fatty acid transport, TAG synthesis, and lipid storage genes were induced only by F. Experiments with missed expected periodic meal and circadian clock mutant Cry1,2^−/− mouse models demonstrated that both the periodic anticipated fasting and the circadian clock contributed to the prevention of fasting-associated hepatic steatosis in CR.

RESULTS

CR prevents fasting-induced accumulation of liver triglycerides

To directly compare hepatic lipid metabolism in CR and unanticipated fasting, we designed the following experiment outlined in Figure 1A. Mice were fed ad libitum (AL) diet until the age of 3 months. At this age, mice were randomly assigned into two groups: CR and AL; these groups had comparable body weight (Figure 1B). CR mice were subjected to 30% CR diet for 2 months. AL mice were continued on AL diet for the same 2 months. At the age of 5 months, AL mice were randomly assigned into two groups: AL and unanticipated fasting (F); mice in these two groups had comparable body weight (Figure 1B). CR mice had significantly reduced body weight compared with AL and F mice (Figure 1B). CR mice received 70% of AL daily intake as a single meal provided at Zeitgeber time 14 (ZT14); they consumed all the food in 2 h by ZT16 and did not have any food for the rest of their day. We started to record the food intake at ZT16 on the day before the start of the experiment. AL and CR mice were on their regular feeding through the duration of the experiment. The unanticipated fasting (F) group was fed AL before their food was unexpectedly removed at ZT16; this time was set up as 0 h without the food. The food consumption data during the day of the experiment and the day before are shown in Figure 1C. AL and F mice have comparable food intake before the food was removed. AL mice continued to eat through the duration of the experiment. CR mice consumed the provided daily meal in 2 h between ZT14 and ZT16. There was no food intake in F mice after the food was removed. Throughout this study, male and female data were analyzed separately due to differences in absolute values.

Consumption of oxygen and production of carbon dioxide is used to estimate the energy expenditure and substrate preference for energy production by calculating the respiratory exchange ratio (RER). RER demonstrated low-amplitude rhythms in AL mice with ratio near 1.0 during the dark phase of the day, an indication of predominantly carbohydrate oxidation, and <1.0 during the light phase, an indication of mixed carbohydrate and fatty acid oxidation (Figure 1D). F mice have RER rhythms similar with AL mice the day before the food was removed. After the food was removed at ZT16 (0 h without food) the RER was near 1.0 and comparable with that of AL mice for few hours; after that, it reduced and remained at 0.75 for the rest of the experiment, indicating that the mice were predominantly oxidizing fatty acids. CR mice demonstrated very distinct RER rhythms (Figure 1D). When the food was provided at ZT14, the RER increased from 0.75 to 1.0 in 15 min. RER was above 1.0 for a few hours, indicating lipid biosynthesis, and after that it gradually reduced to 0.75. The transition to fatty acid oxidation was slightly delayed in CR compared with F. From about 12 to 15 h without food and until the end of the experiment both CR and F mice demonstrated predominantly fatty acid oxidation. We also compared the energy expenditure in all three groups of mice. AL mice demonstrated distinct rhythms with more energy expenditure during activity/feeding phase of the daily cycle (Figure 1E). CR mice demonstrated strong peak of the energy expenditure around feeding time, and the increase of expenditure started a few hours before the feeding in correlation with known food anticipation activity in these mice. F mice demonstrated rhythms similar to AL before the food was removed. Even after the food was removed, the energy expenditure in F mice was still rhythmic—low during the light phase and high during the dark phase. The pattern of energy expenditure was similar to AL mice, but the value was slightly reduced. Indeed, the day before the experiment, AL and F mice demonstrated comparable energy expenditure. Energy expenditure in CR mice was significantly lower compared with that of AL and F mice, in agreement with less food intake in these mice. During 22 h of the experiment, the energy expenditure in F mice was low compared with AL and high compared with CR mice. Thus, total body metabolism was different between AL, CR, and F mice; importantly, both CR and F mice were predominantly utilizing fatty acid oxidation during the last 10 h without the food.

CR and F mice dropped body weight with comparable kinetics in both sexes following the time without food (Figures 1F and S1). Blood glucose was stable in AL and CR mice throughout the duration of the experiment, although the blood glucose concentration was significantly lower in CR compared with AL mice (Figure 1G). Blood glucose progressively reduced in F mice and reached a level comparable with that of CR mice after 14 h time without food (Figure 1G). The reduced body weight and blood glucose concentration further predict a switch in fuel utilization to stored nutrients by both CR and F mice. During fasting, fatty acids are released from the adipose tissue into the blood and are transported to the liver and other metabolic tissues to be used as a source of fuel.²³ Serum NEFAs were low in AL at all time points. NEFA concentration increased with time without food (Figure 1H) in both F and CR mice with comparable kinetics. CR significantly reduced liver TAGs compared with AL in both males and females (Figure 1I). In strong contrast, liver TAGs significantly accumulated in F as early as 6 h of fasting and was high throughout the fasting duration. Although F and CR mice share similar physiological profiles with regard to reduction in blood glucose and increase in serum NEFAs, liver TAGs were accumulated exclusively in F mice but not in CR mice.

Altogether, the data on body weight, blood glucose and NEFAs, and total body metabolism suggested that metabolic fasting response was induced in both F and CR. Importantly, serum NEFAs, which are important sources of liver fatty acids during fasting, increased with comparable kinetics in both CR and F (Figure 1H); thus, serum NEFAs cannot explain the difference in liver TAG accumulation, suggesting liver-intrinsic mechanisms. The transition to fatty acid oxidation started around 3–4 h without food in the F and 5–6 h without food in the CR groups. Therefore, CR and F mice were collected and analyzed at 0, 6, 14, and 22 h without food and AL samples were collected at the same time points.

Fasting induced stronger fatty acid oxidation compared with CR

The schematic of hepatic β-oxidation is shown in Figure 2A. Both CR and F induce hepatic β-oxidation.^28,53 To test whether the reduced lipid accumulation in CR liver was due to a stronger induction of β-oxidation, we assayed the expression of Cpt1a (encodes the rate-limiting enzyme in mitochondrial β-oxidation^54,55) in both male and female mice. Cpt1a was strongly induced in the F liver already after 6 h, whereas in the CR liver the expression was induced only after 22 h (Figures 2B and 2C), and at all time points the expression was significantly higher in F compared with CR. Acetyl coenzyme A (CoA) produced through β-oxidation in the liver can be used in the Krebs cycle or for ketone body synthesis through ketogenesis.⁵⁶ The ketone body, β-hydroxybutyrate (β-HB), is produced by the liver and transported to extrahepatic tissues where it is used as an alternate source of fuel. Ketone body production is proportional to β-oxidation; therefore, ketone bodies can serve as a surrogate marker for hepatic β-oxidation.⁵⁷ We observed a significant increase in the mRNA expression of Hmgcs2 (encodes for the rate-limiting enzyme in ketogenesis⁵⁸) in F liver compared with CR liver (Figures 2B and 2C). Blood β-HB was strongly induced after 6 h in F and only after 22 h in CR, and the concentration of β-HB was in strong correlation with the expression of β-oxidation and ketogenesis genes (Figures 2B and 2C).

