Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling

Adaptation to Aquatic and Terrestrial Environments in Chlorella vulgaris (Chlorophyta)

The globally distributed green microalga Chlorella vulgaris (Chlorophyta) colonizes aquatic and terrestrial habitats, but the molecular mechanisms underpinning survival in these two contrasting environments are far from understood. Here, we compared the authentic strain of C. vulgaris from an aquatic habitat with a strain from a terrestrial high alpine habitat previously determined as Chlorella mirabilis. Molecular phylogeny of SSU rDNA (823 bp) showed that the two strains differed by one nucleotide only. Sequencing of the ITS2 region confirmed that both strains belong to the same species, but to distinct ribotypes. Therefore, the terrestrial strain was re-assessed as C. vulgaris. To study the response to environmental conditions experienced on land, we assessed the effects of irradiance and temperature on growth, of temperature on photosynthesis and respiration, and of desiccation and rehydration on photosynthetic performance. In contrast to the aquatic strain, the terrestrial strain tolerated higher temperatures and light conditions, had a higher photosynthesis-to-respiration ratio at 25°C, still grew at 30°C and was able to fully recover photosynthetic performance after desiccation at 84% relative humidity. The two strains differed most in their response to the dehydration/rehydration treatment, which was further investigated by untargeted GC–MS-based metabolite profiling to gain insights into metabolic traits differentiating the two strains. The two strains differed in their allocation of carbon and nitrogen into their primary metabolites. Overall, the terrestrial strain had higher contents of readily available nitrogen-based metabolites, especially amino acids and the polyamine putrescine. Dehydration and rehydration led to differential regulation of the amino acid metabolism, the tricarboxylic acid cycle and sucrose metabolism. The data are discussed with a view to differences in phenotypic plasticity of the two strains, and we suggest that the two genetically almost identical C. vulgaris strains are attractive models to study mechanisms that protect from abiotic stress factors, which are more frequent in terrestrial than aquatic habitats, such as desiccation and irradiation.

What is new in the exposome?

The exposome concept refers to the totality of exposures from a variety of external and internal sources including chemical agents, biological agents, or radiation, from conception onward, over a complete lifetime. It encompasses also "psychosocial components" including the impact of social relations and socio-economic position on health. In this review we provide examples of recent contributions from exposome research, where we believe their application will be of the greatest value for moving forward. So far, environmental epidemiology has mainly focused on hard outcomes, such as mortality, disease exacerbation and hospitalizations. However, there are many subtle outcomes that can be related to environmental exposures, and investigations can be facilitated by an improved understanding of internal biomarkers of exposure and response, through the application of omic technologies. Second, though we have a wealth of studies on environmental pollutants, the assessment of causality is often difficult because of confounding, reverse causation and other uncertainties. Biomarkers and omic technologies may allow better causal attribution, for example using instrumental variables in triangulation, as we discuss here. Even more complex is the understanding of how social relationships (in particular socio-economic differences) influence health and imprint on the fundamental biology of the individual. The identification of molecular changes that are intermediate between social determinants and disease status is a way to fill the gap. Another field in which biomarkers and omics are relevant is the study of mixtures. Epidemiology often deals with complex mixtures (e.g. ambient air pollution, food, smoking) without fully disentangling the compositional complexity of the mixture, or with rudimentary approaches to reflect the overall effect of multiple exposures or components. From the point of view of disease mechanisms, most models hypothesize that several stages need to be transitioned through health to the induction of disease, but very little is known about the characteristics and temporal sequence of such stages. Exposome models reinforce the idea of a biography-to-biology transition, in that everyone's disease is the product of the individual history of exposures, superimposed on their underlying genetic susceptibilities. Finally, exposome research is facilitated by technological developments that complement traditional epidemiological study designs. We describe in depth one such new tools, adductomics. In general, the development of high-resolution and high-throughput technologies interrogating multiple -omics (such as epigenomics, transcriptomics, proteomics, adductomics and metabolomics) yields an unprecedented perspective into the impact of the environment in its widest sense on disease. The world of the exposome is rapidly evolving, though a huge gap still needs to be filled between the original expectations and the concrete achievements. Perhaps the most urgent need is for the establishment of a new generation of cohort studies with appropriately specified biosample collection, improved questionnaire data (including social variables), and the deployment of novel technologies that allow better characterization of individual environmental exposures, ranging from personal monitoring to satellite based observations.

Integrated metabolomics and lipidomics reveals high accumulation of polyunsaturated lysoglycerophospholipids in human lung fibroblasts exposed to fine particulate matter

Exposure to fine particulate matter (PM) comprising toxic compounds arising from air pollution is a major human health concern. It is linked to increased mortality and incidence of various lung diseases. However, the mechanisms underlying the toxic effects of PM on lung fibroblasts have not been fully explored. We used targeted quantitative metabolomics and lipidomics analysis along with cytotoxicity studies to comprehensively characterize the alterations in the metabolite profiles of human lung fibroblasts (HEL 299) upon exposure to PM_2.5 and PM₁₀. This exposure at 50 μg/mL for 72 h induced an abnormally high apoptotic response via triggering intracellular reactive oxygen species (ROS) production and mitochondrial dysfunction through an imbalance between pro- and anti-apoptotic signaling pathways. The cytotoxic effects of PM_2.5 were more severe than those of PM₁₀. Metabolomics and lipidomics analyses revealed that PM exposure triggered substantial changes in the cellular metabolite profile, which involved reduced mitochondria-related metabolites such as tricarboxylic acid (TCA) cycle intermediates, amino acids, and free fatty acids as well as increased lysoglycerophospholipids (LPLs) containing polyunsaturated fatty acids. The decrease in mitochondria-related metabolites suggested that PM exposure led to reduced TCA cycle capacity and energy production. Apoptotic and inflammatory responses as well as mitochondrial dysfunction were likely to be accelerated because of excessive accumulation of LPLs, contributing to the disruption of membrane rafts and Ca²⁺ homeostasis and causing increased mitochondrial ROS formation. These results provide valuable insights regarding the toxic effects of PM exposure. Our study also provides a new direction for research on PM exposure-related health disorders using different cell lines.

Gut microbial metabolites as multi-kingdom intermediates

The gut microbiota contributes to host physiology through the production of a myriad of metabolites. These metabolites exert their effects within the host as signalling molecules and substrates for metabolic reactions. Although the study of host-microbiota interactions remains challenging due to the high degree of crosstalk both within and between kingdoms, metabolite-focused research has identified multiple actionable microbial targets that are relevant for host health. Metabolites, as the functional output of combined host and microorganism interactions, provide a snapshot in time of an extraordinarily complex multi-organism system. Although substantial work remains towards understanding host-microbiota interactions and the underlying mechanisms, we review the current state of knowledge for each of the major classes of microbial metabolites with emphasis on clinical and translational research implications. We provide an overview of methodologies available for measurement of microbial metabolites, and in addition to discussion of key challenges, we provide a potential framework for integration of discovery-based metabolite studies with mechanistic work. Finally, we highlight examples in the literature where this approach has led to substantial progress in understanding host-microbiota interactions.

Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS

Urine is a popular biofluid for metabolomics studies due to its simple, non-invasive collection and its availability in large quantities, permitting frequent sampling, replicate analyses, and sample banking. The biggest disadvantage with using urine is that it exhibits significant variability in concentration and composition within an individual over relatively short periods of time (arising from various external factors and internal processes regulating the body’s water and solute content). In treating the data from urinary metabolomics studies, one must account for the natural variability of urine concentrations to avoid erroneous data interpretation. Amongst various proposed approaches to account for broadly varying urine sample concentrations, normalization to creatinine has been widely accepted and is most commonly used. MS total useful signal (MSTUS) is another normalization method that has been recently reported for mass spectrometry (MS)-based metabolomics studies. Herein, we explored total useful peak area (TUPA), a modification of MSTUS that is applicable to GC×GC-TOFMS (and data from other separations platforms), for sample normalization in urinary metabolomics studies. Performance of TUPA was compared to the two most common normalization approaches, creatinine adjustment and Total Peak Area (TPA) normalization. Each normalized dataset was evaluated using Principal Component Analysis (PCA). The results showed that TUPA outperformed alternative normalization methods to overcome urine concentration variability. Results also conclusively demonstrate the risks in normalizing data to creatinine.

The role of gut microbiota (GM) and GM-related metabolites in diabetes and obesity. A review of analytical methods used to measure GM-related metabolites in fecal samples with a focus on metabolites' derivatization step

Disruption of gut microbiota (GM) composition is increasingly related to the pathogenesis of various metabolic diseases. Additionally, GM is responsible for the production and transformation of metabolites involved in the development of metabolic disorders, such as obesity and type 2 diabetes mellitus (T2DM). The current state of knowledge regarding the composition of GM and GM-related metabolites in relation to the progress and development of obesity and T2DM is presented in this review. To understand the relationships between GM-related metabolites and the development of metabolic disorders, their accurate qualitative and quantitative measurement in biological samples is needed. Feces represent a valuable biological matrix which composition may reflect the health status of the lower gastrointestinal tract and the whole organism. Mass spectrometry (MS), mainly in combination with gas chromatography (GC) or liquid chromatography (LC), is commonly used to measure fecal metabolites. However, profiling metabolites in such a complex matrix as feces is challenging from both analytical chemistry and biochemistry standpoints. Chemical derivatization is one of the most effective methods used to overcome these problems. In this review, we provide a comprehensive summary of the derivatization methods of GM-related metabolites prior to GC-MS or LC-MS analysis, which have been published in the last five years (2015-2020). Additionally, analytical methods used for the analysis of GM-related metabolites without the derivatization step are also presented.

Cervicovaginal Microbiome Composition Is Associated with Metabolic Profiles in Healthy Pregnancy

Humans have a unique vaginal microbiome compared to other mammals, characterized by low diversity and often dominated by Lactobacillus spp. Dramatic shifts in vaginal microbial communities sometimes contribute to the presence of a polymicrobial overgrowth condition called bacterial vaginosis (BV). However, many healthy women lacking BV symptoms have vaginal microbiomes dominated by microbes associated with BV, resulting in debate about the definition of a healthy vaginal microbiome. Despite substantial evidence that the reproductive health of a woman depends on the vaginal microbiota, future therapies that may improve reproductive health outcomes are stalled due to limited understanding surrounding the ecology of the vaginal microbiome. Here, we use sequencing and metabolomic techniques to show novel associations between vaginal microbes and metabolites during healthy pregnancy. We speculate these associations underlie microbiome dynamics and may contribute to a better understanding of transitions between alternative vaginal microbiome compositions.

Microbes and their metabolic products influence early-life immune and microbiome development, yet remain understudied during pregnancy. Vaginal microbial communities are typically dominated by one or a few well-adapted microbes which are able to survive in a narrow pH range and are adapted to live on host-derived carbon sources, likely sourced from glycogen and mucin present in the vaginal environment. We characterized the cervicovaginal microbiomes of 16 healthy women throughout the three trimesters of pregnancy. Additionally, we analyzed saliva and urine metabolomes using gas chromatography-time of flight mass spectrometry (GC-TOF MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) lipidomics approaches for samples from mothers and their infants through the first year of life. Amplicon sequencing revealed most women had either a simple community with one highly abundant species of Lactobacillus or a more diverse community characterized by a high abundance of Gardnerella, as has also been previously described in several independent cohorts. Integrating GC-TOF MS and lipidomics data with amplicon sequencing, we found metabolites that distinctly associate with particular communities. For example, cervicovaginal microbial communities dominated by Lactobacillus crispatus have high mannitol levels, which is unexpected given the characterization of L. crispatus as a homofermentative Lactobacillus species. It may be that fluctuations in which Lactobacillus dominate a particular vaginal microbiome are dictated by the availability of host sugars, such as fructose, which is the most likely substrate being converted to mannitol. Overall, using a multi-“omic” approach, we begin to address the genetic and molecular means by which a particular vaginal microbiome becomes vulnerable to large changes in composition.

GC-MS-Based Metabolomics for the Smut Fungus Ustilago maydis: A Comprehensive Method Optimization to Quantify Intracellular Metabolites

Ustilago maydis, a smut fungus, is an appealing model in fundamental research and an upcoming cell factory for industrial biotechnology. The genome of U. maydis has been sequenced and some synthesis pathways were biochemically described; however, the operation of the cellular metabolic network is not well-characterized. Thus, we conducted a comprehensive study to optimize the sample preparation procedure for metabolomics of U. maydis using GC-MS/MS. Due to the unique characteristics of U. maydis cell culture, two quenching solutions, different washing steps, eight extraction methods, and three derivatization conditions have been examined. The optimal method was then applied for stable isotope-assisted quantification of low molecular weight hydrophilic metabolites while U. maydis utilized different carbon sources including sucrose, glucose, and fructose. This study is the first report on a methodology for absolute quantification of intracellular metabolites in U. maydis central carbon metabolism such as sugars, sugar phosphates, organic acids, amino acids, and nucleotides. For biotechnological use, this method is crucial to exploit the full production potential of this fungus and can also be used to study other fungi of the family Ustilaginaceae.

Identifying unknown metabolites using NMR-based metabolic profiling techniques

Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.

