MicroRNA-based regulation of epithelial-hybrid-mesenchymal fate determination. Proc Natl Acad Sci U S A. 2013;110:18144-18149. [PMID: 24154725 DOI: 10.1073/pnas.1318192110]

Computational modeling of cancer cell metabolism along the catabolic-anabolic axes

Abnormal metabolism is a hallmark of cancer, this was initially recognized nearly a century ago through the observation of aerobic glycolysis in cancer cells. Mitochondrial respiration can also drive tumor progression and metastasis. However, it remains largely unclear the mechanisms by which cancer cells mix and match different metabolic modalities (oxidative/reductive) and leverage various metabolic ingredients (glucose, fatty acids, glutamine) to meet their bioenergetic and biosynthetic needs. Here, we formulate a phenotypic model for cancer metabolism by coupling master gene regulators (AMPK, HIF-1, MYC) with key metabolic substrates (glucose, fatty acids, and glutamine). The model predicts that cancer cells can acquire four metabolic phenotypes: a catabolic phenotype characterized by vigorous oxidative processes-O, an anabolic phenotype characterized by pronounced reductive activities-W, and two complementary hybrid metabolic states-one exhibiting both high catabolic and high anabolic activity-W/O, and the other relying mainly on glutamine oxidation-Q. Using this framework, we quantified gene and metabolic pathway activity by developing scoring metrics based on gene expression. We validated the model-predicted gene-metabolic pathway association and the characterization of the four metabolic phenotypes by analyzing RNA-seq data of tumor samples from TCGA. Strikingly, carcinoma samples exhibiting hybrid metabolic phenotypes are often associated with the worst survival outcomes relative to other metabolic phenotypes. Our mathematical model and scoring metrics serve as a platform to quantify cancer metabolism and study how cancer cells adapt their metabolism upon perturbations, which ultimately could facilitate an effective treatment targeting cancer metabolic plasticity.

Systems Biology of the Cancer Cell

Questions in cancer have engaged systems biologists for decades. During that time, the quantity of molecular data has exploded, but the need for abstractions, formal models, and simplifying insights has remained the same. This review brings together classic breakthroughs and recent findings in the field of cancer systems biology, focusing on cancer cell pathways for tumorigenesis and therapeutic response. Cancer cells mutate and transduce information from their environment to alter gene expression, metabolism, and phenotypic states. Understanding the molecular architectures that make each of these steps possible is a long-term goal of cancer systems biology pursued by iterating between quantitative models and experiments. We argue that such iteration is the best path to deploying targeted therapies intelligently so that each patient receives the maximum benefit for their cancer.

Integrated mathematical and experimental modeling uncovers enhanced EMT plasticity upon loss of the DLC1 tumor suppressor

Epithelial-mesenchymal transition (EMT) plays an essential role in embryonic development, wound healing, and tumor progression. Partial EMT states have been linked to metastatic dissemination and drug resistance. Several interconnected feedback loops at the RNA and protein levels control the transition between different cellular states. Using a combination of mathematical modeling and experimental analyses in the TGFβ-responsive breast epithelial MCF10A cell model, we identify a central role for the tumor suppressor protein Deleted in Liver Cancer 1 (DLC1) during EMT. By extending a previous model of EMT comprising key transcription factors and microRNAs, our work shows that DLC1 acts as a positive regulator of TGFβ-driven EMT, mainly by promoting SNAIL1 expression. Our model predictions indicate that DLC1 loss impairs EMT progression. Experimental analyses confirm this prediction and reveal the acquisition of a partial EMT phenotype in DLC1-depleted cells. Furthermore, our model results indicate a possible EMT reversion to partial or epithelial states upon DLC1 loss in MCF10A cells induced toward mesenchymal phenotypes. The increased EMT plasticity of cells lacking DLC1 may explain its importance as a tumor suppressor.

Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity

Epithelial-mesenchymal transition (EMT) is a cell state transition co-opted by cancer that drives metastasis via stable intermediate states. Here we study EMT dynamics to identify marker genes of highly metastatic intermediate cells via mathematical modeling with single-cell RNA sequencing (scRNA-seq) data. Across multiple tumor types and stimuli, we identified genes consistently upregulated in EMT intermediate states, many previously unrecognized as EMT markers. Bayesian parameter inference of a simple EMT mathematical model revealed tumor-specific transition rates, providing a framework to quantify EMT progression. Consensus analysis of differential expression, RNA velocity, and model-derived dynamics highlighted SFN and NRG1 as key regulators of intermediate EMT. Independent validation confirmed SFN as an intermediate state marker. Our approach integrates modeling and inference to identify genes associated with EMT dynamics, offering biomarkers and therapeutic targets to modulate tumor-promoting cell state transitions driven by EMT.

A Boolean network model of hypoxia, mechanosensing and TGF-β signaling captures the role of phenotypic plasticity and mutations in tumor metastasis

The tumor microenvironment aids cancer progression by promoting several cancer hallmarks, independent of cancer-related mutations. Biophysical properties of this environment, such as the stiffness of the matrix cells adhere to and local cell density, impact proliferation, apoptosis, and the epithelial to mesenchymal transition (EMT). The latter is a rate-limiting step for invasion and metastasis, enhanced in hypoxic tumor environments but hindered by soft matrices and/or high cell densities. As these influences are often studied in isolation, the crosstalk between hypoxia, biomechanical signals, and the classic EMT driver TGF-β is not well mapped, limiting our ability to predict and anticipate cancer cell behaviors in changing tumor environments. To address this, we built a Boolean regulatory network model that integrates hypoxic signaling with a mechanosensitive model of EMT, which includes the EMT-promoting crosstalk of mitogens and biomechanical signals, cell cycle control, and apoptosis. Our model reproduces the requirement of Hif-1α for proliferation, the anti-proliferative effects of strong Hif-1α stabilization during hypoxia, hypoxic protection from anoikis, and hypoxia-driven mechanosensitive EMT. We offer experimentally testable predictions about the effect of VHL loss on cancer hallmarks, with or without secondary oncogene activation. Taken together, our model serves as a predictive framework to synthesize the signaling responses associated with tumor progression and metastasis in healthy vs. mutant cells. Our single-cell model is a key step towards more extensive regulatory network models that cover damage-response and senescence, integrating most cell-autonomous cancer hallmarks into a single model that can, in turn, control the behavior of in silico cells within a tissue model of epithelial homeostasis and carcinoma.

Role of tristability in the robustness of the differentiation mechanism

During cell differentiation, identical pluripotent cells undergo a specification process marked by changes in the expression of key genes, regulated by transcription factors that can inhibit the transcription of a competing gene or activate their own transcription. This specification is orchestrated by gene regulatory networks (GRNs), encompassing transcription factors, biochemical reactions, and signalling cascades. Mathematical models for these GRNs have been proposed in various contexts, to replicate observed robustness in differentiation properties. This includes reproducible proportions of differentiated cells with respect to parametric or stochastic noise and the avoidance of transitions between differentiated states. Understanding the GRN components controlling these features is crucial. Our study thoroughly explored an extended version of the Toggle Switch model with auto-activation loops. This model represents cells evolving from common progenitors in one out of two fates (A or B, bistable regime) or, additionally, remaining in their progenitor state (C, tristable regime). Such a differentiation into populations with three distinct cell fates is observed during blastocyst formation in mammals, where inner cell mass cells can remain in that state or differentiate into epiblast cells or primitive endoderm. Systematic analysis revealed that the existence of a stable non-differentiated state significantly impacts the GRN's robustness against parametric variations and stochastic noise. This state reduces the sensitivity of cell populations to parameters controlling key gene expression asymmetry and prevents cells from making transitions after acquiring a new identity. Stochastic noise enhances robustness by decreasing sensitivity to initial expression levels and helping the system escape from the non-differentiated state to differentiated cell fates, making the differentiation more efficient.

