Urine metabolomics analysis for biomarker discovery and detection of jaundice syndrome in patients with liver disease

Metabolite profiling and pathway analysis of acute hepatitis rats by UPLC-ESI MS combined with pattern recognition methods

BACKGROUND & AIMS

Metabolomics is comprehensive analysis of low-molecular-weight endogenous metabolites in a biological sample. It could enable mapping of perturbations of early biochemical changes in diseases and hence provide an opportunity to develop predictive biomarkers that could provide valuable insights into the mechanisms of diseases. The aim of this study was to elucidate the changes in endogenous metabolites and to phenotype the metabolic profiling of d-galactosamine (GalN)-inducing acute hepatitis in rats by UPLC-ESI MS.

METHODS

The systemic biochemical actions of GalN administration (ip, 400 mg/kg) have been investigated in male wistar rats using conventional clinical chemistry, liver histopathology and metabolomic analysis of UPLC- ESI MS of urine. The urine was collected predose (-24 to 0 h) and 0-24, 24-48, 48-72, 72-96 h post-dose. Mass spectrometry of the urine was analysed visually and via conjunction with multivariate data analysis.

RESULTS

Results demonstrated that there was a time-dependent biochemical effect of GalN dosed on the levels of a range of low-molecular-weight metabolites in urine, which was correlated with developing phase of the GalN-inducing acute hepatitis. Urinary excretion of beta-hydroxybutanoic acid and citric acid was decreased following GalN dosing, whereas that of glycocholic acid, indole-3-acetic acid, sphinganine, n-acetyl-l-phenylalanine, cholic acid and creatinine excretion was increased, which suggests that several key metabolic pathways such as energy metabolism, lipid metabolism and amino acid metabolism were perturbed by GalN.

CONCLUSION

This metabolomic investigation demonstrates that this robust non-invasive tool offers insight into the metabolic states of diseases.

Comparative metabolomics analysis on hematopoietic functions of herb pair Gui-Xiong by ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry and pattern recognition approach

The compatibility of Angelicae Sinensis Radix (Danggui, DG) and Chuanxiong Rhizoma (Chuanxiong, CX), a famous herb pair Gui-Xiong (GX), can produce synergistic and complementary hematopoiesis. In present study, global metabolic profiling with ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS) combined with pattern recognition method was performed to discover the underlying hematopoietic regulation mechanisms of DG, CX and GX on hemolytic and aplastic anemia rats (HAA) induced by acetyl phenylhydrazine (APH) and cyclophosphamide (CP). Thirteen endogenous metabolites contributing to the separation of model group and control group were tentatively identified. The levels of LPCs including lysoPC (18:0), lysoPC (20:4), lysoPC (16:0) and lysoPC (18:2), sphinganine, nicotinic acid, thiamine pyrophosphate, phytosphingosine, and glycerophosphocholine increased significantly (p<0.05) in HAA, while the levels of oleic acid, 8,11,14-eicosatrienoic acid, ceramides (d18:1/14:0), and 17a-hydroxypregnenolone decreased significantly (p<0.05) in comparison with control rats. Those endogenous metabolites were chiefly involved in thiamine metabolism and sphingolipid metabolism. The metabolic deviations could be regulated closer to normal level after DG, CX and GX intervention. In term of hematopoietic function, GX was the most effective as shown by the relative distance in PLS-DA score plots and relative intensity of metabolomic strategy, reflecting the synergic action between DG and CX. The relative distance calculation was firstly used in metabolomics for semi-quantization.

Metabolomics coupled with pattern recognition and pathway analysis on potential biomarkers in liver injury and hepatoprotective effects of yinchenhao

