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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.

SUBMITTER: Sanchez-Luis E

PROVIDER: S-EPMC7226324 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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

Sánchez-Luis Elena E Joaquín-García Andrea A Campos-Laborie Francisco J FJ Sánchez-Guijo Fermín F Rivas Javier De Las JL

Biomolecules 20200405 4

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 osteob ...[more]

PMID: 32260546

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Project description:BackgroundNeuronal tissue has limited potential to self-renew or repair after neurological diseases. Cellular therapies using stem cells are promising approaches for the treatment of neurological diseases. However, the clinical use of embryonic stem cells or foetal tissues is limited by ethical considerations and other scientific problems. Thus, bone marrow mesenchymal stomal cells (BM-MSC) could represent an alternative source of stem cells for cell replacement therapies. Indeed, many studies have demonstrated that MSC can give rise to neuronal cells as well as many tissue-specific cell phenotypes.MethodsBM-MSC were differentiated in neuron-like cells under specific induction (NPBM + cAMP + IBMX + NGF + Insulin). By day ten, differentiated cells presented an expression profile of real neurons. Functionality of these differentiated cells was evaluated by calcium influx through glutamate receptor AMPA3.ResultsUsing microarray analysis, we compared gene expression profile of these different samples, before and after neurogenic differentiation. Among the 1943 genes differentially expressed, genes down-regulated are involved in osteogenesis, chondrogenesis, adipogenesis, myogenesis and extracellular matrix component (tuftelin, AGC1, FADS3, tropomyosin, fibronectin, ECM2, HAPLN1, vimentin). Interestingly, genes implicated in neurogenesis are increased. Most of them are involved in the synaptic transmission and long term potentialisation as cortactin, CASK, SYNCRIP, SYNTL4 and STX1. Other genes are involved in neurite outgrowth, early neuronal cell development, neuropeptide signaling/synthesis and neuronal receptor (FK506, ARHGAP6, CDKRAP2, PMCH, GFPT2, GRIA3, MCT6, BDNF, PENK, amphiregulin, neurofilament 3, Epha4, synaptotagmin). Using real time RT-PCR, we confirmed the expression of selected neuronal genes: NEGR1, GRIA3 (AMPA3), NEF3, PENK and Epha4. Functionality of these neuron-like cells was demonstrated by Ca2+ influx through glutamate receptor channel (AMPA3) in the presence of two agonist glutamate, AMPA or CNQX antagonist.ConclusionOur results demonstrate that BM-MSC have the potential to differentiate in neuronal cells with specific gene expression and functional properties. BM-MSC are thus promising candidates for cell-based therapy of neurodegenerative diseases.

Project description:Genome-wide libraries of yeast deletion strains have been used to screen for genes that drive phenotypes such as stress response. A surprising observation emerging from these studies is that the genes with the largest changes in mRNA expression during a state transition are not those that drive that transition. Here, we show that integrating gene expression data with context-independent protein interaction networks can help prioritize master regulators that drive biological phenotypes.Genes essential for survival had previously been shown to exhibit high centrality in protein interaction networks. However, the set of genes that drive growth in any specific condition is highly context-dependent. We inferred regulatory networks from gene expression data and transcription factor binding motifs in Saccharomyces cerevisiae, and found that high-degree nodes in regulatory networks are enriched for transcription factors that drive the corresponding phenotypes. We then found that using a metric combining protein interaction and transcriptional networks improved the enrichment for drivers in many of the contexts we examined. We applied this principle to a dataset of gene expression in normal human fibroblasts expressing a panel of viral oncogenes. We integrated regulatory interactions inferred from this data with a database of yeast two-hybrid protein interactions and ranked 571 human transcription factors by their combined network score. The ranked list was significantly enriched in known cancer genes that could not be found by standard differential expression or enrichment analyses.There has been increasing recognition that network-based approaches can provide insight into critical cellular elements that help define phenotypic state. Our analysis suggests that no one network, based on a single data type, captures the full spectrum of interactions. Greater insight can instead be gained by exploring multiple independent networks and by choosing an appropriate metric on each network. Moreover we can improve our ability to rank phenotypic drivers by combining the information from individual networks. We propose that such integrative network analysis could be used to combine clinical gene expression data with interaction databases to prioritize patient- and disease-specific therapeutic targets.

Dataset Information

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

Publications

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

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