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Isotopic Ratio Outlier Analysis (IROA) of the S. cerevisiae metabolome using accurate mass GC-TOF/MS: A new method for discovery
Abstract
Isotopic Ratio Outlier Analysis (IROA) is a 13C metabolomics profiling method that eliminates sample-to-sample variance, discriminates against noise and artifacts, and improves identification of compounds, previously done with accurate mass LC/MS. This is the first report using IROA technology in combination with accurate mass GC-TOFMS, here used to examine the S. cerevisiae metabolome. S. cerevisiae was grown in YNB media, containing randomized 95% 13C, or 5%13C glucose as the single carbon source, in order that the isotopomer pattern of all metabolites would mirror the labeled glucose. When these IROA experiments are combined, the abundance of the heavy isotopologues in the 5%13C extracts, or light isotopologues in the 95%13C extracts, follows the binomial distribution, showing mirrored peak pairs for the molecular ion. The mass difference between the 12C monoisotopic and the 13C monoisotopic equals the number of carbons in the molecules. The IROA-GC/MS protocol developed, using both Chemical and Electron Ionization, extends the information acquired from the isotopic peak patterns for formulae generation, a process that can be formulated as an algorithm, in which the number of carbons, as well as the number of methoximations and silylations, are used as search constraints. In Electron Impact (EI/IROA) spectra, the artifactual peaks are identified and easily removed, which has the potential to generate "clean" EI libraries. The combination of Chemical Ionization (CI) IROA and EI IROA affords a metabolite identification procedure that enables the identification of co-eluting metabolites, and allowed us to characterize 126 metabolites in the current study.
Supplementary resource (1)
... Tracing metabolomics of U 13 C glutamine and glucose identifies anaplerosis of glutamine carbons through both the oxidative and reductive TCA cycle. To determine the contribution of glutamine as a carbon source for the metabolic program of macrophage activation, we used U 13 C glutamine to track its metabolic signature in M. tuberculosis-infected M1-like macrophages by gas chromatography/time-of-flight mass spectrometry (GC-TOF/MS) (50). Infected BMDMs were cultured for 8 h in Dulbecco's modified Eagle's medium (DMEM) supplemented with 4 mM 50% U 13 C glutamine; then, cellular metabolites were extracted, derivatized with silylation reagents, and analyzed for isotope enrichment (50). ...
... To determine the contribution of glutamine as a carbon source for the metabolic program of macrophage activation, we used U 13 C glutamine to track its metabolic signature in M. tuberculosis-infected M1-like macrophages by gas chromatography/time-of-flight mass spectrometry (GC-TOF/MS) (50). Infected BMDMs were cultured for 8 h in Dulbecco's modified Eagle's medium (DMEM) supplemented with 4 mM 50% U 13 C glutamine; then, cellular metabolites were extracted, derivatized with silylation reagents, and analyzed for isotope enrichment (50). Carbons of glutamine can enter the TCA cycle in the form of glutamate-derived a-KG and/or as succinate from the GABA shunt (Fig. 3A). ...
... To confirm that glutamine rather than glucose serves as a major carbon source for the TCA cycle during M1-like polarization, we analyzed the metabolic signature of U 13 C glucose by GC-TOF/MS (50). Infected BMDMs were cultured for 8 h in DMEM supplemented with 25 mM 50% U 13 C glucose, and cellular metabolites were extracted for isotope enrichment analysis. ...
Macrophages play essential roles in determining the progression and final outcome of human infection by Mycobacterium tuberculosis . While upregulation of hypoxia-inducible-factor 1 (HIF-1) and a metabolic reprogramming to the Warburg Effect-like state are known to be critical for immune cell activation in response to M. tuberculosis infection, our overall knowledge about the immunometabolism of M1-like macrophages is poor.
... Recently, Isotopic Ratio Outlier Analysis (IROA) has been developed to enable the characterization of carbon information in a given metabolites or a fragment [6][7][8][9][10]. Unlike other stable isotope labeling methods, rather than utilizing substrates with natural abundance (1.1% of 13 C isotopomer seen in carbon atoms in nature) and 98-99% enrichment for the control and experimental populations, respectively [11][12][13][14][15], IROA with prototrophic yeast uses randomized 95% 12 C glucose (5% 13 C), and 95% randomized 13 C glucose (5% 12 C) as carbon sources. ...
... The promise of IROA for metabolic phenotyping has been demonstrated in model organism studies. Saccharomyces cerevisiae, a prototrophic wild-type strain in the CEN.PK background [16] was grown in minimal yeast nitrogen base (YNB) media, containing either randomized 95% 12 C, or 95% 13 C glucose as the single carbon source, in order that the isotopomer pattern of all metabolites would mirror the labeled glucose [10], a protocol which can easily be adapted for microbial species studies. The abundance of the light isotopologues in the 95% 13 C samples (M n−1 , M n−2 , etc., the 13 C envelope) or the heavy isotopologues in the 95% 12 C samples (M 0+1 , M 0+2 , etc., the 12 C envelope), follows the binomial distribution for 13 C, based on the initial substrate enrichment, in the metabolite products generated. ...
... The mass difference between the 12 C (M 0 ) isotopic peak and the 13 C (M n ) isotopic peak indicates the number of carbons (n) in the metabolite's carbon backbone. This narrows possibilities for chemical formula generation (CFG), and for normalization between control ( 13 C) and treated ( 12 C) groups [9,10]. ...
Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA) workflow using enhanced mass spectrometric data acquired with the ultra-high resolution GC-Orbitrap/MS to determine the identity of non-annotated metabolites. The higher resolution of the GC-Orbitrap/MS, together with its wide dynamic range, resulted in more IROA peak pairs detected, and increased reliability of chemical formulae generation (CFG). IROA uses two different 13C-enriched carbon sources (randomized 95% 12C and 95% 13C) to produce mirror image isotopologue pairs, whose mass difference reveals the carbon chain length (n), which aids in the identification of endogenous metabolites. Accurate m/z, n, and derivatization information are obtained from our GC/MS workflow for unknown metabolite identification, and aids in silico methodologies for identifying isomeric and non-annotated metabolites. We were able to mine more mass spectral information using the same Saccharomyces cerevisiae growth protocol (Qiu et al. Anal. Chem 2016) with the ultra-high resolution GC-Orbitrap/MS, using 10% ammonia in methane as the CI reagent gas. We identified 244 IROA peaks pairs, which significantly increased IROA detection capability compared with our previous report (126 IROA peak pairs using a GC-TOF/MS machine). For 55 selected metabolites identified from matched IROA CI and EI spectra, using the GC-Orbitrap/MS vs. GC-TOF/MS, the average mass deviation for GC-Orbitrap/MS was 1.48 ppm, however, the average mass deviation was 32.2 ppm for the GC-TOF/MS machine. In summary, the higher resolution and wider dynamic range of the GC-Orbitrap/MS enabled more accurate CFG, and the coupling of accurate mass GC/MS IROA methodology with in silico fragmentation has great potential in unknown metabolite identification, with applications for characterizing model organism networks.
... Importantly, a rate-limiting reaction in a particular pathway can be readily identified based on the accumulation of its substrate and depletion of its product. Measurement of absolute metabolite concentrations is challenging due to matrix effects, inefficient extraction, degradation during extraction and variation in the detector sensitivity 28,55,56 . Therefore, to achieve even relative quantitation, the use of internal standards is necessary. ...
... We measured the relative metabolite pools of PCC 11801 and PCC 11802 by isotopic ratio method 29,55,56 . We utilized the intracellular metabolite extract of PCC 11801, which is fully labeled with isotopic 13 C as an internal standard. ...
Cyanobacteria, a group of photosynthetic prokaryotes, are attractive hosts for biotechnological applications. It is envisaged that future biorefineries will deploy engineered cyanobacteria for the conversion of carbon dioxide to useful chemicals via light-driven, endergonic reactions. Fast-growing, genetically amenable, and stress-tolerant cyanobacteria are desirable as chassis for such applications. The recently reported strains such as Synechococcus elongatus UTEX 2973 and PCC 11801 hold promise, but additional strains may be needed for the ongoing efforts of metabolic engineering. Here, we report a novel, fast-growing, and naturally transformable cyanobacterium, S. elongatus PCC 11802, that shares 97% genome identity with its closest neighbor S. elongatus PCC 11801. The new isolate has a doubling time of 2.8 h at 1% CO2, 1000 µmole photons.m−2.s−1 and grows faster under high CO2 and temperature compared to PCC 11801 thus making it an attractive host for outdoor cultivations and eventual applications in the biorefinery. Furthermore, S. elongatus PCC 11802 shows higher levels of key intermediate metabolites suggesting that this strain might be better suited for achieving high metabolic flux in engineered pathways. Importantly, metabolite profiles suggest that the key enzymes of the Calvin cycle are not repressed under elevated CO2 in the new isolate, unlike its closest neighbor.
... Isotopic ratio outlier analysis (IROA) global metabolomics offers potential solutions to these bacterial metabolomics hurdles [30]. IROA LC/MS metabolomics was developed using Caenorhabditis elegans worm models [31,32], and IROA was recently adapted for gas chromatography-mass spectrometry (GC/MS) with Saccharomyces cerevisiae yeast models [33,34]. In these seminal IROA studies, treatment and control organisms were grown in media composed of uniformly 95:5 12 C: 13 C-and 5:95 12 C: 13 C-labeled carbon sources, respectively. ...
... In published IROA metabolomics protocols [32][33][34], organisms from experimental and control groups were grown in media containing uniformly labeled carbon sources with different 12 C: 13 C ratios (i.e., 95:5 vs. 5:95) and pooled prior to chemical extraction and UHPLC/MS or GC/MS. Comparison of the relative abundances of 12 C-and 13 C-labeled signals from isotopic peak pairs revealed differences in metabolite abundances between groups. ...
Actinomycetes are powerhouses of natural product biosynthesis. Full realization of this biosynthetic potential requires approaches for recognizing novel metabolites and determining mediators of metabolite production. Herein, we develop an isotopic ratio outlier analysis (IROA) ultra-high performance liquid chromatography-mass spectrometry (UHPLC/MS) global metabolomics strategy for actinomycetes that facilitates recognition of novel metabolites and evaluation of production mediators. We demonstrate this approach by determining impacts of the iron chelator 2,2′-bipyridyl on the Nocardiopsis dassonvillei metabolome. Experimental and control cultures produced metabolites with isotopic carbon signatures that were distinct from corresponding “standard” culture metabolites, which were used as internal standards for LC/MS. This provided an isotopic MS peak pair for each metabolite, which revealed the number of carbon atoms and relative concentrations of metabolites and distinguished biosynthetic products from artifacts. Principal component analysis (PCA) and random forest (RF) differentiated bipyridyl-treated samples from controls. RF mean decrease accuracy (MDA) values supported perturbation of metabolites from multiple amino acid pathways and novel natural products. Evaluation of bipyridyl impacts on the nocazine/XR334 diketopiperazine (DKP) pathway revealed upregulation of amino acid precursors and downregulation of late stage intermediates and products. These results establish IROA as a tool in the actinomycete natural product chemistry arsenal and support broad metabolic consequences of bipyridyl.