Peroxisome proliferator-activated receptor alpha (PPARα) is a master regulator of liver lipid metabolism, and many fatty acid oxidation genes are under PPARα transcriptional regulation.³³ We evaluated Pparα expression in both male and female mice (Figure 2D). We did not observe changes in Pparα expression in female CR mice, whereas males only showed induction at 22 h time without food. F mice significantly induced the expression of Pparα in the liver, which was evident as early as 6 h in both sexes. We conducted RNA sequencing (RNA-seq) and analyzed the expression of genes involved in fatty acid oxidation and ketogenesis (Figure 2E). Corresponding to our Cpt1a and Hmgcs2 RT-qPCR findings of stronger β-oxidation in the F than in the CR group, we observed more robust induction of other fatty acid oxidation and ketogenesis genes in F liver (Figure 2E). We also evaluated the expression of Pparα target genes^59,60 by RNA-seq and found a much stronger induction of their expression in F compared with CR (Figure 2F). Together, ketone body production and gene expression data suggest that hepatic β-oxidation was stronger in F liver compared with CR and thus cannot explain the differential effect of CR and F on hepatic steatosis.

Transcriptomic analysis reveals hepatic lipid metabolism is significantly different between CR and F

To identify the key candidate genes responsible for the difference in lipid metabolism between the diets, we analyzed liver transcriptomes. Differentially expressed genes (DEGs) between F and AL or between CR and AL at each time point are shown in Figure S2. More genes were affected by F when compared with CR at each time point. About 50% of the genes affected by CR were also affected by F (Figure S2), and changes in the expression were in the same direction in both diets (Figure S3). Overlapping DEGs between CR and F were enriched in various liver lipid metabolism pathways (Figures S2 and S3). CR-specific DEGs were enriched in xenobiotic metabolism and immune response, whereas F-specific DEGs were enriched in lipid metabolism pathways (Figure S2). CR-specific genes cannot explain the difference between the diets, and we hypothesized that candidate genes for hepatic steatosis regulators would be among the F-specific DEGs.

Liver TAGs were accumulated as early as 6 h without food in F and remained elevated at 14 and 22 h (Figure 1I). Therefore, the likely candidates are genes differentially expressed across F. A total of 123 genes were downregulated at all 3 fasting time points in the F mice (Figure S4A). The top pathways were lipid and cholesterol biosynthesis, with some affected genes being Hmgcr, Fdps, and Acly (Figure S4B). The downregulation of many lipogenic genes in F mice (Figures S4A–S4C) suggests that reduced lipogenesis may not contribute to TAG accumulation in the liver of F mice. A total of 135 genes were differentially upregulated in F compared with CR at all three time points (Figures 3A and 3B). Fatty acid transport, glycerolipid metabolic pathway, and lipid storage were identified as pathways differentially induced by F. Interestingly, these pathways are interconnected (Figure 3C). Fatty acids are transported into the liver and converted to acyl-CoA, which can be used in the glycerolipid synthesis pathway to synthesize TAG.⁶¹ The synthesized TAGs are stored in cytoplasmic organelles called LDs.⁶¹ Upregulated lipid metabolism pathways were in line with liver TAG accumulation in F liver; therefore, we focused on these pathways.

Figure 3. — (A and B) RNA-seq analysis: Venn diagram for DEGs between CR and F diets at each time point (A), and bar graph of gene ontology pathway analysis of F-specific DEGs upregulated at 6, 14, and 22 h time without food (B). The number of enriched genes in the pathway analysis is indicated on the bar chart. DEseq2 was performed using AL group as a control across each time point; n = 3 per diet per time point.

(C) Schematic representation of the pathway for FFA transport, TAG synthesis, and lipid storage.

(D) Heatmap of lipid metabolism genes involved in FFA transport, TAG synthesis, and lipid storage (RNA-seq data); n = 3 per diet per time point per.

(E) mRNA expression (RNA-seq) of upregulated F-specific DEGs that were identified in Figure 3B; n = 3 per diet per time point, mean ± SEM.

(F) Liver free fatty acid (FFA) concentration; n = 3 per diet per time point, mean ± SD. Statistical analysis was performed with two-way ANOVA, p <0.05 for a, ALversus CR; b, AL versus F; c, CR versus F.

(G) Liver lipid droplet staining images.

We observed strong induction of the mRNA of several liver fatty acid transporters in fasted mice (Figures 3D and 3E). Among these transporters were Slc27a1 and Slc27a2, which were induced at all three time points in F liver but not in CR. Other fatty acid transporters like Slc27a4 and Cd36 were induced in both F and CR livers, and the induction was significantly stronger in F (Figure 3D). Increased expression of fatty acid transporters suggests an increased fatty acid uptake in F mice. Several major free fatty acids (C16:0, C18:0, C18:1, and C18:2) were significantly increased in the liver of F mice, whereas no significant change was observed in CR liver (Figure 3F). Increased accumulation of fatty acids in F liver is in agreement with upregulated gene expression of fatty acid transporters and could support liver TAG synthesis.

TAG synthesis pathway was upregulated in the F livers (Figures 3D and 3E). Among the genes induced only in F liver was Gpat4 (encodes the enzyme that catalyzes the transfer of an acyl-CoA group to glycerol-3-phosphate)⁶² (Figure 3C). Lpin1 and Lpin2, which code for phosphatidic acid phosphatases,⁶¹ were upregulated in both CR and F (Figure 3D). No significant difference in the expression of Agpat1 and Agpat2 was observed between diets. mRNA expression of several lipid storage genes were upregulated in F liver (Figure 3D). Among these genes were Plin2 and Cidec, which were induced across all time points in F but not in CR (Figures 3D and 3E). The lipid storage gene, Plin5, was upregulated by both diets, with a significantly stronger induction observed in the F mice. (Figure 3D). Significant LD accumulation was observed as early as 6 h in the liver of F mice. The LD content in CR liver was low across all time points (Figures 3G and S5). Together these data revealed significant differences in hepatic lipid metabolism at the level of free fatty acid transport, TAG synthesis, and lipid storage between CR and F, all of which contribute to the differential hepatic TAG accumulation.

