Swine influenza is a highly contagious respiratory viral infection in pigs that is responsible for significant financial losses to pig farmers annually. Current measures to protect herds from ...infection include: inactivated whole-virus vaccines, subunit vaccines, and alpha replicon-based vaccines. As is true for influenza vaccines for humans, these strategies do not provide broad protection against the diverse strains of influenza A virus (IAV) currently circulating in U.S. swine. Improved approaches to developing swine influenza vaccines are needed. Here, we used immunoinformatics tools to identify class I and II T cell epitopes highly conserved in seven representative strains of IAV in U.S. swine and predicted to bind to Swine Leukocyte Antigen (SLA) alleles prevalent in commercial swine. Epitope-specific interferon-gamma (IFNγ) recall responses to pooled peptides and whole virus were detected in pigs immunized with multi-epitope plasmid DNA vaccines encoding strings of class I and II putative epitopes. In a retrospective analysis of the IFNγ responses to individual peptides compared to predictions specific to the SLA alleles of cohort pigs, we evaluated the predictive performance of PigMatrix and demonstrated its ability to distinguish non-immunogenic from immunogenic peptides and to identify promiscuous class II epitopes. Overall, this study confirms the capacity of PigMatrix to predict immunogenic T cell epitopes and demonstrate its potential for use in the design of epitope-driven vaccines for swine. Additional studies that match the SLA haplotype of animals with the study epitopes will be required to evaluate the degree of immune protection conferred by epitope-driven DNA vaccines in pigs.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
T cell epitope prediction tools and associated vaccine design algorithms have accelerated the development of vaccines for humans. Predictive tools for swine and other food animals are not as well ...developed, primarily because the data required to develop the tools are lacking. Here, we overcome a lack of T cell epitope data to construct swine epitope predictors by systematically leveraging available human information. Applying the "pocket profile method", we use sequence and structural similarities in the binding pockets of human and swine major histocompatibility complex proteins to infer Swine Leukocyte Antigen (SLA) peptide binding preferences. We developed epitope-prediction matrices (PigMatrices), for three SLA class I alleles (SLA-1*0401, 2*0401 and 3*0401) and one class II allele (SLA-DRB1*0201), based on the binding preferences of the best-matched Human Leukocyte Antigen (HLA) pocket for each SLA pocket. The contact residues involved in the binding pockets were defined for class I based on crystal structures of either SLA (SLA-specific contacts, Ssc) or HLA supertype alleles (HLA contacts, Hc); for class II, only Hc was possible. Different substitution matrices were evaluated (PAM and BLOSUM) for scoring pocket similarity and identifying the best human match. The accuracy of the PigMatrices was compared to available online swine epitope prediction tools such as PickPocket and NetMHCpan.
PigMatrices that used Ssc to define the pocket sequences and PAM30 to score pocket similarity demonstrated the best predictive performance and were able to accurately separate binders from random peptides. For SLA-1*0401 and 2*0401, PigMatrix achieved area under the receiver operating characteristic curves (AUC) of 0.78 and 0.73, respectively, which were equivalent or better than PickPocket (0.76 and 0.54) and NetMHCpan version 2.4 (0.41 and 0.51) and version 2.8 (0.72 and 0.71). In addition, we developed the first predictive SLA class II matrix, obtaining an AUC of 0.73 for existing SLA-DRB1*0201 epitopes. Notably, PigMatrix achieved this level of predictive power without training on SLA binding data.
Overall, the pocket profile method combined with binding preferences from HLA binding data shows significant promise for developing T cell epitope prediction tools for pigs. When combined with existing vaccine design algorithms, PigMatrix will be useful for developing genome-derived vaccines for a range of pig pathogens for which no effective vaccines currently exist (e.g. porcine reproductive and respiratory syndrome, influenza and porcine epidemic diarrhea).
