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

Advancements in practical and scientific bioanalytical approaches to metabolism studies in drug development

Advancement in metabolism profiling approaches and bioanalytical techniques has been revolutionized over the last two decades. Different in vitro and in vivo approaches along with advanced bioanalytical techniques are enabling the accurate qualitative and quantitative analysis of metabolites. This review summarizes various modern in vitro and in vivo approaches for executing metabolism studies with special emphasis on the recent advancement in the field. Advanced bioanalytical techniques, which can be employed in metabolism studies, have been discussed suggesting their particular application based on specific study objectives. This article can efficiently guide the researchers to scientifically plan metabolism studies and their bioanalysis during drug development programs taking advantage of a detailed understanding of instances of failure in the past.

Comparison of Targeted and Untargeted Approaches in Breath Analysis for the Discrimination of Lung Cancer from Benign Pulmonary Diseases and Healthy Persons

The aim of the present study was to compare the efficiency of targeted and untargeted breath analysis in the discrimination of lung cancer (Ca+) patients from healthy people (HC) and patients with benign pulmonary diseases (Ca−). Exhaled breath samples from 49 Ca+ patients, 36 Ca− patients and 52 healthy controls (HC) were analyzed by an SPME–GC–MS method. Untargeted treatment of the acquired data was performed with the use of the web-based platform XCMS Online combined with manual reprocessing of raw chromatographic data. Machine learning methods were applied to estimate the efficiency of breath analysis in the classification of the participants. Results: Untargeted analysis revealed 29 informative VOCs, from which 17 were identified by mass spectra and retention time/retention index evaluation. The untargeted analysis yielded slightly better results in discriminating Ca+ patients from HC (accuracy: 91.0%, AUC: 0.96 and accuracy 89.1%, AUC: 0.97 for untargeted and targeted analysis, respectively) but significantly improved the efficiency of discrimination between Ca+ and Ca− patients, increasing the accuracy of the classification from 52.9 to 75.3% and the AUC from 0.55 to 0.82. Conclusions: The untargeted breath analysis through the inclusion and utilization of newly identified compounds that were not considered in targeted analysis allowed the discrimination of the Ca+ from Ca− patients, which was not achieved by the targeted approach.

Fecal Metabolomics Reveals Distinct Profiles of Kidney Transplant Recipients and Healthy Controls

Monitoring graft recipients remains dependent on traditional biomarkers and old technologies lacking specificity, sensitivity, or accuracy. Recently, metabolomics is becoming a promising approach that may offer to kidney transplants a more effective and specific monitoring. Furthermore, emerging evidence suggested a fundamental role of gut microbiota as an important determinant of patients’ metabolomes. In the current study, we enrolled forty stable renal allografts recipients compared to twenty healthy individuals. Samples were taken at different time points from patient to patient following transplantation surgery, which varied from 3 months to 22 years post-graft. All patients started the immunosuppression therapy immediately following kidney graft (Day 0). Gas chromatography–mass spectrometry (GC–MS) was employed to perform untargeted analysis of fecal metabolites. Globally, the fecal metabolic signature was significantly different between kidney transplants and the control group. Fecal metabolome was dominated by lipids (sterols and fatty acids) in the stable transplant group compared to the controls (p < 0.05). Overall, 18 metabolites were significantly altered within kidney transplant recipients. Furthermore, the most notable altered metabolic pathways in kidney transplants include ubiquinone and other terpenoid-quinone biosynthesis, tyrosine metabolism, tryptophan biosynthesis, and primary bile acid biosynthesis. Fecal metabolites could effectively distinguish stable transplant recipients from controls, supporting the potential utility of metabolomics in rapid and non-invasive diagnosis to produce relevant biomarkers and to help clinicians in monitoring kidney transplants. Further investigations are needed to clarify the physiological relevance of fecal metabolome and to assess the impact of microbiota modulation.

A GC-MS/Single-Cell Method to Evaluate Membrane Transporter Substrate Specificity and Signaling

Amino acid transporters play a vital role in metabolism and nutrient signaling pathways. Typically, transport activity is investigated using single substrates and competing amounts of other amino acids. We used GC-MS and LC-MS for metabolic screening of Xenopus laevis oocytes expressing various human amino acid transporters incubated in complex media to establish their comprehensive substrate profiles. For most transporters, amino acid selectivity matched reported substrate profiles. However, we could not detect substantial accumulation of cationic amino acids by SNAT4 and ATB0,+ in contrast to previous reports. In addition, comparative substrate profiles of two related sodium neutral amino acid transporters known as SNAT1 and SNAT2, revealed the latter as a significant leucine accumulator. As a consequence, SNAT2, but not SNAT1, was shown to be an effective activator of the eukaryotic cellular growth regulator mTORC1. We propose, that metabolomic profiling of membrane transporters in Xe nopus laevis oocytes can be used to test their substrate specificity and role in intracellular signaling pathways.

Metabolic profiling of 19 amino acids in triptolide-induced liver injured rats by gas chromatography-triple quadrupole mass spectrometry

The liver is an important organ for amino acid metabolism, and its damage can be reflected in the changes of amino acid level in the body. Triptolide (TP) has broad anti-inflammatory and anti-tumor activities, but its clinical application is limited due to hepatotoxicity. In this work, a simple, accurate and sensitive gas chromatography-triple quadrupole mass spectrometry (GC-QqQ-MS/MS) method was developed and validated for evaluating the serum levels of amino acids from control and TP-induced liver injured rats, and chemometric analysis was employed for amino acid metabolic profiles analysis. It was found that 11 amino acids showed significant changes after TP administration, and they were mainly involved in 5 metabolic pathways that are phenylalanine, tyrosine and tryptophan biosynthesis, alanine, aspartate and glutamate metabolism, glutamine and glutamate metabolism, phenylalanine metabolism and arginine biosynthesis. Five amino acids including tyrosine, glutamine, glutamic acid, tryptophan and alanine were identified as biomarkers of TP hepatotoxicity by further analysis. These results indicated that the novel amino acid metabolic profiling study based on the GC-QqQ-MS/MS provided not only exact concentrations of serum amino acids, but also a prospective methodology for evaluation of chemically induced liver injury.

Analytical Methods for the Determination of Neuroactive Steroids

Neuroactive steroids are a family of all steroid-based compounds, of both natural and synthetic origin, which can affect the nervous system functions. Their biosynthesis occurs directly in the nervous system (so-called neurosteroids) or in peripheral endocrine tissues (hormonal steroids). Steroid hormone levels may fluctuate due to physiological changes during life and various pathological conditions affecting individuals. A deeper understanding of neuroactive steroids' production, in addition to reliable monitoring of their levels in various biological matrices, may be useful in the prevention, diagnosis, monitoring, and treatment of some neurodegenerative and psychiatric diseases. The aim of this review is to highlight the most relevant methods currently available for analysis of neuroactive steroids, with an emphasis on immunoanalytical methods and gas, or liquid chromatography combined with mass spectrometry.

