41 research outputs found
Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): An Optimized Statistical Approach for Clustering of <sup>1</sup>H NMR Spectral Data to Reduce Interference and Enhance Robust Biomarkers Selection
We propose a novel statistical approach
to improve the reliability
of <sup>1</sup>H NMR spectral analysis in complex metabolic studies.
The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm
aims to reduce the variation within biological classes by selecting
subsets of homogeneous <sup>1</sup>H NMR spectra that contain specific
spectroscopic metabolic signatures related to each biological class
in a study. In SHOCSY, we used a clustering method to categorize the
whole data set into a number of clusters of samples with each cluster
showing a similar spectral feature and hence biochemical composition,
and we then used an enrichment test to identify the associations between
the clusters and the biological classes in the data set. We evaluated
the performance of the SHOCSY algorithm using a simulated <sup>1</sup>H NMR data set to emulate renal tubule toxicity and further exemplified
this method with a <sup>1</sup>H NMR spectroscopic study of hydrazine-induced
liver toxicity study in rats. The SHOCSY algorithm improved the predictive
ability of the orthogonal partial least-squares discriminatory analysis
(OPLS-DA) model through the use of âtrulyâ representative
samples in each biological class (i.e., homogeneous subsets). This
method ensures that the analyses are no longer confounded by idiosyncratic
responders and thus improves the reliability of biomarker extraction.
SHOCSY is a useful tool for removing irrelevant variation that interfere
with the interpretation and predictive ability of models and has widespread
applicability to other spectroscopic data, as well as other âomicsâ
type of data
Development and Validation of a High-Throughput Ultrahigh-Performance Liquid ChromatographyâMass Spectrometry Approach for Screening of Oxylipins and Their Precursors
Lipid mediators, highly bioactive
compounds synthesized from polyunsaturated
fatty acids (PUFAs), have a fundamental role in the initiation and
signaling of the inflammatory response. Although extensively studied
in isolation, only a limited number of analytical methods offer a
comprehensive coverage of the oxylipin synthetic cascade applicable
to a wide range of human biofluids. We report the development of an
ultrahigh-performance liquid chromatographyâelectrospray ionization
triple quadrupole mass spectrometry (UHPLCâMS) assay to quantify
oxylipins and their PUFA precursors in 100 ÎźL of human serum,
plasma, urine, and cell culture supernatant. A single 15 min UHPLC
run enables the quantification of 43 oxylipins and 5 PUFAs, covering
pro and anti-inflammatory lipid mediators synthesized across the cyclooxygenase
(COX), lipoxygenase (LOX), and cytochrome P450 (CYP450) pathways.
The method was validated in multiple biofluid matrixes (serum, plasma,
urine, and cell supernatant) and suppliers, ensuring its suitability
for large scale metabonomic studies. The approach is accurate, precise,
and reproducible (RSD < 15%) over multiple days and concentrations.
Very high sensitivity is achieved with limits of quantification inferior
to picograms for the majority of analytes (0.05â125 pg) and
linear range spanning up to 5 orders of magnitude. This enabled the
quantification of the great majority of these analytes at their low
endogenous level in human biofluids. We successfully applied the procedure
to individuals undergoing a fasting intervention; oxylipin profiles
highlighted significantly altered PUFA and inflammatory profiles in
accordance with previously published studies as well as offered new
insight on the modulation of the biosynthetic cascade responsible
for the regulation of oxylipins
Pharmacometabonomic Characterization of Xenobiotic and Endogenous Metabolic Phenotypes That Account for Inter-individual Variation in Isoniazid-Induced Toxicological Response
An NMR-based pharmacometabonomic approach was applied
to investigate
inter-animal variation in response to isoniazid (INH; 200 and 400
mg/kg) in male SpragueâDawley rats, alongside complementary
clinical chemistry and histopathological analysis. Marked inter-animal
variability in central nervous system (CNS) toxicity was identified
following administration of a high dose of INH, which enabled characterization
of CNS responders and CNS non-responders. High-resolution post-dose
urinary <sup>1</sup>H NMR spectra were modeled both by their xenobiotic
and endogenous metabolic information sets, enabling simultaneous identification
of the differential metabolic fate of INH and its associated endogenous
metabolic consequences in CNS responders and CNS non-responders. A
characteristic xenobiotic metabolic profile was observed for CNS responders,
which revealed higher urinary levels of pyruvate isonicotinylhydrazone
and β-glucosyl isonicotinylhydrazide and lower levels of acetylisoniazid
compared to CNS non-responders. This suggested that the capacity for
acetylation of INH was lower in CNS responders, leading to increased
metabolism <i>via</i> conjugation with pyruvate and glucose.