Metabolomics can provide an opportunity to develop the systematic analysis of the metabolites in biological samples and has been increasingly applied to discovering and identifying biomarkers and perturbed pathways. It enables us to better understand the metabolic pathways which can clarify the mechanism of traditional Chinese medicines (TCM). Yinchenhao (YCH, Artemisia annua L), a famous TCM plant, has been used clinically for more than a thousand years to relieve liver diseases in Asia, and its mechanisms are not still completely clear. Here, metabolomic techniques may provide additional insight, and our investigation was designed to assess the effects and possible mechanisms of YCH on α-naphthylisothiocyanate (ANIT)-induced liver injury. Metabolite profiling was performed by ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry (UPLC/ESI-Q-TOF/MS) combined with pathway analysis and pattern recognition approaches including independent component analysis (ICA) and partial least squares-discriminant analysis (PLS-DA). Biochemistry test was also performed for the liver tissue and plasma samples. The changes in metabolic profiling were restored to their baseline values after YCH treatment according to the ICA score plots. Of note, YCH has a potential pharmacological effect through regulating multiple perturbed pathways to normal state, correlating well to the assessment of biochemistry test. Five different potential biomarkers in the positive mode contributing to the treatment of YCH were discovered. Pathway analysis showed that these metabolites were associated with perturbations in pyrimidine metabolism, primary bile acid biosynthesis, and propanoate metabolism, which may be helpful to further understand the action mechanisms of YCH. It showed that changed biomarkers and pathways may provide evidence to insight into drug action mechanisms and drug discovery.

Metabolomic analysis of diet-induced type 2 diabetes using UPLC/MS integrated with pattern recognition approach

Metabolomics represents an emerging discipline concerned with comprehensive assessment of small molecule endogenous metabolites in biological systems and provides a powerful approach insight into the mechanisms of diseases. Type 2 diabetes (T2D), called the burden of the 21^st century, is growing with an epidemic rate. However, its precise molecular mechanism has not been comprehensively explored. In this study, we applied urinary metabolomics based on the UPLC/MS integrated with pattern recognition approaches to discover differentiating metabolites, to characterize and explore metabolic pathway disruption in an experimental model for high-fat-diet induced T2D. Six differentiating urinary metabolites were found in the negative mode, and two (2-(4-hydroxy-3-methoxy-phenyl) acetaldehyde sulfate, 2-phenylethanol glucuronide) of which were identified involving the key metabolic pathways linked to pentose and glucuronate interconversions, starch, sucrose metabolism and tyrosine metabolism. Our study provides new insight into pathophysiologic mechanisms and may enhance the understanding of T2D pathogenesis.

Magnetic susceptibility to measure total protein concentration from NMR metabolite spectra: Demonstration on blood plasma

PURPOSE

Accurate metabolite and protein quantification in blood plasma and other body fluids from one single NMR measurement, allowing for improved quantitative metabolic profiling and better assessment of metabolite-protein interactions.

THEORY AND METHODS

The total protein concentration is derived from the common chemical-shift changes-caused by protein-induced bulk magnetic susceptibility (BMS)-measured on well-accessible and exchange-free metabolite resonances. These BMS shifts are simply obtained by external referencing with respect to 3-(trimethylsilyl)propionic-2,2,3,3-d4 acid, sodium salt in a coaxial insert.

RESULTS

Based on blood-plasma data from five volunteers, the estimated accuracy of the BMS method is ≤ 5% with respect and comparable to the 3.8% error of the standard colorimetric, Biuret, method. Valine, alanine, glucose, leucine, and lactate display no exchange-induced shift changes. Their well-accessible signals act as reliable probes for pure protein-induced BMS. The slopes and intercepts of their chemical-shift change versus protein concentration were derived from metabolite mixtures with (fatted) human and bovine albumin acting as blood-plasma mimics.

CONCLUSION

The BMS method, demonstrated on blood plasma, can also be used on other samples containing sufficient protein (> 10 g/L). Also, it allows measurement of the presence and sign of exchange-induced chemical-shift changes.

Analysis of serum metabolites for the discovery of amino acid biomarkers and the effect of galangin on cerebral ischemia

