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Patti, Gary J., Ralf Tautenhahn, Bryan R. Fonslow, Yonghoon Cho, Adam Deutschbauer, Adam Arkin, Trent Northen, and Gary Siuzdak. "Meta-analysis of global metabolomics and proteomics data to link alterations with phenotype." Spectroscopy 26, no. 3 (2011): 151–54. http://dx.doi.org/10.1155/2011/923017.
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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.
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Giebelhaus, Ryland T., Lauren A. E. Erland, and Susan J. Murch. "HormonomicsDB: a novel workflow for the untargeted analysis of plant growth regulators and hormones." F1000Research 11 (October 18, 2022): 1191. http://dx.doi.org/10.12688/f1000research.124194.1.
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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.
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Giebelhaus, Ryland T., Lauren A. E. Erland, and Susan J. Murch. "HormonomicsDB: a novel workflow for the untargeted analysis of plant growth regulators and hormones." F1000Research 11 (April 8, 2024): 1191. http://dx.doi.org/10.12688/f1000research.124194.2.
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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.
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Hartmann, Aaron C., Daniel Petras, Robert A. Quinn, Ivan Protsyuk, Frederick I. Archer, Emma Ransome, Gareth J. Williams, et al. "Meta-mass shift chemical profiling of metabolomes from coral reefs." Proceedings of the National Academy of Sciences 114, no. 44 (October 12, 2017): 11685–90. http://dx.doi.org/10.1073/pnas.1710248114.
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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.
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Phuoc Long, Nguyen, Da Young Heo, Seongoh Park, Nguyen Thi Hai Yen, Yong-Soon Cho, Jae-Gook Shin, Jee Youn Oh, and Dong-Hyun Kim. "Molecular perturbations in pulmonary tuberculosis patients identified by pathway-level analysis of plasma metabolic features." PLOS ONE 17, no. 1 (January 24, 2022): e0262545. http://dx.doi.org/10.1371/journal.pone.0262545.
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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.
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Leite, Debora Farias Batista, Aude-Claire Morillon, Elias F. Melo Júnior, Renato T. Souza, Fergus P. McCarthy, Ali Khashan, Philip Baker, Louise C. Kenny, and Jose Guilherme Cecatti. "Examining the predictive accuracy of metabolomics for small-for-gestational-age babies: a systematic review." BMJ Open 9, no. 8 (August 2019): e031238. http://dx.doi.org/10.1136/bmjopen-2019-031238.
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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.
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Golpour, Navid, Rune L. Brautaset, Flora Hui, Maria Nilsson, Jonas E. Svensson, Pete A. Williams, and James R. Tribble. "Identifying potential key metabolic pathways and biomarkers in glaucoma: a systematic review and meta-analysis." BMJ Open Ophthalmology 10, no. 1 (March 2025): e002103. https://doi.org/10.1136/bmjophth-2024-002103.
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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.
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Kodra, Dritan, Petros Pousinis, Panagiotis A. Vorkas, Katerina Kademoglou, Theodoros Liapikos, Alexandros Pechlivanis, Christina Virgiliou, Ian D. Wilson, Helen Gika, and Georgios Theodoridis. "Is Current Practice Adhering to Guidelines Proposed for Metabolite Identification in LC-MS Untargeted Metabolomics? A Meta-Analysis of the Literature." Journal of Proteome Research 21, no. 3 (December 20, 2021): 590–98. http://dx.doi.org/10.1021/acs.jproteome.1c00841.
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Kim, Hyunju, Emily A. Hu, Kari E Wong, Bing Yu, Lyn M. Steffen, Sara B. Seidelmann, Eric Boerwinkle, Josef Coresh, and Casey M. Rebholz. "Serum Metabolites Associated with Healthy Diets in African Americans and European Americans." Journal of Nutrition 151, no. 1 (November 26, 2020): 40–49. http://dx.doi.org/10.1093/jn/nxaa338.
