Integrative Analysis Unveils the Correlation of Aminoacyl-tRNA Biosynthesis Metabolites with the Methylation of the SEPSECS Gene in Huntington’s Disease Brain Tissue

Round 1

Reviewer 1 Report

This study provides an investigation into the connections between metabolomics and epigenomics in Huntington's disease. However, the experimental data provided by the authors lacks robust support, being either confusing or inadequately labeled. To enhance the overall coherence and clarity of the paper, several improvements are recommended:

Figures and Tables: Several critical figures and tables lack necessary explanations and labels, leading to confusion. A thorough revision is needed to ensure proper clarity and understanding.

Abstract Refinement: The Abstract should be concise and focused, providing a clear overview of the study's scope, methods, and key findings. The section from line 36 to 38 should be relocated to the funding and acknowledgment section.

Introduction Enrichment: The introduction should provide background on the SPESECS gene to ensure readers have sufficient context to understand its relevance to the study.

Line 323-326 Data Provision: Data should be presented to substantiate the information provided in this section.

Table 1 Details: In Table 1, consider including the age details of all subjects to address the significant age difference between the HD patient group and the Control group, thus justifying the comparison.

Figure Numbering Correction: It seems there might be a numbering error in Figure 1b and 1a. This should potentially be Figure 1 and 2. Additionally, ensure that the font sizes of these figures are consistent.

Supplementary Figures Explanation: Supplementary Figures 1, 2, 3, and 4 require clearer explanations of terms such as PMD and thorough labeling of axes. N numbers and error bars should also be incorporated.

Supplementary Figure 6 Details: For Supplementary Figure 6, include N numbers and clarify the significance of dash and solid lines. The wide data variation in Figure 6b should be acknowledged and considered when drawing conclusions.

Supplementary Table 4: Rectify the absence of Supplementary Table 4 to ensure all relevant information is available.

Supplementary Table 5 Clarification: Provide labels for genes mentioned in Line 317-318 to facilitate better comprehension.

 

To ensure a smooth and comprehensible presentation of the findings, addressing the aforementioned concerns is crucial.

Find to read.

Author Response

Reviewer 1: Comments and Suggestions for Authors

This study provides an investigation into the connections between metabolomics and epigenomics in Huntington's disease. However, the experimental data provided by the authors lacks robust support, being either confusing or inadequately labeled. To enhance the overall coherence and clarity of the paper, several improvements are recommended:

1. Figures and Tables: Several critical figures and tables lack necessary explanations and labels, leading to confusion. A thorough revision is needed to ensure proper clarity and understanding.

Response: We thank you for your thorough assessment of our manuscript and for offering valuable insights. The mentioned sections have been enhanced accordingly.

 

2. Abstract Refinement: The Abstract should be concise and focused, providing a clear overview of the study's scope, methods, and key findings. The section from line 36 to 38 should be relocated to the funding and acknowledgment section.

 

Response: Thank you, we have revised the “Abstract” as follows: The impact of environmental factors on epigenetic changes is well established, and cellular function is determined not only by the genome but also by interacting partners such as metabolites. Given the significant impact of metabolism on disease progression, exploring the interaction between the metabolome and epigenome may offer new insights into Huntington's disease (HD) diagnosis and treatment. Using fourteen post-mortem HD cases and fourteen control subjects, we performed metabolomic profiling of human postmortem brain tissue (striatum and frontal lobe) and we performed DNA methylome profiling using the same frontal lobe tissue. Along with finding several perturbed metabolites and differentially methylated loci, Aminoacyl-tRNA biosynthesis (adj p-val = 0.0098) was the most significantly perturbed metabolic pathway with which two CpGs of the SEPSECS gene were correlated. This study improves our understanding of molecular biomarker connections and importantly increases our knowledge of metabolic alterations driving HD progression.

 

3. Introduction Enrichment: The introduction should provide background on the SPESECS gene to ensure readers have sufficient context to understand its relevance to the study.

Response: The following information has been added in the “Introduction” section:

Notably, perturbations in the aminoacyl-tRNA biosynthesis pathway, which is linked to protein synthesis and translation precision [1,2]. A previous study which used cerebrospinal fluid also connected this pathway with HD [3]. Cytosolic tRNAs are implicated in HD, with the elongated Gln repeat in the huntingtin protein driving the disease process [4-6]. One of the genes, the SEPSECS gene, responsible for producing the Sep (O-Phosphoserine) tRNA:Sec (Selenocysteine) tRNA Synthase enzyme, is a focal point. This enzyme facilitates selenocysteine synthesis, a rare amino acid with antioxidant properties [7,8]. Given the oxidative stress implications in HD pathogenesis, exploring selenocysteine's neuroprotective potential is significant [9-11]. Our study sheds light on these interconnected mechanisms and their contribution to HD's complex pathology.

