Derailed protein turnover in the aging mammalian brain

Efficient protein turnover is essential for cellular homeostasis and organ function. Loss of proteostasis is a hallmark of aging culminating in severe dysfunction of protein turnover. To investigate protein turnover dynamics as a function of age, we performed continuous in vivo metabolic stable isotope labeling in mice along the aging continuum. First, we discovered that the brain proteome uniquely undergoes dynamic turnover fluctuations during aging compared to heart and liver tissue. Second, trends in protein turnover in the brain proteome during aging showed sex-specific differences that were tightly tied to cellular compartments. Next, parallel analyses of the insoluble proteome revealed that several cellular compartments experience hampered turnover, in part due to misfolding. Finally, we found that age-associated fluctuations in proteasome activity were associated with the turnover of core proteolytic subunits, which was recapitulated by pharmacological suppression of proteasome activity. Taken together, our study provides a proteome-wide atlas of protein turnover across the aging continuum and reveals a link between the turnover of individual proteasome subunits and the age-associated decline in proteasome activity.

Dear Dr Savas, Thank you again for submitting your work to Molecular Systems Biology.We have now heard back from two of the three reviewers who agreed to evaluate your study.Unfortunately, even thought he have sent them several reminders, we have not received a report from reviewer #2.In the interest of time, we have decided to proceed with making a decision based on the two available reports.As you will see below the two reviewers acknowledge that the presented data and findings seem interesting.They do however raise a series of concerns which we would ask you to address the issues raised in a major revision.
As you will see below most of the reviewers' comments refer to the need to provide further clarifications, include additional references and discuss some of the findings in further detail.A more fundamental issue is raised by reviewer #3 who recommends testing the emerging hypothesis that "reduced UPS activity after 18 months of age is responsible for the changes in the turnover of the catalytic core subunits of the proteasome observed at later ages" by treating 18-month old mice with proteasome inhibitors (e.g.bortezomib) and assessing the effect of the proteasome inhibition.
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Overall, while this study is descriptive in nature, it presents a fascinating dataset that will undoubtedly interest various scientific communities, including those focused on aging, neurodegeneration, and protein turnover.The data quality appears to be high, and although additional experiments may not be necessary in principle, certain terms and statements in the writing require further clarification.Addressing these points would be beneficial before acceptance.
1. Figure 1B and C and also the corresponding text in the results section (2nd page of results, 2nd paragraph): The authors use the term "protein abundance" here.This is a bit misleading as they mean number of identified proteins, while nowadays protein abundance means more protein amount of the proteins.The wording should be adjusted accordingly.This is also important in light of Figure 1D where not the number of IDs are used to calculate FA, but the integrated MS signals, which is more in line with an abundance definition in proteomics.1B: Is the number of ID'd proteins in heart not significantly lower than in cortex or liver?Why was not the same crosssample comparison performed as in 1C? 3.In Figure 1 was the data generated from female and male animals or only from one sex?Please, add the information to the legend.Same for Suppl.Figure 1.2B and C: Relative 15N signal to WHAT?Is it to the median of 12 months as indicated at the bottom of the figure for 14N FA?If to the median of 12 months for each sex then why is the 12 months median for example at 2C not at a log2 value of 0? This is throughout all the figures (see more below).Please define relative to WHAT as it is ambiguous sometimes.Moreover, in several figures it does not seem that it is true that it was standardized to the median of 12 months as then the median of 12months (assuming the line in the boxplot is the median) should be at 1 in linear space or 0 in log2 space.I might be mistaken what you mean by "standardized to the median of 12 months", but in any case, a clarification would help.

