Structural assessment of HLA-A2-restricted SARS-CoV-2 spike epitopes recognized by public and private T-cell receptors.

T cells play a vital role in combatting SARS-CoV-2 and forming long-term memory responses. Whereas extensive structural information is available on neutralizing antibodies against SARS-CoV-2, such information on SARS-CoV-2-specific T-cell receptors (TCRs) bound to their peptide-MHC targets is lacking. Here we determine the structures of a public and a private TCR from COVID-19 convalescent patients in complex with HLA-A2 and two SARS-CoV-2 spike protein epitopes (YLQ and RLQ). The structures reveal the basis for selection of particular TRAV and TRBV germline genes by the public but not the private TCR, and for the ability of the TCRs to recognize natural variants of RLQ but not YLQ. Neither TCR recognizes homologous epitopes from human seasonal coronaviruses. By elucidating the mechanism for TCR recognition of an immunodominant yet variable epitope (YLQ) and a conserved but less commonly targeted epitope (RLQ), this study can inform prospective efforts to design vaccines to elicit pan-coronavirus immunity.

Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction.

Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding cellular immunity, identifying the cause of autoimmune diseases, for immunotherapeutics, and vaccine development. Here, novel high-density peptide microarray technology combined with machine learning techniques were used to address this task at an unprecedented level of high-throughput. Microarrays with over 200,000 defined peptides were assayed with four exemplary HLA-II molecules. Machine learning was applied to mine the signals. The comparison of identified binding motifs, and power for predicting eluted ligands and CD4+ epitope datasets to that obtained using NetMHCIIpan-3.2, confirmed a high quality of the chip readout. These results suggest that the proposed microarray technology offers a novel and unique platform for large-scale unbiased interrogation of peptide binding preferences of HLA-II molecules.

Molecular epidemiology and phylodynamics of the human respiratory syncytial virus fusion protein in northern Taiwan.

BACKGROUND AND AIMS: The glycoprotein (G protein) and fusion protein (F protein) of respiratory syncytial virus (RSV) both show genetic variability, but few studies have examined the F protein gene. This study aimed to characterize the molecular epidemiology and phylodynamics of the F protein gene in clinical RSV strains isolated in northern Taiwan from 2000-2011. METHODS: RSV isolates from children presenting with acute respiratory symptoms between July 2000 and June 2011 were typed based on F protein gene sequences. Phylogeny construction and evaluation were performed using the neighbor-joining (NJ) and maximum likelihood (ML) methods. Phylodynamic patterns in RSV F protein genes were analyzed using the Bayesian Markov Chain Monte Carlo framework. Selection pressure on the F protein gene was detected using the Datamonkey website interface. RESULTS: From a total of 325 clinical RSV strains studied, phylogenetic analysis showed that 83 subgroup A strains (RSV-A) could be further divided into three clusters, whereas 58 subgroup B strains (RSV-B) had no significant clustering. Three amino acids were observed to differ between RSV-A and -B (positions 111, 113, and 114) in CTL HLA-B*57- and HLA-A*01-restricted epitopes. One positive selection site was observed in RSV-B, while none was observed in RSV-A. The evolution rate of the virus had very little change before 2000, then slowed down between 2000 and 2005, and evolved significantly faster after 2005. The dominant subtypes of RSV-A in each epidemic were replaced by different subtypes in the subsequent epidemic. CONCLUSIONS: Before 2004, RSV-A infections were involved in several small epidemics and only very limited numbers of strains evolved and re-emerged in subsequent years. After 2005, the circulating RSV-A strains were different from those of the previous years and continued evolving through 2010. Phylodynamic pattern showed the evolutionary divergence of RSV increased significantly in the recent 5 years in northern Taiwan.

Contraceptive vaccines based on the zona pellucida glycoproteins for dogs and other wildlife population management.

Zona pellucida (ZP) glycoproteins, by virtue of their critical role in fertilization, have been proposed as candidate antigens for the development of contraceptive vaccines. In this review, the potential of a ZP-based contraceptive vaccine for the management of wildlife population, with special reference to street dogs, is discussed. Immunization of various animal species, including female dogs, with native porcine ZP led to inhibition of fertility, which was associated with the ovarian dysfunction. Immunization of female dogs with Escherichia coli-expressed recombinant dog ZP glycoprotein-3 (ZP3) either coupled to diphtheria toxoid or expressed as fusion protein with 'promiscuous' T non-B-cell epitope of tetanus toxoid also led to inhibition of fertility. To improve the contraceptive efficacy of ZP-based contraceptive vaccine, various groups are working on improving the immunogen, use of DNA vaccine as prime-boost strategy, and delivering the zona proteins/peptides presented on either virus-like particles or entrapped in microsphere. Host-specific live vectors such as ectromelia virus and cytomegalovirus have also been used to deliver mouse ZP3 in mice. Various studies show the enormous potential of the ZP-based vaccine for the management of wildlife population, where permanent sterilization may be desirable.

MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides.

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules (proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability (area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets--termed T-cell epitope hotspots. MULTIPRED is available at http://antigen.i2r.a-star.edu.sg/multipred/.

Development of a cost-effective oral vaccination method against viral disease in fish.

Different vaccination methods have been applied to protect fish against the detrimental effects of various pathogens. Several studies have shown the potentials of oral vaccination. In theory oral vaccination is an effortless and stress-free method which can be applied at almost any age. In general, however, the vaccine has to be protected to avoid digestion, which results in high costs for application in aquaculture. In this paper we introduce a cost-effective oral vaccination strategy for viral diseases of fish. The vaccines discussed here include fusion proteins consisting of a gut adhesion molecule and a viral peptide expressed in plants. The adhesion molecule mediates binding to and uptake from the gut, whereas the viral peptide functions as vaccine antigen mediating the induction of a humoral immune response. The first pilot studies using a fusion of the gut adhesion molecule and well-characterised heterologous linear B- and T-cell viral epitopes, produced in potato tubers, showed a promising binding and subsequent uptake in the end gut of carp. The results further indicated that a specific humoral immune response was evoked.

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.

Comprehensive assessment of determinant specificity, frequency, and cytokine signature of the primed CD8 cell repertoire induced by a minor transplantation antigen.

T cell immunity is often focused on one peptide segment of a complex protein Ag, with other epitopes inducing weaker, low frequency responses or no responses at all. Such determinant hierarchy has been well characterized for MHC class II-restricted CD4 cell immunity, but is less well understood for class I-restricted CD8 cell responses. We studied class I determinant recognition in a skin transplant model with beta-galactosidase (beta-gal) as a minor transplantation Ag. CD8 T cells from C57BL/6 mice that rejected congenic C57BL/6 beta-gal transgenic skin were tested in enzyme-linked immunospot assays for recall responses to single-step, overlapping, 9-mer peptides that spanned a 94-aa region of the beta-gal sequence. This approach provided every possible class I-restricted peptide for CD8 cell recognition, allowing us to define the in vivo frequency of CD8 cells specific for each of the 86 individual peptides. While four peptides were predicted to bind to the Kb or Db molecules, only one (beta-gal96-103) actually induced an immune response. No peptides outside of the motifs were recognized. Tolerization to beta-gal96-103 significantly prolonged beta-gal transgenic skin graft survival, confirming its immune dominance. Therefore, single-determinant dominance characterized this CD8 cell response. The data demonstrate the feasibility of large-scale, comprehensive, class I determinant mapping, an approach that should be indispensable in measuring CD8 cell immunity in humans.