Drug‐induced increase in lysobisphosphatidic acid reduces the cholesterol overload in Niemann–Pick type C cells and mice

Abstract Most cells acquire cholesterol by endocytosis of circulating low‐density lipoproteins (LDLs). After cholesteryl ester de‐esterification in endosomes, free cholesterol is redistributed to intracellular membranes via unclear mechanisms. Our previous work suggested that the unconventional phospholipid lysobisphosphatidic acid (LBPA) may play a role in modulating the cholesterol flux through endosomes. In this study, we used the Prestwick library of FDA‐approved compounds in a high‐content, image‐based screen of the endosomal lipids, lysobisphosphatidic acid and LDL‐derived cholesterol. We report that thioperamide maleate, an inverse agonist of the histamine H3 receptor HRH3, increases highly selectively the levels of lysobisphosphatidic acid, without affecting any endosomal protein or function that we tested. Our data also show that thioperamide significantly reduces the endosome cholesterol overload in fibroblasts from patients with the cholesterol storage disorder Niemann–Pick type C (NPC), as well as in liver of Npc1 −/− mice. We conclude that LBPA controls endosomal cholesterol mobilization and export to cellular destinations, perhaps by fluidifying or buffering cholesterol in endosomal membranes, and that thioperamide has repurposing potential for the treatment of NPC.

Thank you for the submission of your research manuscript to our journal. We have now received the full set of referee reports that is copied below.
As you will see the referees acknowledge the potential interest of your findings but they all point out a number of limitations that I briefly summarize below: 1) Limited information on the screen provided 2) Missing information on the cytotoxicity of thioperamide 3) Reproducibility and missing quantification/statistics => how many independent experiments have been performed? 4) The effect of thioperamide on cholesterol storage in NPC fibroblasts are not fully convincing 5) Biochemical evidence on lipid mobilization is missing 6) Insufficient clinical evaluation, more pathological parameters should be assessed in the mouse. 7) Mechanistic insight is limited => how does thioperamide act on LBPA?
I have discussed these points further with the referees and they all emphasized that it will be essential to address points 1-6 to substantiate the findings and strengthen the study. In particular, it will be important to reinforce the translational value of your findings (point 6). Referee 2 suggested to provide at least a minimal survival experiment and to determine lipid levels in the brain after treatment.
However, all referees agreed that further mechanistic insight is not necessary (point 7 above, referee 3) and can be saved for a future study and/or the discussion.
Given these constructive comments and your feedback that you will be able to address these concerns in a reasonable timeframe, we would like to invite you to revise your manuscript with the understanding that the referee concerns (as detailed above and in their reports) must be fully addressed and their suggestions taken on board. Please address all referee concerns in a complete point-by-point response. Acceptance of the manuscript will depend on a positive outcome of a second round of review. It is EMBO reports policy to allow a single round of revision only and acceptance or rejection of the manuscript will therefore depend on the completeness of your responses included in the next, final version of the manuscript.
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Referee #1:
Moreau et al report the results of a high content, image based screen designed to identify small molecules that increase LBPA without concurrently increasing cellular cholesterol. The rationale for the screen is that LBPA plays an important role in mobilizing endolysosomal cholesterol, thereby providing a novel approach to alleviate the storage of unesterified cholesterol in Niemann-Pick type C disease (NPC). The screen was performed using the Prestwick library of FDA approved drugs. The data presented suggest that the compound identified in the screen, thioperamide, has desired activity. However, several notable limitations are present in the data as it is currently presented.
Limited information on the screen is provided. What is the z-factor for the filipin and LBPA readout? What criteria were used to identify hits? Multipoint (8 or 11pt) dose-response curves with thioperamide and pitolisant, as well as a time course of the effect, should be included.
Quantification of cytotoxicity for thioperamide and pitolisant following treatment of HeLa cells and NPC patient fibroblasts (at effective concentrations/durations) should be included.
In almost all of the figures, statistical analysis to assess significance is missing. Additionally, none of the figure legends provide an indication of the number of experimental replicates used for quantification. This latter point is concerning for many figures, including 2C, 3B-D. Together, these deficits raise concerns about the rigor of the analyses.  The authors state no change was observed for "distribution or amounts" of the markers studied, but quantitative data are only shown for amounts. Similar quantification should be shown for distribution or the statement in the text should be modified.  The authors state that Fig 6C shows thioperamide efficiently reduced cholesterol levels in 3 NPC lines, but these changes are significant in only 2 lines. More careful wording would be appropriate.
If thioperamide is working to mobilize endolysosomal cholesterol in NPC cells, as the authors suggest, is there biochemical evidence from lipid analysis or gene expression studies to support this conclusion?
It would be appropriate to include data summarized in Fig 5a as a supplement. Was toxicity noted following treatment of mice with thioperamide as reflected by changes in liver enzymes, renal function or body weight?
In the Discussion, it would be appropriate to mention whether hepatocytes and neurons are known to express HRH3-4?

