Optimality of mutation and selection in germinal centers

A multiscale spatial modeling framework for the germinal center response

The germinal center response or reaction (GCR) is a hallmark event of adaptive humoral immunity. Unfolding in the B cell follicles of the secondary lymphoid organs, a GC culminates in the production of high-affinity antibody-secreting plasma cells along with memory B cells. By interacting with follicular dendritic cells (FDC) and T follicular helper (Tfh) cells, GC B cells exhibit complex spatiotemporal dynamics. Driving the B cell dynamics are the intracellular signal transduction and gene regulatory network that responds to cell surface signaling molecules, cytokines, and chemokines. As our knowledge of the GC continues to expand in depth and in scope, mathematical modeling has become an important tool to help disentangle the intricacy of the GCR and inform novel mechanistic and clinical insights. While the GC has been modeled at different granularities, a multiscale spatial simulation framework - integrating molecular, cellular, and tissue-level responses - is still rare. Here, we report our recent progress toward this end with a hybrid stochastic GC framework developed on the Cellular Potts Model-based CompuCell3D platform. Tellurium is used to simulate the B cell intracellular molecular network comprising NF-κB, FOXO1, MYC, AP4, CXCR4, and BLIMP1 that responds to B cell receptor (BCR) and CD40-mediated signaling. The molecular outputs of the network drive the spatiotemporal behaviors of B cells, including cyclic migration between the dark zone (DZ) and light zone (LZ) via chemotaxis; clonal proliferative bursts, somatic hypermutation, and DNA damage-induced apoptosis in the DZ; and positive selection, apoptosis via a death timer, and emergence of plasma cells in the LZ. Our simulations are able to recapitulate key molecular, cellular, and morphological GC events, including B cell population growth, affinity maturation, and clonal dominance. This novel modeling framework provides an open-source, customizable, and multiscale virtual GC simulation platform that enables qualitative and quantitative in silico investigations of a range of mechanistic and applied research questions on the adaptive humoral immune response in the future.

Repeated vaccination with homologous influenza hemagglutinin broadens human antibody responses to unmatched flu viruses

The on-going diversification of influenza virus necessicates annual vaccine updating. The vaccine antigen, the viral spike protein hemagglutinin (HA), tends to elicit strain-specific neutralizing activity, predicting that sequential immunization with the same HA strain will boost antibodies with narrow coverage. However, repeated vaccination with homologous SARS-CoV-2 vaccine eventually elicits neutralizing activity against highly unmatched variants, questioning this immunological premise. We evaluated a longitudinal influenza vaccine cohort, where each year the subjects received the same, novel H1N1 2009 pandemic vaccine strain. Repeated vaccination gradually enhanced receptor-blocking antibodies (HAI) to highly unmatched H1N1 strains within individuals with no initial memory recall against these historical viruses. An in silico model of affinity maturation in germinal centers integrated with a model of differentiation and expansion of memory cells provides insight into the mechanisms underlying these results and shows how repeated exposure to the same immunogen can broaden the antibody response against diversified targets.

A Multiscale Spatial Modeling Framework for the Germinal Center Response

The germinal center response or reaction (GCR) is a hallmark event of adaptive humoral immunity. Unfolding in the B cell follicles of the secondary lymph organs, a GC culminates in the production of high-affinity antibody-secreting plasma cells along with memory B cells. By interacting with follicular dendritic cells (FDC) and T follicular helper (Tfh) cells, GC B cells exhibit complex spatiotemporal dynamics. Driving the B cell dynamics are the intracellular signal transduction and gene regulatory network that responds to cell surface signaling molecules, cytokines, and chemokines. As our knowledge of the GC continues to expand in depth and in scope, mathematical modeling has become an important tool to help disentangle the intricacy of the GCR and inform novel mechanistic and clinical insights. While the GC has been modeled at different granularities, a multiscale spatial simulation framework - integrating molecular, cellular, and tissue-level responses - is still rare. Here, we report our recent progress toward this end with a hybrid stochastic GC framework developed on the Cellular Potts Model-based CompuCell3D platform. Tellurium is used to simulate the B cell intracellular molecular network comprising NF-κB, FOXO1, MYC, AP4, CXCR4, and BLIMP1 that responds to B cell receptor (BCR) and CD40-mediated signaling. The molecular outputs of the network drive the spatiotemporal behaviors of B cells, including cyclic migration between the dark zone (DZ) and light zone (LZ) via chemotaxis; clonal proliferative bursts, somatic hypermutation, and DNA damage-induced apoptosis in the DZ; and positive selection, apoptosis via a death timer, and emergence of plasma cells in the LZ. Our simulations are able to recapitulate key molecular, cellular, and morphological GC events including B cell population growth, affinity maturation, and clonal dominance. This novel modeling framework provides an open-source, customizable, and multiscale virtual GC simulation platform that enables qualitative and quantitative in silico investigations of a range of mechanic and applied research questions in future.

Antigen presentation dynamics shape the antibody response to variants like SARS-CoV-2 Omicron after multiple vaccinations with the original strain

The Omicron variant of SARS-CoV-2 is not effectively neutralized by most antibodies elicited by two doses of mRNA vaccines, but a third dose increases anti-Omicron neutralizing antibodies. We reveal mechanisms underlying this observation by combining computational modeling with data from vaccinated humans. After the first dose, limited antigen availability in germinal centers (GCs) results in a response dominated by B cells that target immunodominant epitopes that are mutated in an Omicron-like variant. After the second dose, these memory cells expand and differentiate into plasma cells that secrete antibodies that are thus ineffective for such variants. However, these pre-existing antigen-specific antibodies transport antigen efficiently to secondary GCs. They also partially mask immunodominant epitopes. Enhanced antigen availability and epitope masking in secondary GCs together result in generation of memory B cells that target subdominant epitopes that are less mutated in Omicron. The third dose expands these cells and boosts anti-variant neutralizing antibodies.

