METLIN: A Technology Platform for Identifying Knowns and Unknowns

Discovery of metabolic alterations in the serum of patients infected with Plasmodium spp. by high-resolution metabolomics

INTRODUCTION

Despite the advances in diagnosis and treatment, malaria has still not been eradicated. Metabolic interactions between the host and Plasmodium may present novel targets for malaria control, but such interactions are yet to be deciphered. An exploration of metabolic interactions between humans and two Plasmodium species by high-resolution metabolomics may provide fundamental insights that can aid the development of a new strategy for the control of malaria.

OBJECTIVES

This study aimed at exploring the metabolic changes in the sera of patients infected with Plasmodium falciparum and Plasmodium vivax.

METHODS

Uni- and multivariate metabolomic analyses were performed on the sera of four groups of patients, namely normal control (N, n = 100), P. falciparum-infected patients (PF, n = 21), P. vivax-infected patients (PV, n = 74), and non-malarial pyretic patients (Pyr, n = 25).

RESULTS

Univariate and multivariate analyses of N, PF, and PV groups showed differential metabolic phenotypes and subsequent comparisons in pairs revealed significant features. Pathway enrichment test with significant features showed the affected pathways, namely glycolysis/gluconeogenesis for PF and retinol metabolism for PV. The metabolites belonging to the affected pathways included significantly low 2,3-diphosphoglycerate and glyceraldehyde-3-phosphate in the sera of PF. The sera of PV had significantly low levels of retinol but high levels of retinoic acid.

CONCLUSION

Our study reveals metabolic alterations induced by Plasmodium spp. in human serum and would serve as a milestone in the development of novel anti-malarial strategies.

The METLIN small molecule dataset for machine learning-based retention time prediction

Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.

The use of machine learning for identifying small molecules through their retention time’s predictions has been challenging so far. Here the authors combine a large database of liquid chromatography retention time with a deep learning approach to enable accurate metabolites’s identification.

Satureja montana L. Essential Oils: Chemical Profiles/Phytochemical Screening, Antimicrobial Activity and O/W NanoEmulsion Formulations

Chemical fingerprints of four different Satureja montana L. essential oils (SEOs) were assayed by an untargeted metabolomics approach based on Fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometry (MS) coupled with either electrospray ionization or atmospheric pressure chemical ionization ion sources. Analysis and relative quantification of the non-polar volatile fraction were conducted by gas chromatography (GC) coupled to MS. FT-ICR MS confirmed significant differences in the polar metabolite composition, while GC-MS analyses confirmed slight fluctuations in the relative amount of major terpenes and terpenoids, known to play a key role in antimicrobial mechanisms. Oil in eater (O/W) nanoemulsions (NEs) composed by SEOs and Tween 20 or Tween 80 were prepared and analyzed in terms of hydrodynamic diameter, ζ-potential and polydispersity index. The results confirm the formation of stable NEs homogeneous in size. Minimum inhibitory and minimum bactericidal concentrations of SEOs were determined towards Gram-positive (Listeria monocytogenes, Staphylococcus aureus, Staphylococcus haemolyticus) and Gram-negative clinical isolates (Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Serratia marcescens). Commercial SEO showed strongest antibacterial activity, while SEO 3 was found to be the most active among the lab made extractions. MIC and MBC values ranged from 0.39 to 6.25 mg·mL⁻¹. Furthermore, a SEO structured in NEs formulation was able to preserve and improve antimicrobial activity.

Pseudotargeted screening and determination of constituents in Qishen granule based on compound biosynthetic correlation using UHPLC coupled with high-resolution MS

Detection and determination of many known/unknown compounds in traditional Chinese medicines have always been challenging. To comprehensively identify compounds in Qishen granule, which is a widely prescribed herbal formula for treating chronic heart failure, a pseudotargeted screening method was proposed based on compound biosynthetic correlation using ultra high-performance liquid chromatography coupled with high-resolution mass spectrometry. Firstly, all possible compounds of Qishen granule were classified into nine types according to their core skeletons, and potential analogue molecular formulas were predicted according to core compound-related biosynthetic correlations, such as methylation, hydroxylation, and glucosidation. Secondly, nine pseudocompound databases consisting of core compounds, deduced biosynthetic correlations, and predicted analogue molecular formulas were established. Then, compounds of interest were directly located by pseudotargeted screening of high resolution mass spectrometry data and further verified by target tandem mass spectrometry. As a result, 213 constituents were identified and 21 of them were determined as potential new compounds. This demonstrated that pseudotargeted screening based on compound biosynthetic correlations significantly facilitated the processing of extremely large information data and improved the efficiency of compound identification. This research provided essential data for exploration of effective substances in Qishen granule and enriched the methodology for comprehensive characterization of constituents in complex traditional Chinese medicines.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

Identification of the Constituents of Percutaneous Absorption from Duhaldea nervosa Based on UHPLC-Q-Exactive Orbitrap MS and Microdialysis Technique

Duhaldea nervosa (D. nervosa) has been used for treatment of bone fracture by external use. Thus, the percutaneous absorption was crucial to the effect of D. nervosa, especially the constituents of percutaneous absorption. However, the constituents in vivo were never investigated to date. In this study, an efficient method was developed for the identification of constituents of percutaneous absorption using UHPLC-Q-Exactive Orbitrap MS and microdialysis technique. A total of 20 constituents including 15 chlorogenic acid analogues, 3 amino acids, and 2 organic acids were unambiguously or tentatively identified based on high-resolution mass data including MS and MS², chromatography retention time, and bibliography data. To the best of our knowledge, this is the first study to report the constituents of percutaneous absorption from D. nervosa, which will be very helpful for understanding the bioactive compounds and quality control.

Metabolic rewiring of the hypertensive kidney

Hypertension is a persistent epidemic across the developed world that is closely associated with kidney disease. Here, we applied a metabolomic, phosphoproteomic, and proteomic strategy to analyze the effect of hypertensive insults on kidneys. Our data revealed the metabolic aspects of hypertension-induced glomerular sclerosis, including lipid breakdown at early disease stages and activation of anaplerotic pathways to regenerate energy equivalents to counter stress. For example, branched-chain amino acids and proline, required for collagen synthesis, were depleted in glomeruli at early time points. Furthermore, indicators of metabolic stress were reflected by low amounts of ATP and NADH and an increased abundance of oxidized lipids derived from lipid breakdown. These processes were specific to kidney glomeruli where metabolic signaling occurred through mTOR and AMPK signaling. Quantitative phosphoproteomics combined with computational modeling suggested that these processes controlled key molecules in glomeruli and specifically podocytes, including cytoskeletal components and GTP-binding proteins, which would be expected to compete for decreasing amounts of GTP at early time points. As a result, glomeruli showed increased expression of metabolic enzymes of central carbon metabolism, amino acid degradation, and lipid oxidation, findings observed in previously published studies from other disease models and patients with glomerular damage. Overall, multilayered omics provides an overview of hypertensive kidney damage and suggests that metabolic or dietary interventions could prevent and treat glomerular disease and hypertension-induced nephropathy.

