Chris Bailey-Kellogg

Polyfunctional Fc-Effector Profiles Mediated by IgG Subclass Selection Distinguish RV144 and VAX003 Vaccines

RV144 vaccination induced polyfunctional antibody Fc-effector responses, whereas VAX003 vaccination increased inhibitory IgG4 antibodies. More Is Better for Protection Against HIV Recently, results from the first protective HIV phase 2B RV144 vaccine trial pointed to an unexpected signature of protection, not associated with the traditional mechanisms of vaccine-induced immunity, namely, neutralizing antibodies and killer T cell immunity. Instead, protection was associated with specific subpopulations of antibodies that were able to direct killing of HIV-infected cells. However, little is known about the properties of these killer antibodies or their biophysical features. In a new study, Chung et al. functionally profiled antibodies raised by the protective RV144 vaccine trial and its nonprotective predecessor, the VAX003 vaccine trial, both conducted in Thailand. RV144 vaccination uniquely induced antibodies capable of directing several different antiviral functions in a coordinated manner. In contrast, VAX003 vaccination predominantly induced single or uncoordinated antiviral responses. Functional coordination was regulated by the selection of antibody responses directed at vulnerable regions on the HIV envelope that were specifically tuned to enhanced functionality through the selection of a specific antibody subclass, IgG3, known to harbor strong antiviral activity. Collectively, these data suggest that vaccines able to induce broader antibody functional profiles, through the selection of more potent antibody subclasses, which target vulnerable regions of the virus, may represent a new means by which to achieve protection from HIV infection in the absence of neutralization. The human phase 2B RV144 ALVAC-HIV vCP1521/AIDSVAX B/E vaccine trial, held in Thailand, resulted in an estimated 31.2% efficacy against HIV infection. By contrast, vaccination with VAX003 (consisting of only AIDSVAX B/E) was not protective. Because protection within RV144 was observed in the absence of neutralizing antibody activity or cytotoxic T cell responses, we speculated that the specificity or qualitative differences in Fc-effector profiles of nonneutralizing antibodies may have accounted for the efficacy differences observed between the two trials. We show that the RV144 regimen elicited nonneutralizing antibodies with highly coordinated Fc-mediated effector responses through the selective induction of highly functional immunoglobulin G3 (IgG3). By contrast, VAX003 elicited monofunctional antibody responses influenced by IgG4 selection, which was promoted by repeated AIDSVAX B/E protein boosts. Moreover, only RV144 induced IgG1 and IgG3 antibodies targeting the crown of the HIV envelope V2 loop, albeit with limited coverage of breakthrough viral sequences. These data suggest that subclass selection differences associated with coordinated humoral functional responses targeting strain-specific protective V2 loop epitopes may underlie differences in vaccine efficacy observed between these two vaccine trials.

Natural variation in Fc glycosylation of HIV-specific antibodies impacts antiviral activity

While the induction of a neutralizing antibody response against HIV remains a daunting goal, data from both natural infection and vaccine-induced immune responses suggest that it may be possible to induce antibodies with enhanced Fc effector activity and improved antiviral control via vaccination. However, the specific features of naturally induced HIV-specific antibodies that allow for the potent recruitment of antiviral activity and the means by which these functions are regulated are poorly defined. Because antibody effector functions are critically dependent on antibody Fc domain glycosylation, we aimed to define the natural glycoforms associated with robust Fc-mediated antiviral activity. We demonstrate that spontaneous control of HIV and improved antiviral activity are associated with a dramatic shift in the global antibody-glycosylation profile toward agalactosylated glycoforms. HIV-specific antibodies exhibited an even greater frequency of agalactosylated, afucosylated, and asialylated glycans. These glycoforms were associated with enhanced Fc-mediated reduction of viral replication and enhanced Fc receptor binding and were consistent with transcriptional profiling of glycosyltransferases in peripheral B cells. These data suggest that B cell programs tune antibody glycosylation actively in an antigen-specific manner, potentially contributing to antiviral control during HIV infection.

Polyfunctional HIV-Specific Antibody Responses Are Associated with Spontaneous HIV Control

Elite controllers (ECs) represent a unique model of a functional cure for HIV-1 infection as these individuals develop HIV-specific immunity able to persistently suppress viremia. Because accumulating evidence suggests that HIV controllers generate antibodies with enhanced capacity to drive antibody-dependent cellular cytotoxicity (ADCC) that may contribute to viral containment, we profiled an array of extra-neutralizing antibody effector functions across HIV-infected populations with varying degrees of viral control to define the characteristics of antibodies associated with spontaneous control. While neither the overall magnitude of antibody titer nor individual effector functions were increased in ECs, a more functionally coordinated innate immune–recruiting response was observed. Specifically, ECs demonstrated polyfunctional humoral immune responses able to coordinately recruit ADCC, other NK functions, monocyte and neutrophil phagocytosis, and complement. This functionally coordinated response was associated with qualitatively superior IgG3/IgG1 responses, whereas HIV-specific IgG2/IgG4 responses, prevalent among viremic subjects, were associated with poorer overall antibody activity. Rather than linking viral control to any single activity, this study highlights the critical nature of functionally coordinated antibodies in HIV control and associates this polyfunctionality with preferential induction of potent antibody subclasses, supporting coordinated antibody activity as a goal in strategies directed at an HIV-1 functional cure.

