Circadian control of a sex-specific behaviour in Drosophila

Joint Public Review:

Riva et al uncovered the neural substrate underlying the oviposition rhythm in Drosophila melanogaster using a novel device that automates egg collection from individual mated females over the course of multiple days. By systematically knocking down the clock gene period in specific clock neurons the authors show that three cryptochrome (cry) positive dorso-lateral neurons (LNds) present in each hemisphere of the fly brain are critical to generating a female, sex-specific rhythm in oviposition. Interestingly, these neurons are not essential for freerunning locomotor activity. By contrast, the LNvs (lateral ventral neurons), which are essential for freerunning locomotor activity rhythmicity, were not involved in controlling the circadian rhythmicity of oviposition. Thus, this work has identified the first truly sex-specific circadian circuit in Drosophila. Using available Drosophila hemibrain connectome data they identify bidirectional connections between cry-expressing LNd and oviposition-related neurons.

Strengths:

This paper established a new semi-automatic device to register egg-laying activity, in Drosophila and found a specific role for a subset of clock neurons in the control of a female-specific circadian behavior. They also lay the groundwork for understanding how these neurons are connected to the neurons that control egg laying.

Weaknesses:

(1) Controls for the genetic background are incomplete, leaving open the possibility that the observed oviposition timing defects may be due to targeted knockdown of the period (per) gene but from the GAL4, Gal80, and UAS transgenes themselves. To resolve this issue the authors should determine the egg-laying rhythms of the relevant controls (GAL4/+, UAS-RNAi/+, etc); this only needs to be done for those genotypes that produced an arrhythmic egg-laying rhythm.

(2) Reliance on a single genetic tool to generate targeted disruption of clock function leaves the study vulnerable to associated false positive and false negative effects: a) The per RNAi transgene used may only cause partial knockdown of gene function, as suggested by the persistent rhythmicity observed when per RNAi was targeted to all clock neurons. This could indicate that the results in Fig 2C-H underestimate the phenotypes of targeted disruption of clock function. b) Use of a single per RNAi transgene makes it difficult to rule out that off-target effects contributed significantly to the observed phenotypes. We suggest that the authors repeat the critical experiments using a separate UAS-RNAi line (for period or for a different clock gene), or, better yet, use the dominant negative UAS-cycle transgene produced by the Hardin lab (https://doi.org/10.1038/22566).

(3) The egg-laying profiles obtained show clear damping/decaying trends which necessitates careful trend removal from the data to make any sense of the rhythm. Further, the detrending approach used by the authors is not tested for artefacts introduced by the 24h moving average used.

(4) According to the authors the oviposition device cannot sample at a resolution finer than 4 hours, which will compel any experimenter to record egg laying for longer durations to have a suitably long time series which could be useful for circadian analyses.

(5) Despite reducing the interference caused by manually measuring egg-laying, the rhythm does not improve the signal quality such that enough individual rhythmic flies could be included in the analysis methods used. The authors devise a workaround by combining both strongly and weakly rhythmic (LSpower > 0.2 but less than LSpower at p < 0.05) data series into an averaged time series, which is then tested for the presence of a 16-32h "circadian" rhythm. This approach loses valuable information about the phase and period present in the individual mated females, and instead assumes that all flies have a similar period and phase in their "signal" component while the distribution of the "noise" component varies amongst them. This assumption has not yet been tested rigorously and the evidence suggests a lot more variability in the inter-fly period for the egg-laying rhythm.

(6) This variability could also depend on the genotype being tested, as the authors themselves observe between their Canton-S and YW wild-type controls for which their egg-laying profiles show clearly different dynamics. Interestingly, the averaged records for these genotypes are not distinguishable but are reflected in the different proportions of rhythmic flies observed. Unfortunately, the authors also do not provide further data on these averaged profiles, as they did for the wild-type controls in Figure 1, when they discuss their clock circuit manipulations using perRNAi. These profiles could have been included in Supplementary figures, where they would have helped the reader decide for themselves what might have been the reason for the loss of power in the LS periodogram for some of these experimental lines.

(7) By selecting 'the best egg layers' for inclusion in the oviposition analyses an inadvertent bias may be introduced and the results of the assays may not be representative of the whole population.

(8) An approach that measures rhythmicity for groups of individual records rather than separate individual records is vulnerable to outliers in the data, such as the inclusion of a single anomalous individual record. Additionally, the number of individual records that are included in a group may become a somewhat arbitrary determinant for the observed level of rhythmicity. Therefore, the experimental data used to map the clock neurons responsible for oviposition rhythms would be more convincing if presented alongside individual fly statistics, in the same format as used for Figure 1.

