13 research outputs found
Development of Isotope Labeling LC–MS for Human Salivary Metabolomics and Application to Profiling Metabolome Changes Associated with Mild Cognitive Impairment
Saliva is a readily available biofluid that may contain
metabolites
of interest for diagnosis and prognosis of diseases. In this work,
a differential <sup>13</sup>C/<sup>12</sup>C isotope dansylation labeling
method, combined with liquid chromatography Fourier transform ion
cyclotron resonance mass spectrometry (LC–FTICR-MS), is described
for quantitative profiling of the human salivary metabolome. New strategies
are presented to optimize the sample preparation and LC–MS
detection processes. The strategies allow the use of as little of
5 μL of saliva sample as a starting material to determine the
concentration changes of an average of 1058 ion pairs or putative
metabolites in comparative saliva samples. The overall workflow consists
of several steps including acetone-induced protein precipitation, <sup>12</sup>C-dansylation labeling of the metabolites, and LC–UV
measurement of the total concentration of the labeled metabolites
in individual saliva samples. A pooled sample was prepared from all
the individual samples and labeled with <sup>13</sup>C-dansylation
to serve as a reference. Using this metabolome profiling method, it
was found that compatible metabolome results could be obtained after
saliva samples were stored in tubes normally used for genetic material
collection at room temperature, −20 °C freezer, and −80
°C freezer over a period of 1 month, suggesting that many saliva
samples already collected in genomic studies could become a valuable
resource for metabolomics studies, although the effect of much longer
term of storage remains to be determined. Finally, the developed method
was applied for analyzing the metabolome changes of two different
groups: normal healthy older adults and comparable older adults with
mild cognitive impairment (MCI). Top-ranked 18 metabolites successfully
distinguished the two groups, among which seven metabolites were putatively
identified while one metabolite, taurine, was definitively identified
Additional file 1 of Effect of an increase in Lp(a) following statin therapy on cardiovascular prognosis in secondary prevention population of coronary artery disease
Additional file 1: Table S1. Statins used in study subjects. Table S2. Endpoint events for study subjects. Table S3. Univariate COX analysis of risk factors for MACE
Effective Antisense Gene Regulation via Noncationic, Polyethylene Glycol Brushes
Negatively
charged nucleic acids are often complexed with polycationic
transfection agents before delivery. Herein, we demonstrate that a
noncationic, biocompatible polymer, polyethylene glycol, can be used
as a transfection vector by forming a brush polymer-DNA conjugate.
The brush architecture provides embedded DNA strands with enhanced
nuclease stability and improved cell uptake. Because of the biologically
benign nature of the polymer component, no cytotoxicity was observed.
This approach has the potential to address several long-lasting challenges
in oligonucleotide therapeutics
Table1_Impact of epicardial adipose tissue volume on hemodynamically significant coronary artery disease in Chinese patients with known or suspected coronary artery disease.docx
BackgroundEpicardial adipose tissue (EAT) is directly related to coronary artery disease (CAD), but little is known about its role in hemodynamically significant CAD. Therefore, our goal is to explore the impact of EAT volume on hemodynamically significant CAD.MethodsPatients who underwent coronary computed tomography angiography (CCTA) and received coronary angiography within 30 days were retrospectively included. Measurements of EAT volume and coronary artery calcium score (CACs) were performed on a semi-automatic software based on CCTA images, while quantitative flow ratio (QFR) was automatically calculated by the AngioPlus system according to coronary angiographic images.ResultsThis study included 277 patients, 112 of whom had hemodynamically significant CAD and showed higher EAT volume. In multivariate analysis, EAT volume was independently and positively correlated with hemodynamically significant CAD [per standard deviation (SD) cm3; odds ratio (OR), 2.78; 95% confidence interval (CI), 1.86–4.15; P min (per SD cm3; β coefficient, −0.068; 95% CI, −0.109 to −0.027; P = 0.001) after adjustment for traditional risk factors and CACs. Receiver operating characteristics curve analysis demonstrated a significant improvement in predictive value for hemodynamically significant CAD with the addition of EAT volume to obstructive CAD alone (area under the curve, 0.950 vs. 0.891; P ConclusionIn this study, we found that EAT volume correlated substantially and positively with the existence and severity of hemodynamically significant CAD in Chinese patients with known or suspected CAD, which was independent of traditional risk factors and CACs. In combination with obstructive CAD, EAT volume significantly improved diagnostic performance for hemodynamically significant CAD, suggesting that EAT could be a reliable noninvasive indicator of hemodynamically significant CAD.</p
MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification
Identification of unknown metabolites is a major challenge
in metabolomics.
Without the identities of the metabolites, the metabolome data generated
from a biological sample cannot be readily linked with the proteomic
and genomic information for studies in systems biology and medicine.
We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting
mass spectrometry (MS) data against a newly constructed metabolome
library composed of 8 021 known human endogenous metabolites
and their predicted metabolic products (375 809 compounds from
one metabolic reaction and 10 583 901 from two reactions).
