Tie Zhao

Metabonomic variations in the drug-treated type 2 diabetes mellitus patients and healthy volunteers.

The pathological development and the drug intervention of type 2 diabetes mellitus (T2DM) involve altered expression of downstream low molecular weight metabolites including lipids and amino acids, and carbohydrates such as glucose. Currently, a small number of markers used for clinical assessment of T2DM treatment may be insufficient to reflect global variations in pathophysiology. In this study, a metabonomic study was performed to determine metabolic variations associated with T2DM and the drug treatments on 74 patients who were newly diagnosed with T2DM and received a 48 week treatment of a single drug, repaglinide, metformin or rosiglitazone. Fasting overnight and 2 h postprandial blood serum of patients were collected at 24 and 48 weeks to monitor the biochemical indices (FPG, 2hPG, HbA1c, etc.). Gas chromatography/mass spectrometer coupled with multivariate statistical analysis was used to identify the alteration of global serum metabolites associated with T2DM as compared to healthy controls and responses to drug treatment. Significantly altered serum metabolites in diabetic subjects include increased valine, maltose, glutamate, urate, butanoate and long-chain fatty acid (C16:0, C18:1, C18:0, octadecanoate and arachidonate), and decreased glucuronolactone, lysine and lactate. All of the three treatments were able to down-regulate the high level of glutamate to a lower level in serum of T2DM patients, but rosiglitazone treatment was able to reverse more abnormal levels of metabolites, such as valine, lysine, glucuronolactone, C16:0, C18:1, urate, and octadecanoate, suggesting that it is more efficient to alter the metabolism of T2DM patients than the other two drugs.

Metabolite Profiling of Panax notoginseng Using UPLC–ESI-MS

The metabolite profiling of different parts of Panax notoginseng was carried out using rapid ultra-performance liquid chromatography-electrospray ionization mass spectrometry (UPLC-ESI-MS) and multivariate statistical analysis. Principal component analysis (PCA) of the UPLC-ESI-MS data showed a clear separation of compositions among the flower buds, roots and rhizomes of P. notoginseng. The saponins accounting for such variations were identified through the corresponding loadings weights and were further verified by accurate mass, tandem mass and retention times of available standard saponins using UPLC quadrupole time-of-flight mass spectrometer (UPLC-QtofMS). Finally, the influential factors of different metabolic phenotypes of P. notoginseng was elucidated. The currently proposed UPLC-ESI-MS/MS analytical method coupled with multivariate statistical analysis can be further utilized to evaluate chemical components obtained from different parts of the plant and/or the plant of different geographical locations, thereby classifying the medicinal plant resources and potentially elucidating the mechanism of inherent phytochemical diversity.

Metabonomics Approach to Understanding Acute and Chronic Stress in Rat Models

The effects of acute and chronic stress on the production of systemic metabolites were investigated in male Sprague-Dawley (SD) rats. Metabolites excreted in urine were analyzed using GC/MS in conjunction with multivariate and univariate statistical techniques. SD rats were subjected to two kinds of acute stress and chronic unpredictable mild stress, respectively. Metabolic analysis demonstrated that urinary expression of a number of metabolites including glutamate, glutamine, homovanillate, proline, succinate, citrate, and tyrosine altered in the acute stress model in the same way as in the chronic model, while pimelate and hippurate changed in the opposite trend. The results suggested that the stress induced metabolic perturbations were reversible and nonspecific. Metabolic response to chronic combined stress revealed biochemical clues to depression-like symptoms validated by behavior and physiologic results. This study provides a noninvasive and dynamic analytical strategy for the characterization of endogenous metabolic perturbations induced by external stress.

A rapid ultra‐performance liquid chromatography–electrospray Ionisation mass spectrometric method for the analysis of saponins in the adventitious roots of Panax notoginseng

INTRODUCTION Saponins are bioactive compounds employed in the prevention and treatment of cardiovascular and cerebrovascular diseases. The adventitious roots of Panax notoginseng may offer an alternative source of saponins. Identification and determination of saponins in the crude extract is challenging owing to their similar structures and the lack of standards. OBJECTIVE To develop a rapid, sensitive and accurate method based on solid-phase extraction followed by ultra-performance liquid chromatography-electrospray ionisation mass spectrometry (UPLC-ESI-MS) for the identification and quantification of saponins in P. notoginseng. METHODOLOGY Following extraction using Waters Oasis(TM) HLB cartridges, the analytes were subjected to a UPLC system with a Waters Acquity BEH C(18) chromatographic column and a binary mobile phase system consisting of 0.05% formic acid in water and acetonitrile under gradient elution conditions, with final detection by ESI-MS in the positive ion mode. RESULTS The UPLC-ESI-MS method gave limits of detection and quantification within the range 0.015-0.382 and 0.052-1.124 microg/mL, respectively, for 15 studied saponins. The instrumentation/injection precision (RSD) was 4.5% for a low concentration and 3.2% for an intermediate concentration sample. The intra- and inter-day repeatability was less than 2.65% (RSD). The method described was validated using spiked samples with different amounts of saponin standards. CONCLUSION This UPLC-ESI-MS assay provides a suitable quality control method for the tentative identification and determination of major biological active constituents in adventitious and native roots of P. notoginseng.

