Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Statistical Methods in Metabolomics

Metabolomics lies at the fulcrum of the system biology ‘omics’. Metabolic profiling offers researchers new insight into genetic and environmental interactions, responses to pathophysi- ological stimuli and novel biomarker discovery. Metabolomics lacks the simplicity of a single data capturing technique; instead, increasingly sophisticated multivariate statistical techniques are required to tease out useful metabolic features from various complex datasets. In this work, two major metabolomics methods are examined: Nuclear Magnetic Resonance (NMR) Spec- troscopy and Liquid Chromatography-Mass Spectrometry (LC-MS). MetAssimulo, an 1H-NMR metabolic-profile simulator, was developed in part by this author and is described in the Chap- ter 2. Peak positional variation is a phenomenon occurring in NMR spectra that complicates metabolomic analysis so Chapter 3 focuses on modelling the effect of pH on peak position. Analysis of LC-MS data is somewhat more complex given its 2-D structure, so I review existing pre-processing and feature detection techniques in Chapter 4 and then attempt to tackle the issue from a Bayesian viewpoint. A Bayesian Partition Model is developed to distinguish chro- matographic peaks representing useful features from chemical and instrumental interference and noise. Another of the LC-MS pre-processing problems, data binning, is also explored as part of H-MS: a pre-processing algorithm incorporating wavelet smoothing and novel Gaussian and Exponentially Modified Gaussian peak detection. The performance of H-MS is compared alongside two existing pre-processing packages: apLC-MS and XCMS.Open Acces

Diversity in the structures and ligand binding sites of nematode fatty acid and retinol binding proteins revealed by Na-FAR-1 from Necator americanus

Fatty acid and retinol binding proteins (FARs) comprise a family of unusual α-helix rich lipid binding proteins found exclusively in nematodes. They are secreted into host tissues by parasites of plants, animals and humans. The structure of a FAR protein from the free-living nematode Caenorhabditis elegans is available, but this protein (Ce-FAR-7) is from a subfamily of FARs that does not appear to be important at the host-parasite interface. We have therefore examined Na-FAR-1 from the blood-feeding intestinal parasite of humans, Necator americanus . The three dimensional structure of Na-FAR-1 in its ligand-free and ligand-bound forms, determined by nuclear magnetic resonance spectroscopy (NMR) and X-ray crystallography, respectively, reveals an a-helical fold similar to Ce-FAR-7, but Na-FAR-1 possesses a larger and more complex internal ligand binding cavity and an additional C-terminal a-helix. Titration of apo -Na-FAR-1 with oleic acid, analysed by NMR chemical shift perturbation, reveals that at least four distinct protein:ligand complexes can be formed. Na-FAR-1, and possibly other FARs, may have a wider repertoire for hydrophobic ligand binding, as confirmed here by our finding that a range of neutral and polar lipids co-purify with the bacterial recombinant protein. Finally, we show by immunohistochemistry that Na-FAR-1 is present in adult worms with a tissue distribution indicative of possible roles in nutrient acquisition by the parasite and in reproduction in the male

Bayesian estimation of the number of protonation sites for urinary metabolites from NMR spectroscopic data

INTRODUCTION: To aid the development of better algorithms for 11 H NMR data analysis, such as alignment or peak-fitting, it is important to characterise and model chemical shift changes caused by variation in pH. The number of protonation sites, a key parameter in the theoretical relationship between pH and chemical shift, is traditionally estimated from the molecular structure, which is often unknown in untargeted metabolomics applications. OBJECTIVE: We aim to use observed NMR chemical shift titration data to estimate the number of protonation sites for a range of urinary metabolites. METHODS: A pool of urine from healthy subjects was titrated in the range pH 2–12, standard 11 H NMR spectra were acquired and positions of 51 peaks (corresponding to 32 identified metabolites) were recorded. A theoretical model of chemical shift was fit to the data using a Bayesian statistical framework, using model selection procedures in a Markov Chain Monte Carlo algorithm to estimate the number of protonation sites for each molecule. RESULTS: The estimated number of protonation sites was found to be correct for 41 out of 51 peaks. In some cases, the number of sites was incorrectly estimated, due to very close pKa values or a limited amount of data in the required pH range. CONCLUSIONS: Given appropriate data, it is possible to estimate the number of protonation sites for many metabolites typically observed in 11 H NMR metabolomics without knowledge of the molecular structure. This approach may be a valuable resource for the development of future automated metabolite alignment, annotation and peak fitting algorithms

Integrative Approaches in Structural Biology: A More Complete Picture from the Combination of Individual Techniques