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Novel computational tools for swine vaccine development can expand the range of immunization approaches available to prevent economically devastating swine diseases and spillover events between pigs ...and humans. PigMatrix and EpiCC are two new tools for swine T cell epitope identification and vaccine efficacy analysis that have been integrated into an existing computational vaccine design platform named iVAX. The iVAX platform is already in use for the development of human vaccines, thus integration of these tools into iVAX improves and expands the utility of the platform overall by making previously validated immunoinformatics tools, developed for humans, available for use in the design and analysis of swine vaccines. PigMatrix predicts T cell epitopes for a broad array of class I and class II swine leukocyte antigen (SLA) using matrices that enable the scoring of sequences for likelihood of binding to SLA. PigMatrix facilitates the prospective selection of T cell epitopes from the sequences of swine pathogens for vaccines and permits the comparison of those predicted epitopes with "self" (the swine proteome) and with sequences from other strains. Use of PigMatrix with additional tools in the iVAX toolkit also enables the computational design of vaccines
, for testing
. EpiCC uses PigMatrix to analyze existing or proposed vaccines for their potential to protect, based on a comparison between T cell epitopes in the vaccine and circulating strains of the same pathogen. Performing an analysis of T cell epitope relatedness analysis using EpiCC may facilitate vaccine selection when a novel strain emerges in a herd and also permits analysis of evolutionary drift as a means of immune escape. This review of novel computational immunology tools for swine describes the application of PigMatrix and EpiCC in case studies, such as the design of cross-conserved T cell epitopes for swine influenza vaccine or for African Swine Fever. We also describe the application of EpiCC for determination of the best vaccine strains to use against circulating viral variants of swine influenza, swine rotavirus, and porcine circovirus type 2. The availability of these computational tools accelerates infectious disease research for swine and enable swine vaccine developers to strategically advance their vaccines to market.
Zika virus (ZIKV) is a flavivirus primarily transmitted by
Aedes
species mosquitoes, first discovered in Africa in 1947, that disseminated through Southeast Asia and the Pacific Islands in the 2000s. ...The first ZIKV infections in the Americas were identified in 2014, and infections exploded through populations in Brazil and other countries in 2015/16. ZIKV infection during pregnancy can cause severe brain and eye defects in offspring, and infection in adults has been associated with higher risks of Guillain-Barré syndrome. We initiated a study to describe the natural history of Zika (the disease) and the immune response to infection, for which some results have been reported. In this paper, we identify ZIKV-specific CD4+ and CD8+ T cell epitopes that induce responses during infection. Two screening approaches were utilized: an untargeted approach with overlapping peptide arrays spanning the entire viral genome, and a targeted approach utilizing peptides predicted to bind human MHC molecules. Immunoinformatic tools were used to identify conserved MHC class I supertype binders and promiscuous class II binding peptide clusters predicted to bind 9 common class II alleles. T cell responses were evaluated in overnight IFN-γ ELISPOT assays. We found that MHC supertype binding predictions outperformed the bulk overlapping peptide approach. Diverse CD4+ T cell responses were observed in most ZIKV-infected participants, while responses to CD8+ T cell epitopes were more limited. Most individuals developed a robust T cell response against epitopes restricted to a single MHC class I supertype and only a single or few CD8+ T cell epitopes overall, suggesting a strong immunodominance phenomenon. Noteworthy is that many epitopes were commonly immunodominant across persons expressing the same class I supertype. Nearly all of the identified epitopes are unique to ZIKV and are not present in Dengue viruses. Collectively, we identified 31 immunogenic peptides restricted by the 6 major class I supertypes and 27 promiscuous class II epitopes. These sequences are highly relevant for design of T cell-targeted ZIKV vaccines and monitoring T cell responses to Zika virus infection and vaccination.
•Geographically and temporally diverse PCV2 sequences were grouped by cluster and phenotype.•T cell epitope content was identified for 161 selected PCV2 field strain and four PCV2 vaccines.•A T cell ...epitope content comparison (EpiCC score) was made between each field strain and each vaccine.•A bivalent PCV2a-PCV2b vaccine had the highest EpiCC score, and the broadest coverage of field strain T cell epitopes.•Both PCV2a and PCV2b vaccine strains may be required to provide optimal coverage of current and future field isolates.
