The anti-inflammatory effects of Hedyotis diffusa Willd on SLE with STAT3 as a key target

ETHNOPHARMACOLOGICAL RELEVANCE

Hedyotis diffusa Willd, also named Scleromitrion diffusum (Willd.) R.J. Wang, is one medical herb, which has been traditionally used by the She nationality in China. And H. diffusa represents a beneficial effect on Systemic lupus erythematosus (SLE) treatment in clinic.

AIM OF THE STUDY

The underlying mechanisms of the protective effects of H. diffusa on SLE remain unclear. In this study, we treated MRL/lpr mice with H. diffusa water extract (HDW) to assess its therapeutic effects and verified its regulating signalling pathway through cytological experiments.

MATERIALS AND METHODS

In the present study, the constituents of HDW were analysed through ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and SCIEX OS software. The protective activity and underlying mechanisms were studied in a MRL/lpr lupus mouse model. The blood cells, autoantibodies, metabolites and the cytokines in serum were identified with a hematology analyzer, specific ELISA kit, GC/MS system and cytometric assays. The histological and immunohistochemical analysis were engaged in the morphologic, and the expression and translocation of the crucial protein observation. The dual luciferase reporter assay was applied to identifying the regulative activity of HDW. The transcription and translation expression of the protein was studied by real-time PCR and Western blot assays. The network pharmacology analysis was employed to predict the IL-6/STAT3 pathway regulators and the screen the STAT3 inhibitors in HDW.

RESULTS

The results revealed the capability of HDW to attenuate the production of autoantibodies, secretion of inflammatory cytokines (IL-6 and IFN-γ), and suppressed the IgG and C3 deposition, the development of glomerular lesions in MRL/lpr mice. Serum metabolomics study showed the improvement in serum metabolites, especially aminoacyl-tRNA biosynthesis, by HDW. IL-6 was clarified to be highly associated with the significantly changed metabolites in network analysis. We further demonstrated the effects of HDW on the IL-6/STAT3 pathway in vivo and in vitro.

CONCLUSIONS

This study suggested that HDW exerts a therapeutic effect in SLE model mice by suppressing the IL-6/STAT3 pathway.

Employing metabolomic approaches to determine the influence of age on experimental autoimmune encephalomyelitis (EAE)

The immune system plays a critical role not only in homeostasis of the body but also in pathogenesis. Autoimmunity and dysregulation of the immune balance are closely related to age. To examine the influence of age on autoimmunity, the pathophysiological features of experimental autoimmune encephalomyelitis (EAE) induced at different ages were elucidated on the basis of plasma-level metabolic changes. In the present study, female 6 week-old (6 W) and 15 month-old (15 M) C57BL/6 mice were immunized for EAE induction. The plasma and tissue samples were collected to determine the phenotypic characteristics. The activity of NADPH oxidase in plasma and the IL-6 concentrations in the brain and spinal cord were higher in both EAE groups compared to those in the control groups as well as in the 15 M EAE (15 M-E) group compared to those in the 6 W EAE (6 W-E) group. The metabolomic profiles related to characteristics of EAE were characterized by the biosynthesis of unsaturated fatty acids and the metabolism of tryptophan, tyrosine and sphingolipid. The reduced availability of unsaturated fatty acids and perturbations in tryptophan metabolism were high risk factors for EAE development regardless of age. The changes in tyrosine metabolism and sphingolipid metabolites were more dramatic in the 15 M-E group. From these findings, it can be concluded that changes in unsaturated fatty acid and tryptophan metabolism contributed to the development of EAE, whereas changes in sphingolipid and tyrosine metabolism, which corresponded to age, were additional risk factors that influenced the incidence and severity of EAE.

