Nonmodular oscillator and switch based on RNA decay drive regeneration of multimodal gene expression

Elucidation of how the Mir-23-27-24 cluster regulates development and aging

MicroRNAs (miRNAs) are pivotal regulators of gene expression and are involved in biological processes spanning from early developmental stages to the intricate process of aging. Extensive research has underscored the fundamental role of miRNAs in orchestrating eukaryotic development, with disruptions in miRNA biogenesis resulting in early lethality. Moreover, perturbations in miRNA function have been implicated in the aging process, particularly in model organisms such as nematodes and flies. miRNAs tend to be clustered in vertebrate genomes, finely modulating an array of biological pathways through clustering within a single transcript. Although extensive research of their developmental roles has been conducted, the potential implications of miRNA clusters in regulating aging remain largely unclear. In this review, we use the Mir-23-27-24 cluster as a paradigm, shedding light on the nuanced physiological functions of miRNA clusters during embryonic development and exploring their potential involvement in the aging process. Moreover, we advocate further research into the intricate interplay among miRNA clusters, particularly the Mir-23-27-24 cluster, in shaping the regulatory landscape of aging.

Enhancing cell-based therapies with synthetic gene circuits responsive to molecular stimuli

Synthetic biology aims to contribute to the development of next-generation patient-specific cell-based therapies for chronic diseases especially through the construction of sophisticated synthetic gene switches to enhance the safety and spatiotemporal controllability of engineered cells. Indeed, switches that sense and process specific cues, which may be either externally administered triggers or endogenous disease-associated molecules, have emerged as powerful tools for programming and fine-tuning therapeutic outputs. Living engineered cells, often referred to as designer cells, incorporating such switches are delivered to patients either as encapsulated cell implants or by infusion, as in the case of the clinically approved CAR-T cell therapies. Here, we review recent developments in synthetic gene switches responsive to molecular stimuli, spanning regulatory mechanisms acting at the transcriptional, translational, and posttranslational levels. We also discuss current challenges facing clinical translation of cell-based therapies employing these devices.

The art of modeling gene regulatory circuits

The amazing complexity of gene regulatory circuits, and biological systems in general, makes mathematical modeling an essential tool to frame and develop our understanding of their properties. Here, we present some fundamental considerations to develop and analyze a model of a gene regulatory circuit of interest, either representing a natural, synthetic, or theoretical system. A mathematical model allows us to effectively evaluate the logical implications of our hypotheses. Using our models to systematically perform in silico experiments, we can then propose specific follow-up assessments of the biological system as well as to reformulate the original assumptions, enriching both our knowledge and our understanding of the system. We want to invite the community working on different aspects of gene regulatory circuits to explore the power and benefits of mathematical modeling in their system.

Cancer spreading patterns based on epithelial-mesenchymal plasticity

Introduction: Metastasis is a major cause of cancer-related deaths, underscoring the necessity to discern the rules and patterns of cancer cell spreading. Epithelial-mesenchymal plasticity contributes to cancer aggressiveness and metastasis. Despite establishing key determinants of cancer aggressiveness and metastatic ability, a comprehensive understanding of the underlying mechanism is unknown. We aimed to propose a classification system for cancer cells based on epithelial-mesenchymal plasticity, focusing on hysteresis of the epithelial-mesenchymal transition and the hybrid epithelial/mesenchymal phenotype. Methods: We extensively reviewed the concept of epithelial-mesenchymal plasticity, specifically considering the hysteresis of the epithelial-mesenchymal transition and the hybrid epithelial/mesenchymal phenotype. Results: In this review and hypothesis article, based on epithelial-mesenchymal plasticity, especially the hysteresis of epithelial-mesenchymal transition and the hybrid epithelial/mesenchymal phenotype, we proposed a classification of cancer cells, indicating that cancer cells with epithelial-mesenchymal plasticity potential could be classified into four types: irreversible hysteresis, weak hysteresis, strong hysteresis, and hybrid epithelial/mesenchymal phenotype. These four types of cancer cells had varied biology, spreading features, and prognoses. Discussion: Our results highlight that the proposed classification system offers insights into the diverse behaviors of cancer cells, providing implications for cancer aggressiveness and metastasis.

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.

Comprehensive Parameter Space Mapping of Cell Cycle Dynamics under Network Perturbations

Studies of quantitative systems and synthetic biology have extensively utilized models to interpret data, make predictions, and guide experimental designs. However, models often simplify complex biological systems and lack experimentally validated parameters, making their reliability in perturbed systems unclear. Here, we developed a droplet-based synthetic cell system to continuously tune parameters at the single-cell level in multiple dimensions with full dynamic ranges, providing an experimental framework for global parameter space scans. We systematically perturbed a cell-cycle oscillator centered on cyclin-dependent kinase (Cdk1), enabling comprehensive mapping of period landscapes in response to network perturbations. The data allowed us to challenge existing models and refine a new model that matches the observed response. Our analysis demonstrated that Cdk1 positive feedback inhibition restricts the cell cycle frequency range, confirming model predictions; furthermore, it revealed new cellular responses to the inhibition of the Cdk1-counteracting phosphatase PP2A: monomodal or bimodal distributions across varying inhibition levels, underscoring the complex nature of cell cycle regulation that can be explained by our model. This comprehensive perturbation platform may be generalizable to exploring other complex dynamic systems.