In addition, the endogenous metabolic profile of CNS responders revealed
higher urinary levels of lactate and glucose, in comparison to CNS
non-responders. Pharmacometabonomic analysis of the pre-dose <sup>1</sup>H NMR urinary spectra identified a metabolic signature that
correlated with the development of INH-induced adverse CNS effects
and may represent a means of predicting adverse events and acetylation
capacity when challenged with high dose INH. Given the widespread
use of INH for the treatment of tuberculosis, this pharmacometabonomic
screening approach may have translational potential for patient stratification
to minimize adverse events
Metabotyping of Long-Lived Mice using <sup>1</sup>H NMR Spectroscopy
Significant advances in understanding aging have been
achieved
through studying model organisms with extended healthy lifespans.
Employing <sup>1</sup>H NMR spectroscopy, we characterized the plasma
metabolic phenotype (metabotype) of three long-lived murine models:
30% dietary restricted (DR), insulin receptor substrate 1 null (<i>Irs1</i><sup>â/â</sup>), and Ames dwarf (Prop1<sup>df/df</sup>). A panel of metabolic differences were generated for
each model relative to their controls, and subsequently, the three
long-lived models were compared to one another. Concentrations of
mobile very low density lipoproteins, trimethylamine, and choline
were significantly decreased in the plasma of all three models. Metabolites
including glucose, choline, glycerophosphocholine, and various lipids
were significantly reduced, while acetoacetate, d-3-hydroxybutyrate
and trimethylamine-<i>N</i>-oxide levels were increased
in DR compared to <i>ad libitum</i> fed controls. Plasma
lipids and glycerophosphocholine were also decreased in <i>Irs1</i><sup>â/â</sup> mice compared to controls, as were methionine
and citrate. In contrast, high density lipoproteins and glycerophosphocholine
were increased in Ames dwarf mice, as were methionine and citrate.
Pairwise comparisons indicated that differences existed between the
metabotypes of the different long-lived mice models. <i>Irs1</i><sup>â/â</sup> mice, for example, had elevated glucose, acetate, acetone, and creatine
but lower methionine relative to DR mice and Ames dwarfs. Our study
identified several potential candidate biomarkers directionally altered
across all three models that may be predictive of longevity but also
identified differences in the metabolic signatures. This comparative
approach suggests that the metabolic networks underlying lifespan
extension may not be exactly the same for each model of longevity
and is consistent with multifactorial control of the aging process
Development of a Rapid Microbore Metabolic Profiling Ultraperformance Liquid ChromatographyâMass Spectrometry Approach for High-Throughput Phenotyping Studies
A rapid
gradient microbore ultraperformance liquid chromatographyâmass
spectrometry (UPLCâMS) method has been developed to provide
a high-throughput analytical platform for the metabolic phenotyping
of urine from large sample cohorts. The rapid microbore metabolic
profiling (RAMMP) approach was based on scaling a conventional reversed-phase
UPLCâMS method for urinary profiling from 2.1 mm Ă 100
mm columns to 1 mm Ă 50 mm columns, increasing the linear velocity
of the solvent, and decreasing the gradient time to provide an analysis
time of 2.5 min/sample. Comparison showed that conventional UPLCâMS
and rapid gradient approaches provided peak capacities of 150 and
