Untargeted GC-MS Metabolomics

Advances in Chromatographic and Mass Spectrometric Techniques for Analyzing Reducing Monosaccharides and Their Phosphates in Biological Samples

Reducing monosaccharides and their phosphates are critical metabolites in the central carbon metabolism pathway of living organisms. Variations in their content can indicate abnormalities in metabolic pathways and the onset of certain diseases, necessitating their analysis and detection. Reducing monosaccharides and their phosphates exhibit significant variations in content within biological samples and are present in many isomers, which makes the accurate quantification of reducing monosaccharides and their phosphates in biological samples a challenging task. Various analytical methods such as spectroscopy, fluorescence detection, colorimetry, nuclear magnetic resonance spectroscopy, sensor-based techniques, chromatography, and mass spectrometry are employed to detect monosaccharides and phosphates. In comparison, chromatography and mass spectrometry are highly favored for their ability to simultaneously analyze multiple components and their high sensitivity and selectivity. This review thoroughly evaluates the current chromatographic and mass spectrometric methods used for detecting reducing monosaccharides and their phosphates from 2013 to 2023, highlighting their efficacy and the advancements in these analytical technologies.

Advancements in metabolomics research in benign gallbladder diseases: A review

The burgeoning field of metabolomics has piqued the interest of researchers in the context of benign gallbladder diseases, which include conditions such as gallbladder polyps, gallstones, and cholecystitis, which are common digestive system disorders. As metabolomics continues to advance, researchers have increasingly focused their attention on its applicability in the study of benign gallbladder diseases to provide new perspectives for diagnostic, therapeutic, and prognostic evaluation. This comprehensive review primarily describes the techniques of liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance and their respective applications in the study of benign gallbladder disease. Metabolomics has made remarkable progress in various aspects of these diseases, ranging from early diagnosis, etiological research, assessment of disease progression and prognosis, and optimization of therapeutic strategies. However, challenges remain in the field of metabolomics in the study of benign gallbladder diseases. These include issues related to data processing and analysis, biomarker discovery and validation, interdisciplinary research integration, and the advancement of personalized medicine. This article attempts to summarize research findings to date, highlight future research directions, and provide a reference point for metabolomics research in benign gallbladder disease.

Integrated untargeted and targeted testicular metabolomics to reveal the regulated mechanism of Gushudan on the hypothalamic-pituitary-gonadal axis of kidney-yang-deficiency-syndrome rats

Modern studies have shown that neuroendocrine disorders caused by the dysfunction of the hypothalamic-pituitary-gonadal (HPG) axis are one of the important pathogenetic mechanisms of kidney-yang-deficiency-syndrome (KYDS). The preventive effect of Gushudan on KYDS has been reported, but its regulatory mechanisms on the HPG axis have not been elucidated. In this study, we developed an integrated untargeted and targeted metabolomics analysis strategy to investigate the regulatory mechanism of Gushudan on the HPG axis in rats with KYDS. In untargeted metabolomics, we screened 14 potential biomarkers such as glycine, lysine, and glycerol that were significantly associated with the HPG axis. To explore the effect of changes in the levels of potential biomarkers on KYDS, all of them were quantified in targeted metabolomics. With the quantitative results, correlations between potential biomarkers and testosterone, a functional indicator of the HPG axis, were explored. The results showed that oxidative stress, inflammatory response, and energy depletion, induced by metabolic disorders in rats, were responsible for the decrease in testosterone levels. Gushudan improves metabolic disorders and restores testosterone levels, thus restoring HPG axis dysfunction. This finding elucidates the special metabolic characteristics of KYDS and the therapeutic mechanism of Gushudan from a new perspective.

Twenty Years of 1H NMR Plant Metabolomics: A Way Forward toward Assessment of Plant Metabolites for Constitutive and Inducible Defenses to Biotic Stress

Metabolomics has become an important tool in elucidating the complex relationship between a plant genotype and phenotype. For over 20 years, nuclear magnetic resonance (NMR) spectroscopy has been known for its robustness, quantitative capabilities, simplicity, and cost-efficiency. 1H NMR is the method of choice for analyzing a broad range of relatively abundant metabolites, which can be used for both capturing the plant chemical profile at one point in time and understanding the pathways that underpin plant defense. This systematic Review explores how 1H NMR-based plant metabolomics has contributed to understanding the role of various compounds in plant responses to biotic stress, focusing on both primary and secondary metabolites. It clarifies the challenges and advantages of using 1H NMR in plant metabolomics, interprets common trends observed, and suggests guidelines for method development and establishing standard procedures.

A metabolomics study on carcinogenesis of ground-glass nodules

Objective

This study aimed to identify differential metabolites and key metabolic pathways between lung adenocarcinoma (LUAD) tissues and normal lung (NL) tissues using metabolomics techniques, to discover potential biomarkers for the early diagnosis of lung cancer.

Material and Methods

Forty-five patients with primary ground-glass nodules (GGN) identified on computed tomography imaging and who were willing to undergo surgery at Shanghai General Hospital from December 2021 to December 2022 were recruited to the study. All participants underwent video thoracoscopy surgery with segmental or wedge resection of the lung. Tissue samples for pathological examination were collected from the site of ground-glass nodules (GGN) lesion and 3 cm away from the lesion (NL). The pathology results were 35 lung adenocarcinoma (LUAD) cases (13 invasive adenocarcinoma, 14 minimally invasive adenocarcinoma, and eight adenocarcinoma in situ), 10 benign samples, and 45 NL tissues. For the untargeted metabolomics technique, 25 LUAD samples were assigned as the case group and 30 NL tissues as the control group. For the targeted metabolomics technique, ten LUAD samples were assigned as the case group and 15 NL tissues as the control group. Samples were analyzed by untargeted and targeted metabolomics, with liquid chromatography-tandem mass spectrometry detection used as part of the experimental procedure.

Results

Untargeted metabolomics revealed 164 differential metabolites between the case and control groups, comprising 110 up regulations and 54 down regulations. The main metabolic differences found by the untargeted method were organic acids and their derivatives. Targeted metabolomics revealed 77 differential metabolites between the case and control groups, comprising 69 up regulations and eight down regulations. The main metabolic changes found by the targeted method were fatty acids, amino acids, and organic acids. The levels of organic acids such as lactic acid, fumaric acid, and malic acid were significantly increased in LUAD tissue compared to NL. Specifically, an increased level of L-lactic acid was found by both untargeted (variable importance in projection [VIP] = 1.332, fold-change [FC] = 1.678, q = 0.000) and targeted metabolomics (VIP = 1.240, FC = 1.451, q = 0.043). Targeted metabolomics also revealed increased levels of fumaric acid (VIP = 1.481, FC = 1.764, q = 0.106) and L-malic acid (VIP = 1.376, FC = 1.562, q = 0.012). Most of the 20 differential fatty acids identified were downregulated, including dodecanoic acid (VIP = 1.416, FC = 0.378, q = 0.043) and tridecane acid (VIP = 0.880, FC = 0.780, q = 0.106). Furthermore, increased levels of differential amino acids were found in LUAD samples.

