Metabolomic analysis characterizes tissue specific indomethacin-induced metabolic perturbations of rats

In this study, the promising metabolomic approach integrating with ingenuity pathway analysis (IPA) was applied to characterize the tissue specific metabolic perturbation of rats that was induced by indomethacin. The selective pattern recognition analyses were applied to analyze global metabolic profiling of urine of rats treated by indomethacin at an acute dosage of reference that has been proven to induce tissue disorders in rats, evaluated throughout the time-course of -24-72 h. The results preliminarily revealed that modifications of amino acid metabolism, fatty acid metabolism and energetically associated metabolic pathways accounted for metabolic perturbation of the rats that was induced by indomethacin. Furthermore, IPA was applied to deeply analyze the biomarkers and their relations with the metabolic perturbations evidenced by pattern recognition analyses. Specific biochemical functions affected by indomethacin suggested that there is an important correlation of its effects in kidney and liver metabolism, based on the determined metabolites and their pathway-based analysis. The IPA correlation of the three major biomarkers, identified as creatinine, prostaglandin E2 and guanosine, suggested that the administration of indomethacin induced certain levels of toxicity in the kidneys and liver. The changes in the levels of biomarker metabolites allowed the phenotypical determination of the metabolic perturbations induced by indomethacin in a time-dependent manner.

Development of an integrated metabolomic profiling approach for infectious diseases research

Metabolomic profiling offers direct insights into the chemical environment and metabolic pathway activities at sites of human disease. During infection, this environment may receive important contributions from both host and pathogen. Here we apply an untargeted metabolomics approach to identify compounds associated with an E. coli urinary tract infection population. Correlative and structural data from minimally processed samples were obtained using an optimized LC-MS platform capable of resolving ~2300 molecular features. Principal component analysis readily distinguished patient groups and multiple supervised chemometric analyses resolved robust metabolomic shifts between groups. These analyses revealed nine compounds whose provisional structures suggest candidate infection-associated endocrine, catabolic, and lipid pathways. Several of these metabolite signatures may derive from microbial processing of host metabolites. Overall, this study highlights the ability of metabolomic approaches to directly identify compounds encountered by, and produced from, bacterial pathogens within human hosts.

Metabolomic analysis of siderophore cheater mutants reveals metabolic costs of expression in uropathogenic escherichia coli

Bacterial siderophores are a group of chemically diverse, virulence-associated secondary metabolites whose expression exerts metabolic costs. A combined bacterial genetic and metabolomic approach revealed differential metabolomic impacts associated with biosynthesis of different siderophore structural families. Despite myriad genetic differences, the metabolome of a cheater mutant lacking a single set of siderophore biosynthetic genes more closely approximate that of a nonpathogenic K12 strain than its isogenic, uropathogen parent strain. Siderophore types associated with greater metabolomic perturbations are less common among human isolates, suggesting that metabolic costs influence success in a human population. Although different siderophores share a common iron acquisition function, our analysis shows how a metabolomic approach can distinguish their relative metabolic impacts in E.coli.

Discovery of candidate serum proteomic and metabolomic biomarkers in ankylosing spondylitis

Ankylosing Spondylitis (AS) is a common inflammatory rheumatic disease with a predilection for the axial skeleton, affecting 0.2% of the population. Current diagnostic criteria rely on a composite of clinical and radiological changes, with a mean time to diagnosis of 5 to 10 years. In this study we employed nano liquid-chromatography mass spectrometry analysis to detect and quantify proteins and small compounds including endogenous peptides and metabolites in serum from 18 AS patients and nine healthy individuals. We identified a total of 316 proteins in serum, of which 22 showed significant up- or down-regulation (p < 0.05) in AS patients. Receiver operating characteristic analysis of combined levels of serum amyloid P component and inter-α-trypsin inhibitor heavy chain 1 revealed high diagnostic value for Ankylosing Spondylitis (area under the curve = 0.98). We also depleted individual sera of proteins to analyze endogenous peptides and metabolic compounds. We detected more than 7000 molecular features in patients and healthy individuals. Quantitative MS analysis revealed compound profiles that correlate with the clinical assessment of disease activity. One molecular feature identified as a Vitamin D3 metabolite-(23S,25R)-25-hydroxyvitamin D3 26,23-peroxylactone-was down-regulated in AS. The ratio of this vitamin D metabolite versus vitamin D binding protein serum levels was also altered in AS as compared with controls. These changes may contribute to pathological skeletal changes in AS. Our study is the first example of an integration of proteomic and metabolomic techniques to find new biomarker candidates for the diagnosis of Ankylosing Spondylitis

