IL-10 dependent adaptation allows macrophages to adjust inflammatory responses to TLR4 stimulation history

During an infection, innate immune cells must adjust nature and strength of their responses to changing pathogen abundances. To determine how stimulation of the pathogen sensing TLR4 shapes subsequent macrophage responses, we systematically varied priming and restimulation concentrations of its ligand KLA. We find that different priming strengths have very distinct effects at multiple stages of the signaling response, including receptor internalization, MAPK activation, cytokine and chemokine production, and nuclear translocation and chromatin association of NFκB and IκB members. In particular, restimulation-induced TNF-α production required KLA doses equal to or greater than those used for prior exposure, indicating that macrophages can detect and adaptively respond to changing TLR4 stimuli. Interestingly, while such adaptation was dependent on the anti-inflammatory cytokine IL-10, exogenous concentrations of IL-10 corresponding to those secreted after strong priming did not exert suppressive effects on TNF-α without such prior priming, confirming the critical role of TLR4 stimulation history.

Conserved biophysical compatibility among the highly variable germline-encoded regions shapes TCR-MHC interactions

T cells are critically important components of the adaptive immune system primarily responsible for identifying and responding to pathogenic challenges. This recognition of pathogens is driven by the interaction between membrane-bound T cell receptors (TCRs) and antigenic peptides presented on major histocompatibility complex (MHC) molecules. The formation of the TCR-peptide-MHC complex (TCR-pMHC) involves interactions among germline-encoded and hypervariable amino acids. Germline-encoded and hypervariable regions can form contacts critical for complex formation, but only interactions between germline-encoded contacts are likely to be shared across many of all the possible productive TCR-pMHC complexes. Despite this, experimental investigation of these interactions have focused on only a small fraction of the possible interaction space. To address this, we analyzed every possible germline-encoded TCR-MHC contact in humans, thereby generating the first comprehensive characterization of these largely antigen-independent interactions. Our computational analysis suggests that germline-encoded TCR-MHC interactions that are conserved at the sequence level are rare due to the high amino acid diversity of the TCR CDR1 and CDR2 loops, and that such conservation is unlikely to dominate the dynamic protein-protein binding interface. Instead, we propose that binding properties such as the docking orientation are defined by regions of biophysical compatibility between these loops and the MHC surface.

An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator

The adaptive immune system employs an array of receptors designed to respond with high specificity to pathogens or molecular aberrations faced by the host organism. Binding of these receptors to molecular fragments-collectively referred to as antigens-initiates immune responses. These antigenic targets are recognized in their native state on the surfaces of pathogens by antibodies, whereas T cell receptors (TCR) recognize processed antigens as short peptides, presented on major histocompatibility complex (MHC) molecules. Recent research has led to a wealth of immune repertoire data that are key to interrogating the nature of these molecular interactions. However, existing tools for the analysis of these large datasets typically focus on molecular sets of a single type, forcing researchers to separately analyze strongly coupled sequences of interacting molecules. Here, we introduce a software package for the integrated analysis of immune repertoire data, capable of identifying distinct biophysical differences in isolated TCR, MHC, peptide, antibody, and antigen sequence data. This integrated analytical approach allows for direct comparisons across immune repertoire subsets and provides a starting point for the identification of key interaction hotspots in complementary receptor-antigen pairs. The software (AIMS-Automated Immune Molecule Separator) is freely available as an open access package in GUI or command-line form.

SARS-CoV-2 antibodies recognize 23 distinct epitopic sites on the receptor binding domain

The COVID-19 pandemic and SARS-CoV-2 variants have dramatically illustrated the need for a better understanding of antigen (epitope)-antibody (paratope) interactions. To gain insight into the immunogenic characteristics of epitopic sites (ES), we systematically investigated the structures of 340 Abs and 83 nanobodies (Nbs) complexed with the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein. We identified 23 distinct ES on the RBD surface and determined the frequencies of amino acid usage in the corresponding CDR paratopes. We describe a clustering method for analysis of ES similarities that reveals binding motifs of the paratopes and that provides insights for vaccine design and therapies for SARS-CoV-2, as well as a broader understanding of the structural basis of Ab-protein antigen (Ag) interactions.

SARS-CoV-2 antibodies recognize 23 distinct epitopic sites on the receptor binding domain

The COVID-19 pandemic and SARS-CoV-2 variants have dramatically illustrated the need for a better understanding of antigen (epitope)-antibody (paratope) interactions. To gain insight into the immunogenic characteristics of epitopic sites (ES), we systematically investigated the structures of 340 Abs and 83 nanobodies (Nbs) complexed with the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein. We identified 23 distinct ES on the RBD surface and determined the frequencies of amino acid usage in the corresponding CDR paratopes. We describe a clustering method for analysis of ES similarities that reveals binding motifs of the paratopes and that provides insights for vaccine design and therapies for SARS-CoV-2, as well as a broader understanding of the structural basis of Ab-protein antigen (Ag) interactions.

Absolute protein quantitation of the mouse macrophage Toll-like receptor and chemotaxis pathways

The Toll-like receptor (TLR) and chemotaxis pathways are key components of the innate immune system. Subtle variation in the concentration, timing, and molecular structure of the ligands are known to affect downstream signaling and the resulting immune response. Computational modeling and simulation at the molecular interaction level can be used to study complex biological pathways, but such simulations require protein concentration values as model parameters. Here we report the development and application of targeted mass spectrometry assays to measure the absolute abundance of proteins of the mouse macrophage Toll-like receptor 4 (TLR4) and chemotaxis pathways. Two peptides per protein were quantified, if possible. The protein abundance values ranged from 1,332 to 227,000,000 copies per cell. They moderately correlated with transcript abundance values from a previously published mouse macrophage RNA-seq dataset, and these two datasets were combined to make proteome-wide abundance estimates. The datasets produced during this investigation can be used for pathway modeling and simulation, as well as for other studies of the TLR and chemotaxis pathways.

