Differences in body weight in the FA versus FT feeding groups in both Bmal1and WT mice could not be attributed to differences in food consumption, as their food intake was indistinguishable ( Figure 1 C). In the Rev-erbα/β cohort, although food consumption was slightly lower in both FT groups, it was not statistically different and there was no correlation (Pearson and Spearman coefficient) between cumulative food intake and body weight at 12 weeks ( Figure 1 I). Finally, food consumption was similar between the CDKO-FA and CDKO-FT groups, as well as for WT cohorts on FA or FT ( Figure 1 O). Total activity ( Figures 1 E, 1K, and 1Q), assessed by beam breaks in metabolic cages (see Figure 2 ), was also indistinguishable between the FA and FT groups. Furthermore, the light:dark partition of activity was similar in FA and FT (81.05% ± 16.8% versus 84.13% ± 13.8% in the dark in Bmal1FA versus FT [ Figure S1 A]; 50.13% ± 6.6% versus 53.49% ± 2.7% in the dark for Rev-erbα/βFA versus FT [ Figure S1 B]; and 79.33% ± 6.4% versus 68.42% ± 13.3% in the dark for CDKO-FA versus -FT [ Figure S1 C]; two-way ANOVA). To test whether exercise capacity was compromised under FT, mice were subjected to treadmill exercise. All mice on FT, with the exception of CDKO mice, ran significantly longer on a treadmill than mice on FA ( Figures 1 F, 1L, and 1R). Treadmill performance of the CDKO mice on FT was not significantly different from that under FA. Thus, TRF attenuated excessive body weight gain in both WT and circadian mutant mice without changing total caloric intake or activity.

As seen earlier with WT C57BL/6J mice (), the circadian mutant mice adapted to the TRF regimen and consumed an equivalent amount of food as their ad libitum cohorts. All mutant mice and their respective control cohorts exhibited weight gain under the FA condition, and TRF attenuated the weight gain to the same extent in mutants and WT controls ( Figures 1 B, 1H, and 1N). Over the 12-week experiment, Bmal1mice on FA exhibited a 35.5% weight gain (from 26.48 ± 0.28 g to 35.88 ± 0.95 g) compared with only 17.5% for mice on FT (from 27.69 ± 0.39 g to 32.54 ± 0.64 g) ( Figure 1 B). For Rev-erbα/β, weight gain on FA was 79.5% (from 26.09 ± 0.57 g to 46.82 ± 1.58 g) compared with only 31.3% for mice on FT (from 25.77 ± 0.63 g to 33.84 ± 1.61 g) ( Figure 1 H). TRF led to a significant difference in body weight, starting 7 weeks after commencing the feeding regimen. There was no difference in body weight between Bmal1and WT cohorts on FA (35.88 ± 0.95 g versus 37.71 ± 1.0 g, respectively) nor FT (32.54 ± 0.64 g versus 32.50 ± 0.86 g, respectively). Similar body weight trends were also observed in the Rev-erb groups. After 12 weeks, Rev-erbα/βand WT cohorts weighed the same on FA (46.82 ± 1.61 g versus 46.89 ± 1.39 g, respectively) and on FT (33.84 ± 1.61 g versus 37.63 ± 1.26 g, respectively). From 3 weeks onward, TRF significantly prevented body weight gain in Rev-erbα/βmice, as it did in littermate controls. Finally, CDKO mice on TRF were also protected from weight gain, with 39.3% weight gain on FA (from 19.65 ± 0.54 g to 28.86 ± 1.31 g) compared with only 10.4% for mice on FT (from 19.10 ± 0.67 g to 22.57 ± 0.97 g) ( Figure 1 N). As previously described (), the CDKO animals weighed significantly less than their control littermates. The control littermates gained 63.5% of their weight on FA (from 25.16 ± 0.52 g to 41.13 ± 0.98 g), which was significantly higher than the 38% weight gain on FT (from 23.52 ± 0.68 g to 32.48 ± 0.97 g). Body composition was assessed in a subset of mice from each group at the end of the 12-week feeding protocol. All animals on TRF weighed less than their respective littermate controls on FA, and the reduced body weight was accompanied by reduced adiposity ( Figures 1 D, 1J, and 1P).

