MRMPROBS: A Data Assessment and Metabolite Identification Tool for Large-Scale Multiple Reaction Monitoring Based Widely Targeted Metabolomics

Identifying metabolic elements that contribute to productivity of 1-propanol bioproduction using metabolomic analysis

INTRODUCTION

Previously constructed Escherichia coli strains that produce 1-propanol use the native threonine pathway, or a heterologous citramalate pathway. However, based on the energy and cofactor requirements of each pathway, a combination of the two pathways produces synergistic effects that increase the theoretical maximum yield with a simultaneous unexplained increase in productivity.

OBJECTIVE

Identification of key factors that contribute to synergistic effect leading to 1-propanol yield and productivity improvement in E. coli with native threonine pathway and heterologous citramalate pathway.

METHOD

A combination of snapshot metabolomic profiling and dynamic metabolic turnover analysis were used to identify system-wide perturbations that contribute to the productivity improvement.

RESULT AND CONCLUSION

In the presence of both pathways, increased glucose consumption and elevated levels of glycolytic intermediates are attributed to an elevated phosphoenolpyruvate (PEP)/pyruvate ratio that is known to increase the function of the native phosphotransferase. Turnover analysis of nitrogen containing byproducts reveals that ammonia assimilation, required for the threonine pathway, is streamlined when provided with an NAD(P)H surplus in the presence of the citramalate pathway. Our study illustrates the application of metabolomics in identification of factors that alter cellular physiology for improvement of 1-propanol bioproduction.

Widely-targeted quantitative lipidomics method by supercritical fluid chromatography triple quadrupole mass spectrometry

Lipidomics, the mass spectrometry-based comprehensive analysis of lipids, has attracted attention as an analytical approach to provide novel insight into lipid metabolism and to search for biomarkers. However, an ideal method for both comprehensive and quantitative analysis of lipids has not been fully developed. Here, we have proposed a practical methodology for widely targeted quantitative lipidome analysis using supercritical fluid chromatography fast-scanning triple-quadrupole mass spectrometry (SFC/QqQMS) and theoretically calculated a comprehensive lipid multiple reaction monitoring (MRM) library. Lipid classes can be separated by SFC with a normal-phase diethylamine-bonded silica column with high resolution, high throughput, and good repeatability. Structural isomers of phospholipids can be monitored by mass spectrometric separation with fatty acyl-based MRM transitions. SFC/QqQMS analysis with an internal standard-dilution method offers quantitative information for both lipid class and individual lipid molecular species in the same lipid class. Additionally, data acquired using this method has advantages, including reduction of misidentification and acceleration of data analysis. Using the SFC/QqQMS system, alteration of plasma lipid levels in myocardial infarction-prone rabbits to the supplementation of EPA was first observed. Our developed SFC/QqQMS method represents a potentially useful tool for in-depth studies focused on complex lipid metabolism and biomarker discovery.-Takeda, H., Y. Izumi, M. Takahashi, T. Paxton, S. Tamura, T. Koike, Y. Yu, N. Kato, K. Nagase, M. Shiomi, and T. Bamba.

Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery

This work reports the second part of a review intending to give the state of the art of major metabolic phenotyping strategies. It particularly deals with inherent advantages and limits regarding data analysis issues and biological information retrieval tools along with translational challenges. This Part starts with introducing the main data preprocessing strategies of the different metabolomics data. Then, it describes the main data analysis techniques including univariate and multivariate aspects. It also addresses the challenges related to metabolite annotation and characterization. Finally, functional analysis including pathway and network strategies are discussed. The last section of this review is devoted to practical considerations and current challenges and pathways to bring metabolomics into clinical environments.

