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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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Effective response network (ERN) of TORC1. The effective response network is computed for most relevant transcription factors, yielding a network of 1,288 transcription regulations between 17 TFs and 181 target genes. Green nodes represent TFs while blue nodes are the target genes. The size and and color intensity of TFs and target genes represent their relevance score and information flow score, respectively.
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Figure 7: Effective response network (ERN) of TORC1. The effective response network is computed for most relevant transcription factors, yielding a network of 1,288 transcription regulations between 17 TFs and 181 target genes. Green nodes represent TFs while blue nodes are the target genes. The size and and color intensity of TFs and target genes represent their relevance score and information flow score, respectively.

Mentions: To uncover the regulatory mechanisms that mediate the response to TOR inhibition, we construct the effective response network (ERN) of TORC1, which is illustrated in Figure 7 and is available for download as Additional file 5. Node attributes for this network are available for download separately as Additional file 6. This network consists of the most relevant TFs, together with their top-ranked positive targets, with a total of 1,288 regulatory interactions between 17 transcription factors and 181 target genes.


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

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

Effective response network (ERN) of TORC1. The effective response network is computed for most relevant transcription factors, yielding a network of 1,288 transcription regulations between 17 TFs and 181 target genes. Green nodes represent TFs while blue nodes are the target genes. The size and and color intensity of TFs and target genes represent their relevance score and information flow score, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Effective response network (ERN) of TORC1. The effective response network is computed for most relevant transcription factors, yielding a network of 1,288 transcription regulations between 17 TFs and 181 target genes. Green nodes represent TFs while blue nodes are the target genes. The size and and color intensity of TFs and target genes represent their relevance score and information flow score, respectively.
Mentions: To uncover the regulatory mechanisms that mediate the response to TOR inhibition, we construct the effective response network (ERN) of TORC1, which is illustrated in Figure 7 and is available for download as Additional file 5. Node attributes for this network are available for download separately as Additional file 6. This network consists of the most relevant TFs, together with their top-ranked positive targets, with a total of 1,288 regulatory interactions between 17 transcription factors and 181 target genes.

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