 

4. Line 323-326 Data Provision: Data should be presented to substantiate the information provided in this section.

Response: The method to compute metabolome – epigenome interactions was described in the methods. We have sought to clarify the description as follows.

Epigenome-metabolome interactions: To establish epigenome-metabolome interactions within each pathway, metabolites and transcripts pertaining to that pathway were selected. Pathway-protein associations were obtained from SMPDB 63 (https://smpdb.ca). Only CpGs that were associated with any of the transcripts in the pathway were further considered. The concordance of each metabolite-CpG pair was established by fitting a robust linear regression model without an intercept, where the standardized methylation value was the response variable, and standardized metabolite abundance, as well as diagnosis, age, and sex were the independent variables. Effect size and statistical significance of the methylation covariate were taken as estimate of concordance, p values were adjusted using FDR and those with FDR q < 0.2 were reported.

We have provided a “Supplementary table 5” listing all the interactions with FDR q < 0.2 and added the significant pathway interaction details in “Supplementary Table 6”.

5. Table 1 Details: In Table 1, consider including the age details of all subjects to address the significant age difference between the HD patient group and the Control group, thus justifying the comparison.

Response: We have provided the individuals age in the revised table 1.

 

6. Figure Numbering Correction: It seems there might be a numbering error in Figure 1b and 1a. This should potentially be Figure 1 and 2. Additionally, ensure that the font sizes of these figures are consistent.

Response: Thank you, we have updated the figures.

 

7. Supplementary Figures Explanation: Supplementary Figures 1, 2, 3, and 4 require clearer explanations of terms such as PMD and thorough labeling of axes. N numbers and error bars should also be incorporated.

Response: We have expanded each figure caption as detailed below. The figures are intended as experiment quality control testing whether there is potential variance inflation and whether the data follows a normal distribution. The figures confirm that the data adhere to assumptions needed for downstream linear modeling and detection of differential metabolite abundance.

Supplementary Figure 1: As part of experiment quality control, variance inflation has been evaluated in striatum metabolomics data. The left side of the plot shows variance inflation estimates for a full model including all the known covariates (age, gender, condition, postmortem delay [PMD]) whereas the right-side plot shows the variance inflation estimates for the reduced model with one of the inflated variables removed. The X-axis represents the independent variables, and the y-axis represents variance inflation factor values for each of the variables. The plot indicates that sample age and condition are correlated which may lead to incorrect coefficient estimates if all the variables were used in subsequent linear modeling.

 

Supplementary Figure 2: As part of experiment quality control, variance inflation has been evaluated in frontal lobe metabolomics data.  The left side of the plot shows variance inflation estimates for a full model including all the known covariates (age, gender, condition, postmortem delay [PMD]) whereas the right-side plot shows the variance inflation estimates for the reduced model with one of the inflated variables removed. The X-axis represents the independent variables, and the y-axis represents variance inflation factor values for each of the variables. The plot indicates that sample age and condition are correlated which may lead to incorrect coefficient estimates if all the variables were used in subsequent linear modeling.

 

Supplementary Figure 3: Normalized distribution of metabolite abundances. The metabolite abundance data from the striatum was subjected to log transformation and sample-wise quantile normalization. (a) Resulting data distribution.  On the graph, the x-axis displays a continuum of normalized intensity values, while the y-axis represents the density of these values at each point on the x-axis. This density plot effectively illustrates the distribution of normalized intensity values.

(b) Data distribution for each sample. Depicted on the x-axis are the individual samples, whereas the y-axis displays the corresponding normalized intensities. This visualization showcases the dispersion of normalized intensity values across diverse samples. The primary purpose of this plot was to show that the normalized data follows a normal distribution and there are no major differences between individual samples enabling downstream statistical modeling.

 

Supplementary Figure 4: Normalized distribution of metabolite abundances. The metabolite abundance data from the frontal lobe was subjected to log transformation and sample-wise quantile normalization. (a) Resulting data distribution.  On the graph, the x-axis displays a continuum of normalized intensity values, while the y-axis represents the density of these values at each point on the x-axis. This density plot effectively illustrates the distribution of normalized intensity values.

