Coupled reversible and irreversible bistable switches underlying TGFβ-induced epithelial to mesenchymal transition

Heterogeneity and dynamic of EMT through the plasticity of ribosome and mRNA translation

Growing evidence exposes translation and its translational machinery as key players in establishing and maintaining physiological and pathological biological processes. Examining translation may not only provide new biological insight but also identify novel innovative therapeutic targets in several fields of biology, including that of epithelial-to-mesenchymal transition (EMT). EMT is currently considered as a dynamic and reversible transdifferentiation process sustaining the transition from an epithelial to mesenchymal phenotype, known to be mainly driven by transcriptional reprogramming. However, it seems that the characterization of EMT plasticity is challenging, relying exclusively on transcriptomic and epigenetic approaches. Indeed, heterogeneity in EMT programs was reported to depend on the biological context. Here, by reviewing the involvement of translational control, translational machinery and ribosome biogenesis characterizing the different types of EMT, from embryonic and adult physiological to pathological contexts, we discuss the added value of integrating translational control and its machinery to depict the heterogeneity and dynamics of EMT programs.

Epithelial-to-mesenchymal transition proceeds through directional destabilization of multidimensional attractor

How a cell changes from one stable phenotype to another one is a fundamental problem in developmental and cell biology. Mathematically, a stable phenotype corresponds to a stable attractor in a generally multi-dimensional state space, which needs to be destabilized so the cell relaxes to a new attractor. Two basic mechanisms for destabilizing a stable fixed point, pitchfork and saddle-node bifurcations, have been extensively studied theoretically; however, direct experimental investigation at the single-cell level remains scarce. Here, we performed live cell imaging studies and analyses in the framework of dynamical systems theories on epithelial-to-mesenchymal transition (EMT). While some mechanistic details remain controversial, EMT is a cell phenotypic transition (CPT) process central to development and pathology. Through time-lapse imaging we recorded single cell trajectories of human A549/Vim-RFP cells undergoing EMT induced by different concentrations of exogenous TGF-β in a multi-dimensional cell feature space. The trajectories clustered into two distinct groups, indicating that the transition dynamics proceeds through parallel paths. We then reconstructed the reaction coordinates and the corresponding quasi-potentials from the trajectories. The potentials revealed a plausible mechanism for the emergence of the two paths where the original stable epithelial attractor collides with two saddle points sequentially with increased TGF-β concentration, and relaxes to a new one. Functionally, the directional saddle-node bifurcation ensures a CPT proceeds towards a specific cell type, as a mechanistic realization of the canalization idea proposed by Waddington.

Cells with the same genetic code can take on many different formss, or phenotypes, which have distinct roles and appearances. Sometimes cells switch from one phenotype to another as part of healthy growth or during disease. One such change is the epithelial-to-mesenchymal transition (EMT), which is involved in fetal development, wound healing and the spread of cancer cells.

During EMT, closely connected epithelial cells detach from one another and change into mesenchymal cells that are able to migrate. Cells undergo a number of changes during this transition; however, the path they take to reach their new form is not entirely clear. For instance, do all cells follow the same route, or are there multiple ways that cells can shift from one state to the next?

To address this question, Wang et al. studied individual lung cancer cells that had been treated with a protein that drives EMT. The cells were then imaged at regular intervals over the course of two to three days to see how they changed in response to different concentrations of protein. Using a mathematical analysis designed to study chemical reactions, Wang et al. showed that the cells transform into the mesenchymal phenotype through two main routes.

This result suggests that attempts to prevent EMT, in cancer treatment for instance, would require blocking both paths taken by the cells. This information could be useful for biomedical researchers trying to regulate the EMT process. The quantitative approach of this study could also help physicists and mathematicians study other types of transition that occur in biology.

Unraveling the stochastic transition mechanism between oscillation states by landscape and minimum action path theory

Cell fate transitions have been studied from various perspectives, such as the transition between stable states, or the transition between stable states and oscillation states. However, there is a lack...

Cancer Stem Cells: An Ever-Hiding Foe

Cancer stem cells are a population of cells enable to reproduce the original phenotype of the tumor and capable to self-renewal, which is crucial for tumor proliferation, differentiation, recurrence, and metastasis, as well as chemoresistance. Therefore, the cancer stem cells (CSCs) have become one of the main targets for anticancer therapy and many ongoing clinical trials test anti-CSCs efficacy of plenty of drugs. This chapter describes CSCs starting from general description of this cell population, through CSCs markers, signaling pathways, genetic and epigenetic regulation, role of epithelial-mesenchymal transition (EMT) transition and autophagy, cooperation with microenvironment (CSCs niche), and finally role of CSCs in escaping host immunosurveillance against cancer.

Metastatic EMT Phenotype Is Governed by MicroRNA-200-Mediated Competing Endogenous RNA Networks

Epithelial–mesenchymal transition (EMT) is a fundamental physiologically relevant process that occurs during morphogenesis and organ development. In a pathological setting, the transition from epithelial toward mesenchymal cell phenotype is hijacked by cancer cells, allowing uncontrolled metastatic dissemination. The competing endogenous RNA (ceRNA) hypothesis proposes a competitive environment resembling a large-scale regulatory network of gene expression circuits where alterations in the expression of both protein-coding and non-coding genes can make relevant contributions to EMT progression in cancer. The complex regulatory diversity is exerted through an array of diverse epigenetic factors, reaching beyond the transcriptional control that was previously thought to single-handedly govern metastatic dissemination. The present review aims to unravel the competitive relationships between naturally occurring ceRNA transcripts for the shared pool of the miRNA-200 family, which play a pivotal role in EMT related to cancer dissemination. Upon acquiring more knowledge and clinical evidence on non-genetic factors affecting neoplasia, modulation of the expression levels of diverse ceRNAs may allow for the development of novel prognostic/diagnostic markers and reveal potential targets for the disruption of cancer-related EMT.