Missed daily meal impacts CR liver lipid metabolism and induces TAG accumulation

CR mice receive their meal at the same time every day, and they develop strong food anticipation before the meal.^19,21,63 To further investigate the difference in lipid metabolism between CR and F, we designed the following experiment (Figure 4A). The first group of CR mice was collected just before the anticipated meal was provided at ZT14. This group corresponded to CR 22 h time without food; for convenience in this experiment, this group was labeled CR before the meal (CRbm). The second group of CR mice received their regular meal and were collected 4 h after the meal at ZT18 and labeled as CRfed. The third group of CR mice did not receive their expected meal at ZT14 and were collected after 4 h at ZT18; this group was called CR-missed meal (CRmm). As it was expected, the feeding changed the metabolism in CR. The RER changed from 0.75 before the meal to 1.0 after the meal in CRfed (Figures 4B and S6A), indicating a switch from fatty acid to carbohydrate oxidation. The CRmm RER remained unchanged even after the mice did not receive the CR meal, indicating that the mice maintained similar whole-body metabolic status as they were before and after the missed meal. Both CRfed and CRmm had comparable energy expenditure, and the energy expenditure in both groups of mice was reduced when compared with CRbm (Figures 4C and S6B). We also measured body weight between groups and did not observe significant difference between CRbm and CRmm (Figures 4D and S6C). As expected, serum NEFAs significantly decreased after feeding in CRfed (Figures 4E and S6D), whereas there was no significant change in serum NEFA concentration between CRbm and CRmm. The missed meal did not significantly affect the concentration of blood glucose (Figures 4E and S6D). Together, the RER, body weight, blood glucose, and serum NEFA data in both male and female mice suggest that the whole-body metabolic status is comparable between CRmm and CRbm.

Figure 4. — (A) CR feeding plan was modified to test for food anticipation; CRbm represents the time point before the anticipated meal, CRfed represents fed CR mice, and CRmm represents CR mice with the missed meal.

(B and C) Measurements of RER (B) and energy expenditure (C) in CRfed and CRmm mice; n = 5 per diet per time point.

(D) Body weight of mice in all three groups; n = 10 per diet per time point, mean ± SD.

(E) Blood glucose (n = 6 per diet per time point) and serum NEFA (n = 3 per diet per time point) concentration; mean ± SD.

(F) Liver FFA concentration; n = 3 per diet per time point, mean ± SD.

(G) Liver and blood β-HB concentration; n = 3 per diet per time point, mean ± SD.

(H) Liver TAG concentration; n = 3 per diet per time point, mean ± SD.

(I) Liver lipid droplet staining and quantification to evaluate TAG storage; n = 5 per diet per time point, mean ± SD.

(J) Gene expression (RNA-seq) for liver lipid metabolism genes that were identified in the F-specific subset in Figure 3E; n = 3 per diet per time point, mean ± SEM. Statistical analysis was performed with one way ANOVA, *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001, ns = not significant.

At the same time, the missed meal had a strong impact on the liver transcriptome (Figure S6F). The expression of fatty acid and cholesterol biosynthesis genes was reduced in CRmm (Figure S6F). The expression of many β-oxidation genes was induced in CRmm (Figure S6F), and correspondingly, there was an increase in liver and serum β-HB (Figures 4G and S6D). Fatty acid transport, TAG synthesis, and lipid storage genes were also induced in CRmm compared with CRbm (Figure S6F). Many genes induced by F but not CR and identified as candidates for the difference in lipid metabolism between the diets were also affected by the missed meal. The expressions of Slc27a1, Plin2, and Cidec (Figure 4J) were significantly increased just after 4 h of extra fasting in CRmm. Several genes were not affected by the missed meal, namely, Slc27a2 and Gpat4, suggesting that additional mechanisms may be involved in regulating hepatic steatosis in CRmm (Figure 4J). We asked what the impact of the missed meal on liver lipid accumulation in CR will be. Fatty acid concentration was elevated in CRmm liver (Figure 4F). Like F mice (Figure 1I), liver TAG and LDs accumulated in response to the missed CR meal (Figures 4H, 4I, and S6E). Thus, changes in the expression of the genes for fatty acid transporters, TAG synthesis, and LD homeostasis correlated with LD accumulation. These data support that the anticipated periodic feeding/fasting cycle contributed to regulating liver lipid homeostasis and steatosis in CR.

The circadian clock regulates liver lipid metabolism in CR mice

The circadian clocks are intertwined with feeding/fasting cycle. Diets such as high-fat diet (HFD), time-restricted (TR) feeding, or ketogenic diet reprogram circadian rhythms, and in turn, circadian clocks play an important role in organism response to diets.^64,65 CR impacts both the central clock in the SCN and the peripheral clocks in different tissues. In turn, the full health and metabolic benefits of the CR diet depend on the intact circadian clock.^25,46,66,67 In addition, the circadian clocks are involved in the regulation of hepatic steatosis,^68,69 through poorly defined mechanisms. Therefore, we hypothesized the contribution of the circadian clock to the regulation of hepatic lipid homeostasis in CR mice.

Cryptochromes (CRYs) are integral components of the circadian clock, and the deficiency of both Cry1 and Cry2 leads to the disruption of circadian rhythms in behavior, metabolism, and gene expression.^70–72 Cry-deficient mice do not have major health issues.^73,74 Interestingly, Cry1,2^−/− mice on restricted feeding have altered food anticipatory activity.⁷⁵ CRYs are also implicated in the circadian regulation of lipid metabolism through interaction with PPARα.²⁵ Therefore, we decided to use Cry1,2^−/− mice to study the role of the circadian clock in CR lipid metabolism. Like wild-type (WT) mice, Cry1,2^−/− mice were maintained on 30% CR for two months. CR reduced blood glucose in similar ways in Cry1,2^−/− and WT mice (Figure 5A). Serum NEFA was low in both genotypes on AL diet and was induced on CR in both Cry1,2^−/− and WT mice (Figure 5A) with comparable kinetics, but the induction was significantly higher in WT than in Cry1,2^−/− mice. Liver β-HB was low in AL mice and was significantly elevated in both genotypes on CR in a time-dependent manner, with the concentration significantly higher in Cry1,2^−/− mice (Figure 5C), in agreement with our recent publication.²⁵

Figure 5. — (A) Blood glucose and serum NEFA concentration in wild-type and cryptochrome knockout (*Cry1*,2^−/−) mice; n = 6 per diet per time point for blood glucose concentration, and n = 3 per diet per time point for serum NEFA concentration; mean ± SD.

(B) Liver FFA concentration in WT and *Cry1*,2^−/− mice; n = 3 per diet per time point, mean ± SD.

(C) Liver β-HB and liver TAG concentration in WT and *Cry1*,2^−/− mice; n = 3 per diet per time point, mean ± SD.

(D) Gene expression (RT-qPCR) for liver lipid metabolism genes in WT and *Cry1*,2^−/− mice; n = 3 per diet per time point, mean ± SD. (A–D) Statistical analysis was performed with two-way ANOVA, p ≤ 0.05 for a, WT AL versus WT CR; b, WT AL versus *Cry1,2*^−/− AL; c, WT AL versus *Cry1,2*^−/− CR; d, WT CR versus *Cry1,2*^−/− AL; e, WT CR versus *Cry1,2*^−/− CR; and f, *Cry1,2*^−/− AL versus *Cry1,2*^−/− CR.