(b) Data distribution for each sample. Depicted on the x-axis are the individual samples, whereas the y-axis displays the corresponding normalized intensities. This visualization showcases the dispersion of normalized intensity values across diverse samples. The primary purpose of this plot was to show that the normalized data follows a normal distribution and there are no major differences between individual samples enabling downstream statistical modeling.

 

8. Supplementary Figure 6 Details: For Supplementary Figure 6, include N numbers and clarify the significance of dash and solid lines. The wide data variation in Figure 6b should be acknowledged and considered when drawing conclusions.

 

Response: The solid lines connect the hyper-methylated regions, and the dotted line connects the hypo-methylated regions. We have improved the figure caption as follows: Supplementary Figure 6: Enrichment of CpG islands and genomic features with differentially methylated loci in the frontal lobe tissue. Illumina EPIC methylation microarrays use 800k loci spread over various genomic regions and CpG island regions. For each region, we test whether it has received more significantly hyper-modified or hypo-modified loci than could be expected by chance alone. This is achieved by computing an enrichment odds ratio and testing its significance with Fisher’s exact test. The figures indicate odds ratio estimates (log-transformed) for each tested genomic region and corresponding 95% confidence intervals. Negative estimates indicate a lack of significant loci whereas positive estimates indicate enrichment. Statistically significant regions were marked in orange.

 

9. Supplementary Table 4: Rectify the absence of Supplementary Table 4 to ensure all relevant information is available.

Response: The table was provided in pdf format earlier. We have now provided this table in an Excel format. 

 

10. Supplementary Table 5 Clarification: Provide labels for genes mentioned in Line 317-318 to facilitate better comprehension.

Response: Thank you. We have provided the labels as detailed below:

Trafficking Protein Particle Complex Subunit 10 [TRAPPC10], CUB And Sushi Multiple Domains 3 [CSMD3], 5-Methyltetrahydrofolate-Homocysteine Methyltransferase Reductase [MTRR], Pecanex 1 [PCNX], PC-Esterase Domain Containing 1B [PCED1B], Glucose-6-Phosphatase Catalytic Subunit 2 [G6PC2], Protocadherin 7 [PCDH7], Glypican 6 [GPC6], Ribosomal Protein S25 [RPS25], and Casein Kinase 1 Gamma 3 [CSNK1G3].

Author Response File: Author Response.docx

Reviewer 2 Report

Abstract may be rephrased in such a way that it will reflect the theme of the manuscript.

Author may remove the information of funding details from abstract and mention it in acknowledgement section. 

Introduction is quite long and looking like review article. Author may concise the introduction to the title of the manuscript like relevant information about Huntigton disease, aminoacyl-tRNA biosynthesis, their metabolites, methylation and other epigenetic modulation, SEPSECS gene role and relation with Huntington. I didn't find relevant information about aminoacyl-tRNA biosynthesis metabolites, their methylation or other epigenetic modulation and their impact on human health or Huntington, and not a single line about SEPSECS gene in introduction. 

In methodology author may mention about sampling details like, what is the timing of sample collection after death. Which type of medication was running before death, what was the inclusion exclusion criteria for selection of control, is there any recent history of medication for control etc. 

Author mentioned that for H NMR analysis samples sonicated for 20 minutes, please justify the statement. What was the frequency of sonication? 

In methodology author mentioned a section of data analysis which can discuss along with result how data analyzed and which type of result they obtained. 

Is there any other way to display the graphical data to for figure 1 a,b. If not author may give more details in the figure legend.

First appearance of SEPSECS gene on page '8' line no. 327 after abstract.

In figure 3 author mentioned Val and Phe, where as in figure legend there is cytosines and Aminoacyl-tRNA, even text not able to justify the provided figure 3. 

If any other way to represent the figure 3, it will be helpful for the readers.

Results need proper presentation to describe their obtained result.

Author may improve discussion with obtained result and available literature. 

In general epigenetic modulation prone to changes by nutrition, medication, environmental exposure (Effect of mobile phone signal radiation on epigenetic modulation in the hippocampus of Wistar rat 2021,https://doi.org/10.1016/j.envres.2020.110297), nutritional impact, stress impact, drug impact etc. How to justify the HD patient Epigenetic modulation with control when so many factors are constantly working on epigenetic modulation. 

