CAR T cells expressing a bacterial virulence factor trigger potent bystander antitumour responses in solid cancers

Chimeric antigen receptor T cells (CAR T cells) are effective against haematologic malignancies. However, in solid tumours, their potency is hampered by local immunosuppression and by the heterogeneous expression of the antigen that the CAR targets. Here we show that CAR T cells expressing a pluripotent pro-inflammatory neutrophil-activating protein (NAP) from Helicobacter pylori trigger endogenous bystander T-cell responses against solid cancers. In mice with subcutaneous murine pancreatic ductal adenocarcinomas, neuroblastomas or colon carcinomas, CAR(NAP) T cells led to slower tumour growth and higher survival rates than conventional mouse CAR T cells, regardless of target antigen, tumour type and host haplotype. In tumours with heterogeneous antigen expression, NAP secretion induced the formation of an immunologically ‘hot’ microenvironment that supported dendritic cell maturation and bystander responses, as indicated by epitope spreading and infiltration of cytotoxic CD8+ T cells targeting tumour-associated antigens other than the CAR-targeted antigen. CAR T cells armed with NAP neither increased off-tumour toxicity nor hampered the efficacy of CAR T cells, and hence may have advantageous translational potential.

D irect electrical stimulation of the brain is a technique for modulating brain activity that can help treat a variety of brain dysfunctions and facilitate brain functions 1-3 . For example, deep brain stimulation (DBS) is effective in neurological disorders 4 such as Parkinson's disease 5 and epilepsy 6 , and holds promise for neuropsychiatric disorders such as chronic pain 7 , treatment-resistant depression 8 and obsessive-compulsive disorder 9 . Direct electrical stimulation also has the potential to modulate brain functions such as learning 10 , and for use in investigating their neural substrates, for example, in speech production 11 and sensory processing 12 .
Although the mechanism of action by which direct electrical stimulation alters brain activity is still unknown 4 , studies have shown that stimulation alters the activity of multiple brain regions (both local and long range 4,13-17 ) distributed across large-scale brain networks. This network-level stimulation effect has been observed with various signal modalities such as local field potential (LFP) 16 ,electrocorticogram (ECoG) 13,17 , functional magnetic resonance imaging (fMRI) 15 and diffusion tensor imaging (DTI) 14 . These observations highlight the essential need for modelling the effect of stimulation on large-scale multiregional brain network activity, which has largely not been possible to date. Such modelling is especially important when the temporal pattern of stimulation needs to change in real time and when the activity of multiple brain regions needs to be monitored. For example, closed-loop DBS therapies for neurological and neuropsychiatric disorders 1-3,18-21 aim to change the stimulation pattern (for example, the frequency and amplitude of a stimulation pulse train) in real time on the basis of feedback of changes in brain activity. In addition, neural feedback may need to be provided from multiple brain regions 1-3,21-23 , for example, in neuropsychiatric disorders that involve a large-scale multiregional brain network whose functional organization is not well understood [24][25][26] . Despite its importance across a wide range of applications, establishing the ability to predict how ongoing stimulation (input) drives the time evolution (that is, dynamics) of large-scale multiregional brain network activity (output) remains elusive 1,18 .
Computational modelling studies to date have largely focused on building biophysical models of spiking neurons. Biophysical models can provide valuable insights into the mechanisms of action of stimulation-for example, in explaining population-level disease-specific observations especially for Parkinson's disease [27][28][29][30][31] and epilepsy 32,33 -and guide the design of open-loop stimulation patterns using numerical simulations 34,35 . However, biophysical models are typically for disease-specific brain regions, require some knowledge of their functional organization (for example, the cortical-basal-ganglia network in Parkinson's disease [27][28][29]31 ) and involve a large number of nonlinear model parameters that can be challenging to fit to experimental data from an individual 33 . Thus, biophysical models are difficult to generalize to modelling how stimulation drives large-scale multiregional brain network dynamics in an individual, especially in neuropsychiatric disorders where the disease-relevant brain networks are not well characterized [24][25][26] .