Metabolomic and proteomic analysis of breast cancer patient samples suggests that glutamate and 12-HETE in combination with CA15-3 may be useful biomarkers reflecting tumour burden

Evaluation of protein and metabolite expression patterns in blood using mass spectrometry and high-throughput antibody-based screening platforms has potential for the discovery of new biomarkers for managing breast cancer patient treatment. Previously identified blood-based breast cancer biomarkers, including cancer antigen 15.3 (CA15-3) are useful in combination with imaging (computed tomography scans, magnetic resonance imaging, X-rays) and physical examination for monitoring tumour burden in advanced breast cancer patients. However, these biomarkers suffer from insufficient levels of accuracy and with new therapies available for the treatment of breast cancer, there is an urgent need for reliable, non-invasive biomarkers that measure tumour burden with high sensitivity and specificity so as to provide early warning of the need to switch to an alternative treatment. The aim of this study was to identify a biomarker signature of tumour burden using cancer and non-cancer (healthy controls/non-malignant breast disease) patient samples. Results demonstrate that combinations of three candidate biomarkers from Glutamate, 12-Hydroxyeicosatetraenoic acid, Beta-hydroxybutyrate, Factor V and Matrix metalloproteinase-1 with CA15-3, an established biomarker for breast cancer, were found to mirror tumour burden, with AUC values ranging from 0.71 to 0.98 when comparing non-malignant breast disease to the different stages of breast cancer. Further validation of these biomarker panels could potentially facilitate the management of breast cancer patients, especially to assess changes in tumour burden in combination with imaging and physical examination.

A metabolomic approach to studying mechanisms of polymeric gene delivery to retinal pigment epithelial cells

Tiivistelmä ReferatAbstract Metabolomics is a rapidly growing research field that studies the response of biological systems to environmental factors, disease states and genetic modifications. It aims at measuring the complete set of endogenous metabolites, i.e. the metabolome, in a biological sample such as plasma or cells. Because metabolites are the intermediates and end products of biochemical reactions, metabolite compositions and metabolite levels in biological samples can provide a wealth of information on on-going processes in a living system. Due to the complexity of the metabolome, metabolomic analysis poses a challenge to analytical chemistry. Adequate sample preparation is critical to accurate and reproducible analysis, and the analytical techniques must have high resolution and sensitivity to allow detection of as many metabolites as possible. Furthermore, as the information contained in the metabolome is immense, the data set collected from metabolomic studies is very large. In order to extract the relevant information from such large data sets, efficient data processing and multivariate data analysis methods are needed. In the research presented in this thesis, metabolomics was used to study mechanisms of polymeric gene delivery to retinal pigment epithelial (RPE) cells. The aim of the study was to detect differences in metabolomic fingerprints between transfected cells and non-transfected controls, and thereafter to identify metabolites responsible for the discrimination. The plasmid pCMV-β was introduced into RPE cells using the vector polyethyleneimine (PEI). The samples were analyzed using high performance liquid chromatography (HPLC) and ultra performance liquid chromatography (UPLC) coupled to a triple quadrupole (QqQ) mass spectrometer (MS). The software MZmine was used for raw data processing and principal component analysis (PCA) was used in statistical data analysis. The results revealed differences in metabolomic fingerprints between transfected cells and non-transfected controls. However, reliable fingerprinting data could not be obtained because of low analysis repeatability. Therefore, no attempts were made to identify metabolites responsible for discrimination between sample groups. Repeatability and accuracy of analyses can be influenced by protocol optimization. However, in this study, optimization of analytical methods was hindered by the very small number of samples available for analysis. In conclusion, this study demonstrates that obtaining reliable fingerprinting data is technically demanding, and the protocols need to be thoroughly optimized in order to approach the goals of gaining information on mechanisms of gene delivery.