Author Response

Reviewer 2: Comments and Suggestions for Authors

1. Abstract may be rephrased in such a way that it will reflect the theme of the manuscript.

Response: Thank you, we have revised the abstract as follows:

The impact of environmental factors on epigenetic changes is well established, and cellular function is determined not only by the genome but also by interacting partners such as metabolites. Given the significant impact of metabolism on disease progression, exploring the interaction between the metabolome and epigenome may offer new insights into Huntington's disease (HD) diagnosis and treatment. Using fourteen post-mortem HD cases and fourteen control subjects, we performed metabolomic profiling of human postmortem brain tissue (striatum and frontal lobe) and we performed DNA methylome profiling using the same frontal lobe tissue. Along with finding several perturbed metabolites and differentially methylated loci, Aminoacyl-tRNA biosynthesis (adj p-val = 0.0098) was the most significantly perturbed metabolic pathway with which two CpGs of the SEPSECS gene were correlated. This study improves our understanding of molecular biomarker connections and importantly increases our knowledge of metabolic alterations driving HD progression.

 

2. Author may remove the information of funding details from abstract and mention it in acknowledgement section.

Response: We have removed this information from the abstract.

 

3. Introduction is quite long and looking like review article. Author may concise the introduction to the title of the manuscript like relevant information about Huntigton disease, aminoacyl-tRNA biosynthesis, their metabolites, methylation and other epigenetic modulation, SEPSECS gene role and relation with Huntington. I didn't find relevant information about aminoacyl-tRNA biosynthesis metabolites, their methylation or other epigenetic modulation and their impact on human health or Huntington, and not a single line about SEPSECS gene in introduction.

Response: The description of the different regions of the brain has been shortened as follows: HD involves neurodegeneration, mainly in the basal ganglia, particularly the striatum, impacting movement and reward-related neural activity [12]. The neocortex, which connects to the striatum, shrinks during HD, affecting other brain areas later [13].  Disease development involves complex mechanisms like transcription dysfunction and abnormal trafficking [14]. Mood and behavior changes related to frontal lobe dysfunction, serve as early HD indicators  [15-18].

 

Further, we have added information on the significantly perturbed metabolic pathway and the gene of interest, as follows:

Notably, perturbations in the aminoacyl-tRNA biosynthesis pathway, which is linked to protein synthesis and translation precision [1,2]. A previous study which used cerebrospinal fluid also connected this pathway with HD [3]. Cytosolic tRNAs are implicated in HD, with the elongated Gln repeat in the huntingtin protein driving the disease process [4-6]. One of the genes, the SEPSECS gene, responsible for producing the Sep (O-Phosphoserine) tRNA:Sec (Selenocysteine) tRNA Synthase enzyme, is a focal point. This enzyme facilitates selenocysteine synthesis, a rare amino acid with antioxidant properties [7,8]. Given the oxidative stress implications in HD pathogenesis, exploring selenocysteine's neuroprotective potential is significant [9-11]. Our study sheds light on these interconnected mechanisms and their contribution to HD's complex pathology.

 

4. In methodology author may mention about sampling details like, what is the timing of sample collection after death. Which type of medication was running before death, what was the inclusion exclusion criteria for selection of control, is there any recent history of medication for control etc.

Response: Postmortem delay (PMD) details have been included in Table 1. The controls utilized were all deemed pathologically normal for their respective ages. Although some controls may have displayed mild AD pathology or small vessel disease, this aligns with the anticipated aging-related changes. None of the controls exhibited any HD pathology, and there was no recent history of dementia-associated medication use among them.

The above information has been added in the study sample details of the methods section.

 

5. Author mentioned that for H NMR analysis samples sonicated for 20 minutes, please justify the statement. What was the frequency of sonication?

Response: Sonication was employed for 20 minutes at a frequency of 50-60 Hz. The below sentence has been provided in the methods section.

The samples were mixed and sonicated for 20 minutes at a frequency of 50 - 60 Hz to achieve homogenization, cell disruption, and compound extraction.

 

6. In methodology author mentioned a section of data analysis which can discuss along with result how data analyzed and which type of result they obtained.

Response: The data analysis steps have been described in detail in the methods section.

 

7. Is there any other way to display the graphical data to for figure 1 a,b. If not, author may give more details in the figure legend.

