scholarly journals From mass spectral features to molecules in molecular networks: a novel workflow for untargeted metabolomics.

2021 ◽  
Author(s):  
Damien Olivier-Jimenez ◽  
Zakaria Bouchouireb ◽  
Simon Ollivier ◽  
Julia Mocquard ◽  
Pierre-Marie Allard ◽  
...  
Keyword(s):  
Similarity Score ◽  
Untargeted Metabolomics ◽  
Molecular Networks ◽  
Exact Mass ◽  
Mass Spectral ◽  
Calculated Mass ◽  
Single Compound ◽  
Molecular Masses ◽  
Negative Ionization ◽  
Ion Species

In the context of untargeted metabolomics, molecular networking is a popular and efficient tool which organizes and simplifies mass spectrometry fragmentation data (LC-MS/MS), by clustering ions based on a cosine similarity score. However, the nature of the ion species is rarely taken into account, causing redundancy as a single compound may be present in different forms throughout the network. Taking advantage of the presence of such redundant ions, we developed a new method named MolNotator. Using the different ion species produced by a molecule during ionization (adducts, dimers, trimers, in-source fragments), a predicted molecule node (or neutral node) is created by triangulation, and ultimately computing the associated molecule calculated mass. These neutral nodes provide researchers with several advantages. Firstly, each molecule is then represented in its ionization context, connected to all produced ions and indirectly to some coeluted compounds, thereby also highlighting unexpected widely present adduct species. Secondly, the predicted neutrals serve as anchors to merge the complementary positive and negative ionization modes into a single network. Lastly, the dereplication is improved by the use of all available ions connected to the neutral nodes, and the computed molecular masses can be used for exact mass dereplication. MolNotator is available as a Python library and was validated using the lichen database spectra acquired on an Orbitrap, computing neutral molecules for >90% of the 156 molecules in the dataset. By focusing on actual molecules instead of ions, MolNotator greatly facilitates the selection of molecules of interest.

scholarly journals Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

2020 ◽  
Author(s):  
Ivayla Roberts ◽  
Marina Wright Muelas ◽  
Joseph M. Taylor ◽  
Andrew S. Davison ◽  
Yun Xu ◽  
...  
Keyword(s):  
Statistical Significance ◽  
Untargeted Metabolomics ◽  
Patient Treatment ◽  
Intermediary Metabolites ◽  
Qualitative Detection ◽  
Prognostic Tests ◽  
Multiple Predictor ◽  
Negative Ionization ◽  
Bayesian Logistic Regression ◽  
Host Inflammatory Response

AbstractThe diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, existing metabolomics studies are either underpowered, measure only a restricted subset of metabolites (‘targeted metabolomics’), compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model.We here provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict patient’s infection severity (i.e. mild or severe) and potential outcome (i.e. discharged or deceased).High resolution untargeted LC-MS/MS analysis was performed on patient serum using both positive and negative ionization. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model. The predictors were selected for their relevant biological function and include cytosine (reflecting viral load), kynurenine (reflecting host inflammatory response), nicotinuric acid, and multiple short chain acylcarnitines (energy metabolism) among others.Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

scholarly journals Mass spectral similarity for untargeted metabolomics data analysis of complex mixtures

2015 ◽  
Vol 377 ◽  
pp. 719-727 ◽  
Author(s):  
Neha Garg ◽  
Clifford A. Kapono ◽  
Yan Wei Lim ◽  
Nobuhiro Koyama ◽  
Mark J.A. Vermeij ◽  
...  
Keyword(s):  
Data Analysis ◽  
Complex Mixtures ◽  
Untargeted Metabolomics ◽  
Metabolomics Data ◽  
Spectral Similarity ◽  
Mass Spectral

scholarly journals Identification of New Psychoactive Substances in Seized material Using UHPLC-QTOF-MS and An Online Mass Spectral Database

2020 ◽  
Author(s):  
Maria von Cüpper ◽  
Petur Weihe Dalsgaard ◽  
Kristian Linnet
Keyword(s):  
High Performance ◽  
Designer Drugs ◽  
New Psychoactive Substances ◽  
Psychoactive Substances ◽  
Mass Spectral Database ◽  
Exact Mass ◽  
Mass Spectral ◽  
Toxicological Effects ◽  
Flight Mass Spectrometry ◽  
Spectral Database

