Cellular Decisions Research Articles

Wnt Signaling in the Development of Bone Metastasis

Wnt signaling occurs through evolutionarily conserved pathways that affect cellular proliferation and fate decisions during development and tissue maintenance. Alterations in these highly regulated pathways, however, play pivotal roles in various malignancies, promoting cancer initiation, growth and metastasis and the development of drug resistance. The ability of cancer cells to metastasize is the primary cause of cancer mortality. Bone is one of the most frequent sites of metastases that generally arise from breast, prostate, lung, melanoma or kidney cancer. Upon their arrival to the bone, cancer cells can enter a long-term dormancy period, from which they can be reactivated, but can rarely be cured. The activation of Wnt signaling during the bone metastasis process was found to enhance proliferation, induce the epithelial-to-mesenchymal transition, promote the modulation of the extracellular matrix, enhance angiogenesis and immune tolerance and metastasize and thrive in the bone. Due to the complexity of Wnt pathways and of the landscape of this mineralized tissue, Wnt function during metastatic progression within bone is not yet fully understood. Therefore, we believe that a better understanding of these pathways and their roles in the development of bone metastasis could improve our understanding of the disease and may constitute fertile ground for potential therapeutics.

Low-Grade Ovarian Stromal Tumors with Genetic Alterations of the Wnt/β-Catenin Pathway That Is Crucial in Ovarian Follicle Development and Regulation.

The Wnt signaling pathway is important in the normal development and regulation of ovarian follicles throughout the lifecycle of females. Dysregulation of the Wnt signaling pathway, genetically or epigenetically, with subsequent activation of β-catenin has been implicated in tumorigenesis of a spectrum of ovarian neoplasms, from benign to malignant. We review the recent findings of the Wnt signaling pathway involved in regulating normal physiologic processes of the ovarian follicle cycle. We also review the β-catenin mutations in a family of low-grade ovarian stromal tumors, focusing on characterizing their shared morphological features and the utility of immunohistochemistry of β-catenin in facilitating the accurate diagnosis of these ovarian stromal tumors. The Wnt signaling pathway is one of the most critical mechanisms in regulating cell proliferation, differentiation, and morphogenesis. The Wnt signaling pathway comprises a diverse group of glycoproteins that serve as ligands and bind to transmembrane Frizzled family receptors. The ligand-receptor interactions activate the pathway and govern the downstream signaling cascades, ultimately affecting the transcriptional control of the cellular cytoskeleton, organelle dynamics, epithelial-mesenchymal interaction, and tissue remodeling in the ovary. Wnt signaling consists of two major pathways: a canonical pathway that is β-catenin-dependent and a non-canonical Wnt pathway that is β-catenin-independent. Canonical Wnt signaling is governed by the interaction of β-catenin with other molecules to regulate cellular decisions related to proliferation and differentiation. Recent studies have demonstrated that the Wnt signaling pathway plays important roles in the development and regulation of ovarian folliculogenesis and oogenesis.

Using transcription-based detectors to emulate the behavior of sequential probability ratio-based concentration detectors.

The sequential probability ratio test (SPRT) from statistics is known to have the least mean decision time compared to other sequential or fixed-time tests for given error rates. In some circumstances, cells need to make decisions accurately and quickly, therefore it has been suggested that the SPRT may be used to understand the speed-accuracy tradeoff in cellular decision-making. It is generally thought that in order for cells to make use of the SPRT, it is necessary to find biochemical circuits that can compute the log-likelihood ratio needed for the SPRT. However, this paper takes a different approach. We recognize that the high-level behavior of the SPRT is defined by its positive detection or hit rate, and the computation of the log-likelihood ratio is just one way to realize this behavior. In this paper, we will present a method in which a transcription-based detector is used to emulate the hit rate of the SPRT without computing the exact log-likelihood ratio. We consider the problem of using a promoter with multiple binding sites to accurately and quickly detect whether the concentration of a transcription factor is above a target level. We show that it is possible to find binding and unbinding rates of the transcription factor to the promoter's binding sites so that the probability that the amount of mRNA produced will be higher than a threshold is approximately equal to the hit rate of the SPRT detector. Moreover, we show that the average time that this transcription-based detector needs to make a positive detection is less than or equal to that of the SPRT for a wide range of concentrations. We remark that the last statement does not contradict Wald's optimality result because our transcription-based detector uses an open-ended test.