Measurement(s)	molecules per cell
Technology Type(s)	nanoflow high-performance liquid chromatography-electrospray ionisation tandem mass spectrometry
Sample Characteristic - Organism	Mus musculus

Space-time histories approach to fast stochastic simulation of bimolecular reactions

Computational models of reaction-diffusion systems involving low copy numbers or strongly heterogeneous molecular spatial distributions, such as those frequently found in cellular signaling pathways, require approaches that account for the stochastic dynamics of individual particles, as opposed to approaches representing them through their average concentrations. Efforts to remedy the high computational cost associated with particle-based stochastic approaches by taking advantage of Green's functions are hampered by the need to draw random numbers from complicated, and therefore costly, non-standard probability distributions to update particle positions. Here, we introduce an approach that permits the reconstruction of entire molecular trajectories, including bimolecular encounters, in retrospect, after a simulated time step, while avoiding inefficient draws from non-standard distributions. This means that highly accurate stochastic simulations can be performed for system sizes that would be prohibitively costly to simulate with conventional Green's function based methods. The algorithm applies equally well to one, two, and three dimensional systems and can be readily extended to include deterministic forces specified by an interaction potential, such as the Coulomb potential.

Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry

Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multimolecular structures embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computer simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However, it is often difficult to reconcile conflicting computational results that use different approaches to describe the same phenomenon. To address this issue systematically, we have defined a series of computational test cases ranging from very simple to moderately complex, varying key features of dimensionality, reaction type, reaction speed, crowding, and cell size. We then quantified how explicit spatial and/or stochastic implementations alter outcomes, even when all methods use the same reaction network, rates, and concentrations. For simple cases, we generally find minor differences in solutions of the same problem. However, we observe increasing discordance as the effects of localization, dimensionality reduction, and irreversible enzymatic reactions are combined. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision making by researchers developing new models. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.

Cadherin-Mediated Cell Coupling Coordinates Chemokine Sensing across Collectively Migrating Cells

The directed migration of cells sculpts the embryo, contributes to homeostasis in the adult, and, when dysregulated, underlies many diseases [1, 2]. During these processes, cells move singly or as a collective. In both cases, they follow guidance cues, which direct them to their destination [3-6]. In contrast to single cells, collectively migrating cells need to coordinate with their neighbors to move together in the same direction. Recent studies suggest that leader cells in the front sense the guidance cue, relay the directional information to the follower cells in the back, and can pull the follower cells along [7-19]. In this manner, leader cells steer the collective and set the collective's overall speed. However, whether follower cells also participate in steering and speed setting of the collective is largely unclear. Using chimeras, we analyzed the role of leader and follower cells in the collectively migrating zebrafish posterior lateral line primordium. This tissue expresses the chemokine receptor Cxcr4 and is guided by the chemokine Cxcl12a [20-23]. We find that leader and follower cells need to sense the attractant Cxcl12a for efficient migration, are coupled to each other through cadherins, and require coupling to pull Cxcl12a-insensitive cells along. Analysis of cell dynamics in chimeric and protein-depleted primordia shows that Cxcl12a-sensing and cadherin-mediated adhesion contribute jointly to direct migration at both single-cell and tissue levels. These results suggest that all cells in the primordium need to sense the attractant and adhere to each other to coordinate their movements and migrate with robust directionality.

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through “wildcards” representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the “type” concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes a medium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications.

A negative-feedback loop maintains optimal chemokine concentrations for directional cell migration

Chemoattractant gradients frequently guide migrating cells. To achieve the most directional signal, such gradients should be maintained with concentrations around the dissociation constant (Kd)1-6 of the chemoreceptor. Whether this actually occurs in animals is unknown. Here we investigate whether a moving tissue, the zebrafish posterior lateral line primordium, buffers its attractant in this concentration range to achieve robust migration. We find that the Cxcl12 (also known as Sdf1) attractant gradient ranges from 0 to 12 nM, values similar to the 3.4 nM Kd of its receptor Cxcr4. When we increase the Kd of Cxcl12 for Cxcr4, primordium migration is less directional. Furthermore, a negative-feedback loop between Cxcl12 and its clearance receptor Ackr3 (also known as Cxcr7) regulates the Cxcl12 concentrations. Breaking this negative feedback by blocking the phosphorylation of the cytoplasmic tail of Ackr3 also results in less directional primordium migration. Thus, directed migration of the primordium is dependent on a close match between the Cxcl12 concentration and the Kd of Cxcl12 for Cxcr4, which is maintained by buffering of the chemokine levels. Quantitative modelling confirms the plausibility of this mechanism. We anticipate that buffering of attractant concentration is a general mechanism for ensuring robust cell migration.

Mechanistic Models of Cellular Signaling, Cytokine Crosstalk, and Cell-Cell Communication in Immunology

The cells of the immune system respond to a great variety of different signals that frequently reach them simultaneously. Computational models of signaling pathways and cellular behavior can help us explore the biochemical mechanisms at play during such responses, in particular when those models aim at incorporating molecular details of intracellular reaction networks. Such detailed models can encompass hypotheses about the interactions among molecular binding domains and how these interactions are modulated by, for instance, post-translational modifications, or steric constraints in multi-molecular complexes. In this way, the models become formal representations of mechanistic immunological hypotheses that can be tested through quantitative simulations. Due to the large number of parameters (molecular abundances, association-, dissociation-, and enzymatic transformation rates) the goal of simulating the models can, however, in many cases no longer be the fitting of particular parameter values. Rather, the simulations perform sweeps through parameter space to test whether a model can account for certain experimentally observed features when allowing the parameter values to vary within experimentally determined or physiologically reasonable ranges. We illustrate how this approach can be used to explore possible mechanisms of immunological pathway crosstalk. Probing the input-output behavior of mechanistic pathway models through systematic simulated variations of receptor stimuli will soon allow us to derive cell population behavior from single-cell models, thereby bridging a scale gap that currently still is frequently addressed through heuristic phenomenological multi-scale models.

Anosmin1 Shuttles Fgf to Facilitate Its Diffusion, Increase Its Local Concentration, and Induce Sensory Organs

Growth factors induce and pattern sensory organs, but how their distribution is regulated by the extracellular matrix (ECM) is largely unclear. To address this question, we analyzed the diffusion behavior of Fgf10 molecules during sensory organ formation in the zebrafish posterior lateral line primordium. In this tissue, secreted Fgf10 induces organ formation at a distance from its source. We find that most Fgf10 molecules are highly diffusive and move rapidly through the ECM. We identify Anosmin1, which when mutated in humans causes Kallmann Syndrome, as an ECM protein that binds to Fgf10 and facilitates its diffusivity by increasing the pool of fast-moving Fgf10 molecules. In the absence of Anosmin1, Fgf10 levels are reduced and organ formation is impaired. Global overexpression of Anosmin1 slows the fast-moving Fgf10 molecules and results in Fgf10 dispersal. These results suggest that Anosmin1 liberates ECM-bound Fgf10 and shuttles it to increase its signaling range.