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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 <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.
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Casiano, Ashlie Santaliz, Zeynep Madak-Erdogan, Dhruv Meta, Jonna Frasor, Garth Rauscher, and Kent Hoskins. "Abstract C020: Identification of metabolic and molecular mechanisms contributing to ER+ cancer disparities using a machine-learning pipeline." Cancer Epidemiology, Biomarkers & Prevention 32, no. 1_Supplement (January 1, 2023): C020. http://dx.doi.org/10.1158/1538-7755.disp22-c020.
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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.
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Dürholz, K., E. Schmid, M. Frech, M. Linnerbauer, L. Lößlein, S. Lucas, V. Azizov, et al. "POS0002 MICROBIOTA-DERIVED HISTAMINE INDUCES RESOLUTION OF SYNOVIAL INFLAMMATION VIA CELLS OF THE NERVOUS SYSTEM." Annals of the Rheumatic Diseases 82, Suppl 1 (May 30, 2023): 206.1–206. http://dx.doi.org/10.1136/annrheumdis-2023-eular.3681.
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BackgroundIntestinal dysbiosis has been associated with the development and progression of rheumatoid arthritis (RA) [1].Microbiota-derived metabolites such as short-chain fatty acids (SCFA) gained attention in research of inflammatory diseases as promising targets for new therapeutics. Previous work in our lab showed the potent preventive effect of SCFA on the onset of inflammatory arthritis (2, 3). Although effective potential therapeutic approaches are arising, their underlying mechanisms remain elusive [4-6].Cellular derived histamine is considered as pro-inflammatory mediator to induce acute allergic symptoms and to maintain chronicity. Interestingly, we identified an unexpected pro-resolving pathway of SCFA-induced histamine secretion by the gut microbiota. In contrast to the effective, direct Treg inducing 2-week delayed resolving effects of the SCFA propionate, we report on an indirect but ten times faster histamine-mediated pro-resolving mechanism via cells of the nervous system.ObjectivesTo understand the rapid pro-resolving effects of microbiota-derived histamine on synovial inflammation.MethodsWe used 16s rRNA sequencing, HPLC size chromatography exclusion, FMT and meta-transcriptomics to assess propionate-induced changes in microbiota composition and the secreted metabolite profile. Mice with collagen- or serum-induced arthritis (CIA/SIA) were treated orally, i.p. or intrathecally with histamine or specific receptor agonists. Cellular changes in spleen, lymph nodes and joints were assessed by Cytek spectral flow cytometry, inflammatory infiltration and bone erosion were analyzed by histology and µCT. We analyzed differences in CNS and PNS via RNAseq of the spinal cord and peripheral nerves. Effects on vessel leakiness and cell infiltration in the inflamed joints were assessed via lightsheet microscopy and in vivo PET-CT.ResultsHere, we show that therapeutic treatment of CIA mice with the SCFA propionate starting from peak of disease (30 dpi) strongly induced resolution of synovial inflammation after 14 days of treatment. We demonstrate that oral propionate-treatment causes beneficial changes in the microbiota composition and thereby alters the secreted metabolite profile. These metabolites are able to induce rapid resolution of inflammation already after 2 days. By untargeted metabolomics we were able to identify histamine as highly potent metabolite that is increased upon propionate treatment. Oral treatment of CIA mice with histamine or a histamine 3 receptor (H3R) agonist significantly improved clinical symptoms. H3R is mainly expressed on cells of the nervous system. Upon oral H3R treatment we found alterations in activation marker expression in the spinal cord. We further were able to identify changes in the composition and activation of spinal cord (CNS) and the N. plantaris (PNS) of arthritic mice induced by H3R agonist treatment through RNAseq. Moreover, lightsheet microscopy and in vivo PET-CT scans revealed alterations in vessel leakiness and cell infiltration in the inflamed joints.ConclusionIn summary, these data show that the SCFA propionate effectively regulates ongoing inflammation by promoting histamine secretion of the gut microbiota and subsequent H3R-mediated neuronal effector functions that drive the fast resolution of synovial inflammation.References[1]J. U. Scher et al., eLife 2, e01202 (2013).[2]S. Lucas et al., Nat Commun 9, 55 (2018).[3]N. Tajik et al., Nat Commun 11, 1995 (2020).[4]W. Walrabenstein et al., Rheumatology (2023).[5]J. Häger et al., Nutrients 11, (2019).[6]K. Dürholz et al., Nutrients 12, 3207 (2020).Acknowledgements:NIL.Disclosure of InterestsNone Declared.
