A factor VIIIa-mimetic bispecific antibody, Mim8, ameliorates bleeding upon severe vascular challenge in hemophilia A mice.

Hemophilia A is a bleeding disorder resulting from deficient factor VIII (FVIII), which normally functions as a cofactor to activated factor IX (FIXa) that facilitates activation of factor X (FX). To mimic this property in a bispecific antibody format, a screening was conducted to identify functional pairs of anti-FIXa and anti-FX antibodies, followed by optimization of functional and biophysical properties. The resulting bispecific antibody (Mim8) assembled efficiently with FIXa and FX on membranes, and supported activation with an apparent equilibrium dissociation constant of 16 nM. Binding affinity with FIXa and FX in solution was much lower, with equilibrium dissociation constant values for FIXa and FX of 2.3 and 1.5 µM, respectively. In addition, the activity of Mim8 was dependent on stimulatory activity contributed by the anti-FIXa arm, which enhanced the proteolytic activity of FIXa by 4 orders of magnitude. In hemophilia A plasma and whole blood, Mim8 normalized thrombin generation and clot formation, with potencies 13 and 18 times higher than a sequence-identical analogue of emicizumab. A similar potency difference was observed in a tail vein transection model in hemophilia A mice, whereas reduction of bleeding in a severe tail-clip model was observed only for Mim8. Furthermore, the pharmacokinetic parameters of Mim8 were investigated and a half-life of 14 days shown in cynomolgus monkeys. In conclusion, Mim8 is an activated FVIII mimetic with a potent and efficacious hemostatic effect based on preclinical data.

Improved prediction of HLA antigen presentation hotspots: Applications for immunogenicity risk assessment of therapeutic proteins.

Immunogenicity risk assessment is a critical element in protein drug development. Currently, the risk assessment is most often performed using MHC-associated peptide proteomics (MAPPs) and/or T-cell activation assays. However, this is a highly costly procedure that encompasses limited sensitivity imposed by sample sizes, the MHC repertoire of the tested donor cohort and the experimental procedures applied. Recent work has suggested that these techniques could be complemented by accurate, high-throughput and cost-effective prediction of in silico models. However, this work covered a very limited set of therapeutic proteins and eluted ligand (EL) data. Here, we resolved these limitations by showcasing, in a broader setting, the versatility of in silico models for assessment of protein drug immunogenicity. A method for prediction of MHC class II antigen presentation was developed on the hereto largest available mass spectrometry (MS) HLA-DR EL data set. Using independent test sets, the performance of the method for prediction of HLA-DR antigen presentation hotspots was benchmarked. In particular, the method was showcased on a set of protein sequences including four therapeutic proteins and demonstrated to accurately predict the experimental MS hotspot regions at a significantly lower false-positive rate compared with other methods. This gain in performance was particularly pronounced when compared to the NetMHCIIpan-3.2 method trained on binding affinity data. These results suggest that in silico methods trained on MS HLA EL data can effectively and accurately be used to complement MAPPs assays for the risk assessment of protein drugs.

Antigen processing influences HIV-specific cytotoxic T lymphocyte immunodominance.

Although cytotoxic T lymphocytes (CTLs) in people infected with human immunodeficiency virus type 1 can potentially target multiple virus epitopes, the same few are recognized repeatedly. We show here that CTL immunodominance in regions of the human immunodeficiency virus type 1 group-associated antigen proteins p17 and p24 correlated with epitope abundance, which was strongly influenced by proteasomal digestion profiles, affinity for the transporter protein TAP, and trimming mediated by the endoplasmatic reticulum aminopeptidase ERAAP, and was moderately influenced by HLA affinity. Structural and functional analyses demonstrated that proteasomal cleavage 'preferences' modulated the number and length of epitope-containing peptides, thereby affecting the response avidity and clonality of T cells. Cleavage patterns were affected by both flanking and intraepitope CTL-escape mutations. Our analyses show that antigen processing shapes CTL response hierarchies and that viral evolution modifies cleavage patterns and suggest strategies for in vitro vaccine optimization.

Engineering of a membrane-triggered activity switch in coagulation factor VIIa.