An alternative approach to biophysical models is data-driven modelling, as suggested by computer simulations 18,36,37 . However, previous data-driven studies of the brain [38][39][40][41][42] have not aimed at modelling the dynamic response of large-scale multiregional brain networks to ongoing stimulation. Some studies have built models of brain structural connectivity using diffusion-weighted imaging Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation Yuxiao Yang 1,7 , Shaoyu Qiao 2,7 , Omid G. Sani 1 , J. Isaac Sedillo 2 , Breonna Ferrentino 2 , Bijan Pesaran 2,3,4 and Maryam M. Shanechi 1,5,6 Direct electrical stimulation can modulate the activity of brain networks for the treatment of several neurological and neuropsychiatric disorders and for restoring lost function. However, precise neuromodulation in an individual requires the accurate modelling and prediction of the effects of stimulation on the activity of their large-scale brain networks. Here, we report the development of dynamic input-output models that predict multiregional dynamics of brain networks in response to temporally varying patterns of ongoing microstimulation. In experiments with two awake rhesus macaques, we show that the activities of brain networks are modulated by changes in both stimulation amplitude and frequency, that they exhibit damping and oscillatory response dynamics, and that variabilities in prediction accuracy and in estimated response strength across brain regions can be explained by an at-rest functional connectivity measure computed without stimulation. Input-output models of brain dynamics may enable precise neuromodulation for the treatment of disease and facilitate the investigation of the functional organization of large-scale brain networks.
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We hope that you will find the referee reports helpful when revising the work, which we look forward to receive. Please do not hesitate to contact me should you have any questions. The work by Jin and colleagues is a rigorous test of whether expression of NAP derive from H. pylori would increase potency of CAR T. The work is technically well done. Major strengths of the manuscript are the extensive characterization in both hematologic and solid tumor models. In vivo modeling is done in syngeneic mice. A major issue for the field is that CAR T cells have to date not shown to be effective for eliciting endogenous immunity against other antigens in the TME beyond the CAR T-cell target. Here the author's show that CAR T cells encoding NAP in mouse T cells and human T cells have effects through dendritic cells and other cells the microenvironment. The authors conclude with a demonstration that the same approach works in human T cells but they do not model this in immunodeficient mice. This is reasonable because the NSG mouse models would not be expected to mount an immune response through recruitment of endogenous immunity.
The major issue is that in the Discussion the author should add more to the primary limitation of this approach, which is whether immunity either humoral or cellular will prevent or limit the duration of efficacy with the expression of this bacterial protein? Also, authors should comment on whether there is pre-existing immunity in humans to NAP.
Authors should reference some literature where acquired immunity to engineered T cells has been encountered as a limitation: Reviewer #2 (Report for the authors (Required)): Chuan Jin and colleagues demonstrate that arming CAR-Ts to express H. pylori NAP induces bystander immunity via epitope spreading and the system is independent of tumor types and target antigens. The study is interesting and the conclusions are supported by strong data, obtained both in vitro and in vivo. However, I have some comments: 1. In Figure 1D the statistical significance of the difference in tumor size between samples exposed to CAR-T and those exposed to CAR(NAP)-T is not reported, even if in the text authors state that: "only CAR(NAP)-Ts significantly controlled tumor growth and prolonged survival of tumor-bearing mice in both models".
2. Referring to all experiments where tumor size was measured, was the statistical significance calculated at any time point or only at the end?
3. Figure 2H:what is the advantage of using NXS2-mCD19-OVA, in which OVA was introduced as a nontargeted bystander antigen, rather than using GD2 as non-targeted bystander antigen? 4. Why did the authors evaluate the infiltration of the immune cells in A20 tumor model by immunofluorescence ( Figure S8) and not by FACS as they did in Figure 3? And why they did not evaluate the same immune cell populations?
5. Rferring to Figure 3 authors state that the cell type profile obtained by gene expression analysis (panels F-H) was validated also by FACS (I-K). However, increase in cytotoxic NK cells is evidenced by gene expression and not by FACS and vice versa for antigen presenting DCs. 6. Since the analysis shown in Figure 3L did not reveal a robust level of Th1 cytokines, authors could reinforce the conclusion by quantifying the intra-cyto IFNgamma in lymphocytes isolated from the tumor or local lymph nodes. 7. Figure S1D-E-F: to verify the induction of luciferase, authors implanted human cancer cells, expressing or not CD19. It is not clear to me if human CD19-targeting CAR-T cells or murine CD19-targeting CAR-T cells have been used. Moreover, why did the authors not implanted NXS2, expressing or not mCD19, rather than human Daudi (CD19+) and BC-3 (CD19-)? Figure S2, S3, S4, S5 A)? 9. Why did the author choose different target:effector ratios in the experiments shown in Figure S2, S3, S4, S5 and S6 B?

