Comprehensive analytical strategy for biomarker identification based on liquid chromatography coupled to mass spectrometry and new candidate confirmation tools

Changes in the salivary metabolome in patients with chronic erosive gastritis

INTRODUCTION

Chronic erosive gastritis (CEG) is closely related to gastric cancer, which requires early diagnosis and intervention. The invasiveness and discomfort of electronic gastroscope have limited its application in the large-scale screening of CEG. Therefore, a simple and noninvasive screening method is needed in the clinic.

OBJECTIVES

The aim of this study is to screen potential biomarkers that can identify diseases from the saliva samples of CEG patients using metabolomics.

METHODS

Saliva samples from 64 CEG patients and 30 healthy volunteers were collected, and metabolomic analysis was performed using UHPLC-Q-TOF/MS in the positive and negative ion modes. Statistical analysis was performed using both univariate (Student's t-test) and multivariate (orthogonal partial least squares discriminant analysis) tests. Receiver operating characteristic (ROC) analysis was conducted to determine significant predictors in the saliva of CEG patients.

RESULTS

By comparing the saliva samples from CEG patients and healthy volunteers, 45 differentially expressed metabolites were identified, of which 37 were up-regulated and 8 were down-regulated. These differential metabolites were related to amino acid, lipid, phenylalanine metabolism, protein digestion and absorption, and mTOR signaling pathway. In the ROC analysis, the AUC values of 7 metabolites were greater than 0.8, among which the AUC values of 1,2-dioleoyl-sn-glycoro-3-phosphodylcholine and 1-stearoyl-2-oleoyl-sn-glycoro-3-phospholine (SOPC) were greater than 0.9.

CONCLUSIONS

In summary, a total of 45 metabolites were identified in the saliva of CEG patients. Among them, 1,2-dioleoyl-sn-glycoro-3-phosphorylcholine and 1-stearoyl-2-oleoyl-sn-glycoro-3-phosphorine (SOPC) might have potential clinical application value.

Non-targeted metabolomics reveals diagnostic biomarker in the tongue coating of patients with chronic gastritis

Analysis of the properties of the tongue has been used in traditional Chinese medicine for disease diagnosis. Notably, tongue analysis, which is non-invasive and convenient compared with gastroscopy and pathological examination, can be used to assess chronic gastritis (CG). In order to find potential diagnostic biomarkers and study the metabolic mechanisms of the endogenous small molecules in the tongue coating related to CG, a non-targeted metabolomic analysis method was developed using ultra high performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry (UHPLC-Q/TOF-MS). It was performed using two different columns in positive and negative ion scanning modes separately. The stability of the samples was evaluated and the age and gender factors of the subjects were excluded to ensure the reliability of the data in this study. Finally, under the four analysis models, 130, 229, 113 and 92 differential compounds were found using multivariate statistical methods respectively. 37 potential biomarkers were putatively identified after removing the duplicate compounds and five potential diagnostic biomarkers were putatively identified by receiver operating characteristic (ROC) curve analysis, including inosine, oleamide, adenosine, N-acetylglucosamine (GlcNAc) and xanthine. The main metabolic pathways associated with CG were purine metabolism, amino acid metabolism, sphingolipid metabolism and energy metabolism, which suggested that oxygen free radicals and energy metabolism were altered in patients with CG. These results provided a potential new basis for the quantitative diagnosis and pathogenesis of CG.

