Real time, in vivo measurement of neuronal and peripheral clocks in Drosophila melanogaster

  1. Peter S Johnstone
  2. Maite Ogueta
  3. Olga Akay
  4. Inan Top
  5. Sheyum Syed
  6. Ralf Stanewsky
  7. Deniz Top  Is a corresponding author
  1. Department of Biochemistry and Molecular Biology, Dalhousie University, Canada
  2. Department of Pharmacology, Dalhousie University, Canada
  3. Institute of Neuro- and Behavioral Biology, Westfälische Wilhelms University, Germany
  4. You.i Labs Inc, Canada
  5. Department of Physics, University of Miami, United States

Abstract

Circadian clocks are highly conserved transcriptional regulators that control ~24 hr oscillations in gene expression, physiological function, and behavior. Circadian clocks exist in almost every tissue and are thought to control tissue-specific gene expression and function, synchronized by the brain clock. Many disease states are associated with loss of circadian regulation. How and when circadian clocks fail during pathogenesis remains largely unknown because it is currently difficult to monitor tissue-specific clock function in intact organisms. Here, we developed a method to directly measure the transcriptional oscillation of distinct neuronal and peripheral clocks in live, intact Drosophila, which we term Locally Activatable BioLuminescence, or LABL. Using this method, we observed that specific neuronal and peripheral clocks exhibit distinct transcriptional properties. Loss of the receptor for PDF, a circadian neurotransmitter critical for the function of the brain clock, disrupts circadian locomotor activity but not all tissue-specific circadian clocks. We found that, while peripheral clocks in non-neuronal tissues were less stable after the loss of PDF signaling, they continued to oscillate. We also demonstrate that distinct clocks exhibit differences in their loss of oscillatory amplitude or their change in period, depending on their anatomical location, mutation, or fly age. Our results demonstrate that LABL is an effective tool that allows rapid, affordable, and direct real-time monitoring of individual clocks in vivo.

Editor's evaluation

This manuscript will be of broad interest primarily to readers in the field of Chronobiology, but more in general, also Physiology. The reporter construct generated in this study provides a great tool to dissect with cell and tissue specificity the rhythmic transcriptional oscillations orchestrated by circadian clocks in vivo. Here, the authors take full advantage of such a tool showing how neuronal and peripheral clocks might be differentially regulated and possess distinct properties.

https://doi.org/10.7554/eLife.77029.sa0

eLife digest

The daily rhythms in our lives are driven by biological mechanisms called circadian clocks. These biological clocks are protein machines found in almost every cell and organ of the body, in nearly all living things, from fungi and plants to fruit flies and humans. These clocks control 24-hour cycles of gene activity and behaviour, and are kept in-time by so-called ‘master clocks’ in the brain.

Ideally, scientists would be able to observe how circadian clocks work in different parts of the brain in a living animal and track changes throughout the day, as the animal performs different behaviours. However, the tools that are currently available to study circadian clocks do not allow this. To overcome this difficulty, Johnstone et al. used fruit flies to develop a new method that allows scientists to measure the oscillations of the circadian clocks in the brain in real time.

Circadian clocks are composed of proteins called ‘transcription factors’ that activate different genes throughout the day, producing different proteins at different times. Transcription factors control the activity of genes by binding to DNA sequences called ‘promoters’ and switching the genes regulated by these promoters on or off.

Knowing this, Johnstone et al. engineered fruit flies to carry the gene that codes for a protein called luciferase, which emits light, and placed it under the control of the promoter for the period gene, a gene that is regulated by the circadian clock. To prevent all of the cells in the fly from producing luciferase any time the period promoter was active, Johnstone et al. placed a second gene between the promoter and the luciferase gene. This second gene contains ‘stop’ sequences that prevent luciferase from being produced as long as the second gene is present. Importantly, this gene can be genetically removed from specific cells in live flies, so only these cells will produce luciferase.

When Johnstone et al. removed the second gene from specific cells in the fly brain that are involved in controlling behaviours related to the circadian clocks, these cells started emitting light in cycles that reproduced the activity of the circadian clocks. Thus, by monitoring how the brightness of luciferase changed throughout the day in these flies, Johnstone et al. were able to reveal how the circadian clocks work in different parts of the fly brain.

They found that each clock had slightly different cycling lengths, suggesting that the clocks work differently in different parts of the brain to control behaviour. Interestingly, Johnstone et al. found that if a key gene responsible for communication between cells was mutated, the effects of the mutation also varied in different parts of the brain. This suggests that different clocks respond differently to communication cues. Additionally, the results showed that circadian clock activity also changed with age: older flies had weaker circadian behaviours – fewer changes in both behavioural and genetic activity levels between the day and night – than younger animals.

Johnstone et al.’s approach makes it possible to track a living animal’s circadian clocks in different parts of the brain and in different organs in real time without the need to dissect the animal. In the future, this method will help scientists understand the links between different circadian clocks, the genes associated with them, and the behaviours they control.

Introduction

‘Circadian rhythms’ collectively refer to ~24 hr oscillations in an animal’s behavior and physiological responses to daily environmental changes. These rhythms are regulated by the circadian clock, a transcription/translation negative feedback loop that controls the ~24 hr oscillations in expression of hundreds of genes in every tissue. Circadian clocks are highly evolutionarily conserved time-keeping machines, from flies to humans. In both organisms, specialized neurons that express circadian clock components are considered the ‘central clock’; circadian clock components in non-neuronal tissue (hereafter, ‘peripheral clocks’) are widely assumed to respond to the central clock (Brown et al., 2019; Franco et al., 2018; Ito and Tomioka, 2016; Patke et al., 2020; Pilorz et al., 2018), likely through secreted factors (Handler and Konopka, 1979).

In humans, disruption of the circadian clock is associated with a wide range of pathologies, including neurological, cardiovascular, and metabolic disorders, as well as cancer and aging (Acosta-Rodríguez et al., 2021; Bae et al., 2019; Hood and Amir, 2017a; Hood and Amir, 2017b; Leng et al., 2019; Logan and McClung, 2019; Rana et al., 2020; Shimizu et al., 2016; Sulli et al., 2019; Thosar et al., 2018; Tsuchiya et al., 2020; Zhang et al., 2021). Such a broad variety of pathologies associated with compromised circadian rhythms suggests a need for cheap and effective ways to measure tissue-specific circadian clocks directly in model organisms. In animals, locomotion is the simplest and most rapid way to measure circadian clock output, but this output embodies the cumulative activity of many clocks and does not necessarily represent all clocks equally. Moreover, while ablation of neuronal clocks in flies, mice, and humans leads to loss of sleep/activity rhythms and is thought to cause loss of circadian clock function in many tissues, the hierarchy of dysfunction of tissue-specific clocks during specific disease pathogenesis remains unclear. Currently, individual peripheral clocks can be measured in flies by removing the organ and extracting RNA to assess transcriptional oscillations (Erion et al., 2016; Gill et al., 2015; Litovchenko et al., 2021; Wang et al., 2004; Xu et al., 2011). Such terminal qRT-PCR outputs from explanted organs measure clock function only for that time point in the lifespan of the organism and can be time-, cost-, and labor-intensive. Given that circadian clocks appear to be linked to a wide range of physiologies, including metabolism as well as various behavioral disorders, there is a need to monitor distinct cell- and tissue-specific circadian clocks directly, in vivo, and in real time in Drosophila, similar to reporters developed in mouse models (Sinturel et al., 2021; Smith et al., 2022).

We developed a genetically encoded reporter to monitor distinct clocks in Drosophila that we call Locally Activatable BioLuminescence (LABL), offering both high spatial and temporal resolution of clock oscillations in vivo. Our data reveal that tissue-specific clocks have similar but distinct properties of oscillation. To determine if tissue-specific clocks are differentially affected by whole-body mutations, we tested flies lacking a functional PDF (pigment dispersing factor) receptor (han5304, also known as pdfr5304) (Hyun et al., 2005) to demonstrate the properties of clock oscillations LABL can help uncover. PDF is a neuropeptide that elicits a cAMP response from most circadian neurons in the brain and is required to maintain robust rhythmic behaviour in constant dark conditions (Helfrich-Förster, 1995; Helfrich-Förster et al., 2000; Park et al., 2000; Renn et al., 1999; Shafer et al., 2008). While tim01 flies (lacking a functional clock) become completely behaviourally arrythmic and han5304 flies become mostly behaviourally arrhythmic in constant dark conditions, quantification of han5304 fly neuronal clocks reveals infradian oscillations over a narrower range with a mean of ~60 hr, suggesting that loss of different circadian components can disrupt circadian locomotor activity in different ways. When peripheral clocks of a han5304 mutant fly are investigated, they continue to oscillate, but with decreased stability. Here, we demonstrate that LABL reporter flies can be used to measure distinct circadian clocks in different neuronal subpopulations and peripheral tissues in real time and in vivo. The differential changes to distinct clocks caused by the han5304 mutation underscores the assertion that tissue-specific circadian clocks are differentially regulated. We believe that this technology will be critical in the interrogation of distinct circadian clocks in future studies, particularly in monitoring peripheral clock function during disease progression in Drosophila.

Results

Construction of LABL

To monitor distinct clock oscillations in real time, in vivo, we designed a genetically encoded reporter that we call LABL. LABL was constructed into an attB cloning vector for Drosophila embryo injection and PhiC31-mediated genome integration (Bischof et al., 2007; Figure 1—figure supplement 1). LABL is comprised of a per promoter fused to mCherry flanked by FRT sequences, which was subsequently fused to Luc2 (pGL4.10) (Figure 1A). Three stop codons placed 3’ of mCherry are designed to block Luciferase expression. The employed per promoter (~6.7 kb) responds to clock regulation through the CLK/CYC transcription activator complex (Bargiello et al., 1984; Darlington et al., 1998). Tissue-specific expression of Flipase (FLP) triggers recombination at the FRT sites, excising mCherry from the genome and leaving Luciferase under per promoter control. To test the functionality of LABL in vitro, we monitored FLP-driven LABL activity in cultured S2 cells (Figure 1B and C). Since S2 cells do not express CLK, which is required to activate the per promoter, we co-transfected LABL plasmid with Clk-CFP, with and without FLP. These cells were subsequently imaged for CFP and mCherry expression and monitored for luminescence. Cells expressing FLP exhibited increased luminescence and a loss of mCherry fluorescence. Cells lacking FLP exhibited no luminescence but increased mCherry fluorescence. Thus, these data demonstrate that the LABL reporter is functional as designed and can be activated by FLP expression.

Figure 1 with 1 supplement see all
Design and activity of LABL reporter.

(A) The Locally Activatable Bioluminescence (LABL) reporter construct. In architecture, the per promoter (pPer) is fused to mCherry followed by luciferase (Luc). The mCherry gene and three stop codons are flanked by FRT recombination sequences. Expression of Flipase (FLP) excises out mCherry (dashed lines), leaving luciferase under period promoter regulation. (B) Fluorescence image of LABL-expressing S2 cells. Expression of LABL reporter with actin promoter-driven Clk (pAc-Clk-CFP), FLP (pAc-FLP), or both reveals CLK-dependent expression of mCherry in the absence of FLP. Scale bar represents 5 μm. (C) LABL can be activated in cultured S2 cells. Lysed S2 cells emit measurable luminescence in a luciferase assay when expressing LABL reporter in a FLP-dependent manner. ****: p < 0.0001.

Luminescence oscillations of transcription activity reflect behavioural rhythms

Having established its functionality in cultured cells, we proceeded to assess LABL in adult flies. The LABL reporter plasmid was used to generate reporter flies which were then monitored in a luminometer using arenas designed to hold 15 flies on top of fly food supplemented with luciferin (Figure 2A). LABL flies carrying the tim-UAS-Gal4 (TUG) pan-circadian tissue driver were crossed to flies carrying UAS-FLP, and the progeny monitored for luminescence activity in constant darkness (Figure 2B). The raw luminescence data exhibited an oscillating rhythm and a gradual decay, as expected (Brandes et al., 1996; Stanewsky et al., 1997).

Figure 2 with 1 supplement see all
Measurement, quantification, and analysis of luminescence from LABL flies.

(A) LABL activation strategy in Drosophila brain. Fly brain schematic illustrates how LABL can be activated using tissue-specific Gal4 drivers which express UAS-FLP2 to excise mCherry out of the genome in some neurons to permit Luciferase expression (yellow circles) under the regulation of the period promoter, leaving other neurons untouched (red circles). Fifteen LABL flies are placed in custom-made plates containing luciferin mixed with standard fly food. Plates are loaded into a luminometer and luminescence from each cohort is recorded for analysis. (B) Raw luminescence measurements of live flies. Photons were detected from four replicates of 15 flies expressing LABL reporter, UAS-FLP2 and tim-UAS-Gal4 over 9 days at 4-min temporal resolution. (C) Comparison between luminescence signal (upper graph) and locomotor activity (lower graph) of wild-type flies. Luminescence signal from flies described in panel B is normalized to exponential decay of signal, the values are averaged into 30 min bins, and the mean of the four experiments presented, +/-SEM (thickness of curve). Light grey and dark grey backgrounds represent subjective day and subjective night, respectively. Vertical solid black lines divide days. The peaks and troughs of the presented curves are represented by dots, and are the mean of four experiments, +/-SEM. Lines connecting the dots of peaks or troughs are best-fit S-curves. The vertical dashed lines define the point of inflection of each S-curve, which is used to define the A50. Locomotion activity is measured in beam breaks (counts) per 30-min bouts. Curve in lower graph represents rhythmic locomotor activity of 25 flies, +/-SEM (thickness of curve). White background indicates lights on. Vertical solid lines divide days. Light grey and dark grey backgrounds represent subjective day and subjective night, respectively. Blue and yellow dots represent peaks of “morning anticipatory” and “evening anticipatory” locomotion activity, respectively. The vertical dashed lines define the point of inflection of each S-curve, fitted to peaks of activity, which is used to define the decay of amplitude of peaks of behavior. (D) Calculation of oscillation peaks and troughs. A representative single replicate from experiment in panels B and C is plotted. Dots represent averaged luminescence signal in 30-min bins. A sinusoidal curve spanning 2 days is fitted to the data in 1 day increments (distinct colored curves). The peaks and troughs of each curve are calculated (triangles), averaged for both x- and y-values and recorded. This process is repeated for all four replicates and the resulting average is reported as shown in panel C. (E) Changes in oscillation period over time determined by Morlet wavelet fitting. Wavelets of different periods were fitted to luminescence signal from a single representative replicate from experiment in panel B and C, and assigned a confidence interval, across time (upper graph). Periods with highest confidence intervals at a time point (i.e. across the x-axis) were plotted as white dots. Confidence intervals of 25% or less were omitted. These values were replotted along with the other replicates (below), with varying shades of grey representing each of the four experiments. The dotted horizontal line denotes 24 hr as a point of reference. Right panel: All data points without the time dimension are plotted. Bar represents the mean, +/-SD.

We next compared pan-circadian tissue luminescence oscillations with behavioural rhythms. The recorded luminescence activity was normalized to the gradual decay in signal and plotted as an average of four experiments (Figure 2C, top panel). Control flies lacking a driver exhibited no discernable oscillation (white line). To characterize the luminescence oscillations, we first quantified the decay in amplitude (the difference in y-value, between peaks and troughs) of signal. Data points were binned into 30-min time intervals and a 48-hr sinusoidal curve was fitted to the data at 24-hr intervals (Figure 2D). The coordinates of the local minima and maxima were recorded, averaged, and plotted over the decay-normalized luminescence signal to reveal the amplitude of oscillation across time (black circles) (Figure 2C). An S-curve fitted to the changing local minima and maxima (black line) revealed points of inflection coinciding with Day 6 of constant darkness (vertical dashed lines). We used this point of inflection, which is the time at which the amplitude falls to 50% between maximum and minimum (A50), as a measure of clock stability because decay of oscillations into arrhythmic transcription may exceed the timeline of this assay.

Genotypically identical flies were measured for locomotor activity and their behavioral rhythms plotted (Figure 2C, bottom panel). We found that the peaks of morning anticipation (yellow circles) decayed rapidly, allowing the evening anticipation peaks (blue circles) to dominate behavioral oscillations in constant dark conditions. Focusing on the change in evening anticipation peaks, we found that a fitted S-curve revealed a point of inflection (A50) at ~day 6, coincident to the A50 observed in luminescence oscillations. We conclude that the decay of amplitude of oscillation of the ubiquitously expressed molecular clock and behavioural rhythms are consistent with each other.

We next characterized the change of period of luminescence oscillation across time. A Morlet wavelet was fitted onto the measured luminescence oscillations (Figure 2E). Period values with highest confidence intervals revealed a steady ~23.5 hr period of oscillation across time; specifically, oscillations occurred with an average period of 23.57 hr over nine days in constant darkness. This oscillation period also mirrored the behavioural period, with luminescence oscillation and behavioural period statistically the same, at ~23.5 hr. Thus, luminescence could be detected in flies in which LABL was activated using UAS-FLP and the pan-circadian Gal4 driver TUG and revealed parallels between transcription oscillations and behavioral rhythms of the fly.

