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Journal of Translational Medicine Oct 2022Integrative analysis approaches of metagenomics and metabolomics have been widely developed to understand the association between disease and the gut microbiome....
BACKGROUND
Integrative analysis approaches of metagenomics and metabolomics have been widely developed to understand the association between disease and the gut microbiome. However, the different profiling patterns of different metabolic samples in the association analysis make it a matter of concern which type of sample is the most closely associated with gut microbes and disease. To address this lack of knowledge, we investigated the association between the gut microbiome and metabolomic profiles of stool, urine, and plasma samples from ischemic stroke patients and healthy subjects.
METHODS
We performed metagenomic sequencing (feces) and untargeted metabolomics analysis (feces, plasma, and urine) from ischemic stroke patients and healthy volunteers. Differential analyses were conducted to find key differential microbiota and metabolites for ischemic stroke. Meanwhile, Spearman's rank correlation and linear regression analyses were used to study the association between microbiota and metabolites of different metabolic mixtures.
RESULTS
Untargeted metabolomics analysis shows that feces had the most abundant features and identified metabolites, followed by urine and plasma. Feces had the highest number of differential metabolites between ischemic stroke patients and the healthy group. Based on the association analysis between metagenomics and metabolomics of fecal, urine, and plasma, fecal metabolome showed the strongest association with the gut microbiome. There are 1073, 191, and 81 statistically significant pairs (P < 0.05) in the correlation analysis for fecal, urine, and plasma metabolome. Fecal metabolites explained the variance of alpha-diversity of the gut microbiome up to 31.1%, while urine and plasma metabolites only explained the variance of alpha-diversity up to 13.5% and 10.6%. Meanwhile, there were more significant differential metabolites in feces than urine and plasma associated with the stroke marker bacteria.
CONCLUSIONS
The systematic association analysis between gut microbiome and metabolomics reveals that fecal metabolites show the strongest association with the gut microbiome, followed by urine and plasma. The findings would promote the association study between the gut microbiome and fecal metabolome to explore key factors that are associated with diseases. We also provide a user-friendly web server and a R package to facilitate researchers to conduct the association analysis of gut microbiome and metabolomics.
Topics: Feces; Gastrointestinal Microbiome; Humans; Ischemic Stroke; Metabolome; Metabolomics; RNA, Ribosomal, 16S
PubMed: 36209079
DOI: 10.1186/s12967-022-03669-0 -
Nutrients May 2023Obesity is a disorder identified by an inappropriate increase in weight in relation to height and is considered by many international health institutions to be a major... (Review)
Review
Obesity is a disorder identified by an inappropriate increase in weight in relation to height and is considered by many international health institutions to be a major pandemic of the 21st century. The gut microbial ecosystem impacts obesity in multiple ways that yield downstream metabolic consequences, such as affecting systemic inflammation, immune response, and energy harvest, but also the gut-host interface. Metabolomics, a systematized study of low-molecular-weight molecules that take part in metabolic pathways, represents a serviceable method for elucidation of the crosstalk between hosts' metabolism and gut microbiota. In the present review, we confer about clinical and preclinical studies exploring the association of obesity and related metabolic disorders with various gut microbiome profiles, and the effects of several dietary interventions on gut microbiome composition and the metabolome. It is well established that various nutritional interventions may serve as an efficient therapeutic approach to support weight loss in obese individuals, yet no agreement exists in regard to the most effective dietary protocol, both in the short and long term. However, metabolite profiling and the gut microbiota composition might represent an opportunity to methodically establish predictors for obesity control that are relatively simple to measure in comparison to traditional approaches, and it may also present a tool to determine the optimal nutritional intervention to ameliorate obesity in an individual. Nevertheless, a lack of adequately powered randomized trials impedes the application of observations to clinical practice.
Topics: Humans; Gastrointestinal Microbiome; Ecosystem; Obesity; Metabolome; Metabolomics
PubMed: 37242119
DOI: 10.3390/nu15102236 -
Nature Biotechnology Apr 2018Metabolomics, in which small-molecule metabolites (the metabolome) are identified and quantified, is broadly acknowledged to be the omics discipline that is closest to...
Metabolomics, in which small-molecule metabolites (the metabolome) are identified and quantified, is broadly acknowledged to be the omics discipline that is closest to the phenotype. Although appreciated for its role in biomarker discovery programs, metabolomics can also be used to identify metabolites that could alter a cell's or an organism's phenotype. Metabolomics activity screening (MAS) as described here integrates metabolomics data with metabolic pathways and systems biology information, including proteomics and transcriptomics data, to produce a set of endogenous metabolites that can be tested for functionality in altering phenotypes. A growing literature reports the use of metabolites to modulate diverse processes, such as stem cell differentiation, oligodendrocyte maturation, insulin signaling, T-cell survival and macrophage immune responses. This opens up the possibility of identifying and applying metabolites to affect phenotypes. Unlike genes or proteins, metabolites are often readily available, which means that MAS is broadly amenable to high-throughput screening of virtually any biological system.
