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Loregic: a method to characterize the cooperative logic of regulatory factors.

Wang D, Yan KK, Sisu C, Cheng C, Rozowsky J, Meyerson W, Gerstein MB - PLoS Comput. Biol. (2015)

Bottom Line: We validate it with known yeast transcription-factor knockout experiments.Furthermore, we show that MYC, a well-known oncogenic driving TF, can be modeled as acting independently from other TFs (e.g., using OR gates) but antagonistically with repressing miRNAs.Finally, we inter-relate Loregic's gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions, feed-forward loop motifs and global regulatory hierarchy.

View Article: PubMed Central - PubMed

Affiliation: Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America.

ABSTRACT
The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible two-input-one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet's observed gene expression pattern across many conditions. We make Loregic available as a general-purpose tool (github.com/gersteinlab/loregic). We validate it with known yeast transcription-factor knockout experiments. Next, using human ENCODE ChIP-Seq and TCGA RNA-Seq data, we are able to demonstrate how Loregic characterizes complex circuits involving both proximally and distally regulating transcription factors (TFs) and also miRNAs. Furthermore, we show that MYC, a well-known oncogenic driving TF, can be modeled as acting independently from other TFs (e.g., using OR gates) but antagonistically with repressing miRNAs. Finally, we inter-relate Loregic's gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions, feed-forward loop motifs and global regulatory hierarchy.

No MeSH data available.


Related in: MedlinePlus

Distributions by logic gate of all gate-consistent human regulatory triples in acute myeloid leukemia.A—TF-TF-target triplets. The symmetric gate pairs are marked using diamonds on top of bars with identical superscript numbers; B—miRNA-TF-target triplets; C—distTF-TF-triplets. The triplets from B and C have different distributions from A, including notably at symmetric gates because their RF1s are miRNA/distTF. Also, the “T = RF2” gate matches more triplets than any other gate in B and C.
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pcbi.1004132.g004: Distributions by logic gate of all gate-consistent human regulatory triples in acute myeloid leukemia.A—TF-TF-target triplets. The symmetric gate pairs are marked using diamonds on top of bars with identical superscript numbers; B—miRNA-TF-target triplets; C—distTF-TF-triplets. The triplets from B and C have different distributions from A, including notably at symmetric gates because their RF1s are miRNA/distTF. Also, the “T = RF2” gate matches more triplets than any other gate in B and C.

Mentions: Logic operations between TF-TF, miRNA-TF, and distTF-TF across targets in acute myeloid leukemia. Next, we characterized TF-TF, miRNA-TF, and distTF-TF logic operations by integrating ENCODE and TCGA AML datasets using Loregic, where distTF represents a TF regulating its target through a distal regulatory region such as an enhancer, whereas the canonical TF regulation is assumed to occur at the proximal promoter (Materials and Methods, and S2–4 Tables). In total, we identified 50,865 TF1-TF2-target triplets and 821 distTF-TF-target triplets. By integrating miRNA-targets data (Materials and Methods), we were able to identify 56,944 miRNA-TF-target triplets, in which RF1 is an miRNA, RF2 is a TF, and the target is a gene co-regulated by the respective miRNA and TF. Fig. 4 shows the distributions, by logic gate, of these gate-consistent triplets. For example in Fig. 4A, we found that the gate-consistent TF-TF-target triplets preferentially match the OR gate (2505 triplets). The gate-consistent triplets from TF-TF-target, miRNA-TF-target, and distTF-TF-target include 1005 (~55% of 1824 unique targets from 50,865 TF-TF-target triplets), 1672 (~76% of 2210 unique targets from 56,944 miRNA-TF-target triplets), and 66 (~58% of 113 unique targets from 821 distTF-TF-target triplets) unique targets, respectively (Materials and Methods).


Loregic: a method to characterize the cooperative logic of regulatory factors.

Wang D, Yan KK, Sisu C, Cheng C, Rozowsky J, Meyerson W, Gerstein MB - PLoS Comput. Biol. (2015)

Distributions by logic gate of all gate-consistent human regulatory triples in acute myeloid leukemia.A—TF-TF-target triplets. The symmetric gate pairs are marked using diamonds on top of bars with identical superscript numbers; B—miRNA-TF-target triplets; C—distTF-TF-triplets. The triplets from B and C have different distributions from A, including notably at symmetric gates because their RF1s are miRNA/distTF. Also, the “T = RF2” gate matches more triplets than any other gate in B and C.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401777&req=5

pcbi.1004132.g004: Distributions by logic gate of all gate-consistent human regulatory triples in acute myeloid leukemia.A—TF-TF-target triplets. The symmetric gate pairs are marked using diamonds on top of bars with identical superscript numbers; B—miRNA-TF-target triplets; C—distTF-TF-triplets. The triplets from B and C have different distributions from A, including notably at symmetric gates because their RF1s are miRNA/distTF. Also, the “T = RF2” gate matches more triplets than any other gate in B and C.
Mentions: Logic operations between TF-TF, miRNA-TF, and distTF-TF across targets in acute myeloid leukemia. Next, we characterized TF-TF, miRNA-TF, and distTF-TF logic operations by integrating ENCODE and TCGA AML datasets using Loregic, where distTF represents a TF regulating its target through a distal regulatory region such as an enhancer, whereas the canonical TF regulation is assumed to occur at the proximal promoter (Materials and Methods, and S2–4 Tables). In total, we identified 50,865 TF1-TF2-target triplets and 821 distTF-TF-target triplets. By integrating miRNA-targets data (Materials and Methods), we were able to identify 56,944 miRNA-TF-target triplets, in which RF1 is an miRNA, RF2 is a TF, and the target is a gene co-regulated by the respective miRNA and TF. Fig. 4 shows the distributions, by logic gate, of these gate-consistent triplets. For example in Fig. 4A, we found that the gate-consistent TF-TF-target triplets preferentially match the OR gate (2505 triplets). The gate-consistent triplets from TF-TF-target, miRNA-TF-target, and distTF-TF-target include 1005 (~55% of 1824 unique targets from 50,865 TF-TF-target triplets), 1672 (~76% of 2210 unique targets from 56,944 miRNA-TF-target triplets), and 66 (~58% of 113 unique targets from 821 distTF-TF-target triplets) unique targets, respectively (Materials and Methods).

Bottom Line: We validate it with known yeast transcription-factor knockout experiments.Furthermore, we show that MYC, a well-known oncogenic driving TF, can be modeled as acting independently from other TFs (e.g., using OR gates) but antagonistically with repressing miRNAs.Finally, we inter-relate Loregic's gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions, feed-forward loop motifs and global regulatory hierarchy.

View Article: PubMed Central - PubMed

Affiliation: Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America.

ABSTRACT
The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible two-input-one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet's observed gene expression pattern across many conditions. We make Loregic available as a general-purpose tool (github.com/gersteinlab/loregic). We validate it with known yeast transcription-factor knockout experiments. Next, using human ENCODE ChIP-Seq and TCGA RNA-Seq data, we are able to demonstrate how Loregic characterizes complex circuits involving both proximally and distally regulating transcription factors (TFs) and also miRNAs. Furthermore, we show that MYC, a well-known oncogenic driving TF, can be modeled as acting independently from other TFs (e.g., using OR gates) but antagonistically with repressing miRNAs. Finally, we inter-relate Loregic's gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions, feed-forward loop motifs and global regulatory hierarchy.

No MeSH data available.


Related in: MedlinePlus