Integrated analysis of miRNAs and mRNA profiling reveals the potential roles of miRNAs in sheep hair follicle development

BackgroundMerino sheep exhibit high wool production and excellent wool quality. The fleece of Merino sheep is predominantly composed of wool fibers grown from hair follicles (HFs). The HF is a complex biological system involved in a dynamic process governed by gene regulation, and gene expression is regulated by microRNAs (miRNAs). miRNA inhibits posttranscriptional gene expression by specifically binding to target messenger RNA (mRNA) and plays an important role in regulating gene expression, the cell cycle and biological development sequences. The purpose of this study was to examine mRNA and miRNA binding to identify key miRNAs and target genes related to HF development. This will provide new and important insights into fundamental mechanisms that regulate cellular activity and cell fate decisions within and outside of the skin.ResultsWe analyzed miRNA data in skin tissues collected from 18 Merino sheep on four embryonic days (E65, E85, E105 and E135) and two postnatal days (D7 and D30) and identified 87 differentially expressed miRNAs (DE-miRNAs). These six stages were further divided into two longer developmental stages based on heatmap cluster analysis, and the results showed that DE-mRNAs in Stage A were closely related to HF morphogenesis. A coanalysis of Stage A DE-mRNAs and DE-miRNAs revealed that 9 DE-miRNAs and 17 DE-mRNAs presented targeting relationships in Stage A. We found that miR-23b and miR-133 could target and regulate ACVR1B and WNT10A. In dermal fibroblasts, the overexpression of miR-133 significantly reduced the mRNA and protein expression levels of ACVR1B. The overexpression of miR-23b significantly reduced the mRNA and protein expression levels of WNT10A.ConclusionThis study provides a new reference for understanding the molecular basis of HF development and lays a foundation for further improving sheep HF breeding. miRNAs and target genes related to hair follicular development were found, which provided a theoretical basis for molecular breeding for the culture of fine-wool sheep.

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

Elucidating the design principles of regulatory networks driving cellular decision-making has fundamental implications in mapping and eventually controlling cell-fate decisions. Despite being complex, these regulatory networks often only give rise to a few phenotypes. Previously, we identified two 'teams' of nodes in a small cell lung cancer regulatory network that constrained the phenotypic repertoire and aligned strongly with the dominant phenotypes obtained from network simulations (Chauhan et al., 2021). However, it remained elusive whether these 'teams' exist in other networks, and how do they shape the phenotypic landscape. Here, we demonstrate that five different networks of varying sizes governing epithelial-mesenchymal plasticity comprised of two 'teams' of players - one comprised of canonical drivers of epithelial phenotype and the other containing the mesenchymal inducers. These 'teams' are specific to the topology of these regulatory networks and orchestrate a bimodal phenotypic landscape with the epithelial and mesenchymal phenotypes being more frequent and dynamically robust to perturbations, relative to the intermediary/hybrid epithelial/mesenchymal ones. Our analysis reveals that network topology alone can contain information about corresponding phenotypic distributions, thus obviating the need to simulate them. We propose 'teams' of nodes as a network design principle that can drive cell-fate canalization in diverse decision-making processes.

PcnaDeep: a fast and robust single-cell tracking method using deep-learning mediated cell cycle profiling.