LABL signal is comparable to ubiquitously expressed luciferase reporters and is clock dependent

To determine the effectiveness of LABL as a luminescence-based reporter, we compared TUG-activated LABL oscillations to other, ubiquitously expressed luciferase reporters. To this end, we compared our data to data collected from plo (per promoter fused to luciferase) (Brandes et al., 1996) and PER-BG::Luc (per promoter and ~2/3rds of the per gene fused to luciferase) (Stanewsky et al., 1997) flies, two other luciferase-based reporter systems (Figure 3A). Since TUG drives Gal4 expression in all clock cells, we expected TUG-activated LABL luminescence signal to be comparable to plo luminescence signal. Indeed, the oscillation of luminescence signal was comparable in amplitude, period, and phase (Figure 3A). The PER-BG::Luc construct, on the other hand, retains a significant portion of the PER coding region, and since there is a phase delay between per transcription and per mRNA accumulation (So and Rosbash, 1997; Stanewsky et al., 1997), we expected a phase difference between TUG-activated LABL luminescence signal and PER-BG::Luc signal. As anticipated, PER-BG::Luc luminescence signal was phase-delayed compared to both TUG-activated LABL and plo flies (Stanewsky et al., 1997). Thus, LABL is predictably comparable to both plo and PER-BG::Luc flies.

Figure 3 with 1 supplement see all
LABL oscillations are comparable to other luminescence reporters and respond to circadian clock manipulation.

(A) Comparison of luminescence reporters. LABL activated by tim-UAS-Gal4 (black curve) is plotted alongside per promoter fused to luciferase (plo; blue curve) and PER-BG::Luc (pink curve). (B) LABL is activated by tim-UAS-Gal4 in a tim01 genetic background. Top graph represents locomotor activity. Locomotion activity is measured in beam breaks (counts) per 30-min bouts. Curve represents rhythmic locomotor activity of 25 flies, +/-SEM (thickness of curve). White background indicates lights on. Vertical solid lines divide days. Light grey and dark grey backgrounds represent subjective day and subjective night, respectively. Middle graph illustrates luminescence signal over time. Luminescence signal is normalized to exponential decay of signal, values averaged into 30-min bins, as the mean of four experiments (n=15 flies) presented, +/-SEM (thickness of curve). Light grey and dark grey backgrounds represent subjective day and subjective night, respectively. Vertical solid black lines divide days. Lower graph illustrates changes in oscillation period over time determined by Morlet wavelet fitting. Parameters are as described in Figure 2E. Each dot color represents an experimental replicate of wavelet period best-fits in the tim01 genetic background. Inset is a box plot of period best-fits of each of the four experimental replicates, plotted. Experimental replicates (Exp.) are numerically labelled. Grey background represents interquartile range of box plots of the han5304 genetic background. (C) LABL is activated by tim-UAS-Gal4 in a han5304 genetic background. Graphs are as described in panel B. Distribution of period fit in the infradian oscillation range (48 h – 72 hr) is significantly different in han5304 flies when compared to tim01 flies, determined by the Kolmogorov-Smirnov test (****: p < 0.0001).

Eliminating the clock causes behavioural arrhythmicity and loss of transcriptional oscillation. To ensure that the transcription oscillations we observed using LABL were clock-dependent, we monitored TUG-activated LABL luminescence oscillations in a genetic background lacking functional TIM expression (tim01) (Figure 3B). As expected, both locomotor activity and transcription oscillation of tim01 flies were arrhythmic (top and middle panel). Importantly, our attempt to fit Morlet wavelets to the luminescence data revealed periods ranging broadly from 48 hr to 72 hr, consistent with circadian arrhythmicity (bottom panel). Elimination of PDF signaling using han5304 mutant flies (expressing PDF receptor lacking its transmembrane and cytoplasmic tail) permits rhythmic behavior in the first 1–2 days of constant darkness, but ultimately results in arrhythmic behavior in a majority of flies (Hyun et al., 2005). When we characterized LABL oscillations in a han5304 background (Figure 3C), we found that the majority of flies became arrhythmic in their locomotor activity in the second day of constant darkness, as expected. While there was also a rapid decay in transcription oscillation, measured by LABL, closer visual inspection revealed that initial (2–3 days) ~24 hr oscillations decayed into a reproducible 60-hr infradian oscillations with a reliably narrower range of period-fit. Such a decay from an ~24 hr oscillation and coherent (higher confidence wavelet fit) 60-hr infradian oscillation of luminescence was not observable in flies lacking a functional clock (i.e. tim01 flies). A Kolmogorov-Smirnov test reveals that the period distribution within the 48–72 hr range of the han5304 mutant flies was significantly different than that of tim01 flies (Figure 3B and C, inset).

Because of the unusual observation of infradian rhythms in luminescence, we wanted to determine if we could observe these oscillations using other methods, namely by locomotion assay or immunoblotting. We first measured locomotion rhythms of wild-type (iso31), han5304, and tim01 flies, then conducted a Lomb-Scargle analysis for behavioural period determination (Figure 3—figure supplement 1A). In the 16– 32 hr range, wild-type flies yielded robust ~24 hr rhythms with high power of fit, han5304 flies yielded behavioural periods that clustered around 24 hr with a wide distribution and a low power of fit, and tim01 flies yielded no discernable pattern of behavioural period and a low power of fit. We repeated the analysis for the 48–72 hr range and found two clusters of behavioural periods at approximately 60–62 hr and 50 hr for both wild type and han5304 flies. By contrast, tim01 flies showed no identifiable clustering. All flies had an equivalently low power of fit. Although tempting to suggest the 50 hr and 60 hr clustering patterns we observed in wild-type and han5304 flies could be considered infradian rhythms, due to the low power of fit that was comparable to that of tim01 flies, we do not consider these data reliable representations of infradian oscillations.

We next extracted protein from isolated han5304 fly heads, then probed for PER and TIM by immunoblotting to determine if we could observe infradian oscillations (Figure 3—figure supplement 1B). We used protein extracts from heads because protein extracts from whole flies did not yield oscillating protein signal by immunoblot (Figure 3—figure supplement 1C), possibly due to non-oscillating PER protein in ovaries in flies (Hardin, 1994). We observed reliable PER and TIM oscillations from head extracts from both wild-type and the first day of han5304 flies in constant darkness. However, the PER and TIM band intensity profiles did not represent reliable oscillations equivalent to a 60 hr period in these immunoblots. We conclude that the subtle amplitude of luminescence oscillation may be too small to observe by either locomotion or immunoblotting assays.

Circadian drivers used to activate LABL reveal oscillating luminescence

Since the TUG driver can be used to activate LABL in otherwise wild type flies to record oscillating luminescence signal, we wanted to demonstrate that other commonly used circadian drivers can also be used as effectively. We drove FLP expression using Pdf-, DvPdf-, R18H11-, Clk4.1-, Mai179-, and Clk9M-Gal4 (Figure 4A and Figure 4—figure supplement 1). We found that the oscillation with the lowest signal came from flies in which LABL was activated by Pdf-, DvPdf-, and R18H11-Gal4, and this correlates well with the relatively small numbers of neurons that were targeted for LABL activation when compared with TUG-driven LABL (Figure 4A). Clk4.1-, Mai179-, and Clk9M-Gal4 drivers yielded oscillations with higher relative amplitudes than would be expected in proportion to their target neurons (Figure 4—figure supplement 1), suggesting that unintended neurons or tissues may be luminescing (see below). Oscillation periods were maintained at ~24 hr. We conclude that LABL can also be used in conjunction with more tissue-specific drivers.

Figure 4 with 2 supplements see all
Luminescence oscillations of distinct clocks measured in young and aged flies.

Luminescence signal using different Gal4 drivers to activate LABL compared in young (1–3 day old) and old (39–41 day old) flies. Drivers used to activate LABL are TUG (black), Pdf-Gal4 (blue), DvPdf-Gal4 (pink), or R18H11-Gal4 (green) in otherwise wild-type flies. Luminescence values are normalized and averaged into 30 minute bins, and the mean of four experiments presented, +/-SEM (thickness of curve). Light grey and dark grey backgrounds represent subjective day and subjective night, respectively. Vertical solid black lines divide days. The peaks and troughs of the presented curves are represented by dots, which are the mean of four experiments, +/-SEM. Where dots are absent, no peak/trough could be fitted. Lines connecting peaks or troughs are best-fit S-curves. The vertical dashed lines define the point of inflection of each S-curve, which is used to define the A50. Where a vertical dashed line is absent, either no S-curve could be fitted, or no decay could be detected.

Aged flies exhibit rapid loss of amplitude of luminescence oscillation and emergence of infradian oscillations

Since luminescence oscillations in han5304 exhibited differences in amplitude, period and a decay into infradian rhythms, we wanted to determine whether these changes could also be observed using LABL in a different biological context. Aged flies have weaker locomotor behaviour rhythms, lower levels of per transcript, and lower amplitude of luminescence signal from flies expressing luciferase by tim or per promoter, compared to their younger (1–3 day old) counterparts (Koh et al., 2006; Luo et al., 2012; Rakshit et al., 2012; Umezaki et al., 2012; Zhao et al., 2019, Figure 4—figure supplement 2). We therefore reasoned that aged LABL flies in which clocks are no longer robust should display luminescence oscillations with weak or unstable amplitude, and an altered A50, using different drivers. We aged LABL-expressing flies to 39–41 days, measured their luminescence, and compared these values to young flies (Figure 4). In TUG-activated aged LABL flies, we fitted appropriate peaks and troughs to the luminescence signal to determine the A50. We note that in young flies the A50 was at 6.5–7 days, almost identical to our earlier observation (Figure 2C), reflecting the reproducibility of this metric. However, we note that since we do not determine A50 for individual flies, this measure does not readily permit statistical analysis for comparison across genotypes. TUG-activated old LABL flies were too irregular to reliably fit peaks and troughs. When LABL was activated using more specific drivers (i.e. Pdf, DvPdf-, and R18H11-Gal4), the peaks and the troughs of old flies remained low throughout the measurement, and either did not reveal an S-curve of decay or revealed too few peaks and troughs through which to fit a curve. Thus, we conclude that an A50 could not be determined for less stable clock oscillations in aged flies because the amplitude in their endogenous clock oscillations had already decayed to their minimal stable levels.

We also note that the luminescence signal decayed into infradian oscillations in some target neurons in aged flies. On day 3 of constant darkness, Pdf- and R18H11-Gal4 activated LABL flies began to exhibit infradian oscillations. On the other hand, Dvpdf-Gal4 activated LABL flies exhibited circadian oscillations until day 7 of constant conditions, at which point the amplitude of oscillation became too low to draw any further conclusions. Thus, infradian oscillations can develop in older flies that exhibit circadian oscillations when younger, suggesting that infradian oscillations can manifest in flies that are genetically identical but distinct in age.

Phase coupling simulations explain amplitude decay but not infradian oscillations

Dissociated neurons exhibit dampened clock oscillations measured by luminescence (Sabado et al., 2017), suggesting clock coupling may also influence our in vivo luminescence measurements. We therefore wanted to determine whether amplitude decay or ~60 hr infradian luminescence oscillations could emerge from oscillators that are uncoupled. We conducted a simple simulation in which half of the simulated oscillators were phase advanced by different magnitudes and the cumulative oscillation quantified (Figure 5A). Our simulations revealed that the magnitude of a phase advance of half the oscillators changes the amplitude of total observed oscillation but does not change the period. A 12 hr phase advance leads to destructive interference, eliminating the oscillation. We next simulated individual oscillators with the same 24 hr period that were allowed to drift in phase over time. These simulations revealed that permitting the oscillators to randomly assume different phase values dampens the overall oscillation amplitude, with a faster rate of phase drift causing a more rapid dampening effect, but still not altering the period of oscillation (Figure 5B). We noted that such phase drift leads to an S-curve decay of amplitude, as we observed in our biological measurements (Figure 2C). We conclude that allowing for phase-shifted variability of oscillations over time cannot explain the infradian rhythms seen in the data.

Simulations of oscillations with phase decay and measurement of distinct clocks in aged flies reveal rapidly decaying clock amplitude.

(A) Abrupt phase change simulation. Half of 100 simulated oscillations were made to phase advance at 1.5 days (arrow) by the indicated amount of time (red line). Oscillators with no phase change (black line) are illustrated as a reference point. Y-axis represents the peak of oscillations in arbitrary units. The x-axis represents time in days. (B) Gradual phase change simulation. At each time point, each of 100 simulated oscillators were randomly picked from a normal distribution with mean 0 and standard deviation, σ, that increased linearly with time from σ=0 hr at t=0 hr to σ=6 hr (upper graph) or 12 hr (lower graph) at t=336 hr (red line) compared to phase-locked controls (black line). The y-axis of graphs represents the peak of oscillations in arbitrary units, while the x-axis represents time in days. White dots with red borders represent peaks and troughs of local maxima and minima. Black S-curves are fitted to these minima or maxima. The red dashed vertical line represents the point of inflection of the S-curves (A50). Graphs on the right represent the change of oscillator phases across time.

Thus, while rhythmic and clock-dependent, for the purposes of this report, we interpret these infradian oscillations to represent arrhythmia from the 24-hr circadian perspective (i.e. they oscillate with a period on the order of days). These data together demonstrated that the LABL signal is clock-dependent, PDF receptor (PDFR)-dependent, and that it reflects circadian locomotor behavior. Importantly, our method of analysis can quantify mutation-caused changes in transcription oscillations in target cells and tissues robustly and consistently.

Anatomical expression and LABL activation of some commonly used Gal4 lines

Although the anatomical locations targeted by circadian Gal4 drivers are well characterized in the brain (Figure 6A), the amplitude of luminescence oscillations in some of the Gal4 drivers used to activate LABL (e.g. Clk4.1-, Mai179-, and Clk9M-Gal4) suggests that these driver lines may express Gal4 in non-neuronal tissue or non-circadian neurons as well. To establish the anatomical regions in which different Gal4 lines activate LABL, we visualized luminescence in whole animals, distinguishing between Gal4 lines that visibly activate LABL in the body from those which do not. Flies in which LABL was activated using Clk4.1-, Mai179-, and Clk9M-Gal4 exhibited luminescence signal from the body, similar to that of TUG (Figure 6B). On the other hand, the Pdf-, DvPdf-, and R18H11-Gal4 drivers showed no luminescence in peripheral tissues (Figure 6B). We therefore monitored luminescence from explanted live fly brains from Pdf-, DvPdf-, and R18H11-Gal4 activated LABL flies (Figure 6C). We found that Pdf-Gal4 activated LABL flies emitted luminescence from a single point located in the ventral lateral region of the brain, where the LNvs are expected to be. DvPdf-Gal4 activated LABL flies emitted luminescence from a wider area in the lateral region of the brain, suggesting that the sources of light are the LNvs and LNds. R18H11-Gal4 activated LABL fly brains emitted luminescence from the dorsal area, in the region DN1s are located. These observations correlated well with expected expression pattern of these Gal4 lines (Figure 6A). To ensure that the luminescence signal recorded from the brain specifically reflected the circadian neuronal cluster in which the Gal4 drivers were expected to activate LABL, we tested for FLP activity using G-TRACE reporter flies co-stained for TIM expression (Figure 6D). G-TRACE consists of the Ubip63 promoter upstream of stop codons flanked by FRT sequences, followed by the GFP gene (Evans et al., 2009). RedStinger (RFP)-positive neurons revealed Gal4-active sites, as shown. GFP-positive neurons that co-stain with TIM antibody revealed that the LNvs (Pdf-Gal4), the LNvs, 5th s-LNv and 3 LNd neurons (DvPdf-Gal4), and DN1s (R18H11-Gal4) had the expected FLP activity. Thus, Pdf-, DvPdf-, and R18H11-Gal4 lines activated LABL in the predicted neuronal clusters (collectively, ‘brain drivers’), indicating that they are suitable for use with LABL to monitor neuron-specific clocks. The remaining drivers are collectively referred to as ‘body drivers’, due to their activity in the fly body.

Different circadian Gal4 drivers activate LABL in distinct neurons and tissues.

(A) Schematic of a Drosophila brain and Gal4 driver expression patterns. Pdf-Gal4 (blue): LNvs. DvPdf-Gal4 (pink) and Mai179-Gal4 (purple): LNvs, 3 LNds and 5th s-LNv. R18H11-Gal4 (green) and Clk4.1-Gal4 (brown): DN1s. Clk9M-Gal4 (teal): DN2s and small LNvs. (B) Luminescence and fluorescence images of whole flies. Flies are shown in bright field (first column), imaged for luminescence (Luciferase; second column) and fluorescence (mCherry; third column). Drivers described in panel A were used to activate LABL in the flies, as indicated. (C) Luminescence images of explanted brains. Explanted live brains were imaged for luminescence (Luciferase; top row) and imaged in bright field merged with luminescence (bottom row) to determine sites of LABL activation by the indicated Gal4 drivers. White arrow heads denote estimated location of clock neurons: LNvs in Pdf-Gal4 neurons, LNvs and LNds in DvPdf-Gal4 neurons and DN1s in R18H11-Gal4 neurons. Scale bar represents 50 μm. (D) Sites of Flipase activity in fly brains. Fly lines expressing UAS-RedStinger (red fluorescence protein, shown in red) and the indicated driver were crossed into G-TRACE flies and brains were stained for TIM (cyan), RedStinger (red) and GFP (G-TRACE; green). Identity of fluorescence neurons labelled in bottom row. l-LNv: large ventral lateral neurons. s-LNv: small ventral lateral neurons. LNd: dorsal lateral neurons. DN1: dorsal neurons 1. GFP expression is indicative of FLP activity. Pdf-Gal4 driver targets LNvs, DvPdf-Gal4 driver targets LNvs, 5th s-LNv, 3 LNds, and R18H11-Gal4 driver targets DN1s. Scale bar represents 20 μm.

