9 research outputs found
Proteogenomic Biomarkers for Identification of <i>Francisella</i> Species and Subspecies by Matrix-Assisted Laser Desorption Ionization-Time-of-Flight Mass Spectrometry
<i>Francisella tularensis</i> is the causative agent
of tularemia. Because some <i>Francisella</i> strains are
very virulent, this species is considered by the Centers for Disease
Control and Prevention to be a potential category A bioweapon. A mass
spectrometry method to quickly and robustly distinguish between virulent
and nonvirulent <i>Francisella</i> strains is desirable.
A combination of shotgun proteomics and whole-cell matrix-assisted
laser desorption ionization-time-of-flight (MALDI-TOF) mass spectrometry
on the <i>Francisella tularensis</i> subsp. <i>holarctica</i> LVS defined three protein biomarkers that allow such discrimination:
the histone-like protein HU form B, the 10 kDa chaperonin Cpn10, and
the 50S ribosomal protein L24. We established that their combined
detection by whole-cell MALDI-TOF spectrum could enable (i) the identification
of <i>Francisella</i> species, and (ii) the prediction of
their virulence level, i.e., gain of a taxonomical level with the
identification of <i>Francisella tularensis</i> subspecies.
The detection of these biomarkers by MALDI-TOF mass spectrometry is
straightforward because of their abundance and the absence of other
abundant protein species closely related in terms of <i>m</i>/<i>z</i>. The predicted molecular weights for the three
biomarkers and their presence as intense peaks were confirmed with
MALDI-TOF/MS spectra acquired on <i>Francisella philomiragia</i> ATCC 25015 and on <i>Francisella tularensis</i> subsp. <i>tularensis</i> CCUG 2112, the most virulent <i>Francisella</i> subspecies
Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer
Metabolic profiles of biofluids obtained by atmospheric
pressure
ionization mass spectrometry-based technologies contain hundreds to
thousands of features, most of them remaining unknown or at least
not characterized in analytical systems. We report here on the annotation
of the human adult urinary metabolome and metabolite identification
from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics
data sets. Features of biological interest were first of all annotated
using the ESI-MS database of the laboratory. They were also grouped,
thanks to software tools, and annotated using public databases. Metabolite
identification was achieved using two complementary approaches: (i)
formal identification by matching chromatographic retention times,
mass spectra, and also product ion spectra (if required) of metabolites
to be characterized in biological data sets to those of reference
compounds and (ii) putative identification from biological data thanks
to MS/MS experiments for metabolites not available in our chemical
library. By these means, 384 metabolites corresponding to 1484 annotated
features (659 in negative ion mode and 825 in positive ion mode) were
characterized in human urine samples. Of these metabolites, 192 and
66 were formally and putatively identified, respectively, and 54 are
reported in human urine for the first time. These lists of features
could be used by other laboratories to annotate their ESI-MS metabolomics
data sets
Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer
Metabolic profiles of biofluids obtained by atmospheric
pressure
ionization mass spectrometry-based technologies contain hundreds to
thousands of features, most of them remaining unknown or at least
not characterized in analytical systems. We report here on the annotation
of the human adult urinary metabolome and metabolite identification
from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics
data sets. Features of biological interest were first of all annotated
using the ESI-MS database of the laboratory. They were also grouped,
thanks to software tools, and annotated using public databases. Metabolite
identification was achieved using two complementary approaches: (i)
formal identification by matching chromatographic retention times,
mass spectra, and also product ion spectra (if required) of metabolites
to be characterized in biological data sets to those of reference
compounds and (ii) putative identification from biological data thanks
to MS/MS experiments for metabolites not available in our chemical
library. By these means, 384 metabolites corresponding to 1484 annotated
features (659 in negative ion mode and 825 in positive ion mode) were
characterized in human urine samples. Of these metabolites, 192 and
66 were formally and putatively identified, respectively, and 54 are
reported in human urine for the first time. These lists of features
could be used by other laboratories to annotate their ESI-MS metabolomics
data sets
Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer
Metabolic profiles of biofluids obtained by atmospheric
pressure
ionization mass spectrometry-based technologies contain hundreds to
thousands of features, most of them remaining unknown or at least
not characterized in analytical systems. We report here on the annotation
of the human adult urinary metabolome and metabolite identification
from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics
data sets. Features of biological interest were first of all annotated
using the ESI-MS database of the laboratory. They were also grouped,
thanks to software tools, and annotated using public databases. Metabolite
identification was achieved using two complementary approaches: (i)
formal identification by matching chromatographic retention times,
mass spectra, and also product ion spectra (if required) of metabolites
to be characterized in biological data sets to those of reference
compounds and (ii) putative identification from biological data thanks
to MS/MS experiments for metabolites not available in our chemical
library. By these means, 384 metabolites corresponding to 1484 annotated
features (659 in negative ion mode and 825 in positive ion mode) were
characterized in human urine samples. Of these metabolites, 192 and
66 were formally and putatively identified, respectively, and 54 are
reported in human urine for the first time. These lists of features
could be used by other laboratories to annotate their ESI-MS metabolomics
data sets
Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer
Metabolic profiles of biofluids obtained by atmospheric
pressure
ionization mass spectrometry-based technologies contain hundreds to
thousands of features, most of them remaining unknown or at least
not characterized in analytical systems. We report here on the annotation
of the human adult urinary metabolome and metabolite identification
from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics
data sets. Features of biological interest were first of all annotated
using the ESI-MS database of the laboratory. They were also grouped,