How do the authors explain the different expression of the antigen between cultured in vitro cells and cells isolated from the tumor (
10. In Figure S6A is not reported the expression of the antigen by cells isolated from resected tumors. 11. The reduced released of IFNgamma in CAR(NAP)-T with respect to CAR-T ( Figure S6C), is unexpected. Authors should provide a possible explanation.
12. Figure S7, it is not clear for what purpose were used either splenocytes or tumor-infiltrating CD8+ T cells? Legend must include more details on this matter.
13. Since CD19 is expressed by normal B cells, what about the impact on these cells in vitro and in vivo by the exposure/administration of CD19-targeted CAR-T and CAR(NAP)-T? Moreover at least one experiment by using as target cells unrelated and normal cells could be shown.
The authors present a novel approach to enhancing CAR T cell efficacy through the additional introduction of the gene encoding H pylori neutrophil activating protein (NAP) which in turn allows the CAR T cell to introduce NAP to the tumor microenvironment leading to the recruitment of endogenous antitumor effectors including T cells as well as induction of DC maturation which in turn could enhance cross presentation of other tumor antigens to endogenous T cells. The authors demonstrate that these NAP secreting CAR T cells enhance tumor eradication in syngeneic tumor models and further demonstrate that these differences are due in part to modulation of the TME including DC maturation and recruitment of endogenous tumor specific T cells. The authors conclude that this is a proof of principle that bacterial derived proinflammatory factors may be a novel approach to enhance the efficacy of tumor targeted T cells in the context of adoptive cell therapies of cancer. The manuscript is well written and the data largely support the authors' conclusions. However, there are concerns regarding the approach and whether similar results have been demonstrated using other proinflammatory molecules. Critiques 1. A primary concern regarding this approach is the fact that similar outcomes have been demonstrated in the context of, for example, CAR T cells modified to secrete cytokines (i.e. IL-18), or express ligands which enhance DC maturation (i.e. CD40L). Both examples use non-immunogenic reagents. To this end, the work would seem to generate similar outcomes just using a different, but bacterial, proinflammatory molecule (NAP).
2. The authors note in the discussion that this approach may be limited due to the bacterial origin of NAP which quite reasonably would be immunogenic and in fact many patients may potentially already harbor neutralizing antibodies which could impair the efficacy of this approach. Have the authors immunized mice with NAP and then see if these CAR T cells still function well?
3. The authors have not fully studied the safety of this approach especially in the setting of syngeneic tumor models and in the context of systemic tumors. Post mortem analyses of treated mice to assess for systemic inflammation would be helpful. Further does NAP impact the murine immune effectors to a similar degree as in the context of a human immune system? 4. Have the authors looked into the long term persistence of the CAR(NAP) T cells? This is highly relevant given the clinical relevance of CAR T cell persistence and even more so if NAP induces T cell mediated immune responses which could lead to rapid elimination of the CAR T cells. This could readily be explored in the syngeneic mouse models wherein CAR T cell persistence could be evaluated as well as assessment of whether endogenous T cells demonstrate cytotoxicity to CAR(NAP) T cells versus CAR T cells.
5. Some of the presented data does not support the authors' conclusions including figure 1E wherein there is no statistical difference between the CAR-T and CAR(NAP) T cell treated mice in survival, and all long term surviving mice were resistant to tumor rechallange. It is also curious that NSX2 tumor models, the overall survival advantage is small and there are no long term surviving mice.
Fri 04 Feb 2022 Decision on Article nBME-21-1069A Dear Dr Yu, Thank you for your patience in waiting for the feedback on your revised manuscript, "Expression of a pathogenic virulence factor enhances the efficacy of CAR-T cell therapy against solid tumors". Having consulted with Reviewers #2 and #3 (whose comments you will find at the end of this message), I am pleased to write that we shall be happy to publish the manuscript in Nature Biomedical Engineering, provided that the points specified in the attached instructions file are addressed.