Conclusion

Lung cancer is a complex and heterogeneous disease with diverse genetic alterations. The study of metabolic profiles is a promising research field in this cancer type. Targeted and untargeted metabolomics revealed significant differences in metabolites between LUAD and NL tissues, including elevated levels of organic acids, decreased levels of fatty acids, and increased levels of amino acids. These metabolic features provide valuable insights into LUAD pathogenesis and can potentially serve as biomarkers for prognosis and therapy response.

Comprehensive multicomponent characterization and quality assessment of Xiaoyao Wan by UPLC-Q-Orbitrap-MS, HS-SPME-GC-MS and HS-GC-IMS

Xiaoyao Wan (XYW) is a prescription medicine of traditional Chinese medicine (TCM) with the effects of "soothing the liver and relieving depression," and "strengthening spleen and nourishing blood". XYW has been widely concerned in the treatment of depression and has become one of the commonly used classic formulas in clinical practice. However, the pharmacodynamic substance basis and the quality control studies of XYW are hitherto quite limited. Here, we aim to fully utilize an advanced ultra - performance liquid chromatography-quadrupole - Orbitrap mass spectrometry (UPLC-Q-Orbitrap-MS), headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) technique to deep characterization of the pharmacological substance basis and quantitatively evaluate the quality of XYW. Firstly, 299 compounds were identified or tentatively characterized, including 198 non-volatile organic compounds (n-VOCs) and 101 volatile organic compounds (VOCs). Secondly, principal component analysis (PCA) and hierarchical cluster analysis (HCA) was used to analyze quality differences in XYW at different manufacturers. Thirdly, a parallel reaction monitoring (PRM) method was established and validated to quantify the fourteen major effective substances in different manufacturers of XYW, which were chosen as the benchmarked substances to evaluate the quality of XYW. In conclusion, this study shows that the strategy provides a useful method for quality control of TCM and offers a practical workflow for exploring the quality consistency of TCM.

Transcriptomics and metabolomics reveal improved performance of Hu sheep on hybridization with Southdown sheep

Consumers are increasingly demanding high-quality mutton. Cross breeding can improve meat quality and is widely used in sheep breeding. However, little is known about the molecular mechanism of cross breeding sheep meat quality. In this study, male Southdown and female Hu sheep were hybridized. The slaughter performance and longissimus dorsi quality of the 6-month-old hybrid offspring were measured, and the longissimus dorsi of the hybrid offspring was analyzed by transcriptomics and metabolomics to explore the effect of cross breeding on meat quality. The results showed that the production performance of Southdown × Hu F1 sheep was significantly improved, the carcass fat content was significantly decreased, and the eating quality of Southdown × Hu F1 sheep were better. Compared with the HS group (Hu × Hu), the NH group (Southdown × Hu) had 538 differentially expressed genes and 166 differentially expressed metabolites (P < 0.05), which were significantly enriched in amino acid metabolism and other related pathways. Up-regulated genes METTL21C, PPARGC1A and down-regulated gene WFIKKN2 are related to muscle growth and development. Among them, the METTL21C gene, which is related to muscle development, was highly correlated with carnosine, a metabolite related to meat quality (correlation > 0.6 and P < 0.05). Our results provide further understanding of the molecular mechanism of cross breeding for sheep muscle growth and meat quality optimization.

Exploratory optimisation of a LC-HRMS based analytical method for untargeted metabolomic screening of Cannabis Sativa L. through Data Mining

BACKGROUND

Recent increase in public acceptance of cannabis as a natural medical alternative for certain neurological pathologies has led to its approval in different regions of the world. However, due to its previous illegal background, little research has been conducted around its biochemical insights. Therefore, in the current framework, metabolomics may be a suitable approach for deepening the knowledge around this plant species. Nevertheless, experimental methods in metabolomics must be carefully handled, as slight modifications can lead to metabolomic coverage loss. Hence, the main objective of this work was to optimise an analytical method for appropriate untargeted metabolomic screening of cannabis.

RESULTS

We present an empirically optimised experimental procedure through which the broadest metabolomic coverage was obtained, in which extraction solvents for metabolite isolation, chromatographic columns for LC-qOrbitrap analysis and plant-representative biological tissues were compared. By exploratory means, it was determined that the solvent combination composed of CHCl3:H2O:CH3OH (2:1:1, v/v) provided the highest number of features from diverse chemical classes, as it was a two-phase extractant. In addition, a reverse phase 2.6 μm C18 100 Å (150 × 3 mm) chromatographic column was determined as the appropriate choice for adequate separation and further detection of the diverse metabolite classes. Apart from that, overall chromatographic peak quality provided by each column was observed and the need for batch correction methods through quality control (QC) samples was confirmed. At last, leaf and flower tissues resulted to provide complementary metabolic information of the plant, to the detriment of stem tissue, which resulted to be negligible.

SIGNIFICANCE

It was concluded that the optimised experimental procedure could significantly ease the path for future research works related to cannabis metabolomics by LC-HRMS means, as the work was based on previous plant metabolomics literature. Furthermore, it is crucial to highlight that an optimal analytical method can vary depending on the main objective of the research, as changes in the experimental factors can lead to different outcomes, regardless of whether the results are better or worse.

A comparative metabolomics analysis of domestic yak (Bos grunniens) milk with human breast milk

Yaks are tough animals living in Tibet's hypoxic stress environment. However, the metabolite composition of yak milk and its role in hypoxic stress tolerance remains largely unexplored. The similarities and differences between yak and human milk in hypoxic stress tolerance are also unclear. This study explored yak colostrum (YC) and yak mature milk (YMM) using GC-MS, and 354 metabolites were identified in yak milk. A comparative metabolomic analysis of yak and human milk metabolites showed that over 70% of metabolites were species-specific. Yak milk relies mainly on essential amino acids- arginine and essential branched-chain amino acids (BCAAs): L-isoleucine, L-leucine, and L-valine tolerate hypoxic stress. To slow hypoxic stress, human breast milk relies primarily on the neuroprotective effects of non-essential amino acids or derivates, such as citrulline, sarcosine, and creatine. In addition, metabolites related to hypoxic stress were significantly enriched in YC than in YMM. These results reveal the unique metabolite composition of yak and human milk and provide practical information for applying yak and human milk to hypoxic stress tolerance.

Assessment of cold exposure-induced metabolic changes in mice using untargeted metabolomics

Background: Cold exposure (CE) can effectively modulate adipose tissue metabolism and improve metabolic health. Although previous metabolomics studies have primarily focused on analyzing one or two samples from serum, brown adipose tissue (BAT), white adipose tissue (WAT), and liver samples, there is a significant lack of simultaneous analysis of multiple tissues regarding the metabolic changes induced by CE in mice. Therefore, our study aims to investigate the metabolic profiles of the major tissues involved. Methods: A total of 14 male C57BL/6J mice were randomly assigned to two groups: the control group (n = 7) and the CE group (n = 7). Metabolite determination was carried out using gas chromatography-mass spectrometry (GC-MS), and multivariate analysis was employed to identify metabolites exhibiting differential expression between the two groups. Results: In our study, we identified 32 discriminant metabolites in BAT, 17 in WAT, 21 in serum, 7 in the liver, 16 in the spleen, and 26 in the kidney, respectively. Among these metabolites, amino acids, fatty acids, and nucleotides emerged as the most significantly altered compounds. These metabolites were found to be associated with 12 differential metabolic pathways closely related to amino acids, fatty acids, and energy metabolism. Conclusion: Our study may provide valuable insights into the metabolic effects induced by CE, and they have the potential to inspire novel approaches for treating metabolic diseases.

