TRAIL-induced variation of cell signaling states provides nonheritable resistance to apoptosis.

TNFα-related apoptosis-inducing ligand (TRAIL), specifically initiates programmed cell death, but often fails to eradicate all cells, making it an ineffective therapy for cancer. This fractional killing is linked to cellular variation that bulk assays cannot capture. Here, we quantify the diversity in cellular signaling responses to TRAIL, linking it to apoptotic frequency across numerous cell systems with single-cell mass cytometry (CyTOF). Although all cells respond to TRAIL, a variable fraction persists without apoptotic progression. This cell-specific behavior is nonheritable where both the TRAIL-induced signaling responses and frequency of apoptotic resistance remain unaffected by prior exposure. The diversity of signaling states upon exposure is correlated to TRAIL resistance. Concomitantly, constricting the variation in signaling response with kinase inhibitors proportionally decreases TRAIL resistance. Simultaneously, TRAIL-induced de novo translation in resistant cells, when blocked by cycloheximide, abrogated all TRAIL resistance. This work highlights how cell signaling diversity, and subsequent translation response, relates to nonheritable fractional escape from TRAIL-induced apoptosis. This refined view of TRAIL resistance provides new avenues to study death ligands in general

Gene switching rate determines response to extrinsic perturbations in the self-activation transcriptional network motif

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Modeling Dynamics of Cell-to-Cell Variability in TRAIL-Induced Apoptosis Explains Fractional Killing and Predicts Reversible Resistance

International audienceIsogenic cells sensing identical external signals can take markedly different decisions. Such decisions often correlate with pre-existing cell-to-cell differences in protein levels. When not neglected in signal transduction models, these differences are accounted for in a static manner, by assuming randomly distributed initial protein levels. However, this approach ignores the a priori non-trivial interplay between signal transduction and the source of this cell-to-cell variability: temporal fluctuations of protein levels in individual cells, driven by noisy synthesis and degradation. Thus, modeling protein fluctuations, rather than their consequences on the initial population heterogeneity, would set the quantitative analysis of signal transduction on firmer grounds. Adopting this dynamical view on cell-to-cell differences amounts to recast extrinsic variability into intrinsic noise. Here, we propose a generic approach to merge, in a systematic and principled manner, signal transduction models with stochastic protein turnover models. When applied to an established kinetic model of TRAIL-induced apoptosis, our approach markedly increased model prediction capabilities. One obtains a mechanistic explanation of yet-unexplained observations on fractional killing and non-trivial robust predictions of the temporal evolution of cell resistance to TRAIL in HeLa cells. Our results provide an alternative explanation to survival via induction of survival pathways since no TRAIL-induced regulations are needed and suggest that short-lived anti-apoptotic protein Mcl1 exhibit large and rare fluctuations. More generally, our results highlight the importance of accounting for stochastic protein turnover to quantitatively understand signal transduction over extended durations, and imply that fluctuations of short-lived proteins deserve particular attention

Développement d'un système de différenciation modulable par la lumière pour la création et le contrôle de consortiums microbiens dans S. cerevisiae, sa caractérisation en cellule unique pour le développement de modèles prédictifs, et son utilisation pour l'expression hétérologue

Les consortiums microbiens artificiels cherchent à exploiter la division du travail pour optimiser des fonctions et possèdent un immense potentiel pour la bioproduction. Les approches de co-culture, le mode préférentiel pour générer des consortiums, restent limitées dans leur capacité à donner naissance à des consortiums stables ayant des compositions précisément ajustées. J'ai développé ici un système de différenciation artificielle dans la levure boulanger capable de générer à partir d'une seule souche des consortiums microbiens stables avec des fonctionnalités choisies et ayant une composition définie par l'utilisateur dans l'espace et dans le temps, grâce à une modification génétique pilotée par optogénétique. Grâce à une dynamique rapide, reproductible et ajustable par la lumière, mon système permet un contrôle dynamique de la composition des consortiums dans des cultures continues pendant de longues périodes. Je démontre également que notre système peut être étendu de manière simple pour donner naissance à des consortiums avec de multiples sous-populations. Cette stratégie de différenciation artificielle établit un nouveau paradigme pour la création de consortiums microbiens complexes qui sont simples à mettre en oeuvre, contrôlables avec précision et polyvalents à utiliser.En plus de cela, j'ai caractérisé le système au niveau de la cellule unique dans différents contextes en changeant la structure du bruit du facteur de transcription optogénétique qui induit la différenciation. J'ai découvert que le changement de la structure du bruit introduisait un couplage complexe entre les niveaux de la population de cellule et des cellules individuelles, qui ne peut être prédit par un simple modèle d'équations différentielles ordinaires. L'utilisation d'un modèle stochastique bien caractérisé a permis de rétablir la prévisibilité.Enfin, j'ai couplé le système de différenciation avec un system d'arrêt de croissance et de bioproduction de sorte que les cellules différenciées arrêtent de croître et commencent à produire une protéine d'intérêt. J'ai comparé l'efficacité de l'approche basée sur la différenciation avec des équivalents constitutifs et inductibles. J'ai constaté que la production n'était pas monotone par rapport à la fraction de différenciation mais qu'elle pouvait surpasser l'expression induite par un promoteur constitutif fort.Artificial microbial consortia seek to leverage division-of-labour to optimize function and possess immense potential for bioproduction. Co-culturing approaches, the preferred mode of generating a consortium, remain limited in their ability to give rise to stable consortia having finely tuned compositions. Here, I developed an artificial differentiation system in budding yeast capable of generating stable microbial consortia with custom functionalities from a single strain at user-defined composition in space and in time based on optogenetically-driven genetic rewiring. Owing to fast, reproducible, and light-tunable dynamics, my system enables dynamic control of consortia composition in continuous cultures for extended periods independently of the cell density. I further demonstrate that our system can be extended in a straightforward manner to give rise to consortia with multiple subpopulations. This artificial differentiation strategy establishes a novel paradigm for the creation of complex microbial consortia that are simple to implement, precisely controllable, and versatile to use.In addition to this, I characterized the system at the single cell level in different genetic contexts by changing the noise structure of the optogenetic transcription factor that drives differentiation. I found that changing the noise structure introduced complex coupling between the population and the single cell level, which cannot be predicted by a simple population model. A stochastic model of differentiation composed in a stochastic model of plasmid fluctuations not only restored predictability, but revealed mechanistic insights into the functioning of the system. The latter was exploited to demonstrate control of expression of a constitutively expressed gene (proxy for plasmid copy number).Lastly, I coupled the differentiation system with a growth arrest and production module such that differentiated cells stop growing and start producing a protein of interest. Growth arrest was effected via hijacking of the mating pheromone pathway and production was carried out by an orthogonal transcription factor. I developed a light inducible reference to assess the increase in production upon growth arrest. Comparing the efficiency of the differentiation-based approach with constitutive and inducible counterparts, I found that production was non-monotonic with respect to differentiation fraction and could outcompete constitutive expression. However, production did not increase upon growth arrest

