Other examples were described in the results and discussion section, showing that for similar transcriptional responses, different regulatory strategies were implemented in the case of each organism. The considerable differences between the mechanism controlling gene expression and the small set of orthologous genes found in the conditions tested, are a consequence of the large phylogentic distance between these screening assay bacteria. These analyzes also revealed how incomplete
our knowledge still is, concerning gene regulation in B. subtilis. We are aware that processes such as catabolic repression, nitrogen assimilation and sporulation have been extensively analyzed, whereas other functions shared with E. coli, such as certain genes of the main glycolytic pathways, TCA cycle, and respiratory function, are not well Smoothened Agonist in vivo understood. Integrative analysis of transcriptome and transcriptional regulatory data as undertaken here, as well as the comparison between organisms should provide a framework for the future generation of
models. These will help explain the cell’s capaCity to respond to a changing environment and increase understanding of the evolutionary forces, which enable life forms to harmonize their regulatory processes in order to improve their adaptation. Methods Data analysis and identification of differential transcribed genes Transcriptome data was obtained from previously described experiments Methane monooxygenase performed with B. subtilis strain ST100 broth, containing 50 mM potassium phosphate, pH 7.4, and 0.2 mM L-cysteine with (LB+G) or without (LB) 0.4% glucose. The average expression data from three repeated experiments was collected from web http://biology.ucsd.edu/~msaier/regulation2/ of the B. subtilis antisense. DNA arrays used in this work were custom designed and manufactured by Affymetrix (Santa Clara, CA) [8]. As we only had access to the average of the crude expression data, we applied the rank product method [44]. This method is based on the calculation
of rank products, from which significance thresholds can be extracted, in order to distinguish significantly regulated genes. In the case of our data, we chose a RP-value of 3.5 × 10-2 as a cutoff point, and in this way we distinguished the most significant 150 up-regulated and 150 down-regulated genes. However, as we also were interested in the differential expression under both conditions, we picked up those genes exhibiting a > 3-fold change between LB and LB+G. Finally, we took the logical union of such populations. Using this method a set of 503 genes were taken into account for subsequent analysis. As in our previous work, concerning differentially expressed genes of E. coli [13], the terms “”induced”" and “”repressed”" were used in this work to indicate increased or decreased transcript levels, respectively. These terms do not imply a particular mechanism for gene regulation.