Mechanisms that promote the evolution of cross-reactive antibodies upon vaccination with designed influenza immunogens

Immunogens that elicit broadly neutralizing antibodies targeting the conserved receptor-binding site (RBS) on influenza hemagglutinin may serve as candidates for a universal influenza vaccine. Here, we develop a computational model to interrogate antibody evolution by affinity maturation after immunization with two types of immunogens: a heterotrimeric "chimera" hemagglutinin that is enriched for the RBS epitope relative to other B cell epitopes and a cocktail composed of three non-epitope-enriched homotrimers of the monomers that comprise the chimera. Experiments in mice find that the chimera outperforms the cocktail for eliciting RBS-directed antibodies. We show that this result follows from an interplay between how B cells engage these antigens and interact with diverse helper T cells and requires T cell-mediated selection of germinal center B cells to be a stringent constraint. Our results shed light on antibody evolution and highlight how immunogen design and T cells modulate vaccination outcomes.

Moving the needle: Employing deep reinforcement learning to push the boundaries of coarse-grained vaccine models

Highly mutable infectious disease pathogens (hm-IDPs) such as HIV and influenza evolve faster than the human immune system can contain them, allowing them to circumvent traditional vaccination approaches and causing over one million deaths annually. Agent-based models can be used to simulate the complex interactions that occur between immune cells and hm-IDP-like proteins (antigens) during affinity maturation-the process by which antibodies evolve. Compared to existing experimental approaches, agent-based models offer a safe, low-cost, and rapid route to study the immune response to vaccines spanning a wide range of design variables. However, the highly stochastic nature of affinity maturation and vast sequence space of hm-IDPs render brute force searches intractable for exploring all pertinent vaccine design variables and the subset of immunization protocols encompassed therein. To address this challenge, we employed deep reinforcement learning to drive a recently developed agent-based model of affinity maturation to focus sampling on immunization protocols with greater potential to improve the chosen metrics of protection, namely the broadly neutralizing antibody (bnAb) titers or fraction of bnAbs produced. Using this approach, we were able to coarse-grain a wide range of vaccine design variables and explore the relevant design space. Our work offers new testable insights into how vaccines should be formulated to maximize protective immune responses to hm-IDPs and how they can be minimally tailored to account for major sources of heterogeneity in human immune responses and various socioeconomic factors. Our results indicate that the first 3 to 5 immunizations, depending on the metric of protection, should be specially tailored to achieve a robust protective immune response, but that beyond this point further immunizations require only subtle changes in formulation to sustain a durable bnAb response.

Two complementary features of humoral immune memory confer protection against the same or variant antigens

We study an important question in immunology: How is B cell–mediated immune memory recalled upon reexposure to the same or variant antigens? We find that, upon reexposure to the same antigen, high-affinity memory B cells are selectively expanded outside germinal centers (GCs) to quickly provide the best protection possible. Memory B cells also enter GCs and over time produce the highest-affinity antibodies, but GCs also generate diverse B cells, some with low antigen affinity. Upon exposure to a variant antigen, these low-affinity clones can exhibit high affinity for the variant. These clones are expanded rapidly outside the GC to confer immediate protection. Over longer times, secondary GCs produce high-affinity clones tailored for the variant antigen.

The humoral immune response, a key arm of adaptive immunity, consists of B cells and their products. Upon infection or vaccination, B cells undergo a Darwinian evolutionary process in germinal centers (GCs), resulting in the production of antibodies and memory B cells. We developed a computational model to study how humoral memory is recalled upon reinfection or booster vaccination. We find that upon reexposure to the same antigen, affinity-dependent selective expansion of available memory B cells outside GCs (extragerminal center compartments [EGCs]) results in a rapid response made up of the best available antibodies. Memory B cells that enter secondary GCs can undergo mutation and selection to generate even more potent responses over time, enabling greater protection upon subsequent exposure to the same antigen. GCs also generate a diverse pool of B cells, some with low antigen affinity. These results are consistent with our analyses of data from humans vaccinated with two doses of a COVID-19 vaccine. Our results further show that the diversity of memory B cells generated in GCs is critically important upon exposure to a variant antigen. Clones drawn from this diverse pool that cross-react with the variant are rapidly expanded in EGCs to provide the best protection possible while new secondary GCs generate a tailored response for the new variant. Based on a simple evolutionary model, we suggest that the complementary roles of EGC and GC processes we describe may have evolved in response to complex organisms being exposed to evolving pathogen families for millennia.

Multiscale affinity maturation simulations to elicit broadly neutralizing antibodies against HIV

The design of vaccines against highly mutable pathogens, such as HIV and influenza, requires a detailed understanding of how the adaptive immune system responds to encountering multiple variant antigens (Ags). Here, we describe a multiscale model of B cell receptor (BCR) affinity maturation that employs actual BCR nucleotide sequences and treats BCR/Ag interactions in atomistic detail. We apply the model to simulate the maturation of a broadly neutralizing Ab (bnAb) against HIV. Starting from a germline precursor sequence of the VRC01 anti-HIV Ab, we simulate BCR evolution in response to different vaccination protocols and different Ags, which were previously designed by us. The simulation results provide qualitative guidelines for future vaccine design and reveal unique insights into bnAb evolution against the CD4 binding site of HIV. Our model makes possible direct comparisons of simulated BCR populations with results of deep sequencing data, which will be explored in future applications.

Vaccination has saved more lives than any other medical procedure. But, we do not have robust ways to develop vaccines against highly mutable pathogens. For example, there is no effective vaccine against HIV, and a universal vaccine against diverse strains of influenza is also not available. The development of immunization strategies to elicit antibodies that can neutralize diverse strains of highly mutable pathogens (so-called ‘broadly neutralizing antibodies’, or bnAbs) would enable the design of universal vaccines against such pathogens, as well as other viruses that may emerge in the future. In this paper, we present an agent-based model of affinity maturation–the Darwinian process by which antibodies evolve against a pathogen–that, for the first time, enables the in silico investigation of real germline nucleotide sequences of antibodies known to evolve into potent bnAbs, evolving against real amino acid sequences of HIV-based vaccine-candidate proteins. Our results provide new insights into bnAb evolution against HIV, and can be used to qualitatively guide the future design of vaccines against highly mutable pathogens.

