Charting a Path to Chronotherapy: An Atlas of Circadian Metabolism
A circadian rhythm is any biological process driven by endogenous cellular clocks that follows an oscillation of about 24 hours. Disruptions in circadian rhythms resulting from our modern lifestyle are associated with familiar afflictions ranging from jet lag to mood and sleep disorders.
The ability to anticipate daily fluctuations in the environment is a critical adaption across all living organisms – our waking (and sleeping) world revolves around the circadian cycle of the Earth’s rotation. While light exposure is a dominant signal for synchronizing circadian clocks with the environment, post-industrial technology has loosened restrictions on human activity that were previously imposed by the solar day. The resulting shift in sleep-wake cycles parallel alterations to the timing of physical activity and dietary intake, which are potent zeitgebers in their own right. Disruptions in circadian rhythms resulting from our modern lifestyle are associated with familiar afflictions ranging from jet lag to mood and sleep disorders.
The correlation of these cycles with human health and disease is well documented, and recent discoveries have reinforced their biological importance. In 2017, the Nobel Prize in Physiology or Medicine recognized Jeffrey C. Hall, Michael Rosbash and Michael W. Young “for their discoveries of molecular mechanisms controlling the circadian rhythm,” including the identification of multiple proteins involved in the activation and synchronization of known clock genes.1
Unraveling the mechanisms driving circadian rhythms affirms their central role in physiology, and it has sparked further research to determine how external factors that interfere with our clocks might also influence the pathology of disease. Lifestyle risk factors such as diet and exercise are believed to play a role in the susceptibility of diseases such as diabetes, obesity, cardiovascular disease and cancer. Could the elevation or reduction in disease risk be mediated through our internal biological clocks?
The task of describing a phenotype associated with the oscillation of a specific biomarker is complex, and it requires monitoring an elaborate network of metabolites across multiple tissues at various intervals of the circadian cycle. Recently, Paolo Sassone-Corsi’s group at the University of California, Irvine used a global metabolomics approach developed by Metabolon to study circadian metabolism in a range of mouse tissues under varying nutrient conditions.2 Dyar and Lutter, et al. examined the metabolic effects of a high-fat diet (HFD) to study how chronic nutrient stress affects naturally oscillating metabolic processes. This work leverages the study of metabolomics as a critical tool to better understand cellular physiology and to reveal connections between external inputs and pathology. The simultaneous evaluation of a comprehensive panel of multi-tissue metabolites – over multiple time intervals, and under varying environmental conditions – allows a glimpse of the communication pathways between various organs that are essential to whole-organism homeostasis. In this case, the combined power of metabolomics with sophisticated analysis tools provides novel insight into the underpinnings of metabolic processes linked to phenotypic differences in mice, and it validates the use of this approach to support new discoveries in human health and disease.
Temporal and tissue-specific metabolite signatures: The effects of nutrient stress
Several recent studies by the Sassone-Corsi team highlight the effect of nutritional abundance on circadian metabolism and demonstrate its relevance to the development and management of metabolic disease.3 Building on their previous work, they used a systems biology approach to examine several tissues in the context of energy balance. By comparing the patterns of metabolism under a normal chow diet with conditions of nutrient stress imposed by HFD, they assembled a spatial and temporal atlas of circadian mouse metabolism. The atlas maps hundreds of circadian metabolites, revealing the metabolic connections that control daily oscillations in processes that are often mediated by distal organ systems. Furthermore, the study showed that external factors such as chronic nutrient stress can alter communication and coordination between tissue clocks, resulting in metabolic changes associated with pathology.