SBML Level 3 package: Multistate, Multicomponent and Multicompartment Species, Version 1, Release 1

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains (components) and the states such as phosphorylation and binding status of the involved components. Fine-grained spatial information such as the locations of the molecular components relative to a membrane (e.g. whether a modeled molecular domain is embedded into the inner leaflet of the cellular plasma membrane) can also be provided. Through wildcards representing component states entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states and multiple compartments. The SBML Level 3 Multi Package (Multistate, Multicomponent and Multicompartment Species Package for SBML Level 3) extends the SBML Level 3 core with the “type” concept in the Species and Compartment classes and therefore reaction rules may contain species that can be patterns and be in multiple locations in reaction rules. Multiple software tools such as Simmune and BioNetGen support the SBML Level 3 Multi package that thus also becomes a medium for exchanging rule-based models.

A hierarchy of affinities between cytokine receptors and the common gamma chain leads to pathway cross-talk

Cytokines belonging to the common gamma chain (γ_c) family depend on the shared γ_c receptor subunit for signaling. We report the existence of a fast, cytokine-induced pathway cross-talk acting at the receptor level, resulting from a limiting amount of γ_c on the surface of T cells. We found that this limited abundance of γ_c reduced interleukin-4 (IL-4) and IL-21 responses after IL-7 preexposure but not vice versa. Computational modeling combined with quantitative experimental assays indicated that the asymmetric cross-talk resulted from the ability of the "private" IL-7 receptor subunits (IL-7Rα) to bind to many of the γ_c molecules even before stimulation with cytokine. Upon exposure of T cells to IL-7, the high affinity of the IL-7Rα:IL-7 complex for γ_c further reduced the amount of free γ_c in a manner dependent on the concentration of IL-7. Measurements of bioluminescence resonance energy transfer (BRET) between IL-4Rα and γ_c were reduced when IL-7Rα was overexpressed. Furthermore, in a system expressing IL-7Rα, IL-4Rα, and γ_c, BRET between IL-4Rα and γ_c increased after IL-4 binding and decreased when cells were preexposed to IL-7, supporting the assumption that IL-7Rα and the IL-7Rα:IL-7 complex limit the accessibility of γ_c for other cytokine receptor complexes. We propose that in complex inflammatory environments, such asymmetric cross-talk establishes a hierarchy of cytokine responsiveness.

Migrating Myeloid Cells Sense Temporal Dynamics of Chemoattractant Concentrations

Chemoattractant-mediated recruitment of hematopoietic cells to sites of pathogen growth or tissue damage is critical to host defense and organ homeostasis. Chemotaxis is typically considered to rely on spatial sensing, with cells following concentration gradients as long as these are present. Utilizing a microfluidic approach, we found that stable gradients of intermediate chemokines (CCL19 and CXCL12) failed to promote persistent directional migration of dendritic cells or neutrophils. Instead, rising chemokine concentrations were needed, implying that temporal sensing mechanisms controlled prolonged responses to these ligands. This behavior was found to depend on G-coupled receptor kinase-mediated negative regulation of receptor signaling and contrasted with responses to an end agonist chemoattractant (C5a), for which a stable gradient led to persistent migration. These findings identify temporal sensing as a key requirement for long-range myeloid cell migration to intermediate chemokines and provide insights into the mechanisms controlling immune cell motility in complex tissue environments.

Unified path integral approach to theories of diffusion-influenced reactions

Building on mathematical similarities between quantum mechanics and theories of diffusion-influenced reactions, we develop a general approach for computational modeling of diffusion-influenced reactions that is capable of capturing not only the classical Smoluchowski picture but also alternative theories, as is here exemplified by a volume reactivity model. In particular, we prove the path decomposition expansion of various Green's functions describing the irreversible and reversible reaction of an isolated pair of molecules. To this end, we exploit a connection between boundary value and interaction potential problems with δ- and δ^{'}-function perturbation. We employ a known path-integral-based summation of a perturbation series to derive a number of exact identities relating propagators and survival probabilities satisfying different boundary conditions in a unified and systematic manner. Furthermore, we show how the path decomposition expansion represents the propagator as a product of three factors in the Laplace domain that correspond to quantities figuring prominently in stochastic spatially resolved simulation algorithms. This analysis will thus be useful for the interpretation of current and the design of future algorithms. Finally, we discuss the relation between the general approach and the theory of Brownian functionals and calculate the mean residence time for the case of irreversible and reversible reactions.

IRAK1 attenuates TLR signaling and activates the inflammasome in response to dual-TLR stimulation

Macrophages encounter many kinds of pathogen-associated molecules and a coherent immune response involves crosstalk among various immune pathways. How can a cogent response form in the face of crosstalk between various pathways? We address this in the context of combinatorial stimulation of multiple TLRs to assess TLR pathway crosstalk.

We find that IRAK1 forms cytoplasmic clusters that sequester downstream signaling proteins upon dual TLR stimulation. This could explain the less-than additive gene response on dual TLR activation. Accordingly, IRAK1 deficient IMMs and BMDMs have higher phosphoprotein, cytokine and transcriptional responses to dual TLR ligands than WT cells, but their single TLR ligand responses are comparable to WT cells. This suggests that IRAK1-dependent sequestration of key TLR signaling proteins might be a mechanism of modulating the innate immune response to high pathogen load.

We also find that IRAK1 cluster-enriched cells exhibited higher ASC nucleation and increased IL1α/β secretion, which are hallmarks of an active inflammasome. The colocalization of ASC suggests a possible role for dual TLR-induced IRAK1 clustering in diversifying signaling to multiple innate immune signaling pathways.

We conclude that IRAK1 aids in tuning the TLR arm of signaling in response to multi-TLR ligands, allowing the engagement of other PRR pathways like the inflammasome. Thus, the rerouting of immune signals by IRAK1 clusters allows the cell to diversify the immune response to complex microbial signals through activation of multiple host response pathways, and improve the quality of the immune response during an infection.

This work was supported by the Intramural Research Program of NIAID, NIH.