Circadian clock mutants perS and perL reveal features of clock oscillations in distinct neurons

The classical circadian clock mutations, period short (perS) and period long (perL) shorten and lengthen rhythmic behaviour, respectively (Konopka and Benzer, 1971). We wanted to further validate LABL using these clock mutants. The perS mutation produces a residue substitution that blocks phosphorylation of PER by DBT (Chiu et al., 2008; Kivimäe et al., 2008). This mutation in per leads to an early destabilization of the protein before dawn and higher amplitude of luciferase oscillation under the control of the per promoter, causing advanced evening anticipation and short behavioural rhythms (Edery et al., 1994; Li and Rosbash, 2013; Marrus et al., 1996; Top et al., 2018). When assessed using LABL, perS mutant flies indeed exhibited the expected short-period oscillations of luminescence (Figure 7—figure supplement 1A–1D). Each period of luminescence oscillation correlated well with period of behavioural rhythms. The amplitude of luminescence oscillation measured in perS flies in which LABL was activated using TUG driver was greater than their wild-type counterparts (Figure 2 and Figure 7—figure supplement 1A), as expected (Top et al., 2018). This effect was not present in pdf-Gal4 activated and R18H11-Gal4 activated LABL flies (Figure 4A and Figure 7—figure supplement 1B and D). Amplitude stability was also differently affected across different neuronal clusters, with the perS mutation advancing the A50 by a few days in Pdf-Gal4 activated LABL flies and in DvPdf-Gal4 activated LABL flies, while R18H11-Gal4 activated LABL flies maintained a more stable amplitude over time (Figure 4A and Figure 7—figure supplement 1B–1D).

The perL mutation is a residue substitution in the PER PAS domain that interferes with TIM binding (Gekakis et al., 1995). As suggested by its name, this mutation causes a long behavioural period under free-running conditions (Konopka and Benzer, 1971). This mutation also affects temperature compensation ability in the circadian clock and leads to weaker clock oscillations (Huang et al., 1995; Konopka et al., 1989; Rutila et al., 1996). In LABL flies with a perL genetic background, we found that all oscillations were noisier, with lower oscillatory amplitude and longer period, consistent with these earlier reports (Figure 7—figure supplement 1E–1H). The apparent noisiness of the oscillation prevented us from determining an A50, which suggests that this parameter is limited to more stable oscillations. We also found that in DvPdf-Gal4 activated LABL flies, signal from luminescence oscillations became too weak on day six of constant darkness to reliably measure, suggesting that the collective signal from these neurons does not detectably oscillate. Our data confirm the features of clock oscillations in perS and perL genetic backgrounds, but also uncover differences in distinct neuronal clusters, further validating LABL as a reliable tool to measure distinct circadian clocks.

Elimination of PDF signaling has variable effects on neuronal clocks

Elimination of the clock component tim causes arrhythmic locomotor activity and clock oscillations as measured by LABL (Figure 3B). Elimination of PDF signaling leads to arrhythmic locomotor activity in the majority of flies and a transition from ~24 hr to narrow-range infradian oscillations (Figure 3C). This loss of PDF signaling also causes loss of clock synchrony between clock neurons within the fly brain (Lin et al., 2004; Yoshii et al., 2009). We therefore sought to directly measure clock activity in distinct circadian neurons through activation of LABL in mutants lacking PDF signaling (han5304). Pdf- and DvPdf-Gal4 retained transcription oscillation in the absence of PDF signaling, with R18H11-Gal4 retaining oscillations until the end of Day 6, while Clk4.1-, Mai179-, Clk9M-Gal4, and TUG began to deteriorate after one cycle of oscillation (Figure 7A), in parallel with observed development of arrhythmic behaviour in locomotor output (Figure 3C). Thus, signal from LABL flies activated by body drivers appears to deteriorate relatively rapidly. In all cases, loss of PDF signaling in the han5304 mutants revealed higher amplitude of luminescence oscillation, to varying degrees, relative to wild type, consistent with earlier observations of high amplitude PER-LUC oscillations in subsets of clock neurons in a pdf01 genetic background (Versteven et al., 2020; Yoshii et al., 2009).

Figure 7 with 1 supplement see all
Different circadian Gal4 drivers create distinct oscillation patterns that respond to loss of PDF signaling differently.

(A) Luminescence oscillations measured from LABL flies activated by the indicated Gal4 drivers, in a wild-type or PDF signaling deficient genetic background, plotted over time. Colored curves represent signal from flies with no PDF signaling (han5304) compared to their wild-type counterparts (black curves). Drivers used are Pdf-Gal4 (light blue), DvPdf-Gal4 (pink), R18H11-Gal4 (green), Clk4.1-Gal4 (brown), Mai179-Gal4 (purple), and Clk9M-Gal4 (teal). Otherwise, graphs are as described in Figure 2C. Bar above the curve is a visual representation of the amplitude decay across time: color fade represents the A50 calculated from the peaks and troughs. Black bar represents amplitude decay of Pdf-Gal4 activated oscillations in wild-type flies; blue bar represents amplitude decay of Pdf-Gal4 activated oscillations in han5304 mutant flies. Location of A50 with a value close to x=0 that are not readily visible are indicated with a black triangle. Where A50 values are absent, no reliable S-curve fit was made (i.e. too few data points). (B) Stability of luminescence oscillations from LABL flies expressing different drivers are quantified in a wild-type (+) and PDF signaling-deficient (han5304; h) background. Colors correlate with Gal4 drivers described in panel A. Color fade in the bars represent the A50 calculated from the peaks and troughs. Faded color through entirety of bar illustrates no A50 calculation due to too few data points for S-curve fit. Length of bar indicates time at which luminescence oscillation becomes arrhythmic. (C) Differences in average period of luminescence oscillation measured by Morlet wavelet fitting, comparing wild-type (+) and PDF signaling deficient (han5304; h) flies. Colors correlate with Gal4 drivers described in panel A. If arrhythmic (>48 hr) period fits are excluded, periods of oscillations measured in the brain for wild-type and han5304 flies can be averaged, +/-SD. Wild type: Pdf, 24.53 +/-0.61; DvPdf, 25.33 +/-0.58; R18H11, 23.93 +/-0.33 hr. han5304: Pdf, 25.08 +/-0.78; DvPdf, 26.79 +/-1.02; R18H11, 23.58 +/-0.30 hr. The dotted horizontal line denotes 24 hr as a point of reference. Periods of oscillations between han5304 and wild-type flies before decay into arrhythmic or infradian oscillations (i.e., <48 hr) are averaged (black bars), and statistical significance determined between four experimental replicates (t-test). *: p<0.05, **: p<0.01, ***: p<0.001, ****: p<0.0001, NS: not significant. (D) Changes in oscillation period over time determined by Morlet wavelet fitting. Colored dots represent best fits from flies with no PDF signalilng (han5304) compared to their wild-type counterparts (black curves). Colors correlate with Gal4 drivers described in panel A. The dotted horizontal line denotes 24 hr as a point of reference. (E) Schematic summary of changes in period and rhythmicity of luminescence oscillation in distinct parts of the fly brain caused by loss of PDF signaling (han5304 genetic background). LNvs and 3 LNds exhibit longer luminescence oscillation period (top left hemisphere, red) but remain rhythmic (bottom left hemisphere, grey). Luminescence oscillation in DN1s maintain the same period with loss of PDF signaling (top left hemisphere, grey), but become arrhythmic over time (bottom left hemisphere, blue). Anatomical location of s-LNvs (small ventral lateral neurons), l-LNvs (large ventral lateral neurons), 5th s-LNv (5th small ventral lateral neuron), LNds (dorsal lateral neurons), and DN1s, DN2s, DN3s (dorsal neurons 1, 2, 3), as shown (top right hemisphere).

Decay of oscillation amplitude is an indicator of clock robustness. We therefore quantified the A50 value for the different neurons and tissues in which LABL was activated. Stability of luminescence oscillation is represented as a bar (see top of luminescence recordings in Figure 7A), with the dark region representing the early minima/maxima within the S-curves, the light region representing the later minima/maxima within the S-curve, and the gradient representing the difference between points of inflection measured. A comparison of A50 values of different oscillators revealed that it can range from 7 days, in the case of Mai179-Gal4 activated wild-type LABL flies, to 2 days in Clk9M-Gal4 activated wild-type LABL flies (Figure 7B and Figure 4—figure supplement 1). Although we have not determined the extent of influence of the genetic background the GAL4 drivers have on the A50, in all cases elimination of functional PDFR shifted the A50 earlier in constant dark conditions, or eliminated it altogether. han5304 flies with Pdf- and DvPdf-Gal4 activated LABL maintained their oscillations despite an advanced A50 (Figure 7A and B), in agreement with earlier immunofluorescence data that demonstrate LNvs and LNds continuing to oscillate in the absence of PDF signaling (Lin et al., 2004). han5304 flies with Pdf-Gal4 activated LABL exhibited a high amplitude relative to their wild-type counterparts for ~5 days. This is similar to an observed increase in tim promoter activity in s-LNvs in flies lacking PDF signaling (Mezan et al., 2016). On the other hand, DvPdf- and R18H11-Gal4 activated han5304 LABL flies exhibited amplitude peaks similar to their wild-type counterparts after the first cycle of oscillation. Interestingly, with loss of functional PDFR, R18H11-Gal4 activated LABL flies had a relatively unchanged (~half day difference) A50 but relied on PDF signaling to maintain rhythmicity (becoming arrhythmic after Day 6). The large A50 advance observed in DvPDF-Gal4 activated han5304 LABL flies is possibly due to phase dispersal in the s-LNvs and phase advance in the LNds caused by a loss of PDF signaling (Lin et al., 2004). Overall, our data suggest that body-driver activated LABL flies are dependent on functional PDFR to maintain rhythmic oscillations. Of the brain drivers, R18H11-Gal4 activated LABL flies were dependent on functional PDFR to maintain rhythmic oscillations in the long term (beyond 6 days), but Pdf- and DvPdf-Gal4 activated LABL flies continued to oscillate in flies lacking PDF signaling, as also observed in pdf01 flies (Yoshii et al., 2009). From these data, we confirm that PDF signaling is critical for rhythmic locomotor activity and some neuronal clocks, but is not necessary to sustain the ‘master clock’ neurons (LNvs) (Lin et al., 2004; Yoshii et al., 2009).

Flies lacking PDF (pdf01) exhibit shorter behavioural period as well as advanced PER nuclear translocation in LNds (Lin et al., 2004; Renn et al., 1999). We therefore quantified changes in period of transcription oscillations in the different clocks to determine if shortened behavioural rhythms were caused by shortened oscillatory periods in distinct clocks. Examination of the averaged peaks and troughs of transcription oscillation revealed a consistent phase advance in PDF- and R18H11-Gal4 activated LABL flies lacking functional PDFR (Figure 7A). Surprisingly, elimination of functional PDFR caused longer observed clock oscillation periods in different regions of the brain, except those where R18H11-Gal4 was used to activate LABL flies, which maintained wild-type-like oscillatory periods (Figure 7C). This is inconsistent with earlier reports using 8.0-luc which shows a shorter oscillatory period (Versteven et al., 2020) and the short behavioural period of pdf01 and pdfr mutants (Hyun et al., 2005; Renn et al., 1999). However, this reporter lacks the period promoter (but encodes the entire PER protein) and is expressed only in some circadian neurons (Veleri et al., 2003), offering a possible explanation for this discrepancy. Our data are also inconsistent with previously published locomotor data that show a shortened period of pdf01 flies, when arrhythmic animals are excluded (Renn et al., 1999). Therefore, the observed shorter behavioural rhythms may be caused by (1) other clocks that oscillate with a shorter period, (2) a phase change of some of the clocks that normally converge to regulate behavioural rhythms, uncoupled by the loss of PDF (e.g., Figure 5), or (3) elimination of PDFR not entirely reflecting the effect of a pdf01 fly. Indeed, flies lacking a functional PDFR (han5304) or PDF (pdf01) exhibit a phase delay or advance, respectively, of tim or per transcription oscillation in oenocytes, and PDFR is responsive to other neuropeptides (e.g. DH31), which may explain the differences that we have observed (Krupp et al., 2013; Mertens et al., 2005).

Periods of luminescence oscillation of different tissues in han5304 flies were lengthened to different degrees. Luminescence from Mai179-Gal4 activated LABL in han5304 flies exhibited a~30 hr period before becoming arrhythmic on day 3, as compared to a~24 hr period in wild-type flies (Figure 7C). A time course revealed that all oscillators that lose rhythmicity due to loss of PDF signaling appeared to form narrow range infradian rhythms (Figure 7D). Of these, the body drivers (Clk4.1-, Mai179-, Clk9M-Gal4) appeared to show a more disorganized period decay of period in constant darkness (Figure 7D). Importantly, we conclude from these data that LABL can demonstrate that PDF signaling has distinct effects on different neurons and tissues in clock stability, period, and amplitude (Figure 7E).

Peripheral clocks exhibit distinct oscillations and partial PDF-dependence

In mice with suprachiasmatic nucleus (SCN; mammalian master clock neurons) lesions, organs become asynchronous (Sinturel et al., 2021). In flies, the PDF-expressing ventral lateral neurons are considered to be the master clock neurons (Grima et al., 2004; Stoleru et al., 2004). We reasoned that eliminating PDF-signaling would serve to reduce the master clock influence on peripheral organs in flies. This may explain, in part, the difference we observe in LABL activated by brain clocks compared to body clocks (Figure 7). Although PDFR protein has not been shown to be expressed in the peripheral organs, pdfr mRNA has (Li et al., 2022), suggesting that clocks in these tissues may partially depend on PDF-signaling. To characterize tissue-specific peripheral clock drivers, we monitored the stability of tissue-specific clock function in constant dark conditions in both wild-type controls and han5304 mutants. Specifically, we examined LABL oscillations using the neuronal driver elav-Gal4 (Saitoe et al., 2001; Schuster et al., 1996; Sink et al., 2001), intestinal drivers esg- and NP3084-Gal4 (Bonnay et al., 2013; Croker et al., 2007; Lee et al., 2016; Strand and Micchelli, 2011), fat body drivers C564- and LSP2-Gal4 (C564 also expresses in hemocytes and some male reproductive tissues) (Hrdlicka et al., 2002; Paredes et al., 2011; Takeuchi et al., 2015; Zaidman-Rémy et al., 2006), and muscle driver mef2-Gal4 (also expresses in some neurons [LNvs]) (Blanchard et al., 2010; Cripps et al., 1998; Gajewski et al., 1997, Figure 8A). These drivers activated LABL to reveal wild-type clock oscillations with ~24 hr periods (Figure 8—figure supplement 1).

Figure 8 with 1 supplement see all
Peripheral clocks have distinct characteristics in a wild-type and PDF receptor mutant genetic background.

(A) Schematic of target expression pattern of peripheral tissue Gal4 drivers in Drosophila. elav-Gal4 (neurons; orange), mef2-Gal4 (muscle; red), esg-Gal4 and NP3084-Gal4 (intestines; blue), and C564-Gal4 and LSP2-Gal4 (fat body; purple). Note that C564-Gal4 also expresses in hemocytes and some male reproductive tissues and mef2-Gal4 has been reported to express LNv neurons. (B) Luminescence oscillations measured from LABL flies activated by the indicated Gal4 drivers in peripheral tissues, in a wild-type or PDF signaling deficient genetic background, plotted over time. Colored curves represent signal from flies with no PDF signaling (han5304) compared to their wild-type counterparts (black curves). Luminescence signals from wild-type flies are reproduced in Figure 8—figure supplement 1 for clarity. Drivers used are as indicated and described in panel A. Graphs are as described in Figure 2C. Location of A50 with a value close to x=0 that are not readily visible are indicated with a black triangle. (C) Stability of luminescence oscillations from LABL flies expressing different drivers are quantified in a wild-type (+) and PDF signaling deficient (han5304; h) background. Colors correlate with Gal4 drivers described in panel A. Color fade in the bars represent the location of the A50 calculated from the peaks and troughs. Length of bar indicates time at which luminescence oscillation becomes arrhythmic. (D) Differences in average period of luminescence oscillation measured by Morlet wavelet fitting, comparing wild-type (+) and PDF signaling deficient (han5304; h) flies. Colors correlate with Gal4 drivers described in panel A. Black bars represent mean period of ‘rhythmic’ oscillations early in measurement, +/-SEM. Red bars represent mean period of all wavelet fits (including those >48 hr). The dotted and solid horizontal lines denote 24 and 48 hras a point of reference, respectively. Periods of oscillations between han5304 and wild-type flies before decay into arrhythmic or infradian oscillations (i.e. <48 hr) are averaged (black bars), and statistical significance determined between four experimental replicates (t-test). *: p<0.05, NS: not significant. (E) Changes in oscillation period over time determined by Morlet wavelet fitting. Colored dots represent best fits from flies with no PDF signaling (han5304) compared to their wild-type counterparts (black curves). Colors correlate with Gal4 drivers described in panel A. The dotted horizontal line denotes 24 hr as a point of reference.

