Additional file 1 of Strain engineering and metabolic flux analysis of a probiotic yeast Saccharomyces boulardii for metabolizing l-fucose, a mammalian mucin component

Additional file 1. Genome-scale metabolic model of Saccharomyces cerevisiae that metabolizes fucose. Table S1. Sequences of the plasmids constructed in this study

Metabolomic phenotypes integrated with primary metabolic features.

<p>(A) Principal component analysis of metabolomic profiles of 109 metabolites of the wild type (orange) and the homozygous mutant (purple). T1 indicates discriminating vector 1 that explained the largest degree of variation in the dataset. Likewise, T2 indicates principal component 2 with the second largest degree of variation. (B) Hierarchical clustering analysis. Clustering analysis was performed across the metabolites and samples by using Spearman rank correlation and average linkage methods. Each column and each row represent a fly sample and an individual metabolite, respectively.</p

Metabolic networks of biochemical reaction pairs and chemical similarity.

<p>The regulation of all identified metabolites in files is depicted. Blue = down-regulated metabolites, red = up-regulated metabolites in the heterozygous mutants (<i>trpA1</i>^<i>1</i>) compared to the wild-type (Student’s t-test, <i>P</i><0.05). Node sizes reflect the magnitude of differential metabolite expression. Metabolites that did not show significant difference in levels were left unnamed to maintain visual clarity. Dark blue edges represent connections determined via Kegg reaction pair information, and light blue edges represent assemblies as evaluated using Tanimoto scores (score > 0.7)</p

Metabolites showing consensus expression pattern across different genetic settings.

<p>(A) Venn diagram showing allele-specific metabolite changes in <i>trpA1</i>^<i>1</i> and <i>trpA1</i>^<i>GAL4</i> respectively, compared with wild-type (B) Venn diagram showing the chemical overlap between <i>trpA1</i>-expressed strains and <i>trpA1</i>-repressed strains along with two independent comparisons. (C) List of metabolites that are coordinately altered according to <i>trpA1</i> gene expression.</p

A <i>GAL4</i> knock-in to the <i>trpA1</i> locus drives the expression in the digestive system.

<p>(A-B) Brains dissected and stained with anti-GFP from <i>UAS</i>-<i>mCD8</i>::<i>GFP</i>;<i>trpA1</i>^<i>GAL4</i> flies at the adult stage. (C–E) Brains dissected and stained with anti-dILP2 (C) and anti-GFP (D) from <i>UAS</i>-<i>mCD8</i>::<i>GFP</i>;<i>trpA1</i>^<i>GAL4</i> flies in the adult stage. The merged image is shown (E). Broad expression of the <i>trpA1</i> reporter is apparent in the subesophageal ganglions (SOG). Scale bars, 50 m.</p

Proposed functional linkage of <i>trpA1</i> to central carbon metabolism and methionine salvage pathway in <i>Drosophila</i>.

<p>Arrows with filled colors indicate significant changes (<i>P</i><0.05) in <i>trpA1</i> mutants compared to wild-type, and arrows without any colors presented the down-regulation pattern but without statistical significance. Data distributions were displayed by box-whisker plots, giving the mean value, the standard error as the box, and whiskers indicating 1.96 fold the standard.</p

The orthogonal least square-discriminative analysis (OPLS-DA) of metabolomic profiles of synovial fluids of Behcet’s disease (BD) with arthritis and seronegative arthritis (SNA).

<p>(A) The score plot of the OPLS-DA model for the BD with arthritis and SNA groups (t[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135856#pone.0135856.ref001" target="_blank">1</a>], score of the non-orthogonal component; to[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135856#pone.0135856.ref002" target="_blank">2</a>], score of the orthogonal component). The generated explained variation values, 0.45 of <i>R</i>^<i>2</i><i>X</i> and 0.91 of <i>R</i>^<i>2</i><i>Y</i>, and the predictive capability, 0.64 of <i>Q</i>^<i>2</i> indicated the excellence in modeling and prediction of the OPLS-DA model, respectively, with clear discrimination between BD with arthritis and SNA groups. (B) V-plot with p(corr) and VIP values of 123 different metabolites in OPLS-DA. The metabolites with p(corr) < 0 were those decreased in the BD with arthritis group while the metabolites with p(corr) > 0 were those increased in the BD with arthritis group. The metabolites with VIP > 1 were represented in Fig 1B.</p

Identification of 123 metabolites from synovial fluid samples of 24 patients with Behcet’s disease with arthritis and seronegative arthritis using BinBase.

<p>Identification of 123 metabolites from synovial fluid samples of 24 patients with Behcet’s disease with arthritis and seronegative arthritis using BinBase.</p

Receiver operating characteristic (ROC) curve of 3 combined biomarkers for distinguishing Behcet’s disease (BD) with arthritis from seronegative arthritis (SNA) groups.

<p>Glutamate, citramalate, and valine were selected and validated as putative biomarkers for BD with arthritis for distinguishing BD with arthritis from SNA groups by ROC curve analysis. The sensitivity and specificity were 100% and 61.1%, respectively, and the value of the area under curve (AUC) was 0.870.</p

Evaluation and Optimization of Metabolome Sample Preparation Methods for Saccharomyces cerevisiae

Metabolome sampling is one of the most important factors that determine the quality of metabolomics data. The main steps in metabolite sample preparation include quenching and metabolite extraction. Quenching with 60% (v/v) cold methanol at −40 °C has been most commonly used for Saccharomyces cerevisiae, and this method was recently modified as “leakage-free cold methanol quenching” using pure methanol at −40 °C. Boiling ethanol (75%, v/v) and cold pure methanol are the most widely used extraction solvents for S. cerevisiae. In the present study, metabolome sampling protocols, including the above methods, were evaluated by analyzing 110 identified intracellular metabolites of S. cerevisiae using gas chromatography/time-of-flight mass spectrometry. According to our results, fast filtration followed by washing with an appropriate volume of water can minimize the metabolite loss due to cell leakage as well as the contamination by extracellular metabolites. For metabolite extraction, acetonitrile/water mixture (1:1, v/v) at −20 °C was the most effective. These results imply that the systematic evaluation of existing methods and the development of customized methods for each microorganism are critical for metabolome sample preparation to facilitate the reliable and accurate analysis of metabolome