scholarly journals Metabolite discovery through global annotation of untargeted metabolomics data

2021 ◽  
Author(s):  
Li Chen ◽  
Wenyun Lu ◽  
Lin Wang ◽  
Xi Xing ◽  
Xin Teng ◽  
...  
Keyword(s):  
Global Optimization ◽  
High Resolution Mass Spectrometry ◽  
Mass Difference ◽  
Untargeted Metabolomics ◽  
Global Network ◽  
Optimization Approach ◽  
Metabolomics Data ◽  
Isotope Tracer ◽  
Powerful Approach ◽  
Fragmentation Patterns

AbstractA primary goal of metabolomics is to identify all biologically important metabolites. One powerful approach is liquid chromatography-high resolution mass spectrometry (LC-MS), yet most LC-MS peaks remain unidentified. Here, we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. We consider all experimentally observed ion peaks together, and assign annotations to all of them simultaneously so as to maximize a score that considers properties of peaks (known masses, retention times, MS/MS fragmentation patterns) as well network constraints that arise based on mass difference between peaks. Global optimization results in accurate peak assignment and trackable peak-peak relationships. Applying this approach to yeast and mouse data, we identify a half-dozen novel metabolites, including thiamine and taurine derivatives. Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to annotate untargeted metabolomics data, revealing novel metabolites.

scholarly journals Metabolite discovery through global annotation of untargeted metabolomics data

2021 ◽  
Author(s):  
Joshua Rabinowitz ◽  
Li Chen ◽  
Wenyun Lu ◽  
Lin Wang ◽  
Xi Xing ◽  
...  
Keyword(s):  
Global Optimization ◽  
High Resolution Mass Spectrometry ◽  
Mass Difference ◽  
Untargeted Metabolomics ◽  
Global Network ◽  
Optimization Approach ◽  
Metabolomics Data ◽  
Isotope Tracer ◽  
Powerful Approach ◽  
Fragmentation Patterns

Abstract A primary goal of metabolomics is to identify all biologically important metabolites. One powerful approach is liquid chromatography-high resolution mass spectrometry (LC-MS), yet most LC-MS peaks remain unidentified. Here, we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. We consider all experimentally observed ion peaks together, and assign annotations to all of them simultaneously so as to maximize a score that considers properties of peaks (known masses, retention times, MS/MS fragmentation patterns) as well network constraints that arise based on mass difference between peaks. Global optimization results in accurate peak assignment and trackable peak-peak relationships. Applying this approach to yeast and mouse data, we identify a half-dozen novel metabolites, including thiamine and taurine derivatives. Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to annotate untargeted metabolomics data, revealing novel metabolites.

scholarly journals Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data

Metabolites ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 378 ◽  
Author(s):  
Selina Hemmer ◽  
Sascha K. Manier ◽  
Svenja Fischmann ◽  
Folker Westphal ◽  
Lea Wagmann ◽  
...  
Keyword(s):  
Data Processing ◽  
Open Source ◽  
High Resolution Mass Spectrometry ◽  
Liver Microsomes ◽  
Reversed Phase ◽  
Ease Of Use ◽  
Untargeted Metabolomics ◽  
Metabolomics Data ◽  
Metabolomics Study ◽  
High Flexibility

The evaluation of liquid chromatography high-resolution mass spectrometry (LC-HRMS) raw data is a crucial step in untargeted metabolomics studies to minimize false positive findings. A variety of commercial or open source software solutions are available for such data processing. This study aims to compare three different data processing workflows (Compound Discoverer 3.1, XCMS Online combined with MetaboAnalyst 4.0, and a manually programmed tool using R) to investigate LC-HRMS data of an untargeted metabolomics study. Simple but highly standardized datasets for evaluation were prepared by incubating pHLM (pooled human liver microsomes) with the synthetic cannabinoid A-CHMINACA. LC-HRMS analysis was performed using normal- and reversed-phase chromatography followed by full scan MS in positive and negative mode. MS/MS spectra of significant features were subsequently recorded in a separate run. The outcome of each workflow was evaluated by its number of significant features, peak shape quality, and the results of the multivariate statistics. Compound Discoverer as an all-in-one solution is characterized by its ease of use and seems, therefore, suitable for simple and small metabolomic studies. The two open source solutions allowed extensive customization but particularly, in the case of R, made advanced programming skills necessary. Nevertheless, both provided high flexibility and may be suitable for more complex studies and questions.

