A metabolomic data fusion approach to support gliomas grading

Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor-like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject-multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High-Resolution Magic-Angle-Spinning Nuclear Magnetic Resonance (HR-MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T2-filtered, diffusion-filtered and conventional water-presaturated spectra to try to extract as much information as possible, fusing the data gathered by these different NMR experiments and applying a chemometric approach based on Multivariate Curve Resolution (MCR). Biomarkers important for glioma's discrimination were found. In particular, we focused our attention on cystathionine (Cyst) that shows promise as a biomarker for the better prognosis of glioma tumors

A metabolomic data fusion approach to support gliomas grading

Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor-like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject-multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High-Resolution Magic-Angle-Spinning Nuclear Magnetic Resonance (HR-MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T2-filtered, diffusion-filtered and conventional water-presaturated spectra to try to extract as much information as possible, fusing the data gathered by these different NMR experiments and applying a chemometric approach based on Multivariate Curve Resolution (MCR). Biomarkers important for glioma's discrimination were found. In particular, we focused our attention on cystathionine (Cyst) that shows promise as a biomarker for the better prognosis of glioma tumors

Metabolic profiling on 2D NMR TOCSY spectra using machine learning

Due to the dynamicity of biological cells, the role of metabolic profiling in discovering biological fingerprints of diseases, and their evolution, as well as the cellular pathway of different biological or chemical stimuli is most significant. Two-dimensional nuclear magnetic resonance (2D NMR) is one of the fundamental and strong analytical instruments for metabolic profiling. Though, total correlation spectroscopy (2D NMR 1H -1H TOCSY) can be used to improve spectral overlap of 1D NMR, strong peak shift, signal overlap, spectral crowding and matrix effects in complex biological mixtures are extremely challenging in 2D NMR analysis. In this work, we introduce an automated metabolic deconvolution and assignment based on the deconvolution of 2D TOCSY of real breast cancer tissue, in addition to different differentiation pathways of adipose tissue-derived human Mesenchymal Stem cells. A major alternative to the common approaches in NMR based machine learning where images of the spectra are used as an input, our metabolic assignment is based only on the vertical and horizontal frequencies of metabolites in the 1H-1H TOCSY. One- and multi-class Kernel null foley–Sammon transform, support vector machines, polynomial classifier kernel density estimation, and support vector data description classifiers were tested in semi-supervised learning and novelty detection settings. The classifiers’ performance was evaluated by comparing the conventional human-based methodology and automatic assignments under different initial training sizes settings. The results of our novel metabolic profiling methods demonstrate its suitability, robustness, and speed in automated nontargeted NMR metabolic analysis

Metabolic Signatures of Prostate Cancer and Renal Cell Carcinoma using High-Resolution NMR and Hyperpolarized 13C MRI

Non-invasive techniques to assess metabolic reprogramming during cancer progression can be used to improve therapeutic selection and provide an early assessment of therapeutic response or resistance in individual patients. Prior studies have shown that metabolic reprogramming plays a key role in the development of prostate cancer and renal cell carcinoma (RCC). This dissertation further elucidates the metabolic alterations that occur in treatment-resistant prostate cancer and in patient-derived models of RCC using high-resolution nuclear magnetic resonance (NMR) spectroscopy and hyperpolarized (HP) 13C magnetic resonance imaging (MRI), with the goal of identifying new non-invasive diagnostic imaging tools. Glycolysis, metabolism of pyruvate and glutamate via the tricarboxylic acid (TCA) cycle, glutaminolysis, and glutathione synthesis are upregulated in castration-resistant prostate cancer (CRPC) compared to their androgen-dependent counterparts, using human cell lines as well a treatment-driven transgenic murine model. These metabolic alterations were reversed in castration-resistant murine tumors by treatment with a secondary androgen pathway inhibitor, apalutamide, suggesting that early metabolic responses to treatment can be monitored using non-invasive imaging techniques. Furthermore, treatment-emergent small cell neuroendocrine prostate cancer, a consequence of protracted treatment with primary androgen deprivation therapy and secondary androgen pathway inhibitors, exhibits significantly upregulated glycolysis, TCA cycle metabolism of pyruvate and glutamate, and glutaminolysis, as well as significantly altered redox capacity compared to castration-resistant prostate adenocarcinoma using patient-derived xenograft models. Finally, the metabolic differences associated with the tumor microenvironment were compared between various patient-derived models of RCC, finding that RCC patient-derived xenografts (PDXs) displayed higher redox capacity and were more proliferative than cells and tissue slices derived from the PDXs and maintained ex vivo. The work presented in this dissertation suggests that a combination of HP [1-13C]pyruvate, [2-13C]pyruvate, [5-13C]glutamine, and [1-13C]dehydroascorbate can be used to distinguish advanced prostate cancer and RCC subtypes in future HP 13C MRI of patients for improved treatment selection and monitoring

