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iRegulon: from a gene list to a gene regulatory network using large motif and track collections.

Janky R, Verfaillie A, Imrichová H, Van de Sande B, Standaert L, Christiaens V, Hulselmans G, Herten K, Naval Sanchez M, Potier D, Svetlichnyy D, Kalender Atak Z, Fiers M, Marine JC, Aerts S - PLoS Comput. Biol. (2014)

Bottom Line: Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology.Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data.Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.

ABSTRACT
Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying a co-expressed gene set using cis-regulatory sequence analysis. iRegulon implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets. We increase the accuracy of network inference by using very large motif collections of up to ten thousand position weight matrices collected from various species, and linking these to candidate human TFs via a motif2TF procedure. We validate iRegulon on gene sets derived from ENCODE ChIP-seq data with increasing levels of noise, and we compare iRegulon with existing motif discovery methods. Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data. In particular, we over-activate p53 in breast cancer cells, followed by RNA-seq and ChIP-seq, and could identify an extensive up-regulated network controlled directly by p53. Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY. Finally, we generalize our computational framework to include regulatory tracks such as ChIP-seq data and show how motif and track discovery can be combined to map functional regulatory interactions among co-expressed genes. iRegulon is available as a Cytoscape plugin from http://iregulon.aertslab.org.

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Validation of p53 target genes and target CRMs.A. Workflow to generate meta-regulons. Meta-regulons can be obtained directly via the iRegulon Cytoscape plugin. B. Direct targets of p53 in MCF-7 cells. All genes are significantly up-regulated by p53, are predicted as p53 targets by motif discovery in iRegulon and have a significant ChIP peak. In addition, genes in the grey shaded inner circle are part of the p53 meta-regulon, meaning that they are also found as p53 targets across cancer signatures. C. Four new p53 target genes are presented in detail. D. Relative mRNA expression levels of p53 target genes before (−) and 24 h after stimulation with 10 µM Nutlin-3a (N) or after 1 hour pulse of 5 µM Doxorubicin (D). Expression is shown relative to non-treated control and normalized to optimal reference genes for each cell type, assessed by GeNorm [130]. Error bars show standard error of the mean (SEM) of 3 replicates. E. Enhancer-reporter assays of four predicted p53 target CRMs, after transfection into MCF-7 cells before and after induction with Nutlin-3a (5 µM) in Wild Type and p53 Knock-down MCF-7 cells. Error bars represent SEM of 5 replicates. See also Figures S7–S8 and Tables S4,S6.
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pcbi-1003731-g005: Validation of p53 target genes and target CRMs.A. Workflow to generate meta-regulons. Meta-regulons can be obtained directly via the iRegulon Cytoscape plugin. B. Direct targets of p53 in MCF-7 cells. All genes are significantly up-regulated by p53, are predicted as p53 targets by motif discovery in iRegulon and have a significant ChIP peak. In addition, genes in the grey shaded inner circle are part of the p53 meta-regulon, meaning that they are also found as p53 targets across cancer signatures. C. Four new p53 target genes are presented in detail. D. Relative mRNA expression levels of p53 target genes before (−) and 24 h after stimulation with 10 µM Nutlin-3a (N) or after 1 hour pulse of 5 µM Doxorubicin (D). Expression is shown relative to non-treated control and normalized to optimal reference genes for each cell type, assessed by GeNorm [130]. Error bars show standard error of the mean (SEM) of 3 replicates. E. Enhancer-reporter assays of four predicted p53 target CRMs, after transfection into MCF-7 cells before and after induction with Nutlin-3a (5 µM) in Wild Type and p53 Knock-down MCF-7 cells. Error bars represent SEM of 5 replicates. See also Figures S7–S8 and Tables S4,S6.

Mentions: To explore the relevance of the newly identified p53 targets in other tumor types, we applied iRegulon in a meta-analysis to about twenty thousand cancer gene signatures, i.e. differentially expressed genes obtained from cancer specific experiments. We reasoned that those target genes that are recurrently predicted across cancer gene signatures, might contribute to the tumor suppressor role of p53. We used gene signatures from GeneSigDB [70], MSigDB [71] and from gene modules generated across 91 large cancer microarray data sets (see Materials and Methods and Fig. 5A). Out of 23172 signatures, p53 is found as regulator in 709 signatures. We merged the direct p53 targets across all these signatures into a network and weighted the edges according to the recurrence of this p53-target interaction across all signatures. Many previously known p53 targets and many ChIP-Seq derived targets are recovered using this analysis (GSEA NES = 3.01, FDR<0.001) (Fig. S7). Of the 110 predicted p53 targets in MCF-7 cells (as defined above), 44 are also predicted as p53 target in cancer gene signatures (grey area in Fig. 5B). These genes are predicted as p53 targets by iRegulon and show a significant ChIP peak and are represented in the p53 cancer-related meta-regulon. Amongst these 44 genes, 20 were previously indicated as well established p53 targets (genes in squares in Fig. 5B). When extending the analysis and including target genes recently reported in literature [58], [59], [68], it becomes clear that most overlap coincides within this metatargetome (34/44) (Table S6). Keeping in mind that many of the p53 targets reported by others were found using different cell lines, the enriched overlap within this metatargetome can be interpreted as a sign that these genes represent a core set targeted by p53 regardless of the cell type. Interestingly, when looking at targets like RAP2B, NHLH2, SLC12A4, and ALDH3A1, they could not have been identified through motif discovery in proximal promoters only, because the p53 binding sites are located either further upstream (∼1 kb for RAP2B and ∼5 kb for ALDH3A1) or in introns (NHLH2 and SLC12A4) (Fig. 5C).


iRegulon: from a gene list to a gene regulatory network using large motif and track collections.