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Guse, Kylene, Qingqing Mao, Chi Chen, and Andres Gomez. "Meta-Omics Analyses of Conventional and Regenerative Fermented Vegetables: Is There an Impact on Health-Boosting Potential?" Fermentation 11, no. 1 (January 7, 2025): 22. https://doi.org/10.3390/fermentation11010022.
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Fermented vegetables contain probiotic microbes and metabolites, which are transformed from fresh vegetables, potentially providing health benefits. The kind of vegetable used to ferment and how it is grown may determine the types of health-promoting properties. To understand the possible benefits of fermented vegetables under different growing conditions, we compared the microbiomes and metabolomes of three different types of naturally fermented vegetables—carrots, peppers, and radishes—that were grown either under conventional or regenerative growing systems. We profiled bacterial and fungal communities via 16S rRNA short-read (V4 region), long-read, and ITS2 sequencing, in tandem with untargeted metabolomics (LC-MS). The results showed that the microbiomes and metabolomes of the fermented vegetables under each growing system are unique, highlighting distinctions in amino acid content and potentially probiotic microbes (p < 0.05). All fermented vegetables contained high amounts of gamma-aminobutyric acid (GABA), a critical neurotransmitter. However, GABA was found to be in higher abundance in the regenerative fermented vegetables, particularly in carrots (p < 0.01) and peppers (p < 0.05), and was associated with higher abundances of the typically probiotic Lactiplantibacillus plantarum. Our findings indicate that the growing system may impact the microbiome and metabolome of plant-based ferments, encouraging more research on the health-boosting potential of regeneratively grown vegetables.
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Shaver, Amanda O., Brianna M. Garcia, Goncalo J. Gouveia, Alison M. Morse, Zihao Liu, Carter K. Asef, Ricardo M. Borges, et al. "An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics." Frontiers in Molecular Biosciences 9 (November 9, 2022). http://dx.doi.org/10.3389/fmolb.2022.930204.
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Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.
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Yuan, Yu, Liping Huang, Lulu Yu, Xingxu Yan, Siyu Chen, Chenghao Bi, Junjie He, et al. "Clinical metabolomics characteristics of diabetic kidney disease: A meta‐analysis of 1875 cases with diabetic kidney disease and 4503 controls." Diabetes/Metabolism Research and Reviews 40, no. 3 (March 2024). http://dx.doi.org/10.1002/dmrr.3789.
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AbstractAimsDiabetic Kidney Disease (DKD), one of the major complications of diabetes, is also a major cause of end‐stage renal disease. Metabolomics can provide a unique metabolic profile of the disease and thus predict or diagnose the development of the disease. Therefore, this study summarises a more comprehensive set of clinical biomarkers related to DKD to identify functional metabolites significantly associated with the development of DKD and reveal their driving mechanisms for DKD.Materials and MethodsWe searched PubMed, Embase, the Cochrane Library and Web of Science databases through October 2022. A meta‐analysis was conducted on untargeted or targeted metabolomics research data based on the strategy of standardized mean differences and the process of ratio of means as the effect size, respectively. We compared the changes in metabolite levels between the DKD patients and the controls and explored the source of heterogeneity through subgroup analyses, sensitivity analysis and meta‐regression analysis.ResultsThe 34 clinical‐based metabolomics studies clarified the differential metabolites between DKD and controls, containing 4503 control subjects and 1875 patients with DKD. The results showed that a total of 60 common differential metabolites were found in both meta‐analyses, of which 5 metabolites (p < 0.05) were identified as essential metabolites. Compared with the control group, metabolites glycine, aconitic acid, glycolic acid and uracil decreased significantly in DKD patients; cysteine was significantly higher. This indicates that amino acid metabolism, lipid metabolism and pyrimidine metabolism in DKD patients are disordered.ConclusionsWe have identified 5 metabolites and metabolic pathways related to DKD which can serve as biomarkers or targets for disease prevention and drug therapy.