Unraveling the Mechanisms of Ch-SeNP Cytotoxicity against Cancer Cells: Insights from Targeted and Untargeted Metabolomics

Although chitosan-stabilized selenium nanoparticles (Ch-SeNPs) have emerged as a promising chemical form of selenium for anticancer purposes, gathering more profound knowledge related to molecular dysfunctions contributes significantly to the promotion of their evolution as a chemotherapeutic drug. In this sense, metabolites are the end products in the flow of gene expression and, thus, the most sensitive to changes in the physiological state of a biological system. Therefore, metabolomics provides a functional readout of the biochemical activity and cell state. In the present study, we evaluated alterations in the metabolomes of HepG2 cells after the exposure to Ch-SeNPs to elucidate the biomolecular mechanisms involved in their therapeutic effect. A targeted metabolomic approach was conducted to evaluate the levels of four of the main energy-related metabolites (adenosine triphosphate (ATP); adenosine diphosphate (ADP); nicotinamide adenine dinucleotide (NAD+); and 1,4-dihydronicotinamide adenine dinucleotide (NADH)), revealing alterations as a result of exposure to Ch-SeNPs related to a shortage in the energy supply system in the cell. In addition, an untargeted metabolomic experiment was performed, which allowed for the study of alterations in the global metabolic profile as a consequence of Ch-SeNP exposure. The results indicate that the TCA cycle and glycolytic pathways were impaired, while alternative pathways such as glutaminolysis and cysteine metabolism were upregulated. Additionally, increased fructose levels suggested the induction of hypoxia-like conditions. These findings highlight the potential of Ch-SeNPs to disrupt cancer cell metabolism and provide insights into the mechanisms underlying their antitumor effects.

A cross-sectional study: a breathomics based pulmonary tuberculosis detection method

BACKGROUND

Diagnostics for pulmonary tuberculosis (PTB) are usually inaccurate, expensive, or complicated. The breathomics-based method may be an attractive option for fast and noninvasive PTB detection.

METHOD

Exhaled breath samples were collected from 518 PTB patients and 887 controls and tested on the real-time high-pressure photon ionization time-of-flight mass spectrometer. Machine learning algorithms were employed for breathomics analysis and PTB detection mode, whose performance was evaluated in 430 blinded clinical patients.

RESULTS

The breathomics-based PTB detection model achieved an accuracy of 92.6%, a sensitivity of 91.7%, a specificity of 93.0%, and an AUC of 0.975 in the blinded test set (n = 430). Age, sex, and anti-tuberculosis treatment does not significantly impact PTB detection performance. In distinguishing PTB from other pulmonary diseases (n = 182), the VOC modes also achieve good performance with an accuracy of 91.2%, a sensitivity of 91.7%, a specificity of 88.0%, and an AUC of 0.961.

CONCLUSIONS

The simple and noninvasive breathomics-based PTB detection method was demonstrated with high sensitivity and specificity, potentially valuable for clinical PTB screening and diagnosis.

Effects of Fe2+ addition to sugarcane molasses on poly-γ-glutamic acid production in Bacillus licheniformis CGMCC NO. 23967

BACKGROUND

Poly-γ-glutamic acid (γ-PGA) is biodegradable, water-soluble, environment-friendly, and edible. Consequently, it has a variety of industrial applications. It is crucial to control production cost and increase output for industrial production γ-PGA.

RESULTS

Here γ-PGA production from sugarcane molasses by Bacillus licheniformis CGMCC NO. 23967 was studied in shake-flasks and bioreactors, the results indicate that the yield of γ-PGA could reach 40.668 g/L in a 5L stirred tank fermenter. Further study found that γ-PGA production reached 70.436 g/L, γ-PGA production and cell growth increased by 73.20% and 55.44%, respectively, after FeSO₄·7H₂O was added. Therefore, we investigated the metabolomic and transcriptomic changes following FeSO₄·7H₂O addition. This addition resulted in increased abundance of intracellular metabolites, including amino acids, organic acids, and key TCA cycle intermediates, as well as upregulation of the glycolysis pathway and TCA cycle.

CONCLUSIONS

These results compare favorably with those obtained from glucose and other forms of biomass feedstock, confirming that sugarcane molasses can be used as an economical substrate without any pretreatment. The addition of FeSO₄·7H₂O to sugarcane molasses may increase the efficiency of γ-PGA production in intracellular.

Targeting mitochondrial impairment for the treatment of cardiovascular diseases: From hypertension to ischemia-reperfusion injury, searching for new pharmacological targets

Mitochondria and mitochondrial proteins represent a group of promising pharmacological target candidates in the search of new molecular targets and drugs to counteract the onset of hypertension and more in general cardiovascular diseases (CVDs). Indeed, several mitochondrial pathways result impaired in CVDs, showing ATP depletion and ROS production as common traits of cardiac tissue degeneration. Thus, targeting mitochondrial dysfunction in cardiomyocytes can represent a successful strategy to prevent heart failure. In this context, the identification of new pharmacological targets among mitochondrial proteins paves the way for the design of new selective drugs. Thanks to the advances in omics approaches, to a greater availability of mitochondrial crystallized protein structures and to the development of new computational approaches for protein 3D-modelling and drug design, it is now possible to investigate in detail impaired mitochondrial pathways in CVDs. Furthermore, it is possible to design new powerful drugs able to hit the selected pharmacological targets in a highly selective way to rescue mitochondrial dysfunction and prevent cardiac tissue degeneration. The role of mitochondrial dysfunction in the onset of CVDs appears increasingly evident, as reflected by the impairment of proteins involved in lipid peroxidation, mitochondrial dynamics, respiratory chain complexes, and membrane polarization maintenance in CVD patients. Conversely, little is known about proteins responsible for the cross-talk between mitochondria and cytoplasm in cardiomyocytes. Mitochondrial transporters of the SLC25A family, in particular, are responsible for the translocation of nucleotides (e.g., ATP), amino acids (e.g., aspartate, glutamate, ornithine), organic acids (e.g. malate and 2-oxoglutarate), and other cofactors (e.g., inorganic phosphate, NAD⁺, FAD, carnitine, CoA derivatives) between the mitochondrial and cytosolic compartments. Thus, mitochondrial transporters play a key role in the mitochondria-cytosol cross-talk by leading metabolic pathways such as the malate/aspartate shuttle, the carnitine shuttle, the ATP export from mitochondria, and the regulation of permeability transition pore opening. Since all these pathways are crucial for maintaining healthy cardiomyocytes, mitochondrial carriers emerge as an interesting class of new possible pharmacological targets for CVD treatments.