Rate coefficients, binding probabilities, and related quantities for area reactivity models

We further develop the general theory of the area reactivity model that describes the diffusion-influenced reaction of an isolated receptor-ligand pair in terms of a generalized Feynman-Kac equation and that provides an alternative to the classical contact reactivity model. Analyzing both the irreversible and reversible reaction, we derive the equation of motion of the survival probability as well as several relationships between single pair quantities and the reactive flux at the encounter distance. Building on these relationships, we derive the equation of motion of the many-particle survival probability for irreversible pseudo-first-order reactions. Moreover, we show that the usual definition of the rate coefficient as the reactive flux is deficient in the area reactivity model. Numerical tests for our findings are provided through Brownian Dynamics simulations. We calculate exact and approximate expressions for the irreversible rate coefficient and show that this quantity behaves differently from its classical counterpart. Furthermore, we derive approximate expressions for the binding probability as well as the average lifetime of the bound state and discuss on- and off-rates in this context. Throughout our approach, we point out similarities and differences between the area reactivity model and its classical counterpart, the contact reactivity model. The presented analysis and obtained results provide a theoretical framework that will facilitate the comparison of experiment and model predictions.

Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing

Osteoclasts are monocyte-derived multinuclear cells that directly attach to and resorb bone. Sphingosine-1-phosphate (S1P)(1) regulates bone resorption by functioning as both a chemoattractant and chemorepellent of osteoclast precursors through two G-protein coupled receptors that antagonize each other in an S1P-concentration-dependent manner. To quantitatively explore the behavior of this chemosensing pathway, we applied targeted proteomics, transcriptomics, and rule-based pathway modeling using the Simmune toolset. RAW264.7 cells (a mouse monocyte/macrophage cell line) were used as model osteoclast precursors, RNA-seq was used to identify expressed target proteins, and selected reaction monitoring (SRM) mass spectrometry using internal peptide standards was used to perform absolute abundance measurements of pathway proteins. The resulting transcript and protein abundance values were strongly correlated. Measured protein abundance values, used as simulation input parameters, led to in silico pathway behavior matching in vitro measurements. Moreover, once model parameters were established, even simulated responses toward stimuli that were not used for parameterization were consistent with experimental findings. These findings demonstrate the feasibility and value of combining targeted mass spectrometry with pathway modeling for advancing biological insight.

The area reactivity model of geminate recombination

We investigate the reversible diffusion-influenced reaction of an isolated pair in the context of the area reactivity model that describes the reversible binding of a single molecule in the presence of a binding site in terms of a generalized version of the Feynman-Kac equation in two dimensions. We compute the corresponding exact Green's function in the Laplace domain for both the initially unbound and bound molecule. We discuss convolution relations that facilitate the calculation of the binding and survival probabilities. Furthermore, we calculate an exact analytical expression for the Green's function in the time domain by inverting the Laplace transform via the Bromwich contour integral and derive expressions for the binding and survival probability in the time domain as well. We numerically confirm the accuracy of the obtained expressions by propagating the generalized Feynman-Kac equation in the time domain. Our results should be useful for comparing the area reactivity model with the contact reactivity model.

NetworkViewer: visualizing biochemical reaction networks with embedded rendering of molecular interaction rules

Background

Network representations of cell-biological signaling processes frequently contain large numbers of interacting molecular and multi-molecular components that can exist in, and switch between, multiple biochemical and/or structural states. In addition, the interaction categories (associations, dissociations and transformations) in such networks cannot satisfactorily be mapped onto simple arrows connecting pairs of components since their specifications involve information such as reaction rates and conditions with regard to the states of the interacting components. This leads to the challenge of having to reconcile competing objectives: providing a high-level overview without omitting relevant information, and showing interaction specifics while not overwhelming users with too much detail displayed simultaneously. This problem is typically addressed by splitting the information required to understand a reaction network model into several categories that are rendered separately through combinations of visualizations and/or textual and tabular elements, requiring modelers to consult several sources to obtain comprehensive insights into the underlying assumptions of the model.

Results

We report the development of an application, the Simmune NetworkViewer, that visualizes biochemical reaction networks using iconographic representations of protein interactions and the conditions under which the interactions take place using the same symbols that were used to specify the underlying model with the Simmune Modeler. This approach not only provides a coherent model representation but, moreover, following the principle of “overview first, zoom and filter, then details-on-demand,” can generate an overview visualization of the global network and, upon user request, presents more detailed views of local sub-networks and the underlying reaction rules for selected interactions. This visual integration of information would be difficult to achieve with static network representations or approaches that use scripted model specifications without offering simple but detailed symbolic representations of molecular interactions, their conditions and consequences in terms of biochemical modifications.

Conclusions

The Simmune NetworkViewer provides concise, yet comprehensive visualizations of reaction networks created in the Simmune framework. In the near future, by adopting the upcoming SBML standard for encoding multi-component, multi-state molecular complexes and their interactions as input, the NetworkViewer will, moreover, be able to offer such visualization for any rule-based model that can be exported to that standard.

Inflammation-induced interstitial migration of effector CD4⁺ T cells is dependent on integrin αV

Leukocytes must traverse inflamed tissues to effectively control local infection. Although motility in dense tissues appears to be integrin-independent actin-myosin based, during inflammation changes to the extracellular matrix (ECM) may necessitate distinct motility requirements. Indeed, we found that T cell interstitial motility was critically dependent on RGD-binding integrins in the inflamed dermis. Inflammation-induced deposition of fibronectin was functionally linked to increased α_v integrin expression on effector CD4⁺ T cells. Using intravital multi-photon imaging, we found that CD4⁺ T cell motility was dependent on α_v expression. Selective α_v blockade or knockdown arrested T_H1 motility in the inflamed tissue and attenuated local effector function. These data show a context-dependent specificity of lymphocyte movement in inflamed tissues that is essential for protective immunity.

The Simmune Modeler visual interface for creating signaling networks based on bi-molecular interactions

MOTIVATION

Biochemical modeling efforts now frequently take advantage of the possibility to automatically create reaction networks based on the specification of pairwise molecular interactions. Even though a variety of tools exist to visualize the resulting networks, defining the rules for the molecular interactions typically requires writing scripts, which impacts the non-specialist accessibility of those approaches. We introduce the Simmune Modeler that allows users to specify molecular complexes and their interactions as well as the reaction-induced modifications of the molecules through a flexible visual interface. It can take into account the positions of the components of trans-membrane complexes relative to the embedding membranes as well as symmetry aspects affecting the reactions of multimeric molecular structures. Models created with this tool can be simulated using the Simmune Simulator or be exported as SBML code or as files describing the reaction networks as systems of ODEs for import into Matlab.

AVAILABILITY

The Simmune Modeler and the associated simulators as well as extensive additional documentation and tutorials are freely available for Linux, Mac and Windows: http://go.usa.gov/QeH (Note shortened case-sensitive URL!).

