Data correction strategy for metabolomics analysis using gas chromatography–mass spectrometry

Metabolomics: Implication in cardiovascular research and diseases

Cellular metabolism influences all aspects of cellular function and is crucial for overall organismal health. Metabolic disorders related to cardiovascular health can lead to cardiovascular diseases (CVDs). Moreover, associated comorbidities may also damage cardiovascular metabolism, exacerbating CVD and perpetuating a vicious cycle. Given the prominent role of metabolic alterations in CVD, metabolomics has emerged as an imperative technique enabling a comprehensive assessment of metabolites and metabolic architecture within the body. Metabolite profile and metabolic pathways help to deepen and broaden our understanding of complex genomic landscape and pathophysiology of CVD. Here in this review, we aim to overview the experimental and clinical applications of metabolomics in pathogenesis, diagnosis, prognosis, and management of various CVD plus future perspectives and limitations.

Matrix Effects in GC–MS Profiling of Common Metabolites after Trimethylsilyl Derivatization

Metabolite profiling using gas chromatography coupled to mass spectrometry (GC–MS) is one of the most frequently applied and standardized methods in research projects using metabolomics to analyze complex samples. However, more than 20 years after the introduction of non-targeted approaches using GC–MS, there are still unsolved challenges to accurate quantification in such investigations. One particularly difficult aspect in this respect is the occurrence of sample-dependent matrix effects. In this project, we used model compound mixtures of different compositions to simplify the study of the complex interactions between common constituents of biological samples in more detail and subjected those to a frequently applied derivatization protocol for GC–MS analysis, namely trimethylsilylation. We found matrix effects as signal suppression and enhancement of carbohydrates and organic acids not to exceed a factor of ~2, while amino acids can be more affected. Our results suggest that the main reason for our observations may be an incomplete transfer of carbohydrate and organic acid derivatives during the injection process and compound interaction at the start of the separation process. The observed effects were reduced at higher target compound concentrations and by using a more suitable injection-liner geometry.

Unravelling metabolic cross-feeding in a yeast-bacteria community using 13 C-based proteomics

Cross-feeding is fundamental to the diversity and function of microbial communities. However, identification of cross-fed metabolites is often challenging due to the universality of metabolic and biosynthetic intermediates. Here, we use ¹³ C isotope tracing in peptides to elucidate cross-fed metabolites in co-cultures of Saccharomyces cerevisiae and Lactococcus lactis. The community was grown on lactose as the main carbon source with either glucose or galactose fraction of the molecule labelled with ¹³ C. Data analysis allowing for the possible mass-shifts yielded hundreds of peptides for which we could assign both species identity and labelling degree. The labelling pattern showed that the yeast utilized galactose and, to a lesser extent, lactic acid shared by L. lactis as carbon sources. While the yeast provided essential amino acids to the bacterium as expected, the data also uncovered a complex pattern of amino acid exchange. The identity of the cross-fed metabolites was further supported by metabolite labelling in the co-culture supernatant, and by diminished fitness of a galactose-negative yeast mutant in the community. Together, our results demonstrate the utility of ¹³ C-based proteomics for uncovering microbial interactions.

Some Lessons Learned on the Impact of the Storage Conditions, Syringe Wash Solvent, and the Way of GC-MS Injection on the Reproducibility of Metabolomic Studies

Metabolomics based on two-dimensional gas chromatography coupled with mass spectrometry is making high demands on accuracy at all stages of sample preparation, up to the storage and injection into the analytical system. In high sample flow conditions, good repeatability in peak areas and a list of detectable metabolites is sometimes challenging to obtain. In this research, we successfully obtained good repeatability for the peak areas of MSFTA-derivatives of 29 core blood plasma metabolites. Six different strategies of storage and injection were investigated and evaluated for the reproducibility of the obtained data. As the essential factors, we considered popular GC-MS syringe washing solvents (methanol and pyridine); storage conditions (freshly prepared samples and stored for 24 h in ambient temperature or in the refrigerator); scheme of injection (one injection per intact vial or three sequential injections per vial). Our GC×GC-MS results demonstrated that the usage of pyridine as a syringe wash solvent and triple injecting the sample from the same vial was the most appropriate for minimizing the coefficient of variation (CV) of the results obtained (in general, <10%). The prolonged storage of samples does not have a noticeable effect on the change in the areas of chromatographic peaks of metabolites, although it reduces CV in some cases. These storage and injection recommendations can be used in future study protocols for the GC×GC-MS analysis of blood plasma.

Sample Preparation in Microbial Metabolomics: Advances and Challenges

Microbial metabolomics has gained significant interest as it reflects the physiological state of microorganisms. Due to the great variability of biological organisms, in terms of physicochemical characteristics and variable range of concentration of metabolites, the choice of sample preparation methods is a crucial step in the metabolomics workflow and will reflect on the quality and reliability of the results generated. The procedures applied to the preparation of microbial samples will vary according to the type of microorganism studied, the metabolomics approach (untargeted or targeted), and the analytical platform of choice. This chapter aims to provide an overview of the sample preparation workflow for microbial metabolomics, highlighting the pre-analytical factors associated with cultivation, harvesting, metabolic quenching, and extraction. Discussions focus on obtaining intracellular and extracellular metabolites. Finally, we introduced advanced sample preparation methods based on automated systems.

Optimization of a GC-MS Injection-Port Derivatization Methodology to Enhance Metabolomics Analysis Throughput in Biological Samples

Advances in metabolomics analysis and data treatment increase the knowledge of complex biological systems. One of the most used methodologies is gas chromatography-mass spectrometry (GC-MS) due to its robustness, high separation efficiency, and reliable peak identification through curated databases. However, methodologies are not standardized, and the derivatization steps in GC-MS can introduce experimental errors and take considerable time, exposing the samples to degradation. Here, we propose the injection-port derivatization (IPD) methodology to increase the throughput in plasma metabolomics analysis by GC-MS. The IPD method was evaluated and optimized for different families of metabolites (organic acids, amino acids, fatty acids, sugars, sugar phosphates, etc.) in terms of residence time, injection-port temperature, and sample/derivatization reagent ratio. Finally, the method's usefulness was validated in a study consisting of a cohort of obese patients with or without nonalcoholic steatohepatitis. Our results show a fast, reproducible, precise, and reliable method for the analysis of biological samples by GC-MS. Raw data are publicly available at MetaboLights with Study Identifier MTBLS5151.

Operation of a TCA cycle subnetwork in the mammalian nucleus

Nucleic acid and histone modifications critically depend on the tricarboxylic acid (TCA) cycle for substrates and cofactors. Although a few TCA cycle enzymes have been reported in the nucleus, the corresponding pathways are considered to operate in mitochondria. Here, we show that a part of the TCA cycle is operational also in the nucleus. Using 13C-tracer analysis, we identified activity of glutamine-to-fumarate, citrate-to-succinate, and glutamine-to-aspartate routes in the nuclei of HeLa cells. Proximity labeling mass spectrometry revealed a spatial vicinity of the involved enzymes with core nuclear proteins. We further show nuclear localization of aconitase 2 and 2-oxoglutarate dehydrogenase in mouse embryonic stem cells. Nuclear localization of the latter enzyme, which produces succinyl-CoA, changed from pluripotency to a differentiated state with accompanying changes in the nuclear protein succinylation. Together, our results demonstrate operation of an extended metabolic pathway in the nucleus, warranting a revision of the canonical view on metabolic compartmentalization.