All circadian mutant mice fed a normal chow ad libitum develop some aspects of metabolic dysfunction, which exacerbates with age. HFD further accentuates metabolic dysfunctions in circadian mutant mice (), making it easier to compare their phenotypes within a relatively uniform timescale. To test whether a consistent daily cycle of feeding-fasting could attenuate these metabolic dysfunctions, we used three types of clock mutants: (1) CDKO mice, which lacked the circadian clock in all tissues, (2) mice with liver-specific KO of Rev-erbα and Rev-erbβ (Rev-erba;Rev-erbb;Albdenoted Rev-erbα/β), and (3) mice with liver-specific KO of Bmal1 (Bmal1;Alb, denoted Bmal1). Twelve-week-old male mice were fed an HFD (60% energy from fat) either ad libitum (the FA groups), or via TRF, which consisted of a 9–10 hr interval during the night (ZT13–22/23; the FT groups). Bmal1and Rev-erbα/βmice exhibit increased hepatic steatosis and dyslipidemia on HFD (), whereas CDKO mice exhibit accelerated weight gain and impaired glucose homeostasis (). Weekly food intake and body weight of these three mutant lines and their respective littermate controls on FA and FT regimens (12 cohorts as shown in Figures 1 A , 1G, and 1M) were monitored for 12 weeks. The experiment was repeated with three independent mouse cohorts for all mutants, involving 256 mice in total. Rev-erbα/βmice and their WT littermates needed up to 10 hr of food access to eat an equivalent amount of food as their ad libitum counterparts.

(D, J, and P) Body composition (g) in (D) Bmal1 LKO and littermate controls (n = 4–5/group), (J) Rev-erbα/β LDKO and littermate controls (n = 8–11/group), (P) CDKO and littermate controls (n = 8–9/group) after 12 weeks on FA or FT.

(A, G, and M) Schematic of the experimental design depicting the 12 mice cohorts that were studied and the timing of food access relative to a 24 hr day: (A) Bmal1 LKO -FA and -FT, Bmal1 LWT -FA and -FT; (G) Rev-erbα/β LKO -FA and -FT, Rev-erbα/β LWT -FA and -FT; (M) CDKO-FA and -FT, CC-FA and -FT.

To test which aspect of whole-body energy metabolism might contribute to the observed weight stabilization in FT mice, we examined the volume of oxygen (VO) consumed throughout the 24-hr day. Overall, FT mice had higher energy expenditure than FA mice, specifically during the feeding phase ( Figures 2 B, 2F, and 2J). Interestingly, when FT mice were switched to ad libitum eating, they continued to exhibit increased VOconsumption (compared with FA mice) during the first 24–48 hr, supporting the hypothesis that TRF increases overall oxidative metabolism, at least transiently (). This increase in VOconsumption was not associated with differences in total daily food consumption ( Figures 2 D, 2H, 2L, and S1 D–S1F) nor in activity ( Figures 2 C, 2G, and 2K) between FA and FT cohorts. Thus, TRF promotes a rhythm in substrate utilization and is associated with increased energy expenditure in the feeding period without concomitant changes in total daily caloric intake and daily rhythms in activity. As seen earlier with a normal diet (), this rhythm does not require a functional circadian clock.

Bmal1, Rev-erbα/β, and CDKO mice on FA showed a relatively inflexible respiratory exchange ratio (RER) ( Figures 2 A, 2E, and 2I) during a 24-hr day, suggesting diminished capacity to switch fuel usage between day and night. The mice on FT displayed strong diurnal rhythms of the RER with a higher RER during the fed period and lower RER during the fasting period, indicating a relatively higher usage of carbohydrates during feeding and of lipids during fasting. Interestingly, these oscillations and feeding-fasting rhythms were lost as soon as FT mice were released to FA ( Figures 2 A, 2E, and 2I, dotted lines), indicating that the imposed feeding rhythm drives the observed daily rhythm in RER in both whole-body or liver-specific circadian mutant mice, and furthermore that TRF does not lead to a learned eating rhythm in mice.

The effect of TRF in reducing body weight gain in both mutant and WT controls prompted us to test whether TRF affected daily patterns of whole-body fuel utilization. Bmal1, Rev-erbα/β, and CDKO mice were analyzed in metabolic cages (TSE Systems) for 4–5 days (after 48 hr acclimation) after undergoing 6–7 weeks of the indicated feeding regimens. As described for WT mice (), all mutants on FA regimens displayed altered eating behavior, with caloric consumption spread almost evenly between dark and light phases ( Figures 2 D, 2H, 2L, and S1 D–S1F). Bmal1mice on FA consumed 59.97% (±5.6%) of their caloric intake during the light phase and 40.03% (±3.6%) during the dark phase ( Figure S1 D). Light-phase food consumption represented 48.89% (±1.17%) of total food intake for Rev-erbα/βmice ( Figure S1 E) and 46.82% (±12.7%) of total food intake for CDKO mice ( Figure S1 F).

