A computational pipeline for multi-omics regulatory network inference in vascular and glioblastoma models

Regulatory networks are fundamental tools for studying complex diseases, as they allow for the integration and interpretation of interactions between genes, proteins, and non-coding RNAs. In this thesis, we describe the development of a bioinformatics pipeline for the study of gene regulatory networks using multi-omics data applied to two specific cases. In the first project, dedicated to the study of the transcription factor TFEB in HUVECs, a regulatory network integrating protein-coding target genes and miRNAs was first reconstructed based on ChIP-Seq data. The analysis of TFEB-bound differentially expressed genes and miRNAs highlighted a final set of 7 key miRNA candidates, including miR-222, which is involved in controlling the transition from the G1 to the S phase of the cell cycle through the negative regulation of the E2F1 and CDK1 genes. These results reveal a novel TFEB-mediated control of endothelial activity via miRNA-dependent post-transcriptional regulatory circuits. In the second project, we focused on the role of the epigenetic factor LSD1 and its ATF4 cofactor in Glioblastoma Tumor Initiating Cells. The integration of LSD1 ChIP-Seq and mRNA-Seq data allowed the identification of thousands of genomic regions bound by LSD1, many of which are co-occupied by ATF4. Ingenuity Pathway Analysis identified miR-124-3p as an upstream regulator, suggesting a model in which LSD1 suppresses its expression. Consistently, LSD1 inhibition resulted in increased miR-124-3p levels and reduced its target genes, that promote proliferation, invasiveness, and therapeutic resistance in GBM. Integration of public data confirmed the downregulation of miR-124-3p in GBM patients, supporting the hypothesis of an LSD1–miR-124-3p axis involved in tumorigenesis and the maintenance of stem-like properties. Overall, the results highlight the value of multi-omics integration strategies in studying regulatory networks confirming the crucial role of non-coding RNAs.