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In silico prediction of Ebola Zaire GP1,2 immuno-dominant epitopes for the Balb/c mouse

Overview of attention for article published in BMC Immunology, October 2015
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Title
In silico prediction of Ebola Zaire GP1,2 immuno-dominant epitopes for the Balb/c mouse
Published in
BMC Immunology, October 2015
DOI 10.1186/s12865-015-0126-8
Pubmed ID
Authors

Debargh K. Dutta, Kelly Rhodes, Steven C. Wood

Abstract

Ebola is a Filovirus (FV) that induces a highly communicable and deadly hemorrhagic fever. Currently, there are no approved vaccines to treat FV infections. Protection from FV infection requires cell mediated and humoral immunity. Glycoprotein GP1,2 Fc Zaire, a recombinant FV human Fc fusion protein, has been shown to confer protection against mouse adapted Zaire Ebola virus. The present studies are focused upon identifying immunodominant epitopes using in silico methods and developing tetramers as a diagnostic reagent to detect cell mediated immune responses to GP1,2 Fc. The GP1,2 Ebola Zaire sequence from the 1976 outbreak was analyzed by both BIMAS and SYFPEITHI algorithms to identify potential immuno-dominant epitopes. Several peptides were synthesized and screened in flow-based MHC stability studies. Three candidate peptides, P8, P9 and P10, were identified and, following immunization in Balb/c mice, all three peptides induced IFN-γ as detected by ELISpot and intracellular staining. Significantly, P8, P9 and P10 generated robust cytotoxic T-cell responses (CTL) as determined by a flow cytometry-based Caspase assay. Antigen specific cells were also detected, using tetramers. Both P9 and P10 have sequence homology with highly conserved regions of several strains of FV. In sum, three immunodominant sequences of the Ebola GP1,2 have been identified using in silico methods that may confer protection against mouse adapted Ebola Zaire. The development of tetramer reagents will provide unique insight into the potency and durability of medical countermeasure vaccines for known bioterrorism threat agents in preclinical models.

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Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Spain 1 3%
Unknown 35 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 16%
Librarian 5 14%
Other 4 11%
Student > Bachelor 4 11%
Researcher 3 8%
Other 9 24%
Unknown 6 16%
Readers by discipline Count As %
Medicine and Dentistry 9 24%
Agricultural and Biological Sciences 7 19%
Nursing and Health Professions 5 14%
Biochemistry, Genetics and Molecular Biology 2 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 5 14%
Unknown 8 22%