An integrative phenotype-structured partial differential equation model for the population dynamics of epithelial-mesenchymal transition

Phenotypic heterogeneity along the epithelial-mesenchymal (E-M) axis contributes to cancer metastasis and drug resistance. Recent experimental efforts have collated detailed time-course data on the emergence and dynamics of E-M heterogeneity in a cell population. However, it remains unclear how different intra- and inter-cellular processes shape the dynamics of E-M heterogeneity. Here, using Cell Population Balance model, we capture the dynamics of cell density along E-M phenotypic axis resulting from interplay between-(a) intracellular regulatory interaction among biomolecules, (b) cell division and death and (c) stochastic cell-state transition. We find that while the existence of E-M heterogeneity depends on intracellular regulation, heterogeneity gets enhanced with stochastic cell-state transitions and diminished by growth rate differences. Further, resource competition among E-M cells can lead to both bi-phasic growth of the total population and/or bi-stability in the phenotypic composition. Overall, our model highlights complex interplay between cellular processes shaping dynamic patterns of E-M heterogeneity.

Gene regulatory network inference during cell fate decisions by perturbation strategies

With rapid advances in biological technology and computational approaches, inferring specific gene regulatory networks from data alone during cell fate decisions, including determining direct regulations and their intensities between biomolecules, remains one of the most significant challenges. In this study, we propose a general computational approach based on systematic perturbation, statistical, and differential analyses to infer network topologies and identify network differences during cell fate decisions. For each cell fate state, we first theoretically show how to calculate local response matrices based on perturbation data under systematic perturbation analysis, and we also derive the wild-type (WT) local response matrix for specific ordinary differential equations. To make the inferred network more accurate and eliminate the impact of perturbation degrees, the confidence interval (CI) of local response matrices under multiple perturbations is applied, and the redefined local response matrix is proposed in statistical analysis to determine network topologies across all cell fates. Then in differential analysis, we introduce the concept of relative local response matrix, which enables us to identify critical regulations governing each cell state and dominant cell states associated with specific regulations. The epithelial to mesenchymal transition (EMT) network is chosen as an illustrative example to verify the feasibility of the approach. Largely consistent with experimental observations, the differences of inferred networks at the three cell states can be quantitatively identified. The approach presented here can be also applied to infer other regulatory networks related to cell fate decisions.

Fibrotic extracellular matrix preferentially induces a partial Epithelial-Mesenchymal Transition phenotype in a 3-D agent based model of fibrosis

One of the main drivers of fibrotic diseases is epithelial-mesenchymal transition (EMT): a transdifferentiation process in which cells undergo a phenotypic change from an epithelial state to a pro-migratory state. The cytokine transforming growth factor-β1 (TGF-β1) has been previously shown to regulate EMT. TGF-β1 binds to fibronectin (FN) fibrils, which are the primary extracellular matrix (ECM) component in renal fibrosis. We have previously demonstrated experimentally that inhibition of FN fibrillogenesis and/or TGF-β1 tethering to FN inhibits EMT. However, these studies have only been conducted on 2-D cell monolayers, and the role of TGF-β1-FN tethering in 3-D cellular environments is not clear. As such, we sought to develop a 3-D computational model of epithelial spheroids that captured both EMT signaling dynamics and TGF-β1-FN tethering dynamics. We have incorporated the bi-stable EMT switch model developed by Tian et al. (2013) into a 3-D multicellular model to capture both temporal and spatial TGF-β1 signaling dynamics. We showed that the addition of increasing concentrations of exogeneous TGF-β1 led to faster EMT progression, indicated by increased expression of mesenchymal markers, decreased cell proliferation and increased migration. We then incorporated TGF-β1-FN fibril tethering by locally reducing the TGF-β1 diffusion coefficient as a function of EMT to simulate the reduced movement of TGF-β1 when tethered to FN fibrils during fibrosis. We showed that incorporation of TGF-β1 tethering to FN fibrils promoted a partial EMT state, independent of exogenous TGF-β1 concentration, indicating a mechanism by which fibrotic ECM can promote a partial EMT state.

Low dimensionality of phenotypic space as an emergent property of coordinated teams in biological regulatory networks

Cell-fate decisions involve coordinated genome-wide expression changes, typically leading to a limited number of phenotypes. Although often modeled as simple toggle switches, these rather simplistic representations often disregard the complexity of regulatory networks governing these changes. Here, we unravel design principles underlying complex cell decision-making networks in multiple contexts. We show that the emergent dynamics of these networks and corresponding transcriptomic data are consistently low-dimensional, as quantified by the variance explained by principal component 1 (PC1). This low dimensionality in phenotypic space arises from extensive feedback loops in these networks arranged to effectively enable the formation of two teams of mutually inhibiting nodes. We use team strength as a metric to quantify these feedback interactions and show its strong correlation with PC1 variance. Using artificial networks of varied topologies, we also establish the conditions for generating canalized cell-fate landscapes, offering insights into diverse binary cellular decision-making networks.

Design principles of regulatory networks underlying epithelial mesenchymal plasticity in cancer cells

Phenotypic plasticity is a hallmark of cancer and drives metastatic disease and drug resistance. The dynamics of epithelial mesenchymal plasticity is driven by complex interactions involving multiple feedback loops in underlying networks operating at multiple regulatory levels such as transcriptional and epigenetic. The past decade has witnessed a surge in systems level analysis of structural and dynamical traits of these networks. Here, we highlight the key insights elucidated from such efforts-a) multistability in gene regulatory networks and the co-existence of many hybrid phenotypes, thus enabling a landscape with multiple 'attractors', b) mutually antagonistic 'teams' of genes in these networks, shaping the rates of cell state transition in this landscape, and c) chromatin level changes that can alter the landscape, thus controlling reversibility of cell state transitions, allowing cellular memory in the context of epithelial mesenchymal plasticity in cancer cells. Such approaches, in close integration with high-throughput longitudinal data, have improved our understanding of the dynamics of cell state transitions implicated in tumor cell plasticity.

Modeling ncRNA-Mediated Circuits in Cell Fate Decision: From Systems Biology to Synthetic Biology

Noncoding RNAs (ncRNAs) play critical roles in essential cell fate decisions. However, the exact molecular mechanisms underlying ncRNA-mediated bistable switches remain elusive and controversial. In recent years, systematic mathematical and quantitative experimental analyses have made significant contributions to elucidating the molecular mechanisms of controlling ncRNA-mediated cell fate decision processes. In this chapter, we review and summarize the general framework of mathematical modeling of ncRNA in a pedagogical way and the application of this general framework to real biological processes. We discuss the emerging properties resulting from the reciprocal regulation between mRNA, miRNA, and competing endogenous mRNA (ceRNA). We also explore the efforts within the synthetic biology approach to understand the fundamental design principles underlying cell fate decisions. Both the positive feedback loops between ncRNAs and transcription factors and the emerging properties from the miRNA-mRNA reciprocal regulation enable bistable switches to direct cell fate decisions.

