Academic literature on the topic 'Untargeted meta-metabolomics'

Global metabolomics has emerged as a powerful tool to interrogate cellular biochemistry at the systems level by tracking alterations in the levels of small molecules. One approach to define cellular dynamics with respect to this dysregulation of small molecules has been to consider metabolic flux as a function of time. While flux measurements have proven effective for model organisms, acquiring multiple time points at appropriate temporal intervals for many sample types (e.g., clinical specimens) is challenging. As an alternative, meta-analysis provides another strategy for delineating metabolic cause and effect perturbations. That is, the combination of untargeted metabolomic data from multiple pairwise comparisons enables the association of specific changes in small molecules with unique phenotypic alterations. We recently developed metabolomic software called metaXCMS to automate these types of higher order comparisons. Here we discuss the potential of metaXCMS for analyzing proteomic datasets and highlight the biological value of combining meta-results from both metabolomic and proteomic analyses. The combined meta-analysis has the potential to facilitate efforts in functional genomics and the identification of metabolic disruptions related to disease pathogenesis.

Background: Metabolomics is the simultaneous determination of all metabolites in a system. Despite significant advances in the field, compound identification remains a challenge. Prior knowledge of the compound classes of interest can improve metabolite identification. Hormones are a small signaling molecules, which function in coordination to direct all aspects of development, function and reproduction in living systems and which also pose challenges as environmental contaminants. Hormones are inherently present at low levels in tissues, stored in many forms and mobilized rapidly in response to a stimulus making them difficult to measure, identify and quantify. Methods: An in-depth literature review was performed for known hormones, their precursors, metabolites and conjugates in plants to generate the database and an RShiny App developed to enable web-based searches against the database. An accompanying liquid chromatography – mass spectrometry (LC-MS) protocol was developed with retention time prediction in Retip. A meta-analysis of 14 plant metabolomics studies was used for validation. Results: We developed HormonomicsDB, a tool which can be used to query an untargeted mass spectrometry (MS) dataset against a database of more than 200 known hormones, their precursors and metabolites. The protocol encompasses sample preparation, analysis, data processing and hormone annotation and is designed to minimize degradation of labile hormones. The plant system is used a model to illustrate the workflow and data acquisition and interpretation. Analytical conditions were standardized to a 30 min analysis time using a common solvent system to allow for easy transfer by a researcher with basic knowledge of MS. Incorporation of synthetic biotransformations enables prediction of novel metabolites. Conclusions: HormonomicsDB is suitable for use on any LC-MS based system with compatible column and buffer system, enables the characterization of the known hormonome across a diversity of samples, and hypothesis generation to reveal knew insights into hormone signaling networks.

Background Metabolomics is the simultaneous determination of all metabolites in a system. Despite significant advances in the field, compound identification remains a challenge. Prior knowledge of the compound classes of interest can improve metabolite identification. Hormones are a small signaling molecules, which function in coordination to direct all aspects of development, function and reproduction in living systems and which also pose challenges as environmental contaminants. Hormones are inherently present at low levels in tissues, stored in many forms and mobilized rapidly in response to a stimulus making them difficult to measure, identify and quantify. Methods An in-depth literature review was performed for known hormones, their precursors, metabolites and conjugates in plants to generate the database and an RShiny App developed to enable web-based searches against the database. An accompanying liquid chromatography – mass spectrometry (LC-MS) protocol was developed with retention time prediction in Retip. A meta-analysis of 14 plant metabolomics studies was used for validation. Results We developed HormonomicsDB, a tool which can be used to query an untargeted mass spectrometry (MS) dataset against a database of more than 200 known hormones, their precursors and metabolites. The protocol encompasses sample preparation, analysis, data processing and hormone annotation and is designed to minimize degradation of labile hormones. The plant system is used a model to illustrate the workflow and data acquisition and interpretation. Analytical conditions were standardized to a 30 min analysis time using a common solvent system to allow for easy transfer by a researcher with basic knowledge of MS. Incorporation of synthetic biotransformations enables prediction of novel metabolites. Conclusions HormonomicsDB is suitable for use on any LC-MS based system with compatible column and buffer system, enables the characterization of the known hormonome across a diversity of samples, and hypothesis generation to reveal knew insights into hormone signaling networks.

