A proton nuclear magnetic resonance-based metabonomics study of metabolic profiling in immunoglobulin a nephropathy

OBJECTIVES: Immunoglobulin A nephropathy is the most common cause of chronic renal failure among primary glomerulonephritis patients. The ability to diagnose immunoglobulin A nephropathy remains poor. However, renal biopsy is an inconvenient, invasive, and painful examination, and no reliable biomarkers have been developed for use in routine patient evaluations. The aims of the present study were to identify immunoglobulin A nephropathy patients, to identify useful biomarkers of immunoglobulin A nephropathy and to establish a human immunoglobulin A nephropathy metabolic profile. METHODS: Serum samples were collected from immunoglobulin A nephropathy patients who were not using immunosuppressants. A pilot study was undertaken to determine disease-specific metabolite biomarker profiles in three groups: healthy controls (N = 23), low-risk patients in whom immunoglobulin A nephropathy was confirmed as grades I-II by renal biopsy (N = 23), and high-risk patients with nephropathies of grades IV-V (N = 12). Serum samples were analyzed using proton nuclear magnetic resonance spectroscopy and by applying multivariate pattern recognition analysis for disease classification. RESULTS: Compared with the healthy controls, both the low-risk and high-risk patients had higher levels of phenylalanine, myo-Inositol, lactate, L6 lipids ( = CH-CH2-CH = O), L5 lipids (-CH2-C = O), and L3 lipids (-CH2-CH2-C = O) as well as lower levels of β -glucose, α-glucose, valine, tyrosine, phosphocholine, lysine, isoleucine, glycerolphosphocholine, glycine, glutamine, glutamate, alanine, acetate, 3-hydroxybutyrate, and 1-methylhistidine. CONCLUSIONS: These metabolites investigated in this study may serve as potential biomarkers of immunoglobulin A nephropathy. Point scoring of pattern recognition analysis was able to distinguish immunoglobulin A nephropathy patients from healthy controls. However, there were no obvious differences between the low-risk and high-risk groups in our research. These results offer new, sensitive and specific, noninvasive approaches that may be of great benefit to immunoglobulin A nephropathy patients by enabling earlier diagnosis

Development and Application of Chemometric Methods for Modelling Metabolic Spectral Profiles

The interpretation of metabolic information is crucial to understanding the functioning of a biological system. Latent information about the metabolic state of a sample can be acquired using analytical chemistry methods, which generate spectroscopic profiles. Thus, nuclear magnetic resonance spectroscopy and mass spectrometry techniques can be employed to generate vast amounts of highly complex data on the metabolic content of biofluids and tissue, and this thesis discusses ways to process, analyse and interpret these data successfully. The evaluation of J -resolved spectroscopy in magnetic resonance profiling and the statistical techniques required to extract maximum information from the projections of these spectra are studied. In particular, data processing is evaluated, and correlation and regression methods are investigated with respect to enhanced model interpretation and biomarker identification. Additionally, it is shown that non-linearities in metabonomic data can be effectively modelled with kernel-based orthogonal partial least squares, for which an automated optimisation of the kernel parameter with nested cross-validation is implemented. The interpretation of orthogonal variation and predictive ability enabled by this approach are demonstrated in regression and classification models for applications in toxicology and parasitology. Finally, the vast amount of data generated with mass spectrometry imaging is investigated in terms of data processing, and the benefits of applying multivariate techniques to these data are illustrated, especially in terms of interpretation and visualisation using colour-coding of images. The advantages of methods such as principal component analysis, self-organising maps and manifold learning over univariate analysis are highlighted. This body of work therefore demonstrates new means of increasing the amount of biochemical information that can be obtained from a given set of samples in biological applications using spectral profiling. Various analytical and statistical methods are investigated and illustrated with applications drawn from diverse biomedical areas

Integration of transcriptomics and metabonomics: improving diagnostics, biomarker identification and phenotyping in ulcerative colitis