Riboregulation of Enolase 1 activity controls glycolysis and embryonic stem cell differentiation

Differentiating stem cells must coordinate their metabolism and fate trajectories. Here, we report that the catalytic activity of the glycolytic enzyme Enolase 1 (ENO1) is directly regulated by RNAs leading to metabolic rewiring in mouse embryonic stem cells (mESCs). We identify RNA ligands that specifically inhibit ENO1's enzymatic activity in vitro and diminish glycolysis in cultured human cells and mESCs. Pharmacological inhibition or RNAi-mediated depletion of the protein deacetylase SIRT2 increases ENO1's acetylation and enhances its RNA binding. Similarly, induction of mESC differentiation leads to increased ENO1 acetylation, enhanced RNA binding, and inhibition of glycolysis. Stem cells expressing mutant forms of ENO1 that escape or hyper-activate this regulation display impaired germ layer differentiation. Our findings uncover acetylation-driven riboregulation of ENO1 as a physiological mechanism of glycolytic control and of the regulation of stem cell differentiation. Riboregulation may represent a more widespread principle of biological control.

Untargeted Metabolomics in Piper betle Leaf Extracts to Discriminate the Cultivars of Coastal Odisha, India

Betel leaf is consumed as a mouth freshener due to its characteristic flavor, aromaticity, and medicinal values. Abundance of phytochemicals in betel leaf contributes towards unique qualitative features. Screening of metabolites is quintessential for identifying flavoring betel leaves and their origin. Metabolomics presently lays emphasis on the cumulative application of gas chromatography-mass spectrometry and nuclear magnetic resonance spectroscopic approaches. Here we adopted different protocols based on the above-mentioned analytical metabolomics platform for untargeted plant metabolite profiling followed by multivariate analysis methods and a phytochemical characterization of Piper betel leaf cultivars endemic to coastal Odisha, India. Based on variation in the solvent composition, concentration of solvent, extraction temperature, and incubation periods, five extraction methods were followed in GC-MS and NMR spectroscopy of betel leaf extracts. Phytochemical similarities and differences among the species were characterized through multivariate analysis approaches. Principal component analysis, based on the relative abundance of phytochemicals, indicated that the betel cultivars could be grouped into three groups. Our results of FTIR-, GC-MS-, and NMR-based profiling combined with multivariate analyses suggest that untargeted metabolomics can play a crucial role in documenting metabolic signatures of endemic betel leaf varieties.

Mitochondrial protein import determines lifespan through metabolic reprogramming and de novo serine biosynthesis

Sustained mitochondrial fitness relies on coordinated biogenesis and clearance. Both processes are regulated by constant targeting of proteins into the organelle. Thus, mitochondrial protein import sets the pace for mitochondrial abundance and function. However, our understanding of mitochondrial protein translocation as a regulator of longevity remains enigmatic. Here, we targeted the main protein import translocases and assessed their contribution to mitochondrial abundance and organismal physiology. We find that reduction in cellular mitochondrial load through mitochondrial protein import system suppression, referred to as MitoMISS, elicits a distinct longevity paradigm. We show that MitoMISS triggers the mitochondrial unfolded protein response, orchestrating an adaptive reprogramming of metabolism. Glycolysis and de novo serine biosynthesis are causatively linked to longevity, whilst mitochondrial chaperone induction is dispensable for lifespan extension. Our findings extent the pro-longevity role of UPR^mt and provide insight, relevant to the metabolic alterations that promote or undermine survival and longevity.

Mitochondrial function is linked to lifespan. Here the authors show that inhibition of mitochondrial protein import leads to a reduction in mitochondrial abundance and extends lifespan in Caenorhabditis elegans via activation of glycolysis and de novo serine biosynthesis.

Glycolytic flux-signaling controls mouse embryo mesoderm development

How cellular metabolic state impacts cellular programs is a fundamental, unresolved question. Here, we investigated how glycolytic flux impacts embryonic development, using presomitic mesoderm (PSM) patterning as the experimental model. First, we identified fructose 1,6-bisphosphate (FBP) as an in vivo sentinel metabolite that mirrors glycolytic flux within PSM cells of post-implantation mouse embryos. We found that medium-supplementation with FBP, but not with other glycolytic metabolites, such as fructose 6-phosphate and 3-phosphoglycerate, impaired mesoderm segmentation. To genetically manipulate glycolytic flux and FBP levels, we generated a mouse model enabling the conditional overexpression of dominant active, cytoplasmic PFKFB3 (cytoPFKFB3). Overexpression of cytoPFKFB3 indeed led to increased glycolytic flux/FBP levels and caused an impairment of mesoderm segmentation, paralleled by the downregulation of Wnt-signaling, reminiscent of the effects seen upon FBP-supplementation. To probe for mechanisms underlying glycolytic flux-signaling, we performed subcellular proteome analysis and revealed that cytoPFKFB3 overexpression altered subcellular localization of certain proteins, including glycolytic enzymes, in PSM cells. Specifically, we revealed that FBP supplementation caused depletion of Pfkl and Aldoa from the nuclear-soluble fraction. Combined, we propose that FBP functions as a flux-signaling metabolite connecting glycolysis and PSM patterning, potentially through modulating subcellular protein localization.

Metabolic memory underlying minimal residual disease in breast cancer

Tumor relapse from treatment-resistant cells (minimal residual disease, MRD) underlies most breast cancer-related deaths. Yet, the molecular characteristics defining their malignancy have largely remained elusive. Here, we integrated multi-omics data from a tractable organoid system with a metabolic modeling approach to uncover the metabolic and regulatory idiosyncrasies of the MRD. We find that the resistant cells, despite their non-proliferative phenotype and the absence of oncogenic signaling, feature increased glycolysis and activity of certain urea cycle enzyme reminiscent of the tumor. This metabolic distinctiveness was also evident in a mouse model and in transcriptomic data from patients following neo-adjuvant therapy. We further identified a marked similarity in DNA methylation profiles between tumor and residual cells. Taken together, our data reveal a metabolic and epigenetic memory of the treatment-resistant cells. We further demonstrate that the memorized elevated glycolysis in MRD is crucial for their survival and can be targeted using a small-molecule inhibitor without impacting normal cells. The metabolic aberrances of MRD thus offer new therapeutic opportunities for post-treatment care to prevent breast tumor recurrence.

Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion

Adaptive laboratory evolution has proven highly effective for obtaining microorganisms with enhanced capabilities. Yet, this method is inherently restricted to the traits that are positively linked to cell fitness, such as nutrient utilization. Here, we introduce coevolution of obligatory mutualistic communities for improving secretion of fitness-costly metabolites through natural selection. In this strategy, metabolic cross-feeding connects secretion of the target metabolite, despite its cost to the secretor, to the survival and proliferation of the entire community. We thus co-evolved wild-type lactic acid bacteria and engineered auxotrophic Saccharomyces cerevisiae in a synthetic growth medium leading to bacterial isolates with enhanced secretion of two B-group vitamins, viz., riboflavin and folate. The increased production was specific to the targeted vitamin, and evident also in milk, a more complex nutrient environment that naturally contains vitamins. Genomic, proteomic and metabolomic analyses of the evolved lactic acid bacteria, in combination with flux balance analysis, showed altered metabolic regulation towards increased supply of the vitamin precursors. Together, our findings demonstrate how microbial metabolism adapts to mutualistic lifestyle through enhanced metabolite exchange.

