Zika virus-induces metabolic alterations in fetal neuronal progenitors that could influence in neurodevelopment during early pregnancy

  1. Javier Gilbert-Jaramillo
  2. Ujang Purnama
  3. Zoltán Molnár
  4. William S. James

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Abstract

Neuronal progenitor subtypes have distinct fate restrictions regulated by time-dependent activation of energetic pathways. Thus, the hijacking of cellular metabolism by Zika virus (ZIKV) to support its replication may contribute to damage in the developing fetal brain. Here, we showed that ZIKV replicates differently in two glycolytically distinct hiPSC-derived neuronal progenitors that correspond to early and late progenitors in the forebrain. This differential replication alters the transcription of metabolic genes and upregulates the glycolytic capacity of progenitor subtypes. Analysis using Imagestream® revealed that, during early stages of infection, ZIKV replication in early progenitors increases lipid droplet abundance and decreases mitochondrial size and membrane potential. During later stages infection, early progenitors show increased subcellular distribution of lipid droplets, whilst late progenitors show decreased mitochondria size. The finding that there are hi-NPC subtype-specific alterations of cellular metabolism during ZIKV infection may help to explain the differences in brain damage over each trimester.

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    Reply to the reviewers

    Reviewer #1 (Evidence, reproducibility and clarity (Required)):

    This manuscript reports an investigation into the metabolic alterations induced by Zika virus (ZIKV) infection in human neuronal progenitor cells. The authors differentiated human iPSCs to derive neuronal progenitor cells (NPCs) at different days of incubation to represent the different stages of foetal CNS development. They found differences in the levels of ZIKV NS1 proteins as well as marginal differences in ZIKV titres in infected early and late hi-NPCs. Correspondingly, they also showed differences in glucose consumption, lipid metabolism and mitochondrial stress in ZIKV-infected early and late hi-NPCs. They concluded that differences in energy metabolism in neuronal progenitors both before and upon infection may contribute to the brain damage observed in congenital Zika syndrome.

    The evidence supporting a role for dysregulated metabolism in mediating the pathogenesis of congenital Zika syndrome is gaining traction and findings from this study could add to this body of knowledge. However, in its present form, this study has several gaps that limit the extent to which it informs on the clinical pathogenesis of congenital Zika syndrome.

    Major concerns:

    1. The most important concern in this study is the strain of ZIKV used in all of the studies. ZIKV MP1751 was isolated from a mosquito and belongs to the African lineage of ZIKV. Unlike the Asian lineage ZIKV isolated from Latin America and French Polynesia, gestational infection with ZIKV of the African lineage has not been clinically associated with increased risk of foetal abnormality. It is thus uncertain how the changes observed in this study relates to the observed neonatal pathology. Perhaps a way to address this issue is to argue that a difference in these lineages it the ability of the virus to evade systemic and endothelial innate immune responses to cross the placental and blood brain barriers (several papers on attenuated ZIKV have shown this data). Once these barriers are breached, strain differences should not materially affect the similar pathogenic processes in neuronal cells, as also been shown by others using the MR766 strain of ZIKV. Such a discussion would be helpful to contextualise the clinical relevance of this study.

    The authors agree with the reviewer and have now included a discussion paragraph to address this relevant point. In addition, the authors acquired a strain from the Asian lineage (PRVACB59) and performed a single round side-by-side infection in three patient-derived lines with the two different strains of ZIKV. From the infected samples, the authors measured glucose consumption, lactate release and viral output to demonstrate, within the same research, that in in vitro assays, whereas number of ZIKV particles available to infect pools of brain progenitors is not mediated by tissue tropism/advantage. This data has been included in the extended figure 5.

    While the metabolic changes upon ZIKV infection are all interesting, how these changes affect CNS development is unclear. Figure 2F shows marginal impact on productive ZIKV infection and comparable extent of cell death in early and late hi-NPCs. What specific CNS pathology is dependent on the reported metabolic changes?

    The authors acknowledge that a correlation of findings from in vitro research and Zika congenital syndrome would be e.g., observing greater differences in cell survival and/or viral replication kinetics. After revisions of the current data, the authors have reanalysed the datasets corresponding to glucose and lactate metabolism, cell survival and viral output and corrected the results and discussion, accordingly. This correction was done due by calculating cell survival/death using lactate dehydrogenase (LDH) readouts. Thus, the data from CCK-8 was replaced due to the conversion of tetrazolium-based salts are metabolically depending on NAD+ and mitochondrial function– which may well have skewed the results as Zika virus potentially alters mitochondrial homeostasis. The preliminary graphs presented in the previous version of this manuscript are presented in the extended data figure 8 for comparison purposes. Datasets corrected for LDH mirror the in-vitro observations in which Zika-infected early hi-NPCs exhibit greater cell death than late hi-NPCs – this may potentially translate to the fetal pathogenesis observed over different trimesters. The corrected data also shows a significantly greater ZIKV release from late hi-NPCs at 48 and 56 h.p.i. suggesting a window of efficient replication in these cells. Lastly, the authors have expanded the conclusion paragraph highlighting intrauterine pathogenesis correlated with impaired brain metabolism to link how metabolic changes induced by ZIKV may correlate with pathophysiological phenotypes aggravated during early trimesters.

    Figure 2D: The most remarkable virological difference observed is the significant difference in cytoplasmic NS1 levels between early and late hi-NPCs at 56 hpi. Although the data in Fig 2D in general could have been compromised by the quality of the anti-NS1 mAb (the anti-E assay in Fig 2E used polyclonal antibody), it would have been useful to test for NS1 expression using western blot on a denaturing gel (and appropriate anti-NS1 antibody). The mAb used in this study binds a conformational epitope on NS1. The difference in data in Figure 2D and 2E could thus have been misfolding of NS1. Misfolded NS1 could contribute to ER stress that could be important for dysregulated CNS development. A more detailed investigation of the finding in Figure 2D could be highly informative.

    The authors appreciate the comments and hypothesis that NS1 detection may have been compromised due to potential misfolding. This hypothesis was tested by the authors showing no detection of NS1 by denaturing western blot with the referred antibody. However, before using a different NS1 antibody to investigate this potentially relevant phenomenon, the authors attempted to detect NS1 by flow cytometry using fewer markers than in the previous experiments. The authors decided to take this approach due to, although to low levels, NS1 was detected by imaging flow cytometry at early timepoints. When using a combination of markers that did not compromise the signal of NS1 by light compensations and low signal secondary fluorophore, the authors successfully detected NS1 and to similar levels of the Envelope protein. Thus, the authors discarded the possibility that the lowered levels presented in the previous version were due to misfolded NS1. The graphs within Figure 2 have now been corrected with this newly generated dataset.

