Gut microbiota communities have coevolved for millions of years in a symbiotic relationship with their mammalian hosts. Elucidating and understanding the molecular mechanisms by which microbiota ...interacts with its host and how this contributes to the homeostasis of the host is crucial. One of these molecular relationships is the so-called chemical crosstalk between microbiota and host metabolisms, including the poorly explored epigenetic regulation of host tissues by the metabolic activity of gut microbiota in response to changes in diet. DNA methylation and histone modifications are epigenetic marks partly regulated by enzymes such as methylases and acetylases, whose activity depend on host and microbiota metabolites that act as substrates and cofactors for these reactions. However, providing a complete mechanistic description of the regulatory interactions between both metabolisms and the impact on the expression of host genes through an epigenetic modulation, remains elusive. This article presents our perspective on how metabolomic, metagenomic, transcriptomic, and epigenomic data can be used to investigate the "microbiota-nutrient metabolism-epigenetics axis." We also discuss the implications and opportunities this knowledge may have for basic and applied science, such as the impact on the way we structure future research, understand, and prevent diseases like type 2 diabetes or obesity.
Metabolites, the chemical entities that are transformed during metabolism, provide a functional readout of cellular biochemistry. With emerging technologies in mass spectrometry, thousands of ...metabolites can now be quantitatively measured from minimal amounts of biological material, which has thereby enabled systems-level analyses. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanism are being revealed and are shaping our understanding of cell biology, physiology and medicine.
•Mass spectral databases play a key role in metabolomics.•Advantages and limitations of public and commercial databases are underlined.•The overlap of compounds in public and commercial databases is ...calculated.•Future prospects of mass spectral databases are discussed.
At present, mass spectrometry (MS)-based metabolomics has been widely used to obtain new insights into human, plant, and microbial biochemistry; drug and biomarker discovery; nutrition research; and food control. Despite the high research interest, identifying and characterizing the structure of metabolites has become a major drawback for converting raw MS data into biological knowledge. Comprehensive and well-annotated MS-based spectral databases play a key role in serving this purpose via the formation of metabolite annotations. The main characteristics of the mass spectral databases currently used in MS-based metabolomics are reviewed in this study, underlining their advantages and limitations. In addition, the overlap of compounds with MSn (n ≥ 2) spectra from authentic chemical standards in most public and commercial databases has been calculated for the first time. Finally, future prospects of mass spectral databases are discussed in terms of the needs posed by novel applications and instrumental advancements.
FELLA: an R package to enrich metabolomics data Picart-Armada, Sergio; Fernández-Albert, Francesc; Vinaixa, Maria ...
BMC bioinformatics,
12/2018, Letnik:
19, Številka:
1
Journal Article, Publication
Recenzirano
Odprti dostop
Pathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in ...metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects.
We introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites. FELLA builds a hierarchical representation of an organism biochemistry from the Kyoto Encyclopedia of Genes and Genomes (KEGG), containing pathways, modules, enzymes, reactions and metabolites. In addition to providing a list of pathways, FELLA reports intermediate entities (modules, enzymes, reactions) that link the input metabolites to them. This sheds light on pathway cross talk and potential enzymes or metabolites as targets for the condition under study. FELLA has been applied to six public datasets -three from Homo sapiens, two from Danio rerio and one from Mus musculus- and has reproduced findings from the original studies and from independent literature.
The R package FELLA offers an innovative enrichment concept starting from a list of metabolites, based on a knowledge graph representation of the KEGG database that focuses on interpretability. Besides reporting a list of pathways, FELLA suggests intermediate entities that are of interest per se. Its usefulness has been shown at several molecular levels on six public datasets, including human and animal models. The user can run the enrichment analysis through a simple interactive graphical interface or programmatically. FELLA is publicly available in Bioconductor under the GPL-3 license.
Metabolism is vital to every aspect of cell function, yet the metabolome of induced pluripotent stem cells （iPSCs） remains largely unexplored. Here we report, using an untargeted metabolomics ...approach, that human iPSCs share a pluripotent metabolomic signature with embryonic stem cells （ESCs） that is distinct from their parental cells, and that is characterized by changes in metabolites involved in cellular respiration. Examination of cellular bioenergetics corroborated with our metabolomic analysis, and demonstrated that somatic cells convert from an oxidative state to a glycolytic state in pluripotency. Interestingly, the bioenergetics of various somatic cells correlated with their repro- gramming efficiencies. We further identified metabolites that differ between iPSCs and ESCs, which revealed novel metabolic pathways that play a critical role in regulating somatic cell reprogramming. Our findings are the first to globally analyze the metabolome of iPSCs, and provide mechanistic insight into a new layer of regulation involved in inducing pluripotency, and in evaluating iPSC and ESC equivalence.
