BackgroundClostridium autoethanogenum is an acetogenic bacterium capable of producing high value commodity chemicals and biofuels from the C1 gases present in synthesis gas. This common industrial waste gas can act as the sole energy and carbon source for the bacterium that converts the low value gaseous components into cellular building blocks and industrially relevant products via the action of the reductive acetyl-CoA (Wood-Ljungdahl) pathway. Current research efforts are focused on the enhancement and extension of product formation in this organism via synthetic biology approaches. However, crucial to metabolic modelling and directed pathway engineering is a reliable and comprehensively annotated genome sequence.ResultsWe performed next generation sequencing using Illumina MiSeq technology on the DSM10061 strain of Clostridium autoethanogenum and observed 243 single nucleotide discrepancies when compared to the published finished sequence (NCBI: GCA_000484505.1), with 59.1 % present in coding regions. These variations were confirmed by Sanger sequencing and subsequent analysis suggested that the discrepancies were sequencing errors in the published genome not true single nucleotide polymorphisms. This was corroborated by the observation that over 90 % occurred within homopolymer regions of greater than 4 nucleotides in length. It was also observed that many genes containing these sequencing errors were annotated in the published closed genome as encoding proteins containing frameshift mutations (18 instances) or were annotated despite the coding frame containing stop codons, which if genuine, would severely hinder the organism’s ability to survive. Furthermore, we have completed a comprehensive manual curation to reduce errors in the annotation that occur through serial use of automated annotation pipelines in related species. As a result, different functions were assigned to gene products or previous functional annotations rejected because of missing evidence in various occasions.ConclusionsWe present a revised manually curated full genome sequence for Clostridium autoethanogenum DSM10061, which provides reliable information for genome-scale models that rely heavily on the accuracy of annotation, and represents an important step towards the manipulation and metabolic modelling of this industrially relevant acetogen.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2287-5) contains supplementary material, which is available to authorized users.

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To elucidate the biosynthetic pathways for all proteinogenic amino acids in Xanthomonas campestris pv. campestris, this study combines results obtained by in silico genome analysis and by (13)C-NMR-based isotopologue profiling to provide a panoramic view on a substantial section of bacterial metabolism. Initially, biosynthesis pathways were reconstructed from an improved annotation of the complete genome of X. campestris pv. campestris B100. This metabolic reconstruction resulted in the unequivocal identification of biosynthesis routes for 17 amino acids in total: arginine, asparagine, aspartate, cysteine, glutamate, glutamine, histidine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, and valine. Ambiguous pathways were reconstructed from the genome data for alanine, glycine, and isoleucine biosynthesis. (13)C-NMR analyses supported the identification of the metabolically active pathways. The biosynthetic routes for these amino acids were derived from the precursor molecules pyruvate, serine, and pyruvate, respectively. By combining genome analysis and isotopologue profiling, a comprehensive set of biosynthetic pathways covering all proteinogenic amino acids was unraveled for this plant pathogenic bacterium, which plays an important role in biotechnology as a producer of the exopolysaccharide xanthan. The data obtained lay ground for subsequent functional analyses in post-genomics and biotechnology, while the innovative combination of in silico and wet lab technology described here is promising as a general approach to elucidate metabolic pathways.

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Clostridium autoethanogenum is an industrial microbe used for the commercial‐scale production of ethanol from carbon monoxide. While significant progress has been made in the attempted diversification of this bioprocess, further improvements are desirable, particularly in the formation of the high‐value platform chemicals such as 2,3‐butanediol (2,3‐BD). A new, experimentally parameterised genome‐scale model of C. autoethanogenum predicts dramatically increased 2,3‐BD production under non‐carbon‐limited conditions when thermodynamic constraints on hydrogen production are considered.

We have investigated the applicability of commercially available lyophilized spirulina ( Arthrospira platensis), a microorganism uniformly labeled with C, as a readily accessible source of multipleC-labeled metabolites suitable as internal standards for the quantitative determination of intracellular bacterial metabolites. Metabolites of interest were analyzed by hydrophilic-interaction liquid chromatography coupled with high-resolution mass spectrometry. Multiple internal standards obtained from uniformly (U)-C-labeled extracts from spirulina were used to enable isotope-dilution mass spectrometry (IDMS) in the identification and quantification of intracellular metabolites. Extraction of the intracellular metabolites of Clostridium autoethanogenum using 2:1:1 chloroform/methanol/water was found to be the optimal method in comparison with freeze-thaw, homogenization, and sonication methods. The limits of quantification were ≤1 μM with excellent linearity for all of the calibration curves ( R ≥ 0.99) for 74 metabolites. The precision and accuracy were found to be within relative standard deviations (RSDs) of 15% for 49 of the metabolites and within RSDs of 20% for all of the metabolites. The method was applied to study the effects of feeding different levels of carbon monoxide (as a carbon source) on the central metabolism and Wood-Ljungdahl pathway of C. autoethanogenum grown in continuous culture over 35 days. Using LC-IDMS with U-C spirulina allowed the successful quantification of 52 metabolites in the samples, including amino acids, carboxylic acids, sugar phosphates, purines, and pyrimidines. The method provided absolute quantitative data on intracellular metabolites that was suitable for computational modeling to understand and optimize the C. autoethanogenum metabolic pathways active in gas fermentation.

The well-studied plant pathogenic bacterium Xanthomonas campestris pv. campestris (Xcc) synthesizes the biotechnologically important polysaccharide xanthan gum, which is also regarded as a virulence factor in plant interactions. In Xcc, sugars like glucose are utilized as a source to generate energy and biomass for growth and pathogenicity. In this study, we used [1-(13)C]glucose as a tracer to analyze the fluxes in the central metabolism of the bacterium growing in a minimal medium. (13)C-Metabolic flux analysis based on gas chromatography-mass spectrometry (GC-MS) confirmed the prevalent catabolic role of the Entner-Doudoroff pathway. Comparative nuclear magnetic resonance (NMR)-based isotopologue profiling of a mutant deficient in glycolysis gave evidence for a moderate flux via glycolysis in the wild-type. In addition to reconfirming the Entner-Doudoroff pathway as a catabolic main route, this approach affirmed a numerically minor but important flux via the pentose phosphate pathway.

ABSTRACT. New sequencing technologies provide ultra-fast access to novel microbial genome data. For their interpretation, an efficient bioinformatics pipeline that facilitates in silico reconstruction of metabolic networks is highly desirable. The software tool CARMEN performs in silico reconstruction of metabolic networks to interpret genome data in a functional context. CARMEN supports the visualization of automatically derived metabolic networks based on pathway information from the KEGG database or from user-defined SBML templates; this software also enables comparative genomics. The reconstructed networks are stored in standardized SBML format. We demonstrated the functionality of CARMEN with a major application 1661 ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 9 (1): 1660-1672 (2010) In silico metabolic network reconstruction example focusing on the reconstruction of glycolysis and related metabolic reactions of Xanthomonas campestris pv. campestris B100. The curation of such pathways facilitates enhanced visualization of experimental results, simulations and comparative genomics. A second application of this software was performed on a set of corynebacteria to compare and to visualize their carbohydrate metabolism. In conclusion, using CARMEN, we developed highly automated data analysis software that rapidly converts sequence data into new knowledge, replacing the time-consuming manual reconstruction of metabolic networks. This tool is particularly useful for obtaining an overview of newly sequenced genomes and their metabolic blueprints and for comparative genome analysis. The generated pathways provide automated access to modeling and simulation tools that are compliant with the SBML standard. A user-friendly web interface of CARMEN is available at http://carmen.cebitec.uni-bielefeld.de.