Germinal centers are permissive to subdominant antibody responses

Introduction

A protective humoral response to pathogens requires the development of high affinity antibodies in germinal centers (GC). The combination of antigens available during immunization has a strong impact on the strength and breadth of the antibody response. Antigens can display various levels of immunogenicity, and a hierarchy of immunodominance arises when the GC response to an antigen dampens the response to other antigens. Immunodominance is a challenge for the development of vaccines to mutating viruses, and for the development of broadly neutralizing antibodies. The extent by which antigens with different levels of immunogenicity compete for the induction of high affinity antibodies and therefore contribute to immunodominance is not known.

Methods

Here, we perform in silico simulations of the GC response, using a structural representation of antigens with complex surface amino acid composition and topology. We generate antigens with complex domains of different levels of immunogenicity and perform simulations with combinations of these domains.

Results

We found that GC dynamics were driven by the most immunogenic domain and immunodominance arose as affinity maturation to less immunogenic domain was inhibited. However, this inhibition was moderate since the less immunogenic domain exhibited a weak GC response in the absence of the most immunogenic domain. Less immunogenic domains reduced the dominance of GC responses to more immunogenic domains, albeit at a later time point.

Discussion

The simulations suggest that increased vaccine valency may decrease immunodominance of the GC response to strongly immunogenic domains and therefore, act as a potential strategy for the natural induction of broadly neutralizing antibodies in GC reactions.

Assessing the feasibility of statistical inference using synthetic antibody-antigen datasets

Simulation frameworks are useful to stress-test predictive models when data is scarce, or to assert model sensitivity to specific data distributions. Such frameworks often need to recapitulate several layers of data complexity, including emergent properties that arise implicitly from the interaction between simulation components. Antibody-antigen binding is a complex mechanism by which an antibody sequence wraps itself around an antigen with high affinity. In this study, we use a synthetic simulation framework for antibody-antigen folding and binding on a 3D lattice that include full details on the spatial conformation of both molecules. We investigate how emergent properties arise in this framework, in particular the physical proximity of amino acids, their presence on the binding interface, or the binding status of a sequence, and relate that to the individual and pairwise contributions of amino acids in statistical models for binding prediction. We show that weights learnt from a simple logistic regression model align with some but not all features of amino acids involved in the binding, and that predictive sequence binding patterns can be enriched. In particular, main effects correlated with the capacity of a sequence to bind any antigen, while statistical interactions were related to sequence specificity.

Spatial dysregulation of T follicular helper cells impairs vaccine responses in aging

The magnitude and quality of the germinal center (GC) response decline with age, resulting in poor vaccine-induced immunity in older individuals. A functional GC requires the co-ordination of multiple cell types across time and space, in particular across its two functionally distinct compartments: the light and dark zones. In aged mice, there is CXCR4-mediated mislocalization of T follicular helper (TFH) cells to the dark zone and a compressed network of follicular dendritic cells (FDCs) in the light zone. Here we show that TFH cell localization is critical for the quality of the antibody response and for the expansion of the FDC network upon immunization. The smaller GC and compressed FDC network in aged mice were corrected by provision of TFH cells that colocalize with FDCs using CXCR5. This demonstrates that the age-dependent defects in the GC response are reversible and shows that TFH cells support stromal cell responses to vaccines.

Toward real-world automated antibody design with combinatorial Bayesian optimization

Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction

Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.

Reference-based comparison of adaptive immune receptor repertoires

B and T cell receptor (immune) repertoires can represent an individual's immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters. Here, we introduce immuneREF: a quantitative multidimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2,400 datasets from individuals with varying immune states (healthy, [autoimmune] disease, and infection). We discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF enables the population-wide study of adaptive immune response similarity across immune states.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

In silico proof of principle of machine learning-based antibody design at unconstrained scale

Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.

