Heather A. Harrington

Coronary Intraplaque Hemorrhage Evokes a Novel Atheroprotective Macrophage Phenotype

Intraplaque hemorrhage accelerates atherosclerosis via oxidant stress and contributes to lesion development and destabilization. Normally, macrophages scavenge hemoglobin-haptoglobin (HbHp) complexes via CD163, and this process provokes the secretion of the anti-inflammatory atheroprotective cytokine interleukin (IL)-10. We therefore tested the hypothesis that HbHp complexes may drive monocyte differentiation to an atheroprotective phenotype. Examination of the macrophage phenotype in hemorrhaged atherosclerotic plaques revealed a novel hemorrhage-associated macrophage population (HA-mac), defined by high levels of CD163, but low levels of human leukocyte antigen-DR. HA-mac contained more iron, a pro-oxidant catalyst, but paradoxically had less oxidative injury, measured by 8-oxo-guanosine content. Differentiating monocytes with HbHp complexes reproduced the CD163(high) human leukocyte antigen-DR(low) HA-mac phenotype in vitro. These in vitro HA-mac cells cleared Hb more quickly, and consistently showed less hydrogen peroxide release, highly reactive oxygen species and oxidant stress, and increased survival. Differentiation to HA-mac was prevented by neutralizing IL-10 antibodies, indicating that IL-10 mediates an autocrine feedback mechanism in this system. Nonlinear dynamic modeling showed that an IL-10/CD163-positive feedback loop drove a discrete HA-mac lineage. Simulations further indicated an all-or-none switch to HA-mac at threshold levels of HbHp, and this conversion was experimentally verified. These data demonstrate the creation of a novel atheroprotective (HA-mac) macrophage subpopulation in response to intraplaque hemorrhage and raise the possibility that therapeutically reproducing this macrophage phenotype may be cardio-protective in cases of atherosclerosis.

Construction and analysis of a modular model of caspase activation in apoptosis

BackgroundA key physiological mechanism employed by multicellular organisms is apoptosis, or programmed cell death. Apoptosis is triggered by the activation of caspases in response to both extracellular (extrinsic) and intracellular (intrinsic) signals. The extrinsic and intrinsic pathways are characterized by the formation of the death-inducing signaling complex (DISC) and the apoptosome, respectively; both the DISC and the apoptosome are oligomers with complex formation dynamics. Additionally, the extrinsic and intrinsic pathways are coupled through the mitochondrial apoptosis-induced channel via the Bcl-2 family of proteins.ResultsA model of caspase activation is constructed and analyzed. The apoptosis signaling network is simplified through modularization methodologies and equilibrium abstractions for three functional modules. The mathematical model is composed of a system of ordinary differential equations which is numerically solved. Multiple linear regression analysis investigates the role of each module and reduced models are constructed to identify key contributions of the extrinsic and intrinsic pathways in triggering apoptosis for different cell lines.ConclusionThrough linear regression techniques, we identified the feedbacks, dissociation of complexes, and negative regulators as the key components in apoptosis. The analysis and reduced models for our model formulation reveal that the chosen cell lines predominately exhibit strong extrinsic caspase, typical of type I cell, behavior. Furthermore, under the simplified model framework, the selected cells lines exhibit different modes by which caspase activation may occur. Finally the proposed modularized model of apoptosis may generalize behavior for additional cells and tissues, specifically identifying and predicting components responsible for the transition from type I to type II cell behavior.

A roadmap for the computation of persistent homology

Persistent homology (PH) is a method used in topological data analysis (TDA) to study qualitative features of data that persist across multiple scales. It is robust to perturbations of input data, independent of dimensions and coordinates, and provides a compact representation of the qualitative features of the input. The computation of PH is an open area with numerous important and fascinating challenges. The field of PH computation is evolving rapidly, and new algorithms and software implementations are being updated and released at a rapid pace. The purposes of our article are to (1) introduce theory and computational methods for PH to a broad range of computational scientists and (2) provide benchmarks of state-of-the-art implementations for the computation of PH. We give a friendly introduction to PH, navigate the pipeline for the computation of PH with an eye towards applications, and use a range of synthetic and real-world data sets to evaluate currently available open-source implementations for the computation of PH. Based on our benchmarking, we indicate which algorithms and implementations are best suited to different types of data sets. In an accompanying tutorial, we provide guidelines for the computation of PH. We make publicly available all scripts that we wrote for the tutorial, and we make available the processed version of the data sets used in the benchmarking.

