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  1. Author Response

    Reviewer #2 (Public Review):

    Weaknesses:

    1. It is surprising that certain enzymes with established depalmitoylation activity were excluded from BrainPalmSeq data-base (e.g. ABHD4, ABHD11, ABHD12, ABHD6)

    We have now included additional depalmitoylating enzymes in our database and manuscript.

    1. Albeit not essential it will be of great interest to include in the established database enzymes necessary for synthesis of ACYL-CoA (e.g. ACSL enzymes). One improvement may include the ability of future researchers to add such curated analysis to the platform within future research studies.

    We agree with the reviewer there are many expansions of our gene set that would be interesting to include. Given the size of the current manuscript however, for brevity we have decided at present to curate data for the core set of genes that directly regulate dynamic palmitoylation. We have also added a ‘Contact Us’ feature to the website, so that repeatedly requested genes or datasets can be added in future.

    1. The experimental validation presented in figure 6 relies on over-expression of substrates and ZDHHC enzymes. This setup is known to often provide unspecific S-acylation events which result from excess enzyme or substrate availability. Hence, such validation would be greatly strengthened by loss of function experiments.

    We have now done loss-of-function experiments and included results in major discussion point 1 above. If the editors/reviewers think it is appropriate to add to the manuscript, we will comply. However, as our negative data does not negate the fact that ZDHHC9 is able to palmitoylate the myelin proteins tested, but merely suggests it may not be necessary for protein palmitoylation in vivo, we do not think it strengthens the manuscript.

    1. The authors relevantly use in-situ hybridization images from the Allen Brain atlas to validate their predictions. Although it is understandable that an extensive experimental validation of the predictions here established would be out of the scope of the current study, this work could be improved by validating the RNA expression at the protein level of certain abundant ZDHHC enzymes in available neuro-associated cell types.

    We have now validated RNA expression at the protein level for a few palmitoylating and depalmitoylating enzymes.

    1. It would be interesting if the authors would further compare the predicted association clusters (e.g. figure 1), substrates (figures 1 and 2), and S-acylation pairs (figure 4) here determine, with previous determined ZDHHC enzyme associations described in different cell types and biological systems. Alternatively, further relevant validation could include testing whether further established ZDHHC-ZDHHC cascades (e.g. ZDHHC3-7) can be also detected with specific cells or regions of the CNS.

    On our website, all expression data can be downloaded below the heatmaps for each study, and the cell type expression relationships between any 2 genes can be plotted by the user to reveal cell types (if any) within which genes are co-expressed. In response to this comment and that of Reviewer 3 below, we have now performed such analysis on ZDHHC5/ZDHHC20 and ZDHHC6/ZDHHC16, which are to our knowledge the best established ZDHHC cascades. We have included these plots in new Figure 1 – figure supplement 2, along with discussion on line 172. Similar analysis has been performed on the known ZDHHC-accessory protein pairs (see below).

    1. Figure 3B: it is not clear why the cluster of zdhhcs with high layer specific expression displayed at the top of the graph does not follow the low-to-high expression scale of the table.

    The expression data in this figure is grouped by hierarchical clustering, rather than in order of low-to-high expression, in order to be consistent with Figure 2B. While we believe this is the better way to display the data, we are willing to modify if the editors/reviewers have a strong preference.

    1. Figure 4D: the more relevant potential cooperative pairs (ZDHHCs-APTs) could be highlighted in more contrasted colours.

    We thank the reviewer for this suggestion but at this stage would prefer to keep the color scheme as it is so that readers are better able to formulate their own hypotheses when observing these figures.

    Reviewer #3 (Public Review):

    Weaknesses:

    1. There is a vast amount of data available and the description and discussion of this could be endless, but there are a few points that could be brought out in more detail. For example, the correlation (or lack of correlation) of expression of the proposed zDHHC-PAT accessory proteins with their cognate zDHHCs. The dominance of a relatively small number of zDHHC enzymes (20, 2, 17, 3, 21, 8) in the CNS also merits some discussion. Is the combination of a high-capacity, low-specificity enzyme (zDHHC3) with others that are regarded as more 'specific'? I believe none of these are ER-resident - they represent Golgi and PM?

