Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms

Metabolomic profiling is a powerful approach to characterize human metabolism and help understand common disease risk. Although multiple high-throughput technologies have been developed to assay the human metabolome, no technique is capable of capturing the entire human metabolism. Large-scale metabolomics data are being generated in multiple cohorts, but the datasets are typically profiled using different metabolomics platforms. Here, we compared analyses across two of the most frequently used metabolomic platforms, Biocrates and Metabolon, with the aim of assessing how complimentary metabolite profiles are across platforms. We profiled serum samples from 1,001 twins using both targeted (Biocrates, n = 160 metabolites) and non-targeted (Metabolon, n = 488 metabolites) mass spectrometry platforms. We compared metabolite distributions and performed genome-wide association analyses to identify shared genetic influences on metabolites across platforms. Comparison of 43 metabolites named for the same compound on both platforms indicated strong positive correlations, with few exceptions. Genome-wide association scans with high-throughput metabolic profiles were performed for each dataset and identified genetic variants at 7 loci associated with 16 unique metabolites on both platforms. The 16 metabolites showed consistent genetic associations and appear to be robustly measured across platforms. These included both metabolites named for the same compound across platforms as well as unique metabolites, of which 2 (nonanoylcarnitine (C9) [Biocrates]/Unknown metabolite X-13431 [Metabolon] and PC aa C28:1 [Biocrates]/1-stearoylglycerol [Metabolon]) are likely to represent the same or related biochemical entities. The results demonstrate the complementary nature of both platforms, and can be informative for future studies of comparative and integrative metabolomics analyses in samples profiled on different platforms.     

Fly-attracting volatiles produced by Rhodococcus fascians and Mycobacterium aurum isolated from myiatic lesions of sheep.

Bacterial strains isolated from the healthy breech mucosa and myiatic wounds of ewes were tested for their volatile production as fly attractants towards Wohlfahrtia magnifica (Diptera: Sarcophagidae). Cultures were studied as fly baits in field experiments, and strains performing with the best chemotropic effect were selected for further analysis. Static and dynamic headspace samples from shaken cultures were examined by gas chromatography-mass spectrometry (GC-MS). Strains identified as Rhodococcus fascians and Mycobacterium aurum produced various volatile sulfur compounds and benzene, and proved to be the best fly attractants.     

A dynamic programming approach for the alignment of signal peaks in multiple gas chromatography-mass spectrometry experiments

Background  

Gas chromatography-mass spectrometry (GC-MS) is a robust platform for the profiling of certain classes of small molecules in biological samples. When multiple samples are profiled, including replicates of the same sample and/or different sample states, one needs to account for retention time drifts between experiments. This can be achieved either by the alignment of chromatographic profiles prior to peak detection, or by matching signal peaks after they have been extracted from chromatogram data matrices. Automated retention time correction is particularly important in non-targeted profiling studies.     

Combined experimental and statistical strategy for mass spectrometry based serum protein profiling for diagnosis of breast cancer: a case-control study

Serum protein profiling by mass spectrometry is a promising method for early detection of cancer. We have implemented a combined strategy based on matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) and statistical data analysis for serum protein profiling and applied it in a well-described breast cancer case-control study. A rigorous sample collection protocol ensured high quality specimen and reduced bias from preanalytical factors. Preoperative serum samples obtained from 48 breast cancer patients and 28 controls were used to generate MALDI MS protein profiles. A total of nine mass spectrometric protein profiles were obtained for each serum sample. A total of 533 common peaks were defined and represented a 'reference protein profile'. Among these 533 common peaks, we identified 72 peaks exhibiting statistically significant intensity differences ( p < 0.01) between cases and controls. A diagnostic rule based on these 72 mass values was constructed and exhibited a cross-validated sensitivity and specificity of approximately 85% for the detection of breast cancer. With this method, it was possible to distinguish early stage cancers from controls without major loss of sensitivity and specificity. We conclude that optimized serum sample handling and mass spectrometry data acquisition strategies in combination with statistical analysis provide a viable platform for serum protein profiling in cancer diagnosis.     

