Author Response
The following is the authors’ response to the original reviews.
eLife assessment
Receptor tyrosine kinases such as ALK play critical roles during appropriate development and behaviour and are nodal in many disease conditions, through molecular mechanisms that weren't completely understood. This manuscript identifies a previously unknown neuropeptide precursor as a downstream transcriptional target of Alk signalling in Clock neurons in the Drosophila brain. The experiments are well designed with attention to detail, the data are solid and the findings will be useful to those interested in events downstream of signalling by receptor tyrosine kinases.
Authors response: We thank the reviewers for this assessment of our Manuscript. We are happy to accept the current eLife assessment of our manuscript. In our revised manuscript we have addressed all of the major reviewer comments, including additional experiments suggested by the reviewers, which have significantly strengthened the revised version.
Reviewer #1 (Public Review):
Sukumar et al build on a body of work from the Palmer lab that seeks to unravel the transcriptional targets of Alk signaling (a receptor tyrosine kinase). Having uncovered its targets in the mesoderm in an earlier study, they seek to determine its targets in the central nervous system. To do this, they use Targeted DamID (TaDa) in the wild-type and Alk dominant negative background and identify about 1700 genes that might be under the control of Alk signalling. Using their earlier data and applying a set of criteria - upregulated in gain-of-Alk, downregulated in loss-of-Alk, and co-expressed with Alk positive cells in single cell datasets - they arrive upon a single gene, Sparkly, which is predicted to be a neuropeptide precursor.
They generate antibodies and mutants for Sparkly and determine that it is responsive to Alk signalling and is expressed in many neuroendocrine cells, as well as in clock neurons. Though the mutants survive, they have reduced lifespans and are hyperactive. In summary, the authors identify a previously unidentified transcriptional target of Alk signalling, which is likely cleaved into a neuropeptide and is involved in regulating circadian activity.
The data support claims made, are generally well presented and the manuscript clearly written. The link between circadian control of Alk signalling in Clock neurons > Spar expression > ultimately controlling circadian activity, however, was not clear.
Authors response: We thank the reviewer for this through reading of our manuscript and for kindly highlighting the important takeaways from the study. The role of Alk signalling in activity, circadian rhythm and sleep has previously been reported by other groups in the following studies – (Bai and Sehgal, 2015; Weiss et al, 2017; Gouzi, Bouraimi et al 2018), which we have discussed in our manuscript. We also have identified a hyperactivity phenotype in our Alk CNS specific loss-of-function allele, AlkRA, which is similar to the Spar loss-of-function mutant phenotype. We hypothesize that one of ways in which Alk signalling regulates fly activity is through regulating Spar gene expression in neuroendocrine cells. This is supported by our data which shows Alk expression in Clock neurons, as well by the new experimental data showing an activity phenotype in flies expressing Spar RNAi driven by the Clk678-Gal4 driver.
Reviewer #2 (Public Review):
This manuscript illustrates the power of "combined" research, incorporating a range of tools, both old and new to answer a question. This thorough approach identifies a novel target in a well-established signalling pathway and characterises a new player in Drosophila CNS development.
Largely, the experiments are carried out with precision, meeting the aims of the project, and setting new targets for future research in the field. It was particularly refreshing to see the use of multi-omics data integration and Targeted DamID (TaDa) findings to triage scRNA-seq data. Some of the TaDa methodology was unorthodox (and should be justifed/caveats mentioned in the main text), however, this does not affect the main finding of the study.
Their discovery of Spar as a neuropeptide precursor downstream of Alk is novel, as well as its ability to regulate activity and circadian clock function in the fly. Spar was just one of the downstream factors identified from this study, therefore, the potential impact goes beyond this one Alk downstream effector.
Authors response: We thank the reviewer for the positive comments highlighting the strengths of our study. TaDa was used as a semi-quantitative readout of the transcriptional activity in a Alk loss-of-function background with an emphasis on relative differences in peaks close to GATC sites, providing an important dataset for integration with bulk and single cell RNAseq. As the reviewer points out there are important considerations when interpreting this data and we have now added sentences in the discussion to inform readers of possible caveats of our TaDa dataset.
