Combinatorial perturbation analysis reveals divergent regulations of mesenchymal genes during epithelial-to-mesenchymal transition

Epithelial-to-mesenchymal transition (EMT), a fundamental transdifferentiation process in development, produces diverse phenotypes in different physiological or pathological conditions. Many genes involved in EMT have been identified to date, but mechanisms contributing to the phenotypic diversity and those governing the coupling between the dynamics of epithelial (E) genes and that of the mesenchymal (M) genes are unclear. In this study, we employed combinatorial perturbations to mammary epithelial cells to induce a series of EMT phenotypes by manipulating two essential EMT-inducing elements, namely TGF-β and ZEB1. By measuring transcriptional changes in more than 700 E-genes and M-genes, we discovered that the M-genes exhibit a significant diversity in their dependency to these regulatory elements and identified three groups of M-genes that are controlled by different regulatory circuits. Notably, functional differences were detected among the M-gene clusters in motility regulation and in survival of breast cancer patients. We computationally predicted and experimentally confirmed that the reciprocity and reversibility of EMT are jointly regulated by ZEB1. Our integrative analysis reveals the key roles of ZEB1 in coordinating the dynamics of a large number of genes during EMT, and it provides new insights into the mechanisms for the diversity of EMT phenotypes

Jefferson Digital Commons quarterly report: October-December 2018

This quarterly report includes: Articles Dissertations From the Archives Grand Rounds and Lectures Industrial Design Capstones Journals and Newsletters LabArchives Launch Masters of Public Health Capstones Posters Reports Videos What People are Saying About the Jefferson Digital Common

Topological and semantic Web based method for analyzing TGF-β signaling pathways

International audienceTargeting the deleterious effects of Transforming Growth Factor TGF-β without affecting its physiological role is the common goal of therapeutic strategies aiming at curing fibrosis, the final outcome of all chronic liver disease. The pleiotropic effects of TGF-β are linked to the complex nature of its activation and signaling net- works which understanding requires modeling approaches. Our group recently developed a model of TGF-beta signal propagation based on guarded transitions (ref, Andrieux et al, 2014). In this initial work, we explored the combinatorial complexity of cell signaling, developing a discrete formalism based on guarded transitions. We imported the whole database Pathway Interaction Database into a single unified model of signal transduction. We detected 16,000 chains of reactions linking TGF-β to at least one of 159 target genes in the nucleus. The size and complexity of this model place it beyond current understanding. Its analysis requires automated tools for identifying general patterns.Currently, we focus on designing one reasoning method based on Semantic Web technologies for the analysis of signaling pathways. Our method aims at leveraging external domain knowledge represented in biomedical ontologies and linked databases to rank these candidates. We consider a signaling pathway as a set of proteins involved in the respons of a cell to an external stimulus and influencing at least one gene. The underlying reasoning methods are based on graph topological analysis, formal concepts analysis (FCA) and semantic similarity and particularity measures. First, we determine the formal concepts, maximal bi-cliques, between proteins sets and genes. Then, to determine the biological relevance of theses gene clusters, we calculate a similarity score for each cluster based on Wang semantic similarity. Using such approaches, we identify groups of genes sharing signaling networks.Cibler les effets délétères du Transforming Growth Factor, TGF-β, sans affecter son rôle physiologique est l’objectif commun des stratégies thérapeutiques visant à guérir la fibrose, la conséquence finale de toutes les maladies chroniques du foie. Les effets pléiotropiques du TGF-β sont liés à la nature complexe de son activation et du réseaux de signalisation qu’il induit, et dont la compréhension nécessite des approches de modélisation. Notre équipe a développé un modèle de la propagation du signal induit par le TGF-β base ́ sur les transitions gardées. Le développement d’un formalisme discret base ́ sur les transitions gardées permet d’étudier la complexité combinatoire de la signalisation cellulaire. Nous avons formalise ́ l’intégralité de la base de données Pathway Interaction Database en un unique modèle de la propagation du signal. Nous avons détecté 16 000 chaines de réactions reliant le TGF-β à au moins l’un des 159 gènes cibles d’intérêt Pour identifier des propriétés au sein de ces résultats il est nécessaire d’utiliser des outils automatisés.Nous développons actuellement une méthode basée sur le Web sémantique pour l’analyse des voies de signalisation. Cette méthode vise à tirer parti des connaissances de domaine externe représentées dans les ontologies biomédicales et des bases de données pour classer ces candidats. Nous considérons qu’une voie de signalisation est un ensemble des protéines impliquées dans la réaction d’une cellule à un stimulus externe et qui influence au moins un gène. Les méthodes de raisonnement sous-jacentes sont basées sur l’analyse topologique, l’analyse formelle de concepts et les mesures de similarité et de particularité sémantique. Tout d’abord, nous déterminons les concepts formels, c’est-à-dire les bi-cliques maximales, entre les ensembles de protéines et les gènes. Puis, afin de déterminer la pertinence biologique de ces groupes de gènes, nous calculons un score de similarité pour chacun des groupes, base ́ sur la mesure de Wang. La finalité est d’identifier des groupes de gènes similaires influencés par un même ensemble de voies de signalisation

