Improvement of sample classification and metabolite profiling in 1H-NMR by a machine learning-based modelling of signal parameters

RMN és una plataforma analítica utilitzada per quantificar els metabòlits presents en les mostres de metabolòmica. Els espectres de 1H-RMN mostren múltiples senyals de metabòlits amb tres paràmetres específics (desplaçament químic, ample mitjà de banda, intensitat) que poden mostrar reactivitat a les condicions de la mostra. Aquesta reactivitat perjudica l'optimització del fitat dels espectres necessari per a realitzar el perfilat automàtic de metabòlits de les mostres. L'objectiu d'aquesta tesi va ser l'exploració de l'ús de tècniques de tendència basades en Machine Learning (ML) amb l'ús de fluxos de treball robustos per modelar i explotar la informació present en els diferents paràmetres de senyal durant el perfilat de metabòlits dels conjunts de dades 1H-NMR. En particular, les aplicacions considerades van ser la millora de la classificació de les mostres en els estudis de metabolòmica i la millora de la qualitat del perfilat automàtic. A més d'assolir aquests objectius, també es van obtenir èxits addicionals (per exemple, la generació d'una nova eina de codi obert capaç de resoldre els reptes en l'elaboració de perfils de matrius complexes).RMN es una plataforma analítica utilizada para cuantificar los metabolitos presentes en las muestras de metabolómica. Los espectros de 1H-RMN muestran múltiples señales de metabolitos con tres parámetros específicos (desplazamiento químico, ancho medio de banda, intensidad) que pueden mostrar reactividad a las condiciones de la muestra. Esta reactividad perjudica a la optimización del fitado de los espectros necesario para realizar el perfilado automático de metabolitos de las muestras. El objetivo de esta tesis fue la exploración del uso de técnicas de tendencia basadas en Machine Learning (ML) con el uso de flujos de trabajo robustos para modelar y explotar la información presente en los diferentes parámetros de señal durante el perfilado de metabolitos de los conjuntos de datos 1H-NMR. En particular, las aplicaciones consideradas fueron la mejora de la clasificación de las muestras en los estudios de metabolómica y la mejora de la calidad del perfilado automático. Además de lograr estos objetivos, también se obtuvieron logros adicionales (por ejemplo, la generación de una nueva herramienta de código abierto capaz de resolver los retos en la elaboración de perfiles de matrices complejas).NMR is an analytical platform used to quantify the metabolites present in metabolomics samples. 1H-NMR spectra show multiple metabolite signals, each one with three parameters (chemical shift, half bandwidth, intensity) which can show reactivity to the sample conditions. This reactivity is a challenge for the optimization of the lineshape fitting of spectra necessary to perform the automatic metabolite profiling of samples. The aim of this PhD thesis was the exploration of the use of trending machine learning (ML)-based techniques and of robust ML-based workflows to model and then exploit the information present in the different parameters collected for each signal during the metabolite profiling of 1H-NMR datasets. In particular, the applications considered were the enhanced classification of samples in metabolomics studies and the enhancement of the quality of automatic profiling in 1H-NMR datasets. in addition to the achievement of these goals, additional achievements (e.g., the generation of a new open-source tool able to solve challenges in the profiling of complex matrices) was also fulfilled

MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures

<p>Abstract</p> <p>Background</p> <p>One-dimensional ¹H-NMR spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, the accurate identification of individual compounds is still a challenging task, particularly in spectral regions with higher peak densities. The need for automatic tools to facilitate and further improve the accuracy of such tasks, while using increasingly larger reference spectral libraries becomes a priority of current metabolomics research.</p> <p>Results</p> <p>We introduce a web server application, called MetaboHunter, which can be used for automatic assignment of ¹H-NMR spectra of metabolites. MetaboHunter provides methods for automatic metabolite identification based on spectra or peak lists with three different search methods and with possibility for peak drift in a user defined spectral range. The assignment is performed using as reference libraries manually curated data from two major publicly available databases of NMR metabolite standard measurements (HMDB and MMCD). Tests using a variety of synthetic and experimental spectra of single and multi metabolite mixtures show that MetaboHunter is able to identify, in average, more than 80% of detectable metabolites from spectra of synthetic mixtures and more than 50% from spectra corresponding to experimental mixtures. This work also suggests that better scoring functions improve by more than 30% the performance of MetaboHunter's metabolite identification methods.</p> <p>Conclusions</p> <p>MetaboHunter is a freely accessible, easy to use and user friendly ¹H-NMR-based web server application that provides efficient data input and pre-processing, flexible parameter settings, fast and automatic metabolite fingerprinting and results visualization via intuitive plotting and compound peak hit maps. Compared to other published and freely accessible metabolomics tools, MetaboHunter implements three efficient methods to search for metabolites in manually curated data from two reference libraries.</p> <p>Availability</p> <p><url>http://www.nrcbioinformatics.ca/metabohunter/</url></p

Metabolomics Data Processing and Data Analysis—Current Best Practices

Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows

Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications

Schleif F-M, Riemer T, Boerner U, Schnapka-Hille L, Cross M. Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications. Bioinformatics. 2011;27(4):524-533

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

Liquid Chromatography Coupled To Mass Spectrometry Reveals That Aging Affects Lipid Droplet Composition

University of Minnesota Ph.D. dissertation. July 2019. Major: Chemistry. Advisor: Edgar Arriaga. 1 computer file (PDF); ix, 147 pages.Lipid droplets (LD) are intracellular organelles controlling neutral lipid metabolism and storage. One of the recently discovered functions of LDs is the essential role they play in aging process. Alterations in membrane lipid composition are one of the major changes that are shown to take place in many of the aging models. An increase in cholesterol to phospholipid ratio was reported in rat models of aging. A reduction in the level of polyunsaturated fatty acyl is the next important age related variable observed in these aging systems. However, there is no systematic report characterizing the lipid composition of lipid droplets in aging models including Caenorhabditis elegans and mouse liver tissue. In this thesis, ultra-high performance liquid chromatography coupled to mass spectrometry techniques were used to characterize the composition of lipid droplets. The performances of two high resolution mass spectrometers were compared with regards to detection and identification of small hydrophobic molecules. Different software packages and bioinformatics tools were compared to discover possible variation of the extracted information. The selected mass spectrometry platform and optimized data analysis workflow were used to study lipid droplets and identify candidate biomarkers of aging. Preliminary identifications made here could potentially be used as biomarkers in aging diseases and could ultimately lead to treatments for age-related disorders