Landscape of epithelial-mesenchymal plasticity as an emergent property of coordinated teams in regulatory networks

Elucidating the principles of cellular decision-making is of fundamental importance. These decisions are often orchestrated by underlying regulatory networks. While we understand the dynamics of simple network motifs, how do large networks lead to a limited number of phenotypes, despite their complexity, remains largely elusive. Here, we investigate five different networks governing epithelial-mesenchymal plasticity and identified a latent design principles in their topology that limits their phenotypic repertoire - the presence of two 'teams' of nodes engaging in a mutually inhibitory feedback loop, forming a toggle switch. These teams are specific to these networks and directly shape the phenotypic landscape and consequently the frequency and stability of terminal phenotypes vs. the intermediary ones. Our analysis reveals that network topology alone can contain information about phenotypic distributions it can lead to, thus obviating the need to simulate them. We unravel topological signatures that can drive canalization of cell-fates during diverse decision-making processes.

Modeling the C. elegans Germline Stem Cell Genetic Network using Automated Reasoning

Computational methods and tools are a powerful complementary approach to experimental work for studying regulatory interactions in living cells and systems. We demonstrate the use of formal reasoning methods as applied to the Caenorhabditis elegans germ line, which is an accessible model system for stem cell research. The dynamics of the underlying genetic networks and their potential regulatory interactions are key for understanding mechanisms that control cellular decision making between stem cells and differentiation.We model the 'stem cell fate' versus entry into the 'meiotic development' pathway decision circuit in the young adult germ line based on an extensive study of published experimental data and known/hypothesized genetic interactions. We apply a formal reasoning framework to derive predictive networks for control of differentiation. Using this approach we simultaneously specify many possible scenarios and experiments together with potential genetic interactions, and synthesize genetic networks consistent with all encoded experimental observations. In silico analysis of knock-down and overexpression experiments within our model recapitulate published phenotypes of mutant animals and can be applied to make predictions on cellular decision-making. This work lays a foundation for developing realistic whole tissue models of the C. elegans germ line where each cell in the model will execute a synthesized genetic network.

Utilizing Split Fluorescent Proteins to Visualize Binary Cell Fate Decisions

Binary cell fate decisions serve at a cornerstone of cellular decision-making processes during embryonic development. Understanding and studying these decisions require an intimate knowledge of the spatial and temporal expression dynamics of critical genes. Split fluorescent proteins (sFP) can serve as a novel tool to study these binary cell fate decisions, with unique applications such as the potential to amplify weak genetic signals. Ultimately, sFPs can be utilized to revolutionize the study of protein-protein interactions during embryonic development and beyond.

Cullin-RING Ubiquitin Ligase Regulatory Circuits: a Quarter Century Beyond the F-box Hypothesis

Cullin-RING ubiquitin ligases (CRLs) are dynamic modular platforms that regulate myriad biological processes through target-specific ubiquitylation. Our knowledge of this system emerged from the F-box hypothesis, posited a quarter century ago: Numerous interchangeable F-box proteins confer specific substrate recognition for a core CUL1-based RING E3 ubiquitin ligase. This paradigm has been expanded through the evolution of a superfamily of analogous modular CRLs, with five major families and over 200 different substrate-binding receptors in humans. Regulation is achieved by numerous factors organized in circuits that dynamically control CRL activation and substrate ubiquitylation. CRLs also serve as a vast landscape for developing small molecules that reshape interactions and promote targeted ubiquitylation-dependent turnover of proteins of interest. Here, we review molecular principles underlying CRL function, the role of allosteric and conformational mechanisms in controlling substrate timing and ubiquitylation, and how the dynamics of substrate receptor interchange drives the turnover of selected target proteins to promote cellular decision making. Expected final online publication date for the Annual Review of Biochemistry, Volume 90 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A quantitative landscape of cell fate transitions identifies principles of cellular decision-making

Fate decisions in developing tissues involve cells transitioning between a set of discrete cell states, each defined by a distinct gene expression profile. Geometric models, often referred to as Waddington landscapes, in which developmental paths are given by the gradient and cell states by the minima of the model, are an appealing way to describe differentiation dynamics and developmental decisions. To construct and validate accurate dynamical landscapes, quantitative methods based on experimental data are necessary. To this end we took advantage of the differentiation of neural and mesodermal cells from pluripotent mouse embryonic stem cells exposed to different combinations and durations of signalling factors. We developed a principled statistical approach using flow cytometry data to quantify differentiating cell states. Then, using a framework based on Catastrophe Theory and approximate Bayesian computation, we constructed the corresponding dynamical landscape. The result was a quantitative model that accurately predicted the proportions of neural and mesodermal cells differentiating in response to specific signalling regimes. Analysis of the geometry of the landscape revealed two distinct ways in which cells make a binary choice between one of two fates. We discuss the biological relevance of these mechanisms and suggest that they represent general archetypal designs for developmental decisions. Taken together, the approach we describe is broadly applicable for the quantitative analysis of differentiation dynamics and for determining the logic of developmental cell fate decisions.

