Molecular Systems Biology Peer Review Process File
Targeted proteomics reveals strain-specific changes in the
mouse insulin and central metabolic pathways after
sustained high-fat diet
Eduard Sabidó, Yibo Wu, Lucía Bautista, Thomas Porstmann, Ching-Yun Chang, Olga Vitek,
Markus Stoffel, Ruedi Aebersold
Corresponding author: Ruedi Aebersold, ETH Zurich
Review timeline:
Submission date:
Editorial Decision:
Revision received:
Editorial Decision:
Revision received:
Accepted:
18 October 2012
12 December 2012
12 March 2013
13 May 2013
23 May 2013
01 June 2013
Editor: Thomas Lemberger
Transaction Report:
(Note: With the exception of the correction of typographical or spelling errors that could be a source of ambiguity,
letters and reports are not edited. The original formatting of letters and referee reports may not be reflected in this
compilation.)
1st Editorial Decision
12 December 2012
Thank you again for submitting your work to Molecular Systems Biology. First of all, I apologize
for the delay, which was caused by the late arrival of the reports. Unfortunately, the third referee
failed to return a report, so I prefer to make a decision now based on the available evaluations rather
than delaying the process further. As you will see from the reports below, the referees are
moderately supportive. They raise, however, substantial concerns on your work, which should be
convincingly addressed in a major revision.
One of the major issues raised by both reviewers is the need to characterize the activation status of
the insulin signaling pathway by complementing the study with data on protein phosphorylation
levels--at least for relevant signaling components. A second concern, raised by reviewer #2, is that
the time-dependent changes in expression levels are difficult to interpret since they may reflect agedependent changes rather than diet-induced alterations.
If you feel you can satisfactorily deal with these points and those listed by the referees, you may
wish to submit a revised version of your manuscript. Please attach a covering letter giving details of
the way in which you have handled each of the points raised by the referees. A revised manuscript
will be once again subject to review and you probably understand that we can give you no guarantee
at this stage that the eventual outcome will be favorable.
-------------------------------------------------------REFEREE REPORTS:
© European Molecular Biology Organization
1
Molecular Systems Biology Peer Review Process File
Reviewer #1 (Remarks to the Author):
The manuscript by Sabido uses a targeted proteomic analysis to identify changes in proteins linked
to insulin metabolic pathways in liver using two different strains of mice with different
obese/diabetes sensitivity to high fat diet. The idea to use different strains is to gain insights into
proteins that are dysregulated in metabolic diseases and might be causative of this syndrome.
The authors use a targeted mass spectrometry by selected reaction monitoring in different conditions
to generate a large dataset. The results identify significant quantitative changes in proteins that could
contribute to the metabolic phenotypes observed in the different strains.
Overall, the studies are well designed and the technology is adequately used in the different
experimental protocols. It provides a new powerful tool to analyze and quantify changes of proteins
in different conditions. There are, however, some additional points that will strength the manuscript.
1- Some of the changes in proteins using SRM should be validated using western blot and include it
in the manuscript.
2- It is unclear how the quantitation is analyzed, are the relative amount of proteins related to total
protein or mg of tissue or DNA. How these values would change taking into account enormous
changes in liver weight, density or volume.
3- Due to abundancy of the proteins, there is a large list of metabolic enzymes that are cytoplasmic
or mitochondrial? Is any possibility that important regulatory proteins in the nucleus are missed?
Some explanation should be provided in the manuscript.
4- In these large data set, is any information about posttranslational modification, importantly
phosphorylation or acetylation. Since the insulin pathway strongly control kinase signaling, these
modifications should be included as part of datasets.