Narrative review of metabolomics in cardiovascular disease

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

The Microbiome and p-Inulin in Hemodialysis: A Feasibility Study

Background

The intestinal microbiome is an appealing target for interventions in ESKD because of its likely contribution to uremic toxicity. Before conducting clinical trials of microbiome-altering treatments, it is necessary to understand the within-person and between-person variability in the composition and function of the gut microbiome in patients with ESKD.

Methods

We conducted a multicenter, nonrandomized, crossover feasibility study of patients on maintenance hemodialysis consisting of three phases: pretreatment (8 weeks); treatment, during which the prebiotic, p-inulin, was administered at a dosage of 8 g twice daily (12 weeks); and post-treatment (8 weeks). Stool samples were collected 1-2 times per week and blood was collected weekly for 28 weeks. The gut microbiome was characterized using 16S ribosomal-RNA sequencing and metabolomic profiling.

Results

A total of 11 of the 13 participants completed the 28-week study. Interparticipant variability was greater than intraparticipant variability for microbiome composition (P<0.001 by UniFrac distances) and metabolomic composition (P<0.001 by Euclidean distances). p-Inulin was well tolerated by 12 of 13 participants. Adherence to the frequent sample collection and self-aliquoting of stool samples were both 96%. A change in the microbiome composition from pretreatment to post-treatment was evident by the overall shifts in weighted UniFrac distances (P=0.004) and a progressive decrease in prevalence of high intraclass correlations, indicating an increase in intraparticipant microbiome diversity during and after p-inulin treatment. An effect of p-inulin on the metabolomic profile was not evident.

Conclusions

The intraparticipant stability of the gut microbiome under no-treatment conditions, the tolerability of p-inulin, the signals of increased diversity of the microbiome with p-inulin treatment, and the willingness of participants to provide stool samples all support the feasibility of a larger trial to investigate interventions targeting the gut microbiome in patients with ESKD. Whether or not p-inulin has sufficient efficacy as an intervention requires evaluation in larger studies.

Clinical Trial registry name and registration number

Gut Microbiome and p-Inulin in Hemodialysis, NCT02572882.

Enhancing lifespan of budding yeast by pharmacological lowering of amino acid pools

The increasing prevalence of age-related diseases and resulting healthcare insecurity and emotional burden require novel treatment approaches. Several promising strategies seek to limit nutrients and promote healthy aging. Unfortunately, the human desire to consume food means this strategy is not practical for most people but pharmacological approaches might be a viable alternative. We previously showed that myriocin, which impairs sphingolipid synthesis, increases lifespan in Saccharomyces cerevisiae by modulating signaling pathways including the target of rapamycin complex 1 (TORC1). Since TORC1 senses cellular amino acids, we analyzed amino acid pools and identified 17 that are lowered by myriocin treatment. Studying the methionine transporter, Mup1, we found that newly synthesized Mup1 traffics to the plasma membrane and is stable for several hours but is inactive in drug-treated cells. Activity can be restored by adding phytosphingosine to culture medium thereby bypassing drug inhibition, thus confirming a sphingolipid requirement for Mup1 activity. Importantly, genetic analysis of myriocin-induced longevity revealed a requirement for the Gtr1/2 (mammalian Rags) and Vps34-Pib2 amino acid sensing pathways upstream of TORC1, consistent with a mechanism of action involving decreased amino acid availability. These studies demonstrate the feasibility of pharmacologically inducing a state resembling amino acid restriction to promote healthy aging.

Metabolomics analysis reveals a modified amino acid metabolism that correlates with altered oxygen homeostasis in COVID-19 patients

We identified the main changes in serum metabolites associated with severe (n = 46) and mild (n = 19) COVID-19 patients by gas chromatography coupled to mass spectrometry. The modified metabolic profiles were associated to an altered amino acid catabolism in hypoxic conditions. Noteworthy, three α-hydroxyl acids of amino acid origin increased with disease severity and correlated with altered oxygen saturation levels and clinical markers of lung damage. We hypothesize that the enzymatic conversion of α-keto-acids to α- hydroxyl-acids helps to maintain NAD recycling in patients with altered oxygen levels, highlighting the potential relevance of amino acid supplementation during SARS-CoV-2 infection.

Metabolic Reprogramming and Inflammatory Response Induced by D-Lactate in Bovine Fibroblast-Like Synoviocytes Depends on HIF-1 Activity

Acute ruminal acidosis (ARA) occurs after an excessive intake of rapidly fermentable carbohydrates and is characterized by the overproduction of D-lactate in the rumen that reaches the bloodstream. Lameness presentation, one of the primary consequences of ARA in cattle, is associated with the occurrence of laminitis and aseptic polysynovitis. Fibroblast-like synoviocytes (FLS) are predominant cells of synovia and play a key role in the pathophysiology of joint diseases, thus increasing the chances of the release of pro-inflammatory cytokines. Increased D-lactate levels and disturbances in the metabolism of carbohydrates, pyruvates, and amino acids are observed in the synovial fluid of heifers with ARA-related polysynovitis prior to neutrophil infiltration, suggesting an early involvement of metabolic disturbances in joint inflammation. We hypothesized that D-lactate induces metabolic reprogramming, along with an inflammatory response, in bovine exposed FLS. Gas chromatography-mass spectrometry (GC-MS)-based metabolomics revealed that D-lactate disrupts the metabolism of bovine FLS, mainly enhancing glycolysis and gluconeogenesis, pyruvate metabolism, and galactose metabolism. The reverse-transcription quantitative PCR (RT-qPCR) analysis revealed an increased expression of metabolic-related genes, including hypoxia-inducible factor 1 (HIF-1)α, glucose transporter 1 (Glut-1), L-lactate dehydrogenase subunit A (L-LDHA), and pyruvate dehydrogenase kinase 1 (PDK-1). Along with metabolic disturbances, D-lactate also induced an overexpression and the secretion of IL-6. Furthermore, the inhibition of HIF-1, PI3K/Akt, and NF-κB reduced the expression of IL-6 and metabolic-related genes. The results of this study reveal a potential role for D-lactate in bFLS metabolic reprogramming and support a close relationship between inflammation and metabolism in cattle.

AtFAHD1a: A New Player Influencing Seed Longevity and Dormancy in Arabidopsis?

Fumarylacetoacetate hydrolase (FAH) proteins form a superfamily found in Archaea, Bacteria, and Eukaryota. However, few fumarylacetoacetate hydrolase domain (FAHD)-containing proteins have been studied in Metazoa and their role in plants remains elusive. Sequence alignments revealed high homology between two Arabidopsis thaliana FAHD-containing proteins and human FAHD1 (hFAHD1) implicated in mitochondrial dysfunction-associated senescence. Transcripts of the closest hFAHD1 orthologue in Arabidopsis (AtFAHD1a) peak during seed maturation drying, which influences seed longevity and dormancy. Here, a homology study was conducted to assess if AtFAHD1a contributes to seed longevity and vigour. We found that an A. thaliana T-DNA insertional line (Atfahd1a-1) had extended seed longevity and shallower thermo-dormancy. Compared to the wild type, metabolite profiling of dry Atfahd1a-1 seeds showed that the concentrations of several amino acids, some reducing monosaccharides, and δ-tocopherol dropped, whereas the concentrations of dehydroascorbate, its catabolic intermediate threonic acid, and ascorbate accumulated. Furthermore, the redox state of the glutathione disulphide/glutathione couple shifted towards a more reducing state in dry mature Atfahd1a-1 seeds, suggesting that AtFAHD1a affects antioxidant redox poise during seed development. In summary, AtFAHD1a appears to be involved in seed redox regulation and to affect seed quality traits such as seed thermo-dormancy and longevity.

