Jennifer J. Linderman

A Spatial Focusing Model for G Protein Signals REGULATOR OF G PROTEIN SIGNALING (RGS) PROTEIN-MEDIATED KINETIC SCAFFOLDING

Regulators of G protein signaling (RGS) are GTPase-accelerating proteins (GAPs), which can inhibit heterotrimeric G protein pathways. In this study, we provide experimental and theoretical evidence that high concentrations of receptors (as at a synapse) can lead to saturation of GDP-GTP exchange making GTP hydrolysis rate-limiting. This results in local depletion of inactive heterotrimeric G-GDP, which is reversed by RGS GAP activity. Thus, RGS enhances receptor-mediated G protein activation even as it deactivates the G protein. Evidence supporting this model includes a GTP-dependent enhancement of guanosine 5′-3-O-(thio)triphosphate (GTPγS) binding to Gi by RGS. The RGS domain of RGS4 is sufficient for this, not requiring the NH2- or COOH-terminal extensions. Furthermore, a kinetic model including only the GAP activity of RGS replicates the GTP-dependent enhancement of GTPγS binding observed experimentally. Finally in a Monte Carlo model, this mechanism results in a dramatic “spatial focusing” of active G protein. Near the receptor, G protein activity is maintained even with RGS due to the ability of RGS to reduce depletion of local Gα-GDP levels permitting rapid recoupling to receptor and maintained G protein activation near the receptor. In contrast, distant signals are suppressed by the RGS, since Gα-GDP is not depleted there. Thus, a novel RGS-mediated “kinetic scaffolding” mechanism is proposed which narrows the spatial range of active G protein around a cluster of receptors limiting the spill-over of G protein signals to more distant effector molecules, thus enhancing the specificity of Gi protein signals.

Multiscale computational modeling reveals a critical role for TNF-α receptor 1 dynamics in tuberculosis granuloma formation

Multiple immune factors control host responses to Mycobacterium tuberculosis infection, including the formation of granulomas, which are aggregates of immune cells whose function may reflect success or failure of the host to contain infection. One such factor is TNF-α. TNF-α has been experimentally characterized to have the following activities in M. tuberculosis infection: macrophage activation, apoptosis, and chemokine and cytokine production. Availability of TNF-α within a granuloma has been proposed to play a critical role in immunity to M. tuberculosis. However, in vivo measurement of a TNF-α concentration gradient and activities within a granuloma are not experimentally feasible. Further, processes that control TNF-α concentration and activities in a granuloma remain unknown. We developed a multiscale computational model that includes molecular, cellular, and tissue scale events that occur during granuloma formation and maintenance in lung. We use our model to identify processes that regulate TNF-α concentration and cellular behaviors and thus influence the outcome of infection within a granuloma. Our model predicts that TNF-αR1 internalization kinetics play a critical role in infection control within a granuloma, controlling whether there is clearance of bacteria, excessive inflammation, containment of bacteria within a stable granuloma, or uncontrolled growth of bacteria. Our results suggest that there is an interplay between TNF-α and bacterial levels in a granuloma that is controlled by the combined effects of both molecular and cellular scale processes. Finally, our model elucidates processes involved in immunity to M. tuberculosis that may be new targets for therapy.

Differential risk of tuberculosis reactivation among anti-TNF therapies is due to drug binding kinetics and permeability

Increased rates of tuberculosis (TB) reactivation have been reported in humans treated with TNF-α (TNF)-neutralizing drugs, and higher rates are observed with anti-TNF Abs (e.g., infliximab) as compared with TNF receptor fusion protein (etanercept). Mechanisms driving differential reactivation rates and differences in drug action are not known. We use a computational model of a TB granuloma formation that includes TNF/TNF receptor dynamics to elucidate these mechanisms. Our analyses yield three important insights. First, drug binding to membrane-bound TNF critically impairs granuloma function. Second, a higher risk of reactivation induced from Ab-type treatments is primarily due to differences in TNF/drug binding kinetics and permeability. Apoptotic and cytolytic activities of Abs and pharmacokinetic fluctuations in blood concentration of drug are not essential to inducing TB reactivation. Third, we predict specific host factors that, if augmented, would improve granuloma function during anti-TNF therapy. Our findings have implications for the development of safer anti-TNF drugs to treat inflammatory diseases.

