Under-approximating Cut Sets for Reachability in Large Scale Automata Networks

In the scope of discrete finite-state models of interacting components, we present a novel algorithm for identifying sets of local states of components whose activity is necessary for the reachability of a given local state. If all the local states from such a set are disabled in the model, the concerned reachability is impossible. Those sets are referred to as cut sets and are computed from a particular abstract causality structure, so-called Graph of Local Causality, inspired from previous work and generalised here to finite automata networks. The extracted sets of local states form an under-approximation of the complete minimal cut sets of the dynamics: there may exist smaller or additional cut sets for the given reachability. Applied to qualitative models of biological systems, such cut sets provide potential therapeutic targets that are proven to prevent molecules of interest to become active, up to the correctness of the model. Our new method makes tractable the formal analysis of very large scale networks, as illustrated by the computation of cut sets within a Boolean model of biological pathways interactions gathering more than 9000 components

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

Negative correlation of single-cell PAX3:FOXO1 expression with tumorigenicity in rhabdomyosarcoma

Rhabdomyosarcomas (RMS) are phenotypically and functionally heterogeneous. Both primary human RMS cultures and low-passage Myf6Cre,Pax3:Foxo1,p53 mouse RMS cell lines, which express the fusion oncoprotein Pax3:Foxo1 and lack the tumor suppressor Tp53 (Myf6Cre,Pax3:Foxo1,p53), exhibit marked heterogeneity in PAX3:FOXO1 (P3F) expression at the single cell level. In mouse RMS cells, P3F expression is directed by the Pax3 promoter and coupled to eYFP. YFPlow/P3Flow mouse RMS cells included 87% G0/G1 cells and reorganized their actin cytoskeleton to produce a cellular phenotype characterized by more efficient adhesion and migration. This translated into higher tumor-propagating cell frequencies of YFPlow/P3Flow compared with YFPhigh/P3Fhigh cells. Both YFPlow/P3Flow and YFPhigh/P3Fhigh cells gave rise to mixed clones in vitro, consistent with fluctuations in P3F expression over time. Exposure to the anti-tropomyosin compound TR100 disrupted the cytoskeleton and reversed enhanced migration and adhesion of YFPlow/P3Flow RMS cells. Heterogeneous expression of PAX3:FOXO1 at the single cell level may provide a critical advantage during tumor progression

Epitope-engineered human hematopoietic stem cells are shielded from CD123-targeted immunotherapy

Targeted eradication of transformed or otherwise dysregulated cells using monoclonal antibodies (mAb), antibody-drug conjugates (ADC), T cell engagers (TCE), or chimeric antigen receptor (CAR) cells is very effective for hematologic diseases. Unlike the breakthrough progress achieved for B cell malignancies, there is a pressing need to find suitable antigens for myeloid malignancies. CD123, the interleukin-3 (IL-3) receptor alpha-chain, is highly expressed in various hematological malignancies, including acute myeloid leukemia (AML). However, shared CD123 expression on healthy hematopoietic stem and progenitor cells (HSPCs) bears the risk for myelotoxicity. We demonstrate that epitope-engineered HSPCs were shielded from CD123-targeted immunotherapy but remained functional, while CD123-deficient HSPCs displayed a competitive disadvantage. Transplantation of genome-edited HSPCs could enable tumor-selective targeted immunotherapy while rebuilding a fully functional hematopoietic system. We envision that this approach is broadly applicable to other targets and cells, could render hitherto undruggable targets accessible to immunotherapy, and will allow continued posttransplant therapy, for instance, to treat minimal residual disease (MRD)

Dynamic Regulation of Tgf-B Signaling by Tif1γ: A Computational Approach

TIF1γ (Transcriptional Intermediary Factor 1 γ) has been implicated in Smad-dependent signaling by Transforming Growth Factor beta (TGF-β). Paradoxically, TIF1γ functions both as a transcriptional repressor or as an alternative transcription factor that promotes TGF-β signaling. Using ordinary differential-equation models, we have investigated the effect of TIF1γ on the dynamics of TGF-β signaling. An integrative model that includes the formation of transient TIF1γ-Smad2-Smad4 ternary complexes is the only one that can account for TGF-β signaling compatible with the different observations reported for TIF1γ. In addition, our model predicts that varying TIF1γ/Smad4 ratios play a critical role in the modulation of the transcriptional signal induced by TGF-β, especially for short stimulation times that mediate higher threshold responses. Chromatin immunoprecipitation analyses and quantification of the expression of TGF-β target genes as a function TIF1γ/Smad4 ratios fully validate this hypothesis. Our integrative model, which successfully unifies the seemingly opposite roles of TIF1γ, also reveals how changing TIF1γ/Smad4 ratios affect the cellular response to stimulation by TGF-β, accounting for a highly graded determination of cell fate

Existence of reprogrammed lymphoma stem cells in a murine ALCL-like model.