Reviewer #2 (Remarks to the Author):
Overall:
Two mouse strains that show different metabolic phenotypes when fed a high-fat diet were
compared using targeted, SRM-based, proteomics. The use of SRM allowed the measurement of a
set of metabolic pathways and insulin signaling proteins over multiple conditions, mouse strains,
and biological replicates. While the analytical framework is sound, the authors should be much more
specific in describing abundance changes in certain proteins or small groups of proteins and what
conclusions can be drawn from the data. Metabolic pathway visualization would also help to
decipher differences between the mouse strains on a high-fat diet. The experimental layout suffers
from a lack of regular control-diet leading many of the conclusions to be based on the differences
between the 2 mouse strains. Further, it is questionable as to why the insulin pathway proteins were
targeted when this pathway is known to operate as a phosphorylation-dependent signaling cascade
rather than via abundance changes protein components. Westerns for phosphorylation of main nodes
in the insulin signaling cascades (e.g. pAkt, pCREB) should be done.
Specific Points
Summary/Introduction Section
1. the authors fail to emphasize what mouse organ was used in the summary and in the introduction.
Although it is later mentioned that analysis was done on the liver, this should be mentioned earlier.
2. End of paragraph 2, summary section. It is unclear what is meant by the conclusion that
proteomic responses diverged early in the "obesity prone and partially obesity". Also, it would be
better if the conclusions were more specific about what proteins or groups of proteins changed.
Experimental Section
3. In the experimental section, it is unclear for both the age and previous diet when mice were
switched to the high-fat diet.
4. (SRM development section). A very thorough and somewhat novel approach was taken for SRM
development analyzing standard peptides and conducting a search against a spectral library of those
peptides. It is unclear how the SRM-triggered sequencing was done. Is it based on a predicted high
mass y-ion, or empirical spectra from previous datasets?
© European Molecular Biology Organization
2
Molecular Systems Biology Peer Review Process File
5. (Measurements and data analysis section). The rationale behind using correlations iii and iv to
verify transitions don't make sense. In iii, the correlation between light and heavy counterparts will
presumably not be very good if the protein is changing from the experimental conditions. In iv, the
MS2 collected during assay development were not collected from a complex mixture and thus may
not correlate well due to co-eluting peptides in the complex mixture analyses. An additional
assurance is to assess variability among the 4 heavy and light log ratio transitions for each peptide.
If they don't correlate, there is interference. Could the authors comment?
6. The samples were divided into 6 fractions and it is not stated whether the same SRM transitions
were used for each fraction or a new method was done for each fraction based on unique peptides to
each fraction.
Results
7. The authors have 2 sections discussing changes in several groups of proteins ("changes in time"
and "differences between mouse strains"). These sections could be segmented better for readability.
For example, in the first section, discuss specific proteins that changed similarly in both over time in
response to high-fat (leaving out any between-strain comparisons). Then, in the next section, discuss
specific changes occurring in one strain and not the other. There are many sentences that are very
non-specific in these sections as well, essentially saying that protein changes occurred. Those
comments are less informative than describing exactly what proteins changed. Description of the
early-emerging changes would be better in the "changes in time" section.
8. (Influence of high-fat diet section). This experiment lacks a proper control for the 12 week high
fat diet (same for previous section). It could easily be argued that any changes observed between the
normal proteome at time 0 and 12-week high-fat regimen are due to the 12 week difference in age,
not the high-fat diet. Also, in this section some proteins are mentioned to change but it is not clear
whether they increase or decrease, and what the magnitude of changes are without referring to the
figures.
Discussion
9. The author's claims that the experimental set-up used here allows for dissecting the effect of diet
and genetic background are somewhat exaggerated. There is not enough data for such dissection
without many more mouse strains and concurrent genetic analyses of those mice. However, the
findings of increased B-oxidation and lipogenesis are interesting in terms of their relation to diabetes
resistance. This could be more emphasized and put into context current literature on metabolic
disorders and diabetes (likely has something to do with differences in AMPK activation between the
strains).
Figures
General. It would be informative to see some phosphorylation data for some of the main insulin
pathway signaling nodes or AMPK. Perhaps a couple of Westerns would suffice and might explain
some of the B-oxidation and lipogenesis effects.
Fig. 4. a) It would be better to somehow not just see the differences but which direction (up or down
in one versus the other) b) need to show which axis is the B6 or S9 strain.
