The functional genomics of Mycobacterium tuberculosis will be studied and a database will be constructed for both scientific and clinical applications. Representing a new endeavor in microbiology and genomics, this project is important at this time when multidrug-resistant tuberculosis is increasingly a public-health threat and scientists are seeking new drugs with novel mechanisms of action. In light of the tremendous impact of the microarray technology on genomics, the database will store the microarray gene expression data engendered under various designed experimental conditions as well as provides functional annotations of genes based on expression and regulation profiling. M. tuberculosis clinical isolates both drug-sensitive and -resistant that meet experimental criteria will be obtained. A web-based SQL Server relational database will be developed to implement the functional genomics database, providing query and analysis capabilities via a web-based graphical interface. Each gene in the database will be annotated by its expression characteristics, co-regulated genes and associated regulated pathways or networks, and its clinical significance, if appropriate. Furthermore, the database links each gene to major bioinformatics and genomics databases to produce an integrated retrieved report. All functional genomics data and analyses will be placed in the public domain. Working synergistically with other federally funded resource centers, the database is designed to allow other researchers to deposit microarray data, conduct data analysis, and obtain program code for making in-house systems. In this project, a set of differential and coordinated genome-wide gene expression studies will be performed to explore drug targets, drug resistance, and biology. Important anti-tubercular drugs and promising new drug candidates will be assessed using drug-challenged gene expression studies to induce drug-specific gene-expression patterns resulting from drug action. Cell biology will be investigated using synchronized M. tuberculosis culture based on in vitro induced non-replicating persistence so that cycle-dependent genes and pertinent regulatory mechanisms will be identified and gene expression accompanying metabolic reprogramming that occurs during shift from non-replicating to replicating states will be studied. These studies will uncover many co-regulated families of genes and allow the functions of uncharacterized genes to be deduced based on co-expression with genes of known function. Combining cluster analysis, search of cis-regulatory elements upstream of regulons, and use of transcription factor databases will unravel gene regulatory networks and enable inferences about biological pathways and discovery of novel drug targets. Important regulatory genes identified will be subjected to further analysis for confirming their regulatory roles using knockout strains. Partial drug resistance and bacterial persistence, which are two important clinical circumstances often encountered in tuberculosis, will be analyzed using functional-genomics studies. The potential value of the proposed methods has been demonstrated and advantages over previous technology been recognized. Research results will advance molecular biological knowledge and benefit public health management in tuberculosis.
| Fu, Li M; Shinnick, Thomas M (2007) Understanding the action of INH on a highly INH-resistant Mycobacterium tuberculosis strain using Genechips. Tuberculosis (Edinb) 87:63-70 |
| Fu, Li M; Shinnick, Thomas M (2007) Genome-wide exploration of the drug action of capreomycin on Mycobacterium tuberculosis using Affymetrix oligonucleotide GeneChips. J Infect 54:277-84 |
| Fu, Li M; Fu-Liu, Casey S (2007) The gene expression data of Mycobacterium tuberculosis based on Affymetrix gene chips provide insight into regulatory and hypothetical genes. BMC Microbiol 7:37 |
| Fu, Li M (2006) Exploring drug action on Mycobacterium tuberculosis using affymetrix oligonucleotide genechips. Tuberculosis (Edinb) 86:134-43 |
| Fu, Li M; Fu-Liu, Casey S (2005) Evaluation of gene importance in microarray data based upon probability of selection. BMC Bioinformatics 6:67 |
