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Analysis of Aspergillus nidulans metabolism at the genome-scale.

David H, Ozçelik IS, Hofmann G, Nielsen J - BMC Genomics (2008)

Bottom Line: The model was used to simulate metabolic behavior and additionally to integrate, analyze and interpret large-scale gene expression data concerning a study on glucose repression, thereby providing a means of upgrading the information content of experimental data and getting further insight into this phenomenon in A. nidulans.We demonstrate how pathway modeling of A. nidulans can be used as an approach to improve the functional annotation of the genome of this organism.Furthermore we show how the metabolic model establishes functional links between genes, enabling the upgrade of the information content of transcriptome data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Fluxome Sciences A/S, Diplomvej 378, Kgs. 2800 Lyngby, Denmark. hd@fluxome.com

ABSTRACT

Background: Aspergillus nidulans is a member of a diverse group of filamentous fungi, sharing many of the properties of its close relatives with significance in the fields of medicine, agriculture and industry. Furthermore, A. nidulans has been a classical model organism for studies of development biology and gene regulation, and thus it has become one of the best-characterized filamentous fungi. It was the first Aspergillus species to have its genome sequenced, and automated gene prediction tools predicted 9,451 open reading frames (ORFs) in the genome, of which less than 10% were assigned a function.

Results: In this work, we have manually assigned functions to 472 orphan genes in the metabolism of A. nidulans, by using a pathway-driven approach and by employing comparative genomics tools based on sequence similarity. The central metabolism of A. nidulans, as well as biosynthetic pathways of relevant secondary metabolites, was reconstructed based on detailed metabolic reconstructions available for A. niger and Saccharomyces cerevisiae, and information on the genetics, biochemistry and physiology of A. nidulans. Thereby, it was possible to identify metabolic functions without a gene associated, and to look for candidate ORFs in the genome of A. nidulans by comparing its sequence to sequences of well-characterized genes in other species encoding the function of interest. A classification system, based on defined criteria, was developed for evaluating and selecting the ORFs among the candidates, in an objective and systematic manner. The functional assignments served as a basis to develop a mathematical model, linking 666 genes (both previously and newly annotated) to metabolic roles. The model was used to simulate metabolic behavior and additionally to integrate, analyze and interpret large-scale gene expression data concerning a study on glucose repression, thereby providing a means of upgrading the information content of experimental data and getting further insight into this phenomenon in A. nidulans.

Conclusion: We demonstrate how pathway modeling of A. nidulans can be used as an approach to improve the functional annotation of the genome of this organism. Furthermore we show how the metabolic model establishes functional links between genes, enabling the upgrade of the information content of transcriptome data.

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Related in: MedlinePlus

Diagram representing the steps in the annotation process.
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Figure 3: Diagram representing the steps in the annotation process.

Mentions: The approach employed in this work for the annotation of the metabolic genes within the genome of A. nidulans was based on the method previously described by Osterman and Overbeek [16]. The different steps carried out are depicted in Fig. 2 and 3, and described in the following.


Analysis of Aspergillus nidulans metabolism at the genome-scale.

David H, Ozçelik IS, Hofmann G, Nielsen J - BMC Genomics (2008)

Diagram representing the steps in the annotation process.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2386489&req=5

Figure 3: Diagram representing the steps in the annotation process.
Mentions: The approach employed in this work for the annotation of the metabolic genes within the genome of A. nidulans was based on the method previously described by Osterman and Overbeek [16]. The different steps carried out are depicted in Fig. 2 and 3, and described in the following.

Bottom Line: The model was used to simulate metabolic behavior and additionally to integrate, analyze and interpret large-scale gene expression data concerning a study on glucose repression, thereby providing a means of upgrading the information content of experimental data and getting further insight into this phenomenon in A. nidulans.We demonstrate how pathway modeling of A. nidulans can be used as an approach to improve the functional annotation of the genome of this organism.Furthermore we show how the metabolic model establishes functional links between genes, enabling the upgrade of the information content of transcriptome data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Fluxome Sciences A/S, Diplomvej 378, Kgs. 2800 Lyngby, Denmark. hd@fluxome.com

ABSTRACT

Background: Aspergillus nidulans is a member of a diverse group of filamentous fungi, sharing many of the properties of its close relatives with significance in the fields of medicine, agriculture and industry. Furthermore, A. nidulans has been a classical model organism for studies of development biology and gene regulation, and thus it has become one of the best-characterized filamentous fungi. It was the first Aspergillus species to have its genome sequenced, and automated gene prediction tools predicted 9,451 open reading frames (ORFs) in the genome, of which less than 10% were assigned a function.

Results: In this work, we have manually assigned functions to 472 orphan genes in the metabolism of A. nidulans, by using a pathway-driven approach and by employing comparative genomics tools based on sequence similarity. The central metabolism of A. nidulans, as well as biosynthetic pathways of relevant secondary metabolites, was reconstructed based on detailed metabolic reconstructions available for A. niger and Saccharomyces cerevisiae, and information on the genetics, biochemistry and physiology of A. nidulans. Thereby, it was possible to identify metabolic functions without a gene associated, and to look for candidate ORFs in the genome of A. nidulans by comparing its sequence to sequences of well-characterized genes in other species encoding the function of interest. A classification system, based on defined criteria, was developed for evaluating and selecting the ORFs among the candidates, in an objective and systematic manner. The functional assignments served as a basis to develop a mathematical model, linking 666 genes (both previously and newly annotated) to metabolic roles. The model was used to simulate metabolic behavior and additionally to integrate, analyze and interpret large-scale gene expression data concerning a study on glucose repression, thereby providing a means of upgrading the information content of experimental data and getting further insight into this phenomenon in A. nidulans.

Conclusion: We demonstrate how pathway modeling of A. nidulans can be used as an approach to improve the functional annotation of the genome of this organism. Furthermore we show how the metabolic model establishes functional links between genes, enabling the upgrade of the information content of transcriptome data.

Show MeSH
Related in: MedlinePlus