LC-MS based methods for phytosterols determination in oenological matrices. Targeted metabolomic study of Rioja grape varieties

221 p.+ anexos

Relative metabolomic flux: Predicting changes in the environmental metabolome from the metagenome

Background: The world's oceans are home to a diverse array of microbial life whose metabolic activity helps to drive the earth's biogeochemical cycles. Metagenomic analysis has revolutionized our access to these communities, providing a system-scale perspective of microbial community interactions. However, while metagenome sequencing can provide useful estimates of the relative change in abundance of specific genes and taxa between environments or over time, this does not investigate the relative changes in the production or consumption of different metabolites.
Results: We propose a methodology, Predicted Relative Metabolic Turnover (PRMT) that defines and enables exploration of metabolite-space inferred from the metagenome. Our analysis of metagenomic data from a time-series study in the Western English Channel demonstrated considerable correlations between predicted relative metabolic turnover and seasonal changes in abundance of measured environmental parameters as well as with observed seasonal changes in bacterial population structure.
Conclusions: The PRMT method was successfully applied to metagenomic data to explore the Western English Channel microbial metabalome to generate specific, biologically testable hypotheses. Generated hypotheses linked organic phosphate utilization to Gammaproteobactaria, Plantcomycetes, and Betaproteobacteria, chitin degradation to Actinomycetes, and potential small molecule biosynthesis pathways for Lentisphaerae, Chlamydiae, and Crenarchaeota. The PRMT method can be applied as a general tool for the analysis of additional metagenomic or transcriptomic datasets.

Use of NMR metabolomic plasma profiling methodologies to identify illicit growth-promoting administrations

Detection of growth-promoter use in animal production systems still proves to be an analytical challenge despite years of activity in the field. This study reports on the capability of NMR metabolomic profiling techniques to discriminate between plasma samples obtained from cattle treated with different groups of growth-promoting hormones (dexamethasone, prednisolone, oestradiol) based on recorded metabolite profiles. Two methods of NMR analysis were investigated—a Carr–Purcell–Meiboom–Gill (CPMG)-pulse sequence technique and a conventional 1H NMR method using pre-extracted plasma. Using the CPMG method, 17 distinct metabolites could be identified from the spectra. 1H NMR analysis of extracted plasma facilitated identification of 23 metabolites—six more than the alternative method and all within the aromatic region. Multivariate statistical analysis of acquired data from both forms of NMR analysis separated the plasma metabolite profiles into distinct sample cluster sets representative of the different animal study groups. Samples from both sets of corticosteroid-treated animals—dexamethasone and prednisolone—were found to be clustered relatively closely and had similar alterations to identified metabolite panels. Distinctive metabolite profiles, different from those observed within plasma from corticosteroid-treated animal plasma, were observed in oestradiol-treated animals and samples from these animals formed a cluster spatially isolated from control animal plasma samples. These findings suggest the potential use of NMR methodologies of plasma metabolite analysis as a high-throughput screening technique to aid detection of growth promoter use.

Metabolomic Profiling of In Vivo Plasma Responses to DioxinAssociated Dietary Contaminant Exposure in Rats: Implications for Identification of Sources of Animal and Human Exposure

Dioxin contamination of the food chain typically occurs when cocktails of combustion residues or polychlorinated biphenyl (PCB) containing oils become incorporated into animal feed. These highly toxic compounds are bioaccumulative with small amounts posing a major health risk. The ability to identify animal exposure to these compounds prior to their entry into the food chain may be an invaluable tool to safeguard public health. Dioxin-like compounds act by a common mode of action and this suggests that markers or patterns of response may facilitate identification of exposed animals. However, secondary co-contaminating compounds present in typical dioxin sources may affect responses to compounds. This study has investigated for the first time the potential of a metabolomics platform to distinguish between animals exposed to different sources of dioxin contamination through their diet. Sprague-Dawley rats were given feed containing dioxin-like toxins from hospital incinerator soot, a common PCB oil standard and pure 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (normalized at 0.1 µg/kg TEQ) and acquired plasma was subsequently biochemically profiled using ultra high performance liquid chromatography (UPLC) quadropole time-of-flight-mass spectrometry (QTof-MS). An OPLS-DA model was generated from acquired metabolite fingerprints and validated which allowed classification of plasma from individual animals into the four dietary exposure study groups with a level of accuracy of 97-100%. A set of 24 ions of importance to the prediction model, and which had levels significantly altered between feeding groups, were positively identified as deriving from eight identifiable metabolites including lysophosphatidylcholine (16:0) and tyrosine. This study demonstrates the enormous potential of metabolomic-based profiling to provide a powerful and reliable tool for the detection of dioxin exposure in food-producing animals.