Integrative transcriptomics and metabolomics analyses provide hepatotoxicity mechanisms of asarum

Asarum is frequently applied in combination with other agents for prescriptions in practices of Traditional Chinese Medicine. A number of studies have previously indicated that asarum treatment induces lung toxicity by triggering inflammation. However, the potential effects of asarum in the liver and the underlying mechanisms have remained largely elusive. Therefore, transcriptomics and metabolomics approaches were used in the present study to examine the mechanisms of the hepatotoxicity of asarum. Specifically, mRNA and metabolites were obtained from rat liver samples following intragastric administration of asarum powder. RNA sequencing analysis was subsequently performed to screen for differentially expressed genes (DEGs), and a total of 434 DEGs were identified in liver tissue samples, 214 of which were upregulated and 220 were downregulated. Pathway enrichment analysis found that these genes were particularly enriched in processes including the regulation of p53 signaling, metabolic pathways and bile secretion. To investigate potential changes to the metabolic profile as a result of asarum treatment, a metabolomics analysis was performed, which detected 14 significantly altered metabolites in rat liver samples by gas chromatography-mass spectrometry. These metabolites were predominantly members of the taurine, hypotaurine and amino acid metabolic pathways. Metscape network analyses were subsequently performed to integrate the transcriptomics and metabolomics data. Integrative analyis revealed that the DEGs and metabolites were primarily associated with bile acid biosynthesis, amino acid metabolism and the p53 signaling pathway. Taken together, these results provide novel insight into the mechanism of asarum-mediated hepatotoxicity, which may potentially aid the clinical diagnosis and future therapeutic intervention of asarum poisoning.

Proteomics and Metabolomics for Cystic Fibrosis Research

The aim of this review article is to introduce the reader to the state-of-the-art of the contribution that proteomics and metabolomics sciences are currently providing for cystic fibrosis (CF) research: from the understanding of cystic fibrosis transmembrane conductance regulator (CFTR) biology to biomarker discovery for CF diagnosis. Our work particularly focuses on CFTR post-translational modifications and their role in cellular trafficking as well as on studies that allowed the identification of CFTR molecular interactors. We also show how metabolomics is currently helping biomarker discovery in CF. The most recent advances in these fields are covered by this review, as well as some considerations on possible future scenarios for new applications.

Decanoic Acid and Not Octanoic Acid Stimulates Fatty Acid Synthesis in U87MG Glioblastoma Cells: A Metabolomics Study

Medium-chain fatty acids (MCFA) are dietary components with a chain length ranging from 6 to 12 carbon atoms. MCFA can cross the blood-brain barrier and in the brain can be oxidized through mitochondrial β-oxidation. As components of ketogenic diets, MCFA have demonstrated beneficial effects on different brain diseases, such as traumatic brain injury, Alzheimer’s disease, drug-resistant epilepsy, diabetes, and cancer. Despite the interest in MCFA effects, not much information is available about MCFA metabolism in the brain. In this study, with a gas chromatography-mass spectrometry (GC-MS)-based metabolomics approach, coupled with multivariate data analyses, we followed the metabolic changes of U87MG glioblastoma cells after the addition of octanoic (C8), or decanoic (C10) acids for 24 h. Our analysis highlighted significant differences in the metabolism of U87MG cells after the addition of C8 or C10 and identified several metabolites whose amount changed between the two groups of treated cells. Overall, metabolic pathway analyses suggested the citric acid cycle, Warburg effect, glutamine/glutamate metabolism, and ketone body metabolism as pathways influenced by C8 or C10 addition to U87MG cells. Our data demonstrated that, while C8 affected mitochondrial metabolism resulting in increased ketone body production, C10 mainly influenced cytosolic pathways by stimulating fatty acid synthesis. Moreover, glutamine might be the main substrate to support fatty acids synthesis in C10-treated cells. In conclusion, we identified a metabolic signature associated with C8 or C10 addition to U87MG cells that can be used to decipher metabolic responses of glioblastoma cells to MCFA treatment.

Inter-laboratory reproducibility of an untargeted metabolomics GC-MS assay for analysis of human plasma

There is a long-standing concern for the lack of reproducibility of the untargeted metabolomic approaches used in pharmaceutical research. Two types of human plasma samples were split into two batches and analyzed in two individual labs for untargeted GC–MS metabolomic profiling. The two labs used the same silylation sample preparation protocols but different instrumentation, data processing software, and database. There were 55 metabolites annotated reproducibly, independent of the labs. The median coefficient variations (CV%) of absolute spectra ion intensities in both labs were less than 30%. However, the comparison of normalized ion intensity among biological groups, were inconsistent across labs. Predicted power based on annotated metabolites was evaluated post various normalization, data transformation and scaling. For the first time our study reveals the numerical details about the variations in metabolomic annotation and relative quantification using plain inter-laboratory GC–MS untargeted metabolomic approaches. Especially we compare several commonly used post-acquisition strategies and found normalization could not strengthen the annotation accuracy or relative quantification precision of untargeted approach, instead it will impact future experimental design. Standardization of untargeted metabolomics protocols, including sample preparation, instrumentation, data processing, etc., is critical for comparison of untargeted data across labs.

Impact of Vancomycin-Induced Changes in the Intestinal Microbiota on the Pharmacokinetics of Simvastatin

The pharmacokinetic (PK) properties of drugs are affected in several ways by interactions with microbiota. The aim of this study was to investigate the effects of oral vancomycin on the gut microbiota and, consequently, on the PKs of simvastatin. An open-label, single arm, sequential crossover study was conducted in six healthy Korean male subjects. After 6 days on a control diet, simvastatin 40 mg was orally administered to the subjects before and after 1 week of oral vancomycin treatment. Blood samples for PK analysis and fecal samples for metagenomic and metabolomic analyses were collected. After vancomycin treatment, the richness of microbiota considerably decreased, and the composition was altered. In particular, the relative abundance of Bacteroidetes decreased, whereas that of proteobacteria increased. In addition, changes in fecal metabolites, including D-glucuronic acid, were observed. However, systemic exposure of simvastatin was not changed whereas that of hydroxysimvastatin showed a tendency to increase. The relationship between the change of PKs of simvastatin and the change of gut microbiota and fecal metabolites were not clearly observed.

Hepatic NADH reductive stress underlies common variation in metabolic traits

The cellular NADH/NAD+ ratio is fundamental to biochemistry, but the extent to which it reflects versus drives metabolic physiology in vivo is poorly understood. Here we report the in vivo application of Lactobacillus brevis (Lb)NOX1, a bacterial water-forming NADH oxidase, to assess the metabolic consequences of directly lowering the hepatic cytosolic NADH/NAD+ ratio in mice. By combining this genetic tool with metabolomics, we identify circulating α-hydroxybutyrate levels as a robust marker of an elevated hepatic cytosolic NADH/NAD+ ratio, also known as reductive stress. In humans, elevations in circulating α-hydroxybutyrate levels have previously been associated with impaired glucose tolerance2, insulin resistance3 and mitochondrial disease4, and are associated with a common genetic variant in GCKR5, which has previously been associated with many seemingly disparate metabolic traits. Using LbNOX, we demonstrate that NADH reductive stress mediates the effects of GCKR variation on many metabolic traits, including circulating triglyceride levels, glucose tolerance and FGF21 levels. Our work identifies an elevated hepatic NADH/NAD+ ratio as a latent metabolic parameter that is shaped by human genetic variation and contributes causally to key metabolic traits and diseases. Moreover, it underscores the utility of genetic tools such as LbNOX to empower studies of 'causal metabolism'.