The NOESY jigsaw: automated protein secondary structure and main-chain assignment from sparse, unassigned NMR data.

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the JIGSAW algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). JIGSAW applies graph algorithms and probabilistic reasoning techniques, enforcing first-principles consistency rules in order to overcome a 5-10% signal-to-noise ratio. It consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. Deferring assignment eliminates the bottleneck faced by traditional approaches, which begin by correlating peaks among dozens of experiments. JIGSAW utilizes only four experiments, none of which requires 13C-labeled protein, thus dramatically reducing both the amount and expense of wet lab molecular biology and the total spectrometer time. Results for three test proteins demonstrate that JIGSAW correctly identifies 79-100% of alpha-helical and 46-65% of beta-sheet NOE connectivities and correctly aligns 33-100% of secondary structure elements. JIGSAW is very fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations. It could be useful for quick structural assays to speed data to the biologist early in an investigation and could in principle be applied in an automation-like fashion to a large fraction of the proteome.

Adjuvant-dependent innate and adaptive immune signatures of risk of SIVmac251 acquisition.

A recombinant vaccine containing Aventis Pasteurs canarypox vector (ALVAC)–HIV and gp120 alum decreased the risk of HIV acquisition in the RV144 vaccine trial. The substitution of alum with the more immunogenic MF59 adjuvant is under consideration for the next efficacy human trial. We found here that an ALVAC–simian immunodeficiency virus (SIV) and gp120 alum (ALVAC–SIV + gp120) equivalent vaccine, but not an ALVAC–SIV + gp120 MF59 vaccine, was efficacious in delaying the onset of SIVmac251 in rhesus macaques, despite the higher immunogenicity of the latter adjuvant. Vaccine efficacy was associated with alum-induced, but not with MF59-induced, envelope (Env)-dependent mucosal innate lymphoid cells (ILCs) that produce interleukin (IL)-17, as well as with mucosal IgG to the gp120 variable region 2 (V2) and the expression of 12 genes, ten of which are part of the RAS pathway. The association between RAS activation and vaccine efficacy was also observed in an independent efficacious SIV-vaccine approach. Whether RAS activation, mucosal ILCs and antibodies to V2 are also important hallmarks of HIV-vaccine efficacy in humans will require further studies.

The NOESY jigsaw: automated protein secondary structure and main-chain assignment from sparse, unassigned NMR data

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the JIGSAW algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). JIGSAW applies graph algorithms and probabilistic reasoning techniques, enforcing first-principles consistency rules in order to overcome a 5-10% signal-to-noise ratio. It consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. JIGSAWs deferment of assignment until after secondary structure identification differs greatly from traditional approaches, which begin by correlating peaks among dozens of experiments. By deferring assignment, JIGSAW not only eliminates this bottleneck, it also allows the number of experiments to be reduced from dozens to four, none of which requires 13 C-labeled protein. This in turn dramatically reduces the amount and expense of wet lab molecular biology for protein expression and purification, as well as the total spectrometer time to collect data. Our results for three test proteins demonstrate that we are able to identify and align approximately 80 percent of α-helical and 60 percent of β-sheet structure. JIGSAW is very fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations, utilizing a suite of graph analysis algorithms to compensate for the data sparseness. JIGSAW could be used for quick structural assays to speed data to the biologist early in the process of investigation, and could in principle be applied in an automation-like fashion to a large fraction of the proteome.

High-throughput, multiplexed IgG subclassing of antigen-specific antibodies from clinical samples.

In vivo, the activity of antibodies relies critically on properties of both the variable domain, responsible for antigen recognition, and the constant domain, responsible for innate immune recognition. Here, we describe a flexible, microsphere-based array format for capturing information about both functional ends of disease-specific antibodies from complex, polyclonal clinical serum samples. Using minimal serum, we demonstrate IgG subclass profiling of multiple antibody specificities. We further capture and determine the subclass of epitope-specific antibodies. The data generated in this array provides a profile of the humoral immune response with multi-dimensional metrics regarding properties of both variable and constant IgG domains. Significantly, these properties are assessed simultaneously, and therefore information about the relationship between variable and constant domain characteristics is captured, and can be used to predict functions such as antibody effector activity.

