Towards quantitative mass spectrometry-based metabolomics in microbial and mammalian systems

Integrating spatial mapping and metabolomics: A novel platform for bioactive compound discovery and saline land reclamation

Saline lands pose significant environmental and agricultural challenges due to high soil salinity, which disrupts water uptake and ionic balances, limiting conventional crop productivity. Yet, certain endemic plants thrive under these conditions and may offer untapped bioactive compounds. This study proposes a novel platform that integrates species distribution modeling (SDM) and advanced metabolomics to screen for bioactive secondary metabolites, using Buchanania siamensis, a rare native species, as a case study. An ensemble SDM model incorporating environmental and soil parameters identified salinity as a critical factor influencing the species' distribution. Leaf samples were collected from naturally growing trees at both saline (SS) and non-saline (NS) sites. LC-QTOF metabolomic analysis annotated a total of 1106 metabolites across the leaf samples, with 175 found to be significantly different between the groups. Among them, 108 metabolites exhibited higher abundance in the SS group. Additionally, antioxidant assays including DPPH, FRAP, and total phenolic content tests, were conducted. Data were further analyzed using O-PLSR models to identify key metabolites most relevant to antioxidant properties. The results indicated that afzelin was the key metabolite responsible for the antioxidant properties of B. siamensis, with significantly higher levels in SS compared to NS samples (p < 0.05), as determined by peak area. By leveraging this multidisciplinary approach, we propose a framework to support both bioactive compound discovery and saline land reclamation, offering potential environmental and pharmaceutical benefits. This integrated platform may support pharmaceutical research, particularly in drug discovery efforts.

Strategies for Actinobacteria Isolation, Cultivation, and Metabolite Production that Are Biologically Important

Novel antimicrobial agents are urgently needed to combat antimicrobial resistance from multidrug-resistant organisms. Actinobacteria are key sources of bioactive metabolites with diverse biological activities. Despite their contributions to drug discovery, the process from strain identification to drug manufacturing faces many challenges, especially the rediscovery of known compounds. Recent technological and scientific advancements have accelerated drug development. Efforts to isolate and screen rare actinobacterial species could yield novel bioactive compounds. This review summarizes techniques for selectively isolating rare actinobacteria, improving bioactive metabolite production, and discovering potential strains. Notably, new genomic strategies and new discoveries regarding spectroscopic signature-based bioactive natural products containing specific structural motifs are also discussed. Furthermore, this review updates the compounds derived from rare actinobacteria and their biological applications.

The prowess of metabolomics in cancer research: current trends, challenges and future perspectives

Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.

Challenges and recent advances in quantitative mass spectrometry-based metabolomics

The field of metabolomics has gained tremendous interest in recent years. Whether the goal is to discover biomarkers related to certain pathologies or to better understand the impact of a drug or contaminant, numerous studies have demonstrated how crucial it is to understand variations in metabolism. Detailed knowledge of metabolic variabilities can lead to more effective treatments, as well as faster or less invasive diagnostics. Exploratory approaches are often employed in metabolomics, using relative quantitation to look at perturbations between groups of samples. Most metabolomics studies have been based on metabolite profiling using relative quantitation, with very few studies using an approach for absolute quantitation. Using accurate quantitation facilitates the comparison between different studies, as well as enabling longitudinal studies. In this review, we discuss the most widely used techniques for quantitative metabolomics using mass spectrometry (MS). Various aspects will be addressed, such as the use of external and/or internal standards, derivatization techniques, in vivo isotopic labelling, or quantitative MS imaging. The principles, as well as the associated limitations and challenges, will be described for each approach.

SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data

Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming an important approach for studying complex biological systems but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process that is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (standard curve application for determining linear ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signals into absolute quantitative data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from the LC-MS signal. Using a standard mix containing 77 metabolites, we show a close correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate that SCALiR reproducibly calculates concentrations of midrange standards across ten analytical batches (average coefficient of variation 0.091). SCALiR can be used to calculate metabolite concentrations either using external calibration curves or by using internal standards to correct for matrix effects. This open-source and vendor agnostic software offers users several advantages in that (1) it requires only 10 s of analysis time to compute concentrations of >75 compounds, (2) it facilitates automation of quantitative workflows, and (3) it performs deterministic evaluations of compound quantification limits. SCALiR therefore provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

Metabolomic profiling reveals altered phenylalanine metabolism in Parkinson's disease in an Egyptian cohort

Introduction: Parkinson's disease (PD) is the most common motor neurodegenerative disease worldwide. Given the complexity of PD etiology and the different metabolic derangements correlated to the disease, metabolomics profiling of patients is a helpful tool to identify patho-mechanistic pathways for the disease development. Dopamine metabolism has been the target of several previous studies, of which some have reported lower phenylalanine and tyrosine levels in PD patients compared to controls. Methods: In this study, we have collected plasma from 27 PD patients, 18 reference controls, and 8 high-risk controls to perform a metabolomic study using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Results: Our findings revealed higher intensities of trans-cinnamate, a phenylalanine metabolite, in patients compared to reference controls. Thus, we hypothesize that phenylalanine metabolism has been shifted to produce trans-cinnamate via L-phenylalanine ammonia lyase (PAL), instead of producing tyrosine, a dopamine precursor, via phenylalanine hydroxylase (PAH). Discussion: Given that these metabolites are precursors to several other metabolic pathways, the intensities of many metabolites such as dopamine, norepinephrine, and 3-hydroxyanthranilic acid, which connects phenylalanine metabolism to that of tryptophan, have been altered. Consequently, and in respect to Metabolic Control Analysis (MCA) theory, the levels of tryptophan metabolites have also been altered. Some of these metabolites are tryptamine, melatonin, and nicotinamide. Thus, we assume that these alterations could contribute to the dopaminergic, adrenergic, and serotonergic neurodegeneration that happen in the disease.

FNICM: A New Methodology To Identify Core Metabolites Based on Significantly Perturbed Metabolic Subnetworks

Metabolomics has emerged as a powerful tool in biomedical research to understand the pathophysiological processes and metabolic biomarkers of diseases. Nevertheless, it is a significant challenge in metabolomics to identify the reliable core metabolites that are closely associated with the occurrence or progression of diseases. Here, we proposed a new research framework by integrating detection-based metabolomics with computational network biology for function-guided and network-based identification of core metabolites, namely, FNICM. The proposed FNICM methodology is successfully utilized to uncover ulcerative colitis (UC)-related core metabolites based on the significantly perturbed metabolic subnetwork. First, seed metabolites were screened out using prior biological knowledge and targeted metabolomics. Second, by leveraging network topology, the perturbations of the detected seed metabolites were propagated to other undetected ones. Ultimately, 35 core metabolites were identified by controllability analysis and were further hierarchized into six levels based on confidence level and their potential significance. The specificity and generalizability of the discovered core metabolites, used as UC's diagnostic markers, were further validated using published data sets of UC patients. More importantly, we demonstrated the broad applicability and practicality of the FNICM framework in different contexts by applying it to multiple clinical data sets, including inflammatory bowel disease, colorectal cancer, and acute coronary syndrome. In addition, FNICM was also demonstrated as a practicality methodology to identify core metabolites correlated with the therapeutic effects of Clematis saponins. Overall, the FNICM methodology is a new framework for identifying reliable core metabolites for disease diagnosis and drug treatment from a systemic and a holistic perspective.

