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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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Inferred dynamic regulatory networks of 94 genes during cardiac differentiation (ESCs-MES, MES-CPs and CPs-CMs). Expression levels for each gene were normalized to a mean of zero and a standard deviation of one.
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Fig5: Inferred dynamic regulatory networks of 94 genes during cardiac differentiation (ESCs-MES, MES-CPs and CPs-CMs). Expression levels for each gene were normalized to a mean of zero and a standard deviation of one.

Mentions: Figure 5 is a graphic representation of the positive links predicted in the dynamic regulatory networks involving a selected list of 93 key genes in cardiac differentiation. This representation clearly illustrates the changes of gene expression according to the differentiation states as well as the dynamic gene regulatory network involved in this process. Of note, pluripotency genes such as Pou5f1, Klf4 or Zfp281 had the greatest number of links to the predicted down-stream targets in ES to MES transition. The targets included early mesodermal genes such as T, Mesp1, Eomes, Kdr, as well as early lineage specific regulators, such as Etv2, FoxC2, Sox11, Sox18 (endothelial), Nkx2-5, Gata4, Gata6, Hand1, Hand2 and Tbx5 (cardiac), but did not include the cardiac structural genes. Of the identified targets, the early mesodermal genes peaked at the MES stage, however the lineage specific regulators peaked later at the CP stage, although the link was identified in the ES-MES transition. This likely reflects the changes in the histone modification patterns that precede gene activation [1]. In the MES-CP transition, many links from mesodermal genes to cardiac structural genes, as well as from lineage specific genes to cardiac structural genes were identified. In addition, links emanating from several hub genes such as Msx2, Egr1 and Yy1 were prominent. Although the functions of these factors in cardiac development is not well defined, this result suggests the involvement of these factors in the cardiogeneic process [2,59,61-63]. In the CP-CM transition, Tcf3, Egr1, Nkx2-5, Gata4, Srf, Smad3 and Meis2 are predicted to activate many highly expressed genes in cardiomyocytes. Interestingly, although the same target genes (cardiac structural genes) are activated in the MES-CP and CP-CM transitions, the group of activating genes changed from MES and CP, likely reflecting the changes in transcriptional regulatory machinery.Figure 5


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)

Inferred dynamic regulatory networks of 94 genes during cardiac differentiation (ESCs-MES, MES-CPs and CPs-CMs). Expression levels for each gene were normalized to a mean of zero and a standard deviation of one.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig5: Inferred dynamic regulatory networks of 94 genes during cardiac differentiation (ESCs-MES, MES-CPs and CPs-CMs). Expression levels for each gene were normalized to a mean of zero and a standard deviation of one.
Mentions: Figure 5 is a graphic representation of the positive links predicted in the dynamic regulatory networks involving a selected list of 93 key genes in cardiac differentiation. This representation clearly illustrates the changes of gene expression according to the differentiation states as well as the dynamic gene regulatory network involved in this process. Of note, pluripotency genes such as Pou5f1, Klf4 or Zfp281 had the greatest number of links to the predicted down-stream targets in ES to MES transition. The targets included early mesodermal genes such as T, Mesp1, Eomes, Kdr, as well as early lineage specific regulators, such as Etv2, FoxC2, Sox11, Sox18 (endothelial), Nkx2-5, Gata4, Gata6, Hand1, Hand2 and Tbx5 (cardiac), but did not include the cardiac structural genes. Of the identified targets, the early mesodermal genes peaked at the MES stage, however the lineage specific regulators peaked later at the CP stage, although the link was identified in the ES-MES transition. This likely reflects the changes in the histone modification patterns that precede gene activation [1]. In the MES-CP transition, many links from mesodermal genes to cardiac structural genes, as well as from lineage specific genes to cardiac structural genes were identified. In addition, links emanating from several hub genes such as Msx2, Egr1 and Yy1 were prominent. Although the functions of these factors in cardiac development is not well defined, this result suggests the involvement of these factors in the cardiogeneic process [2,59,61-63]. In the CP-CM transition, Tcf3, Egr1, Nkx2-5, Gata4, Srf, Smad3 and Meis2 are predicted to activate many highly expressed genes in cardiomyocytes. Interestingly, although the same target genes (cardiac structural genes) are activated in the MES-CP and CP-CM transitions, the group of activating genes changed from MES and CP, likely reflecting the changes in transcriptional regulatory machinery.Figure 5

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