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Sensory Conflict Disrupts Activity of the Drosophila Circadian Network

Summary Periodic changes in light and temperature synchronize the Drosophila circadian clock, but the question of how the fly brain integrates these two input pathways to set circadian time remains unanswered. We explore multisensory cue combination by testing the resilience of the circadian network to conflicting environmental inputs. We show that misaligned light and temperature cycles can lead to dramatic changes in the daily locomotor activities of wild-type flies during and after exposure to sensory conflict. This altered behavior is associated with a drastic reduction in the amplitude of PERIOD (PER) oscillations in brain clock neurons and desynchronization between light- and temperature-sensitive neuronal subgroups. The behavioral disruption depends heavily on the phase relationship between light and temperature signals. Our results represent a systematic quantification of multisensory integration in the Drosophila circadian system and lend further support to the view of the clock as a network of coupled oscillatory subunits.


Activity Monitoring
Flies were individually placed into small glass recording tubes containing 5% sucrose and 2% agar medium, which occupied approximately one third of the tube. These tubes were then loaded into MB5 activity monitors (Trikinetics, Waltham, USA), with nine infrared beam detectors separated by 3mm directed at each activity tube. An interruption of the infrared light beam by the movement of a fly produced a signal, which was then recorded by a microprocessor. The number of beam breaks was recorded for each fly in 5-minute time bins and summed into bin counts. Thus, 12 activity scans were obtained for each fly per hour.
Monitors were placed in light-and temperature-controllable incubators (Percival) for the duration of the experiments. 12:12 LD was generated through square wave transition between ~2500 and 0 lux respectively. 12:12 TC was achieved through transitions between 26°C (ON) and 16°C (OFF) occurring over ~10min. Environmental conditions were recorded with an environmental monitor placed inside the incubator. These were checked to validate scheduled conditions. (Details of specific experimental designs described at relevant points in Results).

Data Analysis
Activity of individual flies and average activity of the population were plotted as double actograms using the Matlab Flytoolbox library. Period length and proportion of rhythmic animals during free-running conditions, were calculated using autocorrelation in the Matlab Flytoolbox library (Levine et al., 2002). The autocorrelation output 'Rhythms Strength' (RS) serves as an estimate of the rhythm strength associated with each period value. In this study flies with RS values ≥ 1.5 were considered to be rhythmic (Levine et al., 2002, Table S2). To determine and compare the phase of the activity peaks during the two free-run parts of the experiment, circular statistics and phase plots using the same Matlab Flytoolbox library were used (Levine et al., 2002). All other analysis was carried out in Wolfram Mathematica using bespoke programs written for the purposes of this study (details of which are described in relevant sections of this report).

Quantification of Entrained Behavior
Analysis of locomotor behavior under entrained conditions is inherently challenging as observed behavior must be a result of both circadian drive and direct sensory effects (e.g. startle behavior and masking). It is common within the field to assess the anticipatory behavior prior to Zeitgeber offset -the so-called 'evening activity'. A simple measure of this evening behavior is a linear increase of activity prior to Zeitgeber offset.With this in mind, maximum likelihood estimation was used to best fit the linear regression, = + , to the activity bout immediately prior to both light and temperature evening ( Figure S3). The regression analysis was applied only to data points that displayed linearity preceding offset of the Zeitgeber in question. In cases where Zeitgeber offset for light and temperature were in close proximity, care was taken not to include evening activity for one stimulus in the analysis for the second. Thus, during very small or very large conflicts, fewer data points were available for fitting the later Zeitgeber, which translated into larger confidence intervals for these time points.

Immunostaining and Quantification
Flies collected at four time points during the in-phase and out-of-phase conditions (corresponding to ZT3, ZT9, ZT15 and ZT21 of the in-phase condition) were fixed in 4% paraformaldehyde in 0.1M phosphate buffer (PB) with 0.1% Triton X-100 (PBS-T) for 2.5h at room temperature. Flies were then rinsed three times in PB, and the brains subsequently dissected in PB. Brains were then blocked in 5% normal goat serum (NGS) in 0.5% PBS-T at 4˚C for 36h before incubation in primary antibodies for 48h at 4˚C. Double staining was conducted with primary antibodies: rabbit anti-PER (1:1500) (Stanewsky et al., 1997), and mouse anti-PDF (1:500) (DSHB). Secondary fluorescence-conjugated antibodies were alexaFluor 488 and alexaFluor 647 (purchased from Thermo Fisher Scientific), and were both diluted 1:300 in 0.5% PBS-T. Secondary antibodies were applied after washing six times in 0.5% PBS-T. After incubation with secondary antibodies, the brains were washed six times in 0.5% PBS-T and mounted in Vectashield (Vector Labs) mounting medium. The fluorescence signals of the whole mount brains were visualized using a Leica SP8 laser scanning confocal microscope.
Staining was quantified as described previously (Rieger et al., 2006), and a final staining index was calculated for each cell group: The maximum number of cells for the different neuronal groups was as follows: s-LN v , 4; l-LN v , 5; LN d 7; DN 1 , 17; DN 2 , 2. Owing to the large number of DN 3 neurons, SI for this subgroup was calculated as − .

Statistical Methods
All statistical methods are described at relevant points in the text and supplemental information. In brief, phase comparisons of free-running activity rhythms were conducted using circular phase statistics using the Matlab Flytoolbox library (Levine et al., 2002). Here, activity data is smoothed using a low-pass filter, and average peak phase across two consecutive days is calculated for each fly. The results are then plotted in polar coordinates, and a dispersion test used to determine whether the two distributions (FR1 and FR2) differ significantly in angular deviation from their respective means.
For analysis of immunostaining data, one-way ANOVA was used to examine an effect of ZT on PER staining intensity. In addition to this, two-tailed t-test was used to compare staining of neuronal subgroups between time points as shown in Figure 2 and Table S1.