Metabolic model integration of the bibliome, genome, metabolome and reactome of Aspergillus niger

Mikael Rørdam Andersen^1,a, Michael Lynge Nielsen¹ & Jens Nielsen¹

^aMRA performed the reconstruction and the modelling and wrote the manuscript. JN and MLN supervised the work and assisted in manuscript preparation.

Article highlights

Synopsis

It is difficult to think of a filamentous fungus where the metabolic capabilities are of greater interest than Aspergillus niger. As a widely used industrial workhorse, the commercial applications of A. niger range from the high-yield production of citric and gluconic acid, through production of a range of industrial enzymes in high titers and the production of heterologous proteins, such as chymosin or human interferon (van Brunt, 1986; Dunn-Coleman et al, 1991; Punt et al, 2002; Karaffa and Kubicek, 2003; Singh and Kumar, 2007). With such an impressive diversity of high-yield products, A. niger holds the potential to become an even wider used cell factory platform in the future. The recent publishing of the genome sequence of A. niger CBS 513.88 has made it apparent that this fungus holds a genetic diversity that is a potential trove of new products (Pel et al, 2007). As commented by Cullen (2007), there is now a great potential for increasing the yield of both known and novel products by directed metabolic engineering. However, data integration on a systemic level is necessary to fully understand and exploit the potential of the metabolism.

One such way of integrating data on a systems level is reconstructing the metabolic network of the cell and integrating the information with genomic annotation. This provides a way of linking genome-scale data such as transcriptome or fluxome data to the metabolism, which can be a framework for interpreting these complex data types. Additionally, a curated and validated network of this type in tandem with knowledge of biomass composition can be used for modeling growth under conditions defined by the researcher. A complete and accurate model of this type has several uses, e.g. (a) identification of targets for metabolic engineering (Patil et al, 2005); (b) interpretation of transcription data and identification of regulatory features (David et al, 2006); (c) metabolic flux analysis (Christensen and Nielsen, 1999) and (d) evaluation and improvement of gene annotation (Pel et al, 2007).

Bearing in mind how A. niger is applied as a production organism, the applications of such a model for A. niger in a biotechnological context are evident.

Our approach to reconstructing the metabolic network has been to search all articles on A. niger for information on metabolism and compile lists of all reported intracellular enzymatic activities, metabolites and biomass components. These lists were examined for pathways with gaps and these gaps were filled with enzymes reported in other Aspergillus species or by using information from the KEGG pathway database (http://www.genome.jp/kegg/kegg2.html). Gaps remaining after this were closed with the simplest possible reaction(s) (i.e. dehydrogenation or an elimination of water). After the metabolic network was compiled, ORFs were assigned to the included reactions (Figure 7).

Figure 7

Radar plots of the theoretical maximum yields of selected metabolites from glucose and ammonium. Value axis shows percentage of C moles of the metabolite from glucose; 100% denotes a full conversion of glucose to the product. Plot (A) shows the twelve essential precursor metabolites as defined in (Stephanopoulos et al, 1998). Plot (B) shows the 20 common amino acids. Plot (C) shows selected organic acids.

Full figure and legend (237K)Figures & Tables index

The final metabolic network comprises 1190 biochemically unique reactions with no gaps. If reactions for isoenzymes are added based on the gene annotation, the total number is 2240 reactions. In total, 52 enzyme complexes were identified in the network. Overall, 371 cited articles and 871 unique ORFs are associated with the included reactions. The network comprises a total of 1045 metabolites distributed across three compartments; extracellular, cytosolic and mitochondrial. A graphical map of the metabolism has been prepared in the notation known from biochemical textbooks. The metabolic network was also formatted to be a metabolic model of A. niger metabolism (named A. niger iMA871), thus creating the largest metabolic model ever presented for a fungal species.

To evaluate the predictive capabilities of A. niger i MA871 and assess the inference of the modeling results, experimental results from articles were compared to the predictions of simulations of A. niger i MA871. A comparison of predicted yields of selected products compared to those yields reported in the literature showed the model predictions to be in good accordance with experimental values. It is also a property of the metabolic models to produce predictions of intracellular fluxes. This ability was examined by simulating the results of Pedersen et al (2000b), where intracellular carbon fluxes were determined in an oxaloacetate hydrolase-deficient strain and a wild type. The model predicts biomass yields and changes in carbon flow as a cause of the gene deletion in good accordance with the measured values. We also evaluated the prediction of physiological responses with the case example of oxidative phosphorylation during production of citric acid, a bulk chemical produced efficiently by A. niger. Predictions of the activities in the electron transport chain were in good accordance with the known response of the different oxidative pathways (Promper et al, 1993; Kirimura et al, 1999, 2006; Karaffa and Kubicek, 2003). Interestingly, we find three modes of operation (phases A–C of Figure 5) that allow a flexible regulation of the P/O ratio to dispose of excess mitochondrial NADH produced during high-yield citrate production. Finally, we validated the ORF assignment to the reactions by comparing the network to transcription data published with the genome sequencing of A. niger CBS 513.88 (Pel et al, 2007). It was found that which genes were expressed and which were not, correlated nicely with the metabolic pathways essential for biomass production.

Figure 5

Predicted activities of respiratory pathways in citric acid fermentations. In phase A, the standard respiratory pathway is the sole source of NADH turnover. In phase B, the proton-pumping NADH dehydrogenase is substituted for the non-proton-pumping NADH-ubiquinone oxidoreductase, resulting in a pathway that is a meld of the alternative oxidase pathway and the standard oxidative pathway. In phase C, the cytochrome pathway is substituted for the alternative oxidase pathway. The net result is a decreasing amount of ATP produced per metabolized NADH as the citric acid yield increases, resulting in a changing P/O ratio.

Full figure and legend (256K)Figures & Tables index

In conclusion, we have reconstructed a metabolic network and a metabolic network of A. niger that provides a link between reactions, genes and scientific papers and hence represents a comprehensive database of A. niger metabolism. We see this contribution as a possible catalyst for systems-level research in this versatile cell factory in the current development toward a bio-based economy. synopsis

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