Referee #2:
Previous work from these authors showed that LBPA mediates cholesterol flux through endosomes and that interfering with LBPA function causes endosomal cholesterol accumulation phenocopying Niemann Pick type C disease (NPC). Based on these findings the authors screen here for compounds that, by influencing LBPA levels or distribution, could counteract cholesterol accumulation. They identify thioperamide as a novel modulator of LBPA levels in late endosomes that decreases cholesterol overload in fibroblasts from NPC patients and in the liver of NPC ko mice. Finding therapies for a fatal disease like NPC is indeed of interest and the results here presented have translational value. However, there are some important concerns: -About the novelty and significance. The screening method is not new and was used by the authors in a previous study where they identified Wnt pathway as a regulator of intracellular cholesterol transport (Scott et al., EMBO Rep 2015). Same screening method is used here to find modulators of the known cholesterol efflux effector LBPA. While the drug identified, thioperamide, has the potential of becoming a novel therapeutic strategy for NPC, the molecular mechanisms underlying its mode of action on LBPA remain undetermined despite the author efforts. While correlative evidence suggest the involvement of histamine receptors their role is not clear as well as the means by which LBPA facilitates endosomal cholesterol efflux.
-About the preclinical validation. Analysis of the effects of thioperamide treatment in the NPC mouse model is restricted to determine LBPA and cholesterol levels in the liver. This limited analysis weakens the translational potential of the study. Does this drug impact other pathological parameters in liver tissue (i.e. lysosomal size, inflammation) Does it impact the brain, which is particularly affected in NPC? Does it extend life span? -About the data presentation. Results should be more carefully illustrated in the figures. I summarize below instances that need revision: Statistical significance is missing in many graphs (i.e. Figures 1D, 2B, 3B, 4C, 6B, 6D, 6F) Figure 1B. The plot is confusing and needs explanation. Why DMSO and U18666A data appear as a group of dots while thioperamide and trimeprazime treated are shown just like a square? Figure 1E. Accurate quantification is missing. Bar length is not indicated in the figure legend Figure 2B. Why is Pitolisant used here instead of trimeprazine, which was used in Figures 1 and 2C?
Clarity of the manuscript would benefit from more self-explanatory subtitles in the main text and in the figure legends

