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Molecular Omics Apr 2021Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or... (Review)
Review
Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.
Topics: Computational Biology; Diet; Genomics; Humans; Metabolome; Metabolomics; Microbiota; Proteomics; Stress, Physiological; Systems Biology
PubMed: 33598670
DOI: 10.1039/d0mo00176g -
Current Opinion in Microbiology Dec 2022The metabolome lies at the interface of host-microbiome crosstalk. Previous work has established links between chemically diverse microbial metabolites and a myriad of... (Review)
Review
The metabolome lies at the interface of host-microbiome crosstalk. Previous work has established links between chemically diverse microbial metabolites and a myriad of host physiological processes and diseases. Coupled with scalable and cost-effective technologies, metabolomics is thus gaining popularity as a tool for characterization of microbial communities, particularly when combined with metagenomics as a window into microbiome function. A systematic interrogation of microbial community metabolomes can uncover key microbial compounds, metabolic capabilities of the microbiome, and also provide critical mechanistic insights into microbiome-linked host phenotypes. In this review, we discuss methods and accompanying resources that have been developed for these purposes. The accomplishments of these methods demonstrate that metabolomes can be used to functionally characterize microbial communities, and that microbial properties can be used to identify and investigate chemical compounds.
Topics: Metabolomics; Microbiota; Metabolome; Metagenomics
PubMed: 36063685
DOI: 10.1016/j.mib.2022.102195 -
Current Opinion in Biotechnology Oct 2021Single-cell metabolomics (SCM) is currently one of the most powerful tools for performing high-throughput metabolic analysis at the cellular level. The power of... (Review)
Review
Single-cell metabolomics (SCM) is currently one of the most powerful tools for performing high-throughput metabolic analysis at the cellular level. The power of single-cell metabolomics to determine the metabolic profiles of individual cells makes it very suitable for decoding cell heterogeneity. SCM bears great potential in cell type identification and differentiation within cell colonies. With the development of various equipment and techniques, SCM analysis has become possible for a wide range of biological samples. Many fields have incorporated this cutting-edge analytic tool to generate fruitful findings. This review article pays close attention to the prevalent techniques utilized in SCM and the exciting new findings and applications developed by studies in phytology, neurology, and oncology using SCM.
Topics: Metabolome; Metabolomics; Single-Cell Analysis
PubMed: 34339935
DOI: 10.1016/j.copbio.2021.07.015 -
Critical Reviews in Analytical Chemistry 2022Metabolomics is a young field of knowledge that arises linked to other omics such as genomics, transcriptomics, and proteomics. This discipline seeks to understand the... (Review)
Review
Metabolomics is a young field of knowledge that arises linked to other omics such as genomics, transcriptomics, and proteomics. This discipline seeks to understand the performance of metabolites, identifying, quantifying them, and thus understanding its mechanism of action. This new branch of omics science shows high potential, due to its noninvasive character and its close relation with phenotype. Several techniques have been developed to study the metabolome of biological samples, fundamentally nuclear magnetic resonance (NMR), mass spectrometry (MS) and vibrational spectrometry (VS) or a combination of several techniques. These techniques are focused to separate, detect, characterize, and quantify metabolites, as well as elucidate their structures and their function on the metabolic pathways they are involved. However, due to the complexity of the metabolome, in most cases it is necessary to apply several of these techniques to understand completely the whole scenery. This review is aimed to offer a summary of the current knowledge of these analytical techniques for metabolomics and their application to different fields as environmental, food or health sciences. Each technique shows different advantages and drawbacks depending on their technical characteristics and limitations, some factors, such as the aim of the study or the nature of the biological sample will condition the choice. Regarding their applications, NMR has been employed specially to identify new compounds and elucidate structures. The use of MS has gained popularity because of its versatility, easiness to be coupled to separation techniques and its high sensitivity. Whereas VS is widely employed for studies, due to its nondestructive character. Metabolomics applications in different science fields are growing each year, due to advances in analytical techniques and combination with other omics that allow to increase the comprehension of metabolic processes. Further development of analytical tools is necessary to continue exploiting all the possibilities of metabolomics. HighlightsMetabolomics seeks to understand the performance of metabolites and its mechanism of actionDifferent metabolomics techniques have been developed and improved in the last yearsMetabolomics applications cover clinical, pharmaceuticals and food and environmental sciencesThis review is aimed to offer a summary of the current knowledge of these analytical techniques.
Topics: Environmental Health; Food Technology; Mass Spectrometry; Metabolome; Metabolomics
PubMed: 33026841
DOI: 10.1080/10408347.2020.1823811 -
Pediatric Allergy and Immunology :... May 2024Food allergy (FA) is a widespread issue, affecting as many as 10% of the population. Over the past two to three decades, the prevalence of FA has been on the rise,... (Review)
Review
Food allergy (FA) is a widespread issue, affecting as many as 10% of the population. Over the past two to three decades, the prevalence of FA has been on the rise, particularly in industrialized and westernized countries. FA is a complex, multifactorial disease mediated by type 2 immune responses and involving environmental and genetic factors. However, the precise mechanisms remain inadequately understood. Metabolomics has the potential to identify disease endotypes, which could beneficially promote personalized prevention and treatment. A metabolome approach would facilitate the identification of surrogate metabolite markers reflecting the disease activity and prognosis. Here, we present a literature overview of recent metabolomic studies conducted on children with FA.