SWATHtoMRM: Development of High-Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery

The complexity of metabolome presents a great analytical challenge for quantitative metabolite profiling, and restricts the application of metabolomics in biomarker discovery. Targeted metabolomics using multiple-reaction monitoring (MRM) technique has excellent capability for quantitative analysis, but suffers from the limited metabolite coverage. To address this challenge, we developed a new strategy, namely, SWATHtoMRM, which utilizes the broad coverage of SWATH-MS technology to develop high-coverage targeted metabolomics method. Specifically, SWATH-MS technique was first utilized to untargeted profile one pooled biological sample and to acquire the MS² spectra for all metabolites. Then, SWATHtoMRM was used to extract the large-scale MRM transitions for targeted analysis with coverage as high as 1000-2000 metabolites. Then, we demonstrated the advantages of SWATHtoMRM method in quantitative analysis such as coverage, reproducibility, sensitivity, and dynamic range. Finally, we applied our SWATHtoMRM approach to discover potential metabolite biomarkers for colorectal cancer (CRC) diagnosis. A high-coverage targeted metabolomics method with 1303 metabolites in one injection was developed to profile colorectal cancer tissues from CRC patients. A total of 20 potential metabolite biomarkers were discovered and validated for CRC diagnosis. In plasma samples from CRC patients, 17 out of 20 potential biomarkers were further validated to be associated with tumor resection, which may have a great potential in assessing the prognosis of CRC patients after tumor resection. Together, the SWATHtoMRM strategy provides a new way to develop high-coverage targeted metabolomics method, and facilitates the application of targeted metabolomics in disease biomarker discovery. The SWATHtoMRM program is freely available on the Internet ( http://www.zhulab.cn/software.php ).

Iterative cycle of widely targeted metabolic profiling for the improvement of 1-butanol titer and productivity in Synechococcus elongatus

BACKGROUND

Metabolomics is the comprehensive study of metabolites that can demonstrate the downstream effects of gene and protein regulation, arguably representing the closest correlation with phenotypic features. Hence, metabolomics-driven approach offers an effective way to facilitate strain improvement. Previously, targeted metabolomics on the 1-butanol-producing cyanobacterial strain Synechococcus elongatus BUOHSE has revealed the reduction step from butanoyl-CoA to butanal, catalyzed by CoA-acylating propionaldehyde dehydrogenase (PduP), as a rate-limiting step in the CoA-dependent pathway. Moreover, an increase in acetyl-CoA synthesis rate was also observed in this strain, by which the increased rate of release of CoA from butanoyl-CoA was used to enhance formation of acetyl-CoA to feed into the pathway.

RESULTS

In the present study, a new strain (DC7) with an improved activity of PduP enzyme, was constructed using BUOHSE as the background strain. DC7 showed a 33% increase in 1-butanol production compared to BUOHSE. For a deeper understanding of the metabolic state of DC7, widely targeted metabolomics approach using ion-pair reversed-phase LC/MS was performed. Results showed a decreased level of butanoyl-CoA and an increased level of acetyl-CoA in DC7 compared to BUOHSE. This served as an indication that the previous bottleneck has been solved and free CoA regeneration increased upon the improvement of the PduP enzyme. In order to utilize the enhanced levels of acetyl-CoA in DC7 for 1-butanol production, overexpression of acetyl-CoA carboxylase (ACCase) in DC7 was performed by inserting the gene encoding an ACCase subunit from Yarrowia lipolytica into the aldA site. The resulting strain, named DC11, was able to reach a production titer of 418.7 mg/L in 6 days, compared to DC7 that approached a similar titer in 12 days. A maximum productivity of 117 mg/L/day was achieved between days 4 and 5 in DC11.

CONCLUSIONS

In this study, the iterative cycle of genetic modification based on insights from metabolomics successfully resulted in the highest reported 1-butanol productivity for engineered Synechococcus elongatus PCC 7942.

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.

