Mass spectrometry in metabolome analysis

Novel sampling and gas-phase derivatization strategy: Proof-of-concept by profiling ionic polar metabolites using gas chromatography-mass spectrometry

Metabolomic research involves the comprehensive analysis of metabolites in biological samples and has many applications. Gas chromatography-mass spectrometry (GC-MS) is an established and widely used approach for metabolic profiling. However, sample preparation and metabolite derivatization are time-consuming, and derivatization options are limited. We propose gas-solid phase derivatization (GSPD) as a novel sampling and derivatization method that uses a silica monolith substrate and gaseous derivatization reagents for metabolomics using GC-MS. We developed a method to measure the organic acids and sugar phosphates responsible for glycolysis and the tricarboxylic acid (TCA) cycle. GSPD simplifies the sample preparation and can be applied to derivatization reactions that are difficult to perform in solution owing to solvent limitations. The developed method was applied to human plasma and tomato pulp and was shown to have a higher detection performance than the conventional method. This study provides a strategy to simplify sample preparation and expand derivatization options for GC-MS-based metabolomics.

Saliva metabolomics: concepts and applications in oral disorders

OBJECTIVES

The purpose of this review was to present the basic concepts of metabolomics methodology and the use of saliva for diagnostic, prognostic, and predictive strategies.

MATERIAL AND METHODS

This review followed the focus in: "saliva metabolomics" and "oral diseases". The authors searched studies on PubMed database. The inclusion criteria were original studies and reviews that assessed metabolomics techniques. A descriptive analysis was performed considering the study design, approach system, clinical steps, and tools for the determination of profile or biomarkers metabolites, and the advantages and disadvantages.

RESULTS

Metabolomic analyses use a combination of analytical instrumentation and informatic tools to provide information on metabolite characteristics. In this review we described different technologies applied and the advantages and limitations of each technique. Furthermore, in the literature search, we retrieved 25 studies that investigated saliva metabolites in oral diseases: 8 studies used targeted analysis and 17 untargeted metabolomics approaches. Most studies included patients with periodontal diseases, oral squamous cell carcinoma, and Sjögren Syndrome. The most frequently reported metabolites were glycine, leucine, phenylalanine, dipeptides, linoleic acid, arachidonic acid, tyrosine, choline, taurine, lactate, valine, and proline.

CONCLUSIONS

Metabolomics analysis has emerged as a powerful tool for tumor diagnosis and to enhance tumor classification, including salivary gland tumors (SGTs). It also holds promise for developing personalized treatment plans and defining more precise prognostic categories.

CLINICAL RELEVANCE

Metabolomics is the most functional and comprehensive technique for monitoring and understanding gene functions and identifying the biochemical state of an organism in response to genetic and environmental changes.

Protonation State-Induced Unfolding of Protein Secondary Structure in the Gas Phase

The combination of infrared spectroscopy (IR) and ion mobility mass spectrometry (IM-MS) has revealed that protein secondary structures are retained upon transformation from aqueous solution to the gas phase under gentle conditions. Yet the details about where and how these structural elements are embedded in the gas phase remain elusive. In this study, we employ long time scale molecular dynamics (MD) simulations to examine the extent to which proteins retain their solution structures and the impact of protonation state on the stability of secondary structures in the gas phase. Our investigation focuses on two well-studied proteins, myoglobin and β-lactoglobulin, representing typical helical and β-sheet proteins, respectively. Our simulations accurately reproduce the experimental collision cross section (CCS) data measured by IM-MS. Based on accurately reproducing previous experimental collision cross section data and dominant secondary structural species obtained from IM-MS and IR, we confirm that both proteins largely retain their native secondary structural components upon passing from aqueous solution to the gas phase. However, we observe significant reductions in secondary structure contents (19.2 ± 1.2% for myoglobin and 7.3 ± 0.6% for β-lactoglobulin) in specific regions predominantly composed of ionizable residues. Further mechanistic analysis suggests that alterations in protonation states of these residues after phase transition induce changes in their local interaction networks and backbone dihedral angles, which potentially promote the unfolding of secondary structures in the gas phase. We anticipate that similar protonation state induced unfolding may be observed in other proteins possessing distinct secondary structures. Further studies on a broader array of proteins will be essential to refine our understanding of protein structural behavior during the transition to the gas phase.

Bacterial metabolomics: current applications for human welfare and future aspects

An imbalanced microbiome is linked to several diseases, such as cancer, inflammatory bowel disease, obesity, and even neurological disorders. Bacteria and their by-products are used for various industrial and clinical purposes. The metabolites under discussion were chosen based on their biological impacts on host and gut microbiota interactions as established by metabolome research. The separation of bacterial metabolites by using statistics and machine learning analysis creates new opportunities for applications of bacteria and their metabolites in the environmental and medical sciences. Thus, the metabolite production strategies, methodologies, and importance of bacterial metabolites for human well-being are discussed in this review.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

The Influence of Lactobacillus spp. Secondary Metabolites Isolated from Immature Egyptian Honey on Human Pathogens, Transcription of Virulence Genes and Lung Cancer

This work aimed to isolate, and identify Lactic Acid Bacteria LAB from Egyptian immature citrus honey, and characterize their secondary metabolites, as well as determine the antibacterial activities and transcription of virulence genes (stx1, stx2, and eae) influenced by these bacterial secondary metabolites. From twenty hives, twenty immature citrus bee honey samples were taken. Traditional cultural and biochemical testing were used, followed by molecular confirmation. Further, LAB isolates' antibacterial and cytotoxic properties were investigated. 16S rRNA gene sequencing were assessed and, two lactic acid bacterial isolates were identified as Lactobacillus acidophilus Ch2 and Levilactobacillus brevis Ch1. Both isolates have good antagonistic action against clinical pathogens, with Levilactobacillus brevis Ch1 exhibiting the best antibacterial activity against all indicator pathogens examined. When compared to untreated cancer cells, the isolates demonstrated significant cytotoxic activity. Ch1 and Ch2 cell viability percentages were 39.5% and 18.76%, respectively. Furthermore, when exposed to Levilactobacillus brevis Ch1 metabolites, Shiga-producing Escherichia coli (STEC) virulence gene expression was suppressed. To identify bacterial secondary metabolites, a high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC-QTOF) approach was developed. Twenty-seven metabolites from diverse chemical classes were discovered in the crude extracts with antibacterial and anticancer characteristics. This is the first thorough investigation on the metabolic profile of LAB isolated from immature Egyptian honey and the findings suggested that isolates or their secondary metabolites could be used in the food sector as medicinal alternatives or as a biocontrol agent.