Topics: Humans; Metabolic Networks and Pathways; Metabolome; Metabolomics; Phenotype; Proteomics; Systems Biology
PubMed: 29621222
DOI: 10.1038/nbt.4101 -
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 -
The Journal of Steroid Biochemistry and... Nov 2019Advances in technology have allowed for the sensitive, specific, and simultaneous quantitative profiling of steroid precursors, bioactive steroids and inactive... (Review)
Review
Advances in technology have allowed for the sensitive, specific, and simultaneous quantitative profiling of steroid precursors, bioactive steroids and inactive metabolites, facilitating comprehensive characterization of the serum and urine steroid metabolomes. The quantification of steroid panels is therefore gaining favor over quantification of single marker metabolites in the clinical and research laboratories. However, although the biochemical pathways for the biosynthesis and metabolism of steroid hormones are now well defined, a gulf still exists between this knowledge and its application to the measured steroid profiles. In this review, we present an overview of steroid hormone biosynthesis and metabolism by the liver and peripheral tissues, specifically highlighting the pathways linking and differentiating the serum and urine steroid metabolomes. A brief overview of the methodology used in steroid profiling is also provided.
Topics: Humans; Mass Spectrometry; Metabolome; Metabolomics; Steroids
PubMed: 31362062
DOI: 10.1016/j.jsbmb.2019.105439 -
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 -
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 -
ACS Chemical Neuroscience Jan 2018Metabolomics, the characterization of metabolites and their changes within biological systems, has seen great technological and methodological progress over the past... (Review)
Review
Metabolomics, the characterization of metabolites and their changes within biological systems, has seen great technological and methodological progress over the past decade. Most metabolomic experiments involve the characterization of the small-molecule content of fluids or tissue homogenates. While these microliter and larger volume metabolomic measurements can characterize hundreds to thousands of compounds, the coverage of molecular content decreases as sample sizes are reduced to the nanoliter and even to the picoliter volume range. Recent progress has enabled the ability to characterize the major molecules found within specific individual cells. Especially within the brain, a myriad of cell types are colocalized, and oftentimes only a subset of these cells undergo changes in both healthy and pathological states. Here we highlight recent progress in mass spectrometry-based approaches used for single cell metabolomics, emphasizing their application to neuroscience research. Single cell studies can be directed to measuring differences between members of populations of similar cells (e.g., oligodendrocytes), as well as characterizing differences between cell types (e.g., neurons and astrocytes), and are especially useful for measuring changes occurring during different behavior states, exposure to diets and drugs, neuronal activity, and disease. When combined with other omics approaches such as transcriptomics, and with morphological and physiological measurements, single cell metabolomics aids fundamental neurochemical studies, has great potential in pharmaceutical development, and should improve the diagnosis and treatment of brain diseases.
Topics: Animals; Humans; Metabolome; Metabolomics; Neuroglia; Neurons; Single-Cell Analysis
PubMed: 28982006
DOI: 10.1021/acschemneuro.7b00304 -
Biomedicine & Pharmacotherapy =... Jan 2020The aging process is accompanied by changes in the gut microbiota and metabolites. This study aimed to reveal the relationship between gut microbiota and the metabolome...
UNLABELLED
The aging process is accompanied by changes in the gut microbiota and metabolites. This study aimed to reveal the relationship between gut microbiota and the metabolome at different ages, as well as the anti-aging effect of FTZ, which is an effective clinical prescription for the treatment of hyperlipidemia and diabetes.
METHODS
In the present study, mice were randomly divided into different age and FTZ treatment groups. The aging-relevant behavioral phenotype the levels of blood glucose, cholesterol, triglycerides, low density lipoprotein cholesterol, free fatty acids, high density lipoprotein-cholesterol and cytokine TNF-α,IL-6, IL-8 in the serum were measured. Changes of serum metabolties were analyzed by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-Q-TOF/MS). Gut microbiota were identified using 16S rDNA sequencing.
RESULTS
Our results indicated that with age, the aging-relevant behavioral phenotype appeared, glucose and lipid metabolism disordered, secretion levels of cytokine TNF-α, IL-6 and IL-8 increased.The Firmicutes/Bacteroidetes ratio changed with age, first increasing and then decreasing, and the microbial diversity and the community richness of the aging mice were improved by FTZ. The abundance of opportunistic bacteria decreased (Lactobacillus murinus, Erysipelatoclostridium), while the levels of protective bacteria such as Butyricimonas, Clostridium and Akkermansia increased. Metabolic analysis identified 24 potential biomarkers and 10 key pathways involving arachidonic acid metabolism, phospholipid metabolism, fatty acid metabolism, taurine and hypotaurine metabolism. Correlation analysis between the gut microbiota and biomarkers suggested that the relative abundance of protective bacteria was negatively correlated with the levels of leukotriene E4, 20-HETE and arachidonic acid, which was different from protective bacteria.
CONCLUSION
Shifts of gut microbiota and metabolomic profiles were observed in the mice during the normal aging process, and treatment with FTZ moderately corrected the aging, which may be mediated via interference with arachidonic acid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, taurine and hypotaurine metabolism and gut microbiota in mice.
Topics: Aging; Animals; Bacteria; Biomarkers; Drugs, Chinese Herbal; Gastrointestinal Microbiome; Hyperlipidemias; Lipid Metabolism; Male; Metabolome; Metabolomics; Mice; Mice, Inbred C57BL
PubMed: 31704617
DOI: 10.1016/j.biopha.2019.109550