Mass Spectrometry: A Rosetta Stone to Learn How Fungi Interact and Talk

Fungi are a highly diverse group of heterotrophic organisms that play an important role in diverse ecological interactions, many of which are chemically mediated. Fungi have a very versatile metabolism, which allows them to synthesize a large number of still little-known chemical compounds, such as soluble compounds that are secreted into the medium and volatile compounds that are chemical mediators over short and long distances. Mass spectrometry (MS) is currently playing a dominant role in mycological studies, mainly due to its inherent sensitivity and rapid identification capabilities of different metabolites. Furthermore, MS has also been used as a reliable and accurate tool for fungi identification (i.e., biotyping). Here, we introduce the readers about fungal specialized metabolites, their role in ecological interactions and provide an overview on the MS-based techniques used in fungal studies. We particularly present the importance of sampling techniques, strategies to reduce false-positive identification and new MS-based analytical strategies that can be used in mycological studies, further expanding the use of MS in broader applications. Therefore, we foresee a bright future for mass spectrometry-based research in the field of mycology.

Plasma Metabolic Profiling of Human Thyroid Nodules by Gas Chromatography-Mass Spectrometry (GC-MS)-Based Untargeted Metabolomics

One of the challenges in the area of diagnostics of human thyroid cancer is a preoperative diagnosis of thyroid nodules with indeterminate cytology. Herein, we report an untargeted metabolomics analysis to identify circulating thyroid nodule metabolic signatures, to find new novel metabolic biomarkers. Untargeted gas chromatography-quadrupole-mass spectrometry was used to ascertain the specific plasma metabolic changes of thyroid nodule patients, which consisted of papillary thyroid carcinoma (PTC; n = 19), and multinodular goiter (MNG; n = 16), as compared to healthy subjects (n = 20). Diagnostic models were constructed using multivariate analyses such as principal component analysis, orthogonal partial least squares-discriminant analysis, and univariate analysis including One-way ANOVA and volcano plot by MetaboAnalyst and SIMCA software. Because of the multiple-testing issue, false discovery rate p-values were also computed for these functions. A total of 60 structurally annotated metabolites were subjected to statistical analysis. A combination of univariate and multivariate statistical analyses revealed a panel of metabolites responsible for the discrimination between thyroid nodules and healthy subjects, with variable importance in the projection (VIP) value greater than 0.8 and p-value less than 0.05. Significantly altered metabolites between thyroid nodules versus healthy persons are those associated with amino acids metabolism, the tricarboxylic acid cycle, fatty acids, and purine and pyrimidine metabolism, including cysteine, cystine, glutamic acid, α-ketoglutarate, 3-hydroxybutyric acid, adenosine-5-monophosphate, and uracil, respectively. Further, sucrose metabolism differed profoundly between thyroid nodule patients and healthy subjects. Moreover, according to the receiver operating characteristic (ROC) curve analysis, sucrose could discriminate PTC from MNG (area under ROC curve value = 0.92). This study enhanced our understanding of the distinct metabolic pathways associated with thyroid nodules, which enabled us to distinguish between patients and healthy subjects. In addition, our study showed extensive sucrose metabolism in the plasma of thyroid nodule patients, which provides a new metabolic signature of the thyroid nodule’s tumorigenesis. Accordingly, it suggests that sucrose can be considered as a circulating biomarker for differential diagnosis between malignancy and benignity in indeterminate thyroid nodules.

Pre-akinete formation in Zygnema sp. from polar habitats is associated with metabolite re-arrangement

In streptophytic green algae in the genus Zygnema, pre-akinete formation is considered a key survival strategy under extreme environmental conditions in alpine and polar regions. The transition from young, dividing cells to pre-akinetes is associated with morphological changes and the accumulation of storage products. Understanding the underlying metabolic changes could provide insights into survival strategies in polar habitats. Here, GC-MS-based metabolite profiling was used to study the metabolic signature associated with pre-akinete formation in Zygnema sp. from polar regions under laboratory conditions, induced by water and nutrient depletion, or collected in the field. Light microscopy and TEM revealed drastic changes in chloroplast morphology and ultrastructure, degradation of starch grains, and accumulation of lipid bodies in pre-akinetes. Accordingly, the metabolite profiles upon pre-akinete formation reflected a gradual shift in metabolic activity. Compared with young cells, pre-akinetes showed an overall reduction in primary metabolites such as amino acids and intermediates of the tricarboxylic acid (TCA) cycle, consistent with a lower metabolic turnover, while they accumulated lipids and oligosaccharides. Overall, the transition to the pre-akinete stage involves re-allocation of photosynthetically fixed energy into storage instead of growth, supporting survival of extreme environmental conditions.

Rhizosphere engineering through exogenous growth-regulating small molecules improves the colonizing efficiency of a plant growth-promoting rhizobacterium in rice

Enhancing the rhizosphere colonization and persistence of plant growth-promoting rhizobacteria (PGPR) is necessary for maximizing PGPR-mediated benefits for crop growth and fitness in environmentally friendly agriculture. In the present investigation, we attempted manipulation of the rice rhizosphere by spraying of low molecular weight plant-regulating metabolites on the foliage of rice plants to in turn enhance the colonizing efficiency of soil-inoculated PGPR strain. The green fluorescent protein gene-tagged rhizobacterial strain, Pseudomonas chlororaphis ZSB15-M2, was inoculated in sterile plant growth medium (vermiculite coco peat mixture) and non-autoclaved agricultural soil. We sprayed different plant growth-regulating small molecules on the foliage of rice seedlings and monitored the colonizing efficiency of ZSB15-M2 in the rice rhizosphere. Among the chemicals assessed, salicylic acid (SA) at 1 mM or Corynebacterium glutamicum cell extract (CGCE, 0.2% w/v) or Saccharomyces cerevisiae cell extract (SCCE, 0.2% w/v) showed a tenfold increase in rhizosphere colony-forming units of ZSB15-M2 compared to control with a significant decline in non-rhizosphere bulk soil population. Foliar spray of CGCE enhanced soil organic carbon, microbial biomass carbon and soil protein by 21.86%, 9.68% and 11.57% respectively in the rice rhizosphere as compared to mock control. Additionally, CGCE spray enhanced the key soil enzymes, viz., dehydrogenase and acid- and alkaline phosphatase in the rhizosphere ranging 15-36%. The cumulative effect of this engineered rhizosphere resulted in the elevation of nitrogen, phosphorus, potassium and zinc availability by 21.83%, 28.83%, 23.95% and 61.94%, respectively, in rice rhizosphere as compared to control. On the other hand, SCCE and SA spray had an equal influence on the rhizosphere's biological attributes, which is lower than that of GCGE and higher than that of mock control. From the study, we propose that the aboveground management of rice with microbial-based small molecules will modulate the rice rhizosphere to attract more beneficial PGPR-based inoculants, thus improving the crop and soil health.

A review of strategies for untargeted urinary metabolomic analysis using gas chromatography-mass spectrometry

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.

