bioRxivDevelopmental BiologyPreprint
Distinct epicardial gene regulatory programmes drive development and regeneration of the zebrafish heart
Michael WeinbergeriD, Filipa Costa SimõesiD, Tatjana Sauka‐SpengleriD, Paul Richard RileyiD
Abstract
Unlike the adult mammalian heart, which has limited regenerative capacity, the zebrafish heart can fully regenerate following injury. Reactivation of cardiac developmental programmes is considered key to successfully regenerating the heart, yet the regulatory elements underlying the response triggered upon injury and during development remain elusive. Organ-wide activation of the epicardium is essential for zebrafish heart regeneration and is considered a potential regenerative source to target in the mammalian heart. Here we compared the transcriptome and epigenome of the developing and regenerating zebrafish epicardium by integrating gene expression profiles with open chromatin ATAC-seq data. By generating gene regulatory networks associated with epicardial development and regeneration, we inferred genetic programmes driving each of these processes, which were largely distinct. We identified wt1a, wt1b, and the AP-1 subunits junbb, fosab and fosb as central regulators of the developing network, whereas hif1ab, zbtb7a, tbx2b and nrf1 featured as putative central regulators of the regenerating epicardial network. By interrogating developmental gene regulatory networks that drive cell-specific transcriptional heterogeneity, we tested novel subpopulation-related epicardial enhancers in vivo. Taken together, our work revealed striking differences between the regulatory blueprint deployed during epicardial development and regeneration. These findings challenge the dogma that heart regeneration is essentially a reactivation of developmental programmes, and provide important insights into epicardial regulation that can assist in developing therapeutic approaches to enable tissue regeneration in the adult mammalian heart.
- organism: zebrafish
- assay: bulk RNA-seq
- assay: ATAC-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 5
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