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Comprehensive metabolic profiling of Parkinson's disease by liquid chromatography-mass spectrometry.Molecular Neurodegeneration Jan 2021Parkinson's disease (PD) is a prevalent neurological disease in the elderly with increasing morbidity and mortality. Despite enormous efforts, rapid and accurate...
BACKGROUND
Parkinson's disease (PD) is a prevalent neurological disease in the elderly with increasing morbidity and mortality. Despite enormous efforts, rapid and accurate diagnosis of PD is still compromised. Metabolomics defines the final readout of genome-environment interactions through the analysis of the entire metabolic profile in biological matrices. Recently, unbiased metabolic profiling of human sample has been initiated to identify novel PD metabolic biomarkers and dysfunctional metabolic pathways, however, it remains a challenge to define reliable biomarker(s) for clinical use.
METHODS
We presented a comprehensive metabolic evaluation for identifying crucial metabolic disturbances in PD using liquid chromatography-high resolution mass spectrometry-based metabolomics approach. Plasma samples from 3 independent cohorts (n = 460, 223 PD, 169 healthy controls (HCs) and 68 PD-unrelated neurological disease controls) were collected for the characterization of metabolic changes resulted from PD, antiparkinsonian treatment and potential interferences of other diseases. Unbiased multivariate and univariate analyses were performed to determine the most promising metabolic signatures from all metabolomic datasets. Multiple linear regressions were applied to investigate the associations of metabolites with age, duration time and stage of PD. The combinational biomarker model established by binary logistic regression analysis was validated by 3 cohorts.
RESULTS
A list of metabolites including amino acids, acylcarnitines, organic acids, steroids, amides, and lipids from human plasma of 3 cohorts were identified. Compared with HC, we observed significant reductions of fatty acids (FFAs) and caffeine metabolites, elevations of bile acids and microbiota-derived deleterious metabolites, and alterations in steroid hormones in drug-naïve PD. Additionally, we found that L-dopa treatment could affect plasma metabolome involved in phenylalanine and tyrosine metabolism and alleviate the elevations of bile acids in PD. Finally, a metabolite panel of 4 biomarker candidates, including FFA 10:0, FFA 12:0, indolelactic acid and phenylacetyl-glutamine was identified based on comprehensive discovery and validation workflow. This panel showed favorable discriminating power for PD.
CONCLUSIONS
This study may help improve our understanding of PD etiopathogenesis and facilitate target screening for therapeutic intervention. The metabolite panel identified in this study may provide novel approach for the clinical diagnosis of PD in the future.
Topics: Aged; Biomarkers; Case-Control Studies; Chromatography, Liquid; Female; Humans; Male; Mass Spectrometry; Metabolome; Metabolomics; Parkinson Disease
PubMed: 33485385
DOI: 10.1186/s13024-021-00425-8 -
Nature Genetics Jun 2023The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and...
The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (SLC10A2). Shared genetic determinants of 7,073 metabolite-disease combinations represent a resource to better understand metabolic diseases and revealed connections of dipeptidase 1 with circulating digestive enzymes and with hypertension. Extending genetic studies of the metabolome beyond plasma yields unique insights into processes at the interface of body compartments.
Topics: Metabolome; Kidney; Metabolomics
PubMed: 37277652
DOI: 10.1038/s41588-023-01409-8 -
Environmental Science & Technology Nov 2022Metal exposure has been associated with risk of various cardio-metabolic disorders, and investigation on the association between exposure to multiple metals and...
Metal exposure has been associated with risk of various cardio-metabolic disorders, and investigation on the association between exposure to multiple metals and metabolic responses may reveal novel clues to the underlying mechanisms. Based on a metabolome-wide association study of 17 plasma metals with untargeted metabolomic profiling of 189 serum metabolites among 1992 participants within the Dongfeng-Tongji cohort, we replicated two metal-associated pathways, linoleic acid metabolism and aminoacyl-tRNA biosynthesis, with novel metal associations (false discovery rate, FDR < 0.05), and we also identified two novel pathways, including biosynthesis of unsaturated fatty acids and alpha-linolenic acid metabolism, as associated with metal exposure (FDR < 0.05). Moreover, two-way orthogonal partial least-squares analysis showed that five metabolites, including aspartylphenylalanine, free fatty acid 14:1, uridine, carnitine C14:2, and LPC 18:2, contributed most to the joint covariation between the two data matrices (12.3%, 8.3%, 8.0%, 7.4%, and 7.3%, respectively). Further BKMR analysis showed significant positive joint associations of plasma Al, As, Ba, and Zn with aspartylphenylalanine and of plasma Ba, Co, Mn, and Pb with carnitine C14:2, when all the metals were at the 55th percentiles or above, compared with the median. We also found significant interactions between As and Ba in the association with aspartylphenylalanine ( for interaction = 0.048) and between Ba and Pb in the association with carnitine C14:2 ( for interaction < 0.001). Together, these findings may provide new insights into the mechanisms underlying the adverse health effects induced by metal exposure.