Metabolic profiling of femoral muscle from rats at different periods of time after death

Clarification of postmortem metabolite changes can help characterize the process of biological degradation and facilitate investigations of forensic casework, especially in the estimation of postmortem interval (PMI). Metabolomics can provide information on the molecular profiles of tissues, which can aid in investigating postmortem metabolite changes. In this study, liquid chromatography-mass spectrometric (LC-MS) analysis was performed to identify the metabolic profiles of rat femoral muscle at ten periods of time after death within 168 h. The results obtained by reversed-phase liquid chromatography (RPLC)- and hydrophilic interaction liquid chromatography (HILIC)- electrospray ionization (ESI±) have revealed more than 16,000 features from all four datasets. Furthermore, 915 of these features were identified using an in-house database. Principal component analysis (PCA) demonstrated the time-specific features of molecular profiling at each period of time after death. Moreover, results from partial least squares projection to latent structures-discriminant analysis (PLS-DA) disclosed a strong association of metabolic alterations of at least 59 metabolites with the time since death, especially within 48 h after death, which expounds these metabolites as potential indicators in PMI estimation. Altogether, our results illustrate the potentiality of metabolic profiling in the evaluation of PMI and provide candidate metabolite markers with strong correlation with time since death for forensic purpose.

LC-HRMS Metabolomics for Untargeted Diagnostic Screening in Clinical Laboratories: A Feasibility Study

Today’s high-resolution mass spectrometers (HRMS) allow bioanalysts to perform untargeted/global determinations that can reveal unexpected compounds or concentrations in a patient’s sample. This could be performed for preliminary diagnosis attempts when usual diagnostic processes and targeted determinations fail. We have evaluated an untargeted diagnostic screening (UDS) procedure. UDS is a metabolome analysis that compares one sample (e.g., a patient) with control samples (a healthy population). Using liquid chromatography (LC)-HRMS full-scan analysis of human serum extracts and unsupervised data treatment, we have compared individual samples that were spiked with one xenobiotic or a higher level of one endogenous compound with control samples. After the use of different filters that drastically reduced the number of metabolites detected, the spiked compound was eventually revealed in each test sample and ranked. The proposed UDS procedure appears feasible and reliable to reveal unexpected xenobiotics (toxicology) or higher concentrations of endogenous metabolites. HRMS-based untargeted approaches could be useful as preliminary diagnostic screening when canonical processes do not reveal disease etiology nor establish a clear diagnosis and could reduce misdiagnosis. On the other hand, the risk of overdiagnosis of this approach should be reduced with mandatory biomedical interpretation of the patient’s UDS results and with confirmatory targeted and quantitative determinations.

Recent developments in liquid-phase separation techniques for metabolomics

Metabolomics is the comprehensive analysis of low molecular weight compounds in biological samples such as cells, body fluids and tissues. Comprehensive profiling of metabolites in complex sample matrices with the current analytical toolbox remains a huge challenge. Over the past few years, liquid chromatography–mass spectrometry (LC–MS) and capillary electrophoresis–mass spectrometry (CE–MS) have emerged as powerful complementary analytical techniques in the field of metabolomics. This Review provides an update of the most recent developments in LC–MS and CE–MS for metabolomics. Concerning LC–MS, attention is paid to developments in column technology and miniaturized systems, while strategies are discussed to improve the reproducibility and the concentration sensitivity of CE–MS for metabolomics studies. Novel interfacing techniques for coupling CE to MS are also considered. Representative examples illustrate the potential of the recent developments in LC–MS and CE–MS for metabolomics. Finally, some conclusions and perspectives are provided.

Evaluation of horse urine sample preparation methods for metabolomics using LC coupled to HRMS

Background: Horse urine is the medium of choice for the implementation of metabolomic approaches aimed at improving horse doping control. However, drug analysis in this biofluid is a challenging task due to the presence of large amounts of interfering compounds. Methodology & Results: A comparative study of sample preparation has been conducted to evaluate five sample-preparation methods, namely acetonitrile precipitation, proteinase K hydrolysis, membrane filtration and sample dilution with water by factors of five and 20, for metabolome analysis using liquid chromatography coupled to high resolution mass spectrometry. Assessment was performed at both global and targeted levels, by using a few thousand features obtained from peak detection software, and internal standards and 100 annotated or identified metabolites. Conclusion: By considering the number of detected signals, their intensity and their detection repeatability, acetonitrile precipitation was selected as the most efficient sample-preparation method for the analysis of horse urine metabolome in liquid chromatography coupled to high resolution mass spectrometry conditions.

