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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

Loregic workflow.A: Loregic first inputs a gene regulatory network that consists of regulatory factors and their target genes; B: Next, it identifies all possible RF1-RF2-T triplets where RF1 and RF2 co-regulate the target gene T. Note that T can be also a RF; C: Loregic queries binarized gene expression data for each triplet, and D: it extracts the triplet’s binarized gene expression data; E: Loregic matches the triplet’s gene expression against all 16 possible two-input-one-output logic gates based on the binary values, and F: finds the matched logic gate if the triplet is gate-consistent, and calculates the consistency score. Then, Loregic repeats steps C-F for all triplets from Step B in the regulatory network and finds all logic-gate-consistent triplets. In Step G, the gate-consistent triplets can be further mapped onto other regulatory features such as: 1) indirectly bound TFs and 2) feed-forward loops.
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pcbi.1004132.g001: Loregic workflow.A: Loregic first inputs a gene regulatory network that consists of regulatory factors and their target genes; B: Next, it identifies all possible RF1-RF2-T triplets where RF1 and RF2 co-regulate the target gene T. Note that T can be also a RF; C: Loregic queries binarized gene expression data for each triplet, and D: it extracts the triplet’s binarized gene expression data; E: Loregic matches the triplet’s gene expression against all 16 possible two-input-one-output logic gates based on the binary values, and F: finds the matched logic gate if the triplet is gate-consistent, and calculates the consistency score. Then, Loregic repeats steps C-F for all triplets from Step B in the regulatory network and finds all logic-gate-consistent triplets. In Step G, the gate-consistent triplets can be further mapped onto other regulatory features such as: 1) indirectly bound TFs and 2) feed-forward loops.

Mentions: Loregic takes as inputs two types of data: a regulatory network (defined by RFs and their target genes) and a binarized gene expression dataset across multiple samples. The binarized gene expression data (1-on and 0-off) is simple but useful in representing the network RFs’ activities on target genes. The inputs can be chosen from different resources to meet the user’s needs. In this paper, we used BoolNet [29] to obtain binarized gene expressions. Loregic describes each regulatory module (triplet) using a particular type of logic gate; i.e. the gate that best matches the binarized expression data for that triplet across all samples. Formally, a triplet is described as RF1-RF2-T, where RF1 and RF2 are regulators (e.g. TFs) and T is the target. Note, however, that T itself could be a regulator participating in another triplet. Loregic scores the agreement between the triplet’s cross-sample expression and the idealized expression pattern of each of 16 possible logic gates using Laplace’s rule of succession (Materials and Methods). A high score implies a strong cooperation between the activities of the two RFs on the target as described by the matched logic gate. If such a logic gate is found, we define the triplet as “consistent” with the respective logic gate (i.e. the triplet is described as “logic-gate-consistent” or “gate-consistent”). In the case that no best-matching logic gate is found (e.g. all logic gates score low, or there are tied scores between multiple logic gates), we define the triplet as inconsistent with all logic gates (i.e., “gate-inconsistent”). This negative result suggests that the two-input-one-output model cannot appropriately describe the gene regulation, perhaps due to the fact that more RFs are involved and thus a more complex model should be used (Discussion). In this paper, we evaluate Loregic’s ability to analyze transcription factors, miRNAs and their target genes. In detail, our method comprises of six major steps (Fig. 1):


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)

Loregic workflow.A: Loregic first inputs a gene regulatory network that consists of regulatory factors and their target genes; B: Next, it identifies all possible RF1-RF2-T triplets where RF1 and RF2 co-regulate the target gene T. Note that T can be also a RF; C: Loregic queries binarized gene expression data for each triplet, and D: it extracts the triplet’s binarized gene expression data; E: Loregic matches the triplet’s gene expression against all 16 possible two-input-one-output logic gates based on the binary values, and F: finds the matched logic gate if the triplet is gate-consistent, and calculates the consistency score. Then, Loregic repeats steps C-F for all triplets from Step B in the regulatory network and finds all logic-gate-consistent triplets. In Step G, the gate-consistent triplets can be further mapped onto other regulatory features such as: 1) indirectly bound TFs and 2) feed-forward loops.
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getmorefigures.php?uid=PMC4401777&req=5

pcbi.1004132.g001: Loregic workflow.A: Loregic first inputs a gene regulatory network that consists of regulatory factors and their target genes; B: Next, it identifies all possible RF1-RF2-T triplets where RF1 and RF2 co-regulate the target gene T. Note that T can be also a RF; C: Loregic queries binarized gene expression data for each triplet, and D: it extracts the triplet’s binarized gene expression data; E: Loregic matches the triplet’s gene expression against all 16 possible two-input-one-output logic gates based on the binary values, and F: finds the matched logic gate if the triplet is gate-consistent, and calculates the consistency score. Then, Loregic repeats steps C-F for all triplets from Step B in the regulatory network and finds all logic-gate-consistent triplets. In Step G, the gate-consistent triplets can be further mapped onto other regulatory features such as: 1) indirectly bound TFs and 2) feed-forward loops.
Mentions: Loregic takes as inputs two types of data: a regulatory network (defined by RFs and their target genes) and a binarized gene expression dataset across multiple samples. The binarized gene expression data (1-on and 0-off) is simple but useful in representing the network RFs’ activities on target genes. The inputs can be chosen from different resources to meet the user’s needs. In this paper, we used BoolNet [29] to obtain binarized gene expressions. Loregic describes each regulatory module (triplet) using a particular type of logic gate; i.e. the gate that best matches the binarized expression data for that triplet across all samples. Formally, a triplet is described as RF1-RF2-T, where RF1 and RF2 are regulators (e.g. TFs) and T is the target. Note, however, that T itself could be a regulator participating in another triplet. Loregic scores the agreement between the triplet’s cross-sample expression and the idealized expression pattern of each of 16 possible logic gates using Laplace’s rule of succession (Materials and Methods). A high score implies a strong cooperation between the activities of the two RFs on the target as described by the matched logic gate. If such a logic gate is found, we define the triplet as “consistent” with the respective logic gate (i.e. the triplet is described as “logic-gate-consistent” or “gate-consistent”). In the case that no best-matching logic gate is found (e.g. all logic gates score low, or there are tied scores between multiple logic gates), we define the triplet as inconsistent with all logic gates (i.e., “gate-inconsistent”). This negative result suggests that the two-input-one-output model cannot appropriately describe the gene regulation, perhaps due to the fact that more RFs are involved and thus a more complex model should be used (Discussion). In this paper, we evaluate Loregic’s ability to analyze transcription factors, miRNAs and their target genes. In detail, our method comprises of six major steps (Fig. 1):

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