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      Inducible and reversible phenotypes in a novel mouse model of Friedreich’s Ataxia

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          Abstract

          Friedreich's ataxia (FRDA), the most common inherited ataxia, is caused by recessive mutations that reduce the levels of frataxin (FXN), a mitochondrial iron binding protein. We developed an inducible mouse model of Fxn deficiency that enabled us to control the onset and progression of disease phenotypes by the modulation of Fxn levels. Systemic knockdown of Fxn in adult mice led to multiple phenotypes paralleling those observed in human patients across multiple organ systems. By reversing knockdown after clinical features appear, we were able to determine to what extent observed phenotypes represent reversible cellular dysfunction. Remarkably, upon restoration of near wild-type FXN levels, we observed significant recovery of function, associated pathology and transcriptomic dysregulation even after substantial motor dysfunction and pathology were observed. This model will be of broad utility in therapeutic development and in refining our understanding of the relative contribution of reversible cellular dysfunction at different stages in disease.

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          Most cited references130

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          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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              A general framework for weighted gene co-expression network analysis.

              Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                19 December 2017
                2017
                : 6
                : e30054
                Affiliations
                [1 ]deptProgram in Neurogenetics, Department of Neurology, David Geffen School of Medicine University of California, Los Angeles Los AngelesUnited States
                [2 ]deptDepartment of Physiology, David Geffen School of Medicine University of California, Los Angeles Los AngelesUnited States
                [3 ]deptDepartment of Human Genetics, David Geffen School of Medicine University of California, Los Angeles Los AngelesUnited States
                St Jude Children's Research Hospital United States
                St Jude Children's Research Hospital United States
                Author notes
                [‡]

                Department of Pediatrics, School of Medicine, University of Florida, Gainesville, United States.

                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-2469-6263
                https://orcid.org/0000-0003-2896-3450
                Article
                30054
                10.7554/eLife.30054
                5736353
                29257745
                c34666b4-a640-42e1-9b1e-5a1ab35a4ce0
                © 2017, Chandran et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 29 June 2017
                : 20 November 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100002108, Friedreich's Ataxia Research Alliance;
                Award ID: FARA New Investigator Award
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100005984, Dr. Miriam and Sheldon G. Adelson Medical Research Foundation;
                Award ID: Adelson Program in Neural Repair and Regeneration (APNRR)
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100005202, Muscular Dystrophy Association;
                Award ID: Research Grant
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Human Biology and Medicine
                Neuroscience
                Custom metadata
                Restoration of endogenous frataxin levels reverses neurologic and cardiac phenotypes associated with Friedreich's ataxia in adult mice even after significant motor dysfunction.

                Life sciences
                friedreich's ataxia,neurodegeneration,frataxin,human,mouse
                Life sciences
                friedreich's ataxia, neurodegeneration, frataxin, human, mouse

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