Referee #3:
The manuscript by Moreau et al screened the FDA-approved drug library for the chemicals to ameliorate cholesterol accumulation in lysosome in NPC cells. They found that thioperamide maleate (Thio) was able to eliminate lysosomal cholesterol accumulation by elevating the LBPA level. Thio seemed not impair endosomal functions. They further applied Thio to fibroblasts from NPC patients and NPC1-deficient mouse. The results consistently showed that Thio reduced cholesterol accumulation. The current study is well-conducted and the data appear solid. Comments: 1) The mechanism of LBPA and Thio-mediated cholesterol efflux from lysosomes should be further investigated. It has been known that membrane contact sites and sterol transfer proteins play essential role in intracellular cholesterol traffic. The authors may investigate whether LBPA or Thio alter membrane contacts of lysosome-ER, lysosome-peroxisome, et al. Are the STPs changed in Thio treated cells?
2) It is very important for the authors to show that Thio has effect on human patients' cells and NPC disease model. The authors need to further analyze whether Thio improve neuron-muscle function of the NPC1-/-mouse or prolong the life-span of the mice. The rationale for the screen is that LBPA plays an important role in mobilizing endolysosomal cholesterol, thereby providing a novel approach to alleviate the storage of unesterified cholesterol in Niemann-Pick type C disease (NPC). The screen was performed using the Prestwick library of FDA approved drugs. The data presented suggest that the compound identified in the screen, thioperamide, has desired activity. However, several notable limitations are present in the data as it is currently presented.
We thank the reviewer for his/her assessment and we have addressed below the limitations outlined by this reviewer.
Limited information on the screen is provided. What is the z-factor for the filipin and LBPA readout? What criteria were used to identify hits?
We are sorry if information on the screen was missing, partly because of the space constraints of the journal. We wish to emphasize the fact that our goal was not to identify "hits" per se. The previous work of us and others showed that experimental conditions that increase the cholesterol content of endolysosomes, including drugs (e.g. U18666A) or mutations (e.g. NPC1), also increase the LBPA content. Our original goal was thus to identify compounds that modify LBPA but not cholesterol. Using the Prestwick library, we found that thioperamide increased LBPA 3X without affecting cholesterol ( Fig 1D) -not surprisingly, some Prestwick compounds showed a U18666A-like behavior (e.g. trimeprazine) -and we decided to further characterize thioperamide. We have now clarified these issues and added the z-factor for the LBPA and filipin readout.

Multipoint (8 or 11pt) dose-response curves with thioperamide and pitolisant, as well as a time course of the effect, should be included.
As requested, we have added the dose-response curve with thioperamide and pitolisant, as well as a time-courses of the effects. These data show that the dose-response profiles (new Fig EV3A) and timecourses (new Fig EV3B) of LBPA accumulation are very similar. However, these data also show that pitolisant may have some cytotoxic effects after longer times (new Fig EV3C), confirming our decision to center our efforts on thioperamide.

Quantification of cytotoxicity for thioperamide and pitolisant following treatment of HeLa cells and NPC patient fibroblasts (at effective concentrations/durations) should be included.
We apologize if this point was not clear. Previous studies have established that thioperamide is not toxic in animal models (e.g. ref 25). Consistent with these data, thioperamide showed not toxicity in our screen. We have now included new data on the effects of thioperamide in HeLa cells (new Fig  EV3) and in the NPC1 and NPC2 cell-lines that we used (new Fig 5D-E). These data show that thioperamide is not toxic in HeLa and NPC cells. In fact, thioperamide somewhat increased cell survival in NPC cells. This has been clarified in the new version of the paper.

