Effect of the circadian clock time of day and sleep on the human metabolome: identification of metabolite rhythms

The study of metabolomics is a relatively new technique to identify metabolites that are produced by body processes and are involved in regulatory metabolic pathways. It is highly likely that certain metabolites will be altered in disease or during drug therapy and thus, being able to measure and identify these (biomarkers) will be critical for future health and disease diagnostics. For metabolomic profiling to be of value for clinicians in the diagnosis of disease, however, it is essential to establish accurate baseline data from healthy controls. Correct interpretation of metabolomic data will require a thorough knowledge of the impact of time of day as well as the effect of a person's internal biological (circadian) timing system on the metabolomic profile. Biological circadian rhythms and time of day variation occur in most physiological markers e.g. melatonin, cortisol, glucose; metabolites identified in plasma and urine will be no exception. However, to date there has been no systematic study of circadian variation in the human metabolome using established circadian protocols. In addition how a typical living environment (light/dark cycle, sleep/wake cycle, meals) affects metabolomic profiles needs to be determined. Using strictly controlled laboratory studies in healthy volunteers and cutting edge metabolomic technology we thus aim to characterise the effect of the circadian clock, the time of day, the light/dark environment, meals and sleep on rhythmic and non-rhythmic metabolites identified in plasma and urine. Metabolites that show rhythmic circadian and time-of-day variation (cycling) and those that do not (non-cycling metabolites), as well as metabolic processes affected by sleep and sleep deprivation, will be identified through the use of cutting edge, highly sensitive, liquid chromatography-mass spectrometric (LC-MS) techniques. At Surrey we have proven expertise in conducting circadian and sleep deprivation experiments. Using our recently established LC-MS methodology (Surrey and ICR) we have pilot data in healthy volunteers kept in controlled conditions similar to the proposed studies. Significant time of day variation has been observed in at least 20 plasma and 20 urine metabolites, based on Orthogonal Projections to Latent Structures (OPLS) analysis (Simca Software, Waters). Therefore in terms of expertise, clinical and analytical facilities and technical skills the proposal is feasible. Identification of metabolite rhythms and how these are affected by external factors (time of day, wakefulness, sleep, environmental lighting, regular meals) will provide reliable baseline data which will be crucial for the future use, and correct interpretation, of metabolomics in the detection and treatment of human disease. In addition, our Project Partner (Erasmus MC University Medical Center (EUMC), Rotterdam) will perform proteome and transcriptome analysis on selected samples across the 24 h day from both studies with a view to combining the data. The biological samples, metabolomic database and research findings will be shared and disseminated for the benefit of a wide range of professionals involved in disease diagnosis and treatment (e.g. clinicians, clinical biochemists) which will ultimately benefit society.

Metabolomics is a relatively recent technology that has the potential to identify new biomarkers that may be modified in health, disease or during drug therapy. However, the biological variation in the human metabolome due to the internal circadian timing system, sleep/wakefulness, light/dark cycle and time of day variation has not been taken into account. Biological (circadian) rhythms and time-of-day variation occurs in most physiological markers (proteins, mRNA, hormones, metabolites). Disregarding this fundamental aspect of physiology may lead to the incorrect interpretation of metabolomic, transcriptomic and proteomic data. The proposed studies will test the primary hypothesis that there is rhythmic expression of certain metabolites that are linked to a person's internal circadian clock. Secondly the studies will test the hypothesis that these metabolic rhythms will be different in 'real life' conditions (light/dark cycle, meals, sleep). Laboratory studies will be performed in healthy volunteers, firstly under strictly controlled 'constant routine' conditions to minimise the effect of exogenous confounding factors (e.g. light, sleep, posture, activity, meals). Circadian-controlled and non-circadian controlled metabolites will be identified in plasma and urine extracts using highly sensitive liquid chromatography-mass spectrometric (LC-MS) techniques. In addition gene expression, proteome and peptidome profiles will be assessed in blood and urine samples taken under the same conditions (collaboration with EUMC) which will provide substantial added value. This project will generate reliable baseline data across the 24 hour day that will be crucial for the future use of metabolomics, proteomics and genomics in the detection and treatment of human disease. In addition, the findings will provide new insights into the metabolic processes and pathways linked to the circadian timing system and sleep processing.

Scientific advancement and knowledge: The discovery of novel biomarkers for disease detection and progression relies on the ability to identify significant differences between healthy and diseased individuals, and high-quality, accurate baseline data. However, in order for such biomarkers to be identified and have clinical use, it is necessary to establish a reliable set of baseline data as well as a thorough knowledge of how they are influenced by both endogenous and exogenous factors across the day and night. Firstly we will characterise the impact of the internal biological circadian clock on metabolomic profiles in plasma and urine. Secondly we will determine the effect of normal conditions (of light/dark, sleep/wake and meals) on these metabolite rhythms. Key benefits of this study will be: 1. Profiles of an array of metabolite, genetic, protein and peptide biomarkers obtained under strictly controlled conditions (i.e. controlled lighting, wakefulness, isocaloric meals, and physical activity). 2. Baseline profiles of metabolites that do display a circadian rhythm, as well as those that do not (non-cycling). The Institute of Cancer Research (ICR; Raynaud) has a particular interest in identifying non-cycling metabolites that could be used diagnostically and to monitor drug efficacy. 3. Identification of where and how metabolite, protein and peptide pathways interact, thus providing a starting point for future studies with a more in-depth Systems Biology approach. 4. A better appreciation of how time-of-sampling affects metabolomic profiles which will improve the treatment and diagnosis of disease. 5. Determining the effect of sleep per se and sleep deprivation (e.g. as experienced by night shift workers) on the human metabolome. 6. A potential forensic application (thus our collaboration with EUMC) in the accurate determination of sample deposition time at a crime scene, as well as establishment of time-of-death and, in addition, whether the deceased was awake or asleep during the night. 7. The potential discovery of cycling metabolites that can be used as novel biomarkers of peripheral clocks. 8. The potential discovery of novel sleep-controlled metabolites and pathways. Public health: The findings and how these are used by clinicians, associated professions (e.g. clinical biochemists; pathologists; pharmacologists) to improve disease diagnosis and optimise its treatment will ultimately benefit the public. Improved knowledge of the complex processes underlying circadian timing and sleep will lead to more targeted treatment of sleep and circadian rhythm disorders (e.g. as experienced by night shift workers).The detrimental health effects of shift work and disrupted daily biological rhythms and sleep are now well known (demonstrated by the Surrey group and others). However, the biological basis of these detrimental effects is not well understood. Characterising the impact of sleep deprivation as experienced by night shift workers on metabolic profiles will add to our understanding of these damaging effects and ultimately direct changes that will benefit health. Increased production and output: Both the immediate and long-term effects of shift work are generally accepted to negatively affect the health and performance of the worker which has consequences for economic productivity and safety. It is recognised that current shift schedules may not be optimal for the health of the worker or industrial productivity. The ability to non-invasively monitor circadian timing in shift workers in the field using metabolomic profiling will ultimately help in optimising the design of shift schedules.