MARS: A Multipurpose Software for Untargeted LC-MS-Based Metabolomics and Exposomics

Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis.

Cecal metabolomics of 2 divergently selected rabbit lines revealed microbial mechanisms correlated to intramuscular fat deposition

The gastrointestinal microbiota plays a key role in the host physiology and health through a complex host-microbiota co-metabolism. Metabolites produced by microbial metabolism can travel through the bloodstream to reach distal organs and affect their function, ultimately influencing the development of relevant production traits such as meat quality. Meat quality is a complex trait made up of a number of characteristics and intramuscular fat content (IMF) is considered to be one of the most important parameters. In this study, 52 rabbits from 2 lines divergently selected for IMF (high-IMF (H) and low-IMF (L) lines) were used to perform an untargeted metabolomic analysis of their cecal content, with the aim to obtain information on genetically determined microbial metabolism related to IMF. A large, correlated response to selection was found in their cecal metabolome composition. Partial least squares discriminant analysis was used to identify the pathways differentiating the lines, which showed a classification accuracy of 99%. On the other hand, 2 linear partial least squares analyses were performed, one for each line, to extract evidence on the specific pathways associated with IMF deposition within each line, which showed predictive abilities (estimated using the Q2) of approximately 60%. The most relevant pathways differentiating the lines were those related to amino acids (aromatic, branched-chain, and gamma-glutamyl), secondary bile acids, and purines. The higher content of secondary bile acids in the L-line was related to greater lipid absorption, while the differences found in purines suggested different fermentation activities, which could be related to greater nitrogen utilization and energy efficiency in the L-line. The linear analyses showed that lipid metabolism had a greater relative importance for IMF deposition in the L-line, whereas a more complex microbial metabolism was associated with the H-line. The lysophospholipids and gamma-glutamyl amino acids were associated with IMF in both lines; the nucleotide and secondary bile acid metabolisms were mostly associated in the H-line; and the long-chain and branched-chain fatty acids were mostly associated in the L-line. A metabolic signature consisting of 2 secondary bile acids and 2 protein metabolites was found with 88% classification accuracy, pointing to the interaction between lipid absorption and protein metabolism as a relevant driver of the microbiome activity influencing IMF.

Plasma metabolomic profiling in two rabbit lines divergently selected for intramuscular fat content

This study provides a thorough comparison of the plasma metabolome of two rabbit lines divergently selected for intramuscular fat content (IMF). The divergent selection led to a correlated response in the overall adiposity, turning these lines into a valuable animal material to study also the genetics of obesity. Over 900 metabolites were detected, and the adjustment of multivariate models, both discriminant and linear, allowed to identify 322 with differential abundances between lines, which also adjusted linearly to the IMF content. The most affected pathways were those of lipids and amino acids, with differences between lines ranging from 0.23 to 6.04 standard deviations, revealing a limited capacity of the low-IMF line to obtain energy from lipids, and a greater branched-chain amino acids catabolism in the high-IMF line related to its increased IMF content. Additionally, changes in metabolites derived from microbial activity supported its relevant role in the lipid deposition. Future research will focus on the analysis of the metabolomic profile of the cecum content, and on the integration of the several -omics datasets available for these lines, to help disentangle the host and microbiome biological mechanisms involved in the IMF deposition.

SCALiR: a web application for automating absolute quantification of mass spectrometry-based metabolomics data

Metabolomics is an important approach for studying complex biological systems. Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming a mainstream strategy but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process which is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (Standard Curve Application for determining Linear Ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signal data into absolute quantitative data (https://www.lewisresearchgroup.org/software). The algorithm used in SCALiR automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from their LC-MS signal. Using a standard mix containing 77 metabolites, we found excellent correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99) and that SCALiR reproducibly calculated concentrations of mid-range standards across ten analytical batches (average coefficient of variation 0.091). SCALiR offers users several advantages, including that it (1) is open-source and vendor agnostic; (2) requires only 10 seconds of analysis time to compute concentrations of >75 compounds; (3) facilitates automation of quantitative workflows; and (4) performs deterministic evaluation of compound quantification limits. SCALiR provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

The assessment of the potential hepatotoxicity of new drugs by in vitro metabolomics

Drug hepatotoxicity assessment is a relevant issue both in the course of drug development as well as in the post marketing phase. The use of human relevant in vitro models in combination with powerful analytical methods (metabolomic analysis) is a promising approach to anticipate, as well as to understand and investigate the effects and mechanisms of drug hepatotoxicity in man. The metabolic profile analysis of biological liver models treated with hepatotoxins, as compared to that of those treated with non-hepatotoxic compounds, provides useful information for identifying disturbed cellular metabolic reactions, pathways, and networks. This can later be used to anticipate, as well to assess, the potential hepatotoxicity of new compounds. However, the applicability of the metabolomic analysis to assess the hepatotoxicity of drugs is complex and requires careful and systematic work, precise controls, wise data preprocessing and appropriate biological interpretation to make meaningful interpretations and/or predictions of drug hepatotoxicity. This review provides an updated look at recent in vitro studies which used principally mass spectrometry-based metabolomics to evaluate the hepatotoxicity of drugs. It also analyzes the principal drawbacks that still limit its general applicability in safety assessment screenings. We discuss the analytical workflow, essential factors that need to be considered and suggestions to overcome these drawbacks, as well as recent advancements made in this rapidly growing field of research.

Towards inferring absolute concentrations from relative abundance in time-course GC-MS metabolomics data

Metabolomics, the large-scale study of metabolites, has significant appeal as a source of information for metabolic modeling and other scientific applications. One common approach for measuring metabolomics data is gas chromatography-mass spectrometry (GC-MS). However, GC-MS metabolomics data are typically reported as relative abundances, precluding their use with approaches and tools where absolute concentrations are necessary. While chemical standards can be used to help provide quantification, their use is time-consuming, expensive, or even impossible due to their limited availability. The ability to infer absolute concentrations from GC-MS metabolomics data without chemical standards would have significant value. We hypothesized that when analyzing time-course metabolomics datasets, the mass balances of metabolism and other biological information could provide sufficient information towards inference of absolute concentrations. To demonstrate this, we developed and characterized MetaboPAC, a computational framework that uses two approaches-one based on kinetic equations and another using biological heuristics-to predict the most likely response factors that allow translation between relative abundances and absolute concentrations. When used to analyze noiseless synthetic data generated from multiple types of kinetic rate laws, MetaboPAC performs significantly better than negative control approaches when 20% of kinetic terms are known a priori. Under conditions of lower sampling frequency and high noise, MetaboPAC is still able to provide significant inference of concentrations in 3 of 4 models studied. This provides a starting point for leveraging biological knowledge to extract concentration information from time-course intracellular GC-MS metabolomics datasets, particularly for systems that are well-studied and have partially known kinetic structures.