Gas Chromatography-Mass Spectrometry (GC-MS) Metabolites Analysis in Endometriosis Patients: A Prospective Observational Translational Study

BACKGROUND

Endometriosis affects women of reproductive age, and its pathogenesis is still unclear. Typically, it overlaps other similar medical and surgical conditions, determining a delay in early diagnosis. Metabolomics allows studying metabolic changes in different physiological or pathological states to discover new potential biomarkers. We used the gas chromatography-mass spectrometer (GC-MS) to explore metabolic alterations in endometriosis to better understand its pathophysiology and find new biomarkers.

METHODS

Twenty-two serum samples of patients with symptomatic endometriosis and ten without it were collected and subjected to GC-MS analysis. Multivariate and univariate statistical analyses were performed, followed by pathway analysis.

RESULTS

Partial least squares discriminant analysis was performed to determine the differences between the two groups (p = 0.003). Threonic acid, 3-hydroxybutyric acid, and proline increased significantly in endometriosis patients, while alanine and valine decreased. ROC curves were built to test the diagnostic power of metabolites. The pathway analysis identified the synthesis and degradation of ketone bodies and the biosynthesis of phenylalanine, tyrosine, and tryptophan as the most altered pathways.

CONCLUSIONS

The metabolomic approach identifies metabolic alterations in women with endometriosis. These findings may improve our understanding of the pathophysiological mechanisms of disease and the discovery of new biomarkers.

Metabolomic Analysis of Stephania tetrandra-Astragalus membranaceus Herbal Pair-Improving Nephrotic Syndrome Identifies Activation of IL-13/STAT6 Signaling Pathway

The Stephania tetrandra−Astragalus membranaceus herbal pair (FH) is a classic herbal pair widely used in the treatment of nephrotic syndrome (NS). The effects of Stephania tetrandra (FJ) and Astragalus membranaceus (HQ) on NS have been reported, but the mechanism of their combination on the improvement of NS are still unclear. The NS model was established by injecting adriamycin into the tail vein. FH intervention reduced the levels of serum triglyceride, total cholesterol, interleukin-6 (IL-6), blood urea nitrogen (BUN), urinary protein, and the gene expression levels of aquaporin 2 (AQP2) and arginine vasopressin (AVP) in NS rats. In addition, FH improved kidney injury in NS rats by inhibiting the expression of interleukin 13 (IL-13), phospho-signal transducers, and activators of transcription 6 (p-STAT6), Bax, cleaved-caspase3, while promoting the expression of Bcl-2. By comprehensive comparison of multiple indexes, the effects of FH on lipid metabolism, glomerular filtration rate, and inflammation were superior to that of FJ and HQ. Metabonomic studies showed that, compared with FJ and HQ, FH intervention significantly regulated tricarboxylic acid (TCA) cycle, cysteine and methionine metabolism, and alanine, aspartic acid and glutamic acid metabolism. Pearson correlation analysis showed that succinic acid and L-aspartic acid were negatively correlated with urinary protein, cystatin C (Cys C) and BUN (p < 0.05). In summary, FH could reduce renal injury and improve NS through inhibiting the IL-13/STAT6 signal pathway, regulating endogenous metabolic pathways, such as TCA cycle, and inhibiting the expression of AQP2 and AVP genes. This study provides a comprehensive strategy to reveal the mechanism of FH on the treatment of NS, and also provides a reasonable way to clarify the compatibility of traditional Chinese medicine.

Determining the role of microelements for the synthesis of antifungal metabolites of the antagonistic <i>Bacillus velezensis</i> strain

Aim. To study the structure of bacteria exometabolites of the Bacillus velezensis BZR336g strain using the GC×GC‐MS method when cultivated on nutrient media differing in the content of microelements.

Material and Methods. The object of the study is the B. velezensis BZR336g bacterium strain. It shows promise for the development of a biopreparation on its basis for protecting plants against phytopathogenic fungi. Exometabolites were isolated from the liquid culture and their metabolomic profile was analysed with the GC×GC‐MS method using a LECO Pegasus BT‐4D device.

Results. Among the identified compounds one can see the precursors of biologically active metabolites of bacteria of the strain B. velezensis BZR336g. Analysis of the total accumulation of fatty acids and their analogs, as well as of amino acids and peptides, by B. velezensis BZR336g bacteria on nutrient media differing in the content of metal ions, shows that Co2+ ions in combination with Mn2+ and Zn2+ ions play an important role in inducing their synthesis. The presence of the Co2+ ion in the nutrient medium has the greatest influence on the synthesis of peptide components. If it is removed from the nutrient medium, the amount of amino acids and peptides decreases by almost two times. Benzene compounds can act as precursors of aromatic hydrocarbons, which are also of interest in the analysis of bacterial metabolism, since they have antimicrobial activity.

Conclusions. The number of compounds that are precursors of lipopeptide metabolites depends on the content of metal ions in the nutrient medium. This fact may vary depending on the combination in which the metals are present.

Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets

Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal-brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

A conserved role of bam in maintaining metabolic homeostasis via regulating intestinal microbiota in Drosophila

Background

Previous studies have proven that bag-of-marbles (bam) plays a pivotal role in promoting early germ cell differentiation in Drosophila ovary. However, whether it functions in regulating the metabolic state of the host remains largely unknown.

Methods

We utilized GC-MS, qPCR, and some classical kits to examine various metabolic profiles and gut microbial composition in bam loss-of-function mutants and age-paired controls. We performed genetic manipulations to explore the tissue/organ-specific role of bam in regulating energy metabolism in Drosophila. The DSS-induced mouse colitis was generated to identify the role of Gm114, the mammalian homolog of bam, in modulating intestinal homeostasis.

Results

We show that loss of bam leads to an increased storage of energy in Drosophila. Silence of bam in intestines results in commensal microbial dysbiosis and metabolic dysfunction of the host. Moreover, recovery of bam expression in guts almost rescues the obese phenotype in bam loss-of-function mutants. Further examinations of mammalian Gm114 imply a similar biological function in regulating the intestinal homeostasis and energy storage with its Drosophila homolog bam.

Conclusion

Our studies uncover a novel biological function of bam/Gm114 in regulating the host lipid homeostasis.

Optimization of a GC-MS method for the profiling of microbiota-dependent metabolites in blood samples: An application to type 2 diabetes and prediabetes

Changes in serum or plasma metabolome may reflect gut microbiota dysbiosis, which is also known to occur in patients with prediabetes and type 2 diabetes (T2DM). Thus, developing a robust method for the analysis of microbiota-dependent metabolites (MDMs) is an important issue. Gas chromatography with mass spectrometry (GC–MS) is a powerful approach enabling detection of a wide range of MDMs in biofluid samples with good repeatability and reproducibility, but requires selection of a suitable solvents and conditions. For this reason, we conducted for the first time the study in which, we demonstrated an optimisation of samples preparation steps for the measurement of 75 MDMs in two matrices. Different solvents or mixtures of solvents for MDMs extraction, various concentrations and volumes of derivatizing reagents as well as temperature programs at methoxymation and silylation step, were tested. The stability, repeatability and reproducibility of the 75 MDMs measurement were assessed by determining the relative standard deviation (RSD). Finally, we used the developed method to analyse serum samples from 18 prediabetic (PreDiab group) and 24 T2DM patients (T2DM group) from our 1000PLUS cohort. The study groups were homogeneous and did not differ in age and body mass index. To select statistically significant metabolites, T2DM vs. PreDiab comparison was performed using multivariate statistics. Our experiment revealed changes in 18 MDMs belonging to different classes of compounds, and seven of them, based on the SVM classification model, were selected as a panel of potential biomarkers, able to distinguish between patients with T2DM and prediabetes.