    Figure 3A and related text: The fold-change in GLUT1, HK-1 and GAPDH expression are shown in log10 scale. In this scale, 1 would indicate 10-fold increase in expression. The data in Figure 3A are entirely inconsistent with the description in the related text. Which is correct?

    The authors thank the reviewer and would like to highlight that both the Figure 3A and its description were correct in the previous version of this manuscript. The description of the data, however, may have been misleading and unclear thus, the authors have amended the text for clarity. Figure 3 displays the fold-change of key glycolytic genes in early and late Zika infected hi-NPCs, each normalised to their respective controls. The in-text description, besides highlighting this important feature of the ZIKV infection in hi-NPCs, it highlight a more important finding correlated to the significances computed when compare the ratio of fold change between infected early and late hi-NPCs.

    Minor concerns:

    1. Figure 5: The effects of ZIKV infection on the mitochondria of hi-NPCs are interesting and the comparison between ZIKV-infected and uninfected cells in the same culture is a strength of this study. It would be helpful to readers if the authors could include a discussion on the kinetics of ZIKV infection; diminished differences at 48 and 72 hours could be due to the mixture of cells infected at inoculation and hence observed at 24 hours and newly infected cells that were negative for ZIKV E protein at 24 hours. Emphasis should thus be on the 24 h data in Figures 5 C-E.

    The authors thank the reviewer for highlighting the relevance of our experimental approach. The authors also thank for the interpretation of the data focused at early timepoints during the infection kinetics of ZIKV when the metabolic alterations are likely to be exclusive from cells infected at inoculation. The discussion of the data in this new revision of the manuscript emphasises the results based on the kinetics of infection and also clarify and strengthen the findings on Env+ and Env- cells within the pool of infected cells.

    1. Hi-NPCs likely have a diploid genome and thus a finite lifespan. Using the term "cell line" to describe these cells is technically incorrect. Please consider using other terms, such as cell strain.

    The authors appreciate the comment and have amended the text accordingly. The authors decided to use the terminology patient line.

    Discussion section, 3rd paragraph, lines 6-7. The authors suggest thermal decay as an explanation for their observation yet Figure 2B argues against this explanation. Moreover, Kostyuchenko et al (Nature 2016; 533:425-8) have also shown that ZIKV is relatively thermostable. This explanation offered by the authors lack supporting evidence.

    The authors thank the reviewer for the observation. Regarding the discussion on thermal decay, the authors aimed to highlight that circulating virions without available hosts to continue replication (due to cell death) may suffer from thermal decay as the difference in collection timepoints exceeds the tested 3 h reported in this research (Fig 2B). The results from Kostyuchenko et al (Nature 2016; 533:425-8) show thermal stability of ZIKV at different ranges of temperature yet, similar to Fig 2B, only under 2 h. Unpublished data from our group using an FFU assay shows that infectivity of ZIKV virions decrease after 10 h at 37C, knowledge that was used for the discussion. Moreover, the authors have strengthened the discussion by including in the discussion the native immunity of hi-NPCs at different stages of differentiation.

    Discussion section, 3rd paragraph, line 16 and Supplementary Figure 2. I believe the authors are referring to Supplementary Figure 3 and not 2. The indentations observed could be due to ZIKV replication although the data, as presented is not convincing. Co-staining for ZIKV E protein would be useful.

    The authors have corrected the issue and confirm that the reference to potential replication sites of ZIKV was made to the Extended Figure 3 rather than 2. To strengthen these findings, the authors have now acquired nuclear data by confocal imaging of infected late hi-NPCs co-stained for ZIKV E protein and DAPI. Representative images are included in the Extended Figure 6.

    CROSS-CONSULTATION COMMENTS

    • I fully agree with both Reviewers #2 and #3 on the quality of the immunofluorescence images and that these images alone are not sufficiently convincing to support the inferences the authors are making.

    The authors would like to clarify that the confocal imaging displayed in the current manuscript was not used for the interpretation of the data but rather validation of the immunostaining used in fluorescence flow cytometry. The nuclear screening comprised the only result generated from microscopy analysis. The bulk of data presented on this manuscript was generated by imaging flow cytometry (Imagestream) due to the higher degree of unbiased screening and the larger sample size. The authors acknowledge that Imagestream produces low quality immunofluorescence imaging compared to confocal imaging but believe this is justified by the greater data and unbiased analysis offered by imaging flow cytometry. The power of analysis displayed in this research is unlikely to be achieved by confocal microscopy in which no more than 1.000 cells are screened whilst the authors screened 10.000 cells from each patient line to generate each dataset. Lastly, the authors acknowledge the lack of robustness in the images from nuclei and ZIKV replication thus, for late hi-NPCs in which the perinuclear replication sites where evident, data was acquired by confocal imaging; samples co-stained for ZIKV E protein and nuclei DAPI (Extended Figure 6).

    • I also appreciate the first major comment from Reviewer #3. That is important insight and the authors should test their assumption that they have monocultures of human progenitor cells.

    The authors have paraphrased the document for better clarity and accuracy as consider the text was confusing causing misinterpretation of the data. This research is intended to show the impact of ZIKV in two pools of cortical progenitor cells (less and more differentiated/mature) clustered by their distinct metabolic profiles rather than single cell types present at different stages of brain development. Both early and late hi-NPCs comprise pools of cells generated during hiPSC differentiation of cortical progenitors. The authors showed in Fig.1 that both pools have brain identity and express several brain markers to similar levels. When gating these cells by populations present in the developing brain small differences were observed exclusively in one out of three subgroups. Nevertheless, the main distinction of these two populations was due to significant differences in their metabolic profile. Thus, the results obtained in this research are likely to obey to the metabolic maturation of early and late hi-NPCs rather than the percentages of different brain cell types present within these pools.

    Reviewer #1 (Significance (Required)):

    The focus on the metabolism and mitochondrial stress in ZIKV infected neuronal progenitors is interesting and could fill an important gap in knowledge on Zika pathogenesis. The study uses human iPSC derived NPCs instead of animal cells, which is also more clinically relevant than animal models. The findings would thus be of interest to all who are interested in Zika pathogenesis as well as therapeutic/vaccine development. If the above concerns could be addressed, the findings in this study could form the missing links in our current understanding of congenital Zika syndrome.

    Expertise: Flavivirology and immunology. Flavivirus-host interactions.

    The authors thank the reviewer for the comments provided to improve several aspects of the current research. The authors also thank the reviewer for the positive feedback and for highlighting the relevance of our research.