(E) Model of circadian clock–dependent gating of liver lipid metabolism. With the initiation of fasting, blood glucose and insulin are reduced and NEFAs are released as a result of fat mobilization in adipose tissue. Fasting hormones are increased in the blood and cause the activation of fasting-associated transcriptional factors (FATF) such as PPARα, CREB, GR, and FOXO. Depending on the complex composition, the circadian clock transcriptional complex serves as either an activator or a suppressor of transcription, thus enhancing or attenuating the transcription and fasting response.

Next, we analyzed the expression of F-specific DEGs identified as candidates in the liver of Cry1,2^−/− and WT mice on both diets. The expression of Slc27a1 was significantly upregulated in the liver of Cry1,2^−/− on CR diet compared with AL and compared with WT on both diets. The expression of Slc27a2 was not affected by the genotype or diet (Figure 5D). Liver fatty acid concentration was comparable between WT and Cry1,2^−/− mice (Figure 5B). Plin2 and Cidec were significantly induced in the liver of Cry1,2^−/− on CR diet compared with AL, and compared with the WT CR liver (Figure 5D). Interestingly, the effect of CR on the expression of fatty acid transporter, TAG synthesis, and LD homeostasis genes in the liver of Cry1,2^−/− mice strongly mirrored the effect of the missed meal on their expression in the liver of WT mice. The concentration of liver TAGs was comparable between WT and Cry1,2^−/− mice on AL diet. Liver TAGs were reduced by CR in WT mice at all tested time points. Contrary to that, liver TAGs were not reduced by CR in Cry1,2^−/− mice, and they even showed the tendency to be elevated following the time without food. As a result, TAGs were significantly higher in the liver of Cry1,2^−/− mice on CR compared with WT CR at 14 and 18 h without food (Figure 5C). Together, these results reveal that the circadian clock contributes to regulating the expression of key genes that promote hepatic steatosis.

DISCUSSION

We demonstrated that CR and unanticipated fasting (F) induced overlapping but distinct metabolic states in mice. Similarities include reduced blood glucose and increased blood β-HB and serum NEFAs (Figures 1G, 1H, 2B, and 2C). There is also a significant overlap in the liver transcriptome (Figure 3A). The striking difference between the diets was the accumulation of TAGs in the form of LDs in F but not in CR liver (Figures 1F and 3G). In general, the effect of diets on hepatic steatosis in our study was in agreement with previous results on the accumulation of liver TAGs in fasting^22,29 and decreased TAGs in CR.³⁹ The different experimental designs of the cited studies do not allow the direct comparison of lipid metabolism between F and CR, and the molecular mechanisms responsible for the differences were not known. Increased adipose tissue lipolysis and defects in β-oxidation are considered as the main causes, and, therefore, many approaches are focused on them to manage hepatic steatosis^,32,36,76 Our findings suggest an intriguing uncoupling of liver steatosis from adipose tissue lipolysis and from liver β-oxidation. Indeed, serum NEFAs were comparable between the diets (Figure 1H), and, paradoxically, β-oxidation was induced much earlier and was significantly stronger in F compared with CR (Figures 2A–2F).

Through bioinformatic analysis of the liver transcriptome, we identified candidate genes that might explain the difference in liver TAGs between F and CR mice. These include Slc27a1 and Slc27a2, Gpat4, Plin2, and Cidec. Long-chain fatty acid transporters Slc27a1 and Slc27a2 (code for FATP1 and FATP2, respectively) have largely been studied in the context of HFD,^77,78 and their expression is elevated in the fasted liver.⁷⁹ Gpat4 catalyzes the committed step in TAG synthesis; its overexpression induces TAG accumulation in HepG2 cells.⁸⁰ Plin2 and Cidec are involved in lipid storage; the overexpression of Plin2 and Cidec is sufficient to induce lipid accumulation.^81–84 PLIN2 coats LD surface, and CIDEC is located at LD contact sites, where it promotes LD growth.^81,85 PLIN2 and CIDEC play important roles in promoting HFD and fasting-induced hepatic steatosis.^86–89 All these genes were significantly induced in the liver of F mice but not in CR mice (Figure 3). The changes in the expression were consistent with increased liver fatty acid transport, TAG synthesis, and LD accumulation, thus mechanistically differentiating CR and F in regulating liver steatosis.

Our data also suggested that the tight control of lipid homeostasis in CR was achieved, at least in part, through the anticipation of fasting and the circadian clock. Indeed, mice on CR or restricted feeding develop strong food anticipatory behavior expressed as active locomotion few hours before the meal.^18,19,63,90 CR mice were on periodic fasting; therefore, in addition to the well-documented food anticipation, they might also anticipate the fasting and the duration of fasting. Contrary to that, F mice had their food removed unexpectedly, the fasting was not anticipated by them, and the duration of fasting was not known. Missed daily meal added just 4 h of fasting (Figure 4A), but it was an unexpected increase in the duration of fasting for the CR mice. Importantly, the missed meal did not significantly affect blood parameters and total body metabolism (Figures 4B–4E), but it induced transcriptional response in CR mice similar to unanticipated fasting, judged by changes in the candidate gene expression. It also caused accumulation of liver TAGs and LDs (Figures 4H and 4I) in CR mice, which suggests that liver steatosis can be uncoupled from blood NEFAs and β-oxidation and can be transcriptionally programmed.

CR, circadian rhythms, food anticipation, and lipid metabolism are interconnected in a complex way. Circadian rhythms in metabolism and gene expression are reprogrammed by CR,^46,50 indicating that the circadian clock plays an important role in the full benefits of CR. The circadian clock plays a role in the regulation of liver steatosis, but the outcome depends on the model system. Knockout of Rev-erbα promotes hepatic steatosis in mice fed HFD,⁶⁹ and Bmal1^−/− protects against HFD-induced hepatic steatosis.^68,91 Circadian mutants placed on CR had increased expression of the candidate genes similar with F or CR mice with the missed meal, and they accumulated TAGs in the liver (Figures 5C and 5D). Little is known whether the anticipation of the feeding/fasting cycle contributes to the metabolic benefits of CR. There is strong evidence that circadian clocks alone cannot explain food anticipatory behavior, as rhythms in locomotor activity persist even in the absence of a functional clock.^75,92,93 Little is known about other contributors to food anticipation. Cry1,2^−/− mice exhibit altered food anticipatory activity demonstrated by delayed and unstable rhythms in locomotor activity when the mice were placed on TR feeding.⁷⁵ Whether the disrupted hepatic lipid metabolism in Cry1,2^−/− mice on CR is related to altered food anticipatory activity needs to be investigated in the future. TR is a periodic fasting diet whereby mice are given AL access to the meal within a restricted feeding window. TAGs are accumulated in the liver of Per1,2^−/− mice on TR through unknown mechanism,⁹⁴ and we found significant TAG accumulation in the liver of Cry1,2^−/− mice on CR (Figure 5C). PERs and CRYs form the negative arm of the circadian transcription-translation feedback loop; similar effects of the knockout of Per1,2 and Cry1,2 on TAG accumulation in periodic fasting diets suggest some common mechanisms and support the role of the circadian clock in regulating liver lipid homeostasis in periodic fasting diets.