A systems biology approach to multi-faceted diseases has provided an opportunity to establish a holistic understanding of the processes at play. Thus, the current study merges transcriptomics and metabonomics data in order to improve diagnostics, biomarker identification and to explore the possibilities of a molecular phenotyping of ulcerative colitis (UC) patients. Biopsies were obtained from the descending colon of 43 UC patients (22 active UC and 21 quiescent UC) and 15 controls. Genome-wide gene expression analyses were performed using Affymetrix GeneChip Human Genome U133 Plus 2.0. Metabolic profiles were generated using (1)H Nuclear magnetic resonance spectroscopy (Bruker 600 MHz, Bruker BioSpin, Rheinstetten, Germany). Data were analyzed with the use of orthogonal-projection to latent structure-discriminant analysis and a multivariate logistic regression model fitted by lasso. Prediction performance was evaluated using nested Monte Carlo cross-validation. The prediction performance of the merged data sets and that of relative small (<20 variables) multivariate biomarker panels suggest that it is possible to discriminate between active UC, quiescent UC, and controls; between patients with or without steroid dependency, as well as between early or late disease onset. Consequently, this study demonstrates that the novel approach of integrating metabonomics and transcriptomics combines the better of the two worlds, and provides us with clinical applicable candidate biomarker panels. These combined panels improve diagnostics and more importantly also the molecular phenotyping in UC and provide insight into the pathophysiological processes at play, making optimized and personalized medication a possibility. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-013-0580-3) contains supplementary material, which is available to authorized users

Novel biomarkers of renal transplant failure/dysfunction via spectroscopic phenotyping

Successful renal transplantation not only improves patients’ quality and duration of life, but also confers a substantial economic healthcare cost saving. With the growing burden of end-stage renal disease and the requirement for renal replacement therapy, strategies to augment transplant success and subsequent graft survival become more vital than ever. Herein, an objective means of characterising renal function across the transplant journey, and appropriately stratifying in accordance to individual contingencies/factors (including the early detection of renal dysfunction), based on metabolism is explored. Patient pairs, recipients and donors, were metabolically phenotyped prior to (24 h) and post (days 1–5) transplantation using a multi-platform analytical approach (i.e., Nuclear Magnetic Resonance Spectroscopy (NMR) and Mass Spectrometry (MS)) of urine and plasma (n = 50). Using advanced statistics, the resulting metabolic profiles were subsequently modelled, and related to multiple clinical phenotypes (and outcomes), to increase the understanding of molecular changes/signatures across transplantation, capturing valuable information pertinent to transplant type, cause, co-morbidity, modality, immunology and complication (p-value < 0.05) – over donors as well as recipients. An attempt to then develop predictive algorithms for the early detection of renal dysfunction was preliminary defined within the confines of the study design, where integrated NMR and MS metabolic data improved patient stratification for complications over clinical measures (receiver operator characteristic area under curve over 0.900) and potentially replace current measures. While prospective/multicentre studies are imperative for subsequent real-world adoption (qualification/validation), the work conducted herein encompassed much of the first stage of marker development – discovery – where metabolic phenotyping renal transplantation has provided a deeper characterisation of patient journeys with new insights into multiple contingencies/factors (including complication). Such findings infer the value of metabolic phenotyping to augment and potentially replace current measures and methods to better inform decision making in the clinic on the individual/precision level.Open Acces

Chemometric analysis of biofluids from mice experimentally infected with Schistosoma mansoni

BACKGROUND: The urinary metabolic fingerprint of a patent Schistosoma mansoni infection in the mouse has been characterized using spectroscopic methods. However, the temporal dynamics of metabolic alterations have not been studied at the systems level. Here, we investigated the systems metabolic changes in the mouse upon S. mansoni infection by modeling the sequence of metabolic events in urine, plasma and faecal water. METHODS: Ten female NMRI mice, aged 5 weeks, were infected with 80 S. mansoni cercariae each. Ten age- and sex-matched mice remained uninfected and served as a control group. Urine, plasma and faecal samples were collected 1 day before, and on eight time points until day 73 post-infection. Biofluid samples were subjected to 1H nuclear magnetic resonance (NMR) spectroscopy and multivariate statistical analyses. RESULTS: Differences between S. mansoni-infected and uninfected control mice were found from day 41 onwards. One of the key metabolic signatures in urine and faecal extracts was an alteration in several gut bacteria-related metabolites, whereas the plasma reflected S. mansoni infection by changes in metabolites related to energy homeostasis, such as relatively higher levels of lipids and decreased levels of glucose. We identified 12 urinary biomarkers of S. mansoni infection, among which hippurate, phenylacetylglycine (PAG) and 2-oxoadipate were particularly robust with regard to disease progression. Thirteen plasma metabolites were found to differentiate infected from control mice, with the lipid components, D-3-hydroxybutyrate and glycerophosphorylcholine showing greatest consistency. Faecal extracts were highly variable in chemical composition and therefore only five metabolites were found discriminatory of infected mice, of which 5-aminovalerate was the most stable and showed a positive correlation with urinary PAG. CONCLUSIONS: The composite metabolic signature of S. mansoni in the mouse derived from perturbations in urina faecal and plasma composition showed a coherent response in altered energy metabolism and in gut microbial activity. Our findings provide new mechanistic insight into host-parasite interactions across different compartments and identified a set of temporally robust biomarkers of S. mansoni infection, which might assist in derivation of diagnostic assays or metrics for monitoring therapeutic respons