Figure
5. Figure 2D, E: It says that "relative 14N protein abundance from both male and female cohorts NO significant differences..." I think the figure shows the opposite here -that there is a significant difference, doesn't it?Please correct.
6. Figure 2G: K-means clustering.Honestly, I see here more than 2 clusters for the female data.Please explain the rationale, why only 2 clusters were chosen as parameter for the female mice? 7. Figures 3 A, B and C, D -based on the y-axis label the plots in A,B should show the same distribution of values as in C,D.However, they are actually the opposite sign it seems (could it be that one is log2 (A,B) and the other -log2 (C,D)?).Please clarify and if possible, use the same values.Also, at the bottom of the legend it says that "14N FA is standardized to median of 12M group" -this does not seem to be the case for A to D and I. 11. Figure 4G shows turnover values, which have been defined in the text and Figure 2A as "14N / (14N + 15N)" -that means that at 18 months less 14N is left relative to total, so more protein has been turned over.However, the legend says that you see a "decrease in turnover".I do think it is exactly the opposite for the above reason, but could easily misunderstand.Please clarify.12. Suppl.Figure 4: As before -if in B all the values are really standardized to the median of 12 months, why is then the median for 12 months well below a log2 value of 0? Again, I might misunderstand what the authors mean by "standardized to the median of 12 months", but in any case, a clarification would help.
13.I am missing a reference to Kluever et al., Science Advances, 2022 (PMID: 35594347).I think this paper is especially relevant in the context of this manuscript as it looks at protein turnover in young and old animals and especially in the brain.Therefore, the authors might consider referencing it in the manuscript.

Reviewer #3:
In this paper, the authors report a fine-grained study of protein turnover rates measured by metabolic labelling in vivo of three organs.
The study extends previous results from other groups that have performed similar studies on a smaller scale in the brain and points to a role of the proteasome in the regulation of protein turnover in the brain.

General point:
A clear hypothesis emerges from the data: reduced UPS activity after 18 months of age is responsible for the changes in the turnover of the catalytic core subunits of the proteasome observed at later ages.This hypothesis is testable by treating mice of 18 months of age with proteasome inhibitors (e.g.bortezomib) in order directly assess whether reduced proteasome activity is sufficient to mimic the aging-associated changes in turnover of the catalytic subunits.In my opinion, this experiment is a necessary functional validation to warrant publication in a prestigious journal such as Mol Syst Biol.

Specific points
1. Is there a correlation between protein abundance and protein turnover rates?In principle, it is expected that the most abundant proteins should have lower turnover rates.
3. The abundance of proteins in the insoluble fraction should be normalized with respect to the abundance of the same protein in the soluble fraction.In other words, it is expected that highly-abundant proteins are also abundant in the insoluble fraction.

Rao et al. conducted continuous in vivo metabolic labeling to investigate age-related changes in
protein turnover in mice.They compared three tissues -two postmitotic and one mitotic.As expected, the mitotic tissue exhibited greater label incorporation than the postmitotic ones.However, only the cortex displayed age-dependent differences.Intrigued by the findings in the cortex, the researchers delved deeper and performed additional measurements, this time comparing age-related protein turnover changes in males and females.Surprisingly, while proteins from the same cellular compartments exhibited shifts in turnover due to aging, the shifts in female mice occurred at later time points compared to male mice.Part of the age-related shift in protein dynamics could be attributed to the formation of protein aggregates that evade degradation.Interestingly, not all compartments that displayed age-related turnover changes were represented in these insolublesome.Furthermore, the proteasome, a critical player in protein degradation, exhibited differential activity throughout aging.The regulatory proteasome subunits (19S) demonstrated a distinct activity pattern from the core proteasome (20S).
Overall, while this study is descriptive in nature, it presents a fascinating dataset that will undoubtedly interest various scientific communities, including those focused on aging, neurodegeneration, and protein turnover.The data quality appears to be high, and although additional experiments may not be necessary in principle, certain terms and statements in the writing require further clarification.Addressing these points would be beneficial before acceptance.
Response: We thank the Reviewer for carefully reading our manuscript and the positive assessment of our work.We appreciate your comments on the interesting yet descriptive nature of this manuscript.To make our manuscript even stronger we have performed an additional mechanistic experiment to functionally verify our result.
1. Figure 1B and C and also the corresponding text in the results section (2nd page of results, 2nd paragraph): The authors use the term "protein abundance" here.This is a bit misleading as they mean number of identified proteins, while nowadays protein abundance means more protein amount of the proteins.The wording should be adjusted accordingly.This is also important in light of Figure 1D where not the number of IDs are used to calculate FA, but the integrated MS signals, which is more in line with an abundance definition in proteomics.
Response: Thank you for this constructive comment.We agree that use of term ""protein abundance" is not suitable here.To address this concern, we have modified the figure 1B and  C and the corresponding legend and text (please see line 127-130)

Figure 1B: Is the number of ID'd proteins in heart not significantly lower than in cortex or liver? Why was not the same cross-sample comparison performed as in 1C?
Response: We thank the reviewer for this suggestion to perform cross sample comparison in Figure 1B.This analysis has been performed and shows that the N15 protein IDs in heart tissue is significantly lower across aging compared to liver.This analysis has been added to Figure 1B.