Computational methods that track single cells and quantify fluorescent biosensors in time-lapse microscopy images have revolutionized our approach in studying the molecular control of cellular decisions. One barrier that limits the adoption of single-cell analysis in biomedical research is the lack of efficient methods to robustly track single cells over cell division events. Here, we developed an application that automatically tracks and assigns mother-daughter relationships of single cells. By incorporating cell cycle information from a well-established fluorescent cell cycle reporter, we associate mitosis relationships enabling high fidelity long-term single-cell tracking. This was achieved by integrating a deep-learning-based fluorescent proliferative cell nuclear antigen signal instance segmentation module with a cell tracking and cell cycle resolving pipeline. The application offers a user-friendly interface and extensible APIs for customized cell cycle analysis and manual correction for various imaging configurations. pcnaDeep is an open-source Python application under the Apache 2.0 licence. The source code, documentation and tutorials are available at https://github.com/chan-labsite/PCNAdeep. Supplementary data are available at Bioinformatics online.

The molecular architecture of cell cycle arrest

The cellular decision governing the transition between proliferative and arrested states is crucial to the development and function of every tissue. While the molecular mechanisms that regulate the proliferative cell cycle are well established, we know comparatively little about what happens to cells as they diverge into cell cycle arrest. We performed hyperplexed imaging of 47 cell cycle effectors to obtain a map of the molecular architecture that governs cell cycle exit and progression into reversible (“quiescent”) and irreversible (“senescent”) arrest states. Using this map, we found multiple points of divergence from the proliferative cell cycle; identified stress‐specific states of arrest; and resolved the molecular mechanisms governing these fate decisions, which we validated by single‐cell, time‐lapse imaging. Notably, we found that cells can exit into senescence from either G1 or G2; however, both subpopulations converge onto a single senescent state with a G1‐like molecular signature. Cells can escape from this “irreversible” arrest state through the upregulation of G1 cyclins. This map provides a more comprehensive understanding of the overall organization of cell proliferation and arrest.

Preprint Highlight: Probing the rules of cell coordination in live tissues by interpretable machine learning based on graph neural networks

Cell–cell interactions are involved in cellular decision making, but there is a lack of systematic methods to quantify and interpret their role. This study presents a computational methodology that predicts epidermal cell fate at the single cell level using information obtained from live cell images. Specifically, the authors used “graph neural networks” to extract information on cells and their neighbors and predicted the cells' future fates with regard to division or delamination. They then identified the features driving the neural network's prediction to infer potential new mechanisms of cell–cell interactions. This unbiased inference of new cell–cell interactions correlated with cell fates is a powerful approach to decipher cell fate decision making in the context of the microenvironment.

Metabolism, Cellular Decisions and the Language That Unites Them

Metabolism, Cellular Decisions and the Language That Unites Them

Dynamin-Independent Mechanisms of Endocytosis and Receptor Trafficking.

Endocytosis is a fundamental mechanism by which cells perform housekeeping functions. It occurs via a variety of mechanisms and involves many regulatory proteins. The GTPase dynamin acts as a “molecular scissor” to form endocytic vesicles and is a critical regulator among the proteins involved in endocytosis. Some GTPases (e.g., Cdc42, arf6, RhoA), membrane proteins (e.g., flotillins, tetraspanins), and secondary messengers (e.g., calcium) mediate dynamin-independent endocytosis. These pathways may be convergent, as multiple pathways exist in a single cell. However, what determines the specific path of endocytosis is complex and challenging to comprehend. This review summarizes the mechanisms of dynamin-independent endocytosis, the involvement of microRNAs, and factors that contribute to the cellular decision about the specific route of endocytosis.