Impact of a High Protein Intake on the Plasma Metabolome in Elderly Males: 10 Week Randomized Dietary Intervention

High protein diets may improve the maintenance of skeletal muscle mass in the elderly, although it remains less clear what broader impact such diets have on whole body metabolic regulation in the elderly. Non-targeted polar metabolomics analysis using HILIC HPLC-MS was used to profile the circulating plasma metabolome of elderly men (n = 31; 74.7 ± 4.0 years) who were randomized to consume for 10 weeks a diet designed to achieve either protein (RDA; 0.8·g⁻¹·kg⁻¹) or that doubled this recommend intake (2RDA; 1.6.g.kg⁻¹). A limited number of plasma metabolites (n = 24) were significantly differentially regulated by the diet. These included markers of protein anabolism, which increased by the 2RDA diet, including; urea, creatine, and glutarylcarnitine. Whilst in response to the RDA diet; glutamine, glutamic acid, and proline were increased, relative to the 2RDA diet (p < 0.05). Metaboanalyst identified six major metabolic pathways to be influenced by the quantity of protein intake, most notably the arginine and proline pathways. Doubling of the recommended protein intake in older males over 10 weeks exerted only a limited impact on circulating metabolites, as determined by LC-MS. This metabolomic response was almost entirely due to increased circulating abundances of metabolites potentially indicative of altered protein anabolism, without evidence of impact on pathways for metabolic health.

Trial Registration: This trial was registered on 3rd March 2016 at the Australia New Zealand Clinical Trial Registry (www.anzctr.org.au) at ACTRN 12616000310460.

Green Tea Extract Treatment in Obese Mice with Nonalcoholic Steatohepatitis Restores the Hepatic Metabolome in Association with Limiting Endotoxemia-TLR4-NFκB-Mediated Inflammation

SCOPE

Catechin-rich green tea extract (GTE) alleviates nonalcoholic steatohepatitis (NASH) by lowering endotoxin-TLR4 (Toll-like receptor-4)-NFκB (nuclear factor kappa-B) inflammation. This study aimed to define altered MS-metabolomic responses during high-fat (HF)-induced NASH that are restored by GTE utilizing livers from an earlier study in which GTE decreased endotoxin-TLR4-NFκB liver injury.

METHODS AND RESULTS

Mice are fed a low-fat (LF) or HF diet for 12 weeks and then randomized to LF or HF diets containing 0% or 2% GTE for an additional 8 weeks. Global MS-based metabolomics and targeted metabolite profiling of catechins/catechin metabolites are evaluated. GTE in HF mice restores hepatic metabolites implicated in dyslipidemia insulin resistance, and inflammation. These include 122 metabolites: amino acids, lipids, nucleotides, vitamins, bile acids, flavonoids, xenobiotics, and carbohydrates. Hepatic amino acids, B-vitamins, and bile acids are inversely correlated with biomarkers of insulin resistance, liver injury, steatosis, and inflammation. Further, phosphatidylcholine metabolites are positively correlated with biomarkers of liver injury and NFκB inflammation. Thirteen catechin metabolites are identified in livers of GTE-treated mice, mostly as phase II conjugates of parental catechins or microbial-derived valerolactones.

CONCLUSION

The defined anti-inflammatory/metabolic interactions advance an understanding of the mechanism by which GTE catechins protect against NFκB-mediated liver injury in NASH.

Systemic and central nervous system metabolic alterations in Alzheimer's disease

Background

Metabolic alterations, related to cerebral glucose metabolism, brain insulin resistance, and age-induced mitochondrial dysfunction, play an important role in Alzheimer’s disease (AD) on both the systemic and central nervous system level. To study the extent and significance of these alterations in AD, quantitative metabolomics was applied to plasma and cerebrospinal fluid (CSF) from clinically well-characterized AD patients and cognitively healthy control subjects. The observed metabolic alterations were associated with core pathological processes of AD to investigate their relation with amyloid pathology and tau-related neurodegeneration.

Methods

In a case-control study of clinical and biomarker-confirmed AD patients (n = 40) and cognitively healthy controls without cerebral AD pathology (n = 34) with paired plasma and CSF samples, we performed metabolic profiling, i.e., untargeted metabolomics and targeted quantification. Targeted quantification focused on identified deregulated pathways highlighted in the untargeted assay, i.e. the TCA cycle, and its anaplerotic pathways, as well as the neuroactive tryptophan and kynurenine pathway.

Results

Concentrations of several TCA cycle and beta-oxidation intermediates were higher in plasma of AD patients, whilst amino acid concentrations were significantly lower. Similar alterations in these energy metabolism intermediates were observed in CSF, together with higher concentrations of creatinine, which were strongly correlated with blood-brain barrier permeability. Alterations of several amino acids were associated with CSF Amyloidβ1–42. The tryptophan catabolites, kynurenic acid and quinolinic acid, showed significantly higher concentrations in CSF of AD patients, which, together with other tryptophan pathway intermediates, were correlated with either CSF Amyloidβ1–42, or tau and phosphorylated Tau-181.

Conclusions

This study revealed AD-associated systemic dysregulation of nutrient sensing and oxidation and CNS-specific alterations in the neuroactive tryptophan pathway and (phospho)creatine degradation. The specific association of amino acids and tryptophan catabolites with AD CSF biomarkers suggests a close relationship with core AD pathology.

Our findings warrant validation in independent, larger cohort studies as well as further investigation of factors such as gender and APOE genotype, as well as of other groups, such as preclinical AD, to identify metabolic alterations as potential intervention targets.

Isolation and Quantification of Metabolite Levels in Murine Tumor Interstitial Fluid by LC/MS

Cancer is a disease characterized by altered metabolism, and there has been renewed interest in understanding the metabolism of tumors. Even though nutrient availability is a critical determinant of tumor metabolism, there has been little systematic study of the nutrients directly available to cancer cells in the tumor microenvironment. Previous work characterizing the metabolites present in the tumor interstitial fluid has been restricted to the measurement of a small number of nutrients such as glucose and lactate in a limited number of samples. Here we adapt a centrifugation-based method of tumor interstitial fluid isolation readily applicable to a number of sample types and a mass spectrometry-based method for the absolute quantitation of many metabolites in interstitial fluid samples. In this method, tumor interstitial fluid (TIF) is analyzed by liquid chromatography-mass spectrometry (LC/MS) using both isotope dilution and external standard calibration to derive absolute concentrations of targeted metabolites present in interstitial fluid. The use of isotope dilution allows for accurate absolute quantitation of metabolites, as other methods of quantitation are inadequate for determining nutrient concentrations in biological fluids due to matrix effects that alter the apparent concentration of metabolites depending on the composition of the fluid in which they are contained. This method therefore can be applied to measure the absolute concentrations of many metabolites in interstitial fluid from diverse tumor types, as well as most other biological fluids, allowing for characterization of nutrient levels in the microenvironment of solid tumors.

Computational solutions in redox lipidomics - Current strategies and future perspectives

The high chemical diversity of lipids allows them to perform multiple biological functions ranging from serving as structural building blocks of biological membranes to regulation of metabolism and signal transduction. In addition to the native lipidome, lipid species derived from enzymatic and non-enzymatic modifications (the epilipidome) make the overall picture even more complex, as their functions are still largely unknown. Oxidized lipids represent the fraction of epilipidome which has attracted high scientific attention due to their apparent involvement in the onset and development of numerous human disorders. Development of high-throughput analytical methods such as liquid chromatography coupled on-line to mass spectrometry provides the possibility to address epilipidome diversity in complex biological samples. However, the main bottleneck of redox lipidomics, the branch of lipidomics dealing with the characterization of oxidized lipids, remains the lack of optimal computational tools for robust, accurate and specific identification of already discovered and yet unknown modified lipids. Here we discuss the main principles of high-throughput identification of lipids and their modified forms and review the main software tools currently available in redox lipidomics. Different levels of confidence for software assisted identification of redox lipidome are defined and necessary steps toward optimal computational solutions are proposed.