As observed for hepatic steatosis, there were some genotype-specific differences in the severity of serum dyslipidemia. TG serum levels were significantly higher (56%) in Bmal1mice on FA than their control littermates (115.4 ± 5.8 mg/dL versus 74.0 ± 2.7 mg/dL, respectively) ( Figure 3 L), with no difference in cholesterolemia ( Figure 3 O). An opposite phenotype was observed in Rev-erbα/βmice, with 30% higher cholesterol than littermate controls (290.5 ± 14.55 mg/dL versus 203.3 ± 17.87 mg/dL, respectively) ( Figure 3 P). The mutant and WT controls had similarly elevated TG levels under FA ( Figure 3 M).

HFD-induced obesity is ultimately accompanied by serum dyslipidemia. Accordingly, all mice on FA showed serum hypertriglyceridemia ( Figures 3 L–3N) and hypercholesterolemia ( Figures 3 O–3Q). Nevertheless, for all mutants and their control littermates, TRF significantly protected against increases in TG ( Figures 3 L–3N) and cholesterol ( Figures 3 O–3Q) in the serum. Serum TG levels were reduced by 40% (115.4 ± 5.8 mg/dL versus 73.99 ± 4.4 mg/dL) in Bmal1mice FA versus FT, by 17% (49.75 ± 2.0 mg/dL versus 41.19 ± 1.6 mg/dL) in Rev-erbα/βmice FA versus FT, and by 42% (96.41 ± 9.0 mg/dL versus 56.14 ± 4.7 mg/dL) in CDKO-FA versus -FT. In TRF mice, serum cholesterol levels were reduced by 28% in Bmal1mutants (232.9 ± 20.5 mg/dL versus 168.3 ± 8.0 mg/dL), 35% in Rev-erbα/βmutants (290.5 ± 14.6 mg/dL versus 189.9 ± 10.5 mg/dL) and 22% in CDKO mutants (165 ± 5.9 mg/dL versus 128.4 ± 3.2 mg/dL). Taken together, these observations indicate that TRF reduces fat accumulation and supports lipid homeostasis.

Whole-body fat accumulation is often associated with fatty liver disease. Histological examination of livers from WT, Bmal1, and Rev-erbα/βmice on FA showed hepatic accumulation of lipids. TRF protected all of these cohorts from hepatic steatosis ( Figure 3 F). This reduction was confirmed by measuring triglycerides (TG) in whole-liver extracts ( Figures 3 I and 3J). Hepatic TG content was reduced by 66.1% in Bmal1FT compared with FA, and by 67.1% in Rev-erbα/β. Notably, we observed very little accumulation of fat in the liver of CDKO mice under FA ( Figure 3 F), and lower levels of TG in liver extracts ( Figure 3 K), which did not change significantly under FT.

The respective WT cohorts for each genotype also showed similar changes in fat and lean mass upon TRF. The percentage of fat mass ( Figure 3 A) was reduced (27.6% versus 6.7%, p < 0.001 [unpaired t test]) and the percentage of lean mass ( Figure S2 A) was increased (60.5% versus 78.6%, p < 0.01 [unpaired t test]) in the Bmal1cohort subjected to TRF ( Figures 3 A and S2 A). Similarly, Rev-erbα/βon TRF had significantly less fat (35.7% versus 42.6%, p < 0.01) and more lean mass (49.6% versus 54.7%, p < 0.05) than controls fed ad libitum ( Figures 3 B and S2 B). Finally, Cry control (CC)-FT had significantly less fat mass (33.8% versus 39.9%, p < 0.05) and a trend to more lean mass (56.3% versus 51.9%, ns) than CC-FA ( Figures 3 C and S2 C). This reduced fat accumulation was evident in both the adipose depots and the liver. Histological examination of the epididymal white adipose tissue (eWAT) revealed smaller adipocytes and the absence of crown-like structures in FT compared with FA mice of all genotypes ( Figure 3 D), suggesting reduced immune infiltration and inflammation in WAT of TRF mice. H&E staining also revealed that brown adipose tissue (BAT) of TRF mice was protected from “whitening” (i.e., the presence of unilocular fat droplets characteristic of WAT), which is typically observed in FA mice of all genotypes ( Figure 3 E). Finally, consistent with these results, leptin and adiponectin levels, two indicators of whole-body adiposity, were affected by TRF. Serum leptin concentration was significantly lower in all mice on TRF ( Figure 3 G) with a 92.0%, 83.4%, and 82.4% reduction in Bmal1, Rev-erbα/β, and CDKO mice, respectively in FT compared with FA. Serum adiponectin concentrations were higher in all TRF mice ( Figure 3 H). Thus, decreased weight gain in TRF was associated with a consistent reduction in fat mass and an increase in lean mass.