We propose the following mechanistic model (Figure 5E). Transition from the fed to fasting state requires reprogramming of metabolism and gene expression. This reprogramming of the transcriptome is coordinated by fasting-induced transcription factors such as PPARα, glucocorticoid receptor (GR), Fork-head box (FOX)O transcription factors (FOXO), and cyclic AMP response element-binding protein (CREB).²³ These factors drive the expression of fatty acid metabolism genes, including those involved in fatty acid transport, TAG synthesis, and lipid storage. Circadian clock might contribute to the reprogramming by gating this transcriptional response in a manner similar with circadian gating of the cell cycle⁹⁵ and ketogenesis.²⁵ Indeed, circadian transcriptional complex, depending on the stage of the daily cycle, can cooperate or prevent the activation of transcription. For example, the circadian clock interferes with the transcriptional factor PPARα, through CLOCK, BMAL1, and CRY1 binding to circadian E-Box elements in the promoter of several PPARα target genes.²⁵ The model predicts that the expression of lipid metabolism genes with E-Box elements in their promoters will be limited to the stage of the feeding/fasting cycle entrained in CR. We analyzed the promoters of some lipid metabolism genes induced by fasting and identified circadian E-box elements close to their transcription start site (Figure S7): Plin2 (−48 bps), Cidec (−124 bps), and Slc27a1 (−195 bps). Interestingly, Plin2, Cidec, and Slc27a1 were strongly induced upon the missed CR meal and in Cry1,2^−/− CR. Chromatin immunoprecipitation sequencing data confirmed the chromatin association of one or more circadian clock transcriptional factors with the promoters of these genes.⁹⁶ Although chromatin localizations of circadian proteins are not necessarily overlapping, CLOCK, BMAL1, and CRY1 are bound to the promoter of Plin2.⁹⁶ In addition to this model, the circadian clock may also gate the response indirectly by regulating other factors that promote the response to fasting.⁹⁷ For example, glucocorticoids induce hepatic steatosis⁹⁸ and CRY1/2 has been revealed to repress glucocorticoid receptor signaling.⁹⁹ CREB is also implicated in promoting hepatic steatosis,^100,101 and CRY1/2 are regulators of CREB activity.⁷²

In summary, lipid metabolism was significantly different between unanticipated fasting and CR. Two components of CR, the reduced caloric intake and fasting, have been demonstrated to independently contribute to the metabolic benefits of the diet.^17,20,21,102 Here we illustrated that the third component of CR, previously little explored, the anticipation of the feeding/fasting cycle, also contributed to the metabolic benefits of the CR diet by regulating lipid homeostasis and preventing steatosis. With fasting-based diets growing in popularity as an approach to manage cardiometabolic and other diseases, this study suggests considering the consistent feeding schedule to achieve better outcome in metabolism to prevent unwanted side effects such as liver steatosis. Our study also supports the role of circadian clocks in regulating liver lipid homeostasis and preventing liver steatosis in CR mice.

Limitations of the study

There are several limitations to our study. We did not assay other metabolic tissues such as the adipose tissue or skeletal muscles. CR and unanticipated fasting might also impact these tissues differently, which can contribute to the whole-body metabolism and lipid metabolism. Although we cannot exclude the contribution from other tissues, our data highlighted the difference in metabolic reprogramming of the liver between CR and unanticipated fasting. The disrupted hepatic lipid metabolism observed in Cry1,2^−/− CR mice might be due to circadian clock disruption in these animals or might be a result of some CRY-specific defects. The contribution of other tissues and other circadian clock genes needs to be addressed in future studies.

RESOURCE AVAILABILITY

Lead contact

Further information and requests for reagents may be directed to and will be fulfilled by the lead contact, Roman V. Kondratov (r.kondratov@csuohio.edu).

Materials availability

This study did not generate new unique reagents.

Data and code availability

Raw RNA-seq data have been deposited in the Gene Expression Omnibus: GSE278669 and are publicly available as of the date of publication. Processed RNA-seq data are available for quick access in the supplementary files (Table S1). Raw and processed data from the main text and supplemental figures have been deposited in the Mendeley Data: https://doi.org/10.17632/2hdjj5g8y4.1 and are publicly available as of the date of publication. The accession numbers for the datasets are listed in the key resources table.
This study did not generate original code.
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

KEY RESOURCES TABLE.

REAGENT or RESOURCE	SOURCE	IDENTIFIER

Chemicals, peptides, and recombinant proteins

Oil Red	Sigma-Aldrich	O0625
Hematoxylin	Sigma-Aldrich	HHS32-1L
TRIzol^™ Reagent	Invitrogen^™	15596018
D4 - β-hydroxybutyrate	Sigma-Aldrich	904155
Tricarballylic acid	Sigma-Aldrich	T53503
iTaq^™ Universal SYBR^® Green Supermix	Bio-Rad	1725125

Critical commercial assays

Triglyceride quantification Kit	Sigma-Aldrich	MAK266
Free fatty acid assay kit	Sigma-Aldrich	MAK044
RNeasy mini kit	Qiagen	74104
NEBNext Ultra^™ II RNA Library Prep Kit for Illumina	New England Biolabs	E7775
CVS Health advanced blood glucose meter	CVS	402723
Glucose meter test strip	CVS	260964
Precision Xtra blood ketone meter	Abbott	98814-65
Precision Xtra blood ketone test strips	Abbott	75001

Deposited data

RNA seq Raw and Metadata	This paper	GEO: GSE278669
Original data are deposited at Mendeley Data	This paper	Mendeley Data: https://doi.org/10.17632/2hdjj5g8y4.1

Experimental models: Organisms/strains

C57BL/6J mice	Jackson Laboratory	000664
Cry1^−/− mice	Dr. Sancar	IMSR_Jax:016186
Cry2^−/− mice	Dr. Sancar	IMSR_Jax:016185
C57BL/6J mice	Jackson Laboratory	000664