FoodOmicsGR_RI. A Consortium for Comprehensive Molecular Characterisation of Food Products

The national infrastructure FoodOmicsGR_RI coordinates research efforts from eight Greek Universities and Research Centers in a network aiming to support research and development (R&D) in the agri-food sector. The goals of FoodOmicsGR_RI are the comprehensive in-depth characterization of foods using cutting-edge omics technologies and the support of dietary/nutrition studies. The network combines strong omics expertise with expert field/application scientists (food/nutrition sciences, plant protection/plant growth, animal husbandry, apiculture and 10 other fields). Human resources involve more than 60 staff scientists and more than 30 recruits. State-of-the-art technologies and instrumentation is available for the comprehensive mapping of the food composition and available genetic resources, the assessment of the distinct value of foods, and the effect of nutritional intervention on the metabolic profile of biological samples of consumers and animal models. The consortium has the know-how and expertise that covers the breadth of the Greek agri-food sector. Metabolomics teams have developed and implemented a variety of methods for profiling and quantitative analysis. The implementation plan includes the following research axes: development of a detailed database of Greek food constituents; exploitation of "omics" technologies to assess domestic agricultural biodiversity aiding authenticity-traceability control/certification of geographical/genetic origin; highlighting unique characteristics of Greek products with an emphasis on quality, sustainability and food safety; assessment of diet's effect on health and well-being; creating added value from agri-food waste. FoodOmicsGR_RI develops new tools to evaluate the nutritional value of Greek foods, study the role of traditional foods and Greek functional foods in the prevention of chronic diseases and support health claims of Greek traditional products. FoodOmicsGR_RI provides access to state-of-the-art facilities, unique, well-characterised sample sets, obtained from precision/experimental farming/breeding (milk, honey, meat, olive oil and so forth) along with more than 20 complementary scientific disciplines. FoodOmicsGR_RI is open for collaboration with national and international stakeholders.

A perspective on the standards describing mass spectrometry-based metabolic phenotyping (metabolomics/metabonomics) studies in publications

As metabolic phenotyping (metabolomics, metabonomics and also lipidomics) gains in popularity and new investigators enter the field, the need to maintain and improve standards in publication is ever more pressing. In this perspective the requirements for information that should be included in manuscripts published in the Journal of Chromatography B, to ensure that the work is both credible and repeatable, are discussed. These include aspects such as study design, ethics, quality assurance (QA), quality control (QC) and data processing. In addition, aspects such as the level of confidence required for reporting metabolite identification (to a level where they could be subsequently used to develop hypotheses) are discussed.

Quantification of run order effect on chromatography - mass spectrometry profiling data

Chromatographic systems coupled with mass spectrometry detection are widely used in biological studies investigating how levels of biomolecules respond to different internal and external stimuli. Such changes are normally expected to be of low magnitude and therefore all experimental factors that can influence the analysis need to be understood and minimized. Run order effect is commonly observed and constitutes a major challenge in chromatography-mass spectrometry based profiling studies that needs to be addressed before the biological evaluation of measured data is made. So far there is no established consensus, metric or method that quickly estimates the size of this effect. In this paper we demonstrate how orthogonal projections to latent structures (OPLS®) can be used for objective quantification of the run order effect in profiling studies. The quantification metric is expressed as the amount of variation in the experimental data that is correlated to the run order. One of the primary advantages with this approach is that it provides a fast way of quantifying run-order effect for all detected features, not only internal standards. Results obtained from quantification of run order effect as provided by the OPLS can be used in the evaluation of data normalization, support the optimization of analytical protocols and identification of compounds highly influenced by instrumental drift. The application of OPLS for quantification of run order is demonstrated on experimental data from plasma profiling performed on three analytical platforms: GCMS metabolomics, LCMS metabolomics and LCMS lipidomics.