Body composition analysis revealed that the percentage of fat mass ( Figure 3 A) reduced and lean mass ( Figure S2 A) increased when the Bmal1cohort was subjected to TRF (27.0% versus 12.2%, p < 0.01 [unpaired t test] for fat mass, and 63.5% versus 75.5%, p < 0.001 [unpaired t test] for lean mass) ( Figures 3 A and S2 A). Similarly, Rev-erbα/βon TRF had significantly less fat (30.0% versus 43.2%, p < 0.01) and more lean mass (48.6% versus 58.9%, p < 0.05) than controls fed ad libitum fed controls ( Figures 3 B and S2 B). Finally, CDKO-FT had significantly less fat mass (7.5% versus 39.5%, p < 0.01) and more lean mass (78.9% versus 51.4%, p < 0.05) than CDKO-FA ( Figures 3 C and S2 C). A similar trend in reduced fat mass and increased lean mass was also observed in all control littermates ( Figures 3 A–3C and S2 A–S2C).

(D–F) Representative pictures of H&E staining of sections of (D) epididymal WAT, (E) BAT, and (F) liver in the indicated genotype and feeding group.

Bmal1mutants showed different results than the other models. Indeed, as mentioned previously, Bmal1fed ad libitum did not show glucose intolerance ( Figure 4 A), and their insulin levels were elevated under FA ( Figure 4 G), both in the fasted or re-fed state. Nevertheless, TRF reduced serum insulin to a level comparable with littermate controls. In addition, an insulin tolerance test showed that Bmal1mutants were more insulin sensitive than controls ( Figure S2 D). In summary, each of the three mutant models exhibited varying patterns of glucose intolerance during ad libitum HFD feeding; however, TRF improved glucose tolerance in all three genotypes.

We further measured serum glucose levels ( Figures 4 D–4F) and corresponding insulin levels ( Figures 4 G–4I) after 12 hr of fasting (fasted) during the day, and 1 hr after an intraperitoneal injection of glucose (re-fed) to assess insulin sensitivity. In the fasted state, there were no differences in glucose levels between FA and FT groups for any of the mouse cohorts analyzed (i.e., neither in the mutants nor in the control littermates) ( Figures 4 D–4F, fasted). A similar trend was observed for insulin levels ( Figures 4 G–4I), except for Bmal1mice. In the re-fed state, however, glucose levels were always lower in TRF than mice fed ad libitum, with the exception of Bmal1 cohorts ( Figures 4 D–4F), independently confirming the GTT results. In Rev-erbα/β, WT, and CDKO mice, there was a strong correlation between high glucose and high insulin levels in the fed state, demonstrating insulin resistance in FA. Insulin levels were lower in FT, suggesting that TRF supports insulin sensitivity.

To characterize how the circadian clock and eating patterns affect glucose homeostasis, we performed intraperitoneal glucose tolerance tests (ip-GTT). Except for Bmal1mice, all mice on FA showed glucose intolerance ( Figures 4 A–4C ). Interestingly, the severity of glucose intolerance for these FA mutants (compared with controls) was variable. As quantified using the area under the curve (AUC) above baseline, there was no difference between Rev-erbα/βand WT cohorts under FA ( Figure 4 B, insert), whereas CDKO-FA mice, despite their smaller body weight, were much more glucose intolerant than controls ( Figure 4 C, insert). Bmal1mice were not glucose intolerant compared with WT ( Figure 4 A, insert). Glucose intolerance was significantly improved in all mutants and controls on TRF ( Figures 4 A–4C).