Individualized VDJ recombination predisposes the available Ig sequence space

The process of recombination between variable (V), diversity (D), and joining (J) immunoglobulin (Ig) gene segments determines an individual's naive Ig repertoire and, consequently, (auto)antigen recognition. VDJ recombination follows probabilistic rules that can be modeled statistically. So far, it remains unknown whether VDJ recombination rules differ between individuals. If these rules differed, identical (auto)antigen-specific Ig sequences would be generated with individual-specific probabilities, signifying that the available Ig sequence space is individual specific. We devised a sensitivity-tested distance measure that enables inter-individual comparison of VDJ recombination models. We discovered, accounting for several sources of noise as well as allelic variation in Ig sequencing data, that not only unrelated individuals but also human monozygotic twins and even inbred mice possess statistically distinguishable immunoglobulin recombination models. This suggests that, in addition to genetic, there is also nongenetic modulation of VDJ recombination. We demonstrate that population-wide individualized VDJ recombination can result in orders of magnitude of difference in the probability to generate (auto)antigen-specific Ig sequences. Our findings have implications for immune receptor-based individualized medicine approaches relevant to vaccination, infection, and autoimmunity.

Characterization of mechanisms positioning costimulatory complexes in immune synapses

Small immunoglobulin superfamily (sIGSF) adhesion complexes form a corolla of microdomains around an integrin ring and secretory core during immunological synapse (IS) formation. The corolla recruits and retains major costimulatory/checkpoint complexes, such as CD28, making forces that govern corolla formation of particular interest. Here, we investigated the mechanisms underlying molecular reorganization of CD2, an adhesion and costimulatory molecule of the sIGSF family during IS formation. Computer simulations showed passive distal exclusion of CD2 complexes under weak interactions with the ramified F-actin transport network. Attractive forces between CD2 and CD28 complexes relocate CD28 from the IS center to the corolla. Size-based sorting interactions with large glycocalyx components, such as CD45, or short-range CD2 self-attraction successfully explain the corolla 'petals.' This establishes a general simulation framework for complex pattern formation observed in cell-bilayer and cell-cell interfaces, and the suggestion of new therapeutic targets, where boosting or impairing characteristic pattern formation can be pivotal.

Nitric oxide controls proliferation of Leishmania major by inhibiting the recruitment of permissive host cells

Nitric oxide (NO) is an important antimicrobial effector but also prevents unnecessary tissue damage by shutting down the recruitment of monocyte-derived phagocytes. Intracellular pathogens such as Leishmania major can hijack these cells as a niche for replication. Thus, NO might exert containment by restricting the availability of the cellular niche required for efficient pathogen proliferation. However, such indirect modes of action remain to be established. By combining mathematical modeling with intravital 2-photon biosensors of pathogen viability and proliferation, we show that low L. major proliferation results not from direct NO impact on the pathogen but from reduced availability of proliferation-permissive host cells. Although inhibiting NO production increases recruitment of these cells, and thus pathogen proliferation, blocking cell recruitment uncouples the NO effect from pathogen proliferation. Therefore, NO fulfills two distinct functions for L. major containment: permitting direct killing and restricting the supply of proliferation-permissive host cells.

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Direct killing of L. major by NO occurs only during the peak of the immune response

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Efficient L. major proliferation requires newly recruited monocyte-derived cells

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Loss of NO production increases both pathogen proliferation and monocyte recruitment

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NO dampens L. major proliferation indirectly, limiting the pathogen’s cellular niche

Ymir: A 3D structural affinity model for multi-epitope vaccine simulations

Vaccine development is challenged by the hierarchy of immunodominance between target antigen epitopes and the emergence of antigenic variants by pathogen mutation. The strength and breadth of antibody responses relies on selection and mutation in the germinal center and on the structural similarity between antigens. Computational methods for assessing the breadth of germinal center responses to multivalent antigens are critical to speed up vaccine development. Yet, such methods have poorly reflected the 3D antigen structure and antibody breadth. Here, we present Ymir, a new 3D-lattice-based framework that calculates in silico antibody-antigen affinities. Key physiological properties naturally emerge from Ymir such as affinity jumps, cross-reactivity, and differential epitope accessibility. We validated Ymir by replicating known features of germinal center dynamics. We show that combining antigens with mutated but structurally related epitopes enhances vaccine breadth. Ymir opens a new avenue for understanding vaccine potency based on the structural relationship between vaccine antigens.

