1. Author Response

    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.

    We thank the editor for this important comment. As our research is intensively focused on comparative genomics and phylogenetic profiling, we failed to thoroughly detail the concept and rationale of phylogenic profiling. Though it is certainly not the only approach for identifying gene-gene interactions, phylogenic profiling is the most critical part of our analysis and was used to establish the MECP2 co-evolved network. This network was the basis for filtering thousands of drugs to identify possible beneficial effects for RTT phenotypes. In this revision, we have edited the text and performed bioinformatic control experiments to demonstrate how comparative analysis was essential to identifying the specific MECP2-linked genes and compounds tested here. We edited the Introduction section to demonstrate the extensive track record of phylogenic profiling (and cladebased analysis) in accurately predicting gene function and expanding known networks.

    We reanalyze our resulting gene sets with respect to known interactions, showing that while some of our prediction described in the literature many genes we identify are novel.

    We also describe previously published benchmarking of our approach to determine sensitivity and specificity based on established interaction databases, and reference our newly published paper which includes a complete description of our pipeline.

    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?

    We thank the reviewer for this comment and realized that our description of this method in the original manuscript was not well described. We do not use the longest isoform among all isoforms of the gene, but only the longest among the “canonical” isoforms determined by Uniprot (Bateman, 2019), in the rare cases where Uniprot specifies more than one. For all but 29 genes (of over 20,000), Uniprot has a single canonical isoform. For those 29 with multiple canonical isoforms, we choose the longest one. Thus, the “longest isoform” selection affects a tiny number of genes and has very little impact on our results. We have corrected our Methods section (lines 499-505) to describe our method more accurately, and reference our new paper which contains additional details (Tsaban et al., 2021). We apologize for our poor description in the original manuscript and thank the reviewer for bringing this to our attention.

    The reviewer also raises the valid suggestion of evaluating multiple isoforms. This has been investigated, and indeed phylogenetic profiling can benefit from a thorough isoform selection or harmonization scheme. However, such approaches cannot be easily applied to all human genes or cannot be easily scaled to accommodate new genomes (e.g. PALO, IsoSel) (Philippon, Souvane, Brochier-Armanet, & Perrière, 2017; VillanuevaCañas, Laurie, & Albà, 2013), so we have used our standard approach of the Uniprot canonical isoform here, which we have shown to perform well.

    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?

    We and others have successfully applied phylogenetic profiling to identify functionally related genes in multiple systems and pathways. It does provide good specificity, but “specificity” here has a very particular meaning. When a pathway becomes non-functional in a specific lineage (often due to loss of a key gene in the pathway), then other genes involved in the pathway may ose the fitness advantage they provide to the organism if they are not involved in other important pathways/functions. PP captures these pathway-level loss events, providing a quantitative measurement of functional relatedness and prioritizing those genes minimally involved in the same pathway over pleiotropic genes required in multiple pathways. This property is especially attractive for drug development, where the goal to is target the most specific proteins possible. So it does indeed provide a high degree of specificity, under this particular model. We have significantly expanded our description of this method and its complementarity to other methods, on lines 81-98.

    It is clear that many genes with neuronal function have different evolution compared to MECP2. To give one example, we looked at a key pan-neuronal gene synaptobrevin (VAMP2). VAMP2 is conserved much more widely in evolution than MECP2 and is much more strongly linked to other synaptic proteins such as RAB3, than to MECP2. The phylogenetic profiles of these two genes are very different - when we look at the top 200 PP genes linked to each of these, 0 genes are in common among the two lists.

    We performed comparisons to several functional databases to provide comparisons of the 390 MECP2 linked PP genes to baseline. When we analyze these genes with GeneAnalytics (Fuchs et al., 2016), they are most enriched with genes expressed in the brain ( p-value < 0.00024), compared to all other tissues. 79 genes are linked to the cerebral cortex and 54 to the cerebellum. So a substantial fraction are expressed in expected tissue types for MECP2-linked genes. (As an aside, this analysis revealed an unexpected enrichment for testis, and there is a known evolutionary link for eutherian-specific genes to be expressed in the testis and brain (Dunwell, Paps, & Holland, 2017). This new analysis is now Figure 1–figure supplement 1C.

    To investigate another aspect of how the 390 MECP2 linked PP genes compare to baseline, we analyzed them in comparison to the STRING database. In STRING, 1,398 genes are linked to MECP2 in one of three evidence categories: coexpression, experimental, and textmining. 366 of the 390 PP linked genes are not linked to MECP2 in STRING via any of the three evidence categories, indicating the highly unique nature of PP interactions. So while results of PP do show expected functional properties (as evidenced by the GeneAnalytics enrichments), they are quite orthogonal to other methods for prediciting functional interactions.