Optimized Fast Filtration-Based Sampling and Extraction Enables Precise and Absolute Quantification of the Escherichia coli Central Carbon Metabolome

Precise and accurate quantification is a prerequisite for interpretation of targeted metabolomics data, but this task is challenged by the inherent instability of the analytes. The sampling, quenching, extraction, and sample purification conditions required to recover and stabilize metabolites in representative extracts have also been proven highly dependent on species-specific properties. For Escherichia coli, unspecific leakage has been demonstrated for conventional microbial metabolomics sampling protocols. We herein present a fast filtration-based sampling protocol for this widely applied model organism, focusing on pitfalls such as inefficient filtration, selective loss of biomass, matrix contamination, and membrane permeabilization and leakage. We evaluate the effect of and need for removal of extracellular components and demonstrate how residual salts can challenge analytical accuracy of hyphenated mass spectrometric analyses, even when sophisticated correction strategies are applied. Laborious extraction procedures are bypassed by direct extraction in cold acetonitrile:water:methanol (3:5:2, v/v%), ensuring compatibility with sample concentration and thus, any downstream analysis. By applying this protocol, we achieve and demonstrate high precision and low metabolite turnover, and, followingly, minimal perturbation of the inherent metabolic state. This allows us to herein report absolute intracellular concentrations in E. coli and explore its central carbon metabolome at several commonly applied cultivation conditions.

Neotropical mustelids: fecal metabolome diversity and its potential for taxonomic discrimination

Chemical profiles of non-invasive biological material, such as feces, have great potential to study elusive animals or those with low population densities. Here, we use a metabolomic approach to evaluate Neotropical mustelids as a biological model to describe the diversity of the metabolites present in fecal samples, as well as to evaluate the potential of chemical profiles for taxonomic discrimination. We collected fecal samples from captive individuals of five species of mustelids occurring in Brazil and analyzed them by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Over 200 compounds have been annotated; "bile acids, alcohols and derivatives" was the most expressive class in the metabolome of all the species. We successfully discriminated three taxonomic groups: 1 - Tayra (Eira barbara); 2 - otters (Lontra longicaudis and Pteronura brasiliensis; 1); and 3 - grisons (Galictis vittata and Galictis cuja). Several compounds seemed to be associated with food intake and the digestive process, while others were found for the first time in Neotropical mustelids. We concluded that mustelids show high metabolome diversity and that species-specific identification through metabolomic profiles is possible, thus contributing to the development and implementation of additional non-invasive approaches in the study of mustelids. This article is protected by copyright. All rights reserved.

Mass spectrometry-based metabolomics in microbiome investigations

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

Discovery of Polar Ozonation Byproducts via Direct Injection of Effluent Organic Matter with Online LC-FT-ICR-MS

Effluent organic matter (EfOM), a major ozone consumer during wastewater ozonation, is a complex mixture of natural and anthropogenic organic molecules. Ozonation of EfOM adds to molecular complexity by introducing polar and potentially mobile ozonation byproducts (OBPs). Currently, nontargeted direct infusion (DI) ultrahigh resolution mass spectrometry (e.g. FT-ICR-MS) is used to study OBPs but requires sample extraction, limiting the accessible polarity range of OBPs. To better understand the impact of ozonation on EfOM and the formation of polar OBPs, nonextracted effluent was analyzed by direct injection onto a reversed-phase liquid chromatography system (RP-LC) online hyphenated with an FT-ICR-MS. Over four times more OBPs were detected in nonextracted EfOM compared to effluent extracted with solid phase extraction and measured with DI-FT-ICR-MS (13817 vs 3075). Over 1500 highly oxygenated OBPs were detected exclusively in early eluting fractions of nonextracted EfOM, indicating polar OBPs. Oxygenation of these newly discovered OBPs is higher than previously found, with an average molecular DBE-O value of -3.3 and O/C ratio of 0.84 in the earliest eluting OBP fractions. These polar OBPs are consistently lost during extraction but may play an important role in understanding the environmental impact of ozonated EfOM. Moreover, 316 molecular formulas classified as nonreactive to ozone in DI-FT-ICR-MS can be identified with LC-FT-ICR-MS as isomers with varying degrees of reactivity, providing for the first time experimental evidence of differential reactivity of complex organic matter isomers with ozone.

Integrating in vitro metabolomics with a 96-well high-throughput screening platform

INTRODUCTION

High-throughput screening (HTS) is emerging as an approach to support decision-making in chemical safety assessments. In parallel, in vitro metabolomics is a promising approach that can help accelerate the transition from animal models to high-throughput cell-based models in toxicity testing.

OBJECTIVE

In this study we establish and evaluate a high-throughput metabolomics workflow that is compatible with a 96-well HTS platform employing 50,000 hepatocytes of HepaRG per well.

METHODS

Low biomass cell samples were extracted for metabolomics analyses using a newly established semi-automated protocol, and the intracellular metabolites were analysed using a high-resolution spectral-stitching nanoelectrospray direct infusion mass spectrometry (nESI-DIMS) method that was modified for low sample biomass.

RESULTS

The method was assessed with respect to sensitivity and repeatability of the entire workflow from cell culturing and sampling to measurement of the metabolic phenotype, demonstrating sufficient sensitivity (> 3000 features in hepatocyte extracts) and intra- and inter-plate repeatability for polar nESI-DIMS assays (median relative standard deviation < 30%). The assays were employed for a proof-of-principle toxicological study with a model toxicant, cadmium chloride, revealing changes in the metabolome across five sampling times in the 48-h exposure period. To allow the option for lipidomics analyses, the solvent system was extended by establishing separate extraction methods for polar metabolites and lipids.

CONCLUSIONS

Experimental, analytical and informatics workflows reported here met pre-defined criteria in terms of sensitivity, repeatability and ability to detect metabolome changes induced by a toxicant and are ready for application in metabolomics-driven toxicity testing to complement HTS assays.

Implementing Multi-Enzyme Biocatalytic Systems Using Nanoparticle Scaffolds

Interest in multi-enzyme synthesis outside of cells (in vitro) is becoming far more prevalent as the field of cell-free synthetic biology grows exponentially. Such synthesis would allow for complex chemical transformations based on the exquisite specificity of enzymes in a "greener" manner as compared to organic chemical transformations. Here, we describe how nanoparticles, and in this specific case-semiconductor quantum dots, can be used to both stabilize enzymes and further allow them to self-assemble into nanocomplexes that facilitate high-efficiency channeling phenomena. Pertinent protocol information is provided on enzyme expression, choice of nanoparticulate material, confirmation of enzyme attachment to nanoparticles, assay format and tracking, data analysis, and optimization of assay formats to draw the best analytical information from the underlying processes.

Unveiling Metabolic Phenotype Alterations in Anorexia Nervosa through Metabolomics

Anorexia nervosa (AN) is a mental disorder characterized by an intense fear of weight gain that affects mainly young women. It courses with a negative body image leading to altered eating behaviors that have devastating physical, metabolic, and psychological consequences for the patients. Although its origin is postulated to be multifactorial, the etiology of AN remains unknown, and this increases the likelihood of chronification and relapsing. Thus, expanding the available knowledge on the pathophysiology of AN is of enormous interest. Metabolomics is proposed as a powerful tool for the elucidation of disease mechanisms and to provide new insights into the diagnosis, treatment, and prognosis of AN. A review of the literature related to studies of AN patients by employing metabolomic strategies to characterize the main alterations associated with the metabolic phenotype of AN during the last 10 years is described. The most common metabolic alterations are derived from chronic starvation, including amino acid, lipid, and carbohydrate disturbances. Nonetheless, recent findings have shifted the attention to gut-microbiota metabolites as possible factors contributing to AN development, progression, and maintenance. We have identified the areas of ongoing research in AN and propose further perspectives to improve our knowledge and understanding of this disease.