Endosomal pH is an evolutionarily conserved driver of phenotypic plasticity in colorectal cancer

Dysregulated pH is now recognised as a hallmark of cancer. Recent evidence has revealed that the endosomal pH regulator Na+/H+ exchanger NHE9 is upregulated in colorectal cancer to impose a pseudo-starvation state associated with invasion, highlighting an underexplored mechanistic link between adaptive endosomal reprogramming and malignant transformation. In this study, we use a model that quantitatively captures the dynamics of the core regulatory network governing epithelial mesenchymal plasticity. The model recapitulated NHE9-induced calcium signalling and the emergence of migratory phenotypes in colorectal cancer cells. Model predictions were compared with patient data and experimental results from RNA sequencing analysis of colorectal cancer cells with stable NHE9 expression. Mathematical analyses identified that tumours leverage elevated NHE9 levels to delay the transition of cells to a mesenchymal state and allow for metastatic progression. Ectopic expression of NHE9 is sufficient to induce loss of epithelial nature but does not fully couple with gain of mesenchymal state, resulting in a hybrid epithelial-mesenchymal population with increased aggressiveness and metastatic competence. Higher NHE9 expression is associated with cancer cell migration, and the effect appears to be independent of hypoxia status. Our data suggests that alterations in endosomal pH, an evolutionarily conserved starvation response, may be hijacked by colorectal cancer cells to drive phenotypic plasticity and invasion. We propose that cancer cells rewire their endosomal pH not only to meet the demands of rapid cell proliferation, but also to enable invasion, metastasis, and cell survival. Endosomal pH may be an attractive therapeutic target for halting tumour progression.

Transition paths across the EMT landscape are dictated by network logic

During development and cancer metastasis, cells transition reversibly from epithelial to mesenchymal via intermediate cell states during epithelial-mesenchymal transition (EMT). EMT is controlled by gene regulatory networks (GRNs) and can be described by a three-node GRN that permits tristable EMT landscapes. In this GRN, multiple inputs regulate the transcription factor ZEB that induces EMT. It is unknown how to choose the network logic for such regulation. Here we explore the effects of network logic on a tristable EMT network. We discover that the choice of additive vs multiplicative logic affects EMT phenotypes, leading to opposing predictions regarding the factors controlling EMT transition paths. We show that strong inhibition of miR-200 destabilizes the epithelial state and initiates EMT for multiplicative (AND) but not additive (OR) logic, suggesting that AND logic is in better agreement with experimental measurements of the effects of miR-200 regulation on EMT. Using experimental single-cell data, stochastic simulations, and perturbation analysis, we demonstrate how our results can be used to design experiments to infer the network logic of an EMT GRN in live cells. Our results explain how the manipulation of molecular interactions can stabilize or destabilize EMT hybrid states, of relevance during cancer progression and metastasis. More generally, we highlight the importance of the choice of network logic in GRN models in the presence of biological noise and multistability.

A Multicellular Mechanochemical Model to Investigate Tumor Microenvironment Remodeling and Pre-Metastatic Niche Formation

Introduction

Colorectal cancer (CRC) is a major cause of cancer related deaths in the United States, with CRC metastasis to the liver being a common occurrence. The development of an optimal metastatic environment is essential process prior to tumor metastasis. This process, called pre-metastatic niche (PMN) formation, involves activation of key resident liver cells, including fibroblast-like stellate cells and macrophages such as Kupffer cells. Tumor-mediated factors introduced to this environment transform resident cells that secrete additional growth factors and remodel the extracellular matrix (ECM), which is thought to promote tumor colonization and metastasis in the secondary environment.

Methods

To investigate the underlying mechanisms of these dynamics, we developed a multicellular computational model to characterize the spatiotemporal dynamics of the PMN formation in tissue. This modeling framework integrates intracellular and extracellular signaling, and traction and junctional forces into a Cellular Potts model, and represents multiple cell types with varying levels of cellular activation. We perform numerical experiments to investigate the role of key factors in PMN formation and tumor invasiveness, including growth factor concentration, timing of tumor arrival, relative composition of resident cells, and the size of invading tumor cluster.

Results

These parameter studies identified growth factor availability and ECM concentration in the environment as two of the key determinants of tumor invasiveness. We further predict that both the ECM concentration potential and growth factor sensitivity of the stellate cells are key drivers of the PMN formation and associated ECM concentration.

Conclusions

Overall, this modeling framework represents a significant step towards simulating cancer metastasis and investigating the role of key factors on PMN formation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-024-00831-0.

General relationship of local topologies, global dynamics, and bifurcation in cellular networks

Cellular networks realize their functions by integrating intricate information embedded within local structures such as regulatory paths and feedback loops. However, the precise mechanisms of how local topologies determine global network dynamics and induce bifurcations remain unidentified. A critical step in unraveling the integration is to identify the governing principles, which underlie the mechanisms of information flow. Here, we develop the cumulative linearized approximation (CLA) algorithm to address this issue. Based on perturbation analysis and network decomposition, we theoretically demonstrate how perturbations affect the equilibrium variations through the integration of all regulatory paths and how stability of the equilibria is determined by distinct feedback loops. Two illustrative examples, i.e., a three-variable bistable system and a more intricate epithelial-mesenchymal transition (EMT) network, are chosen to validate the feasibility of this approach. These results establish a solid foundation for understanding information flow across cellular networks, highlighting the critical roles of local topologies in determining global network dynamics and the emergence of bifurcations within these networks. This work introduces a novel framework for investigating the general relationship between local topologies and global dynamics of cellular networks under perturbations.

Energy Landscape Reveals the Underlying Mechanism of Cancer-Adipose Conversion in Gene Network Models

Cancer is a systemic heterogeneous disease involving complex molecular networks. Tumor formation involves an epithelial-mesenchymal transition (EMT), which promotes both metastasis and plasticity of cancer cells. Recent experiments have proposed that cancer cells can be transformed into adipocytes via a combination of drugs. However, the underlying mechanisms for how these drugs work, from a molecular network perspective, remain elusive. To reveal the mechanism of cancer-adipose conversion (CAC), this study adopts a systems biology approach by combing mathematical modeling and molecular experiments, based on underlying molecular regulatory networks. Four types of attractors are identified, corresponding to epithelial (E), mesenchymal (M), adipose (A) and partial/intermediate EMT (P) cell states on the CAC landscape. Landscape and transition path results illustrate that intermediate states play critical roles in the cancer to adipose transition. Through a landscape control approach, two new therapeutic strategies for drug combinations are identified, that promote CAC. These predictions are verified by molecular experiments in different cell lines. The combined computational and experimental approach provides a powerful tool to explore molecular mechanisms for cell fate transitions in cancer networks. The results reveal underlying mechanisms of intermediate cell states that govern the CAC, and identified new potential drug combinations to induce cancer adipogenesis.