3.
In Figure 1 was the data generated from female and male animals or only from one sex?Please,  2B and C: Relative 15N signal to WHAT?Is it to the median of 12 months as indicated at the bottom of the figure for 14N FA?If to the median of 12 months for each sex then why is the 12 months median for example at 2C not at a log2 value of 0? This is throughout all the figures (see more below).Please define relative to WHAT as it is ambiguous sometimes.Moreover, in several figures it does not seem that it is true that it was standardized to the median of 12 months as then the median of 12months (assuming the line in the boxplot is the median) should be at 1 in linear space or 0 in log2 space.I might be mistaken what you mean by "standardized to the median of 12 months", but in any case, a clarification would help.

Figure
Response: We thank the Reviewer for the thoughtful comment and acknowledge that the word choices used to describe our data in the first submission had several limitations.The Reviewer is correct, our previous use of 'relative' is not accurate for Figure 2 B and C. In the revised manuscript, we removed 'relative' from the Y-axes in Figures 2B-C, the figure legend, and manuscript text.We can also see how our use of the term "standardization" caused unnecessary confusion.In order to address this limitation, we added an explicit description of the data presented in each figure legend.
We chose against normalizing directly to the 12 M time point for each respective protein.Had we gone that route, then we would be left with values of "1" for all proteins at 12 M, which would prevent proper statistical analyses.The boxplot line for all the plots is mean +/-SEM.
For Figure 2B-C For Figure 2D-E, we extracted the TMT reporter ion intensities for each protein identified in both the 14 N and 15 N channels and calculated the FA (i.e., ( 14 N TMT intensity/( 14 N TMT intensity + 15 N TMT intensity)).Next, we calculated the median FA value for the 12 M time point and adjusted each protein based on this value.This allowed for proper statistical analyses to be performed to compare all groups across aging.
The data presented in Figure 2F-G was prepared as in Figure 2D-E, but the FA value was plotted as scaled by row for heatmap representation.2D, E: It says that "relative 14N protein abundance from both male and female cohorts NO significant differences..." I think the figure shows the opposite here -that there is a significant difference, doesn't it?Please correct.

Figure
Response: We apologize for this mistake; it was a typo and thank the Reviewer for catching this.This has been corrected in Figure 2 legend (please see line 713).2G: K-means clustering.Honestly, I see here more than 2 clusters for the female data.Please explain the rationale, why only 2 clusters were chosen as parameter for the female mice?

Figure
Response: I can see why it may seem as if the female dataset contains more than 2 clusters, however, the male and female k-mean clustering analysis was performed with identical parameters.We have chosen 'k' (i.e., number of clusters) based on the optimal silhouette score.Using the Orange bioinformatics pipeline, we ran 300 iterations to systematically determine how many clusters is ideal while also avoiding overfitting the data.For the female dataset, the optimal number of clusters calculated was 2.

Figures 3 A, B and C, D -based on the y-axis label the plots in A,B should show the same distribution of values as in C,D. However, they are actually the opposite sign it seems (could it be that one is log2 (A,B) and the other -log2 (C,D)?). Please clarify and if possible, use the same values.
Also, at the bottom of the legend it says that "14N FA is standardized to median of 12M group" -this does not seem to be the case for A to D and I.
Response: Thank you for this useful comment for improving the Figure representation.We have now presented the 14 N FA data on the scale of 0 to 1, as these values are calculated based on reconstructed MS1 chromatograms ( 14 N / 14 N + 15 N).

Figure 3I: Is the data from both sexes or only one?
Response: The protein ubiquitination data presented in Figure 3 (Figure 3G in the revised manuscript) is from male cohorts.This information has been added to the figure legend and text (please see line 255 and 748).

