Deciphering Master Gene Regulators and Associated Networks of Human Mesenchymal Stromal Cells

Abstract

Mesenchymal Stromal Cells (MSC) are multipotent cells characterized by self-renewal, multilineage differentiation, and immunomodulatory properties. To obtain a gene regulatory profile of human MSCs, we generated a compendium of more than two hundred cell samples with genome-wide expression data, including a homogeneous set of 93 samples of five related primary cell types: bone marrow mesenchymal stem cells (BM-MSC), hematopoietic stem cells (HSC), lymphocytes (LYM), fibroblasts (FIB), and osteoblasts (OSTB). All these samples were integrated to generate a regulatory gene network using the algorithm ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks; based on mutual information), that finds regulons (groups of target genes regulated by transcription factors) and regulators (i.e., transcription factors, TFs). Furtherly, the algorithm VIPER (Algorithm for Virtual Inference of Protein-activity by Enriched Regulon analysis) was used to inference protein activity and to identify the most significant TF regulators, which control the expression profile of the studied cells. Applying these algorithms, a footprint of candidate master regulators of BM-MSCs was defined, including the genes EPAS1, NFE2L1, SNAI2, STAB2, TEAD1, and TULP3, that presented consistent upregulation and hypomethylation in BM-MSCs. These TFs regulate the activation of the genes in the bone marrow MSC lineage and are involved in development, morphogenesis, cell differentiation, regulation of cell adhesion, and cell structure.

Network-based inference of master regulators in epithelial membrane protein 2-treated human RPE cells

“…The activities of regulators cannot be directly measured by microarray or RNA-seq because these techniques only measure RNA expression level and do not consider gene activity changes by post-translational modifications. Fortunately, activity of regulators can be inferred by its regulons through VIPER, by taking RNA data and a context-specific gene regulatory network (interactome) as inputs [20][21][22].…”

Cellular Heterogeneity of Mesenchymal Stem/Stromal Cells in the Bone Marrow

“…Gene expression in BM-MSCs, HSCs, lymphocytes, fibroblasts, and osteoblasts has been used to create regulatory gene networks ( Roson-Burgo et al, 2016 ). These algorithms were used to identify potential master regulators of genes that are upregulated in BM-MSCs and genes that exhibit hypomethylation (EPAS1, NFE2L1, SNAI2, STAB2, TEAD1, and TULP3) ( Sanchez-Luis et al, 2020 ). Furthermore, the gene encoding Frizzled 5, i.e., FZD5 is highly expressed in undifferentiated human MSCs, indicating that not only canonical but also non-canonical Wnt signaling is important for maintaining stemness ( Harada et al, 2020 ).…”

Network-based Inference of Hub Genes in Epithelial Membrane Protein 2-treated Human RPE cells

“…The Virtual Inference of Protein activity by Enriched Regulon analysis (VIPER) [10][11][12] algorithm has proven to be a useful tool for estimating regulator activity from gene expression data using enriched regulon analysis. VIPER R package was used to compare APs in each group.…”