1. Reviewer #2 (Public Review):

    The authors aimed to address the lack of therapeutic treatments for the Rett Syndrome by (a) identifying novel functional partners of MECP2 (mutations in which underlie Rett Syndrome), and (b) demonstrating the druggability of the partners using in-use drugs. The authors accomplish this by performing phylogenetic profiling across more than thousand species to identify genes that coevolved with MECP2. Using drugs that target three of their top hit genes in RTT models, they demonstrate the potential efficacy of these drugs against RTT and validate their new molecular targets.


    Overall, the manuscript is very well written and easy to follow even for people outside the fields, and provides insights into an important biological process and identifying much needed therapeutic targets. The authors reproduced various RTT phenotypes in human neural cells with reduces MECP2 expression and demonstrated the ability of the three drugs to rescue the phenotypic profiles. In doing so, the authors were able to shed light on some of the potential mechanisms of action through which these drugs operate. Given that all three drugs have approved safety profiles, with further pre-clinical investigation, these drugs could serve as potential therapeutic agents for Rett Syndrome.


    The biggest weakness of the paper is the lack of a strong link between comparative phylogenetic profiling and the identification of potential therapeutic agents. The paper is currently framed as a 'comparative genomic pipeline' to identify novel drug targets, yet the authors didn't demonstrate the robustness of the pipeline using appropriate positive and negative controls. Basic network analyses weren't performed to demonstrate a wide usability of the methodology beyond RTT.

    While the authors do a good job of demonstrating the RTT phenotype-rescuing abilities of the three drugs, they don't exhaustively demonstrate how their comparative evolutionary pipeline was essential for identifying the three drugs. MECP2 forms a complex with HDACs and all three of the drugs selected here have known direct/indirect effects on HDAC activity. It is therefore plausible that the drugs are mediating their effects through HDACs, in which case the comparative genomic pipeline was not required to select these drugs.

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  2. Reviewer #1 (Public Review):

    Major Comments/Concerns

    On line 101 - The use of only the longest transcript for each gene could miss important functional sections of the genome. This could create bias against genes with many isoforms and miss exons that do not happen to lie in the longest transcript. How different would the resulting profiles of conservations be if all coding regions or exons of every gene were used?

    On line 106 - Does this approach create good specificity to our gene of interest rather than just broad functional similarity? For example, with this approach, are there any major neuronal function genes that have NPP very different from MeCP2? Could authors provide a more objective evaluation to baseline/null?

    Minor Comments/Concerns

    On line 132 - It seems fair to examine this set of genes first, but I am not sure this approach to filtering in particular moves us further towards finding a therapeutic for Rett. These genes could be all good potential targets, and your subset of focus are just the best ones for current validation.

    Figure 2C could be made with all 390 co-evolved genes to strengthen the argument that chr19p13.2 is an important region for MeCP2s role.

    Figure 3, 4, 5, 6 - Dynamite plots. While the stats tests are great for understanding the impact of different treatments, box plots or jittered dots would be even more clear.

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  3. Evaluation Summary:

    The manuscript has the potential to be of broad interest to neuroscientists who are aiming to leverage concepts and tools of evolutionary biology to identify novel gene targets and much-needed therapeutic interventions. The follow up experiments are detailed, well thought out, and do a good job of proving the potential of the identified drugs in alleviating molecular signatures in in vitro disease models. However, the link between comparative genomic analysis and identification of specific drugs is not yet sufficiently established and doesn't convincingly demonstrate the usability of the evolutionary pipeline in identifying novel therapeutics.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

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