Analytical techniques for metabolomic studies: a review

Metabolomics is the comprehensive study of small-molecule metabolites. Obtaining a wide coverage of the metabolome is challenging because of the broad range of physicochemical properties of the small molecules. To study the compounds of interest spectroscopic (NMR), spectrometric (MS) and separation techniques (LC, GC, supercritical fluid chromatography, CE) are used. The choice for a given technique is influenced by the sample matrix, the concentration and properties of the metabolites, and the amount of sample. This review discusses the most commonly used analytical techniques for metabolomic studies, including their advantages, drawbacks and some applications.

Gas Chromatography-Mass Spectrometry-Based Metabolite Profiling for the Assessment of Freshness in Gilthead Sea Bream (Sparus aurata)

Gilthead sea bream (Sparus aurata) is one of the most important farmed Mediterranean fish species, and there is considerable interest for the development of suitable methods to assess its freshness. In the present work, gas chromatography–mass spectrometry-based metabolomics was employed to monitor the hydrophilic metabolites of sea bream during storage on ice for 19 days. Additionally, the quality changes were evaluated using two conventional methods: sensory evaluation according to European Union’s grading scheme and K-value, the most widely used chemical index of fish spoilage. With the application of chemometrics, the fish samples were successfully classified in the freshness categories, and a partial least squares regression model was built to predict K-value. A list of differential metabolites were found, which were distinguished according to their evolution profile as potential biomarkers of freshness and spoilage. Therefore, the results support the suitability of the proposed methodology to gain information on seafood quality.

Metabolic reprograming of LPS-stimulated human lung macrophages involves tryptophan metabolism and the aspartate-arginosuccinate shunt

Lung macrophages (LM) are in the first line of defense against inhaled pathogens and can undergo phenotypic polarization to the proinflammatory M1 after stimulation with Toll-like receptor agonists. The objective of the present work was to characterize the metabolic alterations occurring during the experimental M1 LM polarization. Human LM were obtained from resected lungs and cultured for 24 hrs in medium alone or with 10 ng.mL-1 lipopolysaccharide. Cells and culture supernatants were subjected to extraction for metabolomic analysis with high-resolution LC-MS (HILIC and reverse phase -RP- chromatography in both negative and positive ionization modes) and GC-MS. The data were analyzed with R and the Worklow4Metabolomics and MetaboAnalyst online infrastructures. A total of 8,741 and 4,356 features were detected in the intracellular and extracellular content, respectively, after the filtering steps. Pathway analysis showed involvement of arachidonic acid metabolism, tryptophan metabolism and Krebs cycle in the response of LM to LPS, which was confirmed by the specific quantitation of selected compounds. This refined analysis highlighted a regulation of the kynurenin pathway as well as the serotonin biosynthesis pathway, and an involvement of aspartate-arginosuccinate shunt in the malate production. Macrophages M1 polarization is accompanied by changes in the cell metabolome, with the differential expression of metabolites involved in the promotion and regulation of inflammation and antimicrobial activity. The analysis of this macrophage immunometabolome may be of interest for the understanding of the pathophysiology of lung inflammatory disesases.

A Protocol for the Multi-Omic Integration of Cervical Microbiota and Urine Metabolomics to Understand Human Papillomavirus (HPV)-Driven Dysbiosis

The multi-omic integration of microbiota data with metabolomics has gained popularity. This protocol is based on a human multi-omics study, integrating cervicovaginal microbiota, HPV status and neoplasia, with urinary metabolites. Indeed, to understand the biology of the infections and to develop adequate interventions for cervical cancer prevention, studies are needed to characterize in detail the cervical microbiota and understand the systemic metabolome. This article is a detailed protocol for the multi-omic integration of cervical microbiota and urine metabolome to shed light on the systemic effects of cervical dysbioses associated with Human Papillomavirus (HPV) infections. This methods article suggests detailed sample collection and laboratory processes of metabolomics, DNA extraction for microbiota, HPV typing, and the bioinformatic analyses of the data, both to characterize the metabolome, the microbiota, and joint multi-omic analyses, useful for the development of new point-of-care diagnostic tests based on these approaches.

Searching for Drug Synergy Against Cancer Through Polyamine Metabolism Impairment: Insight Into the Metabolic Effect of Indomethacin on Lung Cancer Cells

Non-small cell lung cancer (NSCLC) is the most lethal and prevalent type of lung cancer. In almost all types of cancer, the levels of polyamines (putrescine, spermidine, and spermine) are increased, playing a pivotal role in tumor proliferation. Indomethacin, a non-steroidal anti-inflammatory drug, increases the abundance of an enzyme termed spermidine/spermine-N¹-acetyltransferase (SSAT) encoded by the SAT1 gene. This enzyme is a key player in the export of polyamines from the cell. The aim of this study was to compare the effect of indomethacin on two NSCLC cell lines, and their combinatory potential with polyamine-inhibitor drugs in NSCLC cell lines. A549 and H1299 NSCLC cells were exposed to indomethacin and evaluations included SAT1 expression, SSAT levels, and the metabolic status of cells. Moreover, the difference in polyamine synthesis enzymes among these cell lines as well as the synergistic effect of indomethacin and chemical inhibitors of the polyamine pathway enzymes on cell viability were investigated. Indomethacin increased the expression of SAT1 and levels of SSAT in both cell lines. In A549 cells, it significantly reduced the levels of putrescine and spermidine. However, in H1299 cells, the impact of treatment on the polyamine pathway was insignificant. Also, the metabolic features upstream of the polyamine pathway (i.e., ornithine and methionine) were increased. In A549 cells, the increase of ornithine correlated with the increase of several metabolites involved in the urea cycle. Evaluation of the levels of the polyamine synthesis enzymes showed that ornithine decarboxylase is increased in A549 cells, whereas S-adenosylmethionine-decarboxylase and polyamine oxidase are increased in H1299 cells. This observation correlated with relative resistance to polyamine synthesis inhibitors eflornithine and SAM486 (inhibitors of ornithine decarboxylase and S-adenosyl-L-methionine decarboxylase, respectively), and MDL72527 (inhibitor of polyamine oxidase and spermine oxidase). Finally, indomethacin demonstrated a synergistic effect with MDL72527 in A549 cells and SAM486 in H1299 cells. Collectively, these results indicate that indomethacin alters polyamine metabolism in NSCLC cells and enhances the effect of polyamine synthesis inhibitors, such as MDL72527 or SAM486. However, this effect varies depending on the basal metabolic fingerprint of each type of cancer cell.

