BMAL1 and CLOCK, Two Essential Components of the Circadian Clock, Are Involved in Glucose Homeostasis

1The master clock, which, in mammals, resides in the hypothalamic suprachiasmatic nucleus (SCN), is thought to synchronize multiple peripheral oscillators to ensure temporal coordination of behavior and metabolism. Peripheral clocks amplify or dampen central rhythms or exhibit autonomous behavior to facilitate local adaptive responses. The central clock may communicate to modulate or entrain rhythms in the periphery via hormones or hemodynamic cues. Asynchronous environmental cues, such as eating, also influence the autonomous behavior of peripheral clocks.

2The variation in sleep and wakefulness (activity) is perhaps the most well-known circadian rhythm. Surgical ablation of the SCN in mice and rats abolishes the nocturnal burst in locomotor activity. Similarly, disruption and/or mutation of Bmal1 or Clock, transcription factors that compose the positive limb of an autoregulatory feedback loop in the core molecular clock, also impairs circadian behavior. Bmal1 and Clock may influence behavioral rhythms by regulating the firing rate of SCN neurons.

3Genes relevant to the molecular clock are also expressed in peripheral tissues where approximately 5%–10% of the transcriptome is subject to circadian oscillation. Although the precise role of peripheral clocks and the mechanisms that link them to the SCN remain largely obscure, genetic mutation or deletion has implicated peripheral clocks in the regulation of some aspects of cellular function, including division, estrous cyclicity, and phospholipid metabolism. Glucose and lipid homeostasis are also known to exhibit circadian variation. Surgical ablation of the SCN impairs the control of glucose homeostasis. However, the proximity of satiety centres to the SCN has potentially confounded interpretation of these results. Indeed, there is no direct evidence implicating the molecular clock in the regulation of glycaemia or insulin sensitivity (Si).

4Our studies revealed a profound role for core clock genes—Bmal1 and Clock—in regulating recovery from insulin-induced hypoglycaemia. Furthermore, the impact of a high-fat diet (HF) was to amplify the diurnal variation in glucose tolerance and Si in a manner dependent on the Clock gene. These studies suggest that the temporal distribution of a caloric load may influence the response to insulin and that circadian variability in glucose homeostasis may be subject to modulation by asynchronous dietary cues.

5We examined the role of the molecular clock in glucose homeostasis by using mice in which core clock genes are impaired (Clockmut) or deficient (Bmal1−/−). Both plasma glucose and triglycerides were subject to circadian variation in wild-type (WT) mice, peaking at approximately circadian time point 4 (CT4) and CT28 (where CT0 is subjective day beginning at 7 AM, and CT12 is subjective night beginning at 7 PM) (Figure 1A and 1B), as reported previously. We also observed that corticosterone (Figure 1C), which stimulates gluconeogenesis during hypoglycaemia, and adiponectin (Figure 1D), which has been associated with insulin resistance, oscillated significantly, but out of phase with the glucose and triglyceride rhythms. Diurnal variation in glucose and triglycerides, but not in corticosterone, was disrupted in the mutant mice (Table 1).

6Although there was no clear rhythm in the hypoglyacemic response to insulin, recovery of blood glucose exhibited a robust circadian variation (Figure 2A), with an excessive rebound from the effects of insulin evident at subjective dawn (CT19 and CT25) (Figure 2A). Insulin caused a profound hypoglyacemic response, independent of clock time, in both Bmal1 −/− and Clockmut mice (Figure 2B). This response was more pronounced in the former, consistent with the comparative severity of the molecular and behavioral phenotypes between the Bmal1−/− and Clockmut animals. Despite exacerbation of the hypoglycaemic response to insulin in the mutants, the counterregulatory responses of both corticosterone and glucagon were retained (Figure 2C and 2D).

7Gluconeogenesis also contributes to restoration of blood glucose after insulin-induced hypoglycaemia. Consistent with this observation, conversion of exogenously administered pyruvate to glucose, which reflects gluconeogenesis, was impaired in the Clockmut animals. This impairment was most marked in Bmal1−/− mice, while Bmal1+/− and Clockmut mice exhibited an intermediate phenotype when compared with WT littermate controls (Figure 3A). Furthermore, activity of the key rate-limiting enzyme of gluconeogenesis, phosphoenolpyruvate carboxykinase (PEPCK), exhibited diurnal variation in the liver and aorta that was blunted in Clockmut mice (Figure 3B). PEPCK activity in kidney was antiphasic to the rhythm in aorta and liver and was unimpaired in Clockmut mice (Figure 3B), suggesting tissue-specific regulation of enzyme activity.