Abstract The unpredictable pharmacological and toxicological effects associated with the recreational use of new psychoactive substances (NPS) represent a threat to the public health. Analysts are constantly facing a challenge to identify these designer drugs. In this article, five seized samples were submitted for analysis using ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC–QTOF-MS). To tentatively identify the NPS in the samples, the potential usage of an online mass spectral database (HighResNPS.com) was explored by searching the exact mass of the precursor ion and evaluating the fragmentation profile. This approach successfully identified a suspected candidate compound present in three of the five samples. However, conclusive identification of the remaining two was not possible, due to indistinguishable fragmentation profiles of positional isomers. Therefore, complementary analytical methodologies are of paramount importance. In light of the above, HighResNPS.com is a useful tool in presumptively identifying an NPS without a reference standard.

scholarly journals Studying Autism Using Untargeted Metabolomics in Newborn Screening Samples

2021 ◽  
Author(s):  
Julie Courraud ◽  
Madeleine Ernst ◽  
Susan Svane Laursen ◽  
David M. Hougaard ◽  
Arieh S. Cohen
Keyword(s):  
Newborn Screening ◽  
Biochemical Markers ◽  
Chemical Structure ◽  
Autism Spectrum ◽  
Untargeted Metabolomics ◽  
Future Studies ◽  
Structure Information ◽  
Mass Spectral ◽  
Feature Based ◽  
Broad Level

AbstractMain risk factors of autism spectrum disorder (ASD) include both genetic and non-genetic factors, especially prenatal and perinatal events. Newborn screening dried blood spot (DBS) samples have great potential for the study of early biochemical markers of disease. To study DBS strengths and limitations in the context of ASD research, we analyzed the metabolomic profiles of newborns later diagnosed with ASD. We performed LC-MS/MS-based untargeted metabolomics on DBS from 37 case-control pairs randomly selected from the iPSYCH sample. After preprocessing using MZmine 2.41, metabolites were putatively annotated using mzCloud, GNPS feature-based molecular networking, and MolNetEnhancer. A total of 4360 mass spectral features were detected, of which 150 (113 unique) could be putatively annotated at a high confidence level. Chemical structure information at a broad level could be retrieved for 1009 metabolites, covering 31 chemical classes. Although no clear distinction between cases and controls was revealed, our method covered many metabolites previously associated with ASD, suggesting that biochemical markers of ASD are present at birth and may be monitored during newborn screening. Additionally, we observed that gestational age, age at sampling, and month of birth influence the metabolomic profiles of newborn DBS, which informs us on the important confounders to address in future studies.

scholarly journals Ion Identity Molecular Networking in the GNPS Environment

2020 ◽  
Author(s):  
Robin Schmid ◽  
Daniel Petras ◽  
Louis-Félix Nothias ◽  
Mingxun Wang ◽  
Allegra T. Aron ◽  
...  
Keyword(s):  
Mass Spectra ◽  
Network Connectivity ◽  
Molecular Networks ◽  
Tandem Mass ◽  
Molecular Networking ◽  
Tandem Mass Spectra ◽  
Fragmentation Behavior ◽  
Fragmentation Patterns ◽  
Spectral Libraries ◽  
Ion Species

AbstractMolecular networking connects tandem mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. To connect ion species of the same molecule, we developed Ion Identity Molecular Networking. These new relationships improve network connectivity, are shown to reveal novel ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral libraries.

scholarly journals Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships

2020 ◽  
Author(s):  
Florian Huber ◽  
Lars Ridder ◽  
Stefan Verhoeven ◽  
Jurriaan H. Spaaks ◽  
Faruk Diblen ◽  
...  
Keyword(s):  
Language Processing ◽  
Structural Similarity ◽  
Similarity Score ◽  
Large Set ◽  
Molecular Networking ◽  
Spectral Similarity ◽  
Mass Spectral ◽  
Structural Relationships ◽  
Large Databases ◽  
Using Data

AbstractSpectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm -- Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds.

scholarly journals Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics

Metabolites ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 156 ◽  
Author(s):  
Ellen Kuang ◽  
Matthew Marney ◽  
Daniel Cuevas ◽  
Robert A. Edwards ◽  
Erica M. Forsberg
Keyword(s):  
Mass Spectrometry ◽  
High Resolution Mass Spectrometry ◽  
Metabolic Model ◽  
Mass Spectrometry Data ◽  
Untargeted Metabolomics ◽  
Specific Gene ◽  
Exact Mass ◽  
Metabolomics Data ◽  
Flux Balance ◽  
Balance Analysis