Untargeted metabolomics of environmental samples routinely detects thousands of small molecules, the vast majority of which cannot be identified. Meta-mass shift chemical (MeMSChem) profiling was developed to identify mass differences between related molecules using molecular networks. This approach illuminates metabolome-wide relationships between molecules and the putative chemical groups that differentiate them (e.g., H2, CH2, COCH2). MeMSChem profiling was used to analyze a publicly available metabolomic dataset of coral, algal, and fungal mat holobionts (i.e., the host and its associated microbes and viruses) sampled from some of Earth’s most remote and pristine coral reefs. Each type of holobiont had distinct mass shift profiles, even when the analysis was restricted to molecules found in all samples. This result suggests that holobionts modify the same molecules in different ways and offers insights into the generation of molecular diversity. Three genera of stony corals had distinct patterns of molecular relatedness despite their high degree of taxonomic relatedness. MeMSChem profiles also partially differentiated between individuals, suggesting that every coral reef holobiont is a potential source of novel chemical diversity.

Insight into the metabolic biosignature of tuberculosis (TB) may inform clinical care, reduce adverse effects, and facilitate metabolism-informed therapeutic development. However, studies often yield inconsistent findings regarding the metabolic profiles of TB. Herein, we conducted an untargeted metabolomics study using plasma from 63 Korean TB patients and 50 controls. Metabolic features were integrated with the data of another cohort from China (35 TB patients and 35 controls) for a global functional meta-analysis. Specifically, all features were matched to a known biological network to identify potential endogenous metabolites. Next, a pathway-level gene set enrichment analysis-based analysis was conducted for each study and the resulting p-values from the pathways of two studies were combined. The meta-analysis revealed both known metabolic alterations and novel processes. For instance, retinol metabolism and cholecalciferol metabolism, which are associated with TB risk and outcome, were altered in plasma from TB patients; proinflammatory lipid mediators were significantly enriched. Furthermore, metabolic processes linked to the innate immune responses and possible interactions between the host and the bacillus showed altered signals. In conclusion, our proof-of-concept study indicated that a pathway-level meta-analysis directly from metabolic features enables accurate interpretation of TB molecular profiles.

IntroductionTo date, there is no robust enough test to predict small-for-gestational-age (SGA) infants, who are at increased lifelong risk of morbidity and mortality.ObjectiveTo determine the accuracy of metabolomics in predicting SGA babies and elucidate which metabolites are predictive of this condition.Data sourcesTwo independent researchers explored 11 electronic databases and grey literature in February 2018 and November 2018, covering publications from 1998 to 2018. Both researchers performed data extraction and quality assessment independently. A third researcher resolved discrepancies.Study eligibility criteriaCohort or nested case–control studies were included which investigated pregnant women and performed metabolomics analysis to evaluate SGA infants. The primary outcome was birth weight <10th centile—as a surrogate for fetal growth restriction—by population-based or customised charts.Study appraisal and synthesis methodsTwo independent researchers extracted data on study design, obstetric variables and sampling, metabolomics technique, chemical class of metabolites, and prediction accuracy measures. Authors were contacted to provide additional data when necessary.ResultsA total of 9181 references were retrieved. Of these, 273 were duplicate, 8760 were removed by title or abstract, and 133 were excluded by full-text content. Thus, 15 studies were included. Only two studies used the fifth centile as a cut-off, and most reports sampled second-trimester pregnant women. Liquid chromatography coupled to mass spectrometry was the most common metabolomics approach. Untargeted studies in the second trimester provided the largest number of predictive metabolites, using maternal blood or hair. Fatty acids, phosphosphingolipids and amino acids were the most prevalent predictive chemical subclasses.Conclusions and implicationsSignificant heterogeneity of participant characteristics and methods employed among studies precluded a meta-analysis. Compounds related to lipid metabolism should be validated up to the second trimester in different settings.PROSPERO registration numberCRD42018089985.

