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Integrating phosphorylation network with transcriptional network reveals novel functional relationships.

Wang L, Hou L, Qian M, Deng M - PLoS ONE (2012)

Bottom Line: In spite of its importance, systems-level strategies that couple these two networks have yet to be presented.Here we introduce a novel approach that integrates the physical and functional aspects of phosphorylation network together with the transcription network in S.cerevisiae, and demonstrate that different network motifs are involved in these networks, which should be considered in interpreting and integrating large scale datasets.This strategy is applicable to other organisms as large-scale datasets become available, providing a means to identify the functional relationships between kinases/phosphatases and transcription factors.

View Article: PubMed Central - PubMed

Affiliation: Center for Theoretical Biology, Peking University, Beijing, China.

ABSTRACT
Phosphorylation and transcriptional regulation events are critical for cells to transmit and respond to signals. In spite of its importance, systems-level strategies that couple these two networks have yet to be presented. Here we introduce a novel approach that integrates the physical and functional aspects of phosphorylation network together with the transcription network in S.cerevisiae, and demonstrate that different network motifs are involved in these networks, which should be considered in interpreting and integrating large scale datasets. Based on this understanding, we introduce a HeRS score (hetero-regulatory similarity score) to systematically characterize the functional relevance of kinase/phosphatase involvement with transcription factor, and present an algorithm that predicts hetero-regulatory modules. When extended to signaling network, this approach confirmed the structure and cross talk of MAPK pathways, inferred a novel functional transcription factor Sok2 in high osmolarity glycerol pathway, and explained the mechanism of reduced mating efficiency upon Fus3 deletion. This strategy is applicable to other organisms as large-scale datasets become available, providing a means to identify the functional relationships between kinases/phosphatases and transcription factors.

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Co-function prediction using different datasets suggests distinct regulatory pattern in phosphorylation network and transcriptional network.Shown is the fold change of prediction accuracy using different datasets compared with random levels (the fraction of co-function gene pairs in relevant network). (A) Comparison in phosphorylation networks, KPFN (functional network derived from a microarray study of kinase/phosphatase single deletion strains), KBN (biochemical network derived from in vitro protein chip), and KPIN (physical network of kinase/phosphatase interaction). (B) Comparison in transcriptional regulatory networks, TFBN (transcription factor binding network derived from ChIP-chip experiments) and TFFN (functional networks derived from transcription factor single deletion strains). (C) A linear regulatory model. Regulators R1 and R2 function in a linear regulatory pathway, and T1 and T2 are their targets. R1 and R2 share similar profiles in functional network, but disparate profiles in physical network. (D) A parallel regulatory model. Regulators R1 and R2 function in a parallel regulatory pathway, and T1 and T2 are their targets. R1 and R2 share similar profiles in physical network. However, they have no interaction in functional networks due to genetic buffering. Grey: unobserved data; Green: functional interaction; Blue: physical interaction; Black: no interaction.
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pone-0033160-g001: Co-function prediction using different datasets suggests distinct regulatory pattern in phosphorylation network and transcriptional network.Shown is the fold change of prediction accuracy using different datasets compared with random levels (the fraction of co-function gene pairs in relevant network). (A) Comparison in phosphorylation networks, KPFN (functional network derived from a microarray study of kinase/phosphatase single deletion strains), KBN (biochemical network derived from in vitro protein chip), and KPIN (physical network of kinase/phosphatase interaction). (B) Comparison in transcriptional regulatory networks, TFBN (transcription factor binding network derived from ChIP-chip experiments) and TFFN (functional networks derived from transcription factor single deletion strains). (C) A linear regulatory model. Regulators R1 and R2 function in a linear regulatory pathway, and T1 and T2 are their targets. R1 and R2 share similar profiles in functional network, but disparate profiles in physical network. (D) A parallel regulatory model. Regulators R1 and R2 function in a parallel regulatory pathway, and T1 and T2 are their targets. R1 and R2 share similar profiles in physical network. However, they have no interaction in functional networks due to genetic buffering. Grey: unobserved data; Green: functional interaction; Blue: physical interaction; Black: no interaction.

Mentions: Since various networks of phosphorylation and transcriptional regulation are available, a straightforward question is to test how well different datasets can recapitulate current biological knowledge. We used five datasets to predict co-functional gene pairs, and assessed the accuracy by comparing the predictions with a gold standard dataset (see Materials and methods). The datasets covered both genetic and biochemical aspects of phosphorylation network and transcriptional regulatory networks, including KPFN (functional networks derived from a microarray study of kinase/phosphatase single deletion strains [4]), TFFN (functional networks derived from TF single deletion strains [5]), KBN (biochemical networks derived from in vitro protein chip [1]), KPIN (physical networks of kinase/phosphatase interaction [10]), and TFBN (TF binding network derived from ChIP-chip experiments [12][13]). Except for KPIN, the other networks are directed. In each network, the similarity between regulators is calculated by the Pearson correlation coefficient of their interaction profiles, which measures the extent two regulators share common targets. It is expected that highly correlated pairs are co-functional, however the prediction accuracy varies a lot across the five networks considered (Figure 1 A, B). In phosphorylation network, functional networks (KPFN) are more predictive than biochemical or physical interaction networks (KPIN, KBN); while in the transcriptional regulatory network the opposite is true.