Metabolomic-based clinical studies and murine models for acute pancreatitis disease: A review

Acute pancreatitis (AP) is one of the most common gastroenterological disorders requiring hospitalization and is associated with substantial morbidity and mortality. Metabolomics nowadays not only help us to understand cellular metabolism to a degree that was not previously obtainable, but also to reveal the importance of the metabolites in physiological control, disease onset and development. An in-depth understanding of metabolic phenotyping would be therefore crucial for accurate diagnosis, prognosis and precise treatment of AP. In this review, we summarized and addressed the metabolomics design and workflow in AP studies, as well as the results and analysis of the in-depth of research. Based on the metabolic profiling work in both clinical populations and experimental AP models, we described the metabolites with potential utility as biomarkers and the correlation between the altered metabolites and AP status. Moreover, the disturbed metabolic pathways correlated with biological function were discussed in the end. A practical understanding of current and emerging metabolomic approaches applicable to AP and use of the metabolite information presented will aid in designing robust metabolomics and biological experiments that result in identification of unique biomarkers and mechanisms, and ultimately enhanced clinical decision-making.

Re-routing of Sugar Catabolism Provides a Better Insight Into Fungal Flexibility in Using Plant Biomass-Derived Monomers as Substrates

The filamentous ascomycete Aspergillus niger has received increasing interest as a cell factory, being able to efficiently degrade plant cell wall polysaccharides as well as having an extensive metabolism to convert the released monosaccharides into value added compounds. The pentoses D-xylose and L-arabinose are the most abundant monosaccharides in plant biomass after the hexose D-glucose, being major constituents of xylan, pectin and xyloglucan. In this study, the influence of selected pentose catabolic pathway (PCP) deletion strains on growth on plant biomass and re-routing of sugar catabolism was addressed to gain a better understanding of the flexibility of this fungus in using plant biomass-derived monomers. The transcriptome, metabolome and proteome response of three PCP mutant strains, ΔlarAΔxyrAΔxyrB, ΔladAΔxdhAΔsdhA and ΔxkiA, grown on wheat bran (WB) and sugar beet pulp (SBP), was evaluated. Our results showed that despite the absolute impact of these PCP mutations on pure pentose sugars, they are not as critical for growth of A. niger on more complex biomass substrates, such as WB and SBP. However, significant phenotypic variation was observed between the two biomass substrates, but also between the different PCP mutants. This shows that the high sugar heterogeneity of these substrates in combination with the high complexity and adaptability of the fungal sugar metabolism allow for activation of alternative strategies to support growth.

Systematic evaluation of sample preparation strategy for GC-MS-based plasma metabolomics and its application in osteoarthritis

Sample preparation plays a crucial part in plasma metabolomics. In order to obtain an optimal sample extraction method for gas chromatography mass spectrometry (GC-MS)-based plasma metabolomics, five different extraction strategies including protein precipitation, liquid-liquid extraction and solid-phase extraction were evaluated systematically for both plasma untargeted- and targeted-metabolomics. The comprehensive evaluation revealed that the all-in-one sample preparation method, MeOH-MTBE-H₂O (1:5:1.5, v/v/v), was the optimal extraction method for both untargeted- and targeted-metabolomics. Next, the optimal sample preparation protocol was applied in plasma metabolomics of osteoarthritis (OA). A panel containing cholesterol, lactic acid, stearic acid, alpha-tocopherol and oxalic acid was selected as candidate biomarker to distinguish OA patients from healthy controls (HC) based on the support vector machine (SVM) classification model. The discriminating capability of the candidate biomarker panel was further validated successfully with logistic regression and principal components analysis (PCA) analysis. Therefore, the panel could potentially act as diagnostic biomarker for osteoarthritis.

Mass spectrometry-based metabolomics diagnostics - myth or reality?

ABSTACTIntroduction: Metabolomics, one of the most high-promising technologies, is the most recently developed post-genomics discipline for developing new diagnostic tests for future implementation in medicine. More than 2,000 scientific papers, using mass spectrometry-based (MS-based) metabolomics analysis for human disease diagnostics, have been published during the past two decades, and almost every metabolomics study shows high diagnostic accuracy. However, despite the great results and promising perspectives, there are currently no diagnostic tests based on metabolomics that have been approved and introduced into clinics.Areas covered: In this report, the advantages and challenges of MS-based metabolomics are discussed with a focus on its developing role in diagnostics, and the current trends in implementing metabolomics diagnostics in the clinic.Expert opinion: In the development of new clinical diagnostics tests, MS-based metabolomics has potential as both a preliminary discovery base for routine testing and a multi-test prototype, which is hoped to be introduced into clinical practice in the near future. A laboratory-developed test (LDT) is one possible way that multi-testing could be developed.

Transient phases of OXPHOS inhibitor resistance reveal underlying metabolic heterogeneity in single cells

Cell-to-cell heterogeneity in metabolism plays an unknown role in physiology and pharmacology. To functionally characterize cellular variability in metabolism, we treated cells with inhibitors of oxidative phosphorylation (OXPHOS) and monitored their responses with live-cell reporters for ATP, ADP/ATP, or activity of the energy-sensing kinase AMPK. Across multiple OXPHOS inhibitors and cell types, we identified a subpopulation of cells resistant to activation of AMPK and reduction of ADP/ATP ratio. This resistant state persists transiently for at least several hours and can be inherited during cell divisions. OXPHOS inhibition suppresses the mTORC1 and ERK growth signaling pathways in sensitive cells, but not in resistant cells. Resistance is linked to a multi-factorial combination of increased glucose uptake, reduced protein biosynthesis, and G0/G1 cell-cycle status. Our results reveal dynamic fluctuations in cellular energetic balance and provide a basis for measuring and predicting the distribution of cellular responses to OXPHOS inhibition.

Mass spectrometry-based forest tree metabolomics

Research in forest tree species has advanced slowly when compared with other agricultural crops and model organisms, mainly due to the long-life cycles, large genome sizes, and lack of genomic tools. Additionally, trees are complex matrices, and the presence of interferents (e.g., oleoresins and cellulose) challenges the analysis of tree tissues with mass spectrometry (MS)-based analytical platforms. In this review, advances in MS-based forest tree metabolomics are discussed. Given their economic and ecological significance, particular focus is given to Pinus, Quercus, and Eucalyptus forest tree species to better understand their metabolite responses to abiotic and biotic stresses in the current climate change scenario. Furthermore, MS-based metabolomics technologies produce large and complex datasets that require expertize to adequately manage, process, analyze, and store the data in dedicated repositories. To ensure that the full potential of forest tree metabolomics data are translated into new knowledge, these data should comply with the FAIR principles (i.e., Findable, Accessible, Interoperable, and Re-usable). It is essential that adequate standards are implemented to annotate metadata from forest tree metabolomics studies as is already required by many science and governmental agencies and some major scientific publishers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:126-157, 2021.