BackgroundGlaucoma, a leading cause of irreversible blindness worldwide, is characterised by retinal ganglion cell degeneration. Increasing evidence points to metabolic dysfunction, particularly mitochondrial dysfunction, as a contributing factor to glaucomatous neurodegeneration. This systematic review and meta-analysis aimed to identify key metabolic pathways and biomarkers associated with primary open-angle glaucoma (POAG).MethodsA systematic literature search was conducted to identify studies measuring metabolites in plasma and aqueous humour from patients with POAG using metabolomics techniques. Enrichment analyses for significantly increased metabolites were conducted using MetaboAnalyst. Meta-analyses were performed using random-effects models to calculate effect sizes for metabolites reported in at least three studies.Results17 studies involving patients with POAG were included. Pathway analysis revealed significant enrichment of the arginine and proline metabolism pathway in both aqueous humour and plasma. Additionally, the phenylalanine metabolism pathway was enriched in plasma. These pathways are associated with oxidative stress and neurodegeneration, both of which are key factors in POAG pathology. Meta-analysis identified several significantly elevated metabolites, including lysine, glutamine, alanine, histidine, carnitine and creatinine in aqueous humour, as well as methionine in plasma.ConclusionsThis study underscores the central role of metabolic dysfunction in POAG, highlighting specific metabolites and pathways that could serve as biomarkers for early diagnosis and therapeutic intervention. Future research should prioritise longitudinal studies and untargeted metabolomic profiling to further deepen our understanding of metabolic changes and their contributions to glaucoma progression.PROSPERO registration numberCRD42024512098.

ABSTRACT Background High diet quality is associated with a lower risk of chronic diseases. Metabolomics can be used to identify objective biomarkers of diet quality. Objectives We used metabolomics to identify serum metabolites associated with 4 diet indices and the components within these indices in 2 samples from African Americans and European Americans. Methods We studied cross-sectional associations between known metabolites and Healthy Eating Index (HEI)-2015, Alternative Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension Trial (DASH) diet, alternate Mediterranean diet (aMED), and their components using untargeted metabolomics in 2 samples (n1 = 1,806, n2 = 2,056) of the Atherosclerosis Risk in Communities study (aged 45–64 y at baseline). Dietary intakes were assessed using an FFQ. We used multivariable linear regression models to examine associations between diet indices and serum metabolites in each sample, adjusting for participant characteristics. Metabolites significantly associated with diet indices were meta-analyzed across 2 samples. C-statistics were calculated to examine if these candidate biomarkers improved prediction of individuals in the highest compared with lowest quintile of diet scores beyond participant characteristics. Results Seventeen unique metabolites (HEI: n = 6; AHEI: n = 5; DASH: n = 14; aMED: n = 2) were significantly associated with higher diet scores after Bonferroni correction in sample 1 and sample 2. Six of 17 significant metabolites [glycerate, N-methylproline, stachydrine, threonate, pyridoxate, 3-(4-hydroxyphenyl)lactate)] were associated with ≥1 dietary pattern. Candidate biomarkers of HEI, AHEI, and DASH distinguished individuals with highest compared with lowest quintile of diet scores beyond participant characteristics in samples 1 and 2 (P value for difference in C-statistics &lt;0.02 for all 3 diet indices). Candidate biomarkers of aMED did not improve C-statistics beyond participant characteristics (P value = 0.930). Conclusions A considerable overlap of metabolites associated with HEI, AHEI, DASH, and aMED reflects the similar food components and similar metabolic pathways involved in the metabolism of healthy diets in African Americans and European Americans.

Abstract Background: African American (AA) women are less likely to develop breast cancer but when they do, their mortality rates are 40% higher compared to Non-Hispanic White (NHW) women. This disparity is particularly striking among ER+ breast cancer cases. The purpose of this study is to examine whether there are racial differences in metabolic and molecular pathways typically activated in patients with ER+ positive breast cancer. Methods: We collected plasma from AA and NHW cases and controls to conduct an untargeted metabolomics analysis using gas chromatography-mass spectrometry (GC-MS) to identify metabolites that are possibly altered in the different race groups. Statistical methods combined with multiple feature selection and prediction models were employed to identify race-specific altered metabolic signatures. This was followed by the identification of altered metabolic pathways with a focus on AA patients with breast cancer. The clinical significance of the findings was further examined in the PanCancer Atlas breast cancer data set. Results: We identified differential metabolic signatures between NHW and AA patients. In AA patients, we observed disturbed amino acid metabolism, while fatty acid metabolism was significant in NHW patients. By mapping these metabolites to genes, this study identified significant relations with regulators of metabolism such as methionine adenosyltransferase 1A (MAT1A), DNA Methyltransferases, Histone methyltransferases for AA individuals, and Fatty acid Synthase (FASN) and Monoacylglycerol lipase (MGL) for NHW individuals. Specific histone methyltransferase NELFE was overexpressed and associated with poor survival exclusively in AA individuals. Conclusion: We employ a comprehensive and novel approach that integrates multiple machine learning methods, and statistical methods, coupled with human functional pathway analyses. This metabolic profile of serum samples might be used to assess risk progression in AA individuals with ER+ breast cancer. To our knowledge, this is a novel finding that describes metabolic alterations in AA breast cancer and emphasizes a potential biological basis for breast cancer health disparities. Citation Format: Ashlie Santaliz Casiano, Zeynep Madak-Erdogan, Dhruv Meta, Jonna Frasor, Garth Rauscher, Kent Hoskins. Identification of metabolic and molecular mechanisms contributing to ER+ cancer disparities using a machine-learning pipeline [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C020.