Genomics-based metabolic models of microorganisms currently have no easy way of corroborating predicted biomass with the actual metabolites being produced. This study uses untargeted mass spectrometry-based metabolomics data to generate a list of accurate metabolite masses produced from the human commensal bacteria Citrobacter sedlakii grown in the presence of a simple glucose carbon source. A genomics-based flux balance metabolic model of this bacterium was previously generated using the bioinformatics tool PyFBA and phenotypic growth curve data. The high-resolution mass spectrometry data obtained through timed metabolic extractions were integrated with the predicted metabolic model through a program called MS_FBA. This program correlated untargeted metabolomics features from C. sedlakii with 218 of the 699 metabolites in the model using an exact mass match, with 51 metabolites further confirmed using predicted isotope ratios. Over 1400 metabolites were matched with additional metabolites in the ModelSEED database, indicating the need to incorporate more specific gene annotations into the predictive model through metabolomics-guided gap filling.

scholarly journals Mass Peak Profiling from Selected-Ion Recording Data as a Tool for Regulatory Analyses

1999 ◽  
Vol 82 (6) ◽  
pp. 1443-1457 ◽  
Author(s):  
Andrew H Grange ◽  
G Wayne Sovocool
Keyword(s):  
High Resolution Mass Spectrometry ◽  
High Mass ◽  
Generation Model ◽  
Mass Peak ◽  
Exact Mass ◽  
Temporal Separation ◽  
Molecular Ion ◽  
Mass Spectral ◽  
Elemental Compositions ◽  
Fragment Ions

Abstract Identification of compounds in mixtures of environmental contaminants or synthetic products is essential for regulatory analyses. Exact masses of ions determined by high resolution mass spectrometry provide unique elemental compositions only for low-mass ions (<150 Da). Using mass peak profiling from selected-ion recording data (MPPSIRD) to acquire additional mass spectral data and a profile generation model (PGM) for automated interpretation of the data, provides elemental compositions for ions with m/z up to 600, based on incontestable properties of atoms, their exact masses, isotopic abundances, and valences. In this study, MPPSIRD and a PGM were used to identify intended and unintended products resulting from attempted syntheses of 2 thermolabile, nonionic, phosphorothioate compounds. The products were volatilized from a probe inserted into a VG70-250SE double-focusing mass spectrometer. High mass resolution substituted separation in the mass domain for the temporal separation of most components provided by chromatographic techniques. MPPSIRD and the PGM identified the correct composition for M+• by rejecting all other compositions that were possible within the error limits of the exact mass determinations for M+• MPPSIRD was used with 10 000-24 000 resolution to determine exact masses of ions prominent in mass spectra and to isolate signals from different ions with the same nominal mass. Superposition of volatilization peaks of ions and linked scans (constant magnetic field to electrostatic sector voltage ratio) correlated fragment ions with the molecular ion. The PGM determined the compositions of fragment ions, using the number of atoms of each element in the molecular ion as limits. Fragmentation schemes based on these ions and the tables of exact masses and relative abundances provided a preponderance of evidence for the product identities.

scholarly journals Application of UPLC-QTOF-MS Based Untargeted Metabolomics in Identification of Metabolites Induced in Pathogen-Infected Rice

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 213
Author(s):  
Mira Oh ◽  
SeonJu Park ◽  
Hun Kim ◽  
Gyung Ja Choi ◽  
Seung Hyun Kim
Keyword(s):  
Mass Spectrometry ◽  
Plant Defense ◽  
Metabolic Pathways ◽  
High Resolution Mass Spectrometry ◽  
High Sensitivity ◽  
Untargeted Metabolomics ◽  
Mass Spectral Database ◽  
Mass Spectral ◽  
Flight Mass Spectrometry

Metabolomics is a useful tool for comparing metabolite changes in plants. Because of its high sensitivity, metabolomics combined with high-resolution mass spectrometry (HR-MS) is the most widely accepted metabolomics tools. In this study, we compared the metabolites of pathogen-infected rice (Oryza sativa) with control rice using an untargeted metabolomics approach. We profiled the mass features of two rice groups using a liquid chromatography quadrupole time-of-flight mass spectrometry (QTOF-MS) system. Twelve of the most differentially induced metabolites in infected rice were selected through multivariate data analysis and identified through a mass spectral database search. The role of these compounds in metabolic pathways was finally investigated using pathway analysis. Our study showed that the most frequently induced secondary metabolites are prostanoids, a subclass of eicosanoids, which are associated with plant defense metabolism against pathogen infection. Herein, we propose a new untargeted metabolomics approach for understanding plant defense system at the metabolic level.

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