Recombinant factor VIIa (FVIIa) variants with increased activity offer the promise to improve the treatment of bleeding episodes in patients with inhibitor-complicated hemophilia. Here, an approach was adopted to enhance the activity of FVIIa by selectively optimizing substrate turnover at the membrane surface. Under physiological conditions, endogenous FVIIa engages its cell-localized cofactor tissue factor (TF), which stimulates activity through membrane-dependent substrate recognition and allosteric effects. To exploit these properties of TF, a covalent complex between FVIIa and the soluble ectodomain of TF (sTF) was engineered by introduction of a nonperturbing cystine bridge (FVIIa Q64C-sTF G109C) in the interface. Upon coexpression, FVIIa Q64C and sTF G109C spontaneously assembled into a covalent complex with functional properties similar to the noncovalent wild-type complex. Additional introduction of a FVIIa-M306D mutation to uncouple the sTF-mediated allosteric stimulation of FVIIa provided a final complex with FVIIa-like activity in solution, while exhibiting a two to three orders-of-magnitude increase in activity relative to FVIIa upon exposure to a procoagulant membrane. In a mouse model of hemophilia A, the complex normalized hemostasis upon vascular injury at a dose of 0.3 nmol/kg compared with 300 nmol/kg for FVIIa.

Survey Outcome on Immunogenicity Risk Assessment Tools for Biotherapeutics: an Insight into Consensus on Methods, Application, and Utility in Drug Development.

A survey conducted by the Therapeutic Product Immunogenicity (TPI) community within the American Association of Pharmaceutical Scientists (AAPS) posed questions to the participants on their immunogenicity risk assessment strategies prior to clinical development. The survey was conducted in 2 phases spanning 5 years, and queried information about in silico algorithms and in vitro assay formats for immunogenicity risk assessments and how the data were used to inform early developability effort in discovery, chemistry, manufacturing and control (CMC), and non-clinical stages of development. The key findings representing the trends from a majority of the participants included the use of high throughput in silico algorithms, human immune cell-based assays, and proteomics based outputs, as well as specialized assays when therapeutic mechanism of action could impact risk assessment. Additional insights into the CMC-related risks could also be gathered with the same tools to inform future process development and de-risk critical quality attributes with uncertain and unknown risks. The use of the outputs beyond supporting early development activities was also noted with participants utilizing the risk assessments to drive their clinical strategy and streamline bioanalysis.

Assay format diversity in pre-clinical immunogenicity risk assessment: Toward a possible harmonization of antigenicity assays.

A major impediment to successful use of therapeutic protein drugs is their ability to induce anti-drug antibodies (ADA) that can alter treatment efficacy and safety in a significant number of patients. To this aim, in silico, in vitro, and in vivo tools have been developed to assess sequence and other liabilities contributing to ADA development at different stages of the immune response. However, variability exists between similar assays developed by different investigators due to the complexity of assays, a degree of uncertainty about the underlying science, and their intended use. The impact of protocol variations on the outcome of the assays, i.e., on the immunogenicity risk assigned to a given drug candidate, cannot always be precisely assessed. Here, the Non-Clinical Immunogenicity Risk Assessment working group of the European Immunogenicity Platform (EIP) reviews currently used assays and protocols and discusses feasibility and next steps toward harmonization and standardization.

Porcine major histocompatibility complex (MHC) class I molecules and analysis of their peptide-binding specificities.

In all vertebrate animals, CD8(+) cytotoxic T lymphocytes (CTLs) are controlled by major histocompatibility complex class I (MHC-I) molecules. These are highly polymorphic peptide receptors selecting and presenting endogenously derived epitopes to circulating CTLs. The polymorphism of the MHC effectively individualizes the immune response of each member of the species. We have recently developed efficient methods to generate recombinant human MHC-I (also known as human leukocyte antigen class I, HLA-I) molecules, accompanying peptide-binding assays and predictors, and HLA tetramers for specific CTL staining and manipulation. This has enabled a complete mapping of all HLA-I specificities ("the Human MHC Project"). Here, we demonstrate that these approaches can be applied to other species. We systematically transferred domains of the frequently expressed swine MHC-I molecule, SLA-1*0401, onto a HLA-I molecule (HLA-A*11:01), thereby generating recombinant human/swine chimeric MHC-I molecules as well as the intact SLA-1*0401 molecule. Biochemical peptide-binding assays and positional scanning combinatorial peptide libraries were used to analyze the peptide-binding motifs of these molecules. A pan-specific predictor of peptide-MHC-I binding, NetMHCpan, which was originally developed to cover the binding specificities of all known HLA-I molecules, was successfully used to predict the specificities of the SLA-1*0401 molecule as well as the porcine/human chimeric MHC-I molecules. These data indicate that it is possible to extend the biochemical and bioinformatics tools of the Human MHC Project to other vertebrate species.

