Epigenetic landscape of HEK cells upon epithelial-to-mesenchymal transition
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE105422 | GEO | 2020/10/20
REPOSITORIES: GEO
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Project description:This model is an expansion of the Regan2022 - Mechanosensitive EMT model (MODEL2208050001); it includes a TGFβ signaling module and autocrine signaling in mesenchymal cells. The expanded 150-node (630 link) modular model undergoes EMT triggered by biomechanical and growth signaling crosstalk, or by TGFβ. As its predecessor, this model also reproduces the ability of the core EMT transcriptional network to maintain distinct epithelial, hybrid E/M and mesenchymal states, as well as EMT driven by mitogens such as EGF on stiff ECM. We also reproduce the observed lack of stepwise MET, in that our model's dynamics does not pass through the hybrid E/M state during MET. We show that in the absence of strong autocrine signals such as TGFβ (not included in this version), cells cannot maintain their mesenchymal state in the absence of mitogens, on softer matrices, or at high cell density. In contrast, potent autocrine signaling can stabilize the mesenchymal state in all but very dense monolayers on soft ECM. This expanded model also reproduces the inhibitory effects of TGFβ on proliferation and anoikis resistance in mesenchymal cells, as well as its ability to trigger apoptosis on soft ECM vs. EMT on stiff matrices. The model offers several experimentally testable predictions related to the effect of neighbors on partial vs. full EMT, the tug of war between mitosis and the maintenance of migratory hybrid E/M states, as well as cell cycle defects in dynamic, heterogeneous populations of epithelial, hybrid E/M and mesenchymal cells.
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Project description:In the last decade, new high-throughput sequencing techniques have revealed the complexity of the human transcriptome, allowing the characterization of long non-coding (lnc)RNAs. Since their expression has been reported as very specific to tissue, developmental stage and pathological variations, some lncRNAs have been proposed as biomarkers for diagnosis as well as prognosis of tumors. In this project, we aime in identification of lncRNAs associated with the epithelial-to-mesenchymal transition (EMT), a biological process that promotes metastasis. This was performed on an in vitro model (Castro Vega et al, 2015) in which primary HEK cells naturally undergo EMT. Cells were immortalized prior and after EMT, therefore giving rise to the Epi (HA5-Early) and Mes (HA5-Late) cell-lines, which respectively display epithelial and mesenchymal phenotypes. HOTAIR is one the lcnRNAs functionning as a scaffold to tether PRC2 and Lsd1/REST/coREST complexes to gene promoter to epigenetically repress transcription. Its high expression is associated with poor-prognosis cancer and promotes EMT. To decipher a role of PRC2 and Lsd1 interacting domains in HOTAIR function we generated Epi cell lines expressing full length and truncated versions of HOTAIR missing first 5'-300 bp and last 3'-500 bp interacting with HOTAIR partners. Total RNA form these cell lines was extracted and sequenced to reveal HOTAIR targets dependent of its intercation with PRC2 and/or Lsd1.
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Project description:In the last decade, new high-throughput sequencing techniques have revealed the complexity of the human transcriptome, allowing the characterization of long non-coding (lnc)RNAs. Since their expression has been reported as very specific to tissue, developmental stage and pathological variations, some lncRNAs have been proposed as biomarkers for diagnosis as well as prognosis of tumors. In this project, we investigate a role for the poorly characterized class of lncRNAs, antisense lncRNAs, and their association to the epithelial-to-mesenchymal transition (EMT), a biological process that promotes metastasis. In order to measure the transcription and properly describe the gene-structure of unannotated lncRNAs at the genome-wide level, we adapted a protocol of subcellular fractionation from Gagnon et al. (2014) to isolate nascent chromatin-associated transcript. This was performed on an in vitro model (Castro Vega et al, 2015) in which primary HEK cells naturally undergo EMT. Cells were immortalized prior and after EMT, therefore giving rise to the Epi (HA5-Early) and Mes (HA5-Late) cell-lines, which respectively display epithelial and mesenchymal phenotypes. In addition to the chromatin fraction, the cytoplasmic fraction of the cells was also sequenced in order to decipher the complexity of the transcriptome and assess the composition of mature transcripts.
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Project description:To study the mechanisms of the epithelial-mesenchymal transition (EMT) in prostate cancer metastasis, we first generated an epithelial model cell line derived from prostate cancer epithelial PC3 cells. We isolated a subpopulation of cells that showed a stable epithelial phenotype in culture and designated them PC3-Epi. We next developed mesenchymal cell lines from PC3-Epi cells, through interactions with macrophages. These mesenchymal cell lines were designated PC3-EMT1, PC3-EMT12 and PC3-EMT14. We transfected two of these mesenchymal lines (PC3-EMT1 and PC3-EMT14) with lentiviral ZEB1-shRNA vector (sh4) or the scrambled control (Scr). Consistent with ZEB1's role in EMT and maintenance of the mesenchymal state, silencing of ZEB1 induced the mesenchymal-epithelial transition (MET) in PC3-EMT1 and PC3-EMT14.
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Project description:Epithelial to Mesenchymal Transition (EMT) has been associated with cancer cell heterogeneity, plasticity and metastasis. It has been the subject of several modeling effort. This logical model of the EMT cellular network aims to assess microenvironmental signals controlling cancer-associated phenotypes amid the EMT continuum. Its outcomes relate to the qualitative degrees of cell adhesions by adherent junctions and focal adhesions, two features affected during EMT. Model attractors recover epithelial, mesenchymal and hybrid phenotypes, and simulations show that hybrid phenotypes may arise through independent molecular paths, involving stringent extrinsic signals.
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