Reviewer #3 (Public Review):
Summary:
The receptor tyrosine kinase Anaplastic Lymphoma Kinase (ALK) in humans is nervous system expressed and plays an important role as an oncogene. A number of groups have been signalling ALK signalling in flies to gain mechanistic insight into its various role. In flies, ALK plays a critical role in development, particularly embryonic development and axon targeting. In addition, ALK also was also shown to regulate adult functions including sleep and memory. In this manuscript, Sukumar et al., used a suite of molecular techniques to identify downstream targets of ALK signalling. They first used targeted DamID, a technique that involves a DNA methylase to RNA polymerase II, so that GATC sites in close proximity to PolII binding sites are marked. They performed these experiments in wild-type and ALK loss of function mutants (using an Alk dominant negative ALkDN), to identify Alk responsive loci. Comparing these loci with a larval single-cell RNAseq dataset identified neuroendocrine cells as an important site of Alk action. They further combined these TaDa hits with data from RNA seq in Alk Loss and Gain of Function manipulations to identify a single novel target of Alk signalling - a neuropeptide precursor they named Sparkly (Spar) for its expression pattern. They generated a mutant allele of Spar, raised an antibody against Spar, and characterised its expression pattern and mutant behavioural phenotypes including defects in sleep and circadian function.
Strengths:
The molecular biology experiments using TaDa and RNAseq were elegant and very convincing. The authors identified a novel gene they named Spar. They also generated a mutant allele of Spar (using CrisprCas technology) and raised an antibody against Spar. These experiments are lovely, and the reagents will be useful to the community. The paper is also well written, and the figures are very nicely laid out making the manuscript a pleasure to read.
Weaknesses:
My main concerns were around the genetics and behavioural characterisation which is incomplete. The authors generated a novel allele of Spar - Spar ΔExon1 and examined sleep and circadian phenotypes of this allele. However, they have only one mutant allele of Spar, and it doesn't appear as if this mutant was outcrossed, making it very difficult to rule out off-target effects. To make this data convincing, it would be better if the authors had a second allele, perhaps they could try RNAi?
Further, the sleep and circadian characterisation could be substantially improved. In Fig 8 E-F it appears as if sleep was averaged over 30 days! This is a little bizarre. They then bin the data as day 1 - 12 and 12-30. This is not terribly helpful either. Sleep in flies, as in humans, undergoes ontogenetic changes - sleep is high in young flies, stabilises between day 3-12, and shows defects by around 3 weeks of age (cf Shaw et al., 2000 PMID 10710313). The standard in the sleep field is to average over 3 days or show one representative day. The authors should reanalyse their data as per this standard, and perhaps show data from 310 day old flies, and if they like from 20-30 day old flies. Further, sleep data is usually analysed and presented from lights on to lights on. This allows one to quantify important metrics of sleep consolidation including bout lengths in day and night, and sleep latency. These metrics are of great interest to the community and should be included.
The authors also claim there are defects in circadian anticipatory activity. However, these data, as presented are not solid to me. The standard in the field is to perform eduction analyses and quantify anticipatory activity e.g. using the method of Harrisingh et al. (PMID: 18003827). Further, circadian period could also be evaluated. There are several free software packages to perform these analyses so it should not be hard to do.
Authors response: We thank the reviewer for the thorough reading of our manuscript and for generously praising the positives as well as pointing out the weakness of our study. We have now addressed the highlighted weaknesses in behavioural experiments. In particular, we have reanalysed our data according to the reviewer’s suggestions. In addition, we provide experimental data, driving Spar RNAi in Clock neurons, that support our Spar mutant analysis.
Point-by-point response to the reviewers’ concerns:
Point 1. “My main concerns were around the genetics and behavioural characterisation which is incomplete. The authors generated a novel allele of Spar - Spar ΔExon1 and examined sleep and circadian phenotypes of this allele. However, they have only one mutant allele of Spar, and it doesn't appear as if this mutant was outcrossed, making it very difficult to rule out off-target effects. To make this data convincing, it would be better if the authors had a second allele, perhaps they could try RNAi?”
Authors response: As per the reviewer's suggestion, we conducted a targeted knockdown of Sparkly specifically in clock neurons (Clk-Gal4 > Spar-RNAi) and assessed the circadian phenotypes. Flies were monitored for 5 days in LD followed by a shift to DD, similar to our previous LD-DD experiments. The results revealed a significant disruption in both activity and sleep during the DD transition period upon knockdown of Spar in circadian clock neurons. These findings strongly align with the expression pattern of Spar in clock neurons (Figure 7i-l’’). We have now included a new main figure (Figure 9) together with several supplementary figure (Figure 9 – figure supplements 1 and 2) and discussed these experiments on pages 17-18 of the results section of the revised manuscript.
Point 2. “Further, the sleep and circadian characterisation could be substantially improved. In Fig 8 E-F it appears as if sleep was averaged over 30 days! This is a little bizarre. They then bin the data as day 1 - 12 and 12-30. This is not terribly helpful either. Sleep in flies, as in humans, undergoes ontogenetic changes - sleep is high in young flies, stabilises between day 3-12, and shows defects by around 3 weeks of age (cf Shaw et al., 2000 PMID 10710313). The standard in the sleep field is to average over 3 days or show one representative day. The authors should reanalyse their data as per this standard, and perhaps show data from 3–10-day old flies, and if they like from 20–30-day old flies.”