Orthogonal partial least squares/projections to latent structures regression-based metabolomics approach for identification of gene targets for improvement of 1-butanol production in Escherichia coli

Metabolomics is the comprehensive analysis of metabolites in biological systems that uses multivariate analyses such as principal component analysis (PCA) or partial least squares/projections to latent structures regression (PLSR) to understand the metabolome state and extract important information from biological systems. In this study, orthogonal PLSR (OPLSR) model-based metabolomics approach was applied to 1-butanol producing Escherichia coli to facilitate in strain improvement strategies. Here, metabolite data obtained by liquid chromatography/mass spectrometry (LC/MS) was used to construct an OPLSR model to correlate metabolite changes with 1-butanol production and rationally identify gene targets for strain improvement. Using this approach, acetyl-CoA was determined as the rate-limiting step of the pathway while free CoA was found to be insufficient for 1-butanol production. By resolving the problems addressed by the OPLSR model, higher 1-butanol productivity was achieved. In this study, the usefulness of OPLSR-based metabolomics approach for understanding the whole metabolome state and determining the most relevant metabolites was demonstrated. Moreover, it was able to provide valuable insights for selection of rational gene targets for strain improvement.

Metabolomics-driven approach to solving a CoA imbalance for improved 1-butanol production in Escherichia coli

High titer 1-butanol production in Escherichia coli has previously been achieved by overexpression of a modified clostridial 1-butanol production pathway and subsequent deletion of native fermentation pathways. This strategy couples growth with production as 1-butanol pathway offers the only available terminal electron acceptors required for growth in anaerobic conditions. With further inclusion of other well-established metabolic engineering principles, a titer of 15g/L has been obtained. In achieving this titer, many currently existing strategies have been exhausted, and 1-butanol toxicity level has been surpassed. Therefore, continued engineering of the host strain for increased production requires implementation of alternative strategies that seek to identify non-obvious targets for improvement. In this study, a metabolomics-driven approach was used to reveal a CoA imbalance resulting from a pta deletion that caused undesirable accumulation of pyruvate, butanoate, and other CoA-derived compounds. Using metabolomics, the reduction of butanoyl-CoA to butanal catalyzed by alcohol dehydrogenase AdhE2 was determined as a rate-limiting step. Fine-tuning of this activity and subsequent release of free CoA restored the CoA balance that resulted in a titer of 18.3g/L upon improvement of total free CoA levels using cysteine supplementation. By enhancing AdhE2 activity, carbon flux was directed towards 1-butanol production and undesirable accumulation of pyruvate and butanoate was diminished. This study represents the initial report describing the improvement of 1-butanol production in E. coli by resolving CoA imbalance, which was based on metabolome analysis and rational metabolic engineering strategies.

compMS2Miner: An Automatable Metabolite Identification, Visualization, and Data-Sharing R Package for High-Resolution LC-MS Data Sets

