Metabolite profiling: from diagnostics to systems biology

The concept of metabolite profiling has been around for several decades, but only recent technical innovations have allowed metabolite profiling to be carried out on a large scale - with respect to both the number of metabolites measured and the number of experiments carried out. As a result, the power of metabolite profiling as a technology platform for diagnostics, and the research areas of gene-function analysis and systems biology, is now beginning to be fully realized.     

Gas chromatography mass spectrometry-based metabolite profiling in plants

The concept of metabolite profiling has been around for decades, but technical innovations are now enabling it to be carried out on a large scale with respect to the number of both metabolites measured and experiments carried out. Here we provide a detailed protocol for gas chromatography mass spectrometry (GC-MS)-based metabolite profiling that offers a good balance of sensitivity and reliability, being considerably more sensitive than NMR and more robust than liquid chromatography-linked mass spectrometry. We summarize all steps from collecting plant material and sample handling to derivatization procedures, instrumentation settings and evaluating the resultant chromatograms. We also define the contribution of GC-MS-based metabolite profiling to the fields of diagnostics, gene annotation and systems biology. Using the protocol described here facilitates routine determination of the relative levels of 300-500 analytes of polar and nonpolar extracts in approximately 400 experimental samples per week per machine.     

Metabolite profiling of recombinant CHO cells: Designing tailored feeding regimes that enhance recombinant antibody production

Chinese hamster ovary (CHO) cells are the primary platform for commercial expression of recombinant therapeutic proteins. Obtaining maximum production from the expression platform requires optimal cell culture medium (and associated nutrient feeds). We have used metabolite profiling to define the balance of intracellular and extracellular metabolites during the production process of a CHO cell line expressing a recombinant IgG4 antibody. Using this metabolite profiling approach, it was possible to identify nutrient limitations, which acted as bottlenecks for antibody production, and subsequently develop a simple feeding regime to relieve these metabolic bottlenecks. This metabolite profiling‐based strategy was used to design a targeted, low cost nutrient feed that increased cell biomass by 35% and doubled the antibody titer. This approach, with the potential for utilization in non‐specialized laboratories, can be applied universally to the optimization of production of commercially important biopharmaceuticals. Biotechnol. Bioeng. 2011;108: 3025–3031. © 2011 Wiley Periodicals, Inc.     

Plant metabolomics: towards biological function and mechanism

Metabolite profiling is a fast growing technology and is useful for phenotyping and diagnostic analyses of plants. It is also rapidly becoming a key tool in functional annotation of genes and in the comprehensive understanding of the cellular response to biological conditions. Metabolomics approaches have recently been used to assess the natural variance in metabolite content between individual plants, an approach with great potential for the improvement of the compositional quality of crops. Here, we assess the contribution of metabolite profiling to these areas.     

Extraction parameters for metabolomics from cultured cells

The successful extraction of metabolites is a critical step in metabolite profiling. By optimizing metabolite extraction, the range and quantitative capacity of metabolomics studies can be improved. We considered eight separate extraction protocols for the preparation of a metabolite extract from cultured mammalian cells. Parameters considered included temperature, pH, and cell washing before extraction. The effects on metabolite recovery were studied using a liquid chromatography high-resolution mass spectrometry (LC–HRMS) platform that measures metabolites of diverse chemical classes, including amino acids, lipids, and sugar derivatives. The temperature considered during the extraction or the presence of formic acid, a commonly used additive, was shown to have minimal effects on the measured ion intensities of metabolites. However, washing of samples before metabolite extraction, whether with water or phosphate-buffered saline, exhibited dramatic effects on measured intensities of both intracellular and extracellular metabolites. Together, these findings present a systematic assessment of extraction conditions for metabolite profiling.     

Analytical strategies in metabonomics

To perform metabonomics investigations, it is necessary to generate comprehensive metabolite profiles for complex samples such as biofluids and tissue/tissue extracts. Analytical technologies that can be used to achieve this aim are constantly evolving, and new developments are changing the way in which such profiles' metabolite profiles can be generated. Here, the utility of various analytical techniques for global metabolite profiling, such as, e.g., 1H NMR, MS, HPLC-MS, and GC-MS, are explored and compared.     

LC-MS based global metabolite profiling of grapes: solvent extraction protocol optimisation

Optimal solvent conditions for grape sample preparation were investigated for the purpose of metabolite profiling studies, with the aim of obtaining as many features as possible with the best analytical repeatability. Mixtures of water, methanol and chloroform in different combinations were studied as solvents for the extraction of ground grapes. The experimental design used a two stage study to find the optimum extraction medium. The extracts obtained were further purified using solid phase extraction and analysed using a UPLC full scan TOF MS with both reversed phase and hydrophilic interaction chromatography. The data obtained were processed using data extraction algorithms and advanced statistical software for data mining. The results obtained indicated that a fairly broad optimal area for solvent composition could be identified, containing approximately equal amounts of methanol and chloroform and up to 20% water. Since the water content of the samples was variable, the robustness of the optimal conditions suggests these are appropriate for large scale profiling studies for characterisation of the grape metabolome.     