Tracing Nitrogen Metabolism in Mouse Tissues with Gas Chromatography-Mass Spectrometry

Nitrogen-containing metabolites including ammonia, amino acids, and nucleotides, are essential for cell metabolism, growth, and neural transmission. Nitrogen metabolism is tightly coordinated with carbon metabolism in the breakdown and biosynthesis of amino acids and nucleotides. Both nuclear magnetic resonance spectroscopy and mass spectrometry including gas chromatography-mass spectrometry (GC MS) and liquid chromatography (LC MS) have been used to measure nitrogen metabolism. Here we describe a protocol to trace nitrogen metabolism in multiple mouse tissues using ¹⁵N-ammonia coupled with GC MS. This protocol includes detailed procedures in tracer injection, tissue preparation, metabolite extraction, GC MS analysis and natural abundance corrections. This protocol will provide a useful tool to study tissue-specific nitrogen in metabolically active tissues such as the retina, brain, liver, and tumor.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

The Multiomics Analyses of Fecal Matrix and Its Significance to Coeliac Disease Gut Profiling

Gastrointestinal (GIT) diseases have risen globally in recent years, and early detection of the host’s gut microbiota, typically through fecal material, has become a crucial component for rapid diagnosis of such diseases. Human fecal material is a complex substance composed of undigested macromolecules and particles, and the processing of such matter is a challenge due to the unstable nature of its products and the complexity of the matrix. The identification of these products can be used as an indication for present and future diseases; however, many researchers focus on one variable or marker looking for specific biomarkers of disease. Therefore, the combination of genomics, transcriptomics, proteomics and metabonomics can give a detailed and complete insight into the gut environment. The proper sample collection, sample preparation and accurate analytical methods play a crucial role in generating precise microbial data and hypotheses in gut microbiome research, as well as multivariate data analysis in determining the gut microbiome functionality in regard to diseases. This review summarizes fecal sample protocols involved in profiling coeliac disease.

The potential pathways underlying the association of propyl-paraben exposure with aeroallergen sensitization and EASI score using metabolomics analysis

Propyl-paraben exposure is associated with aeroallergen sensitization, but its association with atopic dermatitis (AD) is inconclusive. No studies have been conducted on the metabolomic pathways underlying these associations. We investigated the associations between propyl-paraben exposure and aeroallergen sensitization, AD, and Eczema Area and Severity Index (EASI) score and identified the underlying pathways using untargeted metabolomics analysis. We enrolled 455 children in a general population study. Skin prick tests were performed with the assessment of EASI score. Urinary propyl-, butyl-, ethyl-, and methyl-paraben levels were measured. Untargeted metabolomics analysis was performed on the first and fifth urine propyl-paraben quintile groups. The highest urine propyl-paraben quintile group was associated with aeroallergen sensitization, but not with AD. Glycine, threonine, serine, ornithine, isoleucine, arabinofuranose, d-lyxofuranose, citrate, and picolinic acid levels were higher, whereas palmitic acid and 2-palmitoylglycerol levels were lower in the highest quintile propyl-paraben group, than in the lowest quintile group. The propyl-paraben-induced metabolic perturbations were associated with serine and glycine metabolisms, branched-chain amino acid metabolism, and ammonia recycling. Propyl-paraben exposure was associated with aeroallergen sensitization and EASI score, partially via metabolomic changes related with oxidative stress, mTOR, peroxisome proliferator-activated receptors pathway, aryl hydrocarbon receptor signaling pathways, and tricarboxylic acid cycle.

Human Milk-Fed Piglets Have a Distinct Small Intestine and Circulatory Metabolome Profile Relative to That of Milk Formula-Fed Piglets

Exclusive HM feeding for newborns is recommended at least for the first 6 months of life. However, when breastfeeding is not possible, MF is recommended as a substitute.

The impact of human milk (HM) feeding compared with cow’s milk formula (MF) feeding on small intestinal and circulatory metabolome patterns has not been fully investigated. Therefore, 2-day-old male piglets were fed HM or MF (n = 26/group) from postnatal day 2 (PND 2) through 21 and were weaned to a solid diet until PND 51. The small intestine (gastrointestinal [GI]) contents, serum, and urine were collected from subsets of piglets at PND 21 and PND 51. Samples were subjected to primary metabolomics analyses at the West Coast Metabolomics Center, UC Davis. The metabolome data assessment and the statistical analyses were performed with MetaboAnalyst software. Compared with MF feeding, at PND 21, HM feeding resulted in a higher abundance of fucose in the jejunum and urine and a greater concentration of myo-inositol in serum. In HM-fed piglets, 1,5-anhydroglucitol was higher in the duodenum, serum, and urine at PND 21. Additionally, the HM group had higher levels of urinary kynurenic acid at PND 21. Correlations between bacterial genera and altered metabolites in ileum revealed that Turicibacter sp. and Campylobacter sp. were positively correlated with maltotriose and panose at PND 21, while ileal Campylobacter sp. was negatively correlated with fumaric acid. At PND 51, no significant metabolites were identified between HM and MF diet groups. The metabolites associated with the neonatal diets may serve as the substrates and signals that contribute to the physiological effects in HM and MF during infancy, with a subset reflecting diet-associated differences in microbial metabolism and ecology.

IMPORTANCE Exclusive HM feeding for newborns is recommended at least for the first 6 months of life. However, when breastfeeding is not possible, MF is recommended as a substitute. Due to the challenges associated with sample collection from infants fed HM or MF, their gut metabolism is poorly understood. Thus, an established piglet model from our team was used to determine the metabolite profile in relation to host, diet, and microbiota. The current study is the first to provide novel insights across the small intestine metabolism and its association with circulatory metabolites in the HM group relative to the MF group at the weaning and postweaning period. Data also demonstrate that during the neonatal period, diet, host, and microbial metabolism contribute to the lumen and circulatory metabolite profile. Furthermore, small intestinal lumen metabolome can be tracked in the urine as a biomarker of dietary differences, which would be a useful tool for clinical interventions.

Comparing Stable Isotope Enrichment by Gas Chromatography with Time-of-Flight, Quadrupole Time-of-Flight, and Quadrupole Mass Spectrometry

Stable isotope tracers are applied for in vivo and in vitro studies to reveal the activity of enzymes and intracellular metabolic pathways. Most often, such tracers are used with gas chromatography coupled to mass spectrometry (GC-MS) owing to its ease of operation and reproducible mass spectral databases. Differences in isotope tracer performance of the classic GC-quadrupole MS instrument and newer time-of-flight instruments are not well studied. Here, we used three commercially available instruments for the analysis of identical samples from a stable isotope labeling study that used [U-13C6] d-glucose to investigate the metabolism of the bacterium Rothia mucilaginosa with respect to 29 amino acids and hydroxyl acids involved in primary metabolism. The prokaryote R. mucilaginosa belongs to the family of Micrococcaceae and is present and metabolically active in the airways and sputum of cystic fibrosis patients. Overall, all three GC-MS instruments (low-resolution GC-SQ MS, low-resolution GC-TOF MS, and high-resolution GC-QTOF MS) can be used to perform stable isotope tracing studies for glycolytic intermediates, tricarboxylic acid (TCA) metabolites, and amino acids, yielding similar biological results, with high-resolution GC-QTOF MS offering additional capabilities to identify the chemical structures of unknown compounds that might show significant isotope enrichments in biological studies.

Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers

BACKGROUND

The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. TEDDY has collected heterogenous data longitudinally to gain insights into the environmental and biological mechanisms driving the progression to persistent islet autoantibodies.

METHODS

We developed a machine learning model to predict imminent transition to the development of persistent islet autoantibodies based on time-varying metabolomics data integrated with time-invariant risk factors (eg, gestational age). The machine learning was initiated with 221 potential features (85 genetic, 5 environmental, 131 metabolomic) and an ensemble-based feature evaluation was utilized to identify a small set of predictive features that can be interrogated to better understand the pathogenesis leading up to persistent islet autoimmunity.

RESULTS

The final integrative machine learning model included 42 disparate features, returning a cross-validated receiver operating characteristic area under the curve (AUC) of 0.74 and an AUC of ~0.65 on an independent validation dataset. The model identified a principal set of 20 time-invariant markers, including 18 genetic markers (16 single nucleotide polymorphisms [SNPs] and two HLA-DR genotypes) and two demographic markers (gestational age and exposure to a prebiotic formula). Integration with the metabolome identified 22 supplemental metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies.

CONCLUSIONS

The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to three pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate, suggesting that these metabolic processes may play a role in triggering islet autoimmunity.

Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model

The kidneys are metabolically active organs with importance in several physiological tasks such as the secretion of soluble wastes into the urine and synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to four compounds with varying levels of nephrotoxicity (acetaminophen, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six and twenty-four hours, and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. For the transcriptomics data, we observed changes in fatty acid metabolism and amino acid metabolism, as well as changes in existing markers of kidney function such as Clu (clusterin). The iRno metabolic network reconstruction was used to predict alterations in these same pathways after integrating transcriptomics data and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism as a step towards identifying novel metabolic biomarkers of kidney function and dysfunction.

Exploring the Use of Gas Chromatography Coupled to Chemical Ionization Mass Spectrometry (GC-CI-MS) for Stable Isotope Labeling in Metabolomics

Isotopic-labeling experiments have been valuable to monitor the flux of metabolic reactions in biological systems, which is crucial to understand homeostatic alterations with disease. Experimental determination of metabolic fluxes can be inferred from a characteristic rearrangement of stable isotope tracers (e.g., 13C or 15N) that can be detected by mass spectrometry (MS). Metabolites measured are generally members of well-known metabolic pathways, and most of them can be detected using both gas chromatography (GC)-MS and liquid chromatography (LC)-MS. In here, we show that GC methods coupled to chemical ionization (CI) MS have a clear advantage over alternative methodologies due to GC's superior chromatography separation efficiency and the fact that CI is a soft ionization technique that yields identifiable protonated molecular ion peaks. We tested diverse GC-CI-MS setups, including methane and isobutane reagent gases, triple quadrupole (QqQ) MS in SIM mode, or selected ion clusters using optimized narrow windows (∼10 Da) in scan mode, and standard full scan methods using high resolution GC-(q)TOF and GC-Orbitrap systems. Isobutane as a reagent gas in combination with both low-resolution (LR) and high-resolution (HR) MS showed the best performance, enabling precise detection of isotopologues in most metabolic intermediates of central carbon metabolism. Finally, with the aim of overcoming manual operations, we developed an R-based tool called isoSCAN that automatically quantifies all isotopologues of intermediate metabolites of glycolysis, TCA cycle, amino acids, pentose phosphate pathway, and urea cycle, from LRMS and HRMS data.

Integrative analyses of TEDDY Omics data reveal lipid metabolism abnormalities, increased intracellular ROS and heightened inflammation prior to autoimmunity for type 1 diabetes

BACKGROUND

The Environmental Determinants of Diabetes in the Young (TEDDY) is a prospective birth cohort designed to study type 1 diabetes (T1D) by following children with high genetic risk. An integrative multi-omics approach was used to evaluate islet autoimmunity etiology, identify disease biomarkers, and understand progression over time.

RESULTS

We identify a multi-omics signature that was predictive of islet autoimmunity (IA) as early as 1 year before seroconversion. At this time, abnormalities in lipid metabolism, decreased capacity for nutrient absorption, and intracellular ROS accumulation are detected in children progressing towards IA. Additionally, extracellular matrix remodeling, inflammation, cytotoxicity, angiogenesis, and increased activity of antigen-presenting cells are observed, which may contribute to beta cell destruction. Our results indicate that altered molecular homeostasis is present in IA-developing children months before the actual detection of islet autoantibodies, which opens an interesting window of opportunity for therapeutic intervention.

CONCLUSIONS

The approach employed herein for assessment of the TEDDY cohort showcases the utilization of multi-omics data for the modeling of complex, multifactorial diseases, like T1D.

Gastric Metabolomics Detects Helicobacter pylori Correlated Loss of Numerous Metabolites in Both the Corpus and Antrum

Helicobacter pylori is a chronic bacterial pathogen that thrives in several regions of the stomach, causing inflammation that can vary by site and result in distinct disease outcomes. Whether the regions differ in terms of host-derived metabolites is not known. We thus characterized the regional variation of the metabolomes of mouse gastric corpus and antrum organoids and tissue. The uninfected secreted organoid metabolites differed between the corpus and antrum in only seven metabolites as follows: lactic acid, malic acid, phosphoethanolamine, alanine, uridine, glycerol, and isoleucine. Several of the secreted chemicals were depleted upon H. pylori infection in both regions, including urea, cholesterol, glutamine, fumaric acid, lactic acid, citric acid, malic acid, and multiple nonessential amino acids. These results suggest a model in which H. pylori preferentially uses carboxylic acids and amino acids in complex environments, and these are found in both the corpus and antrum. When organoid metabolites were compared to mouse tissue, there was little overlap. The tissue corpus and antrum metabolomes were distinct, including antrum-elevated 5-methoxytryptamine, lactic acid, and caprylic acid, and corpus-elevated phospholipid products. The corpus and antrum remained distinct over an 8-month infection time course. The antrum displayed no significant changes between the time points in contrast to the corpus, which exhibited metabolite changes that were consistent with stress, tissue damage, and depletion of key nutrients, such as glutamine and fructose-6-phosphate. Overall, our results suggest that the corpus and antrum have largely but not completely overlapping metabolomes that change moderately upon H. pylori infection.