Ischemic stroke, a devastating disease with a complex pathophysiology, is a leading cause of death and disability worldwide. In our previous study, we reported that galangin provided direct protection against ischemic injury and acted as a potential neuroprotective agent. However, its associated neuroprotective mechanism has not yet been clarified. In this paper, we explored the potential AA biomarkers in the acute phase of cerebral ischemia and the effect of galangin on those potential biomarkers. In our study, 12 AAs were quantified in rat serum and found to be impaired by middle cerebral artery occlusion (MCAO)-induced focal cerebral ischemia. Using partial least squares discriminate analysis (PLS-DA), we identified the following amino acids as potential biomarkers of cerebral ischemia: glutamic acid (Glu), homocysteine (Hcy), methionine (Met), tryptophan (Trp), aspartic acid (Asp), alanine (Ala) and tyrosine (Tyr). Moreover, four amino acids (Hcy, Met, Glu and Trp) showed significant change in galangin-treated (100 and 50 mg kg(-1)) groups compared to vehicle groups. Furthermore, we identified three pathway-related enzymes tyrosine aminotransferase (TAT), glutamine synthetase (GLUL) and monocarboxylate transporter (SLC16A10) by multiplex interactions with Glu and Hcy, which have been previously reported to be closely related to cerebral ischemia. Through an analysis of the metabolite-protein network analysis, we identified 16 proteins that were associated with two amino acids by multiple interactions with three enzymes; five of them may become potential biomarkers of galangin for acute ischemic stroke as the result of molecule docking. Our results may help develop novel strategies to explore the mechanism of cerebral ischemia, discover potential targets for drug candidates and elucidate the related regulatory signal network.

Metabonomics study on the hepatoprotective effect of polysaccharides from different preparations of Angelica sinensis

ETHNOPHARMACOLOGICAL RELEVANCE

Angelica sinensis (AS) has been used for thousands of years in Traditional Chinese Medicine (TCM). Processed products of AS mainly include charred Angelica, parching Angelica with oil, parching Angelica with wine, and parching Angelica with soil, which have been widely used in TCM prescriptions. Polysaccharides are important chemical substances of AS. These compounds effectively treat liver diseases, shows hepatoprotectivity, and contributes directly to the therapeutic effect of AS. However, the precise molecular mechanism of the effects of the different AS products polysaccharide has not been comprehensively explored. The present investigation was designed to assess the effects and possible mechanisms of polysaccharide in the different AS products against carbon tetrachloride-induced liver injury.

MATERIALS AND METHODS

Liver injury was induced by intraperitoneal injection with Carbon tetrachloride (CCl4) in the mice. Gas chromatography-mass spectrometry (GC-MS) combined with pattern recognition approaches, namely, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), were used to determine differentiating metabolites in plasma and liver tissue.

RESULTS

PCA and PLS-DA score plots of the liver injury group clustered separately from that of the control, while groups treated with polysaccharides from charred AS (ASTP), parching AS with soil (ASTUP), parching AS with wine (ASJP), parching AS with Sesame Oil (ASYP) clustered closely with the control. This result indicates that the metabolic profiles of the ASTP, ASTUP, ASJP, and ASYP groups are almost similar to those of the control. Potential metabolite biomarkers (six in the liver homogenates and seven in the plasma) were identified. These biomarkers include citric acid, succinic acid,glycine, palmitelaidic acid, arachidonic acid, fumaric acid, malic acid, valine, ananine, and hexadecanoic acid. Functional pathway analysis revealed that alterations in these metabolites are associated with lipid, amino acid, and energy metabolism. Notably, ASTP exhibited a potential pharmacological effect by regulating multiple perturbed pathways to the normal state.

CONCLUSION

It is likely that ASTP, ASTUP, ASJP, ASYP intervenes the metabolic process of liver injury mice by affecting the lipid and amino acid metabolism. Metabonomics is a robust and promising for the identification of biomarkers and elucidation of the mechanisms of a disease, thereby highlighting its importance in drug discovery.

Microextraction by Packed Sorbent (MEPS) and Solid-Phase Microextraction (SPME) as Sample Preparation Procedures for the Metabolomic Profiling of Urine

For a long time, sample preparation was unrecognized as a critical issue in the analytical methodology, thus limiting the performance that could be achieved. However, the improvement of microextraction techniques, particularly microextraction by packed sorbent (MEPS) and solid-phase microextraction (SPME), completely modified this scenario by introducing unprecedented control over this process. Urine is a biological fluid that is very interesting for metabolomics studies, allowing human health and disease characterization in a minimally invasive form. In this manuscript, we will critically review the most relevant and promising works in this field, highlighting how the metabolomic profiling of urine can be an extremely valuable tool for the early diagnosis of highly prevalent diseases, such as cardiovascular, oncologic and neurodegenerative ones.

Rapidly improved determination of metabolites from biological data sets using the high-efficient TransOmics tool

Detailed analysis workflow of TransOmics informatics for metabolomics data from large biological data sets.