Volumetric compression develops noise-driven single-cell heterogeneity

Recent studies have revealed that extensive heterogeneity of biological systems arises through various routes ranging from intracellular chromosome segregation to spatiotemporally varying biochemical stimulations. However, the contribution of physical microenvironments to single-cell heterogeneity remains largely unexplored. Here, we show that a homogeneous population of non-small-cell lung carcinoma develops into heterogeneous subpopulations upon application of a homogeneous physical compression, as shown by single-cell transcriptome profiling. The generated subpopulations stochastically gain the signature genes associated with epithelial-mesenchymal transition (EMT; VIM, CDH1, EPCAM, ZEB1, and ZEB2) and cancer stem cells (MKI67, BIRC5, and KLF4), respectively. Trajectory analysis revealed two bifurcated paths as cells evolving upon the physical compression, along each path the corresponding signature genes (epithelial or mesenchymal) gradually increase. Furthermore, we show that compression increases gene expression noise, which interplays with regulatory network architecture and thus generates differential cell-fate outcomes. The experimental observations of both single-cell sequencing and single-molecule fluorescent in situ hybridization agrees well with our computational modeling of regulatory network in the EMT process. These results demonstrate a paradigm of how mechanical stimulations impact cell-fate determination by altering transcription dynamics; moreover, we show a distinct path that the ecology and evolution of cancer interplay with their physical microenvironments from the view of mechanobiology and systems biology, with insight into the origin of single-cell heterogeneity.

Regulation of epithelial-to-mesenchymal transition in hypoxia by the HIF-1α network

Epithelial-to-mesenchymal transition (EMT) plays a significant role in cancer metastasis. A series of models have focused on EMT regulation by TGF-β network. However, how EMT is regulated under hypoxia is less understood. We developed a model of HIF-1α network to explore the potential link between EMT and the network topology. Our results revealed that three positive feedback loops, composed of HIF-1α and its three targets SNAIL, TWIST, and miR-210, should be sequentially activated to induce EMT under aggravating hypoxia. We suggested that the number of the positive feedback loops is critical for determining the number of stable states in EMT. Our work may advance the understanding of the significance of network topology in the regulation of EMT.

Incoherent modulation of bi-stable dynamics orchestrates the Mushroom and Isola bifurcations

In biological networks, steady state dynamics of cell-fate regulatory genes often exhibit Mushroom and Isola kind of bifurcations. How these complex bifurcations emerge for these complex networks, and what are the minimal network structures that can generate these bifurcations, remain elusive. Herein, by employing Waddington's landscape theory and bifurcation analysis, we demonstrate that Mushroom and Isola bifurcations can be realized with four minimal network motifs that are constituted by combining a positive feedback motif with various incoherent feed-forward loops. Our study reveals that the intrinsic bi-stable dynamics originating from the positive feedback motif can be fine-tuned by altering the extent of the incoherence of these minimal networks to produce these complex bifurcations. These modeling insights will be useful in identifying the possible network motifs that may give rise to either Mushroom or Isola bifurcation in other biological systems.

Control of tissue homeostasis, tumorigenesis, and degeneration by coupled bidirectional bistable switches

The Hippo-YAP/TAZ signaling pathway plays a critical role in tissue homeostasis, tumorigenesis, and degeneration disorders. The regulation of YAP/TAZ levels is controlled by a complex regulatory network, where several feedback loops have been identified. However, it remains elusive how these feedback loops contain the YAP/TAZ levels and maintain the system in a healthy physiological state or trap the system in pathological conditions. Here, a mathematical model was developed to represent the YAP/TAZ regulatory network. Through theoretical analyses, three distinct states that designate the one physiological and two pathological outcomes were found. The transition from the physiological state to the two pathological states is mechanistically controlled by coupled bidirectional bistable switches, which are robust to parametric variation and stochastic fluctuations at the molecular level. This work provides a mechanistic understanding of the regulation and dysregulation of YAP/TAZ levels in tissue state transitions.

Tissue development and homeostasis require well-controlled cell proliferation. Lack of this control could lead to degenerative or tumorigenic diseases. Signaling pathways have been explored in promoting or inhibiting these diseases. The Hippo signaling pathway is one of these, which has been found to control tissue homeostasis and organ size through cell proliferation and apoptosis, as evidenced by extensive experimental data. However, the question remains of how tissue can transition from a homeostatic state to either a degenerative or tumorigenic state. By theoretically analyzing a mathematical model of its regulatory network, we present a mechanism that underlies Hippo signaling to control tissue transition from a homeostatic state to a disease state. This provides us with a mechanistic understanding of how the parts of the regulatory network are coordinated for the transitions between the homeostasis state and the disease states. In addition, we looked at the role of system noise and found that it could promote the transition to one of the disease states. Our model allows for experimental hypotheses to be generated and could lead to the development of therapeutic strategies by targeting the Hippo signaling pathway.

Landscape and kinetic path quantify critical transitions in epithelial-mesenchymal transition

Epithelial-mesenchymal transition (EMT), a basic developmental process that might promote cancer metastasis, has been studied from various perspectives. Recently, the early warning theory has been used to anticipate critical transitions in EMT from mathematical modeling. However, the underlying mechanisms of EMT involving complex molecular networks remain to be clarified. Especially, how to quantify the global stability and stochastic transition dynamics of EMT and what the underlying mechanism for early warning theory in EMT is remain to be fully clarified. To address these issues, we constructed a comprehensive gene regulatory network model for EMT and quantified the corresponding potential landscape. The landscape for EMT displays multiple stable attractors, which correspond to E, M, and some other intermediate states. Based on the path-integral approach, we identified the most probable transition paths of EMT, which are supported by experimental data. Correspondingly, the results of transition actions demonstrated that intermediate states can accelerate EMT, consistent with recent studies. By integrating the landscape and path with early warning concept, we identified the potential barrier height from the landscape as a global and more accurate measure for early warning signals to predict critical transitions in EMT. The landscape results also provide an intuitive and quantitative explanation for the early warning theory. Overall, the landscape and path results advance our mechanistic understanding of dynamical transitions and roles of intermediate states in EMT, and the potential barrier height provides a new, to our knowledge, measure for critical transitions and quantitative explanations for the early warning theory.

Identification, visualization, statistical analysis and mathematical modeling of high-feedback loops in gene regulatory networks

Background

Feedback loops in gene regulatory networks play pivotal roles in governing functional dynamics of cells. Systems approaches demonstrated characteristic dynamical features, including multistability and oscillation, of positive and negative feedback loops. Recent experiments and theories have implicated highly interconnected feedback loops (high-feedback loops) in additional nonintuitive functions, such as controlling cell differentiation rate and multistep cell lineage progression. However, it remains challenging to identify and visualize high-feedback loops in complex gene regulatory networks due to the myriad of ways in which the loops can be combined. Furthermore, it is unclear whether the high-feedback loop structures with these potential functions are widespread in biological systems. Finally, it remains challenging to understand diverse dynamical features, such as high-order multistability and oscillation, generated by individual networks containing high-feedback loops. To address these problems, we developed HiLoop, a toolkit that enables discovery, visualization, and analysis of several types of high-feedback loops in large biological networks.