(D–F) Serum glucose levels in fasted (ZT22-ZT36) and re-fed mice (1 hr after intraperitoneal injection of glucose (1 mg/g body weight) at ZT36) in (A) Bmal1 LKO and littermate controls (n = 4–6/group), (B) Rev-erbα/β LDKO and littermate controls (n = 4–6/group), and (C) CDKO and littermate controls (n = 4–6/group). ∗ p < 0.05, ∗∗∗∗ p < 0.0001.

(A–C) Glucose tolerance test (ip-GTT) in (A) Bmal1 LKO and littermate controls (n = 8–10/group), (B) Rev-erbα/β LDKO and littermate controls (n = 8–12/group), and (C) CDKO and littermate controls (n = 6–8/group). Quantification of the AUC above baseline is shown in the insert. Unpaired t test, ∗ p < 0.05, ∗∗ p < 0.01.

Third, to analyze the impact of TRF on the temporal profile of metabolites levels, we used Meta2Cycle (meta2d_p value <0.05, see STAR Methods for details) to identify cycling metabolites in each group (results in Table S4 and Figure S4 ). In general, except in Rev-erbα/βwhere the number of cycling metabolites was almost identical (25 in Rev-erbα/β–FA versus 24 in Rev-erbα/β–FT), there were more diurnal metabolites in TRF than ad libitum (9 in WT-FA versus 24 in WT-FT; 7 in Bmal1-FA versus 33 in Bmal1-FT; and 8 in CDKO-FA versus 33 in CDKO-FT). These cycling metabolites were enriched in amino acids (shown in green in Figure S4 ). These metabolites tended to peak after feeding, suggesting that the imposed feeding rhythm could drive their daily rhythms.

Recent studies suggest that NADmetabolism is at the crossroad of circadian regulation, gene expression, and cellular metabolism (). We found that the level of nicotinate (NA) and nicotinamide (NAM), two precursors of NADthrough the salvage pathway, were significantly lower in FT compared with FA ( Figure S3 C). Interestingly, both are upstream of Nampt, the enzyme controlling flux through the salvage pathway whose expression is upregulated in FT compared with FA. Together these results suggest a scenario in which the source of NADmay differ between TRF and mice fed ad libitum, with higher flux in the NADsalvage pathway under TRF. Interestingly, this is reminiscent of what is observed under calorie restriction ().

Second, to identify a TRF metabolite signature, we analyzed metabolomics results using a linear model with the two factors, genotype and feeding group, and a rank analysis. The two pathways that distinguished ad libitum from the time-restricted group were “alanine, aspartate, and glutamate metabolism” and “TCA cycle” (adjusted p = 0.0086 for both). Some TCA intermediates were significantly elevated (α-ketoglutarate and succinate) and some were significantly depleted (fumarate, malate, oxaloacetate, citrate) in FA relative to FT. Placing these intermediates on a schematic of the TCA cycle ( Figure 5 H) revealed ad libitum and time-restricted conditions give rise to the accumulation of metabolites at opposite sides of this cycle. This suggests that eating pattern modifies flux through the TCA cycle, likely by shuttling intermediates to different metabolic pathways in ad libitum and time-restricted conditions. One of the products of the TCA cycle is the ketone body β-hydroxybutyrate (BHBA), which was elevated during the daily fasting period in FT mice ( Figure 5 I). Although FA mice also showed a similar increase in BHBA level upon acute 12 hr fast, the FT mice experienced such daily fast every day.

First, the effect of TRF was analyzed within genotype. Unbiased principal component analyses within each genotype are shown in Figures 5 A–5D . A total of 113 metabolites were differentially abundant between FA and FT in both Bmal1mice and Rev-erbα/β, whereas only 32 metabolites were differentially expressed in CDKO-FA versus -FT. Pathway analysis revealed that the top five pathways significantly altered in FA versus FT in Bmal1and Rev-erbα/βwere identical. They were: (1) biosynthesis of unsaturated fatty acids, (2) the tricarboxylic acid (TCA) cycle, (3) alanine, aspartate, and glutamate metabolism, (4) aminoacyl-tRNA biosynthesis, and (5) glycerophospholipid metabolism. In particular, long-chain fatty acids from 14 carbons to 22 carbons, both saturated and unsaturated, were elevated in Bmal1and Rev-erbα/βon FA ( Figure 5 F). This included pro-inflammatory lipids, such as arachidonate and dihomolinoleate. Although C16 palmitate and C17 margarate were also higher in CDKO-FA, longer-chain fatty acids were not elevated ( Figure 5 G). This absence of long-chain fatty acid accumulation in CDKO-FA was consistent with the observation that these animals did not develop fatty liver ( Figure 3 D). In line with previously described phenotypes, some metabolites showed genotype-specific effects, specifically in CDKO mice. For instance, folate was relatively high in CDKO, whereas it was hardly detected in other mutants ( Figure S3 A). Increased folate could be involved in protecting against hepatosteatosis ().