Identification and profiling of volatile metabolites of the biocontrol fungus Trichoderma atroviride by HS-SPME-GC-MS

In the present study we describe a method, which is based on solid phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS) and which can be used for the profiling of microbial volatile organic compounds (MVOCs) in the headspace (HS) of cultures of filamentous fungi. The method comprises the following successive steps: 1. growth of the fungus on a solid culture medium directly in headspace vials, 2. measurement of volatiles by HS-SPME-GC-MS, 3. deconvolution of mass spectra, 4. identification of volatiles by comparison of measured, deconvoluted mass spectra and linear temperature programmed retention indices (LTPRI) on two stationary GC phases with database entries and LTPRI published in the literature, and 5. profiling of the identified MVOCs.The developed method was successfully applied to cultures of the biocontrol fungus Trichoderma atroviride. An in-house library consisting of mass spectra and LTPRI values of fungal VOCs was established and used to study the profiles of MVOCs of this fungus. In total, 25 different MVOCs were identified by applying strict criteria (spectral match factor at least 90% and a maximum relative deviation of LTPRI of ± 2% from literature values). The MVOCs were assigned to the compound classes of alcohols, ketones, alkanes, furanes, pyrones (mainly the bioactive 6-pentyl-alpha-pyrone), mono- and sesquiterpenes, 13 of which have never been reported to be produced by Trichoderma spp. before. Eleven of these volatiles have been additionally confirmed using authentic standards. Finally, time course experiments and cultivation of T. atroviride in the presence of the mycotoxin fusaric acid demonstrated the potential of the method to study the dynamics of MVOC profiles as well as the effect of different environmental/biological conditions on the expression of MVOCs of filamentous fungi.     

A metabolomics approach to characterise and identify various Mycobacterium species

We investigated the potential use of gas chromatography mass spectrometry (GC-MS), in combination with multivariate statistical data processing, to build a model for the classification of various tuberculosis (TB) causing, and non-TB Mycobacterium species, on the basis of their characteristic metabolite profiles. A modified Bligh-Dyer extraction procedure was used to extract lipid components from Mycobacterium tuberculosis, Mycobacterium avium, Mycobacterium bovis, and Mycobacterium kansasii cultures. Principle component analyses (PCA) of the GC-MS generated data showed a clear differentiation between all the Mycobacterium species tested. Subsequently, the 12 compounds best describing the variation between the sample groups were identified as potential metabolite markers, using PCA and partial least-squares discriminant analysis (PLS-DA). These metabolite markers were then used to build a discriminant classification model based on Bayes' theorem, in conjunction with multivariate kernel density estimation. This model subsequently correctly classified 2 "unknown" samples for each of the Mycobacterium species analysed, with probabilities ranging from 72 to 100%. Furthermore, Mycobacterium species classification could be achieved in less than 16 h, and the detection limit for this approach was 1×10(3)bacteriamL(-1). This study proves the capacity of a GC-MS, metabolomics pattern recognition approach for its possible use in TB diagnostics and disease characterisation.     

Optimization of a sample preparation method for the metabolomic analysis of clinically relevant bacteria

Metabolomics, or metabolite profiling, is an approach that is increasingly used to study the metabolism of diverse organisms, elucidate biological processes and/or find characteristic biomarkers of physiological states. Here, we describe the optimization of a method for global metabolomic analysis of bacterial cultures, with the following steps. Cells are grown to log-phase, starting from an overnight culture and bacterial concentrations are monitored by measuring the optical density of the cultures at 600 nm. At an appropriate density they are harvested by centrifugation, washed three times with NaCl solution and metabolites are extracted using methanol and a bead-mill. Dried extracts are methoxymated and derivatized with methyltrimethylsilyltrifluoroacetamide (MSTFA) then analyzed using gas chromatography coupled to time-of-flight mass spectrometry (GC-MS/TOF). Finally, patterns in the acquired data are examined by multivariate data modeling. This method enabled us to obtain reproducible metabolite profiles of Yersinia pseudotuberculosis, with about 25% compound identification, based on comparison with entries in available GC-MS libraries. To assess the potential utility of the method for comparative analysis of other bacterial species we analyzed cultures of Pseudomonas aeruginosa, Salmonella typhimurium, Escherichia coli and methicillin-sensitive Staphylococcus aureus (MSSA). Multivariate analysis of the acquired data showed that it was possible to differentiate the species according to their metabolic profiles. Our results show that the presented procedure can be used for metabolomic analysis of a wide range of bacterial species of clinical interest.     