A Coarse-Grained Model of Affinity Maturation Indicates the Importance of B-Cell Receptor Avidity in Epitope Subdominance

The elicitation of broadly neutralizing antibodies (bnAbs) is a major goal in the design of vaccines against rapidly-mutating viruses. In the case of influenza, many bnAbs that target conserved epitopes on the stem of the hemagglutinin protein (HA) have been discovered. However, these antibodies are rare, are not boosted well upon reinfection, and often have low neutralization potency, compared to strain-specific antibodies directed to the HA head. Different hypotheses have been proposed to explain this phenomenon. We use a coarse-grained computational model of the germinal center reaction to investigate how B-cell receptor binding valency affects the growth and affinity maturation of competing B-cells. We find that receptors that are unable to bind antigen bivalently, and also those that do not bind antigen cooperatively, have significantly slower rates of growth, memory B-cell production, and, under certain conditions, rates of affinity maturation. The corresponding B-cells are predicted to be outcompeted by B-cells that bind bivalently and cooperatively. We use the model to explore strategies for a universal influenza vaccine, e.g., how to boost the concentrations of the slower growing cross-reactive antibodies directed to the stem. The results suggest that, upon natural reinfections subsequent to vaccination, the protectiveness of such vaccines would erode, possibly requiring regular boosts. Collectively, our results strongly support the importance of bivalent antibody binding in immunodominance, and suggest guidelines for developing a universal influenza vaccine.

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.

A molecular theory of germinal center B cell selection and division

The selection of B cells (BCs) in germinal centers (GCs) is pivotal to the generation of high-affinity antibodies and memory BCs, but it lacks global understanding. Based on the idea of a single Tfh-cell signal that controls BC selection and division, experiments appear contradictory. Here, we use the current knowledge on the molecular pathways of GC BCs to develop a theory of GC BC selection and division based on the dynamics of molecular factors. This theory explains the seemingly contradictory experiments by the separation of signals for BC fate decision from signals controlling the number of BC divisions. Three model variants are proposed and experiments are predicted that allow one to distinguish those. Understanding information processing in molecular BC states is critical for targeted immune interventions, and the proposed theory implies that selection and division can be controlled independently in GC reactions.

Coevolutionary transitions emerging from flexible molecular recognition and eco-evolutionary feedback

Highly mutable viruses evolve to evade host immunity that exerts selective pressure and adapts to viral dynamics. Here, we provide a framework for identifying key determinants of the mode and fate of viral-immune coevolution by linking molecular recognition and eco-evolutionary dynamics. We find that conservation level and initial diversity of antigen jointly determine the timing and efficacy of narrow and broad antibody responses, which in turn control the transition between viral persistence, clearance, and rebound. In particular, clearance of structurally complex antigens relies on antibody evolution in a larger antigenic space than where selection directly acts; viral rebound manifests binding-mediated feedback between ecology and rapid evolution. Finally, immune compartmentalization can slow viral escape but also delay clearance. This work suggests that flexible molecular binding allows a plastic phenotype that exploits potentiating neutral variations outside direct contact, opening new and shorter paths toward highly adaptable states.

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A scale-crossing framework identifies key determinants of viral-immune coevolution

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Fast specific response influences slow broad response by shaping antigen dynamics

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Antibody footprint shift enables breadth acquisition and viral clearance

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Model explains divergent kinetics and outcomes of HCV infection in humans

Survival probability and size of lineages in antibody affinity maturation

Affinity maturation (AM) is the process through which the immune system is able to develop potent antibodies against new pathogens it encounters, and is at the base of the efficacy of vaccines. At its core AM is analogous to a Darwinian evolutionary process, where B cells mutate and are selected on the base of their affinity for an antigen (Ag), and Ag availability tunes the selective pressure. In cases when this selective pressure is high, the number of B cells might quickly decrease and the population might risk extinction in what is known as a population bottleneck. Here we study the probability for a B-cell lineage to survive this bottleneck scenario as a function of the progenitor affinity for the Ag. Using recursive relations and probability generating functions we derive expressions for the average extinction time and progeny size for lineages that go extinct. We then extend our results to the full population, both in the absence and presence of competition for T-cell help, and quantify the population survival probability as a function of Ag concentration and initial population size. Our study suggests the population bottleneck phenomenology might represent a limit case in the space of biologically plausible maturation scenarios, whose characterization could help guide the process of vaccine development.

Mechanisms underlying vaccination protocols that may optimally elicit broadly neutralizing antibodies against highly mutable pathogens

Effective prophylactic vaccines usually induce the immune system to generate potent antibodies that can bind to an antigen and thus prevent it from infecting host cells. B cells produce antibodies by a Darwinian evolutionary process called affinity maturation (AM). During AM, the B cell population evolves in response to the antigen to produce antibodies that bind specifically and strongly to the antigen. Highly mutable pathogens pose a major challenge to the development of effective vaccines because antibodies that are effective against one strain of the virus may not protect against a mutant strain. Antibodies that can protect against diverse strains of a mutable pathogen have high "breadth" and are called broadly neutralizing antibodies (bnAbs). In spite of extensive studies, an effective vaccination strategy that can generate bnAbs in humans does not exist for any highly mutable pathogen. Here we study a minimal model to explore the mechanisms underlying how the selection forces imposed by antigens can be optimally chosen to guide AM to maximize the evolution of bnAbs. For logistical reasons, only a finite number of antigens can be administered in a finite number of vaccinations; that is, guiding the nonequilibrium dynamics of AM to produce bnAbs must be accomplished nonadiabatically. The time-varying Kullback-Leibler divergence (KLD) between the existing B cell population distribution and the fitness landscape imposed by antigens is a quantitative metric of the thermodynamic force acting on B cells. If this force is too small, adaptation is minimal. If the force is too large, contrary to expectations, adaptation is not faster; rather, the B cell population is extinguished for reasons that we describe. We define the conditions necessary for the force to be set optimally such that the flux of B cells from low to high breadth states is maximized. Even in this case we show why the dynamics of AM prevent perfect adaptation. If two shots of vaccination are allowed, the optimal protocol is characterized by a relatively low optimal KLD during the first shot that appropriately increases the diversity of the B cell population so that the surviving B cells have a high chance of evolving into bnAbs upon subsequently increasing the KLD during the second shot. Phylogenetic tree analysis further reveals the evolutionary pathways that lead to bnAbs. The connections between the mechanisms revealed by our analyses and recent simulation studies of bnAb evolution, the problem of generalist versus specialist evolution, and learning theory are discussed.

