Journal of Biological Rhythms最新文献

Activity rhythms of laboratory rodents are usually measured by running wheels, and although wheel running activity-or-rest data enable straightforward rhythmic analyses, it provides limited behavioral information. In subterranean rodents (tuco-tucos), we used bio-loggers (accelerometers) to measure activity rhythms in both lab and field conditions, detecting diverse movements that compose activity. However, understanding these different accelerometer-detected activity components requires more complex analytical tools. Here we used supervised hidden Markov models (HMMs) as a machine learning analysis, to identify behavioral patterns in accelerometer data of tuco-tucos from field enclosures and characterize their behavioral rhythms in this condition. Activity of tuco-tucos was previously video-recorded in the laboratory with simultaneous accelerometer measurements. Video-obtained behavioral data were used in HMM models to refine (train) the classification of accelerometer recordings into different behavioral states. The classification obtained by HMM matched in 93% the one obtained by the video-observed method. Trained models were then used to automatically extract behavior information from accelerometers attached to 20 unobserved tuco-tucos first maintained in field enclosures and then transferred to the laboratory. Activity bouts associated with digging and locomotion were responsible for the diurnal rhythm in field enclosures and the nocturnal rhythm in the laboratory. Bouts of activity spread throughout day and night (cathemeral) were present in both conditions and were associated with feeding, coprophagy, and grooming. Finally, while rest occurs throughout day and night in the laboratory setting, tuco-tucos restrict rest episodes to nighttime under field enclosures, possibly as a behavioral adjustment to challenging environments. HMM models provide more behavioral information from accelerometry data, expanding the scope of activity pattern studies in small mammals under natural conditions.

Chronotype indicates a person's "circadian preference," that is, the time of day when they prefer to perform certain activities (e.g. a "morning" vs "evening" person). Sleep timing is related to chronotype but is also constrained by social requirements. When sleep timing does not align with chronotype, circadian disruption can occur, and circadian disruption impairs cardiometabolic health. There are well-known racial disparities in cardiometabolic health whereby Black adults are at higher risk. It is not well-known, however, whether sleep timing within each chronotype varies between Black and White adults, which was the focus of these analyses. These data are from a cross-sectional sleep study conducted in 2020 to 2023 as an ancillary to the Coronary Artery Risk Development in Young Adults (CARDIA) cohort study, in the United States. The Morningness-Eveningness Questionnaire (MEQ) captured chronotype in 2,373 participants aged 52-70 years. Chronotype was based on both overall MEQ score and question 19 categories. A subset of participants wore a wrist actigraphy monitor for ~7 days to assess sleep timing (n = 720). Our sample included 27% Black women, 17% Black men, 33% White women, and 24% White men. Mean MEQ score and chronotype distribution did not differ among race-gender groups. Among morning types, Black women and men had a later sleep start and midpoint than White women (23-34 minutes later for Black women, 32-53 minutes for Black men). Among intermediate types, Black women had significantly later sleep start (55 minutes later) and midpoint (44 minutes later), and Black men had a later sleep start (50 minutes later) than White women adjusting for age and study site. In summary, regardless of chronotype, Black adults had later sleep timing than White adults.

Circadian disruption is pervasive in modern society and associated with increased risk of disease. Chronic jet lag paradigms are popular experimental tools aiming to emulate human circadian disruption experienced during rotating and night shift work. Chronic jet lag induces metabolic phenotypes tied to liver and systemic functions, yet lack of a clear definition for how rhythmic physiology is impaired under these conditions hinders the ability to identify the underlying molecular mechanisms. Here, we compared 2 common chronic jet lag paradigms and found that neither induced arrythmicity of the liver and each had distinct effects on rhythmicity. Instead, more frequent 8-h forward shifts of the light schedule induced more severe misalignment and non-fasted hyperglycemia. Every other day shifts eventually uncoupled behavioral and hepatic rhythms from the light cycle, reminiscent of free-running conditions. These results point to misalignment, not arrhythmicity, as the initial disturbance tied to metabolic dysfunction in environmental circadian disruption and highlight considerations for the interpretation and design of chronic jet lag studies.

Although the sensitivity of the circadian system to the characteristics of light (e.g., biological timing, intensity, duration, spectrum) has been well studied in adults, data in early childhood remain limited. Utilizing a crossover, within-subjects design, we examined differences in the circadian response to evening light exposure at two different correlated color temperatures (CCT) in preschool-aged children. Healthy, good sleeping children (n = 10, 3.0-5.9 years) completed two 10-day protocols. In each protocol, after maintaining a stable sleep schedule for 7 days, a 3-day in-home dim-light circadian assessment was performed. On the first and third evenings of the in-home protocol, dim-light melatonin onset (DLMO) was assessed. On the second evening, children received a 1-h light exposure of 20 lux from either 2700 K (low CCT) or 5000 K (high CCT) (~9 and ~16 melanopic equivalent daylight illuminance (mEDI lux), respectively) centered around their habitual bedtime. Children received the remaining light condition during their second protocol, with the order counterbalanced across participants. Salivary melatonin was collected to compute melatonin suppression and circadian phase shift resulting from each experimental light condition. Melatonin suppression across the 1-h light stimulus was significantly greater during exposure to the high CCT light (M = 56.3%, SD = 19.25%) than during the low CCT light (M = 23.90%, SD = 41.06%). Both light conditions resulted in marked delays of circadian timing, but only a small difference (d = -0.25) was observed in the delay between the 5000 K (M = 35.3 min, SD = 34.3 min) and 2700 K (M = 26.7 min, SD = 15.9 min) conditions. Together, these findings add to a growing literature demonstrating high responsivity of the circadian clock to evening light exposure in early childhood and provide preliminary evidence of melatonin suppression sensitivity to differences in light spectrum in preschool-aged children.

