Metabolomics coupled with proteomics advancing drug discovery toward more agile development of targeted combination therapies

NMR-based metabolomics coupled with pattern recognition methods in biomarker discovery and disease diagnosis

Molecular biomarkers could detect biochemical changes associated with disease processes. The key metabolites have become an important part for improving the diagnosis, prognosis, and therapy of diseases. Because of the chemical diversity and dynamic concentration range, the analysis of metabolites remains a challenge. Assessment of fluctuations on the levels of endogenous metabolites by advanced NMR spectroscopy technique combined with multivariate statistics, the so-called metabolomics approach, has proved to be exquisitely valuable in human disease diagnosis. Because of its ability to detect a large number of metabolites in intact biological samples with isotope labeling of metabolites using nuclei such as H, C, N, and P, NMR has emerged as one of the most powerful analytical techniques in metabolomics and has dramatically improved the ability to identify low concentration metabolites and trace important metabolic pathways. Multivariate statistical methods or pattern recognition programs have been developed to handle the acquired data and to search for the discriminating features from biosample sets. Furthermore, the combination of NMR with pattern recognition methods has proven highly effective at identifying unknown metabolites that correlate with changes in genotype or phenotype. The research and clinical results achieved through NMR investigations during the first 13 years of the 21st century illustrate areas where this technology can be best translated into clinical practice. In this review, we will present several special examples of a successful application of NMR for biomarker discovery, implications for disease diagnosis, prognosis, and therapy evaluation, and discuss possible future improvements.

Cell metabolomics

Abstract Metabolomics technologies enable the examination and identification of endogenous biochemical reaction products, revealing information on the precise metabolic pathways and processes within a living cell. Metabolism is either directly or indirectly involved with every aspect of cell function, and metabolomics is thus believed to be a reflection of the phenotype of any cell. Metabolomics analysis of cells has many potential applications and advantages compared to currently used methods in the postgenomics era. Cell metabolomics is an emerging field that addresses fundamental biological questions and allows one to observe metabolic phenomena in cells. Cell metabolomics consists of four sequential steps: (a) sample preparation and extraction, (b) metabolic profiles of low-weight metabolites based on MS or NMR spectroscopy techniques, (c) pattern recognition approaches and bioinformatics data analysis, (d) metabolites identification resulting in putative biomarkers and molecular targets. The biomarkers are eventually placed in metabolic networks to provide insight on the cellular biochemical phenomena. This article analyzes the recent developments in use of metabolomics to characterize and interpret the cellular metabolome in a wide range of pathophysiological and clinical contexts, and the putative roles of the endogenous small molecule metabolites in this new frontier of postgenomics biology and systems medicine.

Metabolomics study of type 2 diabetes using ultra-performance LC-ESI/quadrupole-TOF high-definition MS coupled with pattern recognition methods

Type 2 diabetes (T2D), called the burden of the twenty-first century, is growing with an epidemic rate. Here, we explored the differences in metabolite concentrations between T2D patients and healthy volunteers. Metabolomics represents an emerging discipline concerned with comprehensive analysis of small molecule metabolites and provides a powerful approach to discover biomarkers in biological systems. The acquired data were analyzed by ultra-performance liquid chromatography-electrospray ionization/quadrupole time-of-flight high-definition mass spectrometry coupled with pattern recognition approach [principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA)] to identify potential disease-specific biomarkers. PCA showed satisfactory clustering between patients and healthy volunteers. Biomarkers reflected the biochemical events associated with early stages of T2D which were observed in PLS-DA loading plots. These urinary differential metabolites, such as adiponectin, acylcarnitines, citric acid, kynurenic acid, 3-indoxyl sulfate, urate, and glucose, were identified involving several key metabolic pathways such as taurine and hypotaurine metabolism; cysteine and methionine metabolism; valine, leucine, and isoleucine biosynthesis metabolism, etc. Our data suggest that robust metabolomics has the potential as a noninvasive strategy to evaluate the early diagnosis of T2D patients and provides new insight into pathophysiologic mechanisms and may enhance the understanding of its cause of disease.