MetFusion: integration of compound identification strategies

Mass spectrometry (MS) is an important analytical technique for the detection and identification of small compounds. The main bottleneck in the interpretation of metabolite profiling or screening experiments is the identification of unknown compounds from tandem mass spectra. Spectral libraries for tandem MS, such as MassBank or NIST, contain reference spectra for many compounds, but their limited chemical coverage reduces the chance for a correct and reliable identification of unknown spectra outside the database domain. On the other hand, compound databases like PubChem or ChemSpider have a much larger coverage of the chemical space, but they cannot be queried with spectral information directly. Recently, computational mass spectrometry methods and in silico fragmentation prediction allow users to search such databases of chemical structures. We present a new strategy called MetFusion to combine identification results from several resources, in particular, from the in silico fragmenter MetFrag with the spectral library MassBank to improve compound identification. We evaluate the performance on a set of 1062 spectra and achieve an improved ranking of the correct compound from rank 28 using MetFrag alone, to rank 7 with MetFusion, even if the correct compound and similar compounds are absent from the spectral library. On the basis of the evaluation, we extrapolate the performance of MetFusion to the KEGG compound database.

Metabolomic analysis of dynamic response and drug resistance of gastric cancer cells to 5-fluorouracil

Metabolomics has developed as an important new tool in cancer research. It is expected to lead to the discovery of biomarker candidates for cancer diagnosis and treatment. The current study aimed to perform a comprehensive metabolomic analysis of the intracellular dynamic responses of human gastric cancer cells to 5-fluorouracil (5-FU), referencing the mechanisms of drug action and drug resistance. Small metabolites in gastric cancer cells and 5-FU-resistant cells were measured by liquid chromatography-mass spectrometry. Candidates for drug targets were selected according to the presence or absence of resistance, before and after 5-FU treatment. In addition, the gene expression of each candidate was assessed by reverse transcription-polymerase chain reaction. The number of metabolites in cancer cells dramatically changed during short-term treatment with 5-FU. Particularly, proline was reduced to one-third of its original level and glutamate was increased by a factor of 3 after 3 h of treatment. The metabolic production of glutamate from proline proceeds by proline dehydrogenase (PRODH), producing superoxide. After 5-FU treatment, PRODH mRNA expression was upregulated 2-fold and production of superoxide was increased by a factor of 3. In 5-FU-resistant cells, proline and glutamate levels were less affected than in non-resistant cells, and PRODH mRNA expression and superoxide generation were not increased following treatment. In conclusion, the authors identified a candidate biomarker, PRODH, for drug effects using a meta-bolomic approach, a result that was confirmed by conventional methods. In the future, metabolomics will play an important role in the field of cancer research.

OMG: Open Molecule Generator

Computer Assisted Structure Elucidation has been used for decades to discover the chemical structure of unknown compounds. In this work we introduce the first open source structure generator, Open Molecule Generator (OMG), which for a given elemental composition produces all non-isomorphic chemical structures that match that elemental composition. Furthermore, this structure generator can accept as additional input one or multiple non-overlapping prescribed substructures to drastically reduce the number of possible chemical structures. Being open source allows for customization and future extension of its functionality. OMG relies on a modified version of the Canonical Augmentation Path, which grows intermediate chemical structures by adding bonds and checks that at each step only unique molecules are produced. In order to benchmark the tool, we generated chemical structures for the elemental formulas and substructures of different metabolites and compared the results with a commercially available structure generator. The results obtained, i.e. the number of molecules generated, were identical for elemental compositions having only C, O and H. For elemental compositions containing C, O, H, N, P and S, OMG produces all the chemically valid molecules while the other generator produces more, yet chemically impossible, molecules. The chemical completeness of the OMG results comes at the expense of being slower than the commercial generator. In addition to being open source, OMG clearly showed the added value of constraining the solution space by using multiple prescribed substructures as input. We expect this structure generator to be useful in many fields, but to be especially of great importance for metabolomics, where identifying unknown metabolites is still a major bottleneck.