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Leão, Tiago F., Mingxun Wang, Ricardo da Silva, Alexey Gurevich, Anelize Bauermeister, Paulo Wender P. Gomes, Asker Brejnrod, et al. "NPOmix: a machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters." PNAS Nexus, November 26, 2022. http://dx.doi.org/10.1093/pnasnexus/pgac257.
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Abstract Microbial specialized metabolites are an important source of and inspiration for many pharmaceutical, biotechnological products and play key roles in ecological processes. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Efficiently linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs by allowing metabolomics to make use of genetic predictions. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical class). Our new pattern-based genome mining pipeline links biosynthetic genes to metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired datasets that include validated genes-mass spectral links from the Paired omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra to their corresponding previously experimentally validated biosynthetic genes (e.g. via nuclear magnetic resonance or genetic engineering). We illustrated a computational example of how to combine NPOmix with MassQL for mining siderophores that can be reproduced by NPOmix users. We conclude that NPOmix minimizes the need for culturing (it worked well on microbiomes) and facilitates specialized metabolite prioritization based on integrative omics mining.
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Kim, Hyunju, Bing Yu, Xin Li, Kari E. Wong, Eric Boerwinkle, Sara B. Seidelmann, Andrew S. Levey, Eugene P. Rhee, Josef Coresh, and Casey M. Rebholz. "Abstract MP13: Serum Metabolomic Signatures Of Plant-based Diets And Incident Chronic Kidney Disease." Circulation 145, Suppl_1 (March 2022). http://dx.doi.org/10.1161/circ.145.suppl_1.mp13.
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Introduction: Greater adherence to plant-based diets is associated with a lower risk of incident chronic kidney disease (CKD). Metabolomics can help identify blood biomarkers of plant-based diets and understand the underlying mechanisms. Objectives: Using untargeted metabolomics, we aimed to identify metabolites associated with 4 plant-based diet indices (PDI) [overall PDI, provegetarian, healthful PDI, and unhealthful PDI] and incident CKD in 2 subgroups within the Atherosclerosis Risk in Communities Study. Methods: Participants (n 1 =1,762; n 2 =2,152) reported usual dietary intake using a food frequency questionnaire. We used linear regression models to examine the association between 4 PDIs and 374 individual metabolites. We used Cox regression models to evaluate associations between PDI-related metabolites and incident CKD. Estimates were meta-analyzed across 2 subgroups. We calculated C-statistics to assess whether metabolites improved the prediction of those in the highest quintile vs. lower 4 quintiles of PDIs, and whether PDI-related metabolites predicted incident CKD. Results: We identified 82 significant PDI-metabolite associations (overall PDI=27; provegetarian=17; healthful PDI=20; unhealthful PDI=18); 11 metabolites overlapped across the overall PDI, provegetarian, and healthful PDI. The addition of metabolites improved prediction of those in the highest quintile vs. lower 4 quintiles of PDIs compared to participant characteristics alone ( P -value ≤0.001 for all tests). Six PDI-related metabolites representing 2 pathways were significantly associated with incident CKD ( Table ) and improved prediction of incident CKD beyond traditional risk factors (difference in C-statistics for all 6 metabolites=0.005, P -value=0.006). Conclusions: In a community-based study of US adults, we identified metabolites that were related to plant-based diets that predicted incident CKD. These metabolites highlight pathways through which plant-based diets are associated with incident CKD.