A long-standing challenge of untargeted metabolomic profiling by ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is efficient transition from unknown mass spectral features to confident metabolite annotations. The compMS2Miner (Comprehensive MS2 Miner) package was developed in the R language to facilitate rapid, comprehensive feature annotation using a peak-picker-output and MS2 data files as inputs. The number of MS2 spectra that can be collected during a metabolomic profiling experiment far outweigh the amount of time required for pain-staking manual interpretation; therefore, a degree of software workflow autonomy is required for broad-scale metabolite annotation. CompMS2Miner integrates many useful tools in a single workflow for metabolite annotation and also provides a means to overview the MS2 data with a Web application GUI compMS2Explorer (Comprehensive MS2 Explorer) that also facilitates data-sharing and transparency. The automatable compMS2Miner workflow consists of the following steps: (i) matching unknown MS1 features to precursor MS2 scans, (ii) filtration of spectral noise (dynamic noise filter), (iii) generation of composite mass spectra by multiple similar spectrum signal summation and redundant/contaminant spectra removal, (iv) interpretation of possible fragment ion substructure using an internal database, (v) annotation of unknowns with chemical and spectral databases with prediction of mammalian biotransformation metabolites, wrapper functions for in silico fragmentation software, nearest neighbor chemical similarity scoring, random forest based retention time prediction, text-mining based false positive removal/true positive ranking, chemical taxonomic prediction and differential evolution based global annotation score optimization, and (vi) network graph visualizations, data curation, and sharing are made possible via the compMS2Explorer application. Metabolite identities and comments can also be recorded using an interactive table within compMS2Explorer. The utility of the package is illustrated with a data set of blood serum samples from 7 diet induced obese (DIO) and 7 nonobese (NO) C57BL/6J mice, which were also treated with an antibiotic (streptomycin) to knockdown the gut microbiota. The results of fully autonomous and objective usage of compMS2Miner are presented here. All automatically annotated spectra output by the workflow are provided in the Supporting Information and can alternatively be explored as publically available compMS2Explorer applications for both positive and negative modes ( https://wmbedmands.shinyapps.io/compMS2_mouseSera_POS and https://wmbedmands.shinyapps.io/compMS2_mouseSera_NEG ). The workflow provided rapid annotation of a diversity of endogenous and gut microbially derived metabolites affected by both diet and antibiotic treatment, which conformed to previously published reports. Composite spectra (n = 173) were autonomously matched to entries of the Massbank of North America (MoNA) spectral repository. These experimental and virtual (lipidBlast) spectra corresponded to 29 common endogenous compound classes (e.g., 51 lysophosphatidylcholines spectra) and were then used to calculate the ranking capability of 7 individual scoring metrics. It was found that an average of the 7 individual scoring metrics provided the most effective weighted average ranking ability of 3 for the MoNA matched spectra in spite of potential risk of false positive annotations emerging from automation. Minor structural differences such as relative carbon-carbon double bond positions were found in several cases to affect the correct rank of the MoNA annotated metabolite. The latest release and an example workflow is available in the package vignette ( https://github.com/WMBEdmands/compMS2Miner ) and a version of the published application is available on the shinyapps.io site ( https://wmbedmands.shinyapps.io/compMS2Example ).

Technical Challenges in Mass Spectrometry-Based Metabolomics

Metabolomics is a strategy for analysis, and quantification of the complete collection of metabolites present in biological samples. Metabolomics is an emerging area of scientific research because there are many application areas including clinical, agricultural, and medical researches for the biomarker discovery and the metabolic system analysis by employing widely targeted analysis of a few hundred preselected metabolites from 10-100 biological samples. Further improvement in technologies of mass spectrometry in terms of experimental design for larger scale analysis, computational methods for tandem mass spectrometry-based elucidation of metabolites, and specific instrumentation for advanced bioanalysis will enable more comprehensive metabolome analysis for exploring the hidden secrets of metabolism.

Tailored sensitivity reduction improves pattern recognition and information recovery with a higher tolerance to varied sample concentration for targeted urinary metabolomics

Variation in total metabolite concentration among different samples has been a major challenge for urinary metabolomics. Here we investigated the potential of tailored sensitivity reduction of high abundance metabolites for improved targeted urinary metabolomics. Two levels of sensitivity reduction of the 21 predominant urinary metabolites were assessed by employing less sensitive transition or collision energy with level 1 (reduced 1) and 2 (reduced 2) exhibiting 30-90% and 2-20% of the optimal sensitivity, respectively. Five postacquisition normalization methods were compared including no normalization, probabilistic quotient normalization, and normalization to sample median, creatinine intensity, and total intensity. Normalization to total intensity with reduced 2 gave the best pattern recognition and information recovery with a higher tolerance to varied sample concentration. Pareto scaling could improve the performance of tailored sensitivity reduction (reduced 2) for targeted urinary metabolomics while data transformation and autoscaling were susceptible to varied sample concentration. Using controlled spike-in experiments, we demonstrated that tailored sensitivity reduction revealed more differentially expressed markers with higher accuracy than did the conventional optimal sensitivity. This was particularly true when the differences between the sample groups are small. This work also served as an introductory guideline for handling targeted metabolomics data using the open-source software MetaboAnalyst.