... Indeed, the complement of metabolites synthesized by human cells is still not known (Viant et al., 2017). Comprehensive identification of endogenously synthesized compounds have to our knowledge been reported only in microorganisms that can grow on a single carbon source, where fully 13 C cells can been generated, allowing global detection of endogenous metabolites by the isotope ratio outlier analysis (IROA) method (Qiu et al., 2016). However, since mammalian cells require a complex mixture of nutrients, including chemically Cell Chemical Biology 25, 1-9, November 15, 2018 ª 2018 Elsevier Ltd. 1 undefined serum (Niklas et al., 2010), generating fully 13 C mammalian cells is not feasible. ...
... A drawback is that, since the resulting MIDs depend on the metabolic state of the cells, metabolite formulas must be identified from other sources, such as the HMDB database. In contrast, the IROA methodology (Qiu et al., 2016) allows identifying the carbon number directly from MIDs; however, this requires complete 13 C labeling of all biomass, which is only feasible in microorganisms that can grow on a single carbon source, and the resulting data yield no information about pathway activity. It must also be noted that our method cannot distinguish between usages of alternative pathways leading from a labeled nutrient to a labeled product, unless there is at least one unlabeled intermediate. ...
Studying metabolic activities in living cells is crucial for understanding human metabolism, but facile methods for profiling metabolic activities in an unbiased, hypothesis-free manner are still lacking. To address this need, we here introduce the deep-labeling method, which combines a custom 13C medium with high-resolution mass spectrometry. A proof-of-principle study on human cancer cells demonstrates that deep labeling can identify hundreds of endogenous metabolites as well as active and inactive pathways. For example, protein and nucleic acids were almost exclusively de novo synthesized, while lipids were partly derived from serum; synthesis of cysteine, carnitine, and creatine was absent, suggesting metabolic dependencies; and branched-chain keto acids (BCKAs) were formed and metabolized to short-chain acylcarnitines, but did not enter the tricarboxylic acid cycle. Remarkably, BCKAs could substitute for essential amino acids to support growth. The deep-labeling method may prove useful to map metabolic phenotypes across a range of cell types and conditions.
... Moreover, recent developments in mass spectrometry stable isotope labeling has already allowed use for monitoring the fate of metabolites utilizing non-targeted metabolomics protocols; therefore, extending the list of detected and monitored metabolic pathways [49]. Recent progress in isotopic ratio outlier analysis (IROA), a novel method for stable isotope labeling, may find its place in the drug discovery process, since it deals with sample to sample variance, discriminates against noise and artifacts, and improves components identification [50]. It has become clear that advances in emerging Omics technologies are offering a systemic approach to complex disease diagnosis, monitoring and therapeutic intervention. ...
... Metabolomics as a standalone or as a part of systems biology discovery protocols can offer exciting opportunities to discover not only diagnostic, but prognostic and also mechanistic markers for a number of major human diseases [9,37,48,53,54]. It is expected that the ability to identify markers of drug toxicity/efficacy will significantly accelerate drug discovery and assist to delineate the appropriate clinical plan [9,50,[55][56][57]. ...
Metabolomics has emerged as an essential tool for studying metabolic processes, stratification of patients, as well as illuminating the fundamental metabolic alterations in disease onset, progression, or response to therapeutic intervention. Metabolomics materialized within the pharmaceutical industry as a standalone assay in toxicology and disease pathology and eventually evolved towards aiding in drug discovery and pre-clinical studies via supporting pharmacokinetic and pharmacodynamic characterization of a drug or a candidate. Recent progress in the field is illustrated by coining of the new term—Pharmacometabolomics. Integration of data from metabolomics with large-scale omics along with clinical, molecular, environmental and behavioral analysis has demonstrated the enhanced utility of deconstructing the complexity of health, disease, and pharmaceutical intervention(s), which further highlight it as an essential component of systems medicine. This review presents the current state and trend of metabolomics applications in pharmaceutical development, and highlights the importance and potential of clinical metabolomics as an essential part of multi-omics protocols that are directed towards shaping precision medicine and population health.
... Correction for natural labeling was performed using IsoCor (Millard et al., 2012). The isotopic ratio method was utilized for quantification of relative pool sizes of metabolites in S. elongatus strains PCC 11801 and 11802 (Qiu et al., 2016;Stupp et al., 2013). The area under the peak for the monoisotopic m/z of a particular metabolite ( 12 C) was normalized to its respective highest possible isotopolog ( 13 C) present in the fully labeled biomass, which served as an internal standard resulting in area ratios. ...
Synechococcus elongatus PCC 11801 and 11802 are closely related cyanobacterial strains that are fast-growing and tolerant to high light and temperature. These strains hold significant promise as chassis for photosynthetic production of chemicals from carbon dioxide. A detailed quantitative understanding of the central carbon pathways would be a reference for future metabolic engineering studies with these strains. We have used isotopic non-stationary 13 C-metabolic flux analysis (INST-MFA) to quantitively assess the metabolic potential of these two strains. This study highlights key similarities and differences in the central carbon flux distribution between these and other model/non-model strains. The two strains demonstrated a higher Calvin-Benson-Bassham (CBB) cycle flux coupled with negligible flux through the oxidative pentose phosphate (OPP), photorespiratory pathway, and lower anaplerosis fluxes under photoautotrophic conditions. Interestingly, PCC 11802 shows the highest CBB cycle and pyruvate kinase (PK) flux values among those reported in cyanobacteria. The unique tricarboxylic acid (TCA) cycle diversion in PCC 11801 makes it ideal for the large-scale production of TCA cycle-derived chemicals. Additionally, dynamic labeling transients were measured for intermediates of amino acid, nucleotide, and nucleotide-sugar metabolism. Overall, this study provides the first detailed metabolic flux maps of S. elongatus PCC 11801 and 11802 that may aid metabolic engineering efforts in these strains.
... The dried samples were derivatized with a methyl-moximation (with 15 mg/ml methoxy amine in pyridine, 30°C for 90 minutes) and MTBSTFA (at 70°C for 60 minutes). The samples were then analyzed with an Agilent GC-MS, with an electron impact mode and a DB-5 MS column (Agilent) following our protocol [84]. The data were analyzed with Mass Hunter Quantitative Analysis software (Agilent). ...
The contribution of mitochondria to the metabolic function of hypoxic NP cells has been overlooked. We have shown that NP cells contain networked mitochondria and that mitochondrial translocation of BNIP3 mediates hypoxia-induced mitophagy. However, whether BNIP3 also plays a role in governing mitochondrial function and metabolism in hypoxic NP cells is not known. BNIP3 knockdown altered mitochondrial morphology, and number, and increased mitophagy. Interestingly, BNIP3 deficiency in NP cells reduced glycolytic capacity reflected by lower production of lactate/H+ and lower ATP production rate. Widely targeted metabolic profiling and flux analysis using 1-2-13C-glucose showed that the BNIP3 loss resulted in redirection of glycolytic flux into pentose phosphate and hexosamine biosynthesis as well as pyruvate resulting in increased TCA flux. An overall reduction in one-carbon metabolism was noted suggesting reduced biosynthesis. U13C-glutamine flux analysis showed preservation of glutamine utilization to maintain TCA intermediates. The transcriptomic analysis of the BNIP3-deficient cells showed dysregulation of cellular functions including membrane and cytoskeletal integrity, ECM-growth factor signaling, and protein quality control with an overall increase in themes related to angiogenesis and innate immune response. Importantly, we observed strong thematic similarities with the transcriptome of a subset of human degenerative samples. Last, we noted increased autophagic flux, decreased disc height index and aberrant COL10A1/collagen X expression, signs of early disc degeneration in young adult bnip3 knockout mice. These results suggested that in addition to mitophagy regulation, BNIP3 plays a role in maintaining mitochondrial function and metabolism, and dysregulation of mitochondrial homeostasis could promote disc degeneration.Abbreviations: ECAR extracellular acidification rate; HIF hypoxia inducible factor; MFA metabolic flux analysis; NP nucleus pulposus; OCR oxygen consumption rate; ShBnip3 short-hairpin Bnip3.
... Here m i and M i represent the normalized relative and unnormalized isotopolog abundance for each precursor/fragment ion in which i 13 C atoms are incorporated, and n represents the number of carbon atoms in the metabolite/fragment ion (Hasunuma et al., 2010). The relative pool sizes of γ-glutamyl peptides in different strains and conditions were quantified using the isotopic ratio method by normalizing the area under the peak for monoisotopic m/z of a particular peptide ( 12 C) by its respective highest possible isotopolog ( 13 C) present in the fully labeled biomass that acted as internal standard resulting in area ratios (Qiu et al., 2016;Stupp et al., 2013). All graphs presented in this report and its supplementary information were plotted using Origin software (version 9.6.5.169, ...
Cyanobacteria are attractive model organisms for studies of photosynthesis and diurnal metabolism and as hosts for photoautotrophic production of chemicals. Exposure to bright light, environmental pollutants, and diurnal lifestyle of these prokaryotes may result in significant oxidative stress. Glutathione is a widely studied gamma-glutamyl peptide that plays a key role in managing oxidative stress and detoxification of xenobiotics in cyanobacteria. The functional role and biosynthetic pathways of this tripeptide have been studied in detail in various phyla, including cyanobacteria. However, other γ-glutamyl peptides remain largely unexplored. We use an integrated approach to identify a number of γ-glutamyl peptides based on signature mass fragments and mass shifts in them in ¹³C and ¹⁵N enriched metabolite extracts. The newly identified compounds include γ-glutamyl dipeptides and derivatives of glutathione. Carbon backbones of the former turn over much faster than that of glutathione suggesting that they follow a distinct biosynthetic pathway. Further, transients of isotopic ¹³C enrichment show positional labeling in these peptides which allow us to delineate between the alternative biosynthetic pathways. Importantly, the amino acid of γ-glutamyl dipeptides shows much faster turnover compared to the glutamate moiety. The significant accumulation of γ-glutamyl dipeptides under slow-growth conditions combined with the results from dynamic ¹³C labeling suggests that these compounds may act as reservoirs of amino acids in cyanobacteria.
