A. STATEMENT OF HYPOTHESIS AND SPECIFIC AIMS:
This proposal responds to program announcement PA-06-094 that supports informatics research in health related areas. Major advances in genomics and proteomics have led to an explosive growth in the volume of biological information. The field of bioinformatics can assist both informatics and experimental scientists in optimizing their use of these vast data resources. We have been active in HIV research since the disease was first discovered. Our informatics collaborators are computational scientists in the Data Analytical
Bioinformatics Core at the NY State Center of Excellence in Bioinformatics and the Life Sciences (CoE) and the Buffalo Center for Biomedical Computing (BCBC) of the Department of Computer Science and Engineering at UB; the BCBC is a member of the NIH supported National Programs of Excellence in Biomedical
Computing. They will assist us in analyzing the large, complex, clinical, genomic and proteomic database that we are building from our HIV-1 infected patients. This project, using data mining methods, will contribute to a fundamental understanding of the pathogenesis of HIV infections and identify new markers of disease progression and potential new targets for therapy. In this proposal, we focus on a unique cohort of HIV-1 infected patients who are long term non-progressors (LTNP) in comparison with normal progressors (NP).
Susceptibility to and progression of HIV-1 infection is dependent on different virus and host factors (Yue et al.,2005; Campbell et al., 2004., Anzala et al., 1995; Cao et al., 1995; Buchbinder et al., 1994) . Host genetic factors such as human allelic variants, and HLA and HIV co-receptor polymorphisms significantly influence disease outcomes. As the course of an HIV-1 infection is a complex, orchestrated action of many different mechanisms, we propose that a database containing clinical, genomic and proteomic data comparing different patient cohorts may yield significant new biomarkers for susceptibility to HIV-1 infection and disease progression. Thus we hypothesize that using innovative informatics technology we shall develop a large, interactive database containing clinical, genomic and proteomic information on HIV-1 infected patients that can be used to guide rationale, evidence-based, decision making at both the clinical and public health levels.
Specific Aim I.
To identify functional genes and proteins that are unique to our NP and LTNP patient cohorts.
This will be accomplished by:
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Genomic analysis of peripheral blood mononuclear cells (PBMC) from NP and LTNP subjects using cDNA microarrays.
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Proteomic analysis of PBMC from NP and LTNP subjects using 2-dimensional difference gel electrophoresis (2D-DIGE) and protein identification using liquid chromatography-tandem mass spectrometry
( LC-MS/MS). Additionally, label-free proteomic quantification methods such as isotope coded affinity tag
(ICAT), isobaric tag for relative and absolute quantitation (ITRAQ),
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O-incorporation, or stable-isotope labeling by amino acids in cell culture (SILAC) will be explored.
Specific Aim II. To evaluate the role of human allelic variants in influencing the rate of HIV-1 disease progression.
This will be accomplished by:
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Quantitating the gene expression of the allelic variants, RANTES In1.1c, CCR2b-641, CCR5∆32, IL10-
5'A, IL-4 -589T, TNF-
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-238A and HLA-B27, in NP and LTNP patient cohorts using single nucleotide polymorphism (SNP) analysis and real time, quantitative PCR (QPCR).
Specific Aim III. To develop new computational tools to analyze and integrate genomic, proteomic, and clinical data from these HIV-1 patient cohorts and convert them into clinically useful information relating to the pathogenesis, transmission and therapeutic response of HIV-1 infected patients.This will be accomplished by:
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Design flexible, data mining-oriented schemas for integrating genomic, proteomic, and clinical data in a data warehouse. Design and implement flexible and scalable analytical functionality for the data warehouse.
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Develop novel algorithms that are specifically suited for mining interesting and significant expression patterns for gene and protein expression data. Design advanced algorithms to jointly explore genomic and proteomic data sets, and to integrate expert knowledge and exploit the annotation of known genes and proteins to guide the mining process.
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Design metrics and methods to explore the association between genetic variations and haplotype patterns underlying HIV disease.