The tumour ecology of quiescence: Niches across scales of complexity

Quiescence is a state of cell cycle arrest, allowing cancer cells to evade anti-proliferative cancer therapies. Quiescent cancer stem cells are thought to be responsible for treatment resistance in glioblastoma, an aggressive brain cancer with poor patient outcomes. However, the regulation of quiescence in glioblastoma cells involves a myriad of intrinsic and extrinsic mechanisms that are not fully understood. In this review, we synthesise the literature on quiescence regulatory mechanisms in the context of glioblastoma and propose an ecological perspective to stemness-like phenotypes anchored to the contemporary concepts of niche theory. From this perspective, the cell cycle regulation is multiscale and multidimensional, where the niche dimensions extend to extrinsic variables in the tumour microenvironment that shape cell fate. Within this conceptual framework and powered by ecological niche modelling, the discovery of microenvironmental variables related to hypoxia and mechanosignalling that modulate proliferative plasticity and intratumor immune activity may open new avenues for therapeutic targeting of emerging biological vulnerabilities in glioblastoma

Colorectal Cancer Through Simulation and Experiment

Colorectal cancer has continued to generate a huge amount of research interest over several decades, forming a canonical example of tumourigenesis since its use in Fearon and Vogelstein’s linear model of genetic mutation. Over time, the field has witnessed a transition from solely experimental work to the inclusion of mathematical biology and computer-based modelling. The fusion of these disciplines has the potential to provide valuable insights into oncologic processes, but also presents the challenge of uniting many diverse perspectives. Furthermore, the cancer cell phenotype defined by the ‘Hallmarks of Cancer’ has been extended in recent times and provides an excellent basis for future research. We present a timely summary of the literature relating to colorectal cancer, addressing the traditional experimental findings, summarising the key mathematical and computational approaches, and emphasising the role of the Hallmarks in current and future developments. We conclude with a discussion of interdisciplinary work, outlining areas of experimental interest which would benefit from the insight that mathematical and computational modelling can provide

Impaired neurodevelopmental pathways in autism spectrum disorder: a review of signaling mechanisms and crosstalk.

BackgroundThe development of an autistic brain is a highly complex process as evident from the involvement of various genetic and non-genetic factors in the etiology of the autism spectrum disorder (ASD). Despite being a multifactorial neurodevelopmental disorder, autistic patients display a few key characteristics, such as the impaired social interactions and elevated repetitive behaviors, suggesting the perturbation of specific neuronal circuits resulted from abnormal signaling pathways during brain development in ASD. A comprehensive review for autistic signaling mechanisms and interactions may provide a better understanding of ASD etiology and treatment.Main bodyRecent studies on genetic models and ASD patients with several different mutated genes revealed the dysregulation of several key signaling pathways, such as WNT, BMP, SHH, and retinoic acid (RA) signaling. Although no direct evidence of dysfunctional FGF or TGF-β signaling in ASD has been reported so far, a few examples of indirect evidence can be found. This review article summarizes how various genetic and non-genetic factors which have been reported contributing to ASD interact with WNT, BMP/TGF-β, SHH, FGF, and RA signaling pathways. The autism-associated gene ubiquitin-protein ligase E3A (UBE3A) has been reported to influence WNT, BMP, and RA signaling pathways, suggesting crosstalk between various signaling pathways during autistic brain development. Finally, the article comments on what further studies could be performed to gain deeper insights into the understanding of perturbed signaling pathways in the etiology of ASD.ConclusionThe understanding of mechanisms behind various signaling pathways in the etiology of ASD may help to facilitate the identification of potential therapeutic targets and design of new treatment methods