In almost all of the figures, statistical analysis to assess significance is missing. Additionally, none of the figure legends provide an indication of the number of experimental replicates used for quantification. This latter point is concerning for many figures, including 2C, 3B-D. Together, these deficits raise concerns about the rigor of the analyses.
We apologize if the statistical was missing. As requested this information was added, as well as the number of experimental replicates in all Figures. In Fig 2C and 3C-D, the data from the screen are plotted and thus each dot corresponds to the average of replicates in the duplicate plates (as in Fig  1B), and ≥600 cells were analysed per compound (only compounds that showed < 20% toxicity are plotted). In the EM analysis after immuno-gold labelling of cryosections, Fig 3B represents the double-blind quantification of the mean of two replicates for each condition. This was not clarified in the Legends of the Figures.     We thank the reviewer for this comment. After 48h thioperamide treatment of NPC fibroblasts, the increase in LBPA levels is highly significant, close to 2X when compared to DMSO controls (ex- Fig 6B, new Fig 5B). In fact, the effects of the drug are particularly striking, since even before thioperamide addition, LBPA levels are already much higher in NPC cells when compared to controls (e.g. see our quantification of LBPA in NPC mice, new Fig 6B). This issue has been clarified in the text. In addition, the decrease in LBPA levels after 72h is clearly not due to some toxic effect of the compound. In fact, our new data (new Fig 5E) show that thioperamide increases cell survival in the NPC1 and NPC2 cell lines. We could not determine whether the beneficial effects of thioperamide depends on the expression of HRH3-4. Indeed, we were unable to transfect NPC1 or NPC2 fibroblasts with siRNAs. More important, and as noted above, thioperamide acts as an inverse agonist, and thus HRH3-4 KD has the same effect as thioperamide on LBPA levels (new Fig 4F-G), making it impossible to discriminate between the effects of the drug and of the knockdown.
The authors state that Fig 6C shows Fig 5B). Ex- Fig 6C (new Fig 5C) illustrates the effects of the drug on total cellular cholesterol, analyzed by mass spectrometry. The Fig shows that total cellular cholesterol is also reduced, but to a lesser extent: the effects of the drug appear less prominent than at the endosomal level (Fig 5B), because in NPC fibroblasts, the amounts of cholesterol accumulated in endosomes corresponds only to a fraction of total cellular cholesterol. This issue was clarified in the revised manuscript. It should also be noted that thioperamide reduced total cellular cholesterol levels as efficiently as cyclodextrin (Fig 6C), which is considered to be one of the -if not the -most efficient protocol to reduce cholesterol levels in cultured cells.

If thioperamide is working to mobilize endolysosomal cholesterol in NPC cells, as the authors suggest, is there biochemical evidence from lipid analysis or gene expression studies to support this conclusion?
We thank the reviewer for this comment. As requested, we have tested whether endolysosomal cholesterol is mobilized after thioperamide treatment. To this end, we generated NPC1 and NPC2 KO cells using CRISP/Cas9. Our new data show that thioperamide was able to partially correct the defect in transcriptional regulation of two canonical cholesterol-dependent genes, the LDL receptor and HMG CoA reductase, in cells NPC1 or NPC2 KO cells (new Fig 6A).
It would be appropriate to include data summarized in Fig 5a as a supplement. As requested, we have added a supplementary table (Table EV3)  In the Discussion, it would be appropriate to mention whether hepatocytes and neurons are known to express HRH3-4?
We thank the reviewer for this comment. As requested, we now mention in the Discussion that HRH3-4 is expressed in hepatocytes and in neurons. Indeed, an analysis using Genevestigator tools that combine thousands of microarray experiments (https://www.genevestigator.com/gv/index.jsp) shows that HRH3-4 are expressed in most tissues, including liver and brain. This issue has been clarified in the paper.

Referee #2: Previous work from these authors showed that LBPA mediates cholesterol flux through endosomes and that interfering with LBPA function causes endosomal cholesterol accumulation phenocopying Niemann Pick type C disease (NPC). Based on these findings the authors screen here for compounds that, by influencing LBPA levels or distribution, could counteract cholesterol accumulation. They identify thioperamide as a novel modulator of LBPA levels in late endosomes that decreases cholesterol overload in fibroblasts from NPC patients and in the liver of NPC ko mice. Finding therapies for a fatal disease like NPC is indeed of interest and the results here presented have translational value. However, there are some important concerns: -About the novelty and significance. The screening method is not new and was used by the authors in a previous study where they identified Wnt pathway as a regulator of intracellular cholesterol transport (Scott et al., EMBO Rep 2015)
. Same screening method is used here to find modulators of the known cholesterol efflux effector LBPA. While the drug identified, thioperamide, has the potential of becoming a novel therapeutic strategy for NPC, the molecular mechanisms underlying its mode of action on LBPA remain undetermined despite the author efforts. While correlative evidence suggest the involvement of histamine receptors their role is not clear as well as the means by which LBPA facilitates endosomal cholesterol efflux.
This reviewer correctly points out that the mechanism of action of thioperamide is not clear yet, despite much effort. We agree that this issue is important but it extends beyond the present work. However, in response to the comments of referee 3, we have tested whether the expression of proteins involved in sterol transfer or membrane contact sites was changed after thioperamide treatment of control cells, NPC1 KO cells or NPC2 KO cells (we generated NPC1 and NPC2 KO cells using CRIPR/Cas9). We tested the following candidates (from the work of the indicated