    Reviewer #2 (Evidence, reproducibility and clarity (Required)):

    In the manuscript entitled "Zika virus-induces metabolic alterations in fetal neuronal progenitors that could influence in neurodevelopment during early pregnancy", Javier G.-J. and colleagues investigated the role of cellular metabolism during ZIKA virus infection in hiPSC-derived neural progenitor cells (NPC) at different stage of differentiation. Indeed, the authors use a modified protocol of 2D cultures to obtain early-hiNPC and by continuing the cultures for two additional passages, they obtain late-hiNPCs. These two cell populations are characterized by cell morphology and marker expression. Then they test their susceptibility to ZIKV infection and show that late-hiNPCs are more efficient than early- hiNPCs to support viral replication. Moreover, authors demonstrate that the two cell populations are characterized by different cellular metabolism as glucose consumption is higher in early-hiNPCs than in late-hiNPCs although the overall glycolytic capacity is not different between the two subtypes. However, during ZIKV productive infection, late-hNPCs increased the glucose consumption (Fig. 3). The authors examined the mitochondrial alterations during infection showing different kinetics in early vs. late hiNPCs. Then, they show alterations of expression in genes of the lipid metabolism and content of lipid droplets that follow different kinetics of expression during infection in early vs. late hiNPCs. Overall, no significant differences were observed in the lipid droplet homeostasis between the two subtypes. This is a potentially interesting manuscript as they analyze the susceptibility of subtypes of neural progenitors to ZIKV infection and their metabolic alterations before and during infection. However, there are some concerns listed below.

    Major issues:

    1. It is well established that the NPC maturation during neurodevelopment is complex and cells at different stage of maturation play an important role. The authors propose a model that may recapitulate distinct populations of neural progenitors present during neurodevelopment. They use a modified protocol that it is well described. However, the characterization of these NPC subtypes needs to be improved. The pictures selected to be shown in Fig 1B and C do not highlight the morphology of these cells as described in the text.

    The authors thank the reviewer for the comment and have improved the description of the morphology of the cells constituting the two pools of cells at different stages of differentiation. In addition, the authors have included a new figure (Extended Fig. 1) with different magnifications of the acquired brightfield images for better representation of the morphological differences observed between early and late hi-NPCS.

    Late-hiNPC are more efficient than early-hiNPC in supporting ZIKV replication, however the differences are present exclusively at 56 h post-infection and they are modest (ca. 2-fold). Nevertheless, ZIKV cytopathic effects are similar between the two subtypes at 72 h post-infection. Authors should try to lower the MOI and extend the timing of analysis to up 10 days post-infection. They used MOI of 1, but it would be informative to know whether the different efficiency of viral replication is dependent on the MOI. Furthermore, are the differences between early and late-hiNPCs dependent on expression of entry receptor, or a different interferon response to virus infection or state of cell proliferation?

    The authors thank the reviewer for the insights provided on this matter and appreciate the overall positive response towards the research. After several revisions, the authors have corrected the data using a normalisation method that is expected to rely less on cellular metabolism which may well be disturbed during ZIKV infection. Although still modest, differences in virion release between early and late hi-NPCs are observed at 48 and 56 h.p.i. This data matches the increased accumulation of transcripts. Moreover, in the new version of this manuscript, the cytopathic effects are distinct between hi-NPCs. Regarding the reviewer’ comment on MOI. The authors selected the MOI of 1 due to metabolic dysregulations due to viral infection require a good proportion of cells infected with the inoculum. To support this, the authors highlight the comments from reviewer 1 whom encouraged that the main interpretation should be done at the 24 h timepoint as the population is reflecting alterations due to the initial round of infection rather than differences potentially being masked by cells at different stages of ZIKV replication. In addition, MOI of 1 has been reported elsewhere for ZIKV and other flaviviruses. Although it sounds interesting to the authors the lower MOI exposure for longer period of times, this approach is not feasible to conduct in our models as early hi-NPCs have a length of 3-4 days in culture due to exacerbated cell proliferation. After this time, cells need to be passaged. Similar technical complications will be faced if extending the infection times in late hi-NPCs as these cells require passaging/freezing after day 5. Lastly, the authors consider studying the expression of entry receptors in early and late hi-NPCs to be important in explaining potential differences in viral kinetics yet, the lack of differences in virion release at early timepoints of infection suggest the entry and length of the ZIKV replication cycle is conserved between early and late hi-NPCs. Thus, differences during the ZIKV kinetics potentially due to other mechanisms. For this reason, the authors have included a discussion paragraph in which they highlight potential differences may be due to the development of the native immunity of hi-NPCs during differentiation. It is still controversial whether IFN responses are significant in hi-NPCs, with research suggesting that greater IFN responses are observed upon maturation of NPCs.

    The authors state that that this is the first report showing differential changes in nuclear morphology between neural progenitor cells. They show a main finding that is the perinuclear centers only visible in late but not in early-hiNPC in a supplemental figure. These results are not convincing, and an effort should be made in order to support these claims.

    The authors have now addressed this issue by using confocal microscopy and co-stained DAPI (nuclei) and ZIKV envelope protein to better show the perinuclear centres in late hi-NPCs (Extended Fig. 6). Confocal images of early hi-NPCs with non-perinuclear replication centres than late hi-NPCs was not possible to acquire as early hi-NPCs did not adhere to glass coverslips for more than 24 h.

    Gene expression should be supported by data of protein expression (western blot) of some of the enzymes reported in Fig. 5.

    The authors have detected some of the proteins (western blot) involved in lipid metabolism in both early and late hi-NPCs. The authors screened for FASN, PDK2 and ACACA to validate the findings at the mRNA level. The authors selected 56 h.p.i. as a timepoint to measure proteins mainly due to high ZIKV infection levels but not abundant cell death that facilitates obtaining sufficient material for WB. The image of the WB is included in the Extended Fig. 4 of the new version of this manuscript.

    Minor issues:

    1. Since this work is based on in vitro data, I would suggest using the term infection rather than challenge when referring to infection experiments.

    The authors have edited the document and replaced the terminology for what was suggested by the reviewer.

    Improve quality of the graphs. Enlarge symbols as in Fig. 6. Try to use linear scales as the differences are not dramatic and a linear scale would highlight them better.

    The authors thank the reviewer for the observation on the graphs. The authors have enlarged the symbols where possible – in some cases this could not be conducted as otherwise the error bars were not visible for example when displaying the viral output. The authors appreciate the comment on plotting the data on a linear scale to reflect subtle differences to a larger magnitude yet, this may well fit into misleading the interpretation of some results due to the nature of the analysis (e.g., fold-change); where possible, these changes have been applied.

    CROSS-CONSULTATION COMMENTS

    I agree with all comments of Rev#1 and 3. Some/many of the claims are made without the supporting evidence.