Coupled Antigen and BLIMP1 Asymmetric Division With a Large Segregation Between Daughter Cells Recapitulates the Temporal Transition From Memory B Cells to Plasma Cells and a DZ-to-LZ Ratio in the Germinal Center

Memory B cells and antibody-secreting plasma cells are generated within germinal centers during affinity maturation in which B-cell proliferation, selection, differentiation, and self-renewal play important roles. The mechanisms behind memory B cell and plasma cell differentiation in germinal centers are not well understood. However, it has been suggested that cell fate is (partially) determined by asymmetric cell division, which involves the unequal distribution of cellular components to both daughter cells. To investigate what level and/or probability of asymmetric segregation of several fate determinant molecules, such as the antigen and transcription factors (BCL6, IRF4, and BLIMP1) recapitulates the temporal switch and DZ-to-LZ ratio in the germinal center, we implemented a multiscale model that combines a core gene regulatory network for plasma cell differentiation with a model describing the cellular interactions and dynamics in the germinal center. Our simulations show that BLIMP1 driven plasma cell differentiation together with coupled asymmetric division of antigen and BLIMP1 with a large segregation between the daughter cells results in a germinal center DZ-to-LZ ratio and a temporal switch from memory B cells to plasma cells that have been observed in experiments.

Germinal Centre Shutdown

Germinal Centres (GCs) are transient structures in secondary lymphoid organs, where affinity maturation of B cells takes place following an infection. While GCs are responsible for protective antibody responses, dysregulated GC reactions are associated with autoimmune disease and B cell lymphoma. Typically, ‘normal’ GCs persist for a limited period of time and eventually undergo shutdown. In this review, we focus on an important but unanswered question – what causes the natural termination of the GC reaction? In murine experiments, lack of antigen, absence or constitutive T cell help leads to premature termination of the GC reaction. Consequently, our present understanding is limited to the idea that GCs are terminated due to a decrease in antigen access or changes in the nature of T cell help. However, there is no direct evidence on which biological signals are primarily responsible for natural termination of GCs and a mechanistic understanding is clearly lacking. We discuss the present understanding of the GC shutdown, from factors impacting GC dynamics to changes in cellular interactions/dynamics during the GC lifetime. We also address potential missing links and remaining questions in GC biology, to facilitate further studies to promote a better understanding of GC shutdown in infection and immune dysregulation.

Influenza A virus-induced thymus atrophy differentially affects dynamics of conventional and regulatory T-cell development in mice

Foxp3⁺ Treg cells, which are crucial for maintenance of self-tolerance, mainly develop within the thymus, where they arise from CD25⁺ Foxp3^- or CD25^- Foxp3⁺ Treg cell precursors. Although it is known that infections can cause transient thymic involution, the impact of infection-induced thymus atrophy on thymic Treg (tTreg) cell development is unknown. Here, we infected mice with influenza A virus (IAV) and studied thymocyte population dynamics post infection. IAV infection caused a massive, but transient thymic involution, dominated by a loss of CD4⁺ CD8⁺ double-positive (DP) thymocytes, which was accompanied by a significant increase in the frequency of CD25⁺ Foxp3⁺ tTreg cells. Differential apoptosis susceptibility could be experimentally excluded as a reason for the relative tTreg cell increase, and mathematical modeling suggested that enhanced tTreg cell generation cannot explain the increased frequency of tTreg cells. Yet, an increased death of DP thymocytes and augmented exit of single-positive (SP) thymocytes was suggested to be causative. Interestingly, IAV-induced thymus atrophy resulted in a significantly reduced T-cell receptor (TCR) repertoire diversity of newly produced tTreg cells. Taken together, IAV-induced thymus atrophy is substantially altering the dynamics of major thymocyte populations, finally resulting in a relative increase of tTreg cells with an altered TCR repertoire.

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In a dataset of non-redundant antibody-antigen structures, we identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (1) is compact, less than 10⁴ motifs; (2) distinct from non-immune protein-protein interactions; and (3) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work leverages combined structure- and sequence-based learning to demonstrate that machine-learning-driven predictive paratope and epitope engineering is feasible.