Receptor Tyrosine Kinases Mediated Cell Signalling Pathways

A small number of signalling pathways, including receptor tyrosine kinases (RTKs) and G protein-coupled receptors (GPCRs), are activated by plasma membrane receptors in order for cells to respond to external cues. In addition to transmitting, these pathways also process, encode, and integrate both internal and external signals. In recent years, it has become clear that different spatio-temporal activation profiles of the same set of signalling proteins lead to different gene-expression patterns and various physiological responses. These findings suggest that the precise temporal control and relative spatial distribution of activated signal transducers are necessary for important cellular decisions such as cytoskeletal reorganisation, cell-cycle checkpoints, and cell death (apoptosis). Due to their crucial function in the control of embryogenesis, cell survival, motility, proliferation, differentiation, glucose metabolism, and apoptosis, RTK-mediated signalling pathways have drawn a lot of attention from scientists. A significant number of serious human diseases, including diabetes, cancer, chronic inflammatory syndromes, and developmental defects, are caused by RTK signalling dysfunction. RTKs undergo allosteric transitions (the insulin receptor, for instance) or dimerization in response to stimulation, which activates the intrinsic tyrosine-kinase activity. Numerous cytoplasmic proteins receive a biochemical signal from subsequent phosphorylation of numerous tyrosine residues on the receptor, which causes them to move to the cell surface. Through intricate biochemical circuits of protein-protein interactions and covalent modification cascades, the resulting cellular reactions take place. The earlier theories of discrete linear pathways, which connected extracellular signals to the expression of particular genes, have been replaced by an emerging picture of interconnected signalling networks, raising concerns about the specificity of signal-response events. In actuality, all RTK-mediated pathways share a common protein complement that mediates signal transduction downstream of RTKs. The expression of nuclear transcription factors is induced by the activation of kinase and phosphatase cascades, such as the mitogen-activated protein kinase (MAPK) cascades, which are both activated by GPCRs and RTKs. There is no single protein or gene that determines the specificity of a signal for any given receptor pathway. Instead, the temporal and spatial dynamics of the components of downstream signalling control specificity. A classic example is the distinctive biological response to EGF and nerve growth factor stimulation of PC12 cell lines (NGF). Cell proliferation is temporarily induced by EGF, whereas cell differentiation is temporarily triggered by sustained MAPK activation by NGF. However, there are many variables that affect how MAPK cascades behave. Depending heavily on their subcellular localization and their recruitment to scaffold proteins, MAPK cascades can produce oscillations, abrupt switches, bistable dynamics.

Multimodal perception links cellular state to decision-making in single cells.

Individual cells make decisions that are adapted to their internal state and surroundings, but how cells can reliably do this remains unclear. To study the information processing capacity of human cells, we conducted multiplexed quantification of signaling responses and markers of the cellular state. Signaling nodes in a network displayed adaptive information processing, which led to heterogeneous growth factor responses and enabled nodes to capture partially nonredundant information about the cellular state. Collectively, as a multimodal percept this gives individual cells a large information processing capacity to accurately place growth factor concentration within the context of their cellular state and make cellular state-dependent decisions. Heterogeneity and complexity in signaling networks may have coevolved to enable specific and context-aware cellular decision-making in a multicellular setting.

Cell-to-cell ATP differences can modulate cellular decision-making

Cells generate phenotypic diversity both during development and in response to stressful and changing environments, aiding survival. Functionally vital cell fate decisions from a range of phenotypic choices are made by regulatory networks, the dynamics of which rely on gene expression and hence depend on the cellular energy budget (and particularly ATP levels). However, despite pronounced cell-to-cell ATP differences observed across biological systems, the influence of energy availability on regulatory network dynamics is often overlooked as a cellular decision-making modulator, limiting our knowledge of how energy budgets affect cell behaviour. Here, we consider a mathematical model of a highly generalisable, ATP-dependent, decision-making regulatory network, and show that cell-to-cell ATP variability changes the sets of decisions a cell can make. Our model shows that increasing intracellular energy levels can increase the number of supported stable phenotypes, corresponding to increased decision-making capacity. Model cells with sub-threshold intracellular energy are limited to a singular phenotype, forcing the adoption of a specific cell fate. We suggest that energetic differences between cells may be an important consideration to help explain observed variability in cellular decision-making across a broad range of biological systems, including bacteria and the blood stem cell system. *NOTE: Our work is highly interdisciplinary and if you believe it would be better suited to another topic area, then I would be delighted to discuss that further.

