Source: UNIVERSITY OF RHODE ISLAND submitted to NRP
PREDICTING T CELL SPECIFITIES IN SWINE FOR VACCINE DEVELOPMENT
Sponsoring Institution
Agricultural Research Service/USDA
Project Status
ACTIVE
Funding Source
Reporting Frequency
Annual
Accession No.
0423366
Grant No.
(N/A)
Cumulative Award Amt.
(N/A)
Proposal No.
(N/A)
Multistate No.
(N/A)
Project Start Date
Sep 1, 2012
Project End Date
Aug 31, 2013
Grant Year
(N/A)
Program Code
[(N/A)]- (N/A)
Recipient Organization
UNIVERSITY OF RHODE ISLAND
19 WOODWARD HALL 9 EAST ALUMNI AVENUE
KINGSTON,RI 02881
Performing Department
(N/A)
Non Technical Summary
(N/A)
Animal Health Component
20%
Research Effort Categories
Basic
60%
Applied
20%
Developmental
20%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
31135101040100%
Knowledge Area
311 - Animal Diseases;

Subject Of Investigation
3510 - Swine, live animal;

Field Of Science
1040 - Molecular biology;
Goals / Objectives
The objective of this collaboration is to demonstrate the use of computational algorithms to predict epitopes for T cells isolated from genetically defined swine. USDA-ARS and the University of Rhode Island (URI) will independently conduct analysis of the antigenic epitopes recognized by T cells that mediate adaptive immune responses to viruses and viral antigens utilizing different algorithmic techniques.
Project Methods
USDA-ARS has adapted an assay developed at University of Copenhagen (Denmark) for human proteins to swine, which involves expressing the major histocompatibility complex (MHC) proteins in vitro in E. coli, isolating and purifying the proteins and analyzing the capacity of these proteins to bind antigenic peptides derived from pathogenic viruses. This technology has been applied to predictions using an algorithm developed by the Danish collaborators and it has been shown that the algorithm for human proteins successfully predicts swine protein attributes. The University of Rhode Island has independently developed an algorithm for these predictions. Both algorithms will be tested utilizing recombinant proteins of swine and compared for relative accuracy and capacity to identify T cell epitopes in pigs.