Elimination of immunodominant epitopes from multispecific DNA-based vaccines allows induction of CD8 T cells that have a striking antiviral potential.

Immunodominance limits the TCR diversity of specific antiviral CD8 T cell responses elicited by vaccination or infection. To prime multispecific T cell responses, we constructed DNA vaccines that coexpress chimeric, multidomain Ags (with CD8 T cell-defined epitopes of the hepatitis B virus (HBV) surface (S), core (C), and polymerase (Pol) proteins and/or the OVA Ag as stress protein-capturing fusion proteins. Priming of mono- or multispecific, HLA-A*0201- or K(b)-restricted CD8 T cell responses by these DNA vaccines differed. K(b)/OVA(257-264)- and K(b)/S(190-197)-specific CD8 T cell responses did not allow priming of a K(b)/C(93-100)-specific CD8 T cell response in mice immunized with multidomain vaccines. Tolerance to the S- Ag in transgenic Alb/HBs mice (that express large amounts of transgene-encoded S- Ag in the liver) facilitated priming of subdominant, K(b)/C(93-100)-specific CD8 T cell immunity by multidomain Ags. The "weak" (i.e., easily suppressed) K(b)/C(93-100)-specific CD8 T cell response was efficiently elicited by a HBV core Ag-encoding vector in 1.4HBV-S(mut) tg mice (that harbor a replicating HBV genome that produces HBV surface, core, and precore Ag in the liver). K(b)/C(93-100)-specific CD8 T cells accumulated in the liver of vaccinated 1.4HBV-S(mut) transgenic mice where they suppressed HBV replication. Subdominant epitopes in vaccines can hence prime specific CD8 T cell immunity in a tolerogenic milieu that delivers specific antiviral effects to HBV-expressing hepatocytes.

NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11.

NetMHC-3.0 is trained on a large number of quantitative peptide data using both affinity data from the Immune Epitope Database and Analysis Resource (IEDB) and elution data from SYFPEITHI. The method generates high-accuracy predictions of major histocompatibility complex (MHC): peptide binding. The predictions are based on artificial neural networks trained on data from 55 MHC alleles (43 Human and 12 non-human), and position-specific scoring matrices (PSSMs) for additional 67 HLA alleles. As only the MHC class I prediction server is available, predictions are possible for peptides of length 8-11 for all 122 alleles. artificial neural network predictions are given as actual IC(50) values whereas PSSM predictions are given as a log-odds likelihood scores. The output is optionally available as download for easy post-processing. The training method underlying the server is the best available, and has been used to predict possible MHC-binding peptides in a series of pathogen viral proteomes including SARS, Influenza and HIV, resulting in an average of 75-80% confirmed MHC binders. Here, the performance is further validated and benchmarked using a large set of newly published affinity data, non-redundant to the training set. The server is free of use and available at: http://www.cbs.dtu.dk/services/NetMHC.

Peptide binding to HLA class I molecules: homogenous, high-throughput screening, and affinity assays.

The Human MHC Project aims at large-scale description of peptide-HLA binding to a wide range of HLA molecules covering all populations of the world and the accompanying generation of bioinformatics tools capable of predicting binding of any given peptide to any given HLA molecule. Here, the authors present a homogenous, proximity-based assay for detection of peptide binding to HLA class I molecules. It uses a conformation-dependent anti-HLA class I antibody, W6/32, as one tag and a biotinylated recombinant HLA class I molecule as the other tag, and a proximity-based signal is generated through the luminescent oxygen channeling immunoassay technology (abbreviated LOCI and commercialized as AlphaScreen). Compared with an enzyme-linked immunosorbent assay-based peptide-HLA class I binding assay, the LOCI assay yields virtually identical affinity measurements, although having a broader dynamic range, better signal-to-background ratios, and a higher capacity. They also describe an efficient approach to screen peptides for binding to HLA molecules. For the occasional user, this will serve as a robust, simple peptide-HLA binding assay. For the more dedicated user, it can easily be performed in a high-throughput screening mode using standard liquid handling robotics and 384-well plates. We have successfully applied this assay to more than 60 different HLA molecules, leading to more than 2 million measurements.

