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Notes: Rationale:  We have previously demonstrated that bioinformatics tools such as artificial neural networks (ANNs) are capable of performing pathogen-, genome- and HLA-wide predictions of peptide–HLA interactions. These tools may therefore enable a fast and rational approach to epitope identification and thereby assist in the development of vaccines and immunotherapy. A crucial step in the generation of such bioinformatics tools is the selection of data representing the event in question (in casu peptide–HLA interaction). This is particularly important when it is difficult and expensive to obtain data. Herein, we demonstrate the importance in selecting information-rich data and we develop a computational method, query-by-committee, which can perform a global identification of such information-rich data in an unbiased and automated manner. Furthermore, we demonstrate how this method can be applied to an efficient iterative development strategy for these bioinformatics tools.Methods:  A large panel of binding affinities of peptides binding to HLA A*0204 was measured by a radioimmunoassay (RIA). This data was used to develop multiple first generation ANNs, which formed a virtual committee. This committee was used to screen (or ‘queried’) for peptides, where the ANNs agreed (‘low-QBC’), or disagreed (‘high-QBC’), on their HLA-binding potential. Seventeen low-QBC peptides and 17 high-QBC peptides were synthesized and tested. The high- or low-QBC data were added to the original data, and new high- or low-QBC second generation ANNs were developed, respectively. This procedure was repeated 40 times.Results:  The high-QBC-enriched ANN performed significantly better than the low-QBC-enriched ANN in 37 of the 40 tests.Conclusion:  These results demonstrate that high-QBC-enriched networks perform better than low-QBC-enriched networks in selecting informative data for developing peptide–MHC-binding predictors. This improvement in selecting data is not due to differences in network training performance but due to the difference in information content in the high-QBC experiment and in the low-QBC experiment. Finally, it should be noted that this strategy could be used in many contexts where generation of data is difficult and costly.

Notes: An effective SARS vaccine is likely to include components that can induce specific cytotoxic T-cell (CTL) responses. The specificities of such responses are governed by HLA-restricted presentation of SARS-derived peptide epitopes. Exact knowledge of how the immune system handles protein antigens would allow for the identification of such linear sequences directly from genomic/proteomic sequence information. The latter was recently established when a causative coronavirus (SARS CoV) was isolated and full-length sequenced. Here, we have combined advanced bioinformatics and high-throughput immunology to perform an HLA supertype, genome-wide scan for SARS-specific cytotoxic T cell epitopes. The scan includes all nine human HLA supertypes in total covering 〉99% of all major human populations. For each HLA supertype, we have selected the 15 top candidates for test in biochemical-binding assays. At this time (approximately 6 months after the genome was established), we have tested the majority of the HLA supertypes and identified almost 100 potential vaccine candidates. These should be further validated in SARS survivors and used for vaccine formulation. We suggest that immunobioinformatics may become a fast and valuable tool in rational vaccine design.

Notes: Anti-ENA antibody determination by ELISA technique may offer a valuable diagnostic help in the discrimination of patients with mixed connective tissue disease (MCTD) from those with other chronic inflammatory connective tissue diseases. Determination of this antibody was performed in a prospective designed investigation among 101 blood donors, 154 patients with various non-rheumatic internal medical diseases, and 229 patients with chronic inflammatory connective tissue diseases, including five patients with MCTD. A positive titre of anti-ENA antibody was found in approximately 10% of blood donors and patients with various internal medical disorders. A highly positive anti-ribonucleoprotein (RNP) titre was found in the patients with MCTD, but was also observed in patients with other chronic inflammatory connective tissue diseases, giving a predictive value of 56 % for MCTD. We conclude that highly positive anti-RNP antibody values do not automatically indicate the diagnosis MCTD. Other diagnostic possibilities should still be considered.