If sufficient amounts of PDFR protein for signaling efficacy are not expressed in these organs despite measured pdfr transcript (Li et al., 2022), oscillations may be expected to remain undisrupted in the han5304 genetic background. However, our observation of high-amplitude luminescence oscillations in flies lacking PDF signaling is consistent with earlier high-amplitude observations (Versteven et al., 2020; Yoshii et al., 2009) and our own observations (Figure 7A) that PDF signaling is relevant in these tissues (Figure 8B). Furthermore, the noisiness of these oscillations in flies lacking PDF-signaling, similar to hepatocyte oscillations in SCN-lesioned mice (Sinturel et al., 2021), suggests that PDF signaling from the master clocks plays some type of stabilizing role for these peripheral clocks.

The measured peripheral clocks appear to respond differently to a loss of PDF signaling. We found that the fat body drivers, C564-, and LSP2-Gal4, revealed phase-advanced luminescence oscillations in han5304 flies, but these oscillations were otherwise similar to wild-type. NP3084-Gal4 driven LABL lost rhythmic oscillations within 5 days, whether or not PDF-signaling was present. Interestingly, mef2- and esg-Gal4 activated han5304 LABL flies exhibited a loss of measurable oscillation between days 5–6 and 4–6, respectively, followed by a re-established rhythm and also exhibited earlier A50 values compared to their wild-type counterparts (Figure 8B and C). We also note that oscillations in the mef2- and esg-Gal4 driven LABL flies, when rhythms are reestablished, reappear in reverse phase. This echoes an observation made in the cry- LNds, which normally oscillate in reverse phase with respect to the cry+ LNds, but oscillate in synchrony with them in a pdf01 background (Yoshii et al., 2009). Together, our data suggest that PDF signaling has varying influence on different peripheral clocks.

Quantification of clock periods in peripheral tissues revealed distinct periods in wild-type flies (Figure 8D). Importantly, LABL activated by fat body-specific drivers (C564- and LSP2-Gal4), but also for intestine driver NP3084, exhibited oscillation periods greater than 48 hr, suggesting that these clocks may not be as robust (e.g. compared to elav+ clocks) in constant darkness. Elimination of PDF signaling appeared to destabilize the period of oscillation of these and all other measured peripheral clocks to varying degrees (Figure 8D). To uncover the extent of period stability in the absence of PDF signaling, we monitored changes in period of peripheral clocks over time (Figure 8E). Intestine-specific Gal4 drivers (esg- and NP3084-Gal4) revealed transcriptional oscillations that stabilized in infradian oscillations in flies lacking PDF signaling, similar to that observed using body clock drivers (Figure 7D). Of note, NP3084-Gal4 activated LABL flies also exhibited infradian oscillations in a wild type PDFR background. On the other hand, muscle- and fat body-specific drivers (mef2-, C564-, and LSP2-Gal4) appeared to maintain a rhythmic ~24 hr transcription oscillation, though these were unstable, as demonstrated by intermittent deviations (Figure 8E) and observed visually (Figure 8B). Interestingly, periods of clock oscillation in the fat body were shorter in the absence of PDF signaling as compared to the wild type (Figure 8B and D), contrasting the longer periods observed in the same mutant background in Pdf- and DvPdf-Gal4 activated LABL flies (Figure 7C), but similar to shorter behavioural periods measured by locomotion (Hyun et al., 2005). Thus, the LABL reporter system is able to distinguish between loss of rhythmic oscillation and destabilized oscillation, as well as changes in the period of transcription oscillation, of circadian clocks in peripheral tissue.

Discussion

Understanding the links among circadian clocks, the hierarchical organization of circadian clocks within the circadian neuronal network, and the influence which defective clocks have on behavioral/metabolic disorders requires targeted measurement of distinct clocks. Here, we present a method in which we expressed Luciferase under the control of a circadian promoter in targeted cells or tissues, allowing us to directly quantify the oscillatory properties of distinct clocks. Using this method, we found that clocks in different tissues oscillated differently, underscoring two important features of Drosophila circadian clocks: (1) circadian clocks have distinct oscillatory properties; and (2) that individual clocks can respond differently to some mutations, suggesting that they are differently regulated.

Distinct neuronal populations that express circadian clocks have different roles in regulating circadian behaviour. Previous work by others analyzing locomotion patterns found that LNvs regulate morning anticipatory behaviour in LD and rhythmic locomotion in constant darkness, and (CRY+) LNds (and the 5th sLNv) regulate evening anticipation in LD and rhythmic locomotion in constant light conditions in a specific genetic background (Murad et al., 2007; Picot et al., 2007). These different responsibilities for distinct aspects of rhythmic locomotion by distinct neurons suggest that coherent behavioural rhythms are the result of converged function of the entire circadian neuronal network. It follows that distinct neuronal functions may necessitate the need for different molecular mechanisms to regulate individual clocks. Indeed, Casein Kinase II, a regulator of PER/TIM repressor complex nuclear accumulation, is detectably expressed in the LNvs but not in other circadian neurons (Lin et al., 2002; Top et al., 2016). LABL offers an opportunity to explore the effect of mutations and different environmental factors on these distinct clocks.

LABL offers several advantages in its use as a reporter in Drosophila. Unlike other systems that rely on measuring luminescence signal from explanted fly brains, LABL leaves the brain and other tissues in place, allowing them to remain responsive to physiological input from the body of the animal. Current techniques that remove tissues from the context of the whole-body measure clock oscillations in the absence of physiological regulatory factors. For example, explanted brains reveal ~29 hr tim-luc oscillations in contrast to ~24 hr 8.0-luc oscillations measured in a subset of clock neurons (Versteven et al., 2020; Yoshii et al., 2009). Moreover, differences in l-LNv neuronal firing are explained by the presence of eyes (Muraro and Ceriani, 2015), suggesting that a number of external factors maintain physiological oscillations. LABL also allows animals to roam freely, avoiding any aberrations that may arise from tethering the head of the animal to an imaging stage. However, this technique does rely on exclusive expression of Gal4 in the intended target tissues. For example, in characterizing some common circadian Gal4 lines, such as Clk4.1-, Mai179- and Clk9M-Gal4, we found that they expressed Gal4 in peripheral tissues, which disqualifies use of these drivers for neuron-specific LABL-mediated monitoring of transcriptional oscillations (Figure 6).

LABL is also distinct from other in vivo luminescence-based reporters. Reporters such as plo and PER-BG::Luc report the cumulative oscillation of either the period promoter or Period protein in the whole animal (Figure 3A). Other reporters rely on the oscillation of calcium levels (Guo et al., 2017) or the oscillation of secondary signaling such as NFκB or CREB2 (Tanenhaus et al., 2012), which measure cell responses to extracellular signals, but not the circadian clock itself. While these methods are effective in measuring how targeted tissues responds to signals from elsewhere, they do not directly measure the transcription/translation feedback loop. In contrast, LABL permits the direct measurement of the circadian clock in a target cell or target tissue, which is critical to understanding the effect of a mutation or environmental input on distinct clocks.

Using LABL, we confirmed that distinct circadian clocks exhibit distinct characteristics. We noted, for example, a sharp dip in luminescence that bisects the first peak of oscillation in all measured wild-type flies (Figure 2C, Figure 2—figure supplement 1), except with PER-BG::Luc flies (Figure 3A), suggesting that this may be a transcription regulation-specific phenomenon, as previously suggested by others (Brandes et al., 1996). We also observed that not all oscillators are maintained under constant conditions (e.g. NP3084-Gal4 activated LABL) (Figure 8), suggesting that a subset of clocks (i.e. the transcription feedback loop) may at times be sustained by environmental or other forms of input. Such differences in sustaining clock oscillations have also been observed by others (Roberts et al., 2015; Veleri et al., 2003; Veleri and Wülbeck, 2004). For example, the oscillations of the clock protein PER are rapidly dampened in l-LNvs and DN1s in constant dark conditions (compared to other neuronal clusters), while their per transcript oscillations are reportedly relatively unaffected, highlighting differences between PER stability and transcription activity (Roberts et al., 2015; Veleri and Wülbeck, 2004). We too observed oscillations sustained for days in R18H11-Gal4 activated LABL flies (i.e. in the DN1s; Figures 6 and 7). Thus, LABL allowed oscillations to be monitored rapidly, without the need for labor-intensive, low time-resolution immunofluorescence staining or ex vivo luminescence imaging.

We were also able to employ LABL to rapidly assess the effects that loss of PDF signaling have on tissue-specific clocks as a proof of principle for this method to be used in different mutant contexts. We found that transcriptional oscillations became longer to varying degrees when circadian Gal4 lines are used to activate LABL in flies lacking PDF signaling, or shorter in the case of the fat body clocks. R18H11-Gal4 activated LABL flies exhibited no period change of transcription oscillation. Oscillations in peripheral tissue became less stable, while remaining intact. In two Gal4 lines, mef2- and esg-Gal4, circadian oscillations were lost within a few days before they were restored later in the time course, perhaps re-established through signals received from other clocks.

Loss of PDF signaling also reveals infradian oscillations of luminescence in some tissues. These oscillations were clock-dependent, since they were distinct from measurement from flies lacking a functional clock (Figure 3B and C). We are not certain how these infradian oscillations form, but they are unlikely to be caused by non-synchronised individual flies, since flies lacking PDF-signaling (han5304) revealed both ~24 hr oscillations (Pdf-Gal4 activated LABL) and ~60 hr oscillations (R18H11-Gal4 activated LABL), despite being otherwise genotypically identical. Any desynchrony in the fly population should affect these measurements in the same fashion, regardless of where LABL is being activated. These rhythms are also unlikely to be the result of feeding or differences in luciferin absorption into different tissues. If there were to be a 60 hr luminescence oscillation that stemmed from feeding cycles of luciferin supplemented food, the Pdf-Gal4 activated LABL flies would exhibit ~24 hr oscillations embedded into 60 hr oscillations. However, this is not the case. If instead we consider a metabolic explanation wherein some tissues absorb luciferin with a ~60 hr rhythm while others continue to absorb luciferin with a ~24 hr rhythm, we would need to assume the existence of a 60 hr luciferin absorption oscillation that is both dependent on the presence of a clock and absence of PDF-signaling. A third possibility is that subtle genetic differences, either among Gal4 drivers or fly backgrounds, are causing the formation of 60-hr infradian oscillations. We suggest that this is also unlikely, because flies of identical genetic backgrounds that have been aged (compared to young flies) can develop 60-hr infradian oscillations (Figure 4). The observed infradian oscillations are therefore unlikely to be rooted in feeding, metabolism, or genetic differences, and there must be an underlying clock-related explanation to these oscillations.

There are three possible explanations for the observed 60-hr infradian rhythms. The simplest of these explanations is based on the well-known physical phenomenon called ‘beats’, in which short, ultradian oscillators that are tightly coupled in phase can form circadian oscillations (Chance et al., 1967). For beats to produce stable infradian rhythms, the individual oscillators themselves must be held at fixed phase and period in relation to each other. Other mechanisms of producing longer period oscillations involve directly- (Pavlidis, 1969) or indirectly- (Paetkau et al., 2006) coupled faster (i.e. short-period) oscillators. It may therefore be possible to tightly couple circadian oscillators to form infradian oscillators. However, in either case this explanation necessitates that in the absence of PDF-signaling (e.g. han5304 flies) circadian oscillators remain coupled to each other to produce infradian rhythms. With increased temperature, the phase of luciferase oscillations in 8.0-luc flies changes, while the period of oscillation remains the same (Versteven et al., 2020). If this temperature-dependent change in overall phase is caused by a change in phase coupling between distinct oscillators, as we have shown is possible (Figure 5A) and considering that pdf01 flies do not exhibit this change in phase (Versteven et al., 2020), it is possible that a loss of PDF-signaling leads to tighter coupling between oscillators (i.e. less phase drift) to create infradian oscillations. This may also explain why amplitude in flies lacking PDF-signaling is much higher than in wild-type flies (Figures 7 and 8; Veleri et al., 2003; Yoshii et al., 2009). Finally, we cannot rule out that weak 60-hr oscillators exist in flies to begin with, and become exposed as the PDF-dependent oscillators fall into arrhythmia in flies lacking PDF signaling. However, we could not uncover any evidence of such infradian oscillations using modified analysis of behavioural rhythms or by measuring PER and TIM protein levels in fly head protein extracts (Figure 3—figure supplement 1A and B).

The LABL reporter system promises to uncover novel clock mechanisms at increased cellular and temporal resolution. Here, we present data that demonstrate the function and use of this system. LABL has the advantage that it reveals clock oscillations directly, at the intersection of tissues (and cells) in which the per promoter is active (i.e. in clock cells) and Gal4 is expressed, such that cells that express Gal4 but no clock would not interfere with luminescence measurements. LABL reveals distinct clocks that are unique in their characteristics and in their responses to molecular input (e.g. PDF signaling). Together, LABL is an important new tool in directly measuring and quantifying subtle changes to individual circadian neuronal sub-types as well as peripheral clocks in Drosophila, in vivo, in real time.

Materials and methods

Fly strains

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The following lines were used in the study: tim-UAS-Gal4 (A3) (Blau and Young, 1999), Pdf-Gal4 (Park et al., 2000), DvPdf-Gal4 (Guo et al., 2014), R18H11-Gal4 (Guo et al., 2016), Clk4.1-Gal4 (Zhang et al., 2010a; Zhang et al., 2010b), perS and perL (Konopka and Benzer, 1971), elav-Gal4 (gift from Michael W. Young); Pdfr5304 (han5304) (Hyun et al., 2005) (gift from Paul Taghert); Mai179-Gal4 (Picot et al., 2007) (gift from C. Helfrich-Foerster); Clk9M-Gal4 (Kaneko et al., 2012) (gift from O. T. Shafer); 3xUAS-FLP2::pest (Nern et al., 2011) (Janelia Research Campus); PDF receptor mutant han5304 (Hyun et al., 2005; Mertens et al., 2005) (gift from P. H. Taghert); esg-Gal4 (gift from Ben Ohlstein); NP3084-Gal4 (gift from David Walker). G-TRACE flies (32251) Evans et al., 2009; mef2-Gal4 (27390); C564-Gal4 (6982); LSP2-Gal4 (6357) were acquired from BDSC. Genotypes of all flies used in experiments are summarized in Supplementary file 1. Flies were reared on standard cornmeal/agar/yeast/molasses medium at room temperature (22 °C) or 18 °C in ambient laboratory light. Flies were entrained in a light-dark cycle (12 hr:12 h) at 25 °C.

LABL flies

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LABL was constructed into an attB cloning vector for Drosophila embryo injection and PhiC31-mediated genome integration (Bischof et al., 2007; Figure 1—figure supplement 1). The minimal per promoter (Bargiello et al., 1984) (ranging from restriction sites SphI to XbaI) was first cloned into the pattB vector. The 5’ untranslated region, ranging from XbaI site to the per ATG start codon was rebuilt using standard PCR: the ATG was conserved and an FRT sequence followed by a NotI restriction site was added. mCherry was amplified using standard PCR, its start codon eliminated, and a 5’ NotI restriction site, three 3’ stop codons and an EcoRI restriction site added. Luc2 (Promega) was amplified using standard PCR its start codon eliminated, a 5’ EcoRI restriction site followed by FRT sequence added and then 3’ XhoI and KpnI restriction sites added. The plasmid was then assembled and injected into embryos (BestGene Inc).

Luminescence assay

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Luminescence of flies were measured using the LumiCycle 32 Color (Actimetrics). Custom 35 mm plates (designed by Actimetrics) were used to adapt the LumiCycle 32 for Drosophila use. D-luciferin potassium salt (Cayman Chemicals or Gold Biotechnology) was mixed with standard fly food to a final concentration of 15 mM. Volume of food in the plate was sufficient to limit fly movement along the z-axis of the plates. 15 flies were placed in each plate and covered with a cover slip. Each of the four replicates were recorded in a different position in the luminometer to ensure equal representation of data from each of the four photomultiplier tubes. Luminescence from each plate was recorded in 4-min intervals on a standard Windows-operated PC using software by Actimetrics.

Luminescence of transfected S2 cells were measured using a liquid scintillation counter LS6000IC (Beckman) in single-photon collection mode. Cells were transfected in six-well plates at 80% confluency using Effectene transfection reagent (Qiagen) following the manufacturer’s protocol. Cells were transfected with 200 ng total DNA with the indicated plasmids distributed in equivalent amounts. Plasmids used were pAc-Clk-CFP (actin promoter driven clock fused to CFP) (Top et al., 2018), pAc-FLP (actin driven Flipase; gift from Nicholas Stavropoulos), LABL plasmid (above). Cells were fed 24 hr after transfection and lysed another 24 hr later in Cell Culture Lysis Reagent (Promega). Extracts were mixed with Luciferase Assay Reagent (Promega) in a 1:5 ratio and monitored for luminescence.

