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Peer Review

Research article Title: Circadian control of dopaminergic signaling to the mushroom body regulates sleep through rhythmic Pka-C1 transcription in Drosophila

DOI: https://doi.org/10.1101/2025.08.29.673050

Overall statement

This manuscript by Lago Solis and Nagoshi investigates a molecular and circuit mechanism by which the circadian clock modulates sleep using Drosophila melanogaster as a model system. The interplay between circadian and sleep processes has long been a dynamic area of investigation given its broad implications for physiology and behavior. In this study, the authors build upon prior work showing that the mRNA of the PKA catalytic subunit (Pka-C1) exhibits circadian rhythms in the mushroom body (MB) (PMCID: PMC8486785), a key coordination center of the fly brain. Here they add another layer by pinpointing the specific MB neuronal population that displays rhythmic Pka-C1 transcription, using a powerful in vivo reporter approach to monitor gene transcription in specific cell types. They identify a transcription factor regulating Pka-C1 rhythmic expression and show that manipulating this pathway affects sleep. Finally, they explore upstream circuits and propose a two-step pathway linking clock neurons and the MB through calcium and dopaminergic signaling.

Strengths

The manuscript is engagingly written and logically organized. The experiments are clearly described, and the figures are easy to follow. The study is conceptually well grounded with strong evidence for the cell-type specificity of Pka-C1 rhythms and their transcriptional (vs post-transcriptional) regulation. The identification of the transcription factor involved adds mechanistic depth, and the findings reinforce the growing interest in kinase signaling as a key modulator of sleep.

Areas for improvement

The first half of the manuscript presents a well-supported molecular mechanism. The later sections that explore circuit-level regulation and feedback within γ-KCs raise exciting hypotheses but would benefit from additional mechanistic evidence or, alternatively, more cautious phrasing. Integrating some of the ideas below would help reinforce the proposed model and highlight its contribution to our understanding of clock–sleep interactions.

Major Points

1.  Line 176 — The authors report that hyperactivation of γ-KCs disrupts Pka-C1 transcriptional rhythms and interpret this as evidence that rhythmic neuronal activity drives rhythmic transcription. The current experiment demonstrates that continuous activation can override transcriptional rhythms, but it does not directly show that rhythmic activity per se drives transcriptional rhythms. It could be informative to test the converse manipulation—temporarily inhibiting γ-KC activity during the expected circadian peak (for example, using GTACR)—to evaluate whether rhythmic inhibition modulates Pka-C1 expression. Demonstrating that rhythmic, rather than tonic, changes in activity affect transcription would strengthen the conclusion that neuronal activity rhythms directly influence transcriptional rhythms.

2.  Line 180 — “Pka-C1 rhythms then feedback to reinforce activity rhythms”. This statement references findings from Machado et al. The sentence extrapolates beyond the presented data and could be interpreted as a new finding. Consider moving this interpretation to the Discussion, framing it as a hypothesis supported by prior work. This adjustment would help maintain the Results section as a presentation of new evidence and clarify where interpretation begins.

3.  Line 272 — “This trans effect is likely due to altered PKA-C1 rhythms, which disrupt neuronal activity rhythms (Machado Almeida et al., 2021). The text references prior literature to explain the trans effects of indel mutations on PkA-C1 expression. This link is intriguing but currently inferential rather than demonstrated by the new data. If feasible, consider using optogenetic or thermogenetic inhibition of γ-KCs during their activity peak and assessing whether this alters Pka-C1 rhythmic expression. Alternatively, clarifying in the text that this connection remains a working model would also be valuable. Including such data or clarifying the model’s scope would help readers distinguish between observed and inferred relationships.