Nuclear to cytoplasmic shuttling of ERK promotes differentiation of muscle stem/progenitor cells

The transition between the proliferation and differentiation of progenitor cells is a key step in organogenesis, and alterations in this process can lead to developmental disorders. The extracellular signal-regulated kinase 1/2 (ERK) signaling pathway is one of the most intensively studied signaling mechanisms that regulates both proliferation and differentiation. How a single molecule (e.g. ERK) can regulate two opposing cellular outcomes is still a mystery. Using both chick and mouse models, we shed light on the mechanism responsible for the switch from proliferation to differentiation of head muscle progenitors and implicate ERK subcellular localization. Manipulation of the fibroblast growth factor (FGF)-ERK signaling pathway in chick embryos in vitro and in vivo demonstrated that blockage of this pathway accelerated myogenic differentiation, whereas its activation diminished it. We next examined whether the spatial subcellular localization of ERK could act as a switch between proliferation (nuclear ERK) and differentiation (cytoplasmic ERK) of muscle progenitors. A myristoylated peptide that blocks importin 7-mediated ERK nuclear translocation induced robust myogenic differentiation of muscle progenitor/stem cells in both head and trunk. In the mouse, analysis of Sprouty mutant embryos revealed that increased ERK signaling suppressed both head and trunk myogenesis. Our findings, corroborated by mathematical modeling, suggest that ERK shuttling between the nucleus and the cytoplasm provides a switch-like transition between proliferation and differentiation of muscle progenitors.

Topological data analysis of contagion maps for examining spreading processes on networks

Social and biological contagions are influenced by the spatial embeddedness of networks. Historically, many epidemics spread as a wave across part of the Earth’s surface; however, in modern contagions long-range edges—for example, due to airline transportation or communication media—allow clusters of a contagion to appear in distant locations. Here we study the spread of contagions on networks through a methodology grounded in topological data analysis and nonlinear dimension reduction. We construct “contagion maps” that use multiple contagions on a network to map the nodes as a point cloud. By analyzing the topology, geometry, and dimensionality of manifold structure in such point clouds, we reveal insights to aid in the modeling, forecast, and control of spreading processes. Our approach highlights contagion maps also as a viable tool for inferring low-dimensional structure in networks.

Bistability in apoptosis by receptor clustering.

Apoptosis is a highly regulated cell death mechanism involved in many physiological processes. A key component of extrinsically activated apoptosis is the death receptor Fas which, on binding to its cognate ligand FasL, oligomerize to form the death-inducing signaling complex. Motivated by recent experimental data, we propose a mathematical model of death ligand-receptor dynamics where FasL acts as a clustering agent for Fas, which form locally stable signaling platforms through proximity-induced receptor interactions. Significantly, the model exhibits hysteresis, providing an upstream mechanism for bistability and robustness. At low receptor concentrations, the bistability is contingent on the trimerism of FasL. Moreover, irreversible bistability, representing a committed cell death decision, emerges at high concentrations which may be achieved through receptor pre-association or localization onto membrane lipid rafts. Thus, our model provides a novel theory for these observed biological phenomena within the unified context of bistability. Importantly, as Fas interactions initiate the extrinsic apoptotic pathway, our model also suggests a mechanism by which cells may function as bistable life/death switches independently of any such dynamics in their downstream components. Our results highlight the role of death receptors in deciding cell fate and add to the signal processing capabilities attributed to receptor clustering.