The impact of phenanthrene on membrane phospholipids and its biodegradation by Sphingopyxis soli

The direct interactions of bacterial membranes and polycyclic aromatic hydrocarbons (PAHs) strongly influence the biological processes, such as metabolic activity and uptake of substrates due to changes in membrane lipids. However, the elucidation of adaptation mechanisms as well as membrane phospholipid alterations in the presence of phenanthrene (PHE) from α-proteobacteria has not been fully explored. This study was conducted to define the degradation efficiency of PHE by Sphingopyxis soli strain KIT-001 in a newly isolated from Jeonju river sediments and to characterize lipid profiles in the presence of PHE in comparison to cells grown on glucose using quantitative lipidomic analysis. This strain was able to respectively utilize 1-hydroxy-2-naphthoic acid and salicylic acid as sole carbon source and approximately 90% of PHE (50 mg/L) was rapidly degraded via naphthalene route within 1 day incubation. In the cells grown on PHE, strain KIT-001 appeared to dynamically change profiles of metabolite and lipid in comparison to cells grown on glucose. The levels of primary metabolites, phosphatidylethanolamines (PE), and phosphatidic acids (PA) were significantly decreased, whereas the levels of phosphatidylcholines (PC) and phosphatidylglycerols (PG) were significantly increased. The adaptation mechanism of Sphingopyxis sp. regarded mainly the accumulation of bilayer forming lipids and anionic lipids to adapt more quickly under restricted nutrition and toxicity condition. Hence, these findings are conceivable that strain KIT-001 has a good adaptive ability and biodegradation for PHE through the alteration of phospholipids, and will be helpful for applications for effective bioremediation of PAHs-contaminated sites.

[Metabolomic profiling in patients with metabolic syndrome]

OBJECTIVE

To identify biomarkers, which are most specific for patients with metabolic syndrome (MS) using metabolomic profiling.

MATERIALS AND METHODS

Metabolomic profiling of patients with MS and comparison of their profile with the profile of volunteers was performed using high-performance liquid chromatography-mass-spectrometry.

RESULTS

The metabolomic profile of MS patients differed in several amino acids, including choline, cysteine, and serine and in the acylcarnitine group (р<0.05 for all comparisons).

CONCLUSION

The metabolites most specific for MS patients were identified. Increased concentrations of a combination of amino acids and carnitines can be considered as possible additional risk factors for cardiovascular diseases.

Sugar Alcohols Have a Key Role in Pathogenesis of Chronic Liver Disease and Hepatocellular Carcinoma in Whole Blood and Liver Tissues

The major risk factors for hepatocellular carcinoma (HCC) are hepatitis C and B viral infections that proceed to Chronic Liver Disease (CLD). Yet, the early diagnosis and treatment of HCC are challenging because the pathogenesis of HCC is not fully defined. To better understand the onset and development of HCC, untargeted GC-TOF MS metabolomics data were acquired from resected human HCC tissues and their paired non-tumor hepatic tissues (n = 46). Blood samples of the same HCC subjects (n = 23) were compared to CLD (n = 15) and healthy control (n = 15) blood samples. The participants were recruited from the National Liver Institute in Egypt. The GC-TOF MS data yielded 194 structurally annotated compounds. The most strikingly significant alteration was found for the class of sugar alcohols that were up-regulated in blood of HCC patients compared to CLD subjects (p < 2.4 × 10⁻¹²) and CLD compared to healthy controls (p = 4.1 × 10⁻⁷). In HCC tissues, sugar alcohols were the most significant (p < 1 × 10⁻⁶) class differentiating resected HCC tissues from non-malignant hepatic tissues for all HCC patients. Alteration of sugar alcohol levels in liver tissues also defined early-stage HCC from their paired non-malignant hepatic tissues (p = 2.7 × 10⁻⁶). In blood, sugar alcohols differentiated HCC from CLD subjects with an ROC-curve of 0.875 compared to 0.685 for the classic HCC biomarker alpha-fetoprotein. Blood sugar alcohol levels steadily increased from healthy controls to CLD to early stages of HCC and finally, to late-stage HCC patients. The increase in sugar alcohol levels indicates a role of aldo-keto reductases in the pathogenesis of HCC, possibly opening novel diagnostic and therapeutic options after in-depth validation.

Accurate and sensitive quantitation of glucose and glucose phosphates derived from storage carbohydrates by mass spectrometry

The addition of phosphate groups into glycogen modulates its branching pattern and solubility which all impact its accessibility to glycogen interacting enzymes. As glycogen architecture modulates its metabolism, it is essential to accurately evaluate and quantify its phosphate content. Simultaneous direct quantitation of glucose and its phosphate esters requires an assay with high sensitivity and a robust dynamic range. Herein, we describe a highly-sensitive method for the accurate detection of both glycogen-derived glucose and glucose-phosphate esters utilizing gas-chromatography coupled mass spectrometry. Using this method, we observed higher glycogen levels in the liver compared to skeletal muscle, but skeletal muscle contained many more phosphate esters. Importantly, this method can detect femtomole levels of glucose and glucose phosphate esters within an extremely robust dynamic range with excellent accuracy and reproducibility. The method can also be easily adapted for the quantification of plant starch, amylopectin or other biopolymers.

Urinary chemical fingerprint left behind by repeated NSAID administration: Discovery of putative biomarkers using artificial intelligence

Prediction and early detection of kidney damage induced by nonsteroidal anti-inflammatories (NSAIDs) would provide the best chances of maximizing the anti-inflammatory effects while minimizing the risk of kidney damage. Unfortunately, biomarkers for detecting NSAID-induced kidney damage in cats remain to be discovered. To identify potential urinary biomarkers for monitoring NSAID-based treatments, we applied an untargeted metabolomics approach to urine collected from cats treated repeatedly with meloxicam or saline for up to 17 days. Applying multivariate analysis, this study identified a panel of seven metabolites that discriminate meloxicam treated from saline treated cats. Combining artificial intelligence machine learning algorithms and an independent testing urinary metabolome data set from cats with meloxicam-induced kidney damage, a panel of metabolites was identified and validated. The panel of metabolites including tryptophan, tyrosine, taurine, threonic acid, pseudouridine, xylitol and lyxitol, successfully distinguish meloxicam-treated and saline-treated cats with up to 75–100% sensitivity and specificity. This panel of urinary metabolites may prove a useful and non-invasive diagnostic tool for monitoring potential NSAID induced kidney injury in feline patients and may act as the framework for identifying urine biomarkers of NSAID induced injury in other species.

Improved workflow for mass spectrometry-based metabolomics analysis of the heart

MS-based metabolomics methods are powerful techniques to map the complex and interconnected metabolic pathways of the heart; however, normalization of metabolite abundance to sample input in heart tissues remains a technical challenge. Herein, we describe an improved GC-MS-based metabolomics workflow that uses insoluble protein-derived glutamate for the normalization of metabolites within each sample and includes normalization to protein-derived amino acids to reduce biological variation and detect small metabolic changes. Moreover, glycogen is measured within the metabolomics workflow. We applied this workflow to study heart metabolism by first comparing two different methods of heart removal: the Langendorff heart method (reverse aortic perfusion) and in situ freezing of mouse heart with a modified tissue freeze-clamp approach. We then used the in situ freezing method to study the effects of acute β-adrenergic receptor stimulation (through isoproterenol (ISO) treatment) on heart metabolism. Using our workflow and within minutes, ISO reduced the levels of metabolites involved in glycogen metabolism, glycolysis, and the Krebs cycle, but the levels of pentose phosphate pathway metabolites and of many free amino acids remained unchanged. This observation was coupled to a 6-fold increase in phosphorylated adenosine nucleotide abundance. These results support the notion that ISO acutely accelerates oxidative metabolism of glucose to meet the ATP demand required to support increased heart rate and cardiac output. In summary, our MS-based metabolomics workflow enables improved quantification of cardiac metabolites and may also be compatible with other methods such as LC or capillary electrophoresis.