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 patient's 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.

Review: toxicometabolomics

Metabolomics use in toxicology is rapidly increasing, particularly owing to advances in mass spectroscopy, which is widely used in the life sciences for phenotyping disease states. Toxicology has the advantage of having the disease agent, the toxicant, available for experimental induction of metabolomics changes monitored over time and dose. This review summarizes the different technologies employed and gives examples of their use in various areas of toxicology. A prominent use of metabolomics is the identification of signatures of toxicity - patterns of metabolite changes predictive of a hazard manifestation. Increasingly, such signatures indicative of a certain hazard manifestation are identified, suggesting that certain modes of action result in specific derangements of the metabolism. This might enable the deduction of underlying pathways of toxicity, which, in their entirety, form the Human Toxome, a key concept for implementing the vision of Toxicity Testing for the 21st century. This review summarizes the current state of metabolomics technologies and principles, their uses in toxicology and gives a thorough overview on metabolomics bioinformatics, pathway identification and quality assurance. In addition, this review lays out the prospects for further metabolomics application also in a regulatory context.

Metabolomics insights into pathophysiological mechanisms of nephrology

Kidney diseases (KD), a major public health problem that affects about 10 % of the general population, manifest in progressive loss of renal function, which ultimately leads to complete kidney failure. However, current approaches based on renal histopathological results and clinical parameters lack sensitivity and are not sufficient to characterize the category and progression of nephrology or to predict nephrology progression risk reliably or to guide preventive interventions. The high incidence and financial burden of KD make it imperative to diagnose KD at early stages when therapeutic interventions are far more effective. Nowadays, the appearance of metabolomics (the high-throughput measurement and analysis of metabolites) has provided the framework for a comprehensive analysis of KD and serves as a starting point for generating novel molecular diagnostic tools for use in nephrology. Changes in the concentration profiles of a number of small-molecule metabolites found in either blood or urine can be used to localize kidney damage or assess kidneys suffering from injury. The power of metabolomics allows unparalleled opportunity to query the molecular mechanisms of KD. Novel metabolomics technologies have the ability to provide a deeper understanding of the disease beyond classical histopathology, redefine the characteristics of the disease state, and identify novel approaches to reduce renal failure. This review gives an overview of its application to important areas in clinical nephrology, with a particular focus on biomarker discovery. Great strides forward are being made in breaking down important barriers to the successful prevention and treatment of this devastating disorder.

Recent highlights of metabolomics in chinese medicine syndrome research

Chinese medicine syndrome (CMS, “ZHENG” in Chinese) is an understanding of the regularity of disease occurrence and development as well as a certain stage of a comprehensive response of patients with body condition. However, because of the complexity of CMS and the limitation of present investigation method, the research for deciphering the scientific basis and systematic features of CMS is difficult to go further. Metabolomics enables mapping of early biochemical changes in disease and hence provides an opportunity to develop predictive biomarkers. Moreover, its method and design resemble those of traditional Chinese medicine (TCM) which focuses on human disease via the integrity of close relationship between body and syndromes. In the systemic context, metabolomics has a convergence with TCM syndrome; therefore it could provide useful tools for exploring essence of CMS disease, facilitating personalized TCM, and will help to in-depth understand CMS. The integration of the metabolomics and CMS aspects will give promise to bridge the gap between Chinese and Western medicine and help catch the traditional features of CMS. In this paper, particular attention will be paid to the past successes in applications of robust metabolomic approaches to contribute to low-molecular-weight metabolites (biomarkers) discovery in CMS research and development.

Application of (1)h NMR spectroscopy-based metabolomics to sera of tuberculosis patients

