Exploring the Contextual Sensitivity of Factors that Determine Cell-to-Cell Variability in Receptor-Mediated Apoptosis

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

Cell-to-cell variability in cell death: can systems biology help us make sense of it all?

One of the most common observations in cell death assays is that not all cells die at the same time, or at the same treatment dose. Here, using the perspective of the systems biology of apoptosis and the context of cancer treatment, we discuss possible sources of this cell-to-cell variability as well as its implications for quantitative measurements and computational models of cell death. Many different factors, both within and outside of the apoptosis signaling networks, have been correlated with the variable responses to various death-inducing treatments. Systems biology models offer us the opportunity to take a more synoptic view of the cell death process to identify multifactorial determinants of the cell death decision. Finally, with an eye toward ‘systems pharmacology', we discuss how leveraging this new understanding should help us develop combination treatment strategies to compel cancer cells toward apoptosis by manipulating either the biochemical state of cancer cells or the dynamics of signal transduction

Mitochondrial Fission After Traumatic Brain Injury

Mitochondrial dysfunction is a central feature in the pathophysiology of Traumatic Brain Injury (TBI). Loss of mitochondrial function disrupts normal cellular processes in the brain, as well as impedes the ability for repair and recovery, creating a vicious cycle that perpetuates damage after injury. To maintain metabolic homeostasis and cellular health, mitochondria constantly undergo regulated processes of fusion and fission and functionally adapt to changes in the cellular environment. An imbalance of these processes can disrupt the ability for mitochondria to functionally meet the metabolic needs of the cell, therefore resulting in mitochondrial damage and eventual cell death. Excessive fission, in particular, has been identified as a key pathological event in neuronal damage and death in many neurodegenerative disease models. Specifically, dysregulation of the primary protein regulator of mitochondrial fission, Dynamin-related Protein 1 (Drp1), has been implicated as an underlying mechanism associated with excessive fission and neurodegeneration; however, whether dysregulation of Drp1 and excessive fission occur after TBI and contribute to neuropathological outcome is not well known. The studies described in this dissertation investigate the following hypothesis: TBI causes dysregulation of Drp1 and increases mitochondrial fission in the hippocampus, and inhibiting Drp1 will reduce mitochondrial dysfunction, reduce neuronal damage, and improve cognitive function after injury. Results from these studies revealed four key findings: 1) Experimental TBI increases Drp1 association with mitochondria, and 2) causes acute changes in Drp1-mediated mitochondrial morphology that persists post-injury, indicating increased mitochondrial fission acutely after injury. Additionally, 3) post-injury treatment with a pharmacological inhibitor of Drp1, Mdivi-1, improved survival of newly born neurons in the injured hippocampus, and 4) improved hippocampal-dependent cognitive function after experimental TBI. Taken together, results from these studies reveal that TBI causes excessive Drp1-mediated mitochondrial fission and that this pathological fission state may play a key role in hippocampal neuronal death and cognitive deficits after TBI. Furthermore, these findings indicate inhibition of Drp1 and mitochondrial fission as a potential therapeutic strategy to improve neuronal recovery and cognitive function after injury

Fundamental trade-offs between information flow in single cells and cellular populations

Signal transduction networks allow eukaryotic cells to make decisions based on information about intracellular state and the environment. Biochemical noise significantly diminishes the fidelity of signaling: networks examined to date seem to transmit less than 1 bit of information. It is unclear how networks that control critical cell-fate decisions (e.g., cell division and apoptosis) can function with such low levels of information transfer. Here, we use theory, experiments, and numerical analysis to demonstrate an inherent trade-off between the information transferred in individual cells and the information available to control population-level responses. Noise in receptor-mediated apoptosis reduces information transfer to approximately 1 bit at the single-cell level but allows 3–4 bits of information to be transmitted at the population level. For processes such as eukaryotic chemotaxis, in which single cells are the functional unit, we find high levels of information transmission at a single-cell level. Thus, low levels of information transfer are unlikely to represent a physical limit. Instead, we propose that signaling networks exploit noise at the single-cell level to increase population-level information transfer, allowing extracellular ligands, whose levels are also subject to noise, to incrementally regulate phenotypic changes. This is particularly critical for discrete changes in fate (e.g., life vs. death) for which the key variable is the fraction of cells engaged. Our findings provide a framework for rationalizing the high levels of noise in metazoan signaling networks and have implications for the development of drugs that target these networks in the treatment of cancer and other diseases

