Journal of Biological Rhythms最新文献

The analysis of long-term variation patterns in heart rate (HR) and heart rate variability (HRV) provides insights into autonomic nervous system function beyond short-term recordings taken under resting or experimental conditions. Yet, traditional processing pipelines often require time- and labor-intensive visual inspection of electrocardiography (ECG) data and manual artifact removal. This study evaluated the performance of 3 code-based fully automated batch-processing pipelines-NeuroKit2, RHRV, and Systole-against the manual gold standard utilizing Kubios for both (diurnal) HR and HRV estimates derived from raw 48-h ECG recordings. Results illustrate that while automated pipelines yield HR estimates in good agreement to the gold standard (r = 0.91-0.99; α = 0.90-0.99), HRV estimates exhibit greater deviations (r = 0.66-0.87; α = 0.76-0.90). Cosinor analyses of diurnal HR patterns indicate strong consistency between Kubios and NeuroKit2 (r = 0.94-0.99; α = 0.97-0.99), but weaker correlations with RHRV and Systole (r = 0.58-0.87; α = 0.63-0.93). HRV cosinor parameters showed even larger discrepancies, with parameter-dependent correlations ranging from r = 0.41 to 0.86 and Cronbach's alphas from α = 0.59 to 0.91. Findings suggest that automated batch processing of ECG data for analyzing diurnal variation patterns in HR and HRV produces results that show moderate to good agreement with the gold standard including visual inspection and manual processing. However, caution is warranted, as existing toolboxes and pipelines may lead to different results.

Despite evidence for links between circadian dysfunction and mood disorders, previous research has largely reported on single biological markers of circadian alignment. The available evidence on relationships between 2 internal phase markers (e.g., dim light melatonin onset [DLMO] and peak cortisol concentration) suggests these signals may be temporally misaligned in major depressive disorder with greater misalignment associated with more severe depressive symptoms. This study aimed to examine multiple circadian phase markers to determine whether any youth with emerging mood disorders present with clear evidence of internal circadian misalignment, and whether the degree of circadian misalignment is correlated with more severe mood symptoms. Cross-sectional data from 69 youth presenting for mental health care (20.6 ± 3.8 years; 39% male) and 19 healthy controls (24.0 ± 3.6 years; 53% male) included actigraphy monitoring; overnight in-lab measurement of 3 phase markers: DLMO, salivary cortisol peak (CORT), and core body temperature nadir (TEMP); and depressive symptoms (Hamilton Depression Rating Scale). Abnormal phase angles between 2 phase markers were defined as ±2 standard deviations beyond the control mean. In those with emerging mood disorders, earlier TEMP relative to other phase markers (DLMO, CORT, sleep midpoint) was associated with higher depressive symptoms. Sixteen individuals (23%) with emerging mood disorders had abnormal phase angles between at least 1 pair of phase markers, consistent with internal misalignment of the circadian system. The internal misalignment subgroup had later DLMO on average, however presented with a diverse range of individual phase angle abnormalities. Diverse disruptions of circadian alignment occur in youth with mental ill-health. The relative timing of core body temperature and melatonin rhythms may be key circadian features linked to depressive symptoms. Longitudinal research is needed to establish whether correction of circadian misalignment is relevant to treatment of mood syndromes in youth with evidence of disrupted circadian systems.

The circadian neuronal network in the brain comprises central pacemaker neurons and associated input and output pathways. These components work together to generate coherent rhythmicity, synchronize with environmental time cues, and convey circadian information to downstream neurons that regulate behaviors such as the sleep/wake cycle. To mediate these functions, neurotransmitters and neuromodulators play essential roles in transmitting and modulating signals between neurons. In Drosophila melanogaster, approximately 240 brain neurons function as clock neurons. Previous studies have identified several neurotransmitters and neuromodulators, including the Pigment-dispersing factor (PDF) neuropeptide, along with their corresponding receptors in clock neurons. However, our understanding of the neurotransmitters and receptors involved in the circadian system remains incomplete. In this study, we conducted a T2A-GAL4-based screening for neurotransmitter and receptor genes expressed in clock neurons. We identified 2 neurotransmitter-related genes and 22 receptor genes. Notably, while previous studies had reported the expression of 6 neuropeptide receptor genes in large ventrolateral neurons (l-LN_v), we also found that 14 receptor genes-including those for dopamine, serotonin, and γ-aminobutyric acid-are expressed in l-LN_v neurons. These findings suggest that l-LN_v neurons serve as key integrative hubs within the circadian network, receiving diverse external signals.

