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Aspergillus nidulans is a model organism for aspergilli, which are an important …

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- Metabolic network driven analysis of genome-wide transcription data from Aspergillus nidulans

Aspergillus represents a large and important genus of filamentous fungi comprising human pathogens such as A. fumigatus, plant pathogens such as A. flavus, and important cell factories such as A. niger, A. oryzae, and A. terreus. Furthermore, A. nidulans has been extensively used as a model organism for eukaryotic cells. Despite their importance as human and plant pathogens and their extensive use in food, chemical, and pharmaceutical production, it was only recently that an initiative was undertaken to sequence the genomes of several Aspergillus spp. Thus, the genomes of three Aspergillus spp. have been published (A. nidulans [1], A. oryzae [2], and A. fumigatus [3]), and complete genomic sequencing of several other species has been finished or is ongoing. This has enabled analysis of the function of these important organisms at the genome level.

Aspergilli are natural scavengers and hence they have a very flexible metabolism that enables consumption of a wide range of carbon and nitrogen sources. Considering the high degree of flexibility in the metabolism of aspergilli, it is interesting to evaluate the function of the metabolic network in these organisms during growth on different carbon sources. We therefore undertook a study of the metabolism of A. nidulans at the genome level during growth on three different carbon sources: glucose, glycerol, and ethanol. These three carbon sources enter the central carbon metabolism at different locations, and they have been reported to result in widely different regulatory responses [4-8].

Our study involved genome-wide transcription analysis using in situ synthesized oligonucleotide arrays containing probes for 9,371 out of the 9,541 putative genes in the genome of A. nidulans [9]. In order to map the effects of carbon source on transcription, we used well controlled bioreactors to grow the cells. In recent years a few large-scale transcription studies have been conducted in A. nidulans, but so far none has covered the complete set of predicted genes in the genome. Sims and coworkers [10] used spotted DNA arrays to interrogate 2,080 open reading frames (ORFs) within the genome of A. nidulans, using as probes polymerase chain reaction (PCR) products from expressed sequence tags (ESTs), as well as gene sequences deposited in GenBank. The arrays were initially used in connection with an ethanol-to-glucose upshift batch experiment with a reference strain [10], and subsequently modified to study the effect of recombinant protein secretion on gene expression in A. nidulans by comparing the transcription profiles of a recombinant and a reference strain grown in chemostat cultures [11]. For other species of Aspergillus, a few studies on transcription profiling using microarray technology have been reported in the literature. These made use of spotted DNA arrays fabricated from EST sequences of selected genes (for example, A. oryzae [12], A. flavus [13-15], and A. parasiticus [15]) and other types of arrays (for example, for A. terreus [16]). Furthermore, studies similar to ours (aiming to map differences in gene expression during batch growth on different carbon sources, in particular glucose and ethanol) have been performed with other organisms, such as the filamentous fungi A. oryzae [12] and Trichoderma reesei [17], and the yeast Saccharomyces cerevisiae (many studies, with the first being that of DeRisi and coworkers [18]), with only the latter covering the complete genome.

In this work transcriptome data were analyzed using a recently developed consensus clustering algorithm [19]. Clustering of transcription data is valuable with respect to assigning function to genes, and this is particularly pertinent to A. nidulans because less than 10% of the 9,541 putative genes have been assigned a function (more than 90% of the 9,541 putative genes are called hypothetical or predicted proteins), based on automated gene prediction tools [9]. Using consensus clustering, we identified genes specifically relevant to the metabolism of the different carbon sources and, of particular, interest we identified nearly 200 genes that were significantly upregulated only during growth on glycerol versus growth on glucose and ethanol.

In order to study further the transcriptional response to growth on different carbon sources at the level of the metabolism, we used the transcription data to evaluate the operation of the metabolic network. For this purpose, we reconstructed the metabolic network of A. nidulans at the genome level, based on detailed metabolic reconstructions previously developed for A. niger [20], S. cerevisiae [21], and Mus musculus [22], as well as information on the genetics, biochemistry, and physiology of A. nidulans. The metabolic network reconstructed for A. nidulans contains 1,213 reactions and links 666 genes to metabolic functions. In the process of reconstruction, we assigned metabolic functions to 472 ORFs that had not previously been annotated, by employing tools of comparative genomics based on sequence similarity and using public databases of genes and proteins of established function. The metabolic reconstruction provided a framework for the analysis of transcriptome data. In particular, the metabolic network was used in combination with a recently developed algorithm [23] to identify global regulatory responses of the metabolism to variations in carbon source.

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