Luminescence analysis

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Actimetrics analysis software was used to normalize the exponential decay of luminescence signal over days using a polynomial curve fit, with no smoothing. Data were exported into.csv files for analysis. Custom python code was used to organize luminescence data into 30 min bins and quantify peaks, troughs and decay of oscillating luminescence signal (LABLv9.py; https://www.top-lab.org/downloads or https://github.com/deniztop/LABL) (Top, 2022). A sinusoidal curve was fitted to each day,+/-half a day, with the x- and y- coordinates for the local maxima and minima identified and quantified. Overlapping values for each peak and trough were averaged for x- and y- coordinates before being plotted,+/-standard deviation. Graphpad Prism 9 software was used to fit an S-curve to these points, and the calculated value for IC50 was used to identify the point of inflection to identify the reported A50. Custom python code was also used to quantify periods of oscillations using a Morlet wavelet fit (waveletsv4.py; https://www.top-lab.org/downloads or https://github.com/deniztop/LABL) (Top, 2022), as previously described by others (Leise and Harrington, 2011). The bottom 25% of best fits were omitted from analysis. Computer-generated oscillation data were used to confirm the parameters for analysis (data not shown): bandwidth = 3; central frequency = 1; data points in an hour = 15 (i.e. 15x4 min in an hour); zero-hour mark = 10 (10:00 am, subjective dawn); period of shortest wave match = 16; period of longest wave match = 72; increment of wavelet fit = 1; percent threshold for discarding data as a fraction = 0.25. Highest confidence interval values were used to plot period across time. The time (x-value) when an oscillation was considered arrhythmic was defined as the last 25 data points before a wavelet fit exhibited a jump from ~24 hr to ~60 hr, and averaged.

Locomotor activity

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Individual flies were analyzed for locomotor activity for three days in a light-dark cycle, followed by seven days in constant darkness using the Drosophila Activity Monitor System 5 (Trikinetics). Circadian behaviour was determined in the analysis of time in constant darkness. Periodicity of rhythmic locomotor activity was determined using ImageJ plug-in ActogramJ (Lomb-Scargle) (Schmid et al., 2011).

Fluorescence microscopy and immunohistochemistry

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To measure loss of mCherry signal from LABL plasmid in S2 cells, cells were grown in Lab-Tek II chamber slides (Nunc, Rochester, NY). Cells were imaged using a DeltaVision system (Applied Precision, Issaquah, WA) equipped with an inverted Olympus IX70 microscope (60 X oil objective, 1.42 N.A.), a CFP/YFP/mCherry filter set and dichroic mirror (Chroma, Foothill Ranch, CA), a CCD camera (Photometrics, Tucson, AZ), and an XYZ piezoelectric stage for locating and revisiting multiple cells.

Fly brains were collected at ZT23, fixed, mounted, and imaged using Leica confocal microscopy as previously described (Top et al., 2018; Top et al., 2016). Briefly, fly heads were fixed in PBS with 4% paraformaldehyde and 0.5% Triton X-100. Brains were dissected and washed in PBS with 0.5% Triton X-100. Brains were then probed with a 1:1000 dilution of rat anti-TIM (Myers et al., 1996), a 1:1000 dilution of chicken anti-GFP (Aves Labs), and a 1:1000 dilution of rabbit anti-mCherry (Rockland). Washed brains were re-probed using a 1:500 dilution of goat anti-chicken conjugated to Alexa-488, a 1:200 dilution of donkey anti-rat conjugated to Alexa-594, and a 1:200 dilution of donkey anti-rabbit conjugated to Alexa-647 secondary antibodies (Jackson Immunological). Brains were mounted using Fluoromount G (Beckman Coulter, Brea, CA) and imaged using a ZEISS LSM 710 confocal microscopy at ×40 magnification.

Immunoblotting

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Fly heads were collected from iso31 and han5304 flies at 6-hr intervals, specifically CT03, 09, 15, and 21. Protein was extracted from fly heads as described earlier (Top et al., 2016). Protein samples were quantified using a Lowry Assay (Bio-Rad Cat. 5000112) and transferred to PVDF membrane using standard methods. Blots were probed with antibodies against PER (1:500) (gift from Michael W. Young), TIM (1:3000) (Myers et al., 1996), and tubulin (1:20,000) (Sigma, Cat. T6199). HRP-conjugated secondary antibodies against rat (Jackson, Cat. 712-035-153) and mouse (Jackson, Cat. 715-036-151) were used at 1:10,000 dilution.

Oscillation simulations

Abrupt phase change

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Using Matlab we simulated N=100 sinusoidal oscillators at increments of 0.5 hr each with a 24-hr period for 10 days and with no phase difference at t=0 hr. After 1.5 days, we forced half of the oscillators to abruptly phase shift by 0, 0.5, 1, 2, 4, 6, 10, or 12 hr while maintaining the same 24-hr period. Gradual phase change: Using Matlab we simulated N=100 sinusoidal oscillators at increments of 0.5 hr each, each with a 24-hr period for 14 days. At each time point, individual oscillator phases were randomly picked from a normal distribution with mean 0 and standard deviation, σ, that increased linearly with time from σ=0 hr at t=0 hr to either σ=6 hr or 12 hr at t=336 hr (14 days). With these parameters, oscillators had no phase difference at t=0 hr but had a maximum difference of ~40 hr (σ=6 hr) or ~100 hr (σ=12 hr) toward the end of the timeseries.

Luminescence imaging

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Flies were kept at 25 °C under light-dark cycles in vials containing 1% agar and 5% sucrose food supplemented with 15 mM luciferin for at least 3 days prior to the experiment. Images were taken using a Luminoview LV200 (Olympus) bioluminescence imaging system, equipped with an EM-CCD camera (0.688 MHz EM-CCD CAM-ImageEM X2, Hamamatsu, Japan) at a time point corresponding to the peak phase of expression (between ZT 18 and ZT 20).

Adult flies were anesthetized with ether and dry mounted on a CellView glass bottom imaging dish (35x10 mm; Greiner Bio One). Once the position of the fly was determined by bright field microscopy, bioluminescence was measured for 5 min with an EM gain of 400, using a UPLSAPO 20 x Apochromat, followed by 600ms exposure to fluorescence light. After this, another bright field image was captured and compared to the initial image, in order to ensure that the fly had not moved.

Preparation of the brains was similar to that described previously (Schubert et al., 2020; Versteven et al., 2020). Briefly, brains were dissected under red light in ice cold Ca2+ free Ringer’s solution and placed on a CellView glass bottom imaging dish (35x10 mm; Greiner Bio One) treated with Heptane glue, where the culture medium was added. The medium contains 20% heat-inactivated fetal bovine serum, 1% Penicillin-Streptomycin and 0.75 µM Luciferin in Schneider’s medium (Sigma-Aldrich). Bioluminescence images were acquired with a UPLSAPO 40 x by exposing the brains for 5 min with an EM gain of 400. For each brain, a Z-stack of 13 slices was taken, each slice containing a bioluminescence, a fluorescence (200ms), and a bright field image. All images were processed in FIJI (Schindelin et al., 2012) and combined with bright field images in GIMP (https://www.gimp.org/).

Data availability

The codes used in data analysis can be found at https://github.com/deniztop/LABL, (copy archive at swh:1:rev:67f0256ccd5a1469bf33da1a860f1c845c89b330). All data points used in generating the figures can be found at Dryad.

The following data sets were generated
    1. Top D
    (2022) Dryad Digital Repository
    Data from: Real time, in vivo measurement of neuronal and peripheral clocks in Drosophila melanogaster.
    https://doi.org/10.5061/dryad.2v6wwpzpj

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Decision letter

  1. Kristin Tessmar-Raible
    Reviewing Editor; University of Vienna, Austria
  2. K VijayRaghavan
    Senior Editor; National Centre for Biological Sciences, Tata Institute of Fundamental Research, India

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Real time, in vivo measurement of neuronal and peripheral clocks in Drosophila melanogaster" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and K VijayRaghavan as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

There is a unanimous high interest in the technique, but a much more critical perception of the conceptual advances. Thus, we ask the authors to resubmit their manuscript under the "Tools and resource" section of eLife. Addressing the concerns raised towards the validity of your new technique will thus be essential, while we could allow a bit more flexibility on how you will exactly address the concerns raised towards their claimed conceptual advances.

Nevertheless, it is clear that what you will claim as conceptual advances will also need to pass the scientific quality checks.

With this in mind, please address each of the four essential revisions points below. Please also carefully read the individual reviews, as they have been written with great attention to detail and in a highly constructive attitude. While it won't be necessary to address them point-by-point, we believe that they can be helpful to improve your manuscript.

Essential revisions:

1) Better analysis, visualization and further validation of the overall technical approach.

The further validation should include additional experiments, such as the test of circadian clock mutants that slow down/speed up the clock or direct measurements of clock protein/RNA levels.

2) Further validation of the usability of the inflection point of rhythmicity. The reliability of this marker is particularly critical as the interpretation of the PDFR data relies largely on it.

We suggest performing an experiment to demonstrate how the inflection point changes according to clock stability with age. It has been shown in both flies and mice that older individuals exhibit a less stable oscillator. The authors could test their assumption monitoring oscillations in young vs old flies.

3) A critical and careful discussion of the PDFR results in the context of previous work, that clearly explains which results are consistent with or challenge previous models. (See several points of critique about the lack of proper referral to the existing scientific literature in the individual reviews.)

4) There have been several questions/concerns raised concerning the reported 60hr rhythms, such as

– Weak rhythms such as the infradian rhythms need to be better visualized.

– Is there additional evidence (experimental or theoretical) that these are really 60hr rhythms or could these rather represent relatively randomly occurring superpositions of peaks/throughs of de-synchronized cells in tissues? Potentially consistent with the latter hypothesis it appears that these rhythms exhibit a rather large period range (between 48-72hrs)?

– Could such infradian rhythms be artificially driven by feeding or other metabolic rhythms that affect the amount of luciferin in the body?

Reviewer #1 (Recommendations for the authors):

– Currently, a genetically encoded reporter cannot be exploited to assess specific aspects of human diseases for ethical reasons, although in the future this might be possible. Thus, I would strongly encourage the authors to be (1) more focused on the short-term application, and/or (2) more specific (cell culture, organoids, monitoring disease progression but in animal models, etc…). The authors used many references (especially in the introduction) to highlight medical application, but this, in my personal opinion, has to be moderated as such approach would be limited by the current regulations in humans (gene therapy is a different aspect, it is not used to monitor specific aspects of our biology but to potentially improve pathological conditions).

– The authors based their analyses on the identification of the inflection point, considered a readout for clock stability. I would suggest performing an experiment to demonstrate how the inflection point changes according to clock stability. For instance, in both flies and mice, it has been shown that older individuals exhibit a less stable oscillator. The authors could test their assumption monitoring oscillations in young vs old flies. The experiment performed in tim01 background is valuable, but of course in this case the oscillations are severely disrupted. If the inflection point is a true readout, we should expect this to occur later and earlier in young and old flies, respectively. Concerning this point, I am also open to other suggestions from the authors, but I really think that this assumption, although logical from a certain point of view, should be experimentally validated. On the same line, it would make the data more robust by having a demonstration that the inflection point is not influenced by the genetic background (i.e. the GAL4 strains used might differ from these points of view).

– One of my biggest concerns revolves around the experiments and the conclusions on PDF signaling. First, I did not fully understand why the authors chose to perform their experiments in pdfr-/- (han-/-) background instead of pdf-/-, especially knowing that PDFR might be responsive also to other neuropeptides (i.e. DH31) (an aspect correctly reported by the authors). Yet, most importantly, I am not convinced their conclusions about the negligible effects of PDF signaling on peripheral clocks are supported by the experimental plan used. To my knowledge, pdfr has never been shown as expressed in gut, fat bodies, and muscle (flybase reports expression in head and crop). More in general, PDF signaling has not been widely considered to synchronize/coordinate peripheral clocks (outside the head and prothoracic gland). This is a plausible assumption, but without data confirming the expression of pdfr in the tissues included in the analyses, no conclusion based on PDF signaling effects in these peripheral tissues can be drawn (i.e. "This result suggests that the presumed dominance of the brain clock in regulating peripheral clocks needs to be re-examined"). Thus, I encourage the authors to present evidence for such expression pattern (i.e. using immunohistochemistry), if this has not already been reported in the literature.

– My second major concern is about the lack of investigation and explanation of the ~60 h oscillatory dynamics identified. I understand that describing the ~60 h infradian oscillations goes beyond the scope of the study for the authors, yet, this represents such an atypical rhythm that I think the authors should provide a biological explanation about it, at least at the hypothesis/theoretical level. Moreover, I suggest discussing the findings concerning infradian rhythms unveiled in constant conditions in the context of the existing literature on ultradian and infradian rhythms unmasked by DD, LL, etc… On the other hand, graphs suggest infradian oscillations are actually very broad compared to the circadian ones (which are precisely around 24 hours) and range from ~48 h to ~72 h. Thus, I think it is not appropriate to talk about ~60 h rhythms. Moreover, this broad rhythmicity identified at the luminescence level might be of little to no biological meaning, being simply asynchronous, noisy, and stochastic oscillations. Maybe the authors could test whether this rhythmicity is present at the locomotor activity level as well. I think this would demonstrate that this infradian rhythmicity has a real link with Drosophila biology. However, also if this was the case, could we really consider it meaningful rhythmicity in constant conditions?

Reviewer #2 (Recommendations for the authors):

This manuscript describes a novel intersectional approach (LABL) that allows to express a per-luciferase driver in specific cell types or tissues in Drosophila. This is definitely a welcome addition to the arsenal of tools to study Drosophila circadian rhythms. Overall , the authors make a strong case that their approach works well. They are even able to record from a few brain neurons. The authors then use their novel method to test the impact of loss of PDF-receptor (PDFR) signaling on brain and peripheral oscillators. There, I have difficulties with the analysis and interpretation of the data, particularly when it comes to defining some form of hierarchy between circadian pacemaker neurons and peripheral tissues. There is already considerable literature on this, but the authors need to better link their results to current knowledge, and draw clear conclusions on whether their results indeed reveal something new. Results are not always coherent, and I am concerned that one of the main measurements used in these studies, called "amplitude stability constant", is not a reliable method to study the decay in rhythmicity. In summary, this paper brings a novel technology to the study of circadian rhythms in Drosophila, but its use to study PDFR signaling across fruit flies seems to provide rather limited new insight into the hierarchy between brain and body clocks, at least as currently presented.

Specific comments:

1) In the abstract, the authors say their results challenge the notion that brain clocks are dominant over peripheral clocks. However, it has been quite clear for a long time that peripheral clocks in Drosophila function independently. Most have their own photoreceptors, and do not depend on the brain for their oscillations under LD and for a few days at least in DD. There are a few exceptions though. An interesting one is the oenocytes, which receive PDF signaling (Krupp 2013). However, even there, rhythm amplitude is not affected by loss of PDF signaling, only phase. The Prothoracic Gland is however dependent on sLNv signaling for its rhythms, but this is through sNPF (Selcho 2017). The authors need to carefully survey relevant literature and place their results in context. It would be really interesting to try an oenocyte-GAL4 line and test whether LABL can pick up changes in phase of rhythms in the absence of PDFR in oenocytes. This would be a nice validation of this new approach.

2) In the introduction, the authors mention the lack of tissue-specific methods to monitor circadian rhythms in real-time. In flies, such methods are indeed lacking, but not in mammals. Intersectional approaches have been developed by the Schibler (Sinturel 2021) and Weaver (Smith 2022) labs, and those should be mentioned in the introduction. Sinturel should also definitely be mentioned in the discussion as their results are very directly relevant to the present study.

3) The S-curve fitting of peak and through to determine rhythm decay does not appear reliable to me. This approach is going to be very sensitive to experimental noise, given how flat these curves are. Several curves do not look like an S (see Figures 5 and 6). How reproducible were these decay measurements across experiments? There are no statistics on Figure 5B and 6C. I am also not sure that the term "amplitude stability constant" is appropriate. This is not a constant.

4) I am not convinced that the infra-dian oscillations are absent in tim0 flies, just that they are less robust or synchronized than in pdfr-han. These oscillations might be the result of a weak underlying behavioral rhythm such as feeding, which could lead to low amplitude cycles of luciferin in the body. The weak inverted rhythms that appear after a few days in the esg-GAL4 experiment on Figure 6 could also be due to rhythmic ingestion and could explain the inversion of oscillations.

5) In the image for luciferase signal in the LNvs on Figure 4C, the signal seems to be too dorsally located to be from the LNvs. Even in the DvPDF image, the localization seems too dorsal. Would there be a luciferase antibody that could be used, to directly visualize LUC expression by ICC? The G-trace images look good, but they do not show directly luciferase expression.

6) On page 11, the authors seem to conclude that body clocks need PDFR signaling, which contradicts one of the abstract's main statements. Then, the results in figure 6 seem rather different. Overall, loss of PDFR signaling does not seem to have much effect on figure 6 in peripheral clocks. How are those results reconciled?

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Real time, in vivo measurement of neuronal and peripheral clocks in Drosophila melanogaster" for further consideration by eLife. Your revised article has been evaluated by K VijayRaghavan (Senior Editor) and a Reviewing Editor.

The manuscript has improved significantly and all reviewers agree that it is now in principle suitable for publication. However, before we can finally accept your manuscript we would ask you to address some remaining issues, as outlined below:

There are still some concerns on the interpretation of the infradian rhythms from reviewers 1 and 2 that we would ask you to respond to.

Please provide better images of the luciferase signal in the brain of the pdfGAL4 and DvpdfGAL4 flies.

Finally, please respond to the specific comments raised by reviewer 2, as these point out several mistakes or sentences that might need rephrasing for clarity.