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

imputomics: web server and R package for missing values imputation in metabolomics data

MOTIVATION

Missing values are commonly observed in metabolomics data from mass spectrometry. Imputing them is crucial because it assures data completeness, increases the statistical power of analyses, prevents inaccurate results, and improves the quality of exploratory analysis, statistical modeling, and machine learning. Numerous Missing Value Imputation Algorithms (MVIAs) employ heuristics or statistical models to replace missing information with estimates. In the context of metabolomics data, we identified 52 MVIAs implemented across 70 R functions. Nevertheless, the usage of those 52 established methods poses challenges due to package dependency issues, lack of documentation, and their instability.

RESULTS

Our R package, 'imputomics', provides a convenient wrapper around 41 (plus random imputation as a baseline model) out of 52 MVIAs in the form of a command-line tool and a web application. In addition, we propose a novel functionality for selecting MVIAs recommended for metabolomics data with the best performance or execution time.

AVAILABILITY AND IMPLEMENTATION

'imputomics' is freely available as an R package (github.com/BioGenies/imputomics) and a Shiny web application (biogenies.info/imputomics-ws). The documentation is available at biogenies.info/imputomics.

The Exometabolome of Xylella fastidiosa in Contact with Paraburkholderia phytofirmans Supernatant Reveals Changes in Nicotinamide, Amino Acids, Biotin, and Plant Hormones

Microbial competition within plant tissues affects invading pathogens' fitness. Metabolomics is a great tool for studying their biochemical interactions by identifying accumulated metabolites. Xylella fastidiosa, a Gram-negative bacterium causing Pierce's disease (PD) in grapevines, secretes various virulence factors including cell wall-degrading enzymes, adhesion proteins, and quorum-sensing molecules. These factors, along with outer membrane vesicles, contribute to its pathogenicity. Previous studies demonstrated that co-inoculating X. fastidiosa with the Paraburkholderia phytofirmans strain PsJN suppressed PD symptoms. Here, we further investigated the interaction between the phytopathogen and the endophyte by analyzing the exometabolome of wild-type X. fastidiosa and a diffusible signaling factor (DSF) mutant lacking quorum sensing, cultivated with 20% P. phytofirmans spent media. Liquid chromatography-mass spectrometry (LC-MS) and the Method for Metabolite Annotation and Gene Integration (MAGI) were used to detect and map metabolites to genomes, revealing a total of 121 metabolites, of which 25 were further investigated. These metabolites potentially relate to host adaptation, virulence, and pathogenicity. Notably, this study presents the first comprehensive profile of X. fastidiosa in the presence of a P. phytofirmans spent media. The results highlight that P. phytofirmans and the absence of functional quorum sensing affect the ratios of glutamine to glutamate (Gln:Glu) in X. fastidiosa. Additionally, two compounds with plant metabolism and growth properties, 2-aminoisobutyric acid and gibberellic acid, were downregulated when X. fastidiosa interacted with P. phytofirmans. These findings suggest that P. phytofirmans-mediated disease suppression involves modulation of the exometabolome of X. fastidiosa, impacting plant immunity.

Development of a rapid and highly accurate method for 13C tracer-based metabolomics and its application on a hydrogenotrophic methanogen

Microfluidic capillary electrophoresis-mass spectrometry (CE-MS) is a rapid and highly accurate method to determine isotopomer patterns in isotopically labeled compounds. Here, we developed a novel method for tracer-based metabolomics using CE-MS for underivatized proteinogenic amino acids. The method consisting of a ZipChip CE system and a high-resolution Orbitrap Fusion Tribrid mass spectrometer allows us to obtain highly accurate data from 1 μl of 100 nmol/l amino acids comparable to a mere 1 [Formula: see text] 104-105 prokaryotic cells. To validate the capability of the CE-MS method, we analyzed 16 protein-derived amino acids from a methanogenic archaeon Methanothermobacter thermautotrophicus as a model organism, and the mass spectra showed sharp peaks with low mass errors and background noise. Tracer-based metabolome analysis was then performed to identify the central carbon metabolism in M. thermautotrophicus using 13C-labeled substrates. The mass isotopomer distributions of serine, aspartate, and glutamate revealed the occurrence of both the Wood-Ljungdahl pathway and an incomplete reductive tricarboxylic acid cycle for carbon fixation. In addition, biosynthesis pathways of 15 amino acids were constructed based on the mass isotopomer distributions of the detected protein-derived amino acids, genomic information, and public databases. Among them, the presence of alternative enzymes of alanine dehydrogenase, ornithine cyclodeaminase, and homoserine kinase was suggested in the biosynthesis pathways of alanine, proline, and threonine, respectively. To our knowledge, the novel 13C tracer-based metabolomics using CE-MS can be considered the most efficient method to identify central carbon metabolism and amino acid biosynthesis pathways and is applicable to any kind of isolated microbe.

Sample Preparation from Plant Tissue for Gas Chromatography-Mass Spectrometry (GC-MS)we

Metabolites are intermediate products formed during metabolism. Metabolites play different roles, including providing energy, supporting structure, transmitting signals, catalyzing reactions, enhancing defense, and interacting with other species. Plant metabolomics research aims to detect precisely all metabolites found within tissues of plants through GC-MS. This chapter primarily focuses on extracting metabolites using chemicals such as methanol, chloroform, ribitol, MSTFA, and TMCS. The metabolic analysis method is frequently used according to the specific kind of sample or matrix being investigated and the analysis objective. Chromatography (LC, GC, and CE) with mass spectrometry and NMR spectroscopy is used in modern metabolomics to analyze metabolites from plant samples. The most frequently used method for metabolites analysis is the GC-MS. It is a powerful technique that combines gas chromatography's separation capabilities with mass spectrometry, offering detailed information, including structural identification of each metabolite. This chapter contains an easy-to-follow guide to extract plant-based metabolites. The current protocol provides all the information needed for extracting metabolites from a plant, precautions, and troubleshooting.