(I) Serum β-hydroxybutyrate levels in fasted (ZT22-ZT36) and re-fed mice (1 hr after intraperitoneal injection of glucose (1 mg/g body weight) at ZT36) in Bmal1 LKO and littermate controls (n = 4–6/group), Rev-erbα/β LDKO and littermate controls (n = 4–6/group), and CDKO and littermate controls (n = 4–6/group). ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001.

(H) Schematic representation of TCA cycle intermediates and their connection to glucose and lipid metabolic pathways showing liver metabolites that are significantly higher in FT (blue) or FA (red). Bar graphs of the relative levels of indicated metabolites levels are shown below.

(G) Relative levels of medium chain fatty acids in the liver of Bmal1 LKO , Rev-erbα/β LDKO , and CDKO on FA and FT as indicated.

(F) Heatmap representation of the relative expression of indicated fatty acids in Bmal1 LKO on FA and FT and Rev-erbα/β LDKO on FA and FT.

Because the liver is central to nutrient metabolism, to gain insight into the molecular pathways that are regulated by TRF, we performed untargeted metabolomics analysis of liver samples from WT (Bmal1), Bmal1, Rev-erbα/β, and CDKO mice in FA and FT conditions. Liver samples were collected every 4 hr over a 24-hr period for WT and Bmal1mice, and every 3 hr over a 24-hr period for Rev-erbα/βand CDKO mice, representing 56 samples (see STAR Methods for details). For all genotypes and all time points, pooled liver samples from two independent mice from two independent feeding experiments were analyzed. This strategy was chosen to minimize the cost while keeping a representation of biological complexity. Up to 547 metabolites were detected, which covered all major metabolic pathways, including lipid, cholesterol, sugar, and amino acids ( Table S1 ). We used three different strategies to statistically analyze this metabolomics dataset. In the first two approaches, time-series samples were treated as replicates to analyze the effect of TRF either within or across genotypes. In the third approach, time-series samples were treated independently and the MetaCycle package was used to identify cycling metabolites in all groups ().

Liver Transcriptomes Reveal Similarities and Differences between Clock Mutants on TRF

Love et al., 2014 Love M.I.

Huber W.

Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Figure 6 Liver Transcriptomics Unravel Similarities and Differences between Clock Mutants on TRF Show full caption (A) PCA plot of liver transcriptome data. (B and C) Schematic depicting the number of significant genes between each group as indicated. (D) Expression level (normalized read count, log2) of some genes involved in fatty acid oxidation that are significantly lower in Bmal1LKO-FA versus WT-FA. (E) Expression level (normalized read count, log2) of some genes involved in cholesterol metabolism that are significantly lower in Rev-erbα/βLDKO versus WT-FA. (F) Expression level (normalized read count, log2) of enzymes involved in lipid metabolism positioned on a schematic lipid pathway representation as well as the expression level of the master lipid regulators Pparα, Pparγ, and Srebf1. (G) Heatmap representation of the expression levels of 366 cycling genes in WT-FA and 172 cycling genes in WT-FT and their corresponding expression in clock-deficient mice as indicated. (H) Heatmap representation of the expression level of 43 cycling genes in WT-FA and WT-FT and their corresponding expression in clock-deficient mice as indicated. (I) Examples of cycling genes in WT. (B, D, and E) Differentially expressed genes with adjusted p value <0.05. (C and F) Differentially expressed genes with feeding-adjusted p value <0.01 and adjusted p interaction >0.1. Differential expression analyses were conducted using DESeq2 () with designs that accounted for genotype, feeding group, and time as variables (see STAR Methods for details; Table S2 for normalized read counts and Table S3 for results). PCA analyses revealed larger gene expression differences between genotypes, while the effect of eating pattern (FA versus FT) for any given genotype was relatively modest ( Figure 6 A). The CDKO livers under FA or FT showed the fewest differences in gene expression.

