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Inferring the effective TOR-dependent network: a computational study in yeast.

Mohammadi S, Subramaniam S, Grama A - BMC Syst Biol (2013)

Bottom Line: These rankings must be normalized for degree bias, appropriately interpreted, and mapped to associated roles in pathways.We use this framework to identify the most relevant transcription factors in mediating the observed transcriptional response, and to construct the effective response network of the TOR pathway.It also allows us to identify potential network biomarkers for diagnosis and prognosis of age-related pathologies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Purdue University, West Lafayette, Indiana, USA. mohammadi@purdue.edu.

ABSTRACT

Background: Calorie restriction (CR) is one of the most conserved non-genetic interventions that extends healthspan in evolutionarily distant species, ranging from yeast to mammals. The target of rapamycin (TOR) has been shown to play a key role in mediating healthspan extension in response to CR by integrating different signals that monitor nutrient-availability and orchestrating various components of cellular machinery in response. Both genetic and pharmacological interventions that inhibit the TOR pathway exhibit a similar phenotype, which is not further amplified by CR.

Results: In this paper, we present the first comprehensive, computationally derived map of TOR downstream effectors, with the objective of discovering key lifespan mediators, their crosstalk, and high-level organization. We adopt a systematic approach for tracing information flow from the TOR complex and use it to identify relevant signaling elements. By constructing a high-level functional map of TOR downstream effectors, we show that our approach is not only capable of recapturing previously known pathways, but also suggests potential targets for future studies.Information flow scores provide an aggregate ranking of relevance of proteins with respect to the TOR signaling pathway. These rankings must be normalized for degree bias, appropriately interpreted, and mapped to associated roles in pathways. We propose a novel statistical framework for integrating information flow scores, the set of differentially expressed genes in response to rapamycin treatment, and the transcriptional regulatory network. We use this framework to identify the most relevant transcription factors in mediating the observed transcriptional response, and to construct the effective response network of the TOR pathway. This network is hypothesized to mediate life-span extension in response to TOR inhibition.

Conclusions: Our approach, unlike experimental methods, is not limited to specific aspects of cellular response. Rather, it predicts transcriptional changes and post-translational modifications in response to TOR inhibition. The constructed effective response network greatly enhances understanding of the mechanisms underlying the aging process and helps in identifying new targets for further investigation of anti-aging regimes. It also allows us to identify potential network biomarkers for diagnosis and prognosis of age-related pathologies.

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Related in: MedlinePlus

TORC1-dependent regulation of Gap1. The schematic diagram is based on literature evidence for the known interactions. Each node in the signaling pathway is annotated with the rank of its information flow score from TORC1 and colored with its functional classification. Yellow nodes represent kinase associated proteins, red nodes are transcription factors, and blue node (Sit4) is a phosphatase. The rest of nodes have a default color of grey. Ranking of nodes based on their information flow scores coincides with our prior knowledge on the structure of this pathway. Top/bottom ranked nodes are discriminated using the computed cutoff value (l) based on differentially expressed genes. The “?” indicates an unknown underlying mechanism, yet to be discovered, that connects TORC1 to the rest of transcription factors.
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Figure 6: TORC1-dependent regulation of Gap1. The schematic diagram is based on literature evidence for the known interactions. Each node in the signaling pathway is annotated with the rank of its information flow score from TORC1 and colored with its functional classification. Yellow nodes represent kinase associated proteins, red nodes are transcription factors, and blue node (Sit4) is a phosphatase. The rest of nodes have a default color of grey. Ranking of nodes based on their information flow scores coincides with our prior knowledge on the structure of this pathway. Top/bottom ranked nodes are discriminated using the computed cutoff value (l) based on differentially expressed genes. The “?” indicates an unknown underlying mechanism, yet to be discovered, that connects TORC1 to the rest of transcription factors.

Mentions: Figure 6 illustrates this signaling pathway, with each element annotated using its information flow rank. All signaling elements upstream of Gap1 are present among top-ranked scores, yet none of them change their expression levels in response to rapamycin treatment. This partially supports our hypothesis that the top-ranked genes in the random-walk are primarily targets of post-translational modifications. However, a more thorough experimental analysis of the the top-ranked proteins potentially may reveal currently unknown mechanisms by which yeast cells respond to TOR signaling. To this end, our computational studies motivate and provide data for future experimental investigations.