4.  Lines 309–326 — Interpretation of calcium oscillations and circuit logic. The authors describe complementary calcium oscillations between clock neurons and PAM neurons, proposing a two-step circuit leading to rhythmic activity in γ-KCs. The data are suggestive but do not directly demonstrate causality between these neuronal populations. Nevertheless, the abstract, introduction, and discussion currently frame these relationships as established. To bring the text in line with the evidence, consider rephrasing these statements as hypotheses (e.g., “Our data suggest that…”). Alternatively, causal tests could include optogenetic activation of LNds and recording PAM-γ5 or MB activity with GCaMP or Calexa, or manipulating PAMs in a per0 background. Clarifying or further substantiating these links would make the proposed circuit model more convincing and internally consistent

5.  Line 343 — Conclusion about the role of PAM-γ5 neurons as the intermediates of the circuit between clock neurons and MB neuron. Dopamine receptor knockdown experiments indicate dopaminergic modulation of Pka-C1 transcription in γ-KCs, but the intermediary role of PAM-γ5 neurons is inferred. While the results fit this idea, direct evidence connecting PAM-γ5 signaling to Pka-C1 transcription is missing. Experiments where PAM-γ5 are activated with dopamine receptor knockdown, as well as experiments with PAM-γ5 are inhibited could help support the conclusion. In addition, it would be very interesting to integrate the role of Onecut in these signaling pathways. Exploring whether Onecut oscillations are altered with Dop1R1 knockdown—using the thermogenetic or optogenetic setups already in the study—could clarify this pathway. Such data would strengthen the proposed mechanistic bridge between dopaminergic signaling and Pka-C1 transcriptional regulation.

Minor Points

Line 41: All cited references except Cavanaugh et al. are reviews. It may be more cohesive to keep only review references here, while citing Cavanaugh et al. later (as already done).

Line 65: Consider adding PMID: 35446620, which provides detailed activity data for different clock neurons and would complement the discussion of circadian cell types.

Line 78: The following statement lacks proper reference: “This compartmentalization enables sensory information to influence behavior in a context-dependent manner”. The previous paragraph cites a few studies that investigate the role of the MB controlling behavior (Joiner et al.,2006; Modi et al., 2020) and describes in detail the structure of the mushroom body. However, there are no refences about how specific compartments modulate different behaviors. This is key for the context of this article and citations similar to PMDI:33268891 that attribute behaviors to defined MB populations would be ideal.

Line 160: Figure 1H is cited before Figure 1G, and Figure 1G is not referenced in the text; adjusting this will aid clarity.

Line 190: By reading the texts it seems that all the TF (Onecut, Optix, Dorsal, and Twist) have been tested however twi was not. It is understandable that was not tested because not available but was not totally obvious. The sentence could be rephrased to something on the lines of “we tested available RNAis covering three of the four TF”.

Line 208: The sleep increase appears modest, particularly in females. If possible, reporting whether the luminescence data were stronger in males or using multibeam activity monitors might provide more sensitivity.

Line 223: In Figure 4A, indicating that the data were collected in DD would make the figure more accessible to readers less familiar with CT/ ZT terminology.

Line 258: Since the authors infer reduced Onecut binding in promoter mutants, a pull-down or qPCR validation could be considered, or the inference could be acknowledged in the Discussion.

Line 287: Adding a diagram of clock neurons to Figure 6A would make the Trans-tango data easier to interpret.

Figures

Figure 1: The sample sizes vary widely (n = 40–115). A brief note explaining this or showing representative subsets could clarify the dataset composition.

Figures 3D/F and 5C/E: Increasing line thickness or color contrast would improve visibility.

Figure 4: Indicating the normalization method (e.g., intensity/GFP area) on the axis or in Methods would clarify data interpretation.

Figure 5A: A schematic summarizing genotypes would help connect the panels.

Figure 6A: Adding a schematic of clock cells, as mentioned above, would facilitate understanding of the Trans-tango data.

General comments

For rhythmicity index (RI) data, clarifying whether only rhythmic flies were included would be helpful.

For luminescence data, consider a consistent approach: either present rhythmic and arrhythmic flies together in the main figures (with percentages shown) or place one set in supplementary figures for clarity.

Ensure that Greek letters (γ, β, δ) render correctly in all figures.

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they did not use generative AI to come up with new ideas for their review.