Grammar/typos
10. Pg 3, the summary and introduction sections begin and end almost exactly the same way, word
for word
11. Pg 5, end of para. 2, "pre defined" should be "predefined"
12. Pg 9, "elected" should be "selected"
13. Pg 35, supp. figure "died" should be "diet"
1st Revision - authors' response
© European Molecular Biology Organization
12 March 2013
3
We submit a revised version of our manuscript entitled “Targeted proteomics reveals strain-­‐
specific changes in the mouse insulin and central metabolic pathways after sustained high-­‐fat diet” to be considered for publication in Molecular Systems Biology. In this new version, we have addressed the different concerns raised by the reviewers, corrected different parts of the manuscript and we have clarified, either directly in the text or in the reviewer’s letter (see below), all the questions and doubts concerning our work. One of the main concerns of the referees was the incorporation of some additional data showing the phosphorylation state of the system under study. There is no doubt that insulin signaling is mainly driven by phosphorylation events, and protein phosphorylation is indeed a key process for signal transduction in short time courses (within 30 minutes). However, our work focuses on the long-­‐term effects of sustained high-­‐fat diet rather than in acute responses and these effects are supposed to mainly be driven by changes in the signaling network structure, i.e., protein abundances, which favor certain responses or network branches in front of others. Based on this rationale we focused on the study of protein abundances rather than of protein post-­‐translational modifications. Other studies have recently addressed acute responses in the phosphoproteome in response to fed and fasting processes (Grimsrud PA, et al. Cell Metab. 2012 Nov 7;16(5):672-­‐83. doi: 10.1016/j.cmet.2012.10.004.). Moreover, we have controlled the fasted condition not by western blot of key phosphorylation nodes, but by using classical serum biochemical parameters and the abundance of transcription factor SRBP1 that is known to be significantly decreased after a fasting period (Supplementary Table ST2 and Supplementary Figure S5). Finally, the other main concern of the referees was the lack of a proper control for the evaluation of protein changes in different time points for a given mouse strain. Indeed, when comparing these conditions, the observed changes may certainly reflect either diet or age effects, as the second referee pointed out. This possibility has now been clearly stated in the manuscript with the following sentence: “The detected time-­‐dependent changes in expression levels may, however, reflect diet-­‐induced alterations as well as age-­‐dependent changes”. However, note that strain-­‐specific changes were evaluated comparing both mouse strains at 0 weeks, and then comparing them at 12 weeks of high-­‐fat diet. Therefore, as strains are compared always within the same time point, the effect of aging should be ruled out when assessing strain-­‐specific proteome differences. We reduced that part describing the conclusions based on changes susceptible to be influenced by age, and emphasized the section of direct strain comparison and the subsequent conclusions based on strain-­‐specific protein changes. I hope that our efforts, changes and explanations are clear enough to convince the reviewers and you to accept our revised manuscript for publication in Molecular Systems Biology. ANSWERS TO THE REFEREES REFEREE #1 1. Some of the changes in proteins using SRM should be validated using western blot and include it in the manuscript. We have validated by Western Blot some of the most relevant changes observed by mass spectrometry (FAS, DHB4, SRBP1 and KAPCA). We discuss these changes in the text and have included the validation Western Blot results as part of figures 3 and 4. 2. It is unclear how the quantitation is analyzed, are the relative amount of proteins related to total protein or mg of tissue or DNA. How these values would change taking into account enormous changes in liver weight, density or volume. The quantitation has been done based on equal amounts of total protein. This has been emphasized in the text both at the “Materials and Methods” section and in the “Results” section, in which a new sentence has been included. It reads as follows (p.12): “Whole cell homogenates were digested and subsequently fractionated by off-­‐gel electrophoresis, and equal amounts of total protein were analyzed with targeted nLC-­‐SRM”. As the referee states, the liver tissue might change in weight, density and volume among individuals and taking any of these parameters as the normalization factor, would have similar problems than choosing the total protein amount. In this type of experiments it is normally assumed that most proteins present in the liver do not change in abundance, and therefore, total protein abundance is commonly used as the normalization factor among individuals. 