    The reviewer brings up many interesting questions. Indeed, we were hopeful that this type of mining of RNAseq data would bring to light many questions that can be followed up on in future publications.

    We have addressed the correlation in expression of accessory proteins with their cognate ZDHHCs with new data.

    We are unsure how to address the dominance of a relatively small number of ZDHHC enzymes (20, 2, 17, 3, 21, 8) in the CNS, beyond highlighting this expression pattern. We believe that interpretation of the expression of this in any way (e.g. co-expression of high-capacity, low-specificity enzymes (ZDHHC3) with more 'specific' ZDHHCs) would merely be speculative. However, we are open to adding further discussion with some guidance from the reviewer.

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  2. Evaluation Summary:

    This paper will be of broad interest to neuroscientists, providing a rich resource for future research. Using available RNAseq data the authors build an easy-to-work-with web platform which will enable researchers to survey the expression patterns of palmitoylating and de-palmitoylating enzymes and their potential co-expressed substrates within the mouse nervous system. Using this map, the authors test hypotheses about the relationship between these enzymes and neurological diseases and generate hypotheses about enzyme/substrate relationships based on expression correlations.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

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  3. Reviewer #1 (Public Review):

    S-palmitoylation is one of the most common post-translational modifications in the brain. scRNAseq datasets provide crucial data about the diversity of neurons and non-neuronal cells and their regional expression patterns. A recent increase in the publication of these datasets necessitates tools that make a comparison between these possible and easily accessible. This need is addressed in the current work where the authors developed a web tool that curates datasets from several previous publications allowing direct comparison, the evaluation of developmental patterns of expression, and exploration of cell-type diversity.

    The authors use several examples demonstrating the utility of this tool and how it can lead to hypotheses development regarding the role of palmitoylating and depalmitoylating enzymes in various neuronal functions and dysfunctions. Through these examples, the authors make a convincing case for the utility of their web tool. The web tool depicted in this manuscript is user-friendly and provides easy access to a large amount of data. The benefit to the readers is the universal visualization and evaluation tool that will be useful for any lab that studies palmitoylating and depalmitoylating enzymes and their accessory proteins

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  4. Reviewer #2 (Public Review):

    Using available RNAseq data the S. Bamji team builds a web resource that permits assessing patterns and formulating hypothesis regarding the expression of (de)palimitoylating enzymes in the mouse nervous systems. The authors further showcase predictions and validation methodologies.

    Strengths:

    - BrainPalmSeq. The established database provides a curated and easy-to-work platform which will enable researchers to survey the expression patterns of S-acylation enzymes and their potential co-expressed substrates within the mouse nervous system. This constitutes a novel resourceful tool that will be useful not only for research concerning neurobiology-related fields but also to understand the fundamental aspects of S-acylation cascades.

    - This study describes the first RNA expression patterns of ZDHHC and APT enzymes within the nervous system. It highlights a remarkable transcriptional diversity, which shows that ZDHHC enzymes have regional- and cell-specific expression patterns which can be correlated with abundantly co-expressed substrates and tissue and cell specific functions. Furthermore, within the first 4 figures the authors describe an interesting roadmap which led them to uncover novel hypotheses regarding the functional associations between particular ZDHHC groups, the palmitoylated substrates enriched in specific brain regions and associated cell types and finally the cellular functions attributed to different brain compartments and cell types.

    - In figure 5 the authors demonstrate that the expression patterns of ZDHHC 8,9 and PPT1 correlate robustly with the anatomic brain regions and specific cell types affected by loss-of-function mutations in the same enzymes. This analysis further confirms the scope and potential of the BrainPalmSeq database.

    Weaknesses:

    - It is surprising that certain enzymes with established depalmitoylation activity were excluded from BrainPalmSeq data-base (e.g. ABHD4, ABHD11, ABHD12, ABHD6)

    - Albeit not essential it will be of great interest to include in the established database enzymes necessary for synthesis of ACYL-CoA (e.g. ACSL enzymes). One improvement may include the ability of future researchers to add such curated analysis to the platform within future research studies.