Untargeted Plasma Metabolomics Unravels a Metabolic Signature for Tissue Sensitivity to Glucocorticoids in Healthy Subjects: Its Implications in Dietary Planning for a Healthy Lifestyle

In clinical practice, differences in glucocorticoid sensitivity among healthy subjects may influence the outcome and any adverse effects of glucocorticoid therapy. Thus, a fast and accurate methodology that could enable the classification of individuals based on their tissue glucocorticoid sensitivity would be of value. We investigated the usefulness of untargeted plasma metabolomics in identifying a panel of metabolites to distinguish glucocorticoid-resistant from glucocorticoid-sensitive healthy subjects who do not carry mutations in the human glucocorticoid receptor (NR3C1) gene. Applying a published methodology designed for the study of glucocorticoid sensitivity in healthy adults, 101 healthy subjects were ranked according to their tissue glucocorticoid sensitivity based on 8:00 a.m. serum cortisol concentrations following a very low-dose dexamethasone suppression test. Ten percent of the cohort, i.e., 11 participants, on each side of the ranking, with no NR3C1 mutations or polymorphisms, were selected, respectively, as the most glucocorticoid-sensitive and most glucocorticoid-resistant of the cohort to be analyzed and compared with untargeted blood plasma metabolomics using gas chromatography–mass spectrometry (GC–MS). The acquired metabolic profiles were evaluated using multivariate statistical analysis methods. Nineteen metabolites were identified with significantly lower abundance in the most sensitive compared to the most resistant group of the cohort, including fatty acids, sugar alcohols, and serine/threonine metabolism intermediates. These results, combined with a higher glucose, sorbitol, and lactate abundance, suggest a higher Cori cycle, polyol pathway, and inter-tissue one-carbon metabolism rate and a lower fat mobilization rate at the fasting state in the most sensitive compared to the most resistant group. In fact, this was the first study correlating tissue glucocorticoid sensitivity with serine/threonine metabolism. Overall, the observed metabolic signature in this cohort implies a worse cardiometabolic profile in the most glucocorticoid-sensitive compared to the most glucocorticoid-resistant healthy subjects. These findings offer a metabolic signature that distinguishes most glucocorticoid-sensitive from most glucocorticoid-resistant healthy subjects to be further validated in larger cohorts. Moreover, they support the correlation of tissue glucocorticoid sensitivity with insulin resistance and metabolic syndrome-associated pathways, further emphasizing the need for nutritionists and doctors to consider the tissue glucocorticoid sensitivity in dietary and exercise planning, particularly when these subjects are to be treated with glucocorticoids.

Narrative review of metabolomics in cardiovascular disease

Cardiovascular diseases are accompanied by disorders in the cardiac metabolism. Furthermore, comorbidities often associated with cardiovascular disease can alter systemic and myocardial metabolism contributing to worsening of cardiac performance and health status. Biomarkers such as natriuretic peptides or troponins already support diagnosis, prognosis and treatment of patients with cardiovascular diseases and are represented in international guidelines. However, as cardiovascular diseases affect various pathophysiological pathways, a single biomarker approach cannot be regarded as ideal to reveal optimal clinical application. Emerging metabolomics technology allows the measurement of hundreds of metabolites in biological fluids or biopsies and thus to characterize each patient by its own metabolic fingerprint, improving our understanding of complex diseases, significantly altering the management of cardiovascular diseases and possibly personalizing medicine. This review outlines current knowledge, perspectives as well as limitations of metabolomics for diagnosis, prognosis and treatment of cardiovascular diseases such as heart failure, atherosclerosis, ischemic and non-ischemic cardiomyopathy. Furthermore, an ongoing research project tackling current inconsistencies as well as clinical applications of metabolomics will be discussed. Taken together, the application of metabolomics will enable us to gain more insights into pathophysiological interactions of metabolites and disease states as well as improving therapies of patients with cardiovascular diseases in the future.

Calibration-Curve-Locking Database for Semi-Quantitative Metabolomics by Gas Chromatography/Mass Spectrometry

Calibration-Curve-Locking Databases (CCLDs) have been constructed for automatic compound search and semi-quantitative screening by gas chromatography/mass spectrometry (GC/MS) in several fields. CCLD felicitates the semi-quantification of target compounds without calibration curve preparation because it contains the retention time (RT), calibration curves, and electron ionization (EI) mass spectra, which are obtained under stable apparatus conditions. Despite its usefulness, there is no CCLD for metabolomics. Herein, we developed a novel CCLD and semi-quantification framework for GC/MS-based metabolomics. All analytes were subjected to GC/MS after derivatization under stable apparatus conditions using (1) target tuning, (2) RT locking technique, and (3) automatic derivatization and injection by a robotic platform. The RTs and EI mass spectra were obtained from an existing authorized database. A quantifier ion and one or two qualifier ions were selected for each target metabolite. The calibration curves were obtained as plots of the peak area ratio of the target compounds to an internal standard versus the target compound concentration. These data were registered in a database as a novel CCLD. We examined the applicability of CCLD for analyzing human plasma, resulting in time-saving and labor-saving semi-qualitative screening without the need for standard substances.

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.

Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community

Microbial communities often undergo intricate compositional changes yet also maintain stable coexistence of diverse species. The mechanisms underlying long-term coexistence remain unclear as system-wide studies have been largely limited to engineered communities, ex situ adapted cultures or synthetic assemblies. Here, we show how kefir, a natural milk-fermenting community of prokaryotes (predominantly lactic and acetic acid bacteria) and yeasts (family Saccharomycetaceae), realizes stable coexistence through spatiotemporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microorganisms) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a sequential manner in which early members open the niche for the followers by making available metabolites such as amino acids and lactate. Through metabolomics, transcriptomics and large-scale mapping of inter-species interactions, we show how microorganisms poorly suited for milk survive in-and even dominate-the community, through metabolic cooperation and uneven partitioning between grain and milk. Overall, our findings reveal how inter-species interactions partitioned in space and time lead to stable coexistence.