The peptide-binding specificity of HLA-A*3001 demonstrates membership of the HLA-A3 supertype.

Human leukocyte antigen class I (HLA-I) molecules are highly polymorphic peptide receptors, which select and present endogenously derived peptide epitopes to CD8+ cytotoxic T cells (CTL). The specificity of the HLA-I system is an important component of the overall specificity of the CTL immune system. Unfortunately, the large and rapidly increasing number of known HLA-I molecules seriously complicates a comprehensive analysis of the specificities of the entire HLA-I system (as of June 2008, the international HLA registry holds >1,650 unique HLA-I protein entries). In an attempt to reduce this complexity, it has been suggested to cluster the different HLA-I molecules into "supertypes" of largely overlapping peptide-binding specificities. Obviously, the HLA supertype concept is only valuable if membership can be assigned with reasonable accuracy. The supertype assignment of HLA-A*3001, a common HLA haplotype in populations of African descent, has variously been assigned to the A1, A3, or A24 supertypes. Using a biochemical HLA-A*3001 binding assay, and a large panel of nonamer peptides and peptide libraries, we here demonstrate that the specificity of HLA-A*3001 most closely resembles that of the HLA-A3 supertype. We discuss approaches to supertype assignment and underscore the importance of experimental verification.

The effect of acylation with fatty acids and other modifications on HLA class II:peptide binding and T cell stimulation for three model peptides.

Immunogenicity is a major concern in drug development as anti-drug antibodies in many cases affect both patient safety and drug efficacy. Another concern is often the limited half-life of drugs, which can be altered by different chemical modifications, like acylation with fatty acids. However, acylation with fatty acids has been shown in some cases to modulate T cell activation. Therefore, to understand the role of acylation with fatty acids on immunogenicity we tested three immunogenic non-acylated peptides and 14 of their acylated analogues for binding to 26 common HLA class II alleles, and their ability to activate T cells in an ex vivo T cell assay. Changes in binding affinity associated with acylation with fatty acids were typically modest, though a significant decrease was observed for influenza HA acylated with a stearic acid, and affinities for DQ alleles were consistently increased. Importantly, we showed that for all three immunogenic peptides acylation with fatty acids decreased their capacity to activate T cells, a trend particularly evident with longer fatty acids typically positioned within the peptide HLA class II binding core region, or when closer to the C-terminus. With these results we have demonstrated that acylation with fatty acids of immunogenic peptides can lower their stimulatory capacity, which could be important knowledge for drug design and immunogenicity mitigation.

Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction.

BACKGROUND: Reliable predictions of Cytotoxic T lymphocyte (CTL) epitopes are essential for rational vaccine design. Most importantly, they can minimize the experimental effort needed to identify epitopes. NetCTL is a web-based tool designed for predicting human CTL epitopes in any given protein. It does so by integrating predictions of proteasomal cleavage, TAP transport efficiency, and MHC class I affinity. At least four other methods have been developed recently that likewise attempt to predict CTL epitopes: EpiJen, MAPPP, MHC-pathway, and WAPP. In order to compare the performance of prediction methods, objective benchmarks and standardized performance measures are needed. Here, we develop such large-scale benchmark and corresponding performance measures and report the performance of an updated version 1.2 of NetCTL in comparison with the four other methods. RESULTS: We define a number of performance measures that can handle the different types of output data from the five methods. We use two evaluation datasets consisting of known HIV CTL epitopes and their source proteins. The source proteins are split into all possible 9 mers and except for annotated epitopes; all other 9 mers are considered non-epitopes. In the RANK measure, we compare two methods at a time and count how often each of the methods rank the epitope highest. In another measure, we find the specificity of the methods at three predefined sensitivity values. Lastly, for each method, we calculate the percentage of known epitopes that rank within the 5% peptides with the highest predicted score. CONCLUSION: NetCTL-1.2 is demonstrated to have a higher predictive performance than EpiJen, MAPPP, MHC-pathway, and WAPP on all performance measures. The higher performance of NetCTL-1.2 as compared to EpiJen and MHC-pathway is, however, not statistically significant on all measures. In the large-scale benchmark calculation consisting of 216 known HIV epitopes covering all 12 recognized HLA supertypes, the NetCTL-1.2 method was shown to have a sensitivity among the 5% top-scoring peptides above 0.72. On this dataset, the best of the other methods achieved a sensitivity of 0.64. The NetCTL-1.2 method is available at http://www.cbs.dtu.dk/services/NetCTL. All used datasets are available at http://www.cbs.dtu.dk/suppl/immunology/CTL-1.2.php.