A GC-MS metabolic profiling study of plasma samples from mice on low- and high-fat diets

Metabolic profiling of biofluids, based on the quantitative analysis of the concentration profile of their free low molecular mass metabolites, has been playing increasing role employed as a means to gain understanding of the progression of metabolic disorders, including obesity. Chromatographic methods coupled with mass spectrometry have been established as a strategy for metabolic profiling. Among these, GC-MS, targeting mainly the primary metabolism intermediates, offers high sensitivity, good peak resolution and extensive databases. However, the derivatization step required for many involatile metabolites necessitates specific data validation, normalization and analysis protocols to ensure accurate and reproducible performance. In this study, the GC-MS metabolic profiles of plasma samples from mice maintained on 12- or 15-month long low (10 kcal%) or high (60 kcal%) fat diets were obtained. The profiles of the trimethylsilyl(TMS)-methoxime(MeOx) derivatives of the free polar metabolites were acquired through GC-(ion trap)MS, using [U-(13)C]-glucose as the internal standard. After the application of a recently developed data correction and normalization/filtering protocol for GC-MS metabolomic datasets, the profiles of 48 out of the 77 detected metabolites were used in multivariate statistical analysis. Data mining suggested a decrease in the activity of the energy metabolism with age. In addition, the metabolic profiles indicated the presence of subpopulations with different physiology within the high- and low-fat diet mice, which correlated well with the difference in body weight among the animals and current knowledge about hyperglycemic conditions.     

Curie-point pyrolysis mass spectrometry applied to characterization and identification of selected Bacillus species

The use of pyrolysis mass spectrometry in the characterization and identification of Bacillus species was studied. Fifty-three strains of four closely related groups, Bacillus subtilis, B. pumilus, B. licheniformis and 'B. amyloliquefaciens', were used in a study of both sporulated and nonsporulated cultures. Pyrolysis was carried out using a Pyromass 8-80, a novel pyrolysis mass spectrometer specifically designed for fingerprinting complex samples. The pyrolysis data obtained were analysed using multivariate statistical techniques. All four groups could be differentiated using data from non-sporulated cultures but the data from sporulated cultures did not separate B. subtilis from 'B. amyloliquefaciens' or B. pumilus. In contrast, B. licheniformis was more clearly differentiated from the other three species using these data. Culture maturity affected the mass spectra obtained from non-sporulated cultures.     

Identifying and quantifying metabolites by scoring peaks of GC-MS data

Background

Metabolomics is one of most recent omics technologies. It has been applied on fields such as food science, nutrition, drug discovery and systems biology. For this, gas chromatography-mass spectrometry (GC-MS) has been largely applied and many computational tools have been developed to support the analysis of metabolomics data. Among them, AMDIS is perhaps the most used tool for identifying and quantifying metabolites. However, AMDIS generates a high number of false-positives and does not have an interface amenable for high-throughput data analysis. Although additional computational tools have been developed for processing AMDIS results and to perform normalisations and statistical analysis of metabolomics data, there is not yet a single free software or package able to reliably identify and quantify metabolites analysed by GC-MS.