Results

HiLoop not only extracts high-feedback structures and visualize them in intuitive ways, but also quantifies the enrichment of overrepresented structures. Through random parameterization of mathematical models derived from target networks, HiLoop presents characteristic features of the underlying systems, including complex multistability and oscillations, in a unifying framework. Using HiLoop, we were able to analyze realistic gene regulatory networks containing dozens to hundreds of genes, and to identify many small high-feedback systems. We found more than a 100 human transcription factors involved in high-feedback loops that were not studied previously. In addition, HiLoop enabled the discovery of an enrichment of high feedback in pathways related to epithelial-mesenchymal transition.

Conclusions

HiLoop makes the study of complex networks accessible without significant computational demands. It can serve as a hypothesis generator through identification and modeling of high-feedback subnetworks, or as a quantification method for motif enrichment analysis. As an example of discovery, we found that multistep cell lineage progression may be driven by either specific instances of high-feedback loops with sparse appearances, or generally enriched topologies in gene regulatory networks. We expect HiLoop’s usefulness to increase as experimental data of regulatory networks accumulate. Code is freely available for use or extension at https://github.com/BenNordick/HiLoop.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04405-z.

Cell death modes are specified by the crosstalk dynamics within pyroptotic and apoptotic signaling

The crosstalk between pyroptosis and apoptosis pathways plays crucial roles in homeostasis, cancer, and other pathologies. However, its molecular regulatory mechanisms for cell death decision-making remain to be elucidated. Based on the recent experimental studies, we developed a core regulatory network model of the crosstalk between pyroptosis and apoptosis pathways. Sensitivity analysis and bifurcation analysis were performed to assess the death mode switching of the network. Both the approaches determined that only the level of caspase-1 or gasdermin D (GSDMD) has the potential to individually change death modes. The decrease of caspase-1 or GSDMD switches cell death from pyroptosis to apoptosis. Seven biochemical reactions among the 21 reactions in total that are essential for determining cell death modes are identified by using sensitivity analysis. While with bifurcation analysis of state transitions, nine reactions are suggested to be able to efficiently switch death modes. Monostability, bistability, and tristability are observed under different conditions. We found that only the reaction that caspase-1 activation induced by stimuli can trigger tristability. Six and two of the nine reactions are identified to be able to induce bistability and monostability, respectively. Moreover, the concurrence of pyroptosis and apoptosis is observed not only within proper bistable ranges, but also within tristable ranges, implying two potentially distinct regulatory mechanisms. Taken together, this work sheds new light on the crosstalk between pyroptosis and apoptosis and uncovers the regulatory mechanisms of various stable state transitions, which play important roles for the development of potential control strategies for disease prevention and treatment.

A modular approach for modeling the cell cycle based on functional response curves

Modeling biochemical reactions by means of differential equations often results in systems with a large number of variables and parameters. As this might complicate the interpretation and generalization of the obtained results, it is often desirable to reduce the complexity of the model. One way to accomplish this is by replacing the detailed reaction mechanisms of certain modules in the model by a mathematical expression that qualitatively describes the dynamical behavior of these modules. Such an approach has been widely adopted for ultrasensitive responses, for which underlying reaction mechanisms are often replaced by a single Hill function. Also time delays are usually accounted for by using an explicit delay in delay differential equations. In contrast, however, S-shaped response curves, which by definition have multiple output values for certain input values and are often encountered in bistable systems, are not easily modeled in such an explicit way. Here, we extend the classical Hill function into a mathematical expression that can be used to describe both ultrasensitive and S-shaped responses. We show how three ubiquitous modules (ultrasensitive responses, S-shaped responses and time delays) can be combined in different configurations and explore the dynamics of these systems. As an example, we apply our strategy to set up a model of the cell cycle consisting of multiple bistable switches, which can incorporate events such as DNA damage and coupling to the circadian clock in a phenomenological way.

Bistability plays an important role in many biochemical processes and typically emerges from complex interaction patterns such as positive and double negative feedback loops. Here, we propose to theoretically study the effect of bistability in a larger interaction network. We explicitly incorporate a functional expression describing an S-shaped input-output curve in the model equations, without the need for considering the underlying biochemical events. This expression can be converted into a functional module for an ultrasensitive response, and a time delay is easily included as well. Exploiting the fact that several of these modules can easily be combined in larger networks, we construct a cell cycle model consisting of multiple bistable switches and show how this approach can account for a number of known properties of the cell cycle.

Immunofluorescence Image Feature Analysis and Phenotype Scoring Pipeline for Distinguishing Epithelial-Mesenchymal Transition

Epithelial–mesenchymal transition (EMT) is an essential biological process, also implicated in pathological settings such as cancer metastasis, in which epithelial cells transdifferentiate into mesenchymal cells. We devised an image analysis pipeline to distinguish between tissues comprised of epithelial and mesenchymal cells, based on extracted features from immunofluorescence images of differing biochemical markers. Mammary epithelial cells were cultured with 0 (control), 2, 4, or 10 ng/mL TGF-β1, a well-established EMT-inducer. Cells were fixed, stained, and imaged for E-cadherin, actin, fibronectin, and nuclei via immunofluorescence microscopy. Feature selection was performed on different combinations of individual cell markers using a Bag-of-Features extraction. Control and high-dose images comprised the training data set, and the intermediate dose images comprised the testing data set. A feature distance analysis was performed to quantify differences between the treatment groups. The pipeline was successful in distinguishing between control (epithelial) and the high-dose (mesenchymal) groups, as well as demonstrating progress along the EMT process in the intermediate dose groups. Validation using quantitative PCR (qPCR) demonstrated that biomarker expression measurements were well-correlated with the feature distance analysis. Overall, we identified image pipeline characteristics for feature extraction and quantification of immunofluorescence images to distinguish progression of EMT.