Loss of Rb1 Enhances Glycolytic Metabolism in Kras-Driven Lung Tumors In Vivo

Dysregulated metabolism is a hallmark of cancer cells and is driven in part by specific genetic alterations in various oncogenes or tumor suppressors. The retinoblastoma protein (pRb) is a tumor suppressor that canonically regulates cell cycle progression; however, recent studies have highlighted a functional role for pRb in controlling cellular metabolism. Here, we report that loss of the gene encoding pRb (Rb1) in a transgenic mutant Kras-driven model of lung cancer results in metabolic reprogramming. Our tracer studies using bolus dosing of [U-¹³C]-glucose revealed an increase in glucose carbon incorporation into select glycolytic intermediates. Consistent with this result, Rb1-depleted tumors exhibited increased expression of key glycolytic enzymes. Interestingly, loss of Rb1 did not alter mitochondrial pyruvate oxidation compared to lung tumors with intact Rb1. Additional tracer studies using [U-¹³C,¹⁵N]-glutamine and [U-¹³C]-lactate demonstrated that loss of Rb1 did not alter glutaminolysis or utilization of circulating lactate within the tricarboxylic acid cycle (TCA) in vivo. Taken together, these data suggest that the loss of Rb1 promotes a glycolytic phenotype, while not altering pyruvate oxidative metabolism or glutamine anaplerosis in Kras-driven lung tumors.

Metabolomics in the Context of Plant Natural Products Research: From Sample Preparation to Metabolite Analysis

Plant-derived natural products have long been considered a valuable source of lead compounds for drug development. Natural extracts are usually composed of hundreds to thousands of metabolites, whereby the bioactivity of natural extracts can be represented by synergism between several metabolites. However, isolating every single compound from a natural extract is not always possible due to the complex chemistry and presence of most secondary metabolites at very low levels. Metabolomics has emerged in recent years as an indispensable tool for the analysis of thousands of metabolites from crude natural extracts, leading to a paradigm shift in natural products drug research. Analytical methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) are used to comprehensively annotate the constituents of plant natural products for screening, drug discovery as well as for quality control purposes such as those required for phytomedicine. In this review, the current advancements in plant sample preparation, sample measurements, and data analysis are presented alongside a few case studies of the successful applications of these processes in plant natural product drug discovery.

Metabolomic Analysis Reveals Contributions of Citric and Citramalic Acids to Rare Earth Bioleaching by a Paecilomyces Fungus

Conventional methods for extracting rare earth elements from monazite ore require high energy inputs and produce environmentally damaging waste streams. Bioleaching offers a potentially more environmentally friendly alternative extraction process. In order to better understand bioleaching mechanisms, we conducted an exo-metabolomic analysis of a previously isolated rare earth bioleaching fungus from the genus Paecilomyces (GenBank accession numbers KM874779 and KM 874781) to identify contributions of compounds exuded by this fungus to bioleaching activity. Exuded compounds were compared under two growth conditions: growth with monazite ore as the only phosphate source, and growth with a soluble phosphate source (K₂HPO₄) added. Overall metabolite profiling, in combination with glucose consumption and biomass accumulation data, reflected a lag in growth when this organism was grown with only monazite. We analyzed the relationships between metabolite concentrations, rare earth solubilization, and growth conditions, and identified several metabolites potentially associated with bioleaching. Further investigation using laboratory prepared solutions of 17 of these metabolites indicated statistically significant leaching contributions from both citric and citramalic acids. These contributions (16.4 and 15.0 mg/L total rare earths solubilized) accounted for a portion, but not all, of the leaching achieved with direct bioleaching (42 ± 15 mg/L final rare earth concentration). Additionally, citramalic acid released significantly less of the radioactive element thorium than did citric acid (0.25 ± 0.01 mg/L compared to 1.18 ± 0.01 mg/L), suggesting that citramalic acid may have preferable leaching properties for a monazite bioleaching process.

Heat stress effects on source-sink relationships and metabolome dynamics in wheat

Crops such as wheat (Triticum spp.) are predicted to face more frequent exposures to heat stress as a result of climate change. Increasing the yield and sustainability of yield under such stressful conditions has long been a major target of wheat breeding, and this goal is becoming increasingly urgent as the global population increases. Exposure of wheat plants in their reproductive or grain-filling stage to high temperature affects the duration and rate of grain filling, and hence has a negative impact on wheat productivity. Therefore, understanding the plasticity of the response to heat stress that exists between wheat genotypes, especially in source-sink relationships at the reproductive and grain-filling stages, is critical for the selection of germplasm that can maintain high yields under heat stress. A broad understanding of metabolic dynamics and the relationships between metabolism and heat tolerance is required in order to achieve this goal. Here, we review the current literature concerning the effects of heat stress on sink-source relationships in a wide range of wheat genotypes, and highlight the current metabolomic approaches that are used to investigate high temperature responses in wheat.

Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.

Metabolomic Profiling to Identify Molecular Biomarkers of Cellular Response to Methotrexate In Vitro

Variation in methotrexate (MTX) efficacy represents a significant barrier to early and effective disease control in the treatment of autoimmune arthritis. We hypothesize that the utilization of metabolomic techniques will allow for an improved understanding of the biochemical basis for the pharmacological activity of MTX, and can promote the identification and evaluation of novel molecular biomarkers of MTX response. In this work, erythroblastoid cells were exposed to MTX at the physiologic concentration of 1,000 nM and analyzed using three metabolomic platforms to give a broad spectrum of cellular metabolites. MTX pharmacological activity, defined as cellular growth inhibition, was associated with an altered cellular metabolomic profile based on the analysis of 724 identified metabolites. By discriminant analysis, MTX treatment was associated with increases in ketoisovaleric acid, fructose, galactose, and 2-deoxycytidine, and corresponding reductions in 2-deoxyuridine, phosphatidylinositol 32:0, orotic acid, and inosine monophosphate. Inclusion of data from analysis of folate metabolism in combination with chemometric and metabolic network analysis demonstrated that MTX treatment is associated with dysregulated folate metabolism and nucleotide biosynthesis, which is in line with its known mechanism of action. However, MTX treatment was also associated with alterations in a diversity of metabolites, including intermediates of amino acid, carbohydrate, and lipid metabolism. Collectively, these findings support a robust metabolic response following exposure to physiologic concentrations of MTX. They also identify various metabolic intermediates that are associated with the pharmacological activity of MTX, and are, therefore, potential molecular biomarker candidates in future preclinical and clinical studies of MTX efficacy in autoimmune arthritis.

