Dolphin and whale: development, evaluation and application of novel bioinformatics tools for metabolite profiling in high throughput 1H-NMR analysis

El perfilat de metabòlits es la tasca més difícil dins l'anàlisi espectral de RMN. El seu objectiu es comprendre els processos biològics que tenen lloc en un moment concret mitjançant la identificació i quantificació dels metabòlits presents en mescles d' RMN complexes. Un espectre de RMN està compost per ressonàncies d'un gran nombre de metabòlits, i aquestes se solen solapar entre elles, canviar de posició depenent del pH de la mostra i poden quedar emmascarades per senyals de macromolècules. Tots aquests problemes compliquen la identificació i quantificació de metabòlits, pel que obtenir un perfil de metabòlits curat en una mostra pot ser un gran repte inclús per usuaris experts. En aquest context, la motivació d'aquesta tesi va néixer amb l'objectiu de donar automatismes i funcions fàcils de fer servir per al perfilat de metabòlits en RMN, millorant la qualitat dels resultats i reduint el temps d'anàlisi. Per fer-ho, es van implementar un conjunt d'algoritmes que van acabar empaquetats en dos programes, Dolphin i Whale.El perfilado de metabolitos es la tarea más difícil dentro del análisis espectral de RMN. Su objetivo es comprender los procesos biológicos que tienen lugar en un momento concreto a través de la identificación y cuantificación de los metabolitos presentes en mezclas de RMN complejas. Un espectro de RMN está compuesto por resonancias de un gran numero de metabolitos, y éstas a menudo se solapan entre ellas, cambian de posición dependiendo del pH de la muestra y pueden quedar enmascaradas por señales de macromoléculas. Todos estos problemas complican la identificación y cuantificación de metabolitos, por lo que obtener un perfilado de metabolitos curado en una muestra puede ser un gran reto incluso para usuarios expertos. En este contexto, la motivación de esta tesis nació con el objetivo de dar automatismos y funciones fáciles de usar para el perfilado de metabolitos en RMN, mejorando la calidad de los resultados y reduciendo el tiempo de análisis. Para hacerlo, se implementaron un conjunto de algoritmos que acabaron empaquetados en dos programas, Dolphin y Whale.Metabolite profiling is the most challenging approach in NMR spectral analysis. It aims to comprehend biological processes occurring in a certain moment through identifying and quantifying metabolites present in complex NMR mixtures. An NMR spectrum is composed by resonances of a huge number of metabolites, and these resonances often overlap between them, shift position depending on the sample pH and can be masked by macromolecules signals. All these drawbacks hinder metabolite identification and quantification, so obtaining a cured metabolite profile of a sample can be a very big issue even for expert users. In this context, the motivation of this thesis was born with the aim to provide automatisms and user-friendly interactive functions for NMR metabolite profiling, improving the quality of the results and reducing the time span of the analysis. To do so, several algorisms were implemented and embedded into two software packages, Dolphin and Whale

Resting and Functional Magnetic Resonance Spectroscopy of Glutamate in Schizophrenia at 7 Tesla

Schizophrenia is a debilitating disease that affects about 1% of the population. Current therapeutic interventions mostly target dopaminergic neurotransmission but are not effective in treating all symptoms. There is growing evidence to support involvement of glutamatergic neurotransmission, which may better account for the symptomatology of schizophrenia. Glutamate concentrations can be measured in vivo using magnetic resonance spectroscopy (MRS). Stronger MRIs provide benefits for MRS, but they also present challenges. Simulations were designed to examine how MRI field strength influences metabolite quantification. Glutamate and its metabolic precursor, glutamine, were more reliably and independently quantified with higher MRI field strengths, showing a clear benefit for MRS. Using a 7T MRI, voxels were placed within the dorsal anterior cingulate cortex (dACC) and thalamus of volunteers with schizophrenia, a psychiatric control group of volunteers with major depressive disorder (MDD), and healthy controls. Glutamine and glycine, both involved in glutamate neurotransmission, were lower in the thalamus in schizophrenia relative to healthy controls, whereas dACC glutamate concentrations were higher, demonstrating glutamatergic abnormalities in schizophrenia at rest. Prior MRS studies of schizophrenia have been in resting conditions. In a proof of concept study with healthy controls, it was shown that the Stroop Task was able elicit a significant glutamate increase in the dACC when in a functional state (glutamate fMRS) relative to resting conditions using the 7T MRI. This was then explored in the same schizophrenic and MDD subjects as the resting MRS study. Healthy controls significantly increased glutamate concentrations, but the schizophrenic and MDD groups did not significantly. The schizophrenic group had a slower glutamatergic response followed by a slower recovery, and, was the only group to demonstrate significant glutamine increases when activated, indicating potential abnormalities in glutamate dynamics. Using a 7T MRI, glutamate was explored in resting and activated conditions in schizophrenia. Glycine was demonstrated to be lower in schizophrenia using MRS for the first time, and the first functional MRS study was performed in a psychiatric population. The studies were made stronger by inclusion of a psychiatric control group. Future studies of schizophrenia with glutamate fMRS should focus on the delayed glutamatergic response to functional activation and abnormal recovery

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

Denoising single MR spectra by deep learning: Miracle or mirage?