Metabolomics and proteomics annotate therapeutic properties of geniposide: targeting and regulating multiple perturbed pathways

Geniposide is an important constituent of Gardenia jasminoides Ellis, a famous Chinese medicinal plant, and has displayed bright prospects in prevention and therapy of hepatic injury (HI). Unfortunately, the working mechanisms of this compound are difficult to determine and thus remain unknown. To determine the mechanisms that underlie this compound, we conducted a systematic analysis of the therapeutic effects of geniposide using biochemistry, metabolomics and proteomics. Geniposide significantly intensified the therapeutic efficacy as indicated by our modern biochemical analysis. Metabolomics results indicate 9 ions in the positive mode as differentiating metabolites which were associated with perturbations in primary bile acid biosynthesis, butanoate metabolism, citrate cycle (TCA cycle), alanine, aspartate and glutamate metabolism. Of note, geniposide has potential pharmacological effect through regulating multiple perturbed pathways to normal state. In an attempt to address the benefits of geniposide based on the proteomics approaches, the protein-interacting networks were constructed to aid identifying the drug targets of geniposide. Six identified differential proteins appear to be involved in antioxidation and signal transduction, energy production, immunity, metabolism, chaperoning. These proteins were closely related in the protein-protein interaction network and the modulation of multiple vital physiological pathways. These data will help to understand the molecular therapeutic mechanisms of geniposide on hepatic damage rats. We also conclude that metabolomics and proteomics are powerful and versatile tools for both biomarker discovery and exploring the complex relationships between biological pathways and drug response, highlighting insights into drug discovery.

Potentiating therapeutic effects by enhancing synergism based on active constituents from traditional medicine

Shifting current drug discovery tide from 'finding new drugs' to 'screening natural products' may be helpful for overcoming the 'more investment, fewer drugs' challenge. Traditional Chinese medicine (TCM), relying on natural products, has been playing a very important role in health protection and disease control for thousands of years in Asia, whose therapeutic efficacy is based on the 'synergism', that is, the combinational effects to be greater than that of the individual drug. Based on syndromes and patient characteristics and guided by the theories of TCM, formulae are designed to contain a combination of various kinds of crude drugs that, when combined, generally assume that a synergism of all ingredients will bring about the maximum of therapeutic efficacy. The increasing evidence has shown that multiple active component combinations of TCM could amplify the therapeutic efficacy of each agent, representing a new trend for modern medicine. However, the precise mechanism of synergistic action remains poorly understood. The present review highlights the concept of synergy and gives some examples of synergistic effects of TCM, and provides an overview of the recent and potential developments of advancing drug discovery towards more agile development of targeted combination therapies from TCM.

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.

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.

Proteomics analysis of hepatoprotective effects for scoparone using MALDI-TOF/TOF mass spectrometry with bioinformatics

Abstract Scoparone is an active ingredient of Yinchenhao (Artemisia annua L.), a well-known Chinese medicinal plant, and has been utilized in prevention and therapy of liver damage. However, the molecular drug targets associated with the pharmacological effects of scoparone are largely unknown. In the present article, we extend the previous research on Yinchenhao through a study of its active ingredient and thus the putative targets of scoparone. We employed two-dimensional gel electrophoresis, and all proteins expressed were identified by MALDI-TOF/TOF MS and database research. Protein-interacting networks and pathways were also mapped and evaluated. The possible protein network associated with scoparone was constructed, and contribution of these proteins to the protective effect of scoparone against the carbon tetrachloride-induced acute liver injury in rats are discussed herein. Hepatoprotective effects of scoparone on liver injury in rats were associated with regulated expression of six proteins which were closely related in our protein-protein interaction network, and appear to be involved in antioxidation and signal transduction, energy production, immunity, metabolism, and chaperoning. These observations collectively provide new insights on the molecular mechanisms of scoparone action against hepatic damage in rats.