John P. Shockcor

Comprehensive LC−MSE Lipidomic Analysis using a Shotgun Approach and Its Application to Biomarker Detection and Identification in Osteoarthritis Patients

A fast and robust method for lipid profiling utilizing liquid chromatography coupled with mass spectrometry has been demonstrated and validated for the analysis of human plasma. This method allowed quantification and identification of lipids in human plasma using parallel alternating low energy and high energy collision spectral acquisition modes. A total of 275 [corrected] lipids were identified and quantified (as relative concentrations) in both positive and negative ion electrospray ionization mode. The method was validated with five nonendogenous lipids, and the linearity (r(2) better than 0.994) and the intraday and interday repeatability (relative standard deviation, 4-6% and 5-8%, respectively) were satisfactory. The developed lipid profiling method was successfully applied for the analysis of plasma from osteoarthritis (OA) patients. The multivariate statistical analysis by partial least-squares-discrimination analysis suggested an altered lipid metabolism associated with osteoarthritis and the release of arachidonic acid from phospholipids.

Strategy for optimizing LC-MS data processing in metabolomics: a design of experiments approach.

A strategy for optimizing LC-MS metabolomics data processing is proposed. We applied this strategy on the XCMS open source package written in R on both human and plant biology data. The strategy is a sequential design of experiments (DoE) based on a dilution series from a pooled sample and a measure of correlation between diluted concentrations and integrated peak areas. The reliability index metric, used to define peak quality, simultaneously favors reliable peaks and disfavors unreliable peaks using a weighted ratio between peaks with high and low response linearity. DoE optimization resulted in the case studies in more than 57% improvement in the reliability index compared to the use of the default settings. The proposed strategy can be applied to any other data processing software involving parameters to be tuned, e.g., MZmine 2. It can also be fully automated and used as a module in a complete metabolomics data processing pipeline.

Flow Injection Proton Nuclear Magnetic Resonance Spectroscopy Combined With Pattern Recognition Methods: Implications for Rapid Structural Studies and High Throughput Biochemical Screening

The applicability of novel NMR flow probe technology has been tested by the measurement of 300 MHz 1H NMR spectra of a series of rat urine samples. Compared with conventional automatic operation, the method resulted in a significantly increased rate of sample throughput, required minimal spectrometer optimisation before each measurement and avoided the need for expensive and fragile NMR sample tubes. The NMR approach has been coupled with computer methods for spectral data reduction and classification using, in this case, principal components analysis. The flow probe NMR approach offers distinct advantages in situations where large numbers of samples require NMR analysis in a short period of time. These could include routine samples from high throughput chemical synthesis, biofluid samples for drug toxicity monitoring as shown here, samples for clinical diagnosis or real-time analysis in chemical production facilities.

Heteronuclear 19F−1H Statistical Total Correlation Spectroscopy as a Tool in Drug Metabolism: Study of Flucloxacillin Biotransformation

We present a novel application of the heteronuclear statistical total correlation spectroscopy (HET-STOCSY) approach utilizing statistical correlation between one-dimensional 19F/1H NMR spectroscopic data sets collected in parallel to study drug metabolism. Parallel one-dimensional (1D) 800 MHz 1H and 753 MHz 19F{1H} spectra (n = 21) were obtained on urine samples collected from volunteers (n = 6) at various intervals up to 24 h after oral dosing with 500 mg of flucloxacillin. A variety of statistical relationships between and within the spectroscopic datasets were explored without significant loss of the typically high 1D spectral resolution, generating 1H-1H STOCSY plots, and novel 19F-1H HET-STOCSY, 19F-19F STOCSY, and 19F-edited 1H-1H STOCSY (X-STOCSY) spectroscopic maps, with a resolution of approximately 0.8 Hz/pt for both nuclei. The efficient statistical editing provided by these methods readily allowed the collection of drug metabolic data and assisted structure elucidation. This approach is of general applicability for studying the metabolism of other fluorine-containing drugs, including important anticancer agents such as 5-fluorouracil and flutamide, and is extendable to any drug metabolism study where there is a spin-active X-nucleus (e.g., 13C, 15N, 31P) label present.

Systematic Evaluation of Extraction Methods for Multiplatform-Based Metabotyping: Application to the Fasciola hepatica Metabolome

Combining data from multiple analytical platforms is essential for comprehensive study of the molecular phenotype (metabotype) of a given biological sample. The metabolite profiles generated are intrinsically dependent on the analytical platforms, each requiring optimization of instrumental parameters, separation conditions, and sample extraction to deliver maximal biological information. An in-depth evaluation of extraction protocols for characterizing the metabolome of the hepatobiliary fluke Fasciola hepatica, using ultra performance liquid chromatography and capillary electrophoresis coupled with mass spectroscopy is presented. The spectrometric methods were characterized by performance, and metrics of merit were established, including precision, mass accuracy, selectivity, sensitivity, and platform stability. Although a core group of molecules was common to all methods, each platform contributed a unique set, whereby 142 metabolites out of 14,724 features were identified. A mixture design revealed that the chloroform:methanol:water proportion of 15:59:26 was globally the best composition for metabolite extraction across UPLC-MS and CE-MS platforms accommodating different columns and ionization modes. Despite the general assumption of the necessity of platform-adapted protocols for achieving effective metabotype characterization, we show that an appropriately designed single extraction procedure is able to fit the requirements of all technologies. This may constitute a paradigm shift in developing efficient protocols for high-throughput metabolite profiling with more-general analytical applicability.

