The identification of the molecular and cellular basis for adverse events observed in patients immunized against smallpox is of great public health interest. This is especially true today given efforts to defend the U.S. population and military against potential bioterrorism agents. We have shown recently that adverse vents following smallpox vaccination correlate with systemic cytokine patterns, suggesting a role for cytokines in the pathogenesis of adverse events. A challenge to further delineating the immunological mechanisms of adverse events is that cytokines rarely act in isolation to induce an immune response, but rather they work in a complex network to which immune system cells respond. Cytokines are small signaling proteins that integrate the activities of immune system cells. While it is often well understood how they act individually, the behavior of cytokines as part of a signaling network is less well known and likely depends on the nature of the infecting organism. We propose to develop and evaluate a comprehensive strategy to identify detailed kinetic cytokine network models associated with adverse events following smallpox vaccination. This strategy will be developed and evaluated using proteomic time-series data available from 103 volunteers that are part of an ongoing NIAID/NIH-sponsored trial to evaluate the Aventis Pasteur Smallpox Vaccine (APSV). We will develop software tools using machine learning algorithms to automatically discover cytokine signaling network models from observed time-series cytokine expression levels. Once the underlying cytokine network model has been estimated, our goal is to use the dynamic model as a simulation tool to suggest ways to create vaccines that minimize the risk of adverse events associated with vaccination. The software tools developed in this proposal will be generally applicable for biomedical research to understand the biochemical interactions in time-series data, and, thus, a useful and novel software package will be made available to the vaccine research community. The experience and knowledge gained during the collaboration on this important research problem in immunology coupled with the didactic and mentoring portions of the training program will create a firm foundation upon which I can develop and test significant hypotheses for future studies on the immunology of infectious diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Mentored Quantitative Research Career Development Award (K25)
Project #
5K25AI064625-05
Application #
7612652
Study Section
Acquired Immunodeficiency Syndrome Research Review Committee (AIDS)
Program Officer
Prograis, Lawrence J
Project Start
2006-04-01
Project End
2009-08-14
Budget Start
2009-04-01
Budget End
2009-08-14
Support Year
5
Fiscal Year
2009
Total Cost
$70,245
Indirect Cost
Name
University of Alabama Birmingham
Department
Genetics
Type
Schools of Medicine
DUNS #
063690705
City
Birmingham
State
AL
Country
United States
Zip Code
35294
Pandey, A; Davis, N A; White, B C et al. (2012) Epistasis network centrality analysis yields pathway replication across two GWAS cohorts for bipolar disorder. Transl Psychiatry 2:e154
Davis, Nicholas A; Pandey, Ahwan; McKinney, B A (2011) Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS. Bioinformatics 27:284-5
Davis, N A; Crowe Jr, J E; Pajewski, N M et al. (2010) Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine. Genes Immun 11:630-6
McKinney, Brett A (2009) Informatics approaches for identifying biologic relationships in time-series data. Wiley Interdiscip Rev Nanomed Nanobiotechnol 1:60-68
McKinney, Brett A; Crowe, James E; Guo, Jingyu et al. (2009) Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis. PLoS Genet 5:e1000432
Reif, D M; Motsinger-Reif, A A; McKinney, B A et al. (2009) Integrated analysis of genetic and proteomic data identifies biomarkers associated with adverse events following smallpox vaccination. Genes Immun 10:112-9
Reif, David M; McKinney, Brett A; Motsinger, Alison A et al. (2008) Genetic basis for adverse events after smallpox vaccination. J Infect Dis 198:16-22
McKinney, B A; Reif, D M; White, B C et al. (2007) Evaporative cooling feature selection for genotypic data involving interactions. Bioinformatics 23:2113-20
McKinney, B A; Crowe Jr, J E; Voss, H U et al. (2006) Hybrid grammar-based approach to nonlinear dynamical system identification from biological time series. Phys Rev E Stat Nonlin Soft Matter Phys 73:021912
McKinney, Brett A; Reif, David M; Ritchie, Marylyn D et al. (2006) Machine learning for detecting gene-gene interactions: a review. Appl Bioinformatics 5:77-88