ArticleProceedings of the National Academy of Sciences of the United States of America2004
Antigen selection based on expression levels during infection facilitates vaccine development for an intracellular pathogen.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed, 103 citations in OpenAlex.
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Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vaccines effective against intracellular pathogens could save the lives of millions of people every year, but vaccine development has been hampered by the slow largely empirical search for protective antigens. In vivo highly expressed antigens might represent a small attractive antigen subset that could be rapidly evaluated, but experimental evidence supporting this rationale, as well as practical strategies for its application, is largely lacking because of technical difficulties. Here, we used Salmonella strains expressing differential amounts of a fluorescent model antigen during infection to show that, in a mouse typhoid fever model, CD4 T cells preferentially recognize abundant Salmonella antigens. To identify a large number of natural Salmonella antigens with high expression levels during infection, we used a quantitative in vivo screening strategy. Immunization studies with five particularly attractive candidates revealed two highly protective antigens that might permit the development of an improved typhoid fever vaccine. In conclusion, we have established a rationale and an experimental strategy that will substantially facilitate vaccine development for Salmonella and possibly other intracellular pathogens.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.