Green and White Asparagus (Asparagus officinalis): A Source of Developmental, Chemical and Urinary Intrigue

Asparagus (Asparagus officinalis) is one of the world's top 20 vegetable crops. Both green and white shoots (spears) are produced; the latter being harvested before becoming exposed to light. The crop is grown in nearly all areas of the world, with the largest production regions being China, Western Europe, North America and Peru. Successful production demands high farmer input and specific environmental conditions and cultivation practices. Asparagus materials have also been used for centuries as herbal medicine. Despite this widespread cultivation and consumption, we still know relatively little about the biochemistry of this crop and how this relates to the nutritional, flavour, and neutra-pharmaceutical properties of the materials used. To date, no-one has directly compared the contrasting compositions of the green and white crops. In this short review, we have summarised most of the literature to illustrate the chemical richness of the crop and how this might relate to key quality parameters. Asparagus has excellent nutritional properties and its flavour/fragrance is attributed to a set of volatile components including pyrazines and sulphur-containing compounds. More detailed research, however, is needed and we propose that (untargeted) metabolomics should have a more prominent role to play in these investigations.

A Perspective and Framework for Developing Sample Type Specific Databases for LC/MS-Based Clinical Metabolomics

Metabolomics has the potential to greatly impact biomedical research in areas such as biomarker discovery and understanding molecular mechanisms of disease. However, compound identification (ID) remains a major challenge in liquid chromatography mass spectrometry-based metabolomics. This is partly due to a lack of specificity in metabolomics databases. Though impressive in depth and breadth, the sheer magnitude of currently available databases is in part what makes them ineffective for many metabolomics studies. While still in pilot phases, our experience suggests that custom-built databases, developed using empirical data from specific sample types, can significantly improve confidence in IDs. While the concept of sample type specific databases (STSDBs) and spectral libraries is not entirely new, inclusion of unique descriptors such as detection frequency and quality scores, can be used to increase confidence in results. These features can be used alone to judge the quality of a database entry, or together to provide filtering capabilities. STSDBs rely on and build upon several available tools for compound ID and are therefore compatible with current compound ID strategies. Overall, STSDBs can potentially result in a new paradigm for translational metabolomics, whereby investigators confidently know the identity of compounds following a simple, single STSDB search.

Extracellular matrix hydrogel derived from decellularized tissues enables endodermal organoid culture

Organoids have extensive therapeutic potential and are increasingly opening up new avenues within regenerative medicine. However, their clinical application is greatly limited by the lack of effective GMP-compliant systems for organoid expansion in culture. Here, we envisage that the use of extracellular matrix (ECM) hydrogels derived from decellularized tissues (DT) can provide an environment capable of directing cell growth. These gels possess the biochemical signature of tissue-specific ECM and have the potential for clinical translation. Gels from decellularized porcine small intestine (SI) mucosa/submucosa enable formation and growth of endoderm-derived human organoids, such as gastric, hepatic, pancreatic, and SI. ECM gels can be used as a tool for direct human organoid derivation, for cell growth with a stable transcriptomic signature, and for in vivo organoid delivery. The development of these ECM-derived hydrogels opens up the potential for human organoids to be used clinically.

Existing and Emerging Metabolomic Tools for ALS Research

Growing evidence suggests that aberrant energy metabolism could play an important role in the pathogenesis of amyotrophic lateral sclerosis (ALS). Despite this, studies applying advanced technologies to investigate energy metabolism in ALS remain scarce. The rapidly growing field of metabolomics offers exciting new possibilities for ALS research. Here, we review existing and emerging metabolomic tools that could be used to further investigate the role of metabolism in ALS. A better understanding of the metabolic state of motor neurons and their surrounding cells could hopefully result in novel therapeutic strategies.

Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes

Context-specific GEnome-scale metabolic Network REconstructions (GENREs) provide a means to understand cellular metabolism at a deeper level of physiological detail. Here, we use transcriptomics data from chemically-exposed rat hepatocytes to constrain a GENRE of rat hepatocyte metabolism and predict biomarkers of liver toxicity using the Transcriptionally Inferred Metabolic Biomarker Response algorithm. We profiled alterations in cellular hepatocyte metabolism following in vitro exposure to four toxicants (acetaminophen, carbon tetrachloride, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six hour. TIMBR predictions were compared with paired fresh and spent media metabolomics data from the same exposure conditions. Agreement between computational model predictions and experimental data led to the identification of specific metabolites and thus metabolic pathways associated with toxicant exposure. Here, we identified changes in the TCA metabolites citrate and alpha-ketoglutarate along with changes in carbohydrate metabolism and interruptions in ATP production and the TCA Cycle. Where predictions and experimental data disagreed, we identified testable hypotheses to reconcile differences between the model predictions and experimental data. The presented pipeline for using paired transcriptomics and metabolomics data provides a framework for interrogating multiple omics datasets to generate mechanistic insight of metabolic changes associated with toxicological responses.

Metabolite-related dietary patterns and the development of islet autoimmunity

The role of diet in type 1 diabetes development is poorly understood. Metabolites, which reflect dietary response, may help elucidate this role. We explored metabolomics and lipidomics differences between 352 cases of islet autoimmunity (IA) and controls in the TEDDY (The Environmental Determinants of Diabetes in the Young) study. We created dietary patterns reflecting pre-IA metabolite differences between groups and examined their association with IA. Secondary outcomes included IA cases positive for multiple autoantibodies (mAb+). The association of 853 plasma metabolites with outcomes was tested at seroconversion to IA, just prior to seroconversion, and during infancy. Key compounds in enriched metabolite sets were used to create dietary patterns reflecting metabolite composition, which were then tested for association with outcomes in the nested case-control subset and the full TEDDY cohort. Unsaturated phosphatidylcholines, sphingomyelins, phosphatidylethanolamines, glucosylceramides, and phospholipid ethers in infancy were inversely associated with mAb+ risk, while dicarboxylic acids were associated with an increased risk. An infancy dietary pattern representing higher levels of unsaturated phosphatidylcholines and phospholipid ethers, and lower sphingomyelins was protective for mAb+ in the nested case-control study only. Characterization of this high-risk infant metabolomics profile may help shape the future of early diagnosis or prevention efforts.

Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics

The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.

Targeting Pathogenic Lafora Bodies in Lafora Disease Using an Antibody-Enzyme Fusion

Lafora disease (LD) is a fatal childhood epilepsy caused by recessive mutations in either the EPM2A or EPM2B gene. A hallmark of LD is the intracellular accumulation of insoluble polysaccharide deposits known as Lafora bodies (LBs) in the brain and other tissues. In LD mouse models, genetic reduction of glycogen synthesis eliminates LB formation and rescues the neurological phenotype. Therefore, LBs have become a therapeutic target for ameliorating LD. Herein, we demonstrate that human pancreatic α-amylase degrades LBs. We fused this amylase to a cell-penetrating antibody fragment, and this antibody-enzyme fusion (VAL-0417) degrades LBs in vitro and dramatically reduces LB loads in vivo in Epm2a-/- mice. Using metabolomics and multivariate analysis, we demonstrate that VAL-0417 treatment of Epm2a-/- mice reverses the metabolic phenotype to a wild-type profile. VAL-0417 is a promising drug for the treatment of LD and a putative precision therapy platform for intractable epilepsy.