Nuclear magnetic resonance (NMR) spectroscopy is an ideal platform for the metabolic analysis of biofluids due to its high reproducibility, nondestructiveness, nonselectivity in metabolite detection, and the ability to simultaneously quantify multiple classes of metabolites. Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. In this study, we combined multivariate pattern recognition (PR) analytical techniques with (1)H NMR spectroscopy to explore the metabolic profile of sera from TB patients. A total of 77 serum samples obtained from patients with TB (n = 38) and healthy controls (n = 39) were investigated. Orthogonal partial least-squares discriminant analysis (OPLS-DA) was capable of distinguishing TB patients from controls and establishing a TB-specific metabolite profile. A total of 17 metabolites differed significantly in concentration between the two groups. Serum samples from TB patients were characterized by increased concentrations of 1-methylhistidine, acetoacetate, acetone, glutamate, glutamine, isoleucine, lactate, lysine, nicotinate, phenylalanine, pyruvate, and tyrosine, accompanied by reduced concentrations of alanine, formate, glycine, glycerolphosphocholine, and low-density lipoproteins relative to control subjects. Our study reveals the metabolic profile of sera from TB patients and indicates that NMR-based methods can distinguish TB patients from healthy controls. NMR-based metabolomics has the potential to be developed into a novel clinical tool for TB diagnosis or therapeutic monitoring and could contribute to an improved understanding of disease mechanisms.

Dissection of Biological Property of Chinese Acupuncture Point Zusanli Based on Long-Term Treatment via Modulating Multiple Metabolic Pathways

Acupuncture has a history of over 3000 years and is a traditional Chinese medical therapy that uses hair-thin metal needles to puncture the skin at specific points on the body to promote wellbeing, while its molecular mechanism and ideal biological pathways are still not clear. High-throughput metabolomics is the global assessment of endogenous metabolites within a biologic system and can potentially provide a more accurate snap shot of the actual physiological state. We hypothesize that acupuncture-treated human would produce unique characterization of metabolic phenotypes. In this study, UPLC/ESI-HDMS coupled with pattern recognition methods and system analysis were carried out to investigate the mechanism and metabolite biomarkers for acupuncture treatment at “Zusanli” acupoint (ST-36) as a case study. The top 5 canonical pathways including alpha-linolenic acid metabolism, d-glutamine and d-glutamate metabolism, citrate cycle, alanine, aspartate, and glutamate metabolism, and vitamin B6 metabolism pathways were acutely perturbed, and 53 differential metabolites were identified by chemical profiling and may be useful to clarify the physiological basis and mechanism of ST-36. More importantly, network construction has led to the integration of metabolites associated with the multiple perturbation pathways. Urine metabolic profiling might be a promising method to investigate the molecular mechanism of acupuncture.

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.

Combination of injection volume calibration by creatinine and MS signals' normalization to overcome urine variability in LC-MS-based metabolomics studies

It is essential to choose one preprocessing method for liquid chromatography-mass spectrometry (LC-MS)-based metabolomics studies of urine samples in order to overcome their variability. However, the commonly used normalization methods do not substantially reduce the high variabilities arising from differences in urine concentration, especially for signal saturation (abundant metabolites exceed the dynamic range of the instrumentation) or missing values. Herein, a simple preacquisition strategy based on differential injection volumes calibrated by creatinine (to reduce the concentration differences between the samples), combined with normalization to "total useful MS signals" or "all MS signals", is proposed to overcome urine variabilities. This strategy was first systematically compared with other popular normalization methods by application to serially diluted urine samples. Then, the method has been verified using rat urine samples of pre- and postinoculation of Walker 256 carcinoma cells. The results showed that the calibration of injection volumes based on creatinine values could effectively eliminate intragroup differences caused by variations in the concentrations of urinary metabolites, thus giving better parallelism and clustering effects. In addition, peak area normalization could further eliminate intraclass differences. Therefore, the strategy of combining peak area normalization with calibration of injection volumes of urine samples based on their creatinine values is effective for solving problems associated with urinary metabolomics.

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.

Metabolomics in noninvasive breast cancer

Breast cancer remains the most leading cause of death among women worldwide. Common methods for diagnosis and surveillance include mammography, histopathology and blood tests. The major drawback of mammography is the high rate of false reports, aside from the risk from repeated exposure to harmful ionizing radiations; histopathology is time consuming and often prone to subjective interpretations; blood-based tests are attractive, but lack the sensitivity and specificity. Obviously, more sensitive biomarkers for early detection and molecular targets for better treating breast cancer are urgently needed. Fortunately, molecular level 'omics' diagnosis is becoming increasingly popular; metabolomics, diagnosis based on 'metabolic fingerprinting' may provide clinically useful biomarkers applied toward identifying metabolic alterations and has introduced new insights into the pathology of breast cancer. By applying advanced analytical and statistical tools, metabolomics involves the comprehensive profiling of the full complement of low molecular weight compounds in a biological system and could classify the basis of tumor biology of breast cancer, to identify new prognostic and predictive markers and discover new targets for future therapeutic interventions. This advanced bioanalytic methods may now open new avenues for diagnostics in cancer via discovery of biomarkers. In this review we take a closer look at the metabolomics used within the field of breast cancer diagnosis. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples.

