ABSTRACT:
David2008 - Genome-scale metabolic network of
Aspergillus nidulans (iHD666)
This model is described in the article:
Analysis of Aspergillus
nidulans metabolism at the genome-scale.
David H, Ozçelik IS, Hofmann G,
Nielsen J.
BMC Genomics 2008; 9: 163
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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Nicolas Le Novère 
