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.

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

Advancements in capturing and mining mass spectrometry data are transforming natural products research

2021 ◽  
Author(s):  
Scott A. Jarmusch ◽  
Justin J. J. van der Hooft ◽  
Pieter C. Dorrestein ◽  
Alan K. Jarmusch
Keyword(s):  
Mass Spectrometry ◽  
Data Mining ◽  
Natural Products ◽  
Data Analysis ◽  
Community Participation ◽  
Mass Spectrometry Data ◽  
Metabolomics Data ◽  
Analysis Tools ◽  
Public Data ◽  
Potential Use

This review covers the current and potential use of mass spectrometry-based metabolomics data mining in natural products. Public data, metadata, databases and data analysis tools are critical. The value and success of data mining rely on community participation.

Signal Detection in High-Resolution Mass Spectrometry Data

2008 ◽  
Vol 7 (1) ◽  
pp. 276-285 ◽  
Author(s):  
Dale F. McLerran ◽  
Ziding Feng ◽  
O. John Semmes ◽  
Lisa Cazares ◽  
Timothy W. Randolph
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Signal Detection ◽  
High Resolution Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
High Resolution Mass ◽  
Resolution Mass

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 Principal Component Analysis from Mass Spectrometry Data Combined to a Sensory Evaluation as a Suitable Method for Assessing Bitterness of Enzymatic Hydrolysates Produced from Micellar Casein Proteins

Foods ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1354
Author(s):  
Dahlia Daher ◽  
Barbara Deracinois ◽  
Alain Baniel ◽  
Elodie Wattez ◽  
Justine Dantin ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Principal Component Analysis ◽  
High Performance ◽  
High Resolution Mass Spectrometry ◽  
Liquid Fraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mass Spectrometry Data ◽  
Sensory Panel ◽  
Enzymatic Hydrolysates

Enzymatic hydrolysis of food proteins generally changes the techno-functional, nutritional, and organoleptic properties of hydrolyzed proteins. As a result, protein hydrolysates have an important interest in the food industries. However, they tend to be characterized by a bitter taste and some off-flavors, which limit their use in the food industry. These tastes and aromas come from peptides, amino acids, and volatile compounds generated during hydrolysis. In this article, sixteen more or less bitter enzymatic hydrolysates produced from a milk protein liquid fraction enriched in micellar caseins using commercially available, food-grade proteases were subjected to a sensory analysis using a trained and validated sensory panel combined to a peptidomics approach based on the peptide characterization by reverse-phase high-performance liquid chromatography, high-resolution mass spectrometry, and bioinformatics software. The comparison between the sensory characteristics and the principal components of the principal component analysis (PCA) of mass spectrometry data reveals that peptidomics constitutes a convenient, valuable, fast, and economic intermediate method to evaluating the bitterness of enzymatic hydrolysates, as a trained sensory panel can do it.

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