La contamination chimique croissante de l’environnement par les micropolluants représente un risque pour les écosystèmes et leurs fonctions. Les communautés microbiennes aquatiques en contact avec ces contaminants, telles que le périphyton, sont de plus en plus utilisées en écotoxicologie afin de rendre compte des effets des stress environnementaux à des niveaux d’organisation biologique élevés, plus représentatifs des écosystèmes. Malgré un intérêt croissant pour leur étude en raison des fonctions et rôles (p. ex., production primaire, détoxification) qu’ils assurent, un manque de connaissances persiste sur la dynamique temporelle de leur sensibilité au stress chimique, ainsi que sur les mécanismes moléculaires sous-jacents à l’altération de la photosynthèse. Ce travail vise à combler ces lacunes en étudiant les fluctuations de la sensibilité du périphyton à un herbicide dans le temps, en appréhendant à la fois les réponses du méta-métabolome et de la photosynthèse au sein du périphyton. Par ailleurs, ce travail examine également la relation entre ces réponses et la structure de la communauté, ainsi que la modulation par des facteurs environnementaux, tant dans des conditions naturelles que sous stress chimique à court et à long terme. Les résultats suggèrent une fluctuation de la sensibilité du périphyton au cours du temps, avec une sensibilité plus élevée du méta-métabolome par rapport à la photosynthèse, des changements significatifs dans les classes de métabolites (comme les lipides et les acides aminés) se produisant avant les modifications physiologiques. De plus, ces travaux ont mis en évidence, via des réseaux de corrélation identifiés par des analyses statistiques multi-blocs, sept facteurs modulants, au cours de l’année et dans des conditions naturelles, la dynamique moléculaire, physiologique et structurelle du périphyton. Dans un second temps, les analyses ont identifié, dans les réponses du méta-métabolome au stress chimique, trois niveaux distincts de sensibilité influencés par des facteurs environnementaux et des groupes taxonomiques spécifiques. Ces résultats soulignent l’importance de prendre en compte la variabilité temporelle et l’état initial du périphyton lors de l’évaluation de sa réponse au stress chimique
The increasing chemical contamination of the environment by micropollutants represents a risk to ecosystems and their functions. Aquatic microbial communities in contact with these contaminants, such as periphyton, are increasingly used in ecotoxicology to assess the effects of environmental stress at higher levels of biological organization, which are more representative of ecosystems. Despite growing interest in their study due to the functions and roles they play (e.g., primary production, detoxification), there is still a lack of knowledge about the temporal dynamics of their sensitivity to chemical stress and the molecular mechanisms underlying photosynthesis impairment. This work aims to fill these gaps by investigating the fluctuations in periphyton sensitivity to an herbicide over time, examining both meta-metabolome and photosynthesis responses within periphyton. Additionally, this study also explores the relationship between these responses and community structure, as well as their modulation by environmental factors in both natural conditions and under short- and long-term chemical stress. The results suggest a fluctuation in periphyton sensitivity over time, with higher sensitivity of the meta-metabolome compared to photosynthesis, and significant changes in metabolite classes (such as lipids and amino acids) occurring before physiological modifications. Furthermore, this work identified, through correlation networks and multi-block statistical analyses, seven factors modulating periphyton’s molecular, physiological, and structural dynamics throughout the year in natural conditions. In the second phase, the analyses identified three distinct levels of sensitivity in the meta-metabolome’s response to chemical stress, influenced by specific environmental factors and taxonomic groups. These results highlight the importance of considering the temporal variability and initial state of periphyton when assessing its response to chemical stress