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Jayasinghe, O.

Publications and source records attributed to Jayasinghe, O..

2 recordsLinked to original sources

A systemic clock brake: Period1 stabilizes the circadian network under environmental stress

Precise alignment between internal circadian clocks and environmental light cycles is essential for physiological homeostasis and survival. However, the molecular mechanisms that preserve this synchrony across central and peripheral tissues remain poorly defined. Here, we uncover an unexpected role for the core clock gene Period1 (Per1) as a systemic modulator of circadian stability, regulating light-induced re-entrainment across the brain and body. In Per1-deficient mice, we show that loss of Per1 accelerates clock realignment, influencing transcriptomic, metabolic, hormonal, and behavioral indicators of circadian realignment across multiple organ systems, including the suprachiasmatic nucleus (SCN) and peripheral tissues such as the liver, adipose tissue, and adrenal glands. Notably, this accelerated adaptation confers protection against jetlag-induced sleep disturbances, weight gain, and metabolic imbalance, underscoring a systemic role for Per1 in maintaining circadian network stability. Mechanistically, unbiased spatial transcriptomics identified reduced expression of the arginine vasopressin (AVP), a key neuropeptide mediating SCN intercellular coupling, as the driver of circadian network instability. Weakened SCN synchrony permits enhanced flexibility of peripheral oscillator responses, expediting whole-body adaptation to shifted light-dark schedules. These findings position Per1 as a critical regulator of circadian robustness, a buffer against light over-responsiveness, identifying a potential molecular target for mitigating circadian misalignment in contexts such as jetlag, shift work, and metabolic disease. TeaserWhat if beating jetlag was as simple as switching off a gene? Researchers show that disabling Per1, a core circadian regulator, accelerates body clock realignment and protects against sleep and metabolic disruption--highlighting new therapeutic possibilities for jetlag and shift work.

neuroscience↗

GLMMcosinor: Flexible cosinor modeling with a generalized linear mixed modeling framework to characterize rhythmic time series.

BackgroundModeling rhythmic biological processes, such as gene expression and sleep-wake cycles, is critical for understanding physiological mechanisms and their dysregulation in disease. Traditional cosinor analysis, commonly used to model rhythmic data, assumes Gaussian-distributed residuals and does not account for hierarchical data, limiting its applicability in modern biological datasets. ResultsWe present GLMMcosinor, an R package that integrates cosinor modeling into the Generalized Linear Mixed Modeling (GLMM) framework using glmmTMB. GLMMcosinor enables analysis of a broad spectrum of non-Gaussian and hierarchical data structures, including count, positive-only, and zero-inflated distributions. By incorporating mixed-effects modeling, GLMMcosinor improves parameter estimation and biological interpretability. The package includes functions for group comparisons of rhythmic parameters and visualization tools such as polar and time series plots. Additionally, GLMMcosinor is available as a Shiny app for intuitive, code-free analysis. ConclusionsGLMMcosinor significantly extends the flexibility and scope of rhythmic data analysis by incorporating GLMM functionality. It is freely available on GitHub, CRAN, rOpenSci, and the R-universe, with comprehensive documentation and reproducible examples, making it a robust tool for researchers analyzing complex rhythmic datasets.

bioinformatics↗