Annotation of the human adult urinary metabolome and metabolite identification using ultra high performance liquid chromatography coupled to a linear quadrupole ion trap-Orbitrap mass spectrometer

Metabolic profiles of biofluids obtained by atmospheric pressure ionization mass spectrometry-based technologies contain hundreds to thousands of features, most of them remaining unknown or at least not characterized in analytical systems. We report here on the annotation of the human adult urinary metabolome and metabolite identification from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics data sets. Features of biological interest were first of all annotated using the ESI-MS database of the laboratory. They were also grouped, thanks to software tools, and annotated using public databases. Metabolite identification was achieved using two complementary approaches: (i) formal identification by matching chromatographic retention times, mass spectra, and also product ion spectra (if required) of metabolites to be characterized in biological data sets to those of reference compounds and (ii) putative identification from biological data thanks to MS/MS experiments for metabolites not available in our chemical library. By these means, 384 metabolites corresponding to 1484 annotated features (659 in negative ion mode and 825 in positive ion mode) were characterized in human urine samples. Of these metabolites, 192 and 66 were formally and putatively identified, respectively, and 54 are reported in human urine for the first time. These lists of features could be used by other laboratories to annotate their ESI-MS metabolomics data sets.

Metabolomic analysis of the effects of polychlorinated biphenyls in nonalcoholic fatty liver disease

Polychlorinated biphenyls (PCBs) are persistent organic pollutants and have been associated with abnormal liver enzymes and suspected nonalcoholic fatty liver disease (NAFLD), obesity, and the metabolic syndrome in epidemiological studies. In epidemiological surveys of human PCB exposure, PCB 153 has the highest serum levels among PCB congeners. To determine the hepatic effects of PCB 153 in mice, C57BL/6J mice were fed either a control diet (CD) or a high fat diet (HFD) for 12 weeks, with or without PCB 153 coexposure. The metabolite extracts from mouse livers were analyzed using linear trap quadrupole-Fourier transform ion cyclotron resonance mass spectrometer (LTQ-FTICR MS) via direct infusion nanoelectrospray ionization (DI-nESI) mass spectrometry. The metabolomics analysis indicated no difference in the metabolic profile between mice fed the control diet with PCB 153 exposure (CD+PCB 153) and mice fed the control diet (CD) without PCB 153 exposure. However, compared with CD group, levels of 10 metabolites were increased and 15 metabolites were reduced in mice fed HFD. Moreover, compared to CD+PCB 153 group, the abundances of 6 metabolites were increased and 18 metabolites were decreased in the mice fed high fat diet with PCB 153 exposure (HFD+PCB 153). Compared with HFD group, the abundances of 2 metabolites were increased and of 12 metabolites were reduced in HFD+PCB 153 group. These observations agree with the histological results and indicate that the metabolic effects of PCB 153 were highly dependent on macronutrient interactions with HFD. Antioxidant depletion is likely to be an important consequence of this interaction, as this metabolic disturbance has previously been implicated in obesity and NAFLD.

MetSign: a computational platform for high-resolution mass spectrometry-based metabolomics

Data analysis in metabolomics is currently a major challenge, particularly when large sample sets are analyzed. Herein, we present a novel computational platform entitled MetSign for high-resolution mass spectrometry-based metabolomics. By converting the instrument raw data into mzXML format as its input data, MetSign provides a suite of bioinformatics tools to perform raw data deconvolution, metabolite putative assignment, peak list alignment, normalization, statistical significance tests, unsupervised pattern recognition, and time course analysis. MetSign uses a modular design and an interactive visual data mining approach to enable efficient extraction of useful patterns from data sets. Analysis steps, designed as containers, are presented with a wizard for the user to follow analyses. Each analysis step might contain multiple analysis procedures and/or methods and serves as a pausing point where users can interact with the system to review the results, to shape the next steps, and to return to previous steps to repeat them with different methods or parameter settings. Analysis of metabolite extract of mouse liver with spiked-in acid standards shows that MetSign outperforms the existing publically available software packages. MetSign has also been successfully applied to investigate the regulation and time course trajectory of metabolites in hepatic liver.

