Systemic activation of dendritic cells by Toll-like receptor ligands or malaria infection impairs cross-presentation and anti-viral immunity

The mechanisms responsible for the immunosuppression associated with sepsis or some chronic blood infections remain poorly understood. Here we show that infection with a malaria parasite (Plasmodium berghei) or simple systemic exposure to bacterial or viral Toll-like receptor ligands inhibited cross-priming. Reduced cross-priming was a consequence of downregulation of cross-presentation by activated dendritic cells due to systemic activation that did not otherwise globally inhibit T cell proliferation. Although activated dendritic cells retained their capacity to present viral antigens via the endogenous major histocompatibility complex class I processing pathway, antiviral responses were greatly impaired in mice exposed to Toll-like receptor ligands. This is consistent with a key function for cross-presentation in antiviral immunity and helps explain the immunosuppressive effects of systemic infection. Moreover, inhibition of cross-presentation was overcome by injection of dendritic cells bearing antigen, which provides a new strategy for generating immunity during immunosuppressive blood infections.

Cytotoxic T-lymphocyte epitope vaccination protects against human metapneumovirus infection and disease in mice

Human metapneumovirus (hMPV) has emerged as an important human respiratory pathogen causing upper and lower respiratory tract infections in young children and older adults. In addition, hMPV infection is associated with asthma exacerbation in young children. Recent epidemiological evidence indicates that hMPV may cocircullate with human respiratory syncytial virus (hRSV) and mediate clinical disease similar to that seen with hRSV. Therefore, a vaccine for hMPV is highly desirable. In the present study, we used predictive bioinformatics, peptide immunization, and functional T-cell assays to define hMPV cytotoxic T-lymphocyte (CTL) epitopes recognized by mouse T cells restricted through several major histocompatibility complex class I alleles, including HILA-A*0201. We demonstrate that peptide immunization with hMPV CTL epitopes reduces viral load and immunopathollogy in the lungs of hMPV-challenged mice and enhances the expression of Th1-type cytokines (gamma interferon and interleukin-12 [IL-12]) in lungs and regional lymph nodes. In addition, we show that levels of Th2-type cytolkines (IL-10 and IL-4) are significantly lower in hMPV CTL epitope-vaccinated mice challenged with hMPV. These results demonstrate for the first time the efficacy of an hMPV CTL epitope vaccine in the control of hMPV infection in a murine model.

Papillomavirus virus-like particles can deliver defined CTL epitopes to the MHC class I pathway

To evaluate an antigen delivery system in which exogenous antigen can target the major histocompatibility complex (MHC) class I pathway, a single human papillomavirus (HPV) 16 E7 cytotoxic T lymphocyte (CTL) epitope and a single HIV gp160 CTL epitope were separately fused to the C-terminus or bovine papillomavirus 1 (BPV1) L1 sequence to form hybrid BPV1L1 VLPs. Mice immunized with these hybrid VLPs mounted strong CTL responses against the relevant target cells in the absence of any adjuvants. In addition, the CTL responses induced by immunization with BPV1L1/HPV16E7CTL VLPs protected mice against challenge with E7-transformed tumor cells. Furthermore, a high titer-specific antibody response against BPV1L1 VLPs was also induced, and this antiserum could inhibit papillomavirus-induced agglutination of mouse erythrocytes, suggesting that the antibody may recognize conformational determinates relevant to virus neutralization. These data demonstrate that hybrid BPV1L1 VLPs can be used as carriers to target antigenic epitopes to both the MHC class I and class II pathways, providing a promising strategy for the design of vaccines to prevent virus infection, with the potential to elicit therapeutic virus-specific CTL responses. (C) 1998 Academic Press.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

MHC class II expression is regulated in dendritic cells independently of invariant chain degradation

We have investigated the mechanisms that control MHC class II (MHC II) expression in immature and activated dendritic cells (DC) grown from spleen and bone marrow precursors. Degradation of the MHC II chaperone invariant chain (li), acquisition of peptide cargo by MHC II, and delivery of MHC II-peptide complexes to the cell surface proceeded similarly in both immature and activated DC. However, immature DC reendocytosed and then degraded the MHC II-peptide complexes much faster than the activated DC. MHC II expression in DC is therefore not controlled by the activity of the protease(s) that degrade Ii, but by the rate of endocytosis of peptide-loaded MHC II. Late after activation, DC downregulated MHC II synthesis both in vitro and in vivo.

