E 55 TFs could potentially regulate different pathways like stress, oxidative stress, inflammation, coagulation, cell adhesion, obesity and renal function. Furthermore we found that these 443 TFs belong to 124 different families and the core 55 (Table S1) were representatives of 23 TF families.Bioinformatics AnalysisAs shown in the figure 1, promoter analysis to find the core TFs for the 31 biomarkers belonging to different pathways was carried out on MatInspector [24] program from Genomatix software suite. The TFs which had experimental evidence of more than one were chosen for further analysis. The number of binding sites for each TF was noted and the total TF binding sites for each TF family were calculated. This was considered proportional to the number of times the TF binds to the DNA. An entire profile of the Fruquintinib biological activity significant TF binding pattern was generated and was represented by a heat map using Matrix2png [25]. Each TF was assigned to the family they belong and the common TFs (-)-Indolactam V regulating the different pathways were identified using VENNY [26].TheTranscription Factor Expression Modulation in CAD Associated PathwaysThe 443 TFs identified were predicted using Genomatix software. However, it is important to know how many of these predicted TFs were really expressed in the blood samples of CAD affected subjects and controls. Therefore microarray gene expression data was used and was found that only 34 TFs were actually differentially expressed with a significant p-values (p value .0.05) (Table S2) (figure 
2b). The differential expression of theseFigure 1. Methodology for regulome and network module analysis. doi:10.1371/journal.pone.0057193.gTranscriptional Regulation Coronary Artery DiseaseFigure 2. Identification of core transcription factors. a. Venn-diagram of the 443 Transcription factors regulating the pathways in CVD and identification of 55 core transcription factors. b. Mean expression levels of significant transcription factors obtained from microarray between cases and controls. c. Number of binding sites of 34 expressed transcription factors in the biomarkers from 7 
different pathways. d. Venn-diagram of the 34 significant Transcription factors regulating the pathways in CVD and identification of 5 core transcription factors as PPARG, EGR-1, ETV-1, KLF-7, and ESRRA. doi:10.1371/journal.pone.0057193.gTFs might lead to influence on the expression pattern of the biomarkers, thus modifying the pathways.Core Transcription Factors and Regulatory Influence on PathwaysThe number of binding sites for individual TFs in each of the biomarker promoters may influence the level of the expression of the biomarkers. Therefore, we analyzed the number of the binding sites of 34 expressed TFs in the promoter sequences of the biomarkers (figure 2c and Table S3). 5 TFs were found to be binding to all the biomarkers in 7 different pathways (figure 2d). Of the 34 TFs which were found to be expressed in the blood tissue of CAD affected patients and control subjects, the 5 major TFs which seem to regulate the majority of biomarkers associated with CAD have been identified as PPARG, EGR1, ETV1, KLF-7 and ESRRA. We found that Peroxisome proliferator-activated receptor gamma (PPARG) has the highest number of binding sites in majority of the biomarkers with 76 binding sites in total. PPARG seems to regulate cell adhesion (7 binding sites in promoter ofClusterin and 1 in p-selectin), coagulation (7 binding sites in fibrinogen B, 5 in PAI1, 1 in plasminogen.E 55 TFs could potentially regulate different pathways like stress, oxidative stress, inflammation, coagulation, cell adhesion, obesity and renal function. Furthermore we found that these 443 TFs belong to 124 different families and the core 55 (Table S1) were representatives of 23 TF families.Bioinformatics AnalysisAs shown in the figure 1, promoter analysis to find the core TFs for the 31 biomarkers belonging to different pathways was carried out on MatInspector [24] program from Genomatix software suite. The TFs which had experimental evidence of more than one were chosen for further analysis. The number of binding sites for each TF was noted and the total TF binding sites for each TF family were calculated. This was considered proportional to the number of times the TF binds to the DNA. An entire profile of the significant TF binding pattern was generated and was represented by a heat map using Matrix2png [25]. Each TF was assigned to the family they belong and the common TFs regulating the different pathways were identified using VENNY [26].TheTranscription Factor Expression Modulation in CAD Associated PathwaysThe 443 TFs identified were predicted using Genomatix software. However, it is important to know how many of these predicted TFs were really expressed in the blood samples of CAD affected subjects and controls. Therefore microarray gene expression data was used and was found that only 34 TFs were actually differentially expressed with a significant p-values (p value .0.05) (Table S2) (figure 2b). The differential expression of theseFigure 1. Methodology for regulome and network module analysis. doi:10.1371/journal.pone.0057193.gTranscriptional Regulation Coronary Artery DiseaseFigure 2. Identification of core transcription factors. a. Venn-diagram of the 443 Transcription factors regulating the pathways in CVD and identification of 55 core transcription factors. b. Mean expression levels of significant transcription factors obtained from microarray between cases and controls. c. Number of binding sites of 34 expressed transcription factors in the biomarkers from 7 different pathways. d. Venn-diagram of the 34 significant Transcription factors regulating the pathways in CVD and identification of 5 core transcription factors as PPARG, EGR-1, ETV-1, KLF-7, and ESRRA. doi:10.1371/journal.pone.0057193.gTFs might lead to influence on the expression pattern of the biomarkers, thus modifying the pathways.Core Transcription Factors and Regulatory Influence on PathwaysThe number of binding sites for individual TFs in each of the biomarker promoters may influence the level of the expression of the biomarkers. Therefore, we analyzed the number of the binding sites of 34 expressed TFs in the promoter sequences of the biomarkers (figure 2c and Table S3). 5 TFs were found to be binding to all the biomarkers in 7 different pathways (figure 2d). Of the 34 TFs which were found to be expressed in the blood tissue of CAD affected patients and control subjects, the 5 major TFs which seem to regulate the majority of biomarkers associated with CAD have been identified as PPARG, EGR1, ETV1, KLF-7 and ESRRA. We found that Peroxisome proliferator-activated receptor gamma (PPARG) has the highest number of binding sites in majority of the biomarkers with 76 binding sites in total. PPARG seems to regulate cell adhesion (7 binding sites in promoter ofClusterin and 1 in p-selectin), coagulation (7 binding sites in fibrinogen B, 5 in PAI1, 1 in plasminogen.