Identification of systemic immune response markers through metabolomic profiling of plasma from calves given an intra-nasally delivered respiratory vaccine

Vaccination procedures within the cattle industry are important disease control tools to minimize economic and welfare burdens associated with respiratory pathogens. However, new vaccine, antigen and carrier technologies are required to combat emerging viral strains and enhance the efficacy of respiratory vaccines, particularly at the point of pathogen entry. New technologies, specifically metabolomic profiling, could be applied to identify metabolite immune-correlates representative of immune protection following vaccination aiding in the design and screening of vaccine candidates. This study for the first time demonstrates the ability of untargeted UPLC-MS metabolomic profiling to identify metabolite immune correlates characteristic of immune responses following mucosal vaccination in calves. Male Holstein Friesian calves were vaccinated with Pfizer Rispoval® PI3 + RSV intranasal vaccine and metabolomic profiling of post-vaccination plasma revealed 12 metabolites whose peak intensities differed significantly from controls. Plasma levels of glycocholic acid, N-[(3α,5β,12α)-3,12-Dihydroxy-7,24-dioxocholan-24-yl]glycine, uric acid and biliverdin were found to be significantly elevated in vaccinated animals following secondary vaccine administration, whereas hippuric acid significantly decreased. In contrast, significant upregulation of taurodeoxycholic acid and propionylcarnitine levels were confined to primary vaccine administration. Assessment of such metabolite markers may provide greater information on the immune pathways stimulated from vaccine formulations and benchmarking early metabolomic responses to highly immunogenic vaccine formulations could provide a means for rapidly assessing new vaccine formulations. Furthermore, the identification of metabolic systemic immune response markers which relate to specific cell signaling pathways of the immune system could allow for targeted vaccine design to stimulate key pathways which can be assessed at the metabolic level.

Untargeted Metabolomic Analysis of Human Plasma Indicates Differentially Affected Polyamine and L-Arginine Metabolism in Mild Cognitive Impairment Subjects Converting to Alzheimer’s Disease

This study combined high resolution mass spectrometry (HRMS), advanced chemometrics and pathway enrichment analysis to analyse the blood metabolome of patients attending the memory clinic: cases of mild cognitive impairment (MCI; n = 16), cases of MCI who upon subsequent follow-up developed Alzheimer's disease (MCI_AD; n = 19), and healthy age-matched controls (Ctrl; n = 37). Plasma was extracted in acetonitrile and applied to an Acquity UPLC HILIC (1.7μm x 2.1 x 100 mm) column coupled to a Xevo G2 QTof mass spectrometer using a previously optimised method. Data comprising 6751 spectral features were used to build an OPLS-DA statistical model capable of accurately distinguishing Ctrl, MCI and MCI_AD. The model accurately distinguished (R2 = 99.1%; Q2 = 97%) those MCI patients who later went on to develop AD. S-plots were used to shortlist ions of interest which were responsible for explaining the maximum amount of variation between patient groups. Metabolite database searching and pathway enrichment analysis indicated disturbances in 22 biochemical pathways, and excitingly it discovered two interlinked areas of metabolism (polyamine metabolism and L-Arginine metabolism) were differentially disrupted in this well-defined clinical cohort. The optimised untargeted HRMS methods described herein not only demonstrate that it is possible to distinguish these pathologies in human blood but also that MCI patients 'at risk' from AD could be predicted up to 2 years earlier than conventional clinical diagnosis. Blood-based metabolite profiling of plasma from memory clinic patients is a novel and feasible approach in improving MCI and AD diagnosis and, refining clinical trials through better patient stratification.

The application of metabolomic biomarker screening tools for improved Bovine Respiratory Disease diagnosis and management

Bovine Respiratory Disease (BRD) is considered to be one of the most significant causes of economic loss in cattle worldwide. The disease has multifactorial aetiology, where viral induced respiratory damage can predispose animals to developing secondary bacterial infections. Accurate identification of viral infected animals prior to the onset of bacterial infection is necessary to reduce the overuse of antimicrobial treatments and minimize further economic losses from reduced production capacity and death. This research focuses on Bovine Parainfluenza Virus Type 3 (BPIV-3), one of the viruses involved in generating BRD. Vaccination measures for BPIV-3 can induce a level of immunity preventing disease progression, however, not all animals respond equally and immunization can complicate disease diagnosis. Alternative diagnostic approaches are required to identify animals which fail to respond to vaccination during infection outbreaks and are therefore likely to be more susceptible to secondary bacterial infections. Mass spectrometry based metabolomics was employed to identify plasma markers capable of differentiating between vaccinated and non-vaccinated calves after challenge with BPIV-3. Differentiation of vaccinated and non-vaccinated study groups (n=6) was possible as early as day 2 post-BPIV-3 challenge up until day 20 using a panel of potential metabolite markers. This study illustrates the potential for metabolomics to provide more detailed information on animal vaccination status that could be used to develop tools for improved herd health management, reduce economic loss through rapid identification and isolation of animals without immune protection (improving herd level immunity) and help reduce the usage of antimicrobial therapeutic treatments in animals.