My journey into chronobiology began in 1977 with lectures and internships with Wolfgang Engelmann and Hans Erkert at the University of Tübingen in Germany. At that time, the only known animal clock gene was Period, and the location and organization of the master circadian clock in the brain was completely unknown for the model insect Drosophila melanogaster. I was thus privileged to witness and participate in the research that led us from discovering the first clock gene to identifying the clock network in the fly brain and the putative pathways linking it to behavior and physiology. This article highlights my role in these developments and also shows how the successful use of D. melanogaster for studies of circadian rhythms has contributed to the understanding of clock networks in other animals. I also report on my experiences in the German scientific system and hope that my story will be of interest to some of you.

The nature of biological research is changing, driven by the emergence of big data, and new computational models to parse out the information therein. Traditional methods remain the core of biological research but are increasingly either augmented or sometimes replaced by emerging data science tools. This presents a profound opportunity for those circadian researchers interested in incorporating big data and related analyses into their plans. Here, we discuss the emergence of novel sources of big data that could be used to gain real-world insights into circadian biology. We further discuss technical considerations for the biologist interested in including data science approaches in their research. We conversely discuss the biological considerations for data scientists so that they can more easily identify the nuggets of biological rhythms insight that might too easily be lost through application of standard data science approaches done without an appreciation of the way biological rhythms shape the variance of complex data objects. Our hope is that this review will make bridging disciplines in both directions (biology to computational and vice versa) easier. There has never been such rapid growth of cheap, accessible, real-world research opportunities in biology as now; collaborations between biological experts and skilled data scientists have the potential to mine out new insights with transformative impact.

Binge and chronic alcohol intake impair skeletal muscle and liver circadian clocks. Scheduled exercise is suggested to protect against circadian misalignment, like that induced by alcohol. It was tested whether scheduled, voluntary daily wheel running would protect the gastrocnemius and liver clocks against alcohol-induced perturbations. Female C57BL6/Hsd mice were assigned to 1 of 4 groups: control-sedentary (CON SED, n = 26), control-exercise (CON EX, n = 28), alcohol-sedentary (ETOH SED, n = 27), or alcohol-exercise (ETOH EX, n = 25). Exercise mice were granted access to running wheels for 2 h/day (ZT13-15) while ETOH mice consumed alcohol-containing liquid diet for 6 weeks. Tissues were collected every 4 h starting at ZT12 from 4-5 mice/group and were used for RNA/cDNA/RT-PCR (gastrocnemius and liver) and Western blotting (gastrocnemius). A second cohort of mice were weaned off alcohol, given regular chow, and continued daily exercise (2 h/day) for ~2 weeks. Then, all mice (EX and SED) were given 24-h wheel access for 1 week to assess cyclic running behaviors during abstinence. While alcohol differentially disrupted muscle and liver clocks in sedentary mice, differences between exercised groups were minimized. BMAL1 protein expression increased in the nuclear-enriched fraction in the gastrocnemius of both exercise groups compared to both sedentary groups. In the second cohort, wheel running was increased in ETOH EX compared to ETOH SED in the dark cycle. In the light cycle, ETOH mice ran less than CON mice, and EX mice ran less than SED mice despite all mice receiving chow diet and no EtOH. Overall, scheduled wheel running partially offset the alcohol-induced perturbations in the muscle and liver clock while ETOH and EX both influenced the timing of subsequent activity after the dietary intervention ended.

The role of the hierarchical organization of the suprachiasmatic nucleus (SCN) in its functioning, jet lag, and the light treatment of jet lag remains poorly understood. Using the core-shell model, we mimic collective behavior of the core and shell populations of the SCN oscillators in transient states after rapid traveling east and west. The existence of a special region of slow dynamical states of the SCN oscillators can explain phenomena such as the east-west asymmetry of jet lag, instances when entrainment to an advance is via delay shifts, and the dynamics of jet lag recovery time. If jet lag brings the SCN state into this region, it will take a long time to leave it and restore synchronization among oscillators. We show that the population of oscillators in the core responds quickly to a rapid phase shift of the light-dark cycle, in contrast to the shell, which responds slowly. A slow recovery of the synchronization among the shell oscillators in transient states may strongly affect reentrainment in peripheral tissues and behavioral rhythms. We discuss the relationship between molecular, electrical, and behavioral rhythms. We also describe how light pulses affect the SCN and analyze the efficiency of the light treatment in facilitating the adaptation of the SCN to a new time zone. Light pulses of a moderate duration and intensity reduce the recovery time after traveling east, but not west. However, long duration and high intensity of light pulses are more detrimental than beneficial for speeding up reentrainment. The results of the core-shell model are compared with experimental data and other biologically motivated models of the SCN.