-About the preclinical validation. Analysis of the effects of thioperamide treatment in the NPC mouse model is restricted to determine LBPA and cholesterol levels in the liver. This limited analysis weakens the translational potential of the study. Does this drug impact other pathological parameters in liver tissue (i.e. lysosomal size, inflammation) Does it impact the brain, which is particularly affected in NPC? Does it extend life span?
We thank the reviewer for this comment. As requested, we have now included new data in mice (New Fig EV9). In these studies, we analyzed the same mice that we had used in the paper. These mice lack NPC1 and thus model the most aggressive early onset form of the disease. Thioperamide did not significantly improve the life span, motor function/rearing or high frequency tremor, although some benefits were observed when combined with Miglustat ( Fig EV9) -the only compound available as a treatment against NPC in Europe, but not in the US, which prolongs life but does not arrest disease progression. We have now included these new data in mice as new Fig  EV9. Clearly, it will be interesting in the future to test the effects of thioperamide alone or in combination with Miglustat in mice expressing less aggressive NPC mutations.
-About the data presentation. Results should be more carefully illustrated in the figures. I summarize below instances that need revision: As requested, data are more carefully presented in the figures.
Statistical significance is missing in many graphs (i.e. Figures 1D, 2B, 3B  We are sorry if this was not clear. The plot shows the mean of duplicate values for each compound that have been tested in the screen, and therefore thioperamide and trimeprazime each appear as one square. In the screen, each 384-well plates included a full column of 12 wells treated with U18666A, as positive controls. and all these controls appear as the cluster of green dots in the plot. Similarly, each 384-well plates also included a full column of 12 wells treated with DMSO, as negative controls, and these cluster together close to the intercept between X-and Y-axis. In the original plot, the red dots unfortunately disappeared behind the black dots, and this has been changed in the new version of the plot. This issue was clarified in the new version of the paper.

Figure 1E. Accurate quantification is missing. Bar length is not indicated in the figure legend
We are sorry if this was not clear. The data are quantified precisely in Fig 1D, and this issue was clarified in the text. In the original version of the paper, the bar length was indicated on the Fig  itself. As requested, we have now added the bar length in the Legends.

Figure 2B. Why is Pitolisant used here instead of trimeprazine, which was used in Figures 1 and 2C?
We are sorry if this was not clear. We only used trimeprazine as an example of compounds that show U18666A-like effects in the screen itself, e.g. Fig 1B, D-E and Fig 2C. Pitolisant, however, is not in the Prestwick library and it was thus not tested in the screen. We found Pitolisant when searching for other compounds known to target the histamine receptors H3 (HRH3) and H4 (HRH4). We therefore used Pitolisant in Fig 2B because it targets HRH3-4 and because the effects of this compound are close to thioperamide, since it increased LBPA levels without affecting cholesterol. This has been clarified in the paper.

Clarity of the manuscript would benefit from more self-explanatory subtitles in the main text and in the figure legends
As requested, we have added self-explanatory subtitles in text and in legends.

The manuscript by Moreau et al screened the FDA-approved drug library for the chemicals to ameliorate cholesterol accumulation in lysosome in NPC cells. They found that thioperamide maleate (Thio) was able to eliminate lysosomal cholesterol accumulation by elevating the LBPA level. Thio seemed not impair endosomal functions. They further applied Thio to fibroblasts from NPC patients and NPC1-deficient mouse. The results consistently showed that Thio reduced cholesterol accumulation. The current study is well-conducted and the data appear solid.
Comments: 1) The mechanism of LBPA and Thio-mediated cholesterol efflux from lysosomes should be further investigated. It has been known that membrane contact sites and sterol transfer proteins play essential role in intracellular cholesterol traffic. The authors may investigate whether LBPA or Thio alter membrane contacts of lysosome-ER, lysosome-peroxisome, et al.