    Reviewer #2 (Significance (Required)):

    Many papers have reported the efficient ZIKV infection of neural progenitor cells that have been derived from the reprogramming of human pluripotent stem cells (PSC). Most of this literature has not been cited. In fact, ZIKV virus infects human PSC-derived brain neural progenitors causing heightened cell toxicity, dysregulation of cell-cycle and reduced cell growth as reported in many papers. In this manuscript, the advancement consists in having used NPC subtypes that are at different stage of differentiation and having studied their susceptibility to ZIKV infection. Then, the author analyze the fluctations of the glucose and lipid metabolism during infection.

    The audiance that is interested in this manuscript are virologists and , in particular, experts in arboviruses that are for the most part neurotropic viruses. In addition, this is a topic for experts of neurodevelopment.

    My expertise is virology. Key words: Zika virus, neural progenitors, antivirals.

    The authors thank the reviewer for the productive constructivism provided to this research. We would like to highlight that most of the literature in Zika infection and hi-NPCs was not included as this does not directly focuses on metabolism. However, as the document has been amended, some of this literature has now been included to contextualise the current knowledge of ZIKV infectivity in relevant in vitro systems.

    Reviewer #3 (Evidence, reproducibility and clarity (Required)):

    Summary The authors present the analysis of the cellular metabolism in Zika virus-infected human neural cell cultures differentiated from fibroblast-derived hiPSCs. Neural cell cultures from days 12-15 after hiPSC induction to neuronal lineages were designated as 'early neuronal progenitors' (early hi-NPCs), while cultures from days 18-21 post induction were designated as 'late neuronal progenitors' (late hi-NPCs). The outcomes of ZIKV infection in both types of neural cell cultures were analyzed. The authors first characterized several viral parameters of infection including accumulation of viral RNA and proteins and ZIKV replication, as well as nuclear morphology of infected cells. The major body of evidence in the presented study encompasses the comparative analysis of several parameters of glucose and lipid metabolism as well as mitochondrial function and cellular lipid accumulation and storage. The authors postulate that ZIKV replicates differentially in early and late hi-NPCs, inducing some common metabolic responses like upregulated glycolytic capacity, as well as responses unique to either early or late differentiation stage of the neural cultures like the lipid metabolism, lipid droplet homeostasis or mitochondrial function. The authors propose that the differential metabolic responses to ZIKV infection in early and late neural progenitors might help to explain the differences in the fetal brain damage in early or late pregnancy.

    Major comments

    1. The authors refer to the induced neural cell cultures as monocultures of human neural progenitors. This assumption is incorrect and it undermines the proper interpretation of the presented data. The neural lineage induction of iPSC produces neural cell cultures, which depending on the differentiation stage consist of neural stem cells of neuroepithelial-like morphology (Nestin and Sox-2 positive) which differentiate further to more elongated early progenitors with radial glial cell morphology (Nestin, sox2 and PAX-6 positive). Radial glial cells differentiate further into several neural lineages including oligodendrocyte precursors, astrocytes (S100B - positive) and intermediate progenitors (TBR2 - positive). The intermediate progenitors then divide to produce one progenitor and one post mitotic immature neuron (still TBR-positive and also beta II tubulin, Tuj1/TuB3-positive). The neurons then mature further and become NeuN and MAP2-positive. All the above mentioned differentiation markers were used by the authors to characterize the early and late cell cultures. According to the data presented in Fig 1E, both cultures were positive for all the markers indicating that they are not monocultures. The immunofluorescence data provided in Extended Fig.1 in support of the analysis presented in Fig. 1E clearly shows that both cultures stain similarly for Tuj1 (also known as TuB3), a marker of post-mitotic neurons, which are clearly present in both cultures. Abundant MAP2 positive cells (marker of mature neurons) in "early hi-NPCs" presented in extended figure 1B is quite surprising and confusing and is not presented for 1A panel - "late hi-NPCs", which suggests that perhaps figure 1A and 1B were mislabeled. The immunostaining for PAX6 presented in the same figure 1B presents strong cytoplasmic staining while PAX6 is expected to be detected in the cell nucleus, suggesting that the red staining comes most probably from the overexposed background. On a closer look the PAX6 staining presented on panel 1A shows weak and underexposed but most probably positive nuclear staining. Of note, the authors argue that the only significant difference in the staining for differentiation markers was observed for PAX6 (early neural progenitor marker) which was higher in late cultures than the early ones. In the same figure all the pictures in red are generally underexposed except from PAX6, while the DAPI staining is overexposed. This makes the interpretation of the data difficult especially when looking at the merged images. Despite the overall confusion with this part of results it is clear that the early and late cultures consist of different cell types including early and intermediate progenitors as well as astrocytes (S100B - positive), probably glial cells (not tested for) and post-mitotic neurons. The relative ratio of these populations might be different in the two cultures, however the cultures are not monocultures of early or late neural progenitors. They might contain different ratios of both and thus respond differently to the infection. Therefore, the metabolic and virological analysis performed globally on these cell cultures might just as well reflect the cell type ratio related effects rather than the differential responses of the early or late progenitors. This has never been addressed or explained by the authors.

    The authors thank the reviewer for the comment and observation regarding the nomenclature used in the preliminary version of this document. The authors used the terminology “subtypes” not to refer as a monoculture of a particular cell type within the developing brain but to the group of cells that share a metabolic profile. This was due to the abundance of markers to characterize cellular lineages within each population reflected to be similar at the two stages of differentiation. The authors showed cell type differences only in the ratio of glial Pax6 +ve cells (Fig. 1D) whilst greater significant differences were observed in the metabolism between both cultures. Thus, differences to viral responses are most likely to obey to the maturation stage (longer times under culture) rather than different ratio of brain cells. Nevertheless, the authors acknowledge the confusion that the terminology “neuronal subtypes” could have caused and have changed it to “cortical progenitors at different stages of differentiation” or “hi-NPCs”. The authors would like to address the reviewer’ comment on the data presented in Extended Fig. 2 (MAP2 staining in early hi-NPCs). This is not a mislabel between the figure but rather a demonstration that the staining was observed in early hi-NPCs. This staining was not performed in late hi-NPCs thus not showed. Moreover, the data used to quantify the presence of brain markers in early and late hi-NPCs was generated by flow cytometry and not by confocal imaging (ICC). The ICC included in the Extended Fig. 2 was used to demonstrate the antibody staining and to discard potential unspecific antibody binding that may generate false positive detection by flow cytometry. The authors agree with the reviewer that the staining for Pax6 was not clear in the previous version of this manuscript and have redone the figure.

    The data presented is often based on the analysis of the immunofluorescence images, however the quality of the images presented (resolution, magnification, over or under saturation) is often insufficient to support the findings and claims. The most striking example are images supporting the analysis of nuclear morphology in ZIKV-infected cells presented in the Extended figure 2.