The Role of Vts1/Smaug in Adaptive Heritable Gene Expression

The ancient RNA Binding Protein (RBP) Vts1 has been identified to self‐assemble along with the Smaug regulator to produce enhancing non‐amyloid prions in mRNA decay. Seen in Drosophila, the sterile‐alpha‐motif (SAM) domain of Vts and the Smaug regulator acts as an embryonic regulator for maternal transcript degradation through cytoplasmic deadenylase in progeny development. Specifically, of Saccharomyces cerevisiae, the Vts/Smaug regulator forms condensates that self‐template to create prions functioning towards epigenetic heritable inheritance. The RBP Vts1 SAM domain recognizes in RNA hairpins Smaug recognition elements, SREs, initiating the process of degradation and self‐assembly. The structure of RBPs are composed of an ordered RNA binding domain, and an unventured intrinsically disordered region (IDR). At the quaternary level structure, the Vts1 protein matches as a hexamer of 489 kDa dominant peak with an oligomer of more than eight monomers, and the IDR is around 490 kDa as well while the RBD‐Vts1 is around 75 kDa. The condensed version of Vts1 fuse into structures of 10 micrometers due to the IDR driving self‐association to create oligomers. The RNA binding SAM domain and IDR have induced transcription degradation and post‐transcriptional regulation in a new mechanism, prompting adaptive gene expression for developmental decisions in organisms over evolutionary time periods. Though many RBP prion condensates are not heritable and inactivate protein function, the SMAUG+ prions of the condensed Vts1/Smaug protein in S. Cerevisiae instead respond to environmental stress and produce new emerging phenotypes that are actually heritable over a large biological time‐scale. Furthermore, SMAUG+ conversion is reversible and an enhancer of RBP Vts1 function, producing adaptive gene expression systems in fluctuating environments. In various yeast models, the SMAUG+ allows yeast to decide nutrient replenishment after starvation which creates a new adaptive advantage for organisms. The new cellular decision making regulated by the prion is in two forms of rapid mitosis or meiosis creating a non‐stress state; SMAUG+ has created an adaptive memory that can influence developmental and cellular decisions in addition to plant growth in limited nutrients. Such advancements in heritable prion inheritance can uncover the function of intrinsically disordered sequences. The Walton SMART team has designed a 3D model with MSOE Center for Biomolecular Modeling to investigate the SAM domain of Vts1 further and examine the role of SMAUG+ prions in adaptive heritable gene expression.

Proton‐gated Coincidence Detection is a Common Feature of GPCR Signaling

The evolutionary expansion of G protein‐coupled receptors (GPCRs) has produced a rich diversity of transmembrane sensors for many physical and chemical signals. In humans alone, over 800 GPCRs detect stimuli such as light, hormones, and metabolites to guide cellular decision‐making primarily using intracellular G protein signaling networks. This diversity is further enriched by GPCRs that function as molecular sensors capable of discerning multiple inputs to transduce cues encoded in complex, context‐dependent signals. Here, we show that many GPCRs are coincidence detectors that couple proton (H+) binding to GPCR signaling. Using a large panel of receptors covering hundreds of individual GPCR‐Gα coupling combinations, we show that H+ gating both positively and negatively modulates GPCR signaling. Notably, these observations extend to all modes of GPCR pharmacology including ligand efficacy, potency, and cooperativity. Additionally, we show that GPCR antagonism and constitutive activity are regulated by H+ gating and report the discovery of new GPCR H+ sensors that can be activated solely by changes in pH. Together, these findings establish a paradigm for GPCR signaling, biology, and pharmacology applicable to acidified microenvironments such as endosomes, synapses, tumors, and ischemic vasculature.

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 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. A methodological contribution of this work is demonstrating how to effectively model within a formal reasoning framework a complex genetic network with a wealth of known experimental data and constraints. We provide a summary of the steps we have found useful for the development and analysis of this model and can potentially be applicable to other genetic networks. This work also 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.

Protection of nascent DNA at stalled replication forks is mediated by phosphorylation of RIF1 intrinsically disordered region.