... Unfortunately, nonenzymatic chemical degradation and other types of degradation, which occur prior to extraction of the sample, cannot be corrected for via the use of spiked internal standards and thus, degradation and conversion must be prevented or accounted for prior to extraction (Gil et al., 2015). For cell culture studies, an isotopically labeled growth medium incorporated within cells (i.e., fully isotopically labeled reference material similar to that offered by the isotopic ratio outlier analysis (IROA) quantitation kit (Qiu et al., 2016;Stupp et al., 2013)), is useful in accounting for effects of degradation as it would account for the same environmental conditions across time as the endogenous lipid species. In addition to the IROA approach, lipidome isotope labeling of yeast (LILY) has been proposed as an in vivo 13 C labeling technique to produce isotopically labeled eukaryotic lipid standards in yeast (Rampler et al., 2018). ...
Lipidomics is a rapidly growing field, fueled by developments in analytical instrumentation and bioinformatics. To date, most researchers and industries have employed their own lipidomics workflows without a consensus on best practices. Without a community‐wide consensus on best practices for the prevention of lipid degradation and transformations through sample collection and analysis, it is difficult to assess the quality of lipidomics data and hence trust results. Clinical studies often rely on samples being stored for weeks or months until they are analyzed, but inappropriate sampling techniques, storage temperatures, and analytical protocols can result in the degradation of complex lipids and the generation of oxidized or hydrolyzed metabolite artifacts. While best practices for lipid stability are sample dependent, it is generally recommended that strategies during sample preparation capable of quenching enzymatic activity and preventing oxidation should be considered. In addition, after sample preparation, lipid extracts should be stored in organic solvents with antioxidants at −20 °C or lower in an airtight container without exposure to light or oxygen. This will reduce or eliminate sublimation, and chemically and physically induced molecular transformations such as oxidation, enzymatic transformation, and photon/heat‐induced degradation. This review explores the available literature on lipid stability, with a particular focus on human health and/or clinical lipidomic applications. Specifically, this includes a description of known mechanisms of lipid degradation, strategies, and considerations for lipid storage, as well as current efforts for standardization and quality insurance of protocols.
... They found that this method improves data precision more than normalization of metabolite responses group by group using one SIL metabolite per group. Besides, D9-BSTFA was used to verify the number of silylation group per metabolite and also generate a labelled internal standard for every metabolite (Qiu et al. 2016). In another study, d3-MCF was used to develop a GC-MS method for quantitation of almost seventy metabolites of amino acids and non-amino organic acids in rat liver, serum and urine (Kvitvang et al. 2011). ...
Background:
Human urine gives evidence of the metabolism in the body and contains different metabolites at various concentrations. A number of analytical techniques including mass spectrometry (MS) and nuclear magnetic resonance (NMR) have been used to obtain metabolites levels in urine samples. However, gas chromatography-mass spectrometry (GC-MS) is one of the most widely used techniques for urinary metabolomics studies due to its higher sensitivity, resolution, reproducibility, reliability, relatively low cost and ease of operation compared to liquid chromatography-mass spectrometry and NMR.
Aim of review:
This review looks at various aspects of urine preparation prior to analysis by GC-MS including sample storage, urease pretreatment, derivatization, use of internal standard and quality control samples for data correction. In addition, most common types of inlet liners, ionization techniques and columns are discussed and a summary of mass analyzers are also highlighted. Lastly, the role of retention index in metabolite identification and data normalization methods are presented.
Key scientific concepts of review:
The purpose of this review is summarizing methods of sample storage, pretreatment, and GC-MS analysis that are mostly used in urine metabolomics studies. Specific emphasis is given to the critical steps within the GC-MS urine metabolomics that those new to this field need to be aware of and the remaining challenges that require further attention and studies.
... The pool sizes of 59 targeted metabolites comprising intermediates of central carbon metabolism, amino acids, nucleotides, nucleotide sugars, and cofactors were monitored in selected engineered and WT strains. The metabolite levels were quantified under the exponential growth phase by using the isotopic ratio method (Fig. 5, Additional file 1: Figures S7-S13) [50,51] and expressed as fold change with respect to the levels in WT (For the complete list, see Additional file 1: Table S6). Metabolites with fold changes of greater than 1.5 and p-value of less than 0.05 are shown in Fig. 5. ...
Background. Cyanobacteria, a group of photosynthetic prokaryotes, are being increasingly
explored for direct conversion of carbon dioxide to useful chemicals. However, efforts to
engineer these photoautotrophs have resulted in low product titers. This may be ascribed to
the bottlenecks in metabolic pathways, which need to be identified for rational engineering.
We engineered the recently reported, fast-growing and robust cyanobacterium,
Synechococcus elongatus PCC 11801 to produce succinate, an important platform chemical.
Previously, engineering of the model cyanobacterium S. elongatus PCC 7942 has resulted in
succinate titer of 0.43 g.l -1 in 8 days.
Results. Building on the previous report, expression of alpha ketoglutarate decarboxylase,
succinate semialdehyde dehydrogenase and phosphoenol pyruvate carboxylase yielded a
succinate titer of 0.6 g.l -1 in 5 days suggesting that PCC 11801 is better suited as host for
production. Profiling of the engineered strains for 57 intermediate metabolites, a number of enzymes and qualitative analysis of key transcripts revealed potential flux control points. Based on this, we evaluated the effects of overexpression of sedoheptulose-1,7-bisphosphatase, citrate synthase and succinate transporters and knockout of succinate dehydrogenase and glycogen synthase A. The final construct with seven genes overexpressed and two genes knocked out resulted in photoautotrophic production of 0.93 g.l -1 succinate in 5 days.
Conclusion. While the fast-growing strain PCC 11801 yielded a much higher titer than the
model strain, the efficient photoautotrophy of this novel isolate needs to be harnessed further for the production of desired chemicals. Engineered strains of S. elongatus PCC 11801 showed dramatic alterations in the levels of several metabolites suggesting far-reaching effects of pathway engineering. Attempts to overexpress enzymes deemed to be flux controlling led to the emergence of other potential rate-limiting steps. Thus, this process of debottlenecking of the pathway needs to be repeated several times to obtain a significantly superior succinate titer.
... Recent GC-orbitrap applications in pharmacological research and environmental contaminants (Peterson et al. 2014;Baldwin et al. 2016;Postigo et al. 2016). The use of GC-HRMS is particularly advantageous for stable isotope labelling (SIL) approaches incorporating 13 C or 15 N into analytes, two advanced approaches have been reported that include molecular-ion directed acquisition (MIDA) (Peterson et al. 2014) for discovery metabolomics and isotopic ratio outlier analysis (IROA) analysis (Qiu et al. 2016) for metabolite identification. Hyphenated systems and tandem mass spectrometry further open the applicability of GC-MS. ...
Background: Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC–MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and ‘in house’ metabolite databases available.
Aim of review: This review provides an overview of developments in GC–MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC–MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics.
Key scientific concepts of review: The purpose of this review is to both highlight and provide an update on GC–MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC–MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.
... High accuracy mass spectrometers such as orbitrap and time-of-flight MS can be complied in this strategy for disclosing the elemental composition of an unknown metabolite but caution must be taken as such instruments present challenge to precisely determine VOCs below 50 Dalton mass range due to extreme vacuum operation within the analyser. Derivatisation for ketones, acids and aldehydes prior to analysis has been commonly employed for targeted studies, thus improving the detectability of carbonyl functional VOCs via oximation or silylation [38][39][40] . In untargeted breathomics analysis, artifacts potentially introduced during the derivatization reaction would potentially distort the reliability of biomarker discovery. ...
Disease breathomics is gaining importance nowadays due to its usefulness as non-invasive early cancer detection. Mass spectrometry (MS) technique is often used for analysis of volatile organic compounds (VOCs) associated with cancer in the exhaled breath but a long-standing challenge is the uncertainty in mass peak annotation for potential volatile biomarkers. This work describes a cross-platform MS strategy employing selected-ion flow tube mass spectrometry (SIFT-MS), high resolution gas chromatography-mass spectrometry (GC-MS) retrofitted with electron ionisation (EI) and GC-MS retrofitted with positive chemical ionisation (PCI) as orthogonal analytical approaches in order to provide facile identification of the oxygenated VOCs from breath of cancer patients. In addition, water infusion was applied as novel efficient PCI reagent in breathomics analysis, depicting unique diagnostic ions M+or [M-17]+for VOC identification. Identity confirmation of breath VOCs was deduced using the proposed multi-platform workflow, which reveals variation in breath oxygenated VOC composition of oesophageal-gastric (OG) cancer patients with dominantly ketones, followed by aldehydes, alcohols, acids and phenols in decreasing order of relative abundance. Accurate VOC identification provided by cross-platform approach would be valuable for the refinement of diagnostic VOC models and the understanding of molecular drivers of VOC production.
... According to the experimental protocol, 5 experiments with different weights of fecal samples were performed for the analysis of gut microbial host related co-metabolites. After processing the GC/TOFMS data using ChromaTOF software, 440 resolved peaks were detected, 163 of which were annotated to be related to gut microbiota-host cometabolism (see Supplementary material Table 1) according to previous publications [5,15,21]. As shown in Fig. 1, the typical GC/TOFMS total ion chromatograms (TIC) of fecal samples showed changes in endogenous metabolite levels. ...
In this paper, an optimized method based on gas chromatography/time-of-flight mass spectrometry (GC-TOFMS) platform has been developed for the analysis of gut microbial-host related co-metabolites in fecal samples. The optimization was performed with proportion of chloroform (C), methanol (M) and water (W) for the extraction of specific metabolic pathways of interest. Loading Bi-plots from the PLS regression model revealed that high concentration of chloroform emphasized the extraction of short chain fatty acids and TCA intermediates, while the higher concentration of methanol emphasized indole and phenyl derivatives. Low level of organic solution emphasized some TCA intermediates but not for indole and phenyl species. The highest sum of the peak area and the distribution of metabolites corresponded to the extraction of methanol/chloroform/water of 225:75:300 (v/v/v), which was then selected for method validation and utilized in our application. Excellent linearity was obtained with 62 reference standards representing different classes of gut microbial-host related co-metabolites, with correlation coefficients (r ²) higher than 0.99. Limit of detections (LODs) and limit of qualifications (LOQs) for these standards were below 0.9 nmol and 1.6 nmol, respectively. The reproducibility and repeatability of the majority of tested metabolites in fecal samples were observed with RSDs lower than 15%. Chinese rhubarb-treated rats had elevated indole and phenyl species, and decreased levels of polyamine such as putrescine, and several amino acids. Our optimized method has revealed host-microbe relationships of potential importance for intestinal microbial metabolite receptors such as pregnane X receptor (PXR) and aryl hydrocarbon receptor (AHR) activity, and for enzymes such as ornithine decarboxylase (ODC).
