To validate the microarray results, quantitative RT-PCR (qRT-PCR) of selected genes was performed. Five of the genes were selected from the up-regulated group and the other five from the down-regulated group in the polyP-treated P. gingivalis cells. We used 16S rRNA as a reference gene for normalization of the qRT-PCR data. There was a high correlation
between the expression ratios determined by the microarray and the qRT-PCR (r = 0.926) (Figure 2). Figure 1 Differential gene expression in P. gingivalis W83 by polyP75 treatment. Differentially expressed genes with 1.5 fold change and P-value < 0.05 were plotted. X-axis presents fold difference between log2 expression of polyP75 treatment and no treatment, and y-axis shows the –log10 P -value. Up-regulated genes (over-expressed in polyP75 treatment) were represented as red color and down-regulated genes were colored in blue. Figure 2 Comparison SIS3 order of transcription measurements by microarray and qRT-PCR. The relative transcription levels for 10 genes are listed in Table 6. The qRT-PCR log2 values were plotted against the microarray data log2 values.
The Navitoclax concentration correlation coefficient (r) for comparison of the two datasets is 0. 92. To broadly characterize the differentially expressed gene (DEG, up- and down-regulated genes) set, GO category enrichment analysis was performed. This analysis identified distinct biological themes selleck chemicals associated with each group of the up-regulated and the down-regulated genes. The down-regulated genes were associated with GO terms
related to metabolic process (GO:0008152, P = 0.0004), pyridine nucleotide biosynthetic Thiamine-diphosphate kinase process (GO:0019363, P = 0.0012), regulation of cell shape (GO:0008360, P = 0.002), and polysaccharide biosynthetic process (GO:0000271, P = 0.0015). The up-regulated genes were associated with GO terms related to cellular iron ion homeostasis (GO:0006879, P < 0.0001), ribosome (GO:0005840, P = 0.0032), transposase activity (GO:0004803, P < 0.0001), and DNA binding (GO:0003677, P < 0.0001). Using 202 DEGs belonging to the above biological themes, we generated the protein-protein interaction network based on a database of known and predicted protein interactions. The network analysis identified 162 DEGs that have direct interaction with one another (Figure 3), and 5 biological meaningful clusters related to 1) iron/hemin acquisition, 2) energy metabolism and electron carriers, 3) cell envelope and cell division, 4) ribosome, and 5) transposon functions. Figure 3 Protein-protein interaction network of differentially expressed functional genes. The network was constructed based on the STRING database. Nodes (symbolized as circles and square) and edges (linking lines) represent DEGs and interactions among DEGs, respectively. Up-regulated genes were represented as a circular shape and down-regulated genes were presented as a square shape. Node color represents the functional annotation of each gene.