Multi-scale modeling predicts a balance of tumor necrosis factor-α and interleukin-10 controls the granuloma environment during Mycobacterium tuberculosis infection.

Interleukin-10 (IL-10) and tumor necrosis factor-α (TNF-α) are key anti- and pro-inflammatory mediators elicited during the host immune response to Mycobacterium tuberculosis (Mtb). Understanding the opposing effects of these mediators is difficult due to the complexity of processes acting across different spatial (molecular, cellular, and tissue) and temporal (seconds to years) scales. We take an in silico approach and use multi-scale agent based modeling of the immune response to Mtb, including molecular scale details for both TNF-α and IL-10. Our model predicts that IL-10 is necessary to modulate macrophage activation levels and to prevent host-induced tissue damage in a granuloma, an aggregate of cells that forms in response to Mtb. We show that TNF-α and IL-10 parameters related to synthesis, signaling, and spatial distribution processes control concentrations of TNF-α and IL-10 in a granuloma and determine infection outcome in the long-term. We devise an overall measure of granuloma function based on three metrics – total bacterial load, macrophage activation levels, and apoptosis of resting macrophages – and use this metric to demonstrate a balance of TNF-α and IL-10 concentrations is essential to Mtb infection control, within a single granuloma, with minimal host-induced tissue damage. Our findings suggest that a balance of TNF-α and IL-10 defines a granuloma environment that may be beneficial for both host and pathogen, but perturbing the balance could be used as a novel therapeutic strategy to modulate infection outcomes.

Self organization of membrane proteins via dimerization.

Protein-protein dimerization is ubiquitous in biology, but its role in self-organization remains unexplored. Here we use Monte Carlo simulations to demonstrate that under diffusion-limited conditions, reversible dimerization alone can cause membrane proteins to cluster into oligomer-like structures. When multiple distinct protein species are able to form dimers, then heterodimerization and homodimerization can organize proteins into structured clusters that can affect cellular physiology. As an example, we demonstrate how receptor dimerization could provide a physical mechanism for regulating information flow by controlling receptor-receptor cross talk. These results are physically realistic for some membrane proteins, including members of the G-protein coupled receptor family, and may provide a physiological reason as to why many proteins dimerize.

Toward a multiscale model of antigen presentation in immunity

Summary:  A functioning immune system and the process of antigen presentation in particular encompass events that occur at multiple length and time scales. Despite a wealth of information in the biological literature regarding each of these scales, no single representation synthesizing this information into a model of the overall immune response as it depends on antigen presentation is available. In this article, we outline an approach for integrating information over relevant biological and temporal scales to generate such a representation for major histocompatibility complex class II‐mediated antigen presentation. In addition, we begin to address how such models can be used to answer questions about mechanisms of infection and new strategies for treatment and vaccines.

Both ligand- and cell-specific parameters control ligand agonism in a kinetic model of g protein-coupled receptor signaling.

G protein–coupled receptors (GPCRs) exist in multiple dynamic states (e.g., ligand-bound, inactive, G protein–coupled) that influence G protein activation and ultimately response generation. In quantitative models of GPCR signaling that incorporate these varied states, parameter values are often uncharacterized or varied over large ranges, making identification of important parameters and signaling outcomes difficult to intuit. Here we identify the ligand- and cell-specific parameters that are important determinants of cell-response behavior in a dynamic model of GPCR signaling using parameter variation and sensitivity analysis. The character of response (i.e., positive/neutral/inverse agonism) is, not surprisingly, significantly influenced by a ligands ability to bias the receptor into an active conformation. We also find that several cell-specific parameters, including the ratio of active to inactive receptor species, the rate constant for G protein activation, and expression levels of receptors and G proteins also dramatically influence agonism. Expressing either receptor or G protein in numbers several fold above or below endogenous levels may result in system behavior inconsistent with that measured in endogenous systems. Finally, small variations in cell-specific parameters identified by sensitivity analysis as significant determinants of response behavior are found to change ligand-induced responses from positive to negative, a phenomenon termed protean agonism. Our findings offer an explanation for protean agonism reported in β2--adrenergic and α2A-adrenergic receptor systems.