Characterisation of the Immuno-Metabolic Interface in Porcine Models of Nutritional Intervention

There is increasing interest in the idea of using diet for health maintenance. Not only does dietary intake determine the availability of substrates for host metabolism, but it can also shape the composition of the intestinal microbiota, increasingly recognised as an ‘organ’ in its own right, which closely interacts with the mucosal immune system. Alterations in the mammalian-microbial-metabolic axis are associated with disease development and as such it is important to study the systemic consequences of dietary intervention on these interactions in an appropriate animal model such as the pig. The majority of the abundant metabolites present in porcine liver, kidney, serum and urine were assigned by one and two dimensional Nuclear Magnetic Resonance (NMR) spectroscopy and qualitatively compared; inter-compartmental differences in relation to mammalian-microbial co-metabolic representation were identified in the pig, and the applicability of NMR-based urinalysis to interrogate mammalian-microbial co-metabolism in this species confirmed. The initial weaning diet of pigs was found to initiate sustainable metabolic reprogramming in the young pig, leading to a persistent urinary metabolic signature after four weeks; this signature included metabolites linked to microbial metabolic processes and could indicate a diet-induced microbial reprogramming event at weaning. Differences in the initial weaning diet were also found to impact the metabolic and immunologic consequences of Bifidobacterium lactis supplementation on the young pig. The urinary metabolic profile from these animals was significantly correlated with patterns of intestinal mucosal immunoglobulin secretion and thus indicates the potential utility of biofluid-based metabolic profiling to assess mucosal responses to dietary intervention

A Study on the Application of Biophysical Techniques to Terminal Diseases Via the Metabolomic-Based Analyses of Cancers and the Analysis Of Alzheimer’s Disease through the Action of Amyloid-Beta

Honors (Bachelor's)ChemistryUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/112140/1/nirbs.pd

Sample Preparation in Metabolomics

Metabolomics is increasingly being used to explore the dynamic responses of living systems in biochemical research. The complexity of the metabolome is outstanding, requiring the use of complementary analytical platforms and methods for its quantitative or qualitative profiling. In alignment with the selected analytical approach and the study aim, sample collection and preparation are critical steps that must be carefully selected and optimized to generate high-quality metabolomic data. This book showcases some of the most recent developments in the field of sample preparation for metabolomics studies. Novel technologies presented include electromembrane extraction of polar metabolites from plasma samples and guidelines for the preparation of biospecimens for the analysis with high-resolution μ magic-angle spinning nuclear magnetic resonance (HR-μMAS NMR). In the following chapters, the spotlight is on sample preparation approaches that have been optimized for diverse bioanalytical applications, including the analysis of cell lines, bacteria, single spheroids, extracellular vesicles, human milk, plant natural products and forest trees

Hypothermic machine perfusion of cadaveric kidneys: clinical utility, metabolic mechanisms and methods of optimisation

Hypothermic machine perfusion (HMP) is a dynamic method of preserving kidneys ex vivo with established clinical benefits over static cold storage (SCS). The aim of the first part of this thesis was to determine whether HMP influences clinical outcomes in the United Kingdom via registry analysis of a national dataset. In the second part of this thesis, metabolic changes during HMP are explored using nuclear magnetic resonance (NMR) spectroscopy. Chapter 5 describes differences between HMP and SCS conditions using 1D 1H NMR in a porcine donation after circulatory death (DCD) model. Further experimental chapters use perfusion fluid supplemented with the metabolic tracer universally labelled [U13-C] glucose to describe de novo aerobic and anaerobic metabolism in ex vivo kidneys during HMP. Novel developments in tracer-based NMR methodology are discussed early in the thesis. In Chapter 6, perfusion fluid supplemented with [U13-C] glucose was used to demonstrate several benefits of supplementing perfusion fluid with oxygen during HMP in a porcine DCD model. In the final section, modified [U13-C] glucose perfusion fluid demonstrated differences in de novo metabolism in sub-types of cadaveric kidneys prior to transplantation in a clinical study which also aimed to correlate metabolism with clinical outcome

Assinatura metabólica do cancro do pulmão: estudo metabolómico de tecidos e biofluidos humanos

