Xiaoming Sun

A metabolic profiling analysis of symptomatic gout in human serum and urine using high performance liquid chromatography-diode array detector technique

BACKGROUNDnUric acid (UA) is the only biomedical indicator for gout in clinic that always leads to an uncertain diagnose. Due to the lack of reliable metabolites, it is now already highly desirable to diagnose gout definitely.nnnMETHODSnMetabonomics was employed to screen and identify novel biomarkers of gout based on human serum and urine. High performance liquid chromatography-diode array detector (HPLC-DAD) and orthogonal signal correction partial least squares discriminate analysis (OSC-PLS-DA) were also used for metabonomics study.nnnRESULTSnSeveral potential biomarkers including uric acid, creatinine, tryptophan in serum and uric acid, creatinine, guanosine, hippuric acid in urine, were respectively screened and identified. For serum and urine, the predictive levels about the OSC-PLS-DA models of the gout and controls were 95.76% and 100%, and the correction levels about the seriousness of the disease were 90.32% and 87.5%, respectively.nnnCONCLUSIONnCompared with intermittent gout, the acute gout shows clearly the dysfunctions of purine, protein and glucose metabolism. The metabolizing of guanosine to UA increases the levels of UA in serum at the acute stage. Our research contributes to a better understanding of the metabolic mechanism and allowing the targeted therapy of gout at different stages.

Efficient method for the screening and identification of anti-diabetic components in the leaves of Olea europaea L.

In this article, an efficient method of high speed counter-current chromatography (HSCCC) coupled with post-column on-line evaluation was developed to screen, isolate and identify the major anti-diabetic compounds present in the leaves of Olea europaea L. The HSCCC separation employed a two-step process: first, an optimized two-phase system composed of ethyl acetate–water (1u2006:u20061) was used to separate the extraction; then, a solvent system composed of butanol–water–acetic acid (1u2006:u20061u2006:u20060.1) was applied to further separate the anti-diabetic active compounds. The eluant was detected by post-column evaluation with α-amylase used in both steps. It was found that five major constituents of the O. europaea L. leaf extracts displayed potential anti-diabetic activity. Their structures were identified by 1H- and 13C-Nuclear Magnetic Resonance (NMR) as Oleuropein, Ligstroside, Hydroxytyrosol, Tyrosol and Luteolin-7-O-β-D-glucoside.

Metabolite target analysis of human urine combined with pattern recognition techniques for the study of symptomatic gout

Recurrent attacks and irregularity are two important characteristics of gout disease. Uric acid as a single evaluation indicator for clinical diagnosis is insufficient considering the versatile properties of gout. The aim of this work is to identify several endogenous metabolites from urine samples for the elucidation and prediction of gout disease. Metabolite target analysis was established for human urine by high performance liquid chromatography-diode array detection (HPLC-DAD). The targeted metabolites selected included hippuric acid, uracil, phenylalanine, tryptophan, uric acid and creatinine as well as nine purine compounds. Useful information was extracted from multivariate data through Fisher Linear Discriminant Analysis (FDA) and Orthogonal Signal Correction Partial Least Squares Discriminant Analysis (OSC-PLS-DA). Uric acid, hypoxanthine, xanthosine, guanosine, inosine and tryptophan were identified as important metabolites among the acute and chronic gout and controls. Based on OSC-PLS-DA models, the regression equations obtained could discriminate gout from the controls as well as the acute from chronic. The recognition and prediction ability is respectively 100% and 85.0% for the gout, 100% and 83.3% for the acute, and 90.91% and 89.9% for the chronic. Metabolic dysfunction of tryptophan and excessive metabolism of xanthosine and hypoxanthine to xanthine were confirmed for gout disease. Metabolic dysfunction of tryptophan was also proven to be induced by allopurinol in case of Kunming mice with hyperuricemia. Potential biomarkers can be used not only to distinguish gout patients from healthy people, but also to evaluate the disease state.

Chemometric analysis of metabolism disorders in blood plasma of S180 and H22 tumor-bearing mice by high performance liquid chromatography-diode array detection

The aim of this paper is to characterize metabolism disorders in Kunming mice induced by S180 and H22 tumor cells. Metabolic fingerprint based on high performance liquid chromatography‐diode array detector (HPLC‐DAD) was developed to map the disturbed metabolic responses. In vivo testing of the antitumor activity of paclitaxel (Taxol) was carried out by inhibiting the growth of S180 and H22 tumor cells. Based on 27 common peaks, principal component analysis (PCA) and partial least squares‐discriminant analysis (PLS‐DA) were used to distinguish the abnormal from control and to find significant endogenous compounds (SECs) which have significant contributions to classification. The tumor growth inhibition ratios (TIRs) of Taxol groups were used to validate the predictive accuracies of the PLS‐DA models. The predictive accuracies of PLS‐DA models for S180 and H22 tumor model groups were 97.6 and 100%, respectively. Nine (S180) and seven (H22) SECs were discovered, including uric acid and cytidine. In addition, the correlations between relative tumor weights (RTWs) and chromatographic data for the SECs were significant (pu2009<u20090.05). Investigations on the stability and precision of the established metabolic fingerprints demonstrate that the experiment is well controlled and reliable. This work shows that the platform of HPLC‐DAD coupled with chemometric methods provides a promising method for the study of metabolism disorders induced by tumor cells. Copyright