Application of combined omics platforms to accelerate biomedical discovery in diabesity

Diabesity has become a popular term to describe the specific form of diabetes that develops late in life and is associated with obesity. While there is a correlation between diabetes and obesity, the association is not universally predictive. Defining the metabolic characteristics of obesity that lead to diabetes, and how obese individuals who develop diabetes different from those who do not, are important goals. The use of large‐scale omics analyses (e.g., metabolomic, proteomic, transcriptomic, and lipidomic) of diabetes and obesity may help to identify new targets to treat these conditions. This report discusses how various types of omics data can be integrated to shed light on the changes in metabolism that occur in obesity and diabetes.

Use of an Atmospheric Solids Analysis Probe (ASAP) for High Throughput Screening of Biological Fluids: Preliminary Applications on Urine and Bile

A hybrid quadrupole orthogonal time-of-flight mass spectrometer (QToF) equipped with a solids analysis probe (atmospheric solids analysis probe-mass spectrometry (ASAP-MS)) has been applied to the high throughput qualitative analysis of bile (rat and dog) and urine (rat) samples. The metabolic profiles generated by ASAP-MS was less comprehensive than that provided by liquid chromatography (LC) or gas chromatography-mass spectrometry (GC-MS) metabonomic profiling, though simple types of sample preparation were found to increase the range of ions detected for bile (a complex, multicompartment sample type). While unsuited to biomarker discovery, ASAP-MS of these biofluids generated sufficiently complex metabolic fingerprints to enable them to be distinguished from each other using multivariate statistical methods such as principal components analysis (PCA). This ability to provide an effective means of sample classification suggests possible diagnostic applications.

Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping

To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided.

Label-free mass spectrometric profiling of urinary proteins and metabolites from paediatric idiopathic nephrotic syndrome.

Idiopathic nephrotic syndrome (INS) is caused by renal diseases that increase the permeability of the glomerular filtration barrier without evidence of a specific systemic cause. The aim of the present work was to reveal inherent molecular features of INS in children using combined urinary proteomics and metabolomics profiling. In this study, label-free mass spectrometric analysis of urinary proteins and small molecule metabolites was carried out in 12 patients with INS versus 12 sex- and age-matched control subjects with normal renal function. Integration and biological interpretation of obtained results were carried out by Ingenuity IPA software. Validation of obtained proteomics data was carried out by Western blot method. Proteomics data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD000765. This study indicates for the first time that paediatric INS is associated with up-regulation of afamin, hydroxyphenylacetate and uridine, and concomitant down-regulation in glutamine and phenylalanine levels, and many of these molecular species were previously shown to be involved in oxidative stress. Further studies in larger patient population are underway to investigate the role of oxidative stress in renal injury in paediatric INS.

Perturbations in fatty acid metabolism and apoptosis are manifested in calcific coronary artery disease: An exploratory lipidomic study.

BACKGROUND Controversy exists concerning the beneficial or harmful effects of the presence of ectopic calcification in the coronary arteries. Additionally, further elucidation of the exact pathophysiological mechanism is needed. In this study, we sought to identify metabolic markers of vascular calcification that could assist in understanding the disease, monitoring its progress and generating hypotheses describing its pathophysiology. METHODS Untargeted lipid profiling and complementary modeling strategies were employed to compare serum samples from patients with different levels of calcific coronary artery disease (CCAD) based on their calcium score (CS). Subsequently, patients were divided into three groups: no calcification (NC; CS=0; n=26), mild calcification (MC; CS:1-250; n=27) and severe (SC; CS>250; n=17). RESULTS Phosphatidylcholine levels were found to be significantly altered in the disease states (p=0.001-0.04). Specifically, 18-carbon fatty acyl chain (FAC) phosphatidylcholines were detected in lower levels in the SC group, while 20:4 FAC lipid species were detected in higher concentrations. A statistical trend was observed with phosphatidylcholine lipids in the MC group, showing the same tendency as with the SC group. We also observed several sphingomyelin signals present at lower intensities in SC when compared with NC or MC groups (p=0.000001-0.01). CONCLUSIONS This is the first lipid profiling study reported in CCAD. Our data demonstrate dysregulations of phosphatidylcholine lipid species, which suggest perturbations in fatty acid elongation/desaturation. The altered levels of the 18-carbon and 20:4 FAC lipids may be indicative of disturbed inflammation homeostasis. The marked sphingomyelin dysregulation in SC is consistent with profound apoptosis as a potential mechanism of CCAD.