Guangli Yan

Metabolomics Coupled with Proteomics Advancing Drug Discovery toward More Agile Development of Targeted Combination Therapies

To enhance the therapeutic efficacy and reduce the adverse effects of traditional Chinese medicine, practitioners often prescribe combinations of plant species and/or minerals, called formulae. Unfortunately, the working mechanisms of most of these compounds are difficult to determine and thus remain unknown. In an attempt to address the benefits of formulae based on current biomedical approaches, we analyzed the components of Yinchenhao Tang, a classical formula that has been shown to be clinically effective for treating hepatic injury syndrome. The three principal components of Yinchenhao Tang are Artemisia annua L., Gardenia jasminoids Ellis, and Rheum Palmatum L., whose major active ingredients are 6,7-dimethylesculetin (D), geniposide (G), and rhein (R), respectively. To determine the mechanisms underlying the efficacy of this formula, we conducted a systematic analysis of the therapeutic effects of the DGR compound using immunohistochemistry, biochemistry, metabolomics, and proteomics. Here, we report that the DGR combination exerts a more robust therapeutic effect than any one or two of the three individual compounds by hitting multiple targets in a rat model of hepatic injury. Thus, DGR synergistically causes intensified dynamic changes in metabolic biomarkers, regulates molecular networks through target proteins, has a synergistic/additive effect, and activates both intrinsic and extrinsic pathways.

Metabolomic analysis of key regulatory metabolites in hepatitis C virus-infected tree shrews.

Metabolomics is a powerful new technology that allows the assessment of global low-molecular-weight metabolites in a biological system and which shows great potential in biomarker discovery. Analysis of the key metabolites in body fluids has become an important part of improving the diagnosis, prognosis, and therapy of diseases. Hepatitis C virus (HCV) is a major leading cause of liver disease worldwide and a serious burden on public health. However, the lack of a small-animal model has hampered the analysis of HCV pathogenesis. We hypothesize that an animal model (Tupaia belangeri chinensis) of HCV would produce a unique characterization of metabolic phenotypes. Ultra-performance liquid-chromatography/electrospray ionization-SYNAPT-high-definition mass spectrometry (UPLC/ESI-SYNAPT-HDMS) coupled with pattern recognition methods and system analysis was carried out to obtain comprehensive metabolomics profiling and pathways of large biological data sets. Taurine, hypotaurine, ether lipid, glycerophospholipid, arachidonic acid, tryptophan, and primary bile acid metabolism pathways were acutely perturbed, and 38 differential metabolites were identified. More important, five metabolite markers were selected via the “significance analysis for microarrays” method as the most discriminant and interesting biomarkers that were effective for the diagnosis of HCV. Network construction has led to the integration of metabolites associated with the multiple perturbation pathways. Integrated network analysis of the key metabolites yields highly related signaling pathways associated with the differentially expressed proteins, which suggests that the creation of new treatment paradigms targeting and activating these networks in their entirety, rather than single proteins, might be necessary for controlling and treating HCV efficiently.

Ultraperformance Liquid Chromatography–Mass Spectrometry Based Comprehensive Metabolomics Combined with Pattern Recognition and Network Analysis Methods for Characterization of Metabolites and Metabolic Pathways from Biological Data Sets

Metabolomics is the study of metabolic changes in biological systems and provides the small molecule fingerprints related to the disease. Extracting biomedical information from large metabolomics data sets by multivariate data analysis is of considerable complexity. Therefore, more efficient and optimizing metabolomics data processing technologies are needed to improve mass spectrometry applications in biomarker discovery. Here, we report the findings of urine metabolomic investigation of hepatitis C virus (HCV) patients by high-throughput ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) coupled with pattern recognition methods (principal component analysis, partial least-squares, and OPLS-DA) and network pharmacology. A total of 20 urinary differential metabolites (13 upregulated and 7 downregulated) were identified and contributed to HCV progress, involve several key metabolic pathways such as taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, histidine metabolism, arginine and proline metabolism, and so forth. Metabolites identified through metabolic profiling may facilitate the development of more accurate marker algorithms to better monitor disease progression. Network analysis validated close contact between these metabolites and implied the importance of the metabolic pathways. Mapping altered metabolites to KEGG pathways identified alterations in a variety of biological processes mediated through complex networks. These findings may be promising to yield a valuable and noninvasive tool that insights into the pathophysiology of HCV and to advance the early diagnosis and monitor the progression of disease. Overall, this investigation illustrates the power of the UPLC-MS platform combined with the pattern recognition and network analysis methods that can engender new insights into HCV pathobiology.