UPLC-Q-TOF-HDMS analysis of constituents in the root of two kinds of Aconitum using a metabolomics approach

INTRODUCTION

Metabolomics is an 'omics' approach that aims to comprehensively analyse all metabolites in a biological sample, and has great potential for directly elucidating plant metabolic processes. Increasing evidence supports the view that plants produce a broad range of low-molecular-weight secondary metabolites responsible for variation from species to species, thus enabling the use of secondary metabolite profiling in the chemotaxonomy.

OBJECTIVE

To gain deeper insights into the metabolites to increasing plant diversity, we performed systematic untargeted metabolite profiling to exploit the different parts and species of Aconitum as a case study.

METHOD

Application of ultraperformance liquid chromatography-quadrupole time-of-flight-high-definition mass spectrometry (UPLC-QTOF-HDMS) equipped with electrospray ionisation and coupled with pattern recognition analyses to study constituents in the root of two kinds of Aconitum species.

RESULTS

Twenty-two metabolites between the mother root of Aconitum carmichaelii Debx (CHW) and lateral root of Aconitum carmichaelii Debx (SFZ) and 13 metabolites between the CHW and root of Aconitum kusnezoffii Reichb (CW) have been identified. Of note, songorine, carmichaeline and isotalatizidine did not exist in CW, whereas they are present in the SFZ and CHW.

CONCLUSION

Metabolomics based UPLC-QTOF-HDMS with multivariate statistical models was effective for analysis of constituents in the root of two kinds of Aconitum species.

Power of metabolomics in diagnosis and biomarker discovery of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the commonest primary hepatic malignancy and the third most common cause of cancer-related death worldwide. Incidence remains highest in the developing world and is steadily increasing across the developed world. Current diagnostic modalities, of ultrasound and α-fetoprotein, are expensive and lack sensitivity in tumor detection. Because of its asymptomatic nature, HCC is usually diagnosed at late and advanced stages, for which there are no effective therapies. Thus, biomarkers for early detection and molecular targets for treating HCC are urgently needed. Emerging high-throughput metabolomics technologies have been widely applied, aiming at the discovery of candidate biomarkers for cancer staging, prediction of recurrence and prognosis, and treatment selection. Metabolic profiles, which are affected by many physiological and pathological processes, may provide further insight into the metabolic consequences of this severe liver disease. Small-molecule metabolites have an important role in biological systems and represent attractive candidates to understand HCC phenotypes. The power of metabolomics allows an unparalleled opportunity to query the molecular mechanisms of HCC. This technique-driven review aims to demystify the metabolomics pathway, while also illustrating the potential of this technique, with recent examples of its application in HCC.

Serum proteomics in biomedical research: a systematic review

Proteins that are important indicators of physiological or pathological states may contribute to the early diagnosis of disease, which may provide a basis for identifying the underlying mechanism of disease development. Serum, contains an abundance of proteins, offers an easy and inexpensive approach for disease detection and possesses a high potential to revolutionize the diagnostics. These differentially expressed proteins in serum have become an important role to monitoring the state for disease. Availability of emerging proteomic techniques gives optimism that serum can eventually be placed as a biomedium for clinical diagnostics. Advancements have benefited biomarker research to the point where serum is now recognized as an excellent diagnostic medium for the detection of disease. Comprehensive proteome of human serum fluid with high accuracy and availability has the potential to open new doors for disease biomarker discovery and for disease diagnostics, providing insights useful for future study. Thus, this review presents an overview of the value of serum as a credible diagnostic tool, and we aim to summarize the proteomic technologies currently used for global analysis of serum proteins and to elaborate on the application of serum proteomics to the discovery of disease biomarkers, and discuss some of the critical challenges and perspectives for this emerging field.