Application of omics in Sjögren's syndrome

OBJECTIVE

The pathogenesis, diagnosis, and treatment of Sjögren's syndrome (SS) face many challenges, and there is an urgent need to develop new technologies to improve our understanding of SS.

METHODS

By searching the literature published domestically and internationally in the past 20 years, this artical reviewed the research of various omics techniques in SS.

RESULTS

Omics technology provided valuable insights into the pathogenesis, early diagnosis, condition and efficacy evaluation of SS. It is helpful to reveal the pathogenesis of the disease and explore new treatment schemes, which will open a new era for the study of SS.

CONCLUSION

At present, omics research has made some gratifying achievements, but there are still many uncertainties. Therefore, in the future, we should improve research techniques, standardize the collection of samples, and adopt a combination of multi-omics techniques to jointly study the pathogenesis of SS and provide new schemes for its treatment.

Emerging trends and applications of metabolomics in food science and nutrition

The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.

CloMet: A Novel Open-Source and Modular Software Platform That Connects Established Metabolomics Repositories and Data Analysis Resources

The field of metabolomics has witnessed the development of hundreds of computational tools, but only a few have become cornerstones of this field. While MetaboLights and Metabolomics Workbench are two well-established data repositories for metabolomics data sets, Workflows4Metabolomics and MetaboAnalyst are two well-established web-based data analysis platforms for metabolomics. Yet, the raw data stored in the aforementioned repositories lack standardization in terms of the file system format used to store the associated acquisition files. Consequently, it is not straightforward to reuse available data sets as input data in the above-mentioned data analysis resources, especially for non-expert users. This paper presents CloMet, a novel open-source modular software platform that contributes to standardization, reusability, and reproducibility in the metabolomics field. CloMet, which is available through a Docker file, converts raw and NMR-based metabolomics data from MetaboLights and Metabolomics Workbench to a file format that can be used directly either in MetaboAnalyst or in Workflows4Metabolomics. We validated both CloMet and the output data using data sets from these repositories. Overall, CloMet fills the gap between well-established data repositories and web-based statistical platforms and contributes to the consolidation of a data-driven perspective of the metabolomics field by leveraging and connecting existing data and resources.

MetaboDirect: an analytical pipeline for the processing of FT-ICR MS-based metabolomic data

BACKGROUND

Microbiomes are now recognized as the main drivers of ecosystem function ranging from the oceans and soils to humans and bioreactors. However, a grand challenge in microbiome science is to characterize and quantify the chemical currencies of organic matter (i.e., metabolites) that microbes respond to and alter. Critical to this has been the development of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), which has drastically increased molecular characterization of complex organic matter samples, but challenges users with hundreds of millions of data points where readily available, user-friendly, and customizable software tools are lacking.

RESULTS

Here, we build on years of analytical experience with diverse sample types to develop MetaboDirect, an open-source, command-line-based pipeline for the analysis (e.g., chemodiversity analysis, multivariate statistics), visualization (e.g., Van Krevelen diagrams, elemental and molecular class composition plots), and presentation of direct injection high-resolution FT-ICR MS data sets after molecular formula assignment has been performed. When compared to other available FT-ICR MS software, MetaboDirect is superior in that it requires a single line of code to launch a fully automated framework for the generation and visualization of a wide range of plots, with minimal coding experience required. Among the tools evaluated, MetaboDirect is also uniquely able to automatically generate biochemical transformation networks (ab initio) based on mass differences (mass difference network-based approach) that provide an experimental assessment of metabolite connections within a given sample or a complex metabolic system, thereby providing important information about the nature of the samples and the set of microbial reactions or pathways that gave rise to them. Finally, for more experienced users, MetaboDirect allows users to customize plots, outputs, and analyses.

CONCLUSION

Application of MetaboDirect to FT-ICR MS-based metabolomic data sets from a marine phage-bacterial infection experiment and a Sphagnum leachate microbiome incubation experiment showcase the exploration capabilities of the pipeline that will enable the research community to evaluate and interpret their data in greater depth and in less time. It will further advance our knowledge of how microbial communities influence and are influenced by the chemical makeup of the surrounding system. The source code and User's guide of MetaboDirect are freely available through ( https://github.com/Coayala/MetaboDirect ) and ( https://metabodirect.readthedocs.io/en/latest/ ), respectively. Video Abstract.

Data Processing and Analysis in Mass Spectrometry-Based Metabolomics

Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.

How to employ metabolomic analysis to research on functions of prebiotics and probiotics in poultry gut health?

Gut health can be considered one of the major, manageable constituents of the animal immunity and performance. The fast spread of intestinal diseases, and increase of antimicrobial resistance have been observed, therefore the intestinal health has become not only economically relevant, but also highly important subject addressing the interest of public health. It is expected, that the strategies to control infections should be based on development of natural immunity in animals and producing resilient flocks using natural solutions, whilst eliminating antibiotics and veterinary medicinal products from action. Probiotics and prebiotics have been favored, because they have potential to directly or indirectly optimize intestinal health by manipulating the metabolism of the intestinal tract, including the microbiota. Studying the metabolome of probiotics and gut environment, both in vivo, or using the in vitro models, is required to attain the scientific understanding about the functions of bioactive compounds in development of gut health and life lasting immunity. There is a practical need to identify new metabolites being the key bioactive agents regulating biochemical pathways of systems associated with gut (gut-associated axes). Technological advancement in metabolomics studies, and increasing access to the powerful analytical platforms have paved a way to implement metabolomics in exploration of the effects of prebiotics and probiotics on the intestinal health of poultry. In this article, the basic principles of metabolomics in research involving probiotics and probiotics are introduced, together with the overview of existing strategies and suggestions of their use to study metabolome in poultry.