Topics: Humans; Food Hypersensitivity; Metabolomics; Child; Biomarkers; Metabolome; Allergens
PubMed: 38727629
DOI: 10.1111/pai.14133 -
Nature Protocols Jan 2022A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in... (Review)
Review
A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.
Topics: Biomarkers; Data Analysis; Databases, Factual; Humans; Metabolome; Metabolomics; Software
PubMed: 34952956
DOI: 10.1038/s41596-021-00636-9 -
Biology of Sex Differences Jun 2022The sexual dimorphism represents one of the triggers of the metabolic disparities between the organisms, advising about wild implications in research or diagnostics... (Review)
Review
BACKGROUND
The sexual dimorphism represents one of the triggers of the metabolic disparities between the organisms, advising about wild implications in research or diagnostics contexts. Despite the mounting recognition of the importance of sex consideration in the biomedical fields, the identification of male- and female-specific metabolic signatures has not been achieved.
MAIN BODY
This review pointed the focus on the metabolic differences related to the sex, evidenced by metabolomics studies performed on healthy populations, with the leading aim of understanding how the sex influences the baseline metabolome. The main shared signatures and the apparent dissimilarities between males and females were extracted and highlighted from the metabolome of the most commonly analyzed biological fluids, such as serum, plasma, and urine. Furthermore, the influence of age and the significant interactions between sex and age have been taken into account.
CONCLUSIONS
The recognition of sex patterns in human metabolomics has been defined in diverse biofluids. The detection of sex- and age-related differences in the metabolome of healthy individuals are helpful for translational applications from the bench to the bedside to set targeted diagnostic and prevention approaches in the context of personalized medicine.
Topics: Female; Humans; Male; Metabolome; Metabolomics; Sex Characteristics
PubMed: 35706042
DOI: 10.1186/s13293-022-00440-4 -
Metabolomics : Official Journal of the... Oct 2022Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals... (Review)
Review
Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.
Topics: Metabolome; Metabolomics; Proteomics; Workflow
PubMed: 36181583
DOI: 10.1007/s11306-022-01934-3 -
Molecules (Basel, Switzerland) Dec 2022Aging process is characterized by a progressive decline of several organic, physiological, and metabolic functions whose precise mechanism remains unclear. Metabolomics...
Aging process is characterized by a progressive decline of several organic, physiological, and metabolic functions whose precise mechanism remains unclear. Metabolomics allows the identification of several metabolites and may contribute to clarifying the aging-regulated metabolic pathways. We aimed to investigate aging-related serum metabolic changes using a metabolomics approach. Fasting blood serum samples from 138 apparently healthy individuals (20−70 years old, 56% men) were analyzed by Proton Nuclear Magnetic Resonance spectroscopy (1H NMR) and Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS), and for clinical markers. Associations of the metabolic profile with age were explored via Correlations (r); Metabolite Set Enrichment Analysis; Multiple Linear Regression; and Aging Metabolism Breakpoint. The age increase was positively correlated (0.212 ≤ r ≤ 0.370, p < 0.05) with the clinical markers (total cholesterol, HDL, LDL, VLDL, triacylglyceride, and glucose levels); negatively correlated (−0.285 ≤ r ≤ −0.214, p < 0.05) with tryptophan, 3-hydroxyisobutyrate, asparagine, isoleucine, leucine, and valine levels, but positively (0.237 ≤ r ≤ 0.269, p < 0.05) with aspartate and ornithine levels. These metabolites resulted in three enriched pathways: valine, leucine, and isoleucine degradation, urea cycle, and ammonia recycling. Additionally, serum metabolic levels of 3-hydroxyisobutyrate, isoleucine, aspartate, and ornithine explained 27.3% of the age variation, with the aging metabolism breakpoint occurring after the third decade of life. These results indicate that the aging process is potentially associated with reduced serum branched-chain amino acid levels (especially after the third decade of life) and progressively increased levels of serum metabolites indicative of the urea cycle.
Topics: Male; Humans; Young Adult; Adult; Middle Aged; Aged; Female; Leucine; Isoleucine; Aspartic Acid; Metabolomics; Metabolome; Aging; Biomarkers; Valine; Ornithine; Urea
PubMed: 36557788
DOI: 10.3390/molecules27248656 -
Plant Communications Sep 2021Plants produce a variety of metabolites that are essential for plant growth and human health. To fully understand the diversity of metabolites in certain plants, lots of... (Review)
Review
Plants produce a variety of metabolites that are essential for plant growth and human health. To fully understand the diversity of metabolites in certain plants, lots of methods have been developed for metabolites detection and data processing. In the data-processing procedure, how to effectively reduce false-positive peaks, analyze large-scale metabolic data, and annotate plant metabolites remains challenging. In this review, we introduce and discuss some prominent methods that could be exploited to solve these problems, including a five-step filtering method for reducing false-positive signals in LC-MS analysis, QPMASS for analyzing ultra-large GC-MS data, and MetDNA for annotating metabolites. The main applications of plant metabolomics in species discrimination, metabolic pathway dissection, population genetic studies, and some other aspects are also highlighted. To further promote the development of plant metabolomics, more effective and integrated methods/platforms for metabolite detection and comprehensive databases for metabolite identification are highly needed. With the improvement of these technologies and the development of genomics and transcriptomics, plant metabolomics will be widely used in many fields.
Topics: Metabolic Networks and Pathways; Metabolome; Metabolomics; Plants
PubMed: 34746766
DOI: 10.1016/j.xplc.2021.100238