3. Due to abundancy of the proteins, there is a large list of metabolic enzymes that are cytoplasmic or mitochondrial? Is any possibility that important regulatory proteins in the nucleus are missed? Some explanation should be provided in the manuscript. As stated in the manuscript, over one thousand SRM assays were initially designed to quantify 257 proteins, which covered the insulin-­‐signaling pathway and the lipid and carbohydrate metabolism pathways. Nevertheless, after sample fractionation only 144 endogenous proteins were consistently detected in liver samples. When analyzing the observed proteins one can see that there is a clear bias towards abundant proteins which mainly correspond to metabolic pathways (see figure 1), whereas the identification success rate among proteins related to insulin-­‐signaling pathway and downstream effectors (transcription factors) is much lower (see figure 1). This observation is clearly linked to protein abundance and as the referee points out, we are not able to quantify some important low-­‐abundant proteins. We have now stressed this point in the manuscript by adding the following sentence at the first part of the “Results” section: “Proteins that could not be detected even after sample fractionation mostly corresponded to transcription factors and signaling proteins, which are normally present in the cell only at low abundance levels”. 4. In these large data set, is any information about posttranslational modification, importantly phosphorylation or acetylation. Since the insulin pathway strongly control kinase signaling, these modifications should be included as part of datasets. Insulin signaling is mainly driven by phosphorylation events, and protein phosphorylation is a key process for signal transduction in short time courses (within 30 minutes). However, our work focuses on the long-­‐term effects of sustained high-­‐fat diet rather than in short responses and these effects are mainly driven by changes iin the signaling network structure, i.e. protein abundances, that favor certain responses or network branches in front of others. Based on this rationale we focused on the study of protein abundances rather than on protein post-­‐translational modifications. Other studies have recently addressed acute responses in the phosphoproteome (Grimsrud PA, et al. Cell Metab. 2012 Nov 7;16(5):672-­‐83. doi: 10.1016/j.cmet.2012.10.004.). REFEREE #2 General comment Two mouse strains that show different metabolic phenotypes when fed a high-­‐fat diet were compared using targeted, SRM-­‐based, proteomics. The use of SRM allowed the measurement of a set of metabolic pathways and insulin signaling proteins over multiple conditions, mouse strains, and biological replicates. While the analytical framework is sound, the authors should be much more specific in describing abundance changes in certain proteins or small groups of proteins and what conclusions can be drawn from the data. Metabolic pathway visualization would also help to decipher differences between the mouse strains on a high-­‐fat diet. The experimental layout suffers from a lack of regular control-­‐diet leading many of the conclusions to be based on the differences between the 2 mouse strains. Further, it is questionable as to why the insulin pathway proteins were targeted when this pathway is known to operate as a phosphorylation-­‐dependent signaling cascade rather than via abundance changes protein components. Westerns for phosphorylation of main nodes in the insulin signaling cascades (e.g. pAkt, pCREB) should be done. One of the general concerns of the referee was the lack of a proper control for the evaluation of protein changes in different time points for a given mouse strain. Indeed, when comparing these conditions, the observed changes may certainly reflect either diet or age effects, as the second referee pointed out. This possibility has now been clearly stated in the manuscript with the following sentence: “The detected time-­‐
dependent changes in expression levels may, however, reflect diet-­‐induced alterations as well as age-­‐dependent changes”. However, note that strain-­‐specific changes were evaluated comparing both mouse strains at 0 weeks, and then comparing them at 12 weeks of high-­‐fat diet. Therefore, as strains are compared always within the same time point, the effect of aging should be ruled out when assessing strain-­‐specific proteome differences. We reduced that part describing the conclusions based on changes susceptible to be influenced by age, and emphasized the section of direct strain comparison and the subsequent conclusions based on strain-­‐specific protein changes. On the other hand, the referee was also concerned about the choice of studying protein abundance rather than the phosphoproteome. Insulin signaling is indeed mainly driven by phosphorylation events, and protein phosphorylation is a key process for signal transduction in short time courses (within 30 minutes). However, our work focuses on the long-­‐term effects of sustained high-­‐fat diet rather than in short responses and these effects are mainly driven by changes in the signaling network structure, e.g. protein abundances, that favor certain responses or network branches in front of others. Based on this rational we focused on the study of protein abundances rather than protein post-­‐