Metabolome analysis reveals the effect of carbon catabolite control on the poly(γ-glutamic acid) biosynthesis of Bacillus licheniformis ATCC 9945

Poly(γ-glutamic acid) (PGA) is a polymer composed of L- and/or D-glutamic acids that is produced by Bacillus sp. Because the polymer has various features as water soluble, edible, non-toxic and so on, it has attracted attention as a candidate for many applications such as foods, cosmetics and so on. However, although it is well known that the intracellular metabolism of Bacillus sp. is mainly regulated by catabolite control, the effect of the catabolite control on the PGA producing Bacillus sp. is largely unknown. This study is the first report of metabolome analysis on the PGA producing Bacillus sp. that reveals the effect of carbon catabolite control on the metabolism of PGA producing Bacillus licheniformis ATCC 9945. Results showed that the cells cultivated in glycerol-containing medium showed higher PGA production than the cells in glucose-containing medium. Furthermore, metabolome analysis revealed that the activators of CcpA and CodY, global regulatory proteins of the intracellular metabolism, accumulated in the cells cultivated in glycerol-containing and glucose-containing medium, respectively, with CodY apparently inhibiting PGA production. Moreover, the cells seemed to produce glutamate from citrate and ammonium using glutamine synthetase/glutamate synthase. Pulsed addition of di-ammonium hydrogen citrate, as suggested by the metabolome result, was able to achieve the highest value so far for PGA production in B. licheniformis.

DnsID in MyCompoundID for rapid identification of dansylated amine- and phenol-containing metabolites in LC-MS-based metabolomics

High-performance chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) is an enabling technology based on rational design of labeling reagents to target a class of metabolites sharing the same functional group (e.g., all the amine-containing metabolites or the amine submetabolome) to provide concomitant improvements in metabolite separation, detection, and quantification. However, identification of labeled metabolites remains to be an analytical challenge. In this work, we describe a library of labeled standards and a search method for metabolite identification in CIL LC-MS. The current library consists of 273 unique metabolites, mainly amines and phenols that are individually labeled by dansylation (Dns). Some of them produced more than one Dns-derivative (isomers or multiple labeled products), resulting in a total of 315 dansyl compounds in the library. These metabolites cover 42 metabolic pathways, allowing the possibility of probing their changes in metabolomics studies. Each labeled metabolite contains three searchable parameters: molecular ion mass, MS/MS spectrum, and retention time (RT). To overcome RT variations caused by experimental conditions used, we have developed a calibration method to normalize RTs of labeled metabolites using a mixture of RT calibrants. A search program, DnsID, has been developed in www.MyCompoundID.org for automated identification of dansyl labeled metabolites in a sample based on matching one or more of the three parameters with those of the library standards. Using human urine as an example, we illustrate the workflow and analytical performance of this method for metabolite identification. This freely accessible resource is expandable by adding more amine and phenol standards in the future. In addition, the same strategy should be applicable for developing other labeled standards libraries to cover different classes of metabolites for comprehensive metabolomics using CIL LC-MS.

A chemometric-assisted method based on gas chromatography-mass spectrometry for metabolic profiling analysis

An automatic and efficient data analysis method for comprehensive metabolic profiling analysis is urgently required. In this study, a new chemometric-assisted method for metabolic profiling analysis (CAMMPA) was developed to discover potentially valuable metabolites automatically and efficiently. The proposed method mainly consists of three stages. First, automatic chromatographic peak detection is performed based on the total ion chromatograms of samples to extract chromatographic peaks that can be accurately quantified. Second, a novel peak-shift alignment technique based on peak detection results is implemented to resolve time-shift problems across samples. Consequently, aligned results, including aligned chromatograms, and peak area tables, among others, can be successfully obtained. Third, statistical analysis using results from unsupervised and supervised classification results, together with ANOVA and partial least square-discriminate analysis, is performed to extract potential metabolites. To demonstrate the proposed technique, a complex GC-MS metabolic profiling dataset was measured to identify potential metabolites in tobacco plants of different growth stages as well as different plant tissues after maturation. Results indicated that the efficiency of the routine metabolic profiling analysis procedure can be significantly improved and potential metabolites can be accurately identified with the aid of CAMMPA.

MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis

Data-independent acquisition (DIA) in liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) provides comprehensive untargeted acquisition of molecular data. We provide an open-source software pipeline, which we call MS-DIAL, for DIA-based identification and quantification of small molecules by mass spectral deconvolution. For a reversed-phase LC-MS/MS analysis of nine algal strains, MS-DIAL using an enriched LipidBlast library identified 1,023 lipid compounds, highlighting the chemotaxonomic relationships between the algal strains.

MRM-DIFF: data processing strategy for differential analysis in large scale MRM-based lipidomics studies

Based on theoretically calculated comprehensive lipid libraries, in lipidomics as many as 1000 multiple reaction monitoring (MRM) transitions can be monitored for each single run. On the other hand, lipid analysis from each MRM chromatogram requires tremendous manual efforts to identify and quantify lipid species. Isotopic peaks differing by up to a few atomic masses further complicate analysis. To accelerate the identification and quantification process we developed novel software, MRM-DIFF, for the differential analysis of large-scale MRM assays. It supports a correlation optimized warping (COW) algorithm to align MRM chromatograms and utilizes quality control (QC) sample datasets to automatically adjust the alignment parameters. Moreover, user-defined reference libraries that include the molecular formula, retention time, and MRM transition can be used to identify target lipids and to correct peak abundances by considering isotopic peaks. Here, we demonstrate the software pipeline and introduce key points for MRM-based lipidomics research to reduce the mis-identification and overestimation of lipid profiles. The MRM-DIFF program, example data set and the tutorials are downloadable at the "Standalone software" section of the PRIMe (Platform for RIKEN Metabolomics, http://prime.psc.riken.jp/) database website.

Application of Metabolomics for High Resolution Phenotype Analysis

Metabolome, a total profile of whole metabolites, is placed on downstream of proteome. Metabolome is thought to be results of implementation of genomic information. In other words, metabolome can be called as high resolution phenotype. The easiest operation of metabolomics is the integration to the upstream ome information including transcriptome and/or proteome. Those trials have been reported at a certain scientific level. In addition, metabolomics can be operated in stand-alone mode without any other ome information. Among metabolomics tactics, the author's group is particularly focusing on metabolic fingerprinting, in which metabolome information is employed as explanatory variant to evaluate response variant. Metabolic fingerprinting technique is expected not only for analyzing slight difference depending on genotype difference but also for expressing dynamic variation of living organisms. The author introduces several good examples which he performed. Those are useful for easy understanding of the power of metabolomics. In addition, the author mentions the latest technology for analysis of metabolic dynamism. The author's group developed a facile analytical method for semi-quantitative metabolic dynamism. The author introduces the novel method that uses time dependent variation of isotope distribution based on stable isotope dilution.

Challenges of analyzing different classes of metabolites by a single analytical method

Complex biological samples include thousands of metabolites that range widely in both physiochemical properties and concentration. Simultaneously analyzing metabolites with different properties using a single analytical method is very challenging. The analytical process for metabolites comprises multiple steps including sampling, quenching, sample preparation, separation and detection. Each step can have a significant effect on the reliability and precision of ultimate analytic results. The aim of review is a discussion of considerations and challenges for the simultaneous analysis of metabolites using LC– and GC–MS systems. The review discusses available methodology for each analytical step, and presents the limitations and advantages of each method for the large-scale targeted metabolomics analysis of human and animal biological samples.

(Post-)genomics approaches in fungal research

To date, hundreds of fungal genomes have been sequenced and many more are in progress. This wealth of genomic information has provided new directions to study fungal biodiversity. However, to further dissect and understand the complicated biological mechanisms involved in fungal life styles, functional studies beyond genomes are required. Thanks to the developments of current -omics techniques, it is possible to produce large amounts of fungal functional data in a high-throughput fashion (e.g. transcriptome, proteome, etc.). The increasing ease of creating -omics data has also created a major challenge for downstream data handling and analysis. Numerous databases, tools and software have been created to meet this challenge. Facing such a richness of techniques and information, hereby we provide a brief roadmap on current wet-lab and bioinformatics approaches to study functional genomics in fungi.