    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.

    We agree with the reviewer comment however in this paper we try to focus on finding possible drugs using repositioning. The advantage of this approach is that it allows dramatic reduction of drug development time and costs. Other genes could ultimately be even better targets than the genes/proteins that are targeted by known compounds. We have now made available the full gene list as a supplemental table, which could be mined for other potential targets, especially as more genes become druggable over time and present additional opportunities for repurposing. We have also addressed this in the Discussion in lines 420-423.

    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.

    We thank the reviewer for their suggestion. We have updated the figure to include this representation.

    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.

    We agree, and have now produced jitter versions of all barplots in Figures 3-6. We have added an additional replicate for several of the experiments, which very slightly affects pvalues. These changes did not affect the significance on any result except for the weak effect of Pacritinib on NF-κB-dependent luciferase activation (fig 4E), which is now no longer supported by our data. We have amended the text accordingly on line 361.

    Reviewer #2 (Public Review):

    Strengths:

    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.

    We appreciate your recognition of the merits of this work.

    Weakness:

    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.

    We thank the reviewer for this comment, which points to the need for better justification for and validation of our method. We have tried to address this concern substantially in the revision, both by improving our discussion of previous work on the phylogenetic profiling method and by providing new bioinformatic validation experiments comparing our MECP2 protein list with other interaction databases, notably STRING. These are detailed in our responses to specific queries below. Importantly, we have now cited two new technical publications from our group describing the NPP method and benchmarking it against other comparative methods using control datasets as you suggest (Bloch et al., 2020; Tsaban et al., 2021). Overall we have extensive experience in developing and applying phylogenetic profiling, including these more methodological papers.

    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.

    While phylogenetic profiling is highly complementary to other interaction databases (as ndemonstrated by our previously published benchmarking results including Bloch et al., 2020, as well as the STRING comparison to our PP links for MECP2 above), we could not claim that it is the only path to a given drug or even a given protein. We use phylogenetic profiling because it can be automated and systematically applied for prioritization of candidates, using a prioritization logic that is biologically motivated and orthogonal to other techniques.

    This question, along with others by another referee, indicate that we did a poor job of relaying this in the initial version. In the revision, we better describe the benefits of our approach, both in terms of identifying minimally related genes (lines 81-89), as well as providing an unbiased approach which does not depend on experimental datasets (lines 89-98). We also discuss our new methodological publications from our group which describe the NPP method and benchmarking sensitivity and specificity against other comparative methods using control datasets from the CORUM, REACTOME, and KEGG databases (Bloch et al., 2020; Tsaban et al., 2021) in lines 104-109.

    We thank the reviewer for pointing out HDACs, which play a major role in the MECP2 pathway and provide a good case study. Indeed, MECP2 is linked to a number of HDAC and HDAC-associated proteins in the STRING functional network. While associations have been reported in the literature, we searched for direct HDAC interactions for all class 1&2 HDACs in DGIDB, GeneCards and OpenTargets, and found no links to the 3 drugs we identified here. We also used String to provide a ranked prioritization of the top 390 genes linked to MECP2 using the String score (using 390 genes because that is the same number we used for phylogenetic profiling). While this gene list (S390) did indeed contain all the HDACs and associated proteins, we could establish no links to DMF or EPO using these 390 genes and the same databases and methods we used for the phylogenetic gene list. Pacritinib was identified through the same well studied gene that we identified through phylogenetic profiling (IRAK1).

    Thus, while there have been studies linking these drugs to effects on HDACs (which we now discuss in our Introduction on lines 72-80 and our Discussion on lines 462-479), they can not be easily established based on automated searching of the drug target databases. It is possible that digging into more or different databases would provide these links, but it would also produce more false positives. To our knowledge, while several HDACs have been shown to be impacted by EPO and DMF in neural cells, this has not been described as the primary mode of action for either compound, nor have they been shown to impact HDAC6 which is the best established HDAC in the context of Rett Syndrome. The fact that neither DMF nor EPO have been tested up to this point in a Rett model gives some indirect evidence that they have not been highly prioritized by the Rett research community. However, we completely agree with the referee and now clearly state in the text (lines 477-484) the need for future studies to elucidate the direct mode of action we observe for these drugs in MECP2 depleted neural cells, including mediation by HDACs.

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  2. 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.

    Strengths:

    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.

    Weakness:

    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.

    Read the original source
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  3. 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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  4. 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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