Potential drug targets on insomnia and intervention effects of Jujuboside A through metabolic pathway analysis as revealed by UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition approach

Potential metabolites from the metabolic pathways could be therapeutic targets and useful for the discovery of broad spectrum drugs. UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition methods including PCA, PLS-DA, OPLS-DA and Heatmap were integrated to examine the global metabolic signature of insomnia and intervention effects of Jujuboside A (JuA). Six unique pathways of the insomnia were identified using Ingenuity Pathway Analysis (IPA) software. The VIP-value threshold cutoff of the metabolites was set to 10, above this threshold, were filtered out as potential target biomarkers. Sixteen distinct metabolites were identified from these pathways, and 6 of them can be considered for rational drug design. It was further experimental validation that the changes in metabolic profiling were restored to their baseline values after JuA treatment according to the multivariate data analysis. Potential metabolite network of the insomnia was preliminarily predicted JuA-target interaction networks, and could be further explored for in silico docking studies with suitable drugs. Thus, our method is an efficient procedure for drug target identification through metabolic analysis. It can guide testable predictions, provide insights into drug action mechanisms and enable us to increase research productivity toward metabolomic drug discovery.

Metabolomics in diagnosis and biomarker discovery of colorectal cancer

Colorectal cancer (CRC), a major public health concern, is the second leading cause of cancer death in developed countries. There is a need for better preventive strategies to improve the patient outcome that is substantially influenced by cancer stage at the time of diagnosis. Patients with early stage colorectal have a significant higher 5-year survival rates compared to patients diagnosed at late stage. Although traditional colonoscopy remains the effective means to diagnose CRC, this approach generally suffers from poor patient compliance. Thus, it is important to develop more effective methods for early diagnosis of this disease process, also there is an urgent need for biomarkers to diagnose CRC, assess disease severity, and prognosticate course. Increasing availability of high-throughput methodologies open up new possibilities for screening new potential candidates for identifying biomarkers. Fortunately, metabolomics, the study of all metabolites produced in the body, considered most closely related to a patients phenotype, can provide clinically useful biomarkers applied in CRC, and may now open new avenues for diagnostics. It has a largely untapped potential in the field of oncology, through the analysis of the cancer metabolome to identify marker metabolites defined here as surrogate indicators of physiological or pathophysiological states. In this review we take a closer look at the metabolomics used within the field of colorectal cancer. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader.

Rapid and global detection and characterization of aconitum alkaloids in Yin Chen Si Ni Tang, a traditional Chinese medical formula, by ultra performance liquid chromatography-high resolution mass spectrometry and automated data analysis.

An improved method employing Metabolynx XS with mass defect filter (MDF), a post-acquisition data processing software, was developed and applied for global detection of aconitum alkaloids in Yin Chen Si Ni Tang, a traditional Chinese medical formula (TCMF). The full-scan LC-MS/MS data sets with extra mass were acquired using ultra performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF-MS) with the MS(E) mode in a single injection. To remove the interferences, Metabolynx XS was optimized to extract the ions of aconitum alkaloids located at the lower abundance. As a result, 62 ions were assigned rapidly to aconitum alkaloids and identified tentatively by comparing the accurate mass and fragments information with that of the authentic standards or by mass spectrometry analysis and retrieving the reference literatures. Compared with the previous studies on Fuzi-containing TCMF, the report detected more aconitum alkaloids, and the analysis process was accelerated by automated data processing. It is concluded that the screening capability of Metabolynx XS with MDF, together with the utilization of MS(E) in structural elucidation, can facilitate a rapid and comprehensive searching and effective structural characterization of aconitum alkaloids in TCMF.

Urinary metabolic profiling identifies a key role for glycocholic acid in human liver cancer by ultra-performance liquid-chromatography coupled with high-definition mass spectrometry.