Development and validation of a UPLC-MS/MS method for the simultaneous determination of gamma-aminobutyric acid and glutamic acid in human plasma

Gamma-aminobutyric acid (GABA) and its precursor glutamic acid are important neurotransmitters. Both are also present in peripheral tissues and the circulation, where abnormal plasma concentrations have been linked to specific mental disorders. In addition to endogenous synthesis, GABA and glutamic acid can be obtained from dietary sources. An increasing number of studies suggest beneficial cardio-metabolic effects of GABA intake, and therefore GABA is being marketed as a food supplement. The need for further research into their health effects merits accurate and sensitive methods to analyze GABA and glutamic acid in plasma. To this end, an ultra-pressure liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) method was developed and validated for the quantification of GABA and glutamic acid in human plasma. Samples were prepared by a protein precipitation step and subsequent solid phase extraction using acetonitrile. Chromatographic separation was achieved on an Acquity UPLC HSS reversed phase C18 column using gradient elution. Analytes were detected using electrospray ionization and selective reaction monitoring. Standard curve concentrations for GABA ranged from 3.4 to 2500 ng/mL and for glutamic acid from 30.9 ng/mL to 22,500 ng/mL. Within- and between-day accuracy and precision were <10% in quality control samples at low, medium and high concentrations for both GABA and glutamic acid. GABA and glutamic acid were found to be stable in plasma after freeze-thaw cycles and up to 12 months of storage. The validated method was applied to human plasma from 17 volunteers. The observed concentrations ranged between 11.5 and 20.0 ng/ml and 2269 and 7625 ng/ml for respectively GABA and glutamic acid. The reported method is well suited for the measurement of plasma GABA and glutamic acid in pre-clinical or clinical studies.

Metabolic Profiling Indicates Diversity in the Metabolic Physiologies Associated With Maternal Postpartum Depressive Symptoms

Background: Postpartum depression (PPD) is a devastating disease requiring improvements in diagnosis and prevention. Blood metabolomics identifies biological markers discriminatory between women with and those without antenatal depressive symptoms. Whether this cutting-edge method can be applied to postpartum depressive symptoms merits further investigation. Methods: As a substudy within the Biology, Affect, Stress, Imagine and Cognition Study, 24 women with PPD symptom (PPDS) assessment at 6 weeks postpartum were included. Controls were selected as having a score of ≤ 6 and PPDS cases as ≥12 on the Edinburgh Postnatal Depression Scale. Blood plasma was collected at 10 weeks postpartum and analyzed with gas chromatography-mass spectrometry metabolomics. Results: Variations of metabolomic profiles within the PPDS samples were identified. One cluster showed altered kidney function, whereas the other, a metabolic syndrome profile, both previously associated with depression. Five metabolites (glycerol, threonine, 2-hydroxybutanoic acid, erythritol, and phenylalanine) showed higher abundance among women with PPDSs, indicating perturbations in the serine/threonine and glycerol lipid metabolism, suggesting oxidative stress conditions. Conclusions: Alterations in certain metabolites were associated with depressive pathophysiology postpartum, whereas diversity in PPDS physiologies was revealed. Hence, plasma metabolic profiling could be considered in diagnosis and pathophysiological investigation of PPD toward providing clues for treatment. Future studies require standardization of various subgroups with respect to symptom onset, lifestyle, and comorbidities.

Method development and validation for the quantification of organic acids in microbial samples using anionic exchange solid-phase extraction and gas chromatography-mass spectrometry

Organic acids play a key role in central metabolic functions of organisms, are crucial for understanding regulatory processes and are ubiquitous inside the cell. Therefore, quantification of these compounds provides a valuable approach for studying dynamics of metabolic processes, in particular when the organism faces changing environmental conditions. However, the extraction and analysis of organic acids can be challenging and validated methods available in this field are limited. In this study, we developed a method for the extraction and quantification of organic acids from microbial samples based on solid-phase extraction on a strong anionic exchange cartridge and gas chromatographic-mass spectrometric analysis. Full method validation was conducted to determine quality parameters of the new method. Recoveries for 12 of the 15 aromatic and aliphatic acids were between 100 and 111% and detection limits between 3 and 272 ng/mL. The ranges for the regression coefficients and process standard deviations for these compound classes were 0.9874–0.9994 and 0.04–0.69 μg/mL, respectively. Limitations were encountered when targeting aliphatic acids with hydroxy, oxo or enol ester functions. Finally, we demonstrated the applicability of the method on cell extracts of the bacterium Escherichia coli and the dinoflagellate Prorocentrum minimum.

Increased PIP3 activity blocks nanoparticle mRNA delivery

The biological pathways that affect drug delivery in vivo remain poorly understood. We hypothesized that altering cell metabolism with phosphatidylinositol (3,4,5)-triphosphate (PIP3), a bioactive lipid upstream of the metabolic pathway PI3K (phosphatidylinositol 3-kinase)/AKT/ mTOR (mammalian target of rapamycin) would transiently increase protein translated by nanoparticle-delivered messenger RNA (mRNA) since these pathways increase growth and proliferation. Instead, we found that PIP3 blocked delivery of clinically-relevant lipid nanoparticles (LNPs) across multiple cell types in vitro and in vivo. PIP3-driven reductions in LNP delivery were not caused by toxicity, cell uptake, or endosomal escape. Interestingly, RNA sequencing and metabolomics analyses suggested an increase in basal metabolic rate. Higher transcriptional activity and mitochondrial expansion led us to formulate two competing hypotheses that explain the reductions in LNP-mediated mRNA delivery. First, PIP3 induced consumption of limited cellular resources, "drowning out" exogenously-delivered mRNA. Second, PIP3 triggers a catabolic response that leads to protein degradation and decreased translation.

Reductive amination of α-Ketoglutarate in metabolite extracts results in glutamate overestimation

Artifacts due to metabolite extraction, derivatization, and detection techniques can result in aberrant observations that are not accurate representations of actual cell metabolism. Here, we show that α-ketoglutarate (α-KG) is reductively aminated to glutamate in methanol:water metabolite extracts, which introduces an artifact into metabolomics studies. We also identify pyridoxamine and urea as amine donors for α-KG to produce glutamate in methanol:water buffer in vitro, and we demonstrate that the addition of ninhydrin to the methanol:water buffer suppresses the reductive amination of α-KG to glutamate in vitro and in metabolite extracts. Finally, we calculate that glutamate levels have been overestimated by 10-50%, depending on cell line, due to α-KG reductive amination. These findings suggest that precautions to account for α-KG reductive amination should be taken for the accurate quantification of glutamate in metabolomics studies.

Analyses of Cholesterol Metabolites of Optic Nerve Using GC-MS Methods

Gas chromatography-mass spectrometry (GC-MS) is considered the gold standard for analyzing and quantifying the presence of biological compounds in tissue samples due to its high sensitivity, peak resolution, and reproducibility. In this chapter, we describe a step-by-step modified Bligh and Dyer protocol for lipid extraction from the optic nerve tissue and a procedure for GC-MS analyses of the lipid extract. These protocols are based on our experience and can be modified depending on samples and compounds of interest.

