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Comparative analysis of cis-regulation following stroke and seizures in subspaces of conserved eigensystems.

Dabrowski M, Dojer N, Zawadzka M, Mieczkowski J, Kaminska B - BMC Syst Biol (2010)

Bottom Line: It is often desirable to separate effects of different regulators on gene expression, or to identify effects of the same regulator across several systems.We identified a novel antagonistic effect of the motif recognized by the nuclear matrix attachment region-binding protein Satb1 on AP1-driven transcriptional activation, suggesting a link between chromatin loop structure and gene activation by AP1.The effects of motifs binding Satb1 and Creb on gene expression in brain conform to the assumption of the linear response model of gene regulation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Transcription Regulation, Department of Cell Biology, Nencki Institute, Pasteura 3, 02-093 Warsaw, Poland. m.dabrowski@nencki.gov.pl

ABSTRACT

Background: It is often desirable to separate effects of different regulators on gene expression, or to identify effects of the same regulator across several systems. Here, we focus on the rat brain following stroke or seizures, and demonstrate how the two tasks can be approached simultaneously.

Results: We applied SVD to time-series gene expression datasets from the rat experimental models of stroke and seizures. We demonstrate conservation of two eigensystems, reflecting inflammation and/or apoptosis (eigensystem 2) and neuronal synaptic activity (eigensystem 3), between the stroke and seizures. We analyzed cis-regulation of gene expression in the subspaces of the conserved eigensystems. Bayesian networks analysis was performed separately for either experimental model, with cross-system validation of the highest-ranking features. In this way, we correctly re-discovered the role of AP1 in the regulation of apoptosis, and the involvement of Creb and Egr in the regulation of synaptic activity-related genes. We identified a novel antagonistic effect of the motif recognized by the nuclear matrix attachment region-binding protein Satb1 on AP1-driven transcriptional activation, suggesting a link between chromatin loop structure and gene activation by AP1. The effects of motifs binding Satb1 and Creb on gene expression in brain conform to the assumption of the linear response model of gene regulation. Our data also suggest that numerous enhancers of neuronal-specific genes are important for their responsiveness to the synaptic activity.

Conclusion: Eigensystems conserved between stroke and seizures separate effects of inflammation/apoptosis and neuronal synaptic activity, exerted by different transcription factors, on gene expression in rat brain.

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Bayesian network model of fragmented cis-regulatory regions (A, C) Sequence preprocessing consists of extracting instances of composite motifs i.e. sets of (up to three motifs) in the same conserved non-coding sequence (CNS), from the flanks of transcription start sites of all human-rat orthologous genes. (B, D) Expression data preprocessing consists of SVD, followed by discretization of expression into up- and down-regulation in the subspace of a particular conserved eigensystem - based on the sign of its loading. (C, D) Composite motifs and expression data are combined in one dataset, in which the data records correspond to genes. (E) This dataset becomes an input for our Bayesian networks (BN) learning algorithm, which identifies sets of composite motifs most associated with the sign of loadings of a given eigensystem. (F) The final output consists of a ranking of such sets, with conditional probability distributions representing their impact on a given eigensystem.BN learning was performed independently for each of the eigensystems: A2, A3, M2, M3; on the data for all the genes in the respective dataset. Eigensystem A3 is shown as an example.
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Figure 4: Bayesian network model of fragmented cis-regulatory regions (A, C) Sequence preprocessing consists of extracting instances of composite motifs i.e. sets of (up to three motifs) in the same conserved non-coding sequence (CNS), from the flanks of transcription start sites of all human-rat orthologous genes. (B, D) Expression data preprocessing consists of SVD, followed by discretization of expression into up- and down-regulation in the subspace of a particular conserved eigensystem - based on the sign of its loading. (C, D) Composite motifs and expression data are combined in one dataset, in which the data records correspond to genes. (E) This dataset becomes an input for our Bayesian networks (BN) learning algorithm, which identifies sets of composite motifs most associated with the sign of loadings of a given eigensystem. (F) The final output consists of a ranking of such sets, with conditional probability distributions representing their impact on a given eigensystem.BN learning was performed independently for each of the eigensystems: A2, A3, M2, M3; on the data for all the genes in the respective dataset. Eigensystem A3 is shown as an example.

Mentions: • Regulation of gene expression is analysed separately for each conserved eigensystem. In the subspace of a given eigensystem gene expression is binarized into up- and down-regulation, according to the sign of its loading. (Figure 4B, D).