Funder: DJCLS (R14/22) MSCA-ITN-2015-ETN AlkatrasFunder: DFG (CRC850)Funder: DFG (CRC850) BMBF (DeCaRe, FKZ 01ZX1409B)Funder: DJCLS (R14/22) MSCA-ITN-2015-ETN Alkatras DFG (FOR 2033 B1)Funder: DJCLS (R14/22) MSCA-ITN-2015-ETN Alkatras Grant from the Government of Baden-Württemberg (BSL)While cancer stem cells are well established in certain hematologic and solid malignancies, their existence in T cell lymphoma is unclear and the origin of disease is not fully understood. To examine the existence of lymphoma stem cells, we utilized a mouse model of anaplastic large cell lymphoma. Established NPM-ALK+ lymphomas contained heterogeneous cell populations ranging from mature T cells to undifferentiated hematopoietic stem cells. Interestingly, CD4-/CD8- double negative (DN) lymphoma cells aberrantly expressed the T cell receptor α/β chain. Serial transplantation of sorted CD4/CD8 and DN lymphoma subpopulations identified lymphoma stem cells within the DN3/DN4 T cell population, whereas all other subpopulations failed to establish serial lymphomas. Moreover, transplanted lymphoma DN3/DN4 T cells were able to differentiate and gave rise to mature lymphoma T cells. Gene expression analyses unmasked stem-cell-like transcriptional regulation of the identified lymphoma stem cell population. Furthermore, these lymphoma stem cells are characterized by low CD30 expression levels, which might contribute to limited long-term therapeutic success in patients treated with anti-CD30-targeted therapies. In summary, our results highlight the existence of a lymphoma stem cell population in a NPM-ALK-driven CD30+ mouse model, thereby giving the opportunity to test innovative treatment strategies developed to eradicate the origin of disease

Modélisation dynamique de la signalisation cellulaire : aspects différentiels et discrets; application à la signalisation du facteur de croissance TGF-beta dans le cancer

Cell signaling contains the whole biological mecanisms allowing the response of a cell to its microenvironnement in an adapted way. Many extremely intertwinned biological reactions are involved in a network that behaves as a complex system. The understanding of cell response requires the development of data acquisition technics and methods to formalize data into models. This point is the main drive of this thesis. We present here two approaches in order to analyse different granularities of cell signaling. In the first one, we used differential model to study the role of a new component of TGF-β canonical pathway. In the second one, we explored the combinatorial complexity of cell signaling, developing a discrete formalism based on guarded transitions. In this approach, we interpreted the whole database Pathway Interaction Database into a single unified model of signal transduction. Simulation and analysis methods, such as reachability and invariance research, have been developed. The interests are presented through an application on cell cycle regulation by cell signaling, and a global analysis on the regulation of genes compared to experimental data.La signalisation cellulaire regroupe l'ensemble des mécanismes biologiques permettant à une cellule de répondre de façon adaptée à son microenvironnement. Pour ce faire, de nombreuses réactions biologiques entrent en jeux avec un important enchevêtrement, créant ainsi un réseau dont le comportement s'apparente à un système complexe. Le compréhension de la réponse cellulaire à une stimulation passe par le développement conjoint des techniques d'acquisition de données, et des méthodes permettant de formaliser ces données dans un modèle. C'est sur ce dernier point que s'inscrivent les travaux exposés dans cette thèse. Nous présentons ici deux approches visant à répondre à des questions de natures différentes sur la signalisation cellulaire. Dans la première nous utilisons un modèle différentiel pour étudier le rôle d'un nouvel interactant dans la voie canonique du TGF-beta. Dans la seconde nous avons exploré la combinatoire de la signalisation cellulaire en développant un formalisme discret basé sur les transitions gardées. Cette approche regroupe l'interprétation de la base de données Pathway Interaction Database dans un unique modèle dynamique de propagation du signal. Des méthodes de simulations et d'analyses inspirées des techniques de vérification de modèles telles que l'atteignabilité et l'invariance ont été développées. En outre, nous avons étudié la régulation du cycle cellulaire en réponse à la signalisation, ainsi que la régulation des gènes de notre modèle en comparaison avec des données d'expressions

Molecular consequences of SARS-CoV-2 liver tropism

Extrapulmonary manifestations of COVID-19 have gained attention, not only due to their links to clinical outcomes, but also due to their potential long-term sequelae. Recent evidence has shown multi-organ tropism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including heart, kidney and liver. Previous studies have shown that close to 20% of hospitalized patients with COVID-19 develop liver injury, showing an association to disease severity. Here, we characterized SARS-CoV-2 liver tropism in autopsy samples, based on the expression of cell-entry facilitators in parenchymal cells, clinical polymerase chain reaction (PCR) positivity, subgenomic SARS-CoV-2 identification using RNA sequencing, and viral RNA detection by in situ hybridization. Next, we unraveled the transcriptomic landscape of SARS-CoV-2 liver tropism, revealing significant increases in interferon alpha and gamma signaling and compensatory liver-specific metabolic regulation. While these results reflect changes in tissues from patients with severe SARS-CoV-2 infection, the profound molecular alterations raise questions about the potential long-term consequences of COVID-19 infection