Cell-specific responses to the cytokine TGFβ are determined by variability in protein levels

The cytokine TGFβ provides important information during embryonic development, adult tissue homeostasis, and regeneration. Alterations in the cellular response to TGFβ are involved in severe human diseases. To understand how cells encode the extracellular input and transmit its information to elicit appropriate responses, we acquired quantitative time-resolved measurements of pathway activation at the single-cell level. We established dynamic time warping to quantitatively compare signaling dynamics of thousands of individual cells and described heterogeneous single-cell responses by mathematical modeling. Our combined experimental and theoretical study revealed that the response to a given dose of TGFβ is determined cell specifically by the levels of defined signaling proteins. This heterogeneity in signaling protein expression leads to decomposition of cells into classes with qualitatively distinct signaling dynamics and phenotypic outcome. Negative feedback regulators promote heterogeneous signaling, as a SMAD7 knock-out specifically affected the signal duration in a subpopulation of cells. Taken together, we propose a quantitative framework that allows predicting and testing sources of cellular signaling heterogeneity

Paraoxonase 2 deficiency in mice alters motor behavior and causes region-specific transcript changes in the brain

Paraoxonase 2 (PON2) is an intracellular antioxidant enzyme shown to play an important role in mitigating oxidative stress in the brain. Oxidative stress is a common mechanism of toxicity for neurotoxicants and is increasingly implicated in the etiology of multiple neurological diseases. While PON2 deficiency increases oxidative stress in the brain in-vitro, little is known about its effects on behavior in-vivo and what global transcript changes occur from PON2 deficiency. We sought to characterize the effects of PON2 deficiency on behavior in mice, with an emphasis on locomotion, and evaluate transcriptional changes with RNA-Seq. Behavioral endpoints included home-cage behavior (Noldus PhenoTyper), motor coordination (Rotarod) and various gait metrics (Noldus CatWalk). Home-cage behavior analysis showed PON2 deficient mice had increased activity at night compared to wildtype controls and spent more time in the center of the cage, displaying a possible anxiolytic phenotype. PON2 deficient mice had significantly shorter latency to fall when tested on the rotarod, suggesting impaired motor coordination. Minimal gait alterations were observed, with decreased girdle support posture noted as the only significant change in gait with PON2 deficiency. Beyond one home-cage metric, no significant sex-based behavioral differences were found in this study. Finally, A subset of samples were utilized for RNA-Seq analysis, looking at three discrete brain regions: cerebral cortex, striatum, and cerebellum. Highly regional- and sex-specific changes in RNA expression were found when comparing PON2 deficient and wildtype mice, suggesting PON2 may play distinct regional roles in the brain in a sex-specific manner. Taken together, these findings demonstrates that PON2 deficiency significantly alters the brain on both a biochemical and phenotypic level, with a specific impact on motor function. These data have implications for future gene-environment toxicological studies and warrants further investigation of the role of PON2 in the brain

An Integrative Apoptotic Reaction Model for extrinsic and intrinsic stimuli

Apoptosis, a form of programmed cell death central to all multicellular organisms, plays a key role during organism development and is often misregulated in cancer. Devising a single model applicable to distinct stimuli and conditions has been limited by lack of robust observables. Indeed, previous numerical models have been tailored to fit experimental datasets in restricted scenarios, failing to predict response to different stimuli. We quantified the activity of three caspases simultaneously upon intrinsic or extrinsic stimulation to assemble a comprehensive dataset. We measured and modeled the time between maximum activity of intrinsic, extrinsic and effector caspases, a robust observable of network dynamics, to create the first integrated Apoptotic Reaction Model (ARM). Observing how effector caspases reach maximum activity first irrespective of stimuli used, led us to identify and incorporate a missing feedback into a successful model for extrinsic stimulation. By simulating different recently performed experiments, we corroborated that ARM adequately describes them. This integrated model provides further insight into the indispensable feedback from effector caspase to initiator caspases.Fil: Corbat, Agustín Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Silberberg, Mauro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Grecco, Hernan Edgardo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Max Planck Institute of Molecular Physiology; Alemani