How Chaotic Is Genome Chaos?

Simple Summary

Cancer genomes can undergo major restructurings involving many chromosomal locations at key stages in tumor development. This restructuring process has been designated “genome chaos” by some authors. In order to examine how chaotic cancer genome restructuring may be, the cell and molecular processes for DNA restructuring are reviewed. Examination of the action of these processes in various cancers reveals a degree of specificity that indicates genome restructuring may be sufficiently reproducible to enable possible therapies that interrupt tumor progression to more lethal forms.

Abstract

Cancer genomes evolve in a punctuated manner during tumor evolution. Abrupt genome restructuring at key steps in this evolution has been called “genome chaos.” To answer whether widespread genome change is truly chaotic, this review (i) summarizes the limited number of cell and molecular systems that execute genome restructuring, (ii) describes the characteristic signatures of DNA changes that result from activity of those systems, and (iii) examines two cases where genome restructuring is determined to a significant degree by cell type or viral infection. The conclusion is that many restructured cancer genomes display sufficiently unchaotic signatures to identify the cellular systems responsible for major oncogenic transitions, thereby identifying possible targets for therapies to inhibit tumor progression to greater aggressiveness.

Genetic and epigenetic determinants of diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoma and is notorious for its heterogeneity, aggressive nature, and the frequent development of resistance and/or relapse after treatment with standard chemotherapy. To address these problems, a strong emphasis has been placed on researching the molecular origins and mechanisms of DLBCL to develop effective treatments. One of the major insights produced by such research is that DLBCL almost always stems from genetic damage that occurs during the germinal center (GC) reaction, which is required for the production of high-affinity antibodies. Indeed, there is significant overlap between the mechanisms that govern the GC reaction and those that drive the progression of DLBCL. A second important insight is that some of the most frequent genetic mutations that occur in DLBCL are those related to chromatin and epigenetics, especially those related to proteins that “write” histone post-translational modifications (PTMs). Mutation or deletion of these epigenetic writers often renders cells unable to epigenetically “switch on” critical gene sets that are required to exit the GC reaction, differentiate, repair DNA, and other essential cellular functions. Failure to activate these genes locks cells into a genotoxic state that is conducive to oncogenesis and/or relapse.

Defining and Manipulating B Cell Immunodominance Hierarchies to Elicit Broadly Neutralizing Antibody Responses against Influenza Virus

The antibody repertoire possesses near-limitless diversity, enabling the adaptive immune system to accommodate essentially any antigen. However, this diversity explores the antigenic space unequally, allowing some pathogens like influenza virus to impose complex immunodominance hierarchies that distract antibody responses away from key sites of virus vulnerability. We developed a computational model of affinity maturation to map the patterns of immunodominance that evolve upon immunization with natural and engineered displays of hemagglutinin (HA), the influenza vaccine antigen. Based on this knowledge, we designed immunization protocols that subvert immune distraction and focus serum antibody responses upon a functionally conserved, but immunologically recessive, target of human broadly neutralizing antibodies. We tested in silico predictions by vaccinating transgenic mice in which antibody diversity was humanized to mirror clinically relevant humoral output. Collectively, our results demonstrate that complex patterns in antibody immunogenicity can be rationally defined and then manipulated to elicit engineered immunity.

Optimizing immunization protocols to elicit broadly neutralizing antibodies

Natural infections and vaccination with a pathogen typically stimulate the production of potent antibodies specific for the pathogen through a Darwinian evolutionary process known as affinity maturation. Such antibodies provide protection against reinfection by the same strain of a pathogen. A highly mutable virus, like HIV or influenza, evades recognition by these strain-specific antibodies via the emergence of new mutant strains. A vaccine that elicits antibodies that can bind to many diverse strains of the virus-known as broadly neutralizing antibodies (bnAbs)-could protect against highly mutable pathogens. Despite much work, the mechanisms by which bnAbs emerge remain uncertain. Using a computational model of affinity maturation, we studied a wide variety of vaccination strategies. Our results suggest that an effective strategy to maximize bnAb evolution is through a sequential immunization protocol, wherein each new immunization optimally increases the pressure on the immune system to target conserved antigenic sites, thus conferring breadth. We describe the mechanisms underlying why sequentially driving the immune system increasingly further from steady state, in an optimal fashion, is effective. The optimal protocol allows many evolving B cells to become bnAbs via diverse evolutionary paths.

Quantitative modeling of the effect of antigen dosage on B-cell affinity distributions in maturating germinal centers

Affinity maturation is a complex dynamical process allowing the immune system to generate antibodies capable of recognizing antigens. We introduce a model for the evolution of the distribution of affinities across the antibody population in germinal centers. The model is amenable to detailed mathematical analysis and gives insight on the mechanisms through which antigen availability controls the rate of maturation and the expansion of the antibody population. It is also capable, upon maximum-likelihood inference of the parameters, to reproduce accurately the distributions of affinities of IgG-secreting cells we measure in mice immunized against Tetanus Toxoid under largely varying conditions (antigen dosage, delay between injections). Both model and experiments show that the average population affinity depends non-monotonically on the antigen dosage. We show that combining quantitative modeling and statistical inference is a concrete way to investigate biological processes underlying affinity maturation (such as selection permissiveness), hardly accessible through measurements.

Mathematical model of broadly reactive plasma cell production

Strain-specific plasma cells are capable of producing neutralizing antibodies that are essential for clearance of challenging pathogens. These neutralizing antibodies also function as a main defense against disease establishment in a host. However, when a rapidly mutating pathogen infects a host, successful control of the invasion requires shifting the production of plasma cells from strain-specific to broadly reactive. In this study, we develop a mathematical model of germinal center dynamics and use it to predict the events that lead to improved breadth of the plasma cell response. We examine scenarios that lead to germinal centers that are composed of B-cells that come from a single strain-specific clone, a single broadly reactive clone or both clones. We find that the initial B-cell clonal composition, T-follicular helper cell signaling, increased rounds of productive somatic hypermutation, and B-cell selection strength are among the mechanisms differentiating between strain-specific and broadly reactive plasma cell production during infections. Understanding the contribution of these factors to emergence of breadth may assist in boosting broadly reactive plasma cells production.

