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Gaudet, Suzanne

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Gaudet

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Suzanne

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Gaudet, Suzanne
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Now showing 1 - 6 of 6
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    NF-κB signalling and cell fate decisions in response to a short pulse of tumour necrosis factor
    (Nature Publishing Group, 2016) Lee, Robin E. C.; Qasaimeh, Mohammad A.; Xia, Xianfang; Juncker, David; Gaudet, Suzanne
    In tissues and tumours, cell behaviours are regulated by multiple time-varying signals. While in the laboratory cells are often exposed to a stimulus for the duration of the experiment, in vivo exposures may be much shorter. In this study, we monitored NF-κB and caspase signalling in human cancer cells treated with a short pulse of Tumour Necrosis Factor (TNF). TNF is an inflammatory cytokine that can induce both the pro-survival NF-κB-driven gene transcription pathway and the pro-apoptotic caspase pathway. We find that a few seconds of exposure to TNF is sufficient to activate the NF-κB pathway in HeLa cells and induce apoptotic cell death in both HeLa and Kym-1 cells. Strikingly, a 1-min pulse of TNF can be more effective at killing than a 1-hour pulse, indicating that in addition to TNF concentration, duration of exposure also coordinates cell fate decisions.
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    Exploring the Contextual Sensitivity of Factors that Determine Cell-to-Cell Variability in Receptor-Mediated Apoptosis
    (Public Library of Science, 2012) Gaudet, Suzanne; Spencer, Sabrina L.; Chen, William Wei-Lun; Sorger, Peter
    Stochastic fluctuations in gene expression give rise to cell-to-cell variability in protein levels which can potentially cause variability in cellular phenotype. For TRAIL (TNF-related apoptosis-inducing ligand) variability manifests itself as dramatic differences in the time between ligand exposure and the sudden activation of the effector caspases that kill cells. However, the contribution of individual proteins to phenotypic variability has not been explored in detail. In this paper we use feature-based sensitivity analysis as a means to estimate the impact of variation in key apoptosis regulators on variability in the dynamics of cell death. We use Monte Carlo sampling from measured protein concentration distributions in combination with a previously validated ordinary differential equation model of apoptosis to simulate the dynamics of receptor-mediated apoptosis. We find that variation in the concentrations of some proteins matters much more than variation in others and that precisely which proteins matter depends both on the concentrations of other proteins and on whether correlations in protein levels are taken into account. A prediction from simulation that we confirm experimentally is that variability in fate is sensitive to even small increases in the levels of Bcl-2. We also show that sensitivity to Bcl-2 levels is itself sensitive to the levels of interacting proteins. The contextual dependency is implicit in the mathematical formulation of sensitivity, but our data show that it is also important for biologically relevant parameter values. Our work provides a conceptual and practical means to study and understand the impact of cell-to-cell variability in protein expression levels on cell fate using deterministic models and sampling from parameter distributions.
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    The MEK2-binding tumor suppressor hDlg is recruited by E-cadherin to the midbody ring
    (BioMed Central, 2011) Gaudet, Suzanne; Langlois, Marie-Josée; Lue, Robert; Rivard, Nathalie; Viel, Alain
    Background: The human homologue of the Drosophila Discs-large tumor suppressor protein, hDlg, is a multi-domain cytoplasmic protein that localizes to the membrane at intercellular junction sites. At both synaptic junctions and epithelia cell-cell junctions, hDlg is known to recruit several signaling proteins into macromolecular complexes. hDlg is also found at the midbody, a small microtubule-rich structure bridging the two daughter cells during cytokinesis, but its function at this site is not clear. Results: Here we describe the interaction of hDlg with the activated form of MEK2 of the canonical RAF/MEK/ERK pathway, a protein that is found at the midbody during cytokinesis. We show that both proteins localize to a sub-structure of the midbody, the midbody ring, and that the interaction between the PDZ domains of hDlg and the C-terminal portion of MEK2 is dependent on the phosphorylation of MEK2. Finally, we found that E-cadherin also localizes to the midbody and that its expression is required for the isoform-specific recruitment of hDlg, but not activated MEK2, to that structure. Conclusion: Our results suggest that like at other cell-cell junction sites, hDlg is part of a macromolecular complex of structural and signaling proteins at the midbody.
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    Systemic Calibration of a Cell Signaling Network Model
    (BioMed Central, 2010) Spencer, Sabrina Leigh; Albeck, John Gerald; Burke, John M.; Sorger, Peter; Gaudet, Suzanne; Kim, Kyoung Ae; Kim, Do Hyun