Authors response: We have reanalysed these data according to the reviewer's suggestions and revised the sleep data presented. Specifically, we have focused on two 3-day periods, days 5-7 as well as days 20-22. By averaging the sleep mean during these time points, we observed a significant decrease in average sleep duration in the SparΔExon1 and Alk ΔRA mutant flies at a younger age (Figure 8h-h’, Figure 8 – figure supplement 2). However, no significant effect was observed in older flies (Figure 8h-h’, Figure 8 – figure supplement 2). We have incorporated this new data into Figure 8 and provided a detailed description in the results section
(page 16) of the revised manuscript.
Point 3. “Further, sleep data is usually analysed and presented from lights on to lights on. This allows one to quantify important metrics of sleep consolidation including bout lengths in day and night, and sleep latency. These metrics are of great interest to the community and should be included.”
Authors response: We have now reanalysed these data as per the reviewer's suggestion. From the raw data collected over a span of 3 days, we specifically selected the lights on-lights on data and examined the average sleep duration. Notably, we observed a significant downregulation of average sleep in SparΔExon1 and AlkΔRA flies, but only at a younger age (Figure 8h-h’, Figure 8 – figure supplement 2). Furthermore, we assessed the number of sleep bouts using this data and found a significant increase in the number of bouts in younger SparΔExon1 and AlkΔRA flies, with no changes observed at an older age (Figure 8 – figure supplement 2). Additionally, we evaluated the number of bouts in flies that were initially monitored in LD and then shifted to DD, observing a significant decrease in the number of sleep bouts in SparΔExon1 flies following the transition to DD (Figure 9d). This new data is described in detail in the results section (pages 16-18) of the revised manuscript.
Point 4. “The authors also claim there are defects in circadian anticipatory activity. However, these data, as presented are not solid to me. The standard in the field is to perform eduction analyses and quantify anticipatory activity e.g. using the method of Harrisingh et al. (PMID: 18003827).”
Authors response: We appreciate the valuable suggestion provided by the reviewer. In accordance with the referenced paper by Harrisingh et al. (2007), we calculated the "anticipation score" defined as the percentage of activity in the 6hour period preceding the lights-on or lights-off transition that occurs in the 3-hour window just before the transition. To analyse the mean activity of the flies, we selected the data corresponding to the 6 hours before lights-on and the 6 hours before lights-off, averaged over a 14-day period under normal LD conditions. Interestingly, we observed a significant increase in the mean activity of SparΔExon1 flies during both morning anticipation (a.m. anticipation) and evening anticipation (p.m. anticipation) (Figures 8f). Furthermore, we analysed this parameter for flies entrained in DD and found that SparΔExon1 flies exhibited lower mean activity during both morning and evening anticipation (Figures 8g). We have incorporated this new data into Figure 8 and provided a detailed description in the results section
(pages 16-18) of the revised manuscript.
Point 5. Further, circadian period could also be evaluated. There are several free software packages to perform these analyses so it should not be hard to do.
Authors response: We have now evaluated the circadian period as suggested by the reviewer; generating a chi-square periodogram for each fly to calculate the free-running period for the flies that were under normal LD conditions additionally to the ones that were entrained in DD. We calculated the percentage of flies that had a shorter or longer period than 1440 min (24 h) and observed that w1118 and SparΔExon1 flies have a longer circadian period (Figure 8 – figure supplement 4) but following the shift to DD, they tend to have a shorter circadian period (Figure 9 – figure supplement 3). This new data is described in the results (pages 16-18).
Recommendations for the authors:
There are two major concerns that we recommend the authors address:
- The behaviour: There are a number of unconventional representations of the behavioural data in this manuscript. We recommend that the authors revisit their data representation to adhere to conventions in the field - specific suggestions are in the reviews. We also suggest an additional experiment - an RNAi/different allele/rescue experiment to ensure that the phenotypes the authors observe are not due to off-target effects of the mutant they have generated.
Authors response: In the revised manuscript, we have reanalysed the behavioural data according to the reviewers’ recommendations (included in Figures 8 and 9 of the revised version). In addition, we have performed a targeted Spar RNAi experiment in clock neurons (included in Figure 9 of the revised version), identifying a hyperactive behavioural phenotype similar to that of Spar mutants. The inclusion of these new analyses and data strengthens the manuscript and support the conclusion that Spar plays a role in regulation of behaviour.
