Complex diseases have genes which interact and work cooperatively but till date how they associate with diseases is not completely understood. The interactive network of TFs (figure 4) and the 
multiple binding sites for these TFs in different pathway representative biomarker promoters suggests that the regulatory networks work together collaboratively. These collaborative regulome may thus lead to important expression changes of biomarkers (figure 3a and b) in turn associating with CAD. The biomarker expression and interaction is needed as the next step to regulation for onset of the disease. These interactomes (figure 4) of biomarkers might work together in specific modular architecture and in our data we see that cell adhesion pathway molecules (clusterin and P-selectin) form a cluster with biomarkers from oxidative stress (MPO), stress (HSP27), coagulation (PAI1) and obesity (leptin) based on the nodes joining these molecules (figure 4). These biomarkers from different pathways may be working 23727046 in coordination with each other in the early phase of the disease thus forming the risk module for CAD. Similar modular architecture can be found with IL6 associating with oxidative stress (MPO), coagulation (PAI1 or SERPENE1, Factor 3, Fibrinogen beta, stress (HSP27 or HSPB1, HSPD1), cell adhesion (P-selectin) and obesity (Adiponectin and Leptin). Recent published studies also suggest that similar risk modules can exist and interact with neighbors in a collaborative way leading to dysfunction of series of biological processes [40]. In our study the risk modules have biomarkers from different pathways and are not limited to specific pathways. The relationships between the modules might be more with respectto disease but may not with specific pathways to which the biomarkers belong. Therefore our data suggests that biomarkers from different pathways are differentially regulated by combination of core and specific TFs and their interaction may lead to differential expression in the disease condition. Also as seen in our data the disease genes associate through a prescribed communication protocols, like regulome, expression and interactome in shifting the equilibrium in CAD.Supporting InformationTable S55 predicted core transcription factors belonging to 23 families. (DOC)Table S2 Expression levels of significant 10457188 (p value .0.05) transcription factors between Cases and Controls. (Mean D). (DOC) Table S3 Genomatix output for 34 TFs showing the 76932-56-4 web frequency of binding sites for each TF on the promoters of different biomarkers from 7 different pathways. (DOC)AcknowledgmentsThe authors would like to thank the Chairman of Thrombosis Research Institute Prof. Vijay V Kakkar and also the faculty for their kind support.Author ContributionsContributed in designing and performing microarray experiments: JS. Conceived and designed the experiments: RKV VR. Performed the experiments: RKV MG PA HB MS VSR. Analyzed the data: RKV VR. Contributed reagents/materials/analysis tools: RKV VR. Wrote the paper: RKV.
The use of bioethanol as alternative fuel has drawn greater attention than ever due to recent energy crisis and environmental concerns [1], and production of ethanol from microbial fermentation is of practical value in replacing fossil fuel utilization. Different microorganisms, including yeast [2,3], Zymomonas mobilis [4,5] and E. coli [6,7] have been PHCCC chemical information engineered for selective production of ethanol. The highest reported ethanol yield attained through E. coli 
xylos.Complex diseases have genes which interact and work cooperatively but till date how they associate with diseases is not completely understood. The interactive network of TFs (figure 4) and the multiple binding sites for these TFs in different pathway representative biomarker promoters suggests that the regulatory networks work together collaboratively. These collaborative regulome may thus lead to important expression changes of biomarkers (figure 3a and b) in turn associating with CAD. The biomarker expression and interaction is needed as the next step to regulation for onset of the disease. These interactomes (figure 4) of biomarkers might work together in specific modular architecture and in our data we see that cell adhesion pathway molecules (clusterin and P-selectin) form a cluster with biomarkers from oxidative stress (MPO), stress (HSP27), coagulation (PAI1) and obesity (leptin) based on the nodes joining these molecules (figure 4). These biomarkers from different pathways may be working 23727046 in coordination with each other in the early phase of the disease thus forming the risk module for CAD. Similar modular architecture can be found with IL6 associating with oxidative stress (MPO), coagulation (PAI1 or SERPENE1, Factor 3, Fibrinogen beta, stress (HSP27 or HSPB1, HSPD1), cell adhesion (P-selectin) and obesity (Adiponectin and Leptin). Recent published studies also suggest that similar risk modules can exist and interact with neighbors in a collaborative way leading to dysfunction of series of biological processes [40]. In our study the risk modules have biomarkers from different pathways and are not limited to specific pathways. The relationships between the modules might be more with respectto disease but may not with specific pathways to which the biomarkers belong. Therefore our data suggests that biomarkers from different pathways are differentially regulated by combination of core and specific TFs and their interaction may lead to differential expression in the disease condition. Also as seen in our data the disease genes associate through a prescribed communication protocols, like regulome, expression and interactome in shifting the equilibrium in CAD.Supporting InformationTable S55 predicted core transcription factors belonging to 23 families. (DOC)Table S2 Expression levels of significant 10457188 (p value .0.05) transcription factors between Cases and Controls. (Mean D). (DOC) Table S3 Genomatix output for 34 TFs showing the frequency of binding sites for each TF on the promoters of different biomarkers from 7 different pathways. (DOC)AcknowledgmentsThe authors would like to thank the Chairman of Thrombosis Research Institute Prof. Vijay V Kakkar and also the faculty for their kind support.Author ContributionsContributed in designing and performing microarray experiments: JS. Conceived and designed the experiments: RKV VR. Performed the experiments: RKV MG PA HB MS VSR. Analyzed the data: RKV VR. Contributed reagents/materials/analysis tools: RKV VR. Wrote the paper: RKV.
The use of bioethanol as alternative fuel has drawn greater attention than ever due to recent energy crisis and environmental concerns [1], and production of ethanol from microbial fermentation is of practical value in replacing fossil fuel utilization. Different microorganisms, including yeast [2,3], Zymomonas mobilis [4,5] and E. coli [6,7] have been engineered for selective production of ethanol. The highest reported ethanol yield attained through E. coli xylos.