Figure 4B: It is probably my lack of knowledge, but what are the green spots on the right to the western blot (at least they are present in my version of the figure)?
Response: Sorry for the confusion, we have removed the cartoons of 20S and 19S proteasome complexes from the modified figures.

Figure 4D and legend. Please define what NSAF stands for.
Response: NSAF stands for Normalized Spectral Abundance Factor.This term was previously defined in PMC3599300.We have now defined NSAF in Figure 4 legend and modified the figured accordingly.4G shows turnover values, which have been defined in the text and Figure 2A as "14N / (14N + 15N)" -that means that at 18 months less 14N is left relative to total, so more protein has been turned over.However, the legend says that you see a "decrease in turnover".I do think it is exactly the opposite for the above reason, but could easily misunderstand.Please clarify.

Figure
Response: We thank the reviewer for this helpful comment.We agree that the use of the term 'protein turnover' is confusing.To resolve this throughout the manuscript, we have modified the figures to say " 14 N fractional abundance (FA)", where FA = ( 14 N/ 14 N+ 15 N), instead of "protein turnover".12. Suppl.Figure 4: As before -if in B all the values are really standardized to the median of 12 months, why is then the median for 12 months well below a log2 value of 0? Again, I might misunderstand what the authors mean by "standardized to the median of 12 months", but in any case, a clarification would help.Kluever et al., Science Advances, 2022 (PMID: 35594347).I think this paper is especially relevant in the context of this manuscript as it looks at protein turnover in young and old animals and especially in the brain.Therefore, the authors might consider referencing it in the manuscript.

I am missing a reference to
Response: We agree that the findings of Kluever et al. are very relevant to our manuscript and Eugenio Fornasiero is a friend.We apologize for this oversight and have add this citation.(please see lines 59, 315, 368, and 491)

Reviewer #3:
In this paper, the authors report a fine-grained study of protein turnover rates measured by metabolic labelling in vivo of three organs.The study extends previous results from other groups that have performed similar studies on a smaller scale in the brain and points to a role of the proteasome in the regulation of protein turnover in the brain.

General point:
A clear hypothesis emerges from the data: reduced UPS activity after 18 months of age is responsible for the changes in the turnover of the catalytic core subunits of the proteasome observed at later ages.This hypothesis is testable by treating mice of 18 months of age with proteasome inhibitors (e.g.bortezomib) in order directly assess whether reduced proteasome activity is sufficient to mimic the aging-associated changes in turnover of the catalytic subunits.In my opinion, this experiment is a necessary functional validation to warrant publication in a prestigious journal such as Mol Syst Biol.
Response: We thank the Review for this helpful improvement to our manuscript.We agree that showing that manipulating proteasome activity modulates proteasome subunit turnover will strengthen this work.We have performed another in vivo stable isotope labeling experiment in 18 M aged animals in combination with marizomib (a brain penetrant Psmb1/2/5 inhibitor).We have performed several follow up experiments and presented this new data in Figure 5/ Figure EV5.

Specific points 1. Is there a correlation between protein abundance and protein turnover rates? In principle, it is expected that the most abundant proteins should have lower turnover rates.
Response: We appreciate the Review's thoughtful comments on the relationship between protein abundance and protein turnover.We agree some highly abundant proteins are longlived, however not every high-abundance protein is, by default, a long-lived protein (LLP), and not every low-abundance protein is short-lived (Figure 1, left).For this work, we have below a similar example in which we have plotted the relationship between each proteins abundance and the same proteins turnover using MS1 based fractional abundance ( 14 N/ 15 N+ 14 N).The blue "x" symbols show rank ordered protein abundance (high to low) and the pink dots show their respective 14 N FA.Highly abundant proteins do not necessarily have lower turnover rates which is shown by the variable distribution of pink dots.(Nat Neurosci . 2016 Aug;19(8):995-8. doi: 10.1038/nn.4325).Can this explain the changes in the turnover rates of myelin proteins?

It is known that myelin is subject to phagocytosis by microglia
Response: Thank you for this helpful suggestion.We have added discussion about myelin proteins turnover rates and cited the paper suggested by the Reviewer (please see line 403).