Hybrid Search: A Method for Identifying Metabolites Absent from Tandem Mass Spectrometry Libraries

Metabolomics has a critical need for better tools for mass spectral identification. Common metabolites may be identified by searching libraries of tandem mass spectra, which offers important advantages over other approaches to identification. But tandem libraries are not nearly complete enough to represent the full molecular diversity present in complex biological samples. We present a novel hybrid search method that can help identify metabolites not in the library by similarity to compounds that are. We call it "hybrid" searching because it combines conventional, direct peak matching with the logical equivalent of neutral-loss matching. A successful hybrid search requires the library to contain "cognates" of the unknown: similar compounds with a structural difference confined to a single region of the molecule, that does not substantially alter its fragmentation behavior. We demonstrate that the hybrid search is highly likely to find similar compounds under such circumstances.

Degradation of emerging organic pollutants in wastewater effluents by electrochemical photocatalysis on nanostructured TiO2 meshes

An immobilized photoactive TiO₂ coating grown directly on titanium meshes was successfully exploited for the electrochemical photocatalytic degradation of carbamazepine in real secondary wastewater effluent. The catalyst was prepared by Plasma Electrolytic Oxidation and during the photocatalytic water treatment an electrical polarization (bias) was applied to the catalyst. The investigated process was compared with the conventional one employing suspended TiO₂ powder (Degussa P25). Results showed that carbamazepine degradation rate follows the order UV/supported TiO₂+bias ≈ UV/TiO₂ Degussa P25 > UV/supported TiO₂ > UV. The investigation also included the identification of other micropollutants and degradation products. This allowed the detection of 201 compounds present in the secondary wastewater effluent employed for the photocatalysis tests, 51 of them also successfully associated to compounds of emerging concern (CECs), and 194 to transformation products (TPs). The degradation of detected compounds followed first-order kinetics and the mean kinetic constant values of the 51 CECs resulted to be 0.048, 0.035 and 0.043 min^-1 for the TiO₂+Bias + UV, TiO₂+UV and UV, respectively. As for TPs, results showed that the TiO₂+Bias + UV treatment is much more efficient than both TiO₂+UV and UV in minimizing the intensity of the organics in the real wastewater. Such a better performance was more pronounced at higher reaction time reaching 60% reduction of mean peak area of TPs at 90 min of reaction. Among the detected TPs also compounds belonging to known carbamazepine TPs were found. This allowed to propose a degradation pathway of carbamazepine. The supported catalyst was positively tested for 15 cycles demonstrating that it has the potential to be used in real wastewater tertiary steps aimed at removing CECs.

Shielding the Next Generation: Symbiotic Bacteria from a Reproductive Organ Protect Bobtail Squid Eggs from Fungal Fouling

Organisms must have strategies to ensure successful reproduction. Some animals that deposit eggs protect their embryos from fouling/disease with the help of microorganisms. Although beneficial bacteria are hypothesized to contribute to egg defense in some organisms, the mechanisms of this protection remain largely unknown, with the exception of a few recently described systems. Using both experimental and analytical approaches, we demonstrate that symbiotic bacteria associated with a cephalopod reproductive gland and eggs inhibit fungi. Chemical analyses suggest that these bacteria produce antimicrobial compounds that may prevent overgrowth from fungi and other microorganisms. Given the distribution of these symbiotic glands among many cephalopods, similar defensive relationships may be more common in aquatic environments than previously realized. Such defensive symbioses may also be a rich source for the discovery of new antimicrobial compounds.

The importance of defensive symbioses, whereby microbes protect hosts through the production of specific compounds, is becoming increasingly evident. Although defining the partners in these associations has become easier, assigning function to these relationships often presents a significant challenge. Here, we describe a functional role for a bacterial consortium in a female reproductive organ in the Hawaiian bobtail squid, Euprymna scolopes. Bacteria from the accessory nidamental gland (ANG) are deposited into the egg jelly coat (JC), where they are hypothesized to play a defensive role during embryogenesis. Eggs treated with an antibiotic cocktail developed a microbial biomass primarily composed of the pathogenic fungus Fusarium keratoplasticum that infiltrated the JC, resulting in severely reduced hatch rates. Experimental manipulation of the eggs demonstrated that the JC was protective against this fungal fouling. A large proportion of the bacterial strains isolated from the ANG or JC inhibited F. keratoplasticum in culture (87.5%), while a similar proportion of extracts from these strains also exhibited antifungal activity against F. keratoplasticum and/or the human-pathogenic yeast Candida albicans (72.7%). Mass spectral network analyses of active extracts from bacterial isolates and egg clutches revealed compounds that may be involved in preventing microbial overgrowth. Several secondary metabolites were identified from ANG/JC bacteria and egg clutches, including the known antimicrobial lincomycin as well as a suite of glycerophosphocholines and mycinamicin-like compounds. These results shed light on a widely distributed but poorly understood symbiosis in cephalopods and offer a new source for exploring bacterial secondary metabolites with antimicrobial activity.

Investigating the Protective Effect of Gross Saponins of Tribulus terrestris Fruit against Ischemic Stroke in Rat Using Metabolomics and Network Pharmacology

Stroke is one of the leading causes of death and long-term disability worldwide. Gross saponins of Tribulus terrestris fruit (GSTTF) has been used for neuroprotective therapy on convalescents of ischemic stroke. But the related therapeutic mechanisms have not yet been well investigated. This study aimed to investigate the protective effects of GSTTF on ischemic stroke using metabolomics coupled with network pharmacology analysis. The rat urine sample was collected and profiled by an LC-MS-based metabolomics approach. The pathway analysis was performed based on the highlighted biomarkers, then the network pharmacology approach was applied to screen the potential therapeutic targets of GSTTF. Metabolomics analysis showed that a series of metabolic perturbations occurred in the middle cerebral artery occlusion (MCAO) group compared with the sham group. Gross saponins of Tribulus terrestris fruit can change the MCAO-induced urine metabolic deviations in a reverse manner via regulating multiple metabolic pathways. Two proteins, inducible nitric oxide synthase (NOS2) and glycogen synthase kinase-3 beta (GSK3B), were highlighted by the network pharmacology analysis, which may be the potential therapeutic targets for the GSTTF against ischemic stroke. This study provides an overview of the mechanism of MCAO-induced ischemic stroke and investigates the efficacy of GSTTF in the treatment of ischemic stroke. Further study is needed to reveal its underlying mechanisms more clearly.