Prediction and Boolean logical modelling of synergistic microRNA regulatory networks during reprogramming of male germline pluripotent stem cells

Unipotent male germline stem (GS) cells can undergo spontaneous reprogramming to germline pluripotent stem (GPS) cells during in vitro culture. In our previous study, we proposed a Boolean logical model of gene regulatory network (GRN) during reprogramming of GS cells to GPS cells. This study was designed to predict and model synergistic microRNA (miRNA) regulatory network during reprogramming of GS cells into GPS cells. The miRNAs targeting differentially expressed genes (DEGs) among GS and GPS cells were predicted by a novel Gene Ontology (GO) enrichment analysis to construct miRNA synergistic networks (MSN) and identify regulatory miRNA modules. Qualitative Boolean logical model of synergistic miRNAs and its regulated genes was then constructed by considering discrete, asynchronous, multivalued logical formalism using the GINsim modeling and simulation tools. Topology, functional and community overlap studies revealed that mmu-miR-200b-3p, mmu-miR-429-3p and mmu-miR-141-3p, mmu-miR-200a-3p and mmu-miR-200c-3p in MSN belongs to the family of miR-200/429/141 and conjectured to control the pluripotency and reprogramming by promoting Mesenchymal to Epithelial Transition (MET). Synergistic network involving mmu-miR-20b-5p, mmu-miR-20a-5p, mmu-miR-106a-5p, mmu-miR-106b-5p, and mmu-miR-17-5p were found to be essential for the maintenance of GS cells. Logical miRNA regulatory network modelling showed that synergistic miRNAs regulates the gene dynamics of MET during GS-GPS reprogramming, as confirmed by perturbation analysis. Taken together, our study predicted novel synergistic miRNAs involved in the regulation of reprogramming and pluripotency in GPS cells. The Boolean logical model of synergistic miRNAs regulatory network further confirms our previous study that gene dynamics of MET regulates GS-GPS reprogramming.

Computational systems-biology approaches for modeling gene networks driving epithelial-mesenchymal transitions

Epithelial-mesenchymal transition (EMT) is an important biological process through which epithelial cells undergo phenotypic transitions to mesenchymal cells by losing cell-cell adhesion and gaining migratory properties that cells use in embryogenesis, wound healing, and cancer metastasis. An important research topic is to identify the underlying gene regulatory networks (GRNs) governing the decision making of EMT and develop predictive models based on the GRNs. The advent of recent genomic technology, such as single-cell RNA sequencing, has opened new opportunities to improve our understanding about the dynamical controls of EMT. In this article, we review three major types of computational and mathematical approaches and methods for inferring and modeling GRNs driving EMT. We emphasize (1) the bottom-up approaches, where GRNs are constructed through literature search; (2) the top-down approaches, where GRNs are derived from genome-wide sequencing data; (3) the combined top-down and bottom-up approaches, where EMT GRNs are constructed and simulated by integrating bioinformatics and mathematical modeling. We discuss the methodologies and applications of each approach and the available resources for these studies.

The role of mechanical interactions in EMT

The detachment of cells from the boundary of an epithelial tissue and the subsequent invasion of these cells into surrounding tissues is important for cancer development and wound healing, and is strongly associated with the epithelial-mesenchymal transition (EMT). Chemical signals, such as TGF-β, produced by surrounding tissue can be uptaken by cells and induce EMT. In this work, we present a novel cell-based discrete mathematical model of mechanical cellular relaxation, cell proliferation, and cell detachment driven by chemically-dependent EMT in an epithelial tissue. A continuum description of the model is then derived in the form of a novel nonlinear free boundary problem. Using the discrete and continuum models we explore how the coupling of chemical transport and mechanical interactions influences EMT, and postulate how this could be used to help control EMT in pathological situations.

Epigenetic Regulation of Epithelial to Mesenchymal Transition in the Cancer Metastatic Cascade: Implications for Cancer Therapy

Metastasis is the end stage of cancer progression and the direct cause of most cancer-related deaths. The spreading of cancer cells from the primary site to distant organs is a multistep process known as the metastatic cascade, including local invasion, intravasation, survival in the circulation, extravasation, and colonization. Each of these steps is driven by the acquisition of genetic and/or epigenetic alterations within cancer cells, leading to subsequent transformation of metastatic cells. Epithelial–mesenchymal transition (EMT), a cellular process mediating the conversion of cell from epithelial to mesenchymal phenotype, and its reverse transformation, termed mesenchymal–epithelial transition (MET), together endow metastatic cells with traits needed to generate overt metastases in different scenarios. The dynamic shift between these two phenotypes and their transitional state, termed partial EMT, emphasizes the plasticity of EMT. Recent advances attributed this plasticity to epigenetic regulation, which has implications for the therapeutic targeting of cancer metastasis. In this review, we will discuss the association between epigenetic events and the multifaceted nature of EMT, which may provide insights into the steps of the cancer metastatic cascade.

Measuring and Modelling the Epithelial- Mesenchymal Hybrid State in Cancer: Clinical Implications

The epithelial-mesenchymal (E/M) hybrid state has emerged as an important mediator of elements of cancer progression, facilitated by epithelial mesenchymal plasticity (EMP). We review here evidence for the presence, prognostic significance, and therapeutic potential of the E/M hybrid state in carcinoma. We further assess modelling predictions and validation studies to demonstrate stabilised E/M hybrid states along the spectrum of EMP, as well as computational approaches for characterising and quantifying EMP phenotypes, with particular attention to the emerging realm of single-cell approaches through RNA sequencing and protein-based techniques.

MicroRNA governs bistable cell differentiation and lineage segregation via a noncanonical feedback

Positive feedback driven by transcriptional regulation has long been considered a key mechanism underlying cell lineage segregation during embryogenesis. Using the developing spinal cord as a paradigm, we found that canonical, transcription-driven feedback cannot explain robust lineage segregation of motor neuron subtypes marked by two cardinal factors, Hoxa5 and Hoxc8. We propose a feedback mechanism involving elementary microRNA-mRNA reaction circuits that differ from known feedback loop-like structures. Strikingly, we show that a wide range of biologically plausible post-transcriptional regulatory parameters are sufficient to generate bistable switches, a hallmark of positive feedback. Through mathematical analysis, we explain intuitively the hidden source of this feedback. Using embryonic stem cell differentiation and mouse genetics, we corroborate that microRNA-mRNA circuits govern tissue boundaries and hysteresis upon motor neuron differentiation with respect to transient morphogen signals. Our findings reveal a previously underappreciated feedback mechanism that may have widespread functions in cell fate decisions and tissue patterning.