Janky R, Verfaillie A, Imrichová H, Van de Sande B, Standaert L, Christiaens V, Hulselmans G, Herten K, Naval Sanchez M, Potier D, Svetlichnyy D, Kalender Atak Z, Fiers M, Marine JC, Aerts S - PLoS Comput. Biol. (2014)

Validation of p53 target genes and target CRMs.A. Workflow to generate meta-regulons. Meta-regulons can be obtained directly via the iRegulon Cytoscape plugin. B. Direct targets of p53 in MCF-7 cells. All genes are significantly up-regulated by p53, are predicted as p53 targets by motif discovery in iRegulon and have a significant ChIP peak. In addition, genes in the grey shaded inner circle are part of the p53 meta-regulon, meaning that they are also found as p53 targets across cancer signatures. C. Four new p53 target genes are presented in detail. D. Relative mRNA expression levels of p53 target genes before (−) and 24 h after stimulation with 10 µM Nutlin-3a (N) or after 1 hour pulse of 5 µM Doxorubicin (D). Expression is shown relative to non-treated control and normalized to optimal reference genes for each cell type, assessed by GeNorm [130]. Error bars show standard error of the mean (SEM) of 3 replicates. E. Enhancer-reporter assays of four predicted p53 target CRMs, after transfection into MCF-7 cells before and after induction with Nutlin-3a (5 µM) in Wild Type and p53 Knock-down MCF-7 cells. Error bars represent SEM of 5 replicates. See also Figures S7–S8 and Tables S4,S6.
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4109854&req=5

pcbi-1003731-g005: Validation of p53 target genes and target CRMs.A. Workflow to generate meta-regulons. Meta-regulons can be obtained directly via the iRegulon Cytoscape plugin. B. Direct targets of p53 in MCF-7 cells. All genes are significantly up-regulated by p53, are predicted as p53 targets by motif discovery in iRegulon and have a significant ChIP peak. In addition, genes in the grey shaded inner circle are part of the p53 meta-regulon, meaning that they are also found as p53 targets across cancer signatures. C. Four new p53 target genes are presented in detail. D. Relative mRNA expression levels of p53 target genes before (−) and 24 h after stimulation with 10 µM Nutlin-3a (N) or after 1 hour pulse of 5 µM Doxorubicin (D). Expression is shown relative to non-treated control and normalized to optimal reference genes for each cell type, assessed by GeNorm [130]. Error bars show standard error of the mean (SEM) of 3 replicates. E. Enhancer-reporter assays of four predicted p53 target CRMs, after transfection into MCF-7 cells before and after induction with Nutlin-3a (5 µM) in Wild Type and p53 Knock-down MCF-7 cells. Error bars represent SEM of 5 replicates. See also Figures S7–S8 and Tables S4,S6.
Mentions: To explore the relevance of the newly identified p53 targets in other tumor types, we applied iRegulon in a meta-analysis to about twenty thousand cancer gene signatures, i.e. differentially expressed genes obtained from cancer specific experiments. We reasoned that those target genes that are recurrently predicted across cancer gene signatures, might contribute to the tumor suppressor role of p53. We used gene signatures from GeneSigDB [70], MSigDB [71] and from gene modules generated across 91 large cancer microarray data sets (see Materials and Methods and Fig. 5A). Out of 23172 signatures, p53 is found as regulator in 709 signatures. We merged the direct p53 targets across all these signatures into a network and weighted the edges according to the recurrence of this p53-target interaction across all signatures. Many previously known p53 targets and many ChIP-Seq derived targets are recovered using this analysis (GSEA NES = 3.01, FDR<0.001) (Fig. S7). Of the 110 predicted p53 targets in MCF-7 cells (as defined above), 44 are also predicted as p53 target in cancer gene signatures (grey area in Fig. 5B). These genes are predicted as p53 targets by iRegulon and show a significant ChIP peak and are represented in the p53 cancer-related meta-regulon. Amongst these 44 genes, 20 were previously indicated as well established p53 targets (genes in squares in Fig. 5B). When extending the analysis and including target genes recently reported in literature [58], [59], [68], it becomes clear that most overlap coincides within this metatargetome (34/44) (Table S6). Keeping in mind that many of the p53 targets reported by others were found using different cell lines, the enriched overlap within this metatargetome can be interpreted as a sign that these genes represent a core set targeted by p53 regardless of the cell type. Interestingly, when looking at targets like RAP2B, NHLH2, SLC12A4, and ALDH3A1, they could not have been identified through motif discovery in proximal promoters only, because the p53 binding sites are located either further upstream (∼1 kb for RAP2B and ∼5 kb for ALDH3A1) or in introns (NHLH2 and SLC12A4) (Fig. 5C).

Bottom Line: Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology.Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data.Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Computational Biology, KU Leuven Center for Human Genetics, Leuven, Belgium.

ABSTRACT
Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying a co-expressed gene set using cis-regulatory sequence analysis. iRegulon implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets. We increase the accuracy of network inference by using very large motif collections of up to ten thousand position weight matrices collected from various species, and linking these to candidate human TFs via a motif2TF procedure. We validate iRegulon on gene sets derived from ENCODE ChIP-seq data with increasing levels of noise, and we compare iRegulon with existing motif discovery methods. Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data. In particular, we over-activate p53 in breast cancer cells, followed by RNA-seq and ChIP-seq, and could identify an extensive up-regulated network controlled directly by p53. Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY. Finally, we generalize our computational framework to include regulatory tracks such as ChIP-seq data and show how motif and track discovery can be combined to map functional regulatory interactions among co-expressed genes. iRegulon is available as a Cytoscape plugin from http://iregulon.aertslab.org.

Show MeSH
Related in: MedlinePlus