Positive Autoregulation Delays the Expression Phase of Mammalian Clock Gene Per2

In mammals, cellular circadian rhythms are generated by a transcriptional-translational autoregulatory network that consists of clock genes that encode transcriptional regulators. Of these clock genes, Period1 (Per1) and Period2 (Per2) are essential for sustainable circadian rhythmicity and photic entrainment. Intriguingly, Per1 and Per2 mRNAs exhibit circadian oscillations with a 4-hour phase difference, but they are similarly transactivated by CLOCK-BMAL1. In this study, we investigated the mechanism underlying the phase difference between Per1 and Per2 through a combination of mathematical simulations and molecular experiments. Mathematical analyses of a model for the mammalian circadian oscillator demonstrated that the slow synthesis and fast degradation of mRNA tend to advance the oscillation phase of mRNA expression. However, the phase difference between Per1 and Per2 was not reproduced by the model, which implemented a 1.1-fold difference in degradation rates and a 3-fold difference in CLOCK-BMAL1 mediated inductions of Per1 and Per2 as estimated in cultured mammalian cells. Thus, we hypothesized the existence of a novel transcriptional activation of Per2 by PER1/2 such that the Per2 oscillation phase was delayed. Indeed, only the Per2 promoter, but not Per1, was strongly induced by both PER1 and PER2 in the presence of CLOCK-BMAL1 in a luciferase reporter assay. Moreover, a 3-hour advance was observed in the transcriptional oscillation of the delta-Per2 reporter gene lacking cis-elements required for the induction by PER1/2. These results indicate that the Per2 positive feedback regulation is a significant factor responsible for generating the phase difference between Per1 and Per2 gene expression

Circadian Features of Cluster Headache and Migraine: A Systematic Review, Meta-analysis, and Genetic Analysis

BACKGROUND AND OBJECTIVES: Cluster headache and migraine have circadian features at multiple levels (cellular, systems, and behavioral). A thorough understanding of their circadian features informs their pathophysiologies. METHODS: A librarian created search criteria in MEDLINE Ovid, Embase, PsycINFO, Web of Science, and Cochrane Library. Two physicians independently performed the remainder of the systematic review/meta-analysis using Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines. Separate from the systematic review/meta-analysis, we performed a genetic analysis for genes with a circadian pattern of expression (clock-controlled genes or CCGs) by cross-referencing genome-wide association studies (GWASs) of headache, a nonhuman primate study of CCGs in a variety of tissues, and recent reviews of brain areas relevant in headache disorders. Altogether, this allowed us to catalog circadian features at the behavioral level (circadian timing, time of day, time of year, and chronotype), systems level (relevant brain areas where CCGs are active, melatonin and corticosteroid levels), and cellular level (core circadian genes and CCGs). RESULTS: For the systematic review and meta-analysis, 1,513 studies were found, and 72 met the inclusion criteria; for the genetic analysis, we found 16 GWASs, 1 nonhuman primate study, and 16 imaging reviews. For cluster headache behavior, meta-analyses showed a circadian pattern of attacks in 70.5% (3,490/4,953) of participants across 16 studies, with a clear circadian peak between 21:00 and 03:00 and circannual peaks in spring and autumn. Chronotype was highly variable across studies. At the systems level, lower melatonin and higher cortisol levels were reported in cluster headache participants. At the cellular level, cluster headache was associated with core circadian genes DISCUSSION: Cluster headache and migraine are highly circadian at multiple levels, reinforcing the importance of the hypothalamus. This review provides a pathophysiologic foundation for circadian-targeted research into these disorders. TRIAL REGISTRATION INFORMATION: The study was registered with PROSPERO (registration number CRD42021234238)

Inter-individual Variations in Circadian Misalignment-induced Nafld Pathophysiology in Mice

Pathological consequences of circadian misalignment, such as shift work, show considerable individual differences, but the lack of mechanistic understanding hinders precision prevention to prevent and mitigate disease symptoms. Here, we employed an integrative approach involving physiological, transcriptional, and histological phenotypes to examine inter-individual differences in pre-symptomatic pathological progression, preceding irreversible disease onset, in wild-type mice exposed to chronic jet-lag (CJL). We observed that CJL markedly increased the prevalence of hepatic steatosis with pronounced inter-individual differences. Stratification of individual mice based on CJL-induced hepatic transcriptomic signature, validated by histopathological analysis, pinpoints dysregulation of lipid metabolism. Moreover, the period and power of intrinsic behavioral rhythms were found to significantly correlate with CJL-induced gene signatures. Together, our results suggest circadian rhythm robustness of the animals contributes to inter-individual variations in pathogenesis of circadian misalignment-induced diseases and raise the possibility that these physiological indicators may be available for predictive hallmarks of circadian rhythm disorders

