Stéphanie Heux

High-throughput quantitative metabolomics: workflow for cultivation, quenching, and analysis of yeast in a multiwell format.

Metabolomics is a founding pillar of quantitative biology and a valuable tool for studying metabolism and its regulation. Here we present a workflow for metabolomics in microplate format which affords high-throughput and yet quantitative monitoring of primary metabolism in microorganisms and in particular yeast. First, the most critical step of rapid sampling was adapted to a multiplex format by using fritted 96-well plates for cultivation, which ensure fast sample transfer and permit us to use well-established quenching in cold solvents. Second, extensive optimization of large-volume injection on a GC/TOF instrument provided the sensitivity necessary for robust quantification of 30 primary metabolites in 0.6 mg of yeast biomass. The metabolome profiles of bakers yeast cultivated in fritted well plates or in shake flasks were equivalent. Standard deviations of measured metabolites were between 10% and 50% within one plate. As a proof of principle we compared the metabolome of wild-type Saccharomyces cerevisiae and the single-deletion mutant Delta sdh1, which were clearly distinguishable by a 10-fold increase of the intracellular succinate concentration in the mutant. The described workflow allows the production of large amounts of metabolome samples within a day, is compatible with virtually all liquid extraction protocols, and paves the road to quantitative screens.

Engineering a Saccharomyces cerevisiae wine yeast that exhibits reduced ethanol production during fermentation under controlled microoxygenation conditions

ABSTRACT We recently showed that expressing an H2O-NADH oxidase in Saccharomyces cerevisiae drastically reduces the intracellular NADH concentration and substantially alters the distribution of metabolic fluxes in the cell. Although the engineered strain produces a reduced amount of ethanol, a high level of acetaldehyde accumulates early in the process (1 g/liter), impairing growth and fermentation performance. To overcome these undesirable effects, we carried out a comprehensive analysis of the impact of oxygen on the metabolic network of the same NADH oxidase-expressing strain. While reducing the oxygen transfer rate led to a gradual recovery of the growth and fermentation performance, its impact on the ethanol yield was negligible. In contrast, supplying oxygen only during the stationary phase resulted in a 7% reduction in the ethanol yield, but without affecting growth and fermentation. This approach thus represents an effective strategy for producing wine with reduced levels of alcohol. Importantly, our data also point to a significant role for NAD+ reoxidation in controlling the glycolytic flux, indicating that engineered yeast strains expressing an NADH oxidase can be used as a powerful tool for gaining insight into redox metabolism in yeast.

Production of carbon-13-labeled cadaverine by engineered Corynebacterium glutamicum using carbon-13-labeled methanol as co-substrate

Methanol, a one-carbon compound, can be utilized by a variety of bacteria and other organisms as carbon and energy source and is regarded as a promising substrate for biotechnological production. In this study, a strain of non-methylotrophic Corynebacterium glutamicum, which was able to produce the polyamide building block cadaverine as non-native product, was engineered for co-utilization of methanol. Expression of the gene encoding NAD+-dependent methanol dehydrogenase (Mdh) from the natural methylotroph Bacillus methanolicus increased methanol oxidation. Deletion of the endogenous aldehyde dehydrogenase genes ald and fadH prevented methanol oxidation to carbon dioxide and formaldehyde detoxification via the linear formaldehyde dissimilation pathway. Heterologous expression of genes for the key enzymes hexulose-6-phosphate synthase and 6-phospho-3-hexuloisomerase of the ribulose monophosphate (RuMP) pathway in this strain restored growth in the presence of methanol or formaldehyde, which suggested efficient formaldehyde detoxification involving RuMP key enzymes. While growth with methanol as sole carbon source was not observed, the fate of 13C-methanol added as co-substrate to sugars was followed and the isotopologue distribution indicated incorporation into central metabolites and in vivo activity of the RuMP pathway. In addition, 13C-label from methanol was traced to the secreted product cadaverine. Thus, this synthetic biology approach led to a C. glutamicum strain that converted the non-natural carbon substrate methanol at least partially to the non-native product cadaverine.

A novel platform for automated high-throughput fluxome profiling of metabolic variants.

Advances in metabolic engineering are enabling the creation of a large number of cell factories. However, high-throughput platforms do not yet exist for rapidly analyzing the metabolic network of the engineered cells. To fill the gap, we developed an integrated solution for fluxome profiling of large sets of biological systems and conditions. This platform combines a robotic system for (13)C-labelling experiments and sampling of labelled material with NMR-based isotopic fingerprinting and automated data interpretation. As a proof-of-concept, this workflow was applied to discriminate between Escherichia coli mutants with gradual expression of the glucose-6-phosphate dehydrogenase. Metabolic variants were clearly discriminated while pathways that support metabolic flexibility towards modulation of a single enzyme were elucidating. By directly connecting the data flow between cell cultivation and flux quantification, considerable advances in throughput, robustness, release of resources and screening capacity were achieved. This will undoubtedly facilitate the development of efficient cell factories.

Recent advances in high-throughput (13)C-fluxomics.

The rise of high throughput (HT) strain engineering tools accompanying the area of synthetic biology is supporting the generation of a large number of microbial cell factories. A current bottleneck in process development is our limited capacity to rapidly analyze the metabolic state of the engineered strains, and in particular their intracellular fluxes. HT 13C-fluxomics workflows have not yet become commonplace, despite the existence of several HT tools at each of the required stages. This includes cultivation and sampling systems, analytics for isotopic analysis, and software for data processing and flux calculation. Here, we review recent advances in the field and highlight bottlenecks that must be overcome to allow the emergence of true HT 13C-fluxomics workflows.