Calculation of diffusion-limited kinetics for the reactions in collision coupling and receptor cross-linking

Both enzyme (e.g., G-protein) activation via a collision coupling model and the formation of cross-linked receptors by a multivalent ligand involve reactions between two molecules diffusing in the plasma membrane. The diffusion of these molecules is thought to play a critical role in these two early signal transduction events. In reduced dimensions, however, diffusion is not an effective mixing mechanism; consequently, zones in which the concentration of particular molecules (e.g., enzymes, receptors) becomes depleted or enriched may form. To examine the formation of these depletion/ accumulation zones and their effect on reaction rates and ultimately the cellular response, Monte Carlo techniques are used to simulate the reaction and diffusion of molecules in the plasma membrane. The effective reaction rate at steady state is determined in terms of the physical properties of the tissue and ligand for both enzyme activation via collision coupling and the generation of cross-linked receptors. The diffusion-limited reaction rate constant is shown to scale with the mean square displacement of a receptor-ligand complex. The rate constants determined in the simulation are compared with other theoretical predictions as well as experimental data.

Push-pull perfusion sampling with segmented flow for high temporal and spatial resolution in vivo chemical monitoring.

Low-flow push-pull perfusion is a sampling method that yields better spatial resolution than competitive methods like microdialysis. Because of the low flow rates used (50 nL/min), it is challenging to use this technique at high temporal resolution which requires methods of collecting, manipulating, and analyzing nanoliter samples. High temporal resolution also requires control of Taylor dispersion during sampling. To meet these challenges, push-pull perfusion was coupled with segmented flow to achieve in vivo sampling at 7 s temporal resolution at 50 nL/min flow rates. By further miniaturizing the probe inlet, sampling with 200 ms resolution at 30 nL/min (pull only) was demonstrated in vitro. Using this method, L-glutamate was monitored in the striatum of anesthetized rats. Up to 500 samples of 6 nL each were collected at 7 s intervals, segmented by an immiscible oil and stored in a capillary tube. The samples were assayed offline for L-glutamate at a rate of 15 samples/min by pumping them into a reagent addition tee fabricated from Teflon where reagents were added for a fluorescent enzyme assay. Fluorescence of the resulting plugs was monitored downstream. Microinjection of 70 mM potassium in physiological buffered saline evoked l-glutamate concentration transients that had an average maxima of 4.5 ± 1.1 μM (n = 6 animals, 3-4 injections each) and rise times of 22 ± 2 s. These results demonstrate that low-flow push-pull perfusion with segmented flow can be used for high temporal resolution chemical monitoring and in complex biological environments.

Peptide length-based prediction of peptide--MHC class II binding

MOTIVATION Algorithms for predicting peptide-MHC class II binding are typically similar, if not identical, to methods for predicting peptide-MHC class I binding despite known differences between the two scenarios. We investigate whether representing one of these differences, the greater range of peptide lengths binding MHC class II, improves the performance of these algorithms. RESULTS A non-linear relationship between peptide length and peptide-MHC class II binding affinity was identified in the data available for several MHC class II alleles. Peptide length was incorporated into existing prediction algorithms using one of several modifications: using regression to pre-process the data, using peptide length as an additional variable within the algorithm, or representing register shifting in longer peptides. For several datasets and at least two algorithms these modifications consistently improved prediction accuracy. AVAILABILITY http://malthus.micro.med.umich.edu/Bioinformatics