Theory of reversible diffusion-influenced reactions with non-Markovian dissociation in two space dimensions

We investigate the reversible diffusion-influenced reaction of an isolated pair in the presence of a non-Markovian generalization of the backreaction boundary condition in two space dimensions. Following earlier work by Agmon and Weiss, we consider residence time probability densities that decay slower than an exponential and that are characterized by a single parameter 0 < σ ≤ 1. We calculate an exact expression for a Green's function of the two-dimensional diffusion equation subject to a non-Markovian backreaction boundary condition that is valid for arbitrary σ and for all times. We use the obtained expression to derive the survival probability for the initially unbound pair and we calculate an exact expression for the probability S(t[line]*) that the initially bound particle is unbound. Finally, we obtain an approximate solution for long times. In particular, we show that the ultimate fate of the bound state is complete dissociation, as in the Markovian case. However, the limiting value is approached quite differently: Instead of a ~t(-1) decay, we obtain 1 - S(t[line]*) ~ t(-σ)ln t. The derived expressions should be relevant for a better understanding of reversible membrane-bound reactions in cell biology.

Exact Green's function of the reversible diffusion-influenced reaction for an isolated pair in two dimensions

We derive an exact Green's function of the diffusion equation for a pair of disk-shaped interacting particles in two dimensions subject to a backreaction boundary condition. Furthermore, we use the obtained function to calculate exact expressions for the survival probability and the time-dependent rate coefficient for the initially unbound pair and the survival probability of the bound state. The derived expressions will be of particular utility for the description of reversible membrane-bound reactions in cell biology.

Systems biology in immunology: a computational modeling perspective

Systems biology is an emerging discipline that combines high-content, multiplexed measurements with informatic and computational modeling methods to better understand biological function at various scales. Here we present a detailed review of the methods used to create computational models and to conduct simulations of immune function. We provide descriptions of the key data-gathering techniques employed to generate the quantitative and qualitative data required for such modeling and simulation and summarize the progress to date in applying these tools and techniques to questions of immunological interest, including infectious disease. We include comments on what insights modeling can provide that complement information obtained from the more familiar experimental discovery methods used by most investigators and the reasons why quantitative methods are needed to eventually produce a better understanding of immune system operation in health and disease.

RhoH is a key functional determinant of the “stop and go” nature of CD4+ T cells during activation (109.17)

CD4+ T cells spend the majority of their time perusing lymphoid organs in search of cognate antigen presented by APCs, and then cells quickly recirculate through the bloodstream to another lymph node. Therefore regulation of a CD4+ T cell response is dependent upon the ability of cells to arrive in the correct location following chemokine gradients (“go” signals) as well as to receive appropriate TCR activation signals upon cognate antigen recognition (“stop” signals). The mechanism(s) by which a CD4+ T cell regulates these “go” and “stop” signals are not well defined. We find that over-expression of the hematopoietic specific RhoH protein in the presence of chemokine signals results in decreased Rap1-GTP and LFA-1 adhesiveness to ICAM-1, thus impairing T cell chemotaxis, while in the presence of TCR signals there is enhanced and sustained Rap1-GTP and LFA-1 activation as well as prolonged T:APC conjugates. RhoH-/- CD4+ T cells appear to circulate through the blood and lymphoid tissue more quickly corroborating our in vitro migration data. RT-PCR analyses of activated CD4+ T cells and live images of T cell migration and immunological synapse formation have revealed that functions of RhoH take place primarily at the levels of transcription and intracellular distribution. Thus, we hypothesize that RhoH expression may provide a single molecular determinant that allows a CD4+ T cell to switch between sensing chemokine mediated “go” signals and TCR dependent “stop” signals.

Multiscale modeling for biologists

Biomedical research frequently involves performing experiments and developing hypotheses that link different scales of biological systems such as, for instance, the scales of intracellular molecular interactions to the scale of cellular behavior and beyond to the behavior of cell populations. Computational modeling efforts that aim at exploring such multiscale systems quantitatively with the help of simulations have to incorporate several different simulation techniques because of the different time and space scales involved. Here, we provide a nontechnical overview of how different scales of experimental research can be combined with the appropriate computational modeling techniques. We also show that current modeling software permits building and simulating multiscale models without having to become involved with the underlying technical details of computational modeling.

Chemorepulsion by blood S1P regulates osteoclast precursor mobilization and bone remodeling in vivo

Sphingosine-1-phosphate (S1P), a lipid mediator enriched in blood, controls the dynamic migration of osteoclast (OC) precursors (OPs) between the blood and bone, in part via the S1P receptor 1 (S1PR1) which directs positive chemotaxis toward S1P. We show that OPs also express S1PR2, an S1P receptor which mediates negative chemotaxis (or chemorepulsion). OP-positive chemotaxis is prominent in gradients with low maximal concentrations of S1P, whereas such behavior is minimal in fields with high maximal S1P concentrations. This reverse-directional behavior is caused by S1PR2-mediated chemorepulsion acting to override S1PR1 upgradient motion. S1PR2-deficient mice exhibit moderate osteopetrosis as a result of a decrease in osteoclastic bone resorption, suggesting that S1PR2 contributes to OP localization on the bones mediated by chemorepulsion away from the blood where S1P levels are high. Inhibition of S1PR2 function by the antagonist JTE013 changed the migratory behavior of monocytoid cells, including OPs, and relieved osteoporosis in a mouse model by limiting OP localization and reducing the number of mature OCs attached to the bone surface. Thus, reciprocal regulation of S1P-dependent chemotaxis controls bone remodeling by finely regulating OP localization. This regulatory axis may be promising as a therapeutic target in diseases affecting OC-dependent bone remodeling.

