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Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.

Gong W, Koyano-Nakagawa N, Li T, Garry DJ - BMC Bioinformatics (2015)

Bottom Line: Our method not only infers the time-varying networks between different stages of heart development, but it also identifies the TF binding sites associated with promoter or enhancers of downstream genes.Our model also predicted the key regulatory networks for the ESC-MES, MES-CP and CP-CM transitions.This method will allow one to rapidly determine the cis-modules that regulate key genes during cardiac differentiation.

View Article: PubMed Central - PubMed

Affiliation: Lillehei Heart Institute, University of Minnesota, 2231 6th St S.E, 4-165 CCRB, Minneapolis, MN, 55114, USA. gongx030@umn.edu.

ABSTRACT

Background: Decoding the temporal control of gene expression patterns is key to the understanding of the complex mechanisms that govern developmental decisions during heart development. High-throughput methods have been employed to systematically study the dynamic and coordinated nature of cardiac differentiation at the global level with multiple dimensions. Therefore, there is a pressing need to develop a systems approach to integrate these data from individual studies and infer the dynamic regulatory networks in an unbiased fashion.

Results: We developed a two-step strategy to integrate data from (1) temporal RNA-seq, (2) temporal histone modification ChIP-seq, (3) transcription factor (TF) ChIP-seq and (4) gene perturbation experiments to reconstruct the dynamic network during heart development. First, we trained a logistic regression model to predict the probability (LR score) of any base being bound by 543 TFs with known positional weight matrices. Second, four dimensions of data were combined using a time-varying dynamic Bayesian network model to infer the dynamic networks at four developmental stages in the mouse [mouse embryonic stem cells (ESCs), mesoderm (MES), cardiac progenitors (CP) and cardiomyocytes (CM)]. Our method not only infers the time-varying networks between different stages of heart development, but it also identifies the TF binding sites associated with promoter or enhancers of downstream genes. The LR scores of experimentally verified ESCs and heart enhancers were significantly higher than random regions (p <10(-100)), suggesting that a high LR score is a reliable indicator for functional TF binding sites. Our network inference model identified a region with an elevated LR score approximately -9400 bp upstream of the transcriptional start site of Nkx2-5, which overlapped with a previously reported enhancer region (-9435 to -8922 bp). TFs such as Tead1, Gata4, Msx2, and Tgif1 were predicted to bind to this region and participate in the regulation of Nkx2-5 gene expression. Our model also predicted the key regulatory networks for the ESC-MES, MES-CP and CP-CM transitions.

Conclusion: We report a novel method to systematically integrate multi-dimensional -omics data and reconstruct the gene regulatory networks. This method will allow one to rapidly determine the cis-modules that regulate key genes during cardiac differentiation.

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Inferring dynamic networks during cardiac differentiation by a time-varying dynamic Bayesian network model. (A) Number of inferred gene-gene regulatory relationships in ESC-MES, MES-CPs, and CPs-CMs transitions. (B) Number of predicted transcription factor binding sites that are overlapped with known ESC and heart enhancers on genes that have enhancers within their 2 kb, 10 kb or 40 kb regions surrounding the transcriptional start sites. (C) Predicted up- or down-regulated genes on computationally inducing Pou5f1 five-fold in ESCs compared with known up- or down-regulated genes on experimentally inducing Pou5f1. p-values were determined using Fisher's exact test.
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Fig4: Inferring dynamic networks during cardiac differentiation by a time-varying dynamic Bayesian network model. (A) Number of inferred gene-gene regulatory relationships in ESC-MES, MES-CPs, and CPs-CMs transitions. (B) Number of predicted transcription factor binding sites that are overlapped with known ESC and heart enhancers on genes that have enhancers within their 2 kb, 10 kb or 40 kb regions surrounding the transcriptional start sites. (C) Predicted up- or down-regulated genes on computationally inducing Pou5f1 five-fold in ESCs compared with known up- or down-regulated genes on experimentally inducing Pou5f1. p-values were determined using Fisher's exact test.

Mentions: A total of 17,432, 15,491 and 14,339 positive edges (ui,t >0, see Methods) were predicted for the ESC-MES, MES-CP and CP-CM transitions, respectively (Figure 4A). The common links between ESC-MES and MES-CP, and between MES-CP and CP-CM, represented 10.6% and 12.6% of the total number of discovered edges in the corresponding time points. The number of common links between ESC-MES and MES-CP (3,748) and between MES-CP and CP-CM (4,139) were significantly higher than the common ones between ESC-MES and CP-CM (590), suggesting that the common links were captured between adjacent networks.Figure 4


Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.