Porcine circovirus type 2 (PCV2) has one of the highest evolutionary rates among DNA viruses. Traditionally, PCV2 vaccines have been based on the 2a genotype as this was the first genotype discovered. Today, eight genotypes of PCV2 viruses have been identified, and, taken together with the rapid evolutionary rate, propensity to recombine, and high rate of vaccination, further variation in PCV2 is expected. For these reasons, there is a growing genetic gap between available vaccines and field strains. When selecting vaccines, it is important to consider vaccines that contain T cell epitopes that are well-matched to the circulating strains. To quantify the relatedness between PCV2 vaccines and field strains, we predicted and compared their T cell epitope content and calculated Epitope Content Comparison (EpiCC) scores using established in silico tools. T cell epitopes predicted to bind common class I and class II swine leukocyte antigen (SLA) alleles were identified from two major structural proteins, the capsid (encoded by ORF2) and the replicase (encoded by ORF1). The T cell epitope content of three commercial PCV2a-based vaccines (a baculovirus expressed PCV2a ORF2 VacAlt, a PCV1-PCV2a chimeric virus vaccine VacA and a combination cPCV2a-cPCV2b chimeric virus vaccine VacAB) and an experimental PCV2b ORF2-based chimeric virus vaccine VacB (Table 1), were compared to that of 161 PCV2 field strains (representing genotypes a-f).
The T cell epitope content and conservation between vaccine and field strains varied. While all vaccine strains provided broad coverage of the field strains including heterologous genotypes, none of the vaccines covered all the putative T cell epitopes identified in the field strains. PCV2a-based vaccine strains generally scored higher in terms of conserved epitope content against PCV2a field isolates but were not identical. The PCV2b-based vaccine strain had higher scores against PCV2b and PCV2d field strains. The combination PCV2a-PCV2b vaccine (VacAB) had, on average, the highest EpiCC score. PCV2 continues to evolve and EpiCC analysis provides a new tool to assess the possible impact of virus genetic divergence on T cell epitope coverage of vaccine strains. Given that multiple genotypes are currently found and may co-exist on farms, this analysis suggests that a combination of PCV2a and PCV2b vaccine strains may be required to provide optimal coverage of current and future field isolates.
Natural and vaccine-induced SARS-CoV-2 immunity in humans has been described but correlates of protection are not yet defined. T cells support the SARS-CoV-2 antibody response, clear virus-infected ...cells, and may be required to block transmission. In this study, we identified peptide epitopes associated with SARS-CoV-2 T-cell immunity. Using immunoinformatic methods, T-cell epitopes from spike, membrane, and envelope were selected for maximal HLA-binding potential, coverage of HLA diversity, coverage of circulating virus, and minimal potential cross-reactivity with self. Direct restimulation of PBMCs collected from SARS-CoV-2 convalescents confirmed 66% of predicted epitopes, whereas only 9% were confirmed in naive individuals. However, following a brief period of epitope-specific T-cell expansion, both cohorts demonstrated robust T-cell responses to 97% of epitopes. HLA-DR3 transgenic mouse immunization with peptides co-formulated with poly-ICLC generated a potent Th1-skewed, epitope-specific memory response, alleviating safety concerns of enhanced respiratory disease associated with Th2 induction. Taken together, these epitopes may be used to improve our understanding of natural and vaccine-induced immunity, and to facilitate the development of T-cell-targeted vaccines that harness pre-existing SARS-CoV-2 immunity.
Computational vaccinology includes epitope mapping, antigen selection, and immunogen design using computational tools. Tools that facilitate the
prediction of immune response to biothreats, emerging ...infectious diseases, and cancers can accelerate the design of novel and next generation vaccines and their delivery to the clinic. Over the past 20 years, vaccinologists, bioinformatics experts, and advanced programmers based in Providence, Rhode Island, USA have advanced the development of an integrated toolkit for vaccine design called iVAX, that is secure and user-accessible by internet. This integrated set of immunoinformatic tools comprises algorithms for scoring and triaging candidate antigens, selecting immunogenic and conserved T cell epitopes, re-engineering or eliminating regulatory T cell epitopes, and re-designing antigens to induce immunogenicity and protection against disease for humans and livestock. Commercial and academic applications of iVAX have included identifying immunogenic T cell epitopes in the development of a T-cell based human multi-epitope Q fever vaccine, designing novel influenza vaccines, identifying cross-conserved T cell epitopes for a malaria vaccine, and analyzing immune responses in clinical vaccine studies. Animal vaccine applications to date have included viral infections of pigs such as swine influenza A, PCV2, and African Swine Fever. "Rapid-Fire" applications for biodefense have included a demonstration project for Lassa Fever and Q fever. As recent infectious disease outbreaks underscore the significance of vaccine-driven preparedness, the integrated set of tools available on the iVAX toolkit stand ready to help vaccine developers deliver genome-derived, epitope-driven vaccines.