    The authors thank the reviewer and would like to clarify that, although displaying some confocal images within the figure, the graphs were generated from data collected by imaging flow cytometry (Imagestream). In response to the comments from reviewer 1 and 2, the authors explained the advantages and disadvantages of using Imagestream over confocal imaging. The main rationale behind this is the greater sample size and unbiased acquisition of data yet compromising the immunostaining resolution. Regarding the displayed confocal images within the text, the authors have redone the figures and/or acquired new images to correct the issues of oversaturation/overexposure. The authors acknowledge that the data interpretation from the nuclear imaging needs to be done with caution due to its low quality yet, as early hi-NPCs do not adhere to glass as efficiently as late, any confocal acquisition will be limited to plastic-based materials ending in lower resolution. Thus, thanking the reviewers for the observation, the authors have now conducted confocal microscopy co-stained for ZIKV envelope protein and nuclei (DAPI) in late hi-NPCs to better display the nuclear morphology upon infection and the replication centres.

    Some of the claims are made without the supporting evidence. For example in the discussion the authors claim that "Our main finding was that viral perinuclear replication centers (26) (white arrows, Supplementary Figure 2) were only visible in late hi-NPCs and not in early hi-NPCs". This conclusion is made based presumably on Extended Figure 3 (Figure 2 does not have arrows) based on the nuclear morphology of infected cells without staining for any of the viral proteins localizing to the replication centers. Despite low image quality similar crescent-shaped nuclei to the ones indicated by the arrows in "late hi-NPCs" and many more of them are visible in "early hi-NPCs" (Extended Fig.2), however the authors seem ignore them.

    The authors thank the reviewer for the comment and notify that the respective amendments have been done. The data presented in the new version of this manuscript related to the nuclear morphology is from a new dataset of co-stained ZIKV envelope and DAPI (Extended Fig. 6).

    Based on the arguments presented above the conclusions are not convincing, lacking the supporting evidence or ignoring some of the essential facts of the chosen experimental system.

    The authors thank the reviewer for the criticism on the research and notify that several changes throughout the document have been made to support the claims and conclusions of this manuscript.

    The study in presented form, where all the analysis is performed in globally is not informative and would require the characterization of the metabolic and virological responses in different cell populations as characterized by the expression of neural differentiation markers. Alternatively, the population sizes of different types of cells should be determined and accounted for when analyzing the experimental data. It should be determined which types of cells are targeted by the Zika virus and replicate the virus. It could be done by, for example, co-staining for viral and neural differentiation markers. This would however require the entirely different experimental approach from the one presented in this manuscript.

    The authors thank the reviewer for the comments and inputs provided to our research. We would like to highlight that, although it would be highly relevant to distinguish ZIKV infection and metabolic dysregulations in different cell populations; all the published literature in neuronal progenitors and ZIKV infection do not contain insights on the infection per cell populations. This may be due to the difficulties in isolating/sorting populations of immature cells within the pool of in vitro cortical differentiation whilst achieving significant cell survival. The authors would like to address this comment by highlighting that the population sizes of different types of cells within the pools of early and late hi-NPCs was accounted as a starting point of this research (Fig 1D). This characterization was done using a commercially available kit aimed for the sorting of human neural stem cells. These results showed small differences in the ratio of cell types present in early and late hi-NPC cultures. ZIKV has been reported to target all brain cells with lower impact on mature neurons, which arguably will be present in either of the cortical progenitor pools used for this research. Thus, the authors focused on interpreting the results as an impact of ZIKV infection in the overall metabolism of each pool of hi-NPCs used in this study. Metabolism that is likely influenced by most of the cell types present within each hi-NPC pool.

    Some methods are not explained clearly. For the metabolic analysis like Oleic acid oxidation and others, it is not clear at which step of the protocol ZIKV infection was performed. In "Extracellular lactate measurement" freshly made running buffer is mentioned but no composition of the buffer is provided.

    The authors thank the observation of the reviewer and have now amended the method section to clearly state several methods that could have been difficult to understand in the previous version of this manuscript.

    Minor comments

    1. Figure 2G shows the survival of ZIKV-infected hi-NPC subtypes. Clearly for "late hi-NPCs" there is 50% cell death at 56 hours post infection and about 70% death at 72 hours. Subsequent analysis of many metabolic parameters is measured at 56 and sometimes even at 72 hours when the significant differences in responses are observed for example Fig. 3 B and C. The role of cell death in the critical analysis of these parameters is not provided.

    The authors appreciate the reviewers’ comment and would like to emphasise that all the analysis at every timepoint during ZIKV infection was normalised to the cell number present at the time. The use of different timepoints for different measurements (e.g., 56 and 72 h.p.i.) were selected by the authors to adjust the used methodology. In short, 56 h.p.i. was used as the last timepoint to study mitochondrial and lipid dysregulations by imagestream as we observed the highest viral output with sufficient cell survival in early hi-NPCs; 72 h.p.i. will not give sufficient cell number for counting 10.000 cells by Imagestream. However, 72 h.p.i. was used to assess the genetic expression of metabolically relevant genes as the material obtained was sufficient and will signify the interpretation of the latest point during the ZIKV kinetics in our models.

    There are multiple spelling mistakes throughout. The professional terminology of the virology part of the study is often missing. Example the levels of ZIKV RNA measured in the infected cells are designated as "transcriptional levels of ZIKV" which seems incorrect as the level of the genomes is the effect of viral replication and not transcription.

    The authors thank the reviewer for the observation made and have corrected the terminology throughout the document. The spelling has been checked.

    CROSS-CONSULTATION COMMENTS

    Agree with Rev #1 comment on Fig 2D and the levels of NS1. It is striking that the levels of expression drop below the level detected at 48h while the ZIKV E protein continues to accumulate (Fig. 2E) at the same time. Both proteins are translated from the same polyprotein and are processed similarly. It is also confusing that at 48h only about 5% live cells express NS1 while at the same time 15% of live cells express E protein. In my experience both proteins are expressed in all infected cells. The reason why 10% of infected and still alive cells would express only E and not NS1 is difficult to conceive.

    The authors have addressed this issue and highlighted that the limitations of the Imagestream technique may have caused this oddity due to loss of signal detection by compensation. The experiments conducted to correct this manuscript include a new detection by flow cytometry using a smaller panel of markers and labelled-secondary antibodies that will provide greater signal. This approach has demonstrated that detection levels of NS1 mirror those of Envelope.

    I stand with the Rev #1 in asking what specific CNS pathology is dependent on the reported metabolic changes? There is no attempt to link the findings to ZIKV-induced CNS pathologies.