A community resource benchmarking predictions of peptide binding to MHC-I molecules.

Recognition of peptides bound to major histocompatibility complex (MHC) class I molecules by T lymphocytes is an essential part of immune surveillance. Each MHC allele has a characteristic peptide binding preference, which can be captured in prediction algorithms, allowing for the rapid scan of entire pathogen proteomes for peptide likely to bind MHC. Here we make public a large set of 48,828 quantitative peptide-binding affinity measurements relating to 48 different mouse, human, macaque, and chimpanzee MHC class I alleles. We use this data to establish a set of benchmark predictions with one neural network method and two matrix-based prediction methods extensively utilized in our groups. In general, the neural network outperforms the matrix-based predictions mainly due to its ability to generalize even on a small amount of data. We also retrieved predictions from tools publicly available on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools.

Quantitative analysis of the CD4+ T cell response to therapeutic antibodies in healthy donors using a novel T cell:PBMC assay.

Many biopharmaceuticals (BPs) are known to be immunogenic in the clinic, which can result in modified pharmacokinetics, reduced efficacy, allergic reactions and anaphylaxis. During recent years, several technologies to predict immunogenicity have been introduced, but the predictive value is still considered low. Thus, there is an unmet medical need for optimization of such technologies. The generation of T cell dependent high affinity anti-drug antibodies plays a key role in clinical immunogenicity. This study aimed at developing and evaluating a novel in vitro T cell:PBMC assay for prediction of the immunogenicity potential of BPs. To this end, we assessed the ability of infliximab (anti-TNF-α), rituximab (anti-CD20), adalimumab (anti-TNF-α) and natalizumab (anti-α4-integrin), all showing immunogenicity in the clinic, to induce a CD4+ T cells response. Keyhole limpet hemocyanin (KLH) and cytomegalovirus pp65 protein (CMV) were included as neo-antigen and recall antigen positive controls, respectively. By analyzing 26 healthy donors having HLA-DRB1 alleles matching the European population, we calculated the frequency of responding donors, the magnitude of the response, and the frequency of BP-specific T cells, as measured by 3[H]-thymidine incorporation and ELISpot IL-2 secretion. KLH and CMV demonstrated a strong T cell response in all the donors analyzed. The frequency of responding donors to the BPs was 4% for infliximab, 8% for adalimumab, 19% for rituximab and 27% for natalizumab, which is compared to and discussed with their respective observed clinical immunogenicity. This study further complements predictive immunogenicity testing by quantifying the in vitro CD4+ T cell responses to different BPs. Even though the data generated using this modified method does not directly translate to the clinical situation, a high sensitivity and immunogenic potential of most BPs is demonstrated.

Post hoc assessment of the immunogenicity of bioengineered factor VIIa demonstrates the use of preclinical tools.

Immunogenicity is an important consideration in the licensure of a therapeutic protein because the development of neutralizing anti-drug antibodies (ADAs) can affect both safety and efficacy. Neoantigens introduced by bioengineering of a protein drug are a particular cause for concern. The development of a bioengineered recombinant factor VIIa (rFVIIa) analog was discontinued after phase 3 trials because of the development of ADAs. The unmodified parent molecule (rFVIIa), on the other hand, has been successfully used as a drug for more than two decades with no reports of immunogenicity in congenital hemophilia patients with inhibitors. We used computational and experimental methods to demonstrate that the observed ADAs could have been elicited by neoepitopes in the engineered protein. The human leukocyte antigen type of the patients who developed ADAs is consistent with this hypothesis of a neoepitope-driven immune response, a finding that might have implications for the preclinical screening of therapeutic protein analogs.

Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.

In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.

Ca2+ response in neutrophils after exposure to bacterial N-formyl-methionyl-leucyl-phenylalanine: delayed response in ulcerative colitis.