Impact of matrix effects and ionization efficiency in non-quantitative untargeted metabolomics

INTRODUCTION

LC-MS-based untargeted metabolomics has become increasingly popular due to the vast amount of information gained in a single analysis. Many studies utilize metabolomics to profile metabolic changes in various representative biofluids, tissues, or other sample types. Most analyses are performed measuring changes in the metabolic pool of a single biological matrix due to an altered phenotype, such as disease versus normal. Measurements in such experiments are typically highly reproducible with little variation due to analytical and technological advancements in mass spectrometry. With the expanded application of metabolomics into various non-analytical scientific disciplines, the emergence of studies comparing the signal intensities of specific analytes across different biological matrices (e.g. plasma vs. urine) is becoming more common, but the matrix effect between sample types is often neglected. Additionally, the practice of comparing the signal intensities of different analytes and correlating to relative abundance is also increasingly prevalent, but the response ratio between analytes due to differences in ionization efficiency is not always accounted for in data analysis. This report serves to communicate and raise awareness of these two well-recognized issues to prevent improper data interpretation in the field of metabolomics.

OBJECTIVES

We demonstrate the impact of matrix effects and ionization efficiency with labeled analytical standards in human plasma, serum, and urine and describe how the direct comparison of non-quantitative signal intensities between biofluids, as well as between different analytes in the same biofluid, in untargeted metabolomics is inaccurate without proper response corrections.

METHODS

Human plasma, serum, and urine (n = 4 technical replicates per biofluid) were spiked with a panel of labeled internal standards all at identical concentrations, simultaneously extracted, and analyzed by UHPLC-HRMS. Signal intensities were compared for demonstration of the impact of matrix effects in untargeted metabolomics. A neat mixture of two co-eluting, structurally-similar labeled standards at the same concentration was also analyzed to demonstrate the effect of ionization efficiency on signal intensity.

RESULTS

Despite being spiked at identical concentrations, labeled standards we examined in this study showed significant differences in their signal intensities between biofluids, as well as from each other in the same biofluid, due to matrix effects. Co-eluting standards were also found to yield significantly different signal intensities at identical concentrations due to differences in ionization efficiency.

CONCLUSIONS

Due to the presence of matrix effects in untargeted, non-quantitative metabolomics, the signal intensity of any single analyte cannot be directly compared to the signal intensity of that same analyte (or any other analyte) between any two different matrices. Due to differences in ionization efficiency, the signal intensity of any single analyte cannot be directly compared to the signal intensity of any other analyte, even in the same matrix.

Reliable identification of prostate cancer using mass spectrometry metabolomic imaging in needle core biopsies

Metabolomic profiling can aid in understanding crucial biological processes in cancer development and progression and can also yield diagnostic biomarkers. Desorption electrospray ionization coupled to mass spectrometry imaging (DESI-MSI) has been proposed as a potential adjunct to diagnostic surgical pathology, particularly for prostate cancer. However, due to low resolution sampling, small numbers of mass spectra, and little validation, published studies have yet to test whether this method is sufficiently robust to merit clinical translation. We used over 900 spatially resolved DESI-MSI spectra to establish an accurate, high-resolution metabolic profile of prostate cancer. We identified 25 differentially abundant metabolites, with cancer tissue showing increased fatty acids (FAs) and phospholipids, along with utilization of the Krebs cycle, and benign tissue showing increased levels of lyso-phosphatidylethanolamine (PE). Additionally, we identified, for the first time, two lyso-PEs with abundance that decreased with cancer grade and two phosphatidylcholines (PChs) with increased abundance with increasing cancer grade. Importantly, we developed and internally validated a multivariate metabolomic classifier for prostate cancer using 534 spatial regions of interest (ROIs) in the training cohort and 430 ROIs in the test cohort. With excellent statistical power, the training cohort achieved a balanced accuracy of 97% and validation on testing data set demonstrated 85% balanced accuracy. Given the validated accuracy of this classifier and the correlation of differentially abundant metabolites with established patterns of prostate cancer cell metabolism, we conclude that DESI-MSI is an effective tool for characterizing prostate cancer metabolism with the potential for clinical translation.

Unearthing fungal chemodiversity and prospects for drug discovery

Natural products have drastically improved our lives by providing an excellent source of molecules to fight cancer, pathogens, and cardiovascular diseases that have revolutionized medicine. Fungi are prolific producers of diverse natural products and several recent advances in synthetic biology, genetics, bioinformatics, and natural product chemistry have greatly enhanced our ability to efficiently mine their genomes for the discovery of novel drugs. In this article, we provide an overview of improved heterologous expression platforms for targeted production of fungal secondary metabolites, of advances in chemical and bioinformatics dereplication, and of novel bioinformatic platforms to discover biosynthetic genes involved in the production of metabolites with specific bioactivities. These advances, coupled with the presence of vast numbers of biosynthetic gene clusters in fungal genomes whose natural products remain unknown, have revitalized efforts to mine the fungal treasure chest and renewed the promise of discovering new drugs.

In Vivo Microdialysis of Endogenous and 13C-labeled TCA Metabolites in Rat Brain: Reversible and Persistent Effects of Mitochondrial Inhibition and Transient Cerebral Ischemia

Cerebral micro-dialysis allows continuous sampling of extracellular metabolites, including glucose, lactate and pyruvate. Transient ischemic events cause a rapid drop in glucose and a rise in lactate levels. Following such events, the lactate/pyruvate (L/P) ratio may remain elevated for a prolonged period of time. In neurointensive care clinics, this ratio is considered a metabolic marker of ischemia and/or mitochondrial dysfunction. Here we propose a novel, sensitive microdialysis liquid chromatography-mass spectrometry (LC-MS) approach to monitor mitochondrial dysfunction in living brain using perfusion with ¹³C-labeled succinate and analysis of ¹³C-labeled tricarboxylic acid cycle (TCA) intermediates. This approach was evaluated in rat brain using malonate-perfusion (10-50 mM) and endothelin-1 (ET-1)-induced transient cerebral ischemia. In the malonate model, the expected changes upon inhibition of succinate dehydrogenase (SDH) were observed, i.e., an increase in endogenous succinate and decreases in fumaric acid and malic acid. The inhibition was further elaborated by incorporation of ¹³C into specific TCA intermediates from ¹³C-labeled succinate. In the ET-1 model, increases in non-labeled TCA metabolites (reflecting release of intracellular compounds) and decreases in ¹³C-labeled TCA metabolites (reflecting inhibition of de novo synthesis) were observed. The analysis of ¹³C incorporation provides further layers of information to identify metabolic disturbances in experimental models and neuro-intensive care patients.

A fiber-deprived diet disturbs the fine-scale spatial architecture of the murine colon microbiome

Compartmentalization of the gut microbiota is thought to be important to system function, but the extent of spatial organization in the gut ecosystem remains poorly understood. Here, we profile the murine colonic microbiota along longitudinal and lateral axes using laser capture microdissection. We found fine-scale spatial structuring of the microbiota marked by gradients in composition and diversity along the length of the colon. Privation of fiber reduces the diversity of the microbiota and disrupts longitudinal and lateral gradients in microbiota composition. Both mucus-adjacent and luminal communities are influenced by the absence of dietary fiber, with the loss of a characteristic distal colon microbiota and a reduction in the mucosa-adjacent community, concomitant with depletion of the mucus layer. These results indicate that diet has not only global but also local effects on the composition of the gut microbiota, which may affect function and resilience differently depending on location.