With the recent technological and computational advancements, structural biology has begun to tackle more and more difficult questions, including complex biochemical pathways and transient interactions among macromolecules. This has demonstrated that, to approach the complexity of biology, one single technique is largely insufficient and unable to yield thorough answers, whereas integrated approaches have been more and more adopted with successful results. Traditional structural techniques (X-ray crystallography and Nuclear Magnetic Resonance (NMR)) and the emerging ones (cryo-electron microscopy (cryo-EM), Small Angle X-ray Scattering (SAXS)), together with molecular modeling, have pros and cons which very nicely complement one another. In this review, three examples of synergistic approaches chosen from our previous research will be revisited. The first shows how the joint use of both solution and solid-state NMR (SSNMR), X-ray crystallography, and cryo-EM is crucial to elucidate the structure of polyethylene glycol (PEG)ylated asparaginase, which would not be obtainable through any of the techniques taken alone. The second deals with the integrated use of NMR, X-ray crystallography, and SAXS in order to elucidate the catalytic mechanism of an enzyme that is based on the flexibility of the enzyme itself. The third one shows how it is possible to put together experimental data from X-ray crystallography and NMR restraints in order to refine a protein model in order to obtain a structure which simultaneously satisfies both experimental datasets and is therefore closer to the ‘real structure’.Microbial Biotechnolog

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Solution structure of a repeated unit of the ABA-1 nematode polyprotein allergen of ascaris reveals a novel fold and two discrete lipid-binding sites

Parasitic nematode worms cause serious health problems in humans and other animals. They can induce allergic-type immune responses, which can be harmful but may at the same time protect against the infections. Allergens are proteins that trigger allergic reactions and these parasites produce a type that is confined to nematodes, the nematode polyprotein allergens (NPAs). These are synthesized as large precursor proteins comprising repeating units of similar amino acid sequence that are subsequently cleaved into multiple copies of the allergen protein. NPAs bind small lipids such as fatty acids and retinol (Vitamin A) and probably transport these sensitive and insoluble compounds between the tissues of the worms. Nematodes cannot synthesize these lipids, so NPAs may also be crucial for extracting nutrients from their hosts. They may also be involved in altering immune responses by controlling the lipids by which the immune and inflammatory cells communicate. We describe the molecular structure of one unit of an NPA, the well-known ABA-1 allergen of Ascaris and find its structure to be of a type not previously found for lipid-binding proteins, and we describe the unusual sites where lipids bind within this structur

Peaks detection and alignment for mass spectrometry data

The goal of this paper is to review existing methods for protein mass spectrometry data analysis, and to present a new methodology for automatic extraction of significant peaks (biomarkers). For the pre-processing step required for data from MALDI-TOF or SELDI- TOF spectra, we use a purely nonparametric approach that combines stationary invariant wavelet transform for noise removal and penalized spline quantile regression for baseline correction. We further present a multi-scale spectra alignment technique that is based on identification of statistically significant peaks from a set of spectra. This method allows one to find common peaks in a set of spectra that can subsequently be mapped to individual proteins. This may serve as useful biomarkers in medical applications, or as individual features for further multidimensional statistical analysis. MALDI-TOF spectra obtained from serum samples are used throughout the paper to illustrate the methodology

Development of computational tools for the analysis of 2D-nuclear magnetic resonance data

