Academic literature on the topic 'Untargeted Metabolomics approaches'

The authors of this paper conducted a comparative metabolomic analysis of Ophiocordyceps sinensis (OS), providing the metabolic profiles of the stroma (OSBSz) and sclerotia (OSBSh) of OS by widely targeted metabolomics and untargeted metabolomics. The results showed that 778 and 1449 metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. The metabolites in OSBSz and OSBSh are significantly differentiated; 71 and 96 differentially expressed metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. This suggests that these 71 metabolites (riboflavine, tripdiolide, bromocriptine, lumichrome, tetrahymanol, citrostadienol, etc.) and 96 metabolites (sancycline, vignatic acid B, pirbuterol, rubrophen, epalrestat, etc.) are potential biomarkers. 4-Hydroxybenzaldehyde, arginine, and lumichrome were common differentially expressed metabolites. Using the widely targeted metabolomics approach, the key pathways identified that are involved in creating the differentiation between OSBSz and OSBSh may be nicotinate and nicotinamide metabolism, thiamine metabolism, riboflavin metabolism, glycine, serine, and threonine metabolism, and arginine biosynthesis. The differentially expressed metabolites identified using the untargeted metabolomics approach were mainly involved in arginine biosynthesis, terpenoid backbone biosynthesis, porphyrin and chlorophyll metabolism, and cysteine and methionine metabolism. The purpose of this research was to provide support for the assessment of the differences between the stroma and sclerotia, to furnish a material basis for the evaluation of the physical effects of OS, and to provide a reference for the selection of detection methods for the metabolomics of OS.

SUMMARYThe discovery, development and optimal utilization of pharmaceuticals can be greatly enhanced by knowledge of their modes of action. However, many drugs currently on the market act by unknown mechanisms. Untargeted metabolomics offers the potential to discover modes of action for drugs that perturb cellular metabolism. Development of high resolution LC-MS methods and improved data analysis software now allows rapid detection of drug-induced changes to cellular metabolism in an untargeted manner. Several studies have demonstrated the ability of untargeted metabolomics to provide unbiased target discovery for antimicrobial drugs, in particular for antiprotozoal agents. Furthermore, the utilization of targeted metabolomics techniques has enabled validation of existing hypotheses regarding antiprotozoal drug mechanisms. Metabolomics approaches are likely to assist with optimization of new drug candidates by identification of drug targets, and by allowing detailed characterization of modes of action and resistance of existing and novel antiprotozoal drugs.

This mini-review aims to discuss the development and applications of mass spectrometry (MS)-based hybrid approaches in metabolomics. Several recently developed hybrid approaches are introduced. Then, the overall workflow, frequently used instruments, data handling strategies, and applications are compared and their pros and cons are summarized. Overall, the improved repeatability and quantitative capability in large-scale MS-based metabolomics studies are demonstrated, in comparison to either targeted or untargeted metabolomics approaches alone. In summary, we expect this review to serve as a first attempt to highlight the development and applications of emerging hybrid approaches in metabolomics, and we believe that hybrid metabolomics approaches could have great potential in many future studies.

Metabolomics studies of the early-life exposome often use maternal urine specimens to investigate critical developmental windows, including the periconceptional period and early pregnancy. During these windows changes in kidney function can impact urine concentration. This makes accounting for differential urinary dilution across samples challenging. Because there is no consensus on the ideal normalization approach for urinary metabolomics data, this study’s objective was to determine the optimal post-analytical normalization approach for untargeted metabolomics analysis from a periconceptional cohort of 45 women. Urine samples consisted of 90 paired pre- and post-implantation samples. After untargeted mass spectrometry-based metabolomics analysis, we systematically compared the performance of three common approaches to adjust for urinary dilution—creatinine adjustment, specific gravity adjustment, and probabilistic quotient normalization (PQN)—using unsupervised principal components analysis, relative standard deviation (RSD) of pooled quality control samples, and orthogonal partial least-squares discriminant analysis (OPLS-DA). Results showed that creatinine adjustment is not a reliable approach to normalize urinary periconceptional metabolomics data. Either specific gravity or PQN are more reliable methods to adjust for urinary concentration, with tighter quality control sample clustering, lower RSD, and better OPLS-DA performance compared to creatinine adjustment. These findings have implications for metabolomics analyses on urine samples taken around the time of conception and in contexts where kidney function may be altered.