The Impact of Non-coding RNAs in the Epithelial to Mesenchymal Transition

Epithelial to mesenchymal transition (EMT) is a course of action that enables a polarized epithelial cell to undertake numerous biochemical alterations that allow it to adopt features of mesenchymal cells such as high migratory ability, invasive properties, resistance to apoptosis, and importantly higher-order formation of extracellular matrix elements. EMT has important roles in implantation and gastrulation of the embryo, inflammatory reactions and fibrosis, and transformation of cancer cells, their invasiveness and metastatic ability. Regarding the importance of EMT in the invasive progression of cancer, this process has been well studies in in this context. Non-coding RNAs (ncRNAs) have been shown to exert critical function in the regulation of cellular processes that are involved in the EMT. These processes include regulation of some transcription factors namely SNAI1 and SNAI2, ZEB1 and ZEB2, Twist, and E12/E47, modulation of chromatin configuration, alternative splicing, and protein stability and subcellular location of proteins. In the present paper, we describe the influence of ncRNAs including microRNAs and long non-coding RNAs in the EMT process and their application as biomarkers for this process and cancer progression and their potential as therapeutic targets.

Genetic and Non-Genetic Mechanisms Underlying Cancer Evolution

Simple Summary

Our manuscript summarizes the up-to-date data on the complex and dynamic nature of adaptation mechanisms and evolutionary processes taking place during cancer initiation, development and progression. Although for decades cancer has been viewed as a process governed by genetic mechanisms, it is becoming more and more clear that non-genetic mechanisms may play an equally important role in cancer evolution. In this review, we bring together these fundamental concepts and discuss how those tightly interconnected mechanisms lead to the establishment of highly adaptive quickly evolving cancers. Furthermore, we argue that in depth understanding of cancer progression from the evolutionary perspective may allow the prediction and direction of the evolutionary path of cancer populations towards drug sensitive phenotypes and thus facilitate the development of more effective anti-cancer approaches.

Abstract

Cancer development can be defined as a process of cellular and tissular microevolution ultimately leading to malignancy. Strikingly, though this concept has prevailed in the field for more than a century, the precise mechanisms underlying evolutionary processes occurring within tumours remain largely uncharacterized and rather cryptic. Nevertheless, although our current knowledge is fragmentary, data collected to date suggest that most tumours display features compatible with a diverse array of evolutionary paths, suggesting that most of the existing macro-evolutionary models find their avatar in cancer biology. Herein, we discuss an up-to-date view of the fundamental genetic and non-genetic mechanisms underlying tumour evolution with the aim of concurring into an integrated view of the evolutionary forces at play throughout the emergence and progression of the disease and into the acquisition of resistance to diverse therapeutic paradigms. Our ultimate goal is to delve into the intricacies of genetic and non-genetic networks underlying tumour evolution to build a framework where both core concepts are considered non-negligible and equally fundamental.

Potential Landscapes, Bifurcations, and Robustness of Tristable Networks

Bistable switches that produce all-or-none responses have been found to regulate a number of natural cellular decision making processes, and subsequently synthetic switches were designed to exploit their potential. However, an increasing number of studies, particularly in the context of cellular differentiation, highlight the existence of a mixed state that can be explained by tristable switches. The criterion for designing robust tristable switches still remains to be understood from the perspective of network topology. To address such a question, we calculated the robustness of several 2- and 3-component network motifs, connected via only two positive feedback loops, in generating tristable signal response curves. By calculating the effective potential landscape and following its modifications with the bifurcation parameter, we constructed one-parameter bifurcation diagrams of these models in a high-throughput manner for a large combinations of parameters. We report here that introduction of a self-activatory positive feedback loop, directly or indirectly, into a mutual inhibition loop leads to generating the most robust tristable response. The high-throughput approach of our method further allowed us to determine the robustness of four types of tristable responses that originate from the relative locations of four bifurcation points. Using the method, we also analyzed the role of additional mutual inhibition loops in stabilizing the mixed state.

A Computational Systems Biology Approach Identifies SLUG as a Mediator of Partial Epithelial-Mesenchymal Transition (EMT)

Epithelial-mesenchymal plasticity comprises reversible transitions among epithelial, hybrid epithelial/mesenchymal (E/M) and mesenchymal phenotypes, and underlies various aspects of aggressive tumor progression such as metastasis, therapy resistance, and immune evasion. The process of cells attaining one or more hybrid E/M phenotypes is termed as partial epithelial mesenchymal transition (EMT). Cells in hybrid E/M phenotype(s) can be more aggressive than those in either fully epithelial or mesenchymal state. Thus, identifying regulators of hybrid E/M phenotypes is essential to decipher the rheostats of phenotypic plasticity and consequent accelerators of metastasis. Here, using a computational systems biology approach, we demonstrate that SLUG (SNAIL2) - an EMT-inducing transcription factor - can inhibit cells from undergoing a complete EMT and thus stabilize them in hybrid E/M phenotype(s). It expands the parametric range enabling the existence of a hybrid E/M phenotype, thereby behaving as a phenotypic stability factor. Our simulations suggest that this specific property of SLUG emerges from the topology of the regulatory network it forms with other key regulators of epithelial-mesenchymal plasticity. Clinical data suggest that SLUG associates with worse patient prognosis across multiple carcinomas. Together, our results indicate that SLUG can stabilize hybrid E/M phenotype(s).

Cell memory of epithelial-mesenchymal plasticity in cancer

Fundamental biological processes of cell identity and cell fate determination are controlled by complex regulatory networks. These processes require molecular mechanisms that confer cellular phenotypic memory and state persistence. In this minireview, we will summarize mechanisms of cell memory based on regulatory hysteretic feedback loops and explore epigenetic mechanisms widely represented in nature, with special focus on epithelial-to-mesenchymal plasticity. We will also discuss the functional consequences of cell memory and epithelial-to-mesenchymal plasticity dynamics during development and cancer metastasis.

Winner-takes-all resource competition redirects cascading cell fate transitions

Failure of modularity remains a significant challenge for assembling synthetic gene circuits with tested modules as they often do not function as expected. Competition over shared limited gene expression resources is a crucial underlying reason. It was reported that resource competition makes two seemingly separate genes connect in a graded linear manner. Here we unveil nonlinear resource competition within synthetic gene circuits. We first build a synthetic cascading bistable switches (Syn-CBS) circuit in a single strain with two coupled self-activation modules to achieve two successive cell fate transitions. Interestingly, we find that the in vivo transition path was redirected as the activation of one switch always prevails against the other, contrary to the theoretically expected coactivation. This qualitatively different type of resource competition between the two modules follows a ‘winner-takes-all’ rule, where the winner is determined by the relative connection strength between the modules. To decouple the resource competition, we construct a two-strain circuit, which achieves successive activation and stable coactivation of the two switches. These results illustrate that a highly nonlinear hidden interaction between the circuit modules due to resource competition may cause counterintuitive consequences on circuit functions, which can be controlled with a division of labor strategy.