Comparative analysis of cis-regulation following stroke and seizures in subspaces of conserved eigensystems.

Dabrowski M, Dojer N, Zawadzka M, Mieczkowski J, Kaminska B - BMC Syst Biol (2010)

Bayesian network model of fragmented cis-regulatory regions (A, C) Sequence preprocessing consists of extracting instances of composite motifs i.e. sets of (up to three motifs) in the same conserved non-coding sequence (CNS), from the flanks of transcription start sites of all human-rat orthologous genes. (B, D) Expression data preprocessing consists of SVD, followed by discretization of expression into up- and down-regulation in the subspace of a particular conserved eigensystem - based on the sign of its loading. (C, D) Composite motifs and expression data are combined in one dataset, in which the data records correspond to genes. (E) This dataset becomes an input for our Bayesian networks (BN) learning algorithm, which identifies sets of composite motifs most associated with the sign of loadings of a given eigensystem. (F) The final output consists of a ranking of such sets, with conditional probability distributions representing their impact on a given eigensystem.BN learning was performed independently for each of the eigensystems: A2, A3, M2, M3; on the data for all the genes in the respective dataset. Eigensystem A3 is shown as an example.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2902439&req=5

Figure 4: Bayesian network model of fragmented cis-regulatory regions (A, C) Sequence preprocessing consists of extracting instances of composite motifs i.e. sets of (up to three motifs) in the same conserved non-coding sequence (CNS), from the flanks of transcription start sites of all human-rat orthologous genes. (B, D) Expression data preprocessing consists of SVD, followed by discretization of expression into up- and down-regulation in the subspace of a particular conserved eigensystem - based on the sign of its loading. (C, D) Composite motifs and expression data are combined in one dataset, in which the data records correspond to genes. (E) This dataset becomes an input for our Bayesian networks (BN) learning algorithm, which identifies sets of composite motifs most associated with the sign of loadings of a given eigensystem. (F) The final output consists of a ranking of such sets, with conditional probability distributions representing their impact on a given eigensystem.BN learning was performed independently for each of the eigensystems: A2, A3, M2, M3; on the data for all the genes in the respective dataset. Eigensystem A3 is shown as an example.
Mentions: • Regulation of gene expression is analysed separately for each conserved eigensystem. In the subspace of a given eigensystem gene expression is binarized into up- and down-regulation, according to the sign of its loading. (Figure 4B, D).

Bottom Line: It is often desirable to separate effects of different regulators on gene expression, or to identify effects of the same regulator across several systems.We identified a novel antagonistic effect of the motif recognized by the nuclear matrix attachment region-binding protein Satb1 on AP1-driven transcriptional activation, suggesting a link between chromatin loop structure and gene activation by AP1.The effects of motifs binding Satb1 and Creb on gene expression in brain conform to the assumption of the linear response model of gene regulation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Transcription Regulation, Department of Cell Biology, Nencki Institute, Pasteura 3, 02-093 Warsaw, Poland. m.dabrowski@nencki.gov.pl

ABSTRACT

Background: It is often desirable to separate effects of different regulators on gene expression, or to identify effects of the same regulator across several systems. Here, we focus on the rat brain following stroke or seizures, and demonstrate how the two tasks can be approached simultaneously.

Results: We applied SVD to time-series gene expression datasets from the rat experimental models of stroke and seizures. We demonstrate conservation of two eigensystems, reflecting inflammation and/or apoptosis (eigensystem 2) and neuronal synaptic activity (eigensystem 3), between the stroke and seizures. We analyzed cis-regulation of gene expression in the subspaces of the conserved eigensystems. Bayesian networks analysis was performed separately for either experimental model, with cross-system validation of the highest-ranking features. In this way, we correctly re-discovered the role of AP1 in the regulation of apoptosis, and the involvement of Creb and Egr in the regulation of synaptic activity-related genes. We identified a novel antagonistic effect of the motif recognized by the nuclear matrix attachment region-binding protein Satb1 on AP1-driven transcriptional activation, suggesting a link between chromatin loop structure and gene activation by AP1. The effects of motifs binding Satb1 and Creb on gene expression in brain conform to the assumption of the linear response model of gene regulation. Our data also suggest that numerous enhancers of neuronal-specific genes are important for their responsiveness to the synaptic activity.

Conclusion: Eigensystems conserved between stroke and seizures separate effects of inflammation/apoptosis and neuronal synaptic activity, exerted by different transcription factors, on gene expression in rat brain.

Show MeSH
Related in: MedlinePlus