(9) The features in the experimental periodogram data in Figures 3B and D are consistent with weakened complex rhythmicity rather than arrhythmicity. The inclusion of more individual records in the groups might have provided the added statistical power to demonstrate this. Graphs similar to those in 1G and 1I, might have better illustrated qualitative and quantitative aspects of the oviposition rhythms upon per knockdown via MB122B and Mai179; Pdf-Gal80.

Wider context:

The study of the neural basis of oviposition rhythms in Drosophila melanogaster can serve as a model for the analogous mechanisms in other animals. In particular, research in this area can have wider implications for the management of insects with societal impact such as pests, disease vectors, and pollinators. One key aspect of D. melanogaster oviposition that is not addressed here is its strong social modulation (see Bailly et al.. Curr Biol 33:2865-2877.e4. doi:10.1016/j.cub.2023.05.074). It is plausible that most natural oviposition events do not involve isolated individuals, but rather groups of flies. As oviposition is encouraged by aggregation pheromones (e.g., Dumenil et al., J Chem Ecol 2016 https://link.springer.com/article/10.1007/s10886-016-0681-3) its propensity changes upon the pre-conditioning of the oviposition substrates, which is a complication in assays of oviposition rhythms that periodically move the flies to fresh substrate.

Author response:

(1) Controls for the genetic background are incomplete, leaving open the possibility that the observed oviposition timing defects may be due to targeted knockdown of the period (per) gene but from the GAL4, Gal80, and UAS transgenes themselves. To resolve this issue the authors should determine the egg-laying rhythms of the relevant controls (GAL4/+, UAS-RNAi/+, etc); this only needs to be done for those genotypes that produced an arrhythmic egg-laying rhythm.

We agree with this objection, and in the corrected version we plan to provide the assessment of the egg laying rhythms for the missing GAL4 controls as recommended only for Figure 3.

(2) Reliance on a single genetic tool to generate targeted disruption of clock function leaves the study vulnerable to associated false positive and false negative effects: a) The per RNAi transgene used may only cause partial knockdown of gene function, as suggested by the persistent rhythmicity observed when per RNAi was targeted to all clock neurons. This could indicate that the results in Fig 2C-H underestimate the phenotypes of targeted disruption of clock function. b) Use of a single per RNAi transgene makes it difficult to rule out that off-target effects contributed significantly to the observed phenotypes. We suggest that the authors repeat the critical experiments using a separate UAS-RNAi line (for period or for a different clock gene), or, better yet, use the dominant negative UAS-cycle transgene produced by the Hardin lab (https://doi.org/10.1038/22566).

We have recently acquired mutant flies with a dominant negative-cycle transgene (UAS-cycDN, Tanoue et al. 2004), and we plan to repeat our experiments with these mutants, in order to confirm our results.

(3) The egg-laying profiles obtained show clear damping/decaying trends which necessitates careful trend removal from the data to make any sense of the rhythm. Further, the detrending approach used by the authors is not tested for artefacts introduced by the 24h moving average used.

In the revised version we will show that the detrending approach used does not introduce any artefacts. The analysis of numerical simulations with an aperiodic stochastic signal superposed to a decaying signal shows that the detrending method used does not result in a spurious periodic signal. Furthermore, we can show that when the underlying signal is rhythmic, the correct period is obtained even when the moving average is a few hours larger or smaller than 24 h.

(4) According to the authors the oviposition device cannot sample at a resolution finer than 4 hours, which will compel any experimenter to record egg laying for longer durations to have a suitably long time series which could be useful for circadian analyses.

We apologize for not being clear enough. The device can in principle sample at any desired resolution. Notice, however, that the variable we are analyzing (number of eggs laid by a single female) has only a few possible values, which is one of the features that render the assessment of rhythmicity a particularly difficult task. If egg laying is sampled more often (say, at 2 h intervals) more time points will be available, but the values available for each time point will be much less. We will show an example where we compare both rates (2h and 4h). Even though the 2h sampling reveals the rhythmicity of the time series, the significance of the peaks obtained is less than when sampling at 4h intervals. We have found that a 4h sampling seems to provide the best compromise between frequency of the sampling and discreteness of the variable.

On the other hand, it is important to stress that sampling frequency and longer durations are not very correlated (see e.g. Cohen et al. Journal of Theoretical Biology 314, pp 182 [2012]). It has been shown that the best way to make accurate predictions of the period of a rhythmic signal is to have a series spanning many cycles, irrespective of the sampling frequency. In other words, it is not true that with a 2h sampling it would be possible to analyze shorter series than with 4h sampling. Unfortunately, egg laying records are usually less than 5 cycles long, which is one of the reasons for the difficulties in the assessment of their rhythmicity.