Salicylic Acid and Risk of Colorectal Cancer: A Two-Sample Mendelian Randomization Study

Salicylic acid (SA) has observationally been shown to decrease colorectal cancer (CRC) risk. Aspirin (acetylsalicylic acid, that rapidly deacetylates to SA) is an effective primary and secondary chemopreventive agent. Through a Mendelian randomization (MR) approach, we aimed to address whether levels of SA affected CRC risk, stratifying by aspirin use. A two-sample MR analysis was performed using GWAS summary statistics of SA (INTERVAL and EPIC-Norfolk, N = 14,149) and CRC (CCFR, CORECT, GECCO and UK Biobank, 55,168 cases and 65,160 controls). The DACHS study (4410 cases and 3441 controls) was used for replication and stratification of aspirin-use. SNPs proxying SA were selected via three methods: (1) functional SNPs that influence the activity of aspirin-metabolising enzymes; (2) pathway SNPs present in enzymes' coding regions; and (3) genome-wide significant SNPs. We found no association between functional SNPs and SA levels. The pathway and genome-wide SNPs showed no association between SA and CRC risk (OR: 1.03, 95% CI: 0.84-1.27 and OR: 1.08, 95% CI: 0.86-1.34, respectively). Results remained unchanged upon aspirin use stratification. We found little evidence to suggest that an SD increase in genetically predicted SA protects against CRC risk in the general population and upon stratification by aspirin use.

Proline as a Sparker Metabolite of Oxidative Metabolism during the Flight of the Bumblebee, Bombus impatiens

Several insect species use the amino acid proline as a major energy substrate. Although initially thought to be limited to blood-feeding dipterans, studies have revealed this capability is more widespread. Recent work with isolated flight muscle showed that the bumblebee Bombus impatiens can oxidize proline at a high rate. However, its role as a metabolic fuel to power flight is unclear. To elucidate the extent to which proline is oxidized to power flight and how its contribution changes during flight, we profiled 14 metabolites central to energy and proline metabolism at key time points in flight muscle and abdominal tissues. Ultra-high performance liquid chromatography-electrospray ionization-quadrupole time of flight mass spectrometry (UPLC-ESI-QTOF MS) analysis revealed that proline is likely used as a sparker metabolite of the tricarboxylic acid cycle at the onset of flight, whereby it supplements the intermediates of the cycle. Carbohydrates are the major energy substrates, which is evidenced by marked decreases in abdominal glycogen stores and a lack of alanine accumulation to replenish flight muscle proline. The time course of fuel stores and metabolites changes during flight highlights homeostatic regulation of energy substrates and patterns of changes in metabolic intermediates within pathways. This study clarifies the role of proline and carbohydrate metabolism during flight in hymenopterans, such as B. impatiens.

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

In recent years, multi-omic studies have enabled resolving community structure and interrogating community function of microbial communities. Simultaneous generation of metagenomic, metatranscriptomic, metaproteomic, and (meta) metabolomic data is more feasible than ever before, thus enabling in-depth assessment of community structure, function, and phenotype, thus resulting in a multitude of multi-omic microbiome datasets and the development of innovative methods to integrate and interrogate those multi-omic datasets. Specifically, the application of reference-independent approaches provides opportunities in identifying novel organisms and functions. At present, most of these large-scale multi-omic datasets stem from spatial sampling (e.g., water/soil microbiomes at several depths, microbiomes in/on different parts of the human anatomy) or case-control studies (e.g., cohorts of human microbiomes). We believe that longitudinal multi-omic microbiome datasets are the logical next step in microbiome studies due to their characteristic advantages in providing a better understanding of community dynamics, including: observation of trends, inference of causality, and ultimately, prediction of community behavior. Furthermore, the acquisition of complementary host-derived omics, environmental measurements, and suitable metadata will further enhance the aforementioned advantages of longitudinal data, which will serve as the basis to resolve drivers of community structure and function to understand the biotic and abiotic factors governing communities and specific populations. Carefully setup future experiments hold great potential to further unveil ecological mechanisms to evolution, microbe-microbe interactions, or microbe-host interactions. In this article, we discuss the challenges, emerging strategies, and best-practices applicable to longitudinal microbiome studies ranging from sampling, biomolecular extraction, systematic multi-omic measurements, reference-independent data integration, modeling, and validation.

Metabolomics reveals synergy between Ag and g-C3N4 in Ag/g-C3N4 composite photocatalysts: a unique feature among Ag-doped biocidal materials

INTRODUCTION

The silver/graphitic carbon nitride (Ag/g-C₃N₄) composite system exerts biocidal activity against the pathogenic bacterium Escherichia coli 1337-H that is stronger than that of well-known silver and titanium oxide (TiO₂)-based composites. However, whether the Ag/g-C₃N₄ composite system has biocidal properties that the parent components do or do not have as separate chemical entities and whether they differ from those in Ag/TiO₂ composite photocatalysts have not been clarified.

OBJECTIVE

We investigated the chemical (cooperative charge handling and electronic properties) and biological (metabolic) effects exerted by the addition of Ag to g-C₃N₄ and to TiO₂.

METHODS

In this work, we undertook metabolome-wide analysis by liquid chromatography-electrospray ionization-quadrupole-time of flight-mass spectrometry to compare the metabolite profiles of untreated E. coli 1337-H cells or those subjected to disinfection with Ag, g-C₃N₄, 2Ag/g-C₃N₄, TiO₂ and 2Ag/TiO₂.

RESULTS

While Ag or g-C₃N₄ moderately affected microbial metabolism according to the mean of the altered metabolites, multiple cell systems contributing to rapid cell death were immediately affected by the light-triggered radical species produced when Ag and g-C₃N₄ were as xAg/g-C₃N_4. The effects include drastically reduced production of small metabolites essential for detoxifying reactive oxygen species and those that regulate DNA replication fidelity, cell morphology and energy status. These biological consequences were different from those caused by Ag/TiO₂-based biocides, demonstrating the uniqueness of the Ag/g-C₃N₄ system.

CONCLUSIONS

Our results support the idea that the unique Ag/g-C₃N₄ biocidal properties are based on synergistic action and reveal new directions for designing future photocatalysts for use in disinfection and microbial control.

Co-culturing microbial consortia: approaches for applications in biomanufacturing and bioprocessing

The application of microbial co-cultures is now recognized in the fields of biotechnology, ecology, and medicine. Understanding the biological interactions that govern the association of microorganisms would shape the way in which artificial/synthetic co-cultures or consortia are developed. The ability to accurately predict and control cell-to-cell interactions fully would be a significant enabler in synthetic biology. Co-culturing method development holds the key to strategically engineer environments in which the co-cultured microorganism can be monitored. Various approaches have been employed which aim to emulate the natural environment and gain access to the untapped natural resources emerging from cross-talk between partners. Amongst these methods are the use of a communal liquid medium for growth, use of a solid-liquid interface, membrane separation, spatial separation, and use of microfluidics systems. Maximizing the information content of interactions monitored is one of the major challenges that needs to be addressed by these designs. This review critically evaluates the significance and drawbacks of the co-culturing approaches used to this day in biotechnological applications, relevant to biomanufacturing. It is recommended that experimental results for a co-cultured species should be validated with different co-culture approaches due to variations in interactions that could exist as a result of the culturing method selected.