    Background: Mathematical modeling is being applied to increasingly complex biological systems and datasets; however, the process of analyzing and calibrating against experimental data is often challenging and a rate limiting step in model development. To address this problem, we developed a systematic methodology for calibrating quantitative models of dynamic biological processes and illustrate its utility by validating a model of TRAIL (Tumor necrosis factor Related Apoptosis-Inducing Ligand)-induced cell death. Results: We propose a serial framework integrating analysis and calibration modules and we compare various methods for global sensitivity analysis and global parameter estimation. First, adequacy of the network structure is checked by global sensitivity analysis to changes in concentrations of molecular species, validating that the model can reproduce qualitative features of the system behavior derived from experiments or literature surveys. Second, rate parameters are ranked by importance using gradient-based and variance-based sensitivity indices, and we systematically determine the optimal number of parameters to include in model calibration. Third, deterministic, stochastic and hybrid algorithms for global optimization are applied to estimate the values of the most important parameters by fitting to time series data. We compare the performance of these three optimization algorithms. Conclusions: Our proposed framework covers the entire process from validating a proto-model to establishing a realistic model for in silico experiments and thereby provides a generalized workflow for the construction of predictive models of complex network systems.
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    Lyapunov Exponents and Phase Diagrams Reveal Multi-Factorial Control over TRAIL-Induced Apoptosis
    (Nature Publishing Group, 2011) Aldridge, Bree; Gaudet, Suzanne; Lauffenburger, Douglas A.; Sorger, Peter
    Receptor-mediated apoptosis proceeds via two pathways: one requiring only a cascade of initiator and effector caspases (type I behavior) and the second requiring an initiator–effector caspase cascade and mitochondrial outer membrane permeabilization (type II behavior). Here, we investigate factors controlling type I versus II phenotypes by performing Lyapunov exponent analysis of an ODE-based model of cell death. The resulting phase diagrams predict that the ratio of XIAP to pro-caspase-3 concentrations plays a key regulatory role: type I behavior predominates when the ratio is low and type II behavior when the ratio is high. Cell-to-cell variability in phenotype is observed when the ratio is close to the type I versus II boundary. By positioning multiple tumor cell lines on the phase diagram we confirm these predictions. We also extend phase space analysis to mutations affecting the rate of caspase-3 ubiquitylation by XIAP, predicting and showing that such mutations abolish all-or-none control over activation of effector caspases. Thus, phase diagrams derived from Lyapunov exponent analysis represent a means to study multi-factorial control over a complex biochemical pathway.
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    A Reference Map of the Human Binary Protein Interactome
    (Nature Research, 2020-04-08) Luck, Katja; Kim, Dae-Kyum; Lambourne, Luke; Spirohn, Kerstin; Begg, Bridget E; Bian, Wenting; Brignall, Ruth; Cafarelli, Tiziana; Campos-Laborie, Francisco J.; Charloteaux, Benoit; Choi, Dongsic; Coté, Atina; Daley, Meaghan; Deimling, Steven; Desbuleux, Alice; Dricot, Amélie; Gebbia, Marinella; Hardy, Madeleine; Kishore, Nishka; Knapp, Jennifer; Kovács, István A.; Lemmens, Irma; Mee, Miles W.; Mellor, Joseph C.; Pollis, Carl; Pons, Carles; Richardson, Aaron; Schlabach, Sadie; Teeking, Bridget; Yadav, Anupama; Babor, Mariana; Balcha, Dawit; Basha, Omer; Bowman-Colin, Christian; Chin, Suet-Feung; Choi, Soon Gang; Colabella, Claudia; Coppin, Georges; D'Amata, Cassandra; De Ridder, David; De Rouck, Steffi; Duran-Frigola, Miquel; Ennajdaoui, Hanane; Goebels, Florian; Goehring, Liana; Gopal, Anjali; Haddad, Ghazal; Hatchi, Elodie; Helmy, Mohamed; Jacob, Yves; Kassa, Yoseph; Landini, Serena; Li, Roujia; van Lieshout, Natascha; MacWilliams, Andrew; Markey, Dylan; Paulson, Joseph; Rangarajan, Sudharshan; Rasla, John; Rayhan, Ashyad; Rolland, Thomas; San Miguel Delgadillo, Adriana; Shen, Yun; Sheykhkarimli, Dayag; Sheynkman, Gloria; Simonovsky, Eyal; Taşan, Murat; Tejeda, Alexander; Tropepe, Vincent; Twizere, Jean-Claude; Wang, Yang; Weatheritt, Robert; Weile, Jochen; Xia, Yu; Yang, Xinping; Yeger-Lotem, Esti; Zhong, Quan; Aloy, Patrick; Bader, Gary D.; De Las Rivas, Javier; Gaudet, Suzanne; Hao, Tong; Rak, Janusz; Tavernier, Jan; Hill, David; Vidal, Marc; Roth, Frederick P.; Calderwood, Michael
    Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships. Here, we present a human “all-by-all” reference interactome map of human binary protein interactions, or “HuRI”. With ~53,000 high-quality protein-protein interactions (PPIs), HuRI has approximately four times more such interactions than high-quality curated interactions from small-scale studies. Integrating HuRI with genome, transcriptome, and proteome data enables the study of cellular function within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying specific subcellular roles of PPIs. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI represents a systematic proteome-wide reference linking genomic variation to phenotypic outcomes.