Are the STPs changed in Thio treated cells?
As discussed above (answers to the Edito and to referee 2), this reviewer correctly points out that the mechanism of action of thioperamide is not clear yet, despite much effort. We agree that this issue is important but it extends beyond the present work. However, we addressed the comment that membrane contact sites and sterol transfer proteins play a role in intracellular cholesterol traffic. As requested, we tested whether the expression of STPs or MCS proteins that play a role in cholesterol transfer at MCSs was changed after thioperamide treatment of control cells, NPC1 KO cells or NPC2 KO cells (we generated NPC1 and NPC2 KO cells using CRIPR/Cas9). We tested the following candidates (from the work of the indicated group): FYCO1 (H. Stenmark) ANXA1 (C. Futter), STARD3 (F. Alpy), and finally ORP1L, VAPA and VAPB (J. Neefjes), We did see any signifcant change in the expression levels of any candidate in controls or in NPC1 or NPC2 KO cells. These data are now shown in new Fig EV4.

2) It is very important for the authors to show that Thio has effect on human patients' cells and NPC disease model. The authors need to further analyze whether Thio improve neuronmuscle function of the NPC1-/-mouse or prolong the life-span of the mice.
We thank the reviewer for this comment. As requested, we have now included new behavioral data in mice (new Fig EV9). This analysis was carried out in the same mice that we had used in the paper. These mice lack NPC1 and thus model the most aggressive early onset form of the disease. Thioperamide did not significantly improve the life span, motor function/rearing or high frequency tremor, although some benefits were observed when combined with Miglustat ( Fig EV9) -the only compound available as a treatment against NPC in Europe, but not in the US, which prolongs life but does not arrest disease progression. We have now included these new data in mice as new Fig  EV9. Clearly, it will be interesting in the future to test the effects of thioperamide alone or in combination with Miglustat in mice expressing less aggressive NPC mutations. Thank you for the submission of your revised manuscript to EMBO reports. It has been evaluated again by referee 1 and 2 and we have now received the full set of referee reports (copied below).
As you will see, both referees are very positive about the study and request only minor changes to clarify text, figures and data quantification.
From the editorial side, there are also a few things that we need before we can proceed with the official acceptance of your study. -We encourage authors to arrange the figure panels in a manner that they can be called out in the correct order in the text. This is currently not the case for Figure 1. Moreover, Fig 4 F,G are called out before Fig 4D and 5B is discussed after 5E. If possible the panels should be rearranged.
-Moreover, we noticed a callout to Fig 5 G (page 9), which is not present.
- Table EV1, EV2 and EV3 should be supplied as Datasets (Dataset EV1, Dataset EV2, Dataset EV3). Please remove the legend from the main manuscript file and provide it in the first row of the excel file. - Figure 4E, 5B: since the quantification is based on 2 replicates, which is not ideally suited to support a statistical analysis, I would suggest removing the p-values from these panels.
-Finally, EMBO reports papers are accompanied online by A) a short (1-2 sentences) summary of the findings and their significance, B) 2-3 bullet points highlighting key results and C) a synopsis image that is 550x200-400 pixels large (width x height). You can either show a model or key data in the synopsis image. Please note that the size is rather small and that text needs to be readable at the final size. Please send us this information along with the revised manuscript.
We look forward to seeing a final version of your manuscript as soon as possible. Please let me know if you have questions or comments regarding the revision.

