bims-ciryme 2022-04-03 papers

Sci Rep. 2022 Apr 01. 12(1): 5508

Machine learning and expression analyses reveal circadian clock features predictive of anxiety.

Aziz Zafar, Rebeccah Overton, Ziad Attia, Ahmet Ay, Krista Ingram.

Mood disorders, including generalized anxiety disorder, are associated with disruptions in circadian rhythms and are linked to polymorphisms in circadian clock genes. Molecular mechanisms underlying these connections may be direct-via transcriptional activity of clock genes on downstream mood pathways in the brain, or indirect-via clock gene influences on the phase and amplitude of circadian rhythms which, in turn, modulate physiological processes influencing mood. Employing machine learning combined with statistical approaches, we explored clock genotype combinations that predict risk for anxiety symptoms in a deeply phenotyped population. We identified multiple novel circadian genotypes predictive of anxiety, with the PER3(rs17031614)-AG/CRY1(rs2287161)-CG genotype being the strongest predictor of anxiety risk, particularly in males. Molecular chronotyping, using clock gene expression oscillations, revealed that advanced circadian phase and robust circadian amplitudes are associated with high levels of anxiety symptoms. Further analyses revealed that individuals with advanced phases and pronounced circadian misalignment were at higher risk for severe anxiety symptoms. Our results support both direct and indirect influences of clock gene variants on mood: while sex-specific clock genotype combinations predictive of anxiety symptoms suggest direct effects on mood pathways, the mediation of PER3 effects on anxiety via diurnal preference measures and the association of circadian phase with anxiety symptoms provide evidence for indirect effects of the molecular clockwork on mood. Unraveling the complex molecular mechanisms underlying the links between circadian physiology and mood is essential to identifying the core clock genes to target in future functional studies, thereby advancing the development of non-invasive treatments for anxiety-related disorders.

DOI: https://doi.org/10.1038/s41598-022-09421-4

Nat Commun. 2022 Mar 31. 13(1): 1724

A natural timeless polymorphism allowing circadian clock synchronization in "white nights".

Angelique Lamaze, Chenghao Chen, Solene Leleux, Min Xu, Rebekah George, Ralf Stanewsky.

Daily temporal organisation offers a fitness advantage and is determined by an interplay between environmental rhythms and circadian clocks. While light:dark cycles robustly synchronise circadian clocks, it is not clear how animals experiencing only weak environmental cues deal with this problem. Like humans, Drosophila originate in sub-Saharan Africa and spread North up to the polar circle, experiencing long summer days or even constant light (LL). LL disrupts clock function, due to constant activation of CRYPTOCHROME, which induces degradation of the clock protein TIMELESS (TIM), but temperature cycles are able to overcome these deleterious effects of LL. We show here that for this to occur a recently evolved natural timeless allele (ls-tim) is required, encoding the less light-sensitive L-TIM in addition to S-TIM, the only form encoded by the ancient s-tim allele. We show that only ls-tim flies can synchronise their behaviour to semi-natural conditions typical for Northern European summers, suggesting that this functional gain is driving the Northward ls-tim spread.

DOI: https://doi.org/10.1038/s41467-022-29293-6

Front Endocrinol (Lausanne). 2022 ;13 842603

It's What and When You Eat: An Overview of Transcriptional and Epigenetic Responses to Dietary Perturbations in Pancreatic Islets.

Matthew R Brown, Aleksey V Matveyenko.

Our ever-changing modern environment is a significant contributor to the increased prevalence of many chronic diseases, and particularly, type 2 diabetes mellitus (T2DM). Although the modern era has ushered in numerous changes to our daily living conditions, changes in "what" and "when" we eat appear to disproportionately fuel the rise of T2DM. The pancreatic islet is a key biological controller of an organism's glucose homeostasis and thus plays an outsized role to coordinate the response to environmental factors to preserve euglycemia through a delicate balance of endocrine outputs. Both successful and failed adaptation to dynamic environmental stimuli has been postulated to occur due to changes in the transcriptional and epigenetic regulation of pathways associated with islet secretory function and survival. Therefore, in this review we examined and evaluated the current evidence elucidating the key epigenetic mechanisms and transcriptional programs underlying the islet's coordinated response to the interaction between the timing and the composition of dietary nutrients common to modern lifestyles. With the explosion of next generation sequencing, along with the development of novel informatic and -omic approaches, future work will continue to unravel the environmental-epigenetic relationship in islet biology with the goal of identifying transcriptional and epigenetic targets associated with islet perturbations in T2DM.

Keywords: epigenetics; high-fat diet; intermittent fasting; ketogenic diet; low-protein diet; pancreatic islet; time-restricted feeding; type 2 diabetes mellitus

DOI: https://doi.org/10.3389/fendo.2022.842603

Sci Rep. 2022 Apr 01. 12(1): 5544

Quantifying daily rhythms with non-negative matrix factorization applied to mobile phone data.

Talayeh Aledavood, Ilkka Kivimäki, Sune Lehmann, Jari Saramäki.

Human activities follow daily, weekly, and seasonal rhythms. The emergence of these rhythms is related to physiology and natural cycles as well as social constructs. The human body and its biological functions undergo near 24-h rhythms (circadian rhythms). While their frequencies are similar across people, their phases differ. In the chronobiology literature, people are categorized into morning-type, evening-type, and intermediate-type groups called chronotypes based on their tendency to sleep at different times of day. Typically, this typology builds on carefully designed questionnaires or manually crafted features of time series data on people's activity. Here, we introduce a method where time-stamped data from smartphones are decomposed into components using non-negative matrix factorization. The method does not require any predetermined assumptions about the typical times of sleep or activity: the results are fully context-dependent and determined by the most prominent features of the activity data. We demonstrate our method by applying it to a dataset of mobile phone screen usage logs of 400 university students, collected over a year. We find four emergent temporal components: morning activity, night activity, evening activity and activity at noon. Individual behavior can be reduced to weights on these four components. We do not observe any clear categories of people based on the weights, but individuals are rather placed on a continuous spectrum according to the timings of their phone activities. High weights for the morning and night components strongly correlate with sleep and wake-up times. Our work points towards a data-driven way of characterizing people based on their full daily and weekly rhythms of activity and behavior, instead of only focusing on the timing of their sleeping periods.

DOI: https://doi.org/10.1038/s41598-022-09273-y