Response: We agree, this should be Figure 1 and Figure 2. Further details are provided in the figure legends as provided below:

Figure 1: The metabolic pathways that are significantly enriched with metabolites have been identified using the striatum region of HD patients. The x-axis represents the hyper-methylated (+) and hypo-methylated (-) status of pathways, whereas the y-axis provides the pathways that were perturbed. The node size indicates the number of observed hits that are associated with significant pathways. The node color indicates the significance level based on the p-value (<0.05).

Figure 2: The metabolic pathways that are significantly enriched with metabolites have been identified using the frontal lobe region of HD patients. The x-axis represents the hyper-methylated (+) and hypo-methylated (-) status of pathways, whereas the y-axis provides the pathways that were perturbed. The node size indicates the number of observed hits that are associated with significant pathways. The node color indicates the significance level based on p-value (<0.05).

 

8. First appearance of SEPSECS gene on page '8' line no. 327 after abstract.

Response: We have now introduced this in the introduction section.

 

9. In figure 3 author mentioned Val and Phe, whereas in figure legend there is cytosines and Aminoacyl-tRNA, even text not able to justify the provided figure 3.

Response: We have improved the figure legend as follows:

Figure 3: The "circos plot" displays the negative correlation between significantly differentially methylated cytosines and metabolites related to aminoacyl-tRNA biosynthesis pathway.  Metabolites Val and Phe have been downregulated in metabolome analysis as indicated by the blue color on the edge of the circos plot. The cytosines pertaining to SEPSECS gene have been hypo-methylated as well. The blue strips connecting metabolites and cytosines indicate a negative correlation between methylation and metabolite abundance when adjusted for age, sex, postmortem delay, and condition.

 

10. If any other way to represent the figure 3, it will be helpful for the readers.

Response: We have improved the description in the text as provided in the previous response.

 

11. Results need proper presentation to describe their obtained result.

Response: We have updated the figures and tables presentation in the revised manuscript.

 

12. Author may improve discussion with obtained result and available literature.

Response: We concentrated primarily on the aminoacyl-tRNA biosynthesis pathway and the significant gene, SEPSECS, and metabolites Val and Phe. The interaction findings provided insights into possible HD pathogenesis and were backed by available literature.

 

13. In general epigenetic modulation prone to changes by nutrition, medication, environmental exposure (Effect of mobile phone signal radiation on epigenetic modulation in the hippocampus of Wistar rat 2021,https://doi.org/10.1016/j.envres.2020.110297), nutritional impact, stress impact, drug impact etc. How to justify the HD patient Epigenetic modulation with control when so many factors are constantly working on epigenetic modulation.

Response: For the analysis of methylome we have considered all known potentially confounding factors such as sample age, gender, estimated proportion of neurons and postmortem delay. The effects of nutrition or stress are assumed to be equally distributed between sample groups and should not have a significant impact on the outcome. We have also experimented with the use of RUV [19] - the method used to estimate unknown confounding variables, but that did not impact the outcome.

This information has been provided now in the conclusion section of the discussion.

Author Response File: Author Response.docx

Reviewer 3 Report

Using post-mortem frontal lobe and striatal tissue samples from 14 Huntington’s disease (HD) cases and 14 older control subjects, these workers have performed metabolomic and DNA methylome profiling. The striatum contained 4 and the frontal lobe 14 significantly altered metabolites in HD while 11,955 differentially methylated CpGs were found in frontal tissue. The perturbed aminoacyl-tRNA biosynthesis pathway correlated with the presence of two methylated CpGs of the SEPSECS gene in HD. It is concluded that abnormal function of the aminoacyl-tRNA biosynthesis pathway plays a role in the progression of HD. The authors speculate on the mechanisms by which this may be occurring including aberrant accumulation of toxic metabolites and increased oxidative stress and inflammation.  

 

This is the first study to correlate changes in the metabolome and gene methylation in a brain region of HD patients. The study has been rigorously performed but my difficulty is the understandably relatively small number (14) of HD tissue samples investigated and the use of these and 14 non-age matched control samples to both train and validate their support vector models. This approach is unlikely to generate findings that can be generalised. The results are also unexpected as they implicate valine and phenylalanine interactions with two methylated CpGs of the SEPSECS gene as a driver of HD progression. Neither of these aminoacids have been implicated in HD before and it is generally thought that a glutamate-GABA imbalance is a mechanism responsible for driving loss of striatal spiny neurons. Having said that, I agree that  understanding the complex interplay  between metabolites and genes in HD may well have the potential to uncover novel therapeutic targets.