Targeted metabolomics analysis of postoperative delirium

Postoperative delirium is the most common complication among older adults undergoing major surgery. The pathophysiology of delirium is poorly understood, and no blood-based, predictive markers are available. We characterized the plasma metabolome of 52 delirium cases and 52 matched controls from the Successful Aging after Elective Surgery (SAGES) cohort (N = 560) of patients ≥ 70 years old without dementia undergoing scheduled major non-cardiac surgery. We applied targeted mass spectrometry with internal standards and pooled controls using a nested matched case-control study preoperatively (PREOP) and on postoperative day 2 (POD2) to identify potential delirium risk and disease markers. Univariate analyses identified 37 PREOP and 53 POD2 metabolites associated with delirium and multivariate analyses achieved significant separation between the two groups with an 11-metabolite prediction model at PREOP (AUC = 83.80%). Systems biology analysis using the metabolites with differential concentrations rendered "valine, leucine, and isoleucine biosynthesis" at PREOP and "citrate cycle" at POD2 as the most significantly enriched pathways (false discovery rate < 0.05). Perturbations in energy metabolism and amino acid synthesis pathways may be associated with postoperative delirium and suggest potential mechanisms for delirium pathogenesis. Our results could lead to the development of a metabolomic delirium predictor.

Untargeted Metabolomic Analysis Combined With Multivariate Statistics Reveal Distinct Metabolic Changes in GPR40 Agonist-Treated Animals Related to Bile Acid Metabolism

Metabolomics has been increasingly applied to biomarker discovery, as untargeted metabolic profiling represents a powerful exploratory tool for identifying causal links between biomarkers and disease phenotypes. In the present work, we used untargeted metabolomics to investigate plasma specimens of rats, dogs, and mice treated with small-molecule drugs designed for improved glycemic control of type 2 diabetes mellitus patients via activation of GPR40. The in vivo pharmacology of GPR40 is not yet fully understood. Compounds targeting this receptor have been found to induce drug-induced liver injury (DILI). Metabolomic analysis facilitating an integrated UPLC-TWIMS-HRMS platform was used to detect metabolic differences between treated and non-treated animals within two 4-week toxicity studies in rat and dog, and one 2-week toxicity study in mouse. Multivariate statistics of untargeted metabolomics data subsequently revealed the presence of several significantly upregulated endogenous compounds in the treated animals whose plasma level is known to be affected during DILI. A specific bile acid metabolite useful as endogenous probe for drug-drug interaction studies was identified (chenodeoxycholic acid-24 glucuronide), as well as a metabolic precursor indicative of acidic bile acid biosynthesis (7α-hydroxy-3-oxo-4-cholestenoic acid). These results correlate with typical liver toxicity parameters on the individual level.

A multi-omics view of the complex mechanism of vascular calcification

Vascular calcification is a high incidence and high risk disease with increasing morbidity and high mortality, which is considered the consequence of smooth muscle cell transdifferentiation initiating the mechanism of accumulation of hydroxyl calcium phosphate. Vascular calcification is also thought to be strongly associated with poor outcomes in diabetes and chronic kidney disease. Numerous studies have been accomplished; however, the specific mechanism of the disease remains unclear. Development of the genome project enhanced the understanding of life science and has entered the post-genomic era resulting in a variety of omics techniques used in studies and a large amount of available data; thus, a new perspective on data analysis has been revealed. Omics has a broader perspective and is thus advantageous over a single pathway analysis in the study of complex vascular calcification mechanisms. This paper reviews in detail various omics studies including genomics, proteomics, transcriptomics, metabolomics and multiple group studies on vascular calcification. Advances and deficiencies in the use of omics to study vascular calcification are presented in a comprehensive view. We also review the methodology of the omics studies and omics data analysis and processing. In addition, the methodology and data processing presented here can be applied to other areas. An omics landscape perspective across the boundaries between genomics, transcriptomics, proteomics and metabolomics is used to examine the mechanisms of vascular calcification. The perspective combined with various technologies also provides a direction for the subsequent exploration of clinical significance.

A FACS-Free Purification Method to Study Estrogen Signaling, Organoid Formation, and Metabolic Reprogramming in Mammary Epithelial Cells

Few in vitro models are used to study mammary epithelial cells (MECs), and most of these do not express the estrogen receptor α (ERα). Primary MECs can be used to overcome this issue, but methods to purify these cells generally require flow cytometry and fluorescence-activated cell sorting (FACS), which require specialized instruments and expertise. Herein, we present in detail a FACS-free protocol for purification and primary culture of mouse MECs. These MECs remain differentiated for up to six days with >85% luminal epithelial cells in two-dimensional culture. When seeded in Matrigel, they form organoids that recapitulate the mammary gland's morphology in vivo by developing lumens, contractile cells, and lobular structures. MECs express a functional ERα signaling pathway in both two- and three-dimensional cell culture, as shown at the mRNA and protein levels and by the phenotypic characterization. Extracellular metabolic flux analysis showed that estrogens induce a metabolic switch favoring aerobic glycolysis over mitochondrial respiration in MECs grown in two-dimensions, a phenomenon known as the Warburg effect. We also performed mass spectrometry (MS)-based metabolomics in organoids. Estrogens altered the levels of metabolites from various pathways, including aerobic glycolysis, citric acid cycle, urea cycle, and amino acid metabolism, demonstrating that ERα reprograms cell metabolism in mammary organoids. Overall, we have optimized mouse MEC isolation and purification for two- and three-dimensional cultures. This model represents a valuable tool to study how estrogens modulate mammary gland biology, and particularly how these hormones reprogram metabolism during lactation and breast carcinogenesis.

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.

Practical Considerations in Method Development for Gas Chromatography-Based Metabolomic Profiling

This chapter discusses the fundamentals of gas chromatography (GC) to improve method development for metabolic profiling of complex biological samples. The selection of column geometry and phase ratio impacts analyte mass transfer, which must be carefully optimized for fast analysis. Stationary phase selection is critical to obtain baseline resolution of critical pairs, but such selection must consider important aspects of metabolomic protocols, such as derivatization and dependence of analyte identification on existing databases. Sample preparation methods are also addressed depending on the sample matrix, including liquid-liquid extraction and solid-phase microextraction.

Mass Spectrometry-Based Metabolomics Reveals a Concurrent Action of Several Chemical Mechanisms in Arabidopsis-Fusarium oxysporum Compatible and Incompatible Interactions

Fusarium oxysporum is a destructive root-infecting plant pathogen that causes significant yield losses in many economically important crop species. Hence, a deeper understanding of pathogen infection strategies is needed. With liquid chromatography-tandem mass spectrometry and gas chromatography-time of flight mass spectrometry platforms, we analyzed the metabolic changes in a time-course experiment with Arabidopsis accessions either resistant (Col-0) or susceptible (Ler-0) to isolates of Fusarium oxysporum forma specialis matthioli infection. We showed a concurrent effect of Fusarium-derived polyols and the mycotoxin beauvericin in the suppression of the immune response of susceptible hosts. A significant increase in oxidized glutathione in the resistant host was probably associated with effective reactive oxygen species-mediated resistance responses. Through a combination of targeted and untargeted metabolomics, we demonstrated the concurrent action of several Arabidopsis defense systems as well as the concurrent action of several virulence systems in the fungal attack of susceptible Arabidopsis.