NAD
is a vital redox cofactor and a substrate required for activity of various enzyme families, including sirtuins and poly(ADP-ribose) polymerases. Supplementation with NAD
precursors, such as ...nicotinamide mononucleotide (NMN) or nicotinamide riboside (NR), protects against metabolic disease, neurodegenerative disorders and age-related physiological decline in mammals. Here we show that nicotinamide riboside kinase 1 (NRK1) is necessary and rate-limiting for the use of exogenous NR and NMN for NAD
synthesis. Using genetic gain- and loss-of-function models, we further demonstrate that the role of NRK1 in driving NAD
synthesis from other NAD
precursors, such as nicotinamide or nicotinic acid, is dispensable. Using stable isotope-labelled compounds, we confirm NMN is metabolized extracellularly to NR that is then taken up by the cell and converted into NAD
. Our results indicate that mammalian cells require conversion of extracellular NMN to NR for cellular uptake and NAD
synthesis, explaining the overlapping metabolic effects observed with the two compounds.
Mass spectrometry-based metabolomics is the comprehensive study of naturally occurring small molecules collectively known as the metabolome. Given the vast structural diversity and chemical ...properties of endogenous metabolites, biological extraction and chromatography methods bias the number, property, and concentration of metabolites detected by mass spectrometry and creates a challenge for global untargeted studies. In this work, we used Escherichia coli bacterial cells to explore the influence of solvent polarity, temperature, and pH in extracting polar and nonpolar metabolites simultaneously. In addition, we explored chromatographic conditions involving different stationary and mobile phases that optimize the separation and ionization of endogenous metabolite extracts as well as a mixture of synthetic standards. Our results reveal that hot polar solvents are the most efficient in extracting both hydrophilic and hydrophobic metabolites simultaneously. In addition, ammonium fluoride in the mobile phase substantially improved ionization efficiency in negative electrospray ionization mode by an average increase in signal intensity of 5.7 and over a 2-fold increase in the total number of features detected. The improvement in sensitivity with ammonium fluoride resulted in 3.5 times as many metabolite hits in databases compared to ammonium acetate or formic acid enriched mobile phases and allowed for the identification of unique metabolites involved in fundamental cellular pathways.
Structural annotation of metabolites relies mainly on tandem mass spectrometry (MS/MS) analysis. However, approximately 90% of the known metabolites reported in metabolomic databases do not have ...annotated spectral data from standards. This situation has fostered the development of computational tools that predict fragmentation patterns in silico and compare these to experimental MS/MS spectra. However, because such methods require the molecular structure of the detected compound to be available for the algorithm, the identification of novel metabolites in organisms relevant for biotechnological and medical applications remains a challenge. Here, we present iMet, a computational tool that facilitates structural annotation of metabolites not described in databases. iMet uses MS/MS spectra and the exact mass of an unknown metabolite to identify metabolites in a reference database that are structurally similar to the unknown metabolite. The algorithm also suggests the chemical transformation that converts the known metabolites into the unknown one. As a proxy for the structural annotation of novel metabolites, we tested 148 metabolites following a leave-one-out cross-validation procedure or by using MS/MS spectra experimentally obtained in our laboratory. We show that for 89% of the 148 metabolites at least one of the top four matches identified by iMet enables the proper annotation of the unknown metabolites. To further validate iMet, we tested 31 metabolites proposed in the 2012–16 CASMI challenges. iMet is freely available at http://imet.seeslab.net.
Metabolites offer an important unexplored complementary approach to understanding the pluripotency of stem cells. Using MS-based metabolomics, we show that embryonic stem cells are characterized by ...abundant metabolites with highly unsaturated structures whose levels decrease upon differentiation. By monitoring the reduced and oxidized glutathione ratio as well as ascorbic acid levels, we demonstrate that the stem cell redox status is regulated during differentiation. On the basis of the oxidative biochemistry of the unsaturated metabolites, we experimentally manipulated specific pathways in embryonic stem cells while monitoring the effects on differentiation. Inhibition of the eicosanoid signaling pathway promoted pluripotency and maintained levels of unsaturated fatty acids. In contrast, downstream oxidized metabolites (for example, neuroprotectin D1) and substrates of pro-oxidative reactions (for example, acyl-carnitines), promoted neuronal and cardiac differentiation. We postulate that the highly unsaturated metabolome sustained by stem cells allows them to differentiate in response to in vivo oxidative processes such as inflammation.
Abstract
Motivation
The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for ...answering questions as fundamental as, e.g. how many metabolites are there in a given sample.
Results
Here, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools.
Availability and implementation
https://CRAN.R-project.org/package=cliqueMS and https://github.com/osenan/cliqueMS.
Supplementary information
Supplementary data are available at Bioinformatics online.
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