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Please do not hesitate to contact me should you have any questions. Besides accurately addressing all my issues, authors performed new experiments to verify whether preexisting antibodies against NAP affect the therapeutic efficacy of CAR-T cells armed to express NAP. The evidence that the anti-NAP immunity does not interfere with the CAR-T therapy is extremely important considering that the H. pylori infection is still widespread, and NAP is a major antigen. Moreover, a new set of experiments was performed to rule out any possible toxic effect resulting from the treatment and to evaluate the long-term CAR-T persistence. Authors revealed that both CAR-T and CAR(NAP)-T were detectable in about 50% of the mice up to 70 days after administration and repeated treatment with CAR(NAP)-T did not result in elevated toxicity compared to conventional CAR-T when assessing systemic cytokine release or body weight. Collectively, these new data significantly improved the quality of the manuscript but, most of all greatly enhanced the translational potential of this strategy for targeting solid tumors.

Dear Editor and Reviewers
We appreciate your positive feedback on our manuscript (nBME-21-1069) entitled "Expression of a pathogenic virulence factor enhances the efficacy of CAR-T cell therapy against solid tumors". We have performed new animal experiments that specifically address the questions raised by all reviewers and editor. Please find our revised manuscript with changes/additional texts highlighted in red, as well as point-to-point answers (in blue) to each of your comments below.
We believe that the comments from editor and reviewers were highly relevant and have helped us to significantly improve the manuscript. We sincerely hope that it can now be regarded acceptable for publication in Nature Biomedical Engineering.

Responses to all reviewers and editor:
* Evidence and discussion of the in vivo immunogenicity, potential toxicity, and extent of persistence of the CAR-T cells expressing the bacterial protein, as per the comments of all reviewers.
We have performed new animal experiment to evaluate the therapeutic effects of conventional CAR-T and CAR(NAP)-T in tumor-bearing mice with pre-existing anti-NAP antibodies, wherein mice were vaccinated with an adenoviral vector expressing NAP before tumor cell implantation. In repeated experiments we found the overall survival was not affected by the pre-existing anti-NAP immunity. The new data are presented in the new submitted manuscript as a new Supplementary Fig. S8.
We also performed other animal experiments where toxicity and long-term CAR-T persistence were followed. We can see that both CAR-T and CAR(NAP)-T were detectable in about 50% of the mice up to 70 days after administration (Supp Fig. S7). We did not detect a spike in cytokines in the serum related to Cytokine release syndrome after CAR(NAP)-T treatment (Supp Fig. S9). In addition, we also report the mouse body weight in each treatment experiment as safety evaluation (Supp Fig. S10).
* Thorough performance comparisons across groups (with detailed reporting of the statisticalsignificance tests carried out). Also, for all tumour-growth curves, please provide the individualmouse data (in the Supplementary Information, or as overlaid curves in the main figures).
We have now revised the manuscript and reported all statistical method used and the significance for each figure. Individual mouse tumor growth curves are now provided in the corresponding supplementary information.

Response to specific questions to each reviewer
Reviewer #1 (Report for the authors (Required)): The work by Jin and colleagues is a rigorous test of whether expression of NAP derive from H. pylori would increase potency of CAR T. The work is technically well done. Major strengths of the manuscript are the extensive characterization in both hematologic and solid tumor models. In vivo modeling is done in syngeneic mice. A major issue for the field is that CAR T cells have to date not shown to be effective for eliciting endogenous immunity against other antigens in the TME beyond the CAR T-cell target. Here the author's show that CAR T cells encoding NAP in mouse T cells and human T cells have effects through dendritic cells and other cells the microenvironment. The authors conclude with a demonstration that the same approach works in human T cells but they do not model this in immunodeficient mice. This is reasonable because the NSG mouse models would not be expected to mount an immune response through recruitment of endogenous immunity.
The major issue is that in the Discussion the author should add more to the primary limitation of this approach, which is whether immunity either humoral or cellular will prevent or limit the duration of efficacy with the expression of this bacterial protein? Also, authors should comment on whether there is pre-existing immunity in humans to NAP.
Authors should reference some literature where acquired immunity to engineered T cells has been encountered as a limitation: We greatly appreciate the reviewers positive feedback and have now performed new animal experiments addressing the questions regarding pre-existing anti-NAP immunity. As all reviewers mentioned this, we have summarized our response in the section above "Response to all reviewers". We hope that the new data are sufficient.