Coupling mechanism of a GPCR and a heterotrimeric G protein during chemoattractant gradient sensing in Dictyostelium

The coupling of heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptors (GPCRs) with G proteins is fundamental for GPCR signaling; however, the mechanism of coupling is still debated. Moreover, how the proposed mechanisms affect the dynamics of downstream signaling remains unclear. Here, through experiments involving fluorescence recovery after photobleaching and single-molecule imaging, we directly measured the mobilities of cyclic adenosine monophosphate (cAMP) receptor 1 (cAR1), a chemoattractant receptor, and a G protein βγ subunit in live cells. We found that cAR1 diffused more slowly in the plasma membrane than did Gβγ. Upon binding of ligand to the receptor, the mobility of cAR1 was unchanged, whereas the speed of a fraction of the faster-moving Gβγ subunits decreased. Our measurements showed that cAR1 was relatively immobile and Gβγ diffused freely, suggesting that chemoattractant-bound cAR1 transiently interacted with G proteins. Using models of possible coupling mechanisms, we computed the temporal kinetics of G protein activation. Our fluorescence resonance energy transfer imaging data showed that fully activated cAR1 induced the sustained dissociation of G protein α and βγ subunits, which indicated that ligand-bound cAR1 activated G proteins continuously. Finally, simulations indicated that a high-affinity coupling of ligand-bound receptors and G proteins was essential for cAR1 to translate extracellular gradient signals into directional cellular responses. We suggest that chemoattractant receptors use a ligand-induced coupling rather than a precoupled mechanism to control the activation of G proteins during chemotaxis.

Abstract 98: Automated cell tracking and interaction analysis in intra-vital two-photon microscopy image data

The development of two-photon fluorescence microscopy has enabled researchers to investigate dynamic biological processes such as cell migration and interaction behavior with high spatio-temporal resolution in vivo. However, the development of computational high-throughput methods for automated and standardized quantitative analysis of the resulting 4-D image data has lagged behind the experimental advances.

Here, we introduce a novel approach for automated cell migration analysis (“cell tracking”) combined with cellular interaction analysis in 4-D intra-vital two-photon fluorescence microscopy data. Our method accounts for variations of image quality and allows for rapid, standardized, quantitative analysis and comparison of the relevant features in large data sets by integrating adaptive image segmentation and cell tracking modules. It may be used for quantitative data analysis and model validation in basic and tumor immunology studies about, for instance, lymphocyte migration and interaction dynamics in tumor or lymphoid tissues. We demonstrate the utility of our approach by applying our method to the quantification of in-vivo T-cell movement and T-/B- and dendritic cell interaction dynamics in lymph nodes and present results on the impact of a (SAP-) knock-out mutant on synapse formation and T cell function complementing the findings of a recent study.

Such measurements that may yield important information about the molecular mechanisms determining cellular migration and interaction behavior would be very difficult to perform with conventional approaches that rely on manual/semi-automated analyses. Our method should thus be highly valuable to investigators working with cell-biological microscopy who are interested in quantifying cell migration behavior not only, but especially in the context of dynamic interactions between immune and/or cancer cells.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 98.

Computational reconstruction of cell and tissue surfaces for modeling and data analysis

We present a method for the computational reconstruction of the 3-D morphology of biological objects, such as cells, cell conjugates or 3-D arrangements of tissue structures, using data from high-resolution microscopy modalities. The method is based on the iterative optimization of Voronoi representations of the spatial structures. The reconstructions of biological surfaces automatically adapt to morphological features of varying complexity with flexible degrees of resolution. We show how 3-D confocal images of single cells can be used to generate numerical representations of cellular membranes that may serve as the basis for realistic, spatially resolved computational models of membrane processes or intracellular signaling. Another example shows how the protocol can be used to reconstruct tissue boundaries from segmented two-photon image data that facilitate the quantitative analysis of lymphocyte migration behavior in relation to microanatomical structures. Processing time is of the order of minutes depending on data features and reconstruction parameters.

Sphingosine-1-phosphate mobilizes osteoclast precursors and regulates bone homeostasis

Osteoclasts are the only somatic cells with bone-resorbing capacity and, as such, they have a critical role not only in normal bone homeostasis (called 'bone remodelling') but also in the pathogenesis of bone destructive disorders such as rheumatoid arthritis and osteoporosis. A major focus of research in the field has been on gene regulation by osteoclastogenic cytokines such as receptor activator of NF-kappaB-ligand (RANKL, also known as TNFSF11) and TNF-alpha, both of which have been well documented to contribute to osteoclast terminal differentiation. A crucial process that has been less well studied is the trafficking of osteoclast precursors to and from the bone surface, where they undergo cell fusion to form the fully differentiated multinucleated cells that mediate bone resorption. Here we report that sphingosine-1-phosphate (S1P), a lipid mediator enriched in blood, induces chemotaxis and regulates the migration of osteoclast precursors not only in culture but also in vivo, contributing to the dynamic control of bone mineral homeostasis. Cells with the properties of osteoclast precursors express functional S1P(1) receptors and exhibit positive chemotaxis along an S1P gradient in vitro. Intravital two-photon imaging of bone tissues showed that a potent S1P(1) agonist, SEW2871, stimulated motility of osteoclast precursor-containing monocytoid populations in vivo. Osteoclast/monocyte (CD11b, also known as ITGAM) lineage-specific conditional S1P(1) knockout mice showed osteoporotic changes due to increased osteoclast attachment to the bone surface. Furthermore, treatment with the S1P(1) agonist FTY720 relieved ovariectomy-induced osteoporosis in mice by reducing the number of mature osteoclasts attached to the bone surface. Together, these data provide evidence that S1P controls the migratory behaviour of osteoclast precursors, dynamically regulating bone mineral homeostasis, and identifies a critical control point in osteoclastogenesis that may have potential as a therapeutic target.

Computational modeling of signaling networks for eukaryotic chemosensing

The task of developing and simulating computational models of signaling networks for eukaryotic chemosensing confronts the modeler with several challenges: (1) The stimuli that initiate the cellular responses one wishes to study are provided by extracellular concentration gradients. This means that the computational model must have a spatially resolved representation of extracellular molecular concentrations. (2) The intracellular responses consist of the generation of intracellular accumulations and/or translocations of signaling molecules, requiring spatially resolved computational representations of the simulated cells. (3) The signaling networks responsible for eukaryotic chemosensing comprise a multitude of components acting as receptors, adaptors, (lipid- and protein-) kinases (including GTPases), (lipid- and protein-) phosphatases, and molecule types used by others for membrane attachment. Models of such signaling networks may become quite complicated, unless one wishes to rely on abstract functional modules with certain input-output characteristics as modeling "shortcuts" replacing subnetworks of biological signaling molecules. In this chapter, we describe how modelers can use a modeling tool ("simmune") developed to facilitate the design and simulation of detailed computational models of signaling pathways (for eukaryotic chemosensing here), thereby avoiding the technical difficulties typically associated with building and simulating such quantitative models.