Results

Here we introduce a new algorithm, PScore, able to score peaks according to their likelihood of representing metabolites defined in a mass spectral library. We implemented PScore in a R package called MetaBox and evaluated the applicability and potential of MetaBox by comparing its performance against AMDIS results when analysing volatile organic compounds (VOC) from standard mixtures of metabolites and from female and male mice faecal samples. MetaBox reported lower percentages of false positives and false negatives, and was able to report a higher number of potential biomarkers associated to the metabolism of female and male mice.

Conclusions

Identification and quantification of metabolites is among the most critical and time-consuming steps in GC-MS metabolome analysis. Here we present an algorithm implemented in a R package, which allows users to construct flexible pipelines and analyse metabolomics data in a high-throughput manner.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0374-2) contains supplementary material, which is available to authorized users.     

A comparison of direct infusion MS and GC-MS for metabolic footprinting of yeast mutants

Recent technical advances in mass spectrometry (MS) have propelled this technology to the forefront of methods employed in metabolome analysis. Here, we compare two distinct analytical approaches based on MS for their potential in revealing specific metabolic footprints of yeast single-deletion mutants. Filtered fermentation broth samples were analyzed by GC-MS and direct infusion ESI-MS. The potential of both methods in producing specific and, therefore, discriminant metabolite profiles was evaluated using samples from several yeast deletion mutants grown in batch-culture conditions with glucose as the carbon source. The mutants evaluated were cat8, gln3, ino2, opi1, and nil1, all with deletion of genes involved in nutrient sensing and regulation. From the analysis, we found that both methods can be used to classify mutants, but the classification depends on which metabolites are measured. Thus, the GC-MS method is good for classification of mutants with altered nitrogen regulation as it primarily measures amino acids, whereas this method cannot classify mutants involved in regulation of phospholipids metabolism as well as the direct infusion MS (DI-MS) method. From the analysis, we find that it is possible to discriminate the mutants in both the exponential and stationary growth phase, but the data from the exponential growth phase provide more physiological relevant information. Based on the data, we identified metabolites that are primarily involved in discrimination of the different mutants, and hereby providing a link between high-throughput metabolome analysis, strain classification, and physiology.     

Identification of volatile markers for indoor fungal growth and chemotaxonomic classification of Aspergillus species

Microbial volatile organic compounds (MVOCs) were collected in water-damaged buildings to evaluate their use as possible indicators of indoor fungal growth. Fungal species isolated from contaminated buildings were screened for MVOC production on malt extract agar by means of headspace solid-phase microextraction followed by gas chromatography-mass spectrometry (GC-MS) analysis. Some sesquiterpenes, specifically derived from fungal growth, were detected in the sampled environments and the corresponding fungal producers were identified. Statistical analysis of the detected MVOC profiles allowed the identification of species-specific MVOCs or MVOC patterns for Aspergillus versicolor group, Aspergillus ustus, and Eurotium amstelodami. In addition, Chaetomium spp. and Epicoccum spp. were clearly differentiated by their volatile production from a group of 76 fungal strains belonging to different genera. These results are useful in the chemotaxonomic discrimination of fungal species, in aid to the classical morphological and molecular identification techniques.     

Headspace solid-phase microextraction and capillary gas chromatographic-mass spectrometric determination of rivastigmine in canine plasma samples

A simple, rapid and sensitive method for determination of rivastigmine in plasma samples was developed using headspace solid-phase microextraction (HS-SPME) and gas chromatography with mass spectrometry (GC-MS). The optimum conditions for the SPME procedure were: headspace extraction on a 65-microm polydimethylsiloxane/divinylbenzene (PDMS/DVB) fiber; 0.5 ml of plasma modified with 1.0 ml of sodium hydroxide-sodium carbonate solution (0.7 M:0.5M); extraction temperature of 100 degrees C, with stirring at 2000 rpm for 30 min. The calibration curve showed linearity in the range from 0.2 to 80 ng/ml with regression coefficient corresponding to 0.9965 and coefficient of the variation of the points of the calibration curve lower than 10%. The quantification limit for rivastigmine in plasma was 0.2 ng/ml. The method was applied to determination of rivastigmine in canine plasma samples from animals after a single oral administration.     

