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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 gate-consistent human regulatory triples associated with AML-related TFs.The bar color represents-log10(hyper-geometric enrichment p-value) (Materials and Methods). A—The triplets in which RF1 is MYC, RF2 is chosen from other human TFs, and T is a common target. The two most enriched logic gates are “T = RF1” (133 triplets, hyper-geometric test p(133, 2153, 1110, 50865)< 4.3*10-27) and “T = RF1+RF2 (OR)” (211 triplets, hyper-geometric test p(211, 2153, 2505, 50865)< 1.1*10-21), which supports the finding that MYC is a universally amplifier for its target expression; B—the triplets in which RF1 is chosen from AML-related TFs, RF2 is chosen from TFs not relating to AML, and T is a common target as shown in top, and the triplets in which both RF1 and RF2 are chosen from TFs not relating to AML, and T is a common target as shown in bottom. “T = RF1” and “T = ~RF1” are the two most enriched matched logic gates when RF1 is AML-related TF, which implies that AML-related TFs dominate the regulation of their target expression.
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pcbi.1004132.g005: Distributions by logic gate of gate-consistent human regulatory triples associated with AML-related TFs.The bar color represents-log10(hyper-geometric enrichment p-value) (Materials and Methods). A—The triplets in which RF1 is MYC, RF2 is chosen from other human TFs, and T is a common target. The two most enriched logic gates are “T = RF1” (133 triplets, hyper-geometric test p(133, 2153, 1110, 50865)< 4.3*10-27) and “T = RF1+RF2 (OR)” (211 triplets, hyper-geometric test p(211, 2153, 2505, 50865)< 1.1*10-21), which supports the finding that MYC is a universally amplifier for its target expression; B—the triplets in which RF1 is chosen from AML-related TFs, RF2 is chosen from TFs not relating to AML, and T is a common target as shown in top, and the triplets in which both RF1 and RF2 are chosen from TFs not relating to AML, and T is a common target as shown in bottom. “T = RF1” and “T = ~RF1” are the two most enriched matched logic gates when RF1 is AML-related TF, which implies that AML-related TFs dominate the regulation of their target expression.

Mentions: AML-related TFs play a dominant role in regulating target gene expression. Next, we showed that Loregic can make interpretable gate assignments for a cancer-related TF, MYC, which has been found to universally amplify target gene expressions in lymphocytes [33]. We identified 2,153 MYC-TF-target triplets (i.e., RF1 is MYC, RF2 is chosen from other TFs from ENCODE, and T is target), and found that 905 of them are gate-consistent. The two most enriched logic gates are “T = RF1” (133 triplets, hypergeometric test p-value < 4.3*10-27) and “T = RF1+RF2 (OR)” (211 triplets, hypergeometric test p-value < 1.1*10-21) (Fig. 5A). For the 133 triplets consistent with “T = RF1” with RF1 being MYC, our model predicted that high expression of MYC is necessary and sufficient for high target gene expression. For the 211 triplets consistent with “T = RF1+RF2” with RF1 being MYC and RF2 being other TFs, our model predicted that high expression of MYC is sufficient but not necessary for high target expression. Both of the most commonly observed scenarios indicate that high MYC expression is sufficient for high target expression. These results support the recent findings that MYC plays a universal amplifier role in gene expression.


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 gate-consistent human regulatory triples associated with AML-related TFs.The bar color represents-log10(hyper-geometric enrichment p-value) (Materials and Methods). A—The triplets in which RF1 is MYC, RF2 is chosen from other human TFs, and T is a common target. The two most enriched logic gates are “T = RF1” (133 triplets, hyper-geometric test p(133, 2153, 1110, 50865)< 4.3*10-27) and “T = RF1+RF2 (OR)” (211 triplets, hyper-geometric test p(211, 2153, 2505, 50865)< 1.1*10-21), which supports the finding that MYC is a universally amplifier for its target expression; B—the triplets in which RF1 is chosen from AML-related TFs, RF2 is chosen from TFs not relating to AML, and T is a common target as shown in top, and the triplets in which both RF1 and RF2 are chosen from TFs not relating to AML, and T is a common target as shown in bottom. “T = RF1” and “T = ~RF1” are the two most enriched matched logic gates when RF1 is AML-related TF, which implies that AML-related TFs dominate the regulation of their target expression.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004132.g005: Distributions by logic gate of gate-consistent human regulatory triples associated with AML-related TFs.The bar color represents-log10(hyper-geometric enrichment p-value) (Materials and Methods). A—The triplets in which RF1 is MYC, RF2 is chosen from other human TFs, and T is a common target. The two most enriched logic gates are “T = RF1” (133 triplets, hyper-geometric test p(133, 2153, 1110, 50865)< 4.3*10-27) and “T = RF1+RF2 (OR)” (211 triplets, hyper-geometric test p(211, 2153, 2505, 50865)< 1.1*10-21), which supports the finding that MYC is a universally amplifier for its target expression; B—the triplets in which RF1 is chosen from AML-related TFs, RF2 is chosen from TFs not relating to AML, and T is a common target as shown in top, and the triplets in which both RF1 and RF2 are chosen from TFs not relating to AML, and T is a common target as shown in bottom. “T = RF1” and “T = ~RF1” are the two most enriched matched logic gates when RF1 is AML-related TF, which implies that AML-related TFs dominate the regulation of their target expression.
Mentions: AML-related TFs play a dominant role in regulating target gene expression. Next, we showed that Loregic can make interpretable gate assignments for a cancer-related TF, MYC, which has been found to universally amplify target gene expressions in lymphocytes [33]. We identified 2,153 MYC-TF-target triplets (i.e., RF1 is MYC, RF2 is chosen from other TFs from ENCODE, and T is target), and found that 905 of them are gate-consistent. The two most enriched logic gates are “T = RF1” (133 triplets, hypergeometric test p-value < 4.3*10-27) and “T = RF1+RF2 (OR)” (211 triplets, hypergeometric test p-value < 1.1*10-21) (Fig. 5A). For the 133 triplets consistent with “T = RF1” with RF1 being MYC, our model predicted that high expression of MYC is necessary and sufficient for high target gene expression. For the 211 triplets consistent with “T = RF1+RF2” with RF1 being MYC and RF2 being other TFs, our model predicted that high expression of MYC is sufficient but not necessary for high target expression. Both of the most commonly observed scenarios indicate that high MYC expression is sufficient for high target expression. These results support the recent findings that MYC plays a universal amplifier role in gene expression.

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