Tipping points in epithelial-mesenchymal lineages from single-cell transcriptomics data

Understanding cell fate decision-making during complex biological processes is an open challenge that is now aided by high-resolution single-cell sequencing technologies. Specifically, it remains challenging to identify and characterize transition states corresponding to "tipping points" whereby cells commit to new cell states. Here, we present a computational method that takes advantage of single-cell transcriptomics data to infer the stability and gene regulatory networks (GRNs) along cell lineages. Our method uses the unspliced and spliced counts from single-cell RNA sequencing data and cell ordering along lineage trajectories to train an RNA splicing multivariate model, from which cell-state stability along the lineage is inferred based on spectral analysis of the model's Jacobian matrix. Moreover, the model infers the RNA cross-species interactions resulting in GRNs and their variation along the cell lineage. When applied to epithelial-mesenchymal transition in ovarian and lung cancer-derived cell lines, our model predicts a saddle-node transition between the epithelial and mesenchymal states passing through an unstable, intermediate cell state. Furthermore, we show that the underlying GRN controlling epithelial-mesenchymal transition rearranges during the transition, resulting in denser and less modular networks in the intermediate state. Overall, our method represents a flexible tool to study cell lineages with a combination of theory-driven modeling and single-cell transcriptomics data.

A Boolean model explains phenotypic plasticity changes underlying hepatic cancer stem cells emergence

In several carcinomas, including hepatocellular carcinoma, it has been demonstrated that cancer stem cells (CSCs) have enhanced invasiveness and therapy resistance compared to differentiated cancer cells. Mathematical-computational tools could be valuable for integrating experimental results and understanding the phenotypic plasticity mechanisms for CSCs emergence. Based on the literature review, we constructed a Boolean model that recovers eight stable states (attractors) corresponding to the gene expression profile of hepatocytes and mesenchymal cells in senescent, quiescent, proliferative, and stem-like states. The epigenetic landscape associated with the regulatory network was analyzed. We observed that the loss of p53, p16, RB, or the constitutive activation of β-catenin and YAP1 increases the robustness of the proliferative stem-like phenotypes. Additionally, we found that p53 inactivation facilitates the transition of proliferative hepatocytes into stem-like mesenchymal phenotype. Thus, phenotypic plasticity may be altered, and stem-like phenotypes related to CSCs may be easier to attain following the mutation acquisition.

Reconstruction of single-cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition

Exploring the complexity of the epithelial-to-mesenchymal transition (EMT) unveils a diversity of potential cell fates; however, the exact timing and mechanisms by which early cell states diverge into distinct EMT trajectories remain unclear. Studying these EMT trajectories through single-cell RNA sequencing is challenging due to the necessity of sacrificing cells for each measurement. In this study, we employed optimal-transport analysis to reconstruct the past trajectories of different cell fates during TGF-beta-induced EMT in the MCF10A cell line. Our analysis revealed three distinct trajectories leading to low EMT, partial EMT, and high EMT states. Cells along the partial EMT trajectory showed substantial variations in the EMT signature and exhibited pronounced stemness. Throughout this EMT trajectory, we observed a consistent downregulation of the EED and EZH2 genes. This finding was validated by recent inhibitor screens of EMT regulators and CRISPR screen studies. Moreover, we applied our analysis of early-phase differential gene expression to gene sets associated with stemness and proliferation, pinpointing ITGB4, LAMA3, and LAMB3 as genes differentially expressed in the initial stages of the partial versus high EMT trajectories. We also found that CENPF, CKS1B, and MKI67 showed significant upregulation in the high EMT trajectory. While the first group of genes aligns with findings from previous studies, our work uniquely pinpoints the precise timing of these upregulations. Finally, the identification of the latter group of genes sheds light on potential cell cycle targets for modulating EMT trajectories.

Phenotypic Plasticity and Cancer: A System Biology Perspective

Epithelial-to-mesenchymal transition (EMT) is a major axis of phenotypic plasticity not only in diseased conditions such as cancer metastasis and fibrosis but also during normal development and wound healing. Yet-another important axis of plasticity with metastatic implications includes the cancer stem cell (CSCs) and non-CSC transitions. However, in both processes, epithelial (E) and mesenchymal (M) phenotypes are not merely binary states. Cancer cells acquire a spectrum of phenotypes with traits, properties, and markers of both E and M phenotypes, giving rise to intermediary hybrid (E/M) phenotypes. E/M cells play an important role in tumor initiation, metastasis, and disease progression in multiple cancers. Furthermore, the hybrid phenotypes also play a major role in causing therapeutic resistance in cancer. Here, we discuss how a systems biology perspective on the problem, which is implicit in the 'Team Medicine' approach outlined in the theme of this Special Issue of The Journal of Clinical Medicine and includes an interdisciplinary team of experts, is more likely to shed new light on EMT in cancer and help us to identify novel therapeutics and strategies to target phenotypic plasticity in cancer.

Cell-state transitions and density-dependent interactions together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity

Cancer cell populations comprise phenotypes distributed among the epithelial-mesenchymal (E-M) spectrum. However, it remains unclear which population-level processes give rise to the observed experimental distribution and dynamical changes in E-M heterogeneity, including (1) differential growth, (2) cell-state switching, and (3) population density-dependent growth or state-transition rates. Here, we analyze the necessity of these three processes in explaining the dynamics of E-M population distributions as observed in PMC42-LA and HCC38 breast cancer cells. We find that, while cell-state transition is necessary to reproduce experimental observations of dynamical changes in E-M fractions, including density-dependent growth interactions (cooperation or suppression) better explains the data. Further, our models predict that treatment of HCC38 cells with transforming growth factor β (TGF-β) signaling and Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/3) inhibitors enhances the rate of mesenchymal-epithelial transition (MET) instead of lowering that of E-M transition (EMT). Overall, our study identifies the population-level processes shaping the dynamics of spontaneous E-M heterogeneity in breast cancer cells.

NF-κB/RelA signaling in secretoglobin progenitors mediates plasticity and MMP-induced barrier disruption in house dust mite-induced allergic asthma

The mechanisms how aeroallergens induce sensitization are incompletely understood. The house dust mite (HDM) Dermatophagoides pteronyssius (Der p) is a ubiquitous aeroallergen that represents a major cause of allergic rhinitis and asthma. Herein, we tested whether HDM-induced aeroallergen exposure sensitivity is caused by the innate-immune response in small airway epithelial cells. HDM exposure is a rapid activator of NF-κB/RelA in the Secretoglobin (Scgb1a1+) lineage associated with upregulation of NF-κB/RelA-dependent markers of epithelial plasticity. To determine the effect of epithelial NF-κB signaling, NF-κB was depleted in a tamoxifen (TMX)-inducible Scgb1a1-CreERTM mouse within a CL57B/L6 background. Corn oil or TMX-treated/RelA-depleted [RelA knockdown (KD)] mice were repetitively exposed to airway HDM challenges to induce airway hyperresponsiveness (AHR). Strikingly, we observed that HDM induces hallmarks of epithelial plasticity through upregulation of the mesenchymal core factors SNAI1 and ZEB1 and production of metalloproteinase (MMP)9 that are RelA-dependent. Downstream, HDM-induced mucous metaplasia, Th2 polarization, allergen sensitivity, and airway hyperreactivity were all reduced in the RelA-depleted mice. Mechanistically, HDM-induced functional and structural barrier disruption was dependent on RelA signaling and associated with active MMP secretion into the bronchoalveolar lavage fluid. To establish the role of MMP2/9 in barrier disruption, we observe that a small-molecule MMP inhibitor (SB-3CT) blocked HDM-induced barrier disruption and activation of plasticity in naïve wild-type (WT) mice. Loss of functional barrier was associated with MMP disruption of zona occludens (ZO)-1 containing adherens junctions. Overall, this data indicates that host innate signaling in the Scgb1a1+ progenitors is directly linked to epithelial plasticity, MMP9 secretion, and enhanced barrier permeability that allows allergen penetration, sensitization producing allergic asthma (AA) in vivo. We propose that maintenance of epithelial integrity may reduce allergic sensitization and AA.NEW & NOTEWORTHY Allergic asthma from house dust mite (HDM) allergy causes substantial morbidity. This study examines the dynamic changes in small airway epithelial cells in a mouse model of HDM exposure. Our findings indicate that NF-κB/RelA signaling mediates matrix metalloproteinase production, disrupting the epithelial barrier resulting in allergic sensitization. Our findings bring new insight into mechanisms for epithelial cell-state change in the allergen response, creating a potential therapeutic pathway for maintaining barrier function in asthma.

