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Trimming of mammalian transcriptional networks using network component analysis.

Tran LM, Hyduke DR, Liao JC - BMC Bioinformatics (2010)

Bottom Line: Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data.Our method retained 1074 edges (i.e. 75% of the original edge number) and identified 17 TFs as being significantly perturbed under the experimental condition.Identifying the important TF-gene interactions is crucial for understanding the roles of pleiotropic global regulators, such as p53.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA.

ABSTRACT

Background: Network Component Analysis (NCA) has been used to deduce the activities of transcription factors (TFs) from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies.

Results: We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number) and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network.

Conclusions: The advantage of our new algorithm, relative to the original NCA, is that our algorithm can identify the important TF-gene interactions. Identifying the important TF-gene interactions is crucial for understanding the roles of pleiotropic global regulators, such as p53. Also, our algorithm has been developed to overcome NCA's inability to analyze large networks where multiple TFs regulate a single gene. Thus, our algorithm extends the applicability of NCA to the realm of mammalian regulatory network analysis.

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Flow diagram illustrating the modified forward stepwise regression method as applied to a gene (gx) which has three candidate regulators (TFi, TFj, TFn).
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Figure 2: Flow diagram illustrating the modified forward stepwise regression method as applied to a gene (gx) which has three candidate regulators (TFi, TFj, TFn).

Mentions: Edge-trimming of the network is accomplished using the forward stepwise regression method (Figure 2). The expression of individual genes is modeled in Eq. (1) as:


Trimming of mammalian transcriptional networks using network component analysis.

Tran LM, Hyduke DR, Liao JC - BMC Bioinformatics (2010)

Flow diagram illustrating the modified forward stepwise regression method as applied to a gene (gx) which has three candidate regulators (TFi, TFj, TFn).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2967563&req=5

Figure 2: Flow diagram illustrating the modified forward stepwise regression method as applied to a gene (gx) which has three candidate regulators (TFi, TFj, TFn).
Mentions: Edge-trimming of the network is accomplished using the forward stepwise regression method (Figure 2). The expression of individual genes is modeled in Eq. (1) as:

Bottom Line: Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data.Our method retained 1074 edges (i.e. 75% of the original edge number) and identified 17 TFs as being significantly perturbed under the experimental condition.Identifying the important TF-gene interactions is crucial for understanding the roles of pleiotropic global regulators, such as p53.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA.

ABSTRACT

Background: Network Component Analysis (NCA) has been used to deduce the activities of transcription factors (TFs) from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies.

Results: We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number) and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network.

Conclusions: The advantage of our new algorithm, relative to the original NCA, is that our algorithm can identify the important TF-gene interactions. Identifying the important TF-gene interactions is crucial for understanding the roles of pleiotropic global regulators, such as p53. Also, our algorithm has been developed to overcome NCA's inability to analyze large networks where multiple TFs regulate a single gene. Thus, our algorithm extends the applicability of NCA to the realm of mammalian regulatory network analysis.

Show MeSH
Related in: MedlinePlus