Graphical Models of Residue Coupling in Protein Families

Many statistical measures and algorithmic techniqueshave been proposed for studying residue coupling inprotein families. Generally speaking, two residue positions areconsidered coupled if, in the sequence record, some of theiramino acid type combinations are significantly more commonthan others. While the proposed approaches have proven useful infinding and describing coupling, a significant missing componentis a formal probabilistic model that explicates and compactlyrepresents the coupling, integrates information about sequence,structure, and function, and supports inferential procedures foranalysis, diagnosis, and prediction.We present an approach to learning and using probabilisticgraphical models of residue coupling. These models capturesignificant conservation and coupling constraints observable ina multiply-aligned set of sequences. Our approach can place astructural prior on considered couplings, so that all identifiedrelationships have direct mechanistic explanations. It can alsoincorporate information about functional classes, and therebylearn a differential graphical model that distinguishes constraintscommon to all classes from those unique to individual classes.Such differential models separately account for class-specificconservation and family-wide coupling, two different sourcesof sequence covariation. They are then able to perform interpretablefunctional classification of new sequences, explainingclassification decisions in terms of the underlying conservationand coupling constraints. We apply our approach in studies ofboth G protein-coupled receptors and PDZ domains, identifyingand analyzing family-wide and class-specific constraints, andperforming functional classification. The results demonstrate thatgraphical models of residue coupling provide a powerful toolfor uncovering, representing, and utilizing significant sequencestructure-function relationships in protein families.

Optimization algorithms for functional deimmunization of therapeutic proteins

BackgroundTo develop protein therapeutics from exogenous sources, it is necessary to mitigate the risks of eliciting an anti-biotherapeutic immune response. A key aspect of the response is the recognition and surface display by antigen-presenting cells of epitopes, short peptide fragments derived from the foreign protein. Thus, developing minimal-epitope variants represents a powerful approach to deimmunizing protein therapeutics. Critically, mutations selected to reduce immunogenicity must not interfere with the proteins therapeutic activity.ResultsThis paper develops methods to improve the likelihood of simultaneously reducing the anti-biotherapeutic immune response while maintaining therapeutic activity. A dynamic programming approach identifies optimal and near-optimal sets of conservative point mutations to minimize the occurrence of predicted T-cell epitopes in a target protein. In contrast with existing methods, those described here integrate analysis of immunogenicity and stability/activity, are broadly applicable to any protein class, guarantee global optimality, and provide sufficient flexibility for users to limit the total number of mutations and target MHC alleles of interest. The input is simply the primary amino acid sequence of the therapeutic candidate, although crystal structures and protein family sequence alignments may also be input when available. The output is a scored list of sets of point mutations predicted to reduce the proteins immunogenicity while maintaining structure and function. We demonstrate the effectiveness of our approach in a number of case study applications, showing that, in general, our best variants are predicted to be better than those produced by previous deimmunization efforts in terms of either immunogenicity or stability, or both factors.ConclusionsBy developing global optimization algorithms leveraging well-established immunogenicity and stability prediction techniques, we provide the protein engineer with a mechanism for exploring the favorable sequence space near a targeted protein therapeutic. Our mechanism not only helps identify designs more likely to be effective, but also provides insights into the interrelated implications of design choices.

The two-faced T cell epitope: examining the host-microbe interface with JanusMatrix.

Advances in the field of T cell immunology have contributed to the understanding that cross-reactivity is an intrinsic characteristic of the T cell receptor (TCR), and that each TCR can potentially interact with many different T cell epitopes. To better define the potential for TCR cross-reactivity between epitopes derived from the human genome, the human microbiome, and human pathogens, we developed a new immunoinformatics tool, JanusMatrix, that represents an extension of the validated T cell epitope mapping tool, EpiMatrix. Initial explorations, summarized in this synopsis, have uncovered what appear to be important differences in the TCR cross-reactivity of selected regulatory and effector T cell epitopes with other epitopes in the human genome, human microbiome, and selected human pathogens. In addition to exploring the T cell epitope relationships between human self, commensal and pathogen, JanusMatrix may also be useful to explore some aspects of heterologous immunity and to examine T cell epitope relatedness between pathogens to which humans are exposed (Dengue serotypes, or HCV and Influenza, for example). In Hand-Foot-Mouth disease (HFMD) for example, extensive enterovirus and human microbiome cross-reactivity (and limited cross-reactivity with the human genome) seemingly predicts immunodominance. In contrast, more extensive cross-reactivity with proteins contained in the human genome as compared to the human microbiome was observed for selected Treg epitopes. While it may be impossible to predict all immune response influences, the availability of sequence data from the human genome, the human microbiome, and an array of human pathogens and vaccines has made computationally–driven exploration of the effects of T cell epitope cross-reactivity now possible. This is the first description of JanusMatrix, an algorithm that assesses TCR cross-reactivity that may contribute to a means of predicting the phenotype of T cells responding to selected T cell epitopes. Whether used for explorations of T cell phenotype or for evaluating cross-conservation between related viral strains at the TCR face of viral epitopes, further JanusMatrix studies may contribute to developing safer, more effective vaccines.