Journal of Biological Rhythms最新文献

Disrupted diurnal rest-activity rhythms (RAR), that is, daily 24-h patterns of rest and activity, have been associated with fatigue and decreased quality of life among survivors of colorectal cancer (CRC). To identify potential targets for interventions to improve RAR, we investigated longitudinal associations of time spent in sedentary behavior and physical activity with RAR parameters after CRC treatment. In a prospective cohort study, repeated measurements were performed among 268 survivors of stage I-III CRC at 6 weeks, 6 months, and 1, 2, and 5 years after treatment. Thigh-worn accelerometers were used to determine hours/day spent in sedentary behavior, standing, and total physical activity during waking time, as well as RAR parameters including mesor, amplitude, circadian quotient (CQ), dichotomy index (I < O) and 24 h-autocorrelation (R24). Self-reported light-intensity physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) were determined via the validated SQUASH questionnaire. Longitudinal associations were analyzed using confounder-adjusted linear mixed models. More sedentary time was statistically significantly associated with a lower mesor, amplitude, I < O and R24 over the 5-year post-treatment period. More standing time was associated with a higher mesor, amplitude, CQ, and I < O but not with R24. Higher levels of objectively assessed total physical activity as well as self-reported MVPA were associated with higher values for all RAR parameters. LPA was not associated with any of the RAR parameters. In the years after CRC treatment, less sedentary behavior and more standing and physical activity were generally associated with higher RAR parameters indicating a more robust rhythm. Future studies should provide more insight into causality of these associations as RAR may be a potential new target for interventions to reduce fatigue after CRC.Trial registration: EnCoRe study NL6904 (https://www.Onderzoekmetmensen.nl/).

The brain is the most energy-demanding organ, yet whether cerebral energy homeostasis exhibits seasonal rhythmicity remains unclear. In this study, 432 healthy men underwent a health checkup program with fasting-state brain [¹⁸F]fluorodeoxyglucose positron emission tomography (PET) scanning twice: first at the baseline and then at the 5-year follow-up. We analyzed the effect of day length on brain glucose uptake separately for both time points. In both baseline and follow-up scans, day length on the day of imaging significantly predicted glucose uptake in the socio-emotional circuit. A longer day length was associated with increased glucose uptake in the cuneus, precuneus, orbitofrontal cortex, pre- and postcentral gyrus, superior and middle temporal gyrus, posterior cingulate cortex, insula, and frontal pole. This large-scale longitudinal PET study provides landmark evidence for the impact of daylight exposure on brain glucose metabolism. Findings disclose the baseline seasonal variation of brain energy consumption in men.

Biomarkers are valuable tools in a wide range of human health areas including circadian medicine. Valid, low-burden, multivariate molecular approaches to assess circadian phase at scale in people living and working in the real world hold promise for translating basic circadian knowledge to practical applications. However, standards for the development and evaluation of these circadian biomarkers have not yet been established, even though several publications report such biomarkers and claim that the methods are universal. Here, we present a basic exploration of some of the determinants and confounds of blood-based biomarker development for suprachiasmatic nucleus (SCN) phase by reanalysing publicly available data sets. We compare performance of biomarkers based on three feature-selection methods: Partial Least Squares Regression, ZeitZeiger, and Elastic Net, as well as performance of a standard set of clock genes. We explore the effects of training sample size and the impact of the experimental protocols from which training samples are drawn and on which performance is tested. Approaches based on small sample sizes used for training are prone to poor performance due to overfitting. Performance to some extent depends on the feature-selection method, but at least as much on the experimental conditions from which the biomarker training samples were drawn. Performance of biomarkers developed under baseline conditions does not necessarily translate to protocols that mimic real-world scenarios such as shiftwork in which sleep may be restricted or desynchronized from the endogenous circadian SCN phase. The molecular features selected by the various approaches to develop biomarkers for the SCN phase show very little overlap although the processes associated with these features have common themes with response to steroid hormones, that is, cortisol being the most prominent. Overall, the findings indicate that establishment of circadian biomarkers should be guided by established biomarker-development concepts and foundational principles of human circadian biology.

The circadian clock enables organisms to optimize their metabolism, physiology, and behavior with the time-of-day. However, circadian rhythms benefit organisms only if they are properly synchronized with the day/night cycle; circadian misalignment can have detrimental effects on animals' wellbeing and survival. We previously showed that in Drosophila, loss of the microRNA miR-124 advances the phase of circadian evening locomotor activity by several hours under constant darkness conditions. Interestingly, we now report that loss of miR-124 also delays morning activity under a light/dark cycle with a short photoperiod. We recapitulated these opposite phase phenotypes by eliminating miR-124 during larval development, but not when this microRNA is lost during pupation to adulthood. The loss of miR-124 results in significant miswiring within the circadian neural network and severely alters neural activity rhythms in the ventral Lateral Neurons (s-LNvs) and the posterior Dorsal Neurons 1 (DN1ps), which control the timing of morning and evening activity. Silencing the s-LNvs in miR-124 mutant flies restores the phase of evening activity, while activating the DN1ps rescues the phases of both morning and evening activities. Our findings thus reveal the pivotal role of miR-124 in sculpting the Drosophila circadian neural network during development and its long-lasting impact on circuit activity and adult circadian behavior.