Oligonucleotides

18S rRNA	IDT	Forward 5’ GCTTAATTTGACTCAACACGGGA 3’ Reverse 5’ AGCTATCAATCTGTCAATCCTGTC3’
Cidec	IDT	Forward 5’ GGGGAGGTCCAACACAATCC 3’ Reverse 5’ CTTCCGATCTGCGGTGCTAA 3’
Cptla	IDT	Forward 5’ ACTCCGCTCGCTCATTCCG 3’ Reverse 5’ CACACCCACCACCACGAT AA 3’
Hmgcs2	IDT	Forward 5’ TACACCTCTTCCCTCTATGG 3’ Reverse 5’ TTGGACACTCGGAATGAAAA 3’
Plin2	IDT	Forward 5’ AGGTAGGTCCTGCACCAGAT 3’ Reverse 5’ ACCACAGAAGGACGTGCAAA 3 ’
Slc27a1	IDT	Forward 5’ACTTCTGTGAGAACCTGCGAG 3’ Reverse 5’ CAGACGATACGCAGAAAGCG 3’
Slc27a2	IDT	Forward 5’- AGCGGAGACCTCCTGATGAT 3’ Reverse 5’ GGCACGCCATACACATTCAC 3’
Pparα	IDT	Forward 5’ CCACCATCACTGTATCT 3’ Reverse 5’ CAGGACCTACTCTCTATG 3’
β-actin	IDT	Forward 5’ CCAGCCTTCCTTCTTGGGTA 3’ Reverse 5’ CAATGCCTGGGTACATGGTG 3’

Software and algorithms

GraphPad Prism version 6.0	GraphPad Prism	http://www.graphpad.com/scientific-software/prism
Rstudio	Rstudio	https://www.r-project.org/
ImageJ	ImageJ¹⁰³	https://imagej.net/
Gene ontology	David web server (2021 update)^104,105	https://davidbioinformatics.nih.gov/
Comprehensive Laboratory Animal Monitoring System (CLAMS) and CI-Link software	Columbus Instruments (version 1.13.0)	https://www.colinst.com/
Bowtie	Langmead et al. Version 1.3.1¹⁰⁶	N/A
RSEM (v1.2.3)	Li and Dewey. Version 1.2.3¹⁰⁷	N/A
EBSeq	Leng et al.¹⁰⁸	N/A
DEseq2 R package	Love et al.¹⁰⁹	N/A

Other

Ohio Super Computer	Dell, Intel Xeon	Owens Cluster
Illumina NovaSeq 6000 platform	Novogene Corporation	NovaSeq 6000
Nano Drop 2000	Thermo Fisher Scientific	ND 2000
Bioanalyzer 2100	Agilent Technologies	N/A
Scientific Imaging film and Odyssey FC imaging system	LI-COR Biosciences	Odyssey FC
GC-MS: Agilent GC system 7890B coupled with Agilent MSD 5977A Mass Spectrometer	Agilent Technologies	N/A
Agilent J&W HP-5ms column	Agilent Technologies	N/A
Graphical abstract – created using BioRender	This paper	https://BioRender.com/heyrr00

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STAR★METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Experimental animals and dietary regimens

All experiments involving animals were conducted in accordance with federal and university guidelines, and all procedures were approved by Institutional Animal Care and Use Committee (IACUC) at Cleveland State University (Protocol No. 21160-KON-S). Both male and female mice were of C57BL/6 background, housed under 12:12 h light/dark cycles with light on at ZT0. All animals were fed 5008 LabDiet (proteins 26.5%, fat 16.9%, carbohydrates 56.5%), and given unlimited access to water.

AL mice received unlimited access to food. At 3 months of age, mice were placed on calorie restriction (CR), and these mice received 70% of Ad libitum (AL) daily food at ZT14, given once per day for the duration of the experiment. The mice were placed on CR for two months, before samples were collected for analysis. Sample collection was performed at circadian time points of the day following the absence of the CR meal, we called this ‘Time without food’. The timepoint when CR mice consumed all their food, ZT16, was set as 0 h’ time without food. Another group of mice were maintained on AL diet for 5 months, and on the last day, they were fasted, and samples were analyzed following the time without food. This group of mice were simply denoted as fasted (F). The fasted mice had their food removed at ZT16, which is the same time CR mice finish their daily meal. Thus, the time without food was synchronized to begin at the same time for CR and F. Samples were collected for analysis following 0, 6, 14, and 22 h without food, while the AL group that were never fasted were analyzed at equivalent circadian timepoints to serve as control group. For circadian clock experiment, Cry1,2^−/− mice were used. These mice were previously described.⁷⁰ All mice were 5 months of age at the time of sample analysis.

METHOD DETAILS

Metabolic cage measurements

Respiratory exchange ratio (RER) and Energy expenditure (EE) in mice were assessed using the Comprehensive Laboratory Animal Monitoring System (CLAMS, Columbus Instruments). Mice were housed individually in the metabolic cages and were provided free access to water. Prior to recording data, mice were acclimatized in the metabolic cages for 3 days. Respiratory exchange ratio (RER), energy expenditure, and food intake data was collected using the CI-Link software (version 1.13.0) provided by the manufacturer. Final data for RER and EE was graphically presented at 1 h intervals.

Blood glucose and β-hydroxybutyrate detection

Blood glucose and ketone body concentrations was evaluated from the tail vein. Glucose was measured using CVS Health Advanced Blood Glucose Meter with CVS Health advanced Glucose Meter Strips (CVS Pharmacy, Woonsocket, RI). Blood β-hydroxybutyrate was measured using the Precision Xtra Blood Glucose and Ketone meter (Abbott Laboratories).

Serum non-esterified fatty acids detection

Blood was collected from the tail vein and centrifuged at 6500 rpm at 4°C, serum was collected. Serum NEFA was measured using a colorimetric kit (Sigma-Aldrich).

Liver TAG analysis

Liver triglyceride was assayed using commercially available kit (Sigma-Aldrich). Per manufacturer’s instruction, 20mg of frozen liver tissue was homogenized in 200 μL of 5% NP-40. Samples were processed through repeated heating in boiling water for 2–5 min, cooled to solubilize triglycerides, and centrifuged at 10,000 rpm for 2 min. The supernatant was diluted 10-fold with H₂0, and all subsequent manufacturer procedures were strictly followed.

Liver β-hydroxybutyrate and fatty acid analysis by GC-MS

Sample preparation: Sample preparation was performed on ice. Mice liver tissues (~25 mg) were spiked with 100 μL of 1 mM D4 - β-hydroxybutyrate (Sigma-Aldrich) and 50 μL of 1 mM tricarballylic acid (Sigma – Aldrich). D4 - β-hydroxybutyrate and tricarballylic acid working solutions were prepared in H2O and were used as internal standards for β–hydroxybutyrate and fatty acids respectively. Each tissue was homogenized in 500 μL of “Extraction Matrix” (1:1 v/v) methanol: 5% acetic acid (aq) using a VWR pellet mixer and centrifuged at 10000 rpm for 10 min. The supernatant was transferred to an 8 mL glass tube. The pellet was resuspended in 500 μL of the Extraction Matrix and centrifuged again. The combined supernatants were dried under N2 evaporator at room temperature and then subjected to derivatization. Derivatization was performed with methoxamine (2 mg/mL in pyridine) (MOX) and N,O-Bis(trimethylsilyl)trifluoroacetamide (BSTFA) reagents (Sigma-Aldrich). 40 μL of MOX was added to the dried samples and incubated at 80°C for 1 h, followed by 60 μL of BSTFA with incubation at 70°C for 30 min 10-fold dilution of the samples with BSTFA was performed before insertion into the GC-MS.