The abundance of proteins in the insoluble fraction should be normalized with respect to the abundance of the same protein in the soluble fraction. In other words, it is expected that highlyabundant proteins are also abundant in the insoluble fraction.
Response: The Reviewer raises an important point.
Please note, we never exclusively analyzed the "soluble fraction" of the proteome.For Figures 1 and 2, the tissues were thawed on ice and homogenized using a bead-based Precellys in 500 μL of homogenization buffer (4 mM HEPES, 0.32 M sucrose, 0.1 mM MgCl 2 with Halt protease inhibitor cocktail, and 1 mM PMSF) and methanol and chloroform precipitated.The pellets were solubilized in 8 M urea or 6M GuHCl, reduced, alkylated, and digested with trypsin or trypsin Lys-C.Thus, we never extracted the soluble fraction.
For the insolubleome analysis presented in Figure 3, cortical homogenates were sonicated and incubated with 1% SDS and the insoluble material was isolated by ultracentrifugation at 100,000 x g at 4 °C for 60 minutes.Our thinking was that maybe the differences in protein turnover identified across aging in Figure 2 are driven by misfolded proteins.By again using  the tissues from the continuously 15 N labeled mice, we are able to calculate the 14 N (old) protein level relative to the total insoluble pool to each respective protein.This provides a robust method for overcoming any variability in biochemical separation of insoluble fractions.
To further address the Reviewer's comment we have also extracted high and low abundance proteins in the insolubleome (Table 1), and presented their FA values from insolubleome and global analysis.These are examples of proteins who's higher FA value in the insolubeome is indicative of a deficit in turnover due to phase-separation/misfolding.We have provided the list of proteins that possess higher FA values in the insolubleome compared to global (or not identified in global) across times points in the Dataset EV3 and have provided examples below as well.

Related to Fig. 3 C and D, the GO overrepresentation of the two "pink" clusters should be shown separately
Response: Thank you for this helpful suggestion.In our revised manuscript, we now have presented the two pink clusters separately.

5
. By comparing Fig. 3 C and D, it appears that the "dynamic" clusters in the Fig. 3 D are comparable to the "static" clusters in Fig. 3 C.It is necessary to show confidence interval for the three clusters and to provide a statistical assessment of the their dynamic nature (e.g. via two-way ANOVA) Response: We thank the Reviewer for this critical suggestion.We agree that this is necessary to show the significant differences between the three clusters.Accordingly, we have performed the two-way ANOVA and have added it to the Figure 3.  Dear Jeff, Thank you again for sending us your revised manuscript.We are now satisfied with the modifications made and I am pleased to inform you that your paper has been accepted for publication.Your manuscript will be processed for publication by EMBO Press.It will be copy edited and you will receive page proofs prior to publication.Please note that you will be contacted by Springer Nature Author Services to complete licensing and payment information.
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protein turnover in the aging mammalian brain.
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8. Figure3I: Is the data from both sexes or only one?9. Figure 4B: It is probably my lack of knowledge, but what are the green spots on the right to the western blot (at least they are present in my version of the figure)?10. Figure 4D and legend.Please define what NSAF stands for.
4. Related to Fig.3C and D, the GO overrepresentation of the two "pink" clusters should be shown separately 5.By comparing Fig.3C and D, it appears that the "dynamic" clusters in the Fig.3D are comparable to the "static" clusters in Fig.3C.It is necessary to show confidence interval for the three clusters and to provide a statistical assessment of the their dynamic nature (e.g. via two-way ANOVA) add the information to the legend.Same for Suppl.Figure1.Response: The data generated in Figure1is from female cohorts.This information has been added in the legend of Figure1and S1 and text (please see line 115).
, we extracted the TMT reporter ion intensities for each identified 15 N protein and plotted them based on a log 2 scale.Next, we calculated the median value for the 12 M timepoint point and adjusted each protein based on this value.

Figure 1 .
Figure 1.Comparison of protein abundance based on 14 N and 15 N spectral counts.

Figure 2 .
Figure 2. Comparison of protein abundance based on spectral counts compared to MS1-based fractional abundance.
protein turnover in the aging mammalian brain.

Table 1 .
Comparison of FA values high and low abundant proteins identified in the insolubleome and the corresponding global proteome FA.

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