Multiscale Modeling of Germinal Center Recapitulates the Temporal Transition From Memory B Cells to Plasma Cells Differentiation as Regulated by Antigen Affinity-Based Tfh Cell Help

Germinal centers play a key role in the adaptive immune system since they are able to produce memory B cells and plasma cells that produce high affinity antibodies for an effective immune protection. The mechanisms underlying cell-fate decisions are not well understood but asymmetric division of antigen, B-cell receptor affinity, interactions between B-cells and T follicular helper cells (triggering CD40 signaling), and regulatory interactions of transcription factors have all been proposed to play a role. In addition, a temporal switch from memory B-cell to plasma cell differentiation during the germinal center reaction has been shown. To investigate if antigen affinity-based Tfh cell help recapitulates the temporal switch we implemented a multiscale model that integrates cellular interactions with a core gene regulatory network comprising BCL6, IRF4, and BLIMP1. Using this model we show that affinity-based CD40 signaling in combination with asymmetric division of B-cells result in switch from memory B-cell to plasma cell generation during the course of the germinal center reaction. We also show that cell fate division is unlikely to be (solely) based on asymmetric division of Ag but that BLIMP1 is a more important factor. Altogether, our model enables to test the influence of molecular modulations of the CD40 signaling pathway on the production of germinal center output cells.

Evaluation of CD8 T cell killing models with computer simulations of 2-photon imaging experiments

In vivo imaging of cytotoxic T lymphocyte (CTL) killing activity revealed that infected cells have a higher observed probability of dying after multiple contacts with CTLs. We developed a three-dimensional agent-based model to discriminate different hypotheses about how infected cells get killed based on quantitative 2-photon in vivo observations. We compared a constant CTL killing probability with mechanisms of signal integration in CTL or infected cells. The most likely scenario implied increased susceptibility of infected cells with increasing number of CTL contacts where the total number of contacts was a critical factor. However, when allowing in silico T cells to initiate new interactions with apoptotic target cells (zombie contacts), a contact history independent killing mechanism was also in agreement with experimental datasets. The comparison of observed datasets to simulation results, revealed limitations in interpreting 2-photon data, and provided readouts to distinguish CTL killing models.

Agent-Based Modeling of T Cell Receptor Cooperativity

Immunological synapse (IS) formation is a key event during antigen recognition by T cells. Recent experimental evidence suggests that the affinity between T cell receptors (TCRs) and antigen is actively modulated during the early steps of TCR signaling. In this work, we used an agent-based model to study possible mechanisms for affinity modulation during IS formation. We show that, without any specific active mechanism, the observed affinity between receptors and ligands evolves over time and depends on the density of ligands of the antigen peptide presented by major histocompatibility complexes (pMHC) and TCR molecules. A comparison between the presence or absence of TCR–pMHC centrally directed flow due to F-actin coupling suggests that centripetal transport is a potential mechanism for affinity modulation. The model further suggests that the time point of affinity measurement during immune synapse formation is critical. Finally, a mathematical model of F-actin foci formation incorporated in the agent-based model shows that TCR affinity can potentially be actively modulated by positive/negative feedback of the F-actin foci on the TCR-pMHC association rate k_on.

Staphylococcus aureus Alpha-Toxin Limits Type 1 While Fostering Type 3 Immune Responses

Staphylococcus aureus can cause life-threatening diseases, and hospital- as well as community-associated antibiotic-resistant strains are an emerging global public health problem. Therefore, prophylactic vaccines or immune-based therapies are considered as alternative treatment opportunities. To develop such novel treatment approaches, a better understanding of the bacterial virulence and immune evasion mechanisms and their potential effects on immune-based therapies is essential. One important staphylococcal virulence factor is alpha-toxin, which is able to disrupt the epithelial barrier in order to establish infection. In addition, alpha-toxin has been reported to modulate other cell types including immune cells. Since CD4⁺ T cell-mediated immunity is required for protection against S. aureus infection, we were interested in the ability of alpha-toxin to directly modulate CD4⁺ T cells. To address this, murine naïve CD4⁺ T cells were differentiated in vitro into effector T cell subsets in the presence of alpha-toxin. Interestingly, alpha-toxin induced death of Th1-polarized cells, while cells polarized under Th17 conditions showed a high resistance toward increasing concentrations of this toxin. These effects could neither be explained by differential expression of the cellular alpha-toxin receptor ADAM10 nor by differential activation of caspases, but might result from an increased susceptibility of Th1 cells toward Ca²⁺-mediated activation-induced cell death. In accordance with the in vitro findings, an alpha-toxin-dependent decrease of Th1 and concomitant increase of Th17 cells was observed in vivo during S. aureus bacteremia. Interestingly, corresponding subsets of innate lymphoid cells and γδ T cells were similarly affected, suggesting a more general effect of alpha-toxin on the modulation of type 1 and type 3 immune responses. In conclusion, we have identified a novel alpha-toxin-dependent immunomodulatory strategy of S. aureus, which can directly act on CD4⁺ T cells and might be exploited for the development of novel immune-based therapeutic approaches to treat infections with antibiotic-resistant S. aureus strains.