50, respectively, with the conventional method detecting approximately
19âŻ000 features compared to the âź6âŻ000 found
using the rapid gradient method. Similar levels of repeatability were
seen for both methods. Despite the reduced peak capacity and the reduction
in ions detected, the RAMMP method was able to achieve similar levels
of group discrimination as conventional UPLCâMS when applied
to rat urine samples obtained from investigative studies on the effects
of acute 2-bromophenol and chronic acetaminophen administration. When
compared to a direct infusion MS method of similar analysis time the
RAMMP method provided superior selectivity. The RAMMP approach provides
a robust and sensitive method that is well suited to high-throughput
metabonomic analysis of complex mixtures such as urine combined with
a 5-fold reduction in analysis time compared with the conventional
UPLCâMS method
Gut Microbiota Modulate the Metabolism of Brown Adipose Tissue in Mice
A two by two experimental study has been designed to determine the effect of gut microbiota on energy metabolism in mouse models. The metabolic phenotype of germ-free (GF, <i>n</i> = 20) and conventional (<i>n</i> = 20) mice was characterized using a NMR spectroscopy-based metabolic profiling approach, with a focus on sexual dimorphism (20 males, 20 females) and energy metabolism in urine, plasma, liver, and brown adipose tissue (BAT). Physiological data of age-matched GF and conventional mice showed that male animals had a higher weight than females in both groups. In addition, conventional males had a significantly higher total body fat content (TBFC) compared to conventional females, whereas this sexual dimorphism disappeared in GF animals (i.e., male GF mice had a TBFC similar to those of conventional and GF females). Profiling of BAT hydrophilic extracts revealed that sexual dimorphism in normal mice was absent in GF animals, which also displayed lower BAT lactate levels and higher levels of (<i>D</i>)-3-hydroxybutyrate in liver, plasma, and BAT, together with lower circulating levels of VLDL. These data indicate that the gut microbiota modulate the lipid metabolism in BAT, as the absence of gut microbiota stimulated both hepatic and BAT lipolysis while inhibiting lipogenesis. We also demonstrated that <sup>1</sup>H NMR metabolic profiles of BAT were excellent predictors of BW and TBFC, indicating the potential of BAT to fight against obesity
Robust Data Processing and Normalization Strategy for MALDI Mass Spectrometric Imaging
Matrix-assisted laser desorption/ionization (MALDI) mass
spectrometry
imaging (MSI) provides localized information about the molecular content
of a tissue sample. To derive reliable conclusions from MSI data,
it is necessary to implement appropriate processing steps in order
to compare peak intensities across the different pixels comprising
the image. Here, we review commonly used normalization methods, and
propose a rational data processing strategy, for robust evaluation
and modeling of MSI data. The approach includes newly developed heuristic
methods for selecting biologically relevant peaks and pixels to reduce
the size of a data set and remove the influence of the applied MALDI
matrix. The methods are demonstrated on a MALDI MSI data set of a
sagittal section of rat brain (4750 bins, <i>m</i>/<i>z</i> = 50â1000, 111 Ă 185 pixels) and the proposed
preferred normalization method uses the median intensity of selected
peaks, which were determined to be independent of the MALDI matrix.