A computational modelling of cellular and supra-cellular networks to unravel the control of EMT

"Over the last decade, Epithelial-to-Mesenchymal Transition (EMT) has gained the attention of cancer researchers due to its potential to promote cancer migration and metastasis. However, the complexity of EMT intertwined regulation and the involvement of multiple signals in the tumour microenvironment have been limiting the understanding of how this process can be controlled. Cell-cell adhesion and focal adhesion dynamics are two critical properties that change during EMT, which provide a simple way to characterize distinct modes of cancer migration. Therefore, the main focus of this thesis is to provide a framework to predict critical microenvironment and de-regulations in cancer that drive interconversion between adhesion phenotypes, accounting for main microenvironment signals and signalling pathways in EMT. Here, we address this issue through a systems approach using the logical modelling framework to generate new testable predictions for the field.(...)"Instituto Gulbenkian de Ciência (FCG-IGC

Advanced technologies to target cardiac cell fate plasticity for heart regeneration

The adult human heart can only adapt to heart diseases by starting a myocardial remodeling process to compensate for the loss of functional cardiomyocytes, which ultimately develop into heart failure. In recent decades, the evolution of new strategies to regenerate the injured myocardium based on cellular reprogramming represents a revolutionary new paradigm for cardiac repair by targeting some key signaling molecules governing cardiac cell fate plasticity. While the indirect reprogramming routes require an in vitro engineered 3D tissue to be transplanted in vivo, the direct cardiac reprogramming would allow the administration of reprogramming factors directly in situ, thus holding great potential as in vivo treatment for clinical applications. In this framework, cellular reprogramming in partnership with nanotechnologies and bioengineering will offer new perspectives in the field of cardiovascular research for disease modeling, drug screening, and tissue engineering applications. In this review, we will summarize the recent progress in developing innovative therapeutic strategies based on manipulating cardiac cell fate plasticity in combination with bioengineering and nanotechnology-based approaches for targeting the failing heart

Evolutionary underpinnings of microgeographic adaptation in song sparrows distributed along a steep climate gradient

2021 Summer.Includes bibliographical references.Understanding how evolutionary processes interact to maintain adaptive variation in natural populations has been a fundamental goal of evolutionary biology. Yet, despite adaptation remaining at the forefront of evolutionary theory and empirical studies, there remains a lack of consensus about the evolutionary conditions that enable adaptation to persist in natural populations, especially when considering complex phenotypes in response to multivariate selection regimes. In my dissertation, I disentangle the evolutionary mechanisms that shape adaptive divergence in song sparrows (Melospiza melodia) distributed along a climate gradient on the California Channel Islands and nearby coastal California. First, I found evidence that climate, and neither vegetation nor selection for increased foraging efficiency, likely drive adaptive divergence in bill morphology among insular populations. Second, I used an integrated population and landscape genomics approach to infer that bill variation is indicative of microgeographic local adaptation to temperature. Lastly, I tested whether the distinct climate gradient facilitates adaptative divergence in other thermoregulatory traits and found evidence to support environmental temperatures result in fixed population differences in many complementary phenotypes, including plumage color, feather microstructure, and thermal physiology. Collectively, these results find support for microgeographic climate adaptation in a suite of complex phenotypes and demonstrate the utility of integrative approaches to infer local adaptation in natural populations. Finally, by developing a more holistic understanding of climate adaptation in natural populations, my results inform conservation management of this species of special concern

Reconstructing equations of motion for cell phenotypic transitions: integration of data science and dynamical systems theory

Dynamical systems theory has long been fruitfully applied to describe cellular processes, while a main challenge is lack of quantitative information for constraining models. Advances of quantitative approaches, especially single cell techniques, have accelerated the emergence of a new direction of reconstructing the equations of motion of a cellular system from quantitative single cell data, thus places studies under the framework of dynamical systems theories, as compared to the currently dominant statistics-based approaches. Here I review a selected number of recent studies using live- and fixed- cell data, and provide my perspective on the future development.Comment: 18 pages, 4 figure