    The authors have included discussion paragraphs to link the observed phenotypes during ZIKV infection to relevant CNS pathologies.

    In relation to major comment 2 from the Rev #2, I disagree that Late-hiNPC are more efficient than early-hiNPC in supporting ZIKV replication. 2-fold difference in a viral plaquing assay falls within the error of the assay which is usually quite substantial for the plaquing assay. Lack of error bars for late hi-NPCs 56 h raise my suspicion as to how real is this effect in viral replication.

    The authors would like to clarify that the error bars were present in the graph but the size of the symbol difficulted the visualisation. After correcting all the datasets to a less dependent metabolic assay (LDH based survival), the differences in virion release are observed at 48 and 56 h.p.i., like that of ZIKV replication measured by qPCR. The authors also clarify that, although a 2-fold difference may fall within the technical error of plaque assay, minor differences observed in our research are potentially greater if displayed in a different form as our analysis comprises three independent ZIKV infection conducted in three patients’ lines and further normalisation to cell number.

    I stand behind Rev #2 major comment 2 there there is no evidence to support the claims about the nuclear morphology or the replication centers

    This issue has now been addressed (Extended Fig. 6).

    Reviewer #3 (Significance (Required)):

    Despite global research efforts the course of Zika virus infection of the fetal brain during pregnancy is not clear. Among many knowledge gaps, the molecular determinants of differential outcomes of Zika virus infection during early or late pregnancy are unknown. The study aims to address this highly significant issue by focusing on the metabolic responses of cells to infection. In the presented form the study however, fails to deliver significant progress in our understanding of Zika virus infection of developing fetal brain. The experimental design and the quality of presented data does not allow to make unbiased conclusions and to support the claims. My expertise is in iPSC-derived neural cell cultures, molecular virology, in particular that of Flaviviruses like Zika virus and hepatitis c virus and confocal microscopy. I am not familiar with metabolic techniques and I find the description of methods for this part of the study insufficient to fully understand the experimental approach.

    The authors thank the reviewer for the valuable inputs provided to the research. We would like to highlight that, whereas possible, new sets of experiments have been conducted to better support some of the conclusions and claims. Lastly, the authors would like to mention that the method section has been corrected for a better understanding of the metabolic assays and data normalisation. Within the text, paragraphs have been added to clarify the nature of the results and data acquisition.

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    Referee #3

    Evidence, reproducibility and clarity

    Summary

    The authors present the analysis of the cellular metabolism in Zika virus-infected human neural cell cultures differentiated from fibroblast-derived hiPSCs. Neural cell cultures from days 12-15 after hiPSC induction to neuronal lineages were designated as 'early neuronal progenitors' (early hi-NPCs), while cultures from days 18-21 post induction were designated as 'late neuronal progenitors' (late hi-NPCs). The outcomes of ZIKV infection in both types of neural cell cultures were analyzed. The authors first characterized several viral parameters of infection including accumulation of viral RNA and proteins and ZIKV replication, as well as nuclear morphology of infected cells. The major body of evidence in the presented study encompasses the comparative analysis of several parameters of glucose and lipid metabolism as well as mitochondrial function and cellular lipid accumulation and storage. The authors postulate that ZIKV replicates differentially in early and late hi-NPCs, inducing some common metabolic responses like upregulated glycolytic capacity, as well as responses unique to either early or late differentiation stage of the neural cultures like the lipid metabolism, lipid droplet homeostasis or mitochondrial function. The authors propose that the differential metabolic responses to ZIKV infection in early and late neural progenitors might help to explain the differences in the fetal brain damage in early or late pregnancy.

    Major comments

    1. The authors refer to the induced neural cell cultures as monocultures of human neural progenitors. This assumption is incorrect and it undermines the proper interpretation of the presented data. The neural lineage induction of iPSC produces neural cell cultures, which depending on the differentiation stage consist of neural stem cells of neuroepithelial-like morphology (Nestin and Sox-2 positive) which differentiate further to more elongated early progenitors with radial glial cell morphology (Nestin, sox2 and PAX-6 positive). Radial glial cells differentiate further into several neural lineages including oligodendrocyte precursors, astrocytes (S100B - positive) and intermediate progenitors (TBR2 - positive). The intermediate progenitors then divide to produce one progenitor and one post mitotic immature neuron (still TBR-positive and also beta II tubulin, Tuj1/TuB3-positive). The neurons then mature further and become NeuN and MAP2-positive. All the above mentioned differentiation markers were used by the authors to characterize the early and late cell cultures. According to the data presented in Fig 1E, both cultures were positive for all the markers indicating that they are not monocultures. The immunofluorescence data provided in Extended Fig.1 in support of the analysis presented in Fig. 1E clearly shows that both cultures stain similarly for Tuj1 (also known as TuB3), a marker of post-mitotic neurons, which are clearly present in both cultures. Abundant MAP2 positive cells (marker of mature neurons) in "early hi-NPCs" presented in extended figure 1B is quite surprising and confusing and is not presented for 1A panel - "late hi-NPCs", which suggests that perhaps figure 1A and 1B were mislabeled. The immunostaining for PAX6 presented in the same figure 1B presents strong cytoplasmic staining while PAX6 is expected to be detected in the cell nucleus, suggesting that the red staining comes most probably from the overexposed background. On a closer look the PAX6 staining presented on panel 1A shows weak and underexposed but most probably positive nuclear staining. Of note, the authors argue that the only significant difference in the staining for differentiation markers was observed for PAX6 (early neural progenitor marker) which was higher in late cultures than the early ones. In the same figure all the pictures in red are generally underexposed except from PAX6, while the DAPI staining is overexposed. This makes the interpretation of the data difficult especially when looking at the merged images. Despite the overall confusion with this part of results it is clear that the early and late cultures consist of different cell types including early and intermediate progenitors as well as astrocytes (S100B - positive), probably glial cells (not tested for) and post-mitotic neurons. The relative ratio of these populations might be different in the two cultures, however the cultures are not monocultures of early or late neural progenitors. They might contain different ratios of both and thus respond differently to the infection. Therefore, the metabolic and virological analysis performed globally on these cell cultures might just as well reflect the cell type ratio related effects rather than the differential responses of the early or late progenitors. This has never been addressed or explained by the authors.
    2. The data presented is often based on the analysis of the immunofluorescence images, however the quality of the images presented (resolution, magnification, over or under saturation) is often insufficient to support the findings and claims. The most striking example are images supporting the analysis of nuclear morphology in ZIKV-infected cells presented in the Extended figure 2.
    3. Some of the claims are made without the supporting evidence. For example in the discussion the authors claim that "Our main finding was that viral perinuclear replication centers (26) (white arrows, Supplementary Figure 2) were only visible in late hi-NPCs and not in early hi-NPCs". This conclusion is made based presumably on Extended Figure 3 (Figure 2 does not have arrows) based on the nuclear morphology of infected cells without staining for any of the viral proteins localizing to the replication centers. Despite low image quality similar crescent-shaped nuclei to the ones indicated by the arrows in "late hi-NPCs" and many more of them are visible in "early hi-NPCs" (Extended Fig.2), however the authors seem ignore them.
    4. Based on the arguments presented above the conclusions are not convincing, lacking the supporting evidence or ignoring some of the essential facts of the chosen experimental system.
    5. The study in presented form, where all the analysis is performed in globally is not informative and would require the characterization of the metabolic and virological responses in different cell populations as characterized by the expression of neural differentiation markers. Alternatively, the population sizes of different types of cells should be determined and accounted for when analyzing the experimental data. It should be determined which types of cells are targeted by the Zika virus and replicate the virus. It could be done by, for example, co-staining for viral and neural differentiation markers. This would however require the entirely different experimental approach from the one presented in this manuscript.
    6. Some methods are not explained clearly. For the metabolic analysis like Oleic acid oxidation and others, it is not clear at which step of the protocol ZIKV infection was performed. In "Extracellular lactate measurement" freshly made running buffer is mentioned but no composition of the buffer is provided.