Black Walnut (Juglans nigra) Extracts Inhibit Proinflammatory Cytokine Production From Lipopolysaccharide-Stimulated Human Promonocytic Cell Line U-937

Black walnut (Juglans nigra L.) is an excellent source of health-promoting compounds. Consumption of black walnuts has been linked to many health benefits (e.g., anti-inflammatory) stemming from its phytochemical composition and medicinal properties, but these effects have not been systematically studied or characterized. In this study, potential anti-inflammatory compounds found in kernel extracts of 10 black walnut cultivars were putatively identified using a metabolomic profiling analysis, revealing differences in potential anti-inflammatory capacities among examined cultivars. Five cultivars were examined for activities in the human promonocytic cell line U-937 by evaluating the effects of the extracts on the expression of six human inflammatory cytokines/chemokines using a bead-based, flow cytometric multiplex assay. The methanolic extracts of these cultivars were added at four concentrations (0.1, 0.3, 1, and 10 mg/ml) either before and after the addition of lipopolysaccharide (LPS) to human U-937 cells to examine their effect on cytokine production. Results from cytotoxicity and viability assays revealed that the kernel extracts had no toxic effect on the U-937 cells. Of the 13 cytokines [interleukin (IL)-1β, tumor necrosis factor alpha (TNF-α), monocyte chemoattractant protein (MCP)-1, IL-6, IL-8, IL-10, IL-12, IL-17, IL-18, IL-23, IL-33, interferon (IFN)-α, IFN-γ] measured, only six were detected under the culture conditions. The production of the six detected cytokines by phorbol 12-myristate 13-acetate (PMA)-differentiated, LPS-stimulated U-937 was significantly inhibited by the kernel extracts from two cultivars Surprise and Sparrow when the extracts were added before the addition of LPS. Other cultivars (Daniel, Mystry, and Sparks) showed weak or no significant effects on cytokine production. In contrast, no inhibitory effect was observed on the production of cytokines by PMA-differentiated, LPS-stimulated U-937 when the kernel extracts were added after the addition of LPS. The findings suggest that the extracts from certain black walnut cultivars, such as Sparrow and Surprise, are promising biological candidates for potentially decreasing the severity of inflammatory disease.

NMR metabolomics: A look ahead

NMR has been used to perform metabolic studies, metabolic profiling and metabolomics in biofluids and tissues for more than 40 years. This close connection between metabolic measurements and NMR has flourished because of NMR's many unique strengths for characterizing the chemical composition of complex mixtures. However, a number of other technologies, including mass spectrometry, have appeared in the past few years that are encroaching on NMR's dominance in metabolomics and metabolic studies. In this brief review, some of the current strengths and existing limitations of NMR-based metabolomics are highlighted. Additionally, a number of recent advances in NMR hardware, methodology and software are also described and these advancements are used to speculate about where NMR-based metabolomics is going, what needs to be done to make it more popular and how it will evolve in the next 5-10 years.

Impact of short-term exposure to fine particulate matter air pollution on urinary metabolome: A randomized, double-blind, crossover trial

BACKGROUND

Metabolomics is a novel tool to explore the biological mechanisms of the health effects of fine particulate matter (PM_2.5) air pollution. Very few studies have examined the urinary metabolomic changes associated with PM_2.5 exposure.

OBJECTIVE

To assess the alternation in urine metabolomics in response to short-term PM_2.5 exposure.

METHODS

We conducted a randomized, double-blind, crossover trial of 9-day real or sham indoor air purification among 45 healthy college students in Shanghai, China. Urine samples were collected immediately at the end of each intervention stage and were analyzed for metabolomics using ultrahigh performance liquid chromatography-mass spectrometry. Orthogonal partial least square-discriminant analysis and linear mixed effect models were used to examine metabolomic changes between interventional scenarios and their associations with continuous PM_2.5 exposure.

RESULTS

The time-weighted average personal PM_2.5 exposure in the real-purified scenario was 50% lower than in the sham-purified air scenario (28.3 μg/m³ VS 56.9 μg/m³). A total of 40 differentiated urinary metabolites at a false discovery rate <0.05 were identified for the effects of both intervention and continuous PM_2.5 exposure, including 16 lipids, 5 purine metabolites, 2 neurotransmitters, and 3 coenzymes.

CONCLUSIONS

This real-world randomized crossover trial demonstrated that short-term PM_2.5 exposure could result in significant changes in urinary metabolomic profile, which may further lead to perturbation in energy metabolism, oxidative stress and inflammation.

Lipid Coverage in Nanospray Desorption Electrospray Ionization Mass Spectrometry Imaging of Mouse Lung Tissues

Lipids are a naturally occurring group of molecules that not only contribute to the structural integrity of the lung preventing alveolar collapse but also play important roles in the anti-inflammatory responses and antiviral protection. Alteration in the type and spatial localization of lipids in the lung plays a crucial role in various diseases, such as respiratory distress syndrome (RDS) in preterm infants and oxidative stress-influenced diseases, such as pneumonia, emphysema, and lung cancer following exposure to environmental stressors. The ability to accurately measure spatial distributions of lipids and metabolites in lung tissues provides important molecular insights related to lung function, development, and disease states. Nanospray desorption electrospray ionization (nano-DESI) and other ambient ionization mass spectrometry techniques enable label-free imaging of complex samples in their native state with minimal to absolutely no sample preparation. However, lipid coverage obtained in nano-DESI mass spectrometry imaging (MSI) experiments has not been previously characterized. In this work, the depth of lipid coverage in nano-DESI MSI of mouse lung tissues was compared to liquid chromatography tandem mass spectrometry (LC-MS/MS) lipidomics analysis of tissue extracts prepared using two different procedures: standard Folch extraction method of the whole lung samples and extraction into a 90% methanol/10% water mixture used in nano-DESI MSI experiments. A combination of positive and negative ionization mode nano-DESI MSI identified 265 unique lipids across 20 lipids subclasses and 19 metabolites (284 in total) in mouse lung tissues. Except for triacylglycerols (TG) species, nano-DESI MSI provided comparable coverage to LC-MS/MS experiments performed using methanol/water tissue extracts and up to 50% coverage in comparison with the Folch extraction-based whole lung lipidomics analysis. These results demonstrate the utility of nano-DESI MSI for comprehensive spatially resolved analysis of lipids in tissue sections. A combination of nano-DESI MSI and LC-MS/MS lipidomics is particularly useful for exploring changes in lipid distributions during lung development, as well as resulting from disease or exposure to environmental toxicants.

On-Line Analysis of Exhaled Breath Focus Review

On-line analysis of exhaled breath offers insight into a person's metabolism without the need for sample preparation or sample collection. Due to its noninvasive nature and the possibility to sample continuously, the analysis of breath has great clinical potential. The unique features of this technology make it an attractive candidate for applications in medicine, beyond the task of diagnosis. We review the current methodologies for on-line breath analysis, discuss current and future applications, and critically evaluate challenges and pitfalls such as the need for standardization. Special emphasis is given to the use of the technology in diagnosing respiratory diseases, potential niche applications, and the promise of breath analysis for personalized medicine. The analytical methodologies used range from very small and low-cost chemical sensors, which are ideal for continuous monitoring of disease status, to optical spectroscopy and state-of-the-art, high-resolution mass spectrometry. The latter can be utilized for untargeted analysis of exhaled breath, with the capability to identify hitherto unknown molecules. The interpretation of the resulting big data sets is complex and often constrained due to a limited number of participants. Even larger data sets will be needed for assessing reproducibility and for validation of biomarker candidates. In addition, molecular structures and quantification of compounds are generally not easily available from on-line measurements and require complementary measurements, for example, a separation method coupled to mass spectrometry. Furthermore, a lack of standardization still hampers the application of the technique to screen larger cohorts of patients. This review summarizes the present status and continuous improvements of the principal on-line breath analysis methods and evaluates obstacles for their wider application.