Lack of standardized applications of bioinformatics and statistical approaches for pre- and postprocessing of global metabolomic profiling data sets collected using high-resolution mass spectrometry platforms remains an inadequately addressed issue in the field. Several publications now recognize that data analysis outcome variability is caused by different data treatment approaches. Yet, there is a lack of interlaboratory reproducibility studies that have looked at the contribution of data analysis techniques toward variability/overlap of results. The goal of our study was to identify the contribution of data pre- and postprocessing methods on metabolomics analysis results. We performed urinary metabolomics from samples obtained from mice exposed to 5 Gray of external beam gamma rays and those exposed to sham irradiation (control group). The data files were made available to study participants for comparative analysis using commonly used bioinformatics and/or biostatistics approaches in their laboratories. The participants were asked to report back the top 50 metabolites/features contributing significantly to the group differences. Herein we describe the outcome of this study which suggests that data preprocessing is critical in defining the outcome of untargeted metabolomic studies.

Untargeted metabolomics enables the identification of key changes to standard pathways, but also aids in revealing other important and possibly novel metabolites or pathways for further analysis. Much progress has been made in this field over the past decade and yet plant metabolomics seems to still be an emerging approach because of the high complexity of plant metabolites and the number one challenge of untargeted metabolomics, metabolite identification. This final and critical stage remains the focus of current research. The intention of this review is to give a brief current state of LC–MS based untargeted metabolomics approaches for plant specific samples and to review the emerging solutions in mass spectrometer hardware and computational tools that can help predict a compound's molecular structure to improve the identification rate.

Forensic toxicology and forensic medicine are unique among all other medical fields because of their essential legal impact, especially in civil and criminal cases. New high-throughput technologies, borrowed from chemistry and physics, have proven that metabolomics, the youngest of the “omics sciences”, could be one of the most powerful tools for monitoring changes in forensic disciplines. Metabolomics is a particular method that allows for the measurement of metabolic changes in a multicellular system using two different approaches: targeted and untargeted. Targeted studies are focused on a known number of defined metabolites. Untargeted metabolomics aims to capture all metabolites present in a sample. Different statistical approaches (e.g., uni- or multivariate statistics, machine learning) can be applied to extract useful and important information in both cases. This review aims to describe the role of metabolomics in forensic toxicology and in forensic medicine.

Aging of a living organism is closely related to systemic metabolic changes. But due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, this problem is solved using one of the main approaches of metabolomics — untargeted metabolome profiling. The purpose of this publication is to systematize the results of metabolomic studies based on such profiling, both in animal models and in humans.

Abstract Summary Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines. Availability and implementation The AlpsNMR R package and tutorial is freely available to download from http://github.com/sipss/AlpsNMR under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