... same biological source. For example, IROA is often employed using a labeled yeast extract spiked into samples from other species [60 ]. While this strategy has some advantages, one may miss large segments of metabolism that are not present in the labeled standard as the experimental and standard materials constitutionally diverge. ...
... These metabolites, and the biochemical pathways that produce them, represent a huge opportunity for new biological discovery. It was in response to this challenge that the international Metabolomics Society launched a Metabolite Identification task group in 2013 [24], with goals to develop robust reporting standards for metabolite identification and to advertise best-practice to the metabolomics community using a range of analytical approaches [25][26][27][28]. The challenge of identifying all metabolites in all sample types is currently-and into the foreseeable future-not achievable. ...
Model organisms are an essential component of biological and biomedical research that can be used to study specific biological processes. These organisms are in part selected for facile experimental study. However, just as importantly, intensive study of a small number of model organisms yields important synergies as discoveries in one area of science for a given organism shed light on biological processes in other areas, even for other organisms. Furthermore, the extensive knowledge bases compiled for each model organism enable systems-level understandings of these species, which enhance the overall biological and biomedical knowledge for all organisms, including humans. Building upon extensive genomics research, we argue that the time is now right to focus intensively on model organism metabolomes. We propose a grand challenge for metabolomics studies of model organisms: to identify and map all metabolites onto metabolic pathways, to develop quantitative metabolic models for model organisms, and to relate organism metabolic pathways within the context of evolutionary metabolomics, i.e., phylometabolomics. These efforts should focus on a series of established model organisms in microbial, animal and plant research.
Ion suppression is a major problem in mass spectrometry (MS)-based metabolomics; it can dramatically decrease measurement accuracy, precision, and signal-to-noise sensitivity. Here we report a new method, the IROA TruQuant Workflow, that uses a stable isotope-labeled internal standard (IROA-IS) plus novel companion algorithms to 1) measure and correct for ion suppression, and 2) perform Dual MSTUS normalization of MS metabolomic data. We have evaluated the method across ion chromatography (IC), hydrophilic interaction liquid chromatography (HILIC), and reverse phase liquid chromatography (RPLC)-MS systems in both positive and negative ionization modes, with clean and unclean ion sources, and across different biological matrices. Across the broad range of conditions tested, all detected metabolites exhibited ion suppression ranging from 1% to 90+% and coefficient of variations ranging from 1% to 20%, but the Workflow and companion algorithms were highly effective at nulling out that suppression and error. Overall, the Workflow corrects ion suppression across diverse analytical conditions and produces robust normalization of non-targeted metabolomic data.
Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics and lipidomics have recently been used to show that MYC-amplified group 3 medulloblastoma tumors are driven by metabolic reprogramming. Here, we present a protocol to extract metabolites and lipids from human medulloblastoma brain tumor-initiating cells and normal neural stem cells. We describe untargeted LC-MS methods that can be used to achieve extensive coverage of the polar metabolome and lipidome. Finally, we detail strategies for metabolite identification and data analysis.
For complete details on the use and execution of this protocol, please refer to Gwynne et al.¹
While the combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS) is commonly used for feature annotation in untargeted omics experiments, ensuring these prioritized features originate from endogenous metabolism remains challenging. Isotopologue workflows, such as isotopic ratio outlier analysis (IROA), mass isotopomer ratio analysis of U-13C labeled extracts (MIRACLE), and credentialing incorporate isotopic labels directly into metabolic precursors, guaranteeing that all features of interest are unequivocal byproducts of cellular metabolism. Furthermore, comprehensive separation and annotation of small molecules continue to challenge the metabolomics field, particularly for isomeric systems. In this paper, we evaluate the analytical utility of incorporating ion mobility spectrometry (IMS) as an additional separation mechanism into standard LC-MS/MS isotopologue workflows. Since isotopically labeled molecules codrift in the IMS dimension with their 12C versions, LC-IMS-CID-MS provides four dimensions (LC, IMS, MS, and MS/MS) to directly investigate the metabolic activity of prioritized untargeted features. Here, we demonstrate this additional selectivity by showcasing how a preliminary data set of 30 endogeneous metabolites are putatively annotated from isotopically labeled Escherichia coli cultures when analyzed by LC-IMS-CID-MS. Metabolite annotations were based on several molecular descriptors, including accurate mass measurement, carbon number, annotated fragmentation spectra, and collision cross section (CCS), collectively illustrating the importance of incorporating IMS into isotopologue workflows. Overall, our results highlight the enhanced separation space and increased annotation confidence afforded by IMS for metabolic characterization and provide a unique perspective for future developments in isotopically labeled MS experiments.
It is urgent to develop disease models to dissect mechanisms regulating SARS-CoV-2 infection. Here, we derive airway organoids from human pluripotent stem cells (hPSC-AOs). The hPSC-AOs, particularly ciliated-like cells, are permissive to SARS-CoV-2 infection. Using this platform, we perform a high content screen and identify GW6471, which blocks SARS-CoV-2 infection. GW6471 can also block infection of the B.1.351 SARS-CoV-2 variant. RNA-seq analysis suggests that GW6471 blocks SARS-CoV-2 infection at least in part by inhibiting HIF1α, which is further validated by chemical inhibitor and genetic perturbation targeting HIF1α. Metabolic profiling identifies decreased rates of glycolysis upon GW6471 treatment, consistent with transcriptome profiling. Finally, xanthohumol, 5-(Tetradecyloxy)-2-furoic acid, and ND-646, three compounds that suppress fatty acid biosynthesis, also block SARS-CoV-2 infection. Together, a high content screen coupled with transcriptome and metabolic profiling reveals a key role of the HIF1α-glycolysis axis in mediating SARS-CoV-2 infection of human airway epithelium.
High-resolution mass spectrometry (MS) has advanced the study of metabolism in living systems by allowing many metabolites to be measured in a single experiment. Although improvements in mass detector sensitivity have facilitated the detection of greater numbers of analytes, compound identification strategies, feature reduction software, and data sharing have not kept up with the influx of MS data. Here, we discuss the ongoing challenges with MS-based metabolomics, including de novo metabolite identification from mass spectra, differentiation of metabolites from environmental contamination, chromatographic separation of isomers, and incomplete MS databases. Because of their popularity and sensitive detection of small molecules, this review focuses on the challenges of liquid chromatography-mass spectrometry–based methods. We then highlight important instrumentational, experimental, and computational tools that have been created to address these challenges and how they have enabled the advancement of metabolomics research.
Stable isotope tracers are applied for in vivo and in vitro studies to reveal the activity of enzymes and intracellular metabolic pathways. Most often, such tracers are used with gas chromatography coupled to mass spectrometry (GC–MS) owing to its ease of operation and reproducible mass spectral databases. Differences in isotope tracer performance of the classic GC–quadrupole MS instrument and newer time-of-flight instruments are not well studied. Here, we used three commercially available instruments for the analysis of identical samples from a stable isotope labeling study that used [U-13C6] d-glucose to investigate the metabolism of the bacterium Rothia mucilaginosa with respect to 29 amino acids and hydroxyl acids involved in primary metabolism. The prokaryote R. mucilaginosa belongs to the family of Micrococcaceae and is present and metabolically active in the airways and sputum of cystic fibrosis patients. Overall, all three GC–MS instruments (low-resolution GC-SQ MS, low-resolution GC-TOF MS, and high-resolution GC-QTOF MS) can be used to perform stable isotope tracing studies for glycolytic intermediates, tricarboxylic acid (TCA) metabolites, and amino acids, yielding similar biological results, with high-resolution GC-QTOF MS offering additional capabilities to identify the chemical structures of unknown compounds that might show significant isotope enrichments in biological studies.
Identification of unknown metabolites is one of the major issues for untargeted metabolomics studies. Isotopic ratio outlier analysis (IROA) technique uses specially designed isotopic patterns to reveal the carbon number of each metabolite and distinguish the biological originated metabolites from artifacts. This chapter describes the procedure to utilize the IROA technique with high accurate mass GC-MS to identify unknown metabolites.
Cyanobacteria are gaining importance both as hosts for photoautotrophic production of chemicals and as model systems for studies of diurnal lifestyle. While the proteome and transcriptome of cyanobacteria have been closely examined under diurnal growth, the downstream effects on the intermediary metabolism have not received sufficient attention. The present study focuses on identifying the cellular metabolites whose inventories undergo dramatic changes in a fast-growing cyanobacterium, Synechococcus elongatus PCC 11801. We identified and quantified 67 polar metabolites, whose inventory changes significantly during diurnal growth, with some metabolites changing by 100-fold. The Calvin-Benson-Bassham cycle intermediates peak at midday to support fast growth. The hitherto unexplored γ-glutamyl peptides act as reservoirs of amino acids. Interestingly, several storage molecules or their precursors accumulate during the dark phase, dispelling the notion that all biosynthetic activity takes place in the light phase. Our results will guide metabolic modeling and strain engineering of cyanobacteria.
Untargeted metabolomics can detect more than 10,000 peaks in a single LC-MS run. The correspondence between these peaks and metabolites, however, remains unclear. Here we introduce a Peak Annotation and Verification Engine (PAVE) for systematically annotating untargeted microbial metabolomics data. The workflow involves growing cells in ¹³C and ¹⁵N isotope-labeled media to identify peaks from biological compounds and their carbon and nitrogen atom counts. Improved de-isotoping and de-adducting is enabled by algorithms that integrate positive mode, negative mode and labeling data. To distinguish metabolites and their fragments, PAVE experimentally measures the response of each peak to a weak in-source collision-induced-dissociation voltage, which increases the peak intensity for fragments while decreasing it for their parent ions. The molecular formulae of the putative metabolites are then assigned based on database searching using both m/z and C/N atom counts. Application of this procedure to S. cerevisiae and E. coli revealed that more than 80% peaks do not label, i.e. are environmental contaminants. More than 70% of the biological peaks are isotopic variants, adducts, fragments, or mass spectrometry artifacts yielding ~2,000 apparent metabolites across the two organisms. About 650 match to a known metabolite formula based on m/z and C/N atom counts, with 220 assigned structures based on MS/MS and/or retention time to match to authenticated standards. Thus, PAVE provides an efficient route to systematically annotate LC-MS metabolomics data.