A Sensitivity Analysis Comparison of Three Models for the Dynamics of Germinal Centers

Germinal centers (GCs) are transient anatomical microenvironments where antibody affinity maturation and memory B cells generation takes place. In the past, models of Germinal Center (GC) dynamics have focused on understanding antibody affinity maturation rather than on the main mechanism(s) driving their rise-and-fall dynamics. Here, based on a population dynamics model core, we compare three mechanisms potentially responsible for this GC biphasic behavior dependent on follicular dendritic cell (FDC) maturation, follicular T helper (Tfh) cell maturation, and antigen depletion. Analyzing the kinetics of B and T cells, as well as its parameter sensitivities, we found that only the FDC-maturation-based model could describe realistic GC dynamics, whereas the simple Tfh-maturation and antigen-depletion mechanisms, as implemented here, could not. We also found that in all models the processes directly related to Tfh cell kinetics have the highest impact on GC dynamics. This suggests the existence of some still unknown mechanism(s) tuning GC dynamics by affecting Tfh cell response to proliferation-inducing stimuli.

Evolution on the Biophysical Fitness Landscape of an RNA Virus

Viral evolutionary pathways are determined by the fitness landscape, which maps viral genotype to fitness. However, a quantitative description of the landscape and the evolutionary forces on it remain elusive. Here, we apply a biophysical fitness model based on capsid folding stability and antibody binding affinity to predict the evolutionary pathway of norovirus escaping a neutralizing antibody. The model is validated by experimental evolution in bulk culture and in a drop-based microfluidics that propagates millions of independent small viral subpopulations. We demonstrate that along the axis of binding affinity, selection for escape variants and drift due to random mutations have the same direction, an atypical case in evolution. However, along folding stability, selection and drift are opposing forces whose balance is tuned by viral population size. Our results demonstrate that predictable epistatic tradeoffs between molecular traits of viral proteins shape viral evolution.

The low spike density of HIV may have evolved because of the effects of T helper cell depletion on affinity maturation

The spikes on virus surfaces bind receptors on host cells to propagate infection. High spike densities (SDs) can promote infection, but spikes are also targets of antibody-mediated immune responses. Thus, diverse evolutionary pressures can influence virus SDs. HIV's SD is about two orders of magnitude lower than that of other viruses, a surprising feature of unknown origin. By modeling antibody evolution through affinity maturation, we find that an intermediate SD maximizes the affinity of generated antibodies. We argue that this leads most viruses to evolve high SDs. T helper cells, which are depleted during early HIV infection, play a key role in antibody evolution. We find that T helper cell depletion results in high affinity antibodies when SD is high, but not if SD is low. This special feature of HIV infection may have led to the evolution of a low SD to avoid potent immune responses early in infection.

Role of framework mutations and antibody flexibility in the evolution of broadly neutralizing antibodies

Eliciting antibodies that are cross reactive with surface proteins of diverse strains of highly mutable pathogens (e.g., HIV, influenza) could be key for developing effective universal vaccines. Mutations in the framework regions of such broadly neutralizing antibodies (bnAbs) have been reported to play a role in determining their properties. We used molecular dynamics simulations and models of affinity maturation to study specific bnAbs against HIV. Our results suggest that there are different classes of evolutionary lineages for the bnAbs. If germline B cells that initiate affinity maturation have high affinity for the conserved residues of the targeted epitope, framework mutations increase antibody rigidity as affinity maturation progresses to evolve bnAbs. If the germline B cells exhibit weak/moderate affinity for conserved residues, an initial increase in flexibility via framework mutations may be required for the evolution of bnAbs. Subsequent mutations that increase rigidity result in highly potent bnAbs. Implications of our results for immunogen design are discussed.

A Population Dynamics Model for Clonal Diversity in a Germinal Center

Germinal centers (GCs) are micro-domains where B cells mature to develop high affinity antibodies. Inside a GC, B cells compete for antigen and T cell help, and the successful ones continue to evolve. New experimental results suggest that, under identical conditions, a wide spectrum of clonal diversity is observed in different GCs, and high affinity B cells are not always the ones selected. We use a birth, death and mutation model to study clonal competition in a GC over time. We find that, like all evolutionary processes, diversity loss is inherently stochastic. We study two selection mechanisms, birth-limited and death limited selection. While death limited selection maintains diversity and allows for slow clonal homogenization as affinity increases, birth limited selection results in more rapid takeover of successful clones. Finally, we qualitatively compare our model to experimental observations of clonal selection in mice.

Rational design of vaccine targets and strategies for HIV: a crossroad of statistical physics, biology, and medicine

Vaccination has saved more lives than any other medical procedure. Pathogens have now evolved that have not succumbed to vaccination using the empirical paradigms pioneered by Pasteur and Jenner. Vaccine design strategies that are based on a mechanistic understanding of the pertinent immunology and virology are required to confront and eliminate these scourges. In this perspective, we describe just a few examples of work aimed to achieve this goal by bringing together approaches from statistical physics with biology and clinical research.

A Perspective on the Role of Computational Models in Immunology

This is an exciting time for immunology because the future promises to be replete with exciting new discoveries that can be translated to improve health and treat disease in novel ways. Immunologists are attempting to answer increasingly complex questions concerning phenomena that range from the genetic, molecular, and cellular scales to that of organs, whole animals or humans, and populations of humans and pathogens. An important goal is to understand how the many different components involved interact with each other within and across these scales for immune responses to emerge, and how aberrant regulation of these processes causes disease. To aid this quest, large amounts of data can be collected using high-throughput instrumentation. The nonlinear, cooperative, and stochastic character of the interactions between components of the immune system as well as the overwhelming amounts of data can make it difficult to intuit patterns in the data or a mechanistic understanding of the phenomena being studied. Computational models are increasingly important in confronting and overcoming these challenges. I first describe an iterative paradigm of research that integrates laboratory experiments, clinical data, computational inference, and mechanistic computational models. I then illustrate this paradigm with a few examples from the recent literature that make vivid the power of bringing together diverse types of computational models with experimental and clinical studies to fruitfully interrogate the immune system.