translational modifications. Other studies have recently addressed acute responses in the phosphoproteome (Grimsrud PA, et al. Cell Metab. 2012 Nov 7;16(5):672-­‐83. doi: 10.1016/j.cmet.2012.10.004.). Specific Points 1. The authors fail to emphasize what mouse organ was used in the summary and in the introduction. Although it is later mentioned that analysis was done on the liver, this should be mentioned earlier. This has been clarified both in the summary and in the introduction and a statement saying that murine liver tissue was used has been added in both sections. 2. End of paragraph 2, summary section. It is unclear what is meant by the conclusion that proteomic responses diverged early in the "obesity prone and partially obesity". Also, it would be better if the conclusions were more specific about what proteins or groups of proteins changed. We rephrased the sentence and highlighted some more specific conclusions that were discussed in the text. 3. In the experimental section, it is unclear for both the age and previous diet when mice were switched to the high-­‐fat diet. This has now been clarified and a new sentence has been added that reads as follows: “Three-­‐weeks old C57BL/6J and 129Sv mice were obtained from Charles River Laboratories International, Inc. Mice were maintained in chow diet for one additional week before starting the experiments with high-­‐fat diet in a pathogen-­‐free facility at the Institute of Molecular Systems Biology, ETH Zürich, complying with official ETH Zürich ethical guidelines. Mice from both strains were kept on a 12 h light/dark cycle and fed with a high-­‐fat rodent diet (60 % fat content) once the experiments started”. 4. (SRM development section). A very thorough and somewhat novel approach was taken for SRM development analyzing standard peptides and conducting a search against a spectral library of those peptides. It is unclear how the SRM-­‐triggered sequencing was done. Is it based on a predicted high mass y-­‐ion, or empirical spectra from previous datasets? As the referee states this point was not clear in our previous version of the manuscript. We used indeed predicted high mass y-­‐ions to trigger MS2 that we then used to build a reference spectral library. We have clarified this in the “Materials and Methods” section with the sentence: “Fragment ion spectra were collected for each peptide using SRM-­‐
triggered MS2 mode and two predicted high mass y-­‐ions per peptide in a QTRAP 4000 instrument (AB/Sciex)”. 5. (Measurements and data analysis section). The rationale behind using correlations iii and iv to verify transitions don't make sense. In iii, the correlation between light and heavy counterparts will presumably not be very good if the protein is changing from the experimental conditions. In iv, the MS2 collected during assay development were not collected from a complex mixture and thus may not correlate well due to co-­‐eluting peptides in the complex mixture analyses. An additional assurance is to assess variability among the 4 heavy and light log ratio transitions for each peptide. If they don't correlate, there is interference. Could the authors comment? To evaluate the transition groups we used i) co-­‐elution of the transition traces associated with a targeted peptide, both in its light and heavy version; ii) presence of at least four co-­‐eluting transition traces for a given peptide exceeding a signal-­‐to-­‐noise ratio of 3; iii) rank correlation between the light SRM relative intensities and the heavy counterparts; iv) rank correlation between the SRM relative intensities and the intensities obtained in the MS2 spectra during the SRM assay development; and v) consistence among replicates. The referee had concerns about points iii and iv. Point iii checks that the order of the transitions (not the absolute intensity) is the same for the light and heavy versions of the same peptide. This is based on the fact that light and heavy versions have the same fragmentation properties and thus, given a true pair, the most intense product ion for the light peptide, should also be the most intense for the heavy peptide; the same for the second most intense; and the same for the third, etc. If this order is not maintained it means that either we are considering a false peak pair or that there is interference that can alter the peptide quantitation. In both cases, the trace should not be considered or at least, processed with caution. To stress that it is the order of transitions what is evaluated here and not the absolute intensity, the word rank correlation has been added to the description. Concerning point iv, the referee pointed out that the different matrix between the assay development and the measurement of liver samples may alter the rank correlation between the MS2 collected during assay development and the SRM traces acquired in the real sample. Although this is certainly a possibility, experimental evidence shows that the MS2 vs SRM rank correlation is in general quite good and it can be used as quality parameter for peak assessment (Reiter L, et al. Nat Methods. 2011 May;8(5):430-­‐
5.; and Picotti P, et al. Nat Methods. 2010 Jan;7(1):43-­‐6.). Nevertheless, it is less powerful than co-­‐elution (i), number of traces (ii) and rank correlation between the endogenous transitions and the heavy reference (iii), and this is the reason why this point is only in the fourth place when evaluating SRM traces. To stress the weight of each point a new clarification has been included in the main text that reads as follows: “Transition groups corresponding to the targeted peptides were evaluated with MultiQuant v.