RNA regulatory mechanisms controlling TGF-β signaling and EMT in cancer

Epithelial-mesenchymal transition (EMT) is a major contributor to metastatic progression and is prominently regulated by TGF-β signalling. Both EMT and TGF-β pathway components are tightly controlled by non-coding RNAs - including microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) - that collectively have major impacts on gene expression and resulting cellular states. While miRNAs are the best characterised regulators of EMT and TGF-β signaling and the miR-200-ZEB1/2 feedback loop plays a central role, important functions for lncRNAs and circRNAs are also now emerging. This review will summarise our current understanding of the roles of non-coding RNAs in EMT and TGF-β signaling with a focus on their functions in cancer progression.

Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer

Breast cancer is one of the prevailing cancers globally, with a high mortality rate. Metastatic breast cancer (MBC) is an advanced stage of cancer, characterised by a highly nonlinear, heterogeneous process involving numerous singling pathways and regulatory interactions. Epithelial-mesenchymal transition (EMT) emerges as a key mechanism exploited by cancer cells. Transforming Growth Factor-β (TGFβ)-dependent signalling is attributed to promote EMT in advanced stages of breast cancer. A comprehensive regulatory map of TGFβ induced EMT was developed through an extensive literature survey. The network assembled comprises of 312 distinct species (proteins, genes, RNAs, complexes), and 426 reactions (state transitions, nuclear translocations, complex associations, and dissociations). The map was developed by following Systems Biology Graphical Notation (SBGN) using Cell Designer and made publicly available using MINERVA ( http://35.174.227.105:8080/minerva/?id=Metastatic_Breast_Cancer_1 ). While the complete molecular mechanism of MBC is still not known, the map captures the elaborate signalling interplay of TGFβ induced EMT-promoting MBC. Subsequently, the disease map assembled was translated into a Boolean model utilising CaSQ and analysed using Cell Collective. Simulations of these have captured the known experimental outcomes of TGFβ induced EMT in MBC. Hub regulators of the assembled map were identified, and their transcriptome-based analysis confirmed their role in cancer metastasis. Elaborate analysis of this map may help in gaining additional insights into the development and progression of metastatic breast cancer.

Decoding the principle of cell-fate determination for its reverse control

Understanding and manipulating cell fate determination is pivotal in biology. Cell fate is determined by intricate and nonlinear interactions among molecules, making mathematical model-based quantitative analysis indispensable for its elucidation. Nevertheless, obtaining the essential dynamic experimental data for model development has been a significant obstacle. However, recent advancements in large-scale omics data technology are providing the necessary foundation for developing such models. Based on accumulated experimental evidence, we can postulate that cell fate is governed by a limited number of core regulatory circuits. Following this concept, we present a conceptual control framework that leverages single-cell RNA-seq data for dynamic molecular regulatory network modeling, aiming to identify and manipulate core regulatory circuits and their master regulators to drive desired cellular state transitions. We illustrate the proposed framework by applying it to the reversion of lung cancer cell states, although it is more broadly applicable to understanding and controlling a wide range of cell-fate determination processes.

The role of alternative polyadenylation in epithelial-mesenchymal transition of non-small cell lung cancer

The metastatic non-small cell lung cancer (NSCLC) is one of the cancers with high incidence, poor survival, and limited treatment. Epithelial-mesenchymal transition (EMT) is the first step by which an early tumor converts to an invasive one. Studying the underlying mechanisms of EMT can help the understanding of cancer metastasis and improve the treatment. In this study, 1013 NSCLC patients and 123 NSCLC cell lines are deeply analyzed for the potential roles of alternative polyadenylation (APA) in the EMT process. A trend of shorter 3'-UTRs (three prime untranslated region) is discovered in the mesenchymal samples. The identification of EMT-related APA events highlights the proximal poly(A) selection of CARM1. It is a pathological biomarker of mesenchymal tumor and cancer metastasis through losing miRNA binding to upregulate the EMT inducer of CARM1 and releasing miRNAs to downregulate the EMT inhibitor of RBM47. The crucial role of this APA event in EMT also guides its effect on drug responses. The patients with shorter 3'-UTR of CARM1 are more benefit from chemotherapy drugs, especially cisplatin. A stratification of NSCLC patients based on this APA event is useful for chemotherapy design in future clinics.

Data- and theory-driven approaches for understanding paths of epithelial-mesenchymal transition

Reversible transitions between epithelial and mesenchymal cell states are a crucial form of epithelial plasticity for development and disease progression. Recent experimental data and mechanistic models showed multiple intermediate epithelial-mesenchymal transition (EMT) states as well as trajectories of EMT underpinned by complex gene regulatory networks. In this review, we summarize recent progress in quantifying EMT and characterizing EMT paths with computational methods and quantitative experiments including omics-level measurements. We provide perspectives on how these studies can help relating fundamental cell biology to physiological and pathological outcomes of EMT.

Combinatorial Cooperativity in miR200-Zeb Feedback Network can Control Epithelial-Mesenchymal Transition

Carcinomas often utilize epithelial-mesenchymal transition (EMT) programs for cancer progression and metastasis. Numerous studies report SNAIL-induced miR200/Zeb feedback circuit as crucial in regulating EMT by placing cancer cells in at least three phenotypic states, viz. epithelial (E), hybrid (h-E/M), mesenchymal (M), along the E-M phenotypic spectrum. However, a coherent molecular-level understanding of how such a tiny circuit controls carcinoma cell entrance into and residence in various states is lacking. Here, we use molecular binding data and mathematical modeling to report that the miR200/Zeb circuit can essentially utilize combinatorial cooperativity to control E-M phenotypic plasticity. We identify minimal combinatorial cooperativities that give rise to E, h-E/M, and M phenotypes. We show that disrupting a specific number of miR200 binding sites on Zeb as well as Zeb binding sites on miR200 can have phenotypic consequences-the circuit can dynamically switch between two (E, M) and three (E, h-E/M, M) phenotypes. Further, we report that in both SNAIL-induced and SNAIL knock-out miR200/Zeb circuits, cooperative transcriptional feedback on Zeb as well as Zeb translation inhibition due to miR200 are essential for the occurrence of intermediate h-E/M phenotype. Finally, we demonstrate that SNAIL can be dispensable for EMT, and in the absence of SNAIL, the transcriptional feedback can control cell state transition from E to h-E/M, to M state. Our results thus highlight molecular-level regulation of EMT in miR200/Zeb circuit and we expect these findings to be crucial to future efforts aiming to prevent EMT-facilitated dissemination of carcinomas.