Selective and Accurate Quantification of N-Acetylglucosamine in Biotechnological Cell Samples via GC-MS/MS and GC-TOFMS

N-Acetylglucosamine is a key component of bacterial and fungal cell walls and of the extracellular matrix of animal cells. It plays a variety of roles at the cell surface structure and is under discussion to be involved in signaling pathways. The presence of a number of N-acetylhexosamine stereoisomers in samples of biological or biotechnological origin demands for dedicated high efficiency separation methods, due to identical exact mass and similar fragmentation patterns of the stereoisomers. Gas chromatography offers high sample capacity, separation efficiency, and precision under repeatability conditions of measurement, which is a necessity for the analysis of low abundant stereoisomers in biological samples. Automated online derivatization facilitates to overcome the main obstacle for the use of gas chromatography in metabolomics, namely, the derivatization of polar metabolites prior to analysis. Using alkoximation and subsequent trimethylsilylation, carbohydrates and their derivatives are known to show several derivatives, since derivatization is incomplete as well as highly matrix dependent inherent to the high number of functional groups present in carbohydrates. A method based on efficient separation of ethoximated and trimethylsilylated N-acetylglucosamines was developed. Accurate absolute quantification is enabled using biologically derived ¹³C labeled internal standards eliminating systematic errors related to sample pretreatment and analysis. Due to the lack of certified reference materials, a methodological comparison between tandem and time-of-flight mass spectrometric instrumentation was performed for mass spectrometric assessment of trueness. Both methods showed limits of detection in the lower femtomol range. The methods were applied to biological samples of Penicillium chrysogenum cultivations with different matrices revealing excellent agreement of both mass spectrometric techniques.

Blood plasma metabolic profiling of pregnant women with antenatal depressive symptoms

Antenatal depression affects ~9-19% of pregnant women and can exert persistent adverse effects on both mother and child. There is a need for a deeper understanding of antenatal depression mechanisms and the development of tools for reliable diagnosis and early identification of women at high risk. As the use of untargeted blood metabolomics in the investigation of psychiatric and neurological diseases has increased substantially, the main objective of this study was to investigate whether untargeted gas chromatography-mass spectrometry (GC-MS) plasma metabolomics in 45 women in late pregnancy, residing in Uppsala, Sweden, could indicate metabolic differences between women with and without depressive symptoms. Furthermore, seasonal differences in the metabolic profiles were explored. When comparing the profiles of cases with controls, independently of season, no differences were observed. However, seasonal differences were observed in the metabolic profiles of control samples, suggesting a favorable cardiometabolic profile in the summer vs. winter, as indicated by lower glucose and sugar acid concentrations and lactate to pyruvate ratio, and higher abundance of arginine and phosphate. Similar differences were identified between cases and controls among summer pregnancies, indicating an association between a stressed metabolism and depressive symptoms. No depression-specific differences were apparent among depressed and non-depressed women, in the winter pregnancies; this could be attributed to an already stressed metabolism due to the winter living conditions. Our results provide new insights into the pathophysiology of antenatal depression, and warrant further investigation of the use of metabolomics in antenatal depression in larger cohorts.

Oxidized alginate hydrogels with the GHK peptide enhance cord blood mesenchymal stem cell osteogenesis: A paradigm for metabolomics-based evaluation of biomaterial design

Oxidized alginate hydrogels are appealing alternatives to natural alginate due to their favourable biodegradability profiles and capacity to self-crosslink with amine containing molecules facilitating functionalization with extracellular matrix cues, which enable modulation of stem cell fate, achieve highly viable 3-D cultures, and promote cell growth. Stem cell metabolism is at the core of cellular fate (proliferation, differentiation, death) and metabolomics provides global metabolic signatures representative of cellular status, being able to accurately identify the quality of stem cell differentiation. Herein, umbilical cord blood mesenchymal stem cells (UCB MSCs) were encapsulated in novel oxidized alginate hydrogels functionalized with the glycine-histidine-lysine (GHK) peptide and differentiated towards the osteoblastic lineage. The ADA-GHK hydrogels significantly improved osteogenic differentiation compared to gelatin-containing control hydrogels, as demonstrated by gene expression, alkaline phosphatase activity and bone extracellular matrix deposition. Metabolomics revealed the high degree of metabolic heterogeneity in the gelatin-containing control hydrogels, captured the enhanced osteogenic differentiation in the ADA-GHK hydrogels, confirmed the similar metabolism between differentiated cells and primary osteoblasts, and elucidated the metabolic mechanism responsible for the function of GHK. Our results suggest a novel paradigm for metabolomics-guided biomaterial design and robust stem cell bioprocessing. STATEMENT OF SIGNIFICANCE: Producing high quality engineered bone grafts is important for the treatment of critical sized bone defects. Robust and sensitive techniques are required for quality assessment of tissue-engineered constructs, which result to the selection of optimal biomaterials for bone graft development. Herein, we present a new use of metabolomics signatures in guiding the development of novel oxidised alginate-based hydrogels with umbilical cord blood mesenchymal stem cells and the glycine-histidine-lysine peptide, demonstrating that GHK induces stem cell osteogenic differentiation. Metabolomics signatures captured the enhanced osteogenesis in GHK hydrogels, confirmed the metabolic similarity between differentiated cells and primary osteoblasts, and elucidated the metabolic mechanism responsible for the function of GHK. In conclusion, our results suggest a new paradigm of metabolomics-driven design of biomaterials.

Comparison of human isogeneic Wharton's jelly MSCs and iPSC-derived MSCs reveals differentiation-dependent metabolic responses to IFNG stimulation

Variability among donors, non-standardized methods for isolation, and characterization contribute to mesenchymal stem/stromal cell (MSC) heterogeneity. Induced pluripotent stem cell (iPSCs)-derived MSCs would circumvent many of current issues and enable large-scale production of standardized cellular therapy. To explore differences between native MSCs (nMSCs) and iPSC-derived MSCs (iMSCs), we developed isogeneic lines from Wharton’s jelly (WJ) from the umbilical cords of two donors (#12 and #13) under xeno-free conditions. Next, we reprogrammed them into iPSCs (iPSC12 and iPSC13) and subsequently differentiated them back into iMSCs (iMSC12 and iMSC13) using two different protocols, which we named ARG and TEX. We assessed their differentiation capability, transcriptome, immunomodulatory potential, and interferon-γ (IFNG)-induced changes in metabolome. Our data demonstrated that although both differentiation protocols yield iMSCs similar to their parental nMSCs, there are substantial differences. The ARG protocol resulted in iMSCs with a strong immunomodulatory potential and lower plasticity and proliferation rate, whereas the TEX protocol raised iMSCs with a higher proliferation rate, better differentiation potential, though weak immunomodulatory response. Our data suggest that, following a careful selection and screening of donors, nMSCs from umbilical’s cord WJ can be easily reprogrammed into iPSCs, providing an unlimited source of material for differentiation into iMSCs. However, the differentiation protocol should be chosen depending on their clinical use.