Doutoramento em QuímicaThis 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.A presente tese reporta a aplicação da metabolómica ao estudo de tecidos e biofluidos humanos (plasma sanguíneo e urina), com o intuito de caracterizar a assinatura metabólica do cancro pulmonar primário. No Capítulo 1, apresenta-se uma breve introdução sobre a epidemiologia e a patogénese deste tipo de cancro, bem como um sumário das principais alterações metabólicas tipicamente associadas ao cancro em geral. Descreve-se ainda a abordagem metabolómica, nomeadamente os métodos analíticos e estatísticos utilizados, assim como o estado da arte da sua aplicação em estudos clínicos do cancro do pulmão. No Capítulo 2, apresentam-se os detalhes experimentais deste trabalho, no que diz respeito ao grupo de indivíduos envolvidos, à colheita e análise das amostras e ao posterior tratamento dos dados. O Capítulo 3 descreve a caracterização metabólica de tecidos do pulmão (de 56 doentes) por espetroscopia de Ressonância Magnética Nuclear (RMN) de alta resolução com rotação no ângulo mágico. Após a otimização cuidada das condições de aquisição e a identificação detalhada dos sinais espetrais (mais de 50 metabolitos identificados), os perfis metabólicos dos tumores e dos tecidos adjacentes não envolvidos (controlos) foram comparados por análise multivariada, tendo sido discriminados com uma exatidão de 97%. Os metabolitos que mais significativamente contribuíram para esta diferenciação foram: glucose e acetato (diminuídos nos tumores), lactato, alanina, glutamato, GSH, taurina, creatina, fosfocolina, glicerofosfocolina, fosfoetanolamina, nucleótidos de uracilo e péptidos (aumentados nos tumores). Algumas destas variações corroboraram alterações típicas do metabolismo do cancro (e.g., glicólise e glutaminólise aumentadas), enquanto outras sugeriram novas pistas sobre a possível relevância de processos como a proteção antioxidante e a degradação proteica. Um outro resultado novo e importante descrito neste capítulo foi a dependência da assinatura metabólica em relação ao tipo histológico do tumor. Enquanto as principais alterações observadas nos adenocarcinomas (AdC) se relacionaram com o metabolismo fosfolipídico e proteico, os carcinomas de células escamosas (SqCC) apresentaram perfis glicolíticos e glutaminolíticos mais pronunciados, sendo possível construir um modelo válido para a discriminação destes subtipos. No Capítulo 4, apresenta-se o estudo metabolómico por RMN de plasma sanguíneo de mais de 100 doentes e quase 100 controlos saudáveis, do qual resultou um modelo multivariado com uma taxa de classificação de 87%. A distinção entre os grupos foi feita essencialmente com base nos níveis de lactato, piruvato, acetoacetato, lipoproteínas LDL+VLDL e glicoproteínas (aumentados nos doentes), juntamente com os níveis de glutamina, histidina, valina, metanol, lipoproteínas HDL e dois compostos não identificados (diminuídos nos doentes). Estas variações foram detetadas desde os estádios iniciais da doença e a magnitude de algumas delas dependeu do tipo histológico, embora não permitindo discriminar AdC de SqCC. Para além disso, mostra-se neste capítulo que o desequilíbrio dos grupos controlo e cancro em termos da idade dos indivíduos poderá ter alguma influência nos resultados, e apresenta-se uma tentativa exploratória de validação externa, que resultou numa taxa de classificação de 85%. O estudo por RMN do perfil metabólico da urina dos doentes com cancro do pulmão e dos controlos é apresentado no Capítulo 5. Comparativamente ao plasma, o modelo construído com os perfis urinários apresentou uma taxa de classificação superior (97%). Após uma avaliação cuidada da possível influência do género, idade e hábitos tabágicos, um conjunto de 19 metabolitos foi proposto como estando relacionado com a doença (incluindo 3 compostos desconhecidos e 6 parcialmente identificados como metabolitos N-acetilados). Tal como no caso do plasma, estas variações foram detetadas em doentes no estádio inicial e mostraram alguma dependência em relação ao tipo histológico, obtendo-se um modelo válido para a discriminação AdC vs. SqCC, ainda que com um poder preditivo modesto. Para além disso, o teste preliminar de validação externa revelou 100% de sensibilidade e 90% de especificidade, o que é um resultado bastante promissor em termos da potencial utilização dos perfis urinários em aplicações clínicas futuras. No Capitulo 6, descreve-se a caracterização dos perfis metabólicos da urina (de um subgrupo de indivíduos) por cromatografia líquida de ultra-eficiência acoplada a espetrometria de massa (UPLC-MS). Embora não avançando muito na identificação estrutural de possíveis marcadores, este estudo reforçou o valor diagnóstico da urina, já que os modelos multivariados resultantes apresentaram taxa de classificação e poder preditivo elevados. Finalmente, no Capítulo 7, apresentam-se as principais conclusões deste trabalho, realçando o contributo da metabolómica integrada de tecidos e biofluidos para a compreensão do metabolismo alterado do cancro do pulmão e para a deteção de novos perfis marcadores com valor diagnóstico