A high-throughput method for the quantitative analysis of auxins

Auxin measurements in plants are critical to understanding both auxin signaling and metabolic homeostasis. The most abundant natural auxin is indole-3-acetic acid (IAA). This protocol is for the precise, high-throughput determination of free IAA in plant tissue by isotope dilution analysis using gas chromatography-mass spectrometry (GC-MS). The steps described are as follows: harvesting of plant material; amino and polymethylmethacrylate solid-phase purification followed by derivatization with diazomethane (either manual or robotic); GC-MS analysis; and data analysis. [13C?]IAA is the standard used. The amount of tissue required is relatively small (25 mg of fresh weight) and one can process more than 500 samples per week using an automated system. To extract eight samples, this procedure takes ～3 h, whether performed manually or robotically. For processing more than eight samples, robotic extraction becomes substantially more time efficient, saving at least 0.5 h per additional batch of eight samples.     

Metabolomic profiling of biomarkers of liver X receptor-induced toxicity in mouse liver tissue

Methods based on matrix-assisted laser desorption/ionisation mass spectrometry (MALDI-MS), liquid chromatography coupled to an LTQ-Orbitrap mass spectrometer (LC-MS) and gas chromatography-mass spectrometry (GC-MS) were used to investigate changes in the small molecule profiles of mouse liver in response to administration of an LXR agonist. Mice were treated with either 0.3 mg/kg, 1 mg/kg, 10 mg/kg, 30 mg/kg or 60 mg/kg of an LXR test compound or saline (control) once daily, over a 5 day period, to investigate the effects of the drug on metabolism in the liver. It was possible to detect triacylglycerol accumulation in the livers of animals treated with the drug, even at the lowest concentrations using, in the first instance, MALDI MS. There was also an increase in the relative degree of triacylglycerol saturation in the drug-treated samples. Changes in the profiles of phosphatidylcholine lipids were also observed. The changes in lipid profiles were also confirmed by LC-MS and GC-MS, the latter revealing a large increase in the level of the free fatty acid oleic acid (C18:1) in the treated samples. All of the changes were dose-related. Polar metabolites in the samples were analysed by hydrophilic interaction (HILIC) chromatography in combination with an LTQ-Orbitrap mass spectrometer. There were many changes in the metabolite profiles, some of which might simply be related to generalised toxicity. The clearest marker compounds, which showed very marked changes with dose, were methylglutaryl carnitine (MGC) and hydroxymethylglutaryl carnitine (HMGC). Another marker of some interest was uridine diphosphate N-acetylglucosamine (UNGA).     

Diagnosis of Bacteria In Vitro by Mass Spectrometric Fingerprinting:A Pilot Study

The identification of bacteria by using conventional microbiological techniques can be very time-consuming and circumstantial. In contrast, the headspace screening of bacterial cultures by analyzing their emitted volatile compounds using mass spectrometry might provide a novel approach in diagnostic microbiology. In the present study different strains of Escherichia coli, Klebsiella, Citrobacter, Pseudomonas aeruginosa, Staphylococcus aureus, and Helicobacter pylori were investigated. The volatile compounds emitted by these bacteria in vitro were analyzed using proton-transfer-reaction mass spectrometry, which allows rapid and sensitive measurement. The detected patterns of volatile compounds produced by the investigated bacteria were compared and substantial differences regarding both quantity and quality were observed. In conclusion, the present study is the first to describe headspace screening of bacterial cultures as a potential diagnostic approach in medical microbiology.     

