Physiological Consequences of Targeting 14-3-3 and Its Interacting Partners in Neurodegenerative Diseases

Round 1

Reviewer 1 Report

It has been found that 14-3-3 proteins are a family of proteins expressed throughout the body and implicated in many diseases from cancer to neurodegenerative disorders. The author nicely describes molecular interactions of 14-3-3 in silico and several experimental approaches. The paper is highly organized and my suggestion is to accept the manuscript after checking overall contents.

Author Response

We would like to thank the reviewers for their detailed and helpful critiques.  Most of the comments have resulted in improvements to the manuscript. Our detailed response to critiques follows below. 

 

Reviewer 1

It has been found that 14-3-3 proteins are a family of proteins expressed throughout the body and implicated in many diseases from cancer to neurodegenerative disorders. The author nicely describes molecular interactions of 14-3-3 in silico and several experimental approaches. The paper is highly organized and my suggestion is to accept the manuscript after checking overall contents.

We are grateful for the detailed comments, and for recognizing and recommending our manuscript for publication.  

Reviewer 2 Report

In eukaryotes, 14-3-3 paralogs are conserved adapter proteins involved in multiple physiological processes such as signal transduction, translation, protein trafficking and apoptosis. Previous studies have documented critical roles played by 14-3-3 proteins in diverse neurological and other age-associated disorders. In the current study Akshatha Ganne et al. used computational methods to predict disordered regions of 14-3-3 paralogs that fail to attain stable conformations on their own, resulting in indeterminate (or partner-determined) structures. In order to target protein-protein interaction interfaces for the treatment of neurodegenerative diseases, including Alzheimer’s disease, the authors proposed to use small molecules as protein-protein interaction inhibitors, to counteract aggregate progression by breaking critical interactions needed for aggregate growth. They screened FDA-approved drugs in silico for structures that could 26 target the 14-3-3G/hexokinase interface, an interaction specific to aggregates and AD.

The authors have demonstrated that drugs targeting the interfaces of 14-3-3 paralogs with their interacting partners show promise to reduce aggregation and improve associated physiological functions. Such disease-specific protein-protein interaction inhibitors have the potential to prevent, slow, or reverse aggregation associated with neurodegen-359 erative diseases and other age-progressive disorders.

Overall, the study was well thought out and logically executed. Dr. Ayyadevara Srinivas is the reputed researcher of the mechanisms of aging and age-related diseases. He uses model organisms due to their easy genetics (mainly C. elegans). He has a great experience in proteomics and analysis of post-translational modifications.

However, I have some doubts about the statistical analysis of the data. The authors show on a graph with multiple groups the significant differences founded by the t-test, which can only be used when comparing the means of two groups (so-called pairwise comparison). Why Fisher-Behrens heteroscedastic t-test was chosen, please give examples of its application in biology or give at least a link. Although the multi-group graphs compare the control and treatment group in pairs, you still need to apply tests for multiple comparisons and apply a correction for these comparisons. Perhaps this is why the authors have such a small p-value....10-7 or 10-17

 

I did not see anywhere information about the number of samples. Strange graph 2b....last bar is the smallest relative to control, but there is no significant difference. 

Please note, the number of stars (****) does not match in the legend to figure 2 and in Figure 2b-2c itself.

The scale is not indicated in Figures 2a and 3b. I don't see any difference in fluorescence between the groups at all in Figure 3. Please provide more representative images.

Please note common typos!

Author Response

We would like to thank the reviewers for their detailed and helpful critiques.  Most of the comments have resulted in improvements to the manuscript. Our detailed response to critiques follows below.

 

Reviewer 2

The authors have demonstrated that drugs targeting the interfaces of 14-3-3 paralogs with their interacting partners show promise to reduce aggregation and improve associated physiological functions. Such disease-specific protein-protein interaction inhibitors have the potential to prevent, slow, or reverse aggregation associated with neurodegenerative diseases and other age-progressive disorders.

Overall, the study was well thought out and logically executed. Dr. Ayyadevara Srinivas is the reputed researcher of the mechanisms of aging and age-related diseases. He uses model organisms due to their easy genetics (mainly C. elegans). He has a great experience in proteomics and analysis of post-translational modifications.

We would like to thank the reviewers for all the positive comments.

However, I have some doubts about the statistical analysis of the data. The authors show on a graph with multiple groups the significant differences founded by the t-test, which can only be used when comparing the means of two groups (so-called pairwise comparison). Why Fisher-Behrens heteroscedastic t-test was chosen, please give examples of its application in biology or give at least a link. 

Please see reference 37 (cited in line 490).

Although the multi-group graphs compare the control and treatment group in pairs, you still need to apply tests for multiple comparisons and apply a correction for these comparisons. Perhaps this is why the authors have such a small p-value....10-7 or 10-17.

The P values shown were calculated without any adjustment, but thresholds were shifted to α=0.01 to reduce type-I errors (see lines 494 – 496). We have made this nominal adjustment for multiple endpoints when present, but we caution that strict Bonferroni correction may be excessive due to correlation among assessed measures, since all reflect a common underlying process — protein aggregation.

I did not see anywhere information about the number of samples. Strange graph 2b....last bar is the smallest relative to control, but there is no significant difference. 

We apologize for the omission of N values, which have now been added in multiple locations (see lines 158, 183, 264, 267, 279, 284, 305, and 312).

Please note, the number of stars (****) does not match in the legend to figure 2 and in Figure 2b-2c itself. 

We have made numerous corrections to the figures to ensure that the indicated significances are consistent.

 

The scale is not indicated in Figures 2a and 3b. 

Scale bars have been added to the figures. 

I don't see any difference in fluorescence between the groups at all in Figure 3. Please provide more representative images.

Thank you; this has been corrected. 

Please note common typos! 

Done.

 

 

 

Author Response File: Author Response.pdf