RIF1 is a multifunctional protein that plays key roles in the regulation of DNA processing. During repair of DNA double-strand breaks (DSBs), RIF1 functions in the 53BP1-Shieldin pathway that inhibits resection of DNA ends to modulate the cellular decision on which repair pathway to engage. Under conditions of replication stress, RIF1 protects nascent DNA at stalled replication forks from degradation by the DNA2 nuclease. How these RIF1 activities are regulated at the post-translational level has not yet been elucidated. Here, we identified a cluster of conserved ATM/ATR consensus SQ motifs within the intrinsically disordered region (IDR) of mouse RIF1 that are phosphorylated in proliferating B lymphocytes. We found that phosphorylation of the conserved IDR SQ cluster is dispensable for the inhibition of DSB resection by RIF1, but is essential to counteract DNA2-dependent degradation of nascent DNA at stalled replication forks. Therefore, our study identifies a key molecular feature that enables the genome-protective function of RIF1 during DNA replication stress.

Intermediate Filaments in Cellular Mechanoresponsiveness: Mediating Cytoskeletal Crosstalk From Membrane to Nucleus and Back.

The mammalian cytoskeleton forms a mechanical continuum that spans across the cell, connecting the cell surface to the nucleus via transmembrane protein complexes in the plasma and nuclear membranes. It transmits extracellular forces to the cell interior, providing mechanical cues that influence cellular decisions, but also actively generates intracellular forces, enabling the cell to probe and remodel its tissue microenvironment. Cells adapt their gene expression profile and morphology to external cues provided by the matrix and adjacent cells as well as to cell-intrinsic changes in cytoplasmic and nuclear volume. The cytoskeleton is a complex filamentous network of three interpenetrating structural proteins: actin, microtubules, and intermediate filaments. Traditionally the actin cytoskeleton is considered the main contributor to mechanosensitivity. This view is now shifting owing to the mounting evidence that the three cytoskeletal filaments have interdependent functions due to cytoskeletal crosstalk, with intermediate filaments taking a central role. In this Mini Review we discuss how cytoskeletal crosstalk confers mechanosensitivity to cells and tissues, with a particular focus on the role of intermediate filaments. We propose a view of the cytoskeleton as a composite structure, in which cytoskeletal crosstalk regulates the local stability and organization of all three filament families at the sub-cellular scale, cytoskeletal mechanics at the cellular scale, and cell adaptation to external cues at the tissue scale.

Cell Responsiveness to Physical Energies: Paving the Way to Decipher a Morphogenetic Code.

We discuss emerging views on the complexity of signals controlling the onset of biological shapes and functions, from the nanoarchitectonics arising from supramolecular interactions, to the cellular/multicellular tissue level, and up to the unfolding of complex anatomy. We highlight the fundamental role of physical forces in cellular decisions, stressing the intriguing similarities in early morphogenesis, tissue regeneration, and oncogenic drift. Compelling evidence is presented, showing that biological patterns are strongly embedded in the vibrational nature of the physical energies that permeate the entire universe. We describe biological dynamics as informational processes at which physics and chemistry converge, with nanomechanical motions, and electromagnetic waves, including light, forming an ensemble of vibrations, acting as a sort of control software for molecular patterning. Biomolecular recognition is approached within the establishment of coherent synchronizations among signaling players, whose physical nature can be equated to oscillators tending to the coherent synchronization of their vibrational modes. Cytoskeletal elements are now emerging as senders and receivers of physical signals, “shaping” biological identity from the cellular to the tissue/organ levels. We finally discuss the perspective of exploiting the diffusive features of physical energies to afford in situ stem/somatic cell reprogramming, and tissue regeneration, without stem cell transplantation.

Transitions in development - an interview with Antonio Scialdone.

Antonio Scialdone is a Junior Group Leader at the Helmholtz Zentrum München, Institute of Epigenetics and Stem Cells, in Germany. Trained as a theoretical physicist, Antonio now uses multidisciplinary approaches, combining both computational methods and physical models, to study cellular decision making. We met Antonio over Teams to learn more about his experiences moving between different research fields and the transition to becoming a group leader.