A simple and efficient method to extract polar metabolites from guar leaves (Cyamopsis tetragonoloba (L.) Taub.) for GC-MS metabolome analysis

Guar (Cyamopsis tetragonoloba (L.) Taub.) is an agricultural crop species new to Russia and is in demand by the gas, oil and food industries. Due to the progress of “omics” technologies and the marker-assisted selection, there is a huge interest in the studies that compare the metabolites of various guar varieties, employing metabolomics as a method of functional genomics. For a large-scale screening of guar germplasm from the VIR collection, it is important to choose an efficient method to extract metabolites from samples. The accuracy of the assessment of the content of metabolites in samples is crucial for distinguishing varieties within the crop, since the metabolome profiles of plants within the same species differ mainly in the quantitative ratio of metabolites, and not in their qualitative composition. In metabolome practice, two methods of extracting polar compounds are usually employed in the preparation of samples for GC-MS analysis. One of the widely used methods of sample preparation is the long-term extraction of metabolites from whole tissues with the aid of a methanol solvent. Another method of sample preparation is based on the short-term methanol extraction of metabolites from frozen and homogenized material. The advantages and disadvantages of these two methods revealed in the course of our work have prompted us to develop a new approach that avoids some difficulties in analyzing the metabolic profiles of leaves of various guar varieties. The method we suggested combines the advantages of the two above-mentioned approaches of sample preparation, namely eliminates the loss of metabolites due to centrifugation and ensures the complete destruction of all cell walls, ensuring the maximum extraction level of polar metabolites. The essence of the new method is that the leaf is rapidly frozen in liquid nitrogen with subsequent thawing in cold methanol. Thus, leaf tissues retain morphological integrity, and subsequent centrifugation, necessary for homogenization, is skipped. We have checked the effectiveness of this improved method by experiments with leaf samples of three guar genotypes. It has been shown that the amount of extracted metabolites increases more than 5-fold compared to extraction with methanol from fresh unfrozen leaf tissues and more than 2-fold compared to extraction with methanol after freezing and homogenization. Extraction of metabolites using the new method allows the GC-MS analysis of guar samples to be conducted with the least loss and high accuracy required to distinguish varieties.

Review of recent developments in GC-MS approaches to metabolomics-based research

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.

Comparison of sequential derivatization with concurrent methods for GC/MS-based metabolomics

The gas chromatography/mass spectrometry (GC/MS)-based metabolomics requires a two-step derivatization procedure consisting of oximation and silylation. However, due to the incomplete derivatization and degeneration of the metabolites, good repeatability is difficult to obtain during the batch derivatization, as the time between completing the derivatization process and GC analysis differs from sample to sample. In this research, we successfully obtained good repeatability for the peak areas of 52 selected metabolites by sequential derivatization and interval injection, in which the oximation and silylation times were maintained at constant values. In addition, the derivatization times and amount of reagents employed were varied to confirm that the optimal derivatization conditions differed for the various metabolites. In conventional batch derivatization, six metabolites, viz. glutamine, glutamic acid, histidine, alanine, asparagine, and tryptophan, exhibited fluctuations in their peak areas. Indeed, we found that for all six metabolites these differences originated from the silylation process, while the variations for glutamine and glutamic acid were related to the oximation process.

Changes in metabolic profiling of sugarcane leaves induced by endophytic diazotrophic bacteria and humic acids

Plant growth-promoting bacteria (PGPB) and humic acids (HA) have been used as biostimulants in field conditions. The complete genomic and proteomic transcription of Herbaspirillum seropedicae and Gluconacetobacter diazotrophicus is available but interpreting and utilizing this information in the field to increase crop performance is challenging. The identification and characterization of metabolites that are induced by genomic changes may be used to improve plant responses to inoculation. The objective of this study was to describe changes in sugarcane metabolic profile that occur when HA and PGPB are used as biostimulants. Inoculum was applied to soil containing 45-day old sugarcane stalks. One week after inoculation, the methanolic extracts from leaves were obtained and analyzed by gas chromatography coupled to time-of-flight mass spectrometry; a total of 1,880 compounds were observed and 280 were identified in all samples. The application of HA significantly decreased the concentration of 15 metabolites, which generally included amino acids. HA increased the levels of 40 compounds, and these included metabolites linked to the stress response (shikimic, caffeic, hydroxycinnamic acids, putrescine, behenic acid, quinoline xylulose, galactose, lactose proline, oxyproline and valeric acid) and cellular growth (adenine and adenosine derivatives, ribose, ribonic acid and citric acid). Similarly, PGPB enhanced the level of metabolites identified in HA-treated soils; e.g., 48 metabolites were elevated and included amino acids, nucleic acids, organic acids, and lipids. Co-inoculation (HA+PGPB) boosted the level of 110 metabolites with respect to non-inoculated controls; these included amino acids, lipids and nitrogenous compounds. Changes in the metabolic profile induced by HA+PGPB influenced both glucose and pentose pathways and resulted in the accumulation of heptuloses and riboses, which are substrates in the nucleoside biosynthesis and shikimic acid pathways. The mevalonate pathway was also activated, thus increasing phytosterol synthesis. The improvement in cellular metabolism observed with PGPB+HA was compatible with high levels of vitamins. Glucuronate and amino sugars were stimulated in addition to the products and intermediary compounds of tricarboxylic acid metabolism. Lipids and amino acids were the main compounds induced by co-inoculation in addition to antioxidants, stress-related metabolites, and compounds involved in cellular redox. The primary compounds observed in each treatment were identified, and the effect of co-inoculation (HA+PGPB) on metabolite levels was discussed.

Quantification of run order effect on chromatography - mass spectrometry profiling data

Chromatographic systems coupled with mass spectrometry detection are widely used in biological studies investigating how levels of biomolecules respond to different internal and external stimuli. Such changes are normally expected to be of low magnitude and therefore all experimental factors that can influence the analysis need to be understood and minimized. Run order effect is commonly observed and constitutes a major challenge in chromatography-mass spectrometry based profiling studies that needs to be addressed before the biological evaluation of measured data is made. So far there is no established consensus, metric or method that quickly estimates the size of this effect. In this paper we demonstrate how orthogonal projections to latent structures (OPLS®) can be used for objective quantification of the run order effect in profiling studies. The quantification metric is expressed as the amount of variation in the experimental data that is correlated to the run order. One of the primary advantages with this approach is that it provides a fast way of quantifying run-order effect for all detected features, not only internal standards. Results obtained from quantification of run order effect as provided by the OPLS can be used in the evaluation of data normalization, support the optimization of analytical protocols and identification of compounds highly influenced by instrumental drift. The application of OPLS for quantification of run order is demonstrated on experimental data from plasma profiling performed on three analytical platforms: GCMS metabolomics, LCMS metabolomics and LCMS lipidomics.

Laboratory evolution reveals regulatory and metabolic trade-offs of glycerol utilization in Saccharomyces cerevisiae

Most microbial species, including model eukaryote Saccharomyces cerevisiae, possess genetic capability to utilize many alternative nutrient sources. Yet, it remains an open question whether these manifest into assimilatory phenotypes. Despite possessing all necessary pathways, S. cerevisiae grows poorly or not at all when glycerol is the sole carbon source. Here we discover, through multiple evolved lineages, genetic determinants underlying glycerol catabolism and the associated fitness trade-offs. Most evolved lineages adapted through mutations in the HOG pathway, but showed hampered osmotolerance. In the other lineages, we find that only three mutations cause the improved phenotype. One of these contributes counter-intuitively by decoupling the TCA cycle from oxidative phosphorylation, and thereby hampers ethanol utilization. Transcriptomics, proteomics and metabolomics analysis of the re-engineered strains affirmed the causality of the three mutations at molecular level. Introduction of these mutations resulted in improved glycerol utilization also in industrial strains. Our findings not only have a direct relevance for improving glycerol-based bioprocesses, but also illustrate how a metabolic pathway can remain unexploited due to fitness trade-offs in other, ecologically important, traits.