8The frequent sampling intravenous glucose tolerance test (FSIGT) was performed to assess more precisely the impact of the molecular clock on sensitivity to insulin. This test provides an estimate of Si, consistent with that obtained by the euglycaemic clamp. Additionally, data modeling provides estimates of glucose-mediated glucose disposal (Sg), insulin secretion, and Si. Si and insulin secretion, but not Sg, exhibited a diurnal variation in WT mice fed a regular chow diet (RC) (Table 2). Circadian variation of glucose and lipid homeostasis might condition the metabolic response to asynchronous environmental cues, such as diet, that impinge on Si. Dyslipidemia coincides with insulin resistance in the metabolic syndrome, and a diet high in fat impairs Si. Both HF-fed WT and HF-fed Clockmut mice increased body weight significantly and to a similar degree in comparison to their age-matched, RC-fed controls (Table 3). Body fat composition averaged 17.6% of lean body mass in RC-fed WT mice, rising to 27.7% (p < 0.002) on high-fat feeding. Again, fat composition was not significantly altered by the presence of the Clock mutation (Table 3).

9Glucose tolerance on an RC trended towards an intolerant phenotype at CT1 versus CT13, but this difference did not attain significance (Figure 4A). This is consistent with the temporal variation in insulin secretion observed in RC-fed WT mice in the FSIGT experiment (Table 2). However, when the mice were fed HF for 2 mo, this glycaemic excursion at CT1 evoked by the environmental challenge was amplified and significant (two way analysis of variance [ANOVA]; F = 63.2, p < 0.001) (Figure 4A). Similarly, although the hypoglycaemic response to insulin was not different in mice fed regular chow at CT1 and CT13 (Figure 4B), the HF induced a significant temporal variation (Figure 4B). Thus, the impact of a high fat intake on carbohydrate metabolism in WTs includes an amplification of the diurnal variation in the response to both glucose and insulin. This coincides with a modest impairment in the ability to restore euglycaemia after insulin. A similar impairment resulting from a defect in gluconeogenesis has been observed in rats. The mutants failed to exhibit a significant time-dependent variation in their response to glucose or insulin, again reminiscent of the RC-fed, WT phenotype (Figure 4).

10Mice were also subjected to extended high-fat feeding (11 mo versus 2 mo). Long-term hyperlipidemia is known to induce frank diabetes with impaired release of insulin, in contrast to short-term, high-fat feeding, which increases release of insulin, but impairs the response to it. The extended HF impaired insulin secretion, reflected by its marked reduction to negative values in HF-fed WT mice (Table 2). The lack of insulin secretion resulted in calculated values of Si that were imperceptibly low (see Material and Methods). However, Sg, insulin secretion, and Si were restored to a WT phenotype in Clockmut mice that were also HF-fed for 11 mo (Table 2). This is, again, consistent with a role for the molecular clock in conditioning the response of glucose metabolism to the intake of dietary fat. Remarkably, mutation of the molecular clock protected against the development of frank diabetes caused by chronic high-fat feeding.

11Maintenance of blood glucose levels within a narrow range is critical to mammalian survival, and environmental cues can trigger appropriate tissue disposition of glucose through adaptive behaviors such as in hibernation or the “fight-or-flight” response. In this sense, glucose regulation is a fundamental and ancestral defence mechanism. Our studies suggest that Bmal1 and Clock, core components of the molecular clock, contribute substantially to regulation of recovery from the hypoglycaemic response to insulin. However, other mechanisms also impinge on this ancient adaptive response. Thus, impaired recovery from insulin-induced hypoglycaemia is observed in mice lacking proopiomelanocortin. These animals lack adrenal glands and melanocortins and exhibit a defective glucagon response to insulin-induced hypoglycaemia. They contrast with the Bmal1 −/− and Clockmut mice, where the counterregulatory hormone response is unimpaired. Thus, steroids, epinephrine, and glucagon appear to facilitate recovery from insulin-induced hypoglycaemia in a manner distinct from, but complementary to, the molecular clock.