PURPOSE The inherently poor SNR of MRS measurements presents a significant hurdle to its clinical application. Denoising by machine or deep learning (DL) was proposed as a remedy. It is investigated whether such denoising leads to lower estimate uncertainties or whether it essentially reduces noise in signal-free areas only. METHODS Noise removal based on supervised DL with U-nets was implemented using simulated 1 H MR spectra of human brain in two approaches: (1) via time-frequency domain spectrograms and (2) using 1D spectra as input. Quality of denoising was evaluated in three ways: (1) by an adapted fit quality score, (2) by traditional model fitting, and (3) by quantification via neural networks. RESULTS Visually appealing spectra were obtained; hinting that denoising is well-suited for MRS. However, an adapted denoising score showed that noise removal is inhomogeneous and more efficient for signal-free areas. This was confirmed by quantitative analysis of traditional fit results as well as DL quantitation following DL denoising. DL denoising, although apparently successful as judged by mean squared errors, led to substantially biased estimates in both implementations. CONCLUSION The implemented DL-based denoising techniques may be useful for display purposes, but do not help quantitative evaluations, confirming expectations based on estimation theory: Cramér Rao lower bounds defined by the original data and the appropriate fitting model cannot be circumvented in an unbiased way for single data sets, unless additional prior knowledge can be incurred in the form of parameter restrictions/relations or applicable substates

Practical Applications of NMR to Solve Real-World Problems

Nuclear magnetic resonance spectroscopy (NMR) has developed from primarily a method of academic study into a recognized technology that has advanced measurement capabilities within many different industrial sectors. These sectors include areas such as national security, energy, forensics, life sciences, pharmaceuticals, etc. Despite this diversity, these applications have many shared technical challenges and regulatory burdens, yet interdisciplinary cross-talk is often limited. To facilitate the sharing of knowledge, this Special Issue presents technical articles from four different areas, including the oil industry, nanostructured systems and materials, metabolomics, and biologics. These areas use NMR or magnetic resonance imaging (MRI) technologies that range from low-field relaxometry to magnetic fields as high as 700 MHz. Each article represents a practical application of NMR. A few articles are focused on basic research concepts, which will likely have the cross-cutting effect of advancing multiple disciplinary areas

Understanding the Metabolic and Genetic Regulation of Breast Cancer Recurrence Using Magnetic Resonance-Based Integrative Metabolomics

Breast cancer is the most commonly diagnosed malignancy in women and is the leading cause of cancer-related death in the female population worldwide. In these women, breast cancer recurrence--local, regional, or distant--represents the principal cause of death from this disease. The mechanisms underlying tumor recurrence remain largely unknown. To dissect those mechanisms, our laboratory has developed inducible transgenic mouse models that accurately recapitulate key features of the natural history of human breast cancer progression: primary tumor development, tumor dormancy and recurrence. Dysregulated metabolism has long been known to be a key feature in tumorigenesis. Yet, very little is known about the connection, if any, between cellular metabolic changes and breast cancer recurrence. In this work, I design and implement a systems engineering-based approach, magnetic resonance-based integrative metabolomics, to better understand the metabolic and genetic regulation of breast cancer recurrence. Through a combination of 1H and 13C magnetic resonance spectroscopy (MRS), mass spectrometry (MS) as well as gene expression profiling and functional metabolic and genetic studies, I aim to identify the metabolic profile of mammary tumors during breast cancer progression, identify the molecular basis and role of differential glutamine uptake and metabolism in breast cancer recurrence and finally, investigate the molecular basis and role of differential lactate production in breast cancer recurrence. The findings suggest an evolving metabolic phenotype of tumors during breast cancer progression as well as metabolic dysregulation in some of the key regulatory nodes that control that evolution. Identifying the metabolic changes associated with tumor recurrence can pave the way for identifying novel diagnostic strategies and therapeutic targets that can contribute to improved clinical management and outcome for breast cancer patients

The impact of spectral basis set composition on estimated levels of cingulate glutamate and its associations with different personality traits

Background: 1H-MRS is increasingly used in basic and clinical research to explain brain function and alterations respectively. In psychosis research it is now one of the main tools to investigate imbalances in the glutamatergic system. Interestingly, however, the findings are extremely variable even within patients of similar disease states. One reason may be the variability in analysis strategies, despite suggestions for standardization. Therefore, our study aimed to investigate the extent to which the basis set configuration– which metabolites are included in the basis set used for analysis– would affect the spectral fit and estimated glutamate (Glu) concentrations in the anterior cingulate cortex (ACC), and whether any changes in levels of glutamate would be associated with psychotic-like experiences and autistic traits. Methods: To ensure comparability, we utilized five different exemplar basis sets, used in research, and two different analysis tools, r-based spant applying the ABfit method and Osprey using the LCModel. Results: Our findings revealed that the types of metabolites included in the basis set significantly affected the glutamate concentration. We observed that three basis sets led to more consistent results across different concentration types (i.e., absolute Glu in mol/kg, Glx (glutamate + glutamine), Glu/tCr), spectral fit and quality measurements. Interestingly, all three basis sets included phosphocreatine. Importantly, our findings also revealed that glutamate levels were differently associated with both schizotypal and autistic traits depending on basis set configuration and analysis tool, with the same three basis sets showing more consistent results. Conclusions: Our study highlights that scientific results may be significantly altered depending on the choices of metabolites included in the basis set, and with that emphasizes the importance of carefully selecting the configuration of the basis set to ensure accurate and consistent results, when using MR spectroscopy. Overall, our study points out the need for standardized analysis pipelines and reporting