Synthetic gene circuits may not function as expected due to the resource competition between modules. Here the authors build cascading bistable switches to achieve two successive cell fate transitions but found a ‘winner-takes-all’ behaviour, which is overcome by a division of labour strategy.

Autocrine TGF-β in Cancer: Review of the Literature and Caveats in Experimental Analysis

Autocrine signaling is defined as the production and secretion of an extracellular mediator by a cell followed by the binding of that mediator to receptors on the same cell to initiate signaling. Autocrine stimulation often operates in autocrine loops, a type of interaction, in which a cell produces a mediator, for which it has receptors, that upon activation promotes expression of the same mediator, allowing the cell to repeatedly autostimulate itself (positive feedback) or balance its expression via regulation of a second factor that provides negative feedback. Autocrine signaling loops with positive or negative feedback are an important feature in cancer, where they enable context-dependent cell signaling in the regulation of growth, survival, and cell motility. A growth factor that is intimately involved in tumor development and progression and often produced by the cancer cells in an autocrine manner is transforming growth factor-β (TGF-β). This review surveys the many observations of autocrine TGF-β signaling in tumor biology, including data from cell culture and animal models as well as from patients. We also provide the reader with a critical discussion on the various experimental approaches employed to identify and prove the involvement of autocrine TGF-β in a given cellular response.

Mathematical Modeling of Plasticity and Heterogeneity in EMT

The epithelial-mesenchymal transition (EMT) and the corresponding reverse process, mesenchymal-epithelial transition (MET), are dynamic and reversible cellular programs orchestrated by many changes at both biochemical and morphological levels. A recent surge in identifying the molecular mechanisms underlying EMT/MET has led to the development of various mathematical models that have contributed to our improved understanding of dynamics at single-cell and population levels: (a) multi-stability-how many phenotypes can cells attain during an EMT/MET?, (b) reversibility/irreversibility-what time and/or concentration of an EMT inducer marks the "tipping point" when cells induced to undergo EMT cannot revert?, (c) symmetry in EMT/MET-do cells take the same path when reverting as they took during the induction of EMT?, and (d) non-cell autonomous mechanisms-how does a cell undergoing EMT alter the tendency of its neighbors to undergo EMT? These dynamical traits may facilitate a heterogenous response within a cell population undergoing EMT/MET. Here, we present a few examples of designing different mathematical models that can contribute to decoding EMT/MET dynamics.

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

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.

Simulation of multiple microenvironments shows a pivot role of RPTPs on the control of Epithelial-to-Mesenchymal Transition

Epithelial-to-Mesenchymal Transition (EMT) is a natural and reversible process involved in embryogenesis, wound healing and thought to participate in the process of metastasis. Multiple signals from the microenvironment have been reported to drive EMT. However, the tight control of this process on physiological scenarios and how it is disrupted during cancer progression is not fully understood. Here, we analysed a regulatory network of EMT accounting for 10 key microenvironment signals focusing on the impact of two cell contact signals on the reversibility of EMT and the stability of resulting phenotypes. The analysis showed that the microenvironment is not enough for stabilizing Hybrid and Amoeboid-like phenotypes, requiring intracellular de-regulations as reported during cancer progression. Our simulations demonstrated that RPTP activation by cell contacts have the potential to inhibit the process of EMT and trigger its reversibility under tissue growth and chronic inflammation scenarios. Simulations also showed that hypoxia inhibits the capacity of RPTPs to control EMT. Our analysis further provided a theoretical explanation for the observed correlation between hypoxia and metastasis under chronic inflammation, and predicted that de-regulations in FAT4 signalling may promote Hybrid stabilization. Taken together, we propose a natural control mechanism of EMT that supports the idea that EMT is tightly regulated by the microenvironment.

Systems biology approach suggests new miRNAs as phenotypic stability factors in the epithelial-mesenchymal transition

The epithelial-mesenchymal transition (EMT) is a cellular programme on which epithelial cells undergo a phenotypic transition to mesenchymal ones acquiring metastatic properties such as mobility and invasion. TGF-β signalling can promote the EMT process. However, the dynamics of the concentration response of TGF-β-induced EMT is not well explained. In this work, we propose a logical model of TGF-β dose dependence of EMT in MCF10A breast cells. The model outcomes agree with experimentally observed phenotypes for the wild-type and perturbed/mutated cases. As important findings of the model, it predicts the coexistence of more than one hybrid state and that the circuit between TWIST1 and miR-129 is involved in their stabilization. Thus, miR-129 should be considered as a phenotypic stability factor. The circuit TWIST1/miR-129 associates with ZEB1-mediated circuits involving miRNAs 200, 1199, 340, and the protein GRHL2 to stabilize the hybrid state. Additionally, we found a possible new autocrine mechanism composed of the circuit involving TGF-β, miR-200, and SNAIL1 that contributes to the stabilization of the mesenchymal state. Therefore, our work can extend our comprehension of TGF-β-induced EMT in MCF10A cells to potentially improve the strategies for breast cancer treatment.

Emerging role of tumor cell plasticity in modifying therapeutic response

Resistance to cancer therapy is a major barrier to cancer management. Conventional views have proposed that acquisition of resistance may result from genetic mutations. However, accumulating evidence implicates a key role of non-mutational resistance mechanisms underlying drug tolerance, the latter of which is the focus that will be discussed here. Such non-mutational processes are largely driven by tumor cell plasticity, which renders tumor cells insusceptible to the drug-targeted pathway, thereby facilitating the tumor cell survival and growth. The concept of tumor cell plasticity highlights the significance of re-activation of developmental programs that are closely correlated with epithelial-mesenchymal transition, acquisition properties of cancer stem cells, and trans-differentiation potential during drug exposure. From observations in various cancers, this concept provides an opportunity for investigating the nature of anticancer drug resistance. Over the years, our understanding of the emerging role of phenotype switching in modifying therapeutic response has considerably increased. This expanded knowledge of tumor cell plasticity contributes to developing novel therapeutic strategies or combination therapy regimens using available anticancer drugs, which are likely to improve patient outcomes in clinical practice.