Detection included a wide range of metabolite classes from 8 tissue types (i.e., serum, liver, skeletal muscle, brain, brown and white fat, and sperm). Alterations in the relative abundance of several metabolites were characteristic of known tissue-specific pathology. For example, carbohydrates comprised 53% of total altered liver metabolites of mice fed normal chow compared with only 8% in the HFD group. Lipid metabolites exhibited an inverse proportion, with 11% altered in mice on normal chow versus 52% in HFD-fed mice. The accumulation of lipids in liver relative to carbohydrates is suggestive of HFD-induced hepatic steatosis and may have relevance to the progression of NASH. A similar shift in lipid accumulation in skeletal muscle, a prominent glucose sink, suggests the potential for development of insulin resistance. Supporting these observations, several epidemiological studies have correlated an increased risk for insulin resistance and fatty liver with night shift work.4, 5
To create a visual atlas of the metabolites under study, the group applied algorithms that plotted the significant temporal correlations according to metabolite class and tissue type. The resulting atlas revealed both temporal and tissue-specific signatures of metabolic pathways over the 24-hour cycle. When examining correlations according to metabolite class, serum lipids showed the greatest degree of synchronization with other metabolites under normal chow, consistent with a role for the vasculature in integrating biochemical networks. However, under HFD nutrient stress, these correlations were lost or significantly reduced, affirming the impact of energy balance on circadian misalignment.
Bench to bedside: Actionable insights
In addition to elucidating key spatial and temporal elements of energy metabolism, the work by the Sassone-Corsi team provides a model for examining the relationship between other external factors and normal coherent networks across tissues. Their results evoke the rallying cry of translational science – how might these observed differences in metabolism be converted to clinical interventions? The proposed model is not limited to examining the effect of nutritional behavior on metabolic disease, or other behavioral interventions such as exercise. Integrated analysis of the circadian metabolome using tools like Metabolon’s global metabolomics platform offers potential for further discovery in disease pathways to reveal novel biomarkers and therapeutic targets, as well as fine-tuning clinical diagnostics.
Diagnostic measures and drug dosing are typically scheduled irrespective of circadian metabolism. Constraints of the clinician’s timetable, ensuring appropriate time intervals between medication doses, or the requirements of sample collection (e.g., fasting plasma or morning urine) dictate scheduling rather than coordinating with biological clocks. Many commonly prescribed drugs work by targeting the products of circadian genes, and since their half-lives are often less than 6 hours, timing of administration might have a significant impact on their action or influence potential side effects.6 Metabolite comparisons across multiple tissues may provide insight that has been missing from studies of single biomarkers that represent only one tissue at a specific time point of the circadian metabolome.
The relationship between coordination of peripheral clocks and pathology remains mostly unknown, but the circadian atlas presented by Dyar and Lutter, et al. demonstrates how global metabolomics is beginning to fill this information gap. Exploiting known oscillations might permit actionable insights including optimization of other external behaviors, improving the accuracy of diagnostics, and targeting specific time points to administer therapeutics. In the future, this same approach could be used on human samples to unlock biological discoveries hidden within the temporal dysregulation of metabolic processes, and provide insights to develop personalized chronotherapy.
 Press release, NobelPrize.org. Nobel Media AB 2018. Updated 2018. https://www.nobelprize.org/prizes/medicine/2017/press-release. Accessed October 10, 2018.
 Dyar KA, Lutter D, et al. Atlas of circadian metabolism reveals system-wide coordination and communication between clocks. Cell. 2018;174:1571-1585. doi: 10.1016/j.cell.2018.08.042
 Asher G, Sassone-Corsi P. Time for food: the intimate interplay between nutrition, metabolism, and the circadian clock. Cell. 2015; 161:84-92. doi: 10.1016/j.cell.2015.03.015
 Guo Y, Rong Y, et al. Shift work and the relationship with metabolic syndrome in Chinese aged workers. PloS One. 2015; 0120632. doi: 10.1371/journal.pone.0120632
 Konturek PC, Brzozowski T, Konturek SJ. Gut clock: implication of circadian rhythms in the gastrointestinal tract. J Physiol Pharmaco. 2011; 62:139-150.
 Zhang R, Lahens N, et al. A circadian gene expression atlas in mammals. PNAS. 2014; 111:16219-16224. doi: 10.1073/pnas.1408886111