El fum de tabac residual (thirdhand smoke en anglès, THS) és una via d'exposició a compostos tòxics de fum tabac poc estudiada fins ara. El THS es produeix per la deposició de parEcules i gasos en superFcies i pols, on es poden reemetre i/o reaccionar produint nous compostos tòxics, alguns d'ells carcinògens. Malgrat les creixents evidències, els riscos inherents a l'exposició a THS encara no s'han descrit completament. L'objecKu principal d'aquesta tesi és avançar en la caracterització química del THS i dels efectes per a la salut derivats d'aquesta exposició mitjançant l'aplicació de mètodes analíKcs dirigits i no dirigits. Aquesta tesi presenta el desenvolupament de un nou mètode analíKc per determinar simultàniament tòxics del tabac en pols domèsKca, mitjançant cromatografia líquida (UHPLC). En aquesta tesi, també s'ha desenvolupat un mètode d'anàlisi no dirigit basat en l'adquisició de mostres per UHPLC acoblada a espectrometria de masses d'alta resolució (HR-MS), amb l'aplicació d'estratègies avançades de processament de dades, la priorització estadísKca d’ions rellevants i una nova estratègia per a l'anotació de compostos. La combinació d'aquests dos mètodes va proporcionar per primera vegada l'anotació de dotzenes de tòxics relacionats amb la contaminació per THS adherits a la pols domèsKca. Pel que fa als efectes sobre la salut, presentem el primer estudi metabolòmic no dirigit en fetge de ratolins exposats a THS. L'aplicació de les tècniques UHPLC-HRMS i ressonància magnèKca nuclear (RMN) va permetre idenKficar dotzenes de metabòlits hepàKcs alterats, mentre que les imatges d'espectrometria de masses (MSI) van mostrar la distribució espacial diferencial de lípids en fetge induïda per THS. Aquests resultats confirmen els perills de l'exposició a THS i el paper clau de la introducció de noves estratègies metodològiques en la invesKgació en salut ambiental.
El humo de tabaco residual (thirdhand smoke en inglés, THS) es una vía de exposición a compuestos tóxicos del humo del tabaco poco estudiada hasta la fecha. El THS se produce por la deposición de parBculas y gases en superficies y polvo, dónde se pueden reemiEr y/o reaccionar produciendo nuevos compuestos tóxicos, algunos de ellos carcinógenos. A pesar de las crecientes evidencias, los riesgos inherentes a la exposición a THS aún no se han descrito por completo. El objeEvo principal de esta tesis es avanzar en la caracterización química del THS y de los efectos para la salud derivados de esta exposición mediante la aplicación de métodos analíEcos dirigidos y no dirigidos. Esta tesis presenta el desarrollo de un nuevo método analíEco para determinar simultáneamente tóxicos del tabaco en polvo domésEco mediante cromatograMa líquida (UHPLC). En esta tesis, también se ha desarrollado un método de análisis no dirigido basado en la adquisición de muestras por UHPLC acoplada a espectrometría de masas de alta resolución (HR-MS), con la aplicación de estrategias avanzadas de procesamiento de datos, la priorización estadísEca de iones relevantes y una nueva estrategia para la anotación de compuestos. La combinación de estos dos métodos proporcionó por primera vez la anotación de docenas de tóxicos relacionados con la contaminación por THS adheridos al polvo domésEco.Respecto a los efectos sobre la salud, presentamos el primer estudio metabolómico no dirigido en hígado de ratones expuestos a THS. La aplicación de las técnicas UHPLC-HRMS y resonancia magnéEca nuclear (RMN) permiEó idenEficar docenas de metabolitos hepáEcos alterados, mientras que las imágenes de espectrometría de masas (MSI) mostraron la distribución espacial diferencial de lípidos en hígado inducida por THS. Estos resultados confirman los peligros de la exposición a THS y el papel clave de nuevos enfoques metodológicos en la invesEgación en salud ambiental.
Thirdhand tobacco smoke (THS) is a novel and poorly understood pathway of tobacco exposure produced by the deposi=on and ageing of tobacco smoke par=cles and toxicants in surfaces and dust. This aged tobacco smoke could reemit into the air or react with other atmospheric chemicals to yield new toxicants, including carcinogens and becoming increasingly toxic with age. Although growing evidences of THS hazards, its chemical characteriza=on and the related health effects remain to be fully elucidated. Hence, this thesis aims to advance on the current knowledge on THS chemical characteriza=on and on the health effects derived from THS exposure by applying novel targeted and untargeted approaches. To advance on THS chemical characteriza=on, we have developed an efficient, quick and robust analy=cal method for simultaneously determining tobacco-specific compounds in household dust by ultra-highperformance liquid-chromatography coupled to tandem mass spectrometry (UHPLC-MSMS). We applied this target method in combina=on with untargeted strategies for a comprehensive characteriza=on of THS toxicants aNached to household dust. The developed untargeted workflow combines the sample acquisi=on by UHPLC coupled to high-resolu=on mass spectrometry (HR-MS) with the applica=on of advanced data processing strategies, the sta=s=cal priori=za=on of relevant features and a novel strategy for compound annota=on. The combina=on of these two approaches provided for the first =me the annota=on of dozens of toxicants related to THS contamina=on. To advance on the health effects, this thesis presents the first mul=plaQorm untargeted metabolomics study to unravel the molecular altera=ons of liver from mice exposed to THS. UHPLC-HRMS and nuclear magne=c resonance (NMR) revealed dozens of hepa=c metabolites altered in THS-exposed mice whilst mass spectrometry imaging (MSI) showed the differen=al spa=al distribu=on of lipids induced by THS. The results presented here confirm the hazards of THS exposure and the key role of combined untargeted and targeted methods in environmental health research.