 

 

Author Response

Reviewer 3: Comments and Suggestions for Authors

1. Using post-mortem frontal lobe and striatal tissue samples from 14 Huntington’s disease (HD) cases and 14 older control subjects, these workers have performed metabolomic and DNA methylome profiling. The striatum contained 4 and the frontal lobe 14 significantly altered metabolites in HD while 11,955 differentially methylated CpGs were found in frontal tissue. The perturbed aminoacyl-tRNA biosynthesis pathway correlated with the presence of two methylated CpGs of the SEPSECS gene in HD. It is concluded that abnormal function of the aminoacyl-tRNA biosynthesis pathway plays a role in the progression of HD. The authors speculate on the mechanisms by which this may be occurring including aberrant accumulation of toxic metabolites and increased oxidative stress and inflammation.

This is the first study to correlate changes in the metabolome and gene methylation in a brain region of HD patients. The study has been rigorously performed but my difficulty is the understandably relatively small number (14) of HD tissue samples investigated and the use of these and 14 non-age matched control samples to both train and validate their support vector models. This approach is unlikely to generate findings that can be generalised. The results are also unexpected as they implicate valine and phenylalanine interactions with two methylated CpGs of the SEPSECS gene as a driver of HD progression. Neither of these amino acids have been implicated in HD before and it is generally thought that a glutamate-GABA imbalance is a mechanism responsible for driving loss of striatal spiny neurons. Having said that, I agree that understanding the complex interplay between metabolites and genes in HD may well have the potential to uncover novel therapeutic targets.

Response: Thank you for your thoughts on the manuscript and its novel findings. The limitation in the sample size is primarily due to the difficulty in obtaining brain tissues. However, as a proof-of-concept study, we aimed to demonstrate the feasibility of the proposed methodology using combined metabolomics and DNA methylation, which is the first of its kind in studying the pathogenesis of HD.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Please provide the individuals age in the table 1, other comments were addressed.

Author Response

Reviewer 1:

Comments and Suggestions for Authors

1. Please provide the individuals age in the table 1, other comments were addressed.

Response: Please find below the updated Table 1.

Table 1: Demographic characteristics of Huntington’s disease individuals versus cognitively healthy individuals.

	HD patients	Controls	p-value
Number of subjects	14	14	n/a
Age, Mean (SD) Individual age in years: Patient/control – 1 Patient/control – 2 Patient/control – 3 Patient/control – 4 Patient/control – 5 Patient/control – 6 Patient/control – 7 Patient/control – 8 Patient/control – 9 Patient/control – 10 Patient/control – 11 Patient/control – 12 Patient/control – 13 Patient/control – 14	54.64 (12.39)   70 57 51 52 67 51 33 47 48 na 50 68 72 75	78.5 (13.46)   84 84 81 87 90 89 89 54 53 84 60 89 83 90	< 0.0001
Sex
Males	8 (57.1)	8 (57.1)	0.45
Females	6 (42.8)	6 (42.8)
Postmortem delay (PMD) - Minutes
Mean (SD)	77.35 (71.63)	69.28 (38.09)	0.65

 

 

Author Response File: Author Response.docx

Reviewer 2 Report

Section 1 Introduction: Second page first paragraph last line, Author should check the reference no.

Section 1 Introduction: Second page, Third paragraph, author should shift this to discussion, as here author describing his findings.

Section 1 Introduction: Second page, 4th paragraph: Notably, perturbations in the aminoacyl-tRNA biosynthesis pathway, which is linked to protein synthesis and translation precision [30,31]. A previous study which used cerebrospinal fluid also connected this pathway with HD [32]. Cytosolic tRNAs are implicated in HD, with the elongated Gln repeat in the huntingtin protein driving the disease process [33-35]. One of the genes, the SEPSECS gene, responsible for producing the Sep (O-Phosphoserine) tRNA:Sec (Selenocysteine) tRNA Synthase enzyme, is a focal point. This enzyme facilitates selenocysteine synthesis, a rare amino acid with antioxidant properties [36,37]. Given the oxidative stress implications in HD pathogenesis, exploring selenocysteine's neuroprotective potential is significant [38-40]. Our study sheds light on these interconnected mechanisms and their contribution to HD's complex pathology"

Section 3. Result: Author stated that "Our study aimed to identify strong associations between DNA methylation and metabolic measures, with the goal of discovering significant diagnostic markers and understanding the interaction between the methylome and metabolome in HD".  Author should club these statement with discussion if suitable there. 