1.1 Beta (Applied Biosystems, USA) based on different parameters (in order of importance): i)
co-elution […]” 6. The samples were divided into 6 fractions and it is not stated whether the same SRM transitions were used for each fraction or a new method was done for each fraction based on unique peptides to each fraction. This point has been clarified in the text with the addition of the following sentence: “Targeted peptides were only acquired in their corresponding OFFGEL fraction and three replicates for each time point were used in these measurements”. 7. The authors have 2 sections discussing changes in several groups of proteins ("changes in time" and "differences between mouse strains"). These sections could be segmented better for readability. For example, in the first section, discuss specific proteins that changed similarly in both over time in response to high-­‐fat (leaving out any between-­‐strain comparisons). Then, in the next section, discuss specific changes occurring in one strain and not the other. There are many sentences that are very non-­‐specific in these sections as well, essentially saying that protein changes occurred. Those comments are less informative than describing exactly what proteins changed. Description of the early-­‐emerging changes would be better in the "changes in time" section. As suggested by the referee, we have re-­‐organized the text to improve the readability of the section. Some paragraphs have been simplified, additional information has been added to explain specific protein changes, and western blots have been performed to validate the protein abundance changes observed for some key proteins (see figures). 8. (Influence of high-­‐fat diet section). This experiment lacks a proper control for the 12 week high fat diet (same for previous section). It could easily be argued that any changes observed between the normal proteome at time 0 and 12-­‐week high-­‐fat regimen are due to the 12 week difference in age, not the high-­‐fat diet. Also, in this section some proteins are mentioned to change but it is not clear whether they increase or decrease, and what the magnitude of changes are without referring to the figures. When evaluating protein changes in different time points for a certain mouse strain, the observed changes may certainly reflect either diet or age effects, as the referee pointed out. This possibility has now been clearly stated in the manuscript with the following sentence: “The detected time-­‐dependent changes in expression levels may, however, reflect diet-­‐induced alterations as well as age-­‐dependent changes”. However, note that strain-­‐
specific changes were evaluated comparing both mouse strains at 0 weeks, and then comparing them at 12 weeks. Therefore, as strains are compared always within the same time point, the effect of aging should be ruled out when assessing strain-­‐specific proteome differences. Some additional comments have been added in this section specifying the trends of the protein changes in each strain so that the reader can have a first description before checking the corresponding figures, as suggested by the referee. 9. The author's claims that the experimental set-­‐up used here allows for dissecting the effect of diet and genetic background are somewhat exaggerated. There is not enough data for such dissection without many more mouse strains and concurrent genetic analyses of those mice. However, the findings of increased B-­‐oxidation and lipogenesis are interesting in terms of their relation to diabetes resistance. This could be more emphasized and put into context current literature on metabolic disorders and diabetes (likely has something to do with differences in AMPK activation between the strains). We have rephrased the last paragraph of the discussion to make more conservative claims and we have improved the link of our findings to the current literature on metabolic disorders and diabetes. Figures Fig. 4. a) It would be better to somehow not just see the differences but which direction (up or down in one versus the other) b) need to show which axis is the B6 or S9 strain. Figure 4 directly compares both mouse strains and it shows the strain-­‐specific differences in time. As there are two factors being evaluated at the same time (strain, time) raw fold-­‐changes are not very informative e.g. a protein that is increasing at the same rate in both mouse strains in time, would yield a strain-­‐specific difference of zero; the same result would be obtained with a protein that is decreasing at the same rate in both mouse strains in time. This is the reason why no fold-­‐changes were represented in plots 4A and 4B, but instead, full protein abundance dynamics were plotted in 4C and Supplementary Figure S3. 