Boolean model of anchorage dependence and contact inhibition points to coordinated inhibition but semi-independent induction of proliferation and migration

Epithelial cells respond to their physical neighborhood with mechano-sensitive behaviors required for development and tissue maintenance. These include anchorage dependence, matrix stiffness-dependent proliferation, contact inhibition of proliferation and migration, and collective migration that balances cell crawling with the maintenance of cell junctions. While required for development and tissue repair, these coordinated responses to the microenvironment also contribute to cancer metastasis. Predictive models of the signaling networks that coordinate these behaviors are critical in controlling cell behavior to halt disease. Here we propose a Boolean regulatory network model that synthesizes mechanosensitive signaling that links anchorage to a matrix of varying stiffness and cell density sensing to contact inhibition, proliferation, migration, and apoptosis. Our model can reproduce anchorage dependence and anoikis, detachment-induced cytokinesis errors, the effect of matrix stiffness on proliferation, and contact inhibition of proliferation and migration by two mechanisms that converge on the YAP transcription factor. In addition, we offer testable predictions related to cell cycle-dependent anoikis sensitivity, the molecular requirements for abolishing contact inhibition, and substrate stiffness dependent expression of the catalytic subunit of PI3K. Moreover, our model predicts heterogeneity in migratory vs. non-migratory phenotypes in sub-confluent monolayers, and co-inhibition but semi-independent induction of proliferation vs. migration as a function of cell density and mitogenic stimulation. Our model serves as a stepping-stone towards modeling mechanosensitive routes to the epithelial to mesenchymal transition, capturing the effects of the mesenchymal state on anoikis resistance, and understanding the balance between migration versus proliferation at each stage of the epithelial to mesenchymal transition.

Local and global features of genetic networks supporting a phenotypic switch

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.

Dynamical gene regulatory networks are tuned by transcriptional autoregulation with microRNA feedback

Concepts from dynamical systems theory, including multi-stability, oscillations, robustness and stochasticity, are critical for understanding gene regulation during cell fate decisions, inflammation and stem cell heterogeneity. However, the prevalence of the structures within gene networks that drive these dynamical behaviours, such as autoregulation or feedback by microRNAs, is unknown. We integrate transcription factor binding site (TFBS) and microRNA target data to generate a gene interaction network across 28 human tissues. This network was analysed for motifs capable of driving dynamical gene expression, including oscillations. Identified autoregulatory motifs involve 56% of transcription factors (TFs) studied. TFs that autoregulate have more interactions with microRNAs than non-autoregulatory genes and 89% of autoregulatory TFs were found in dual feedback motifs with a microRNA. Both autoregulatory and dual feedback motifs were enriched in the network. TFs that autoregulate were highly conserved between tissues. Dual feedback motifs with microRNAs were also conserved between tissues, but less so, and TFs regulate different combinations of microRNAs in a tissue-dependent manner. The study of these motifs highlights ever more genes that have complex regulatory dynamics. These data provide a resource for the identification of TFs which regulate the dynamical properties of human gene expression.

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

Epithelial-mesenchymal transition (EMT) and its reverse process mesenchymal-epithelial transition (MET) are central to metastatic aggressiveness and therapy resistance in solid tumors. While molecular determinants of both processes have been extensively characterized, the heterogeneity in the response of tumor cells to EMT and MET inducers has come into focus recently, and has been implicated in the failure of anti-cancer therapies. Recent experimental studies have shown that some cells can undergo an irreversible EMT depending on the duration of exposure to EMT-inducing signals. While the irreversibility of MET, or equivalently, resistance to EMT, has not been studied in as much detail, evidence supporting such behavior is slowly emerging. Here, we identify two possible mechanisms that can underlie resistance of cells to undergo EMT: epigenetic feedback in ZEB1/GRHL2 feedback loop and stochastic partitioning of biomolecules during cell division. Identifying the ZEB1/GRHL2 axis as a key determinant of epithelial-mesenchymal plasticity across many cancer types, we use mechanistic mathematical models to show how GRHL2 can be involved in both the abovementioned processes, thus driving an irreversible MET. Our study highlights how an isogenic population may contain subpopulation with varying degrees of susceptibility or resistance to EMT, and proposes a next set of questions for detailed experimental studies characterizing the irreversibility of MET/resistance to EMT.

Cell plasticity in cancer cell populations

In this review, we propose a recension of biological observations on plasticity in cancer cell populations and discuss theoretical considerations about their mechanisms.

A self-sustaining endocytic-based loop promotes breast cancer plasticity leading to aggressiveness and pro-metastatic behavior

The subversion of endocytic routes leads to malignant transformation and has been implicated in human cancers. However, there is scarce evidence for genetic alterations of endocytic proteins as causative in high incidence human cancers. Here, we report that Epsin 3 (EPN3) is an oncogene with prognostic and therapeutic relevance in breast cancer. Mechanistically, EPN3 drives breast tumorigenesis by increasing E-cadherin endocytosis, followed by the activation of a β-catenin/TCF4-dependent partial epithelial-to-mesenchymal transition (EMT), followed by the establishment of a TGFβ-dependent autocrine loop that sustains EMT. EPN3-induced partial EMT is instrumental for the transition from in situ to invasive breast carcinoma, and, accordingly, high EPN3 levels are detected at the invasive front of human breast cancers and independently predict metastatic rather than loco-regional recurrence. Thus, we uncover an endocytic-based mechanism able to generate TGFβ-dependent regulatory loops conferring cellular plasticity and invasive behavior.

Cancer Biology and Prevention in Diabetes

The available evidence suggests a complex relationship between diabetes and cancer. Epidemiological data suggest a positive correlation, however, in certain types of cancer, a more complex picture emerges, such as in some site-specific cancers being specific to type I diabetes but not to type II diabetes. Reports share common and differential mechanisms which affect the relationship between diabetes and cancer. We discuss the use of antidiabetic drugs in a wide range of cancer therapy and cancer therapeutics in the development of hyperglycemia, especially antineoplastic drugs which often induce hyperglycemia by targeting insulin/IGF-1 signaling. Similarly, dipeptidyl peptidase 4 (DPP-4), a well-known target in type II diabetes mellitus, has differential effects on cancer types. Past studies suggest a protective role of DPP-4 inhibitors, but recent studies show that DPP-4 inhibition induces cancer metastasis. Moreover, molecular pathological mechanisms of cancer in diabetes are currently largely unclear. The cancer-causing mechanisms in diabetes have been shown to be complex, including excessive ROS-formation, destruction of essential biomolecules, chronic inflammation, and impaired healing phenomena, collectively leading to carcinogenesis in diabetic conditions. Diabetes-associated epithelial-to-mesenchymal transition (EMT) and endothelial-to-mesenchymal transition (EndMT) contribute to cancer-associated fibroblast (CAF) formation in tumors, allowing the epithelium and endothelium to enable tumor cell extravasation. In this review, we discuss the risk of cancer associated with anti-diabetic therapies, including DPP-4 inhibitors and SGLT2 inhibitors, and the role of catechol-o-methyltransferase (COMT), AMPK, and cell-specific glucocorticoid receptors in cancer biology. We explore possible mechanistic links between diabetes and cancer biology and discuss new therapeutic approaches.