Engineering a microbial biosynthesis platform for de novo production of tropane alkaloids

Tropane alkaloids (TAs) are a class of phytochemicals produced by plants of the nightshade family used for treating diverse neurological disorders. Here, we demonstrate de novo production of tropine, a key intermediate in the biosynthetic pathway of medicinal TAs such as scopolamine, from simple carbon and nitrogen sources in yeast (Saccharomyces cerevisiae). Our engineered strain incorporates 15 additional genes, including 11 derived from diverse plants and bacteria, and 7 disruptions to yeast regulatory or biosynthetic proteins to produce tropine at titers of 6 mg/L. We also demonstrate the utility of our engineered yeast platform for the discovery of TA derivatives by combining biosynthetic modules from distant plant lineages to achieve de novo production of cinnamoyltropine, a non-canonical TA. Our engineered strain constitutes a starting point for future optimization efforts towards realizing industrial fermentation of medicinal TAs and a platform for the synthesis of TA derivatives with enhanced bioactivities.

Tropane alkaloids (TAs) are a group of phytochemicals that are used to treat neurological disorders. Here, the authors engineer baker’s yeast to produce tropine, a key intermediate in the biosynthetic pathway of TAs, and cinnamoyltropine, a non-canonical TA, from simple carbon and nitrogen sources.

Integrating a generalized data analysis workflow with the Single-probe mass spectrometry experiment for single cell metabolomics

We conducted single cell metabolomics studies of live cancer cells through online single cell mass spectrometry (SCMS) experiments combined with a generalized comprehensive data analysis workflow. The SCMS experiments were carried out using the Single-probe device coupled with a mass spectrometer to measure molecular profiles of cells in response to two mitotic inhibitors, taxol and vinblastine, under a series of treatment conditions. SCMS metabolomic data were analyzed using a comprehensive approach, including data pre-treatment, visualization, statistical analysis, machine learning, and pathway enrichment analysis. For comparative studies, traditional liquid chromatography-MS (LC-MS) experiments were conducted using lysates prepared from bulk cell samples. Metabolomic profiles of single cells were visualized through Partial Least Square-Discriminant Analysis (PLS-DA), and the phenotypic biomarkers associated with emerging phenotypes induced by drug treatment were discovered and compared through a series of rigorous statistical analysis. Species of interest were further identified at both the single cell and population levels. In addition, four biological pathways potentially involved in the drug treatment were determined through pathway enrichment analysis. Our work demonstrated the capability of comprehensive pipeline studies of single cell metabolomics. This method can be potentially applied to broader types of SCMS datasets for future pharmaceutical and chemotherapeutic research.

Fabric Phase Sorptive Extraction-A Metabolomic Preprocessing Approach for Ionizing Radiation Exposure Assessment

The modern application of mass spectrometry-based metabolomics to the field of radiation assessment and biodosimetry has allowed for the development of prompt biomarker screenings for radiation exposure. Our previous work on radiation assessment, in easily accessible biofluids (such as urine, blood, saliva), has revealed unique metabolic perturbations in response to radiation quality, dose, and dose rate. Nevertheless, the employment of swift injury assessment in the case of a radiological disaster still remains a challenge as current sample processing can be time consuming and cause sample degradation. To address these concerns, we report a metabolomics workflow using a mass spectrometry-compatible fabric phase sorptive extraction (FPSE) technique. FPSE employs a matrix coated with sol-gel poly(caprolactone-b-dimethylsiloxane-b-caprolactone) that binds both polar and nonpolar metabolites in whole blood, eliminating serum processing steps. We confirm that the FPSE preparation technique combined with liquid chromatography-mass spectrometry can distinguish radiation exposure markers such as taurine, carnitine, arachidonic acid, α-linolenic acid, and oleic acid found 24 h after 8 Gy irradiation. We also note the effect of different membrane fibers on both metabolite extraction efficiency and the temporal stabilization of metabolites in whole blood at room temperature. These findings suggest that the FPSE approach could work in future technology to triage irradiated individuals accurately, via biomarker screening, by providing a novel method to stabilize biofluids between collection and sample analysis.

Novel applications of mass spectrometry-based metabolomics in herbal medicines and its active ingredients: Current evidence

Current evidence shows that herbal medicines could be beneficial for the treatment of various diseases. However, the complexities present in chemical compositions of herbal medicines are currently an obstacle for the progression of herbal medicines, which involve unclear bioactive compounds, mechanisms of action, undetermined targets for therapy, non-specific features for drug metabolism, etc. To overcome those issues, metabolomics can be a great to improve and understand herbal medicines from the small-molecule metabolism level. Metabolomics could solve scientific difficulties with herbal medicines from a metabolic perspective, and promote drug discovery and development. In recent years, mass spectrometry-based metabolomics was widely applied for the analysis of herbal constituents in vivo and in vitro. In this review, we highlight the value of mass spectrometry-based metabolomics and metabolism to address the complexity of herbal medicines in systems pharmacology, and to enhance their biomedical value in biomedicine, to shed light on the aid that mass spectrometry-based metabolomics can offer to the investigation of its active ingredients, especially, to link phytochemical analysis with the assessment of pharmacological effect and therapeutic potential. © 2019 Wiley Periodicals, Inc. Mass Spec Rev.

Gut microbiome and serum metabolome analyses identify molecular biomarkers and altered glutamate metabolism in fibromyalgia

BACKGROUND

Fibromyalgia is a complex, relatively unknown disease characterised by chronic, widespread musculoskeletal pain. The gut-brain axis connects the gut microbiome with the brain through the enteric nervous system (ENS); its disruption has been associated with psychiatric and gastrointestinal disorders. To gain an insight into the pathogenesis of fibromyalgia and identify diagnostic biomarkers, we combined different omics techniques to analyse microbiome and serum composition.

METHODS

We collected faeces and blood samples to study the microbiome, the serum metabolome and circulating cytokines and miRNAs from a cohort of 105 fibromyalgia patients and 54 age- and environment-matched healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from faeces samples. UPLC-MS metabolomics and custom multiplex cytokine and miRNA analysis (FirePlex™ technology) were used to examine sera samples. Finally, we combined the different data types to search for potential biomarkers.

RESULTS

We found that the diversity of bacteria is reduced in fibromyalgia patients. The abundance of the Bifidobacterium and Eubacterium genera (bacteria participating in the metabolism of neurotransmitters in the host) in these patients was significantly reduced. The serum metabolome analysis revealed altered levels of glutamate and serine, suggesting changes in neurotransmitter metabolism. The combined serum metabolomics and gut microbiome datasets showed a certain degree of correlation, reflecting the effect of the microbiome on metabolic activity. We also examined the microbiome and serum metabolites, cytokines and miRNAs as potential sources of molecular biomarkers of fibromyalgia.

CONCLUSIONS

Our results show that the microbiome analysis provides more significant biomarkers than the other techniques employed in the work. Gut microbiome analysis combined with serum metabolomics can shed new light onto the pathogenesis of fibromyalgia. We provide a list of bacteria whose abundance changes in this disease and propose several molecules as potential biomarkers that can be used to evaluate the current diagnostic criteria.

Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome

The gut microbiome has been shown to play a significant role in human healthy and diseased states. The dynamic signaling that occurs between the host and microbiome is critical for the maintenance of host homeostasis. Analyzing the human microbiome with metaproteomics, metabolomics, and integrative multi-omics analyses can provide significant information on markers for healthy and diseased states, allowing for the eventual creation of microbiome-targeted treatments for diseases associated with dysbiosis. Metaproteomics enables functional activity information to be gained from the microbiome samples, while metabolomics provides insight into the overall metabolic states affecting/representing the host-microbiome interactions. Combining these functional -omic platforms together with microbiome composition profiling allows for a holistic overview on the functional and metabolic state of the microbiome and its influence on human health. Here the benefits of metaproteomics, metabolomics, and the integrative multi-omic approaches to investigating the gut microbiome in the context of human health and diseases are reviewed.

Reactive Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Using an Intrinsically Photoreactive Paternò-Büchi Matrix for Double-Bond Localization in Isomeric Phospholipids

The location and identity of phospholipids (PLs) within tissues can serve as diagnostic markers for tissue types or diseases. Whereas mass spectrometry imaging (MSI) has emerged as a powerful bioanalytical tool to visualize PL distributions, inferring PL identities from MSI experiments is challenging. Especially, C═C double-bond (DB) positions are not identifiable in most MSI experiments. Herein, we introduce benzophenone (BPh) as a novel reactive matrix for matrix-assisted laser desorption/ionization (MALDI). BPh promotes desorption/ionization and simultaneously serves as derivatization reagent that allows functionalization of unsaturated PLs during the MALDI process via a laser-light driven Paternò-Büchi (PB) reaction without the need for additional equipment. Using BPh, PB product ions of numerous PL classes are readily generated to pinpoint the location of DBs. High lateral resolution MSI results of DB-position isomers are presented, highlighting the capabilities of BPh as a PB-reactive MALDI matrix to potentially unveil the impact of DB-position isomers in PL metabolism.

MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools

Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR), and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.

Tutorial on lipidomics

The mainstream of lipidomics involves mass spectrometry-based, systematic, and large-scale studies of the structure, composition, and quantity of lipids in biological systems such as organs, cells, and body fluids. As increasingly more researchers in broad fields are beginning to pay attention to and actively learn about the lipidomic technology, some introduction on the topic is needed to help the newcomers to better understand the field. This tutorial seeks to introduce the basic knowledge about lipidomics and to provide readers with some core ideas and the most important approaches for studying the field.

Deep Metabolomics of a High-Grade Serous Ovarian Cancer Triple-Knockout Mouse Model

High-grade serous carcinoma (HGSC) is the most common and deadliest ovarian cancer (OC) type, accounting for 70-80% of OC deaths. This high mortality is largely due to late diagnosis. Early detection is thus crucial to reduce mortality, yet the tumor pathogenesis of HGSC remains poorly understood, making early detection exceedingly difficult. Faithfully and reliably representing the clinical nature of human HGSC, a recently developed triple-knockout (TKO) mouse model offers a unique opportunity to examine the entire disease spectrum of HGSC. Metabolic alterations were investigated by applying ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) to serum samples collected from these mice at premalignant, early, and advanced stages of HGSC. This comprehensive analysis revealed a panel of 29 serum metabolites that distinguished mice with HGSC from controls and mice with uterine tumors with over 95% accuracy. Meanwhile, our panel could further distinguish early-stage HGSC from controls with 100% accuracy and from advanced-stage HGSC with over 90% accuracy. Important identified metabolites included phospholipids, sphingomyelins, sterols, N-acyltaurine, oligopeptides, bilirubin, 2(3)-hydroxysebacic acids, uridine, N-acetylneuraminic acid, and pyrazine derivatives. Overall, our study provides insights into dysregulated metabolism associated with HGSC development and progression, and serves as a useful guide toward early detection.

Using a high-resolution mass spectrometry-based metabolomics strategy for comprehensively screening and identifying biomarkers of phthalate exposure: Method development and application

Di-(2-propylheptyl) phthalate (DPHP) is an alternative plasticizer that can replace other phthalates currently being scrutinized, and its use and production volumes are increasing. This study aimed to develop a high-resolution mass spectrometry (HRMS)-based metabolomics strategy to comprehensively screen urinary biomarkers of DPHP exposure and filter out potentially useful DPHP exposure markers for human exposure assessments. This strategy included three stages: screening of biomarkers, verification of dose-response relationships in laboratory animals, and application in human subjects. The multivariate data analysis method known as orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to screen and find meaningful signals in an MS dataset generated from urine samples collected from DPHP-administered rats. Thirty-six MS signals were verified as exposure marker candidates by assessing dose-response relationships in an animal feeding study. A biotransformation product of DPHP, mono-(2-propyl-7-dihydroxy-heptyl) phthalate, was suggested as a DPHP exposure marker for general human exposure assessments after the human application study and chemical structure identification. Three previously oxidized DPHP biotransformation products might be suitable for human exposure assessments in high-level exposure groups but not in the general population due to their low sensitivity.

Systematic Isolation and Structure Elucidation of Urinary Metabolites Optimized for the Analytical-Scale Molecular Profiling Laboratory

Annotation and identification of metabolite biomarkers is critical for their biological interpretation in metabolic phenotyping studies, presenting a significant bottleneck in the successful implementation of untargeted metabolomics. Here, a systematic multistep protocol was developed for the purification and de novo structural elucidation of urinary metabolites. The protocol is most suited for instances where structure elucidation and metabolite annotation are critical for the downstream biological interpretation of metabolic phenotyping studies. First, a bulk urine pool was desalted using ion-exchange resins enabling large-scale fractionation using precise iterations of analytical scale chromatography. Primary urine fractions were collected and assembled into a “fraction bank” suitable for long-term laboratory storage. Secondary and tertiary fractionations exploited differences in selectivity across a range of reversed-phase chemistries, achieving the purification of metabolites of interest yielding an amount of material suitable for chemical characterization. To exemplify the application of the systematic workflow in a diverse set of cases, four metabolites with a range of physicochemical properties were selected and purified from urine and subjected to chemical formula and structure elucidation by respective magnetic resonance mass spectrometry (MRMS) and NMR analyses. Their structures were fully assigned as tetrahydropentoxyline, indole-3-acetic-acid-O-glucuronide, p-cresol glucuronide, and pregnanediol-3-glucuronide. Unused effluent was collected, dried, and returned to the fraction bank, demonstrating the viability of the system for repeat use in metabolite annotation with a high degree of efficiency.

"-Omics" workflow for paleolimnological and geological archives: A review

"-Omics" is a powerful screening method with applications in molecular biology, toxicology, wildlife biology, natural product discovery, and many other fields. Genomics, proteomics, metabolomics, and lipidomics are common examples included under the "-omics" umbrella. This screening method uses combinations of untargeted, semi-targeted, and targeted analyses paired with data mining to facilitate researchers' understanding of the genome, proteins, and small organic molecules in biological systems. Recently, however, the use of "-omics" has expanded into the fields of geology, specifically petrology, and paleolimnology. Specifically, untargeted analyses stand to transform these fields as petroleomics, and sediment-"omics" become more prevalent. "-Omics" facilitates the visualization of small molecule profiles from environmental matrices (i.e. oil and sediment). Small molecule profiles can provide improved understanding of small molecules distributions throughout the environment, and how those compositions can change depending on conditions (i.e. climate change, weathering, etc.). "-Omics" also facilities discovery of next-generation biomarkers that can be used for oil source identification and as proxies for reconstructing past environmental changes. Untargeted analyses paired with data mining and multivariate statistical analyses represents a powerful suite of tools for hypothesis generation, and new method development for environmental reconstructions. Here we present an introduction to "-omics" methodology, technical terms, and examples of applications to paleolimnology and petrology. The purpose of this review is to highlight the important considerations at each step in the "-omics" workflow to produce high quality and statistically powerful data for petrological and paleolimnological applications.