GC-MS Metabolomic Profiling of Protic Metabolites Following Heptafluorobutyl Chloroformate Mediated Dispersive Liquid Microextraction Sample Preparation Protocol

A simple analytical workflow is described for gas chromatographic-mass spectrometry (GC-MS)-based metabolomic profiling of protic metabolites, particularly amino-carboxylic species in biological matrices. The sample preparation is carried out directly in aqueous samples and uses simultaneous in situ heptafluorobutyl chloroformate (HFBCF) derivatization and dispersive liquid-liquid microextraction (DLLME), followed by GC-MS analysis in single-ion monitoring (SIM) mode. The protocol involves ten simple pipetting steps and provides quantitative analysis of 132 metabolites by using two internal standards. A comment on each analytical step and explaining notes are provided with particular attention to the GC-MS analysis of 112 physiological metabolites in human urine.

Untargeted GC-MS Metabolomics

Untargeted metabolomics refers to the high-throughput analysis of the metabolic state of a biological system (e.g., tissue, biological fluid, cell culture) based on the concentration profile of all measurable free low molecular weight metabolites. Gas chromatography-mass spectrometry (GC-MS), being a highly sensitive and high-throughput analytical platform, has been proven a useful tool for untargeted studies of primary metabolism in a variety of applications. As an omic analysis, GC-MS metabolomics is a multistep procedure; thus, standardization of an untargeted GC-MS metabolomics protocol requires the integrated optimization of pre-analytical, analytical, and computational steps. The main difference of GC-MS metabolomics compared to other metabolomics analytical platforms, including liquid chromatography-MS, is the need for the derivatization of the metabolite extracts into volatile and thermally stable derivatives, the latter being quantified in the metabolic profiles. This analytical step requires special care in the optimization of the untargeted GC-MS metabolomics experimental protocol. Moreover, both the derivatization of the original sample and the compound fragmentation that takes place in GC-MS impose specialized GC-MS metabolomic data identification, quantification, normalization and filtering methods. In this chapter, we describe the integrated protocol of untargeted GC-MS metabolomics with both the analytical and computational steps, focusing on the GC-MS specific parts, and provide details on any sample depending differences.

The impact of succinate trace on pWW0 and ortho-cleavage pathway transcription in Pseudomonas putida mt-2 during toluene biodegradation

Toluene is a pollutant catabolised through the interconnected pWW0 (TOL) and ortho-cleavage pathways of Pseudomonas putida mt-2, while upon succinate and toluene mixtures introduction in batch cultures grown on M9 medium, succinate was previously reported as non-repressing. The effect of a 40 times lower succinate concentration, as compared to literature values, was explored through systematic real-time qPCR monitoring of transcriptional kinetics of the key TOL Pu, Pm and ortho-cleavage PbenR, PbenA promoters in mixed-substrate experiments. Even succinate trace inhibited transcription leading to bi-modal promoters expression. Potential carbon catabolite repression mechanisms and novel expression patterns of promoters were unfolded. Lag phase was shortened and biomass growth levels increased compared to sole toluene biodegradation suggesting enhanced pollutant removal efficiency. The study stressed the noticeable effect of a preferred compound's left-over on the main route of a bioprocess, revealing the beneficiary supply of low preferred substrates concentrations to design optimal bioremediation strategies.

Metabolomics Analysis of the Osteogenic Differentiation of Umbilical Cord Blood Mesenchymal Stem Cells Reveals Differential Sensitivity to Osteogenic Agents

Mesenchymal stem cells (MSCs) of fetal origin, such as umbilical cord blood MSCs (UCB MSCs), have emerged as a promising cell source for musculoskeletal tissue regeneration because of their higher proliferation potential, lack of donor site morbidity, and their off-the-shelf potential. MSCs differentiated toward the osteogenic lineage exhibit a specific metabolic phenotype characterized by reliance to oxidative phosphorylation for energy production and reduced glycolytic rates. Currently, limited information exists on the metabolic transitions at different stages of the osteogenic process after osteoinduction with different agents. Herein, the osteoinduction efficiency of BMP-2 and dexamethasone on UCB MSCs was assessed using gas chromatography-mass spectrometry (GC-MS) metabolomics analysis, revealing metabolic discrepancies at 7, 14, and 21 days of induction. Whereas both agents when administered individually were able to induce collagen I, osteocalcin, and osteonectin expression, BMP-2 was less effective than dexamethasone in promoting alkaline phosphatase expression. The metabolomics analysis revealed that each agent induced distinct metabolic alterations, including changes in amino acid pools, glutaminolysis, one-carbon metabolism, glycolysis, and tricarboxylic acid cycle. Importantly, we showed that in vitro-differentiated UCB MSCs acquire a metabolic physiology similar to primary osteoblasts when induced with dexamethasone but not with BMP-2, highlighting the fact that metabolomics analysis is sensitive enough to reveal potential differences in the osteogenic efficiency and can be used as a quality control assay for evaluating the osteogenic process.

Human embryonic and induced pluripotent stem cells maintain phenotype but alter their metabolism after exposure to ROCK inhibitor

Human pluripotent stem cells (hPSCs) are adhesion-dependent cells that require cultivation in colonies to maintain growth and pluripotency. Robust differentiation protocols necessitate single cell cultures that are achieved by use of ROCK (Rho kinase) inhibitors. ROCK inhibition enables maintenance of stem cell phenotype; its effects on metabolism are unknown. hPSCs were exposed to 10 μM ROCK inhibitor for varying exposure times. Pluripotency (TRA-1-81, SSEA3, OCT4, NANOG, SOX2) remained unaffected, until after prolonged exposure (96 hrs). Gas chromatography–mass spectrometry metabolomics analysis identified differences between ROCK-treated and untreated cells as early as 12 hrs. Exposure for 48 hours resulted in reduction in glycolysis, glutaminolysis, the citric acid (TCA) cycle as well as the amino acids pools, suggesting the adaptation of the cells to the new culture conditions, which was also reflected by the expression of the metabolic regulators, mTORC1 and tp53 and correlated with cellular proliferation status. While gene expression and protein levels did not reveal any changes in the physiology of the cells, metabolomics revealed the fluctuating state of the metabolism. The above highlight the usefulness of metabolomics in providing accurate and sensitive information on cellular physiological status, which could lead to the development of robust and optimal stem cell bioprocesses.

Quantification of Microbial Phenotypes

Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis.

A metabolomics investigation of the function of the ESX-1 gene cluster in mycobacteria

The ESX-1 gene cluster, encoding the Type-VII secretion (T7S) system and its virulence associated proteins, ESAT-6 and CFP-10, is thought to be responsible for the transport of extracellular proteins across the hydrophobic and highly impermeable, cell envelope of Mycobacterium, and is involved in virulence in Mycobacterium tuberculosis, the causative agent of tuberculosis. Using a GCxGC-TOFMS metabolomics approach, a M. smegmatis ESX-1 knock-out strain (ΔESX-1_ms) was compared to that of the M. smegmatis wild-type parent strain, and the metabolite markers due to the presence or absence of the ESX-1 gene cluster were identified. A general increase in specific metabolites in the ΔESX-1_ms, confirmed the roles previously described for ESX-1 in mycolic acid biosynthesis and cell wall integrity. However, a number of other metabolite markers identified indicates ESX-1 has an additional role the in cell envelope structure, altering the levels of antioxidants and energy metabolism. Furthermore, the metabolome profiles correlated with the metabolomic variation observed when comparing a hyper- and hypo-virulent Beijing strain of M. tuberculosis, suggesting that the pathways which modulate virulence in M. tuberculosis are also influenced by ESX-1, reaffirming the previously described association of ESX-1 with virulence and cell envelope biogenesis.