Identifying inhibitors of epithelial-mesenchymal plasticity using a network topology-based approach

Metastasis is the cause of over 90% of cancer-related deaths. Cancer cells undergoing metastasis can switch dynamically between different phenotypes, enabling them to adapt to harsh challenges, such as overcoming anoikis and evading immune response. This ability, known as phenotypic plasticity, is crucial for the survival of cancer cells during metastasis, as well as acquiring therapy resistance. Various biochemical networks have been identified to contribute to phenotypic plasticity, but how plasticity emerges from the dynamics of these networks remains elusive. Here, we investigated the dynamics of various regulatory networks implicated in Epithelial–mesenchymal plasticity (EMP)—an important arm of phenotypic plasticity—through two different mathematical modelling frameworks: a discrete, parameter-independent framework (Boolean) and a continuous, parameter-agnostic modelling framework (RACIPE). Results from either framework in terms of phenotypic distributions obtained from a given EMP network are qualitatively similar and suggest that these networks are multi-stable and can give rise to phenotypic plasticity. Neither method requires specific kinetic parameters, thus our results emphasize that EMP can emerge through these networks over a wide range of parameter sets, elucidating the importance of network topology in enabling phenotypic plasticity. Furthermore, we show that the ability to exhibit phenotypic plasticity correlates positively with the number of positive feedback loops in a given network. These results pave a way toward an unorthodox network topology-based approach to identify crucial links in a given EMP network that can reduce phenotypic plasticity and possibly inhibit metastasis—by reducing the number of positive feedback loops.

Sequential Wnt Agonist Then Antagonist Treatment Accelerates Tissue Repair and Minimizes Fibrosis

Tissue fibrosis compromises organ function and occurs as a potential long-term outcome in response to acute tissue injuries. Currently, lack of mechanistic understanding prevents effective prevention and treatment of the progression from acute injury to fibrosis. Here, we combined quantitative experimental studies with a mouse kidney injury model and a computational approach to determine how the physiological consequences are determined by the severity of ischemia injury and to identify how to manipulate Wnt signaling to accelerate repair of ischemic tissue damage while minimizing fibrosis. The study reveals that memory of prior injury contributes to fibrosis progression and ischemic preconditioning reduces the risk of death but increases the risk of fibrosis. Furthermore, we validated the prediction that sequential combination therapy of initial treatment with a Wnt agonist followed by treatment with a Wnt antagonist can reduce both the risk of death and fibrosis in response to acute injuries.

Hybrid Epithelial-Mesenchymal Phenotypes Are Controlled by Microenvironmental Factors

Epithelial-to-mesenchymal transition (EMT) has been associated with cancer cell heterogeneity, plasticity, and metastasis. However, the extrinsic signals supervising these phenotypic transitions remain elusive. To assess how selected microenvironmental signals control cancer-associated phenotypes along the EMT continuum, we defined a logical model of the EMT cellular network that yields qualitative degrees of cell adhesions by adherens junctions and focal adhesions, two features affected during EMT. The model attractors recovered epithelial, mesenchymal, and hybrid phenotypes. Simulations showed that hybrid phenotypes may arise through independent molecular paths involving stringent extrinsic signals. Of particular interest, model predictions and their experimental validations indicated that: (i) stiffening of the extracellular matrix was a prerequisite for cells overactivating FAK_SRC to upregulate SNAIL and acquire a mesenchymal phenotype and (ii) FAK_SRC inhibition of cell-cell contacts through the receptor-type tyrosine-protein phosphatases kappa led to acquisition of a full mesenchymal, rather than a hybrid, phenotype. Altogether, these computational and experimental approaches allow assessment of critical microenvironmental signals controlling hybrid EMT phenotypes and indicate that EMT involves multiple molecular programs. SIGNIFICANCE: A multidisciplinary study sheds light on microenvironmental signals controlling cancer cell plasticity along EMT and suggests that hybrid and mesenchymal phenotypes arise through independent molecular paths.

Computational models to explore the complexity of the epithelial to mesenchymal transition in cancer

Epithelial to mesenchymal transition (EMT) is a complex biological process that plays a key role in cancer progression and metastasis formation. Its activation results in epithelial cells losing adhesion and polarity and becoming capable of migrating from their site of origin. At this step the disease is generally considered incurable. As EMT execution involves several individual molecular components, connected by nontrivial relations, in vitro techniques are often inadequate to capture its complexity. Computational models can be used to complement experiments and provide additional knowledge difficult to build up in a wetlab. Indeed in silico analysis gives the user total control on the system, allowing to identify the contribution of each independent element. In the following, two kinds of approaches to the computational study of EMT will be presented. The first relies on signal transduction networks description and details how changes in gene expression could influence this process, both focusing on specific aspects of the EMT and providing a general frame for this phenomenon easily comparable with experimental data. The second integrates single cell and population level descriptions in a multiscale model that can be considered a more accurate representation of the EMT. The advantages and disadvantages of each approach will be highlighted, together with the importance of coupling computational and experimental results. Finally, the main challenges that need to be addressed to improve our knowledge of the role of EMT in the neoplastic disease and the scientific and translational value of computational models in this respect will be presented. This article is categorized under: Analytical and Computational Methods > Computational Methods.

A plausible accelerating function of intermediate states in cancer metastasis

Epithelial-to-mesenchymal transition (EMT) is a fundamental cellular process and plays an essential role in development, tissue regeneration, and cancer metastasis. Interestingly, EMT is not a binary process but instead proceeds with multiple partial intermediate states. However, the functions of these intermediate states are not fully understood. Here, we focus on a general question about how the number of partial EMT states affects cell transformation. First, by fitting a hidden Markov model of EMT with experimental data, we propose a statistical mechanism for EMT in which many unobservable microstates may exist within one of the observable macrostates. Furthermore, we find that increasing the number of intermediate states can accelerate the EMT process and that adding parallel paths or transition layers may accelerate the process even further. Last, a stabilized intermediate state traps cells in one partial EMT state. This work advances our understanding of the dynamics and functions of EMT plasticity during cancer metastasis.