Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions.

Metabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.

Gsmodutils: a python based framework for test-driven genome scale metabolic model development.

MOTIVATION: Genome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle. RESULTS: As part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions. AVAILABILITY AND IMPLEMENTATION: The software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Engineering of vitamin prototrophy in Clostridium ljungdahlii and Clostridium autoethanogenum.

Clostridium autoethanogenum and Clostridium ljungdahlii are physiologically and genetically very similar strict anaerobic acetogens capable of growth on carbon monoxide as sole carbon source. While exact nutritional requirements have not been reported, we observed that for growth, the addition of vitamins to media already containing yeast extract was required, an indication that these are fastidious microorganisms. Elimination of complex components and individual vitamins from the medium revealed that the only organic compounds required for growth were pantothenate, biotin and thiamine. Analysis of the genome sequences revealed that three genes were missing from pantothenate and thiamine biosynthetic pathways, and five genes were absent from the pathway for biotin biosynthesis. Prototrophy in C. autoethanogenum and C. ljungdahlii for pantothenate was obtained by the introduction of plasmids carrying the heterologous gene clusters panBCD from Clostridium acetobutylicum, and for thiamine by the introduction of the thiC-purF operon from Clostridium ragsdalei. Integration of panBCD into the chromosome through allele-coupled exchange also conveyed prototrophy. C. autoethanogenum was converted to biotin prototrophy with gene sets bioBDF and bioHCA from Desulfotomaculum nigrificans strain CO-1-SRB, on plasmid and integrated in the chromosome. The genes could be used as auxotrophic selection markers in recombinant DNA technology. Additionally, transformation with a subset of the genes for pantothenate biosynthesis extended selection options with the pantothenate precursors pantolactone and/or beta-alanine. Similarly, growth was obtained with the biotin precursor pimelate combined with genes bioYDA from C. acetobutylicum. The work raises questions whether alternative steps exist in biotin and thiamine biosynthesis pathways in these acetogens.

Progress towards platform chemical production using Clostridium autoethanogenum.

Since 2013, there has been an explosion in the number of research articles published on Clostridium autoethanogenum, an acetogen capable of producing platform chemicals such as ethanol and 2,3-butanediol from greenhouse gases. However, no review focusing solely on C. autoethanogenum has appeared in the literature. This review outlines the research conducted into this organism in three broad categories (Enzymology, Genetics, and Systems Biology) and suggestions for future research are offered.

Whole genome sequence and manual annotation of Clostridium autoethanogenum, an industrially relevant bacterium.

BACKGROUND: Clostridium 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. RESULTS: We 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. CONCLUSIONS: We 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.

Quantitative Bioreactor Monitoring of Intracellular Bacterial Metabolites in Clostridium autoethanogenum Using Liquid Chromatography-Isotope Dilution Mass Spectrometry.

We report a liquid chromatography-isotope dilution mass spectrometry method for the simultaneous quantification of 131 intracellular bacterial metabolites of Clostridium autoethanogenum. A comprehensive mixture of uniformly 13C-labeled internal standards (U-13C IS) was biosynthesized from the closely related bacterium Clostridium pasteurianum using 4% 13C-glucose as a carbon source. The U-13C IS mixture combined with 12C authentic standards was used to validate the linearity, precision, accuracy, repeatability, limits of detection, and quantification for each metabolite. A robust-fitting algorithm was employed to reduce the weight of the outliers on the quantification data. The metabolite calibration curves were linear with R 2 ≥ 0.99, limits of detection were ≤1.0 µM, limits of quantification were ≤10 µM, and precision/accuracy was within RSDs of 15% for all metabolites. The method was subsequently applied for the daily monitoring of the intracellular metabolites of C. autoethanogenum during a CO gas fermentation over 40 days as part of a study to optimize biofuel production. The concentrations of the metabolites were estimated at steady states of different pH levels using the robust-fitting mathematical approach, and we demonstrate improved accuracy of results compared to conventional regression. Metabolic pathway analysis showed that reactions of the incomplete (branched) tricarboxylic acid "cycle" were the most affected pathways associated with the pH shift in the bioreactor fermentation of C. autoethanogenum and the concomitant changes in ethanol production.