Integrative Bioinformatics Analysis Reveals a Transcription Factor EB-Driven MicroRNA Regulatory Network in Endothelial Cells.
Teresa GravinaFrancesco FaveroStefania RosanoSushant ParabAlejandra Diaz AlcaldeFederico BussolinoGabriella DoronzoDavide CoràPublished in: International journal of molecular sciences (2024)
Various human diseases are triggered by molecular alterations influencing the fine-tuned expression and activity of transcription factors, usually due to imbalances in targets including protein-coding genes and non-coding RNAs, such as microRNAs (miRNAs). The transcription factor EB (TFEB) modulates human cellular networks, overseeing lysosomal biogenesis and function, plasma-membrane trafficking, autophagic flux, and cell cycle progression. In endothelial cells (ECs), TFEB is essential for the maintenance of endothelial integrity and function, ensuring vascular health. However, the comprehensive regulatory network orchestrated by TFEB remains poorly understood. Here, we provide novel mechanistic insights into how TFEB regulates the transcriptional landscape in primary human umbilical vein ECs (HUVECs), using an integrated approach combining high-throughput experimental data with dedicated bioinformatics analysis. By analyzing HUVECs ectopically expressing TFEB using ChIP-seq and examining both polyadenylated mRNA and small RNA sequencing data from TFEB-silenced HUVECs, we have developed a bioinformatics pipeline mapping the different gene regulatory interactions driven by TFEB. We show that TFEB directly regulates multiple miRNAs, which in turn post-transcriptionally modulate a broad network of target genes, significantly expanding the repertoire of gene programs influenced by this transcription factor. These insights may have significant implications for vascular biology and the development of novel therapeutics for vascular disease.
Keyphrases
- transcription factor
- endothelial cells
- bioinformatics analysis
- genome wide identification
- cell cycle
- high throughput
- dna binding
- single cell
- genome wide
- high glucose
- public health
- cell proliferation
- electronic health record
- binding protein
- poor prognosis
- rna seq
- healthcare
- high resolution
- oxidative stress
- dna methylation
- machine learning
- network analysis
- air pollution
- artificial intelligence
- deep learning
- social media