Sample Preparation Strategies for the Effective Quantitation of Hydrophilic Metabolites in Serum by Multi-Targeted HILIC-MS/MS

The effect of endogenous interferences of serum in multi-targeted metabolite profiling HILIC-MS/MS analysis was investigated by studying different sample preparation procedures. A modified QuEChERS dispersive SPE protocol, a HybridSPE protocol, and a combination of liquid extraction with protein precipitation were compared to a simple protein precipitation. Evaluation of extraction efficiency and sample clean-up was performed for all methods. SPE sorbent materials tested were found to retain hydrophilic analytes together with endogenous interferences, thus additional elution steps were needed. Liquid extraction was not shown to minimise matrix effects. In general, it was observed that a balance should be reached in terms of recovery, efficient clean-up, and sample treatment time when a wide range of metabolites are analysed. A quick step for removing phospholipids prior to the determination of hydrophilic endogenous metabolites is required, however, based on the results from the applied methods, further studies are needed to achieve high recoveries for all metabolites.

Investigation of the derivatization conditions for GC-MS metabolomics of biological samples

AIM

Metabolomics applications represent an emerging field where significant efforts are directed. Derivatization consists prerequisite for GC-MS metabolomics analysis.

METHODS

Common silylation agents were tested for the derivatization of blood plasma. Optimization of methoxyamination and silylation reactions was performed on a mixture of reference standards, consisting of 46 different metabolites. Stability of derivatized metabolites was tested at 4°C.

RESULTS

Optimum results were achieved using N-methyl-N-(trimethylsilyl)trifluoroacetamide. Methoxyamination at room temperature for 24 h followed by 2-h silylation at high temperature lead to efficient derivatization.

CONCLUSION

Formation and stability of derivatives among metabolites differ greatly, so derivatization should be studied before application in metabolomics studies.

Development of a Rapid Microbore Metabolic Profiling Ultraperformance Liquid Chromatography-Mass Spectrometry Approach for High-Throughput Phenotyping Studies

A rapid gradient microbore ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) method has been developed to provide a high-throughput analytical platform for the metabolic phenotyping of urine from large sample cohorts. The rapid microbore metabolic profiling (RAMMP) approach was based on scaling a conventional reversed-phase UPLC-MS method for urinary profiling from 2.1 mm × 100 mm columns to 1 mm × 50 mm columns, increasing the linear velocity of the solvent, and decreasing the gradient time to provide an analysis time of 2.5 min/sample. Comparison showed that conventional UPLC-MS and rapid gradient approaches provided peak capacities of 150 and 50, respectively, with the conventional method detecting approximately 19 000 features compared to the ∼6 000 found using the rapid gradient method. Similar levels of repeatability were seen for both methods. Despite the reduced peak capacity and the reduction in ions detected, the RAMMP method was able to achieve similar levels of group discrimination as conventional UPLC-MS when applied to rat urine samples obtained from investigative studies on the effects of acute 2-bromophenol and chronic acetaminophen administration. When compared to a direct infusion MS method of similar analysis time the RAMMP method provided superior selectivity. The RAMMP approach provides a robust and sensitive method that is well suited to high-throughput metabonomic analysis of complex mixtures such as urine combined with a 5-fold reduction in analysis time compared with the conventional UPLC-MS method.

A batch correction method for liquid chromatography-mass spectrometry data that does not depend on quality control samples

The need for reproducible and comparable results is of increasing importance in non-targeted metabolomic studies, especially when differences between experimental groups are small. Liquid chromatography-mass spectrometry spectra are often acquired batch-wise so that necessary calibrations and cleaning of the instrument can take place. However this may introduce further sources of variation, such as differences in the conditions under which the acquisition of individual batches is performed. Quality control (QC) samples are frequently employed as a means of both judging and correcting this variation. Here we show that the use of QC samples can lead to problems. The non-linearity of the response can result in substantial differences between the recorded intensities of the QCs and experimental samples, making the required adjustment difficult to predict. Furthermore, changes in the response profile between one QC interspersion and the next cannot be accounted for and QC based correction can actually exacerbate the problems by introducing artificial differences. "Background correction" methods utilise all experimental samples to estimate the variation over time rather than relying on the QC samples alone. We compare non-QC correction methods with standard QC correction and demonstrate their success in reducing differences between replicate samples and their potential to highlight differences between experimental groups previously hidden by instrumental variation.