GC-MS analysis: The derivatized samples were analyzed using Agilent GC system 7890B coupled with Agilent MSD 5977A Mass Spectrometer (Agilent Technologies Inc.). Chromatographic separation was achieved with Agilent J&W HP-5ms is a (5%- phenyl)-methylpolysiloxane column (30 m × 250 μm × 0.25 μm) (Agilent Technologies Inc). The temperature program was as the following: starting temperature of 800 C for three minutes following by the ramp at 80 C/min till temperature reached 320°C, with a final hold of two minutes. Total run time was thirty-five minutes. Mass spectrometer was operated in a positive scan mode. The temperature of the inlet was set at 275°C, and the injection volume was 1 μL in splitless mode. Metabolites’ identification was performed based on retention times of appropriate standards and NIST library. For the quantification, specific ions were extracted for each analyte of interest as following: m/z 233 for β-hydroxybutyrate, m/z 237 for D4- β-hydroxybutyrate, m/z 377 for tricarballylic acid, m/z 313 for C16:0, m/z 341 for C18:0, m/z 339 for C18:1, and m/z 337 for C18:2.

Hepatic lipid staining

Mouse liver was perfused with 1× PBS and fixed in 10% buffered formalin. Samples were embedded in OCT and flash-frozen. Samples were cut onto superfrost plus microscopic slides (Fisher Scientific) using a cryostat at 10μm thickness. Sample slides were immersed in several solutions as follows: H₂O (30 s), H₂O (30 s), 60% isopropanol (30 s), Oil Red O for 18 min (Oil Red O powder was purchased from Sigma-Aldrich, prepared in 250 mL of isopropanol, after stirring for 10 min, 150 mL of H₂O was added, left to stand for 10 min, then filtered), 60% isopropanol (30 s), 2× wash in H₂O (30 s), hematoxylin (2 min) (Sigma-Aldrich), ammonium hydroxide (10 dips) (Fisher Scientific), and 3× wash with H₂O (30 s). Coverslips were place on the slides using aqua mount (Epredia), and the stained sections were imaged and quantified by assessing the percentage of red stain to the total area of the image using ImageJ¹⁰³ software.

RNA isolation and RT-qPCR analysis

Total RNA was isolated from frozen liver using Trizol kit. The purity of RNA was checked using nanodrop (Thermo Fisher Scientific), with 260/280 and 260/230 ratios ≥2.0. RNA was reverse transcribed using SuperScript IV Reverse Transcriptase. qPCR was performed using the iTaq Universal SYBR Green Supermix (Bio-Rad). Fold change was calculated by the ΔΔCt method, and B-Actin was used for normalization. All primers were ordered from IDT.

RNA sequencing

Total RNA was isolated from female mouse liver tissue using mini spin column QIAGEN RNeasy Mini Kit according to the manufacturer’s protocol. Sample purity was checked on Nano Drop (Thermo Fisher Scientific) with 260/280 and 260/230 ratios ≥2.0. The RNA sequencing was performed by Novogene Corporation Inc (Sacramento, CA, USA). After sample quality control, non-directional – polyA enrichment library was prepared using NEBNext Ultra II RNA Library Prep Kit for Illumina (New England BioLabs), and 50M reads (150bp, paired end) per sample were generated on Illumina NovaSeq 6000 platform. RNA-seq reads were mapped to the mouse protein coding genes (Ensembl: Mus_musculus; GRCm38) using Bowtie¹⁰⁶ allowing up to 2-mismatches. The gene expected read counts and Transcripts Per Million (TPM) were estimated by RSEM (v1.2.3).¹⁰⁷ The TPMs were further normalized by EBSeq¹⁰⁸ R package to correct potential batch effect.

Gene ontology analysis

The Database for Annotation, Visualization, and Integrated Discovery (DAVID) Ver. 2021 (https://davidbioinformatics.nih.gov/)^104,105 was used for Gene Ontology analysis to identify top enriched pathways. Official gene symbols (common gene names) were used for the input for each subset of genes analyzed.

Gene promoter analysis

The gene transcription start site (TSS) was determined on the mouse genomic sequence from the National Center for Biotechnology Information (NCBI) database. 2 kilobases upstream of the TSS was pooled and manually screened for circadian E-box elements (CANNTG).

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analysis

Data was analyzed by two-way analysis of variance (ANOVA) with Bonferroni correction using GraphPad Prism Version 6.0 (GraphPad Software, Boston, MA), with p value ≤0.05. Differential expression (DEseq) was performed using DEseq2¹⁰⁹ R package. Genes with adjusted p value ≤0.01 and log 2-fold change of |≥ 0.585 and ≤ −0.585| were regarded as differentially expressed. Output files from DEseq2 analysis are available in Table S2.

Supplementary Material

NIHMS2107289-supplement-1.pdf^{(613.3KB, pdf)}

NIHMS2107289-supplement-2.xlsx^{(18.7MB, xlsx)}

NIHMS2107289-supplement-3.xlsx^{(9.9MB, xlsx)}

SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.celrep.2025.116141.

Highlights.

Periodic fasting is an essential component of calorie restriction (CR)
CR differentially affects liver lipid homeostasis compared with unanticipated fasting
The metabolic effects of CR depend on the anticipation of the feeding/fasting cycle
Circadian clocks gate fasting transcriptional response in CR to regulate lipid homeostasis

ACKNOWLEDGMENTS

This work was supported by the National Institute of Health (NIH) grant (R01AG039547 to R.V.K.) and funds from the Center of Gene Regulation in Health and Disease (Cleveland State University) to R.V.K. We thank Alicia Traughber for technical assistance with the cryostat operation and Brendon M. Rubel and Gena Asi for assistance with tissue staining and quantification. The graphical abstract was created using BioRender (https://BioRender.com/heyrr00).

Footnotes

DECLARATION OF INTERESTS

The authors declare no competing interests.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

NIHMS2107289-supplement-1.pdf^{(613.3KB, pdf)}

NIHMS2107289-supplement-2.xlsx^{(18.7MB, xlsx)}

NIHMS2107289-supplement-3.xlsx^{(9.9MB, xlsx)}

Data Availability Statement

Raw RNA-seq data have been deposited in the Gene Expression Omnibus: GSE278669 and are publicly available as of the date of publication. Processed RNA-seq data are available for quick access in the supplementary files (Table S1). Raw and processed data from the main text and supplemental figures have been deposited in the Mendeley Data: https://doi.org/10.17632/2hdjj5g8y4.1 and are publicly available as of the date of publication. The accession numbers for the datasets are listed in the key resources table.
This study did not generate original code.
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

KEY RESOURCES TABLE.