    - The experimental validation presented in figure 6 relies on over-expression of substrates and ZDHHC enzymes. This setup is known to often provide unspecific S-acylation events which result from excess enzyme or substrate availability. Hence, such validation would be greatly strengthened by loss of function experiments.

    - The authors relevantly use in-situ hybridization images from the Allen Brain atlas to validate their predictions. Although it is understandable that an extensive experimental validation of the predictions here established would be out of the scope of the current study, this work could be improved by validating the RNA expression at the protein level of certain abundant ZDHHC enzymes in available neuro-associated cell types.

    - It would be interesting if the authors would further compare the predicted association clusters (e.g. figure 1), substrates (figures 1 and 2), and S-acylation pairs (figure 4) here determine, with previous determined ZDHHC enzyme associations described in different cell types and biological systems. Alternatively, further relevant validation could include testing whether further established ZDHHC-ZDHHC cascades (e.g. ZDHHC3-7) can be also detected with specific cells or regions of the CNS.

    - Figure 3B: it is not clear why the cluster of zdhhcs with high layer specific expression displayed at the top of the graph does not follow the low-to-high expression scale of the table.

    - Figure 4D: the more relevant potential cooperative pairs (ZDHHCs-APTs) could be highlighted in more contrasted colours.

    Conclusions are justified by their data:
    This study demonstrates that cellular and tissue localization of ZDHHCs and APTs enzymes is controlled by different transcriptional networks, may underly their tissue and cellular functions, and be used to predict and determine enzyme-substrate associations The future use of the database will further explore this potential.

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  5. Reviewer #3 (Public Review):

    Wild and co-workers present a compendium of expression data for enzymes regulating protein palmitoylation based on single cell RNA seq data from different brain regions. The compendium has been developed into an online tool to facilitate user engagement and access. Using the tool, the authors have visualised expression of the palmitoylating zDHHC enzymes throughout the mouse nervous system and provide detailed snapshots of these enzymes expression and co-expression with palmitoylated proteins in the hippocampus and pyramidal neurons. Regional zDHHC enzyme expression data is used to explain pathologies associated with mutations in the zDHHCs and to predict enzyme / substrate relationships. As the authors themselves acknowledge that their analysis of this dataset is merely the tip of the iceberg, and much of the value of this investigation lies in the opportunities it will afford others in the field to explore expression patterns of palmitoylating and depalmitoylating enzymes in the brain.

    Strengths:
    The visual representation of the data is strong and emphasises the eye-catching dominance of certain zDHHCs throughout the nervous system. The correlation between the expression of particular zDHHCs with particular neurotransmitters is highly likely to seed future investigations. Correlations (and lack of correlations) between the expression of particular zDHHCs and accessory proteins will clearly be of interest to the field and suggest multiple future lines of research.

    The detailed cell-type specific maps of zDHHC expression in the hippocampus and somatosensory cortex highlight the heterogeneity of zDHHC expression and provide evidence about the processes controlled by these enzymes to which palmitoylation likely contributes. Again, this will be an incredibly useful resource for the field in the future.

    A particular highlight of the investigation is the insight offered by the analysis of phenotypes associated with loss of function mutations of some zDHHC-PATs. By highlighting the brain region(s) most 'dependent' on a particular zDHHC-PAT, the authors offer insight into the likely driving forces behind disease pathogenesis.

    Weaknesses:

    There is a vast amount of data available and the description and discussion of this could be endless, but there are a few points that could be brought out in more detail. For example, the correlation (or lack of correlation) of expression of the proposed zDHHC-PAT accessory proteins with their cognate zDHHCs. The dominance of a relatively small number of zDHHC enzymes (20, 2, 17, 3, 21, 8) in the CNS also merits some discussion. Is the combination of a high-capacity, low-specificity enzyme (zDHHC3) with others that are regarded as more 'specific'? I believe none of these are ER-resident - they represent Golgi and PM?

    Was this evaluation helpful?