Downregulation of Tryptophan-related Metabolomic Profile in Rheumatoid Arthritis Synovial Fluid

OBJECTIVE

Synovial fluid (SF) is one of the most important materials that reflect the pathophysiological process of arthritis. A metabolomic and lipidomic study of SF was performed with the aim of identifying tentative diagnostic markers or therapeutic candidates for rheumatoid arthritis (RA).

METHODS

SF was aspirated from 10 patients with RA and 10 patients with osteoarthritis (OA). RA SF and OA SF were collected and analyzed by ultraperformance liquid chromatography quadruple time-of-flight mass spectrometry. Associations among clinical variables, laboratory results, and metabolic profiles were investigated.

RESULTS

The metabolic pathways for carnitine, tryptophan, phenylalanine, arachidonic acid, and glycophospholipid were significantly upregulated in OA SF. The metabolic pathways for taurine, cholesterol ester, and the β-oxidation of pristine acid, linolenic acid, and sphingolipid were activated more in RA SF than in OA SF. In particular, the tryptophan pathway, which comprises kynurenine, indoleacetic acid, indole acetaldehyde, and N'-formylkynurenine, was downregulated. Interestingly, the levels of tryptophan metabolites kynurenine and N'-formylkynurenine, which are involved in immune tolerance, were significantly lower in RA SF compared with OA SF (p < 0.05), but the opposite pattern was observed for erythrocyte sedimentation rate (p < 0.01) and the levels of C-reactive protein (CRP; p < 0.01), rheumatoid factor (p < 0.01), and anticyclic citrullinated peptide antibody (p < 0.05). Kynurenine concentration correlated inversely with CRP concentration in RA SF but not in OA SF (r -0.65, p < 0.05).

CONCLUSION

Advances in metabolomic techniques enabled us to delineate distinctive metabolic and lipidomic profiles in RA SF and OA SF. RA SF and OA SF showed distinct metabolic profiles.

Statistical methods for handling unwanted variation in metabolomics data

Metabolomics experiments are inevitably subject to a component of unwanted variation, due to factors such as batch effects, long runs of samples, and confounding biological variation. Although the removal of this unwanted variation is a vital step in the analysis of metabolomics data, it is considered a gray area in which there is a recognized need to develop a better understanding of the procedures and statistical methods required to achieve statistically relevant optimal biological outcomes. In this paper, we discuss the causes of unwanted variation in metabolomics experiments, review commonly used metabolomics approaches for handling this unwanted variation, and present a statistical approach for the removal of unwanted variation to obtain normalized metabolomics data. The advantages and performance of the approach relative to several widely used metabolomics normalization approaches are illustrated through two metabolomics studies, and recommendations are provided for choosing and assessing the most suitable normalization method for a given metabolomics experiment. Software for the approach is made freely available.

Metabolomics data normalization with EigenMS

Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminants in the MS ion source and MS sensitivity among others. In this study we aimed to test a singular value decomposition-based method, called EigenMS, for normalization of metabolomics data. We analyzed a clinical human dataset where LC-MS serum metabolomics data and physiological measurements were collected from thirty nine healthy subjects and forty with type 2 diabetes and applied EigenMS to detect and correct for any systematic bias. EigenMS works in several stages. First, EigenMS preserves the treatment group differences in the metabolomics data by estimating treatment effects with an ANOVA model (multiple fixed effects can be estimated). Singular value decomposition of the residuals matrix is then used to determine bias trends in the data. The number of bias trends is then estimated via a permutation test and the effects of the bias trends are eliminated. EigenMS removed bias of unknown complexity from the LC-MS metabolomics data, allowing for increased sensitivity in differential analysis. Moreover, normalized samples better correlated with both other normalized samples and corresponding physiological data, such as blood glucose level, glycated haemoglobin, exercise central augmentation pressure normalized to heart rate of 75, and total cholesterol. We were able to report 2578 discriminatory metabolite peaks in the normalized data (p<0.05) as compared to only 1840 metabolite signals in the raw data. Our results support the use of singular value decomposition-based normalization for metabolomics data.