Integrating phosphorylation network with transcriptional network reveals novel functional relationships.

Wang L, Hou L, Qian M, Deng M - PLoS ONE (2012)

Co-function prediction using different datasets suggests distinct regulatory pattern in phosphorylation network and transcriptional network.Shown is the fold change of prediction accuracy using different datasets compared with random levels (the fraction of co-function gene pairs in relevant network). (A) Comparison in phosphorylation networks, KPFN (functional network derived from a microarray study of kinase/phosphatase single deletion strains), KBN (biochemical network derived from in vitro protein chip), and KPIN (physical network of kinase/phosphatase interaction). (B) Comparison in transcriptional regulatory networks, TFBN (transcription factor binding network derived from ChIP-chip experiments) and TFFN (functional networks derived from transcription factor single deletion strains). (C) A linear regulatory model. Regulators R1 and R2 function in a linear regulatory pathway, and T1 and T2 are their targets. R1 and R2 share similar profiles in functional network, but disparate profiles in physical network. (D) A parallel regulatory model. Regulators R1 and R2 function in a parallel regulatory pathway, and T1 and T2 are their targets. R1 and R2 share similar profiles in physical network. However, they have no interaction in functional networks due to genetic buffering. Grey: unobserved data; Green: functional interaction; Blue: physical interaction; Black: no interaction.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3303811&req=5

pone-0033160-g001: Co-function prediction using different datasets suggests distinct regulatory pattern in phosphorylation network and transcriptional network.Shown is the fold change of prediction accuracy using different datasets compared with random levels (the fraction of co-function gene pairs in relevant network). (A) Comparison in phosphorylation networks, KPFN (functional network derived from a microarray study of kinase/phosphatase single deletion strains), KBN (biochemical network derived from in vitro protein chip), and KPIN (physical network of kinase/phosphatase interaction). (B) Comparison in transcriptional regulatory networks, TFBN (transcription factor binding network derived from ChIP-chip experiments) and TFFN (functional networks derived from transcription factor single deletion strains). (C) A linear regulatory model. Regulators R1 and R2 function in a linear regulatory pathway, and T1 and T2 are their targets. R1 and R2 share similar profiles in functional network, but disparate profiles in physical network. (D) A parallel regulatory model. Regulators R1 and R2 function in a parallel regulatory pathway, and T1 and T2 are their targets. R1 and R2 share similar profiles in physical network. However, they have no interaction in functional networks due to genetic buffering. Grey: unobserved data; Green: functional interaction; Blue: physical interaction; Black: no interaction.
Mentions: Since various networks of phosphorylation and transcriptional regulation are available, a straightforward question is to test how well different datasets can recapitulate current biological knowledge. We used five datasets to predict co-functional gene pairs, and assessed the accuracy by comparing the predictions with a gold standard dataset (see Materials and methods). The datasets covered both genetic and biochemical aspects of phosphorylation network and transcriptional regulatory networks, including KPFN (functional networks derived from a microarray study of kinase/phosphatase single deletion strains [4]), TFFN (functional networks derived from TF single deletion strains [5]), KBN (biochemical networks derived from in vitro protein chip [1]), KPIN (physical networks of kinase/phosphatase interaction [10]), and TFBN (TF binding network derived from ChIP-chip experiments [12][13]). Except for KPIN, the other networks are directed. In each network, the similarity between regulators is calculated by the Pearson correlation coefficient of their interaction profiles, which measures the extent two regulators share common targets. It is expected that highly correlated pairs are co-functional, however the prediction accuracy varies a lot across the five networks considered (Figure 1 A, B). In phosphorylation network, functional networks (KPFN) are more predictive than biochemical or physical interaction networks (KPIN, KBN); while in the transcriptional regulatory network the opposite is true.

Bottom Line: In spite of its importance, systems-level strategies that couple these two networks have yet to be presented.Here we introduce a novel approach that integrates the physical and functional aspects of phosphorylation network together with the transcription network in S.cerevisiae, and demonstrate that different network motifs are involved in these networks, which should be considered in interpreting and integrating large scale datasets.This strategy is applicable to other organisms as large-scale datasets become available, providing a means to identify the functional relationships between kinases/phosphatases and transcription factors.

View Article: PubMed Central - PubMed

Affiliation: Center for Theoretical Biology, Peking University, Beijing, China.

ABSTRACT
Phosphorylation and transcriptional regulation events are critical for cells to transmit and respond to signals. In spite of its importance, systems-level strategies that couple these two networks have yet to be presented. Here we introduce a novel approach that integrates the physical and functional aspects of phosphorylation network together with the transcription network in S.cerevisiae, and demonstrate that different network motifs are involved in these networks, which should be considered in interpreting and integrating large scale datasets. Based on this understanding, we introduce a HeRS score (hetero-regulatory similarity score) to systematically characterize the functional relevance of kinase/phosphatase involvement with transcription factor, and present an algorithm that predicts hetero-regulatory modules. When extended to signaling network, this approach confirmed the structure and cross talk of MAPK pathways, inferred a novel functional transcription factor Sok2 in high osmolarity glycerol pathway, and explained the mechanism of reduced mating efficiency upon Fus3 deletion. This strategy is applicable to other organisms as large-scale datasets become available, providing a means to identify the functional relationships between kinases/phosphatases and transcription factors.

Show MeSH
Related in: MedlinePlus