HLA class II antigens positively and negatively associated with hepatosplenic schistosomiasis in a Chinese population

To identify possible associations between host genetic factors and the onset of liver fibrosis following Schistosoma japonicum infection, the major histocompatibility class II alleles of 84 individuals living on an island (Jishan) endemic for schistosomiasis japonica in the Poyang Lake Region of Southern China were determined. Forty patients exhibiting advanced schistosomiasis, characterised by extensive liver fibrosis, and 44 age and sex-matched control subjects were assessed for the class II haplotypes HLA-DRBI and HLA-DQB1. Two HLA-DRB1 alleles, HLA-DRB1*0901 (P = 0.012) and *1302 (P = 0.039), and two HLA-DQB1 alleles, HLA-DQB1*0303 (P = 0.012) and *0609 (P = 0.037), were found to be significantly associated with susceptibility to fibrosis. These associated DRB1 and DQB1 alleles are in very strong linkage disequilibrium, with DRB1*0901-DQB1*0303 and DRB1*1302-DQB1*0609 found as: common haplotypes in this population. In contrast, the alleles HLA-DRB1*1501 (P = 0.025) and HLA-DQB 1*0601 (P = 0.022) were found to be associated with resistance to hepatosplenic disease. Moreover, the alleles DQB1*0303 and DRB1*0901 did not increase susceptibility in the presence of DQB1*0601, indicating that DQB1*0601 is dominant over DQB1*0303 and DRB1*0901. The study has thus identified both positive and negative associations between HLA class II alleles and the risk of individuals developing moderate to severe liver fibrosis following schistosome infection. (C) 2001 Australian Society for Parasitology Inc. Published by Elsevier Science Ltd. All rights reserved.

PREDBALB/c: a system for the prediction of peptide binding to H2(d) molecules, a haplotype of the BALB/c mouse

PREDBALB/c is a computational system that predicts peptides binding to the major histocompatibility complex-2 (H2(d)) of the BALB/c mouse, an important laboratory model organism. The predictions include the complete set of H2(d) class I ( H2-K-d, H2-L-d and H2-D-d) and class II (I-E-d and I-A(d)) molecules. The prediction system utilizes quantitative matrices, which were rigorously validated using experimentally determined binders and non-binders and also by in vivo studies using viral proteins. The prediction performance of PREDBALB/c is of very high accuracy. To our knowledge, this is the first online server for the prediction of peptides binding to a complete set of major histocompatibility complex molecules in a model organism (H2(d) haplotype). PREDBALB/c is available at http://antigen.i2r.a-star.edu.sg/predBalbc/.

Inhibition of natural killer cells by a cytomegalovirus MHC class I homologue in vivo

Herpesviruses, such as murine and human cytomegalovirus (MCMV and HCMV), can establish a persistent infection within the host and have diverse mechanisms as protection from host immune defences'. Several herpesvirus genes that are homologous to host immune modulators have been identified, and are implicated in viral evasion of the host immune response(2,3). The discovery of a viral major histocompatibility complex (MHC) class I homologue, encoded by HCMV(4), led to speculation that it might function as an immune modulator and disrupt presentation of peptides by MHC class I to cytotoxic T cells(5). However, there is no evidence concerning the biological significance of this gene during viral infection. Recent analysis of the MCMV genome has also demonstrated the presence of a MHC class I homologue(6). Here we show that a recombinant MCMV,in which. the gene encoding the class I homologue has been disrupted, has severely restricted replication during the acute stage of infection compared with wild-type MCMV, We demonstrate by in vivo depletion studies that natural killer (NK) cells are responsible for the attenuated phenotype of the mutant. Thus the viral MHC dass I homologue contributes to immune evasion through interference with NK cell-mediated clearance.

Provision of 4-1BB ligand enhances effector and memory CTL responses generated by immunization with dendritic cells expressing a human tumor-associated antigen

