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Integrative bioinformatics analysis of genomic and proteomic approaches to understand the transcriptional regulatory program in coronary artery disease pathways.

Vangala RK, Ravindran V, Ghatge M, Shanker J, Arvind P, Bindu H, Shekar M, Rao VS - PLoS ONE (2013)

Bottom Line: Our present work focuses on understanding the regulatory mechanisms at transcriptional level by identifying the core and specific transcription factors that regulate the coronary artery disease associated pathways.These network modules in turn comprise of biomarkers from different pathways showing that the core regulatory transcription factors may work together in differential regulation of several pathways potentially leading to the disease.This kind of analysis can enhance the elucidation of mechanisms in the disease and give better strategies of developing multimarker module based risk predictions.

View Article: PubMed Central - PubMed

Affiliation: Tata Proteomics and Coagulation Department, Thrombosis Research Institute, Bangalore, Karnataka, India. rajani@triindia.org.in

ABSTRACT
Patients with cardiovascular disease show a panel of differentially regulated serum biomarkers indicative of modulation of several pathways from disease onset to progression. Few of these biomarkers have been proposed for multimarker risk prediction methods. However, the underlying mechanism of the expression changes and modulation of the pathways is not yet addressed in entirety. Our present work focuses on understanding the regulatory mechanisms at transcriptional level by identifying the core and specific transcription factors that regulate the coronary artery disease associated pathways. Using the principles of systems biology we integrated the genomics and proteomics data with computational tools. We selected biomarkers from 7 different pathways based on their association with the disease and assayed 24 biomarkers along with gene expression studies and built network modules which are highly regulated by 5 core regulators PPARG, EGR1, ETV1, KLF7 and ESRRA. These network modules in turn comprise of biomarkers from different pathways showing that the core regulatory transcription factors may work together in differential regulation of several pathways potentially leading to the disease. This kind of analysis can enhance the elucidation of mechanisms in the disease and give better strategies of developing multimarker module based risk predictions.

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Methodology for regulome and network module analysis.
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pone-0057193-g001: Methodology for regulome and network module analysis.

Mentions: As 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].The network between the significant TFs and the biomarkers was built on STRING [27].


Integrative bioinformatics analysis of genomic and proteomic approaches to understand the transcriptional regulatory program in coronary artery disease pathways.

Vangala RK, Ravindran V, Ghatge M, Shanker J, Arvind P, Bindu H, Shekar M, Rao VS - PLoS ONE (2013)

Methodology for regulome and network module analysis.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585295&req=5

pone-0057193-g001: Methodology for regulome and network module analysis.
Mentions: As 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].The network between the significant TFs and the biomarkers was built on STRING [27].

Bottom Line: Our present work focuses on understanding the regulatory mechanisms at transcriptional level by identifying the core and specific transcription factors that regulate the coronary artery disease associated pathways.These network modules in turn comprise of biomarkers from different pathways showing that the core regulatory transcription factors may work together in differential regulation of several pathways potentially leading to the disease.This kind of analysis can enhance the elucidation of mechanisms in the disease and give better strategies of developing multimarker module based risk predictions.

View Article: PubMed Central - PubMed

Affiliation: Tata Proteomics and Coagulation Department, Thrombosis Research Institute, Bangalore, Karnataka, India. rajani@triindia.org.in

ABSTRACT
Patients with cardiovascular disease show a panel of differentially regulated serum biomarkers indicative of modulation of several pathways from disease onset to progression. Few of these biomarkers have been proposed for multimarker risk prediction methods. However, the underlying mechanism of the expression changes and modulation of the pathways is not yet addressed in entirety. Our present work focuses on understanding the regulatory mechanisms at transcriptional level by identifying the core and specific transcription factors that regulate the coronary artery disease associated pathways. Using the principles of systems biology we integrated the genomics and proteomics data with computational tools. We selected biomarkers from 7 different pathways based on their association with the disease and assayed 24 biomarkers along with gene expression studies and built network modules which are highly regulated by 5 core regulators PPARG, EGR1, ETV1, KLF7 and ESRRA. These network modules in turn comprise of biomarkers from different pathways showing that the core regulatory transcription factors may work together in differential regulation of several pathways potentially leading to the disease. This kind of analysis can enhance the elucidation of mechanisms in the disease and give better strategies of developing multimarker module based risk predictions.

Show MeSH
Related in: MedlinePlus