a-computational-method-to-quantify-fly-circadian-activity

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. ...

University of Miami

We used fluorescence imaging with one nanometer accuracy (FIONA) to analyze organelle movement by conventional kinesin and cytoplasmic dynein in a cell. We located a green fluorescence protein (GFP)-tagged peroxisome in cultured Drosophila S2 cells to within 1.5 nanometers in 1.1 milliseconds, a 400-fold improvement in temporal resolution, sufficient to determine the average step size to be approximately 8 nanometers for both dynein and kinesin. Furthermore, we found that dynein and kinesin do not work against each other in vivo during peroxisome transport. Rather, multiple kinesins or multiple dyneins work together, producing up to 10 times the in vitro speed.

Kinesin and dynein move a peroxisome in vivo: a tug-of-war or coordinated movement?

We studied the structural dynamics of chicken myosin V by combining the localization power of fluorescent imaging with one nanometer accuracy (FIONA) with the ability to detect angular changes of a fluorescent probe. The myosin V was labeled with bifunctional rhodamine on one of its calmodulin light chains. For every 74 nm translocation, the probe exhibited two reorientational motions, associated with alternating smaller and larger translational steps. Molecules previously identified as stepping alternatively 74-0 nm were found to actually step 64-10 nm. Additional tilting often occurred without full steps, possibly indicating flexibility of the attached myosin heads or probing of their vicinity. Processive myosin V molecules sometimes shifted from the top to the side of actin, possibly to avoid an obstacle. The data indicate marked adaptability of this molecular motor to a nonuniform local environment and provide strong support for a straight-neck model of myosin V in which the lever arm of the leading head is tilted backwards at the prepowerstoke angle.

Adaptability of myosin V studied by simultaneous detection of position and orientation.

The centroid of a fluorophore can be determined within approximately 1.5-nm accuracy from its focused image through fluorescence imaging with one-nanometer accuracy (FIONA). If, instead, the sample is moved away from the focus, the point-spread-function depends on both the position and 3D orientation of the fluorophore, which can be calculated by defocused orientation and position imaging (DOPI). DOPI does not always yield position accurately, but it is possible to switch back and forth between focused and defocused imaging, thereby getting the centroid and the orientation with precision. We have measured the 3D orientation and stepping behavior of single bifunctional rhodamine probes attached to one of the calmodulins of the light-chain domain (LCD) of myosin V as myosin V moves along actin. Concomitant with large and small steps, the LCD rotates and then dwells in the leading and trailing position, respectively. The probe angle relative to the barbed end of the actin (beta) averaged 128 degrees while the LCD was in the leading state and 57 degrees in the trailing state. The angular difference of 71 degrees represents rotation of LCD around the bound motor domain and is consistent with a 37-nm forward step size of myosin V. When beta changes, the probe rotates +/-27 degrees azimuthally around actin and then rotates back again on the next step. Our results remove degeneracy in angles and the appearance of nontilting lever arms that were reported.

Defocused orientation and position imaging (DOPI) of myosin V.

Hidden Markov models (HMMs) provide an excellent analysis of recordings with very poor signal/noise ratio made from systems such as ion channels which switch among a few states. This method has also recently been used for modeling the kinetic rate constants of molecular motors, where the observable variable-the position-steadily accumulates as a result of the motor's reaction cycle. We present a new HMM implementation for obtaining the chemical-kinetic model of a molecular motor's reaction cycle called the variable-stepsize HMM in which the quantized position variable is represented by a large number of states of the Markov model. Unlike previous methods, the model allows for arbitrary distributions of step sizes, and allows these distributions to be estimated. The result is a robust algorithm that requires little or no user input for characterizing the stepping kinetics of molecular motors as recorded by optical techniques.

Improved hidden Markov models for molecular motors, part 1: basic theory.

Unbiased interpretation of noisy single molecular motor recordings remains a challenging task. To address this issue, we have developed robust algorithms based on hidden Markov models (HMMs) of motor proteins. The basic algorithm, called variable-stepsize HMM (VS-HMM), was introduced in the previous article. It improves on currently available Markov-model based techniques by allowing for arbitrary distributions of step sizes, and shows excellent convergence properties for the characterization of staircase motor timecourses in the presence of large measurement noise. In this article, we extend the VS-HMM framework for better performance with experimental data. The extended algorithm, variable-stepsize integrating-detector HMM (VSI-HMM) better models the data-acquisition process, and accounts for random baseline drifts. Further, as an extension, maximum a posteriori estimation is provided. When used as a blind step detector, the VSI-HMM outperforms conventional step detectors. The fidelity of the VSI-HMM is tested with simulations and is applied to in vitro myosin V data where a small 10 nm population of steps is identified. It is also applied to an in vivo recording of melanosome motion, where strong evidence is found for repeated, bidirectional steps smaller than 8 nm in size, implying that multiple motors simultaneously carry the cargo.