A hybrid model for tumor-induced angiogenesis in the cornea in the presence of inhibitors

The present work formulates and analyzes, by means of numerical experiments, a model for tumor-induced angiogenesis in the presence of inhibitors in the cornea. Our model is a generalization of the earlier work of Tong and Yuan [S. Tong, F. Yuan, Numerical simulations of angiogenesis in the cornea, Microvascular Research 61 (2001) 14-27] to incorporate the role of inhibitors, relevant to experimental assays. The derived set of hybrid equations consists of partial differential equations for the tumor angiogenic factors and the inhibitors with a particle model for the motion of the endothelial cells. This is analyzed numerically in the two-dimensional setting. The relevant results are discussed and qualitative agreement with the experimental work is illustrated.

Cellular Compartments Cause Multistability and Allow Cells to Process More Information

Many biological, physical, and social interactions have a particular dependence on where they take place; e.g., in living cells, protein movement between the nucleus and cytoplasm affects cellular responses (i.e., proteins must be present in the nucleus to regulate their target genes). Here we use recent developments from dynamical systems and chemical reaction network theory to identify and characterize the key-role of the spatial organization of eukaryotic cells in cellular information processing. In particular, the existence of distinct compartments plays a pivotal role in whether a system is capable of multistationarity (multiple response states), and is thus directly linked to the amount of information that the signaling molecules can represent in the nucleus. Multistationarity provides a mechanism for switching between different response states in cell signaling systems and enables multiple outcomes for cellular-decision making. We combine different mathematical techniques to provide a heuristic procedure to determine if a system has the capacity for multiple steady states, and find conditions that ensure that multiple steady states cannot occur. Notably, we find that introducing species localization can alter the capacity for multistationarity, and we mathematically demonstrate that shuttling confers flexibility for and greater control of the emergence of an all-or-nothing response of a cell.

Parameter-free methods distinguish Wnt pathway models and guide design of experiments

Significance The canonical Wnt/β-catenin signaling pathway is important for essential cellular functions and is implicated in many diseases. We introduce a new mathematical model that focuses on β-catenin degradation and protein shuttling between cellular compartments. We compare our model to others and show that all fit to time-dependent experimental data. To evade this parameter problem, we use algebraic methods and characterize model features that are independent of the choice of parameter values. We find that multiple responses to Wnt are feasible under certain conditions for the new model, but not for the others; moreover, we provide dependencies between species (variables) that inform future experiments and model discrimination. We also highlight the wide applicability of these tools across problems in systems biology. The canonical Wnt signaling pathway, mediated by β-catenin, is crucially involved in development, adult stem cell tissue maintenance, and a host of diseases including cancer. We analyze existing mathematical models of Wnt and compare them to a new Wnt signaling model that targets spatial localization; our aim is to distinguish between the models and distill biological insight from them. Using Bayesian methods we infer parameters for each model from mammalian Wnt signaling data and find that all models can fit this time course. We appeal to algebraic methods (concepts from chemical reaction network theory and matroid theory) to analyze the models without recourse to specific parameter values. These approaches provide insight into aspects of Wnt regulation: the new model, via control of shuttling and degradation parameters, permits multiple stable steady states corresponding to stem-like vs. committed cell states in the differentiation hierarchy. Our analysis also identifies groups of variables that should be measured to fully characterize and discriminate between competing models, and thus serves as a guide for performing minimal experiments for model comparison.

Parameter-free model discrimination criterion based on steady-state coplanarity

We introduce a procedure for deciding when a mass-action model is incompatible with observed steady-state data that does not require any parameter estimation. Thus, we avoid the difficulties of nonlinear optimization typically associated with methods based on parameter fitting. Instead, we borrow ideas from algebraic geometry to construct a transformation of the model variables such that any set of steady states of the model under that transformation lies on a common plane, irrespective of the values of the model parameters. Model rejection can then be performed by assessing the degree to which the transformed data deviate from coplanarity. We demonstrate our method by applying it to models of multisite phosphorylation and cell death signaling. Our framework offers a parameter-free perspective on the statistical model selection problem, which can complement conventional statistical methods in certain classes of problems where inference has to be based on steady-state data and the model structures allow for suitable algebraic relationships among the steady-state solutions.