Detection of batch effects in liquid chromatography-mass spectrometry metabolomic data using guided principal component analysis

Metabolomics based on liquid chromatography-mass spectrometry (LC-MS) is a powerful tool for studying dynamic responses of biological systems to different physiological or pathological conditions. Differences in the instrumental response within and between batches introduce unwanted and uncontrolled data variation that should be removed to extract useful information. This work exploits a recently developed method for the identification of batch effects in high throughput genomic data based on the calculation of a δ statistic through principal component analysis (PCA) and guided PCA. Its applicability to LC-MS metabolomic data was tested on two real examples. The first example involved the repeated analysis of 42 plasma samples and 6 blanks in three independent batches, and the second data set involved the analysis of 101 plasma and 18 blank samples in a single batch with a total runtime of 50h. The first and second data set were used to evaluate between and within-batch effects using the δ statistic, respectively. Results obtained showed the usefulness of using the δ statistic together with other approaches such as summary statistics of peak intensity distributions, PCA scores plots or the monitoring of IS peak intensities, to detect and identify instrumental instabilities in LC-MS.

Serum lipid profiling of patients with chronic hepatitis B, cirrhosis, and hepatocellular carcinoma by ultra fast LC/IT-TOF MS

In this study, an ultra fast LC/IT-TOF MS (UFLC/IT-TOF MS)-based serum lipidomics method was employed to characterize the serum lipid profile of patients with chronic hepatitis B, cirrhosis, and hepatocellular carcinoma (HCC). After data collection and processing, 96 lipids including lysophosphatidylcholines, phosphatidylcholines, sphingomyelins, triacylglycerides, and cholesterol esters were identified and used for subsequent data analysis. Partial least squares-discriminant analysis revealed that patients with liver diseases had distinctly different serum lipid profile from that of healthy controls; while cirrhosis and HCC patients had a similar serum lipid profile, but different from that of hepatitis patients. The ANOVA analysis found 75 of the 96 identified lipids to be abnormally regulated, among which most of these lipids were downregulated in cirrhosis and HCC patients compared with those of healthy controls and hepatitis patients, while hepatitis patients induced several lipids downregulated and others upregulated compared with those of healthy controls, indicating the aberrant lipid metabolism in patients with liver diseases. This work demonstrated the utility of UFLC/IT-TOF MS-based serum lipidomics as a powerful tool to investigate the lipid metabolism of liver diseases.

Phosphorus and sulfur metabonomic profiling of tissue and plasma obtained from tumour-bearing mice using ultra-performance liquid chromatography/inductively coupled plasma mass spectrometry

RATIONALE

Metabonomic studies use complex biological samples (blood plasma/serum, tissues, etc.) that when analysed with high-performance liquid chromatography/mass spectrometry (HPLC/MS) or nuclear magnetic resonance (NMR) generate profiles that may contain many thousands of features. These profiles can be difficult to interpret with the majority of the features contributing little to the study. As such there is an argument for the development of techniques that can simplify the problem by targeting particular classes of compounds.

METHODS

In this study ultra-performance liquid chromatography/inductively coupled plasma mass spectrometry (UPLC/ICP-MS) was used to profile tumour tissue and plasma samples for phosphorus- and sulfur-containing metabolites. These samples were xenograft tumours (derived from breast, lung and colon cell lines) and plasma obtained from nude mice. Plasma was also obtained from non-tumour-bearing mice as a control. Due to isobaric interferences this method took advantage of the dynamic reaction cell within the ICP-MS system to react the phosphorus and sulfur ions with oxygen. The PO⁺ and SO⁺ ions were then monitored free of interferences. The total phosphorus and sulfur within each sample was also recorded using flow injection ICP-MS. A robust quality control system based on pooled sample replicate analysis was used throughout the study.