Establishment and application of quantitative method for 22 organic acids in honey based on SPE-GC–MS

Honey, a natural healthy liquid bee product, is rich in amino acids, vitamins, and other essential nutrients. Different origin honeys also varied in organic acids. The objective of this study is to establish an efficient solid-phase extraction-gas chromatography–mass spectrometry (SPE-GC–MS) method to eliminate interference of sugar and other impurities for accurately estimating 22 organic acids in honey by optimizing extraction, purification, derivatization, and gas chromatography–mass spectrometry (GC–MS) analytical conditions. After being extracted, purified and derivatized, organic acids in honey were qualified and quantified by GC–MS. This method was evaluated experimentally, and the results showed that, within a certain concentration range, the standard curve linear relationship was satisfactory (R2 > 0.9942), and the target organic acid recovery rate was 86.74% ~ 118.68%. Besides, precision (relative standard deviation, RSD = 2.98% ~ 13.42%), detection limit (LOD = 0.002 ~ 0.2 mg kg−1), and quantification limit (LOQ = 0.008 ~ 0.5 mg kg−1) met the target requirements. Also, based on this analytical method, the organic acids in five types of honey (acacia, jujube, vitex, canola and linden honey) were estimated. Notably, they all contained 22 different kinds of organic acids, and significant differences (p < 0.05) in the organic acid content and composition among different honey varieties were observed. PCA analysis showed that the five honeys could be differentiated based on the content of 22 organic acids.

Metabolomics and modelling approaches for systems metabolic engineering

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

Differentiation of meat species of raw and processed meat based on polar metabolites using 1H NMR spectroscopy combined with multivariate data analysis

Meat species of raw meat and processed meat products were investigated by 1H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on aqueous extraction combined with ultrafiltration in order to reduce macromolecular components in the extracts. 1H NMR data was analyzed by using a non-targeted approach followed by principal component analysis (PCA), linear discrimination analysis (LDA), and cross-validation (CV) embedded in a Monte Carlo (MC) resampling approach. A total of 379 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, poultry) were collected between the years 2018 and 2021. A 99% correct prediction rate was achieved if the raw meat samples were classified according to meat species. Predicting processed meat products was slightly less successful (93 %) with this approach. Furthermore, identification of spectral regions that are relevant for the classification via polar chemical markers was performed. Finally, data on polar metabolites were fused with previously published 1H NMR data on non-polar metabolites in order to build a broader classification model and to improve prediction accuracy.

Antiviral COVID-19 protein and molecular docking: In silico characterization of various antiviral compounds extracted from Arisaema jacquemontii Blume

Arisaema jacquemontii Blume is a highly medicinal and poisonous plant belong to the family Araceae. It is used to treat several deadly diseases, including viral infections. It has antioxidant, anti-cancerous, antimalarial, anti-vermicidal, and antiviral activities. Therefore, five parts of the Arisaema jacquemontii Blume plant, such as leaf, seed, stem, pulp, and rhizome extract, were evaluated for metabolic and in silico characterization of probable compounds using gas chromatography-mass spectrometry (GC-MS) analysis. A total of 22 compounds were isolated from the methanolic extracts of A. jacquemontii Blume. A selected antiviral COVID-19 protein i.e., protease (6LU7) was docked against the obtained compounds. Different affinities were obtained through various compounds. The best results were shown by three different compounds identified in the rhizome. The maximum binding affinity of these compounds is 8.1 kJ/mol. Molecular docking (MD) indicate that these molecules have the highest binding energies and hydrogen bonding interactions. The binding mode of interaction was discovered to be reasonably effective for counteracting the SARS virus COVID-19. The findings of this study could be extremely useful in the development of more phytochemical-based COVID-19 therapeutics.

Bioactive Compounds from Marine Sponges and Algae: Effects on Cancer Cell Metabolome and Chemical Structures

Metabolomics represent the set of small organic molecules generally called metabolites, which are located within cells, tissues or organisms. This new “omic” technology, together with other similar technologies (genomics, transcriptomics and proteomics) is becoming a widely used tool in cancer research, aiming at the understanding of global biology systems in their physiologic or altered conditions. Cancer is among the most alarming human diseases and it causes a considerable number of deaths each year. Cancer research is one of the most important fields in life sciences. In fact, several scientific advances have been made in recent years, aiming to illuminate the metabolism of cancer cells, which is different from that of healthy cells, as suggested by Otto Warburg in the 1950s. Studies on sponges and algae revealed that these organisms are the main sources of the marine bioactive compounds involved in drug discovery for cancer treatment and prevention. In this review, we analyzed these two promising groups of marine organisms to focus on new metabolomics approaches for the study of metabolic changes in cancer cell lines treated with chemical extracts from sponges and algae, and for the classification of the chemical structures of bioactive compounds that may potentially prove useful for specific biotechnological applications.

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.

Anti-inflammatory and Immunostimulant Therapy with Lactobacillus fermentum and Lactobacillus plantarum in COVID-19: A Literature Review

Inflammatory diseases are diseases characterized by inflammatory symptoms. Acute inflammatory disease can cause dysregulation of the inflammatory immune response, thereby inhibiting the development of protective immunity against infection. Among the acute inflammatory disease is COVID-19. The initial viral infection causes the antigen-presenting cells to detect the virus through a phagocytosis mechanism in the form of macrophage and dendritic cells. Lactobacillus fermentum and L. plantarum are gram-positive bacteria potentially serving as immunomodulators caused by inflammation and immune system response. Short-chain fatty acids (SCFA) produced by Lactobacillus can induce immune response through tolerogenic dendritic cells. This probiotic bacterium can induce the production of different cytokines or chemokines. Following the results of in vitro and in vivo tests, L. fermentum and L. plantarum can induce IL-10 release to activate regulatory T-cell and inhibit tumor necrosis factor-α (TNF-α) binding activity of nuclear factor kappa B (NF-κB). Literature review showed that dysregulation of inflammatory immune response disorders due to inflammatory disease could be treated using probiotic bacteria L. fermentum and L. plantarum. Therefore, it is necessary to conduct further studies on the potential of indigenous Indonesian strains of these two bacteria as anti-inflammatory and immunostimulants.