Genetic improvement for stress tolerance requires a solid understanding of biochemical processes involved with different physiological mechanisms and their relationships with different traits. The objective of this study was to demonstrate genetic variability in altered metabolic levels in a panel of six wheat genotypes in contrasting temperature regimes, and to quantify the correlation between those metabolites with different traits. In a controlled environment experiment, heat stress (35:28 ± 0.08°C) was initiated 10 days after anthesis. Flag leaves were collected 10 days after heat treatment to employ an untargeted metabolomics profiling using LC-HRMS based technique called IROA. High temperature stress produced significant genetic variations for cell and thylakoid membrane damage, and yield related traits. 64 known metabolites accumulated 1.5 fold of higher or lower due to high temperature stress. In general, metabolites that increased the most under heat stress (L-tryptophan, pipecolate) showed negative correlation with different traits. Contrary, the metabolites that decreased the most under heat stress (drummondol, anthranilate) showed positive correlation with the traits. Aminoacyl-tRNA biosysnthesis and plant secondary metabolite biosynthesis pathways were most impacted by high temperature stress. The robustness of metabolic change and their relationship with phenotypes renders those metabolites as potential bio-markers for genetic improvement.
Functional metabolomics is a new concept, which studies the functions of metabolites and related enzymes focused on metabolomics. It overcomes the shortcomings of traditional discovery metabolomics of mainly relying on literatures for biological interpretation. Functional metabolomics has many advantages. Firstly, the functional roles of metabolites and related metabolic enzymes are focused. Secondly, the in vivo and in vitro experiments are conducted to validate the metabolomics findings, therefore, increasing the reliability of metabolomics study and producing the new knowledge. Thirdly, functional metabolomics can be used by biologists to investigate functions of metabolites, and related genes and proteins. In this review, we summarize the analytical, biological and clinical platforms used in functional metabolomics studies. Recent progresses of functional metabolomics in cancer, metabolic diseases and biological phenotyping are reviewed, and future development is also predicted. Because of the tremendous advantages of functional metabolomics, it will have a bright future.
Drought stress at the reproductive stage causes severe damage to productivity of wheat. However, little is known about the metabolites associated with drought tolerance. The objectives of this study were to elucidate changes in metabolite levels in wheat under drought, and to identify potential metabolites associated with drought stress through untargeted metabolomic profiling using a liquid chromatography-high resolution mass spectrometry (LC-HRMS)-based technique called Isotopic Ratio Outlier Analysis. Metabolomic analysis was performed on flag leaves of drought-stressed and control (well-watered) plants after 18 days of post-anthesis drought stress at three-hour intervals over a 24-hour period. Out of 723 peaks detected in leaves, 221 were identified as known metabolites. Sixty known metabolites were identified as important metabolites by 3 different methods, PLS-DA, RF and SAM. The most pronounced accumulation due to drought stress was demonstrated by tryptophan, proline, pipecolate and linamarin, whereas the most pronounced decrease was demonstrated by serine, trehalose, N-acetyl-glutamic acid, DIBOA-glucoside etc. Three different patterns of metabolite accumulation were observed over 24-hour period. The increased accumulated metabolites remained higher during all 8 time points in drought stressed leaves. On the contrary, metabolites that showed decreased level remained significantly lower during all or the most time points. However, the levels of some decreased metabolites were lower during the day, but higher during night in drought stressed leaves. Both univariate and multivariate analyses predicted thatN-acetyl-glutamic acid, proline, pipecolate, linamarin, tryptophan, and DIBOA-glucoside could be potential metabolite biomarkers, and their levels could serve as indicators of drought tolerance in wheat.
The metabolome describes the full complement of the tens to hundreds of thousands of low molecular weight metabolites present within a biological system. Identification of the metabolome is critical for discovering the maximum amount of biochemical knowledge from metabolomics datasets. Yet no exhaustive experimental characterisation of any organismal metabolome has been reported to date, dramatically contrasting with the genome sequencing of thousands of plants, animals and microbes. Here, we review the status of metabolome annotation and describe advances in the analytical methodologies being applied. In part through new international coordination, we conclude that we are now entering a new era of metabolome annotation.
Thermal processes are widely used in small molecule chemical analysis and metabolomics for derivatization, vaporization, chromatography, and ionization especially in gas chromatography mass spectrometry (GC/MS). In this study the effect of heating was examined on a set of 64 small molecule standards and, separately, on human plasma metabolites. The samples, either derivatized or underivatized, were heated at three different temperatures (60, 100, and 250°C) at different exposure times (30s, 60s, and 300s). All the samples were analyzed by liquid chromatography coupled to electrospray ionization mass spectrometry (LC/MS) and the data processed by XCMS Online (xcmsonline.scripps.edu). The results showed that heating at an elevated temperature of 100°C had an appreciable effect on both the underivatized and derivatized molecules, and heating at 250°C created substantial changes in the profile. For example, over 40% of the molecular peaks were altered in the plasma metabolite analysis after heating (250°C, 300s) with a significant formation of upregulated, degradation and transformation products. Derivatized samples were similarly affected by thermal degradation. The analysis of the 64 small molecule standards validated the temperature-induced changes observed on the plasma metabolites, where most of the small molecules degraded at elevated temperatures even after minimal exposure times (30s). For example, tri- and di-organophosphates (e.g., adenosine triphosphate and adenosine diphosphate) were readily degraded into a mono-organophosphate (e.g., adenosine monophosphate) during heating. Nucleosides and nucleotides (e.g., inosine and inosine monophosphate) were also found to be transformed into purine derivatives (e.g., hypoxanthine). A newly formed transformation product, oleoyl ethyl amide, was also identified in both the underivatized and derivatized of the plasma metabolites and small molecule standard mixture, and was likely generated from reaction(s) with oleic acid. Overall these analyses show that small molecules and metabolites undergo significant time-sensitive alterations when exposed to elevated temperatures, especially those conditions consistent with GC/MS experiments.
Compound identification is a major bottleneck in metabolomics studies. In nuclear magnetic resonance (NMR) investigations, resonance overlap often hinders unambiguous database matching or de novo compound identification. In liquid chromatography-mass spectrometry (LC-MS), discriminating between biological signals and background artifacts and reliable determination of molecular formulae are not always straightforward. We have designed and implemented several NMR and LC-MS approaches that utilize ¹³C, either enriched or at natural abundance, in metabolomics applications. For LC-MS applications, we describe a technique called isotopic ratio outlier analysis (IROA), which utilizes samples that are isotopically labeled with 5% (test) and 95% (control) ¹³C. This labeling strategy leads to characteristic isotopic patterns that allow the differentiation of biological signals from artifacts and yield the exact number of carbons, significantly reducing possible molecular formulae. The relative abundance between the test and control samples for every IROA feature can be determined simply by integrating the peaks that arise from the 5 and 95% channels. For NMR applications, we describe two ¹³C-based approaches. For samples at natural abundance, we have developed a workflow to obtain ¹³C–¹³C and ¹³C–¹H statistical correlations using 1D ¹³C and ¹H NMR spectra. For samples that can be isotopically labeled, we describe another NMR approach to obtain direct ¹³C–¹³C spectroscopic correlations. These methods both provide extensive information about the carbon framework of compounds in the mixture for either database matching or de novo compound identification. We also discuss strategies in which ¹³C NMR can be used to identify unknown compounds from IROA experiments. By combining technologies with the same samples, we can identify important biomarkers and corresponding metabolites of interest.
Motivation: Metabolite databases provide a unique window into metabolome research allowing the most commonly searched biomarkers to be catalogued. Omic scale metabolite profiling, or metabolomics, is finding increased utility in biomarker discovery largely driven by improvements in analytical technologies and the concurrent developments in bioinformatics. However, the successful translation of biomarkers into clinical or biologically relevant indicators is limited.
Results: With the aim of improving the discovery of translatable metabolite biomarkers, we present search analytics for over one million METLIN metabolite database queries. The most common metabolites found in METLIN were cross-correlated against XCMS Online, the widely used cloud-based data processing and pathway analysis platform. Analysis of the METLIN and XCMS common metabolite data has two primary implications: these metabolites, might indicate a conserved metabolic response to stressors and, this data may be used to gauge the relative uniqueness of potential biomarkers.
Gas chromatography-mass spectrometry (GC/MS) has long been considered one of the premiere analytical tools for small molecule analysis. Recently, a number of GC/MS systems equipped with high-resolution mass analyzers have been introduced. These hybrid systems provide analysts with a new dimension of information - accurate mass measurement to the third or fourth decimal place; however, existing data processing tools do not capitalize on this information. Beyond that, GC/MS spectral reference libraries, which have been curated over the last several decades, contain almost exclusively unit resolution MS spectra making integration of accurate mass data dubious. Here we present an informatic approach, called High-Resolution Filtering (HRF), which bridges this gap. During HRF, high-resolution mass spectra are assigned putative identifications through traditional spectral matching at unit resolution. Once candidate identities have been assigned, all unique combinations of atoms from these candidate precursors are generated and matched to m/z peaks using narrow mass tolerances. The total amount of measured signal that is annotated is used as a metric of plausibility for the presumed identification. Here we demonstrate that the HRF approach is both feasible and highly specific towards correct identifications.
This review provides an overview of two complementary approaches to identify biologically active compounds for studies in chemical ecology. The first is activity-guided fractionation and the second is metabolomics, particularly focusing on a new liquid chromatography-mass spectrometry-based method called isotopic ratio outlier analysis. To illustrate examples using these approaches, we review recent experiments using Caenorhabditis elegans and related free-living nematodes.
© The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.
Robust quantification of analytes is a prerequisite for meaningful metabolomics experiments. In non-targeted metabolomics it is still hard to compare measurements across multiple batches or instruments. For targeted analyses isotope dilution mass spectrometry is used to provide a robust normalization reference. Here, we present an approach that allows for the automated semi-quantification of metabolites relative to a fully stable isotope-labeled metabolite extract. Unlike many previous approaches, we include both identified and unidentified compounds in the data analysis. The internal standards are detected in an automated manner using the non-targeted tracer fate detection algorithm. The ratios of the light and heavy form of these compounds serve as a robust measure to compare metabolite levels across different mass spectrometric platforms. As opposed to other methods which require high resolution mass spectrometers, our methodology works with low resolution mass spectrometers as commonly used in gas chromatography electron impact mass spectrometry (GC-EI-MS)-based metabolomics. We demonstrate the validity of our method by analyzing compound levels in different samples and show that it outperforms conventional normalization approaches in terms of intra- and inter-instrument reproducibility. We show that a labeled yeast metabolite extract can also serve as a reference for mammalian metabolite extracts where complete stable isotope labeling is hard to achieve.
Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.
Metabolic phenotyping has provided important biomarker findings, which, unfortunately, are rarely replicated across different sample sets due to the variations from different analytical and clinical protocols used in the studies. To date, very few metabolic hallmarks in a given cancer type have been confirmed and validated by use of a metabolomic approach and other clinical modalities. Here, we report a metabolomics study to identify potential metabolite biomarkers of colorectal cancer (CRC) with potential theranostic value.