Baitmet,a computational approach for GC–MS library-driven metabolite profiling

Introduction

Current computational tools for gas chromatography—mass spectrometry (GC–MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual data reviewing. Metabolomics advent has fostered the development of public metabolite repositories containing mass spectra and retention indices, two orthogonal properties needed for metabolite identification. Such libraries can be used for library-driven compound profiling of large datasets produced in metabolomics, a complementary approach to current GC–MS non-targeted data analysis solutions that can eventually help to assess metabolite identities more efficiently.

Results

This paper introduces Baitmet, an integrated open-source computational tool written in R enclosing a complete workflow to perform high-throughput library-driven GC–MS profiling in complex samples. Baitmet capabilities were assayed in a metabolomics study involving 182 human serum samples where a set of 61 metabolites were profiled given a reference library.

Conclusions

Baitmet allows high-throughput and wide scope interrogation on the metabolic composition of complex samples analyzed using GC–MS via freely available spectral data. Baitmet is freely available at http://CRAN.R-project.org/package=baitmet.

     

Metabolic profiling of human saliva before and after induced physiological stress by ultra-high performance liquid chromatography–ion mobility–mass spectrometry

A method has been developed for metabolite profiling of the salivary metabolome based on protein precipitation and ultra-high performance liquid chromatography coupled with ion mobility-mass spectrometry (UHPLC–IM–MS). The developed method requires 0.5 mL of human saliva, which is easily obtainable by passive drool. Standard protocols have been established for the collection, storage and pre-treatment of saliva. The use of UHPLC allows rapid global metabolic profiling for biomarker discovery with a cycle time of 15 min. Mass spectrometry imparts the ability to analyse a diverse number of species reproducibly over a wide dynamic range, which is essential for profiling of biofluids. The combination of UHPLC with IM–MS provides an added dimension enabling complex metabolic samples to be separated on the basis of retention time, ion mobility and mass-to-charge ratio in a single chromatographic run. The developed method has been applied to targeted metabolite identification and untargeted metabolite profiling of saliva samples collected before and after exercise-induced physiological stress. δ-Valerolactam has been identified as a potential biomarker on the basis of retention time, MS/MS spectrum and ion mobility drift time.     

A unique automation platform for measuring low level radioactivity in metabolite identification studies

Generation and interpretation of biotransformation data on drugs, i.e. identification of physiologically relevant metabolites, defining metabolic pathways and elucidation of metabolite structures, have become increasingly important to the drug development process. Profiling using (14)C or (3)H radiolabel is defined as the chromatographic separation and quantification of drug-related material in a given biological sample derived from an in vitro, preclinical in vivo or clinical study. Metabolite profiling is a very time intensive activity, particularly for preclinical in vivo or clinical studies which have defined limitations on radiation burden and exposure levels. A clear gap exists for certain studies which do not require specialized high volume automation technologies, yet these studies would still clearly benefit from automation. Use of radiolabeled compounds in preclinical and clinical ADME studies, specifically for metabolite profiling and identification are a very good example. The current lack of automation for measuring low level radioactivity in metabolite profiling requires substantial capacity, personal attention and resources from laboratory scientists. To help address these challenges and improve efficiency, we have innovated, developed and implemented a novel and flexible automation platform that integrates a robotic plate handling platform, HPLC or UPLC system, mass spectrometer and an automated fraction collector.     

An approach towards method development for untargeted urinary metabolite profiling in metabonomic research using UPLC/QToF MS

The application of LC-MS for untargeted urinary metabolite profiling in metabonomic research has gained much interest in recent years. However, the effects of varying sample pre-treatments and LC conditions on generic metabolite profiling have not been studied. We aimed to evaluate the effects of varying experimental conditions on data acquisition in untargeted urinary metabolite profiling using UPLC/QToF MS. In-house QC sample clustering was used to monitor the performance of the analytical platform. In terms of sample pre-treatment, results showed that untreated filtered urine yielded the highest number of features but dilution with methanol provided a more homogenous urinary metabolic profile with less variation in number of features and feature intensities. An increased cycle time with a lower flow rate (400mul/min vs 600mul/min) also resulted in a higher number of features with less variability. The step elution gradient yielded the highest number of features and the best chromatographic resolution among three different elution gradients tested. The maximum retention time and mass shift were only 0.03min and 0.0015Da respectively over 600 injections. The analytical platform also showed excellent robustness as evident by tight QC sample clustering. To conclude, we have investigated LC conditions by studying variability and repeatability of LC-MS data for untargeted urinary metabolite profiling.     