RESULTS

Determination of the total phosphorus and sulfur content of each sample was sufficient in itself for statistical differentiation between the majority of the cell lines analysed. Subsequent reversed-phase chromatographic profiling of the organic tumour and plasma extracts revealed the presence of a number of well-retained phosphorus-containing compounds that showed tumour-specific profiles. Reversed-phase profiling was not suitable for the sulfur-containing compounds which eluted with the solvent front.

CONCLUSIONS

This study has shown the potential use of UPLC/ICP-MS to differentiate between tumour cell lines, using both plasma and tumour tissue samples, based solely on metabolites that contain phosphorus or sulfur. Whilst further work is required to identify these compounds this methodology shows the ability of the described methods to provide targets for future biomarker discovery studies. Copyright © 2013 John Wiley & Sons, Ltd.

Batch profiling calibration for robust NMR metabonomic data analysis

Metabonomic studies involve the analysis of large numbers of samples to identify significant changes in the metabolic fingerprints of biological systems, possibly with sufficient statistical power for analysis. While procedures related to sample preparation and spectral data acquisition generally include the use of independent sample batches, these might be sources of systematic variation whose effects should be removed to focus on phenotyping the relevant biological variability. In this work, we describe a grouped-batch profile (GBP) calibration strategy to adjust nuclear magnetic resonance (NMR) metabolomic data-sets for batch effects either introduced during NMR experiments or samples work-up. We show how this method can be applied to data calibration in the context of a large-scale NMR epidemiological study where quality control samples are available. We also illustrate the efficiency of a batch profile correction for NMR metabonomic investigation of cell extracts, where GBP can significantly improve the predictive power of multivariate statistical models for discriminant analysis of the cell infection status. The method is applicable to a broad range of NMR metabolomic/metabonomic cohort studies.

A rough guide to metabolite identification using high resolution liquid chromatography mass spectrometry in metabolomic profiling in metazoans

Compound identification in mass spectrometry based metabolomics can be a problem but sometimes the problem seems to be presented in an over complicated way. The current review focuses on metazoans where the range of metabolites is more restricted than for example in plants. The focus is on liquid chromatography with high resolution mass spectrometry where it is proposed that most of the problems in compound identification relate to structural isomers rather than to isobaric compounds. Thus many of the problems faced relate to separation of isomers, which is usually required even if fragmentation is used to support structural identification. Many papers report the use of MS/MS or MS² as an adjunct to the identification of known metabolites but there a few examples in metabolomics studies of metazoans of complete structure elucidation of novel metabolites or metabolites where no authentic standards are available for comparison.

Statistical analysis of metabolomics data

Statistical matters form an integral part of a metabolomics experiment. In this chapter we describe several important aspects in the analysis of metabolomics data such as the removal of unwanted variation and the identification of differentially abundant metabolites, along with a number of other essential statistical considerations.

Metabolome analysis of gram-positive bacteria such as Staphylococcus aureus by GC-MS and LC-MS

The field of metabolomics has become increasingly important in the context of functional genomics. Together with other "omics" data, the investigation of the metabolome is an essential part of systems biology. Beside the analysis of human and animal biofluids, the investigation of the microbial physiology by methods of metabolomics has gained increased attention. For example, the analysis of metabolic processes during growth or virulence factor expression is crucially important to understand pathogenesis of bacteria. Common bioanalytical techniques for metabolome analysis include liquid and gas chromatographic methods coupled to mass spectrometry (LC-MS and GC-MS) and spectroscopic approaches such as NMR. In order to achieve metabolome data representing the physiological status of a microorganism, well-verified protocols for sampling and analysis are necessary. This chapter presents a detailed protocol for metabolome analysis of the Gram-positive bacterium Staphylococcus aureus. A detailed manual for cell sampling and metabolite extraction is given, followed by the description of the analytical procedures GC-MS and LC-MS. The advantages and limitations of each experimental setup are discussed. Here, a guideline specified for S. aureus metabolomics and information for important protocol steps are presented, to avoid common pitfalls in microbial metabolome analysis.