OBJECTIVE: In acute stages of ulcerative colitis (UC), neutrophils migrate from the circulation into inflamed colonic tissue, initiated by yet unknown stimuli. The bacterial peptide N-formyl-methionyl-leucyl-phenylalanine (FMLP) is a component of the surface membrane of colonic bacteria such as Escherichia coli and stimulates Ca2+ influx into neutrophils, reflecting the fact that ionized calcium is an important secondary messenger for several neutrophil functions, including locomotion, phagocytosis and free oxygen radical production. Recent studies have revealed that Ca2+ dependent ICAM-1/beta 2-integrin mediated neutrophil migration is impaired in UC patients. The aim of the present work was to study the influx of Ca2+ into peripheral blood neutrophils of UC patients after exposure to FMLP and after binding of either beta 2-integrins or intercellular adhesion molecule-1 (ICAM-1). METHODS: The relative intracellular Ca2+ levels ([Ca2+]i ) were measured spectrofluorometrically in neutrophils isolated from eight UC patients and eight controls. The cells were exposed to 1 nm FMLP, 5 pm free ICAM-1, or antibodies binding ICAM-1 or the beta 2-integrins CD11a, CD11b, CD11c and CD18. RESULTS: A pronounced increase in [Ca2+]i was observed by exposure of cells to FMLP, and neutrophils from UC patients showed a consistent and significant delayed response as compared to cells from control subjects (P < 0.01). Antibody mediated cross-linking of CD18 triggered a small but detectable increase in [Ca2+]i, which did not differ between patients and controls. CONCLUSION: A delayed response to bacterial peptides appears to be a phenotypic trait for neutrophils of UC patients. A connection between FMLP stimulated Ca2+ influx and CD11/CD18 upregulation is discussed.

Antibody-mediated targeting of the Orai1 calcium channel inhibits T cell function.

Despite the attractiveness of ion channels as therapeutic targets, there are no examples of monoclonal antibodies directed against ion channels in clinical development. Antibody-mediated inhibition of ion channels could offer a directed, specific therapeutic approach. To investigate the potential of inhibiting ion channel function with an antibody, we focused on Orai1, the pore subunit of the calcium channel responsible for store-operated calcium entry (SOCE) in T cells. Effector T cells are key drivers of autoimmune disease pathogenesis and calcium signaling is essential for T cell activation, proliferation, and cytokine production. We show here the generation of a specific anti-human Orai1 monoclonal antibody (mAb) against an extracellular loop of the plasma membrane-spanning protein. The anti-Orai1 mAb binds native Orai1 on lymphocytes and leads to cellular internalization of the channel. As a result, T cell proliferation, and cytokine production is inhibited in vitro. In vivo, anti-Orai1 mAb is efficacious in a human T cell-mediated graft-versus host disease (GvHD) mouse model. This study demonstrates the feasibility of antibody-mediated inhibition of Orai1 function and, more broadly, reveals the possibility of targeting ion channels with biologics for the treatment of autoimmunity and other diseases.

Human leukocyte antigen (HLA) class I restricted epitope discovery in yellow fewer and dengue viruses: importance of HLA binding strength.

Epitopes from all available full-length sequences of yellow fever virus (YFV) and dengue fever virus (DENV) restricted by Human Leukocyte Antigen class I (HLA-I) alleles covering 12 HLA-I supertypes were predicted using the NetCTL algorithm. A subset of 179 predicted YFV and 158 predicted DENV epitopes were selected using the EpiSelect algorithm to allow for optimal coverage of viral strains. The selected predicted epitopes were synthesized and approximately 75% were found to bind the predicted restricting HLA molecule with an affinity, K(D), stronger than 500 nM. The immunogenicity of 25 HLA-A*02:01, 28 HLA-A*24:02 and 28 HLA-B*07:02 binding peptides was tested in three HLA-transgenic mice models and led to the identification of 17 HLA-A*02:01, 4 HLA-A*2402 and 4 HLA-B*07:02 immunogenic peptides. The immunogenic peptides bound HLA significantly stronger than the non-immunogenic peptides. All except one of the immunogenic peptides had K(D) below 100 nM and the peptides with K(D) below 5 nM were more likely to be immunogenic. In addition, all the immunogenic peptides that were identified as having a high functional avidity had K(D) below 20 nM. A*02:01 transgenic mice were also inoculated twice with the 17DD YFV vaccine strain. Three of the YFV A*02:01 restricted peptides activated T-cells from the infected mice in vitro. All three peptides that elicited responses had an HLA binding affinity of 2 nM or less. The results indicate the importance of the strength of HLA binding in shaping the immune response.