Ammonia-lowering activities and carbamoyl phosphate synthetase 1 (Cps1) induction mechanism of a natural flavonoid

OBJECTIVE: Ammonia detoxification is essential for physiological well-being, and the urea cycle in liver plays a predominant role in ammonia disposal. Nobiletin (NOB), a natural dietary flavonoid, is known to exhibit various physiological efficacies. In the current study, we investigated a potential role of NOB in ammonia control and the underlying cellular mechanism. MATERIALS/METHODS: C57BL/6 mice were fed with regular chow (RC), high-fat (HFD) or high-protein diet (HPD) and treated with either vehicle or NOB. Serum and/or urine levels of ammonia and urea were measured. Liver expression of genes encoding urea cycle enzymes and C/EBP transcription factors was determined over the circadian cycle. Luciferase reporter assays were carried out to investigate function of CCAAT consensus elements on the carbamoyl phosphate synthetase (Cps1) gene promoter. A circadian clock-deficient mouse mutant, Clock(Δ19/Δ19), was utilized to examine a requisite role of the circadian clock in mediating NOB induction of Cps1. RESULTS: NOB was able to lower serum ammonia levels in mice fed with RC, HFD or HPD. Compared with RC, HFD repressed the mRNA and protein expression of Cps1, encoding the rate-limiting enzyme of the urea cycle. Interestingly, NOB rescued CPS1 protein levels under the HFD condition via induction of the transcription factors C/EBPα and C/EBPβ. Expression of other urea cycle genes was also decreased by HFD relative to RC and again restored by NOB to varying degrees, which, in conjunction with Cps1 promoter reporter analysis, suggested a C/EBP-dependent mechanism for the co-induction of urea cycle genes by NOB. In comparison, HPD markedly increased CPS1 levels relative to RC, yet NOB did not further enrich CPS1 to a significant extent. Using the circadian mouse mutant Clock(Δ19/Δ19), we also showed that a functional circadian clock, known to modulate C/EBP and CPS1 expression, was required for NOB induction of CPS1 under the HFD condition. CONCLUSION: NOB, a dietary flavonoid, exhibits a broad activity in ammonia control across varying diets, and regulates urea cycle function via C/EBP-and clock-dependent regulatory mechanisms. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12986-015-0020-7) contains supplementary material, which is available to authorized users

Xcvatr: Detection and Characterization of Variant Impact on the Embeddings of Single -Cell and Bulk Rna-Sequencing Samples

BACKGROUND: RNA-sequencing has become a standard tool for analyzing gene activity in bulk samples and at the single-cell level. By increasing sample sizes and cell counts, this technique can uncover substantial information about cellular transcriptional states. Beyond quantification of gene expression, RNA-seq can be used for detecting variants, including single nucleotide polymorphisms, small insertions/deletions, and larger variants, such as copy number variants. Notably, joint analysis of variants with cellular transcriptional states may provide insights into the impact of mutations, especially for complex and heterogeneous samples. However, this analysis is often challenging due to a prohibitively high number of variants and cells, which are difficult to summarize and visualize. Further, there is a dearth of methods that assess and summarize the association between detected variants and cellular transcriptional states. RESULTS: Here, we introduce XCVATR (eXpressed Clusters of Variant Alleles in Transcriptome pRofiles), a method that identifies variants and detects local enrichment of expressed variants within embedding of samples and cells in single-cell and bulk RNA-seq datasets. XCVATR visualizes local clumps of small and large-scale variants and searches for patterns of association between each variant and cellular states, as described by the coordinates of cell embedding, which can be computed independently using any type of distance metrics, such as principal component analysis or t-distributed stochastic neighbor embedding. Through simulations and analysis of real datasets, we demonstrate that XCVATR can detect enrichment of expressed variants and provide insight into the transcriptional states of cells and samples. We next sequenced 2 new single cell RNA-seq tumor samples and applied XCVATR. XCVATR revealed subtle differences in CNV impact on tumors. CONCLUSIONS: XCVATR is publicly available to download from https://github.com/harmancilab/XCVATR

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding ‘big data’ that is conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them