For your information, please find the full reviews below.

Reviewer #1 (Recommendations for the authors):

I personally consider the manuscript entitled "Real time, in vivo measurement of neuronal and peripheral clocks in Drosophila melanogaster" significantly improved overall. The authors conducted many suggested experiments and strengthened at the same time the weak points in the interpretation of generated data, phrasing, and literature gaps.

The authors tackled with appreciated effort all the 4 major points of concern raised by the reviewers. Particularly, the authors effectively answered (a) my criticism concerning the misleading statements on the applicability/potential of the methodology developed in disease contexts, and (b) all the points I raised in my initial review.

Concerning my criticism of the inflection point, the authors definitely strengthened the reproducibility of their analysis but failed to provide convincing evidence on clock stability in aged flies and the effect of the genetic background. However, I appreciated the dedication and effort in the experiments I suggested. I understand the failed aged flies` experiment is caused by insurmountable technical aspects, and I am satisfied with the transparency they used in openly addressing the genetic background issue I raised.

Similarly, I positively value how the authors addressed my concerns regarding their interpretation of the role of PDF signaling. I understand the "genetically-convenience" reasons (considering their major goal), and the motivations for why they did not directly tackle PDFR expression, and I appreciate the implemented changes in the phrasing of some potentially misleading sentences.

Concerning the ~60 h rhythm concerns, although the experiments did not provide positive evidence, I again appreciate the authors´ investment in experimentally addressing these criticisms. At the same time, from a reader standpoint, the manuscript transparency is significantly improved, thanks to the inclusion of these data as supplementary information, and the theoretical explanations provided in the text. To be completely honest with the authors, I still feel the genetic background might have a bigger role than we think, yet, I do think the oscillator coupling argument is very valid and definitely the most plausible hypothesis, and I share their enthusiasm about future studies in this direction.

Finally, I agree with the editors about moving the manuscript to the "Tools and Resource" section of eLife. As much as the authors conducted interesting/impactful research and careful analyses, I still feel the advancement provided in the manuscript from a biological standpoint is still surpassed by the potential of the methodology the authors developed.

All considered I endorse the publication of this manuscript on eLife.

Reviewer #2 (Recommendations for the authors):

The authors have improved their manuscript, though some issues remain. I will first go through the four main issues that were brought up in the first round of reviews.

1) Validation of the approach: This has certainly been improved with the use of clock mutants and old flies. LABL should prove to be a potent new tool for studying circadian rhythms in Drosophila.

2) I still have concerns with the reliability of the inflection point. It is reassuring that the authors observed a similar A50 in two independent sets of experiments done with wild-type flies. However, they acknowledge that there is important variability between individual experiments. I am not sure that inflection points will prove reliable beyond robustly rhythmic genotypes. For example, in figure 7, it does not appear that perL can be fitted with an S shape. Also, by not being able to use individual flies or even individual experiments, there is no way to test differences between genotypes with statistical analysis. Some of the differences that were observed might not hold in future experiments. The authors should acknowledge those important limitations.

3) Discussion of previous results on PDF and PDFR signaling has been improved, but I noticed issues with referencing that will be detailed below.

4) The lengthy response of the authors to the questions about the infradian rhythms is at times difficult to follow. In particular, I do not understand why they exclude some form of metabolic/feeding rhythm. These rhythms might simply be revealed when circadian control is lost or severely impaired. They might be more clustered around 60 hrs in han mutant flies compared to tim0 , because tim0 flies have no circadian clock at all.

Reviewer #3 (Recommendations for the authors):

The authors have done a good job in responding to my previous critiques. They have added new experiments, new analyses, new model simulations, and new text. The changes to figures have made them clearer. While it remains unclear to this reviewer how the 60-hour LABL rhythms arise or what they mean, the discussion of possible explanations is thoughtful and the source/meaning of these infradian rhythms remains outside the scope of this paper. The genetic tools generated in this paper have been well validated and characterized and will be appropriately published in the Tools and Methodology section of eLife. Without a doubt, these tools will be highly useful for Drosophila researchers in circadian biology and this manuscript is likely to be well cited.

https://doi.org/10.7554/eLife.77029.sa1

Author response

Essential revisions:

1) Better analysis, visualization and further validation of the overall technical approach.

The further validation should include additional experiments, such as the test of circadian clock mutants that slow down/speed up the clock or direct measurements of clock protein/RNA levels.

We have conducted further experiments to better analyze, visualize and validate our overall technical approach in four ways, using (1) the perS mutant (loss of DBT phosphorylation site), (2) the perL mutant (low PER protein expression), (3) aged flies compared to young flies and (4) measuring protein levels. These data confirm that LABL is a useful tool that allows for the measurement of clock oscillations in target cells and tissues and is versatile in its use.

(1 and 2) We have added a text section to the results entitled “Circadian clock mutants perL and perS reveal features of clocks in distinct neurons.” and also added Supplemental Figure 5, which illustrate these data, pp 14-16 in manuscript.

Our data reveal that each of the different neuronal clusters tested indeed shorten or lengthen clock oscillations as measured by our LABL method. We also demonstrate that clock oscillations in a perL genetic background are dampened, as previously reported by Rutila et al., 1996 (cited in revised manuscript).

Further to this, we once again find that each of the neuronal clusters exhibit different rates of amplitude decay, and slight variability in their period, further validating our overall technical approach.

(3) We measured luminescence oscillations in aged flies. Aged flies are known to lose robustness in their behavioural rhythms (and Supplementary Figure 4). Using LABL in aged flies, we demonstrate that clock oscillations in distinct cells oscillate with a low amplitude, reflecting the lost robustness observed in behavioural rhythms. Further to this, luminescence oscillations in aged flies exhibit no decrease in amplitude over time (as observed with young flies) or the oscillations become infradian. The infradianlike oscillations prevent us from fitting an S-curve to the peaks and troughs to measure the A50 (point of inflection of the S-curve), but also demonstrate that infradian oscillations can appear in aged flies in genetic backgrounds that don’t exhibit infradian oscillations in younger flies. This validates the observed infradian oscillations, but also suggests that genetic background is less likely to be a factor in our observation of infradian oscillations. Thus, our method of luminescence measurement describes a second biological phenomenon (age-caused decay of clock oscillations), further validating our overall technical approach. We added an additional figure (Figure 4 and Figure S4) and text (pp. 10-11) to describe our results. We also expanded our discussion to reflect on this data.

(4) We attempted to visualize 24-hour protein oscillations or decay into 60-hour infradian rhythms using protein extracts from fly heads and probing for PER and TIM. We demonstrated that we can observe PER and TIM oscillation across 24 hours, as expected. However, immunoblotting proved too insensitive to observe the amplitude decay that reveal the infradian rhythms we observe with luminescence, in a han5304 genetic background: since changes in amplitude that we observe in our luminescence oscillations were low, and since immunoblotting requires collection of different samples across time at low time resolution (i.e., one data point is not dependent on the previous data point and is collected independently), we concluded that we did not have the time resolution necessary to observe infradian rhythms. We added Supplementary Figures 2B and 2C and text (pp. 9-10) to reflect our results.

Overall, we believe that our data shows that LABL-mediated luminescence oscillations (1) predictably shorten or lengthen in different mutant backgrounds, (2) reveal low-amplitude oscillations in aged flies, as expected, and (3) correlate well with TIM and PER protein oscillations across 24 hours as revealed by immunoblot.

2) Further validation of the usability of the inflection point of rhythmicity. The reliability of this marker is particularly critical as the interpretation of the PDFR data relies largely on it.

We suggest performing an experiment to demonstrate how the inflection point changes according to clock stability with age. It has been shown in both flies and mice that older individuals exhibit a less stable oscillator. The authors could test their assumption monitoring oscillations in young vs old flies.

The question regarding the reliability of the inflection point is a good one, and we thank the reviewers for encouraging us to test this.

We conducted an additional locomotor behaviour experiment with aged flies to demonstrate that in our hands aged flies also exhibit weakened locomotor rhythms (Supplementary Figure 4).

We repeated the LABL experiment in Figure 2C when conducting the experiment for aged flies and comparing them to young flies. We show that LABL flies tested for luminescence oscillations reveal nearly identical points of inflection in the new experiment using the tim-UAS-Gal4 driver: compare Figure 2C and Figure 4A. We believe this speaks to the reproducibility of the point of inflection. We added the text “We note that in young flies, the A50 fell to 6.5-7 days, almost identical to our first observation (Figure 2C), reflecting the reproducibility of this calculation.”, p. 11 to reflect this. We additionally refer the reviewers to point 4 below, under “Experiments, 3. Oscillation Simulations”. Here we describe how the amplitude decay may occur and how it fits into an S-curvelike decay.

Testing aged flies is a very good suggestion, which we have done. Our data reveal that the immediate low amplitude of older flies correlates well with the amplitude decay observed in young flies after many days in DD, as show in Figure 4. Importantly, we do not observe a decay of amplitude in which we could fit an S-curve, either because the number of data points (fitted peaks and troughs) were too few, or the data points appeared to already be decayed. In an unexpected result, we found that Pdf- and R18H11-Gal4-driven LABL flies exhibited rapid decay into infradian rhythms, which is not observed in younger flies.

We believe that this experiment validates two things. (1) The decay of amplitude that we define is indeed reliable (and reproducible) and that this decay is not observed in older flies. We assume this is because the decay has already happened earlier in the flies’ lifetime, or is not observable in our experimental setup. (2) Infradian oscillations are observed in clocks in tissues in older flies that are not observed in younger flies, suggesting that the observed infradian rhythms are not an artefact of genetic background, driver, etc., but can be age-dependent (expanded further below, point 4).

Additionally, we conducted simulations of phase uncoupling of simulated oscillators. We demonstrate that abruptly uncoupled oscillators do not exhibit changed period, only changed amplitude (Figure 5A). However, if phase uncoupling is allowed to grow randomly and linearly over time, we find that we can reproduce the amplitude decay that we observe in our data without affecting period, once again. In our simulations, the S-curve fit for peaks and troughs eventually approaches zero, because all oscillators are being uncoupled. In our experimental data (e.g. young flies in Figure 4), the bottom part of the Scurve always approaches a non-zero value, suggesting that either some oscillators do not phase shift relative to each other, or the phase shift does not continue indefinitely as it does in our simulations.

Overall with our simulations, we have shown how the decay of amplitude may occur, and we further support the hypothesis that infradian rhythms must indeed be a biological phenomenon (expanded below, point 4).

We have included additional text (pp. 11-13) and include a new figure (Figure 5) to describe our results.

3) A critical and careful discussion of the PDFR results in the context of previous work, that clearly explains which results are consistent with or challenge previous models. (See several points of critique about the lack of proper referral to the existing scientific literature in the individual reviews.)

We thank the reviewers for this point. We have expanded our discussion of our results to include appropriate context of PDF signaling and PDFR from previous work; we highlight where our data is consistent with, or challenges earlier work and we have expanded our references in support of some of our data and conclusion.

Since our initial submission, Li et al., have published a paper that describes all detectable transcripts that are expressed in various organs in flies. They report the expression of pdfr in a number of peripheral organs. We have cited this paper and incorporated it into our results (p. 19). We now refer to this paper as evidence that some peripheral organs express pdfr mRNA, but we underscore that mRNA expression does not reflect how much PDFR protein (or whether an effective amount of PDFR) is expressed.

One thing that we also would like to draw attention to is that pdfr expresses different spliced variants. Although Im et al., (2010, J Comp Neuro) developed some antibodies against PDFR, it is likely that this antibody is limited in its detection of different PDFR variants. Indeed, it appears that flies express three alternatively spliced versions of PDFR, which complicates this endeavour (Paul Taghert, personal communication, SRBR 2022). Unentangling which PDFR is expressed in which tissue will require considerable effort and is being undertaken by the Taghert group.

In the context of PDFR expression in peripheral organs, we also highlight our observation of a change in phase of oscillation after a few days in constant darkness in some of the peripheral tissues we tested for luminescence oscillation (i.e., fat body and muscle). We discuss how our measurements suggest that PDF plays a role in maintaining phase coupling in these tissues, and also discuss how this may be analogous to the PDF influence on coupling between cry- and cry+ LNds (pp. 20-21) (as described in Yoshii et al., 2009).

We now have added an additional section in which we underscore the differences between luminescence oscillations revealed by LABL and other systems that use universal luciferase reporters (i.e., 8.0-luc) (pp. 18-19). This is in addition to Figure 3A. Specifically, we point out that we observe longer luminescence periods in han mutants, while others observe shorter periods using 8.0-luc in pdf01 flies (p. 18) (Versteven 2020). We discuss that this difference may be due to 8.0-luc expression in a select number of neurons in the fly (Veleri et al., 2003). Despite this, we observe shorter behavioural rhythms in the rhythmic han5304 flies, similar to others and to the shorter behavioural rhythms observed in rhythmic pdf01 flies (Renn et al., 1999). Related to this, we also cite and highlight that pdf01 and han5304 flies have been shown to exhibit differences in phase advance and phase delay in fly behaviour (Krupp et al., 2013), which is a possible explanation for some of the discrepancies between our luminescence data and other published luminescence data.

We corrected an issue where we inadvertently called han5304 (and pdf01) mutant flies arrhythmic. They are mostly arrhythmic, not completely. We also expanded these sections to highlight that the flies that carry these mutations exhibit shorter behavioural rhythms. (pp. 8) (and previous point)

We introduce the concept of neuronal synchrony (and coupling) in the context of PDF (Lin et al., 2004 and Yoshii et al., 2009) (p. 13). We also described how pdf may work to keep some oscillators uncoupled and highlight how cry- and cry+ LNds become phase coupled in a pdf01 genetic background fly (Yoshii et al., 2009). We also suggest that the increased amplitude of luminescence oscillation that we observe in han5304 flies (and the same observation in pdf01 flies described by others), compared to wild type flies, may be a result of clocks that are more tightly coupled when PDF-signaling is eliminated (pp. 13, 16, 18 and 20).

Additionally, we have included other references throughout the manuscript, listed below:

LNvs and LNds continue to oscillate in the absence of pdf (Lin et al., 2004, Yoshii et al., 2009) (p. 17).

s-LNvs have enhanced tim promoter activity in the absence of functional PDFR (han5304) (Mezan et al., 2016) (p.17).

Pdf01 exhibits shorter behavioural period and advanced LNd nuclear translocation (Lin et al., 2004, Renn et al., 1999) (p.18).

We have also included additional references suggested by the reviewers, (e.g., oenocytes, pp 18-19).

4) There have been several questions/concerns raised concerning the reported 60hr rhythms, such as

– Weak rhythms such as the infradian rhythms need to be better visualized.

– Is there additional evidence (experimental or theoretical) that these are really 60hr rhythms or could these rather represent relatively randomly occurring superpositions of peaks/throughs of de-synchronized cells in tissues? Potentially consistent with the latter hypothesis it appears that these rhythms exhibit a rather large period range (between 48-72hrs)?

– Could such infradian rhythms be artificially driven by feeding or other metabolic rhythms that affect the amount of luciferin in the body?

The question of what these infradian rhythms are is a good one, and we thank the reviewers for pushing us to dig deeper into this. We have conducted three experiments, including two different simulations, included citations from theoretical physicists to explain this phenomenon and make five theoretical arguments about the probable explanation for this phenomenon.

Additional Experiments:

1. Immunoblotting:

We measured PER oscillations across 6 days at 6-hour resolution by immunoblot, to reveal 60-hour rhythms. First, we extracted protein from whole flies, since our 60-hour data relied on luminescence signal from the whole flies (TUG driving LABL) (Supplementary Figure 2C). Arrhythmic PER expression in ovaries prevented us from demonstrating PER oscillation in these protein extracts from a mixed-sex fly population (Hardin, 1994 MCB). We noticed that in the SDS-PAGE wells, we were left with lipid-like impurities during the experiment, and we reasoned that these impurities may be interfering with the immunoblotting.

We therefore next extracted protein from fly heads in a reattempt of the experiment. We were able to demonstrate an ~24-hour period in the first day of constant darkness in han5304 mutant flies, which we were expecting and had shown to be the case in both locomotion and luminescence oscillations in timUAS-Gal4 driven LABL flies (Figure 3C). However, we lost reliable and reproducible patterns of PER expression at later time points. We concluded from this that immunoblotting may not be sufficiently sensitive or quantitative enough to reveal the subtle amplitude changes that would reveal a lowamplitude 60-hour period. Even if we had been successful, there would still be reason for caution: it has been reported that clocks in heads and bodies can differ in their oscillation (Hardin et al., 1994 MCB). We therefore include our new immunoblot data in the paper to demonstrate that while we are able to detect oscillations of PER and TIM protein in wildtype flies and in the first day of han5304 mutant flies, we are not able to reveal a 60-hour rhythm in these data.