Author should rephrase this statement in such a way that, they propose their hypothesis instead of finding strong association. Their association may be weak or strong it will reflect in data and discussion. It can't be author's aim.

 

Author Response

Reviewer 2:

Comments and Suggestions for Authors

1. Section 1 Introduction: Second page first paragraph last line, Author should check the reference no.

Response: Thank you, the said line states that, “Mood and behavior changes related to frontal lobe dysfunction, serve as early HD indicators” citing four references [10-13]

These references are appropriately numbered. The cited sources delve into the background of mood and behavioral discrepancies, regulated by the frontal lobe. They also discuss the classification of HD in relation to the frontal lobe and the symptoms linked to its dysfunction.

Consequently, we confirm the accuracy of both the provided references and their numbering as 10-13.

 

10. Chow, T.W. Personality in frontal lobe disorders. Current psychiatry reports 2000, 2, 446-451, doi:10.1007/s11920-000-0031-5.
11. Duff, K.; Paulsen, J.S.; Beglinger, L.J.; Langbehn, D.R.; Wang, C.; Stout, J.C.; Ross, C.A.; Aylward, E.; Carlozzi, N.E.; Queller, S. "Frontal" behaviors before the diagnosis of Huntington's disease and their relationship to markers of disease progression: evidence of early lack of awareness. The Journal of neuropsychiatry and clinical neurosciences 2010, 22, 196-207, doi:10.1176/jnp.2010.22.2.196.
12. Vonsattel, J.P.; Myers, R.H.; Stevens, T.J.; Ferrante, R.J.; Bird, E.D.; Richardson, E.P., Jr. Neuropathological classification of Huntington's disease. Journal of neuropathology and experimental neurology 1985, 44, 559-577, doi:10.1097/00005072-198511000-00003.
13. Aylward, E.H.; Anderson, N.B.; Bylsma, F.W.; Wagster, M.V.; Barta, P.E.; Sherr, M.; Feeney, J.; Davis, A.; Rosenblatt, A.; Pearlson, G.D.; et al. Frontal lobe volume in patients with Huntington's disease. Neurology 1998, 50, 252-258.

 

 

2. Section 1 Introduction: Second page, Third paragraph, author should shift this to discussion, as here author describing his findings.

Response: This has been moved to section 4.1, end of 1^st paragraph (Page 10).

 

3. Section 1 Introduction: Second page, 4th paragraph: Notably, perturbations in the aminoacyl-tRNA biosynthesis pathway, which is linked to protein synthesis and translation precision [30,31]. A previous study which used cerebrospinal fluid also connected this pathway with HD [32]. Cytosolic tRNAs are implicated in HD, with the elongated Gln repeat in the huntingtin protein driving the disease process [33-35]. One of the genes, the SEPSECS gene, responsible for producing the Sep (O-Phosphoserine) tRNA:Sec (Selenocysteine) tRNA Synthase enzyme, is a focal point. This enzyme facilitates selenocysteine synthesis, a rare amino acid with antioxidant properties [36,37]. Given the oxidative stress implications in HD pathogenesis, exploring selenocysteine's neuroprotective potential is significant [38-40]. Our study sheds light on these interconnected mechanisms and their contribution to HD's complex pathology".

Response: This has been moved to section 4.1, to the end of 4^th paragraph before conclusion section.

 

4. Section 3. Result: Author stated that "Our study aimed to identify strong associations between DNA methylation and metabolic measures, with the goal of discovering significant diagnostic markers and understanding the interaction between the methylome and metabolome in HD".  Author should club these statement with discussion if suitable there.

 

Response: Thank you, we have removed these lines from results section, and we have rephrased and merged it in the discussion section. Further details are provided in the next response.

 

5. Author should rephrase this statement in such a way that, they propose their hypothesis instead of finding strong association. Their association may be weak or strong it will reflect in data and discussion. It can't be author's aim.

 

Response: We have rephrased this statement as provided below and provided it in the 2^nd paragraph of “Discussion” section (Page-9).

We hypothesized that there exists a robust connection between DNA methylation patterns and metabolic measures. We aimed to discover diagnostic indicators and understand the methylome-metabolome interplay in HD. Our study reveals the complex relationship between the epigenome and metabolome, potentially impacting HD development.

Author Response File: Author Response.docx