Meta-analysis of immune epitope data for all Plasmodia: overview and applications for malarial immunobiology and vaccine-related issues

Vaughan, K., Blythe, M., Greenbaum, J., Zhang, Q., Peters, B., Doolan, D.L., and Sette, A. (2009) Meta-analysis of immune epitope data for all Plasmodia: overview and applications for malarial immunobiology and vaccine-related issues. Parasite Immunology, 31 (2). pp. 78-97.

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Abstract

We present a comprehensive meta-analysis of more than 500 references, describing nearly 5000 unique B cell and T cell epitopes derived from the Plasmodium genus, and detailing thousands of immunological assays. This is the first inventory of epitope data related to malaria-specific immunology, plasmodial pathogenesis, and vaccine performance. The survey included host and pathogen species distribution of epitopes, the number of antibody vs. CD4⁺ and CD8⁺ T cell epitopes, the genomic distribution of recognized epitopes, variance among epitopes from different parasite strains, and the characterization of protective epitopes and of epitopes associated with parasite evasion of the host immune response. The results identify knowledge gaps and areas for further investigation. This information has relevance to issues, such as the identification of epitopes and antigens associated with protective immunity, the design and development of candidate malaria vaccines, and characterization of immune response to strain polymorphisms.

Item ID: 42733
Item Type: Article (Research - C1)
ISSN: 1365-3024
Keywords: malaria; epitope; Plasmodium; vaccine
Funders: National Institutes of Health (NIH), National Institute of Allergy and Infectious Diseases (NIAID), Pfizer Australia Senior Research Fellowship (PASRF)
Projects and Grants: NIH/NIAID contract number HHSN26620040006C
Date Deposited: 24 Mar 2016 00:21
FoR Codes: 11 MEDICAL AND HEALTH SCIENCES > 1107 Immunology > 110799 Immunology not elsewhere classified @ 100%
SEO Codes: 92 HEALTH > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920109 Infectious Diseases @ 100%
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