Epigenetic feedback and stochastic partitioning during cell division can drive resistance to EMT

Jia, Wen; Tripathi, Shubham; Chakraborty, Priyanka; Chedere, Adithya; Rangarajan, Annapoorni; Levine, Herbert; Jolly, Mohit Kumar

doi:10.18632/oncotarget.27651

Cited by 34 publications

(29 citation statements)

References 83 publications

(117 reference statements)

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“…Besides, the Mbd3/NuRD complex, in combination with activities of HDACs, and Tet2 hydroxylase, are important regulators of epithelial-mesenchymal plasticity in EMT models of murine breast cancer [ 178 ]. Thus, similar to irreversible EMT, the possibility of irreversible MET (or resistance to EMT) has been recently proposed, owing to epigenetic modulation mediated by GRHL2 [ 172 , 179 ]. Thus, epigenetic feedback operated by various EMT-TFs and/or MET-TFs may alter the dynamics and reversibility of these bidirectional transitions.…”

Section: Stabilizers Of Partial Emt Phenotypementioning

confidence: 99%

Hypoxia, partial EMT and collective migration: Emerging culprits in metastasis

Saxena

Jolly

Balamurugan

2020

Translational Oncology

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141

112

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Epithelial-mesenchymal transition (EMT) is a cellular biological process involved in migration of primary cancer cells to secondary sites facilitating metastasis. Besides, EMT also confers properties such as stemness, drug resistance and immune evasion which can aid a successful colonization at the distant site. EMT is not a binary process; recent evidence suggests that cells in partial EMT or hybrid E/M phenotype(s) can have enhanced stemness and drug resistance as compared to those undergoing a complete EMT. Moreover, partial EMT enables collective migration of cells as clusters of circulating tumor cells or emboli, further endorsing that cells in hybrid E/M phenotypes may be the ‘fittest’ for metastasis. Here, we review mechanisms and implications of hybrid E/M phenotypes, including their reported association with hypoxia. Hypoxia-driven activation of HIF-1α can drive EMT. In addition, cyclic hypoxia, as compared to acute or chronic hypoxia, shows the highest levels of active HIF-1α and can augment cancer aggressiveness to a greater extent, including enriching for a partial EMT phenotype. We also discuss how metastasis is influenced by hypoxia, partial EMT and collective cell migration, and call for a better understanding of interconnections among these mechanisms. We discuss the known regulators of hypoxia, hybrid EMT and collective cell migration and highlight the gaps which needs to be filled for connecting these three axes which will increase our understanding of dynamics of metastasis and help control it more effectively.

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Section: Stabilizers Of Partial Emt Phenotypementioning

confidence: 99%

Hypoxia, partial EMT and collective migration: Emerging culprits in metastasis

Saxena

Jolly

Balamurugan

2020

Translational Oncology

Self Cite

141

112

View full text Add to dashboard Cite

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“…We have extended the modeling of EMT to a heterogeneous population of cells, while still incorporating gene regulatory networks, offering a convenient framework to explore cell proliferation by monitoring the changes in gene expressions prompted by interactions between various EMT agents, which is important for cancer studies ( 23 , 60 , 61 ). Our model explicitly incorporates stochastic effects caused by each cell division ( 62 , 63 ) that may affect cell fates. Our model can also easily incorporate different assumptions on proliferative dynamics of each cell state.…”

Section: Discussionmentioning

confidence: 99%

Inference and multiscale model of epithelial-to-mesenchymal transition via single-cell transcriptomic data

Sha

Wang

Zhou

et al. 2020

Nucleic Acids Research

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Rapid growth of single-cell transcriptomic data provides unprecedented opportunities for close scrutinizing of dynamical cellular processes. Through investigating epithelial-to-mesenchymal transition (EMT), we develop an integrative tool that combines unsupervised learning of single-cell transcriptomic data and multiscale mathematical modeling to analyze transitions during cell fate decision. Our approach allows identification of individual cells making transition between all cell states, and inference of genes that drive transitions. Multiscale extractions of single-cell scale outputs naturally reveal intermediate cell states (ICS) and ICS-regulated transition trajectories, producing emergent population-scale models to be explored for design principles. Testing on the newly designed single-cell gene regulatory network model and applying to twelve published single-cell EMT datasets in cancer and embryogenesis, we uncover the roles of ICS on adaptation, noise attenuation, and transition efficiency in EMT, and reveal their trade-off relations. Overall, our unsupervised learning method is applicable to general single-cell transcriptomic datasets, and our integrative approach at single-cell resolution may be adopted for other cell fate transition systems beyond EMT.

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“…This view is consistent with a host of recent experimental results. ZEB1 was shown to have multiple connections to other feedback loops in the network, such as with GRHL2, OVOL2 and ESRP1 [ 40 , 41 ]. The set of genes inhibited during EMT is enriched with genes that contain ZEB1 binding sites in their promoters [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

Local and global features of genetic networks supporting a phenotypic switch

2020

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Phenotypic switches are associated with alterations in the cell's gene expression profile and are vital to many aspects of biology. Previous studies have identified local motifs of the genetic regulatory network that could underlie such switches. Recent advancements allowed the study of networks at the global, many-gene, level; however, the relationship between the local and global scales in giving rise to phenotypic switches remains elusive. In this work, we studied the epithelial-mesenchymal transition (EMT) using a gene regulatory network model. This model supports two clusters of stable steady-states identified with the epithelial and mesenchymal phenotypes, and a range of intermediate less stable hybrid states, whose importance in cancer has been recently highlighted. Using an array of network perturbations and quantifying the resulting landscape, we investigated how features of the network at different levels give rise to these landscape properties. We found that local connectivity patterns affect the landscape in a mostly incremental manner; in particular, a specific previously identified double-negative feedback motif is not required when embedded in the full network, because the landscape is maintained at a global level. Nevertheless, despite the distributed nature of the switch, it is possible to find combinations of a few local changes that disrupt it. At the level of network architecture, we identified a crucial role for peripheral genes that act as incoming signals to the network in creating clusters of states. Such incoming signals are a signature of modularity and are expected to appear also in other biological networks. Hybrid states between epithelial and mesenchymal arise in the model due to barriers in the interaction between genes, causing hysteresis at all connections. Our results suggest emergent switches can neither be pinpointed to local motifs, nor do they arise as typical properties of random network ensembles. Rather, they arise through an interplay between the nature of local interactions, and the core-periphery structure induced by the modularity of the cell.

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Epigenetic feedback and stochastic partitioning during cell division can drive resistance to EMT

Cited by 34 publications

References 83 publications

Hypoxia, partial EMT and collective migration: Emerging culprits in metastasis

Hypoxia, partial EMT and collective migration: Emerging culprits in metastasis

Inference and multiscale model of epithelial-to-mesenchymal transition via single-cell transcriptomic data

Local and global features of genetic networks supporting a phenotypic switch

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