    Minor comments

    1. Figure 2G shows the survival of ZIKV-infected hi-NPC subtypes. Clearly for "late hi-NPCs" there is 50% cell death at 56 hours post infection and about 70% death at 72 hours. Subsequent analysis of many metabolic parameters is measured at 56 and sometimes even at 72 hours when the significant differences in responses are observed for example Fig. 3 B and C. The role of cell death in the critical analysis of these parameters is not provided.
    2. There are multiple spelling mistakes throughout. The professional terminology of the virology part of the study is often missing. Example the levels of ZIKV RNA measured in the infected cells are designated as "transcriptional levels of ZIKV" which seems incorrect as the level of the genomes is the effect of viral replication and not transcription.

    Referees cross-commenting

    Agree with Rev #1 comment on Fig 2D and the levels of NS1. It is striking that the levels of expression drop below the level detected at 48h while the ZIKV E protein continues to accumulate (Fig. 2E) at the same time. Both proteins are translated from the same polyprotein and are processed similarly. It is also confusing that at 48h only about 5% live cells express NS1 while at the same time 15% of live cells express E protein. In my experience both proteins are expressed in all infected cells. The reason why 10% of infected and still alive cells would express only E and not NS1 is difficult to conceive.

    I stand with the Rev #1 in asking what specific CNS pathology is dependent on the reported metabolic changes? There is no attempt to link the findings to ZIKV-induced CNS pathologies.

    In relation to major comment 2 from the Rev #2, I disagree that Late-hiNPC are more efficient than early-hiNPC in supporting ZIKV replication. 2-fold difference in a viral plaquing assay falls within the error of the assay which is usually quite substantial for the plaquing assay. Lack of error bars for late hi-NPCs 56 h raise my suspicion as to how real is this effect in viral replication.

    I stand behind Rev #2 major comment 2 there there is no evidence to support the claims about the nuclear morphology or the replication centers

    Significance

    Despite global research efforts the course of Zika virus infection of the fetal brain during pregnancy is not clear. Among many knowledge gaps, the molecular determinants of differential outcomes of Zika virus infection during early or late pregnancy are unknown. The study aims to address this highly significant issue by focusing on the metabolic responses of cells to infection. In the presented form the study however, fails to deliver significant progress in our understanding of Zika virus infection of developing fetal brain. The experimental design and the quality of presented data does not allow to make unbiased conclusions and to support the claims.

    My expertise is in iPSC-derived neural cell cultures, molecular virology, in particular that of Flaviviruses like Zika virus and hepatitis c virus and confocal microscopy. I am not familiar with metabolic techniques and I find the description of methods for this part of the study insufficient to fully understand the experimental approach.

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    Referee #2

    Evidence, reproducibility and clarity

    In the manuscript entitled "Zika virus-induces metabolic alterations in fetal neuronal progenitors that could influence in neurodevelopment during early pregnancy", Javier G.-J. and colleagues investigated the role of cellular metabolism during ZIKA virus infection in hiPSC-derived neural progenitor cells (NPC) at different stage of differentiation. Indeed, the authors use a modified protocol of 2D cultures to obtain early-hiNPC and by continuing the cultures for two additional passages, they obtain late-hiNPCs. These two cell populations are characterized by cell morphology and marker expression. Then they test their susceptibility to ZIKV infection and show that late-hiNPCs are more efficient than early- hiNPCs to support viral replication. Moreover, authors demonstrate that the two cell populations are characterized by different cellular metabolism as glucose consumption is higher in early-hiNPCs than in late-hiNPCs although the overall glycolytic capacity is not different between the two subtypes. However, during ZIKV productive infection, late-hNPCs increased the glucose consumption (Fig. 3). The authors examined the mitochondrial alterations during infection showing different kinetics in early vs. late hiNPCs. Then, they show alterations of expression in genes of the lipid metabolism and content of lipid droplets that follow different kinetics of expression during infection in early vs. late hiNPCs. Overall, no significant differences were observed in the lipid droplet homeostasis between the two subtypes.
    This is a potentially interesting manuscript as they analyze the susceptibility of subtypes of neural progenitors to ZIKV infection and their metabolic alterations before and during infection. However, there are some concerns listed below.

    Major issues:

    1. It is well established that the NPC maturation during neurodevelopment is complex and cells at different stage of maturation play an important role. The authors propose a model that may recapitulate distinct populations of neural progenitors present during neurodevelopment. They use a modified protocol that it is well described. However, the characterization of these NPC subtypes needs to be improved. The pictures selected to be shown in Fig 1B and C do not highlight the morphology of these cells as described in the text.
    2. Late-hiNPC are more efficient than early-hiNPC in supporting ZIKV replication, however the differences are present exclusively at 56 h post-infection and they are modest (ca. 2-fold). Nevertheless, ZIKV cytopathic effects are similar between the two subtypes at 72 h post-infection. Authors should try to lower the MOI and extend the timing of analysis to up 10 days post-infection. They used MOI of 1, but it would be informative to know whether the different efficiency of viral replication is dependent on the MOI. Furthermore, are the differences between early and late-hiNPCs dependent on expression of entry receptor, or a different interferon response to virus infection or state of cell proliferation?
    3. The authors state that that this is the first report showing differential changes in nuclear morphology between neural progenitor cells. They show a main finding that is the perinuclear centers only visible in late but not in early-hiNPC in a supplemental figure. These results are not convincing, and an effort should be made in order to support these claims.
    4. Gene expression should be supported by data of protein expression (western blot) of some of the enzymes reported in Fig. 5.