La maladie de la sharka, causée par le Plum pox virus (PPV), est responsable d’importantes pertes économiques chez les Prunus. Toutefois, aucun traitement préventif ou curatif n’est à ce jour disponible et peu de sources de résistance naturelle ont été retrouvées. En France, une approche prophylactique, qui repose essentiellement sur la détection et l’élimination rapide des arbres infectés, a été adoptée afin de réduire la propagation du virus. Néanmoins, certaines contraintes technico-économiques ne permettent pas la détection précoce et efficace du PPV à grande échelle par des méthodes conventionnelles. Le département des Pyrénées Orientales (France) est le plus touché par cette maladie (85% des contaminations). Ces enjeux ont motivé la création du projet Antishark, issu d'une collaboration entre AkiNaO, l'Université de Perpignan Via Domitia, la FDGDON66 et les producteurs locaux. L'objectif du projet consiste à développer une méthode innovante de détection précoce, en ciblant les réponses métaboliques de Prunus persica à un stade précoce de l'infection. Par conséquence, deux études en conditions contrôlées utilisant une approche métabolomique non-ciblée (UHPLC-HRMS) ont été réalisées. Cette approche constitue un outil prometteur pour mettre en évidence les interactions métaboliques entre le PPV et son hôte. Dans une première étude, la réponse métabolique globale à l'infection par le PPV (souches Dideron et Marcus), intégrant les feuilles symptomatiques et asymptomatiques, a permis de discriminer les profils métaboliques provenant de feuilles infectées par le PPV et de feuilles saines. Bien qu’il existe une réponse commune aux deux souches, des différences métaboliques ont également été révélées, mettant en évidence des altérations métaboliques souche-dépendante. De fait, cette observation pourrait amener à terme, la possibilité d’identifier la ou les souches virales responsables d’une infection. De plus, il est possible de discriminer les plants infectés par le PPV (feuilles symptomatiques et asymptomatiques) des plants sains et des plants infectés par un autre virus phytopathogène. Ces observations suggèrent l’existence d’une réponse spécifique potentielle à la maladie de la sharka. L’ensemble de nos résultats corroborent l'hypothèse selon laquelle les arbres asymptomatiques mais infectés par le PPV, pourraient être détectés via les altérations métaboliques provoquées le virus. Par ailleurs, les réponses métaboliques observées sur les feuilles asymptomatiques pourraient être considérées comme des réponses précoces, déclenchées avant l’apparition des symptômes. Dans un deuxième temps, des altérations métaboliques précoces, avant l’apparition des symptômes sharka, ont été confirmées par une étude cinétique et ce, malgré des tests moléculaires négatives (RT-qPCR). Nos résultats indiquent que la détection précoce des plantes infectées par le PPV, en ciblant les réponses métaboliques de Prunus persica, est de facto une stratégie prometteuse. Finalement, des corrélations statistiques entre les deux études ont été retrouvées. Bien que les cultivars présentent des profils métaboliques significativement différents, certaines variables discriminantes sont communes entre les différents cultivars testés (GF-305, nectarine jaune, pêche jaune) et également entre les différents stades d’infection du virus (symptomatique et asymptomatique). Cependant, une co-infection PPV et oïdium observée le long de l’étude cinétique en conditions contrôlées, serait susceptible d’altérer l'impact de l'infection par le PPV. Par conséquent, une nouvelle étude cinétique sans co-infection est en cours pour confirmer ou infirmer ces observations. De plus, l'identification de biomarqueurs liés à la maladie, également en cours, permettrait de mieux comprendre les interactions métaboliques entre la pêche et le PPV. Enfin, d'autres expérimentations en conditions naturelles sont en cours afin d'évaluer la robustesse de nos potentiels biomarqueurs
Sharka disease, caused by Plum pox virus (PPV), is responsible for significant economic losses in Prunus. However, no preventive or curative treatments are currently available and only a few sources of natural resistance have been found. In France, a prophylactic approach has been adopted in an attempt to limit the spread of the PPV, which is essentially based on the rapid detection and removal of infected trees. However, certain technical and economic limitations do not allow the early andeffective detection of PPV on a large scale by conventional methods. The department of Pyrénées Orientales (France) is the most affected by this disease (85% of infections). These issues motivated the creation of the Antishark project, which is the result of a collaboration between AkiNaO, the University of Perpignan Via Domitia, FDGDON66 and local producers. The objective of the project was to develop an innovative method of early detection, targeting the metabolic responses of Prunuspersica at an early stage of the infection. Consequently, two studies under monitored conditions using an untargeted metabolomics approach (UHPLC-HRMS) were carried out. This approach is a promising tool to reveal the metabolic interactions between PPV and its host. In a first study, the global metabolic response to PPV-infection (Dideron and Marcus strains), including symptomatic and asymptomatic leaves, allowed the discrimination of metabolic profiles from PPV-infected and healthy leaves. Although there was a common response between the two strains, metabolic differences were also revealed, notably highlighting strain-specific metabolic alterations. In fact, this novel result could eventually lead to the possibility of identifying the viral strain(s) responsible for the infection. Furthermore, it was possible to discriminate PPV-infected plants (symptomatic and asymptomatic leaves) from healthy plants and from plants infected by another plant pathogenic virus. These observations suggest the existence of a potential specific response to the sharka disease. Based on all these findings, the hypothesis that asymptomatic PPVinfected trees could be detected through virus-induced metabolic alterations is supported.Furthermore, the metabolic responses collected from asymptomatic leaves could be considered as early responses to PPV-infection, i.e., before the appearance of symptoms. In a second step, early metabolic alterations, before the appearance of sharka symptoms, were confirmed by a kinetic study, despite negative molecular tests (RT-qPCR). Our results indicate that early detection of PPVinfected plants by targeting metabolic responses in Prunus persica was a promising strategy. Finally,statistical correlations between the two studies were found. Although the cultivars showed significantly different metabolic profiles, some discriminant features were common between the different cultivars tested (GF-305, yellow nectarine, yellow peach) and also between the different stages of the virus infection (symptomatic and asymptomatic). Nevertheless, a co-infection of PPV and powdery mildew observed during the kinetic experiment under monitored conditions could alter the impact of PPV-infection. Consequently, a new kinetic study without co-infection, is ongoing to confirm or refute these first observations. In addition, the identification of biomarkers related to the sharka disease, also in progress, would provide a betterunderstanding of the metabolic interactions between peach and PPV. Finally, other experiments under natural conditions are underway to evaluate the robustness of our potential biomarkers