REAGENT or RESOURCE	SOURCE	IDENTIFIER

Chemicals, peptides, and recombinant proteins

Oil Red	Sigma-Aldrich	O0625
Hematoxylin	Sigma-Aldrich	HHS32-1L
TRIzol^™ Reagent	Invitrogen^™	15596018
D4 - β-hydroxybutyrate	Sigma-Aldrich	904155
Tricarballylic acid	Sigma-Aldrich	T53503
iTaq^™ Universal SYBR^® Green Supermix	Bio-Rad	1725125

Critical commercial assays

Triglyceride quantification Kit	Sigma-Aldrich	MAK266
Free fatty acid assay kit	Sigma-Aldrich	MAK044
RNeasy mini kit	Qiagen	74104
NEBNext Ultra^™ II RNA Library Prep Kit for Illumina	New England Biolabs	E7775
CVS Health advanced blood glucose meter	CVS	402723
Glucose meter test strip	CVS	260964
Precision Xtra blood ketone meter	Abbott	98814-65
Precision Xtra blood ketone test strips	Abbott	75001

Deposited data

RNA seq Raw and Metadata	This paper	GEO: GSE278669
Original data are deposited at Mendeley Data	This paper	Mendeley Data: https://doi.org/10.17632/2hdjj5g8y4.1

Experimental models: Organisms/strains

C57BL/6J mice	Jackson Laboratory	000664
Cry1^−/− mice	Dr. Sancar	IMSR_Jax:016186
Cry2^−/− mice	Dr. Sancar	IMSR_Jax:016185
C57BL/6J mice	Jackson Laboratory	000664

Oligonucleotides

18S rRNA	IDT	Forward 5’ GCTTAATTTGACTCAACACGGGA 3’ Reverse 5’ AGCTATCAATCTGTCAATCCTGTC3’
Cidec	IDT	Forward 5’ GGGGAGGTCCAACACAATCC 3’ Reverse 5’ CTTCCGATCTGCGGTGCTAA 3’
Cptla	IDT	Forward 5’ ACTCCGCTCGCTCATTCCG 3’ Reverse 5’ CACACCCACCACCACGAT AA 3’
Hmgcs2	IDT	Forward 5’ TACACCTCTTCCCTCTATGG 3’ Reverse 5’ TTGGACACTCGGAATGAAAA 3’
Plin2	IDT	Forward 5’ AGGTAGGTCCTGCACCAGAT 3’ Reverse 5’ ACCACAGAAGGACGTGCAAA 3 ’
Slc27a1	IDT	Forward 5’ACTTCTGTGAGAACCTGCGAG 3’ Reverse 5’ CAGACGATACGCAGAAAGCG 3’
Slc27a2	IDT	Forward 5’- AGCGGAGACCTCCTGATGAT 3’ Reverse 5’ GGCACGCCATACACATTCAC 3’
Pparα	IDT	Forward 5’ CCACCATCACTGTATCT 3’ Reverse 5’ CAGGACCTACTCTCTATG 3’
β-actin	IDT	Forward 5’ CCAGCCTTCCTTCTTGGGTA 3’ Reverse 5’ CAATGCCTGGGTACATGGTG 3’

Software and algorithms

GraphPad Prism version 6.0	GraphPad Prism	http://www.graphpad.com/scientific-software/prism
Rstudio	Rstudio	https://www.r-project.org/
ImageJ	ImageJ¹⁰³	https://imagej.net/
Gene ontology	David web server (2021 update)^104,105	https://davidbioinformatics.nih.gov/
Comprehensive Laboratory Animal Monitoring System (CLAMS) and CI-Link software	Columbus Instruments (version 1.13.0)	https://www.colinst.com/
Bowtie	Langmead et al. Version 1.3.1¹⁰⁶	N/A
RSEM (v1.2.3)	Li and Dewey. Version 1.2.3¹⁰⁷	N/A
EBSeq	Leng et al.¹⁰⁸	N/A
DEseq2 R package	Love et al.¹⁰⁹	N/A

Other

Ohio Super Computer	Dell, Intel Xeon	Owens Cluster
Illumina NovaSeq 6000 platform	Novogene Corporation	NovaSeq 6000
Nano Drop 2000	Thermo Fisher Scientific	ND 2000
Bioanalyzer 2100	Agilent Technologies	N/A
Scientific Imaging film and Odyssey FC imaging system	LI-COR Biosciences	Odyssey FC
GC-MS: Agilent GC system 7890B coupled with Agilent MSD 5977A Mass Spectrometer	Agilent Technologies	N/A
Agilent J&W HP-5ms column	Agilent Technologies	N/A
Graphical abstract – created using BioRender	This paper	https://BioRender.com/heyrr00

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PERMALINK

Circadian clocks and periodic anticipated fasting prevent fasting-associated hepatic steatosis in calorie restriction

Oghogho P Ebeigbe

Volha Mezhnina

Artem Astafev

Nikkhil Velingkaar

Jillian Kodger

Allan Poe

Jonathan Fritz

Kadaia Z Williams

Evelina Trokhimenko

Josefa-Marie B Rom

Yana Sandlers

Roman V Kondratov

SUMMARY

In brief

Graphical Abstract

INTRODUCTION

RESULTS

CR prevents fasting-induced accumulation of liver triglycerides

Figure 1. Effect of F and CR diets on mice physiology.

Fasting induced stronger fatty acid oxidation compared with CR

Figure 2. Hepatic fatty acid oxidation is stronger in F than in CR mice.

Transcriptomic analysis reveals hepatic lipid metabolism is significantly different between CR and F

Figure 3. Hepatic fatty acid transport, TAG synthesis, and lipid storage are significantly different between CR and F.

Missed daily meal impacts CR liver lipid metabolism and induces TAG accumulation

Figure 4. Missed daily periodic meal impacts CR liver lipid homeostasis and induces lipid accumulation.

The circadian clock regulates liver lipid metabolism in CR mice

Figure 5. Circadian clock disruption affects CR liver lipid homeostasis.

DISCUSSION

Limitations of the study

RESOURCE AVAILABILITY

Lead contact

Materials availability

Data and code availability

KEY RESOURCES TABLE.

STAR★METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Experimental animals and dietary regimens

METHOD DETAILS

Metabolic cage measurements

Blood glucose and β-hydroxybutyrate detection

Serum non-esterified fatty acids detection

Liver TAG analysis

Liver β-hydroxybutyrate and fatty acid analysis by GC-MS

Hepatic lipid staining

RNA isolation and RT-qPCR analysis

RNA sequencing

Gene ontology analysis

Gene promoter analysis

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analysis

Supplementary Material

Highlights.

ACKNOWLEDGMENTS

Footnotes

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

KEY RESOURCES TABLE.

ACTIONS

PERMALINK

RESOURCES

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