Mass Spectrometry Data Repository Enhances Novel Metabolite Discoveries with Advances in Computational Metabolomics

Mass spectrometry raw data repositories, including Metabolomics Workbench and MetaboLights, have contributed to increased transparency in metabolomics studies and the discovery of novel insights in biology by reanalysis with updated computational metabolomics tools. Herein, we reanalyzed the previously published lipidomics data from nine algal species, resulting in the annotation of 1437 lipids achieving a 40% increase in annotation compared to the previous results. Specifically, diacylglyceryl-carboxyhydroxy-methylcholine (DGCC) in Pavlova lutheri and Pleurochrysis carterae, glucuronosyldiacylglycerol (GlcADG) in Euglena gracilis, and P. carterae, phosphatidylmethanol (PMeOH) in E. gracilis, and several oxidized phospholipids (oxidized phosphatidylcholine, OxPC; phosphatidylethanolamine, OxPE; phosphatidylglycerol, OxPG; phosphatidylinositol, OxPI) in Chlorella variabilis were newly characterized with the enriched lipid spectral databases. Moreover, we integrated the data from untargeted and targeted analyses from data independent tandem mass spectrometry (DIA-MS/MS) acquisition, specifically the sequential window acquisition of all theoretical fragment-ion MS/MS (SWATH-MS/MS) spectra, to increase the lipidomic annotation coverage. After the creation of a global library of precursor and diagnostic ions of lipids by the MS-DIAL untargeted analysis, the co-eluted DIA-MS/MS spectra were resolved in MRMPROBS targeted analysis by tracing the specific product ions involved in acyl chain compositions. Our results indicated that the metabolite quantifications based on DIA-MS/MS chromatograms were somewhat inferior to the MS1-centric quantifications, while the annotation coverage outperformed those of the untargeted analysis of the data dependent and DIA-MS/MS data. Consequently, integrated analyses of untargeted and targeted approaches are necessary to extract the maximum amount of metabolome information, and our results showcase the value of data repositories for the discovery of novel insights in lipid biology.

An elusive electron shuttle from a facultative anaerobe

Some anaerobic bacteria use insoluble minerals as terminal electron acceptors and discovering the ways in which electrons move through the membrane barrier to the exterior acceptor forms an active field of research with implications for both bacterial physiology and bioenergy. A previous study suggested that Shewanella oneidensis MR-1 utilizes a small, polar, redox active molecule that serves as an electron shuttle between the bacteria and insoluble acceptors, but the shuttle itself has never been identified. Through isolation and synthesis, we identify it as ACNQ (2-amino-3-carboxy-1,4-naphthoquinone), a soluble analog of menaquinone. ACNQ is derived from DHNA (1,4-dihydroxy-2-naphthoic acid) in a non-enzymatic process that frustrated genetic approaches to identify the shuttle. Both ACNQ and DHNA restore reduction of AQDS under anaerobic growth in menaquinone-deficient mutants. Bioelectrochemistry analyses reveal that ACNQ (−0.32 V_Ag/AgCl) contributes to the extracellular electron transfer (EET) as an electron shuttle, without altering menaquinone generation or EET related cytochrome c expression.

In order to survive, we break down food through a series of chemical reactions that release energy to power our cells. In these metabolic reactions, small electrically charged particles called electrons are removed from the food molecule, and transferred, via a series of reactions, to a terminal electron acceptor.

For humans and many other organisms, oxygen is the terminal electron acceptor. Bacteria generate energy through a similar series of chemical reactions, but many species of bacteria live in environments where oxygen is absent. Some bacteria solve this problem by transferring the electrons released in their metabolic reactions to acceptor compounds in the external environment. These species must therefore employ a small molecule ‘shuttle’ to carry the electrons to the acceptor.

Previous work has shown the bacterial strain Shewanella oneidensis MR-1 releases a small molecule into its surrounding environment, which serves as its electron shuttle. Despite identifying a mutant strain of MR-1 that cannot produce this shuttle, researchers have been unable to determine the exact chemical identity of this critical molecule.

Now, Mevers, Su et al. have identified this elusive electron shuttle. This involved growing MR-1 and isolating the active molecule which restores the mutant bacteria’s ability to shuttle electrons. Further experiments characterizing the structure of this compound using techniques involving analytical and synthetic organic chemistry revealed it be a small molecule known as ACNQ.

Mevers, Su et al. showed MR-1 produces this elusive electron shuttle by releasing a precursor structure into the environment where it spontaneously converts into ACNQ. As a result, there are no genes present in the genome of MR-1 or other bacterial strains that are required for the production of ACNQ. This genetic absence and low production levels of ACNQ has frustrated previous attempts to identify MR-1’s electron shuttle.

Bacterial metabolism is studied for its applications in bioenergy (producing renewable energy using living organisms) and bioremediation (detoxification of substances using the reactions of bacterial metabolism). A better understanding of bacterial metabolism is thus essential for the continued development of these useful technologies.

Combined Markers to Assess Meat Intake-Human Metabolomic Studies of Discovery and Validation

SCOPE

Biomarkers of red meat may clarify the relationship between meat intake and health. This paper explores the discovery of biomarkers of intake for three types of meat with varying heme iron content. Candidate biomarkers for red and general meat are further evaluated based on defined validation criteria.

METHODS AND RESULTS

In a randomized cross-over meal study, healthy volunteers consume a randomized sequence of four test meals: chicken, pork, beef, and a control made of egg white and pea. Fasting and postprandial urine samples are collected to cover 48 h and profiled by untargeted LC-ESI-qTOF-MS metabolomics. The profiles following the meal challenges are explored by univariate and multivariate analyses. Nine red, four white, and eight general meat biomarkers are selected as putative biomarkers, originating from collagen degradation, flavour compounds, and amino acid metabolism. Heme-related metabolites are masked by the chlorophyll content of the control meal. The candidate biomarkers are confirmed in an independent meal study and validated for plausibility, robustness, time-response, and prediction performance. Combinations of biomarkers are more efficient than single markers in predicting meat intake.

CONCLUSION

New combinations of partially validated biomarkers are proposed to assess terrestrial meat intake and thus help disentangle the effects of meat consumption on human health.

Challenges in Identifying the Dark Molecules of Life

Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.

Translational Metabolomics: Current Challenges and Future Opportunities

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.

Identification of bioactive metabolites using activity metabolomics

The metabolome, the collection of small-molecule chemical entities involved in metabolism, has traditionally been studied with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolome analysis (metabolomics) has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other 'omics' levels, including the genome, epigenome, transcriptome and proteome. In this Review, we focus on recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites - which we term activity metabolomics - is already having a broad impact on biology.