Gas chromatography-Time-of-Flight mass spectrometry (GC-TOFMS) based metabolomics was used to analyze 376 surgical specimens, which were collected from 4 independent cohorts of CRC patients at 3 hospitals located in China and City of Hope Comprehensive Cancer Center in the US. Differential metabolites were identified and evaluated as potential prognostic markers. A targeted transcriptomic analysis of 29 CRC and 27 adjacent non-tumor tissues was applied to analyze the gene expression levels for key enzymes associated with these shared metabolites.
A panel of 15 significantly altered metabolites was identified, which demonstrates the ability to predict the rate of recurrence and survival for patients after surgery and chemotherapy. The targeted transcriptomic analysis suggests that the differential expression of these metabolites is due to robust metabolic adaptations in cancer cells to increased oxidative stress as well as demand for energy, and macromolecular substrates for cell growth and proliferation.
These CRC patients, despite their varied genetic background, mutations, pathological stages, and geographical locations, shared a metabolic signature that is of great prognostic and therapeutic potential.
Metabolite profiling is commonly performed by GC–MS of methoximated trimethylsilyl derivatives. The popularity of this technique
owes much to the robust, library searchable spectra produced by electron ionization (EI). However, due to extensive fragmentation,
EI spectra of trimethylsilyl derivatives are commonly dominated by trimethylsilyl fragments (e.g. m/z 73 and 147) and higher m/z fragment ions with structural information are at low abundance. Consequently different metabolites can have similar EI spectra,
and this presents problems for identification of “unknowns” and the detection and deconvolution of overlapping peaks. The
aim of this work is to explore use of positive chemical ionization (CI) as an adjunct to EI for GC–MS metabolite profiling.
Two reagent gases differing in proton affinity (CH4 and NH3) were used to analyse 111 metabolite standards and extracts from plant samples. NH3-CI mass spectra were simple and generally dominated by [MH]+ and/or the adduct [M+NH4]+. For the 111 metabolite standards, m/z 73 and 147 were less than 3% of basepeak in NH3-CI and less than 30% of basepeak in CH4-CI. With CH4-CI, [MH]+ was generally present but at lower relative abundance than for NH3-CI. CH4-CI spectra were commonly dominated by losses of CH4 [M+1-16]+, 1–3 TMSOH [M+1-nx90]+, and combinations of CH4 and TMSOH losses [M+1-nx90-16]+. CH4-CI and NH3-CI mass spectra are presented for 111 common metabolites, and CI is used with real samples to help identify overlapping peaks
and aid identification via determination of the pseudomolecular ion with NH3-CI and structural information with CH4-CI.
Diabesity has become a popular term to describe the specific form of diabetes that develops late in life and is associated with obesity. While there is a correlation between diabetes and obesity, the association is not universally predictive. Defining the metabolic characteristics of obesity that lead to diabetes, and how obese individuals who develop diabetes different from those who do not, are important goals. The use of large-scale omics analyses (e.g., metabolomic, proteomic, transcriptomic, and lipidomic) of diabetes and obesity may help to identify new targets to treat these conditions. This report discusses how various types of omics data can be integrated to shed light on the changes in metabolism that occur in obesity and diabetes.
>Metabolomics or biochemical profiling is a fast emerging science; however, there are still many associated bottlenecks to overcome before measurements will be considered robust. Advances in MS resolution and sensitivity, ultra pressure LC-MS, ESI, and isotopic approaches such as flux analysis and stable-isotope dilution, have made it easier to quantitate biochemicals. The digitization of mass spectrometers has simplified informatic aspects. However, issues of analytical variability, ion suppression and metabolite identification still plague metabolomics investigators. These hurdles need to be overcome for accurate metabolite quantitation not only for in vitro systems, but for complex matrices such as biofluids and tissues, before it is possible to routinely identify biomarkers that are associated with the early prediction and diagnosis of diseases. In this report, we describe a novel isotopic-labeling method that uses the creation of distinct biochemical signatures to eliminate current bottlenecks and enable accurate metabolic profiling.
Alternative metabolic pathways inside a cell can be deduced using stable isotopically labeled substrates. One prerequisite is accurate measurement of the labeling pattern of targeted metabolites. Experiments are generally limited to the use of single-element isotopes, mainly 13C. Here, we demonstrate the application of direct infusion nanospray, ultrahigh-resolution Fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) for metabolic studies using differently labeled elemental isotopes simultaneously—i.e., 13C and 15N—in amino acids of a total protein hydrolysate. The optimized strategy for the analysis of metabolism by a hybrid linear ion trap-FTICR-MS comprises the collection of multiple adjacent selected ion monitoring scans. By limiting both the width of the mass range and the number of ions entering the ICR cell with automated gain control, sensitive measurements of isotopologue distribution were possible without compromising mass accuracy and isotope intensity mapping. The required mass-resolving power of more than 60,000 is only achievable on a routine basis by FTICR and Orbitrap mass spectrometers. Evaluation of the method was carried out by comparison of the experimental data to the natural isotope abundances of selected amino acids and by comparison to GC/MS results obtained from a labeling experiment with 13C-labeled glucose. The developed method was used to shed light on the complexity of the yeast Saccharomyces cerevisiae carbon–nitrogen co-metabolism by administering both 13C-labeled glucose and 15N-labeled alanine. The results indicate that not only glutamate but also alanine acts as an amino donor during alanine and valine synthesis. Metabolic studies using FTICR-MS can exploit new possibilities by the use of multiple-labeled elemental isotopes.
Figure
Analysis of carbon and nitrogen co-metabolism in yeast by nanospray-FTICR-MS determination of 13C and 15N incorporation into proteinogenic amino acids
Driven by the advent of metabolomics, recent years have seen renewed interest in the investigation of yeast metabolism. Here we provide a practical guide to metabolomic analysis of yeast using liquid chromatography-mass spectrometry (LC-MS). We begin with background on LC-MS and its utility in studying yeast metabolism. We then describe key issues involved at each step of a typical yeast metabolomics experiment: in experimental design, cell culture, metabolite extraction, LC-MS, and data processing and analysis. Throughout, we highlight interdependencies between the steps that are relevant to developing an integrated workflow which effectively leverages LC-MS to reveal yeast biology.
Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at http://gmd.mpimp-golm.mpg.de/. All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities.
At least two independent parameters are necessary for compound identification in metabolomics. We have compiled 2 212 electron impact mass spectra and retention indices for quadrupole and time-of-flight gas chromatography/mass spectrometry (GC/MS) for over 1000 primary metabolites below 550 Da, covering lipids, amino acids, fatty acids, amines, alcohols, sugars, amino-sugars, sugar alcohols, sugar acids, organic phosphates, hydroxyl acids, aromatics, purines, and sterols as methoximated and trimethylsilylated mass spectra under electron impact ionization. Compounds were selected from different metabolic pathway databases. The structural diversity of the libraries was found to be highly overlapping with metabolites represented in the BioMeta/KEGG pathway database using chemical fingerprints and calculations using Instant-JChem. In total, the FiehnLib libraries comprised 68% more compounds and twice as many spectra with higher spectral diversity than the public Golm Metabolite Database. A range of unique compounds are present in the FiehnLib libraries that are not comprised in the 4345 trimethylsilylated spectra of the commercial NIST05 mass spectral database. The libraries can be used in conjunction with GC/MS software but also support compound identification in the public BinBase metabolomic database that currently comprises 5598 unique mass spectra generated from 19,032 samples covering 279 studies of 47 species (plants, animals, and microorganisms).
Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for the identification and quantification of trace chemicals in complex mixtures. When complex samples are analyzed by GC-MS it is common to observe co-elution of two or more components, resulting in an overlap of signal peaks observed in the total ion chromatogram. In such situations manual signal analysis is often the most reliable means for the extraction of pure component signals; however, a systematic manual analysis over a number of samples is both tedious and prone to error. In the past 30 years a number of computational approaches were proposed to assist in the process of the extraction of pure signals from co-eluting GC-MS components. This includes empirical methods, comparison with library spectra, eigenvalue analysis, regression and others. However, to date no approach has been recognized as best, nor accepted as standard. This situation hampers general GC-MS capabilities, and in particular has implications for the development of robust, high-throughput GC-MS analytical protocols required in metabolic profiling and biomarker discovery. Here we first discuss the nature of GC-MS data, and then review some of the approaches proposed for the extraction of pure signals from co-eluting components. We summarize and classify different approaches to this problem, and examine why so many approaches proposed in the past have failed to live up to their full promise. Finally, we give some thoughts on the future developments in this field, and suggest that the progress in general computing capabilities attained in the past two decades has opened new horizons for tackling this important problem.
Multiparallel analyses of mRNA and proteins are central to today's functional genomics initiatives. We describe here the use of metabolite profiling as a new tool for a comparative display of gene function. It has the potential not only to provide deeper insight into complex regulatory processes but also to determine phenotype directly. Using gas chromatography/mass spectrometry (GC/MS), we automatically quantified 326 distinct compounds from Arabidopsis thaliana leaf extracts. It was possible to assign a chemical structure to approximately half of these compounds. Comparison of four Arabidopsis genotypes (two homozygous ecotypes and a mutant of each ecotype) showed that each genotype possesses a distinct metabolic profile. Data mining tools such as principal component analysis enabled the assignment of "metabolic phenotypes" using these large data sets. The metabolic phenotypes of the two ecotypes were more divergent than were the metabolic phenotypes of the single-loci mutant and their parental ecotypes. These results demonstrate the use of metabolite profiling as a tool to significantly extend and enhance the power of existing functional genomics approaches.
Abstract Background Metabolomic studies are targeted at identifying and quantifying all metabolites in a given biological context. Among the tools used for metabolomic research, mass spectrometry is one of the most powerful tools. However, metabolomics by mass spectrometry always reveals a high number of unknown compounds which complicate in depth mechanistic or biochemical understanding. In principle, mass spectrometry can be utilized within strategies of de novo structure elucidation of small molecules, starting with the computation of the elemental composition of an unknown metabolite using accurate masses with errors
Structure elucidation of unknown small molecules by mass spectrometry is a challenge despite advances in instrumentation. The first crucial step is to obtain correct elemental compositions. In order to automatically constrain the thousands of possible candidate structures, rules need to be developed to select the most likely and chemically correct molecular formulas.