Power of metabolomics in biomarker discovery and mining mechanisms of obesity

Obesity, the prevalence of which is increasing rapidly worldwide, is recognized as a risk factor for diabetes, cardiovascular disease, liver disease and renal disease. Unfortunately, the mechanisms underlying it have not been well characterized. Fortunately, metabolomics - the systematic study of metabolites, which are small molecules generated by the process of metabolism - has been important in elucidating the pathways underlying obesity. Small-molecule metabolites have an important role in biological system and represent attractive candidates to understand obesity phenotypes. Metabolomic analysis is a valid and powerful tool with which to further define the mechanisms. Recent attention has focused on identifying biomarkers that would propose a better non-invasive way to detect or visualize obesity and prevent its events. The discovery of the biomarkers has become a key breakthrough towards a better molecular understanding of obesity. Thus, this review covers how recent metabolomic studies have advanced biomarker discovery and the elucidation of mechanisms underlying obesity and its comorbidities. The importance of identifying metabolic markers and pathways of disease-associated intermediate phenotypes is also emphasized. These biomarkers would be applicable as diagnostic tools in a personalized healthcare setting and may also open door to biomarker discovery, disease diagnosis and novel therapeutic avenues.

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.

Recent advances in natural products from plants for treatment of liver diseases

Liver disease is any condition that may cause liver inflammation or tissue damage and affects liver function. Natural products that are found in vegetables, fruits, plant extracts, herbs, insects, and animals, have been traditionally used for treating liver diseases. They are chemical compounds that usually have biological activities for use in drug discovery and design. Many natural products have been clinically available as potent hepatoprotective agents against commonly occurring liver diseases. This review summarizes the current progress in the basic, clinical, and translational research on natural products in treatment of various liver diseases. Furthermore, we will focus on the discovery and biological evaluation of the natural products, which shows potential as a new therapeutic agent of liver diseases.

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.

Proteomic identification network analysis of haptoglobin as a key regulator associated with liver fibrosis

Liver fibrosis (LF) is the final stage of liver dysfunction, characterized by diffuse fibrosis which is the main response to the liver injury. Haptoglobin (HP) protein, produced as an acute phase reactant during LF, preventing liver damage, may be potential molecular targets for early LF diagnostics and therapeutic applications. However, protein networks associated with the HP are largely unknown. To address this issue, we used a pathological mouse model of LF that was induced by treatment with carbon tetrachloride for 8 days. HP protein was separated and identified by two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight/time-of-flight mass spectrometry. HP protein was subjected to functional pathway analysis using STRING and Cytoscape software for better understanding of the protein-protein interaction (PPI) networks in biological context. Bioinformatics analyses revealed that HP expression associated with fibrosis was upregulated, and suggested that HP responsible for fibrosis may precede the onset and progression of LF. Using the web-based database, functional pathway analysis suggested the modulation of multiple vital physiological pathways, including antioxidation immunity, signal transduction, metabolic process, energy production, cell apoptosis, oxidation reduction, DNA repair process, cell communication, and regulation of cellular process. The generation of protein interaction networks clearly enhances the interpretation and understanding of the molecular mechanisms of HP. HP protein represents targets for further experimental investigation that will provide biological insight and potentially could be exploited for novel therapeutic approaches to combat LF.

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.

Proteomics study on the hepatoprotective effects of traditional Chinese medicine formulae Yin-Chen-Hao-Tang by a combination of two-dimensional polyacrylamide gel electrophoresis and matrix-assisted laser desorption/ionization-time of flight mass spectrometry

Proteomics can bring breakthroughs in the study of traditional Chinese medicine (TCM). Yin-Chen-Hao-Tang (YCHT), a famous TCM formulae, has been used to alleviate various types of liver injury. However, the underlying mechanisms and drug targets of YCHT associated with the hepatic injury are largely unknown. To identify the possible target proteins of YCHT, two-dimensional gel electrophoresis (2-DE)-based proteomics was performed and proteins altered after YCHT treatment were identified by MALDI-TOF/TOF-MS. Interestingly, 15 modulated proteins were identified, out of which 7 were found to be significantly altered by YCHT. YCHT treatment caused a statistically significant down-regulation of zinc finger protein 407, haptoglobin, macroglobulin, alpha-1-antitrypsin; significant up-regulation of transthyretin, vitamin D-binding protein, and prothrombin, appear to be involved in metabolism, energy generation, chaperone, antioxidation, signal transduction, protein folding and apoptosis. Finally, interaction network from 7 differentially expressed protein to the signal-related proteins was established using bioinformatic analysis. Of note, these signal-related proteins could be included in a network together with 7 proteins through direct interaction or only one intermediate partner. Functional pathway analysis suggested that these proteins were closely related in the protein-protein interaction network and the modulation of multiple vital physiological pathways. Thus, our data will help to understand the molecular mechanisms of hepatoprotective effects of YCHT.