BACKGROUND Metabolomics has been proposed to be a hallmark of cancer, yet a systematic characterization of a metabolite and metabolic pathways in human hepatocarcinoma (HCC) remains a challenge. METHODS Using ultra-performance liquid-chromatography/quadrupole-time-of-flight coupled with high-definition mass spectrometry (UPLC-Q-TOF-HDMS) in conjunction with multivariate data analysis methods, we identified and measured the metabolite profile of glycocholic acid from urine samples obtained from patients with HCC diseases. Bioinformatic tools were used to construct the metabolite network that can identify a key role for glycocholic acid in HCC. RESULTS Biochemical analyses revealed that glycocholic acid expression was up-regulated in urine samples associated with HCC. Its pathway analysis suggested the modulation of multiple vital physiological pathways, including primary bile acid biosynthesis, secondary bile acid biosynthesis, metabolic pathways, and bile secretion. The network generation clearly enhances the interpretation and understanding of mechanisms for glycocholic acid. CONCLUSIONS Metabolomics can contribute to evaluating the potential of metabolites in HCC patients and may provide new insight into pathophysiologic mechanisms.

Urinary metabolic biomarker and pathway study of hepatitis B virus infected patients based on UPLC-MS system.

Hepatitis B virus (HBV) is the fatal consequence of chronic hepatitis, and lack of biomarkers has been a long standing bottleneck in the clinical diagnosis. Metabolomics concerns with comprehensive analysis of small molecules and provides a powerful approach to discover biomarkers in biological systems. Here, we present metabolomics analysis applying ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry. (UPLC-Q-TOF-HDMS) to determine metabolite alterations in HBV patients. Most important permutations are elaborated using multivariate statistical analysis and network analysis that was used to select the metabolites for the noninvasive diagnosis of HBV. In this study, the total 11 urinary differential metabolites were identified and contributed to HBV progress involving several key metabolic pathways by using pathway analysis with MetPA, which are promising biomarker candidates for diagnostic research. More importantly, of 11 altered metabolites, 4 metabolite markers were effective for the diagnosis of human HBV, achieved a satisfactory accuracy, sensitivity and specificity, respectively. It demonstrates that metabolomics has the potential as a non-invasive tool to evaluate the potential of these metabolites in the early diagnosis of HBV patients. These findings may be promising to yield a valuable insight into the pathophysiology of HBV and to advance the approaches of diagnosis, treatment, and prevention.

Rapid discovery and global characterization of chemical constituents and rats metabolites of Phellodendri amurensis cortex by ultra-performance liquid chromatography-electrospray ionization/quadrupole-time-of-flight mass spectrometry coupled with pattern recognition approach

To discover and screen the constituents or metabolites absorbed into blood after oral administration of herbal medicines tends to be more and more difficult. In this work, an integrative pattern recognition approach of principal component analysis (PCA) and orthogonal partial least squared discriminant analysis (OPLS-DA) was successfully applied for rapid discovery of natural compounds from herbal medicines. A rapid, sensitive, and reliable ultra performance liquid chromatography coupled with electrospray ionization/quadrupole-time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS) method with Masslynx™ software was established to characterize the chemical constituents and rats metabolites of Phellodendri amurensis cortex (Guan Huangbai, GHB). The analysis was performed on a Waters UPLC HSS T3 column (2.1 × 100 mm, 1.8 μm) using gradient elution system. A hyphenated electrospray ionization and quadrupole-time-of-flight analyzer was used for the determination of accurate mass of the protonated or deprotonated molecule and fragment ion in both negative and positive modes. A total of 46 peaks were obtained, 41 of which were tentatively characterized from GHB. In the S-plot of OPLS-DA, 24 interested ions (17 ions in positive mode and 6 ions in negative mode) were extracted, among them, 12 absorbed prototype components of GHB and 12 metabolites were identified in vivo. Major metabolic reactions of GHB were demethylation, methylation and glucuronidation. This is the first report on systematic analysis of chemical constituents and in vivo metabolites of GHB. It is concluded that UPLC-MS coupled with pattern recognition approach for the identification of herbal constituents in biological samples has been successfully developed. The method can also be applied to rapid discovery and global characterization of the constituents in rat serum after oral administration of other herbal medicines.

Metabolomics for Biomarker Discovery: Moving to the Clinic

To improve the clinical course of diseases, more accurate diagnostic and assessment methods are required as early as possible. In order to achieve this, metabolomics offers new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has become an important tool in clinical research and the diagnosis of human disease and has been applied to discovery and identification of the perturbed pathways. It provides a powerful approach to discover biomarkers in biological systems and offers a holistic approach with the promise to clinically enhance diagnostics. When carried out properly, it could provide insight into the understanding of the underlying mechanisms of diseases, help to identify patients at risk of disease, and predict the response to specific treatments. Currently, metabolomics has become an important tool in clinical research and the diagnosis of human disease and becomes a hot topic. This review will highlight the importance and benefit of metabolomics for identifying biomarkers that accurately screen potential biomarkers of diseases.