An algorithm for filtering molecular formulas is derived from seven heuristic rules: (1) restrictions for the number of elements, (2) LEWIS and SENIOR chemical rules, (3) isotopic patterns, (4) hydrogen/carbon ratios, (5) element ratio of nitrogen, oxygen, phosphor, and sulphur versus carbon, (6) element ratio probabilities and (7) presence of trimethylsilylated compounds. Formulas are ranked according to their isotopic patterns and subsequently constrained by presence in public chemical databases. The seven rules were developed on 68,237 existing molecular formulas and were validated in four experiments. First, 432,968 formulas covering five million PubChem database entries were checked for consistency. Only 0.6% of these compounds did not pass all rules. Next, the rules were shown to effectively reducing the complement all eight billion theoretically possible C, H, N, S, O, P-formulas up to 2000 Da to only 623 million most probable elemental compositions. Thirdly 6,000 pharmaceutical, toxic and natural compounds were selected from DrugBank, TSCA and DNP databases. The correct formulas were retrieved as top hit at 80-99% probability when assuming data acquisition with complete resolution of unique compounds and 5% absolute isotope ratio deviation and 3 ppm mass accuracy. Last, some exemplary compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and by gas chromatography-time of flight mass spectrometry. In each case, the correct formula was ranked as top hit when combining the seven rules with database queries.
The seven rules enable an automatic exclusion of molecular formulas which are either wrong or which contain unlikely high or low number of elements. The correct molecular formula is assigned with a probability of 98% if the formula exists in a compound database. For truly novel compounds that are not present in databases, the correct formula is found in the first three hits with a probability of 65-81%. Corresponding software and supplemental data are available for downloads from the authors' website.
Multivariate resolution technique is a set of mathematical tools that uncovers the underlying profiles from a set of measurements of time evolving chemical systems. This technique was proposed for resolving the overlapping GC-MS peaks into pure chromatogram and mass spectra. In this paper, several common resolution chemometric techniques in GC-MS resolution such as mean field-independent component analysis (MF-ICA), multivariate curve resolution-alternating least squares (MCR-ALS), and multivariate curve resolution-objective function minimization (MCR-FMIN) were investigated. The obtained solutions using chemometric methods are assessed by lack of fit (LOF) and R(2). Results show that all solutions by fulfilling the same constraints have same performance in resolving high overlapping peaks. Also, the differences obtained in each case should be related to the unresolved rotational ambiguity. Among the different ambiguities such as intensity, permutation and rotation in resolution methods, rotational ambiguity is the most difficult and critical one. Because of rotational ambiguity, there is a set of feasible MCR solutions, which explain equally well the observed experimental data, and fulfill sufficiently the imposed constraints of the system. So in these methods, a range of feasible solutions exist. The rotational ambiguities of the profiles are a challenging fact which complicates the development of stable and universal self-modeling curve resolution (SMCR) algorithms. The relative component contribution (RCC) function values for the component profiles obtained by the different methods are calculated by MCR-BANDS. The values of RCC for these three methods are equivalent. Rotational ambiguities of the solutions of SMCR methods can be reduced by applying suitable constraints. The obtained results show, using data sets, which are arranged in a single augmented data matrix could be the best solution for reducing or removing of rotational ambiguity.
Identification of unknown compounds is of critical importance in GC/MS applications (metabolomics, environmental toxin identification, sports doping, petroleomics, and biofuel analysis, among many others) and remains a technological challenge. Derivation of elemental composition is the first step to determining the identity of an unknown compound by MS, for which high accuracy mass and isotopomer distribution measurements are critical. Here, we report on the development of a dedicated, applications-grade GC/MS employing an Orbitrap mass analyzer, the GC/Quadrupole-Orbitrap. Built from the basis of the benchtop Orbitrap LC/MS, the GC/Quadrupole-Orbitrap maintains the performance characteristics of the Orbitrap, enables quadrupole-based isolation for sensitive analyte detection, and includes numerous analysis modalities to facilitate structural elucidation. We detail the design and construction of the instrument, discuss its key figures-of-merit, and demonstrate its performance for the characterization of unknown compounds and environmental toxins.
Identification of unknown peaks in gas chromatography/mass spectrometry (GC/MS)-based discovery metabolomics remains challenging. Identification of these peaks is necessary to permit discovery of novel or unexpected metabolites that may elucidate disease processes and/or further our understanding of how genotypes relate to phenotypes. Here, we introduce two new technologies and an analytical workflow that can facilitate the identification of unknown peaks. First, we report on a GC/quadrupole-Orbitrap mass spectrometer that provides high mass accuracy, high resolution, and high sensitivity analyte detection. Second, with an "intelligent" data-dependent algorithm, termed molecular-ion directed acquisition (MIDA), we maximize the information content generated from unsupervised tandem MS (MS/MS) and selected ion monitoring (SIM) by directing the MS to target the ions of greatest information content, that is, the most-intact ionic species. We combine these technologies with 13C- and 15N-metabolic labeling, multiple derivatization and ionization types, and heuristic filtering of candidate elemental compositions to achieve: 1) MS/MS spectra of intact ion species for structural elucidation, 2) knowledge of carbon and nitrogen atom content for every ion in MS and MS/MS spectra, 3) relative quantification between alternatively labeled samples, and 4) unambiguous annotation of elemental composition.
We demonstrate the global metabolic analysis of Caenorhabditis elegans stress responses using a mass spectrometry-based technique called Isotopic Ratio Outlier Analysis (IROA). In an IROA protocol, control and experimental samples are isotopically labeled with 95% and 5% 13C, and the two sample populations are mixed together for uniform extraction, sample preparation, and LC-MS analysis. This labeling strategy provides several advantages over conventional approaches: 1) compounds arising from biosynthesis are easily distinguished from artifacts, 2) errors from sample extraction and preparation are minimized because the control and experiment are combined into a single sample, 3) measurement of both the molecular weight and the exact number of carbon atoms in each molecule provides extremely accurate molecular formulae, and 4) relative concentrations of all metabolites are easily determined. A heat shock perturbation was conducted on C. elegans to demonstrate this approach. We identified many compounds that significantly changed upon heat shock, including many compounds from the purine metabolism pathway, which we use to demonstrate the approach. The metabolomic response information for C. elegans provided by IROA may be interpreted in the context of a wealth of genetic and proteomic information available. Furthermore, the IROA protocol can be applied to any organism that can be isotopically labeled, making it a powerful new tool in a global metabolomics pipeline.
ADAP-GC 2.0 has been developed to deconvolute coeluting metabolites that frequently exist in real biological samples of metabolomics studies. Deconvolution is based on a chromatographic model peak approach that combines five metrics of peak qualities for constructing/selecting model peak features. Prior to deconvolution, ADAP-GC 2.0 takes raw mass spectral data as input, extracts ion chromatograms for all the observed masses, and detects chromatographic peak features. After deconvolution, it aligns components across samples and exports the qualitative and quantitative information of all of the observed components. Centered on the deconvolution, the entire data analysis workflow is fully automated. ADAP-GC 2.0 has been tested using three different types of samples. The testing results demonstrate significant improvements of ADAP-GC 2.0, compared to the previous ADAP 1.0, to identify and quantify metabolites from gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS) data in untargeted metabolomics studies.
One of the major obstacles in metabolomics is the identification of unknown metabolites. We tested constraints for reidentifying the correct structures of 29 known metabolite peaks from GCT premier accurate mass chemical ionization GC-TOF mass spectrometry data without any use of mass spectral libraries. Correct elemental formulas were retrieved within the top-3 hits for most molecular ion adducts using the "Seven Golden Rules" algorithm. An average of 514 potential structures per formula was downloaded from the PubChem chemical database and in-silico-derivatized using the ChemAxon software package. After chemical curation, Kovats retention indices (RI) were predicted for up to 747 potential structures per formula using the NIST MS group contribution algorithm and corrected for contribution of trimethylsilyl groups using the Fiehnlib RI library. When matching the range of predicted RI values against the experimentally determined peak retention, all but three incorrect formulas were excluded. For all remaining isomeric structures, accurate mass electron ionization spectra were predicted using the MassFrontier software and scored against experimental spectra. Using a mass error window of 10 ppm for fragment ions, 89% of all isomeric structures were removed and the correct structure was reported in 73% within the top-5 hits of the cases.
Many metabolomic applications use gas chromatography/mass spectrometry (GC/MS) under standard 70 eV electron ionization (EI) parameters. However, the abundance of molecular ions is often extremely low, impeding the calculation of elemental compositions for the identification of unknown compounds. On changing the beam-steering voltage of the ion source, the relative abundances of molecular ions at 70 eV EI were increased up to ten-fold for alkanes, fatty acid methyl esters and trimethylsilylated metabolites, concomitant with 2-fold absolute increases in ion intensities. We have compared the abundance, mass accuracy and isotope ratio accuracy of molecular species in EI with those in chemical ionization (CI) with methane as reagent gas under high-mass tuning. Thirty-three peaks of a diverse set of trimethylsilylated metabolites were analyzed in triplicate, resulting in 342 ion species ([M+H](+), [M-CH(3)](+) for CI and [M](+.), [M-CH(3)](+.) for EI). On average, CI yielded 8-fold more intense molecular species than EI. Using internal recalibration, average mass errors of 1.8 +/- 1.6 mm/z units and isotope ratio errors of 2.3 +/- 2.0% (A+1/A ratio) and 1.7 +/- 1.8% (A+2/A ratio) were obtained. When constraining lists of calculated elemental compositions by chemical and heuristic rules using the Seven Golden Rules algorithm and PubChem queries, the correct formula was retrieved as top hit in 60% of the cases and within the top-3 hits in 80% of the cases.
After our serum metabonomic study of colorectal cancer (CRC) patients recently published in J. Proteome Res., we profiled urine metabolites from the same group of CRC patients (before and after surgical operation) and 63 age-matched healthy volunteers using gas chromatography-mass spectrometry (GC-MS) in conjunction with a multivariate statistics technique. A parallel metabonomic study on a 1,2-dimethylhydrazine (DMH)-treated Sprague-Dawley rat model was also performed to identify significantly altered metabolites associated with chemically induced precancerous colorectal lesion. The orthogonal partial least-squares-discriminant analysis (OPLS-DA) models of metabonomic results demonstrated good separations between CRC patients or DMH-induced model rats and their healthy counterparts. The significantly increased tryptophan metabolism, and disturbed tricarboxylic acid (TCA) cycle and the gut microflora metabolism were observed in both the CRC patients and the rat model. The urinary metabolite profile of postoperative CRC subjects altered significantly from that of the preoperative stage. The significantly down-regulated gut microflora metabolism and TCA cycle were observed in postoperative CRC subjects, presumably due to the colon flush involved in the surgical procedure and weakened physical conditions of the patients. The expression of 5-hydroxytryptophan significantly decreased in postsurgery samples, suggesting a recovered tryptophan metabolism toward healthy state. Abnormal histamine metabolism and glutamate metabolism were found only in the urine samples of CRC patients, and the abnormal polyamine metabolism was found only in the rat urine. This study assessed the important metabonomic variations in urine associated with CRC and, therefore, provided baseline information complementary to serum/plasma and tissue metabonomics for the complete elucidation of the underlying metabolic mechanisms of CRC.