Central carbon metabolism remodeling as a mechanism to develop drug tolerance and drug resistance in Mycobacterium tuberculosis

Suboptimal efficacy of the current antibiotic regimens and frequent emergence of antibiotic-resistant Mycobacterium tuberculosis (Mtb), an etiological agent of tuberculosis (TB), render TB the world’s deadliest infectious disease before the COVID-19 outbreak. Our outdated TB treatment method is designed to eradicate actively replicating populations of Mtb. Unfortunately, accumulating evidence suggests that a small population of Mtb can survive antimycobacterial pressure of antibiotics by entering a “persister” state (slowly replicating or non-replicating and lacking a stably heritable antibiotic resistance, termed drug tolerance). The formation of drug-tolerant Mtb persisters is associated with TB treatment failure and is thought to be an adaptive strategy for eventual development of permanent genetic mutation-mediated drug resistance. Thus, the molecular mechanisms behind persister formation and drug tolerance acquisition are a source of new antibiotic targets to eradicate both Mtb persisters and drug-resistant Mtb. As Mtb persisters are genetically identical to antibiotic susceptible populations, metabolomics has emerged as a vital biochemical tool to differentiate these populations by determining phenotypic shifts and metabolic reprogramming. Metabolomics, which provides detailed insights into the molecular basis of drug tolerance and resistance in Mtb, has unique advantages over other techniques by its ability to identify specific metabolic differences between the two genetically identical populations. This review summarizes the recent advances in our understanding of the metabolic adaptations used by Mtb persisters to achieve intrinsic drug tolerance and facilitate the emergence of drug resistance. These findings present metabolomics as a powerful tool to identify previously unexplored antibiotic targets and improved combinations of drug regimens against drug-resistant TB infection.

Autophagic event and metabolomic disorders unveil cellular toxicity of environmental microplastics on marine polychaete Hediste diversicolor

Although the hazards of microplastics (MPs) have been quite well explored, the aberrant metabolism and the involvement of the autophagy pathway as an adverse response to environmental MPs in benthic organisms are still unclear. The present work aims to assess the impact of different environmental MPs collected from the south coast of the Mediterranean Sea, composed by polyethylene (PE), polyethylene vinyl acetate (PEVA), low-density polyethylene (LDPE), high-density polyethylene (HDPE), polypropylene (PP) and polyamide (PA) on the metabolome and proteome of the marine polychaete Hediste diversicolor. As a result, all the microplastic types were detected with Raman microspectroscopy in polychaetes tissues, causing cytoskeleton damage and induced autophagy pathway manifested by immunohistochemical labeling of specific targeted proteins, through Tubulin (Tub), Microtubule-associated protein light chain 3 (LC3), and p62 (also named Sequestosome 1). Metabolomics was conducted to further investigate the metabolic alterations induced by the environmental MPs-mixture in polychaetes. A total of 28 metabolites were differentially expressed between control and MPs-treated polychaetes, which showed elevated levels of amino acids, glucose, ATP/ADP, osmolytes, glutathione, choline and phosphocholine, and reduced concentration of aspartate. These novel findings extend our understanding given the toxicity of environmental microplastics and unravel their underlying mechanisms.

Follicular Fluid: A Powerful Tool for the Understanding and Diagnosis of Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) represents one of the leading causes of anovulatory infertility and affects 5% to 20% of women worldwide. Until today, both the subsequent etiology and pathophysiology of PCOS remain unclear, and patients with PCOS that undergo assisted reproductive techniques (ART) might present a poor to exaggerated response, low oocyte quality, ovarian hyperstimulation syndrome, as well as changes in the follicular fluid metabolites pattern. These abnormalities originate a decrease of Metaphase II (MII) oocytes and decreased rates for fertilization, cleavage, implantation, blastocyst conversion, poor egg to follicle ratio, and increased miscarriages. Focus on obtaining high-quality embryos has been taken into more consideration over the years. Nowadays, the use of metabolomic analysis in the quantification of proteins and peptides in biological matrices might predict, with more accuracy, the success in assisted reproductive technology. In this article, we review the use of human follicular fluid as the matrix in metabolomic analysis for diagnostic and ART predictor of success for PCOS patients.

Bacterial Metabolomics: Sample Preparation Methods

Metabolomics is a comprehensive analysis of metabolites existing in biological systems. As one of the important "omics" tools, the approach has been widely employed in various fields in helping to better understand the complex cellular metabolic states and changes. Bacterial metabolomics has gained a significant interest as bacteria serve to provide a better subject or model at systems level. The approach in metabolomics is categorized into untargeted and targeted which serves different paradigms of interest. Nevertheless, the bottleneck in metabolomics has been the sample or metabolite preparation method. A custom-made method and design for a particular species or strain of bacteria might be necessary as most studies generally refer to other bacteria or even yeast and fungi that may lead to unreliable analysis. The paramount aspect of metabolomics design comprises sample harvesting, quenching, and metabolite extraction procedures. Depending on the type of samples and research objective, each step must be at optimal conditions which are significantly important in determining the final output. To date, there are no standardized nor single designated protocols that have been established for a specific bacteria strain for untargeted and targeted approaches. In this paper, the existing and current developments of sample preparation methods of bacterial metabolomics used in both approaches are reviewed. The review also highlights previous literature of optimized conditions used to propose the most ideal methods for metabolite preparation, particularly for bacterial cells. Advantages and limitations of methods are discussed for future improvement of bacterial metabolomics.

Non-invasive oocyte quality assessment

Oocyte quality is perhaps the most important limiting factor in female fertility; however, the current methods of determining oocyte competence are only marginally capable of predicting a successful pregnancy. We aim to review the predictive value of non-invasive techniques for the assessment of human oocytes and their related cells and biofluids that pertain to their developmental competence. Investigation of the proteome, transcriptome, and hormonal makeup of follicular fluid, as well as cumulus-oocyte complexes are currently underway; however, prospective randomized non-selection-controlled trials of the future are needed before determining their prognostic value. The biological significance of polar body morphology and genetics are still unknown and the subject of debate. The predictive utility of zygotic viscoelasticity for embryo development has been demonstrated, but similar studies performed on oocytes have yet to be conducted. Metabolic profiling of culture media using human oocytes are also limited and may require integration of automated, high-throughput targeted metabolomic assessments in real time with microfluidic platforms. Light exposure to oocytes can be detrimental to subsequent development and utilization of time-lapse imaging and morphometrics of oocytes is wanting. Polarized light, Raman microspectroscopy, and coherent anti-Stokes Raman scattering are a few novel imaging tools that may play a more important role in future oocyte assessment. Ultimately, the integration of chemistry, genomics, microfluidics, microscopy, physics, and other biomedical engineering technologies into the basic studies of oocyte biology, and in testing and perfecting practical solutions of oocyte evaluation, are the future for non-invasive assessment of oocytes.