A Metabolite Array Technology for Precision Medicine

The application of metabolomics in translational research suffers from several technological bottlenecks, such as data reproducibility issues and the lack of standardization of sample profiling procedures. Here, we report an automated high-throughput metabolite array technology that can rapidly and quantitatively determine 324 metabolites including fatty acids, amino acids, organic acids, carbohydrates, and bile acids. Metabolite identification and quantification is achieved using the Targeted Metabolome Batch Quantification (TMBQ) software, the first cross-vendor data processing pipeline. A test of this metabolite array was performed by analyzing serum samples from patients with chronic liver disease (N = 1234). With high detection efficiency and sensitivity in serum, urine, feces, cell lysates, and liver tissue samples and suitable for different mass spectrometry systems, this metabolite array technology holds great potential for biomarker discovery and high throughput clinical testing. Additionally, data generated from such standardized procedures can be used to generate a clinical metabolomics database suitable for precision medicine in next-generation healthcare.

Applying metabolomics to veterinary pharmacology and therapeutics

Metabolomics is the large-scale study of low-molecular-weight substances in a biological system in a given physiological state at a given time point. Metabolomics can be applied to identify predictors of inter-individual variability in drug response, provide clinicians with data useful for decision-making processes in drug selection, and inform about the pharmacokinetics and pharmacodynamics of a drug. It is, therefore, an exceptional approach for gaining new understanding effects in the field of comparative veterinary pharmacology. However, the incorporation of metabolomics into veterinary pharmacology and toxicology is not yet widespread, and this is probably, at least in part, a result of its highly multidisciplinary nature. This article reviews the potential applications of metabolomics in veterinary pharmacology and therapeutics. It integrates key concepts for designing metabolomics studies and analyzing and interpreting metabolomics data, providing solid foundations for applying metabolomics to the study of drugs in all veterinary species.

Metabolomics for Biomarker Discovery: Key Signatory Metabolic Profiles for the Identification and Discrimination of Oat Cultivars

The first step in crop introduction-or breeding programmes-requires cultivar identification and characterisation. Rapid identification methods would therefore greatly improve registration, breeding, seed, trade and inspection processes. Metabolomics has proven to be indispensable in interrogating cellular biochemistry and phenotyping. Furthermore, metabolic fingerprints are chemical maps that can provide detailed insights into the molecular composition of a biological system under consideration. Here, metabolomics was applied to unravel differential metabolic profiles of various oat (Avena sativa) cultivars (Magnifico, Dunnart, Pallinup, Overberg and SWK001) and to identify signatory biomarkers for cultivar identification. The respective cultivars were grown under controlled conditions up to the 3-week maturity stage, and leaves and roots were harvested for each cultivar. Metabolites were extracted using 80% methanol, and extracts were analysed on an ultra-high performance liquid chromatography (UHPLC) system coupled to a quadrupole time-of-flight (qTOF) high-definition mass spectrometer analytical platform. The generated data were processed and analysed using multivariate statistical methods. Principal component analysis (PCA) models were computed for both leaf and root data, with PCA score plots indicating cultivar-related clustering of the samples and pointing to underlying differential metabolic profiles of these cultivars. Further multivariate analyses were performed to profile differential signatory markers, which included carboxylic acids, amino acids, fatty acids, phenolic compounds (hydroxycinnamic and hydroxybenzoic acids, and associated derivatives) and flavonoids, among the respective cultivars. Based on the key signatory metabolic markers, the cultivars were successfully distinguished from one another in profiles derived from both leaves and roots. The study demonstrates that metabolomics can be used as a rapid phenotyping tool for cultivar differentiation.

Adaptation of central metabolite pools to variations in growth rate and cultivation conditions in Saccharomyces cerevisiae

Background

Saccharomyces cerevisiae is a well-known popular model system for basic biological studies and serves as a host organism for the heterologous production of commercially interesting small molecules and proteins. The central metabolism is at the core to provide building blocks and energy to support growth and survival in normal situations as well as during exogenous stresses and forced heterologous protein production. Here, we present a comprehensive study of intracellular central metabolite pool profiling when growing S. cerevisiae on different carbon sources in batch cultivations and at different growth rates in nutrient-limited glucose chemostats. The latest versions of absolute quantitative mass spectrometry-based metabolite profiling methodology were applied to cover glycolytic and pentose phosphate pathway metabolites, tricarboxylic acid cycle (TCA), complete amino acid, and deoxy-/nucleoside phosphate pools.

Results

Glutamate, glutamine, alanine, and citrate were the four most abundant metabolites for most conditions tested. The amino acid is the dominant metabolite class even though a marked relative reduction compared to the other metabolite classes was observed for nitrogen and phosphate limited chemostats. Interestingly, glycolytic and pentose phosphate pathway (PPP) metabolites display the largest variation among the cultivation conditions while the nucleoside phosphate pools are more stable and vary within a closer concentration window. The overall trends for glucose and nitrogen-limited chemostats were increased metabolite pools with the increasing growth rate. Next, comparing the chosen chemostat reference growth rate (0.12 h⁻¹, approximate one-fourth of maximal unlimited growth rate) illuminates an interesting pattern: almost all pools are lower in nitrogen and phosphate limited conditions compared to glucose limitation, except for the TCA metabolites citrate, isocitrate and α-ketoglutarate.

Conclusions

This study provides new knowledge-how the central metabolism is adapting to various cultivations conditions and growth rates which is essential for expanding our understanding of cellular metabolism and the development of improved phenotypes in metabolic engineering.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01557-8.

Targeted Metabolomic Assessment of the Sub-Lethal Toxicity of Halogenated Acetic Acids (HAAs) to Daphnia magna

Halogenated acetic acids (HAAs) are amongst the most frequently detected disinfection by-products in aquatic environments. Despite this, little is known about their toxicity, especially at the molecular level. The model organism Daphnia magna, which is an indicator species for freshwater ecosystems, was exposed to sub-lethal concentrations of dichloroacetic acid (DCAA), trichloroacetic acid (TCAA) and dibromoacetic acid (DBAA) for 48 h. Polar metabolites extracted from Daphnia were analyzed using liquid chromatography hyphened to a triple quadrupole mass spectrometer (LC-MS/MS). Multivariate analyses identified shifts in the metabolic profile with exposure and pathway analysis was used to identify which metabolites and associated pathways were disrupted. Exposure to all three HAAs led to significant downregulation in the nucleosides: adenosine, guanosine and inosine. Pathway analyses identified perturbations in the citric acid cycle and the purine metabolism pathways. Interestingly, chlorinated and brominated acetic acids demonstrated similar modes of action after sub-lethal acute exposure, suggesting that HAAs cause a contaminant class-based response which is independent of the type or number of halogens. As such, the identified metabolites that responded to acute HAA exposure may serve as suitable bioindicators for freshwater monitoring programs.

Development of an LC-MS Targeted Metabolomics Methodology to Study Proline Metabolism in Mammalian Cell Cultures

A growing interest in metabolomics studies of cultured cells requires development not only untargeted methods capable of fingerprinting the complete metabolite profile but also targeted methods enabling the precise and accurate determination of a selected group of metabolites. Proline metabolism affects many crucial processes at the cellular level, including collagen biosynthesis, redox balance, energetic processes as well as intracellular signaling. The study aimed to develop a robust and easy-to-use targeted metabolomics method for the determination of the intracellular level of proline and the other two amino acids closely related to proline metabolism: glutamic acid and arginine. The method employs hydrophilic interaction liquid chromatography followed by high-resolution, accurate-mass mass spectrometry for reliable detection and quantification of the target metabolites in cell lysates. The sample preparation consisted of quenching by the addition of ice-cold methanol and subsequent cell scraping into a quenching solution. The method validation showed acceptable linearity (r > 0.995), precision (%RSD < 15%), and accuracy (88.5–108.5%). Pilot research using HaCaT spontaneously immortalized human keratinocytes in a model for wound healing was performed, indicating the usefulness of the method in studies of disturbances in proline metabolism. The developed method addresses the need to determine the intracellular concentration of three key amino acids and can be used routinely in targeted mammalian cell culture metabolomics research.

Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures

Biological organisms are constantly exposed to an immense repertoire of molecules that cover environmental or food-derived molecules and drugs, triggering a continuous flow of stimuli-dependent adaptations. The diversity of these chemicals as well as their concentrations contribute to the multiplicity of induced effects, including activation, stimulation, or inhibition of physiological processes and toxicity. Metabolism, as the foremost phenotype and manifestation of life, has proven to be immensely sensitive and highly adaptive to chemical stimuli. Therefore, studying the effect of endo- or xenobiotics over cellular metabolism delivers valuable knowledge to apprehend potential cellular activity of individual molecules and evaluate their acute or chronic benefits and toxicity. The development of modern metabolomics technologies such as mass spectrometry or nuclear magnetic resonance spectroscopy now offers unprecedented solutions for the rapid and efficient determination of metabolic profiles of cells and more complex biological systems. Combined with the availability of well-established cell culture techniques, these analytical methods appear perfectly suited to determine the biological activity and estimate the positive and negative effects of chemicals in a variety of cell types and models, even at hardly detectable concentrations. Metabolic phenotypes can be estimated from studying intracellular metabolites at homeostasis in vivo, while in vitro cell cultures provide additional access to metabolites exchanged with growth media. This article discusses analytical solutions available for metabolic phenotyping of cell culture metabolism as well as the general metabolomics workflow suitable for testing the biological activity of molecular compounds. We emphasize how metabolic profiling of cell supernatants and intracellular extracts can deliver valuable and complementary insights for evaluating the effects of xenobiotics on cellular metabolism. We note that the concepts and methods discussed primarily for xenobiotics exposure are widely applicable to drug testing in general, including endobiotics that cover active metabolites, nutrients, peptides and proteins, cytokines, hormones, vitamins, etc.

Multi-omics analysis to examine microbiota, host gene expression and metabolites in the intestine of black tiger shrimp (Penaeus monodon) with different growth performance

Understanding the correlation between shrimp growth and their intestinal bacteria would be necessary to optimize animal's growth performance. Here, we compared the bacterial profiles along with the shrimp's gene expression responses and metabolites in the intestines between the Top and the Bottom weight groups. Black tiger shrimp (Penaeus monodon) were collected from the same population and rearing environments. The two weight groups, the Top-weight group with an average weight of 36.82 ± 0.41 g and the Bottom-weight group with an average weight of 17.80 ± 11.81 g, were selected. Intestines were aseptically collected and subjected to microbiota, transcriptomic and metabolomic profile analyses. The weighted-principal coordinates analysis (PCoA) based on UniFrac distances showed similar bacterial profiles between the two groups, suggesting similar relative composition of the overall bacterial community structures. This observed similarity was likely due to the fact that shrimp were from the same genetic background and reared under the same habitat and diets. On the other hand, the unweighted-distance matrix revealed that the bacterial profiles associated in intestines of the Top-weight group were clustered distinctly from those of the Bottom-weight shrimp, suggesting that some unique non-dominant bacterial genera were found associated with either group. The key bacterial members associated to the Top-weight shrimp were mostly from Firmicutes (Brevibacillus and Fusibacter) and Bacteroidetes (Spongiimonas), both of which were found in significantly higher abundance than those of the Bottom-weight shrimp. Transcriptomic profile of shrimp intestines found significant upregulation of genes mostly involved in nutrient metabolisms and energy storage in the Top-weight shrimp. In addition to significantly expressed metabolic-related genes, the Bottom-weight shrimp also showed significant upregulation of stress and immune-related genes, suggesting that these pathways might contribute to different degrees of shrimp growth performance. A non-targeted metabolome analysis from shrimp intestines revealed different metabolic responsive patterns, in which the Top-weight shrimp contained significantly higher levels of short chain fatty acids, lipids and organic compounds than the Bottom-weight shrimp. The identified metabolites included those that were known to be produced by intestinal bacteria such as butyric acid, 4-indolecarbaldehyde and L-3-phenyllactic acid as well as those produced by shrimp such as acyl-carnitines and lysophosphatidylcholine. The functions of these metabolites were related to nutrient absorption and metabolisms. Our findings provide the first report utilizing multi-omics integration approach to investigate microbiota, metabolic and transcriptomics profiles of the host shrimp and their potential roles and relationship to shrimp growth performance.

Confrontation between Penicillium rubens and Aspergillus terreus: Investigating the production of fungal secondary metabolites in submerged co-cultures

The production of secondary metabolites in the submerged co-cultures of Penicillium rubens Wisconsin 54-1255 and Aspergillus terreus ATCC 20542 was evaluated. The biosynthetic capabilities of the two strains were compared in a set of diverse liquid media that differed with respect to the initial levels of glucose, lactose and yeast extract, contained carrot juice or vegetable/turkey puree as additional nutrient sources or were supplemented with phenylacetic acid, the side-chain precursor of penicillin G. The main goal of the study was to investigate the interactions between A. terreus and P. rubens that might contribute to the changes of secondary metabolite titers. Briefly, the biosynthesis of octaketide metabolites (+)-geodin and asterric acid was visibly enhanced as a result of replacing the conventional monocultures with the co-culture systems, but solely in the media containing not more than 5 g L^-1 of yeast extract. By contrast, no marked enhancement was observed with respect to the biosynthesis of penicillin G, lovastatin, chrysogine, 4a,5-dihydromevinolinic acid and 3α-hydroxy-3,5-dihydromonacolin L acid. It was shown that the relationships between medium composition and product titers were clearly different in monoculture variants than in the corresponding co-cultures. Finally, it was demonstrated that the utilization of penicillin precursors by P. rubens can be blocked under the conditions of co-cultivation.

Concepts and Software Package for Efficient Quality Control in Targeted Metabolomics Studies: MeTaQuaC

Targeted quantitative mass spectrometry metabolite profiling is the workhorse of metabolomics research. Robust and reproducible data are essential for confidence in analytical results and are particularly important with large-scale studies. Commercial kits are now available which use carefully calibrated and validated internal and external standards to provide such reliability. However, they are still subject to processing and technical errors in their use and should be subject to a laboratory's routine quality assurance and quality control measures to maintain confidence in the results. We discuss important systematic and random measurement errors when using these kits and suggest measures to detect and quantify them. We demonstrate how wider analysis of the entire data set alongside standard analyses of quality control samples can be used to identify outliers and quantify systematic trends to improve downstream analysis. Finally, we present the MeTaQuaC software which implements the above concepts and methods for Biocrates kits and other target data sets and creates a comprehensive quality control report containing rich visualization and informative scores and summary statistics. Preliminary unsupervised multivariate analysis methods are also included to provide rapid insight into study variables and groups. MeTaQuaC is provided as an open source R package under a permissive MIT license and includes detailed user documentation.

