Metabolic discrimination of synovial fluid between rheumatoid arthritis and osteoarthritis using gas chromatography/time-of-flight mass spectrometry.

INTRODUCTION: Rheumatoid arthritis (RA) and osteoarthritis (OA) are clinicopathologically different. OBJECTIVES: We aimed to assess the feasibility of metabolomics in differentiating the metabolite profiles of synovial fluid between RA and OA using gas chromatography/time-of-flight mass spectrometry. METHODS: We first compared the global metabolomic changes in the synovial fluid of 19 patients with RA and OA. Partial least squares-discriminant, hierarchical clustering, and univariate analyses were performed to distinguish metabolites of RA and OA. These findings were then validated using synovial fluid samples from another set of 15 patients with RA and OA. RESULTS: We identified 121 metabolites in the synovial fluid of the first 19 samples. The score plot of PLS-DA showed a clear separation between RA and OA. Twenty-eight crucial metabolites, including hypoxanthine, xanthine, adenosine, citrulline, histidine, and tryptophan, were identified to be capable of distinguishing RA metabolism from that of OA; these were found to be associated with purine and amino acid metabolism. CONCLUSION: Our results demonstrated that metabolite profiling of synovial fluid could clearly discriminate between RA and OA, suggesting that metabolomics may be a feasible tool to assist in the diagnosis and advance the comprehension of pathological processes for diseases.

Strain engineering and metabolic flux analysis of a probiotic yeast Saccharomyces boulardii for metabolizing L-fucose, a mammalian mucin component.

BACKGROUND: Saccharomyces boulardii is a probiotic yeast that exhibits antimicrobial and anti-toxin activities. Although S. boulardii has been clinically used for decades to treat gastrointestinal disorders, several studies have reported weak or no beneficial effects of S. boulardii administration in some cases. These conflicting results of S. boulardii efficacity may be due to nutrient deficiencies in the intestine that make it difficult for S. boulardii to maintain its metabolic activity. RESULTS: To enable S. boulardii to overcome any nutritional deficiencies in the intestine, we constructed a S. boulardii strain that could metabolize L-fucose, a major component of mucin in the gut epithelium. The fucU, fucI, fucK, and fucA from Escherichia coli and HXT4 from S. cerevisiae were overexpressed in S. boulardii. The engineered S. boulardii metabolized L-fucose and produced 1,2-propanediol under aerobic and anaerobic conditions. It also produced large amounts of 1,2-propanediol under strict anaerobic conditions. An in silico genome-scale metabolic model analysis was performed to simulate the growth of S. boulardii on L-fucose, and elementary flux modes were calculated to identify critical metabolic reactions for assimilating L-fucose. As a result, we found that the engineered S. boulardii consumes L-fucose via (S)-lactaldehyde-(S)-lactate-pyruvate pathway, which is highly oxygen dependent. CONCLUSION: To the best of our knowledge, this is the first study in which S. cerevisiae and S. boulardii strains capable of metabolizing L-fucose have been constructed. This strategy could be used to enhance the metabolic activity of S. boulardii and other probiotic microorganisms in the gut.

Variation in the synovial fluid metabolome according to disease activity of rheumatoid arthritis.

OBJECTIVES: Because genetic and environmental factors both contribute to rheumatoid arthritis (RA), metabolomics could be a very useful tool to elucidate the pathophysiology of RA, and to predict response to treatment. This study was carried out to investigate synovial fluid (SF) metabolic perturbation in RA patients according to the degree of disease activity using gas chromatography/time-of-flight mass spectrometry (GC/TOF MS). METHODS: SF samples were obtained from 48 RA patients. Disease activity was assessed using DAS28-ESR(3). SF metabolomics profiling was performed using GC/TOF-MS, in conjunction with multivariate statistical analyses and pathway analyses. RESULTS: Significant discrimination of metabolite profiles between moderate and high disease activity groups was shown by PLS-DA, which provided evidence that SF metabolic profiles predicted disease activity. We found the significant correlation between DAS28-ESR(3) value and the intensities of 12 metabolites. The intensities of glycocyamine and indol-3-lactate positively correlated with DAS28-ESR(3) value. On the other hand, ß-alanine, asparagine, citrate, cyano-L-alanine, leucine, nicotinamide, citrulline, methionine, oxoproline, and salicylaldehyde negatively correlated with DAS28-ESR(3) value. We found fifteen pathways that were significantly associated with disease activity in RA and that the higher the disease activity, the more amino acid metabolic processes were affected. CONCLUSIONS: We found the SF metabolic alterations in RA patients according to disease activity by using GC/TOF MS and identified 12 candidate metabolic biomarkers that may well reflect the disease activity of RA. SF metabolomic approaches based on GC/TOF MS might provide additional information relating to monitoring disease activity in RA.