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.

The human antigen R as an actionable super-hub within the network of cancer cell persistency and plasticity

In this perspective article, a clinically inspired phenotype-driven experimental approach is put forward to address the challenge of the adaptive response of solid cancers to small-molecule targeted therapies. A list of conditions is derived, including an experimental quantitative assessment of cell plasticity and an information theory-based detection of in vivo dependencies, for the discovery of post-transcriptional druggable mechanisms capable of preventing at multiple levels the emergence of plastic dedifferentiated slow-proliferating cells. The approach is illustrated by the author's own work in the example case of the adaptive response of BRAFV600-melanoma to BRAF inhibition. A bench-to-bedside and back to bench effort leads to a therapeutic strategy in which the inhibition of the baseline activity of the interferon-γ-activated inhibitor of translation (GAIT) complex, incriminated in the expression insufficiency of the RNA-binding protein HuR in a minority of cells, results in the suppression of the plastic, intermittently slow-proliferating cells involved in the adaptive response. A similar approach is recommended for the validation of other classes of mechanisms that we seek to modulate to overcome this complex challenge of modern cancer therapy.

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.

Involvement of Epithelial-Mesenchymal Transition Genes in Small Cell Lung Cancer Phenotypic Plasticity

Small cell lung cancer (SCLC) is an aggressive cancer recalcitrant to treatment, arising predominantly from epithelial pulmonary neuroendocrine (NE) cells. Intratumor heterogeneity plays critical roles in SCLC disease progression, metastasis, and treatment resistance. At least five transcriptional SCLC NE and non-NE cell subtypes were recently defined by gene expression signatures. Transition from NE to non-NE cell states and cooperation between subtypes within a tumor likely contribute to SCLC progression by mechanisms of adaptation to perturbations. Therefore, gene regulatory programs distinguishing SCLC subtypes or promoting transitions are of great interest. Here, we systematically analyze the relationship between SCLC NE/non-NE transition and epithelial to mesenchymal transition (EMT)-a well-studied cellular process contributing to cancer invasiveness and resistance-using multiple transcriptome datasets from SCLC mouse tumor models, human cancer cell lines, and tumor samples. The NE SCLC-A2 subtype maps to the epithelial state. In contrast, SCLC-A and SCLC-N (NE) map to a partial mesenchymal state (M1) that is distinct from the non-NE, partial mesenchymal state (M2). The correspondence between SCLC subtypes and the EMT program paves the way for further work to understand gene regulatory mechanisms of SCLC tumor plasticity with applicability to other cancer types.

A quantitative evaluation of topological motifs and their coupling in gene circuit state distributions

One of the major challenges in biology is to understand how gene interactions collaborate to determine overall functions of biological systems. Here, we present a new computational framework that enables systematic, high-throughput, and quantitative evaluation of how small transcriptional regulatory circuit motifs, and their coupling, contribute to functions of a dynamical biological system. We illustrate how this approach can be applied to identify four-node gene circuits, circuit motifs, and motif coupling responsible for various gene expression state distributions, including those derived from single-cell RNA sequencing data. We also identify seven major classes of four-node circuits from clustering analysis of state distributions. The method is applied to establish phenomenological models of gene circuits driving human neuron differentiation, revealing important biologically relevant regulatory interactions. Our study will shed light on a better understanding of gene regulatory mechanisms in creating and maintaining cellular states.

Using Single-Cell RNA Sequencing and MicroRNA Targeting Data to Improve Colorectal Cancer Survival Prediction

Colorectal cancer has proven to be difficult to treat as it is the second leading cause of cancer death for both men and women worldwide. Recent work has shown the importance of microRNA (miRNA) in the progression and metastasis of colorectal cancer. Here, we develop a metric based on miRNA-gene target interactions, previously validated to be associated with colorectal cancer. We use this metric with a regularized Cox model to produce a small set of top-performing genes related to colon cancer. We show that using the miRNA metric and a Cox model led to a meaningful improvement in colon cancer survival prediction and correct patient risk stratification. We show that our approach outperforms existing methods and that the top genes identified by our process are implicated in NOTCH3 signaling and general metabolism pathways, which are essential to colon cancer progression.

Epigenetic memory acquired during long-term EMT induction governs the recovery to the epithelial state

Epithelial-mesenchymal transition (EMT) and its reverse mesenchymal-epithelial transition (MET) are critical during embryonic development, wound healing and cancer metastasis. While phenotypic changes during short-term EMT induction are reversible, long-term EMT induction has been often associated with irreversibility. Here, we show that phenotypic changes seen in MCF10A cells upon long-term EMT induction by TGFβ need not be irreversible, but have relatively longer time scales of reversibility than those seen in short-term induction. Next, using a phenomenological mathematical model to account for the chromatin-mediated epigenetic silencing of the miR-200 family by ZEB family, we highlight how the epigenetic memory gained during long-term EMT induction can slow the recovery to the epithelial state post-TGFβ withdrawal. Our results suggest that epigenetic modifiers can govern the extent and time scale of EMT reversibility and advise caution against labelling phenotypic changes seen in long-term EMT induction as 'irreversible'.

Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics

Gene expression in mammalian cells is highly variable and episodic, resulting in a series of discontinuous bursts of mRNAs. A challenge is to understand how static promoter architecture and dynamic feedback regulations dictate bursting on a genome-wide scale. Although single-cell RNA sequencing (scRNA-seq) provides an opportunity to address this challenge, effective analytical methods are scarce. We developed an interpretable and scalable inference framework, which combined experimental data with a mechanistic model to infer transcriptional burst kinetics (sizes and frequencies) and feedback regulations. Applying this framework to scRNA-seq data generated from embryonic mouse fibroblast cells, we found Simpson's paradoxes, i.e. genome-wide burst kinetics exhibit different characteristics in two cases without and with distinguishing feedback regulations. We also showed that feedbacks differently modulate burst frequencies and sizes and conceal the effects of transcription start site distributions on burst kinetics. Notably, only in the presence of positive feedback, TATA genes are expressed with high burst frequencies and enhancer-promoter interactions mainly modulate burst frequencies. The developed inference method provided a flexible and efficient way to investigate transcriptional burst kinetics and the obtained results would be helpful for understanding cell development and fate decision.

Cooperative RNA degradation stabilizes intermediate epithelial-mesenchymal states and supports a phenotypic continuum

Multiple intermediate epithelial-mesenchymal transition (EMT) states reflecting hybrid epithelial and mesenchymal phenotypes were observed in physiological and pathological conditions. Previous theoretical models explaining multiple EMT states rely on regulatory loops involving transcriptional feedback, which produce three or four attractors. This is incompatible with the observed continuum-like EMT spectrum. Here, we used mass-action-based models to describe post-transcriptional regulations, finding that cooperative RNA degradation via multiple microRNA binding sites can generate four-attractor systems without transcriptional feedback. Furthermore, the newly identified intermediates-enabling circuits are common in the EMT regulatory network, and they can synergize with transcriptional feedback to support phenotypic continuum. Finally, our model predicted a role of miR-101 in multistate EMT, and we identified evidence from single-cell RNA-sequencing data that support the prediction. Our work reveals a previously unknown role of cooperative RNA degradation and microRNAs in EMT, providing a framework that can bridge the gap between mechanistic models and single-cell experiments.

CELLoGeNe - An energy landscape framework for logical networks controlling cell decisions

Experimental and computational efforts are constantly made to elucidate mechanisms controlling cell fate decisions during development and reprogramming. One powerful computational method is to consider cell commitment and reprogramming as movements in an energy landscape. Here, we develop Computation of Energy Landscapes of Logical Gene Networks (CELLoGeNe), which maps Boolean implementation of gene regulatory networks (GRNs) into energy landscapes. CELLoGeNe removes inadvertent symmetries in the energy landscapes normally arising from standard Boolean operators. Furthermore, CELLoGeNe provides tools to visualize and stochastically analyze the shapes of multi-dimensional energy landscapes corresponding to epigenetic landscapes for development and reprogramming. We demonstrate CELLoGeNe on two GRNs governing different aspects of induced pluripotent stem cells, identifying experimentally validated attractors and revealing potential reprogramming roadblocks. CELLoGeNe is a general framework that can be applied to various biological systems offering a broad picture of intracellular dynamics otherwise inaccessible with existing methods.

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CELLoGeNe – Computation of Energy Landscapes of Logical Gene Networks

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Cell states as landscape attractors

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Maintenance and acquisition of cell pluripotency applications

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Single cell stochastic landscape navigation and visualization tool

Comparative single-cell transcriptomes of dose and time dependent epithelial–mesenchymal spectrums

Epithelial–mesenchymal transition (EMT) is a cellular process involved in development and disease progression. Intermediate EMT states were observed in tumors and fibrotic tissues, but previous in vitro studies focused on time-dependent responses with single doses of signals; it was unclear whether single-cell transcriptomes support stable intermediates observed in diseases. Here, we performed single-cell RNA-sequencing with human mammary epithelial cells treated with multiple doses of TGF-β. We found that dose-dependent EMT harbors multiple intermediate states at nearly steady state. Comparisons of dose- and time-dependent EMT transcriptomes revealed that the dose-dependent data enable higher sensitivity to detect genes associated with EMT. We identified cell clusters unique to time-dependent EMT, reflecting cells en route to stable states. Combining dose- and time-dependent cell clusters gave rise to accurate prognosis for cancer patients. Our transcriptomic data and analyses uncover a stable EMT continuum at the single-cell resolution, and complementary information of two types of single-cell experiments.