LWT). The number of genes that were differentially expressed (adjusted p ≤ 0.05) between Bmal1LKO, Rev-erbα/βLDKO, and CDKO versus WT were 2,922, 5,003, and 6,448, respectively ( First, because the different mutants displayed genotype-specific differences on FA, we compared WT with each mutant on FA to identify metabolic pathways that were specifically affected by each clock component with the caveat that we only used one WT cohort (Bmal1). The number of genes that were differentially expressed (adjusted p ≤ 0.05) between Bmal1, Rev-erbα/β, and CDKO versus WT were 2,922, 5,003, and 6,448, respectively ( Figure 6 B). Among them, 1,402 genes were commonly modulated in all three mutants, suggesting that a functional clock is required for their normal expression. Examples of those include increased expression of the tumor suppressor Pten in all the mutants and decreased expression of genes involved in lipid metabolism, such as Apoe, Fabp1, and Slc25a1 in all the mutants compared with the WT ( Figure S5 A).

LKO livers revealed that many genes involved in lipid metabolism, particularly fatty acid oxidation (Cpt2, Acads, Acadm, Acadl, Acadvl, Hadh, Hadha, Gpd1, Gpd1l, Lpin2) were downregulated in Bmal1LKO on FA ( Joshi et al., 2014 Joshi M.

Eagan J.

Desai N.K.

Newton S.A.

Towne M.C.

Marinakis N.S.

Esteves K.M.

De Ferranti S.

Bennett M.J.

McIntyre A.

et al. A compound heterozygous mutation in GPD1 causes hepatomegaly, steatohepatitis, and hypertriglyceridemia. LDKO-FA compared with WT-FA, many genes belonging to cholesterol homeostasis were downregulated (LDKO mice. Those changes in gene expression between Rev-erb knockout and WT animals fed HFD were in agreement with those observed when these mice were fed a normal chow in a published study ( Cho et al., 2012 Cho H.

Zhao X.

Hatori M.

Yu R.T.

Barish G.D.

Lam M.T.

Chong L.W.

DiTacchio L.

Atkins A.R.

Glass C.K.

et al. Regulation of circadian behaviour and metabolism by REV-ERB-alpha and REV-ERB-beta. LKO, and Rev-erbα/βLDKO under FA. Such CDKO-specific gene expression changes correlated with the lack of fatty liver diseases in CDKO mice. In summary, these genotype-specific expression differences likely contributed to genotype-specific differences in metabolic defects under FA. Many of these changes in the expression of metabolic genes are also found in circadian mutant mice fed a normal chow. The genotype-specific differences in gene expression may explain genotype-specific metabolic defects. Functional annotation of transcripts ( Figure S6 ) that were differentially expressed between WT and Bmal1livers revealed that many genes involved in lipid metabolism, particularly fatty acid oxidation (Cpt2, Acads, Acadm, Acadl, Acadvl, Hadh, Hadha, Gpd1, Gpd1l, Lpin2) were downregulated in Bmal1on FA ( Figures 6 D and S5 C). Their expression changes correlated with relatively severe hyperlipidemia observed in Bmal1 mutant animals ( Figure 3 ). For instance, Gpd1 loss of function is associated with hypertriglyceridemia in infants (). In Rev-erbα/β-FA compared with WT-FA, many genes belonging to cholesterol homeostasis were downregulated ( Figures 6 E and S5 C). In particular, reduced expression of Fxr, the master regulator of the conversion of cholesterol to bile acids and of rate-limiting enzymes of the pathway (Cyp27a1, Cyp7a1), as well as rate-limiting enzymes involved in the conversion of cholesterol to steroid hormones (Hsd11b1, Hsd17b2, Hsd17b12, Srd5a1, Fdx1), may explain hypercholesterolemia of Rev-erbα/βmice. Those changes in gene expression between Rev-erb knockout and WT animals fed HFD were in agreement with those observed when these mice were fed a normal chow in a published study () ( Figure S5 E). The expression levels of genes encoding key regulators and enzymes of fat metabolism ( Figure 6 F, see Pparγ, Cidec, Gpat2) were not as elevated in the CDKO-FA mice as they were in the livers of WT, Bmal1, and Rev-erbα/βunder FA. Such CDKO-specific gene expression changes correlated with the lack of fatty liver diseases in CDKO mice. In summary, these genotype-specific expression differences likely contributed to genotype-specific differences in metabolic defects under FA. Many of these changes in the expression of metabolic genes are also found in circadian mutant mice fed a normal chow.