F-Actin-Driven CD28-CD80 Localization in the Immune Synapse

During immunological synapse (IS) formation, T cell receptor (TCR) signaling complexes, integrins, and costimulatory molecules exhibit a particular spatial localization. Here, we develop an agent-based model for the IS formation based on TCR peptide-bound major histocompatibility complex (pMHC) and leukocyte-function-associated antigen 1 (LFA-1) intracellular activation molecule 1 (ICAM-1) dynamics, including CD28 binding to a costimulatory ligand, coupling of molecules to the centripetal actin flow, and size-based segregation (SBS). A radial gradient of LFA-1 in the peripheral supramolecular activation cluster (pSMAC) toward the central supramolecular activation cluster (cSMAC) emerged as a combined consequence of actin binding and diffusion and modified the positioning of other molecules. The simulations predict a mechanism of CD28 movement, according to which CD28-CD80 complexes passively follow TCR-pMHC microclusters. However, the characteristic CD28-CD80 localization in a ring pattern around the cSMAC only emerges with a particular CD28-actin coupling strength that induces a centripetal motion. These results have implications for the understanding of T cell activation and fate decisions.

Synchronous Germinal Center Onset Impacts the Efficiency of Antibody Responses

The germinal center reaction is an important target for modulating antibody responses. Antibody production from germinal centers is regulated by a negative feedback mechanism termed antibody feedback. By imposing antibody feedback, germinal centers can interact and regulate the output of other germinal centers. Using an agent-based model of the germinal center reaction, we studied the impact of antibody feedback on kinetics and efficiency of a germinal center. Our simulations predict that high feedback of antibodies from germinal centers reduces the production of plasma cells and subsequently the efficiency of the germinal center reaction by promoting earlier termination. Affinity maturation is only weakly improved by increased antibody feedback and ultimately interrupted because of premature termination of the reaction. The model predicts that the asynchronous onset and changes in number of germinal centers could alter the efficiency of antibody response due to changes in feedback by soluble antibodies. Consequently, late initialized germinal centers have a compromised output due to higher antibody feedback from the germinal centers formed earlier. The results demonstrate potential effects of germinal center intercommunication and highlight the importance of understanding germinal center interactions for optimizing the antibody response, in particular, in the elderly and in the context of vaccination.

Class-Switch Recombination Occurs Infrequently in Germinal Centers

Class-switch recombination (CSR) is a DNA recombination process that replaces the immunoglobulin (Ig) constant region for the isotype that can best protect against the pathogen. Dysregulation of CSR can cause self-reactive BCRs and B cell lymphomas; understanding the timing and location of CSR is therefore important. Although CSR commences upon T cell priming, it is generally considered a hallmark of germinal centers (GCs). Here, we have used multiple approaches to show that CSR is triggered prior to differentiation into GC B cells or plasmablasts and is greatly diminished in GCs. Despite finding a small percentage of GC B cells expressing germline transcripts, phylogenetic trees of GC BCRs from secondary lymphoid organs revealed that the vast majority of CSR events occurred prior to the onset of somatic hypermutation. As such, we have demonstrated the existence of IgM-dominated GCs, which are unlikely to occur under the assumption of ongoing switching.