Saliva Metabolomics in Dry Mouth Patients with Head and Neck Cancer or Sjögren’s Syndrome

The etiology of dry mouth conditions is multi-faceted. Patients radiated after head and neck cancer (HNC) and those with primary Sjögren’s syndrome (pSS) share many of the same symptoms despite different causes. With the aim of better understanding the pathophysiology and biochemical processes behind dry mouth with different etiologies, we investigated the metabolic profile of 10 HNC patients, 9 pSS patients and 10 healthy controls using high-performance liquid chromatography-high resolution mass spectrometry (HPLC-MS) metabolomics. Principal component analysis (PCA) revealed different metabolic profiles when comparing all subjects included in the study. Both patient groups showed higher ratios of several pyrimidine nucleotides and nucleosides when compared to controls. This finding may indicate that purinergic signaling plays a role in dry mouth conditions. Moreover, significantly increased levels of DL-3-aminoisobutyric acid were found in HNC patients when compared to controls, and a similar tendency was observed in the pSS patients. Furthermore, a dysregulation in amino acid metabolism was observed in both patient groups. In conclusion, metabolomics analysis showed separate metabolic profiles for HNC and pSS patients as compared to controls that could be useful in diagnostics and for elucidating the different pathophysiologies. The demonstrated dysregulation of pyrimidine nucleotides and levels of metabolites derived from amino acids in the patient groups should be studied further.

Phytoconstituents from Moringa oleifera fruits target ACE2 and open spike glycoprotein to combat SARS-CoV-2: An integrative phytochemical and computational approach

Therapeutic drugs based on natural products for the treatment of SARS-CoV-2 are currently unavailable. This study was conducted to develop an anti-SARS-CoV-2 herbal medicine to face the urgent need for COVID-19 treatment. The bioactive components from ethanolic extract of Moringa oleifera fruits (MOFs) were determined by gas chromatography-mass spectroscopy (GC-MS). Molecular-docking analyses elucidated the binding effects of identified phytocomponents against SARS-CoV-2 spike glycoprotein (PDB ID: 6VYB) and human ACE2 receptor (PDB ID: 1R42) through the Glide module of Maestro software. GC-MS analysis unveiled the presence of 33 phytocomponents. Eighteen phytocomponents exhibited good binding affinity toward ACE2 receptor, and thirteen phytocomponents had a high affinity with spike glycoprotein. This finding suggests that the top 11 hits (Docking score ≥ -3.0 kcal/mol) could inhibit SARS-CoV-2 propagation. Intriguingly, most of the phytoconstituents displayed drug-likeness with no predicted toxicity. However, further studies are needed to validate their effects and mechanisms of action. PRACTICAL APPLICATIONS: Moringa oleifera (MO) also called "drumstick tree" has been used as an alternative food source to combat malnutrition and may act as an immune booster. GC-MS analysis unveiled that ethanolic extract of Moringa oleifera fruits (MOFs) possessed 33 active components of pyridine, aromatic fatty acid, oleic acid, tocopherol, methyl ester, diterpene alcohol, triterpene and fatty acid ester and their derivatives, which have various pharmacological and medicinal values. Virtual screening study of phytocomponents of MOF with human ACE2 receptor and SARS-CoV-2 spike glycoprotein exhibited good binding affinity. Based on molecular docking, the top 11 hits (Docking score ≥-3.0 kcal/mol) might serve as potential lead molecules in antiviral drug development. Intriguingly, most of the phytoconstituents displayed drug-likeness with no predicted toxicity. Thus, MOF might be used as a valuable source for antiviral drug development to combat COVID-19, an ongoing pandemic.

Metabolomics Tools Assisting Classic Screening Methods in Discovering New Antibiotics from Mangrove Actinomycetia in Leizhou Peninsula

Mangrove actinomycetia are considered one of the promising sources for discovering novel biologically active compounds. Traditional bioactivity- and/or taxonomy-based methods are inefficient and usually result in the re-discovery of known metabolites. Thus, improving selection efficiency among strain candidates is of interest especially in the early stage of the antibiotic discovery program. In this study, an integrated strategy of combining phylogenetic data and bioactivity tests with a metabolomics-based dereplication approach was applied to fast track the selection process. A total of 521 actinomycetial strains affiliated to 40 genera in 23 families were isolated from 13 different mangrove soil samples by the culture-dependent method. A total of 179 strains affiliated to 40 different genera with a unique colony morphology were selected to evaluate antibacterial activity against 12 indicator bacteria. Of the 179 tested isolates, 47 showed activities against at least one of the tested pathogens. Analysis of 23 out of 47 active isolates using UPLC-HRMS-PCA revealed six outliers. Further analysis using the OPLS-DA model identified five compounds from two outliers contributing to the bioactivity against drug-sensitive A. baumannii. Molecular networking was used to determine the relationship of significant metabolites in six outliers and to find their potentially new congeners. Finally, two Streptomyces strains (M22, H37) producing potentially new compounds were rapidly prioritized on the basis of their distinct chemistry profiles, dereplication results, and antibacterial activities, as well as taxonomical information. Two new trioxacarcins with keto-reduced trioxacarcinose B, gutingimycin B (16) and trioxacarcin G (20), together with known gutingimycin (12), were isolated from the scale-up fermentation broth of Streptomyces sp. M22. Our study demonstrated that metabolomics tools could greatly assist classic antibiotic discovery methods in strain prioritization to improve efficiency in discovering novel antibiotics from those highly productive and rich diversity ecosystems.

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.

Human impacts on deep-sea sponge grounds: Applying environmental omics to monitoring

Sponges (Phylum Porifera) are the oldest extant Metazoans. In the deep sea, sponges can occur at high densities forming habitats known as sponge grounds. Sponge grounds can extend over large areas of up to hundreds of km² and are biodiversity hotspots. However, as human activities, including deep-water hydrocarbon extraction, continue to expand into areas harbouring sponge grounds, understanding how anthropogenic impacts affect sponges and the ecosystem services they provide at multiple biological scales (community, individual and (sub)cellular levels) is key for achieving sustainable management. This chapter (1) provides an update to the chapter of Advances in Marine Biology Volume 79 entitled "Potential Impacts of Offshore Oil and Gas Activities on Deep-Sea Sponges and the Habitats They Form" and (2) discusses the use of omics as a future tool for deep-sea ecosystem monitoring. While metagenomics and (meta)transcriptomics studies have contributed to improve our understanding of sponge biology in recent years, metabolomics analysis has mostly been used to identify natural products. The sponge metabolome, therefore, remains vastly unknown despite the fact that the metabolome is a key link between the genotype and phenotype, giving us a unique new insight to how key components of an ecosystem are functioning. As the fraction of the metabolome released into the seawater, the sponge exometabolome has only just started to be characterised in comparative environmental metabolomic studies. Yet, the sponge exometabolome constitute a unique opportunity for the identification of biomarkers of sponge health as compounds can be measured in seawater, bypassing the need for physical samples which can still be difficult to collect in the deep sea. Within sponge grounds, the characterisation of a shared sponge exometabolome could lead to the identification of biomarkers of ecosystem functioning and overall health. Challenges remain in establishing omics approaches in environmental monitoring but constant technological advances and reduction in costs means these techniques will become widely available in the future.