Systematic impacts of fluoride exposure on the metabolomics of rats

Fluoride is widely present in the environment. Excessive fluoride exposure leads to fluorosis, which has become a global public health problem and will cause damage to various organs and tissues. Only a few studies focus on serum metabolomics, and there is still a lack of systematic metabolomics associated with fluorosis within the main organs. Therefore, in the current study, a non-targeted metabolomics method using gas chromatography-mass spectrometry (GC-MS) was used to research the effects of fluoride exposure on metabolites in different organs, to uncover potential biomarkers and study whether the affected metabolic pathways are related to the mechanism of fluorosis. Male Sprague-Dawley rats were randomly divided into two groups: a control group and a fluoride exposure group. GC-MS technology was used to identify metabolites. Multivariate statistical analysis identified 16, 24, 20, 20, 24, 13, 7, and 13 differential metabolites in the serum, liver, kidney, heart, hippocampus, cortex, kidney fat, and brown fat, respectively, in the two groups of rats. Fifteen metabolic pathways were affected, involving toxic mechanisms such as oxidative stress, mitochondrial damage, inflammation, and fatty acid, amino acid and energy metabolism disorders. This study provides a new perspective on the understanding of the mechanism of toxicity associated with sodium fluoride, contributing to the prevention and treatment of fluorosis.

UHPLC/MS-Based Serum Metabolomics Reveals the Mechanism of Radiation-Induced Thrombocytopenia in Mice

Radiation-induced thrombocytopenia is a common and life-threatening side effect of ionizing radiation (IR) therapy. However, the underlying pathological mechanisms remain unclear. In the present study, irradiation was demonstrated to significantly reduce platelet levels, inhibit megakaryocyte differentiation, and promote the apoptosis of bone marrow (BM) cells. A metabolomics approach and a UHPLC-QTOF MS system were subsequently employed for the comprehensive analysis of serum metabolic profiles of normal and irradiated mice. A total of 66 metabolites were significantly altered, of which 56 were up-regulated and 10 were down-regulated in irradiated mice compared to normal mice on day 11 after irradiation. Pathway analysis revealed that disorders in glycerophospholipid metabolism, nicotinate and nicotinamide metabolism, sphingolipid metabolism, inositol phosphate metabolism, and tryptophan metabolism were involved in radiation-induced thrombocytopenia. In addition, three important differential metabolites, namely L-tryptophan, LysoPC (17:0), and D-sphinganine, which were up-regulated in irradiated mice, significantly induced the apoptosis of K562 cells. L-tryptophan inhibited megakaryocyte differentiation of K562 cells. Finally, serum metabolomics was performed on day 30 (i.e., when the platelet levels in irradiated mice recovered to normal levels). The contents of L-tryptophan, LysoPC (17:0), and D-sphinganine in normal and irradiated mice did not significantly differ on day 30 after irradiation. In conclusion, radiation can cause metabolic disorders, which are highly correlated with the apoptosis of hematopoietic cells and inhibition of megakaryocyte differentiation, ultimately resulting in thrombocytopenia. Further, the metabolites, L-tryptophan, LysoPC (17:0), and D-sphinganine can serve as biomarkers for radiation-induced thrombocytopenia.

Comprehensive evaluation of caloric restriction-induced changes in the metabolome profile of mice

Objects

Caloric restriction (CR) is known to extend lifespan and exert a protective effect on organs, and is thus a low-cost and easily implemented approach to the health maintenance. However, there have been no studies that have systematically evaluated the metabolic changes that occur in the main tissues affected by CR. This study aimed to explore the target tissues metabolomic profile in CR mice.

Methods

Male C57BL/6J mice were randomly allocated to the CR group (n = 7) and control group (n = 7). A non-targeted gas chromatography–mass spectrometry approach and multivariate analysis were used to identify metabolites in the main tissues (serum, heart, liver, kidney, cortex, hippocampus, lung, muscle, and white adipose) in model of CR.

Results

We identified 10 metabolites in the heart that showed differential abundance between the 2 groups, along with 9 in kidney, 6 in liver, 6 in lung, 6 in white adipose, 4 in hippocampus, 4 in serum, 3 in cortex, and 2 in muscle. The most significantly altered metabolites were amino acids (AAs) (glycine, aspartic acid, l-isoleucine, l-proline, l-aspartic acid, l-serine, l-hydroxyproline, l-alanine, l-valine, l-threonine, l-glutamic acid, and l-phenylalanine) and fatty acids (FAs) (palmitic acid, 1-monopalmitin, glycerol monostearate, docosahexaenoic acid, 16-octadecenoic acid, oleic acid, stearic acid, and hexanoic acid). These metabolites were associated with 7 different functional pathways related to the metabolism of AAs, lipids, and energy.

Conclusion

Our results provide insight into the specific metabolic changes that are induced by CR and can serve as a reference for physiologic studies on how CR improves health and extends lifespan.

Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC)

INTRODUCTION

The metabolomics quality assurance and quality control consortium (mQACC) is enabling the identification, development, prioritization, and promotion of suitable reference materials (RMs) to be used in quality assurance (QA) and quality control (QC) for untargeted metabolomics research.

OBJECTIVES

This review aims to highlight current RMs, and methodologies used within untargeted metabolomics and lipidomics communities to ensure standardization of results obtained from data analysis, interpretation and cross-study, and cross-laboratory comparisons. The essence of the aims is also applicable to other 'omics areas that generate high dimensional data.

RESULTS

The potential for game-changing biochemical discoveries through mass spectrometry-based (MS) untargeted metabolomics and lipidomics are predicated on the evolution of more confident qualitative (and eventually quantitative) results from research laboratories. RMs are thus critical QC tools to be able to assure standardization, comparability, repeatability and reproducibility for untargeted data analysis, interpretation, to compare data within and across studies and across multiple laboratories. Standard operating procedures (SOPs) that promote, describe and exemplify the use of RMs will also improve QC for the metabolomics and lipidomics communities.

CONCLUSIONS

The application of RMs described in this review may significantly improve data quality to support metabolomics and lipidomics research. The continued development and deployment of new RMs, together with interlaboratory studies and educational outreach and training, will further promote sound QA practices in the community.

