The unexpected diversity of the human microbiome and metabolome far exceeds the complexity of the human genome. Although we now understand microbial taxonomic and genetic repertoires in some ...populations, we are just beginning to assemble the necessary computational and experimental tools to understand the metabolome in comparable detail. However, even with the limited current state of knowledge, individual connections between microbes and metabolites, between microbes and immune function, and between metabolites and immune function are being established. Here, we provide our perspective on these connections and outline a systematic research program that could turn these individual links into a broader network that allows us to understand how these components interact. This program will enable us to exploit connections among the microbiome, metabolome, and host immune system to maintain health and perhaps help us understand how to reverse the processes that lead to a wide range of immune and other diseases.
Recent research has developed specific connections among particular microbes, metabolites, and host immune processes, but a generalized map of this interaction network is still lacking. Dorrestein et al. describe these connections and outline how we could move toward a global view.
The microbiota, and the genes that comprise its microbiome, play key roles in human health. Host-microbe interactions affect immunity, metabolism, development, and behavior, and dysbiosis of gut ...bacteria contributes to disease. Despite advances in correlating changes in the microbiota with various conditions, specific mechanisms of host-microbiota signaling remain largely elusive. We discuss the synthesis of microbial metabolites, their absorption, and potential physiological effects on the host. We propose that the effects of specialized metabolites may explain present knowledge gaps in linking the gut microbiota to biological host mechanisms during initial colonization, and in health and disease.
Although eukaryotic nuclei contain distinct architectural structures associated with noncoding RNAs (ncRNAs), their potential relationship to regulated transcriptional programs remains poorly ...understood. Here, we report that methylation/demethylation of Polycomb 2 protein (Pc2) controls relocation of growth-control genes between Polycomb bodies (PcGs) and interchromatin granules (ICGs) in response to growth signals. This movement is the consequence of binding of methylated and unmethylated Pc2 to the ncRNAs
TUG1 and
MALAT1/NEAT2, located in PcGs and ICGs, respectively. These ncRNAs mediate assembly of multiple corepressors/coactivators and can serve to switch mark recognition by “readers” of the histone code. Additionally, binding of
NEAT2 to unmethylated Pc2 promotes E2F1 SUMOylation, leading to activation of the growth-control gene program. These observations delineate a molecular pathway linking the actions of subnuclear structure-specific ncRNAs and nonhistone protein methylation to relocation of transcription units in the three-dimensional space of the nucleus, thus achieving coordinated gene expression programs.
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► Suv39h1 methylates Pc2 that is bound to growth-control genes ► Pc2 interacts with NEAT2 RNA in interchromatin granules ► Methylated Pc2 associates with TUG1 RNA in Polycomb bodies ► Regulated ncRNA-Pc2 interactions are required for activation of growth-control genes
Methylation of Polycomb 2 protein (Pc2) determines its association with noncoding RNAs in distinct subnuclear structures. Growth signals change the methylation status of Pc2, leading to relocalization of Pc2-bound genes within the nucleus and activation of the growth control program.
The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural ...annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
It is a common problem in natural product therapeutic lead discovery programs that despite good bioassay results in the initial extract, the active compound(s) may not be isolated during subsequent ...bioassay-guided purification. Herein, we present the concept of bioactive molecular networking to find candidate active molecules directly from fractionated bioactive extracts. By employing tandem mass spectrometry, it is possible to accelerate the dereplication of molecules using molecular networking prior to subsequent isolation of the compounds, and it is also possible to expose potentially bioactive molecules using bioactivity score prediction. Indeed, bioactivity score prediction can be calculated with the relative abundance of a molecule in fractions and the bioactivity level of each fraction. For that reason, we have developed a bioinformatic workflow able to map bioactivity score in molecular networks and applied it for discovery of antiviral compounds from a previously investigated extract of Euphorbia dendroides where the bioactive candidate molecules were not discovered following a classical bioassay-guided fractionation procedure. It can be expected that this approach will be implemented as a systematic strategy, not only in current and future bioactive lead discovery from natural extract collections but also for the reinvestigation of the untapped reservoir of bioactive analogues in previous bioassay-guided fractionation efforts.
Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries ...or databases, restricting analysis and interpretation of experimental data. Here we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computational tool for systematic compound class annotation. CANOPUS uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available and predicts classes lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four baseline methods. We demonstrate the broad utility of CANOPUS by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level.
Molecular networking is a tandem mass spectrometry (MS/MS) data organizational approach that has been recently introduced in the drug discovery, metabolomics, and medical fields. The chemistry of ...molecules dictates how they will be fragmented by MS/MS in the gas phase and, therefore, two related molecules are likely to display similar fragment ion spectra. Molecular networking organizes the MS/MS data as a relational spectral network thereby mapping the chemistry that was detected in an MS/MS-based metabolomics experiment. Although the wider utility of molecular networking is just beginning to be recognized, in this review we highlight the principles behind molecular networking and its use for the discovery of therapeutic leads, monitoring drug metabolism, clinical diagnostics, and emerging applications in precision medicine.
Illuminating the dark matter in metabolomics da Silva, Ricardo R.; Dorrestein, Pieter C.; Quinn, Robert A.
Proceedings of the National Academy of Sciences - PNAS,
10/2015, Letnik:
112, Številka:
41
Journal Article
Molecular cartography of the human skin surface in 3D Bouslimani, Amina; Porto, Carla; Rath, Christopher M. ...
Proceedings of the National Academy of Sciences - PNAS,
04/2015, Letnik:
112, Številka:
17
Journal Article
Recenzirano
Odprti dostop
Significance The paper describes the implementation of an approach to study the chemical makeup of human skin surface and correlate it to the microbes that live in the skin. We provide the ...translation of molecular information in high-spatial resolution 3D to understand the body distribution of skin molecules and bacteria. In addition, we use integrative analysis to interpret, at a molecular level, the large scale of data obtained from human skin samples. Correlations between molecules and microbes can be obtained to further gain insights into the chemical milieu in which these different microbial communities live.
The human skin is an organ with a surface area of 1.5–2 m ² that provides our interface with the environment. The molecular composition of this organ is derived from host cells, microbiota, and external molecules. The chemical makeup of the skin surface is largely undefined. Here we advance the technologies needed to explore the topographical distribution of skin molecules, using 3D mapping of mass spectrometry data and microbial 16S rRNA amplicon sequences. Our 3D maps reveal that the molecular composition of skin has diverse distributions and that the composition is defined not only by skin cells and microbes but also by our daily routines, including the application of hygiene products. The technological development of these maps lays a foundation for studying the spatial relationships of human skin with hygiene, the microbiota, and environment, with potential for developing predictive models of skin phenotypes tailored to individual health.
Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in ...structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries.
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