thanks to software tools, and annotated using public databases. Metabolite
identification was achieved using two complementary approaches: (i)
formal identification by matching chromatographic retention times,
mass spectra, and also product ion spectra (if required) of metabolites
to be characterized in biological data sets to those of reference
compounds and (ii) putative identification from biological data thanks
to MS/MS experiments for metabolites not available in our chemical
library. By these means, 384 metabolites corresponding to 1484 annotated
features (659 in negative ion mode and 825 in positive ion mode) were
characterized in human urine samples. Of these metabolites, 192 and
66 were formally and putatively identified, respectively, and 54 are
reported in human urine for the first time. These lists of features
could be used by other laboratories to annotate their ESI-MS metabolomics
data sets
Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer
Metabolic profiles of biofluids obtained by atmospheric
pressure
ionization mass spectrometry-based technologies contain hundreds to
thousands of features, most of them remaining unknown or at least
not characterized in analytical systems. We report here on the annotation
of the human adult urinary metabolome and metabolite identification
from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics
data sets. Features of biological interest were first of all annotated
using the ESI-MS database of the laboratory. They were also grouped,
thanks to software tools, and annotated using public databases. Metabolite
identification was achieved using two complementary approaches: (i)
formal identification by matching chromatographic retention times,
mass spectra, and also product ion spectra (if required) of metabolites
to be characterized in biological data sets to those of reference
compounds and (ii) putative identification from biological data thanks
to MS/MS experiments for metabolites not available in our chemical
library. By these means, 384 metabolites corresponding to 1484 annotated
features (659 in negative ion mode and 825 in positive ion mode) were
characterized in human urine samples. Of these metabolites, 192 and
66 were formally and putatively identified, respectively, and 54 are
reported in human urine for the first time. These lists of features
could be used by other laboratories to annotate their ESI-MS metabolomics
data sets
Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer
Metabolic profiles of biofluids obtained by atmospheric
pressure
ionization mass spectrometry-based technologies contain hundreds to
thousands of features, most of them remaining unknown or at least
not characterized in analytical systems. We report here on the annotation
of the human adult urinary metabolome and metabolite identification
from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics
data sets. Features of biological interest were first of all annotated
using the ESI-MS database of the laboratory. They were also grouped,
thanks to software tools, and annotated using public databases. Metabolite
identification was achieved using two complementary approaches: (i)
formal identification by matching chromatographic retention times,
mass spectra, and also product ion spectra (if required) of metabolites
to be characterized in biological data sets to those of reference
compounds and (ii) putative identification from biological data thanks
to MS/MS experiments for metabolites not available in our chemical
library. By these means, 384 metabolites corresponding to 1484 annotated
features (659 in negative ion mode and 825 in positive ion mode) were
characterized in human urine samples. Of these metabolites, 192 and
66 were formally and putatively identified, respectively, and 54 are
reported in human urine for the first time. These lists of features
could be used by other laboratories to annotate their ESI-MS metabolomics
data sets
Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer
Metabolic profiles of biofluids obtained by atmospheric
pressure
ionization mass spectrometry-based technologies contain hundreds to
thousands of features, most of them remaining unknown or at least
not characterized in analytical systems. We report here on the annotation
of the human adult urinary metabolome and metabolite identification
from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics
data sets. Features of biological interest were first of all annotated
using the ESI-MS database of the laboratory. They were also grouped,
thanks to software tools, and annotated using public databases. Metabolite
identification was achieved using two complementary approaches: (i)
formal identification by matching chromatographic retention times,
mass spectra, and also product ion spectra (if required) of metabolites
to be characterized in biological data sets to those of reference
compounds and (ii) putative identification from biological data thanks
to MS/MS experiments for metabolites not available in our chemical
library. By these means, 384 metabolites corresponding to 1484 annotated
features (659 in negative ion mode and 825 in positive ion mode) were
characterized in human urine samples. Of these metabolites, 192 and
66 were formally and putatively identified, respectively, and 54 are
reported in human urine for the first time. These lists of features
could be used by other laboratories to annotate their ESI-MS metabolomics
data sets
Bacterial Detection Using Unlabeled Phage Amplification and Mass Spectrometry through Structural and Nonstructural Phage Markers
According to the World Health Organization,
food safety is an essential
public health priority. In this context, we report a relevant proof
of feasibility for the indirect specific detection of bacteria in
food samples using unlabeled phage amplification coupled to ESI mass
spectrometry analysis and illustrated with the model phage systems
T4 and SPP1. High-resolving power mass spectrometry analysis (including
bottom-up and top-down protein analysis) was used for the discovery
of specific markers of phage infection. Structural components of the
viral particle and nonstructural proteins encoded by the phage genome
were identified. Then, targeted detection of these markers was performed
on a triple quadrupole mass spectrometer operating in the selected
reaction monitoring mode. <i>E. coli</i> at 1 × 10<sup>5</sup>, 5 × 10<sup>5</sup>, and 1 × 10<sup>6</sup> CFU/mL
concentrations was successfully detected after only a 2 h infection
time by monitoring phage T4 structural markers in Luria–Bertani
broth, orange juice, and French bean stew (“cassoulet”)
matrices. Reproducible detection of nonstructural markers was also
demonstrated, particularly when a high titer of input phages was required
to achieve successful amplification. This strategy provides a highly
time-effective and sensitive assay for bacterial detection