Colorectal carcinogenesis involves the overexpression of many immediate-early response genes associated with growth and inflammation, which significantly alters downstream protein synthesis and small-molecule metabolite production. We have performed a serum metabolic analysis to test the hypothesis that the distinct metabolite profiles of malignant tumors are reflected in biofluids. In this study, we have analyzed the serum metabolites from 64 colorectal cancer (CRC) patients and 65 healthy controls using gas chromatography time-of-flight mass spectrometry (GC-TOFMS) and Acquity ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (Acquity UPLC-QTOFMS). Orthogonal partial least-squares discriminate analysis (OPLS-DA) models generated from GC-TOFMS and UPLC-QTOFMS metabolic profile data showed robust discrimination from CRC patients and healthy controls. A total of 33 differential metabolites were identified using these two analytical platforms, five of which were detected in both instruments. These metabolites potentially reveal perturbation of glycolysis, arginine and proline metabolism, fatty acid metabolism and oleamide metabolism, associated with CRC morbidity. These results suggest that serum metabolic profiling has great potential in detecting CRC and helping to understand its underlying mechanisms.
This protocol provides a method for quantitating the intracellular concentrations of endogenous metabolites in cultured cells. The cells are grown in stable isotope-labeled media to near-complete isotopic enrichment and then extracted in organic solvent containing unlabeled internal standards in known concentrations. The ratio of endogenous metabolite to internal standard in the extract is determined using mass spectrometry (MS). The product of this ratio and the unlabeled standard amount equals the amount of endogenous metabolite present in the cells. The cellular concentration of the metabolite can then be calculated on the basis of intracellular volume of the extracted cells. The protocol is exemplified using Escherichia coli and primary human fibroblasts fed uniformly with (13)C-labeled carbon sources, with detection of (13)C-assimilation by liquid chromatography-tandem MS. It enables absolute quantitation of several dozen metabolites over approximately 1 week of work.
Mass isotopomer distribution analysis (MIDA) is a technique for measuring biosynthesis and turnover of polymers in vivo. A stable isotopically enriched precursor is administered, and the relative abundances of different mass isotopomers in the polymer of interest are measured by mass spectrometry (MS). By comparison of statistical distributions predicted from the binomial or multinomial expansion to the pattern of excess isotopomer frequencies observed in the polymer, the enrichment of the biosynthetic precursor subunits (p) for newly synthesized polymers is calculated. MIDA thereby provides a solution to the problem of determining the isotope content in the actual precursor molecules that entered a particular polymeric product (the "true" precursor). The fraction of polymer molecules in a mixture that were newly synthesized during an isotopic experiment (fractional synthesis) can then be calculated. We describe some mathematical characteristics of MIDA and point out certain advantageous features. For example, mathematical estimates of p remain valid even if there does not exist a single anatomic or functional precursor pool. The interpretation of decay curves of endogenously labeled polymers may be improved by the use of higher mass isotopomers, which better fulfill the assumption of flash labeling. By combining fractional synthesis values with rate constants of decay, absolute endogenous synthesis rates can be calculated. Thus, by using probability logic combined with MS analysis, MIDA allows dynamic measurements to be made through analyses on a polymer alone during both isotopic incorporation and decay phases. The method has been applied to fatty acids, cholesterol, and glucose and is potentially applicable to nucleic acids, porphyrins, perhaps proteins, and many other classes of polymers.
Important syntheses in living systems occur by condensation reactions of the type nA----1B (where n is the number of A molecules needed to synthesize 1 molecule of B). Quantitative relationships for estimating the rate of synthesis of B from radioactive and stable isotope tracers are compared. With radioisotope tracers, only a single quantity is detected, the amount of radioactivity in B. In contrast, isotopes of varying mass produce multiple mass isotopomers B that are detected using mass spectrometry. The analysis demonstrates that the rate of synthesis of B is identifiable from stable isotope data but not from radioisotope data. This results because the isotopomer distribution of B at any time after tracer addition is a function of only the multinomial distribution representing the synthesis of B from n molecules of A and two parameters representing the fractional fluxes of isotopically enriched molecules to the sampled compartment of B. The model considers the possibility that the sampled compartment of B may not reach isotopic steady state during the experiment. A graphical method for obtaining initial estimates of the two parameters is presented.
Unknown compounds in polar fractions of Arabidopsis thaliana crude leaf extracts were identified on the basis of calculations of elemental compositions obtained from gas chromatography/low-resolution quadrupole mass spectrometric data. Plant metabolites were methoximated and silylated prior to analysis. All known peaks were used as internal references to construct polynomial recalibration curves of from raw mass spectrometric data. Mass accuracies of 0.005 +/- 0.003 amu and isotope ratio errors of 0.5 +/- 0.3% (A + 1/A), respectively, 0.3 +/- 0.2% (A + 2/A), could be achieved. Both masses and isotope ratios were combined when the elemental compositions of unknown peaks were calculated. After calculation, compound identities were elucidated by searching metabolic databases, interpreting spectra, and, finally, by comparison with reference compounds. Sum formulas of more than 70 peaks were determined throughout single GC/MS chromatograms. Exact masses were confirmed by high-resolution mass spectrometric data. More than 15 uncommon plant metabolites were identified, some of which are novel in Arabidopsis, such as tartronate semialdehyde, citramalic acid, allothreonine, or glycolic amide.
First, we report the application of stable isotope dilution theory in metabolome characterization of aerobic glucose limited chemostat culture of S. cerevisiae CEN.PK 113-7D using liquid chromatography-electrospray ionization MS/MS (LC-ESI-MS/MS). A glucose-limited chemostat culture of S. cerevisiae was grown to steady state at a specific growth rate (mu)=0.05 h(-1) in a medium containing only naturally labeled (99% U-12C, 1% U-13C) carbon source. Upon reaching steady state, defined as 5 volume changes, the culture medium was switched to chemically identical medium except that the carbon source was replaced with 100% uniformly (U) 13C labeled stable carbon isotope, fed for 4 h, with sampling every hour. We observed that within a period of 1 h approximately 80% of the measured glycolytic metabolites were U-13C-labeled. Surprisingly, during the next 3 h no significant increase of the U-13C-labeled metabolites occurred. Second, we demonstrate for the first time the LC-ESI-MS/MS-based quantification of intracellular metabolite concentrations using U-13C-labeled metabolite extracts from chemostat cultivated S. cerevisiae cells, harvested after 4 h of feeding with 100% U-13C-labeled medium, as internal standard. This method is hereby termed "Mass Isotopomer Ratio Analysis of U-13C Labeled Extracts" (MIRACLE). With this method each metabolite concentration is quantified relative to the concentration of its U-13C-labeled equivalent, thereby eliminating drawbacks of LC-ESI-MS/MS analysis such as nonlinear response and matrix effects and thus leads to a significant reduction of experimental error and work load (i.e., no spiking and standard additions). By coextracting a known amount of U-13C labeled cells with the unlabeled samples, metabolite losses occurring during the sample extraction procedure are corrected for.
A novel method was developed for the quantitative analysis of the microbial metabolome using a mixture of fully uniformly (U) (13)C-labeled metabolites as internal standard (IS) in the metabolite extraction procedure the subsequent liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) analysis. This mixture of fully U (13)C-labeled metabolites was extracted from biomass of Saccharomyces cerevisiae cultivated in a fed-batch fermentation on fully U (13)C-labeled substrates. The obtained labeled cell extract contained, in principle, the whole yeast metabolome, allowing the quantification of any intracellular metabolite of interest in S. cerevisiae. We have applied the labeled cell extract as IS in the analysis of glycolytic and tricarboxylic acid (TCA) cycle intermediates in S. cerevisiae sampled in both steady-state and transient conditions following a glucose pulse. The use of labeled IS effectively reduced errors due to variations occurring in the analysis and sample processing. As a result, the linearity of calibration lines and the precision of measurements were significantly improved. Coextraction of the labeled cell extract with the samples also eliminates the need to perform elaborate recovery checks for each metabolite to be analyzed. In conclusion, the method presented leads to less workload, more robustness, and a higher precision in metabolome analysis.
Gas chromatography-mass spectrometry (GC-MS) based metabolite profiling of biological samples is one of the key technologies for metabolite profiling and substantially contributes to our understanding of the metabolome. While the technology is in increasing use it is challenged with novel demands. Increasing the number of metabolite identifications within existing profiling platforms is prerequisite for a substantially improved scope of profiling studies. Clear, reproducible strategies for metabolite identification and exchange of identifications between laboratories will facilitate further developments, such as the extension of profiling technologies towards metabolic signals and other technically demanding trace compound analysis. Using GC-MS technology as an example the concept of mass spectral tags (MSTs) is presented. A mass spectral tag is defined by the chemometric properties, molecular mass to charge ratio, chromatographic retention index and an induced mass fragmentation pattern such as an electron impact mass spectrum (EI-MS) or secondary fragmentation (MS(2)). These properties if properly documented will allow identification of hitherto non-identified MSTs by standard addition experiments of authenticated reference substances even years after first MST description. Strategies are discussed for MST identification and enhanced MST characterization utilizing experimental schemes such as in vivo stable isotope labelling of whole organisms and open access information distribution, for example the GMG internet platform initiated in 2004 (GMD, http://www.csbdb.mpimp-golm.mpg.de/gmd.html).
This work describes an approach to differential metabolomics that involves stable isotope labeling for relative quantification as part of sample analysis by two-dimensional gas chromatography/mass spectrometry (GCxGC/MS). The polar metabolome in control and experimental samples was extracted and differentially derivatized using isotopically light and heavy (D6) forms of the silylation reagent N-methyl-N-tert-butyldimethylsilyl)trifluoroacetamide (MTBSTFA). MTBSTFA derivatives are of much greater hydrolytic stability than the more common trimethylsilyl derivatives, thus diminishing the possibility of isotopomer scrambling during GC analysis. Subsequent to derivatization with MTBSTFA, differentially labeled samples were mixed and analyzed by GCxGC/MS. Metabolites were identified, and the isotope ratio of isotopomers was quantified. The method was tested using three classes of metabolites; amino acids, fatty acids, and organic acids. The relative concentration of isotopically labeled metabolites was determined by isotope ratio analysis. The accuracy and precision, respectively, in quantification of standard mixtures was 9.5 and 4.77% for the 16 amino acids, 9.7 and 2.83% for the mixture of 19 fatty acids, and 14 and 4.53% for the 20 organic acids. Suitability of the method for the examination of complex samples was demonstrated in analyses of the spiked blood serum samples. This differential isotope coding method proved to be an effective means to compare the concentration of metabolites between two samples simultaneously.
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