Bladder cancer determination via two urinary metabolites: a biomarker pattern approach

The purpose of this study was to use metabonomic profiling to identify a potential specific biomarker pattern in urine as a noninvasive bladder cancer (BC) detection strategy. A liquid chromatography-mass spectrometry based method, which utilized both reversed phase liquid chromatography and hydrophilic interaction chromatography separations, was performed, followed by multivariate data analysis to discriminate the global urine profiles of 27 BC patients and 32 healthy controls. Data from both columns were combined, and this combination proved to be effective and reliable for partial least squares-discriminant analysis. Following a critical selection criterion, several metabolites showing significant differences in expression levels were detected. Receiver operating characteristic analysis was used for the evaluation of potential biomarkers. Carnitine C9:1 and component I, were combined as a biomarker pattern, with a sensitivity and specificity up to 92.6% and 96.9%, respectively, for all patients and 90.5% and 96.9%, respectively for low-grade BC patients. Metabolic pathways of component I and carnitine C9:1 are discussed. These results indicate that metabonomics is a practicable tool for BC diagnosis given its high efficacy and economization. The combined biomarker pattern showed better performance than single metabolite in discriminating bladder cancer patients, especially low-grade BC patients, from healthy controls.

LC-MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery

A LC-MS based method, which utilizes both reversed-performance (RP) chromatography and hydrophilic interaction chromatography (HILIC) separations, has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of renal cell carcinoma (RCC) patients and healthy controls. The HILIC was found necessary for a comprehensive serum metabonomic profiling as well as RP separation. The feasibility of using serum metabonomics for the diagnosis and staging of RCC has been evaluated. One-hundred percent sensitivity in detection has been achieved, and a satisfactory clustering between the early stage and advanced-stage patients is observed. The results suggest that the combination of LC-MS analysis with multivariate statistical analysis can be used for RCC diagnosis and has potential in the staging of RCC. The MS/MS experiments have been carried out to identify the biomarker patterns that made great contribution to the discrimination. As a result, 30 potential biomarkers for RCC are identified. It is possible that the current biomarker patterns are not unique to RCC but just the result of any malignancy disease. To further elucidate the pathophysiology of RCC, related metabolic pathways have been studied. RCC is found to be closely related to disturbed phospholipid catabolism, sphingolipid metabolism, phenylalanine metabolism, tryptophan metabolism, fatty acid beta-oxidation, cholesterol metabolism, and arachidonic acid metabolism.

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.

Computational mass spectrometry for metabolomics: identification of metabolites and small molecules

The identification of compounds from mass spectrometry (MS) data is still seen as a major bottleneck in the interpretation of MS data. This is particularly the case for the identification of small compounds such as metabolites, where until recently little progress has been made. Here we review the available approaches to annotation and identification of chemical compounds based on electrospray ionization (ESI-MS) data. The methods are not limited to metabolomics applications, but are applicable to any small compounds amenable to MS analysis. Starting with the definition of identification, we focus on the analysis of tandem mass and MS(n) spectra, which can provide a wealth of structural information. Searching in libraries of reference spectra provides the most reliable source of identification, especially if measured on comparable instruments. We review several choices for the distance functions. The identification without reference spectra is even more challenging, because it requires approaches to interpret tandem mass spectra with regard to the molecular structure. Both commercial and free tools are capable of mining general-purpose compound libraries, and identifying candidate compounds. The holy grail of computational mass spectrometry is the de novo deduction of structure hypotheses for compounds, where method development has only started thus far. In a case study, we apply several of the available methods to the three compounds, kaempferol, reserpine, and verapamil, and investigate whether this results in reliable identifications.