Extraction methods for the removal of phospholipids and other endogenous material from a biological fluid

Background: A comparison of three different sample preparation techniques for the analysis of plasma samples has been investigated to highlight the effect that these approaches have on the removal of endogenous material. The three techniques under investigation are: SPE, support assisted liquid–liquid extraction and nonspecific solvent-based protein precipitation. Results: Comparisons are made on the practicalities of each approach and to allow a semiquantitative assessment between the effectiveness of these different techniques the relative amounts of phospholipids present within the sample are analyzed. Total ion chromatograms are also obtained to further study the effects of different extraction techniques in the removal of endogenous components from a biological matrix. Both of these approaches provide a very coarse measure of the cleanliness of the extracts and demonstrate that support assisted liquid–liquid extraction and an optimized SPE approach remove a greater amount of endogenous material. Conclusion: This study highlights the importance of sample preparation in removing endogenous material, which may have a detrimental effect on the performance of a bioanalytical assay.

Mass spectrometry-based holistic analytical approaches for metabolite profiling in systems biology studies

Metabonomics and metabolomics represent one of the three major platforms in systems biology. To perform metabolomics it is necessary to generate comprehensive "global" metabolite profiles from complex samples, for example, biological fluids or tissue extracts. Analytical technologies based on mass spectrometry (MS), and in particular on liquid chromatography-MS (LC-MS), have become a major tool providing a significant source of global metabolite profiling data. In the present review we describe and compare the utility of the different analytical strategies and technologies used for MS-based metabolomics with a particular focus on LC-MS. Both the advantages offered by the technology and also the challenges and limitations that need to be addressed for the successful application of LC-MS in metabolite analysis are described. Data treatment and approaches resulting in the detection and identification of biomarkers are considered. Special emphasis is given to validation issues, instrument stability, and QA/quality control (QC) procedures.

Sample preparation prior to the LC–MS-based metabolomics/metabonomics of blood-derived samples

Blood represents a very important biological fluid and has been the target of continuous and extensive research for diagnostic, or health and drug monitoring reasons. Recently, metabonomics/metabolomics have emerged as a new and promising ‘omics’ platform that shows potential in biomarker discovery, especially in areas such as disease diagnosis, assessment of drug efficacy or toxicity. Blood is collected in various establishments in conditions that are not standardized. Next, the samples are prepared and analyzed using different methodologies or tools. When targeted analysis of key molecules (e.g., a drug or its metabolite[s]) is the aim, enforcement of certain measures or additional analyses may correct and harmonize these discrepancies. In omics fields such as those performed by holistic analytical approaches, no such rules or tools are available. As a result, comparison or correlation of results or data fusion becomes impractical. However, it becomes evident that such obstacles should be overcome in the near future to allow for large-scale studies that involve the assaying of samples from hundreds of individuals. In this case the effect of sample handling and preparation becomes very serious, in order to avoid wasting months of work from experts and expensive instrument time. The present review aims to cover the different methodologies applied to the pretreatment of blood prior to LC–MS metabolomic/metabonomic studies. The article tries to critically compare the methods and highlight issues that need to be addressed.