We have also added the references mentioned.
Reviewer #2 (Report for the authors (Required)): Chuan Jin and colleagues demonstrate that arming CAR-Ts to express H. pylori NAP induces bystander immunity via epitope spreading and the system is independent of tumor types and target antigens. The study is interesting and the conclusions are supported by strong data, obtained both in vitro and in vivo. However, I have some comments: 1. In Figure 1D the statistical significance of the difference in tumor size between samples exposed to CAR-T and those exposed to CAR(NAP)-T is not reported, even if in the text authors state that: "only CAR(NAP)-Ts significantly controlled tumor growth and prolonged survival of tumor-bearing mice in both models".
We thank the reviewer's carefully pointing out this mistake. There is no statistic difference between the CAR-T and CAR(NAP)-T groups; or between the CAR-T and Mock-T groups. We have added the statistics in the new submitted figure and rephrased the text in the results.
2. Referring to all experiments where tumor size was measured, was the statistical significance calculated at any time point or only at the end?
The statistics were calculated when the first mice had to be sacrificed in the experiment (thus, the last day reported in the graphs showing mean tumors sizes). We have now clarified this in the Figure  Legends. 3. Figure 2H:what is the advantage of using NXS2-mCD19-OVA, in which OVA was introduced as a non-targeted bystander antigen, rather than using GD2 as non-targeted bystander antigen?
We thank the reviewer for this comment. OVA is a protein antigen while GD2 is a disialoganglioside. Thus, using OVA as a bystander antigen allowed us to use established molecular tools, such as tetramer staining to investigate OVA-specific T cell response. In addition, protein-based antigen also allows us to detect a diversified T cell response against different TCR epitopes using peptide pool and ELISA as read-out ( Fig 2G). Lack of specific detection method obstructed us from using GD2 as a bystander antigen.
4. Why did the authors evaluate the infiltration of the immune cells in A20 tumor model by immunofluorescence ( Figure S8) and not by FACS as they did in Figure 3? And why they did not evaluate the same immune cell populations?
The reason for using immunofluorescence (now Fig S14) or FACS in Fig 3 is mainly based on the material available. A20 tumors can easily be cryo-sectioned while for NXS2 tumors it is more difficult to obtain good cryosection and therefore add an extra difficulty to sample analysis. In addition, we used different methods to detect immune cell infiltration as we believe that it increases the confidence of the reported data if different methods yield similar results. Figure 3 authors state that the cell type profile obtained by gene expression analysis (panels F-H) was validated also by FACS (I-K). However, increase in cytotoxic NK cells is evidenced by gene expression and not by FACS and vice versa for antigen presenting DCs.

Rferring to
We thank the reviewer for pointing out this mistake. We have now re-phrased the sentence in our new submitted manuscript. 6. Since the analysis shown in Figure 3L did not reveal a robust level of Th1 cytokines, authors could reinforce the conclusion by quantifying the intra-cyto IFNgamma in lymphocytes isolated from the tumor or local lymph nodes.
We greatly appreciate this valid point by the reviewer and have performed intracellular staining of IFN-gamma and IL-2 on tumor-infiltrating CD3+ T cells. Higher percentage of IFN-gamma and IL-2 producing T cells were found in tumor after CAR(NAP)-T treatment, and the new data are also shown in the new submitted manuscript as Fig. 3M, N. 7. Figure S1D-E-F: to verify the induction of luciferase, authors implanted human cancer cells, expressing or not CD19. It is not clear to me if human CD19-targeting CAR-T cells or murine CD19targeting CAR-T cells have been used. Moreover, why did the authors not implanted NXS2, expressing or not mCD19, rather than human Daudi (CD19+) and BC-3 (CD19-)?
We thank the reviewer for this comment and have now clarified in the figure legend of Fig S1 that it was human CAR-T injected into nude mice with human cancers (Duadi or BC-3). We did not actively disregard to use NXS2 and NXS2-mCD19 cells, it is rather a result of how the project developed. This experiment was performed many years ago when we started the project. At that time, our focus was on proof-of-concept experiments to see whether our idea worked or not. Daudi and BC-3 cells, as well as the nLuc-expressing CAR-T were injected intravenously, which we believe is a "cleaner" experimental setup compared to using the NXS2 model that involves subcutaneous tumor development. We believe that our model can answer the question whether the transgene can be inducible expressed or not, and facilitate our further development of CAR(NAP)-T constructs.