Combined metabolomics with transcriptomics reveals potential plasma biomarkers correlated with non-small-cell lung cancer proliferation through the At pathway

BACKGROUND

Non-small-cell lung cancer (NSCLC) is one of the main types of lung cancer. Due to lack of effective biomarkers for early detection of NSCLC, the therapeutic effect is not ideal. This study aims to reveal potential biomarkers for clinical diagnosis.

METHODS

The plasma metabolic profiles of the patients were characterized by liquid chromatography-mass spectrometry (LC-MS). Differential metabolites were screened by p < 0.05 and VIP > 1. Multivariate statistical analysis was used to search for potential biomarkers. Receiver operating characteristic (ROC) curve was used to evaluate the predictors of potential biomarkers. Pathway enrichment analysis was performed on metabolomics data by Ingenuity Pathway Analysis (IPA) and transcriptomics data from GEO were used for validation.

RESULTS

A plasma metabolite biomarker panel including 13(S)-hydroxyoctadecadienoic acid (13(S)-HODE) and arachidonic acid was chose. The area under the ROC curve were 0.917, 0.900 and 0.867 for the panel in the different algorithm like Partial Least Squares Discrimination Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF). The candidate biomarkers were associated with the Akt pathway. Genes involved in the biological pathway had significant changes in the expression levels.

CONCLUSION

13(S)-HODE and arachidonic acid may be potential biomarkers of NSCLC. The Akt pathway was associated with this biomarker panel in NSCLC. Further studies are needed to clarify the mechanisms of disruption in this pathway.

Mass spectrometry-based metabolomics in microbiome investigations

Microbiotas are a malleable part of ecosystems, including the human ecosystem. Microorganisms affect not only the chemistry of their specific niche, such as the human gut, but also the chemistry of distant environments, such as other parts of the body. Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota, and to understand the functional role of these microbial metabolites. This Review provides a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Data analysis remains an obstacle; therefore, the emphasis is placed on data analysis approaches and integrative analysis, including the integration of microbiome sequencing data.

Urinary metabolomics for discovering metabolic biomarkers of bladder cancer by UPLC-MS

Bladder cancer (BC) is one of the most frequent cancer in the world, and its incidence is rising worldwide, especially in developed countries. Urine metabolomics is a powerful approach to discover potential biomarkers for cancer diagnosis. In this study, we applied an ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) method to profile the metabolites in urine from 29 bladder cancer patients and 15 healthy controls. The differential metabolites were extracted and analyzed by univariate and multivariate analysis methods. Together, 19 metabolites were discovered as differently expressed biomarkers in the two groups, which mainly related to the pathways of phenylacetate metabolism, propanoate metabolism, fatty acid metabolism, pyruvate metabolism, arginine and proline metabolism, glycine and serine metabolism, and bile acid biosynthesis. In addition, a subset of 11 metabolites of those 19 ones were further filtered as potential biomarkers for BC diagnosis by using logistic regression model. The results revealed that the area under the curve (AUC) value, sensitivity and specificity of receiving operator characteristic (ROC) curve were 0.983, 95.3% and 100%, respectively, indicating an excellent discrimination power for BC patients from healthy controls. It was the first time to reveal the potential diagnostic markers of BC by metabolomics, and this will provide a new sight for exploring the biomarkers of the other disease in the future work.

Differential Metabolites and Metabolic Pathways Involved in Aerobic Exercise Improvement of Chronic Fatigue Symptoms in Adolescents Based on Gas Chromatography-Mass Spectrometry

Studies have found that the prevalence of chronic fatigue syndrome (CFS) in adolescents has continued to increase over the years, affecting learning and physical health. High school is a critical stage for adolescents to grow and mature. There are inadequate detection and rehabilitation methods for CFS due to an insufficient understanding of the physiological mechanisms of CFS. The purpose of this study was to evaluate the effect and metabolic mechanisms of an aerobic running intervention program for high school students with CFS. Forty-six male high school students with CFS were randomly assigned to the exercise intervention group (EI) and control group (CFS). Twenty-four age- and sex-matched healthy male students were recruited as healthy controls (HCs). The EI group received the aerobic intervention for 12 weeks, three times a week, in 45-min sessions; the CFS group maintained their daily routines as normal. The outcome measures included fatigue symptoms and oxidation levels. Keratin was extracted from the nails of all participants, and the oxidation level was assessed by measuring the content of 3-Nitrotyrosine (3-NT) in the keratin by ultraviolet spectrophotometry. All participants’ morning urine was collected to analyze urinary differential metabolites by the GC-MS technique before and after the intervention, and MetaboAnalyst 5.0 was used for pathway analysis. Compared with before the intervention, the fatigue score and 3-NT level in the EI group were significantly decreased after the intervention. The CFS group was screened for 20 differential metabolites involving the disruption of six metabolic pathways, including arginine biosynthesis, glycerolipid metabolism, pentose phosphate pathway, purine metabolism, β-alanine metabolism, and arginine and proline metabolism. After the intervention, 21 differential metabolites were screened, involved in alterations in three metabolic pathways: beta-alanine metabolism, pentose phosphate metabolism, and arginine and proline metabolism. Aerobic exercise was found to lessen fatigue symptoms and oxidative levels in students with CFS, which may be related to the regulation of putrescine (arginine and proline metabolism), 6-Phospho-D-Gluconate (starch and sucrose metabolism pathway), and Pentose (phosphate metabolism pathway).

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.

Use of metabolomics in refining the effect of an organic food intervention on biomarkers of exposure to pesticides and biomarkers of oxidative damage in primary school children in Cyprus: A cluster-randomized cross-over trial

BACKGROUND

Exposure to pesticides has been associated with oxidative stress in animals and humans. Previously, we showed that an organic food intervention reduced pesticide exposure and oxidative damage (OD) biomarkers over time; however associated metabolic changes are not fully understood yet.

OBJECTIVES

We assessed perturbations of the urine metabolome in response to an organic food intervention for children and its association with pesticides biomarkers [3-phenoxybenzoic acid (3-PBA) and 6-chloronicotinic acid (6-CN)]. We also evaluated the molecular signatures of metabolites associated with biomarkers of OD (8-iso-PGF2a and 8-OHdG) and related biological pathways.

METHODS

We used data from the ORGANIKO LIFE + trial (NCT02998203), a cluster-randomized cross-over trial conducted among primary school children in Cyprus. Participants (n = 149) were asked to follow an organic food intervention for 40 days and their usual food habits for another 40 days, providing up to six first morning urine samples (>850 samples in total). Untargeted GC-MS metabolomics analysis was performed. Metabolites with RSD ≤ 20% and D-ratio ≤ 50% were retained for analysis. Associations were examined using mixed-effect regression models and corrected for false-discovery rate of 0.05. Pathway analysis followed.

RESULTS

Following strict quality checks, 156 features remained out of a total of 610. D-glucose was associated with the organic food intervention (β = -0.23, 95% CI: -0.37,-0.10), aminomalonic acid showed a time-dependent increase during the intervention period (β_int = 0.012; 95% CI:0.002, 0.022) and was associated with the two OD biomarkers (β = -0.27, 95% CI:-0.34,-0.20 for 8-iso-PGF2a and β = 0.19, 95% CI:0.11,0.28 for 8-OHdG) and uric acid with 8-OHdG (β = 0.19, 95% CI:0.11,0.26). Metabolites were involved in pathways such as the starch and sucrose metabolism and pentose and glucuronate interconversions.