Trabalho Final de Mestrado Integrado, Ciências Farmacêuticas, Universidade de Lisboa, Faculdade de Farmácia, 2014
Metabolomics is a growing field of analytical science. Because of developments in instrumentation and enhancement of data processing, untargeted approaches are being used to their best potential yet, as tools mainly in the discovery and monitoring of biomarkers. Alzheimer’s disease (AD) is a major cause of dementia and general health decline in most developed countries. The onset for the disease is silent but development of a method of detection in its early phase is likely to yield better results in its treatment. In this project, a new superficially-porous particle column (SPP) is tested for an LC-MS study to define a global metabolic profile that can be used to identify biomarkers for AD. In parallel, a classic targeted GC-MS method is developed to track 2,4-dihydroxybutyric acid in biological samples and ascertain the viability of its study as a biomarker candidate.
A metabolómica é um ramo em crescimento da química analítica. Devido aos desenvolvimentos na instrumentação e a uma melhoria no processamento de dados, abordagens untargeted estão a ser usadas no seu maior potencial até hoje como ferramentas principalmente para a descoberta e monitorização de biomarcadores. A Doença de Alzheimer (AD) é uma causa eminente de demência e declínio da saúde em geral, na maioria dos países desenvolvidos. A instalação da doença é silenciosa mas o desenvolvimento de um método de deteção na sua fase inicial é provável que gere melhores resultados no seu tratamento. Neste projeto, uma coluna de partículas superficialmente-porosas (SPP) é testada para um estudo de LC-MS que pretende definir um perfil metabólico geral que possa ser usado para identificar biomarcadores para AD. Em paralelo, um método clássico targeted de GC-MS será desenvolvido para detetar o ácido 2,4-dihidroxibutirico em amostras biológicas e verificar a viabilidade do seu estudo como um candidato a biomarcador.

Metabolomics is an emerging and rapidly evolving technology tool, which involves quantitative and qualitative metabolite assessments science. It offers tremendous promise for different applications in various fields such as medical, environmental, nutrition, and agricultural sciences. Metabolomic approach is based on global identification of a high number of metabolites present in a biological fluid. This allows to characterize the metabolic profile of a given condition and to identify which metabolites or metabolite patterns may be useful in the discrimination between different groups. The use of one mass spectrometry (MS) platform from targeted quantification to untargeted metabolomics will make more efficient workflows in many fields and should allow projects to be more easily undertaken and realized. Metabolomics can be divided into non-targeted and targeted. The first one can analyze metabolites derived from the organisms comprehensively and systematically, so it is an unbiased metabolomics analysis that can discover new biomarkers. Targeted metabolomics, on the other hand, is the study and analysis of specific metabolites. Both have their own advantages and disadvantages, and are often used in combination for discovery and accurate weight determination of differential metabolites, and allow in-depth research and analysis of subsequent metabolic molecular markers. Targeted and non-targeted metabolomics are involved in food identification, disease research, animal model verification, biomarker discovery, disease diagnosis, drug development, drug screening, drug evaluation, clinical plant metabolism and microbial metabolism research. The aim of this chapter is to highlight the versatility of metabolomic analysis due to both the enormous variety of samples and the no strict barriers between quantitative and qualitative analysis. For this purpose, two examples from our group will be considered. Using non-targeted metabolomics in opposite Antarctic cryptoendolytic communities exposed to the sun, we revealed specific adaptations. Instead, through the targeted metabolomics applied to the urine during childbirth, we identified a different distribution of specific metabolites and the metabolic differences allowed us to discriminate between the two phases of labor, highlighting the metabolites most involved in the discrimination. The choice of these two approaches is to highlight that metabolomic analysis can be applied to any sample, even physiologically and metabolomically very distant, as can be microorganisms living on Antarctic rocks and biological fluids such as urine.