8. How do the authors explain the different expression of the antigen between cultured in vitro cells and cells isolated from the tumor ( Figure S2, S3, S4, S5 A)?
We thank the reviewer for this question. Cells engineered to ectopically express a protein can gradually reduce expression and even after some time lose the protein expression if a selection pressure is not presented which is the case in vivo, unlike cells cultured in vitro in the presence of puromycin (a selection marker co-expressed with the antigen during engineering). This phenomenon has also been reported by others (PMID: 16140581), and is most likely due to epigenetic silencing of the promoter. However, our key message is to convey that we do see expression of the CAR-target antigens in the different models examined, thus ruling out that lack of efficacy of the conventional CAR-T is due to loss of the antigen. 9. Why did the author choose different target:effector ratios in the experiments shown in Figure S2, S3, S4, S5 and S6 B?
The reason we use different E:T ratio is due to that different CARs have different efficacy and that the different retrovirus batches resulted in different transduction efficacy. Otherwise, there was no specific reason to select different target:effector ratios. Figure S6A is not reported the expression of the antigen by cells isolated from resected tumors.

In
We thank the reviewer for observing this. Due to miscommunication, the resected tumors were not saved. We have now performed new experiment assessing the human PSCA expression on resected tumor, and the new data are embedded in the updated Fig. S6A. 11. The reduced released of IFNgamma in CAR(NAP)-T with respect to CAR-T ( Figure S6C), is unexpected. Authors should provide a possible explanation.
We agree with the reviewer that it is unexpected that the hPSCA-directed CAR(NAP)-T yielded somewhat lower IFN-g release than the conventional hPSCA-directed CAR-T. We have no plausible explanation as to why. We would like to point that hPSCA-directed CAR(NAP)-T release significantly higher IFN-g release than Mock-T, illustrating the function of CAR(NAP)-T in vitro.
12. Figure S7, it is not clear for what purpose were used either splenocytes or tumor-infiltrating CD8+ T cells? Legend must include more details on this matter.
We thank the reviewer for this comment. It is the splenocytes used in panel A and CD8+ TILs for the rest of the panels (B-F). To avoid confusion we have now spilt the figures in new Sup Fig S12 (old S7 A) and new Sup Fig. S13 (Old S7B-F). We have updated the new figure legends with clear information.
13. Since CD19 is expressed by normal B cells, what about the impact on these cells in vitro and in vivo by the exposure/administration of CD19-targeted CAR-T and CAR(NAP)-T? Moreover at least one experiment by using as target cells unrelated and normal cells could be shown.
We appreciate the reviewer's comment. We observe that CAR(NAP)-T treated mice have a lower number of normal B-cells (CD19+ cells) in the blood, which is not observed for mice treated with conventional CAR-T (also included as Sup Fig S2F). This data is in line with observation reported by Pegram et. al. (PMID: 22354001) that conventional CAR-T requires chemotherapy pre-conditioning to achieve B-cell aplasia.
Regarding using irrelevant target cell as control. We think that the data presented in Supplementary Figure S1D, where we used both Daudi and BC-3 could explain the specificity, provided that we have always included a Mock-T control.
Reviewer #3 (Report for the authors (Required)): The authors present a novel approach to enhancing CAR T cell efficacy through the additional introduction of the gene encoding H pylori neutrophil activating protein (NAP) which in turn allows the CAR T cell to introduce NAP to the tumor microenvironment leading to the recruitment of endogenous antitumor effectors including T cells as well as induction of DC maturation which in turn could enhance cross presentation of other tumor antigens to endogenous T cells. The authors demonstrate that these NAP secreting CAR T cells enhance tumor eradication in syngeneic tumor models and further demonstrate that these differences are due in part to modulation of the TME including DC maturation and recruitment of endogenous tumor specific T cells. The authors conclude that this is a proof of principle that bacterial derived proinflammatory factors may be a novel approach to enhance the efficacy of tumor targeted T cells in the context of adoptive cell therapies of cancer. The manuscript is well written and the data largely support the authors' conclusions. However, there are concerns regarding the approach and whether similar results have been