Tuning sensitivity to IL-4 and IL-13: differential expression of IL-4Ralpha, IL-13Ralpha1, and gammac regulates relative cytokine sensitivity

Interleukin (IL)-4 and -13 are related cytokines sharing functional receptors. IL-4 signals through the type I (IL-4Rα/common γ-chain [γc]) and the type II (IL-4Rα/-13Rα1) IL-4 receptors, whereas IL-13 utilizes only the type II receptor. In this study, we show that mouse bone marrow–derived macrophages and human and mouse monocytes showed a much greater sensitivity to IL-4 than to IL-13. Lack of functional γc made these cells poorly responsive to IL-4, while retaining full responsiveness to IL-13. In mouse peritoneal macrophages, IL-4 potency exceeds that of IL-13, but lack of γc had only a modest effect on IL-4 signaling. In contrast, IL-13 stimulated greater responses than IL-4 in fibroblasts. Using levels of receptor chain expression and known binding affinities, we modeled the assemblage of functional type I and II receptor complexes. The differential expression of IL-4Rα, IL-13Rα1, and γc accounted for the distinct IL-4–IL-13 sensitivities of the various cell types. These findings provide an explanation for IL-13's principal function as an “effector” cytokine and IL-4's principal role as an “immunoregulatory” cytokine.

Locally controlled inhibitory mechanisms are involved in eukaryotic GPCR-mediated chemosensing

Gprotein-coupled receptor (GPCR) signaling mediates a balance of excitatory and inhibitory activities that regulate Dictyostelium chemosensing to cAMP. The molecular nature and kinetics of these inhibitors are unknown. We report that transient cAMP stimulations induce PIP3 responses without a refractory period, suggesting that GPCR-mediated inhibition accumulates and decays slowly. Moreover, exposure to cAMP gradients leads to asymmetric distribution of the inhibitory components. The gradients induce a stable accumulation of the PIP3 reporter PHCrac-GFP in the front of cells near the cAMP source. Rapid withdrawal of the gradient led to the reassociation of G protein subunits, and the return of the PIP3 phosphatase PTEN and PHCrac-GFP to their pre-stimulus distribution. Reapplication of cAMP stimulation produces a clear PHCrac-GFP translocation to the back but not to the front, indicating that a stronger inhibition is maintained in the front of a polarized cell. Our study demonstrates a novel spatiotemporal feature of currently unknown inhibitory mechanisms acting locally on the PI3K activation pathway.

Progressive CD4+ central memory T cell decline results in CD4+ effector memory insufficiency and overt disease in chronic SIV infection

Primary simian immunodeficiency virus (SIV) infections of rhesus macaques result in the dramatic depletion of CD4⁺ CCR5⁺ effector–memory T (T_EM) cells from extra-lymphoid effector sites, but in most infections, an increased rate of CD4⁺ memory T cell proliferation appears to prevent collapse of effector site CD4⁺ T_EM cell populations and acute-phase AIDS. Eventually, persistent SIV replication results in chronic-phase AIDS, but the responsible mechanisms remain controversial. Here, we demonstrate that in the chronic phase of progressive SIV infection, effector site CD4⁺ T_EM cell populations manifest a slow, continuous decline, and that the degree of this depletion remains a highly significant correlate of late-onset AIDS. We further show that due to persistent immune activation, effector site CD4⁺ T_EM cells are predominantly short-lived, and that their homeostasis is strikingly dependent on the production of new CD4⁺ T_EM cells from central–memory T (T_CM) cell precursors. The instability of effector site CD4⁺ T_EM cell populations over time was not explained by increasing destruction of these cells, but rather was attributable to progressive reduction in their production, secondary to decreasing numbers of CCR5⁻ CD4⁺ T_CM cells. These data suggest that although CD4⁺ T_EM cell depletion is a proximate mechanism of immunodeficiency, the tempo of this depletion and the timing of disease onset are largely determined by destruction, failing production, and gradual decline of CD4⁺ T_CM cells.

Understanding diseases by mouse click: the promise and potential of computational approaches in Systems Biology

Computational modelling approaches can nowadays build large-scale simulations of cellular behaviour based on data describing detailed molecular level interactions, thus performing the space- and time-scale integrations that would be impossible just by intuition. Recent progress in the development of both experimental methods and computational tools has provided the means to generate the necessary quantitative data and has made computational methods accessible even to non-theorists, thereby removing a major hurdle that has in the past made many experimentalists hesitate to invest serious effort in formulating quantitative models. We describe how computational biology differs from classical bioinformatics, how it emerged from mathematical biology and elucidate the role it plays for the integration of traditionally separated areas of biomedical research within the larger framework of Systems Biology.

Keratinocyte growth factor augments immune reconstitution after autologous hematopoietic progenitor cell transplantation in rhesus macaques

Opportunistic infections contribute to morbidity and mortality after peripheral blood progenitor cell (PBPC) transplantation and are related to a deficient T-cell compartment. Accelerated T-cell reconstitution may therefore be clinically beneficent. Keratinocyte growth factor (KGF) has been shown to protect thymic epithelial cells in mice. Here, we evaluated immune reconstitution after autologous CD34(+) PBPC transplantation in rhesus macaques conditioned with myeloablative total body irradiation in the absence or presence of single pretotal body irradiation or repeated peritransplant KGF administration. All KGF-treated animals exhibited a well-preserved thymic architecture 12 months after graft. In contrast, thymic atrophy was observed in the majority of animals in the control group. The KGF-treated animals showed higher frequencies of naive T cells in lymph nodes after transplantation compared with the control animals. The animals given repeated doses of KGF showed the highest levels of T-cell receptor excision circles (TRECs) and the lowest frequencies of Ki67(+) T cells, which suggest increased thymic-dependent reconstitution in these animals. Of note, the humoral response to a T-cell-dependent neo-antigen was significantly higher in the KGF-treated animals compared with the control animals. Thus, our findings suggest that KGF may be a useful adjuvant therapy to augment T-cell reconstitution after human PBPC transplantation.