    Minor issues:

    1. Since this work is based on in vitro data, I would suggest using the term infection rather than challenge when referring to infection experiments.
    2. Improve quality of the graphs. Enlarge symbols as in Fig. 6. Try to use linear scales as the differences are not dramatic and a linear scale would highlight them better.

    Referees cross-commenting

    I agree with all comments of Rev#1 and 3. Some/many of the claims are made without the supporting evidence.

    Significance

    Many papers have reported the efficient ZIKV infection of neural progenitor cells that have been derived from the reprogramming of human pluripotent stem cells (PSC). Most of this literature has not been cited. In fact, ZIKV virus infects human PSC-derived brain neural progenitors causing heightened cell toxicity, dysregulation of cell-cycle and reduced cell growth as reported in many papers. In this manuscript, the advancement consists in having used NPC subtypes that are at different stage of differentiation and having studied their susceptibility to ZIKV infection. Then, the author analyze the fluctations of the glucose and lipid metabolism during infection.

    The audiance that is interested in this manuscript are virologists and , in particular, experts in arboviruses that are for the most part neurotropic viruses. In addition, this is a topic for experts of neurodevelopment.

    My expertise is virology. Key words: Zika virus, neural progenitors, antivirals.

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    Referee #1

    Evidence, reproducibility and clarity

    This manuscript reports an investigation into the metabolic alterations induced by Zika virus (ZIKV) infection in human neuronal progenitor cells. The authors differentiated human iPSCs to derive neuronal progenitor cells (NPCs) at different days of incubation to represent the different stages of foetal CNS development. They found differences in the levels of ZIKV NS1 proteins as well as marginal differences in ZIKV titres in infected early and late hi-NPCs. Correspondingly, they also showed differences in glucose consumption, lipid metabolism and mitochondrial stress in ZIKV-infected early and late hi-NPCs. They concluded that differences in energy metabolism in neuronal progenitors both before and upon infection may contribute to the brain damage observed in congenital Zika syndrome.

    The evidence supporting a role for dysregulated metabolism in mediating the pathogenesis of congenital Zika syndrome is gaining traction and findings from this study could add to this body of knowledge. However, in its present form, this study has several gaps that limit the extent to which it informs on the clinical pathogenesis of congenital Zika syndrome.

    Major concerns:

    1. The most important concern in this study is the strain of ZIKV used in all of the studies. ZIKV MP1751 was isolated from a mosquito and belongs to the African lineage of ZIKV. Unlike the Asian lineage ZIKV isolated from Latin America and French Polynesia, gestational infection with ZIKV of the African lineage has not been clinically associated with increased risk of foetal abnormality. It is thus uncertain how the changes observed in this study relates to the observed neonatal pathology. Perhaps a way to address this issue is to argue that a difference in these lineages it the ability of the virus to evade systemic and endothelial innate immune responses to cross the placental and blood brain barriers (several papers on attenuated ZIKV have shown this data). Once these barriers are breached, strain differences should not materially affect the similar pathogenic processes in neuronal cells, as also been shown by others using the MR766 strain of ZIKV. Such a discussion would be helpful to contextualise the clinical relevance of this study.
    2. While the metabolic changes upon ZIKV infection are all interesting, how these changes affect CNS development is unclear. Figure 2F shows marginal impact on productive ZIKV infection and comparable extent of cell death in early and late hi-NPCs. What specific CNS pathology is dependent on the reported metabolic changes?
    3. Figure 2D: The most remarkable virological difference observed is the significant difference in cytoplasmic NS1 levels between early and late hi-NPCs at 56 hpi. Although the data in Fig 2D in general could have been compromised by the quality of the anti-NS1 mAb (the anti-E assay in Fig 2E used polyclonal antibody), it would have been useful to test for NS1 expression using western blot on a denaturing gel (and appropriate anti-NS1 antibody). The mAb used in this study binds a conformational epitope on NS1. The difference in data in Figure 2D and 2E could thus have been misfolding of NS1. Misfolded NS1 could contribute to ER stress that could be important for dysregulated CNS development. A more detailed investigation of the finding in Figure 2D could be highly informative.
    4. Figure 3A and related text: The fold-change in GLUT1, HK-1 and GAPDH expression are shown in log10 scale. In this scale, 1 would indicate 10-fold increase in expression. The data in Figure 3A are entirely inconsistent with the description in the related text. Which is correct?

    Minor concerns:

    1. Figure 5: The effects of ZIKV infection on the mitochondria of hi-NPCs are interesting and the comparison between ZIKV-infected and uninfected cells in the same culture is a strength of this study. It would be helpful to readers if the authors could include a discussion on the kinetics of ZIKV infection; diminished differences at 48 and 72 hours could be due to the mixture of cells infected at inoculation and hence observed at 24 hours and newly infected cells that were negative for ZIKV E protein at 24 hours. Emphasis should thus be on the 24 h data in Figures 5 C-E.
    2. Hi-NPCs likely have a diploid genome and thus a finite lifespan. Using the term "cell line" to describe these cells is technically incorrect. Please consider using other terms, such as cell strain.
    3. Discussion section, 3rd paragraph, lines 6-7. The authors suggest thermal decay as an explanation for their observation yet Figure 2B argues against this explanation. Moreover, Kostyuchenko et al (Nature 2016; 533:425-8) have also shown that ZIKV is relatively thermostable. This explanation offered by the authors lack supporting evidence.
    4. Discussion section, 3rd paragraph, line 16 and Supplementary Figure 2. I believe the authors are referring to Supplementary Figure 3 and not 2. The indentations observed could be due to ZIKV replication although the data, as presented is not convincing. Co-staining for ZIKV E protein would be useful.

    Referees cross-commenting

    • I fully agree with both Reviewers #2 and #3 on the quality of the immunofluorescence images and that these images alone are not sufficiently convincing to support the inferences the authors are making.
    • I also appreciate the first major comment from Reviewer #3. That is important insight and the authors should test their assumption that they have monocultures of human progenitor cells.

    Significance

    The focus on the metabolism and mitochondrial stress in ZIKV infected neuronal progenitors is interesting and could fill an important gap in knowledge on Zika pathogenesis. The study uses human iPSC derived NPCs instead of animal cells, which is also more clinically relevant than animal models. The findings would thus be of interest to all who are interested in Zika pathogenesis as well as therapeutic/vaccine development. If the above concerns could be addressed, the findings in this study could form the missing links in our current understanding of congenital Zika syndrome.

    Expertise: Flavivirology and immunology. Flavivirus-host interactions.

  5. Version published to 10.1101/2022.08.24.505080v1 on bioRxiv