2. Locomotor Behaviour:

We conducted additional locomotor behaviour experiments to try to measure 60-hour oscillations of clocks through another method means. We thank the reviewer who suggested looking for this period in locomotor activity in wt and han5304 flies. We conducted this experiment using wild type flies (24hour luminescence rhythms), han5304 (eventual 60-hour luminescence rhythms) and tim01 as a control (no rhythms). Behavioural period of all flies was measured in a 16 – 32-hour range or 48 – 72-hour range (Supplementary Figure 2A), without consideration of whether the actograms were arrhythmic (visually or by power of fit in a Lomb-Scargle method of generating a periodogram). Wild type flies demonstrated a behavioural period just under 24 hours, as expected, with a Power of ~800. han5304 flies exhibited an average of ~24-hour behavioural period with wide variability of behavioural period, and dramatically lower Power. (We would like to point out that we used all living flies to estimate behavioural period, regardless of whether or not they were arrhythmic by eye or by low Power. This may explain the discrepancy between the ~24-hour behavioural period here and slightly short behavioural periods reported in “rhythmic” flies.) tim01 flies showed high variability of behavioural period fit with no clear clustering and a very low Power. This demonstrates that overall wt flies are rhythmic, tim01 flies are arrhythmic and some han5304 flies retain weakly rhythmic behaviour.

The same data was analyzed for behavioural period fits ranging between 48 – 72 hours (Supplementary Figure 2A). Both wild type and han5304 flies demonstrated some clustering of behavioural period into two ~60 and ~50-hour clusters, while tim0 demonstrated a uniform range of variability of fits, suggesting that in wild type and han mutants there may be a behavioural rhythm that fits within a 50 – 60-hour period. However, the Power of these fits was very low, similar to that observed for tim01 flies. We concluded that these data are not reliable enough to draw conclusions from. We include these data in the manuscript as Supplemental Figure 2A to inform readers that we explored identifying a 60-hour behavioural rhythm.

3. Oscillation Simulations:

We simulated oscillations in which oscillators were abruptly uncoupled or gradually uncoupled in an attempt to produce infradian rhythms. While this simulation did not reveal infradian rhythms, it did demonstrate that phase changes of some oscillators cause a decay in amplitude (and overall phase). In other words, clock uncoupling and phase change does not explain the 60-hour oscillation (Figure 4) but does explain amplitude decay. The abrupt changes in phase of some oscillators caused an immediate change in amplitude and a change in phase of the total signal, whereas a gradual shift in phase of some oscillators cause a decay of amplitude that tracked an S-curve. We therefore conclude that phase uncoupling would only lead to a decline of the signal while maintaining constant period, even if the periods of oscillation were slightly varied (e.g., 23-hour periods and 24-hour periods in the same animal). In our biological system, the amplitudes of luminescence oscillation in the top end and bottom end of the S-curve are steady, declining to a stable but non-zero value. We therefore conclude that either some clocks do not change phase amongst each other, or the amount of phase change is limited (i.e., the phase can drift, but stops at a given value, unlike in our simulations where it is allowed to drift indefinitely).

Theoretical Arguments:

Refutation or support of possible explanations for the 60-hour infradian argument.

1. The Population Argument:

It has been raised that flies within the population that we are testing may “uncouple” from each other, causing a phase drift that creates the amplitude decay or infradian rhythms. Our simulations suggest that this would lead to an amplitude decay, rather than a period change. However, population uncoupling is still very unlikely because we see 24-hour rhythms in Pdf-Gal4 driven LABL han5304 flies and 60-hour rhythms in R18H11-Gal4 driven LABL han5304 flies. Since the flies are otherwise genetically identical (except for their Gal4 driver), any desynchrony in the population should affect both measurements the same. Thus, this is a highly unlikely explanation for infradian rhythms.

2. The Metabolism Argument:

Feeding: It has been suggested that feeding patterns may influence the formation of 60-hour infradian rhythms. We feel this is unlikely. If there were to be a 60-hour feeding oscillation, the Pdf-Gal4 activated LABL han5304 flies would exhibit ~24-hour oscillations embedded into 60-hour oscillations. This is not the case, suggesting that feeding rhythms do not contribute to the 60-hour infradian oscillations.

Luciferin absorption: A second possibility for a metabolism explanation is that some tissues absorb luciferin with a ~60-hour rhythm while others continue to absorb luciferin with a ~24-hour rhythm. However, this explanation creates the requirement of a 60-hour luciferin absorption oscillation instead of a 60-hour clock oscillation that also must depend on both the absence of PDF signalling and presence of a clock. Furthermore, this argument moves the 60-hour oscillation problem away from a clock-mechanism and places it on a clock-dependent luciferin absorption mechanism, “kicking the can down the road”. Importantly, it assumes that an endogenous 60-hour oscillation must exist in the background (see below, oscillator coupling argument).

3. The Genetic Background Argument:

It is also suggested that subtle genetic differences in fly backgrounds may underlie the development of 60-hour infradian oscillations. We also suggest that this is unlikely, because flies of identical genetic backgrounds that have been aged (compared to young flies) develop 60-hour infradian oscillations as well (Figure 4C). While we cannot rule out that the driver-dependent subtle genetic backgrounds combined with age may cause the development of infradian rhythms, we suggest that this is an unlikely explanation.

4. The Oscillator Coupling Argument:

A few theoretical papers describe how short-period ultradian rhythms, tightly coupled, could create 24hour oscillations. By extension, it is possible that multiple ~24-hour oscillators could create a longerperiod oscillation if they are coupled more tightly in the absence of PDF signaling. This possibility requires that PDF in wild type flies keeps some clocks uncoupled (or enforces weak coupling), which would suggest an as-yet unknown function of PDF. Versteven et al., (iScience, 2020) show change in phases of oscillation in luminescence signal in a temperature-dependent manner. The same authors observe this phase change (and amplitude reduction) disappear in a pdf01 genetic background. If increased temperature is what is uncoupling some clocks in this study, and since our simulation data suggests that abrupt phase uncoupling of oscillators can cause overall phase changes in total measured oscillators without affecting period (Figure 5A), it is possible that PDF indeed decreases the strength of coupling between oscillators (i.e., pdf01 increases coupling strength). If loss of PDF signaling causes stronger coupling between clocks, this may both explain the 60-hour infradian oscillation as well as increased amplitude of luminescence oscillation. An additional observation by Yoshii et al., (2009) demonstrates that cry- LNds normally oscillate in reverse phase with cry+ LNds – only to become synchronized (coupled) when PDF is lost. This once again suggests that PDF may indeed function to uncouple some oscillators, causing tighter coupling when it is lost, leading to longer infradian periods of luminescence oscillation.

It is possible that shorter behavioural periods are observed in flies lacking PDF, either because of differences in coupling in locomotor-specific circuits, or because shorter locomotor periods are the result of exclusion of arrhythmic flies with low Power that biases the data in one direction (see 2. Locomotor Behaviour).

5. The Endogenous Infradian Clock Argument:

It is possible that 60-hour oscillators already exist in damped form in flies. This possibility would suggest that these infradian rhythms appear when the 24-hour clocks become uncoupled when PDFsignaling is lost, decaying rapidly, and exposing these infradian oscillations. We offer this as a possible, but unsatisfying scenario for explaining the observed 60-hour infradian oscillations.

We added text to the discussion describing these possibilities, pp. 25-26.

We also added the statement “We also point to seemingly long-period oscillations observed in the DN1s, in constant light conditions in a cryb genetic background (Nave et al., 2021). Although this mechanism is likely different, it is another example of long-period oscillations in the DN1s, which we observe in a han5304 background in DN1s after 6 days of constant darkness (Figure 6D).”, to the Discussion, to underscore a biological precedent for long-period oscillations in some neurons under specific conditions p 26.

We once again thank all three reviewers for their constructive and helpful criticisms, and we feel that they have helped us strengthen the manuscript considerably.

We would also like to address the specific points raised by the reviewers below. Where relevant, we refer the reviewers to our explanations above.

Reviewer #1 (Recommendations for the authors):

– Currently, a genetically encoded reporter cannot be exploited to assess specific aspects of human diseases for ethical reasons, although in the future this might be possible. Thus, I would strongly encourage the authors to be (1) more focused on the short-term application, and/or (2) more specific (cell culture, organoids, monitoring disease progression but in animal models, etc…). The authors used many references (especially in the introduction) to highlight medical application, but this, in my personal opinion, has to be moderated as such approach would be limited by the current regulations in humans (gene therapy is a different aspect, it is not used to monitor specific aspects of our biology but to potentially improve pathological conditions).

We agree with the reviewer. We did not mean to imply that this reporter can or should be used in humans. We have made the following changes to clarify this confusion:

p.3 “Such a broad variety of pathologies associated with compromising circadian rhythms suggests a need for cheap and effective ways to measure tissue-specific circadian clocks directly in model organisms.” The sentence was changed to include the phrase “in model organisms”.

p.4 “Given that circadian clocks appear to be linked to a wide range of physiologies, including metabolism, as well as various behavioral disorders, there is a need to monitor distinct cell- and tissue-specific circadian clocks directly, in vivo, and in real time in model organisms.” The sentence was changed to include the phrase “in model organisms”.

p.5 “We believe that this technology will be critical in the interrogation of distinct circadian clocks in future studies, particularly in monitoring peripheral clock function during disease progression in Drosophila.” The sentence was changed to include “in Drosophila

p.22 In the sentence “Using LABL, we were able to rapidly assess the effects of loss of PDF signaling on tissue-specific clocks as a proof of principle for this method in different mutant contexts.” We removed the phrase “or disease contexts” at the end of the sentence to avoid confusion regarding application to human disease.

– The authors based their analyses on the identification of the inflection point, considered a readout for clock stability. I would suggest performing an experiment to demonstrate how the inflection point changes according to clock stability. For instance, in both flies and mice, it has been shown that older individuals exhibit a less stable oscillator. The authors could test their assumption monitoring oscillations in young vs old flies. The experiment performed in tim01 background is valuable, but of course in this case the oscillations are severely disrupted. If the inflection point is a true readout, we should expect this to occur later and earlier in young and old flies, respectively. Concerning this point, I am also open to other suggestions from the authors, but I really think that this assumption, although logical from a certain point of view, should be experimentally validated. On the same line, it would make the data more robust by having a demonstration that the inflection point is not influenced by the genetic background (i.e. the GAL4 strains used might differ from these points of view).

We thank the reviewer for the thoughtful and constructive comment. We refer the reviewer to our responses to the four main points above.

First, we point out that we have now shown reproducibility of the point of inflection (Figure 2C and Figure 4C).

We have conducted the experiments on aged flies and demonstrate that S-curve fit is not possible, either due to an already-decayed amplitude of oscillation, or a decay in amplitude so rapid that an Scurve fit is not possible due to the formation of infradian rhythms (i.e., there are too few peaks and troughs to fit an S-curve to).

With regard to the point about genetic backgrounds, we also demonstrate that the point of inflection in DvPdf-Gal4 driven LABL flies that falls to ~5 days is absent in aged flies, suggesting that the presence of this point of inflection is dependent on age and not exclusively on the genetic background of the fly expressing the Gal4 driver in this experiment. However, we agree that genetic background can certainly have an influence on our data when comparing between flies under the same conditions, and we therefore highlight in our manuscript that we cannot rule out the possibility that the contributions of genetic background influence where the point of inflection falls in the statements:

“Although we have not determined the extent of influence the genetic background the GAL4 drivers have on the A50, in all cases elimination of functional PDFR (all other genetic background being the same) shifted the A50 earlier in constant dark conditions.” (p. 15)

“A third possibility is that the differences of Gal4 drivers, or subtle genetic differences in fly backgrounds are causing the formation of 60-hour infradian oscillations.” (p. 23).

– One of my biggest concerns revolves around the experiments and the conclusions on PDF signaling. First, I did not fully understand why the authors chose to perform their experiments in pdfr-/- (han-/-) background instead of pdf-/-, especially knowing that PDFR might be responsive also to other neuropeptides (i.e. DH31) (an aspect correctly reported by the authors). Yet, most importantly, I am not convinced their conclusions about the negligible effects of PDF signaling on peripheral clocks are supported by the experimental plan used. To my knowledge, pdfr has never been shown as expressed in gut, fat bodies, and muscle (flybase reports expression in head and crop). More in general, PDF signaling has not been widely considered to synchronize/coordinate peripheral clocks (outside the head and prothoracic gland). This is a plausible assumption, but without data confirming the expression of pdfr in the tissues included in the analyses, no conclusion based on PDF signaling effects in these peripheral tissues can be drawn (i.e. "This result suggests that the presumed dominance of the brain clock in regulating peripheral clocks needs to be re-examined"). Thus, I encourage the authors to present evidence for such expression pattern (i.e. using immunohistochemistry), if this has not already been reported in the literature.

The reviewer makes a good point regarding our assumptions of PDFR expression in organs and with how PDFR and peripheral clocks interact. We have clarified our language in the text.

Since our initial submission, Li et al., have published a paper that describes all detectable transcripts that are expressed in various organs in flies. They report the expression of pdfr in a number of peripheral organs. We have cited this paper and incorporated it into our results (p. 19). We now refer to this paper as evidence that some peripheral organs express pdfr mRNA, but we underscore that mRNA expression does not reflect how much PDFR protein (or whether an effective amount of PDFR) is expressed. (pp. 20-21)

Our use of han5304 flies instead of pdf01 flies was for genetic reasons. Our supplemental table illustrates the number of transgenes involved in the genotypes of some of the flies that we used in our system. Using pdf01 would have required us to generate a large number of recombinant lines, and since our intention with this manuscript is to show that LABL is a functional reporter that generates reproducible luminescence signal to describe distinct clocks, we reasoned that the han5304 mutant would be an acceptable and a genetically convenient mutant candidate.

We agree that PDFR protein has not been shown to be expressed in the gut, fat body and muscle and we had not intended to imply that it had been. To avoid confusion by other readers, we changed the language in this section of the results to reflect that PDFR has not been shown to be expressed in these organs to make clear to the readers that we did not intend to suggest that we thought these organs expressed PDFR. We do however suggest that PDFR must play some sort of role in regulating peripheral clocks, not least because of transcripts reported in a number of peripheral organs in flies (Li et al., 2022), but also because luminescence oscillations from LABL flies that have been activated using organ-specific drivers reveal changed oscillation characteristics (relative to wild type).

In the abstract, the sentence that the reviewer highlighted “This result suggests that the presumed dominance of the brain clock in regulating peripheral clocks needs to be re-examined” was changed to simply state “we found that, while peripheral clocks in non-neuronal tissues were less stable after the loss of PDF signaling, they continued to oscillate.” Other parts of this region of the abstract were changed further to reflect the reworked Results section.

We changed the subtitle in the Results section from “Peripheral clocks exhibit distinct oscillations and variable PDF-dependence.” to “Peripheral clocks exhibit distinct oscillations and partial PDF-dependence.”.

Although we are excited by the prospect of showing PDFR expression in these organs as the reviewer suggests, given the difficulty in raising antibodies against PDFR, and given that flies express three alternatively spliced versions of PDFR, which complicates this endeavour (Paul Taghert, personal communication, SRBR 2022), and most importantly, since Paul Taghert’s group at Washington University is focused on this endeavour, we will not look for PDFR expression in peripheral organs.

We hope that the reviewer will find our changes satisfactory. We would like to underscore that the Taghert group themselves had difficulty in identifying expression patterns of PDFR in the brain for years and had to infer PDFR expression through the use of mutants and change in cell responses. We therefore think it is reasonable for us to demonstrate that there is an effect on clocks in peripheral tissue in the absence of functional PDFR, and emphasize that we do not know if this effect is direct, since we cannot demonstrate that PDFR is expressed in these tissues.

– My second major concern is about the lack of investigation and explanation of the ~60 h oscillatory dynamics identified. I understand that describing the ~60 h infradian oscillations goes beyond the scope of the study for the authors, yet, this represents such an atypical rhythm that I think the authors should provide a biological explanation about it, at least at the hypothesis/theoretical level. Moreover, I suggest discussing the findings concerning infradian rhythms unveiled in constant conditions in the context of the existing literature on ultradian and infradian rhythms unmasked by DD, LL, etc… On the other hand, graphs suggest infradian oscillations are actually very broad compared to the circadian ones (which are precisely around 24 hours) and range from ~48 h to ~72 h. Thus, I think it is not appropriate to talk about ~60 h rhythms. Moreover, this broad rhythmicity identified at the luminescence level might be of little to no biological meaning, being simply asynchronous, noisy, and stochastic oscillations. Maybe the authors could test whether this rhythmicity is present at the locomotor activity level as well. I think this would demonstrate that this infradian rhythmicity has a real link with Drosophila biology. However, also if this was the case, could we really consider it meaningful rhythmicity in constant conditions?

We thank the reviewer for the insightful comments and the suggestion of looking for infradian oscillations in locomotor behaviour – this was a very good idea that we had not considered. We believe that we have addressed the reviewer’s points in the manuscript, and conducted additional experiments, as detailed above in the main points. We refer the reviewer to our response under the four major points, above.

Ultimately, we agree about the infradian oscillations that we see, which is why in the original iteration of this manuscript, we “considered them arrhythmic from the circadian point of view”. However, we admit that these data are nonetheless interesting, which is why, with the encouragement of the reviews, we have expanded on them. We respectfully disagree that these signals can be noise. We see the long oscillations in the luminescence measurements visually, and when we quantify them, they are consistently around the ~60-hour period mark. We do not see how stochastic and noisy and synchronous oscillations can collect in a range reproducibly, only in han5304 mutants and not in tim01 mutants. Further to this, we do not see the han5304 pattern in tim01 flies (Figure 3B) nor in per0 flies (data not shown), suggesting that there is indeed a biological relevance to our observation. We also point out that wild type period fits range from 23 hour to 24.5 hours, while the majority of period fits for infradian rhythms fall between 55 and 63 hours (Figure 3C, inset, grey area).