Metabolomic and Transcriptomic Analyses of Escherichia coli for Efficient Fermentation of L-Fucose.

L-Fucose, one of the major monomeric sugars in brown algae, possesses high potential for use in the large-scale production of bio-based products. Although fucose catabolic pathways have been enzymatically evaluated, the effects of fucose as a carbon source on intracellular metabolism in industrial microorganisms such as Escherichia coli are still not identified. To elucidate the effects of fucose on cellular metabolism and to find clues for efficient conversion of fucose into bio-based products, comparative metabolomic and transcriptomic analyses were performed on E. coli on L-fucose and on D-glucose as a control. When fucose was the carbon source for E. coli, integration of the two omics analyses revealed that excess gluconeogenesis and quorum sensing led to severe depletion of ATP, resulting in accumulation and export of fucose extracellularly. Therefore, metabolic engineering and optimization are needed for E. coil to more efficiently ferment fucose. This is the first multi-omics study investigating the effects of fucose on cellular metabolism in E. coli. These omics data and their biological interpretation could be used to assist metabolic engineering of E. coli producing bio-based products using fucose-containing brown macroalgae.

Evaluation and optimization of quantitative analysis of cofactors from yeast by liquid chromatography/mass spectrometry.

Cofactors play pivotal roles in catabolism and anabolism in all living organisms. Many studies have investigated the concentration of cofactors in living organisms to understand their metabolic status, which can be used to produce valuable chemicals or to understand the pathophysiology of diseases. Among various analytical platforms, liquid chromatography/mass spectrometry (LC/MS) is the most frequently used method for the quantification of cofactors. Several studies have reported various analytical methods for cofactors using LC/MS. However, the lack of optimal LC/MS methods makes it challenging to analyze various cofactors simultaneously. In addition, the method of extracting cofactors from cells needs to be optimized because conventional protocols probably have low extraction efficiency, which makes it difficult to reflect the actual concentration of cofactors in cells. In this study, we systematically compared various analytical methods and suggested optimal methods for the analysis of cofactors using LC/MS. In addition, we systematically compared quenching methods and extraction solvents and suggested optimal methods for the extraction of cofactors from Saccharomyces cerevisiae. The optimized methods can be used as standard protocols for LC/MS analysis and the extraction of cofactors from S. cerevisiae.

Elucidation of the fucose metabolism of probiotic Lactobacillus rhamnosus GG by metabolomic and flux balance analyses.

Lactobacillus rhamnosus GG (LGG) is one of the most widely used probiotics because of its health benefits and safety. Fucose is among the most abundant hexoses in the human intestine, and LGG consumes fucose to produce energy or proliferate. However, no study has elucidated the metabolism by which LGG metabolizes fucose to produce energy, biomass, and extracellular metabolites. We used metabolomics and flux balance analysis to elucidate these mechanisms and highlight how they might affect the host. We found three different metabolic flux modes by which LGG anaerobically metabolizes fucose to produce energy and biomass. These metabolic flux modes differ from homolactic or heterolactic fermentation and account for the production of lactic acid, 1,2-propanediol, acetic acid, formic acid, and carbon dioxide as a result of fucose metabolism in LGG. We also used gas chromatography/time-of-flight mass spectrometry to identify a variety of short-chain fatty acids and organic acids secreted during fucose metabolism by LGG. Our study is the first to elucidate the unique fucose metabolism of LGG in anaerobic condition.

GC-TOF/MS-Based Metabolomics for Comparison of Volar and Non-Volar Skin Types.