Oral Gavage Delivery of Stable Isotope Tracer for In Vivo Metabolomics

Stable isotope-resolved metabolomics (SIRM) is a powerful tool for understanding disease. Advances in SIRM techniques have improved isotopic delivery and expanded the workflow from exclusively in vitro applications to in vivo methodologies to study systemic metabolism. Here, we report a simple, minimally-invasive and cost-effective method of tracer delivery to study SIRM in vivo in laboratory mice. Following a brief fasting period, we orally administered a solution of [U-13C] glucose through a blunt gavage needle without anesthesia, at a physiological dose commonly used for glucose tolerance tests (2 g/kg bodyweight). We defined isotopic enrichment in plasma and tissue at 15, 30, 120, and 240 min post-gavage. 13C-labeled glucose peaked in plasma around 15 min post-gavage, followed by period of metabolic decay and clearance until 4 h. We demonstrate robust enrichment of a variety of central carbon metabolites in the plasma, brain and liver of C57/BL6 mice, including amino acids, neurotransmitters, and glycolytic and tricarboxylic acid (TCA) cycle intermediates. We then applied this method to study in vivo metabolism in two distinct mouse models of diseases known to involve dysregulation of glucose metabolism: Alzheimer's disease and type II diabetes. By delivering [U-13C] glucose via oral gavage to the 5XFAD Alzheimer's disease model and the Lepob/ob type II diabetes model, we were able to resolve significant differences in multiple central carbon pathways in both model systems, thus providing evidence of the utility of this method to study diseases with metabolic components. Together, these data clearly demonstrate the efficacy and efficiency of an oral gavage delivery method, and present a clear time course for 13C enrichment in plasma, liver and brain of mice following oral gavage of [U-13C] glucose-data we hope will aid other researchers in their own 13C-glucose metabolomics study design.

Plasma amino acids profile in first-episode psychosis, unaffected siblings and community-based controls

Investigations of plasma amino acids in early psychosis and their unaffected siblings are rare. We measured plasma amino acids involved in the co-activation of dopaminergic, GABAergic, glutamatergic, and serotoninergic neurotransmitters in first-episode psychosis (FEP) patients (n = 166), unaffected siblings (n = 76), and community-based controls (n = 166) included in a cross-sectional study. Plasma levels of glutamic acid (GLU), glutamine, glycine, proline (PRO), tryptophan (TRP), tyrosine, serine and GABA were quantified by gas-chromatography-mass spectrometry. We used the generalized linear model adjusted by sex, age, and body mass index for group comparison and paired t-test for FEP-Sibling pairs. FEP had reduced GABA plasma levels compared to siblings and controls (p < 0.05 for both). Siblings had lower GLU, Glx and PRO (p < 0.05 for all) but increased TRP compared to patients and controls (p < 0.05 for both). FEP patients with longer duration of pharmacological treatment and medicated only with antipsychotics had increased GLU compared to FEP with shorter periods, or with those treated with a combination of medications (p < 0.05 for both). Finally, FEP patients treated only with antipsychotics presented higher Glx compared to those with mixed medications (p = 0.026). Our study suggests that FEP have low a GABA plasma profile. Unaffected siblings may be a possible risk group for metabolic abnormalities.

Neonatal Diet Impacts the Large Intestine Luminal Metabolome at Weaning and Post-Weaning in Piglets Fed Formula or Human Milk

The impact of human milk (HM) or dairy milk-based formula (MF) on the large intestine’s metabolome was not investigated. Two-day old male piglets were randomly assigned to HM or MF diet (n = 26/group), from postnatal day (PND) 2 through 21 and weaned to a solid diet until PND 51. Piglets were euthanized at PND 21 and PND 51, luminal contents of the cecum, proximal (PC) and distal colons (DC), and rectum were collected and subjected to metabolomics analysis. Data analyses were performed using Metaboanalyst. In comparison to MF, the HM diet resulted in higher levels of fatty acids in the lumen of the cecum, PC, DC, and rectum at PND 21. Glutamic acid was greater in the lumen of cecum, PC, and DC relative to the MF group at PND 21. Also, spermidine was higher in the DC and rectal contents of HM relative to MF at PND 21. MF diet resulted in greater abundances of amino acids in the cecal lumen relative to HM diet at PND 21. Additionally, several sugar metabolites were higher in various regions of the distal gut of MF fed piglets relative to HM group at PND 21. In contrast, at PND 51, in various regions there were higher levels of erythritol, maltotriose, isomaltose in HM versus MF fed piglets. This suggests a post weaning shift in sugar metabolism that is impacted by neonatal diet. The data also suggest that infant diet type and host-microbiota interactions likely influence the lower gut metabolome.

A rapid GC method coupled with quadrupole or time of flight mass spectrometry for metabolomics analysis

Gas chromatography-mass spectrometry (GC-MS) is an ideal tool for analyzing the intermediates of tricarboxylic acid cycle and glycolysis, sugars, organic acids and amino acids, etc. High-throughput metabolomics methods are required by large-scale clinical researches, and time of flight mass spectrometry (TOF MS) having fast scanning rate is preferable for rapid GC. Quadrupole MS (qMS) instruments have 95% market share, and their potential in rapid metabolomics is worth being studied. In this work, a within 15-min GC program was established and matched by qMS scanning for plasma metabolome analysis after N-methyl-N-(trimethylsilyl)-trifluoroacetamide derivatization. Compared to the longer-time program GC-qMS method, the rapid GC-qMS method had nearly no metabolome information loss, and it had excellent profile performance in repeatability, intra-day and inter-day precision, sampling range, linearity and extraction recovery. Compared to TOF MS, qMS achieved similar results in investigating lung cancer serum metabolic disruptions. Partial least squares-discriminant analysis revealed that the two datasets acquired by qMS and TOF MS had very similar model parameters, and most of top ranked differential metabolites were the same. This study provides a rapid and economical GC-qMS metabolomics method for researchers. Still, MS having faster scanning rate and higher sensitivity are recommended, if possible, to detect more small peaks and some co-eluted peaks.

An Overview of the Application of Systems Biology in an Understanding of Chronic Rhinosinusitis (CRS) Development

Chronic rhinosinusitis (CRS) is an inflammatory disease of the paranasal sinuses. It is defined as the presence of a minimum of two out of four main symptoms such as hyposmia, facial pain, nasal blockage, and discharge, which last for 8–12 weeks. CRS significantly impairs a patient’s quality of life. It needs special treatment mainly focusing on preventing local infection/inflammation with corticosteroid sprays or improving sinus drainage using nasal saline irrigation. When other treatments fail, endoscopic sinus surgery is considered an effective option. According to the state-of-the-art knowledge of CRS, there is more evidence suggesting that it is more of an inflammatory disease than an infectious one. This condition is also treated as a multifactorial inflammatory disorder as it may be triggered by various factors, such as bacterial or fungal infections, airborne irritants, defects in innate immunity, or the presence of concomitant diseases. Due to the incomplete understanding of the pathological processes of CRS, there is a continuous search for new indicators that are directly related to the pathogenesis of this disease—e.g., in the field of systems biology. The studies adopting systems biology search for possible factors responsible for the disease at genetic, transcriptomic, proteomic, and metabolomic levels. The analyses of the changes in the genome, transcriptome, proteome, and metabolome may reveal the dysfunctional pathways of inflammatory regulation and provide a clear insight into the pathogenesis of this disease. Therefore, in the present paper, we have summarized the state-of-the-art knowledge of the application of systems biology in the pathology and development of CRS.