Understanding the fundamentals of microbial remediation with emphasize on metabolomics

The post-genomic tool metabolomics is a great advancement in science and technology which acquires novel strategies and pathways to analyze various biological compounds. Metabolomics aids in retrieving the qualitative and quantitative data from the various biological system. The current review is focused on the application of metabolomics in bioremediation and helps to focus on the xenobiotic compounds which are discharged into the environment and have long term impact. The microbial based biodegradation can be effectively used along with the combination of metabolomic approach for a better understanding of the breakdown of certain recalcitrant. Additionally, this review also discusses the candidate gene approach which helps to comprehend the functional analysis of microbial genes in response to different contaminants. Therefore, this review intends to discuss the metabolomics in bioremediation by studying the complete set of metabolites involved during the process of degradation and their interaction with the environment.

Characterizing Multidevice Capillary Vibrating Sharp-Edge Spray Ionization for In-Droplet Hydrogen/Deuterium Exchange to Enhance Compound Identification

Multidevice capillary vibrating sharp-edge spray ionization (cVSSI) source parameters have been examined to determine their effects on conducting in-droplet hydrogen/deuterium exchange (HDX) experiments. Control experiments using select compounds indicate that the observed differences in mass spectral isotopic distributions obtained upon initiation of HDX result primarily from solution-phase reactions as opposed to gas-phase exchange. Preliminary studies have determined that robust HDX can only be achieved with the application of same-polarity voltage to both the analyte and the deuterium oxide reagent (D₂O) cVSSI devices. Additionally, a similar HDX reactivity dependence on the voltage applied to the D₂O device for various analytes is observed. Analyte and reagent flow experiments show that, for the multidevice cVSSI setup employed, there is a nonlinear dependence on the D₂O reagent flow rate; increasing the D₂O reagent flow by 100% results in only an ∼10-20% increase in deuterium incorporation for this setup. Instantaneous (subsecond) response times have been demonstrated in the initiation or termination of HDX, which is achieved by turning on or off the reagent cVSSI device piezoelectric transducer. The ability to distinguish isomeric species by in-droplet HDX is presented. Finally, a demonstration of a three-component cVSSI device setup to perform multiple (successive or in combination) in-droplet chemistries to enhance compound ionization and identification is presented and a hypothetical metabolomics workflow consisting of successive multidevice activation is briefly discussed.

Neurodevelopmental exposome: The effect of in utero co-exposure to heavy metals and phthalates on child neurodevelopment

In this study, the exposome paradigm has been applied on a mother-child cohort adopting an optimised untargeted metabolomics approach for human urine followed by advanced bioinformatics analysis. Exposome-wide association algorithms were used to draw links between in utero co-exposure to metals and phthalates, metabolic pathways deregulation, and clinically observed phenotypes of neurodevelopmental disorders such as problems in linguistic, motor development and cognitive capacity. Children (n = 148) were tested at the first and second year of their life using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III). Their mothers had been exposed to metals and phthalates during the pregnancy, according to human biomonitoring results from previously performed studies. Untargeted metabolomics analysis of biobanked urine samples from the mothers was performed using a combination of the high throughput analytical methods liquid chromatography-high resolution mass spectrometry (LC-HRMS) and nuclear magnetic resonance (NMR). Most perturbed metabolic pathways from co-exposure heavy metals and phthalates were pathways related to the tricarboxylic acid cycle (TCA cycle) and oxidative phosphorylation, indicating the possibility of disruption of mitochondrial respiration. Overproduction of reactive oxygen species (ROS); the presence of glutathione peroxidase 3 (GPx3) during pregnancy and presence of glutathione peroxidase 1 (GPx1) in the umbilical cord were linked to verbal development problems. Another finding of the study is that in real life, adverse outcomes occur as a combination of environmental and social factors, all of them acting synergistically towards the deployment of an observed phenotype. Finally, the two-steps association process (exposure to pathways and pathways to adverse outcomes) was able to (a) provide associations that are not evident by directly associating exposure to outcomes and (b) provides additional insides on the mechanisms of environmental disease.

Omic methodologies for assessing metal(-loid)s-host-microbiota interplay: A review

Omic methodologies have become key analytical tools in a wide number of research topics such as systems biology, environmental analysis, biomedicine or food analysis. They are especially useful when they are combined providing a new perspective and a holistic view of the analytical problem. Methodologies for microbiota analysis have been mostly focused on genome sequencing. However, information provided by these metagenomic studies is limited to the identification of the presence of genes, taxa and their inferred functionality. To achieve a deeper knowledge of microbial functionality in health and disease, especially in dysbiosis conditions related to metal and metalloid exposure, the introduction of additional meta-omic approaches including metabolomics, metallomics, metatranscriptomics and metaproteomics results essential. The possible impact of metals and metalloids on the gut microbiota and their effects on gut-brain axis (GBA) only begin to be figured out. To this end new analytical workflows combining powerful tools are claimed such as high resolution mass spectrometry and heteroatom-tagged proteomics for the absolute quantification of metal-containing biomolecules using the metal as a "tag" in a sensitive and selective detector (e.g. ICP-MS). This review focus on current analytical methodologies related with the analytical techniques and procedures available for metallomics and microbiota analysis with a special attention on their advantages and drawbacks.