IFN-γ Producing Th1 Cells Induce Different Transcriptional Profiles in Microglia and Astrocytes

Autoreactive T cells that infiltrate into the central nervous system (CNS) are believed to have a significant role in mediating the pathology of neuroinflammatory diseases like multiple sclerosis. Their interaction with microglia and astrocytes in the CNS is crucial for the regulation of neuroinflammatory processes. Our previous work demonstrated that effectors secreted by Th1 and Th17 cells have different capacities to influence the phenotype and function of glial cells. We have shown that Th1-derived effectors altered the phenotype and function of both microglia and astrocytes whereas Th17-derived effectors induced direct effects only on astrocytes but not on microglia. Here we investigated if effector molecules associated with IFN-γ producing Th1 cells induced different gene expression profiles in microglia and astrocytes. We performed a microarray analysis of RNA isolated from microglia and astrocytes treated with medium and Th-derived culture supernatants and compared the gene expression data. By using the criteria of 2-fold change and a false discovery rate of 0.01 (corrected p < 0.01), we demonstrated that a total of 2,106 and 1,594 genes were differentially regulated in microglia and astrocytes, respectively, in response to Th1-derived factors. We observed that Th1-derived effectors induce distinct transcriptional changes in microglia and astrocytes in addition to commonly regulated transcripts. These distinct transcriptional changes regulate peculiar physiological functions, and this knowledge can help to better understand T cell mediated neuropathologies.

Characterization of CTLA4 Trafficking and Implications for Its Function

CTLA4 is an essential negative regulator of T-cell immune responses and a key checkpoint regulating autoimmunity and antitumor responses. Genetic mutations resulting in quantitative defects in the CTLA4 pathway are also associated with the development of immune dysregulation syndromes in humans. It has been proposed that CTLA4 functions to remove its ligands CD80 and CD86 from opposing cells by a process known as transendocytosis. A quantitative characterization of CTLA4 synthesis, endocytosis, degradation, and recycling and how these affect its function is currently lacking. In a combined in vitro and in silico study, we developed a mathematical model and identified these trafficking parameters. Our model predicts optimal ligand removal in an intermediate affinity range. The intracellular CTLA4 pool as well as fast internalization, recovery of free CTLA4 from internalized complexes, and recycling is critical for sustained functionality. CD80-CTLA4 interactions are predicted to dominate over CD86-CTLA4. Implications of these findings in the context of control of antigen-presenting cells by regulatory T cells and of pathologic genetic deficiencies are discussed. The presented mathematical model can be reused in the community beyond these questions to better understand other trafficking receptors and study the impact of CTLA4 targeting drugs.

Multidimensional Analysis Integrating Human T-Cell Signatures in Lymphatic Tissues with Sex of Humanized Mice for Prediction of Responses after Dendritic Cell Immunization

Mice transplanted with human cord blood-derived hematopoietic stem cells (HSCs) became a powerful experimental tool for studying the heterogeneity of human immune reconstitution and immune responses in vivo. Yet, analyses of human T cell maturation in humanized models have been hampered by an overall low immune reactivity and lack of methods to define predictive markers of responsiveness. Long-lived human lentiviral induced dendritic cells expressing the cytomegalovirus pp65 protein (iDCpp65) promoted the development of pp65-specific human CD8⁺ T cell responses in NOD.Cg-Rag1^tm1Mom-Il2rγ^tm1Wj humanized mice through the presentation of immune-dominant antigenic epitopes (signal 1), expression of co-stimulatory molecules (signal 2), and inflammatory cytokines (signal 3). We exploited this validated system to evaluate the effects of mouse sex in the dynamics of T cell homing and maturation status in thymus, blood, bone marrow, spleen, and lymph nodes. Statistical analyses of cell relative frequencies and absolute numbers demonstrated higher CD8⁺ memory T cell reactivity in spleen and lymph nodes of immunized female mice. In order to understand to which extent the multidimensional relation between organ-specific markers predicted the immunization status, the immunophenotypic profiles of individual mice were used to train an artificial neural network designed to discriminate immunized and non-immunized mice. The highest accuracy of immune reactivity prediction could be obtained from lymph node markers of female mice (77.3%). Principal component analyses further identified clusters of markers best suited to describe the heterogeneity of immunization responses in vivo. A correlation analysis of these markers reflected a tissue-specific impact of immunization. This allowed for an organ-resolved characterization of the immunization status of individual mice based on the identified set of markers. This new modality of multidimensional analyses can be used as a framework for defining minimal but predictive signatures of human immune responses in mice and suggests critical markers to characterize responses to immunization after HSC transplantation.