Inferring the effective TOR-dependent network: a computational study in yeast.

Mohammadi S, Subramaniam S, Grama A - BMC Syst Biol (2013)

TORC1-dependent regulation of Gap1. The schematic diagram is based on literature evidence for the known interactions. Each node in the signaling pathway is annotated with the rank of its information flow score from TORC1 and colored with its functional classification. Yellow nodes represent kinase associated proteins, red nodes are transcription factors, and blue node (Sit4) is a phosphatase. The rest of nodes have a default color of grey. Ranking of nodes based on their information flow scores coincides with our prior knowledge on the structure of this pathway. Top/bottom ranked nodes are discriminated using the computed cutoff value (l) based on differentially expressed genes. The “?” indicates an unknown underlying mechanism, yet to be discovered, that connects TORC1 to the rest of transcription factors.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4016608&req=5

Figure 6: TORC1-dependent regulation of Gap1. The schematic diagram is based on literature evidence for the known interactions. Each node in the signaling pathway is annotated with the rank of its information flow score from TORC1 and colored with its functional classification. Yellow nodes represent kinase associated proteins, red nodes are transcription factors, and blue node (Sit4) is a phosphatase. The rest of nodes have a default color of grey. Ranking of nodes based on their information flow scores coincides with our prior knowledge on the structure of this pathway. Top/bottom ranked nodes are discriminated using the computed cutoff value (l) based on differentially expressed genes. The “?” indicates an unknown underlying mechanism, yet to be discovered, that connects TORC1 to the rest of transcription factors.
Mentions: Figure 6 illustrates this signaling pathway, with each element annotated using its information flow rank. All signaling elements upstream of Gap1 are present among top-ranked scores, yet none of them change their expression levels in response to rapamycin treatment. This partially supports our hypothesis that the top-ranked genes in the random-walk are primarily targets of post-translational modifications. However, a more thorough experimental analysis of the the top-ranked proteins potentially may reveal currently unknown mechanisms by which yeast cells respond to TOR signaling. To this end, our computational studies motivate and provide data for future experimental investigations.

Bottom Line: These rankings must be normalized for degree bias, appropriately interpreted, and mapped to associated roles in pathways.We use this framework to identify the most relevant transcription factors in mediating the observed transcriptional response, and to construct the effective response network of the TOR pathway.It also allows us to identify potential network biomarkers for diagnosis and prognosis of age-related pathologies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Purdue University, West Lafayette, Indiana, USA. mohammadi@purdue.edu.

ABSTRACT

Background: Calorie restriction (CR) is one of the most conserved non-genetic interventions that extends healthspan in evolutionarily distant species, ranging from yeast to mammals. The target of rapamycin (TOR) has been shown to play a key role in mediating healthspan extension in response to CR by integrating different signals that monitor nutrient-availability and orchestrating various components of cellular machinery in response. Both genetic and pharmacological interventions that inhibit the TOR pathway exhibit a similar phenotype, which is not further amplified by CR.

Results: In this paper, we present the first comprehensive, computationally derived map of TOR downstream effectors, with the objective of discovering key lifespan mediators, their crosstalk, and high-level organization. We adopt a systematic approach for tracing information flow from the TOR complex and use it to identify relevant signaling elements. By constructing a high-level functional map of TOR downstream effectors, we show that our approach is not only capable of recapturing previously known pathways, but also suggests potential targets for future studies.Information flow scores provide an aggregate ranking of relevance of proteins with respect to the TOR signaling pathway. These rankings must be normalized for degree bias, appropriately interpreted, and mapped to associated roles in pathways. We propose a novel statistical framework for integrating information flow scores, the set of differentially expressed genes in response to rapamycin treatment, and the transcriptional regulatory network. We use this framework to identify the most relevant transcription factors in mediating the observed transcriptional response, and to construct the effective response network of the TOR pathway. This network is hypothesized to mediate life-span extension in response to TOR inhibition.

Conclusions: Our approach, unlike experimental methods, is not limited to specific aspects of cellular response. Rather, it predicts transcriptional changes and post-translational modifications in response to TOR inhibition. The constructed effective response network greatly enhances understanding of the mechanisms underlying the aging process and helps in identifying new targets for further investigation of anti-aging regimes. It also allows us to identify potential network biomarkers for diagnosis and prognosis of age-related pathologies.

Show MeSH
Related in: MedlinePlus