Mass Spectrometry-based Metabolomics in Translational Research

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

Metabolomics of Mycobacterium tuberculosis

Enzymes fuel the biochemical activities of all cells. Their substrates and products thus represent a potential window into the physiologic state of a cell. Metabolomics focuses on the global, or systems-level, study of small molecules in a given biological system and has thus provided an experimental tool with which to study cellular physiology, including the biochemistry within pathogenic microorganisms. While metabolomic studies of Mycobacterium tuberculosis are still in their infancy, recent studies have begun to deliver unique insights into the composition, organization, activity, and regulation of the bacterium's physiologic network not accessible by other approaches. Here, we outline practical methods for the culture, collection, and analysis of metabolomic samples from M. tuberculosis that emphasize minimally perturbing sample handling, broad and native metabolite recovery, and sensitive, biologically agnostic metabolite detection.

The Comprehensive and Reliable Detection of Secondary Metabolites in Trichoderma reesei: A Tool for the Discovery of Novel Substances

A method based on reversed phase high-performance liquid chromatography coupled with electrospray ionization high-resolution mass spectrometry (RP-HPLC-ESI-HRMS) for the comprehensive and reliable detection of secondary metabolites of Trichoderma reesei cultured in synthetic minimal liquid medium is presented. A stable isotope-assisted (SIA) workflow is used, which allows the automated, comprehensive extraction of truly fungal metabolite-derived LC-MS signals from the acquired chromatographic data. The subsequent statistical data analysis and a typical outcome of such a metabolomics data evaluation are shown by way of example in a previously published study on the influence of the pleiotropic regulator transcription factor Xylanase promoter binding protein 1 (Xpp1) in T. reesei on secondary metabolism.

Metabolomics: An emerging potential approach to decipher critical illnesses

Critical illnesses contribute to the maximum morbidity and mortality of hospitalized patients. Acute respiratory distress syndrome (ARDS) and sepsis/septic shock are the two most common acute illnesses associated with intensive care unit (ICU) admission. Once triggered, both have an identical underlying mechanism, portrayed by inflammation and endothelial dysfunction. The diagnosis of ARDS is based on clinical findings, laboratory tests, and radiological imaging. Blood cultures remain the gold standard for the diagnosis of sepsis, with the limitation of time delay and low positive yield. A combination of biomarkers has been proposed to diagnose and prognosticate these acute disorders with strengths and limitations, but still, the gold standard has been elusive to clinicians. In this review article, we illustrate the potential of metabolomics to unravel biomarkers that can be clinically utilized as a rapid prognostic and diagnostic tool associated with specific patient populations (ARDS and sepsis/septic shock) based on the available scientific data.

Untargeted metabolomics changes on Gammarus pulex induced by propranolol, triclosan, and nimesulide pharmaceutical drugs

The presence of pharmaceuticals and personal care products (PPCPs) in natural water resources due to incomplete removal in Wastewater Treatment Plants (WWTPs) is a serious environmental concern at present. In this work, the effects of three pharmaceuticals (propranolol, triclosan, and nimesulide) on Gammarus pulex metabolic profiles at different doses and times of exposure have been investigated by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The complex data sets generated in the different exposure experiments were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest (ROI) data and on their analysis by the Multivariate Curve-Resolution Alternating Least Squares (MCR-ALS) chemometrics method. This approach, allowed the resolution and identification of the metabolites present in the analyzed samples, as well as the estimation of their concentration changes due to the exposure experiments. ANOVA Simultaneous Component Analysis (ASCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were then conducted to assess the changes in the concentration of the metabolites for the three pharmaceuticals at the different conditions of exposure. The three tested pharmaceuticals changed the concentrations of metabolites, which were related to different KEGG functional classes. These changes summarize the biochemical response of Gammarus pulex to the exposure by the three investigated pharmaceuticals. Possible pathway alterations related to protein synthesis and oxidative stress were observed in the concentration of identified metabolites.

Development and validation of TOF/Q-TOF MS/MS, HPLC method and in vitro bio-strategy for aflatoxin mitigation

Some secondary metabolites produced by fungi are carcinogenic, hepatotoxic, and/or cause birth defects in humans and animals. We developed and optimised bio-analytical tools for detection of metabolites, aflatoxins and evaluated the effectiveness of the methods in co-infected maize tissues. Isolate KSM012 (atoxigenic) demonstrated no peaks and no blue fluorescence on HPLC and TLC plates respectively confirming non-toxicity. AFB1 and AFB2 were produced by Isolate KSM015 in addition to AFG1 and AFG2, which is an indication of possible S_BG morphotype. The limits of quantification and detection ranged from 0.02 to 35.81 µg/mL and 0.01-6.8 µg/mL, respectively. The best mass spectrum with lowest noise was obtained at 100% ACN and sterile water spiked with 0.1% formic acid at a flow rate of 0.3 mL/min. The positive ion mode with electrospray ionisation application exhibited better fragmentation for mycotoxins. In total 17 metabolites were detected by targeted and formula mass. KDVI maize line exhibited high fungal colonisation in comparison to GAF4 at equal co-infection ratio 50:50. AFB1 and AFG2 were remarkably higher in GAF4 in comparison to sensitive KDV1 (p ˂ 0.05). The detection limits, linearity and sensitivity showed the method developed was suitable for the determination of mycotoxin in comparisons to the guidelines of European Commission 657/EC 2002.

Gas chromatography-mass spectrometry (GC/MS) reveals urine metabolites associated to light and heavy infections by Schistosoma mansoni in mice

High-throughput profiling of metabolites has been used to identify metabolic changes in murine models as a response to the infection by the parasitic trematode Schistosoma. These investigations have contributed to our understanding on the pathogenesis of this tropical neglected disease, with a potential of helping diagnosis. Here, our study aimed to investigate the application of gas chromatography-mass spectrometry (GC/MS) on the profiling of urine metabolites from mice carrying infections by Schistosoma mansoni. Two larval infection doses created distinctive infection intensities in mice, whereby the heavily infected animals were found to release 25 times more eggs in faeces than lightly infected animals. Over 200 urine metabolites were identified from these animals by GC/MS, following two complementary derivatisation methods. A list of 14 individual metabolites with altered relative abundances between groups were identified. Most of the altered metabolites showed a trend of increased abundances in response to infection intensity, indicating host-specific metabolic alterations as a result of the disease. Hippurate, a metabolite which concentration is intimately modulated by the gut microbiota, was found to be highly correlated to infection intensity. Our study showed that urine metabolic profiling by GC/MS can distinguish non-infected animals from those carrying light and heavy infections by S. mansoni, revealing metabolites associated to the infection and providing insights on the pathogenesis of schistosomiasis.