Key role of local regulation in chemosensing revealed by a new molecular interaction-based modeling method

The signaling network underlying eukaryotic chemosensing is a complex combination of receptor-mediated transmembrane signals, lipid modifications, protein translocations, and differential activation/deactivation of membrane-bound and cytosolic components. As such, it provides particularly interesting challenges for a combined computational and experimental analysis. We developed a novel detailed molecular signaling model that, when used to simulate the response to the attractant cyclic adenosine monophosphate (cAMP), made nontrivial predictions about Dictyostelium chemosensing. These predictions, including the unexpected existence of spatially asymmetrical, multiphasic, cyclic adenosine monophosphate–induced PTEN translocation and phosphatidylinositol-(3,4,5)P₃ generation, were experimentally verified by quantitative single-cell microscopy leading us to propose significant modifications to the current standard model for chemoattractant-induced biochemical polarization in this organism. Key to this successful modeling effort was the use of “Simmune,” a new software package that supports the facile development and testing of detailed computational representations of cellular behavior. An intuitive interface allows user definition of complex signaling networks based on the definition of specific molecular binding site interactions and the subcellular localization of molecules. It automatically translates such inputs into spatially resolved simulations and dynamic graphical representations of the resulting signaling network that can be explored in a manner that closely parallels wet lab experimental procedures. These features of Simmune were critical to the model development and analysis presented here and are likely to be useful in the computational investigation of many aspects of cell biology.

Cells can orient their migration in response to small local differences in the concentration of extracellular chemicals (chemoattractants). Understanding this process (chemosensing) requires analyzing the time and position-dependent behavior of the signaling molecules within the responding cell, making it an especially interesting challenge for both experimental and computational investigation. Here, the authors report the development and testing of a new detailed molecular model of the chemosensing apparatus of the amoeba Dictyostelium discoidium reacting to the chemoattractant cyclic adenosine monophosphate. Computer simulations performed using this model predicted unexpected and previously unreported patterns of changes in the concentration and location of two important intracellular signaling molecules. These predictions were experimentally verified using microscopy, suggesting the need for modifications to the current “standard” model of eukaryotic chemosensing. The high degree of detail in their model was made possible by a new software suite called “Simmune,” which allows biologists to enter information about molecular interactions using a graphical interface. Without requiring the user to write any equations, the software automatically constructs the overall reaction network, simulates the model, and provides several ways to view the biochemistry of simulated cells. This new tool should help biologists to translate qualitative representations of cell biological processes into quantitative, predictive models.

Pathogenesis of HIV infection: what the virus spares is as important as what it destroys

Upon transmission to a new host, HIV targets CCR5+ CD4+ effector memory T cells, resulting in acute, massive depletion of these cells from mucosal effector sites. This depletion does not initially compromise the regenerative capacity of the immune system because naive and most central memory T cells are spared. Here, we discuss evidence suggesting that frequent activation of these spared cells during the chronic phase of HIV infection supplies mucosal tissues with short-lived CCR5+ CD4+ effector cells that prevent life-threatening infections. This immune activation also facilitates continued viral replication, but infection and killing of target T cells by HIV are selective and the impact on effector-cell lifespan is limited. We propose, however, that persistent activation progressively disrupts the functional organization of the immune system, reducing its regenerative capacity and facilitating viral evolution that leads to loss of the exquisite target cell-sparing selectivity of viral replication, ultimately resulting in AIDS.

Quantitative imaging of single live cells reveals spatiotemporal dynamics of multistep signaling events of chemoattractant gradient sensing in Dictyostelium

Activation of G-protein-coupled chemoattractant receptors triggers dissociation of Galpha and Gbetagamma subunits. These subunits induce intracellular responses that can be highly polarized when a cell experiences a gradient of chemoattractant. Exactly how a cell achieves this amplified signal polarization is still not well understood. Here, we quantitatively measure temporal and spatial changes of receptor occupancy, G-protein activation by FRET imaging, and PIP3 levels by monitoring the dynamics of PH(Crac)-GFP translocation in single living cells in response to different chemoattractant fields. Our results provided the first direct evidence that G-proteins are activated to different extents on the cell surface in response to asymmetrical stimulations. A stronger, uniformly applied stimulation triggers not only a stronger G-protein activation but also a faster adaptation of downstream responses. When naive cells (which have not experienced chemoattractant) were abruptly exposed to stable cAMP gradients, G-proteins were persistently activated throughout the entire cell surface, whereas the response of PH(Crac)-GFP translocation surprisingly consisted of two phases, an initial transient and asymmetrical translocation around the cell membrane, followed by a second phase producing a highly polarized distribution of PH(Crac)-GFP. We propose a revised model of gradient sensing, suggesting an important role for locally controlled components that inhibit PI3Kinase activity.

Spontaneous proliferation, a response of naive CD4 T cells determined by the diversity of the memory cell repertoire

T cell numbers are maintained within narrow ranges in vivo. Introduction of naïve cells into lymphopenic environments results in proliferation and differentiation driven by the recognition of peptide/MHC complexes and by cytokine signaling. This process, often described as homeostatic proliferation, is here referred to as spontaneous proliferation. We show that, although the presence of memory CD4 T cells of broad repertoire efficiently inhibits proliferation/differentiation of naïve CD4 T cells, a memory population of similar size comprised of cells with a repertoire of limited diversity fails to do so, implying that cells of a given specificity prevent responses of cells of the same or related specificity. This finding suggests that the immune system has evolved mechanisms to attain a memory cell repertoire of great diversity independently of foreign antigens.

CD4 T cell depletion is linked directly to immune activation in the pathogenesis of HIV-1 and HIV-2 but only indirectly to the viral load

The causal relationships among CD4 cell depletion, HIV replication, and immune activation are not well understood. HIV-2 infection, "nature's experiment" with inherently attenuated HIV disease, provides additional insights into this issue. We report the finding that in HIV-2 and HIV-1 patients with a comparable degree of CD4 depletion the imbalance in the relative sizes of the naive and memory T cell populations and the up-regulation of CD4 and CD8 cell activation markers (HLA-DR, CD38, CD69, Fas molecules) are similar, even though the viral load in the plasma of HIV-2-infected patients is two orders of magnitude lower than in HIV-1 patients and HIV-2 patients are known to have slower rates of CD4 T cell decline and a better clinical prognosis. Moreover, we found a similar increase in the frequency of cycling CD4 T cells (Ki67+), which was in strong correlation with the expression of activation markers. Finally, the level of T cell anergy, as assessed by the proliferative responses to CD3 stimulation and to a panel of microbial Ags, proved to be comparable in HIV-1 and HIV-2 patients with a similar degree of CD4 depletion despite large differences in viral load. Our data are consistent with a direct causal relationship between immune activation and CD4 cell depletion in HIV disease and an only indirect relation of these parameters to the virus replication rate. Invoking the concept of proximal immune activation and virus transmission, which links efficient transmission of virus to local cell activation and proliferation in response to Ags and inflammation, we propose an integrative interpretation of the data and suggest that strongly elevated immune activation induces CD4 cell depletion and not vice versa, with potential implications for the choice of treatment strategies.