We would also like to mention that Nave et al., (J Neuro, 2021) has recently shown that DN1s can oscillate with a very long period in constant light conditions in a cryb genetic background. We highlight this paper in the Discussion with the statement “We also point to seemingly long-period oscillations observed in the DN1s, in constant light conditions in a cryb genetic background (Nave et al., 2021). Although this mechanism is likely different, it is another example of longperiod oscillations in the DN1s, which we observe in a han5304 background in DN1s after 6 days of constant darkness (Figure 6D).”, p 24.

Having argued this point though, we are grateful that the reviewers asked us to dig deeper into how these rhythms might occur. We hope that the reviewer feels that our explanations above of how an infradian rhythm may arise is satisfactory, particularly the possibility of strengthened coupling causing these longer rhythms – a hypothesis we are very excited about testing in the future.

Reviewer #2 (Recommendations for the authors):

This manuscript describes a novel intersectional approach (LABL) that allows to express a per-luciferase driver in specific cell types or tissues in Drosophila. This is definitely a welcome addition to the arsenal of tools to study Drosophila circadian rhythms. Overall , the authors make a strong case that their approach works well. They are even able to record from a few brain neurons. The authors then use their novel method to test the impact of loss of PDF-receptor (PDFR) signaling on brain and peripheral oscillators. There, I have difficulties with the analysis and interpretation of the data, particularly when it comes to defining some form of hierarchy between circadian pacemaker neurons and peripheral tissues. There is already considerable literature on this, but the authors need to better link their results to current knowledge, and draw clear conclusions on whether their results indeed reveal something new. Results are not always coherent, and I am concerned that one of the main measurements used in these studies, called "amplitude stability constant", is not a reliable method to study the decay in rhythmicity. In summary, this paper brings a novel technology to the study of circadian rhythms in Drosophila, but its use to study PDFR signaling across fruit flies seems to provide rather limited new insight into the hierarchy between brain and body clocks, at least as currently presented.

Specific comments:

1) In the abstract, the authors say their results challenge the notion that brain clocks are dominant over peripheral clocks. However, it has been quite clear for a long time that peripheral clocks in Drosophila function independently. Most have their own photoreceptors, and do not depend on the brain for their oscillations under LD and for a few days at least in DD. There are a few exceptions though. An interesting one is the oenocytes, which receive PDF signaling (Krupp 2013). However, even there, rhythm amplitude is not affected by loss of PDF signaling, only phase. The Prothoracic Gland is however dependent on sLNv signaling for its rhythms, but this is through sNPF (Selcho 2017). The authors need to carefully survey relevant literature and place their results in context. It would be really interesting to try an oenocyte-GAL4 line and test whether LABL can pick up changes in phase of rhythms in the absence of PDFR in oenocytes. This would be a nice validation of this new approach.

The reviewer makes a very good point, and we have corrected our language surrounding this issue. The abstract was changed to more correctly reflect our conclusions. We have also made changes regarding our discussion involving PDF, as outlined above, under the four major points.

We agree with the reviewer that the oenocyte data would be very interesting. Since we are focusing this manuscript as a method and to turn our revisions around within a reasonable amount of time, we will conduct these (and other) experiments for reporting in a future manuscript, or in collaboration with interested labs.

2) In the introduction, the authors mention the lack of tissue-specific methods to monitor circadian rhythms in real-time. In flies, such methods are indeed lacking, but not in mammals. Intersectional approaches have been developed by the Schibler (Sinturel 2021) and Weaver (Smith 2022) labs, and those should be mentioned in the introduction. Sinturel should also definitely be mentioned in the discussion as their results are very directly relevant to the present study.

We have included the suggested references in our manuscript. We thank the reviewer particularly for the recommendation of the paper from the Schibler lab. We have modified the Results section, pp 19-20 to include the paper by Sinturel et al., 2021. We have included both Sinturel et al., and Smith et al., in our introduction (p 4)

3) The S-curve fitting of peak and through to determine rhythm decay does not appear reliable to me. This approach is going to be very sensitive to experimental noise, given how flat these curves are. Several curves do not look like an S (see Figures 5 and 6). How reproducible were these decay measurements across experiments? There are no statistics on Figure 5B and 6C. I am also not sure that the term "amplitude stability constant" is appropriate. This is not a constant.

When conducting experiments for the old and young flies, we had an opportunity to test the reproducibility of the point of inflection measure and S-curve measure for TUG-driven LABL flies (Figure 2C and Figure 4C). We note that these graphs are nearly identical, speaking to their reproducibility.

We went a bit further and attempted to measure the S-curve fit in individual experiments. This had mixed results. The robustness of the data is in the numbers, and S-curve fits into single, or two data points is very unreliable and non-informative. We therefore avoided such fits. Importantly, when we simultaneously run internal controls with experimental flies, we consistently reproduce trends: For example, an advanced point of inflection when flies are lacking a PDFR is always advanced.

Because of the suggestion of the reviewer, we have changed the “amplitude stability constant” to a more reflective A50, for Amplitude and 50%, inspired by IC50. This point represents the halfway point between the maximum and minimum values of the S-curve, where the point of inflection lies. We have made the changes to the manuscript accordingly.

4) I am not convinced that the infra-dian oscillations are absent in tim0 flies, just that they are less robust or synchronized than in pdfr-han. These oscillations might be the result of a weak underlying behavioral rhythm such as feeding, which could lead to low amplitude cycles of luciferin in the body. The weak inverted rhythms that appear after a few days in the esg-GAL4 experiment on Figure 6 could also be due to rhythmic ingestion and could explain the inversion of oscillations.

We feel we have addressed the reviewers concerns above, under the main points.

However, we thank the reviewer for their keen eye in noticing the inverted rhythms. We noted this, and speculated in-house that this may be similar to inverted rhythms observed by Yoshii et al., 2009 in pdf01 flies, but felt that we should follow up this point in a follow up manuscript. We have since decided to highlight this point and cite the Yoshii paper. (p. 21)

We have also added statistical analysis for graphs in insets of Figures 3B and 3C to demonstrate statistically that the infradian rhythms observed in data from han5304 flies are distinct from the fits in data from tim01 flies.

5) In the image for luciferase signal in the LNvs on Figure 4C, the signal seems to be too dorsally located to be from the LNvs. Even in the DvPDF image, the localization seems too dorsal. Would there be a luciferase antibody that could be used, to directly visualize LUC expression by ICC? The G-trace images look good, but they do not show directly luciferase expression.

We tried very hard to do this experiment over the span of a year. We tested four commercially available luciferase antibodies from abcam, novusbio, sigma and thermo-fisher. These did not work (we tested these antibodies in non-LABL controls, such as plo flies and UAS-Luc flies as well, and these too did not work). We were careful to collect flies for ICC imaging at the peak of measured luminescence to maximize luciferase expression. We then spent 6 months to try to produce our own Luciferase antibody, twice. Unfortunately, this also did not yield reliable ICC visualization. This is when we turned to G-trace to monitor FLP activity in target neurons and to luminescence image brains, as a compromise.

6) On page 11, the authors seem to conclude that body clocks need PDFR signaling, which contradicts one of the abstract's main statements. Then, the results in figure 6 seem rather different. Overall, loss of PDFR signaling does not seem to have much effect on figure 6 in peripheral clocks. How are those results reconciled?

We agree with the reviewer that our writing was confusing. We have cleaned up the language in this section, as described above.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Reviewer #1 (Recommendations for the authors):

I personally consider the manuscript entitled "Real time, in vivo measurement of neuronal and peripheral clocks in Drosophila melanogaster" significantly improved overall. The authors conducted many suggested experiments and strengthened at the same time the weak points in the interpretation of generated data, phrasing, and literature gaps.

The authors tackled with appreciated effort all the 4 major points of concern raised by the reviewers. Particularly, the authors effectively answered (a) my criticism concerning the misleading statements on the applicability/potential of the methodology developed in disease contexts, and (b) all the points I raised in my initial review.

Concerning my criticism of the inflection point, the authors definitely strengthened the reproducibility of their analysis but failed to provide convincing evidence on clock stability in aged flies and the effect of the genetic background. However, I appreciated the dedication and effort in the experiments I suggested. I understand the failed aged flies` experiment is caused by insurmountable technical aspects, and I am satisfied with the transparency they used in openly addressing the genetic background issue I raised.

We thank the reviewer for this feedback. We also hope that our response to Reviewer 2’s item #2 reflects our acknowledgment of the point made by the Reviewer regarding genetic background and our measurement of point of inflection (A50).

Similarly, I positively value how the authors addressed my concerns regarding their interpretation of the role of PDF signaling. I understand the "genetically-convenience" reasons (considering their major goal), and the motivations for why they did not directly tackle PDFR expression, and I appreciate the implemented changes in the phrasing of some potentially misleading sentences.

We thank the reviewer for this feedback and in helping us clarify our statements.

Concerning the ~60 h rhythm concerns, although the experiments did not provide positive evidence, I again appreciate the authors´ investment in experimentally addressing these criticisms. At the same time, from a reader standpoint, the manuscript transparency is significantly improved, thanks to the inclusion of these data as supplementary information, and the theoretical explanations provided in the text. To be completely honest with the authors, I still feel the genetic background might have a bigger role than we think, yet, I do think the oscillator coupling argument is very valid and definitely the most plausible hypothesis, and I share their enthusiasm about future studies in this direction.

We once again thank the reviewer for guiding us to strengthen our manuscript and their careful insights. We have taken the suggestions to heart.

Finally, I agree with the editors about moving the manuscript to the "Tools and Resource" section of eLife. As much as the authors conducted interesting/impactful research and careful analyses, I still feel the advancement provided in the manuscript from a biological standpoint is still surpassed by the potential of the methodology the authors developed.

All considered I endorse the publication of this manuscript on eLife.

We thank the reviewer for their positive and constructive feedback in helping us strengthen our manuscript.

Reviewer #2 (Recommendations for the authors):

The authors have improved their manuscript, though some issues remain. I will first go through the four main issues that were brought up in the first round of reviews.

1) Validation of the approach: This has certainly been improved with the use of clock mutants and old flies. LABL should prove to be a potent new tool for studying circadian rhythms in Drosophila.

We thank the reviewer for this feedback.

2) I still have concerns with the reliability of the inflection point. It is reassuring that the authors observed a similar A50 in two independent sets of experiments done with wild-type flies. However, they acknowledge that there is important variability between individual experiments. I am not sure that inflection points will prove reliable beyond robustly rhythmic genotypes. For example, in figure 7, it does not appear that perL can be fitted with an S shape. Also, by not being able to use individual flies or even individual experiments, there is no way to test differences between genotypes with statistical analysis. Some of the differences that were observed might not hold in future experiments. The authors should acknowledge those important limitations.

We agree with the reviewer’s assessment, and we hope that the additional modifications also satisfy any remaining concern from Reviewer 1.

On page 11, we added the sentence, “However we note that since we do not determine A50 for individual flies, this measure does not readily permit statistical analysis for comparison across genotypes.” to underscore the statistical limitation of determining A50 on a collection of flies rather than individual flies.

On page 11, we changed the sentence “Thus, we conclude that an A50 could not be determined for aged flies because the amplitude in their endogenous clock oscillations had already decayed to their minimal stable levels.” to “Thus, we conclude that an A50 could not be determined for less stable clock oscillations in aged flies because the amplitude in their endogenous clock oscillations had already decayed to their minimal stable levels.” We believe this underscores the caveat for A50 analysis.

On page 15, in the section under perL analysis, we added the sentence “The apparent noisiness of the oscillation prevented us from determining an A50, which suggests that this parameter is limited to more stable oscillations.” To underscore the limitation for A50 analysis.

3) Discussion of previous results on PDF and PDFR signaling has been improved, but I noticed issues with referencing that will be detailed below.

We hope that we have addressed all concerns below.

4) The lengthy response of the authors to the questions about the infradian rhythms is at times difficult to follow. In particular, I do not understand why they exclude some form of metabolic/feeding rhythm. These rhythms might simply be revealed when circadian control is lost or severely impaired. They might be more clustered around 60 hrs in han mutant flies compared to tim0 , because tim0 flies have no circadian clock at all.

We apologize for not making our argument clearer. We suggest that metabolic/feeding rhythm is an unlikely explanation for the observed 60-hour rhythms in han5304 flies.

Reason 1: LABL activated in pdf+ neurons reveal luminescence oscillations with a period in the vicinity of 24 hours. LABL activated in R18H11+ neurons reveal a decay to a ~60-hour rhythm on day 5 of constant darkness. Other drivers (e.g., clk9M, Mai179) exhibit different days at which the 24-hour rhythm falls into an ~60-hour rhythm. Since the main genetic difference between them is the driver that they carry, we expect each type of fly to exhibit eating patterns similar to each other. We therefore conclude that ~24-hour oscillations and ~60-hour oscillations from flies that are otherwise genetically identical are unlikely to be caused by a change in eating behaviour.

Reason 2: Another explanation for a 60-hour luminescence oscillation may be that luciferin is absorbed into the different tissues differently, rather than being exclusively correlated with luciferase expression. However, this would require that differences in luciferin absorption in different tissues must exist, that it is also clock dependent (as tim0 does not show 60-hour oscillations as the Reviewer correctly points out), and that the 24-hour transcription oscillation and rhythmic luciferin absorption conspire to reveal a 60-hour rhythm.

While we cannot rule these possibilities out, we suggest that changes to coupling strength of different 24-hour transcription oscillation offers a simpler and more likely explanation.

We hope that this clarifies our thinking. We do agree that our arguments do not rule out contribution from metabolism, but we hope that we are able to express that we suspect that it is not a major contributing factor.

Reviewer #3 (Recommendations for the authors):

The authors have done a good job in responding to my previous critiques. They have added new experiments, new analyses, new model simulations, and new text. The changes to figures have made them clearer. While it remains unclear to this reviewer how the 60-hour LABL rhythms arise or what they mean, the discussion of possible explanations is thoughtful and the source/meaning of these infradian rhythms remains outside the scope of this paper. The genetic tools generated in this paper have been well validated and characterized and will be appropriately published in the Tools and Methodology section of eLife. Without a doubt, these tools will be highly useful for Drosophila researchers in circadian biology and this manuscript is likely to be well cited.

We thank the Reviewer for their positive and constructive feedback. We believe their feedback has significantly improved this manuscript.

https://doi.org/10.7554/eLife.77029.sa2

Article and author information

Author details

  1. Peter S Johnstone

    1. Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, Canada
    2. Department of Pharmacology, Dalhousie University, Halifax, Canada
    Contribution
    Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9421-811X
  2. Maite Ogueta

    Institute of Neuro- and Behavioral Biology, Westfälische Wilhelms University, Münster, Germany
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  3. Olga Akay

    1. Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, Canada
    2. Department of Pharmacology, Dalhousie University, Halifax, Canada
    Contribution
    Data curation, Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Inan Top

    You.i Labs Inc, Ottawa, Canada
    Contribution
    Software, Methodology, Writing – review and editing
    Competing interests
    is an employee of You.i Labs Inc
  5. Sheyum Syed

    Department of Physics, University of Miami, Miami, United States
    Contribution
    Formal analysis, Validation
    Competing interests
    No competing interests declared
  6. Ralf Stanewsky

    Institute of Neuro- and Behavioral Biology, Westfälische Wilhelms University, Münster, Germany
    Contribution
    Resources, Data curation, Funding acquisition, Visualization, Methodology, Writing - original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8238-6864
  7. Deniz Top

    1. Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, Canada
    2. Department of Pharmacology, Dalhousie University, Halifax, Canada
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing – review and editing
    For correspondence
    dtop@dal.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1042-8460

Funding

Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-06101)

  • Deniz Top

National Science Foundation (IOS 1656603)

  • Sheyum Syed

Deutsche Forschungsgemeinschaft (INST 211/835-1 FUGG)

  • Ralf Stanewsky

Deutsche Forschungsgemeinschaft (STA421/7-1)

  • Ralf Stanewsky

Canadian Institutes of Health Research (FRN-178220)

  • Deniz Top

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by a grant from NSERC (RGPIN-2019–06101) to DT, from NSF (IOS 1656603) to SS. and from the Deutsche Forschungsgemeinschaft (DFG) (INST 211/835–1 FUGG, and STA421/7-1) to RS.

Senior Editor

  1. K VijayRaghavan, National Centre for Biological Sciences, Tata Institute of Fundamental Research, India

Reviewing Editor

  1. Kristin Tessmar-Raible, University of Vienna, Austria

Publication history

  1. Preprint posted: January 12, 2022 (view preprint)
  2. Received: January 13, 2022
  3. Accepted: September 30, 2022
  4. Accepted Manuscript published: October 3, 2022 (version 1)
  5. Version of Record published: November 14, 2022 (version 2)

Copyright

© 2022, Johnstone et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Peter S Johnstone
  2. Maite Ogueta
  3. Olga Akay
  4. Inan Top
  5. Sheyum Syed
  6. Ralf Stanewsky
  7. Deniz Top
(2022)
Real time, in vivo measurement of neuronal and peripheral clocks in Drosophila melanogaster
eLife 11:e77029.
https://doi.org/10.7554/eLife.77029
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