Optimal Sequential Immunization Can Focus Antibody Responses against Diversity Loss and Distraction

Affinity maturation is a Darwinian process in which B lymphocytes evolve potent antibodies to encountered antigens and generate immune memory. Highly mutable complex pathogens present an immense antigenic diversity that continues to challenge natural immunity and vaccine design. Induction of broadly neutralizing antibodies (bnAbs) against this diversity by vaccination likely requires multiple exposures to distinct but related antigen variants, and yet how affinity maturation advances under such complex stimulation remains poorly understood. To fill the gap, we present an in silico model of affinity maturation to examine two realistic new aspects pertinent to vaccine development: loss in B cell diversity across successive immunization periods against different variants, and the presence of distracting epitopes that entropically disfavor the evolution of bnAbs. We find these new factors, which introduce additional selection pressures and constraints, significantly influence antibody breadth development, in a way that depends crucially on the temporal pattern of immunization (or selection forces). Curiously, a less diverse B cell seed may even favor the expansion and dominance of cross-reactive clones, but only when conflicting selection forces are presented in series rather than in a mixture. Moreover, the level of frustration due to evolutionary conflict dictates the degree of distraction. We further describe how antigenic histories select evolutionary paths of B cell lineages and determine the predominant mode of antibody responses. Sequential immunization with mutationally distant variants is shown to robustly induce bnAbs that focus on conserved elements of the target epitope, by thwarting strain-specific and distracted lineages. An optimal range of antigen dose underlies a fine balance between efficient adaptation and persistent reaction. These findings provide mechanistic guides to aid in design of vaccine strategies against fast mutating pathogens.

Humanized Immunoglobulin Mice: Models for HIV Vaccine Testing and Studying the Broadly Neutralizing Antibody Problem

A vaccine that can effectively prevent HIV-1 transmission remains paramount to ending the HIV pandemic, but to do so, will likely need to induce broadly neutralizing antibody (bnAb) responses. A major technical hurdle toward achieving this goal has been a shortage of animal models with the ability to systematically pinpoint roadblocks to bnAb induction and to rank vaccine strategies based on their ability to stimulate bnAb development. Over the past 6 years, immunoglobulin (Ig) knock-in (KI) technology has been leveraged to express bnAbs in mice, an approach that has enabled elucidation of various B-cell tolerance mechanisms limiting bnAb production and evaluation of strategies to circumvent such processes. From these studies, in conjunction with the wealth of information recently obtained regarding the evolutionary pathways and paratopes/epitopes of multiple bnAbs, it has become clear that the very features of bnAbs desired for their function will be problematic to elicit by traditional vaccine paradigms, necessitating more iterative testing of new vaccine concepts. To meet this need, novel bnAb KI models have now been engineered to express either inferred prerearranged V(D)J exons (or unrearranged germline V, D, or J segments that can be assembled into functional rearranged V(D)J exons) encoding predecessors of mature bnAbs. One encouraging approach that has materialized from studies using such newer models is sequential administration of immunogens designed to bind progressively more mature bnAb predecessors. In this review, insights into the regulation and induction of bnAbs based on the use of KI models will be discussed, as will new Ig KI approaches for higher-throughput production and/or altering expression of bnAbs in vivo, so as to further enable vaccine-guided bnAb induction studies.

Optimal immunization cocktails can promote induction of broadly neutralizing Abs against highly mutable pathogens

Strategies to elicit Abs that can neutralize diverse strains of a highly mutable pathogen are likely to result in a potent vaccine. Broadly neutralizing Abs (bnAbs) against HIV have been isolated from patients, proving that the human immune system can evolve them. Using computer simulations and theory, we study immunization with diverse mixtures of variant antigens (Ags). Our results show that particular choices for the number of variant Ags and the mutational distances separating them maximize the probability of inducing bnAbs. The variant Ags represent potentially conflicting selection forces that can frustrate the Darwinian evolutionary process of affinity maturation. An intermediate level of frustration maximizes the chance of evolving bnAbs. A simple model makes vivid the origin of this principle of optimal frustration. Our results, combined with past studies, suggest that an appropriately chosen permutation of immunization with an optimally designed mixture (using the principles that we describe) and sequential immunization with variant Ags that are separated by relatively large mutational distances may best promote the evolution of bnAbs.

Sustained antigen availability during germinal center initiation enhances antibody responses to vaccination

Natural infections expose the immune system to escalating antigen and inflammation over days to weeks, whereas nonlive vaccines are single bolus events. We explored whether the immune system responds optimally to antigen kinetics most similar to replicating infections, rather than a bolus dose. Using HIV antigens, we found that administering a given total dose of antigen and adjuvant over 1-2 wk through repeated injections or osmotic pumps enhanced humoral responses, with exponentially increasing (exp-inc) dosing profiles eliciting >10-fold increases in antibody production relative to bolus vaccination post prime. Computational modeling of the germinal center response suggested that antigen availability as higher-affinity antibodies evolve enhances antigen capture in lymph nodes. Consistent with these predictions, we found that exp-inc dosing led to prolonged antigen retention in lymph nodes and increased Tfh cell and germinal center B-cell numbers. Thus, regulating the antigen and adjuvant kinetics may enable increased vaccine potency.

Immune perturbations in HIV-1-infected individuals who make broadly neutralizing antibodies

Induction of broadly neutralizing antibodies (bnAbs) is a goal of HIV-1 vaccine development. bnAbs occur in some HIV-1-infected individuals and frequently have characteristics of autoantibodies. We have studied cohorts of HIV-1-infected individuals who made bnAbs and compared them with those who did not do so, and determined immune traits associated with the ability to produce bnAbs. HIV-1-infected individuals with bnAbs had a higher frequency of blood autoantibodies, a lower frequency of regulatory CD4+ T cells, a higher frequency of circulating memory T follicular helper CD4+ cells, and a higher T regulatory cell level of programmed cell death-1 expression compared with HIV-1-infected individuals without bnAbs. Thus, induction of HIV-1 bnAbs may require vaccination regimens that transiently mimic immunologic perturbations in HIV-1-infected individuals.