Metabolomics: A Tool for Cultivar Phenotyping and Investigation of Grain Crops

The quality of plants is often enhanced for diverse purposes such as improved resistance to environmental pressures, better taste, and higher yields. Considering the world’s dependence on plants (nutrition, medicine, or biofuel), developing new cultivars with superior characteristics is of great importance. As part of the ‘omics’ approaches, metabolomics has been employed to investigate the large number of metabolites present in plant systems under well-defined environmental conditions. Recent advances in the metabolomics field have greatly expanded our understanding of plant metabolism, largely driven by potential application to agricultural systems. The current review presents the workflow for plant metabolome analyses, current knowledge, and future directions of such research as determinants of cultivar phenotypes. Furthermore, the value of metabolome analyses in contemporary crop science is illustrated. Here, metabolomics has provided valuable information in research on grain crops and identified significant biomarkers under different conditions and/or stressors. Moreover, the value of metabolomics has been redefined from simple biomarker identification to a tool for discovering active drivers involved in biological processes. We illustrate and conclude that the rapid advances in metabolomics are driving an explosion of information that will advance modern breeding approaches for grain crops and address problems associated with crop productivity and sustainable agriculture.

Absolute Quantification of the Central Carbon Metabolome in Eight Commonly Applied Prokaryotic and Eukaryotic Model Systems

Absolute quantification of intracellular metabolite pools is a prerequisite for modeling and in-depth biological interpretation of metabolomics data. It is the final step of an elaborate metabolomics workflow, with challenges associated with all steps—from sampling to quantifying the physicochemically diverse metabolite pool. Chromatographic separation combined with mass spectrometric (MS) detection is the superior platform for high coverage, selective, and sensitive detection of metabolites. Herein, we apply our quantitative MS-metabolomics workflow to measure and present the central carbon metabolome of a panel of commonly applied biological model systems. The workflow includes three chromatographic methods combined with isotope dilution tandem mass spectrometry to allow for absolute quantification of 68 metabolites of glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, and the amino acid and (deoxy) nucleoside pools. The biological model systems; Bacillus subtilis, Saccharomyces cerevisiae, two microalgal species, and four human cell lines were all cultured in commonly applied culture media and sampled in exponential growth phase. Both literature and databases are scarce with comprehensive metabolite datasets, and existing entries range over several orders of magnitude. The workflow and metabolite panel presented herein can be employed to expand the list of reference metabolomes, as encouraged by the metabolomics community, in a continued effort to develop and refine high-quality quantitative metabolomics workflows.

The β-Lactamase Inhibitor Boronic Acid Derivative SM23 as a New Anti-Pseudomonas aeruginosa Biofilm

Pseudomonas aeruginosa is a Gram-negative nosocomial pathogen, often causative agent of severe device-related infections, given its great capacity to form biofilm. P. aeruginosa finely regulates the expression of numerous virulence factors, including biofilm production, by Quorum Sensing (QS), a cell-to-cell communication mechanism used by many bacteria. Selective inhibition of QS-controlled pathogenicity without affecting bacterial growth may represent a novel promising strategy to overcome the well-known and widespread drug resistance of P. aeruginosa. In this study, we investigated the effects of SM23, a boronic acid derivate specifically designed as β-lactamase inhibitor, on biofilm formation and virulence factors production by P. aeruginosa. Our results indicated that SM23: (1) inhibited biofilm development and production of several virulence factors, such as pyoverdine, elastase, and pyocyanin, without affecting bacterial growth; (2) decreased the levels of 3-oxo-C₁₂-HSL and C₄-HSL, two QS-related autoinducer molecules, in line with a dampened lasR/lasI system; (3) failed to bind to bacterial cells that had been preincubated with P. aeruginosa-conditioned medium; and (4) reduced both biofilm formation and pyoverdine production by P. aeruginosa onto endotracheal tubes, as assessed by a new in vitro model closely mimicking clinical settings. Taken together, our results indicate that, besides inhibiting β-lactamase, SM23 can also act as powerful inhibitor of P. aeruginosa biofilm, suggesting that it may have a potential application in the prevention and treatment of biofilm-associated P. aeruginosa infections.

Integrative omics approaches provide biological and clinical insights: examples from mitochondrial diseases

High-throughput technologies for genomics, transcriptomics, proteomics, and metabolomics, and integrative analysis of these data, enable new, systems-level insights into disease pathogenesis. Mitochondrial diseases are an excellent target for hypothesis-generating omics approaches, as the disease group is mechanistically exceptionally complex. Although the genetic background in mitochondrial diseases is in either the nuclear or the mitochondrial genome, the typical downstream effect is dysfunction of the mitochondrial respiratory chain. However, the clinical manifestations show unprecedented variability, including either systemic or tissue-specific effects across multiple organ systems, with mild to severe symptoms, and occurring at any age. So far, the omics approaches have provided mechanistic understanding of tissue-specificity and potential treatment options for mitochondrial diseases, such as metabolome remodeling. However, no curative treatments exist, suggesting that novel approaches are needed. In this Review, we discuss omics approaches and discoveries with the potential to elucidate mechanisms of and therapies for mitochondrial diseases.

Modified Protocol of Harvesting, Extraction, and Normalization Approaches for Gas Chromatography Mass Spectrometry-Based Metabolomics Analysis of Adherent Cells Grown Under High Fetal Calf Serum Conditions

A gas chromatography mass spectrometry (GC-MS) metabolomics protocol was modified for quenching, harvesting, and extraction of metabolites from adherent cells grown under high (20%) fetal calf serum conditions. The reproducibility of using either 50% or 80% methanol for quenching of cells was compared for sample harvest. To investigate the efficiency and reproducibility of intracellular metabolite extraction, different volumes and ratios of chloroform were tested. Additionally, we compared the use of total protein amount versus cell mass as normalization parameters. We demonstrate that the method involving 50% methanol as quenching buffer followed by an extraction step using an equal ratio of methanol:chloroform:water (1:1:1, v/v/v) followed by the collection of 6 mL polar phase for GC-MS measurement was superior to the other methods tested. Especially for large sample sets, its comparative ease of measurement leads us to recommend normalization to protein amount for the investigation of intracellular metabolites of adherent human cells grown under high (or standard) fetal calf serum conditions. To avoid bias, care should be taken beforehand to ensure that the ratio of total protein to cell number are consistent among the groups tested. For this reason, it may not be suitable where culture conditions or cell types have very different protein outputs (e.g., hypoxia vs. normoxia). The full modified protocol is available in the Supplementary Materials.

The applications of metabolomics in the molecular diagnostics of cancer

Introduction: Metabolomics, the study of metabolites, is a promising research field for cancers. The metabolic pathway in a tumor cell is different from a normal tissue cell. There are two approaches to study the metabolism, targeted and untargeted. The general approach is that metabolomic data are interpreted by bioinformatics tools correlating with metabolomic databases to obtain significant findings. With the use of specific analysis tools, such as nuclear magnetic resonance (NMR) and mass spectrometer (MS) combined with chromatography, metabolic profile or metabolic fingerprint of various biological specimens could be obtained. The applications of metabolomics are used to discover potential cancer biomarkers and monitor the metastatic state, therapeutic and drug response for better patient management. Areas covered: In this review, the author introduce metabolomics and discuss the use of metabolomics approaches in different cancers, including the study of colorectal cancer, prostate cancer, liver cancer, pancreatic cancer and breast cancer using NMR and MS. Expert opinion: Knowledge on the molecular basis of cancer metabolism and its potential clinical applications has been improving recently. However, there are still many challenges for the technological development and integration of metabolomics with other omics spaces such as genomics. In the near future, it is expected that metabolomics will play an important role in cancer molecular diagnostics.