Increased heterogeneity in expression of genes associated with cancer progression and drug resistance

Fluctuations in the number of regulatory molecules and differences in timings of molecular events can generate variation in gene expression among genetically identical cells in the same environmental condition. This variation, termed as expression noise, can create differences in metabolic state and cellular functions, leading to phenotypic heterogeneity. Expression noise and phenotypic heterogeneity have been recognized as important contributors to intra-tumor heterogeneity, and have been associated with cancer growth, progression, and therapy resistance. However, how expression noise changes with cancer progression in actual cancer patients has remained poorly explored. Such an analysis, through identification of genes with increasing expression noise, can provide valuable insights into generation of intra-tumor heterogeneity, and could have important implications for understanding immune-suppression, drug tolerance and therapy resistance. In this work, we performed a genome-wide identification of changes in gene expression noise with cancer progression using single-cell RNA-seq data of lung adenocarcinoma patients at different stages of cancer. We identified 37 genes in epithelial cells that showed an increasing noise trend with cancer progression, many of which were also associated with cancer growth, EMT and therapy resistance. We found that expression of several of these genes was positively associated with expression of mitochondrial genes, suggesting an important role of mitochondria in generation of heterogeneity. In addition, we uncovered substantial differences in sample-specific noise profiles which could have implications for personalized prognosis and treatment.

How important is EMT for cancer metastasis?

Epithelial-to-mesenchymal transition (EMT), a biological phenomenon of cellular plasticity initially reported in embryonic development, has been increasingly recognized for its importance in cancer progression and metastasis. Despite tremendous progress being made in the past 2 decades in our understanding of the molecular mechanism and functional importance of EMT in cancer, there are several mysteries around EMT that remain unresolved. In this Unsolved Mystery, we focus on the variety of EMT types in metastasis, cooperative and collective EMT behaviors, spatiotemporal characterization of EMT, and strategies of therapeutically targeting EMT. We also highlight new technical advances that will facilitate the efforts to elucidate the unsolved mysteries of EMT in metastasis.

Identifying critical regulatory interactions in cell fate decision and transition by systematic perturbation analysis

One of the most significant challenges in biology is to elucidate the roles of various regulatory interactions in cell fate decision and transition. However, it remains to be fully clarified how they cooperate and determine fate transition. Here, a general framework based on statistical analysis and bifurcation theory is proposed to identify crucial regulatory interactions and how they play decisive roles in fate transition. More exactly, specific feedback loops determine occurrence of bifurcations by which cell fate transition can be realized. While regulatory interactions in the feedback loops determine the direction of transition. In addition, two-parameter bifurcation analysis further provides detailed understanding of how the fate transition based on statistical analysis occurs. Statistical analysis can also be used to reveal synergistic combinatorial perturbations by which fate transition can be more efficiently realized. The integrative analysis approach can be used to identify critical regulatory interactions in cell fate transition and reveal how specific cell fate transition occurs. To verify feasibility of the approach, the epithelial to mesenchymal transition (EMT) network is chosen as an illustrative example. In agreement with experimental observations, the approach reveals some critical regulatory interactions and underlying mechanisms in cell fate determination and transitions between three states. The approach can also be applied to analyze other regulatory networks related to cell fate decision and transition.

Expression profiles of lncRNAs, miRNAs, and mRNAs and interaction analysis indicate their potential involvement during testicular fusion in Spodoptera litura

Testicular fusion of Spodoptera litura occures during metamorphosis, which benefits sperms development. Previous research identified involvement of ECM-integrin interaction pathways, MMPs in testicular fusion, but the regulatory mechanism remains unclear. RNA-seq was performed to analyze long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in testes, aiming to uncover potential regulatory mechanisms of testicular fusion. 2150 lncRNAs, 2742 targeted mRNAs, and 347 miRNAs were identified in testes at three different developmental stages. Up-regulated DElncRNAs and DEmRNAs, as well as down-regulated DEmiRNAs, were observed during testicular fusion, while the opposite expression pattern was observed after fusion. Enrichment analysis of DEmRNAs revealed that cAMP signal pathway, ECM remodeling enzymes, ECM-integrin interaction pathways, and cell adhesion molecules were potentially associated with testicular fusion. The identified DElncRNA-DEmiRNA-DEmRNA regulatory network related to cAMP signal pathway, ECM remodeling enzymes suggests their roles during testicular fusion. Our research will provide new targets for studying the mechanism of testicular fusion.

Subconfluent ARPE-19 Cells Display Mesenchymal Cell-State Characteristics and Behave like Fibroblasts, Rather Than Epithelial Cells, in Experimental HCMV Infection Studies

Human cytomegalovirus (HCMV) has a broad cellular tropism and epithelial cells are important physiological targets during infection. The retinal pigment epithelial cell line ARPE-19 has been used to model HCMV infection in epithelial cells for decades and remains a commonly used cell type for studying viral entry, replication, and the cellular response to infection. We previously found that ARPE-19 cells, despite being derived from an epithelial cell explant, express extremely low levels of canonical epithelial proteins, such as E-cadherin and EpCAM. Here, we perform comparative studies of ARPE-19 and additional epithelial cell lines with strong epithelial characteristics. We find that ARPE-19 cells cultured under subconfluent conditions resemble mesenchymal fibroblasts, rather than epithelial cells; this is consistent with previous studies showing that ARPE-19 cultures require extended periods of high confluency culture to maintain epithelial characteristics. By reanalyzing public gene expression data and using machine learning, we find evidence that ARPE-19 cultures maintained across many labs exhibit mesenchymal characteristics and that the majority of studies employing ARPE-19 use them in a mesenchymal state. Lastly, by performing experimental HCMV infections across mesenchymal and epithelial cell lines, we find that ARPE-19 cells behave like mesenchymal fibroblasts, producing logarithmic yields of cell-free infectious progeny, while cell lines with strong epithelial character exhibit an atypical infectious cycle and naturally restrict the production of cell-free progeny. Our work highlights important characteristics of the ARPE-19 cell line and suggests that subconfluent ARPE-19 cells may not be optimal for modeling epithelial infection with HCMV or other human viruses. It also suggests that HCMV biosynthesis and/or spread may occur quite differently in epithelial cells compared to mesenchymal cells. These differences could contribute to viral persistence or pathogenesis in epithelial tissues.

Morphological and cytoskeleton changes in cells after EMT

Epithelial cells undergoing EMT experience significant alterations at transcriptional and morphological levels. However, changes in the cytoskeleton, especially cytoskeleton dynamics are poorly described. Addressing the question we induced EMT in three cell lines (MCF-7, HaCaT and A-549) and analyzed morphological and cytoskeletal changes there using immunostaining and life cell imaging of cells transfected with microtubule and focal adhesion markers. In all studied cell lines, cell area after EMT increased, MCF-7 and A-549 cells became elongated, while HaCaT cells kept the aspect ratio the same. We next analyzed three components of the cytoskeleton: microtubules, stress fibers and focal adhesions. The following changes were observed after EMT in cultured cells: (i) Organization of microtubules becomes more radial; and the growth rate of microtubule plus ends was accelerated; (ii) Actin stress fibers become co-aligned forming the longitudinal cell axis; and (iii) Focal adhesions had decreased area in all cancer cell lines studied and became more numerous in HaCaT cells. We conclude that among dynamic components of the cytoskeleton, the most significant changes during EMT happen in the regulation of microtubules.