DISCUSSION

This is the first metabolomics study providing evidence of differential expression of metabolites by an organic food intervention, corroborating the reduction in biomarkers of OD. Further mechanistic evidence is warranted to better understand the biological plausibility of an organic food treatment on children's health outcomes.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

Integration of Transcriptomics and Metabolomics to Reveal the Molecular Mechanisms Underlying Rhodium Nanoparticles-Based Photodynamic Cancer Therapy

Rhodium nanoparticles have recently been described as promising photosensitizers due to their low toxicity in the absence of near-infrared irradiation, but their high cytotoxicity when irradiated. Irradiation is usually carried out with a laser source, which allows the treatment to be localized in a specific area, thus avoiding undesirable side effects on healthy tissues. In this study, a multi-omics approach based on the combination of microarray-based transcriptomics and mass spectrometry-based untargeted and targeted metabolomics has provided a global picture of the molecular mechanisms underlying the anti-tumoral effect of rhodium nanoparticle-based photodynamic therapy. The results have shown the ability of these nanoparticles to promote apoptosis by suppressing or promoting anti- and pro-apoptotic factors, respectively, and by affecting the energy machinery of tumor cells, mainly blocking the β-oxidation, which is reflected in the accumulation of free fatty acids and in the decrease in ATP, ADP and NAD+ levels.

Metabolomics and metabolites in ischemic stroke

Stroke is a major reason for disability and the second highest cause of death in the world. When a patient is admitted to a hospital, it is necessary to identify the type of stroke, and the likelihood for development of a recurrent stroke, vascular dementia, and depression. These factors could be determined using different biomarkers. Metabolomics is a very promising strategy for identification of biomarkers. The advantage of metabolomics, in contrast to other analytical techniques, resides in providing low molecular weight metabolite profiles, rather than individual molecule profiles. Technically, this approach is based on mass spectrometry and nuclear magnetic resonance. Furthermore, variations in metabolite concentrations during brain ischemia could alter the principal neuronal functions. Different markers associated with ischemic stroke in the brain have been identified including those contributing to risk, acute onset, and severity of this pathology. In the brain, experimental studies using the ischemia/reperfusion model (IRI) have shown an impaired energy and amino acid metabolism and confirmed their principal roles. Literature data provide a good basis for identifying markers of ischemic stroke and hemorrhagic stroke and understanding metabolic mechanisms of these diseases. This opens an avenue for the successful use of identified markers along with metabolomics technologies to develop fast and reliable diagnostic tools for ischemic and hemorrhagic stroke.

Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices

Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography- and gas chromatography-mass spectrometry-based metabolomics-derived data.

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.

Biomarker identification and pathway analysis of rheumatoid arthritis based on metabolomics in combination with ingenuity pathway analysis

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease worldwide, but understanding its pathogenesis is still limited. In this study, plasma untargeted metabolomics of a discovery cohort and targeted analysis of a verification cohort were performed by gas chromatograph mass spectrometry (GC/MS). Univariate and multivariate statistical analysis were utilized to reveal differential metabolites, followed by the construction of biomarker panel through random forest (RF) algorithm. The pathways involved in RA were enriched by differential metabolites using Ingenuity Pathway Analysis (IPA) suite. Untargeted metabolomics revealed eighteen significantly altered metabolites in RA. Among these metabolites, a three-metabolite marker panel consisting of L-cysteine, citric acid and L-glutamine was constructed, using random forest algorithm that could predict RA with high accuracy, sensitivity and specificity based on a multivariate exploratory receiver operator characteristic (ROC) curve analysis. The panel was further validated by support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) algorithms, and also verified with targeted metabolomics using a verification cohort. Additionally, the dysregulated taurine biosynthesis pathway in RA was revealed by an integrated analysis of metabolomics and transcriptomics. Our findings in this study not only provided a mechanism underlying RA pathogenesis, but also offered alternative therapeutic targets for RA.

Combined Metabolomics with Transcriptomics Reveals Important Serum Biomarkers Correlated with Lung Cancer Proliferation through a Calcium Signaling Pathway

Lung cancer (LC) is one of the most malignant cancers in the world, but currently, it lacks effective noninvasive biomarkers to assist its early diagnosis. Our study aims to discover potential serum diagnostic biomarkers for LC. In our study, untargeted serum metabolomics of a discovery cohort and targeted analysis of a test cohort were performed based on gas chromatography-mass spectrometry. Both univariate and multivariate statistical analyses were employed to screen for differential metabolites between LC and healthy control (HC), followed by the selection of candidate biomarkers through multiple algorithms. The results showed that 15 metabolites were significantly dysregulated between LC and HC, and a panel, comprising cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid, was demonstrated to have excellent differentiating capability for LC based on multiple classification modelings. In addition, the molecular interaction analysis combined with transcriptomics revealed a close correlation between the candidate biomarkers and LC proliferation via a Ca²⁺ signaling pathway. Our study discovered that cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid in combination could be a promising diagnostic biomarker for LC, and most importantly, our results will shed some light on the pathophysiological mechanism underlying LC to understand it deeply. The data that support the findings of this study are openly available in MetaboLights at https://www.ebi.ac.uk/metabolights/, reference number MTBLS1517.

Cognitive analysis of metabolomics data for systems biology

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.

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.

Comparative genomics and metabolomics analysis of Riemerella anatipestifer strain CH-1 and CH-2

Riemerella anatipestifer is a major pathogenic microorganism in poultry causing serositis with significant mortality. Serotype 1 and 2 were most pathogenic, prevalent, and liable over the world. In this study, the intracellular metabolites in R. anatipestifer strains RA-CH-1 (serotype 1) and RA-CH-2 (serotype 2) were identified by gas chromatography-mass spectrometer (GC–MS). The metabolic profiles were performed using hierarchical clustering and partial least squares discriminant analysis (PLS-DA). The results of hierarchical cluster analysis showed that the amounts of the detected metabolites were more abundant in RA-CH-2. RA-CH-1 and RA-CH-2 were separated by the PLS-DA model. 24 potential biomarkers participated in nine metabolisms were contributed predominantly to the separation. Based on the complete genome sequence database and metabolite data, the first large-scale metabolic models of iJL463 (RA-CH-1) and iDZ470 (RA-CH-2) were reconstructed. In addition, we explained the change of purine metabolism combined with the transcriptome and metabolomics data. The study showed that it is possible to detect and differentiate between these two organisms based on their intracellular metabolites using GC–MS. The present research fills a gap in the metabolomics characteristics of R. anatipestifer.

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.