Referee #1:
This revised manuscript is much improved and the modifications address many of the concerns raised in the prior review. A few points remain: Fig 3B: How many cells were used for quantification? Analysis of biological triplicates with assessment of statistical significance would be most appropriate. As the data are currently presented (mean values), there is no indication of variation between samples.
Fig EV9B, C: How many mice/group? Include error bars and statistical analysis to support conclusion in panel C that thioperamide plus miglustat is more beneficial than either treatment alone. Include thioperamide alone group.
Legend to EV9: Delete mention of rearing data, which is not included in figure.
Legend to Fig 1E: add scale bar size Page 9: LBPA quatification in NPC1 mice is shown in Fig 6B rather than 5G

Referee #2:
The authors have addressed my queries and made an effort to further characterise thioperamide mode of action and the effects of the treatment in the mouse brain. Although, unfortunately, the effects in the brain and life span are not significant the underlying reasons for that are discussed. Overall, the results encourage further research on LBPA function and open perspectives on its use as a target for NPC treatment.
There are only two minor points that should be addressed but do not need re-review from my part.
1-Fix the apparent inconsistency on the following: While the subtitle of Figure 3 claims that Thioperamide affects endosome morphology, the text in page 5 states "analysis by electron microscopy showed that the ultrastructure of individual endosomes looked very similar in thioperamide-treated cells when compared to controls".
2-Statistical analysis is still missing for the data on the total cholesterol levels in liver shown in Figure 6B. We are sorry if the quantification was not clear. Immuno-EM is not easy and we tried to do the best we could. The experiment was done in Switzerland, and sent to Brisbane Australia. The data were quantified in a double-blind analysis of two sets of 16 micrographs for each condition (control cells and thioperamide-treated cells). The number of gold particles per endosome was quantified and is now shown in a scatter plot for each endosome identified without any bias in each micrograph of control and thioperamide-treated samples. I hope that this clarifies the issue.
Fig EV9B, C: How many mice/group? Include error bars and statistical analysis to support conclusion in panel C that thioperamide plus miglustat is more beneficial than either treatment alone. Include thioperamide alone group.
We are sorry about these omissions. In these experiments we used 6 mice per condition. The Legends have been corrected as well as the error bars. We have also included the thioperamide alone group.
Legend to EV9: Delete mention of rearing data, which is not included in figure.
Sorry about the confusion! The data was included in the new version of the figure.
Legend to Fig 1E: add scale bar size The Legend has been corrected.
Page 9: LBPA quatification in NPC1 mice is shown in Fig 6B rather than 5G. This has been corrected.
Referee #2: The authors have addressed my queries and madean effort to further characterise thioperamide mode of action and the effects of the treatment in the mouse brain. Although, unfortunately, the effects in the brain and life span are not significant the underlying reasons for that are discussed. Overall, the results encourage further research on LBPA function and open perspectives on its use as a target for NPC treatment.
There are only two minor points that should be addressed but do not need re-review from my part.
1-Fix the apparent inconsistency on the following: While the subtitle of Figure 3 claims that Thioperamide affects endosome morphology, the text in page 5 states "analysis by electron microscopy showed that the ultrastructure of individual endosomes looked very similar in thioperamide-treated cells when compared to controls". Oups! Again, sorry for this mistake. The title of the Legendwas corrected and changed to: "Thioperamide does not affect endosome morphology or distribution".
2-Statistical analysis is still missing for the data on the total cholesterol levels in liver shown in Figure 6B. As mentioned above (reviewer one), we are sorry about these omissions. In these experiments we used 6 mice per condition. The Legends have been corrected as well as the error bars. We have also included the thioperamide alone group common tests, such as t-test (please specify whether paired vs. unpaired), simple χ2 tests, Wilcoxon and Mann-Whitney tests, can be unambiguously identified by name only, but more complex techniques should be described in the methods section; are tests one-sided or two-sided? are there adjustments for multiple comparisons? exact statistical test results, e.g., P values = x but not P values < x; definition of 'center values' as median or average; definition of error bars as s.d. or s.e.m.
1.a. How was the sample size chosen to ensure adequate power to detect a pre-specified effect size? 1.b. For animal studies, include a statement about sample size estimate even if no statistical methods were used.
2. Describe inclusion/exclusion criteria if samples or animals were excluded from the analysis. Were the criteria preestablished?
3. Were any steps taken to minimize the effects of subjective bias when allocating animals/samples to treatment (e.g. randomization procedure)? If yes, please describe.
For animal studies, include a statement about randomization even if no randomization was used.
4.a. Were any steps taken to minimize the effects of subjective bias during group allocation or/and when assessing results (e.g. blinding of the investigator)? If yes please describe. Do the data meet the assumptions of the tests (e.g., normal distribution)? Describe any methods used to assess it.
Is there an estimate of variation within each group of data?
Is the variance similar between the groups that are being statistically compared?
Yes, all statistical analysis have been performed with Graphpad Prism 8, assessing first the normalization of the data, followed by runing the proper statistical test.
Yes, all data set have been tested for the normal distribution befor runing statistical analysis Yes Yes, the variance is similar. With our high-throughput automated microscopy set-up, sample preparation (preparation of the 96-or 384-well plates), acquisition of the data and analysis are fully automated. This ensures extremely robust processing, which in turn ensures high consistency from experiment to experiment. Moreover, the size of each samples is very large (2000 cells) which also contributes significantly to robustness and consistency from experiment to experiment.