Incorporating standardised drift-tube ion mobility to enhance non-targeted assessment of the wine metabolome (LC×IM-MS)

Liquid chromatography with drift-tube ion mobility spectrometry-mass spectrometry (LCxIM-MS) is emerging as a powerful addition to existing LC-MS workflows for addressing a diverse range of metabolomics-related questions [1,2]. Importantly, excellent precision under repeatability and reproducibility conditions of drift-tube IM separations [3] supports the development of non-targeted approaches for complex metabolome assessment such as wine characterisation [4]. In this work, fundamentals of this new analytical metabolomics approach are introduced and application to the analysis of 90 authentic red and white wine samples originating from Macedonia is presented. Following measurements, intersample alignment of metabolites using non-targeted extraction and three-dimensional alignment of molecular features (retention time, collision cross section, and high-resolution mass spectra) provides confidence for metabolite identity confirmation. Applying a fingerprinting metabolomics workflow allows statistical assessment of the influence of geographic region, variety, and age. This approach is a state-of-the-art tool to assess wine chemodiversity and is particularly beneficial for the discovery of wine biomarkers and establishing product authenticity based on development of fingerprint libraries

Exploring gut microbiome – host interactions in the extremes of health and disease

Introduction: Multi ‘omics analyses, including metabonomic and metagenomic profiling techniques, have enabled new insights into systems biology over the past decade. Using two extremes of a continuum between health and disease – elite athletes and obese patients undergoing bariatric surgery – the work in this thesis aims to apply metabolic phenotyping to further understand the impact of exercise, diet and obesity on human metabolism. Furthermore, through combinatorial analysis of metabonomic and gut microbiome data sets, host – gut microbiome co-metabolism and its influence on health is explored in these two extreme populations. Methods: Biofluids were collected from three cohorts: i) elite athletes and age and sex matched controls, ii) healthy individuals before and after a high protein diet, exercise regime or both, and iii) obese subjects pre and post bariatric surgery. Multiple analytical platforms were utilised for metabolic profiling including 1H-NMR spectroscopy, UPLC-MS and GC-MS. Gut microbiome analysis was performed using next generation metagenomic sequencing. After pre-processing the metabonomic and metagenomic data; univariate, unsupervised and supervised multivariate analyses were performed as well as gut microbiome-metabolite association studies. Results: Distinct metabolic and microbial phenotypes existed between both athletes and controls and between obese patients before and after bariatric surgery. Discriminatory metabolites higher in athletes include metabolites associated with muscle turnover, vitamins and recovery supplements, a high protein diet and those derived from gut microbes. Interestingly, increased bacterial diversity seen in athletes correlated with a specific subset of metabolites. Similarly, bariatric surgery resulted in large changes to circulating metabolites. A number of these metabolites were linked to changes in the gut microbiome, including bile acids, short-chain fatty acids and amino acids. Conclusion: This thesis extends existing knowledge of the gut microbiome’s influence on human health through small molecule signalling. Mechanistic studies are now needed to establish causal links between gut microbes, changes to circulating metabolites and disease status.Open Acces

Metabolomics of urinary tract infection : a multiplatform approach

Urinary tract infection is a complex clinical entity a common infectious disease that encompasses a variety of clinical syndromes with a positive bacterial culture as common denominator. This thesis provides an exhaustive exploratory study of the metabolic pattern of patients affected by urinary tract infection and Here this complex clinical entity was investigated with a multiplatform approach. Each of the used platforms added a unique perspective to the further understanding of the infection process. The assessment of the bacterial growth (NMR), of the host response (LC__MS) and of the physiological status (GC-APCI-MS) could eventually be useful during the assessment of the disease severity and/or decision makingUBL - phd migration 201