The objective of this program project is to integrate three different genomic disciplines to advance our working understanding of clinical kidney transplantation. Patients with acute rejection and chronic allograft nephropathy will be compared to patients without rejection and long term, well-functioning transplants. Specifically, we propose to use both kidney transplant biopsies and peripheral blood lymphocytes. Gene expression profiling will be done using Affymetrix oligonucleotide-based microarrays (Project 1). Proteomics will involve the use of liquid chromatography coupled with linear ion trap mass spectrometry (Project 2). Gene candidates identified by data generated with these two technologies will comprise a set of 500 genes for which we will perform complex trait SNP genetic analysis (Project 3). The Bioinformatics and Statistics Core will provide the bioinformatics and statistical support for all projects. All data will flow to the Core for both advanced analysis and archiving. At the first level this support will include monitoring experimental designs for statistical integrity, organizing complex data sets generated in each Project so that they are more accessible to the Principal Investigators for interpretation and discovery and perform data mining using bioinformatic tools. Clinical data entered into the web site at Scripps will also be integrated with these efforts in a real-time fashion. At the second level the Core will supervise the selection and statistical validation of the 500 gene candidate set based on gene expression and proteomic data intended for complex trait genetics in Project 3. We will use supervised and unsupervised data mining methods such as clustering and class prediction tools. We will also identify differentially expressed genes and proteins in the different kidney transplant group comparisons. At the third level the Core will integrate data generated in all three Projects so that connections, pathways and mechanisms can be recognized, defined and validated including possibly novel relationships between patient and donor genetics.
Showing the most recent 10 out of 80 publications
