Circadian analysis of peripheral and brain samples in epilepsy patients

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PROJECT SUMMARY Nearly every process that occurs in the brain and the body follows a 24 hour cycle. Moreover, adverse health events such as heart attacks, stroke and seizures tend to occur at particular times of day. Seizures often follow a rhythmic pattern, but this pattern can be very different from patient to patient. If one can determine the circadian factors that predict that a seizure is likely to occur, then this allows proper timing of treatments to provide the most therapeutic impact with fewest side effects. Indeed, several groups are starting to apply this chronotherapy as a personalized approach to predict and treat a number of disorders including seizures. While sleep/wake activity measures are a gross predictor of the optimal time of day for medication, they are confounded by a variety of environmental factors and do not always align with endogenous rhythms. The use of peripheral markers in blood or urine could serve to fine tune chronotherapeutic approaches as they are easily obtained in the clinic. Indeed, researchers have now been able to accurately predict the phase of the master clock in the suprachiasmatic nucleus (SCN) using as little as 1-2 blood samples. Moreover, better understanding of phase relationships between peripheral markers, SCN phase, and molecular rhythms in parts of the brain that experience seizures, would help not only in the administration of chronotherapy, but also in our understanding of why the brain might experience a seizure at a particular time of day. In this study, we have the rare opportunity to obtain human brain tissue, blood, and urine samples at the same time of day from male and female adolescents and young adults (~25 subjects) undergoing surgical resection for the treatment of seizures. In addition, prior to surgery, we will collect sleep/wake data, behavioral data, cognitive assessments, assessments of seizure patterns and psychiatric evaluations. This will allow us for the first time to measure transcripts and metabolites with known rhythms across multiple peripheral and brain samples at the same time of day from the same individuals, giving us precise data regarding how markers of phase in the peripheral samples align with markers in brain regions in which seizures occur. We will also be able to determine how these molecular and metabolic measures align with sleep/wake measures and various clinical features, seizure patterns, and differences between healthy and disease tissue, paying particular attention to any sex and developmental differences. This data will inform future mechanistic studies of why seizures occur at particular times of day, and optimize the use of peripheral samples to serve as biomarkers for the most appropriate timing for treatment.