Endothelial gene regulatory elements associated with cardiopharyngeal lineage differentiation.
Ilaria AurigemmaOlga LanzettaAndrea CirinoSara AllegrettiGabriella LaniaRosa FerrentinoVarsha Poondi KrishnanClaudia AngeliniElizabeth IllingworthAntonio BaldiniPublished in: Communications biology (2024)
Endothelial cells (EC) differentiate from multiple sources, including the cardiopharyngeal mesoderm, which gives rise also to cardiac and branchiomeric muscles. The enhancers activated during endothelial differentiation within the cardiopharyngeal mesoderm are not completely known. Here, we use a cardiogenic mesoderm differentiation model that activates an endothelial transcription program to identify endothelial regulatory elements activated in early cardiogenic mesoderm. Integrating chromatin remodeling and gene expression data with available single-cell RNA-seq data from mouse embryos, we identify 101 putative regulatory elements of EC genes. We then apply a machine-learning strategy, trained on validated enhancers, to predict enhancers. Using this computational assay, we determine that 50% of these sequences are likely enhancers, some of which are already reported. We also identify a smaller set of regulatory elements of well-known EC genes and validate them using genetic and epigenetic perturbation. Finally, we integrate multiple data sources and computational tools to search for transcriptional factor binding motifs. In conclusion, we show EC regulatory sequences with a high likelihood to be enhancers, and we validate a subset of them using computational and cell culture models. Motif analyses show that the core EC transcription factors GATA/ETS/FOS is a likely driver of EC regulation in cardiopharyngeal mesoderm.
Keyphrases
- transcription factor
- endothelial cells
- single cell
- rna seq
- gene expression
- genome wide identification
- dna binding
- pluripotent stem cells
- genome wide
- machine learning
- big data
- electronic health record
- dna methylation
- high throughput
- drinking water
- high glucose
- artificial intelligence
- heart failure
- dna damage
- left ventricular
- quality improvement
- binding protein
- deep learning
- atrial fibrillation