Circadian rhythms are endogenous oscillations that occur with a 24-h periodicity and support organismal homeostasis. While the role of the circadian clock in systemic vasculature is well known, its role in pulmonary vasculature, specifically in the pulmonary endothelium, has remained unexplored. We hypothesized that the circadian clock directly regulates pulmonary endothelium to control lung inflammation. Using pulmonary artery segments and endothelial cells isolated from lungs of mPer2luciferase transgenic mice, we monitored circadian rhythms and observed that lipopolysaccharide (LPS) treatment disrupted rhythmicity. This disruption was mediated by reactive oxygen species (ROS) generated via NADPH oxidase 2 (NOX2). Remarkably, the pharmacologic inhibition of NOX2 before LPS exposure restored circadian rhythmicity in the pulmonary endothelium. In wild-type (WT) mice, LPS activated a NOX2-NLRP3 signaling axis that drove inflammation as evidenced by increased polymorphonuclear neutrophil (PMN) accumulation and intercellular adhesion molecule-1 (ICAM-1) expression on the pulmonary endothelium. In contrast, disruption of the clock using two different clock mutants (Bmal1^-/- and Cry1/2^-/-) resulted in a sustained baseline elevation of PMN and ICAM-1, which changed minimally with LPS. This effect was attributed to aberrant activation of the NLRP3 inflammasome at baseline in the clock mutants, as supported by lung transcriptomic data and reversal of the phenotype with an NLRP3 inhibitor. Importantly, these findings also reveal an intriguing bidirectional relationship: while the circadian clock modulates inflammatory responses, inflammatory stimuli in turn alter circadian rhythmicity via the NOX2 pathway. Together, our results identify a novel mechanism by which circadian control of pulmonary endothelial inflammation may be leveraged to mitigate the consequences of clock disruption in lung disease.

In the cyanobacterial circadian clock, a core oscillator comprising the proteins KaiA, KaiB, and KaiC keeps time based on a rhythmic phosphorylation of KaiC, and histidine protein kinases relay temporal information from the KaiABC complex to regulate gene expression. The kinases SasA and CikA engage directly with the oscillator and are responsible for modulating the phosphorylation and dephosphorylation throughout the circadian day of the response-regulator transcription factor RpaA; the phosphorylation state of RpaA in turn determines circadian gene expression. We recently showed that either CikA or SasA can drive rhythmic phosphorylation and DNA binding of RpaA in an in vitro system. However, when SasA is absent in vivo, a bioluminescence reporter gene shows a very low expression and amplitude rhythm, indicating CikA kinase activity is not sufficient to activate gene expression. We questioned why CikA cannot serve as a robust kinase for RpaA in the absence of SasA in the cell. Here, we investigated post-translational modifications of CikA and found KaiC-dependent phosphorylation sites of CikA that dramatically affect its activity. Phosphomimetic mutants of these sites showed that the phosphorylated version of CikA is not functional. Our data show that inverse correlation of KaiC levels and these inhibitory phosphorylation sites can explain the lower CikA activity in a SasA knockout background. We conclude that these phosphorylation sites act as a rheostat for CikA activity and are regulated by KaiC levels.

Response to: "Mathematical Analysis of Light-sensitivity Related Challenges in Assessment of the Intrinsic Period of the Human Circadian Pacemaker".

Measuring and analyzing personal light exposure has become increasingly important in circadian and myopia research. Very small measurement values in light exposure patterns, especially zero, are regularly recorded in field studies. These zero-lux values are problematic for commonly applied logarithmic transformations and should neither be dismissed nor be unduly influential in visualizations and statistical modeling. We compare 4 ways to visualize such data on a linear, logarithmic, hybrid, or symlog scale, and we model the light exposure patterns with a generalized additive model by removing zero-lux values, adding a very small or -1 log₁₀ lux value to the dataset, or using the Tweedie error distribution. We show that a symlog-transformed visualization, implemented in LightLogR, displays relevant features of light exposure across scales, including zero-lux, while reducing the emphasis on the small values (<1 lux). Symlog is well-suited to visualize differences in light exposure covering heavy-tailed negative values. We further show that small but not negligible value additions to the light exposure data of -1 log₁₀ lux for statistical modeling allow for acceptable models on a logarithmic scale, while very small values distort results. We also demonstrate the utility of the Tweedie distribution, which does not require prior transformations, models data on a logarithmic scale, and includes zero-lux values, capturing personal light exposure patterns satisfactorily. Data from field studies of personal light exposure require appropriate handling of zero-lux values in a logarithmic context. Symlog scales for visualizations and an appropriate addition to input values for modeling, or the Tweedie distribution, provide a solid basis. Beyond light exposure, other time-series data relevant to biological rhythms, such as accelerometry for ambulatory sleep scoring in humans or wheel-running in animal models, exhibit zero inflation and can benefit from the methods introduced here.

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.