Shall I compare thee to a GM potato?

A fundamental issue in the safety assessment of genetically modified crops is the question of whether unintentional changes have occurred in the crop plant as a consequence of the genetic modification. This question was addressed recently by using a powerful metabolite fingerprinting and metabolite profiling method to assess whether genetically modified potatoes are substantially similar to their corresponding conventional cultivars.     

Analytical aspects of plant metabolite profiling platforms: current standings and future aims

Over the past years, metabolic profiling has been established as a comprehensive systems biology tool. Mass spectrometry or NMR spectroscopy-based technology platforms combined with unsupervised or supervised multivariate statistical methodologies allow a deep insight into the complex metabolite patterns of plant-derived samples. Within this review, we provide a thorough introduction to the analytical hard- and software requirements of metabolic profiling platforms. Methodological limitations are addressed, and the metabolic profiling workflow is exemplified by summarizing recent applications ranging from model systems to more applied topics.     

Lipidomic analysis can distinguish between two morphologically similar strains of Nannochloropsis oceanica

The two morphologically similar microalgae NMBluh014 and NMBluh‐X belong to two different strains of Nannochloropsis oceanica. They possess obviously different feeding effects on bivalves, but are indistinguishable by 18S rRNA and morphological features. In this work, lipidomic analysis followed by principal component analysis and orthogonal projections to latent structures discriminant analysis provided a clear distinction between these strains. Metabolites that definitively contribute to the classification were selected as potential biomarkers. The most important difference in polar lipids were sulfoquinovosyldiacylglycerol (containing 18:1/16:0 and 18:3/16:0) and monogalactosyldiacylglycerol (containing 18:3/16:3 and 20:5/14:0), which were detected only in NMBluh‐X. Additionally, an exhaustive qualitative and quantitative profiling of the neutral lipid triacylglycerol (TAG) in the two strains was carried out. The predominant species of TAG containing 16:1/16:1/16:1 acyl groups was detected only in NMBluh‐X with a content of ~93.67 ± 11.85 nmol · mg^?1 dry algae at the onset of stationary phase. Meanwhile, TAG containing 16:0/16:0/16:0 was the main TAG in NMBluh014 with a content of 40.25 ± 3.92 nmol · mg^?1. These results provided the most straightforward evidence for differentiating the two species. The metabolomic profiling indicated that NMBluh‐X underwent significant chemical and physiological changes during the growth process, whereas NMBluh014 did not show such noticeable time‐dependent metabolite change. This study is the first using Ultra Performance Liquid Chromatography coupled with Electrospray ionization‐Quadrupole‐Time of Flight Mass Spectrometry (UPLC‐Q‐TOF‐MS) for lipidomic profiling with multivariate statistical analysis to explore lipidomic differences of plesiomorphous microalgae. Our results demonstrate that lipidomic profiling is a valid chemotaxonomic tool in the study of microalgal systematics.     

Quantitative analysis of metabolite concentrations in human urine samples using 13C{1H} NMR spectroscopy

Targeted profiling is a library-based method of using mathematically modeled reference spectra for quantification of metabolite concentrations in NMR mixture analysis. Metabolomics studies of biofluids, such as urine, represent a highly complex problem in this area, and for this reason targeted profiling of ¹H NMR spectra can be hampered. A number of the issues relating to ¹H NMR spectroscopy can be overcome using ¹³C{¹H} NMR spectroscopy. In this work, a ¹³C{¹H} NMR database was created using Chenomx NMR Suite, incorporating 120 metabolites. The ¹³C{¹H} NMR database was standardized through the analysis of a series of metabolite solutions containing varying concentrations of 19 distinct metabolites, where the metabolite concentrations were varied across a range of values including biological ranges. Subsequently, the NMR spectra of urine samples were collected using ¹³C{¹H} NMR spectroscopy and profiled using the ¹³C{¹H} NMR library. In total, about 30 metabolites were conclusively identified and quantified in the urine samples using ¹³C{¹H} NMR targeted profiling. The proton decoupling and larger spectral window provided easier identification and more accurate quantification for specific classes of metabolites, such as sugars and amino acids with overlap in the aliphatic region of the ¹H NMR spectrum. We discuss potential application areas in which ¹³C{¹H} NMR targeted profiling may be superior to ¹H NMR targeted profiling.