Development of escherichia coli DNA microarray and global gene expression profiling in escherichia coli

Abstract

Gene expression monitoring by DNA microarray can provide important information about cell physiology and has the potential to identify connections between regulatory or metabolic pathways that were not previously known. However, due to huge amount of gene expression data and insufficient knowledge on physiology and metabolism of target organisms, the meaningful biological information is hard to get and is often focused on the concerned cellular functions. In this thesis, I carried out gene expression monitoring and transcriptome analysis. To do these, A microarrayer system was developed for manufacturing E. coli microarrays. The 3-axis robot was designed to automatically collect samples from 96- or 384-well microtitre plates using up to 16 simultaneously moving pens and to deposit them on a surface-modified slide glass. This is followed by a wash/dry operation in a clean station. The cycle is repeated with a new set of samples. This system can deposit cDNA or oligonucleotides with spot intervals of $150 \mu m$ and the spot size of $80 \mu m$ , thus allowing a high density DNA chip containing about 5,000 spots per cm2 to be made. The entire procedure is controlled by the Visual C++ program that was written in our laboratory by using a personal computer with Pentium 100 CPU. For manufacturing E. coli microarrays, 2,850 genes including all functionally known and putative ones were selected amplified by the polymerase chain reaction (PCR), and spotted onto poly-L-lysine treated slide glasses. Fed-batch fermentation of Escherichia coli was carried out by exponential feeding until the cell density reached 74 g dry cell weight/L. Transcriptome analysis was carried out to understand metabolic and physiological changes of E. coli during the high cell density cultivation (HCDC). Out of 2,850 genes analyzed, 690 genes showed gene expression level variation greater than two-fold, and these genes were analysed by self-organizing map and hierarchical clustering. It was ...