Altered Phenotypes of Breast Epithelial × Breast Cancer Hybrids after ZEB1 Knock-Out

ZEB1 plays a pivotal role in epithelial-to-mesenchymal transition (EMT), (cancer) cell stemness and cancer therapy resistance. The M13HS tumor hybrids, which were derived from spontaneous fusion events between the M13SV1-EGFP-Neo breast epithelial cells and HS578T-Hyg breast cancer cells, express ZEB1 and exhibit prospective cancer stem cell properties. To explore a possible correlation between the ZEB1 and stemness/ EMT-related properties in M13HS tumor hybrids, ZEB1 was knocked-out by CRISPR/Cas9. Colony formation, mammosphere formation, cell migration, invasion assays, flow cytometry and Western blot analyses were performed for the characterization of ZEB1 knock-out cells. The ZEB1 knock-out in M13HS tumor cells was not correlated with the down-regulation of the EMT-related markers N-CADHERIN (CDH2) and VIMENTIN and up-regulation of miR-200c-3p. Nonetheless, both the colony formation and mammosphere formation capacities of the M13HS ZEB1 knock-out cells were markedly reduced. Interestingly, the M13HS-2 ZEB1-KO cells harbored a markedly higher fraction of ALDH1-positive cells. The Transwell/ Boyden chamber migration assay data indicated a reduced migratory activity of the M13HS ZEB1-knock-out tumor hybrids, whereas in scratch/ wound-healing assays only the M13SH-8 ZEB1-knock-out cells possessed a reduced locomotory activity. Similarly, only the M13HS-8 ZEB1-knock-out tumor hybrids showed a reduced invasion capacity. Although the ZEB1 knock-out resulted in only moderate phenotypic changes, our data support the role of ZEB1 in EMT and stemness.

Identifying key factors in cell fate decisions by machine learning interpretable strategies

Cell fate decisions and transitions are common in almost all developmental processes. Therefore, it is important to identify the decision-making mechanisms and important individual molecules behind the fate decision processes. In this paper, we propose an interpretable strategy based on systematic perturbation, unsupervised hierarchical cluster analysis (HCA), machine learning (ML), and Shapley additive explanation (SHAP) analysis for inferring the contribution and importance of individual molecules in cell fate decision and transition processes. In order to verify feasibility of the approach, we apply it to the core epithelial to mesenchymal transition (EMT)-metastasis network. The key factors identified in EMT-metastasis are consistent with relevant experimental observations. The approach presented here can be applied to other biological networks to identify important factors related to cell fate decisions and transitions.

Effects of microRNA-mediated negative feedback on gene expression noise

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression post-transcriptionally in eukaryotes by binding with target mRNAs and preventing translation. miRNA-mediated feedback motifs are ubiquitous in various genetic networks that control cellular decision making. A key question is how such a feedback mechanism may affect gene expression noise. To answer this, we have developed a mathematical model to study the effects of a miRNA-dependent negative-feedback loop on mean expression and noise in target mRNAs. Combining analytics and simulations, we show the existence of an expression threshold demarcating repressed and expressed regimes in agreement with earlier studies. The steady-state mRNA distributions are bimodal near the threshold, where copy numbers of mRNAs and miRNAs exhibit enhanced anticorrelated fluctuations. Moreover, variation of negative-feedback strength shifts the threshold locations and modulates the noise profiles. Notably, the miRNA-mRNA binding affinity and feedback strength collectively shape the bimodality. We also compare our model with a direct auto-repression motif, where a gene produces its own repressor. Auto-repression fails to produce bimodal mRNA distributions as found in miRNA-based indirect repression, suggesting the crucial role of miRNAs in creating phenotypic diversity. Together, we demonstrate how miRNA-dependent negative feedback modifies the expression threshold and leads to a broader parameter regime of bimodality compared to the no-feedback case.

Cusp bifurcation in a metastatic regulatory network

Understanding the potential for cancers to metastasize is still relatively unknown. While many predictive methods may use deep learning or stochastic processes, we highlight a long standing mathematical concept that may be useful for modeling metastatic breast cancer systems. Ordinary differential equations (ODEs) can model cell state transitions by considering the pertinent environmental variables as well as the paths systems take over time. Bifurcation theory is a branch of dynamical systems which studies changes in the behavior of an ODE system while one or more parameters are varied. Many studies have applied concepts in one-parameter bifurcation theory to model biological network dynamics, and cell division. However, studies of two-parameter bifurcations are much more rare. Two-parameter bifurcations have not been studied in metastatic systems. Here we show how a specific two-parameter bifurcation phenomenon called a cusp bifurcation separates two qualitatively different metastatic cell state transitions modalities and propose a new perspective on defining such transitions based on mathematical theory. We hope the observations and verification methods detailed here may help in the understanding of metastatic potential from a basic biological perspective and in clinical settings.

Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.

Dynamics, statistics, and task allocation of foraging ants

Ant foraging is one of the most fascinating examples of cooperative behavior observed in nature. It is well studied from an entomology viewpoint, but there is currently a lack of mathematical synthesis of this phenomenon. We address this by constructing an ant foraging model that incorporates simple behavioral rules within three task groups of the ant colony during foraging (foragers, transporters, and followers), pheromone trails, and memory effects. The motion of an ant is modeled as a discrete correlated random walk, with a characteristic zigzag path that is congruent with experimental data. We simulate the foraging cycle, which consists of ants searching for food, transporting food, and depositing chemical trails to recruit and orient more ants (en masse) to the food source. This allows us to gain insights into the basic mechanism of the cooperative interactions between ants and the dynamical division of labor within an ant colony during foraging to achieve optimal efficiency. We observe a disorder-order phase transition from the start to the end of a foraging process, signaling collective motion at the population level. Finally, we present a set of time delay ODEs that corroborates with numerical simulations.

Understanding and leveraging phenotypic plasticity during metastasis formation

Cancer metastasis is the process of detrimental systemic spread and the primary cause of cancer-related fatalities. Successful metastasis formation requires tumor cells to be proliferative and invasive; however, cells cannot be effective at both tasks simultaneously. Tumor cells compensate for this trade-off by changing their phenotype during metastasis formation through phenotypic plasticity. Given the changing selection pressures and competitive interactions that tumor cells face, it is poorly understood how plasticity shapes the process of metastasis formation. Here, we develop an ecology-inspired mathematical model with phenotypic plasticity and resource competition between phenotypes to address this knowledge gap. We find that phenotypically plastic tumor cell populations attain a stable phenotype equilibrium that maintains tumor cell heterogeneity. Considering treatment types inspired by chemo- and immunotherapy, we highlight that plasticity can protect tumors against interventions. Turning this strength into a weakness, we corroborate current clinical practices to use plasticity as a target for adjuvant therapy. We present a parsimonious view of tumor plasticity-driven metastasis that is quantitative and experimentally testable, and thus potentially improving the mechanistic understanding of metastasis at the cell population level, and its treatment consequences.