Up-regulation of receptor-ligand pairs during interaction of an MHC-presented epitope on dendritic cells (DCs) with cognate TCR may amplify, sustain, and drive diversity in the ensuing T cell immune response. Members of the TNF ligand superfamily and the TNFR superfamily contribute to this costimulatory molecule signaling. In this study, we used replication deficient adenoviruses to introduce a model tumor-associated Ag (the E7 oncoprotein of human papillomavirus 16) and the T cell costimulatory molecule 4-IBBL into murine DCs, and monitored the ability of these recombinant DO to elicit E7-directed T cell responses following immunization. Splenocytes from mice immunized with DCs expressing E7 alone elicited E7-directed effector and memory CTL responses. Coexpression of 4-1BBL in these E7-expressing DO increased effector and memory CTL responses when they were used for immunization. 4-1BBL expression up-regulated CD80 and CD86 second signaling molecules in DO. We also report an additive effect of 4-IBBL and receptor activator of NF-kappaB/receptor activator of NF-kappaB ligand coexpression in E7-transduced DC inummogens on E7-directed effector and memory CTL responses and on MHC class II and CD80/86 expression in DCs. Additionally, expression of 4-1BBL in E7-transduced DCs reduced nonspecific T cell activation characteristic of adenovirus vector-associated immunization. The results have generic implications for improved or tumor Ag-expressing DC vaccines by incorporation of exogenous 4-1BBL. There are also specific implications for an improved DC-based vaccine for human papillomavirus 16-associated cervical carcinoma.

Prediction of HLA-DQ3.2 beta ligands: evidence of multiple registers in class II binding peptides

Motivation: While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models. Methods: We describe a structure-based prediction model for modeling peptide-DQ3.2 beta complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2 beta receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2 beta binding and non-binding peptides obtained from biochemical and functional studies. Results: Our model predicts DQ3.2 beta binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve A(ROC) > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2 beta peptides and illustrate that several registers exist within a candidate binding peptide. Further analysis reveals that peptides with multiple registers occur predominantly for high-affinity binders.

Methods for prediction of peptide binding to MHC molecules: A comparative study

Background: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. Materials and Methods: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. Results: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. Conclusion: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.

Virtual models of the HLA class I antigen processing pathway

Antigen recognition by cytotoxic CD8 T cells is dependent upon a number of critical steps in MHC class I antigen processing including proteosomal cleavage, TAP transport into the endoplasmic reticulum, and MHC class 1 binding. Based on extensive experimental data relating to each of these steps there is now the capacity to model individual antigen processing steps with a high degree of accuracy. This paper demonstrates the potential to bring together models of individual antigen processing steps, for example proteosome cleavage, TAP transport, and MHC binding, to build highly informative models of functional pathways. In particular, we demonstrate how an artificial neural network model of TAP transport was used to mine a HLA-binding database so as to identify H LA-binding peptides transported by TAP. This integrated model of antigen processing provided the unique insight that HLA class I alleles apparently constitute two separate classes: those that are TAP-efficient for peptide loading (HLA-B27, -A3, and -A24) and those that are TAP-inefficient (HLA-A2, -B7, and -B8). Hence, using this integrated model we were able to generate novel hypotheses regarding antigen processing, and these hypotheses are now capable of being tested experimentally. This model confirms the feasibility of constructing a virtual immune system, whereby each additional step in antigen processing is incorporated into a single modular model. Accurate models of antigen processing have implications for the study of basic immunology as well as for the design of peptide-based vaccines and other immunotherapies. (C) 2004 Elsevier Inc. All rights reserved.

The crystal structure of a bacterial Class II ketol-acid reductolsomerase: Domain conservation and evolution

Ketol-acid reductoisomerase (KARI; EC 1.1.1.86) catalyzes two steps in the biosynthesis of branched-chain amino acids. Amino acid sequence comparisons across species reveal that there are two types of this enzyme: a short form (Class 1) found in fungi and most bacteria, and a long form (Class 11) typical of plants. Crystal structures of each have been reported previously. However, some bacteria such as Escherichia coli possess a long form, where the amino acid sequence differs appreciably from that found in plants. Here, we report the crystal structure of the E. coli enzyme at 2.6 A resolution, the first three-dimensional structure of any bacterial Class 11 KARI. The enzyme consists of two domains, one with mixed alpha/beta structure, which is similar to that found in other pyridine nucleotide-dependent dehydrogenases. The second domain is mainly alpha-helical and shows strong evidence of internal duplication. Comparison of the active sites between KARI of E. coli, Pseudomonas aeruginosa, and spinach shows that most residues occupy conserved positions in the active site. E. coli KARI was crystallized as a tetramer, the likely biologically active unit. This contrasts with P. aeruginosa KARI, which forms a dodecamer, and spinach KARI, a dimer. In the E. coli KARI tetramer, a novel subunit-to-subunit interacting surface is formed by a symmetrical pair of bulbous protrusions.