Summary Pyrazinamide is one of the most important drugs in the treatment of latent Mycobacterium tuberculosis infection. The emergence of strains resistant to pyrazinamide represents an important ...public health problem, as both first- and second-line treatment regimens include pyrazinamide. The accepted mechanism of action states that after the conversion of pyrazinamide into pyrazinoic acid by the bacterial pyrazinamidase enzyme, the drug is expelled from the bacteria by an efflux pump. The pyrazinoic acid is protonated in the extracellular environment and then re-enters the mycobacterium, releasing the proton and causing a lethal disruption of the membrane. Although it has been shown that mutations causing significant loss of pyrazinamidase activity significantly contribute to pyrazinamide resistance, the mechanism of resistance is not completely understood. The pyrazinoic acid efflux rate may depend on multiple factors, including pyrazinamidase activity, intracellular pyrazinamidase concentration, and the efficiency of the efflux pump. Whilst the importance of the pyrazinoic acid efflux rate to the susceptibility to pyrazinamide is recognized, its quantitative effect remains unknown. Thirty-four M. tuberculosis clinical isolates and a Mycobacterium smegmatis strain (naturally resistant to PZA) were selected based on their susceptibility to pyrazinamide, as measured by Bactec 460TB and the Wayne method. For each isolate, the initial velocity at which pyrazinoic acid is released from the bacteria and the initial velocity at which pyrazinamide enters the bacteria were estimated. The data indicated that pyrazinoic acid efflux rates for pyrazinamide-susceptible M. tuberculosis strains fell within a specific range, and M. tuberculosis strains with a pyrazinoic acid efflux rate below this range appeared to be resistant. This finding contrasts with the high pyrazinoic acid efflux rate for M. smegmatis , which is innately resistant to pyrazinamide: its pyrazinoic acid efflux rate was found to be 900 fold higher than the average efflux rate for M. tuberculosis strains. No significant variability was observed in the pyrazinamide flux rate. The pyrazinoic acid efflux rate explained 61% of the variability in Bactec pyrazinamide susceptibility, 24% of Wayne activity, and 51% of the Bactec 460TB growth index. In contrast, pyrazinamidase activity accounted for only 27% of the Bactec pyrazinamide susceptibility. This finding suggests that mechanisms other than pncA mutations (reduction of pyrazinamidase activity) are also implicated in pyrazinamide resistance, and that pyrazinoic acid efflux rate acts as a better proxy for pyrazinamide resistance than the presence of pncA mutations. This is relevant to the design of molecular diagnostics for pyrazinamide susceptibility, which currently rely on pncA gene mutation detection.
Tuberculosis control efforts are hampered by a mismatch in diagnostic technology: modern optimal diagnostic tests are least available in poor areas where they are needed most. Lack of adequate early ...diagnostics and MDR detection is a critical problem in control efforts. The Microscopic Observation Drug Susceptibility (MODS) assay uses visual recognition of cording patterns from Mycobacterium tuberculosis (MTB) to diagnose tuberculosis infection and drug susceptibility directly from a sputum sample in 7-10 days with a low cost. An important limitation that laboratories in the developing world face in MODS implementation is the presence of permanent technical staff with expertise in reading MODS. We developed a pattern recognition algorithm to automatically interpret MODS results from digital images. The algorithm using image processing, feature extraction and pattern recognition determined geometrical and illumination features used in an object-model and a photo-model to classify TB-positive images. 765 MODS digital photos were processed. The single-object model identified MTB (96.9% sensitivity and 96.3% specificity) and was able to discriminate non-tuberculous mycobacteria with a high specificity (97.1% M. avium, 99.1% M. chelonae, and 93.8% M. kansasii). The photo model identified TB-positive samples with 99.1% sensitivity and 99.7% specificity. This algorithm is a valuable tool that will enable automatic remote diagnosis using Internet or cellphone telephony. The use of this algorithm and its further implementation in a telediagnostics platform will contribute to both faster TB detection and MDR TB determination leading to an earlier initiation of appropriate treatment.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PDF