FindPath: a Matlab solution for in silico design of synthetic metabolic pathways

SUMMARY Several methods and computational tools have been developed to design novel metabolic pathways. A major challenge is evaluating the metabolic efficiency of the designed pathways in the host organism. Here we present FindPath, a unified system to predict and rank possible pathways according to their metabolic efficiency in the cellular system. This tool uses a chemical reaction database to generate possible metabolic pathways and exploits constraint-based models (CBMs) to identify the most efficient synthetic pathway to achieve the desired metabolic function in a given host microorganism. FindPath can be used with common tools for CBM manipulation and uses the standard SBML format for both input and output files. AVAILABILITY AND IMPLEMENTATION http://metasys.insa-toulouse.fr/software/findpath/. CONTACT heux@insa-toulouse.fr SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

A high-throughput metabolomics method to predict high concentration cytotoxicity of drugs from low concentration profiles

A major source of drug attrition in pharmacological development is drug toxicity, which eventually manifests itself in detrimental physiological effects. These effects can be assessed in large sample cohorts, but generating rich sets of output variables that are necessary to predict toxicity from lower drug dosages is problematic. Currently the throughput of methods that enable multi-parametric cellular readouts over many drugs and large ranges of concentrations is limited. Since metabolism is at the core of drug toxicity, we develop here a high-throughput intracellular metabolomics platform for relative measurement of 50–100 targeted metabolites by flow injection-tandem mass spectrometry. Specifically we focused on central metabolism of the yeast Saccharomyces cerevisiae because potential cytotoxic effects of drugs can be expected to affect this ubiquitous core network. By machine learning based on intracellular metabolite responses to 41 drugs that were administered at seven concentrations over three orders of magnitude, we demonstrate prediction of cytotoxicity in yeast from intracellular metabolome patterns obtained at much lower drug concentrations that exert no physiological toxicity. Furthermore, the 13C-determined intracellular response of metabolic fluxes to drug treatment demonstrates the functional performance of the network to be rather robust, until growth was compromised. Thus we provide evidence that phenotypic robustness to drug challenges is achieved by a flexible make-up of the metabolome.

Functional characterization of a gene locus from an uncultured gut Bacteroides conferring xylo-oligosaccharides utilization to Escherichia coli.

In prominent gut Bacteroides strains, sophisticated strategies have been evolved to achieve the complete degradation of dietary polysaccharides such as xylan, which is one of the major components of the plant cell wall. Polysaccharide Utilization Loci (PULs) consist of gene clusters encoding different proteins with a vast arsenal of functions, including carbohydrate binding, transport and hydrolysis. Transport is often attributed to TonB‐dependent transporters, although major facilitator superfamily (MFS) transporters have also been identified in some PULs. However, until now, few of these transporters have been biochemically characterized. Here, we targeted a PUL‐like system from an uncultivated Bacteroides species that is highly prevalent in the human gut metagenome. It encodes three glycoside‐hydrolases specific for xylo‐oligosaccharides, a SusC/SusD tandem homolog and a MFS transporter. We combined PUL rational engineering, metabolic and transcriptional analysis in Escherichia coli to functionally characterize this genomic locus. We demonstrated that the SusC and the MFS transporters are specific for internalization of linear xylo‐oligosaccharides of polymerization degree up to 3 and 4 respectively. These results were strengthened by the study of growth dynamics and transcriptional analyses in response to XOS induction of the PUL in the native strain, Bacteroides vulgatus.

High-throughput workflow for monitoring and mining bioprocess data and its application to inferring the physiological response of Escherichia coli to perturbations.

ABSTRACT Miniaturization and high-throughput screening are currently the focus of emerging research areas such as systems biology and systems biotechnology. A fluorescence-based screening assay for the online monitoring of oxygen and pH and a numerical method to mine the resulting online process data are described. The assay employs commercial phosphorescent oxygen- and pH-sensitive probes in standard 48- or 96-well plates on a plate reader equipped with a shaker. In addition to dual parametric analysis of both pH and oxygen in a single well, the assay allows monitoring of growth, as measured by absorbance. Validation of the assay is presented and compared with commercially available plates equipped with optical sensors for oxygen and pH. By using model-free fitting to the readily available online measurements, the length and rate of each phase such as the duration of lag and transition phase or acidification, growth, and oxygen consumption rates are automatically detected. In total, nine physiological descriptors, which can be used for further statistical and comparison analysis, are extracted from the pH, oxygen partial pressure (pO2), and optical density (OD) profiles. The combination of a simple mix-and-measure procedure with an automatic data mining method allows high sample throughput and good reproducibility while providing a physiological state identification and characterization of test cells. As a proof of concept, the utility of the workflow in assessing the physiological response of Escherichia coli to environmental and genetic perturbations is demonstrated.

MetExplore: collaborative edition and exploration of metabolic networks

Abstract Metabolism of an organism is composed of hundreds to thousands of interconnected biochemical reactions responding to environmental or genetic constraints. This metabolic network provides a rich knowledge to contextualize omics data and to elaborate hypotheses on metabolic modulations. Nevertheless, performing this kind of integrative analysis is challenging for end users with not sufficiently advanced computer skills since it requires the use of various tools and web servers. MetExplore offers an all-in-one online solution composed of interactive tools for metabolic network curation, network exploration and omics data analysis. In particular, it is possible to curate and annotate metabolic networks in a collaborative environment. The network exploration is also facilitated in MetExplore by a system of interactive tables connected to a powerful network visualization module. Finally, the contextualization of metabolic elements in the network and the calculation of over-representation statistics make it possible to interpret any kind of omics data. MetExplore is a sustainable project maintained since 2010 freely available at https://metexplore.toulouse.inra.fr/metexplore2/.