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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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Using iRegulon to map a p53-dependent gene regulatory network.A. MCF-7 breast cancer cells were treated with Nutlin-3a to stabilize p53, followed by RNA-Seq after 24 h. iRegulon results shows p53 as top regulator in a set of 801 up-regulated genes, represented by 6 significantly enriched motifs, and 307 predicted direct targets. The top regulator in the set of down-regulated genes is E2F, with 653/790 predicted direct targets. B. Regulatory network for up-regulated target genes showing the overlap between the p53 regulon and regulons of predicted co-factors (AP-1, NFY, FOX) and regulatory network for down-regulated target genes showing a strong overlap between the predicted E2F and NF-Y regulons. Targets are in grey circle nodes and TF in black hexagon nodes. Regulons for each TF are represented by different edge colours. See also Tables S2–S5.
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pcbi-1003731-g003: Using iRegulon to map a p53-dependent gene regulatory network.A. MCF-7 breast cancer cells were treated with Nutlin-3a to stabilize p53, followed by RNA-Seq after 24 h. iRegulon results shows p53 as top regulator in a set of 801 up-regulated genes, represented by 6 significantly enriched motifs, and 307 predicted direct targets. The top regulator in the set of down-regulated genes is E2F, with 653/790 predicted direct targets. B. Regulatory network for up-regulated target genes showing the overlap between the p53 regulon and regulons of predicted co-factors (AP-1, NFY, FOX) and regulatory network for down-regulated target genes showing a strong overlap between the predicted E2F and NF-Y regulons. Targets are in grey circle nodes and TF in black hexagon nodes. Regulons for each TF are represented by different edge colours. See also Tables S2–S5.

Mentions: We first determined a p53-dependent gene signature in the MCF-7 human breast cancer cell line by RNA-seq upon stabilization of p53 by the non-genotoxic small molecule Nutlin-3a [57]. This treatment resulted in significant up-regulation of 801 genes and down-regulation of 790 genes. Both up- and down-regulated gene sets were subsequently analyzed with iRegulon (Fig. 3A). The top-scoring regulon in the list of up-regulated genes is confirmed as the p53 regulon, with 307 genes predicted to be direct targets (Fig. 3A and Table S2). This indicates that p53 itself is the master regulator of the downstream network and directly controls many up-regulated genes, but not all of them (at least 38%). A Gene Ontology (GO) enrichment analysis of the 307 predicted direct targets identifies p53-related processes and pathways, such as “p53 signaling pathway” (adjusted pvalue = 3.18e-21) or “Apoptosis” (adjusted p-value = 6.76e-07), while the set with the remaining 494 up-regulated genes show no significant GO term enrichment (data not shown).


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)

Using iRegulon to map a p53-dependent gene regulatory network.A. MCF-7 breast cancer cells were treated with Nutlin-3a to stabilize p53, followed by RNA-Seq after 24 h. iRegulon results shows p53 as top regulator in a set of 801 up-regulated genes, represented by 6 significantly enriched motifs, and 307 predicted direct targets. The top regulator in the set of down-regulated genes is E2F, with 653/790 predicted direct targets. B. Regulatory network for up-regulated target genes showing the overlap between the p53 regulon and regulons of predicted co-factors (AP-1, NFY, FOX) and regulatory network for down-regulated target genes showing a strong overlap between the predicted E2F and NF-Y regulons. Targets are in grey circle nodes and TF in black hexagon nodes. Regulons for each TF are represented by different edge colours. See also Tables S2–S5.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003731-g003: Using iRegulon to map a p53-dependent gene regulatory network.A. MCF-7 breast cancer cells were treated with Nutlin-3a to stabilize p53, followed by RNA-Seq after 24 h. iRegulon results shows p53 as top regulator in a set of 801 up-regulated genes, represented by 6 significantly enriched motifs, and 307 predicted direct targets. The top regulator in the set of down-regulated genes is E2F, with 653/790 predicted direct targets. B. Regulatory network for up-regulated target genes showing the overlap between the p53 regulon and regulons of predicted co-factors (AP-1, NFY, FOX) and regulatory network for down-regulated target genes showing a strong overlap between the predicted E2F and NF-Y regulons. Targets are in grey circle nodes and TF in black hexagon nodes. Regulons for each TF are represented by different edge colours. See also Tables S2–S5.
Mentions: We first determined a p53-dependent gene signature in the MCF-7 human breast cancer cell line by RNA-seq upon stabilization of p53 by the non-genotoxic small molecule Nutlin-3a [57]. This treatment resulted in significant up-regulation of 801 genes and down-regulation of 790 genes. Both up- and down-regulated gene sets were subsequently analyzed with iRegulon (Fig. 3A). The top-scoring regulon in the list of up-regulated genes is confirmed as the p53 regulon, with 307 genes predicted to be direct targets (Fig. 3A and Table S2). This indicates that p53 itself is the master regulator of the downstream network and directly controls many up-regulated genes, but not all of them (at least 38%). A Gene Ontology (GO) enrichment analysis of the 307 predicted direct targets identifies p53-related processes and pathways, such as “p53 signaling pathway” (adjusted pvalue = 3.18e-21) or “Apoptosis” (adjusted p-value = 6.76e-07), while the set with the remaining 494 up-regulated genes show no significant GO term enrichment (data not shown).

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