Improved hidden Markov models for molecular motors, part 2: extensions and application to experimental data.

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.

Kinetics of doubletime kinase-dependent degradation of the Drosophila period protein.

Sleep research in Drosophila is not only here to stay, but is making impressive strides towards helping us understand the biological basis for and the purpose of sleep-perhaps one of the most complex and enigmatic of behaviors. Thanks to over a decade of sleep-related studies in flies, more molecular methods are being applied than ever before towards understanding the genetic basis of sleep disorders. The advent of high-throughput technologies that can rapidly interrogate whole genomes, epigenomes and proteomes, has also revolutionized our ability to detect genetic variants that might be causal for a number of sleep disorders. In the coming years, mutational studies in model organisms such as Drosophila will need to be functionally connected to information being generated from these whole-genome approaches in humans. This will necessitate the development of appropriate methods for interpolating data and increased analytical power to synthesize useful network(s) of sleep regulatory pathways-including appropriate discriminatory and predictive capabilities. Ultimately, such networks will also need to be interpreted in the context of fundamental neurobiological substrates for sleep in any given species. In this review, we highlight some emerging approaches, such as network analysis and mathematical modeling of sleep distributions, which can be applied to contemporary sleep research as a first step to achieving these aims. These methodologies should favorably impact not only a mechanistic understanding of sleep, but also future pharmacological intervention strategies to manage and treat sleep disorders in humans.

Modeling the genetic basis for human sleep disorders in Drosophila.

Coupling between cyclically varying external light and an endogenous biochemical oscillator known as the circadian clock, modulates a rhythmic pattern with two prominent peaks in the locomotion of Drosophila melanogaster. A morning peak appears around the time lights turn on and an evening peak appears just before lights turn off. The close association between the peaks and the external 12:12 hour light/dark photoperiod means that respective morning and evening peaks of individual flies are well-synchronized in time and, consequently, feature prominently in population-averaged data. Here, we report on a brief but strong stochastic burst in fly activity that, in contrast to morning and evening peaks, is detectable only in single fly recordings. This burst was observed across 3 wild-type strains of Drosophila melanogaster. In a single fly recording, the burst is likely to appear once randomly within 0.5-5 hours after lights turn on, last for only 2-3 minutes and yet show 5 times greater activity compared to the maximum of morning peak with data binned in 3 minutes. Owing to its variable timing and short duration, the burst is virtually undetectable in population-averaged data. We use a locally-built illumination system to study the burst and find that its incidence in a population correlates with light intensity, with ~85% of control flies showing the behavior at 8000 lux (1942 μW/cm2). Consistent with that finding, several mutant flies with impaired vision show substantially reduced frequency of the burst. Additionally, we find that genetic ablation of the clock has insignificant effect on burst frequency. Together, these data suggest that the pronounced burst is likely generated by a light-activated circuit that is independent of the circadian clock.

A Stochastic Burst Follows the Periodic Morning Peak in Individual Drosophila Locomotion.

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.

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

The molecular clock relies on a delayed negative feedback loop of transcriptional regulation to generate oscillating gene expression. Although the principal components of the clock are present in all circadian neurons, different neuronal clusters have varying effects on rhythmic behavior, suggesting that the clocks they house are differently regulated. Combining biochemical and genetic techniques in Drosophila, we identify a phosphorylation program native to the master pacemaker neurons that regulates the timing of nuclear accumulation of the Period/Timeless repressor complex. GSK-3/SGG binds and phosphorylates Period-bound Timeless, triggering a CK2-mediated phosphorylation cascade. Mutations that block the hierarchical phosphorylation of Timeless in vitro also delay nuclear accumulation in both tissue culture and in vivo and predictably change rhythmic behavior. This two-kinase phosphorylation cascade is anatomically restricted to the eight master pacemaker neurons, distinguishing the regulatory mechanism of the molecular clock within these neurons from the other clocks that cooperate to govern behavioral rhythmicity.

Sheyum Syed

Department of Physics

University of Miami

A Computational Method to Quantify Fly Circadian Activity