Systematic analysis of differential rhythmic liver gene expression mediated by the circadian clock and feeding rhythms.

The circadian clock and feeding rhythms are both important regulators of rhythmic gene expression in the liver. To further dissect the respective contributions of feeding and the clock, we analyzed differential rhythmicity of liver tissue samples across several conditions. We developed a statistical method tailored to compare rhythmic liver messenger RNA (mRNA) expression in mouse knockout models of multiple clock genes, as well as PARbZip output transcription factors (Hlf/Dbp/Tef). Mice were exposed to ad libitum or night-restricted feeding under regular light-dark cycles. During ad libitum feeding, genetic ablation of the core clock attenuated rhythmic-feeding patterns, which could be restored by the night-restricted feeding regimen. High-amplitude mRNA expression rhythms in wild-type livers were driven by the circadian clock, but rhythmic feeding also contributed to rhythmic gene expression, albeit with significantly lower amplitudes. We observed that Bmal1 and Cry1/2 knockouts differed in their residual rhythmic gene expression. Differences in mean expression levels between wild types and knockouts correlated with rhythmic gene expression in wild type. Surprisingly, in PARbZip knockout mice, the mean expression levels of PARbZip targets were more strongly impacted than their rhythms, potentially due to the rhythmic activity of the D-box-repressor NFIL3. Genes that lost rhythmicity in PARbZip knockouts were identified to be indirect targets. Our findings provide insights into the diurnal transcriptome in mouse liver as we identified the differential contributions of several core clock regulators. In addition, we gained more insights on the specific effects of the feeding-fasting cycle.

The circadian oscillator analysed at the single-transcript level.

The circadian clock is an endogenous and self-sustained oscillator that anticipates daily environmental cycles. While rhythmic gene expression of circadian genes is well-described in populations of cells, the single-cell mRNA dynamics of multiple core clock genes remain largely unknown. Here we use single-molecule fluorescence in situ hybridisation (smFISH) at multiple time points to measure pairs of core clock transcripts, Rev-erbα (Nr1d1), Cry1 and Bmal1, in mouse fibroblasts. The mean mRNA level oscillates over 24 h for all three genes, but mRNA numbers show considerable spread between cells. We develop a probabilistic model for multivariate mRNA counts using mixtures of negative binomials, which accounts for transcriptional bursting, circadian time and cell-to-cell heterogeneity, notably in cell size. Decomposing the mRNA variability into distinct noise sources shows that clock time contributes a small fraction of the total variability in mRNA number between cells. Thus, our results highlight the intrinsic biological challenges in estimating circadian phase from single-cell mRNA counts and suggest that circadian phase in single cells is encoded post-transcriptionally.

Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling.

Wearable biosensors and smartphone applications can measure physiological variables over multiple days in free-living conditions. We measure food and drink ingestion, glucose dynamics, physical activity, heart rate (HR), and heart rate variability (HRV) in 25 healthy participants over 14 days. We develop a Bayesian inference framework to learn personal parameters that quantify circadian rhythms and physiological responses to external stressors. Modeling the effects of ingestion events on glucose levels reveals that slower glucose decay kinetics elicit larger postprandial glucose spikes, and we uncover a circadian baseline rhythm for glucose with high amplitudes in some individuals. Physical activity and circadian rhythms explain as much as 40%-65% of the HR variance, whereas the variance explained for HRV is more heterogeneous across individuals. A more complex model incorporating activity, HR, and HRV explains up to 15% of additional glucose variability, highlighting the relevance of integrating multiple biosensors to better predict glucose dynamics.

Memory and relatedness of transcriptional activity in mammalian cell lineages.

Phenotypically identical mammalian cells often display considerable variability in transcript levels of individual genes. How transcriptional activity propagates in cell lineages, and how this varies across genes is poorly understood. Here we combine live-cell imaging of short-lived transcriptional reporters in mouse embryonic stem cells with mathematical modelling to quantify the propagation of transcriptional activity over time and across cell generations in phenotypically homogenous cells. In sister cells we find mean transcriptional activity to be strongly correlated and transcriptional dynamics tend to be synchronous; both features control how quickly transcriptional levels in sister cells diverge in a gene-specific manner. Moreover, mean transcriptional activity is transmitted from mother to daughter cells, leading to multi-generational transcriptional memory and causing inter-family heterogeneity in gene expression.

microRNA input into a neural ultradian oscillator controls emergence and timing of alternative cell states.

Progenitor maintenance, timed differentiation and the potential to enter quiescence are three fundamental processes that underlie the development of any organ system. In the nervous system, progenitor cells show short-period oscillations in the expression of the transcriptional repressor Hes1, while neurons and quiescent progenitors show stable low and high levels of Hes1, respectively. Here we use experimental data to develop a mathematical model of the double-negative interaction between Hes1 and a microRNA, miR-9, with the aim of understanding how cells transition from one state to another. We show that the input of miR-9 into the Hes1 oscillator tunes its oscillatory dynamics, and endows the system with bistability and the ability to measure time to differentiation. Our results suggest that a relatively simple and widespread network of cross-repressive interactions provides a unifying framework for progenitor maintenance, the timing of differentiation and the emergence of alternative cell states.