LKO mice on FA versus FT (adjusted p ≤ 0.1). Among these, 68 were previously identified as altered in Bmal1LKO. There were 1,505 differentially expressed genes in the livers of Rev-erbα/βLDKO mice on FA versus FT (adjusted p ≤ 0.1) (771 up in FA, 734 up in FT). Among these, 554 were previously identified as altered in Rev-erbα/βLDKO. There were 30 genes different between CDKO on FA versus FT (adjusted p ≤ 0.1); 14 were previously identified as altered in CDKO (relative to WT). We hypothesized that, within genotype, TRF could alleviate genotype-specific gene expression differences by sustaining the expression of some transcripts to WT levels. In other words, we analyzed genes with a “favorable” profile, namely those genes for which the TRF effect was identical (either up or down) in WT and mutants (examples are shown in LKO, 368 in Rev-erbα/βLDKO, and 4 in CDKO. Functional annotation of these transcripts did not explain a significant portion of TRF benefits. In summary, these analyses suggest that TRF did not correct the majority of genotype-specific gene expression changes found under ad libitum feeding conditions. Next, we interrogated gene expression changes between FA and FT cohorts that might explain the beneficial effects of FT within each genotype ( Figure 6 B). One simple hypothesis was that the genotype-specific gene expression changes in each mutant relative to WT, as described in the previous paragraph, were reversed by TRF. There were 257 differentially expressed genes in the livers of Bmal1mice on FA versus FT (adjusted p ≤ 0.1). Among these, 68 were previously identified as altered in Bmal1. There were 1,505 differentially expressed genes in the livers of Rev-erbα/βmice on FA versus FT (adjusted p ≤ 0.1) (771 up in FA, 734 up in FT). Among these, 554 were previously identified as altered in Rev-erbα/β. There were 30 genes different between CDKO on FA versus FT (adjusted p ≤ 0.1); 14 were previously identified as altered in CDKO (relative to WT). We hypothesized that, within genotype, TRF could alleviate genotype-specific gene expression differences by sustaining the expression of some transcripts to WT levels. In other words, we analyzed genes with a “favorable” profile, namely those genes for which the TRF effect was identical (either up or down) in WT and mutants (examples are shown in Figure S5 D). There were only 33 genes with a favorable profile in Bmal1, 368 in Rev-erbα/β, and 4 in CDKO. Functional annotation of these transcripts did not explain a significant portion of TRF benefits. In summary, these analyses suggest that TRF did not correct the majority of genotype-specific gene expression changes found under ad libitum feeding conditions.

TRF normalized body weight, adiposity, and metabolic profiles in all genotypes. DIO over 12 weeks does not reach a steady state for these health parameters, and TRF over the same time period does not leave legacy benefits. Therefore, gene expression patterns in these mice reflect active molecular processes that promote or prevent metabolic diseases under FA and FT. We thus hypothesized that the gene expression changes (FT versus FA) that were shared among the four genotypes could underlie TRF benefits. To isolate the generic effect of TRF regardless of the genotype, we introduced an interaction term in the design formula used for statistical analysis. There were 6,178 genes different between FA versus FT (adjusted p < 0.1) in at least one genotype. Among these, 2,142 genes showed a similar trend in all genotypes (i.e., they did not exhibit a significant interaction between genotype and feeding condition; adjusted p interaction >0.1); 927 had higher expression under FA, and 1,215 were increased under FT ( Figure 6 C). Pathway enrichment analysis was carried out using Metascape on each set, and the data were manually curated to control overlapping or redundant GO terms (there were no more than three genes that were shared between any two clusters). Genes that were significantly upregulated in FA and reduced in FT were enriched in lipid metabolism genes ( Figure 6 F), including β-oxidation (Acox1, Acad10, Hadh, Acaa1b), de novo lipogenesis (Fasn, Scd3, Elovl5), triglyceride synthesis (Mogat1, Gpam, Gpat12, Agpat14), and TG storage (Plin2, Cidec). Master regulators of lipid metabolism, such as Pparα, Pparγ, and Srebf1, were also higher in FA ( Figure 6 F, insert).