10. Pg 3, the summary and introduction sections begin and end almost exactly the same way, word for word We have rephrased the summary to make it different than the introduction section. 11. Pg 5, end of para. 2, "pre defined" should be "predefined" Corrected. 12. Pg 9, "elected" should be "selected" Corrected. 13. Pg 35, supp. figure "died" should be "diet" Corrected. Molecular Systems Biology Peer Review Process File
2nd Editorial Decision
13 May 2013
Thank you again for submitting your work to Molecular Systems Biology. We have now heard back
from the two referees who accepted to evaluate the study. As you will see, the referees are now
supportive and I am pleased to inform you that we will be able to accept your manuscript for
publication pending the last minor amendments:
- Reviewer #1 notes that information about the phosphorylation status of signalling proteins would
be important, even if the present study focuses on long-term effects. We do not wish to delay this
study further and suggest thus to include a brief discussion of this point since potential changes
reflecting insulin resistance might be relevant in the context of this study.
- Thank you for providing the SRM data as supplementary datasets. If the data has in addition been
deposited into PASSEL, we would kindly ask you to include the respective accession numbers in
Materials & Methods.
---------------------------------------------------------------------------REFEREE REPORTS
Reviewer #1 (Remarks to the Author):
The authors have adequately addressed most of the concerns. In my view, however, the point raised
regarding the inclusion of post-translational modification will strength the manuscript and provide
important data sets. Because the authors have the data sets
they should include data about the phosphorylation of proteins. Although the authors claims that
insulin induces changes at short term, there are many modifications that could be consequence of
insulin resistance and should be reported.
Reviewer #2 (Remarks to the Author):
The authors have adequately addressed my comments. I find the manuscript suitable for publication.
2nd Revision - authors' response
© European Molecular Biology Organization
23 May 2013
4
Dear Editor
We introduced the suggested changes to the manuscript entitled “Targeted proteomics reveals
strain-specific changes in the mouse insulin and central metabolic pathways after sustained
high-fat diet” that we submitted for publication in Molecular Systems Biology. Find below our
comments.
Reviewer #1 notes that information about the phosphorylation status of signalling proteins would
be important, even if the present study focuses on long-term effects. We do not wish to delay
this study further and suggest thus to include a brief discussion of this point since potential
changes reflecting insulin resistance might be relevant in the context of this study.
We have now introduced a sentence that states the importance potential phosphorylation
alteration and the convenience of performing further phosphorylation analysis to complement
existing data. The sentence reads as follows: “These results point to potential changes in the
liver protein phosphorylation status induced by long-term high-fat diet and, therefore, follow-up
phosphoproteome studies might be relevant in this context”.
Thank you for providing the SRM data as supplementary datasets. If the data has in addition
been deposited into PASSEL, we would kindly ask you to include the respective accession
numbers in Materials & Methods.
We are providing the detected intensities for each quantified transition as a supplementary table
(Suppl. Table ST1) and we uploaded the raw data (wiff files) in the PASSEL repository. A
sentence has been added in Materials & Methods: “Raw data was deposited in the PASSEL
repository with the dataset identifier PASS00244”
I would be grateful if you could prepare a supplementary information file according to our
instructions (www.nature.com/msb/authors/index.html#a3.4.6). Essentially, supplementary
figures and supplementary text items should be included in a single PDF file that starts with a
Table of Content.
Supplementary information has now been prepared as suggested by the editorial guidelines.