A unique cell population expressing the Epithelial-Mesenchymal Transition-transcription factor Snail moderates microglial and astrocyte injury responses

Insults to the central nervous system (CNS) elicit common glial responses including microglial activation evidenced by functional, morphological, and phenotypic changes, as well as astrocyte reactions including hypertrophy, altered process orientation, and changes in gene expression and function. However, the cellular and molecular mechanisms that initiate and modulate such glial response are less well-defined. Here we show that an adult cortical lesion generates a population of ultrastructurally unique microglial-like cells that express Epithelial-Mesenchymal Transcription factors including Snail. Knockdown of Snail with antisense oligonucleotides results in a postinjury increase in activated microglial cells, elevation in astrocyte reactivity with increased expression of C3 and phagocytosis, disruption of astrocyte junctions and neurovascular structure, increases in neuronal cell death, and reduction in cortical synapses. These changes were associated with alterations in pro-inflammatory cytokine expression. By contrast, overexpression of Snail through microglia-targeted an adeno-associated virus (AAV) improved many of the injury characteristics. Together, our results suggest that the coordination of glial responses to CNS injury is partly mediated by epithelial-mesenchymal transition-factors (EMT-Fsl).

The epithelial-mesenchymal plasticity landscape: principles of design and mechanisms of regulation

Epithelial-mesenchymal plasticity (EMP) enables cells to interconvert between several states across the epithelial-mesenchymal landscape, thereby acquiring hybrid epithelial/mesenchymal phenotypic features. This plasticity is crucial for embryonic development and wound healing, but also underlies the acquisition of several malignant traits during cancer progression. Recent research using systems biology and single-cell profiling methods has provided novel insights into the main forces that shape EMP, which include the microenvironment, lineage specification and cell identity, and the genome. Additionally, key roles have emerged for hysteresis (cell memory) and cellular noise, which can drive stochastic transitions between cell states. Here, we review these forces and the distinct but interwoven layers of regulatory control that stabilize EMP states or facilitate epithelial-mesenchymal transitions (EMTs) and discuss the therapeutic potential of manipulating the EMP landscape.

Theoretical and computational tools to model multistable gene regulatory networks

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics.

Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity

The Epithelial-to-Mesenchymal Transition (EMT) is a hallmark of cancer metastasis and morbidity. EMT is a non-binary process, and cells can be stably arrested en route to EMT in an intermediate hybrid state associated with enhanced tumor aggressiveness and worse patient outcomes. Understanding EMT progression in detail will provide fundamental insights into the mechanisms underlying metastasis. Despite increasingly available single-cell RNA sequencing (scRNA-seq) data that enable in-depth analyses of EMT at the single-cell resolution, current inferential approaches are limited to bulk microarray data. There is thus a great need for computational frameworks to systematically infer and predict the timing and distribution of EMT-related states at single-cell resolution. Here, we develop a computational framework for reliable inference and prediction of EMT-related trajectories from scRNA-seq data. Our model can be utilized across a variety of applications to predict the timing and distribution of EMT from single-cell sequencing data.

ZHX2 deficiency enriches hybrid MET cells through regulating E-cadherin expression

Growing evidence indicates that the epithelial to mesenchymal (E/M) hybrid state plays a key role in tumorigenesis. Importantly, a hybrid mesenchymal to epithelial transition (MET) state in which individual cells express both epithelial and mesenchymal markers was recently identified in vivo, further strengthening the bonds between the hybrid EMT state and cancer progression. However, the role and the molecular mechanisms by which the hybrid MET state is maintained in triple-negative breast cancer cells (TNBC) remain elusive. Here, we find that loss of ZHX2 expression results in the hybrid MET phenotype in mesenchymal TNBC cells. Mechanistically, through directly binding to the CDH1 promoter, depletion of ZHX2 specifically reactivates expression of CDH1 encoding E-cadherin, an epithelial marker that is crucial for maintaining epithelial phenotype. Functionally, loss of ZHX2 expression enriches the hybrid MET cells and inhibits the migration and dissemination of TNBC cells or organoids, which could be reversed by restoration of E-cadherin. Moreover, depletion of ZHX2 suppresses lung metastasis in preclinical models of TNBC. In patients with TNBC, ZHX2 expression was amplified and negatively correlated with the expression of E-cadherin. These findings suggest that loss of ZHX2 promotes the hybrid MET state to impair TNBC progression.

Genomic targets of the IRE1-XBP1s pathway in mediating metabolic adaptation in epithelial plasticity

Epithelial mesenchymal plasticity (EMP) is a complex cellular reprogramming event that plays a major role in tissue homeostasis. Recently we observed the unfolded protein response (UPR) triggers EMP through the inositol-requiring protein 1 (IRE1α)-X-box-binding protein 1 spliced (XBP1s) axis, enhancing glucose shunting to protein N glycosylation. To better understand the genomic targets of XBP1s, we identified its genomic targets using Cleavage Under Targets and Release Using Nuclease (CUT&RUN) of a FLAG-epitope tagged XBP1s in RSV infection. CUT&RUN identified 7086 binding sites in chromatin that were enriched in AP-1 motifs and GC-sequences. Of these binding sites, XBP1s peaks mapped to 4827 genes controlling Rho-GTPase signaling, N-linked glycosylation and ER-Golgi transport. Strikingly, XBP1s peaks were within 1 kb of transcription start sites of 2119 promoters. In addition to binding core mesenchymal transcription factors SNAI1 and ZEB1, we observed that hexosamine biosynthetic pathway (HBP) enzymes were induced and contained proximal XBP1s peaks. We demonstrate that IRE1α -XBP1s signaling is necessary and sufficient to activate core enzymes by recruiting elongation-competent phospho-Ser2 CTD modified RNA Pol II. We conclude that the IRE1α-XBP1s pathway coordinately regulates mesenchymal transcription factors and hexosamine biosynthesis in EMP by a mechanism involving recruitment of activated pSer2-Pol II to GC-rich promoters.

Dynamics of Epithelial-Mesenchymal Plasticity: What Have Single-Cell Investigations Elucidated So Far?

Epithelial-mesenchymal plasticity (EMP) is a key driver of cancer metastasis and therapeutic resistance, through which cancer cells can reversibly and dynamically alter their molecular and functional traits along the epithelial-mesenchymal spectrum. While cells in the epithelial phenotype are usually tightly adherent, less metastatic, and drug-sensitive, those in the hybrid epithelial/mesenchymal and/or mesenchymal state are more invasive, migratory, drug-resistant, and immune-evasive. Single-cell studies have emerged as a powerful tool in gaining new insights into the dynamics of EMP across various cancer types. Here, we review many recent studies that employ single-cell analysis techniques to better understand the dynamics of EMP in cancer both in vitro and in vivo. These single-cell studies have underlined the plurality of trajectories cells can traverse during EMP and the consequent heterogeneity of hybrid epithelial/mesenchymal phenotypes seen at both preclinical and clinical levels. They also demonstrate how diverse EMP trajectories may exhibit hysteretic behavior and how the rate of such cell-state transitions depends on the genetic/epigenetic background of recipient cells, as well as the dose and/or duration of EMP-inducing growth factors. Finally, we discuss the relationship between EMP and patient survival across many cancer types. We also present a next set of questions related to EMP that could benefit much from single-cell observations and pave the way to better tackle phenotypic switching and heterogeneity in clinic.

Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.