How to Simulate a Germinal Center

Germinal centers host a mini-evolutionary environment where B cells can mutate their receptor and be selected depending on its affinity to target antigens in a process called affinity maturation. Starting from founder cells with a weak B cell receptor affinity, germinal centers release output cells as antibody-secreting cells or memory cells with a very high affinity, a property which is essential for pathogen clearance and immune memory. Therapeutic interventions on the germinal centers are tantalizing approaches to improve vaccines or to support rejection of chronic pathogens such as HIV. However, the complexity of the selection processes makes it very hard to make reliable predictions. Here, we present in detail how to build an agent-based model (hyphasma), accounting for the dynamics of the germinal center. It encompasses the core quantitative traits of affinity maturation, and allowed to make reliable predictions in previous studies.

Glutamine-dependent α-ketoglutarate production regulates the balance between T helper 1 cell and regulatory T cell generation

T cell activation requires that the cell meet increased energetic and biosynthetic demands. We showed that exogenous nutrient availability regulated the differentiation of naïve CD4(+) T cells into distinct subsets. Activation of naïve CD4(+) T cells under conditions of glutamine deprivation resulted in their differentiation into Foxp3(+) (forkhead box P3-positive) regulatory T (Treg) cells, which had suppressor function in vivo. Moreover, glutamine-deprived CD4(+) T cells that were activated in the presence of cytokines that normally induce the generation of T helper 1 (TH1) cells instead differentiated into Foxp3(+) Treg cells. We found that α-ketoglutarate (αKG), the glutamine-derived metabolite that enters into the mitochondrial citric acid cycle, acted as a metabolic regulator of CD4(+) T cell differentiation. Activation of glutamine-deprived naïve CD4(+) T cells in the presence of a cell-permeable αKG analog increased the expression of the gene encoding the TH1 cell-associated transcription factor Tbet and resulted in their differentiation into TH1 cells, concomitant with stimulation of mammalian target of rapamycin complex 1 (mTORC1) signaling. Together, these data suggest that a decrease in the intracellular amount of αKG, caused by the limited availability of extracellular glutamine, shifts the balance between the generation of TH1 and Treg cells toward that of a Treg phenotype.

A signal integration model of thymic selection and natural regulatory T cell commitment

The extent of TCR self-reactivity is the basis for selection of a functional and self-tolerant T cell repertoire and is quantified by repeated engagement of TCRs with a diverse pool of self-peptides complexed with self-MHC molecules. The strength of a TCR signal depends on the binding properties of a TCR to the peptide and the MHC, but it is not clear how the specificity to both components drives fate decisions. In this study, we propose a TCR signal-integration model of thymic selection that describes how thymocytes decide among distinct fates, not only based on a single TCR-ligand interaction, but taking into account the TCR stimulation history. These fates are separated based on sustained accumulated signals for positive selection and transient peak signals for negative selection. This spans up the cells into a two-dimensional space where they are either neglected, positively selected, negatively selected, or selected as natural regulatory T cells (nTregs). We show that the dynamics of the integrated signal can serve as a successful basis for extracting specificity of thymocytes to MHC and detecting the existence of cognate self-peptide-MHC. It allows to select a self-MHC-biased and self-peptide-tolerant T cell repertoire. Furthermore, nTregs in the model are enriched with MHC-specific TCRs. This allows nTregs to be more sensitive to activation and more cross-reactive than conventional T cells. This study provides a mechanistic model showing that time integration of TCR-mediated signals, as opposed to single-cell interaction events, is needed to gain a full view on the properties emerging from thymic selection.

Ethylene spectroscopy using a quasi-room-temperature quantum cascade laser

Spectroscopic measurements on ethylene were performed using a quasi-room-temperature quantum cascade (QC) laser operated in pulsed mode in the 10.3 microm range. Using transmission spectroscopy, a broadening of the ethylene absorption spectrum was observed with increasing laser pulse duration, due to an increase of the laser linewidth. This linewidth was determined from the measured absorption spectra, showing a value of 0.04 cm(-1) for a 20 ns pulse duration and an enhancement coefficient of 6.5 x 10(-3) cm(-1) per ns in the 20-50 ns pulse length range. Photoacoustic (PA) detection of ethylene was also performed using the QC laser and a resonant PA cell, with a detection limit of 60 ppm.

Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly

A new method of evaluating the characteristics of postural transition (PT) and their correlation with falling risk in elderly people is described. The time of sit-to-stand and stand-to-sit transitions and their duration were measured using a miniature gyroscope attached to the chest and a portable recorder placed on the waist. Based on a simple model and the discrete wavelet transform, three parameters related to the PT were measured, namely, the average and standard deviation of transition duration and the occurrence of abnormal successive transitions (number of attempts to have a successful transition). The comparison between two groups of elderly subjects (with high and low fall-risk) showed that the computed parameters were significantly correlated with the falling risk as determined by the record of falls during the previous year, balance and gait disorders (Tinetti score), visual disorders, and cognitive and depressive disorders (p < 0.01). In this study, the wavelet transform has provided a powerful technique for enhancing the pattern of PT, which was mainly concentrated into the frequency range of 0.04-0.68 Hz. The system is especially adapted for long-term ambulatory monitoring of elderly people.

Psychotropic drug prescription in a psychiatric university hospital

A retrospective survey on drug prescription over a one-year period (1989) in 1083 patients (48.3% of whom were male) hospitalized in a psychiatric university hospital in Switzerland and a 35-day prospective study (1992) on the prescription of "as needed" (prn) medication in a closed and an open ward were carried out. Their aim was to establish a basis for a monitoring of prescription habits and for pharmacoeconomic considerations. In the retrospective study, 48.3% of the patients were male. The mean duration of hospitalization of the patients was 47.0 +/- 68.1 days (mean +/- s.d.). Only 11 out of the 1083 patients (1%) were without psychotropic medication. The mean (+/- SD) number of drugs/day the patients were prescribed was 4.6 +/- 2.8, including 3.2 +/- 1.7 psychotropic drugs. Patients suffering from schizophrenia (67 d) or from unipolar depression (67.4 d) were hospitalized for the longest periods. Antipsychotics (67.5% of the patients) were the most frequently prescribed psychotropic drugs, followed by anxiolytics (42.2%), antidepressants (28.3%), hypnotics (31.4%) and mood stabilizers (7.1%). Antiparkinsonian agents accounted for 4.6% of all prescriptions. Levomepromazine, haloperidol (30.9% of all patients) and clotiapine were the most often prescribed neuroleptics, and clozapine was administered to only 6.4% of all patients. Among the antidepressants, maprotiline (11.9% of all patients) was more frequently prescribed than the classical tricyclic antidepressant amitriptyline, while the only available SSRI fluvoxamine and MAO inhibitors were rarely used. The most frequently prescribed anxiolytics were clorazepate (28.2% of all patients), lorazepam, bromazepam, and prazepam. Among the hypnotic drugs, chloral hydrate (11.5%) was more frequently administered than the first-ranking benzodiazepine flunitrazepam (7.8%). In the prospective study, 97% and 77% of the patients (n = 55) of the closed (n = 29) and of the open ward, respectively, were prescribed "as needed" (prn) drugs. However, only 71 and 80%, respectively, of these patients finally received the drug. The frequency of prescription was 34.9% for neuroleptics, 15.1% for anxiolytic drugs, 8.2% for non-benzodiazepine hypnotics and only 2.1% for benzodiazepine hypnotics. The most frequently prescribed neuroleptic drug was clotiapine (18% of all patients), but finally, only 29% of the prescribed doses were administered. Studies of this type are biased by the fact that local habits of prescription do not allow generalisation of the findings. Such surveys should be carried out more frequently and simultaneously in different centers. Critical comparisons could help to optimize treatment.

Optical fiber ring resonator characterization by optical time-domain reflectometry

A novel method for characterization of optical fiber resonators by an optical time-domain reflectometry (OTDR) technique is reported. This easy-to-use technique yields accurate results for cavity lengths ranging from a few meters to several kilometers. A simple relationship is established between the round-trip cavity loss and the position where the OTDR signal is maximum. The value obtained for the round-trip cavity loss turns out to be quite insensitive to uncertainties in the determination of the OTDR maximum position.

Reversibility of photoinduced birefringence in ultralow-birefringence fibers

New measurements of the birefringence induced in ultralow-birefringence fibers by a succession of orthogonally polarized UV side exposures show the reversibility of the process. Moreover, it is shown that the birefringence growth depends on two parameters, the total irradiation dose and the nature of the successive polarized UV side exposures. These observations indicate that geometric asymmetry is not the major cause of the birefringence and confirm a model of birefringence growth based on dipole alignment.