- TaDa analyses: We were concerned that the authors might be picking up false positives with the way they have analysed their data. While this may not matter for this study, it will be useful to reason out their approach and keep this in mind for any other targets they choose from these data for further studies.
Authors response: In line with the reviewers concerns we have now highlighted the potential caveats and drawbacks of our TaDa dataset in the discussion section of the revised manuscript (detailed in response to Reviewer #2 below).
Reviewer #1 (Recommendations For The Authors):
Though generally well written, I felt that some sections could be written in more detail. For example, the text around Figure 5 was not very informative. Many of the other approaches to the analyses and details of datasets used were glossed over. Since the manuscript uses a lot of previously published data, it would be nice to give more details about them in the context of the results.
Authors response: We thank the reviewer for this recommendation. We have now added additional information about peptidomics analysis in the results and in the legend of Figure 5. We have also included a table in the Methods that summarised the datasets used in this study, including the Dataset name, brief description and reference.
In the panels where co-localisations have been represented, it would be nice to include enlarged insets depicting the co-labelling. It is not always obvious in the way the figures have currently been represented. For example, in Fig 2G, Alk stain appears to be everywhere, but the authors make the point that it is enriched in neuroendocrine cells (as labelled by dimmed), but the co-localisation isn't evident. Similar issues come up with the sparkly colocalisations.
Authors response: As suggested by the reviewer, we have now added additional panels to complement the stainings in Figure 2G. These new data are included as Figure 2 – figure supplement 1 (Alk/Dimm-Gal4>UAS-GFPcaax staining) and as Figure 4 – figure supplement 1 (Alk/Spar staining), which indicate colocalization in the central brain and ventral nerve cord prosecretory cells with enlarged panels.
Supplementary figures S3C and 3F appear garbled to me? Maybe it didn't upload properly?
Authors response: Unfortunately, this issue is not apparent to us. However, we have now re-uploaded these Figures.
Sparkly's responsiveness to Alk signalling: Visually, there does not seem to be an increase or decrease in spar levels in the images in Fig 4F-H. How was the quantification done? I would suggest a more detailed interpretation of their results related to spar's responsiveness to Alk signalling - at the mRNA vs protein levels and the GOF vs LOF conditions.
Authors response: We thank the reviewer for this constructive recommendation. In the revised manuscript, we have now repeated this experiment with increased numbers of larval CNS followed by blinded image analysis. These results also show an increased fluorescence intensity as measured by corrected total cell fluorescence (CTCF), confirming our previous observation of increased Spar protein expression in in Alk gain-of-function conditions compared to controls. In this analysis, changed in Spar levels in Alk loss-of-function remained non-significant compared to control, in agreement with our previous data. As suggested by the reviewer, we have now included several additional sentences discussing the possible reasons for these observations. This following text is now included on Page 11 of the results section:
“While our bulk RNA-seq and TaDa datasets show a reduction in Spar transcript levels in Alk loss-of-function conditions, this reduction is not reflected at the protein level. This observation may reflect additional uncharacterised pathways that regulate Spar mRNA levels as well as translation and protein stability. Taken together, these observations confirm that Spar expression is responsive to Alk signaling in CNS, although Alk is not critically required to maintain Spar protein levels.”
We have also added an additional Image analysis method section explaining the methodology of the CTCF fluorescent intensity quantification on Page 28.
Reviewer #2 (Recommendations For The Authors):
It was surprising to see that the authors did not use Dam-only controls. This is to control for background methylation by Dam (i.e. accessible chromatin). This does not invalidate the main results of the manuscript, however, there could be false positives in the dataset for genes that are seen to be up-regulated in the mutant condition (e.g. if accessibility is increased in the mutant but not transcription, then it would look like increased Pol II binding, when it isn't). As the study was focusing on genes down-regulated in the mutant, this is less of an issue, as it is very unlikely to see an increase in transcription with a decrease in accessibility (that could provide a false positive). The authors should explain their rationale for not using Dam-only controls, and the associated caveats, in the manuscript.