As an example, in the analysis of a simple extract of human urine
or plasma and the whole human urine by liquid chromatography-mass
spectrometry and MS/MS, we are able to identify at least two times
more metabolites in these samples than by using a standard human metabolome
library. In addition, it is shown that the evidence-based metabolome
library (EML) provides a much superior performance in identifying
putative metabolites from a human urine sample, compared to the use
of the ChemPub and KEGG libraries
MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification
Identification of unknown metabolites is a major challenge
in metabolomics.
Without the identities of the metabolites, the metabolome data generated
from a biological sample cannot be readily linked with the proteomic
and genomic information for studies in systems biology and medicine.
We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting
mass spectrometry (MS) data against a newly constructed metabolome
library composed of 8 021 known human endogenous metabolites
and their predicted metabolic products (375 809 compounds from
one metabolic reaction and 10 583 901 from two reactions).
As an example, in the analysis of a simple extract of human urine
or plasma and the whole human urine by liquid chromatography-mass
spectrometry and MS/MS, we are able to identify at least two times
more metabolites in these samples than by using a standard human metabolome
library. In addition, it is shown that the evidence-based metabolome
library (EML) provides a much superior performance in identifying
putative metabolites from a human urine sample, compared to the use
of the ChemPub and KEGG libraries
MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification
Identification of unknown metabolites is a major challenge
in metabolomics.
Without the identities of the metabolites, the metabolome data generated
from a biological sample cannot be readily linked with the proteomic
and genomic information for studies in systems biology and medicine.
We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting
mass spectrometry (MS) data against a newly constructed metabolome
library composed of 8 021 known human endogenous metabolites
and their predicted metabolic products (375 809 compounds from
one metabolic reaction and 10 583 901 from two reactions).
As an example, in the analysis of a simple extract of human urine
or plasma and the whole human urine by liquid chromatography-mass
spectrometry and MS/MS, we are able to identify at least two times
more metabolites in these samples than by using a standard human metabolome
library. In addition, it is shown that the evidence-based metabolome
library (EML) provides a much superior performance in identifying
putative metabolites from a human urine sample, compared to the use
of the ChemPub and KEGG libraries
MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification
Identification of unknown metabolites is a major challenge
in metabolomics.
Without the identities of the metabolites, the metabolome data generated
from a biological sample cannot be readily linked with the proteomic
and genomic information for studies in systems biology and medicine.
We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting
mass spectrometry (MS) data against a newly constructed metabolome
library composed of 8 021 known human endogenous metabolites
and their predicted metabolic products (375 809 compounds from
one metabolic reaction and 10 583 901 from two reactions).
As an example, in the analysis of a simple extract of human urine
or plasma and the whole human urine by liquid chromatography-mass
spectrometry and MS/MS, we are able to identify at least two times
more metabolites in these samples than by using a standard human metabolome
library. In addition, it is shown that the evidence-based metabolome
library (EML) provides a much superior performance in identifying
putative metabolites from a human urine sample, compared to the use
of the ChemPub and KEGG libraries
MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification
Identification of unknown metabolites is a major challenge
in metabolomics.
Without the identities of the metabolites, the metabolome data generated
from a biological sample cannot be readily linked with the proteomic
and genomic information for studies in systems biology and medicine.
We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting
mass spectrometry (MS) data against a newly constructed metabolome
library composed of 8 021 known human endogenous metabolites
and their predicted metabolic products (375 809 compounds from
one metabolic reaction and 10 583 901 from two reactions).
As an example, in the analysis of a simple extract of human urine
or plasma and the whole human urine by liquid chromatography-mass
spectrometry and MS/MS, we are able to identify at least two times
more metabolites in these samples than by using a standard human metabolome
library. In addition, it is shown that the evidence-based metabolome
library (EML) provides a much superior performance in identifying
putative metabolites from a human urine sample, compared to the use
of the ChemPub and KEGG libraries
MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification
Identification of unknown metabolites is a major challenge
in metabolomics.
Without the identities of the metabolites, the metabolome data generated
from a biological sample cannot be readily linked with the proteomic
and genomic information for studies in systems biology and medicine.
We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting
mass spectrometry (MS) data against a newly constructed metabolome
library composed of 8 021 known human endogenous metabolites
and their predicted metabolic products (375 809 compounds from
one metabolic reaction and 10 583 901 from two reactions).
As an example, in the analysis of a simple extract of human urine
or plasma and the whole human urine by liquid chromatography-mass
spectrometry and MS/MS, we are able to identify at least two times
more metabolites in these samples than by using a standard human metabolome
library. In addition, it is shown that the evidence-based metabolome
library (EML) provides a much superior performance in identifying
putative metabolites from a human urine sample, compared to the use
of the ChemPub and KEGG libraries