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 Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Michiel Bongaerts ◽  
Ramon Bonte ◽  
Serwet Demirdas ◽  
Edwin H. Jacobs ◽  
Esmee Oussoren ◽  
...  
Keyword(s):  
Regression Model ◽  
Inborn Errors Of Metabolism ◽  
Untargeted Metabolomics ◽  
Detection Accuracy ◽  
Inborn Errors ◽  
Mixed Effect ◽  
Metabolomics Data ◽  
Normalization Methods ◽  
Metabolite Concentrations ◽  
Reference Samples

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.

scholarly journals HPLC-MS/MS method applied to an untargeted metabolomics approach for the diagnosis of “olive quick decline syndrome”

2021 ◽  
Author(s):  
Sabrina Di Masi ◽  
Giuseppe E. De Benedetto ◽  
Cosimino Malitesta ◽  
Maria Saponari ◽  
Cinzia Citti ◽  
...  
Keyword(s):  
Bacterial Infections ◽  
High Performance ◽  
High Resolution Mass Spectrometry ◽  
Xylella Fastidiosa ◽  
Long Chain Fatty Acids ◽  
Powerful Approach ◽  
Diffusible Signal Factor ◽  
Profile Changes ◽  
Olive Quick Decline Syndrome ◽  
Olive Trees

AbstractOlive quick decline syndrome (OQDS) is a disorder associated with bacterial infections caused by Xylella fastidiosa subsp. pauca ST53 in olive trees. Metabolic profile changes occurring in infected olive trees are still poorly investigated, but have the potential to unravel reliable biomarkers to be exploited for early diagnosis of infections. In this study, an untargeted metabolomic method using high-performance liquid chromatography coupled to quadrupole-time-of-flight high-resolution mass spectrometry (HPLC-ESI-Q-TOF-MS) was used to detect differences in samples (leaves) from healthy (Ctrl) and infected (Xf) olive trees. Both unsupervised and supervised data analysis clearly differentiated the groups. Different metabolites have been identified as potential specific biomarkers, and their characterization strongly suggests that metabolism of flavonoids and long-chain fatty acids is perturbed in Xf samples. In particular, a decrease in the defence capabilities of the host after Xf infection is proposed because of a significant dysregulation of some metabolites belonging to flavonoid family. Moreover, oleic acid is confirmed as a putative diffusible signal factor (DSF). This study provides new insights into the host-pathogen interactions and confirms LC-HRMS-based metabolomics as a powerful approach for disease-associated biomarkers discovery in plants. Graphical abstract

Chromatography hyphenated to high resolution mass spectrometry in untargeted metabolomics for investigation of food (bio)markers

2021 ◽  
Vol 135 ◽  
pp. 116161
Author(s):  
Leticia Lacalle-Bergeron ◽  
David Izquierdo-Sandoval ◽  
Juan V. Sancho ◽  
Francisco J. López ◽  
Félix Hernández ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
High Resolution Mass Spectrometry ◽  
Untargeted Metabolomics ◽  
High Resolution Mass ◽  
Resolution Mass

scholarly journals Ultra-Performance Liquid Chromatography-Ion Mobility Separation-Quadruple Time-of-Flight MS (UHPLC-IMS-QTOF MS) Metabolomics for Short-Term Biomarker Discovery of Orange Intake: A Randomized, Controlled Crossover Study

Nutrients ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1916
Author(s):  
Leticia Lacalle-Bergeron ◽  
Tania Portolés ◽  
Francisco J. López ◽  
Juan Vicente Sancho ◽  
Carolina Ortega-Azorín ◽  
...  
Keyword(s):  
Ion Mobility ◽  
New Technologies ◽  
Biomarker Discovery ◽  
High Resolution Mass Spectrometry ◽  
Collision Cross Section ◽  
Ultra Performance Liquid Chromatography ◽  
Untargeted Metabolomics ◽  
Mobility Separation ◽  
Randomized Controlled ◽  
Ion Mobility Separation

A major problem with dietary assessments is their subjective nature. Untargeted metabolomics and new technologies can shed light on this issue and provide a more complete picture of dietary intake by measuring the profile of metabolites in biological samples. Oranges are one of the most consumed fruits in the world, and therefore one of the most studied for their properties. The aim of this work was the application of untargeted metabolomics approach with the novel combination of ion mobility separation coupled to high resolution mass spectrometry (IMS-HRMS) and study the advantages that this technique can bring to the area of dietary biomarker discovery, with the specific case of biomarkers associated with orange consumption (Citrus reticulata) in plasma samples taken during an acute intervention study (consisting of a randomized, controlled crossover trial in healthy individuals). A total of six markers of acute orange consumption, including betonicines and conjugated flavonoids, were identified with the experimental data and previous literature, demonstrating the advantages of ion mobility in the identification of dietary biomarkers and the benefits that an additional structural descriptor, as the collision cross section value (CCS), can provide in this area.

Sign in / Sign up

Export Citation Format

Share Document