Plasma and urine metabolomic analyses in aortic valve stenosis reveal shared and biofluid-specific changes in metabolite levels

Aortic valve stenosis (AVS) is a prevalent condition among the elderly population that eventually requires aortic valve replacement. The lack of reliable biomarkers for AVS poses a challenge for its early diagnosis and the application of preventive measures. Untargeted gas chromatography mass spectrometry (GC-MS) metabolomics was applied in 46 AVS cases and 46 controls to identify plasma and urine metabolites underlying AVS risk. Multivariate data analyses were performed on pre-processed data (e.g. spectral peak alignment), in order to detect changes in metabolite levels in AVS patients and to evaluate their performance in group separation and sensitivity of AVS prediction, followed by regression analyses to test for their association with AVS. Through untargeted analysis of 190 urine and 130 plasma features that could be detected and quantified in the GC-MS spectra, we identified contrasting levels of 22 urine and 21 plasma features between AVS patients and control subjects. Following metabolite assignment, we observed significant changes in the concentration of known metabolites in urine (n = 14) and plasma (n = 15) that distinguish the metabolomic profiles of AVS patients from healthy controls. Associations with AVS were replicated in both plasma and urine for about half of these metabolites. Among these, 2-Oxovaleric acid, elaidic acid, myristic acid, palmitic acid, estrone, myo-inositol showed contrasting trends of regulation in the two biofluids. Only trans-Aconitic acid and 2,4-Di-tert-butylphenol showed consistent patterns of regulation in both plasma and urine. These results illustrate the power of metabolomics in identifying potential disease-associated biomarkers and provide a foundation for further studies towards early diagnostic applications in severe heart conditions that may prevent surgery in the elderly.

Integration of non-target metabolomics and sensory analysis unravels vegetable plant metabolite signatures associated with sensory quality: A case study using dill (Anethum graveolens)

Using dill (Anethum graveolens L.) as a model herb, we reveal novel associations between metabolite profile and sensory quality, by integrating non-target metabolomics with sensory data. Low night temperatures and exposure to UV-enriched light was used to modulate plant metabolism, thereby improving sensory quality. Plant age is a crucial factor associated with accumulation of dill ether and α-phellandrene, volatile compounds associated with dill flavour. However, sensory analysis showed that neither of these compounds has any strong association with dill taste. Rather, amino acids alanine, phenylalanine, glutamic acid, valine, and leucine increased in samples exposed to eustress and were positively associated with dill and sour taste. Increases in amino acids and organic acids changed the taste from lemon/grass to a more bitter/pungent dill-related taste. Our procedure reveals a novel approach to establish links between effects of eustressors on sensory quality and may be applicable to a broad range of crops.

EGYVIR: An immunomodulatory herbal extract with potent antiviral activity against SARS-CoV-2

Due to the challenges for developing vaccines in devastating pandemic situations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), developing and screening of novel antiviral agents are peremptorily demanded. Herein, we developed EGYVIR as a potent immunomodulatory herbal extract with promising antiviral activity against SARS-CoV-2. It constitutes of a combination of black pepper extract with curcumin extract. The antiviral effect of EGYVIR extract is attributed to the two key phases of the disease in severe cases. First, the inhibition of the nuclear translocation of NF-kβ p50, attenuating the SARS-CoV-2 infection-associated cytokine storm. Additionally, the EGYVIR extract has an in vitro virucidal effect for SARS-CoV-2. The in vitro study of EGYVIR extract against SARS-CoV-2 on Huh-7 cell lines, revealed the potential role of NF-kβ/TNFα/IL-6 during the infection process. EGYVIR antagonizes the NF-kβ pathway in-silico and in-vitro studies. Consequently, it has the potential to hinder the release of IL-6 and TNFα, decreasing the production of essential cytokines storm elements.

Effect of Zinc Supplementation on the Serum Metabolites Profile at the Early Stage of Breast Cancer in Rats

The cytotoxic properties of zinc nanoparticles have been evaluated in vitro against several types of cancer. However, there is a lack of significant evidence of their activity in vivo, and a potential therapeutic application remains limited. Herein we report the effective inhibition of tumor growth by zinc nanoparticles in vivo, as the effect of the dietary intervention, after the chemical induction in a rodent model of breast cancer. Biopsy images indicated grade 1 tumors with multiple inflammatory infiltrates in the group treated with zinc nanoparticles, whereas, in the other groups, a moderately differentiated grade 2 adenocarcinoma was identified. Moreover, after the supplementation with zinc nanoparticles, the levels of several metabolites associated with cancer metabolism, important to its survival, were found to have been altered. We also revealed that the biological activity of zinc in vivo depends on the size of applied particles, as the treatment with zinc microparticles has not had much effect on cancer progression.

Breath markers for therapeutic radiation

Radiation dose is important in radiotherapy. Too little, and the treatment is not effective, too much causes radiation toxicity. A biochemical measurement of the effect of radiotherapy would be useful in personalisation of this treatment. This study evaluated changes in exhaled breath volatile organic compounds (VOC) associated with radiotherapy with thermal desorption gas chromatography mass-spectrometry followed by data processing and multivariate statistical analysis. Further the feasibility of adopting gas chromatography ion mobility spectrometry for radiotherapy point-of-care breath was assessed. A total of 62 participants provided 240 end-tidal 1 dm³ breath samples before radiotherapy and at 1, 3, and 6 h post-exposure, that were analysed by thermal-desorption/gas-chromatography/quadrupole mass-spectrometry. Data were registered by retention-index and mass-spectra before multivariate statistical analyses identified candidate markers. A panel of sulfur containing compounds (thio-VOC) were observed to increase in concentration over the 6 h following irradiation. 3-methylthiophene (80 ng.m^-3 to 790 ng.m^-3) had the lowest abundance while 2-thiophenecarbaldehyde(380 ng.m^-3 to 3.85 μg.m^-3) the highest; note, exhaled 2-thiophenecarbaldehyde has not been observed previously. The putative tumour metabolite 2,4-dimethyl-1-heptene concentration reduced by an average of 73% over the same time. Statistical scoring based on the signal intensities thio-VOC and 3-methylthiophene appears to reflect individuals' responses to radiation exposure from radiotherapy. The thio-VOC are hypothesised to derive from glutathione and Maillard-based reactions and these are of interest as they are associated with radio-sensitivity. Further studies with continuous monitoring are needed to define the development of the breath biochemistry response to irradiation and to determine the optimum time to monitor breath for radiotherapy markers. Consequently, a single 0.5 cm³ breath-sample gas chromatography-ion mobility approach was evaluated. The calibrated limit of detection for 3-methylthiophene was 10 μg.m^-3 with a lower limit of the detector's response estimated to be 210 fg.s^-1; the potential for a point-of-care radiation exposure study exists.