Computing autocatalytic sets to unravel inconsistencies in metabolic network reconstructions.

MOTIVATION: Genome-scale metabolic network reconstructions have been established as a powerful tool for the prediction of cellular phenotypes and metabolic capabilities of organisms. In recent years, the number of network reconstructions has been constantly increasing, mostly because of the availability of novel (semi-)automated procedures, which enabled the reconstruction of metabolic models based on individual genomes and their annotation. The resulting models are widely used in numerous applications. However, the accuracy and predictive power of network reconstructions are commonly limited by inherent inconsistencies and gaps. RESULTS: Here we present a novel method to validate metabolic network reconstructions based on the concept of autocatalytic sets. Autocatalytic sets correspond to collections of metabolites that, besides enzymes and a growth medium, are required to produce all biomass components in a metabolic model. These autocatalytic sets are well-conserved across all domains of life, and their identification in specific genome-scale reconstructions allows us to draw conclusions about potential inconsistencies in these models. The method is capable of detecting inconsistencies, which are neglected by other gap-finding methods. We tested our method on the Model SEED, which is the largest repository for automatically generated genome-scale network reconstructions. In this way, we were able to identify a significant number of missing pathways in several of these reconstructions. Hence, the method we report represents a powerful tool to identify inconsistencies in large-scale metabolic networks. AVAILABILITY AND IMPLEMENTATION: The method is available as source code on http://users.minet.uni-jena.de/∼m3kach/ASBIG/ASBIG.zip. CONTACT: christoph.kaleta@uni-jena.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Regioselective Dichlorination of a Non-Activated Aliphatic Carbon Atom and Phenolic Bismethylation by a Multifunctional Fungal Flavoenzyme.

The regioselective functionalization of non-activated carbon atoms such as aliphatic halogenation is a major synthetic challenge. A novel multifunctional enzyme catalyzing the geminal dichlorination of a methyl group was discovered in Aspergillus oryzae (Koji mold), an important fungus that is widely used for Asian food fermentation. A biosynthetic pathway encoded on two different chromosomes yields mono- and dichlorinated polyketides (diaporthin derivatives), including the cytotoxic dichlorodiaporthin as the main product. Bioinformatic analyses and functional genetics revealed an unprecedented hybrid enzyme (AoiQ) with two functional domains, one for halogenation and one for O-methylation. AoiQ was successfully reconstituted in vivo and in vitro, unequivocally showing that this FADH2 -dependent enzyme is uniquely capable of the stepwise gem-dichlorination of a non-activated carbon atom on a freestanding substrate. Genome mining indicated that related hybrid enzymes are encoded in cryptic gene clusters in numerous ecologically relevant fungi.

Biosynthesis of the halogenated mycotoxin aspirochlorine in koji mold involves a cryptic amino acid conversion.

Aspirochlorine (1) is an epidithiodiketopiperazine (ETP) toxin produced from koji mold (Aspergillus oryzae), which has been used in the oriental cuisine for over two millennia. Considering its potential risk for food safety, we have elucidated the molecular basis of aspirochlorine biosynthesis. By a combination of genetic and chemical analyses we found the acl gene locus and identified the key role of AclH as a chlorinase. Stable isotope labeling, biotransformation, and mutational experiments, analysis of intermediates and an in vitro adenylation domain assay gave totally unexpected insights into the acl pathway: Instead of one Phe and one Gly, two Phe units are assembled by an iterative non-ribosomal peptide synthetase (NRPS, AclP), followed by halogenation and an unprecedented Phe to Gly amino acid conversion. Biological assays showed that both amino acid transformations are required to confer cytotoxicity and antifungal activity to the mycotoxin.