Nature CommunicationsCell BiologyPeer-reviewed journal article
Identification of epigenetic regulators of fibrotic transformation in cardiac fibroblasts through bulk and single-cell CRISPR screens
Laura Pilar Aguado‐AlvaroiD, Nerea Garitano, Wolfgang Esser‐SkalaiD, Judith R. SayersiD, Cynthia del Valle, Daniel AlamedaiD, Julen Mendieta-EstebaniD, María Eréndira Calleja-CervantesiD, Ainhoa Goñi‐SalaverriiD, Jon Zazpe, Anna Rosaria de Vito, Francesco P. MarcheseiD, Diego AlignaniiD, Juliana CudiniiD, Torsten Groß, Gregorio RábagoiD, Nisha NarayaniD, Laura Martinez, Sonia Martinez, Brian J.P. HuntlyiD, Paul Richard RileyiD, Arantxa GonzáleziD, Jake P. Taylor‐KingiD, Nikolaus FortelnyiD, Beatriz PelachoiD, David Lara‐AstiasoiD
Abstract
Cardiac fibrosis is mediated by the persistent activity of myofibroblasts, which differentiate from resident cardiac fibroblasts in response to tissue damage and stress signals. The signaling pathways and transcription factors regulating fibrotic transformation have been thoroughly studied. In contrast, the roles of chromatin factors in myofibroblast differentiation and their contribution to pathogenic cardiac fibrosis remain poorly understood. Here, we combined bulk and single-cell CRISPR screens to characterize the roles of chromatin factors in the fibrotic transformation of primary cardiac fibroblasts. We uncover strong regulators of fibrotic states including Srcap and Kat5 chromatin remodelers. We confirm that these factors are required for functional processes underlying fibrosis including collagen synthesis and cell contractility. Using chromatin profiling in perturbed cardiac fibroblasts, we demonstrate that pro-fibrotic chromatin complexes facilitate the activity of well-characterized pro-fibrotic transcription factors. Finally, we show that KAT5 inhibition alleviates fibrotic responses in patient-derived human fibroblasts.
- organism: human
- assay: CRISPR screen
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
- Citations
- 11
- FWCI
- 7.64
- Field percentile
- top 2%
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