Topics: Adult; Humans; Middle Aged; Aged; Lead; Metabolome; Metabolomics; Carnitine; China
PubMed: 36269707
DOI: 10.1021/acs.est.2c05547 -
Current Protocols in Bioinformatics Dec 2019MetaboAnalyst (https://www.metaboanalyst.ca) is an easy-to-use web-based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with...
MetaboAnalyst (https://www.metaboanalyst.ca) is an easy-to-use web-based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever-expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta-analysis, and network-based multi-omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web-based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Data uploading, processing, and normalization Basic Protocol 2: Identification of significant variables Basic Protocol 3: Multivariate exploratory data analysis Basic Protocol 4: Functional interpretation of metabolomic data Basic Protocol 5: Biomarker analysis based on receiver operating characteristic (ROC) curves Basic Protocol 6: Time-series and two-factor data analysis Basic Protocol 7: Sample size estimation and power analysis Basic Protocol 8: Joint pathway analysis Basic Protocol 9: MS peaks to pathway activities Basic Protocol 10: Biomarker meta-analysis Basic Protocol 11: Knowledge-based network exploration of multi-omics data Basic Protocol 12: MetaboAnalystR introduction.
Topics: Biomarkers; Computational Biology; Databases, Factual; Humans; Internet; Metabolome; Metabolomics; Principal Component Analysis; Software; User-Computer Interface
PubMed: 31756036
DOI: 10.1002/cpbi.86 -
Magnetic Resonance in Chemistry : MRC Dec 2023Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred... (Review)
Review
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
Topics: Humans; Metabolome; Reproducibility of Results; Metabolomics; Multivariate Analysis; Principal Component Analysis
PubMed: 37005774
DOI: 10.1002/mrc.5350 -
Advances in Experimental Medicine and... 2021Metabolomics is the systematic study of metabolite profiles of complex biological systems, and involves the systematic identification and quantification of metabolites.... (Review)
Review
Metabolomics is the systematic study of metabolite profiles of complex biological systems, and involves the systematic identification and quantification of metabolites. Metabolism is integrated with all biochemical reactions in biological systems; thus metabolite profiles provide collective information on biochemical processes induced by genetic or environmental perturbations. Transcriptomes or proteomes may not be functionally active and not always reflect phenotypic variations. The metabolome, however, consists of the biomolecules closest to the phenotype of living organisms, and is often called the molecular phenotype of biological systems. Thus, metabolome alterations can easily result in disease states, providing important clues to understand pathophysiological mechanisms contributing to various biomedical symptoms. The metabolome and metabolomics have been emphasized in translational research related to biomarker discovery, drug target discovery, drug responses, and disease mechanisms. This review describes the basic concepts, workflows, and applications of mass spectrometry-based metabolomics in translational research.
Topics: Mass Spectrometry; Metabolome; Metabolomics; Phenotype; Translational Research, Biomedical
PubMed: 33834448
DOI: 10.1007/978-981-33-6064-8_19 -
Life Sciences Nov 2023Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are... (Review)
Review
Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.
Topics: Humans; Epigenesis, Genetic; Metabolomics; Metabolome; Mitochondria; Cardiovascular Diseases
PubMed: 37788764
DOI: 10.1016/j.lfs.2023.122137 -
Database : the Journal of Biological... Jan 2020Breathomics is a special branch of metabolomics that quantifies volatile organic compounds (VOCs) from collected exhaled breath samples. Understanding how breath...