Trade-offs in antibody repertoires to complex antigens

Pathogens vary in their antigenic complexity. While some pathogens such as measles present a few relatively invariant targets to the immune system, others such as malaria display considerable antigenic diversity. How the immune response copes in the presence of multiple antigens, and whether a trade-off exists between the breadth and efficacy of antibody (Ab)-mediated immune responses, are unsolved problems. We present a theoretical model of affinity maturation of B-cell receptors (BCRs) during a primary infection and examine how variation in the number of accessible antigenic sites alters the Ab repertoire. Naive B cells with randomly generated receptor sequences initiate the germinal centre (GC) reaction. The binding affinity of a BCR to an antigen is quantified via a genotype-phenotype map, based on a random energy landscape, that combines local and distant interactions between residues. In the presence of numerous antigens or epitopes, B-cell clones with different specificities compete for stimulation during rounds of mutation within GCs. We find that the availability of many epitopes reduces the affinity and relative breadth of the Ab repertoire. Despite the stochasticity of somatic hypermutation, patterns of immunodominance are strongly shaped by chance selection of naive B cells with specificities for particular epitopes. Our model provides a mechanistic basis for the diversity of Ab repertoires and the evolutionary advantage of antigenically complex pathogens.

Affinity Inequality among Serum Antibodies That Originate in Lymphoid Germinal Centers

Upon natural infection with pathogens or vaccination, antibodies are produced by a process called affinity maturation. As affinity maturation ensues, average affinity values between an antibody and ligand increase with time. Purified antibodies isolated from serum are invariably heterogeneous with respect to their affinity for the ligands they bind, whether macromolecular antigens or haptens (low molecular weight approximations of epitopes on antigens). However, less is known about how the extent of this heterogeneity evolves with time during affinity maturation. To shed light on this issue, we have taken advantage of previously published data from Eisen and Siskind (1964). Using the ratio of the strongest to the weakest binding subsets as a metric of heterogeneity (or affinity inequality), we analyzed antibodies isolated from individual serum samples. The ratios were initially as high as 50-fold, and decreased over a few weeks after a single injection of small antigen doses to around unity. This decrease in the effective heterogeneity of antibody affinities with time is consistent with Darwinian evolution in the strong selection limit. By contrast, neither the average affinity nor the heterogeneity evolves much with time for high doses of antigen, as competition between clones of the same affinity is minimal.

The evolution within us

The B-cell immune response is a remarkable evolutionary system found in jawed vertebrates. B-cell receptors, the membrane-bound form of antibodies, are capable of evolving high affinity to almost any foreign protein. High germline diversity and rapid evolution upon encounter with antigen explain the general adaptability of B-cell populations, but the dynamics of repertoires are less well understood. These dynamics are scientifically and clinically important. After highlighting the remarkable characteristics of naive and experienced B-cell repertoires, especially biased usage of genes encoding the B-cell receptors, we contrast methods of sequence analysis and their attempts to explain patterns of B-cell evolution. These phylogenetic approaches are currently unlinked to explicit models of B-cell competition, which analyse repertoire evolution at the level of phenotype, the affinities and specificities to particular antigenic sites. The models, in turn, suggest how chance, infection history and other factors contribute to different patterns of immunodominance and protection between people. Challenges in rational vaccine design, specifically vaccines to induce broadly neutralizing antibodies to HIV, underscore critical gaps in our understanding of B cells' evolutionary and ecological dynamics.

Manipulating the selection forces during affinity maturation to generate cross-reactive HIV antibodies

Generation of potent antibodies by a mutation-selection process called affinity maturation is a key component of effective immune responses. Antibodies that protect against highly mutable pathogens must neutralize diverse strains. Developing effective immunization strategies to drive their evolution requires understanding how affinity maturation happens in an environment where variants of the same antigen are present. We present an in silico model of affinity maturation driven by antigen variants which reveals that induction of cross-reactive antibodies often occurs with low probability because conflicting selection forces, imposed by different antigen variants, can frustrate affinity maturation. We describe how variables such as temporal pattern of antigen administration influence the outcome of this frustrated evolutionary process. Our calculations predict, and experiments in mice with variant gp120 constructs of the HIV envelope protein confirm, that sequential immunization with antigen variants is preferred over a cocktail for induction of cross-reactive antibodies focused on the shared CD4 binding site epitope.

Autoreactivity in HIV-1 broadly neutralizing antibodies: implications for their function and induction by vaccination

PURPOSE OF REVIEW

This review discusses progress in understanding the impact of immune tolerance on inducing broadly neutralizing antibodies (bnAbs), and how such knowledge can be incorporated into novel immunization approaches.

RECENT FINDINGS

Over 120 bnAbs have now been isolated, all of which bear unusual features associated with host tolerance controls, but paradoxically may also be required for their function. Evidence that poly/autoreactivity of membrane proximal external region bnAbs can invoke such controls has been demonstrated by knock-in technology, highlighting its potential for studying the impact of tolerance in the generation of bnAb lineages to distinct HIV-1 envelope targets. The requirement for extensive affinity maturation in developing neutralization breadth/potency during infection is being examined, and similar studies in the setting of immunization will be aided by testing novel vaccine approaches in knock-in models that either selectively express reverted V(D)J rearrangements, or unrearranged germline segments, from which bnAb lineages originate.

SUMMARY

It is increasingly apparent that immune tolerance, sometimes invoked by self-reactivity that overlaps with bnAb epitope specificity, adds to a formidable set of roadblocks impeding bnAb induction. The path to an effective HIV-1 vaccine may thus benefit from a deeper understanding of host controls, including categorizing those that are unique or common at distinct bnAb targets, and ranking those most feasible to overcome by immunization. Ultimately, such emerging information will be critical to incorporate into new vaccine approaches that can be tested in human trials.

