Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): An Optimized Statistical Approach for Clustering of ¹H NMR Spectral Data to Reduce Interference and Enhance Robust Biomarkers Selection

We propose a novel statistical approach to improve the reliability of ¹H 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 ¹H 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 ¹H NMR data set to emulate renal tubule toxicity and further exemplified this method with a ¹H 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 “truly” 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 “omics” type of data

Development and Validation of a High-Throughput Ultrahigh-Performance Liquid Chromatography–Mass 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 chromatography–electrospray ionization triple quadrupole mass spectrometry (UHPLC–MS) assay to quantify oxylipins and their PUFA precursors in 100 μL 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 (0.05–125 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 Sprague–Dawley 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 ¹H 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 ¹H 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 ¹H NMR Spectroscopy

Significant advances in understanding aging have been achieved through studying model organisms with extended healthy lifespans. Employing ¹H 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>^–/–), and Ames dwarf (Prop1^df/df). 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>^–/– 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>^–/– 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 Chromatography–Mass Spectrometry Approach for High-Throughput Phenotyping Studies

A rapid gradient microbore ultraperformance liquid chromatography–mass spectrometry (UPLC–MS) 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 UPLC–MS method for urinary profiling from 2.1 mm × 100 mm columns to 1 mm × 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 UPLC–MS and rapid gradient approaches provided peak capacities of 150 and 50, respectively, with the conventional method detecting approximately 19 000 features compared to the ∼6 000 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 UPLC–MS 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 UPLC–MS 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 ¹H NMR metabolic profiles of BAT were excellent predictors of BW and TBFC, indicating the potential of BAT to fight against obesity

Chiral Metabonomics: ¹H 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 ¹H 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

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> = 50–1000, 111 × 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

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 “STOCSY editing” 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^S). STOCSY^S 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^S: 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^S 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^S 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 ¹H NMR metabolic profiles of BAT were excellent predictors of BW and TBFC, indicating the potential of BAT to fight against obesity