YOU MUST COMPLETE ALL CELLS WITH A PINK BACKGROUND ê
In the high content compound screen, the sample size has been optimized for the screening experiment during the assay development, by evaluating Z' factor using positive and negative controls. For all other microscopy experiment a minimum of 2000 cells per biological replicate have been imaged and analyzed using our high-through microscopy set-up.
Sample size estimate was based on previous power calculations and experience of effect size and reproducibility of the animal model (widely used and published by the Platt group).
Cells: In the high content compound screen, we excluded all compound showing more than 20% toxicity on cells. Mice: N/A Randomized to group and blinded Randomisiation is routinely conducted and importantly for this model weaning body weights standardised as much as possible between groups.
All light microscopy imaging experiments have been carried out by automated microscopy, so that image selection is entirely random with no possibility for the experimentator to choose specific cells. In our electron microscopy analysis, the quantitation of LBPA labeling of cryo-sections was performed afer number-coding of the samples in a double-blind fashion.
In mice experiments, investigator wre blinded. Blinding for animal staff performing functional analysis 1. Data the data were obtained and processed according to the field's best practice and are presented to reflect the results of the experiments in an accurate and unbiased manner. figure panels include only data points, measurements or observations that can be compared to each other in a scientifically meaningful way. graphs include clearly labeled error bars for independent experiments and sample sizes. Unless justified, error bars should not be shown for technical replicates. if n< 5, the individual data points from each experiment should be plotted and any statistical test employed should be justified the exact sample size (n) for each experimental group/condition, given as a number, not a range; Each figure caption should contain the following information, for each panel where they are relevant:

Captions
The data shown in figures should satisfy the following conditions: Source Data should be included to report the data underlying graphs. Please follow the guidelines set out in the author ship guidelines on Data Presentation.
Please fill out these boxes ê (Do not worry if you cannot see all your text once you press return) a specification of the experimental system investigated (eg cell line, species name).

B-Statistics and general methods
the assay(s) and method(s) used to carry out the reported observations and measurements an explicit mention of the biological and chemical entity(ies) that are being measured. an explicit mention of the biological and chemical entity(ies) that are altered/varied/perturbed in a controlled manner. a statement of how many times the experiment shown was independently replicated in the laboratory.
Any descriptions too long for the figure legend should be included in the methods section and/or with the source data.
In the pink boxes below, please ensure that the answers to the following questions are reported in the manuscript itself. Every question should be answered. If the question is not relevant to your research, please write NA (non applicable). We encourage you to include a specific subsection in the methods section for statistics, reagents, animal models and human subjects. This checklist is used to ensure good reporting standards and to improve the reproducibility of published results. These guidelines are consistent with the Principles and Guidelines for Reporting Preclinical Research issued by the NIH in 2014. Please follow the journal's authorship guidelines in preparing your manuscript.