Authors response: We agree with the reviewer’s comment on possibility of identifying false positive candidates from our TaDa dataset. Especially, if one is seeking to find a gene with increased Pol II occupancy in a Alk dominant negative condition. However, our analysis only focuses on genes which are responsive to Alk-manipulation, namely, genes which are downregulated in the Alk dominant negative condition. One of the rationales for not using a Dam-only control was that in our previous Mendoza-Garcia et al, 2021 study, we employed a similar method and were able to successfully identify already known and novel targets of Alk signalling in embryonic mesoderm comparing the Dam-Pol II versus Dam-Pol II; Alk Dominant negative conditions. In the current version of the manuscript, we have expanded our discussion of these caveats as follows (Discussion, Page 19-20):
“A potential drawback of our TaDa dataset is the identification of false positives, due to non-specific methylation of GATC sites at accessible regions in the genome by Dam protein. Hence, our experimental approach likely more reliably identifies candidates which are downregulated upon Alk inhibition. In our analysis, we have limited this drawback by focusing on genes downregulated upon Alk inhibition and integrating our analysis with additional datasets, followed by experimental validation. This approach is supported by the identification of numerous previously iden-
tied Alk targets in our TaDa candidate list.”
Related to this, could the authors make it clear/justify why they chose to use peakbased analysis of the Dam-Pol II data rather than looking at signals across whole transcripts? For example, this could result in false positives if a gene switches from having no Pol II to having paused Pol II.
Authors response: In our opinion, a peak based analysis is dependable in this context. We chose to prioritize peaks close (+/- 1kb) to transcription start sites (TSS) to increase the chances of finding true Pol II occupancy peaks. Also, during bioinformatics analysis using Damid-seq pipeline (Maksimov et al, 2016) fragments not aligning to GATC borders are excluded. Therefore, a whole transcript Pol II occupancy peak analysis may not be always feasible. We agree with the reviewer that a paused Pol II will result in false positives, however, it will only result in an increase of a specific peak and in our case, we are seeking to identify peaks with lower pol II occupancy as a result of Alk knockdown. Furthermore, we depend on additional integration with additional relevant datasets to minimise false positive candidates for detailed analysis. In the current version of the manuscript these caveats have been mentioned and discussed (see point above).
Do the authors have any theories about the mode of action of Spar? Or ideas about how this might be followed up? If so, that could be included in the Discussion.
Authors response: Other than identifying modified Spar derived peptides, which suggest a target receptor, possibly a GPCR, were have no other data currently that allows us to speculate more on the mode of action of Spar. We are currently working hard to try to identify a receptor, but this is a challenging and ongoing process. In the discussion we speculate regarding the identity of the Spar receptor, as well as its location, which is likely in the CNS, and body muscle, however, these are open questions that we can hopefully answer in a future study.
Reviewer #3 (Recommendations For The Authors):
Spar protein expression was unchanged in Alk loss of function. This is a curious result as the authors used RNA seq data from Alk loss of function to identify Spar.
This could be commented on in the discussion.
Authors response: We thank the reviewer for this comment, and they are correct in noticing this. We have also thought about this, and reviewer #1 also commented. To confirm this result, we repeated this experiment with increased numbers of larval CNS followed by blinded image analysis for the revised version. These results also show an increased fluorescence intensity as measured by corrected total cell fluorescence (CTCF), confirming our previous observation of increased Spar protein expression in in Alk gain-of-function conditions compared to controls. In this analysis, changed in Spar levels in Alk loss-of-function remained non-significant compared to control, in agreement with our previous data. As suggested by reviewer #1, we have now included several additional sentences discussing the possible reasons for these observations. This following text is now included on Page 11 of the results section:
“While our bulk RNA-seq and TaDa datasets show a reduction in Spar transcript levels in Alk loss-of-function conditions, this reduction is not reflected at the protein level. This observation may reflect additional uncharacterised pathways that regulate Spar mRNA levels as well as translation and protein stability. Taken together, these observations confirm that Spar expression is responsive to Alk signaling in CNS, although Alk is not critically required to maintain Spar protein levels.”
Pg 19: Spar is expressed in the Mushroom Bodies (MBs). Do they mean in Kenyon Cells (KCs)? I don't see this expression in the figures. Maybe this could be highlighted in the figure. It would definitely be of interest if this were true.
Authors response: We agree with the reviewer that this would be interesting. We have not performed detailed staining of the mushroom bodies at this point, however, Spar mRNA expression in a transcriptomics analysis performed by Crocker et al, 2016, identifies Spar in all cell types, including Kenyon cells. We have now included this and cited this reference in the discussion.
Spar is also expressed in multiple potential sleep regulatory sites including clock neurons, the PI, AstA cells and so on. Some of these might be arousal-promoting and some sleep-promoting. Taking out Spar in both sleep and arousal-promoting subsets might have complex effects. The authors might want to knock down Alk in different subsets of neurons to make more targeted manipulations.
Authors response: We thank the reviewer for this suggestion regarding interesting experiments to further investigate Spar function. We are planning to follow up and study the role of Alk signalling in different neuronal subsets, with a specific interest in neuroendocrine/prosecretory cells.