This was found to effectively compensate for a range of known limitations
associated with the MALDI process and irregularities in MS image sampling
routines. This new approach is relevant for processing of all MALDI
MSI data sets, and thus likely to have impact in biomarker profiling,
preclinical drug distribution studies, and studies addressing underlying
molecular mechanisms of tissue pathology
Chiral Metabonomics: <sup>1</sup>H NMR-Based Enantiospecific Differentiation of Metabolites in Human Urine via Direct Cosolvation with β-Cyclodextrin
Differences in molecular chirality remain an important
issue in
drug metabolism and pharmacokinetics for the pharmaceutical industry
and regulatory authorities, and chirality is an important feature
of many endogenous metabolites. We present a method for the rapid,
direct differentiation and identification of chiral drug enantiomers
in human urine without pretreatment of any kind. Using the well-known
anti-inflammatory chemical ibuprofen as one example, we demonstrate
that the enantiomers of ibuprofen and the diastereoisomers of one
of its main metabolites, the glucuronidated carboxylate derivative,
can be resolved by <sup>1</sup>H NMR spectroscopy as a consequence
of direct addition of the chiral cosolvating agent (CSA) β-cyclodextrin
(βCD). This approach is simple, rapid, and robust, involves
minimal sample manipulation, and does not require derivatization or
purification of the sample. In addition, the method should allow the
enantiodifferentiation of endogenous chiral metabolites, and this
has potential value for differentiating metabolites from mammalian
and microbial sources in biofluids. From these initial findings, we
propose that more extensive and detailed enantiospecific metabolic
profiling could be possible using CSA-NMR spectroscopy than has been
previously reported
Statistical Total Correlation Spectroscopy Scaling for Enhancement of Metabolic Information Recovery in Biological NMR Spectra
The high level of complexity in nuclear magnetic resonance
(NMR)
metabolic spectroscopic data sets has fueled the development of experimental
and mathematical techniques that enhance latent biomarker recovery
and improve model interpretability. We previously showed that statistical
total correlation spectroscopy (STOCSY) can be used to <i>edit</i> NMR spectra to remove drug metabolite signatures that obscure metabolic
variation of diagnostic interest. Here, we extend this âSTOCSY
editingâ concept to a generalized scaling procedure for NMR
data that enhances recovery of latent biochemical information and
improves biological classification and interpretation. We call this
new procedure STOCSY-scaling (STOCSY<sup>S</sup>). STOCSY<sup>S</sup> exploits the fixed proportionality in a set of NMR spectra between
resonances from the same molecule to suppress or enhance features
correlated with a resonance of interest. We demonstrate this new approach
using two exemplar data sets: (a) a streptozotocin rat model (<i>n</i> = 30) of type 1 diabetes and (b) a human epidemiological
study utilizing plasma NMR spectra of patients with metabolic syndrome
(<i>n</i> = 67). In both cases significant biomarker discovery
improvement was observed by using STOCSY<sup>S</sup>: the approach
successfully suppressed interfering NMR signals from glucose and lactate
that otherwise dominate the variation in the streptozotocin study,
which then allowed recovery of biomarkers such as glycine, which were
otherwise obscured. In the metabolic syndrome study, we used STOCSY<sup>S</sup> to enhance variation from the high-density lipoprotein cholesterol
peak, improving the prediction of individuals with metabolic syndrome
from controls in orthogonal projections to latent structures discriminant
analysis models and facilitating the biological interpretation of
the results. Thus, STOCSY<sup>S</sup> is a versatile technique that
is applicable in any situation in which variation, either biological
or otherwise, dominates a data set at the expense of more interesting
or important features. This approach is generally appropriate for
many types of NMR-based complex mixture analyses and hence for wider
applications in bioanalytical science
Gut Microbiota Modulate the Metabolism of Brown Adipose Tissue in Mice
A two by two experimental study has been designed to determine the effect of gut microbiota on energy metabolism in mouse models. The metabolic phenotype of germ-free (GF, <i>n</i> = 20) and conventional (<i>n</i> = 20) mice was characterized using a NMR spectroscopy-based metabolic profiling approach, with a focus on sexual dimorphism (20 males, 20 females) and energy metabolism in urine, plasma, liver, and brown adipose tissue (BAT). Physiological data of age-matched GF and conventional mice showed that male animals had a higher weight than females in both groups. In addition, conventional males had a significantly higher total body fat content (TBFC) compared to conventional females, whereas this sexual dimorphism disappeared in GF animals (i.e., male GF mice had a TBFC similar to those of conventional and GF females). Profiling of BAT hydrophilic extracts revealed that sexual dimorphism in normal mice was absent in GF animals, which also displayed lower BAT lactate levels and higher levels of (<i>D</i>)-3-hydroxybutyrate in liver, plasma, and BAT, together with lower circulating levels of VLDL. These data indicate that the gut microbiota modulate the lipid metabolism in BAT, as the absence of gut microbiota stimulated both hepatic and BAT lipolysis while inhibiting lipogenesis. We also demonstrated that <sup>1</sup>H NMR metabolic profiles of BAT were excellent predictors of BW and TBFC, indicating the potential of BAT to fight against obesity