Dissertação de mestrado em BioinformaticsMetabolomics is one of the omics’ sciences that has been gaining a lot of interest due to its potential on correlating an organism’s biochemical activity and its phenotype. The applications of metabolomics are being extended as new techniques reveal new information on metabolic profiles and molecules, thus elucidating biological, chemical and functional knowledge. The main techniques that collect data are based on mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy. The last one has the advantage of analyzing a sample in vivo without damaging it and while its sensitivity is pointed out as a disadvantage, multidimensional NMR delivers a solution to this issue. It adds layers of information, generating new data that requires advanced bioinformatics methods in order to extract biological meaning. Since multidimensional NMR has different approaches within itself, the need to estab lish an integrated framework that allows a researcher to load its data and extract relevant knowledge has become more imperative over the years. Also, establishing common data analysis pipelines on one-dimensional and multidimensional NMR remains a challenge in current scientific research hindering reproducibility across research groups. In recent work from the host group, specmine, an R package for metabolomics and spectral data analysis/mining, has been developed to wrap and deliver key metabolomic methods that allow a researcher to perform a complete analysis. In this dissertation, tools integrated in specmine were developed to read, visualize and analyze two-dimensional (2D) NMR. A new specmine structure was created for this type of data, easing interpretation and data visualization. In terms of visualization a novel approach towards three-dimensional environments enables users to interact with their data allowing peak hovering or identification of rich resonance regions. The selection of which samples to plot, when the user does not specify an input, is based on a signal-to-noise ratio scale which plots samples with opposite signal-to-noise ratios. A method to perform peak detection on 2D NMR based on local maximum search was implemented to obtain a data structure that best benefits from specmine’s functionalities. These include preprocessing, univariate and multivariate analysis as well as machine learning and feature selection methods. The 2D NMR functions were validated using experimental data from two scientific papers, available on metabolomic databases and applying the necessary preprocessing steps to compare spectra and results. These data originated two case studies from different NMR sources, Bruker and Varian, which reinforces specmine’s flexibility. The case studies were carried out using mainly specmine and other packages for specific processing steps, such as, probabilistic quotient normalization. A pipeline to analyze 2D NMR was added to specmine, in a form of a vignette, to provide a guideline for the newly developed functionalities.A metabolómica é uma das ciências ómicas que tem vindo a ganhar muito interesse devido ao seu potencial para correlacionar a atividade bioquímica de um organismo com o seu fenótipo. As aplicações da metabolómica estão em constante crescimento à medida que novas técnicas revelam nova informação sobre perfis metabólicos e moleculares, elucidando conhecimento biológico, químico e funcional. As principais técnicas para recolher este tipo de dados são baseadas em espectrometria de massa e em ressonância magnética nuclear (RMN). Esta última tem a vantagem de analisar uma amostra in vivo sem a danificar e enquanto a sensibilidade da mesma tem sido apontada como uma desvantagem, surge a abordagem de RMN multidimensional melhorando a versão tradicional. Através da medição de outros núcleos adiciona camadas de informação, gerando um novo tipo de dados que requere métodos bioinformáticos avançados para se extrair significado biológico. A existência de várias abordagens para realizar RMN multidimensional leva à crescente necessidade da existência de uma ferramenta que integre este tipo de dados, de forma a permitir ao investigador executar a sua análise de forma eficaz. Adicionalmente, a consolidação de pipelines comuns para analisar dados de RMN uni- e multidimensional permanece um desafio a investigação científica, dificultando a reprodutibilidade de resultados por diferentes grupos de investigação. Em trabalhos recentes do grupo de acolhimento foi desenvolvido um package para o programa R focado na metabolómica e na análise/mineração de dados. Este package, specmine, tem sido melhorado desde o seu desenvolvimento funcionando como uma ferramenta que engloba diferentes métodos permitindo uma análise total a um determinado conjunto de dados. Baseado neste package, mais recentemente foi desenvolvida uma plataforma web integrada, WebSpecmine, com o mesmo propósito que providencia ao utilizador uma interface de utilizador mais fácil e amigável. Nesta dissertação, ferramentas que permitem a leitura, visualização e análise de NMR bidimensional (2D) foram desenvolvidas tendo em conta a sua integração no specmine. Uma nova estrutura foi adicionada ao package, facilitando a interpretação e esquemetazição dos dados. Quanto a visualização, uma abordagem inovadora para ambientes tridimensionais permite ao utilizador interagir com os seus dados através da identificação de regiões espectrais de interesse ou reconhecimento de picos. A visualização de espectros 2D, sem especificação por parte do utilizador, tem por base uma escala de relação sinal/ruído que permite numa primeira instância visualizar as amostras com uma maior e menor diferença entre sinal e ruído. Foi também implementado um método para realizar a deteção de picos em RMN 2D baseado na procura por valores máximos locais. Esta operação tem por objectivo obter uma estrutura de dados simplificada que melhor beneficia das funcionalidades do specmine. Estas incluem operações de pré-processamento, análises uni- e multivariada, métodos de seleção de variáveis e aprendizagem máquina. As funções desenvolvidas para RMN 2D foram validadas com dados experimentais recolhidos de dois artigos científicos, disponíveis em bases de dados de metabolómica e sobre os quais foram aplicados os passos de pré-processamento que permitissem a comparação de resultados. Estes dados originaram dois casos de estudos que abordavam diferentes instrumentos utilizados em RMN, Bruker e Varian, reforçando desta forma a flexibilidade do specmine relativamente as tipologias de dados capazes de serem lidas. Estes casos foram realizados utilizando principalmente o specmine, no entanto, a utilização de packages externos foi necessária para passos de processamento específicos, como por exemplo, a normalização por quociente probabilístico. Uma pipeline para analise de dados RMN 2D foi adicionada ao specmine, sob a forma de vignette, um formato de documentação longa adequado a packages implementados no programa R. Desta forma e proporcionado ao utilizador um conjunto de procedimentos, orientados a utilização correta das funcionalidades implementadas