Quantifying information accumulation encoded in the dynamics of biochemical signaling

Cellular responses to environmental changes are encoded in the complex temporal patterns of signaling proteins. However, quantifying the accumulation of information over time to direct cellular decision-making remains an unsolved challenge. This is, in part, due to the combinatorial explosion of possible configurations that need to be evaluated for information in time-course measurements. Here, we develop a quantitative framework, based on inferred trajectory probabilities, to calculate the mutual information encoded in signaling dynamics while accounting for cell-cell variability. We use it to understand NFκB transcriptional dynamics in response to different immune threats, and reveal that some threats are distinguished faster than others. Our analyses also suggest specific temporal phases during which information distinguishing threats becomes available to immune response genes; one specific phase could be mapped to the functionality of the IκBα negative feedback circuit. The framework is generally applicable to single-cell time series measurements, and enables understanding how temporal regulatory codes transmit information over time.

A quantitative model of cellular decision making in direct neuronal reprogramming

The direct reprogramming of adult skin fibroblasts to neurons is thought to be controlled by a small set of interacting gene regulators. Here, we investigate how the interaction dynamics between these regulating factors coordinate cellular decision making in direct neuronal reprogramming. We put forward a quantitative model of the governing gene regulatory system, supported by measurements of mRNA expression. We found that nPTB needs to feed back into the direct neural conversion network most likely via PTB in order to accurately capture quantitative gene interaction dynamics and correctly predict the outcome of various overexpression and knockdown experiments. This was experimentally validated by nPTB knockdown leading to successful neural conversion. We also proposed a novel analytical technique to dissect system behaviour and reveal the influence of individual factors on resulting gene expression. Overall, we demonstrate that computational analysis is a powerful tool for understanding the mechanisms of direct (neuronal) reprogramming, paving the way for future models that can help improve cell conversion strategies.

Synchronized emergence under diatom sperm competition

Appropriate timing of mating is crucial for the success of individuals. However, we know little about factors that explain variation in mating time in unicellular organisms. Unicellular eukaryotes often have facultative sexuality, that is, the less frequent sex is occasionally induced after long clonal reproduction. Thus, males originated from clonemates could be non-negligible mating rivals. Using a centric diatom whose clonal cells differentiate into either male or female, we analysed whether males (spermatogonium) compete or cooperate with each other. By analysing differentiation timing with hypotheses based on evolutionary game theory, we estimated that a substantial part of the variation in the mating timing of the diatom can be explained by results of optimization through interactions among selfish individuals rather than cooperation among clonemates. However, the competition is fiercer than expected owing to excessive synchronization, which was realized by adjustment of meiotic duration: cells completed mitotic division in the earlier mating phase took longer to enter into meiosis, whereas late-dividing cells entered into meiosis more quickly. Adjacent cells tended to synchronize, and model analyses suggest that cell–cell interaction can create a gap between the optimal and actual decisions. Our results provide insights into the evolution of cellular decision making and its restriction.

Multi-stability in cellular differentiation enabled by a network of three mutually repressing master regulators

Identifying the design principles of complex regulatory networks driving cellular decision-making remains essential to decode embryonic development as well as enhance cellular reprogramming. A well-studied network motif involved in cellular decision-making is a toggle switch—a set of two opposing transcription factors A and B, each of which is a master regulator of a specific cell fate and can inhibit the activity of the other. A toggle switch can lead to two possible states—(high A, low B) and (low A, high B)—and drives the ‘either-or' choice between these two cell fates for a common progenitor cell. However, the principles of coupled toggle switches remain unclear. Here, we investigate the dynamics of three master regulators A, B and C inhibiting each other, thus forming three-coupled toggle switches to form a toggle triad. Our simulations show that this toggle triad can lead to co-existence of cells into three differentiated ‘single positive' phenotypes—(high A, low B, low C), (low A, high B, low C) and (low A, low B, high C). Moreover, the hybrid or ‘double positive' phenotypes—(high A, high B, low C), (low A, high B, high C) and (high A, low B, high C)—can coexist together with ‘single positive' phenotypes. Including self-activation loops on A, B and C can increase the frequency of ‘double positive' states. Finally, we apply our results to understand cellular decision-making in terms of differentiation of naive CD4 + T cells into Th1, Th2 and Th17 states, where hybrid Th1/Th2 and hybrid Th1/Th17 cells have been reported in addition to the Th1, Th2 and Th17 ones. Our results offer novel insights into the design principles of a multi-stable network topology and provide a framework for synthetic biology to design tristable systems.