Metabolomics study on the hepatoprotective effect of scoparone using ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry

Scoparone is an important constituent of Yinchenhao (Artemisia annua L.), a famous medicinal plant, and displayed bright prospects in the prevention and therapy of liver injury. However, the precise molecular mechanism of hepatoprotective effects has not been comprehensively explored. Here, metabolomics techniques are the comprehensive assessment of endogenous metabolites in a biological system and may provide additional insight into the mechanisms. The present investigation was designed to assess the effects and possible mechanisms of scoparone against carbon tetrachloride-induced liver injury. Ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry (UPLC/ESI-Q-TOF/MS) combined with pattern recognition approaches including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were integrated to discover differentiating metabolites. Results indicate five ions in the positive mode as differentiating metabolites. Functional pathway analysis revealed that the alterations in these metabolites were associated with primary bile acid biosynthesis, pyrimidine metabolism. Of note, scoparone has a potential pharmacological effect through regulating multiple perturbed pathways to the normal state. Our findings also showed that the robust metabolomics techniques are promising for getting biomarkers and clarifying mechanisms of disease, highlighting insights into drug discovery.

Salivary proteomics in biomedical research

Proteins that are important indicators of physiological or pathological states, can provide information for the identification of early and differential markers for disease. Saliva, contains an abundance of proteins, offers an easy, inexpensive, safe, and non-invasive approach for disease detection, and possesses a high potential to revolutionize the diagnostics. Discovery of salivary biomarkers could be used to scrutinize health and disease surveillance. The impact of human saliva proteome analysis in the search for clinically relevant disease biomarkers will be realized through advances made using proteomic technologies. The advancements of emerging proteomic techniques have benefited biomarker research to the point where saliva is now recognized as an excellent diagnostic medium for the detection of disease. This review presents an overview of the value of saliva as a credible diagnostic tool and we aim to summarize the proteomic technologies currently used for global analysis of saliva proteins and to elaborate on the application of saliva proteomics to the discovery of disease biomarkers, and discuss some of the critical challenges and perspectives in this field.

Metabolomics study of intervention effects of Wen-Xin-Formula using ultra high-performance liquid chromatography/mass spectrometry coupled with pattern recognition approach

Metabolomics is a new approach based on the systematic study of the full complement of small molecular metabolites in a biological sample. It could map the perturbations of early biochemical changes on diseases and hence provides an opportunity to develop predictive biomarkers that can result in earlier intervention and possess valuable insights into the mechanisms of diseases. Given the poor diagnosis of myocardial ischemia syndrome (heart-qi deficiency, HQD), biomarkers of great significance are urgently needed. Fortunately, metabolomics may offer the possibility of identifying marker metabolites and pathways activated in HQD. This paper was designed to explore globally metabolomics characters of the HQD and the therapeutic effects of traditional Chinese medicine Wen-Xin-Formula (WXF). Serum biochemical analysis and histopathological examinations were simultaneously performed. Global metabolic profiling with UHPLC/MS (ultra high-performance liquid chromatography-mass spectrometry), multivariate analysis and database searching were performed to discover differentiating metabolites. Seventeen biomarkers were identified and pathway analysis tools suggest that the glycolysis or gluconeogenesis metabolism, biosynthesis of unsaturated fatty acids metabolism, fatty acid biosynthesis and purine metabolism networks were acutely perturbed by HQD. Of note, WXF has potential pharmacological effect through regulating multiple perturbed pathways to normal state, correlates well to the assessment of biochemistry and histopathological assay. Overall, this study successfully demonstrated that the power of metabolomics in unraveling protective effects of WXF and these findings may help better understand the mechanisms of disease, and the underlying pathophysiologic processes.