Metabolomic Analysis in Brain Research: Opportunities and Challenges

Metabolism being a fundamental part of molecular physiology, elucidating the structure and regulation of metabolic pathways is crucial for obtaining a comprehensive perspective of cellular function and understanding the underlying mechanisms of its dysfunction(s). Therefore, quantifying an accurate metabolic network activity map under various physiological conditions is among the major objectives of systems biology in the context of many biological applications. Especially for CNS, metabolic network activity analysis can substantially enhance our knowledge about the complex structure of the mammalian brain and the mechanisms of neurological disorders, leading to the design of effective therapeutic treatments. Metabolomics has emerged as the high-throughput quantitative analysis of the concentration profile of small molecular weight metabolites, which act as reactants and products in metabolic reactions and as regulatory molecules of proteins participating in many biological processes. Thus, the metabolic profile provides a metabolic activity fingerprint, through the simultaneous analysis of tens to hundreds of molecules of pathophysiological and pharmacological interest. The application of metabolomics is at its standardization phase in general, and the challenges for paving a standardized procedure are even more pronounced in brain studies. In this review, we support the value of metabolomics in brain research. Moreover, we demonstrate the challenges of designing and setting up a reliable brain metabolomic study, which, among other parameters, has to take into consideration the sex differentiation and the complexity of brain physiology manifested in its regional variation. We finally propose ways to overcome these challenges and design a study that produces reproducible and consistent results.

Improved stability of TMS derivatives for the robust quantification of plant polar metabolites by gas chromatography-mass spectrometry

Plant metabolite profiling is commonly carried out by GC-MS of methoximated trimethylsilyl (TMS) derivatives. This technique is robust and enables a library search for spectra produced by electron ionization. However, recent articles have described problems associated with the low stability of some TMS derivatives. This limits the use of GC-MS for metabolomic studies that need large sets of qualitative and quantitative analyses. The aim of this work is to determine the experimental conditions in which the stability of TMS derivatives could be improved. This would facilitate the analysis of the large-scale experimental designs needed in the metabolomics approach. For good repeatability, the sampling conditions and the storage temperature of samples during analysis were investigated. Multiple injections of one sample from one vial led to high variations while injection of one sample from different vials improved the analysis. However, before injection, some amino acid TMS derivatives were degraded during the storage of vials in the autosampler. Only 10% of the initial quantity of glutamine 3 TMS and glutamate 3 TMS and 66% of α-alanine 2 TMS was detected 48 h after derivatization. When stored at 4 °C until injection, all TMS derivatives remained stable for 12 h; at -20 °C, they remained stable for 72 h. From the integration of all these results, a detailed analytical procedure is thus proposed. It enables a robust quantification of polar metabolites, useful for further plant metabolomics studies using GC-MS.

A modified data normalization method for GC-MS-based metabolomics to minimize batch variation

The goal of metabolomics data pre-processing is to eliminate systematic variation, such that biologically-related metabolite signatures are detected by statistical pattern recognition. Although several methods have been developed to tackle the issue of batch-to-batch variation, each method has its advantages and disadvantages. In this study, we used a reference sample as a normalization standard for test samples within the same batch, and each metabolite value is expressed as a ratio relative to its counterpart in the reference sample. We then applied this approach to a large multi-batch data set to facilitate intra- and inter-batch data integration. Our results demonstrate that normalization to a single reference standard has the potential to minimize batch-to-batch data variation across a large, multi-batch data set.

An artifact in LC-MS/MS measurement of glutamine and glutamic acid: in-source cyclization to pyroglutamic acid

Advances in metabolomics, particularly for research on cancer, have increased the demand for accurate, highly sensitive methods for measuring glutamine (Gln) and glutamic acid (Glu) in cell cultures and other biological samples. N-terminal Gln and Glu residues in proteins or peptides have been reported to cyclize to pyroglutamic acid (pGlu) during liquid chromatography (LC)-mass spectrometry (MS) analysis, but cyclization of free Gln and Glu to free pGlu during LC-MS analysis has not been well-characterized. Using an LC-MS/MS protocol that we developed to separate Gln, Glu, and pGlu, we found that free Gln and Glu cyclize to pGlu in the electrospray ionization source, revealing a previously uncharacterized artifact in metabolomic studies. Analysis of Gln standards over a concentration range from 0.39 to 200 μM indicated that a minimum of 33% and maximum of almost 100% of Gln was converted to pGlu in the ionization source, with the extent of conversion dependent on fragmentor voltage. We conclude that the sensitivity and accuracy of Gln, Glu, and pGlu quantitation by electrospray ionization-based mass spectrometry can be improved dramatically by using (i) chromatographic conditions that adequately separate the three metabolites, (ii) isotopic internal standards to correct for in-source pGlu formation, and (iii) user-optimized fragmentor voltage for acquisition of the MS spectra. These findings have immediate impact on metabolomics and metabolism research using LC-MS technologies.

Plasma metabolomic profiling suggests early indications for predisposition to latent insulin resistance in children conceived by ICSI

BACKGROUND

There have been increasing indications about an epigenetically-based elevated predisposition of assisted reproductive technology (ART) offspring to insulin resistance, which can lead to an unfavorable cardio-metabolic profile in adult life. However, the relevant long-term systematic molecular studies are limited, especially for the IntraCytoplasmic Sperm Injection (ICSI) method, introduced in 1992. In this study, we carefully defined a group of 42 prepubertal ICSI and 42 naturally conceived (NC) children. We assessed differences in their metabolic profile based on biochemical measurements, while, for a subgroup, plasma metabolomic analysis was also performed, investigating any relevant insulin resistance indices.

METHODS & RESULTS

Auxological and biochemical parameters of 42 6.8±2.1 yrs old ICSI-conceived and 42 age-matched controls were measured. Significant differences between the groups were determined using univariate and multivariate statistics, indicating low urea and low-grade inflammation markers (YKL-40, hsCRP) and high triiodothyronine (T3) in ICSI-children compared to controls. Moreover, plasma metabolomic analysis carried out for a subgroup of 10 ICSI- and 10 NC girls using Gas Chromatography-Mass Spectrometry (GC-MS) indicated clear differences between the two groups, characterized by 36 metabolites linked to obesity, insulin resistance and metabolic syndrome. Notably, the distinction between the two girl subgroups was accentuated when both their biochemical and metabolomic measurements were employed.

CONCLUSIONS

The present study contributes a large auxological and biochemical dataset of a well-defined group of pre-pubertal ICSI-conceived subjects to the research of the ART effect to the offspring's health. Moreover, it is the first time that the relevant usefulness of metabolomics was investigated. The acquired results are consistent with early insulin resistance in ICSI-offspring, paving the way for further systematic investigations. These data support that metabolomics may unravel metabolic differences before they become clinically or biochemically evident, underlining its utility in the ART research.