Urinary metabolomics as a potentially novel diagnostic and stratification tool for knee osteoarthritis

Metabolomics has been used as a tool in disease diagnosis and phenotype prediction. A urinary metabolomic study based on GC–MS in combination with multivariate statistics was used here to classify between knee osteoarthritis (OA) and healthy controls. OPLS-DA of the spectral data showed distinct metabolic profile variations between OA patients and healthy controls and between two OA phenotypes. Differential metabolites reveal up-regulated TCA cycle associated with OA and histamine metabolism disorders accompanied with knee effusion symptoms. This metabolomic method is potentially applicable as a novel strategy for OA diagnosis and patient stratification.

Trace Element Profiling Using Inductively Coupled Plasma Mass Spectrometry and its Application in an Osteoarthritis Study

In this study, a novel method of quantitatively measuring serum trace elements using inductively coupled plasma mass spectrometry (ICP-MS) coupled with multivariate statistical analysis was developed and applied successfully to the study of osteoarthritis (OA). This technology provides potential advantages over conventional targeted elemental analysis in that it achieves high throughput measurement, small sample volume, and simple operational procedure. Such an unbiased method is particularly suitable for large scale discovery research on trace element based biomarkers. The method optimization and validation study involved accuracy and perturbation testing which focused on estimating the ability of the method to resist interferences in ICP-MS analysis, particularly those of mass <82 amu, in the serum sample. The developed method was successfully applied to the study of serum samples from OA patients. As a result, the serum trace element profiles of OA patients were distinctively separated from those of the healthy controls (HC) using an Orthogonal Projections to Latent Structures Discriminant Analysis (OPLSDA) model. Additionally, significantly differential elements correlated with OA, such as Li and Sn, were identified as potential elemental-based biomarkers.

Metabonomics approach to assessing the modulatory effects of St John's Wort, ginsenosides, and clomipramine in experimental depression

The protective effects of St Johns Wort extract (SJ), ginsenosides (GS), and clomipramine (CPM) on chronic unpredictable mild stress (CUMS)-induced depression in rats were investigated by using a combination of behavioral assessments and metabonomics. Metabonomic analyses were performed using gas chromatography/mass spectrometry in conjunction with multivariate and univariate statistical analyses. During and at the end point of the chronic stress experiment, food consumption, body weight, adrenal gland, thymus and spleen indices, behavior scores, sucrose consumption, and stress hormone levels were measured. Changes in these parameters reflected characteristic phenotypes of depression in rats. Metabonomic analysis of serum, urine, and brain tissue revealed that CPM and SJ mainly attenuated the alteration of monoamine neurotransmitter metabolites, while GS affected both excitatory/inhibitory amino acids and monoamine neurotransmitter metabolites. GS also attenuated the stress-induced alterations in cerebrum and peripheral metabolites to a greater extent than CPM and SJ. These results provide important mechanistic insights into the protective effects of GS against CUMS-induced depression and metabolic dysfunction.

Human gouty arthritis is associated with a distinct serum trace elemental profile

Homeostatic imbalance of essential trace elements is deeply involved in many pathophysiological states, especially in joint disorders such as gout. A total of 64 elements were measured in the serum samples in three regionally independent groups of patients with gouty arthritis (n = 100) and an age-matched healthy control group (n = 40) by inductively coupled plasma-mass spectrometry (ICP-MS). A distinct elemental profile of gouty arthritis encompassing significantly altered Li, Al, Ti, Fe, Cu, Se, Sr, Ta, Hg, Bi, Th, and U was obtained with a sensitivity of 0.97 (95% confidence interval (CI): 0.92-0.99) and a specificity of 0.95 (95% CI: 0.83-0.99) for gout diagnosis. An independent group of 52 subjects (39 gout patients and 13 healthy controls) was further used to validate the elemental signature, yielding a sensitivity of 1.00 (95% CI: 0.91-1.00) and a specificity of 1.00 (95% CI: 0.75-1.00) for gout prediction. It is also noteworthy that we were able to achieve ≥95.7% correct classification rate in both discovery and validation groups using only three elemental markers, Li, Al, and U. We also observed a good correlation between Li, Zn, and Cu and the other two risk factors, age and serum urate concentration, in gout patients. Our findings underscore that gouty arthritis possesses a unique elemental expression profile regardless of many other nutritional and environmental factors.