Mass Spectrometry-based Metabolomics in Translational Research

Metabolomics is the systematic study of metabolite profiles of complex biological systems, and involves the systematic identification and quantification of metabolites. Metabolism is integrated with all biochemical reactions in biological systems; thus metabolite profiles provide collective information on biochemical processes induced by genetic or environmental perturbations. Transcriptomes or proteomes may not be functionally active and not always reflect phenotypic variations. The metabolome, however, consists of the biomolecules closest to the phenotype of living organisms, and is often called the molecular phenotype of biological systems. Thus, metabolome alterations can easily result in disease states, providing important clues to understand pathophysiological mechanisms contributing to various biomedical symptoms. The metabolome and metabolomics have been emphasized in translational research related to biomarker discovery, drug target discovery, drug responses, and disease mechanisms. This review describes the basic concepts, workflows, and applications of mass spectrometry-based metabolomics in translational research.

Probing the Response of the Amphibious Plant Butomus umbellatus to Nutrient Enrichment and Shading by Integrating Eco-Physiological With Metabolomic Analyses

Amphibious plants, living in land-water ecotones, have to cope with challenging and continuously changing growth conditions in their habitats with respect to nutrient and light availability. They have thus evolved a variety of mechanisms to tolerate and adapt to these changes. Therefore, the study of these plants is a major area of ecophysiology and environmental ecological research. However, our understanding of their capacity for physiological adaptation and tolerance remains limited and requires systemic approaches for comprehensive analyses. To this end, in this study, we have conducted a mesocosm experiment to analyze the response of Butomus umbellatus, a common amphibious species in Denmark, to nutrient enrichment and shading. Our study follows a systematic integration of morphological (including plant height, leaf number, and biomass accumulation), ecophysiological (photosynthesis-irradiance responses, leaf pigment content, and C and N content in plant organs), and leaf metabolomic measurements using gas chromatography-mass spectrometry (39 mainly primary metabolites), based on bioinformatic methods. No studies of this type have been previously reported for this plant species. We observed that B. umbellatus responds to nutrient enrichment and light reduction through different mechanisms and were able to identify its nutrient enrichment acclimation threshold within the applied nutrient gradient. Up to that threshold, the morpho-physiological response to nutrient enrichment was profound, indicating fast-growing trends (higher growth rates and biomass accumulation), but only few parameters changed significantly from light to shade [specific leaf area (SLA); quantum yield (φ)]. Metabolomic analysis supported the morpho-physiological results regarding nutrient overloading, indicating also subtle changes due to shading not directly apparent in the other measurements. The combined profile analysis revealed leaf metabolite and morpho-physiological parameter associations. In this context, leaf lactate, currently of uncertain role in higher plants, emerged as a shading acclimation biomarker, along with SLA and φ. The study enhances both the ecophysiology methodological toolbox and our knowledge of the adaptive capacity of amphibious species. It demonstrates that the educated combination of physiological with metabolomic measurements using bioinformatic approaches is a promising approach for ecophysiology research, enabling the elucidation of discriminatory metabolic shifts to be used for early diagnosis and even prognosis of natural ecosystem responses to climate change.

Integration of untargeted and targeted mass spectrometry-based metabolomics provides novel insights into the potential toxicity associated to surfynol

The intake of toxic compounds through the diet as a result of migration processes from food packaging is of increasing concern. It has been shown that the surfactant commercially known as surfynol, which is commonly used in food-contact materials, is capable of migrating from multilayer containers into the food, reaching potentially harmful concentration levels. In the present study, the integration of an untargeted and a targeted metabolomics approach has been carried out using NTERA-2 germinal cells as in-vitro model, to make further progress in elucidating the molecular mechanisms associated with the toxicity of surfynol. This study has allowed the identification of different altered metabolites mainly related with energy-acquiring, cell development and cellular defense mechanisms. While glutamine, L-threonine, propanoate, octadecanoate and carbamate were found at higher concentrations in cells exposed tu surfynol, L-valine, oxalate, phosphate, phenylalanine and myoinositol were found inhibited. Additionally, concentrations of ATP, ADP and NAD⁺ were found significantly inhibited, supporting the idea that surfynol induces glycolysis inactivation. The results obtained strengthen the evidence of the toxicity associated to surfynol; therefore, reinforcing the need for a more comprehensive study on the viability of its use in food packaging.

Metabolomics profiling reveals Echinops latifolius Tausch improves the trabecular micro-architecture of ovariectomized rats mainly via intervening amino acids and glycerophospholipids metabolism

ETHNOPHARMACOLOGICAL RELEVANCE

Echinops latifolius Tausch (ELT) is traditional Mongolian medicine in China, and often used to against osteoporosis, strengthen tendons and bones, clear bones heat.

AIM OF THE STUDY

To study efficacy of ELT on ovariectomized (OVX) rats and underly metabolic pathways related to trabecular micro-architecture changing of OVX.

MATERIALS AND METHODS

Three-month-old female Wistar rats were randomly divided into 4 groups (n = 6) including normal group (without surgery), sham group (bilateral laparotomy), OVX group (bilateral ovariectomy), and ELT-treated groups (ELT-treated after bilateral ovariectomy). The effects of ELT on trabecular micro-architecture and biochemical markers of OVX rat were investigated by dual-energy X-ray absorptiometry machine and Enzyme-linked immunosorbent assay (ELISA), respectively. Untargeted metabolomics strategy was applied to discover the potential biomarkers and related metabolic pathways involving the progression of OVX-induced osteoporosis.

RESULTS

The trabecular micro-architecture and biochemical markers of OVX rats were improved by ELT. We found 36 potential biomarkers and 21 related metabolic pathways were involved in progression of OVX-induced osteoporosis. Amino acids metabolism and glycerophospholipids metabolism were mainly intervened in ELT treatment on ovariectomized rats. The disordered amino acids and glycerophospholipids metabolism closely related to the imbalance between bone resorption and formation were reversed by administration of ELT, indicating that the influences of ELT on OVX rats' trabecular micro-architecture may possible be associated with intervening amino acids and glycerophospholipids metabolism.

CONCLUSIONS

This approach may provide the metabolomic perspective to link metabolic alterations and anti-osteoporosis action of ELT, to further explain how ELT works in postmenopausal patients with bone loss.

GC-MS Based Metabolite Profiling to Monitor Ripening-Specific Metabolites in Pineapple (Ananas comosus)

Pineapple is one of the most cultivated tropical, non-climacteric fruits in the world due to its high market value and production volume. Since non-climacteric fruits do not ripen after harvest, the ripening stage at the time of harvest is an important factor that determines sensory quality and shelf life. The objective of this research was to investigate metabolite changes in the pineapple ripening process by metabolite profiling approach. Pineapple (Queen variety) samples from Indonesia were subjected to GC-MS analysis. A total of 56, 47, and 54 metabolites were annotated from the crown, flesh, and peel parts, respectively. From the principal component analysis (PCA) plot, separation of samples based on ripening stages from C0-C2 (early ripening stages) and C3-C4 (late ripening stages) was observed for flesh and peel parts, whereas no clear separation was seen for the crown part. Furthermore, orthogonal projection to latent structures (OPLS) analysis suggested metabolites that were associated with the ripening stages in flesh and peel parts of pineapple. This study indicated potentially important metabolites that are correlated to the ripening of pineapple that would provide a basis for further study on pineapple ripening process.