Increased H3K27 trimethylation contributes to cone survival in a mouse model of cone dystrophy

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Abstract

Inherited retinal diseases (IRDs) are a heterogeneous group of blinding disorders, which result in dysfunction or death of the light-sensing cone and rod photoreceptors. Despite individual IRDs (Inherited retinal disease) being rare, collectively, they affect up to 1:2000 people worldwide, causing a significant socioeconomic burden, especially when cone-mediated central vision is affected. This study uses the Pde6c ^cpfl1 mouse model of achromatopsia, a cone-specific vision loss IRD (Inherited retinal disease), to investigate the potential gene-independent therapeutic benefits of a histone demethylase inhibitor GSK-J4 on cone cell survival. We investigated the effects of GSK-J4 treatment on cone cell survival in vivo and ex vivo and changes in cone-specific gene expression via single-cell RNA sequencing. A single intravitreal GSK-J4 injection led to transcriptional changes in pathways involved in mitochondrial dysfunction, endoplasmic reticulum stress, among other key epigenetic pathways, highlighting the complex interplay between methylation and acetylation in healthy and diseased cones. Furthermore, continuous administration of GSK-J4 in retinal explants increased cone survival. Our results suggest that IRD (Inherited retinal disease)-affected cones respond positively to epigenetic modulation of histones, indicating the potential of this approach in developing a broad class of novel therapies to slow cone degeneration.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00018-022-04436-6.

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Most cited references 78

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Integrating single-cell transcriptomic data across different conditions, technologies, and species

Rahul Satija, Efthymia Papalexi, Peter Smibert … (2018)

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

Edward Chen, Christopher M Tan, Yan Kou … (2013)

Background System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Results Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Conclusions Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

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REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

Fran Supek, Matko Bošnjak, Nives Škunca … (2011)

Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret. REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.

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Author and article information

Contributors

Livia S. Carvalho:

ORCID: http://orcid.org/0000-0002-3909-5778

liviacarvalho@lei.org.au

Dragana Trifunović: draganatri@yahoo.com

Journal

Journal ID (nlm-ta): Cell Mol Life Sci

Journal ID (iso-abbrev): Cell Mol Life Sci

Title: Cellular and Molecular Life Sciences

Publisher: Springer International Publishing (Cham )

ISSN (Print): 1420-682X

ISSN (Electronic): 1420-9071

Publication date (Electronic): 10 July 2022

Publication date PMC-release: 10 July 2022

Publication date (Print): 2022

Volume: 79

Issue: 8

Electronic Location Identifier: 409

Affiliations

[1 ]GRID grid.1489.4, ISNI 0000 0000 8737 8161, Retinal Genomics and Therapy Group, , Lions Eye Institute Ltd, ; 2 Verdun Street, Nedlands, WA 6009 Australia

[2 ]GRID grid.1012.2, ISNI 0000 0004 1936 7910, Centre for Ophthalmology and Visual Science, , The University of Western Australia, ; 35 Stirling Hwy, Crawley, WA 6009 Australia

[3 ]GRID grid.10392.39, ISNI 0000 0001 2190 1447, Institute for Ophthalmic Research, , Tübingen University, ; Elfriede-Aulhorn-Straße 7, 72076 Tübingen, Germany

[4 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Lab for Retinal Cell Biology, Department of Ophthalmology, University Hospital Zürich, , University of Zürich, ; Zurich, Switzerland

[5 ]Analytical Computing Solutions, Willetton, WA 6155 Australia

[6 ]GRID grid.411806.a, ISNI 0000 0000 8999 4945, Department of Microbiology and Immunology, Faculty of Medicine, , Minia University, ; Minia, Egypt

[7 ]GRID grid.1025.6, ISNI 0000 0004 0436 6763, Institute for Immunology and Infectious Diseases, , Murdoch University, ; Murdoch, WA Australia

[8 ]GRID grid.412807.8, ISNI 0000 0004 1936 9916, Department of Medicine, , Vanderbilt University Medical Centre, ; Nashville, TN USA

Author information

Livia S. Carvalho http://orcid.org/0000-0002-3909-5778

Article

Publisher ID: 4436

DOI: 10.1007/s00018-022-04436-6

PMC ID: 9271452

PubMed ID: 35810394

SO-VID: 128124b7-2370-4b57-b486-f0106f178d07

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 11 February 2022

Date revision received : 16 June 2022

Date accepted : 17 June 2022

Funding

Funded by: Deutsche Forschungsgemeinschaft

Award ID: DFG TR 1238/4-1

Award Recipient : Dragana Trifunović

Funded by: Swiss National Science Foundation

Award ID: 31003A 173008

Award Recipient : Dragana Trifunović

Funded by: The Lindsay & Heather Payne Medical Research Charitable Foundation

Award ID: IPAP2020/1082

Award Recipient : Livia S. Carvalho

Funded by: Australian Government

Funded by: Tahija Foundation

Funded by: Future Health Research and Innovation Fund Scheme

Funded by: Kerstan Foundation

Funded by: ProRetina Foundation

Funded by: University of Western Australia

Open Access :

Open Access funding enabled and organized by CAUL and its Member Institutions

Custom metadata

ScienceOpen disciplines: Molecular biology

Keywords: inherited retinal disease,cone photoreceptors,achromatopsia,h3k27me3,gsk-j4,single-cell rna sequencing

Data availability:

ScienceOpen disciplines: Molecular biology

Keywords: inherited retinal disease, cone photoreceptors, achromatopsia, h3k27me3, gsk-j4, single-cell rna sequencing

Increased H3K27 trimethylation contributes to cone survival in a mouse model of cone dystrophy

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Abstract

Supplementary Information

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Higher order chromatin architecture

Most cited references 78

Integrating single-cell transcriptomic data across different conditions, technologies, and species

Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

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