Evaluation of polydopamine supported nano-polytetrafluoroethylene as a novel material for solid phase extraction

A novel material for solid phase extraction (SPE) was prepared by the deposition of polydopamine onto nano-polytetrafluoroethylene (PTFE) using a simple self-assembly approach. The coated nano-PTFE was characterized by X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM). The adsorption mechanism of the prepared material, as a SPE adsorbent, was investigated through static and dynamic adsorption experiments. Four alkaloids and two amino acids were used as model analytes. Several operation parameters including adsorption time and solution pH were optimized in the static adsorption mode. The coated nano-PTFE displayed a strong adsorption capability for four alkaloids, and isocorydione could be selectively adsorbed under the optimized pH. Hydrophobic and hydrogen-bonding interactions are the two main driving forces for the adsorption of the analytes. When the two coexist, the hydrogen-bonding interactions are more important than the hydrophobic effects. The prepared material exhibited almost opposite adsorption behavior compared with C18 silica without end-capping treatment. Therefore, polydopamine coated nano-PTFE can be used as packing material for a reverse phase HPLC column, instead of C18 with a tedious treatment of end-capping.

Spiral counter‐current chromatography: Design, development, application, and challenges

Depending on the rapid growth in the radial gradient of the centrifugal force, spiral counter-current chromatography can greatly improve the retention of stationary phase, especially for the aqueous two-phase systems with ultra-polar and high viscosity that are not well retained in the conventional multilayer coils counter-current chromatography. As a result, it is an attractive and alternative technology that is suited for separation of hydrophilic compounds and has led to many exciting progress in recent years. This review presents the recent advances and applications of spiral counter-current chromatography, including its major benefits and limitations, some novel methods to improve the separation efficiency and its applications in separation of real samples. In addition, the remaining challenges and future perspectives on development of spiral counter-current chromatography also are proposed in this article.

Evaluation of an efficient and selective adsorbent based on multi-walled carbon nanotubes coated silica microspheres for detecting nucleobases and nucleosides in human urine

Multi-walled carbon nanotubes (MWCNTs) can be used in analytical chemistry for separation and purification and offer the opportunity to determine low concentration compounds in complex systems. Novel multi-walled carbon nanotube coated silica microspheres (MWCNTs/SiO2) were synthesized by the covalent bonding of amino bonds. The characteristics results obtained from emission scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared (FTIR) spectroscopy and Brunauer–Emmett–Teller (BET) surface area analysis showed that MWCNTs/SiO2 was successfully prepared. By evaluating the static adsorption capacities of ten nucleobases and nucleosides, the adsorption capacity of the microspheres for adenine (A), guanine (G), uric acid (UA) and xanthosine (X) was found to be significantly stronger than for others. Moreover, the adsorption capacity depends on the pH value, salinity and contact time. It was shown that π–π conjugation and hydrogen bonding interactions were the two main driving forces for the adsorption of target compounds. Adsorption kinetics and adsorption isotherms showed that the adsorption process was a chemical and multilayer adsorption. They could be determined within the test ranges with a good correlation coefficient (r > 0.997). The limits of detection (LOD) for A, G, UA and X were 1.22, 2.02, 0.32 and 2.28 ng mL−1, respectively. The intra- and inter-day relative standard deviations (RSDs) were no more than 6.5%. This procedure therefore afforded a convenient, sensitive and accurate method with a high extraction efficiency for the determination of A, G, UA and X in human urine.

Variable selection and chemometric models for discriminating symptomatic gout based on a metabolic target analysis: Discriminating Symptomatic Gout Based On Metabolic Target Analysis

In clinical practice, uric acid is frequently used as a diagnostic criterion in gout. However, gout is commonly confused with other diseases, including rheumatoid arthritis, soft tissue joint injury, and hyperuricosuric calcium oxalate urolithiasis. Two new strategies—graphical index of separation and subwindow permutation analysis—were applied to understand the metabolic changes induced by gout. Metabolic target analysis was performed using high performance liquid chromatography with a diode array detector. Compared with the nongout samples, the concentrations of uric acid, uracil, inosine, adenosine, and tryptophan are different in gout samples, and these metabolites could be used as important diagnostic markers. However, the uric acid, uracil, phenylalanine, tryptophan, and adenine concentrations differed between acute and chronic gout. We confirmed the metabolic disorder of uracil during the basic development of gout. In the gout and nongout groups, the recognition rate of the model reached 0.98, whereas the value of recognition ability was only 0.79 when uric acid was used as a single variable. In the acute and chronic class of gout, the recognition rate of the model was 0.90 and that of uric acid was only 0.62. Variable selection combined with chemometric models can be used as a supplementary method for the diagnosis and prognosis of gout in clinical practice.