Metabolic signature of lung cancer: a metabolomic study of human tissues and biofluids

This thesis reports the application of metabolomics to human tissues and biofluids (blood plasma and urine) to unveil the metabolic signature of primary lung cancer. In Chapter 1, a brief introduction on lung cancer epidemiology and pathogenesis, together with a review of the main metabolic dysregulations known to be associated with cancer, is presented. The metabolomics approach is also described, addressing the analytical and statistical methods employed, as well as the current state of the art on its application to clinical lung cancer studies. Chapter 2 provides the experimental details of this work, in regard to the subjects enrolled, sample collection and analysis, and data processing. In Chapter 3, the metabolic characterization of intact lung tissues (from 56 patients) by proton High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy is described. After careful assessment of acquisition conditions and thorough spectral assignment (over 50 metabolites identified), the metabolic profiles of tumour and adjacent control tissues were compared through multivariate analysis. The two tissue classes could be discriminated with 97% accuracy, with 13 metabolites significantly accounting for this discrimination: glucose and acetate (depleted in tumours), together with lactate, alanine, glutamate, GSH, taurine, creatine, phosphocholine, glycerophosphocholine, phosphoethanolamine, uracil nucleotides and peptides (increased in tumours). Some of these variations corroborated typical features of cancer metabolism (e.g., upregulated glycolysis and glutaminolysis), while others suggested less known pathways (e.g., antioxidant protection, protein degradation) to play important roles. Another major and novel finding described in this chapter was the dependence of this metabolic signature on tumour histological subtype. While main alterations in adenocarcinomas (AdC) related to phospholipid and protein metabolisms, squamous cell carcinomas (SqCC) were found to have stronger glycolytic and glutaminolytic profiles, making it possible to build a valid classification model to discriminate these two subtypes. Chapter 4 reports the NMR metabolomic study of blood plasma from over 100 patients and near 100 healthy controls, the multivariate model built having afforded a classification rate of 87%. The two groups were found to differ significantly in the levels of lactate, pyruvate, acetoacetate, LDL+VLDL lipoproteins and glycoproteins (increased in patients), together with glutamine, histidine, valine, methanol, HDL lipoproteins and two unassigned compounds (decreased in patients). Interestingly, these variations were detected from initial disease stages and the magnitude of some of them depended on the histological type, although not allowing AdC vs. SqCC discrimination. Moreover, it is shown in this chapter that age mismatch between control and cancer groups could not be ruled out as a possible confounding factor, and exploratory external validation afforded a classification rate of 85%. The NMR profiling of urine from lung cancer patients and healthy controls is presented in Chapter 5. Compared to plasma, the classification model built with urinary profiles resulted in a superior classification rate (97%). After careful assessment of possible bias from gender, age and smoking habits, a set of 19 metabolites was proposed to be cancer-related (out of which 3 were unknowns and 6 were partially identified as N-acetylated metabolites). As for plasma, these variations were detected regardless of disease stage and showed some dependency on histological subtype, the AdC vs. SqCC model built showing modest predictive power. In addition, preliminary external validation of the urine-based classification model afforded 100% sensitivity and 90% specificity, which are exciting results in terms of potential for future clinical application. Chapter 6 describes the analysis of urine from a subset of patients by a different profiling technique, namely, Ultra-Performance Liquid Chromatography coupled to Mass Spectrometry (UPLC-MS). Although the identification of discriminant metabolites was very limited, multivariate models showed high classification rate and predictive power, thus reinforcing the value of urine in the context of lung cancer diagnosis. Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the potential of integrated metabolomics of tissues and biofluids to improve current understanding of lung cancer altered metabolism and to reveal new marker profiles with diagnostic value.