Metabolomics for biomarker discovery in gastroenterological cancer

The study of the omics cascade, which involves comprehensive investigations based on genomics, transcriptomics, proteomics, metabolomics, etc., has developed rapidly and now plays an important role in life science research. Among such analyses, metabolome analysis, in which the concentrations of low molecular weight metabolites are comprehensively analyzed, has rapidly developed along with improvements in analytical technology, and hence, has been applied to a variety of research fields including the clinical, cell biology, and plant/food science fields. The metabolome represents the endpoint of the omics cascade and is also the closest point in the cascade to the phenotype. Moreover, it is affected by variations in not only the expression but also the enzymatic activity of several proteins. Therefore, metabolome analysis can be a useful approach for finding effective diagnostic markers and examining unknown pathological conditions. The number of studies involving metabolome analysis has recently been increasing year-on-year. Here, we describe the findings of studies that used metabolome analysis to attempt to discover biomarker candidates for gastroenterological cancer and discuss metabolome analysis-based disease diagnosis.

Metabolome analysis for discovering biomarkers of gastroenterological cancer

Improvements in analytical technologies have made it possible to rapidly determine the concentrations of thousands of metabolites in any biological sample, which has resulted in metabolome analysis being applied to various types of research, such as clinical, cell biology, and plant/food science studies. The metabolome represents all of the end products and by-products of the numerous complex metabolic pathways operating in a biological system. Thus, metabolome analysis allows one to survey the global changes in an organism's metabolic profile and gain a holistic understanding of the changes that occur in organisms during various biological processes, e.g., during disease development. In clinical metabolomic studies, there is a strong possibility that differences in the metabolic profiles of human specimens reflect disease-specific states. Recently, metabolome analysis of biofluids, e.g., blood, urine, or saliva, has been increasingly used for biomarker discovery and disease diagnosis. Mass spectrometry-based techniques have been extensively used for metabolome analysis because they exhibit high selectivity and sensitivity during the identification and quantification of metabolites. Here, we describe metabolome analysis using liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and capillary electrophoresis-mass spectrometry. Furthermore, the findings of studies that attempted to discover biomarkers of gastroenterological cancer are also outlined. Finally, we discuss metabolome analysis-based disease diagnosis.

Highly sensitive and selective analysis of widely targeted metabolomics using gas chromatography/triple-quadrupole mass spectrometry

In metabolomics studies, gas chromatography coupled with time-of-flight or quadrupole mass spectrometry has frequently been used for the non-targeted analysis of hydrophilic metabolites. However, because the analytical platform employs the deconvolution method to extract single-metabolite information from co-eluted peaks and background noise, the extracted peak is artificial product depending on the mathematical parameters and is not completely compatible with the pure component obtained by analyzing a standard compound. Moreover, it has insufficient ability for quantitative metabolomics. Therefore, highly sensitive and selective methods capable of pure peak extraction without any complicated mathematical techniques are needed. For this purpose, we have developed a novel analytical method using gas chromatography coupled with triple-quadrupole mass spectrometry (GC-QqQ/MS). We developed a selected reaction monitoring (SRM) method to analyze the trimethylsilyl derivatives of 110 metabolites, using electron ionization. This methodology enables us to utilize two complementary techniques-non-targeted and widely targeted metabolomics in the same sample preparation protocol, which would facilitate the formulation or verification of novel hypotheses in biological sciences. The GC-QqQ/MS analysis can accurately identify a metabolite using multichannel SRM transitions and intensity ratios in the analysis of living organisms. In addition, our methodology offers a wide dynamic range, high sensitivity, and highly reproducible metabolite profiles, which will contribute to the biomarker discoveries and quality evaluations in biology, medicine, and food sciences.