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Kim, Hyunju, Bing Yu, Xin Li, Kari E. Wong, Eric Boerwinkle, Sara B. Seidelmann, Andrew S. Levey, Eugene P. Rhee, Josef Coresh, and Casey M. Rebholz. "Serum metabolomic signatures of plant-based diets and incident chronic kidney disease." American Journal of Clinical Nutrition, February 26, 2022. http://dx.doi.org/10.1093/ajcn/nqac054.
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ABSTRACT Background Greater adherence to plant-based diets is associated with a lower risk of incident chronic kidney disease (CKD). Metabolomics can help identify blood biomarkers of plant-based diets and enhance understanding of underlying mechanisms. Objectives Using untargeted metabolomics, we aimed to identify metabolites associated with 4 plant-based diet indices (PDIs) (overall PDI, provegetarian diet, healthful PDI, and unhealthful PDI) and incident CKD in 2 subgroups within the Atherosclerosis Risk in Communities study. Methods We calculated 4 PDIs based on participants’ responses on an FFQ. We used multivariable linear regression to examine the association between 4 PDIs and 374 individual metabolites, adjusting for confounders. We used Cox proportional hazards regression to evaluate associations between PDI-related metabolites and incident CKD. Estimates were meta-analyzed across 2 subgroups (n1 = 1762; n2 = 1960). We calculated C-statistics to assess whether metabolites improved the prediction of those in the highest quintile compared to the lower 4 quintiles of PDIs, and whether PDI- and CKD-related metabolites predicted incident CKD beyond the CKD prediction model. Results We identified 82 significant PDI–metabolite associations (overall PDI = 27; provegetarian = 17; healthful PDI = 20; unhealthful PDI = 18); 11 metabolites overlapped across the overall PDI, provegetarian diet, and healthful PDI. The addition of metabolites improved prediction of those in the highest quintile as opposed to the lower 4 quintiles of PDIs compared with participant characteristics alone (range of differences in C-statistics = 0.026–0.104; P value ≤ 0.001 for all tests). Six PDI-related metabolites (glycerate, 1,5-anhydroglucitol, γ-glutamylalanine, γ-glutamylglutamate, γ-glutamylleucine, γ-glutamylvaline), involved in glycolysis, gluconeogenesis, pyruvate metabolism, and γ-glutamyl peptide metabolism, were significantly associated with incident CKD and improved prediction of incident CKD beyond the CKD prediction model (difference in C-statistics for 6 metabolites = 0.005; P value = 0.006). Conclusions In a community-based study of US adults, we identified metabolites that were related to plant-based diets and predicted incident CKD. These metabolites highlight pathways through which plant-based diets are associated with incident CKD.
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Sterrett, John D., Kevin D. Quinn, Katrina A. Doenges, Nichole M. Nusbacher, Cassandra L. Levens, Mike L. Armstrong, Richard M. Reisdorph, et al. "Appearance of green tea compounds in plasma following acute green tea consumption is modulated by the gut microbiome in mice." Microbiology Spectrum, January 10, 2025. https://doi.org/10.1128/spectrum.01799-24.
