Regulation of proteostasis by sleep through autophagy in <i>Drosophila</i> models of Alzheimer’s disease

Sleep and circadian rhythm dysfunctions are common clinical features of Alzheimer’s disease (AD). Increasing evidence suggests that in addition to being a symptom, sleep disturbances can also drive the progression of neurodegeneration. Protein aggregation is a pathological hallmark of AD; however, the molecular pathways behind how sleep affects protein homeostasis remain elusive. Here we demonstrate that sleep modulation influences proteostasis and the progression of neurodegeneration in Drosophila models of tauopathy. We show that sleep deprivation enhanced Tau aggregational toxicity resulting in exacerbated synaptic degeneration. In contrast, sleep induction using gaboxadol led to reduced toxic Tau accumulation in neurons as a result of modulated autophagic flux and enhanced clearance of ubiquitinated Tau, suggesting altered protein processing and clearance that resulted in improved synaptic integrity and function. These findings highlight the complex relationship between sleep and regulation of protein homeostasis and the neuroprotective potential of sleep-enhancing therapeutics to slow the progression or delay the onset of neurodegeneration

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A mathematical model provides mechanistic links to temporal patterns in Drosophila daily activity

BACKGROUND: Circadian clocks are endogenous biochemical oscillators that control daily behavioral rhythms in all living organisms. In fruit fly, the circadian rhythms are typically studied using power spectra of multiday behavioral recordings. Despite decades of study, a quantitative understanding of the temporal shape of Drosophila locomotor rhythms is missing. Locomotor recordings have been used mostly to extract the period of the circadian clock, leaving these data-rich time series largely underutilized. The power spectra of Drosophila and mouse locomotion often show multiple peaks in addition to the expected at T ~ 24 h. Several theoretical and experimental studies have previously used these data to examine interactions between the circadian and other endogenous rhythms, in some cases, attributing peaks in the T < 24 h regime to ultradian oscillators. However, the analysis of fly locomotion was typically performed without considering the shape of time series, while the shape of the signal plays important role in its power spectrum. To account for locomotion patterns in circadian studies we construct a mathematical model of fly activity. Our model allows careful analysis of the temporal shape of behavioral recordings and can provide important information about biochemical mechanisms that control fly activity. RESULTS: Here we propose a mathematical model with four exponential terms and a single period of oscillation that closely reproduces the shape of the locomotor data in both time and frequency domains. Using our model, we reexamine interactions between the circadian and other endogenous rhythms and show that the proposed single-period waveform is sufficient to explain the position and height of >88 % of spectral peaks in the locomotion of wild-type and circadian mutants of Drosophila. In the time domain, we find the timescales of the exponentials in our model to be ~1.5 h(−1) on average. CONCLUSIONS: Our results indicate that multiple spectral peaks from fly locomotion are simply harmonics of the circadian period rather than independent ultradian oscillators as previously reported. From timescales of the exponentials we hypothesize that model rates reflect activity of the neuropeptides that likely transduce signals of the circadian clock and the sleep–wake homeostat to shape behavioral outputs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12868-016-0248-9) contains supplementary material, which is available to authorized users

A Computational Method to Quantify Fly Circadian Activity

In most animals and plants, circadian clocks orchestrate behavioral and molecular processes and synchronize them to the daily light-dark cycle. Fundamental mechanisms that underlie this temporal control are widely studied using the fruit fly Drosophila melanogaster as a model organism. In flies, the clock is typically studied by analyzing multiday locomotor recording. Such a recording shows a complex bimodal pattern with two peaks of activity: a morning peak that happens around dawn, and an evening peak that happens around dusk. These two peaks together form a waveform that is very different from sinusoidal oscillations observed in clock genes, suggesting that mechanisms in addition to the clock have profound effects in producing the observed patterns in behavioral data. Here we provide instructions on using a recently developed computational method that mathematically describes temporal patterns in fly activity. The method fits activity data with a model waveform that consists of four exponential terms and nine independent parameters that fully describe the shape and size of the morning and evening peaks of activity. The extracted parameters can help elucidate the kinetic mechanisms of substrates that underlie the commonly observed bimodal activity patterns in fly locomotor rhythms

Data from: A Stochastic Burst Follows the Periodic Morning Peak in Individual Drosophila Locomotion

The data is associated to the article A Stochastic Burst Follows the Periodic Morning Peak in Individual Drosophila Locomotion published in PLoS ONE. The full text article can be found at https://doi.org/10.1371/journal.pone.0140481</p

MOESM1 of A mathematical model provides mechanistic links to temporal patterns in Drosophila daily activity

Additional file 1. Supplementary material for the main manuscript. The file contains additional data that support our findings, detailed mathematical derivation of the model power spectrum, and mathematical analysis of effects of the Dirichlet kernel and Butterworth filter on power spectra

Kinetics of Doubletime Kinase-dependent Degradation of the Drosophila Period Protein

Robust circadian oscillations of the proteins PERIOD (PER) and TIMELESS (TIM) are hallmarks of a functional clock in the fruit fly Drosophila melanogaster . Early morning phosphorylation of PER by the kinase Doubletime (DBT) and subsequent PER turnover is an essential step in the functioning of the Drosophila circadian clock. Here using time-lapse fluorescence microscopy we study PER stability in the presence of DBT and its short, long, arrhythmic, and inactive mutants in S2 cells. We observe robust PER degradation in a DBT allele-specific manner. With the exception of doubletime-short (DBT S ), all mutants produce differential PER degradation profiles that show direct correspondence with their respective Drosophila behavioral phenotypes. The kinetics of PER degradation with DBT S in cell culture resembles that with wild-type DBT and posits that, in flies DBT S likely does not modulate the clock by simply affecting PER degradation kinetics. For all the other tested DBT alleles, the study provides a simple model in which the changes in Drosophila behavioral rhythms can be explained solely by changes in the rate of PER degradation

Behavioral Studies in Drosophila Models of Human Diseases

Drosophila melanogaster, commonly known as the fruit fly, is one of the most widely used model organisms in biological studies. Although Drosophila differs significantly from vertebrates in terms of their gross appearance, the genes and neural circuits that control essential behaviors are functionally conserved during evolution. In addition, Drosophila is highly amenable for genetic manipulations, making it ideal to dissect genetic and circuitry underpinnings of normal behavior, and establish disease models that can largely recapitulate the pathological hallmarks and behavioral changes in humans. Therefore, the power of Drosophila genetics and the large repertoire of Drosophila behaviors provide an unprecedented opportunity for in-depth mechanistic studies of human disease pathology as well as identification of potential therapeutic drug targets through large-scale behavioral screens. In this chapter, we will discuss the well-established Drosophila behavioral assays, including locomotion, learning & memory, sleep, and grooming, and highlight their application in Drosophila models of human diseases