B-cell-lineage immunogen design in vaccine development with HIV-1 as a case study

Failure of immunization with the HIV-1 envelope to induce broadly neutralizing antibodies against conserved epitopes is a major barrier to producing a preventive HIV-1 vaccine. Broadly neutralizing monoclonal antibodies (BnAbs) from those subjects who do produce them after years of chronic HIV-1 infection have one or more unusual characteristics, including polyreactivity for host antigens, extensive somatic hypermutation and long, variable heavy-chain third complementarity-determining regions, factors that may limit their expression by host immunoregulatory mechanisms. The isolation of BnAbs from HIV-1-infected subjects and the use of computationally derived clonal lineages as templates provide a new path for HIV-1 vaccine immunogen design. This approach, which should be applicable to many infectious agents, holds promise for the construction of vaccines that can drive B cells along rare but desirable maturation pathways.

HIV-1 gp120 vaccine induces affinity maturation in both new and persistent antibody clonal lineages

Most antibodies that broadly neutralize HIV-1 are highly somatically mutated in antibody clonal lineages that persist over time. Here, we describe the analysis of human antibodies induced during an HIV-1 vaccine trial (GSK PRO HIV-002) that used the clade B envelope (Env) gp120 of clone W6.1D (gp120(W6.1D)). Using dual-color antigen-specific sorting, we isolated Env-specific human monoclonal antibodies (MAbs) and studied the clonal persistence of antibodies in the setting of HIV-1 Env vaccination. We found evidence of V(H) somatic mutation induced by the vaccine but only to a modest level (3.8% ± 0.5%; range 0 to 8.2%). Analysis of 34 HIV-1-reactive MAbs recovered over four immunizations revealed evidence of both sequential recruitment of naïve B cells and restimulation of previously recruited memory B cells. These recombinant antibodies recapitulated the anti-HIV-1 activity of participant serum including pseudovirus neutralization and antibody-dependent cell-mediated cytotoxicity (ADCC). One antibody (3491) demonstrated a change in specificity following somatic mutation with binding of the inferred unmutated ancestor to a linear C2 peptide while the mutated antibody reacted only with a conformational epitope in gp120 Env. Thus, gp120(W6.1D) was strongly immunogenic but over four immunizations induced levels of affinity maturation below that of broadly neutralizing MAbs. Improved vaccination strategies will be needed to drive persistent stimulation of antibody clonal lineages to induce affinity maturation that results in highly mutated HIV-1 Env-reactive antibodies.

Antibody V(h) repertoire differences between resolving and chronically evolving hepatitis C virus infections

Despite the production of neutralizing antibodies to hepatitis C virus (HCV), many patients fail to clear the virus and instead develop chronic infection and long-term complications. To understand how HCV infection perturbs the antibody repertoire and to identify molecular features of antibody genes associated with either viral clearance or chronic infection, we sequenced the V(D)J region of naïve and memory B cells of 6 persons who spontaneously resolved an HCV infection (SR), 9 patients with a newly diagnosed chronically evolving infection (CE), and 7 healthy donors. In both naïve and memory B cells, the frequency of use of particular antibody gene subfamilies and segments varied among the three clinical groups, especially between SR and CE. Compared to CE, SR antibody genes used fewer VH, D and JH gene segments in naïve B cells and fewer VH segments in memory B cells. SR and CE groups significantly differed in the frequency of use of 7 gene segments in naïve B cell clones and 3 gene segments in memory clones. The nucleotide mutation rates were similar among groups, but the pattern of replacement and silent mutations in memory B cell clones indicated greater antigen selection in SR than CE. Greater clonal evolution of SR than CE memory B cells was revealed by analysis of phylogenetic trees and CDR3 lengths. Pauciclonality of the peripheral memory B cell population is a distinguishing feature of persons who spontaneously resolved an HCV infection. This finding, previously considered characteristic only of patients with HCV-associated lymphoproliferative disorders, suggests that the B cell clones potentially involved in clearance of the virus may also be those susceptible to abnormal expansion.

Short-time evolution in the adaptive immune system

We exploit a simple model to numerically and analytically investigate the effect of enforcing a time constraint for achieving a system-wide goal during an evolutionary dynamics. This situation is relevant to finding antibody specificities in the adaptive immune response as well as to artificial situations in which an evolutionary dynamics is used to generate a desired capability in a limited number of generations. When the likelihood of finding the target phenotype is low, we find that the optimal mutation rate can exceed the error threshold, in contrast to conventional evolutionary dynamics. We also show how a logarithmic correction to the usual inverse scaling of population size with mutation rate arises. Implications for natural and artificial evolutionary situations are discussed.

[Proteomics and peptidomics in fundamental and applied medical studies]

The review is focused on current issues of biomedical proteomics and peptidomics. The main attention is paid to modem proteomics technologies applied in medical research--extraction, detection and data analysis techniques. The use of chromatography, mass spectrometry and chromato mass spectrometry in proteogenomic, biomedical studies and biomarker discovery is discussed in detail.

A multi-scale model for correlation in B cell VDJ usage of zebrafish

The zebrafish (Danio rerio) is one of the model animals used for the study of immunology because the dynamics in the adaptive immune system of zebrafish are similar to that in higher animals. In this work, we built a multi-scale model to simulate the dynamics of B cells in the primary and secondary immune responses of zebrafish. We use this model to explain the reported correlation between VDJ usage of B cell repertoires in individual zebrafish. We use a delay ordinary differential equation (ODE) system to model the immune responses in the 6-month lifespan of a zebrafish. This mean field theory gives the number of high-affinity B cells as a function of time during an infection. The sequences of those B cells are then taken from a distribution calculated by a 'microscopic' random energy model. This generalized NK model shows that mature B cells specific to one antigen largely possess a single VDJ recombination. The model allows first-principle calculation of the probability, p, that two zebrafish responding to the same antigen will select the same VDJ recombination. This probability p increases with the B cell population size and the B cell selection intensity. The probability p decreases with the B cell hypermutation rate. The multi-scale model predicts correlations in the immune system of the zebrafish that are highly similar to that from experiment.