Breathomics is a special branch of metabolomics that quantifies volatile organic compounds (VOCs) from collected exhaled breath samples. Understanding how breath molecules are related to diseases, mechanisms and pathways identified from experimental analytical measurements is challenging due to the lack of an organized resource describing breath molecules, related references and biomedical information embedded in the literature. To provide breath VOCs, related references and biomedical information, we aim to organize a database composed of manually curated information and automatically extracted biomedical information. First, VOCs-related disease information was manually organized from 207 literature linked to 99 VOCs and known Medical Subject Headings (MeSH) terms. Then an automated text mining algorithm was used to extract biomedical information from this literature. In the end, the manually curated information and auto-extracted biomedical information was combined to form a breath molecule database-the Human Breathomics Database (HBDB). We first manually curated and organized disease information including MeSH term from 207 literatures associated with 99 VOCs. Then, an automatic pipeline of text mining approach was used to collect 2766 literatures and extract biomedical information from breath researches. We combined curated information with automatically extracted biomedical information to assemble a breath molecule database, the HBDB. The HBDB is a database that includes references, VOCs and diseases associated with human breathomics. Most of these VOCs were detected in human breath samples or exhaled breath condensate samples. So far, the database contains a total of 913 VOCs in relation to human exhaled breath researches reported in 2766 publications. The HBDB is the most comprehensive HBDB of VOCs in human exhaled breath to date. It is a useful and organized resource for researchers and clinicians to identify and further investigate potential biomarkers from the breath of patients. Database URL: https://hbdb.cmdm.tw.
Topics: Breath Tests; Data Mining; Database Management Systems; Exhalation; Humans; Metabolome; Metabolomics; Volatile Organic Compounds
PubMed: 31976536
DOI: 10.1093/database/baz139 -
International Journal of Molecular... Jun 2022In the past two decades, the post-genomic era envisaged high-throughput technologies, resulting in more species with available genome sequences. In-depth multi-omics... (Review)
Review
In the past two decades, the post-genomic era envisaged high-throughput technologies, resulting in more species with available genome sequences. In-depth multi-omics approaches have evolved to integrate cellular processes at various levels into a systems biology knowledge base. Metabolomics plays a crucial role in molecular networking to bridge the gaps between genotypes and phenotypes. However, the greater complexity of metabolites with diverse chemical and physical properties has limited the advances in plant metabolomics. For several years, applications of liquid/gas chromatography (LC/GC)-mass spectrometry (MS) and nuclear magnetic resonance (NMR) have been constantly developed. Recently, ion mobility spectrometry (IMS)-MS has shown utility in resolving isomeric and isobaric metabolites. Both MS and NMR combined metabolomics significantly increased the identification and quantification of metabolites in an untargeted and targeted manner. Thus, hyphenated metabolomics tools will narrow the gap between the number of metabolite features and the identified metabolites. Metabolites change in response to environmental conditions, including biotic and abiotic stress factors. The spatial distribution of metabolites across different organs, tissues, cells and cellular compartments is a trending research area in metabolomics. Herein, we review recent technological advancements in metabolomics and their applications in understanding plant stress biology and different levels of spatial organization. In addition, we discuss the opportunities and challenges in multiple stress interactions, multi-omics, and single-cell metabolomics.
Topics: Chromatography, Liquid; Gas Chromatography-Mass Spectrometry; Mass Spectrometry; Metabolome; Metabolomics; Plants
PubMed: 35805979
DOI: 10.3390/ijms23136985 -
Journal of Chromatography. B,... Aug 2020Metabolomics involvement in the study of foods is steadily growing. Such a rise is a consequence of the increasing demand in the food sector to address challenges... (Review)
Review
Metabolomics involvement in the study of foods is steadily growing. Such a rise is a consequence of the increasing demand in the food sector to address challenges regarding the issues of food safety, quality, and authenticity in a more comprehensive way. Virgin olive oil (VOO) is a key product of the Mediterranean diet, with a globalized consumer interest as it may be associated with various nutritional and health benefits. Despite the strict legislation to protect this high added-value agricultural commodity and offer guarantees to consumers and honest producers, there are still analytical issues needing to be further addressed. Thus, this review aims to present the efforts made using targeted and untargeted metabolomics approaches, namely nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry-based techniques (mainly LC/GC-MS) combined with multivariate statistical analysis. Case-studies focusing on geographical/varietal classification and detection of adulteration are discussed with regards to the identification of possible markers. The advantages and limitations of each of the aforementioned techniques applied to VOO analysis are also highlighted.
Topics: Chromatography; Magnetic Resonance Spectroscopy; Mass Spectrometry; Metabolome; Metabolomics; Olive Oil
PubMed: 32505112
DOI: 10.1016/j.jchromb.2020.122161