12An assumption intrinsic to our studies is that the phenotypes revealed in the mutant mice are attributable to their function as core elements of the molecular clock. However, as trans-activators, both Clock and Bmal1 may have pleiotropic effects independent of the circadian clock that could impinge on metabolism. Several lines of evidence argue against this hypothesis. First, genes relevant to these metabolic phenotypes display circadian oscillations in their steady-state mRNA levels. In addition, the mRNA levels of many of these key proteins are phase-aligned with Per1 (e.g., Enolase 3, Pgam, Transketolase, Lipase, Lpl, Dgat1, Ppar alpha) or Per2 (e.g., Mod1, Lpl, Pepck, lipin 1) (unpublished data). In addition, many of their mRNAs are at lower levels in Bmal1 −/− (e.g., Mod1, Pepck, Enolase3, Pgam) (unpublished data), consistent with a direct role of Bmal1 in their transcription. Thirdly, these metabolic parameters are disrupted in both circadian mutants with the same rank order of potency as the locomotor activity phenotypes (Bmal1−/− > Clockmut). Thus, the most parsimonious interpretation is that the observed metabolic deficiencies in the Bmal1 −/− and Clockmut mice are due to their roles in the circadian clock, rather than to “off-clock” effects.

13We observed that the impact of HF on glucose homeostasis was apparently to emphasize the role of the molecular clock. Diet has previously been shown to interact with peripheral clocks. Changes in feeding shift the circadian pattern of gene expression in the liver, but not in the master clock in the SCN, demonstrating the importance of food as a cue to circadian control. Individual constituents of food could also provide discrete stimuli. For example, glucose alone can induce rhythmic gene expression in isolated fibroblasts. Thus, dietary composition, the size and timing of a feed might all be expected to interact differentially with an underlying circadian regulation of metabolic control.

14Alterations in dietary content, the availability of “fast food,” inactivity, and sociocultural factors have all been implicated in the emergence of the metabolic syndrome as a major challenge to the public health. However, while mechanistic integration of the diverse elements of the syndrome has proven elusive, our studies suggest that timing may influence the functional consequences of ingesting a caloric load.

15Mice were acclimatized for 2 wk in 12 h light–12 h dark cycles before being subjected to a 36-h period of constant darkness followed by experimentation in darkness. Experimental chronology is measured in CT, subjective day beginning at 7 AM (CT0), and subjective night beginning at 7 PM (CT12).

16WT and Clockmut mice were placed on an HF (Teklad, TD02435) and compared to age-matched WT mice on a regular chow diet (RC). Mice were on RC for 8 wk except for those subjected to FSIGT where they received RC for 11 mo. Body mass composition was measured by dual energy X-ray absorptiometry at 10 mo.

17Tests were performed as described with a diminution in the glucose bolus (0.1 g/kg).

18The tolerance test was performed as described in unanesthetized mice, and the minimal model of was applied to the data using MINMOD software. The derived values were Si, Sg, and acute insulin response to glucose, which measures insulin secretion. Si is the ratio of insulin delivery rate to the interstitium to insulin extraction rate from the interstitium. Long-term feeding of HF to WT mice resulted in imperceptibly small insulin sensitivity values. This could be the consequence of impaired delivery of insulin to the interstitium, exacerbated extraction rate, or a combination of both factors. Insulin secretion is derived from area under the insulin curve, above basal, from 0 to 10 min after glucose infusion; and disposition index, which equals the product of insulin sensitivity multiplied by insulin secretion and measures the degree to which insulin sensitivity can be compensated for by elevated insulin secretion.

19Insulin, leptin, corticosterone, and glucagon levels were measured by immunoassays from Crystalchem (Downers Grove, Illinois, United States), ICN Biochemicals (Costa Mesa, California, United States), and Linco Research (St. Charles, Missouri, United States). Plasma glucose was measured by the glucose oxidase method using a glucose analyzer machine for FSIGT and by glucometer for the intraperitoneal tolerance test. PEPCK activity was quantitated by a bioluminescent method.

20The significance of differences amongst the tolerance test curves was assessed by distribution-free two-way ANOVA with a Bonferroni correction. FSIGT data were tested by one-way ANOVA with the Kruskal-Wallis test. Paired Student's t-tests were used to perform comparisons of corticosterone levels before and after insulin injection in Bmal−/− mice and between WT and Clockmut mice. Plasma samples for glucagon analysis were pooled and were thus not compared by a formal statistical analysis. Results are presented as mean ± standard error of the mean (SEM), except for the FSIGT data (Table 2), presented as mean ± fractional standard deviation. Differences were considered significant when p < 0.05.