(5) Despite reducing the interference caused by manually measuring egg-laying, the rhythm does not improve the signal quality such that enough individual rhythmic flies could be included in the analysis methods used. The authors devise a workaround by combining both strongly and weakly rhythmic (LSpower > 0.2 but less than LSpower at p < 0.05) data series into an averaged time series, which is then tested for the presence of a 16-32h "circadian" rhythm. This approach loses valuable information about the phase and period present in the individual mated females, and instead assumes that all flies have a similar period and phase in their "signal" component while the distribution of the "noise" component varies amongst them. This assumption has not yet been tested rigorously and the evidence suggests a lot more variability in the inter-fly period for the egg-laying rhythm.

The assumption is difficult to test rigorously, since for individual flies the records seem to be so noisy that no information can be extracted. As shown in the paper, it is even very difficult to assess the presence of rhythmicity at the individual level. We consider that the appearance of a rhythm after averaging several records shows the presence of this rhythm at the individual level. But it could be argued that the presence of rhythmicity in the average record could be due to only a few (or even a single) rhythmic individuals. In order to show that this is probably not the case, in the revised version we will show that, when the individuals that are rhythmic are left out, the average of the remaining flies still shows a rhythm (albeit a weaker one, as was to be expected).

Regarding our assumption that all flies have the “same” period, the results on Fig. 1 F cannot really rule out this possibility, because with so few cycles, the determination of the period is not very accurate (see e.g. Cohen et al. Journal of Theoretical Biology 314, pp 182 [2012]). In our case, the error for the period is related to the width of the corresponding peak in the periodogram, which is typically 4 hs. In any case, in the revised version we will try to show, by using numerical simulations, that when the individual periods are not the same, but are distributed approximately as in Fig 1F, the average series is still rhythmic with the correct period.

(6) This variability could also depend on the genotype being tested, as the authors themselves observe between their Canton-S and YW wild-type controls for which their egg-laying profiles show clearly different dynamics. Interestingly, the averaged records for these genotypes are not distinguishable but are reflected in the different proportions of rhythmic flies observed. Unfortunately, the authors also do not provide further data on these averaged profiles, as they did for the wild-type controls in Figure 1, when they discuss their clock circuit manipulations using perRNAi. These profiles could have been included in Supplementary figures, where they would have helped the reader decide for themselves what might have been the reason for the loss of power in the LS periodogram for some of these experimental lines.

Even though we think that the individual records are in general too noisy to be really informative, we will provide all the individual egg profiles in the Supplementary Material of the revised version, in order to let the reader, check this for herself/himself.

(7) By selecting 'the best egg layers' for inclusion in the oviposition analyses an inadvertent bias may be introduced and the results of the assays may not be representative of the whole population.

We agree that this may introduce some bias in the results. But in our opinion this bias is very difficult to avoid, since for females that lay very few eggs, rhythmicity can even be difficult to define (some females can spend a whole day without laying a single egg). On the other hand, even when the results may not be representative of the whole population, they would be representative of the flies that lay most of the eggs in a population, which seems to be very relevant in ecological terms.

(8) An approach that measures rhythmicity for groups of individual records rather than separate individual records is vulnerable to outliers in the data, such as the inclusion of a single anomalous individual record. Additionally, the number of individual records that are included in a group may become a somewhat arbitrary determinant for the observed level of rhythmicity. Therefore, the experimental data used to map the clock neurons responsible for oviposition rhythms would be more convincing if presented alongside individual fly statistics, in the same format as used for Figure 1.

The question of possible rhythmic outliers has been addressed above, in question 5, where we discuss why we think that such outliers are not “determinant for the observed level of rhythmicity”. As also mentioned above, even though we think that they are too noisy to be informative, we plan to include all individual profiles in the Supplementary Material.

(9) The features in the experimental periodogram data in Figures 3B and D are consistent with weakened complex rhythmicity rather than arrhythmicity. The inclusion of more individual records in the groups might have provided the added statistical power to demonstrate this. Graphs similar to those in 1G and 1I, might have better illustrated qualitative and quantitative aspects of the oviposition rhythms upon per knockdown via MB122B and Mai179; Pdf-Gal80.

We assume that the features mentioned refer to the appearance in the periodograms of two small peaks under the significance lines. We are aware that in the studies of the rhythmicity of locomotor activity such features are usually interpreted as “complex rhythms”, i.e. as evidence of the existence of two different mechanisms producing two different rhythms in the same individual. In our case, however, at least two other possibilities should be taken into account. Since the periodograms we show assess the rhythmicity of the average time series of several individuals, the two small peaks could correspond to the periods of two different subpopulations. Another possibility could be that such peaks are simply an artifact of the method in the analysis of time series that consist of very few cycles (as explained above) and also few points per cycle. A cursory examination of the individual profiles, that will be provided in the new version, do not seem to support any of the first two possibilities mentioned. On the other hand, we will show evidence that the analysis of series that are perfectly random sometimes result in periodograms with some small peaks.