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      Reprogramming to recover youthful epigenetic information and restore vision

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          Abstract

          Ageing is a degenerative process that leads to tissue dysfunction and death. A proposed cause of ageing is the accumulation of epigenetic noise that disrupts gene expression patterns, leading to decreases in tissue function and regenerative capacity 13 . Changes to DNA methylation patterns over time form the basis of ageing clocks 4 , but whether older individuals retain the information needed to restore these patterns—and, if so, whether this could improve tissue function—is not known. Over time, the central nervous system (CNS) loses function and regenerative capacity 57 . Using the eye as a model CNS tissue, here we show that ectopic expression of Oct4 (also known as Pou5f1), Sox2 and Klf4 genes (OSK) in mouse retinal ganglion cells restores youthful DNA methylation patterns and transcriptomes, promotes axon regeneration after injury, and reverses vision loss in a mouse model of glaucoma and in aged mice. The beneficial effects of OSK-induced reprogramming in axon regeneration and vision require the DNA demethylases TET1 and TET2. These data indicate that mammalian tissues retain a record of youthful epigenetic information—encoded in part by DNA methylation—that can be accessed to improve tissue function and promote regeneration in vivo.

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          Most cited references72

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Is Open Access

            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                4 December 2020
                02 December 2020
                December 2020
                02 June 2021
                : 588
                : 7836
                : 124-129
                Affiliations
                [1. ]Department of Genetics, Blavatnik Institute, Paul F. Glenn Center for Biology of Aging Research, Harvard Medical School, MA, USA;
                [2. ]Department of Neurology, Boston Children’s Hospital, Harvard Medical School, MA, USA;
                [3. ]Department of Ophthalmology, Harvard Medical School, Boston, MA, USA;
                [4. ]Schepens Eye Research Institute of Mass Eye & Ear, Harvard Medical School, MA, USA;
                [5. ]Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, MA, USA;
                [6. ]Department of Pathology, Yale School of Medicine, New Haven, CT, USA;
                [7. ]Department of Genetics, Wyss Institute for Biologically Inspired Engineering, Harvard University, MA, USA;
                [8. ]Department of Molecular Biology, Cancer Center and Center for Regenerative Medicine, Massachusetts General Hospital, MA, USA;
                [9. ]Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, CA, USA;
                [10. ]Laboratory for Aging Research, Department of Pharmacology, School of Medical Sciences, The University of New South Wales, Sydney, Australia;
                [11. ]These authors contributed equally: B. B., X. T., A. K., M. M.;
                Author notes
                [*]

                Senior authors;

                Contributions

                Y.L. and D.A.S. conceived the project. Y.L., X.T., and D.A.S. wrote the manuscript with input from all co-authors. Y.L. was involved in all experiments and analyses. M.S.B. and J.-H.Y. provided early training to Y.L.. B.B., C.W., Q.Z., D.Y., S.Z., and Z.H. contributed to the optic nerve crush studies and imaging. A.K., D.Y., Q.Z., E.M.H., E.K., M.S.G.-K., and B.R.K. contributed to the glaucoma and ageing studies. M.M.K. and B.R.K. performed OCT imaging and analysis. M.M. and V.N.G. conducted ribosomal DNAm age analysis for mouse RGCs. M.E.L. developed DNAm ageing signature. D.L.V. performed the RNA-seq and gene association analysis. X.T. conducted human neuron experiments. S.H. conducted human methylation clock analysis. X.T., J.-H.Y., and K.H. helped with transgenic mouse fibroblasts work. M.S.B., X.T., M.B.S., A.E.K., L.A.R., helped with systemic AAV9 experiments. N.D., and G.M.C. helped with plasmid constructs and AAV9 production. K.C. helped with grant applications and project management. M.S.G.-K., B.R.K., Z.H., and D.A.S. jointly supervised this work.

                Article
                NIHMS1640389
                10.1038/s41586-020-2975-4
                7752134
                33268865
                fffdf345-1c46-4e06-9727-b680b9f03937

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