Gong W, Koyano-Nakagawa N, Li T, Garry DJ - BMC Bioinformatics (2015)

Inferring dynamic networks during cardiac differentiation by a time-varying dynamic Bayesian network model. (A) Number of inferred gene-gene regulatory relationships in ESC-MES, MES-CPs, and CPs-CMs transitions. (B) Number of predicted transcription factor binding sites that are overlapped with known ESC and heart enhancers on genes that have enhancers within their 2 kb, 10 kb or 40 kb regions surrounding the transcriptional start sites. (C) Predicted up- or down-regulated genes on computationally inducing Pou5f1 five-fold in ESCs compared with known up- or down-regulated genes on experimentally inducing Pou5f1. p-values were determined using Fisher's exact test.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4359553&req=5

Fig4: Inferring dynamic networks during cardiac differentiation by a time-varying dynamic Bayesian network model. (A) Number of inferred gene-gene regulatory relationships in ESC-MES, MES-CPs, and CPs-CMs transitions. (B) Number of predicted transcription factor binding sites that are overlapped with known ESC and heart enhancers on genes that have enhancers within their 2 kb, 10 kb or 40 kb regions surrounding the transcriptional start sites. (C) Predicted up- or down-regulated genes on computationally inducing Pou5f1 five-fold in ESCs compared with known up- or down-regulated genes on experimentally inducing Pou5f1. p-values were determined using Fisher's exact test.
Mentions: A total of 17,432, 15,491 and 14,339 positive edges (ui,t >0, see Methods) were predicted for the ESC-MES, MES-CP and CP-CM transitions, respectively (Figure 4A). The common links between ESC-MES and MES-CP, and between MES-CP and CP-CM, represented 10.6% and 12.6% of the total number of discovered edges in the corresponding time points. The number of common links between ESC-MES and MES-CP (3,748) and between MES-CP and CP-CM (4,139) were significantly higher than the common ones between ESC-MES and CP-CM (590), suggesting that the common links were captured between adjacent networks.Figure 4

Bottom Line: Our method not only infers the time-varying networks between different stages of heart development, but it also identifies the TF binding sites associated with promoter or enhancers of downstream genes.Our model also predicted the key regulatory networks for the ESC-MES, MES-CP and CP-CM transitions.This method will allow one to rapidly determine the cis-modules that regulate key genes during cardiac differentiation.

View Article: PubMed Central - PubMed

Affiliation: Lillehei Heart Institute, University of Minnesota, 2231 6th St S.E, 4-165 CCRB, Minneapolis, MN, 55114, USA. gongx030@umn.edu.

ABSTRACT

Background: Decoding the temporal control of gene expression patterns is key to the understanding of the complex mechanisms that govern developmental decisions during heart development. High-throughput methods have been employed to systematically study the dynamic and coordinated nature of cardiac differentiation at the global level with multiple dimensions. Therefore, there is a pressing need to develop a systems approach to integrate these data from individual studies and infer the dynamic regulatory networks in an unbiased fashion.

Results: We developed a two-step strategy to integrate data from (1) temporal RNA-seq, (2) temporal histone modification ChIP-seq, (3) transcription factor (TF) ChIP-seq and (4) gene perturbation experiments to reconstruct the dynamic network during heart development. First, we trained a logistic regression model to predict the probability (LR score) of any base being bound by 543 TFs with known positional weight matrices. Second, four dimensions of data were combined using a time-varying dynamic Bayesian network model to infer the dynamic networks at four developmental stages in the mouse [mouse embryonic stem cells (ESCs), mesoderm (MES), cardiac progenitors (CP) and cardiomyocytes (CM)]. Our method not only infers the time-varying networks between different stages of heart development, but it also identifies the TF binding sites associated with promoter or enhancers of downstream genes. The LR scores of experimentally verified ESCs and heart enhancers were significantly higher than random regions (p <10(-100)), suggesting that a high LR score is a reliable indicator for functional TF binding sites. Our network inference model identified a region with an elevated LR score approximately -9400 bp upstream of the transcriptional start site of Nkx2-5, which overlapped with a previously reported enhancer region (-9435 to -8922 bp). TFs such as Tead1, Gata4, Msx2, and Tgif1 were predicted to bind to this region and participate in the regulation of Nkx2-5 gene expression. Our model also predicted the key regulatory networks for the ESC-MES, MES-CP and CP-CM transitions.

Conclusion: We report a novel method to systematically integrate multi-dimensional -omics data and reconstruct the gene regulatory networks. This method will allow one to rapidly determine the cis-modules that regulate key genes during cardiac differentiation.

Show MeSH
Related in: MedlinePlus