By Karen Kreeger

The timepiece a person wears on his wrist keeps one record of the day’s 24-hour cycle. The tissues and cells inside that person’s body keep many more. Fine-tuned by environmental cues, such as light, to the 24-hour solar cycle, the body’s molecular circadian clock coordinates its rhythms. A master clock in the brain communicates that control to molecular clocks in peripheral tissues. In humans, many aspects of physiology, including body temperature, levels of blood sugar, insulin, hormones, and neurotransmitters vary on a daily cycle.

And it is becoming abundantly clear that those rhythms matter when it comes to health. Physicians tell patients to take their cholesterol-lowering statin drugs at bedtime because the related liver enzymes are more active during sleep. Studies have also identified that most heart attacks occur in the early morning as the body jolts awake. And many more disease symptoms and treatment strategies are affected by the cycle of the clock: The incidence or severity of conditions such as asthma, stroke, and depression exhibit daily variation. Similarly, the levels of molecular targets of many drugs oscillate, as do enzymes and transporters relevant to drug metabolism.

Researchers are paying close attention.

An Algorithm to Find and Timestamp Hidden Cycles

Researchers from Penn Medicine and Cincinnati Children’s Hospital Medical Center have developed a powerful tool for detecting and characterizing some of these molecular rhythms. They developed a machine learning-type algorithm called CYCLOPS that can sift through existing data on gene activity in human tissue samples to identify genes whose activity varies with a daily rhythm. (The acronym CYCLOPS stands for “CYCLic Ordering by Periodic Structure.”)

Described in the Proceedings of the National Academy of Sciences in April 2017, CYCLOPS at least partly overcomes what has been one of the major obstacles to studying circadian rhythms in humans.

“It’s just impractical and dangerous to take tissue samples from an individual around the clock to see how gene activity in a particular cell type varies,” said lead author Ron C. Anafi, MD, PhD, an assistant professor of Sleep Medicine at Penn.

CYCLOPS instead is meant to use the enormous amount of existing data on gene activity in different human tissues and cells—data obtained from people at biopsies and autopsies, in scientific as well as medical settings, and made available through databases like the federal Gene Expression Omnibus repository.

Such data almost never includes the time of day when tissue samples were taken. But CYCLOPS doesn’t need to know sampling times. If the dataset is large enough, it can detect any strong 24-hour pattern in the activity level of a given gene, and can then assign a likely clock time to each measurement.

Anafi and his colleagues first demonstrated CYCLOPS to analyze gene activity levels in mouse liver cells using a dataset for which sampling times were available. Then they raised the difficulty level, asking the algorithm to generate new scientific data on human molecular rhythms. In a first-ever analysis of human lung and liver tissue, the algorithm revealed the strongly cyclic activity in thousands of lung-cell and liver-cell genes. These included hundreds of drug targets and disease genes.

“For many of these genes, the daily variability in activity turned out to be larger than the variability due to all other environmental and genetic factors,” said study co-author John Hogenesch, a former professor of Pharmacology at Penn Medicine now at the Cincinnati Children's Hospital Medical Center.

Underscoring the potential medical relevance of this research, CYCLOPS found strong cycling in several genes whose proteins are targeted by common drugs. In one case, CYCLOPS detected a strong circadian-type rhythm in the activity of the gene for angiotensin converting enzyme (ACE), a protein in lung vessels that is targeted by blood pressure-lowering drugs. Prior studies have found that ACE inhibitor drugs appear to work better at controlling blood pressure when given at night. “Our discovery of daily cycling in the ACE gene could explain those findings,” Anafi said.

Anafi and his colleagues are now using CYCLOPS to generate an atlas of cycling genes in different human tissues, in order to find other drugs whose dosing could be optimized by altering the time of day they are given.

Quantified Self Meets Chronobiology

When it comes to clinical studies that track 24-hour rhythms in humans, researchers often focus on a few parameters at a time and enroll many participants to see the impact of the circadian cycles across the broad population. But a recent study at the Perelman School of Medicine set this approach on its head.

The Penn team instead studied six healthy young male volunteers to collect physiological information as they went about their normal daily lives. They collected data on thousands of physiological indicators.

“We integrated data from remote sensors, wearables, and physiological samples to see how feasible it would be to detect an oscillatory phenotype, the chronobiome, of an individual, despite the ‘noise’ of everyday life,” said Carsten Skarke, MD, a research assistant professor of Medicine who was first author of the study published in Scientific Reports in December 2017.

The study’s senior author, Garret FitzGerald, MD, director of the Institute for Translational Medicine and Therapeutics, coined the term “chronobiome” to describe the collection of an individual’s physiological traits over a 24-hour rhythmic pattern.

In their study, the majority—62 percent—of sensor readouts showed time-specific variability, including the expected variation in blood pressure, heart rate, and the hormone cortisol. Those expected results were an important baseline for the proof of concept and a necessary prelude to detect differences in the chronobiome. The team hopes to ultimately find therapeutic value in patients with circadian time-dependent diseases, such as non-dipping hypertension, nocturnal asthma, depression, and night-eating syndrome. Despite the long-recognized, time-dependent variation in the effectiveness of many commonly used drugs, there has been little use of chronotherapy in clinical practice.

The Penn team now has similar online pilot studies with surgical, HIV, heart disease, and asthma patients, as well as shift workers. The next phase of study will include 200 volunteers of both sexes and different ages, studied across seasons and when exposed to a variety of stressors.

Skarke and FitzGerald see potential for chronotherapy to become integrated into clinical care in many ways. For instance, if it’s assumed that a drug should be taken at bedtime, what does that mean for an individual chronotype? Should it be a different regimen for morning larks versus night owls? They propose that patients’ chronobiomes could be characterized using a wearable device, their cell phones, and biomarkers from their blood, urine, saliva, and feces. Then a drug could be dosed according to an individual’s chronobiome.

How soon will that become a reality in medical practice? It’s hard to say. But the clock is ticking.

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