EXCEED THE SPACE PROVIDED. Recent experimentation in functional genomics is generating new data at an unprecedented rate. The challenge now is to integrate this large amount of new information into a mechanistic understanding of how biological systems work. We have developed a bioinformatics algorithm, CLASSIFI, which uses gene ontology information to classify microarray gene clusters based on coordinated expression patterns. CLASSIFI was used to analyze expression microarray data from splenic B cells stimulated with two ligands, BAFF and CD40L, which play important roles in regulating B cell homeostasis and function. One cluster of probes/genes up-regulated in response to BAFF contains a high proportion of sequences that have not been previously characterized, suggesting that BAFF is inducing the expression of a panel of new genes that may be involved in novel biological processes. Two general goals will be pursued in this project - to develop objective approaches for the selection and characterization of previously uncharacterized genes, and to investigate the molecular processes controlled by BAFF that regulate B lymphocyte homeostasis and function. These goals will be achieved through a combination of data mining and hypothesis-driven experimentation.
The specific aims of this project are to characterize the B cell responses controlled by BAFF, to filter and prioritize the uncharacterized probe/gene set through a combination of bioinformatics approaches and tissue expression studies, to develop hypotheses regarding possible biological functions of the candidate genes using bioinformatics analysis, and to investigate possible functions of the candidate genes through a determination of interacting proteins, an investigation of possible biochemical activities and a measurement of expression perturbation effects on BAFF-mediated B cell responses. Mechanistic studies of B cell responses to BAFF are medically relevant since dysregulation of BAFF is associated with the development of autoimmune disease in both animal models and humans. PERFORMANCE SITE ========================================Section End===========================================