Simulating tissue mechanics with Agent Based Models: concepts and perspectives

International audienceIn this paper we present an overview of agent based models that are used to simulate mechanical and physiological phenomena in cells and tissues, and we discuss underlying concepts, limitations and future perspectives of these models. As the interest in cell and tissue mechanics increase, agent based models are becoming more common the modeling community. We overview the physical aspects, complexity, shortcomings and capabilities of the major agent based model categories: lattice-based models (cellular automata, lattice gas cellular automata, cellular Potts models), off-lattice models (center based models, deformable cell models, vertex models), and hybrid discrete-continuum models. In this way, we hope to assist future researchers in choosing a model for the phenomenon they want to model and understand. The article also contains some novel results

Characterisation of the role of the NEDD8 E3 ligase DCNL5 in the apoptosis response

Defective in cullin neddylation-like (DCNL) proteins are known to coordinate the addition of NEDD8 to the cullin subunit of the largest family of ubiquitin E3 ligases, the Cullin-RING ligases (CRLs), in a process known as neddylation. The human genome encodes five DCNL proteins which are thought to exhibit a large degree of overlap in function, with only a few neddylation processes having been definitively ascribed to a single DCNL homologue. It currently remains unclear whether these DCNL proteins have functions that extend beyond their roles in cullin neddylation. In the present study we now describe a novel role for one of the family members, DCNL5, in the programmed cell death response known as apoptosis. We have shown that cells lacking DCNL5 function fail to promote caspase 8 cleavage – an important early activation step - in response to various inducers of the extrinsic apoptosis pathway. Caspase 8 cleavage and activation requires polyubiquitination which is known to be mediated by cullin 3 in coordination with the dual ubiquitin and NEDD8 ligase RBX1. This process is thought to occur in lipid rafts at the plasma membrane and in the cytosol. In the present work, we provide the first indication that DCNL5 is able to translocate out of the nucleus where it was previously thought to be exclusively located, and this occurs in response to TNFα-related apoptosis-inducing ligand (TRAIL) stimulation. In addition, we present evidence for the first known interaction between DCNL5 and cullin 3 in U2OS cells under endogenous conditions. The DCNL5 KO cells demonstrated a lack of a polyubiquitination event that occurs in WT cells; unmodified caspase 8 was shown to associate with a polyubiquitinated protein (the identity of which we were unable to determine) in response to TRAIL and this interaction was absent in KO cells, perhaps representing the key mechanism underlying DCNL5 involvement. This emerging function for DCNL5 in promoting caspase 8 cleavage was confirmed in multiple cancer cell lines including U2OS, H460 and HeLa cells. Importantly, we demonstrated that siRNA-mediated silencing of DCNL5 prevented CASP8 cleavage. A lot of our work suggested that DCNL5’s role in CASP8 activation is mediated by the cullin CUL3. However, treatment with the neddylation inhibitor MLN4924 caused a reduction, but not a total loss of caspase 8 cleavage, suggesting that if cullin 3 is involved, it may be independent of its neddylation status. This hints at the surprising possibility that a CRL complex exists that that does not require neddylation for some of its function. Furthermore, while our data suggests that DCNL5 may regulate apoptosis via cullin 3, we were unable to exclude a cullin neddylation-independent role for DCNL5 in this process. Future work will need to answer this question by identifying and characterising the molecular target of DCNL5 in apoptosis signalling to ascertain the precise mechanism underlying DCNL5 regulation of this clinically important signalling event