Development of a high throughput screening method to test flavour-forming capabilities of anaerobic micro-organisms

AIM: Development of a fast, automated and reliable screening method for screening of large collections of bacterial strains with minimal handling time. METHODS AND RESULTS: The method is based on the injection of a small headspace sample (100 microl) from culture vials (2 ml) in 96-well format directly into the mass spectrometry (MS). A special sample tray has been developed for liquid media, and anaerobically grown cultures. In principle, all volatile components can be measured, but a representative mass fragment has to be obtained in the MS. Representative masses for 3-methylbutanal, 2-methylpropanal and benzaldehyde are 58, 72 and 105, respectively. In 1 day over 1500 samples could be analysed and the coefficient of variation for the response was <5%. CONCLUSION: Screening of 72 strains belonging to the genus Lactococcus in quadruple on the production of the key-flavour compound 3-methylbutanal illustrated the effectiveness of the method. Furthermore, knowledge of the biochemistry and physiology of 3-methylbutanal formation was used to optimize the composition of the growth medium to enhance 3-methylbutanal production, and thereby improve the screening. SIGNIFICANCE AND IMPACT OF THE STUDY: A commonly used method to control flavour formation in fermented food products is the selection of bacterial strains, which are able to produce the desired flavour compounds. As large collections of strains are available for such screenings, studying biodiversity of micro-organisms on the level of metabolic routes is strongly facilitated by highly automated high throughput screening methods for measuring enzyme activities or production of metabolites. Therefore, this method will be a useful tool for selecting flavour-producing strains and for enhancing starter culture development.     

Quantitative proteomics targeting classes of motif-containing peptides using immunoaffinity-based mass spectrometry

The development of high-performance technology platforms for generating detailed protein expression profiles, or protein atlases, is essential. Recently, we presented a novel platform that we termed global proteome survey, where we combined the best features of affinity proteomics and mass spectrometry, to probe any proteome in a species independent manner while still using a limited set of antibodies. We used so called context-independent-motif-specific antibodies, directed against short amino acid motifs. This enabled enrichment of motif-containing peptides from a digested proteome, which then were detected and identified by mass spectrometry. In this study, we have demonstrated the quantitative capability, reproducibility, sensitivity, and coverage of the global proteome survey technology by targeting stable isotope labeling with amino acids in cell culture-labeled yeast cultures cultivated in glucose or ethanol. The data showed that a wide range of motif-containing peptides (proteins) could be detected, identified, and quantified in a highly reproducible manner. On average, each of six different motif-specific antibodies was found to target about 75 different motif-containing proteins. Furthermore, peptides originating from proteins spanning in abundance from over a million down to less than 50 copies per cell, could be targeted. It is worth noting that a significant set of peptides previously not reported in the PeptideAtlas database was among the profiled targets. The quantitative data corroborated well with the corresponding data generated after conventional strong cation exchange fractionation of the same samples. Finally, several differentially expressed proteins, with both known and unknown functions, many relevant for the central carbon metabolism, could be detected in the glucose- versus ethanol-cultivated yeast. Taken together, the study demonstrated the potential of our immunoaffinity-based mass spectrometry platform for reproducible quantitative proteomics targeting classes of motif-containing peptides.     

Proteomic technologies for identification of serum potential biomarkers of autoimmune demyelinating polyneuropathies

Time-of-flight MALDI mass spectrometry (MALDI-TOF-MS) profiling of blood serum of patients with Guillain-Barré syndrome (GBS, 36 samples), chronic inflammatory demyelinating polyneuropathy (CIDP, 24 samples) and practically healthy donors (HD) (35 samples) was carried out in order to identify potential biomarkers of autoimmune demyelinating polyneuropathies (ADP). To simplify the peptide-protein mixture of serum prior to MALDI-TOF-MS analysis samples were pre-fractionated on magnetic microparticles with a weak cation-exchange (MB-WCX) surface. Comparative analysis of mass spectrometric data using the classification algorithms (genetic and neural network-controlled) revealed a characteristic set of peaks, agreed change area with a high specificity and sensitivity of the differentiated mass spectrometry profiles of the blood serum of patients with DPNP and healthy donors (for GBS values of these characteristics reached 100 and 100, and for CIDP 94.1 and 100% respectively). Comparative analysis of mass spectrometric profiles of serum samples obtained from patients with GBS and CIDP, allowed to build a classification model to differentiate these diseases from each other, with a specificity of 88.9 and a sensitivity of 80%.