The circadian clock maintains oscillations in gene expression with a 24-hour periodicity in nearly every cell of the body and confers rhythmic patterns to many aspects of behavior and physiology. The presence of circadian rhythms in tumors leads to the question of whether tumors may respond differently to chemotherapy given at different times of day. We addressed this question using a male mouse model of hepatoma by treating mice in the morning (ZT2) or evening (ZT14) with cisplatin, and measuring gross effects on body weight, blood counts and chemistry, gene expression, and cellular proliferation. We found that among cisplatin-treated mice, there was a reduction in expression of the proliferation marker protein Ki-67 in tumors of mice treated at ZT14 as compared to ZT2. Corresponding hepatotoxicity, as measured by elevated serum alanine aminotransferase (ALT), and body weight loss were also reduced at ZT14. Overall gene expression at ZT14 was more similar to healthy liver than expression at ZT2. Mitogen-activated protein kinase (MAPK) and Ras-related protein-1 (Rap-1) signaling pathways were specifically downregulated in tumors following treatment at ZT14, which may be related to the decreased proliferation, at this treatment time. These findings align with the possible use of timed chemotherapy to enhance drug efficacy.

Accurately capturing the temporal distribution of polysomnographic sleep stages is critical for the study of sleep function, regulation, and disorders in higher vertebrates. In laboratory rodents, scoring of electrocorticography (ECoG) and electromyography (EMG) recordings is usually performed manually by categorizing 5- to 10-sec epochs as 1 of 3 specific stages: wakefulness, rapid-eye-movement (REM) sleep, and non-REM (NREM) sleep. This process is laborious, time-consuming, and particularly impractical for large experimental cohorts with recordings lasting longer than 24 h, which are critical for the study of the circadian regulation of sleep. To circumvent this problem, we developed an open-source Python toolkit, Sleep Identification Enabled by Supervised Training Algorithms (SIESTA), that automates the detection of these 3 main behavioral stages in mice. We used a supervised machine learning algorithm that extracts features from the ECoG and EMG signals and autonomously scores recordings with a hierarchical classifier based on using logistic regression. We evaluated this approach on data collected from wild-type mice housed under both normal and different lighting conditions, as well as from mutant mouse lines with abnormal sleep phenotypes and from rats. We obtained mean F₁ scores 0.94 for wakefulness, 0.94 for NREM, and 0.74 for REM, and followed up by validating SIESTA with manually scored data from 3 other laboratories. SIESTA has a user-friendly interface that can be used without coding expertise. To our knowledge, this is the first time that such a strategy has been developed using all open-source and freely available resources. Our aim is that SIESTA becomes a useful tool that facilitates further research in sleep on rodent models.

Postmenopausal shift workers face increased cardiometabolic risk due to estrogen decline and shift work-induced circadian misalignment. Yet, their combined effects remain poorly understood, especially in hypertensive individuals. This study investigated whether circadian misalignment worsens cardiometabolic parameters in a hypertensive ovariectomized rat model. Female spontaneously hypertensive rats (SHR) were ovariectomized or sham-operated (7-week-old), and then exposed to a chronic phase shift (CPS) protocol or a control light schedule for 10 weeks (n = 9 per group). Measurements included body mass, food and water intake, blood pressure (BP), fasting glucose, glucose tolerance, organ masses, and low-density lipoprotein (LDL) concentration. Ovariectomized rats were heavier and had greater food intake and organ masses than sham-operated rats. However, food intake and organ masses were reduced relative to body mass. CPS rats had greater water intake and reduced liver mass than control light rats. In addition, ovariectomized rats showed lower glucose concentration than sham-operated rats, whereas CPS rats showed higher glucose concentration than control light rats during the oral glucose tolerance test. Moreover, the CPS rats had higher systolic BP. The LDL and fasting glucose concentrations were similar. No interaction between ovariectomy and CPS was observed. These findings suggest that estrogen deficiency increases body mass, but does not worsen cardiometabolic parameters in female SHR. CPS-induced circadian misalignment altered water intake, liver mass, systolic BP and glucose tolerance in the CPS condition in female SHR. This study was unable to monitor physiological or behavioral indicators to confirm circadian misalignment by the CPS protocol. However, the findings provide novel insights into how CPSs independently impair cardiometabolic outcomes in female SHR, with implications for understanding risk in postmenopausal shift workers.