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ABSTRACT Studies have suggested that phytochemicals in green tea have systemic anti-inflammatory and neuroprotective effects. However, the mechanisms behind these effects are poorly understood, possibly due to the differential metabolism of phytochemicals resulting from variations in gut microbiome composition. To unravel this complex relationship, our team utilized a novel combined microbiome analysis and metabolomics approach applied to low complexity microbiome (LCM) and human colonized (HU) gnotobiotic mice treated with an acute dose of powdered matcha green tea. A total of 20 LCM mice received 10 distinct human fecal slurries for an n = 2 mice per human gut microbiome; 9 LCM mice remained un-colonized with human slurries throughout the experiment. We performed untargeted metabolomics on green tea and plasma to identify green tea compounds that were found in the plasma of LCM and HU mice that had consumed green tea. 16S ribosomal RNA gene sequencing was performed on feces of all mice at study end to assess microbiome composition. We found multiple green tea compounds in plasma associated with microbiome presence and diversity (including acetylagmatine, lactiflorin, and aspartic acid negatively associated with diversity). Additionally, we detected strong associations between bioactive green tea compounds in plasma and specific gut bacteria, including associations between spiramycin and Gemmiger and between wildforlide and Anaerorhabdus . Notably, some of the physiologically relevant green tea compounds are likely derived from plant-associated microbes, highlighting the importance of considering foods and food products as meta-organisms. Overall, we describe a novel workflow for discovering relationships between individual food compounds and the composition of the gut microbiome. IMPORTANCE Foods contain thousands of unique and biologically important compounds beyond the macro- and micro-nutrients listed on nutrition facts labels. In mammals, many of these compounds are metabolized or co-metabolized by the community of microbes in the colon. These microbes may impact the thousands of biologically important compounds we consume; therefore, understanding microbial metabolism of food compounds will be important for understanding how foods impact health. We used metabolomics to track green tea compounds in plasma of mice with and without complex microbiomes. From this, we can start to recognize certain groups of green tea-derived compounds that are impacted by mammalian microbiomes. This research presents a novel technique for understanding microbial metabolism of food-derived compounds in the gut, which can be applied to other foods.
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Tramice, A., D. Paris, A. Manca, F. A. Guevara Agudelo, S. Petrosino, L. Siracusa, M. Carbone, D. Melck, F. Raymond, and F. Piscitelli. "Analysis of the oral microbiome during hormonal cycle and its alterations in menopausal women: the “AMICA” project." Scientific Reports 12, no. 1 (December 21, 2022). http://dx.doi.org/10.1038/s41598-022-26528-w.
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AbstractThe maintenance of human health is dependent on a symbiotic relationship between humans and associated bacteria. The diversity and abundance of each habitat’s signature microbes vary widely among body areas and among them the oral microbiome plays a key role. Significant changes in the oral cavity, predominantly at salivary and periodontal level, have been associated with changes in estrogen levels. However, whether the oral microbiome is affected by hormonal level alterations is understudied. Hence the main objective pursued by AMICA project was to characterize the oral microbiome (saliva) in healthy women through: profiling studies using "omics" technologies (NMR-based metabolomics, targeted lipidomics by LC–MS, metagenomics by NGS); SinglePlex ELISA assays; glycosidase activity analyses and bioinformatic analysis. For this purpose, thirty-nine medically healthy women aged 26–77 years (19 with menstrual cycle and 20 in menopause) were recruited. Participants completed questionnaires assessing detailed medical and medication history and demographic characteristics. Plasmatic and salivary levels of sexual hormones were assessed (FSH, estradiol, LH and progesteron) at day 3 and 14 for women with menstrual cycle and only once for women in menopause. Salivary microbiome composition was assessed through meta-taxonomic 16S sequencing and overall, the salivary microbiome of most women remained relatively stable throughout the menstrual cycle and in menopause. Targeted lipidomics and untargeted metabolomics profiling were assessed through the use of LC–MS and NMR spectroscopy technologies, respectively and significant changes in terms of metabolites were identified in saliva of post-menopausal women in comparison to cycle. Moreover, glycosyl hydrolase activities were screened and showed that the β-D-hexosaminidase activity was the most present among those analyzed. Although this study has not identified significant alterations in the composition of the oral microbiome, multiomics analysis have revealed a strong correlation between 2-AG and α-mannosidase. In conclusion, the use of a multidisciplinary approach to investigate the oral microbiome of healthy women provided some indication about microbiome-derived predictive biomarkers that could be used in the future for developing new strategies to help to re-establish the correct hormonal balance in post-menopausal women.