Skin has heterogenous identities on different body sites despite similar cellular compositions. There are two types of skin, volar (palmoplantar) and non-volar (dorsal), which are characterized by epidermal thickness, pigmentation, and presence of hair follicles. However, the mechanisms underlying the development of these different skin types remain unclear. To investigate these, we profiled the cellular metabolites of volar and non-volar skin in mice using gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS), and further assessed the metabolic differences between them. In total, 96 metabolites from both volar and non-volar skin of mice were identified using the BinBase database system. Metabolomics analysis revealed important differences associated with amino acid metabolism (phenylalanine, tyrosine, and tryptophan biosynthesis; aspartate and glutamate metabolism), sugar metabolism (pentose phosphate pathway), and nucleotide metabolism (pyrimidine metabolism) in volar skin. Fifty metabolites were identified as potential biomarkers differentiating the physiological characteristics of these skin types. Of these, nine were highly increased whereas 41 were significantly decreased in volar skin compared with those in non-volar skin. Overall, these results provide valuable information for understanding the metabolic differences between volar and non-volar skin.

Activation of ectopic olfactory receptor 544 induces GLP-1 secretion and regulates gut inflammation.

Olfactory receptors are ectopically expressed in extra-nasal tissues. The gut is constantly exposed to high levels of odorants where ectopic olfactory receptors may play critical roles. Activation of ectopic olfactory receptor 544 (Olfr544) by azelaic acid (AzA), an Olfr544 ligand, reduces adiposity in mice fed a high-fat diet (HFD) by regulating fuel preference to fats. Herein, we investigated the novel function of Olfr544 in the gut. In GLUTag cells, AzA induces the cAMP-PKA-CREB signaling axis and increases the secretion of GLP-1, an enteroendocrine hormone with anti-obesity effects. In mice fed a HFD and orally administered AzA, GLP-1 plasma levels were elevated in mice. The induction of GLP-1 secretion was negated in cells with Olfr544 gene knockdown and in Olfr544-deficient mice. Gut microbiome analysis revealed that AzA increased the levels of Bacteroides acidifaciens and microbiota associated with antioxidant pathways. In fecal metabolomics analysis, the levels of succinate and trehalose, metabolites correlated with a lean phenotype, were elevated by AzA. The function of Olfr544 in gut inflammation, a key feature in obesity, was further investigated. In RNA sequencing analysis, AzA suppressed LPS-induced activation of inflammatory pathways and reduced TNF-α and IL-6 expression, thereby improving intestinal permeability. The effects of AzA on the gut metabolome, microbiome, and colon inflammation were abrogated in Olfr544-KO mice. These results collectively demonstrated that activation of Olfr544 by AzA in the gut exerts multiple effects by regulating GLP-1 secretion, gut microbiome and metabolites, and colonic inflammation in anti-obesogenic phenotypes and, thus, may be applied for obesity therapeutics.

Antidiabetic Effect of Noodles Containing Fermented Lettuce Extracts.

The aim of the current study was to examine the antidiabetic effect of noodle containing fermented lettuce extract (FLE) on diabetic mice as a pre-clinical study. The γ-aminobutyric acid (GABA) content, antioxidant capacity, and total polyphenol content of the FLE noodles were analyzed and compared with those of standard noodles. In addition, oral glucose and sucrose tolerance, and fasting blood glucose tests were performed using a high-fat diet/streptozotocin-mediated diabetic mouse model. Serum metabolite profiling of mice feed standard or FLE noodles was performed using gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) to understand the mechanism changes induced by the FLE noodles. The GABA content, total polyphenols, and antioxidant activity were high in FLE noodles compared with those in the standard noodles. In vivo experiments also showed that mice fed FLE noodles had lower blood glucose levels and insulin resistance than those fed standard noodles. Moreover, glycolysis, purine metabolism, and amino acid metabolism were altered by FLE as determined by GC-TOF-MS-based metabolomics. These results demonstrate that FLE noodles possess significant antidiabetic activity, suggesting the applicability of fermented lettuce extract as a potential food additive for diabetic food products.

Systematic re-evaluation of the long-used standard protocol of urease-dependent metabolome sample preparation.

In the urinary metabolomics for finding biomarkers in urine, owing to high concentrations of urea, for chromatography-based metabolomic analysis, urea needed to be degraded by urease. This urease pretreatment has been the key step of sample preparation for standard urinary metabolomics until today even for mass spectrometry-based analysis. The urease pretreatment involving incubation of urine with urease contradicts the concept of metabolome sampling, which should immediately arrest metabolic reactions to prevent alterations of a metabolite profile. Nonetheless, the impact of urease pretreatment has not been clearly elucidated yet. We found that activities of urease and endogenous urinary enzymes and metabolite contaminants from the urease preparations introduce artefacts into metabolite profiles, thus leading to misinterpretation.