Biomarker discovery in biological specimens (plasma, hair, liver and kidney) of diabetic mice based upon metabolite profiling using ultra-performance liquid chromatography with electrospray ionization time-of-flight mass spectrometry

BACKGROUND

The number of diabetic patients has recently been increasing worldwide. Diabetes is a multifactorial disorder based on environmental factors and genetic background. In many cases, diabetes is asymptomatic for a long period and the patient is not aware of the disease. Therefore, the potential biomarker(s), leading to the early detection and/or prevention of diabetes mellitus, are strongly required. However, the diagnosis of the prediabetic state in humans is a very difficult issue, because the lifestyle is variable in each person. Although the development of a diagnosis method in humans is the goal of our research, the extraction and structural identification of biomarker candidates in several biological specimens (i.e., plasma, hair, liver and kidney) of ddY strain mice, which undergo naturally occurring diabetes along with aging, were carried out based upon a metabolite profiling study.

METHODS

The low-molecular-mass compounds including metabolites in the biological specimens of diabetic mice (ddY-H) and normal mice (ddY-L) were globally separated by ultra-performance liquid chromatography (UPLC) using different reversed-phase columns (i.e., T3-C18 and HS-F5) and detected by electrospray ionization time-of-flight mass spectrometry (ESI-TOF-MS). The biomarker candidates related to diabetes mellitus were extracted from a multivariate statistical analysis, such as an orthogonal partial least-squares-discriminant analysis (OPLS-DA), followed by a database search, such as ChemSpider, KEGG and HMDB.

RESULTS

Many metabolites and unknown compounds in each biological specimen were detected as the biomarker candidates related to diabetic mellitus. Among them, the elucidation of the chemical structures of several possible metabolites, including more than two biological specimens, was carried out along with the comparison of the tandem MS/MS analyses using authentic compounds. One metabolite was clearly identified as N-acetyl-L-leucine based upon the MS/MS spectra and the retention time on the chromatograms.

CONCLUSIONS

N-acetyl-L-leucine is an endogenous compound included in all biological specimens (plasma, hair, liver and kidney). Therefore, this metabolite appears to be a potential biomarker candidate related to diabetes. Although the structures of other biomarker candidates have still not yet determined, the present approach based upon a metabolite profiling study using UPLC-ESI-TOF-MS could be helpful for understanding the abnormal state of various diseases.

Practical analytical approach for the identification of biomarker candidates in prediabetic state based upon metabonomic study by ultraperformance liquid chromatography coupled to electrospray ionization time-of-flight mass spectrometry

The number of diabetic patients has recently been increasing worldwide. Thus, the discovery of potential diabetic biomarker(s), leading to the early detection and/or prevention of diabetes mellitus, is strongly required. The diagnosis of the prediabetic state in humans is a very difficult issue because of the lifestyle differences in each person and ethical consideration. Upon the basis of these considerations, animal experiments using ddY strain mice (ddY-H), which undergo naturally occurring diabetes along with age, were carried out in this study. Biomarker discovery based upon a metabonome study is now quite common, the same as that in the proteome analysis. Reversed-phase liquid chromatography-mass spectrometry (LC-MS) has mainly been used for the extensive analysis of low-molecular mass compounds including metabolites. The metabolites in the plasma of diabetic mice (ddY-H) and normal mice (ddY-L) were exhaustively separated and detected by ultraperformance liquid chromatography along with electrospray ionization time-of-flight mass spectrometry (UPLC-ESI-TOF-MS) using T3-C18 and HS-F5 columns. The biomarker candidates related to diabetes mellitus were extracted from the metabolite profiling of ddY-H and ddY-L at 5, 9 13, and 20 weeks old using a multivariate statistical analysis such as orthogonal partial least-squares-discriminant analysis (OPLS-DA). Various metabolites and unknown compounds were detected as biomarker candidates related to diabetic mellitus. Furthermore, the concentration of several metabolites on Lysine biosynthesis and Lysine degradation pathways were remarkably changed between the 9-week old ddY-H and ddY-L mice. Because a couple of biomarker candidates related to the prediabetic state were identified using the present approach, the metabolite profiling study could be helpful for understanding the abnormal state of various diseases.