Harnessing Biological Insight to Accelerate Tuberculosis Drug Discovery

Tuberculosis (TB) is the leading cause of mortality globally resulting from an infectious disease, killing almost 1.6 million people annually and accounting for approximately 30% of deaths attributed to antimicrobial resistance (AMR). This despite the widespread administration of a neonatal vaccine, and the availability of an effective combination drug therapy against the causative agent, Mycobacterium tuberculosis (Mtb). Instead, TB prevalence worldwide is characterized by high-burden regions in which co-epidemics, such as HIV, and social and economic factors, undermine efforts to control TB. These elements additionally ensure conditions that favor the emergence of drug-resistant Mtb strains, which further threaten prospects for future TB control. To address this challenge, significant resources have been invested in developing a TB drug pipeline, an initiative given impetus by the recent regulatory approval of two new anti-TB drugs. However, both drugs have been reserved for drug-resistant disease, and the seeming inevitability of new resistance plus the recognized need to shorten the duration of chemotherapy demands continual replenishment of the pipeline with high-quality "hits" with novel mechanisms of action. This represents a massive challenge, which has been undermined by key gaps in our understanding of Mtb physiology and metabolism, especially during host infection. Whereas drug discovery for other bacterial infections can rely on predictive in vitro assays and animal models, for Mtb, inherent metabolic flexibility and uncertainties about the nutrients available to infecting bacilli in different host (micro)environments instead requires educated predictions or demonstrations of efficacy in animal models of arguable relevance to human disease. Even microbiological methods for enumeration of viable mycobacterial cells are fraught with complication. Our research has focused on elucidating those aspects of mycobacterial metabolism that contribute to the robustness of the bacillus to host immunological defenses and applied antibiotics and that, possibly, drive the emergence of drug resistance. This work has identified a handful of metabolic pathways that appear vulnerable to antibiotic targeting. Those highlighted, here, include the inter-related functions of pantothenate and coenzyme A biosynthesis and recycling and nucleotide metabolism-the last of which reinforces our view that DNA metabolism constitutes an under-explored area for new TB drug development. Although nonessential functions have traditionally been deprioritized for antibiotic development, a common theme emerging from this work is that these very functions might represent attractive targets because of the potential to cripple mechanisms critical to bacillary survival under stress (for example, the RelMtb-dependent stringent response) or to adaptability under unfavorable, potentially lethal, conditions including antibiotic therapy (for example, DnaE2-dependent SOS mutagenesis). The bar, however, is high: demonstrating convincingly the likely efficacy of this strategy will require innovative models of human TB disease. In the concluding section, we focus on the need for improved techniques to elucidate mycobacterial metabolism during infection and its impact on disease outcomes. Here, we argue that developments in other fields suggest the potential to break through this barrier by harnessing chemical-biology approaches in tandem with the most advanced technologies. As researchers based in a high-burden country, we are impelled to continue participating in this important endeavor.

Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders

Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.

Individual metabolomic signatures of circadian misalignment during simulated night shifts in humans

Misalignment of the daily sleep-wake and fasting-feeding cycles with the endogenous circadian timing system is an inevitable consequence of night shift work and is associated with adverse metabolic health effects. However, a detailed characterisation of the effects of night shifts on 24-h rhythms in the metabolome is missing. We performed targeted metabolomic profiling on plasma samples collected every 2 h from healthy human subjects during two 24-h measurement periods at baseline and on the fourth day of a simulated night shift protocol, in which the habitual sleep-wake cycle was delayed by 10 h. Thirty-two out of the 130 detected metabolites showed a 24-h rhythm both at baseline and during the night shift condition. Among these, 75% were driven by sleep-wake and fasting-feeding cycles rather than by the endogenous circadian clock, showing an average phase delay of 8.8 h during the night shift condition. Hence, the majority of rhythmic metabolites were misaligned relative to the endogenous circadian system during the night shift condition. This could be a key mechanism involved in the increased prevalence of adverse metabolic health effects observed in shift workers. On the individual level, the response to the night shift protocol was highly diverse, with phase shifts of rhythmic metabolite profiles ranging from a 0.2-h advance in one subject to a 12-h delay in another subject, revealing an individual metabolomic signature of circadian misalignment. Our findings provide insight into the overall and individual responses of the metabolome to circadian misalignment associated with night schedules and may thereby contribute to the development of individually tailored strategies to minimise the metabolic impacts of shift work.

A study of simulated night shifts in humans shows that 24-hour rhythms in plasma metabolites are primarily influenced by behavioural cycles rather than the endogenous clock; in addition, a large degree of variability between individuals suggests the existence of individual metabolomic signatures of circadian misalignment.

Metabolomics in the study of retinal health and disease

Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.

Quenching for Microalgal Metabolomics: A Case Study on the Unicellular Eukaryotic Green Alga Chlamydomonas reinhardtii

Capturing a valid snapshot of the metabolome requires rapid quenching of enzyme activities. This is a crucial step in order to halt the constant flux of metabolism and high turnover rate of metabolites. Quenching with cold aqueous methanol is treated as a gold standard so far, however, reliability of metabolomics data obtained is in question due to potential problems connected to leakage of intracellular metabolites. Therefore, we investigated the influence of various parameters such as quenching solvents, methanol concentration, inclusion of buffer additives, quenching time and solvent to sample ratio on intracellular metabolite leakage from Chlamydomonas reinhardtii. We measured the recovery of twelve metabolite classes using gas chromatography mass spectrometry (GC-MS) in all possible fractions and established mass balance to trace the fate of metabolites during quenching treatments. Our data demonstrate significant loss of intracellular metabolites with the use of the conventional 60% methanol, and that an increase in methanol concentration or quenching time also resulted in higher leakage. Inclusion of various buffer additives showed 70 mM HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) to be suitable. In summary, we recommend quenching with 60% aqueous methanol supplemented with 70 mM HEPES (−40 °C) at 1:1 sample to quenching solvent ratio, as it resulted in higher recoveries for intracellular metabolites with subsequent reduction in the metabolite leakage for all metabolite classes.

Functional metabolomics approach reveals the reduced biosynthesis of fatty acids and TCA cycle is required for pectinase activity in Bacillus licheniformis

Increase of pectinase activity is especially important in fermentation industry. Understanding of the metabolic mechanisms can find metabolic modulation approach to promote high yield of pectinase. Higher activity of pectinase was detected in DY1 than DY2, two strains of Bacillus licheniformis. GC-MS-based metabolomics identified differential metabolome of DY2 compared with DY1, characterizing the increased TCA cycle and biosynthesis of fatty acids. Elevated activity of pyruvate dehydrogenase (PDH), α-ketoglutaric dehydrogenase (KGDH) and succinate dehydrogenase (SDH) showed global elevation of carbon metabolism, which is consistent with the result that lowers glucose in DY2 than DY1. Inhibitors malonate, furfural and triclosan, of PDH, SDH and biosynthesis of fatty acids, promoted pectinase activity, where triclosan increased pectinase activity by 179%. These results indicate that functional metabolomics is an effective approach to understand metabolic mechanisms of fermentation production and provides clues to develop new methods for changing bacterial physiology and production.