Alpsnmr: an r package for signal processing of fully untargeted nmr-based metabolomics

Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines

An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data

<p>Abstract</p> <p>Background</p> <p>Nuclear magnetic resonance spectroscopy (NMR) is a powerful technique to reveal and compare quantitative metabolic profiles of biological tissues. However, chemical and physical sample variations make the analysis of the data challenging, and typically require the application of a number of preprocessing steps prior to data interpretation. For example, noise reduction, normalization, baseline correction, peak picking, spectrum alignment and statistical analysis are indispensable components in any NMR analysis pipeline.</p> <p>Results</p> <p>We introduce a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra. For each aligned NMR data point the ratio of the between-group and within-group sum of squares (BW-ratio) is calculated to quantify the difference in variability between and within predefined groups of NMR spectra. This differential analysis is related to the calculation of the F-statistic or a one-way ANOVA, but without distributional assumptions. Statistical inference based on the BW-ratio is achieved by bootstrapping the null distribution from the experimental data.</p> <p>Conclusions</p> <p>The workflow performance was evaluated using a previously published dataset. Correlation maps, spectral and grey scale plots show clear improvements in comparison to other methods, and the down-to-earth quantitative analysis works well for the CluPA-aligned spectra. The whole workflow is embedded into a modular and statistically sound framework that is implemented as an R package called "speaq" ("spectrum alignment and quantitation"), which is freely available from <url>http://code.google.com/p/speaq/</url>.</p

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field

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

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

Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies

NMR spectroscopy in wine authentication: an official control perspective

6FESR 2014-2020 Program of the Autonomous Province of Trento (Italy) with EU co-financing. (Fruitomics)openInternationalInternational coauthor/editorWine authentication is vital in identifying malpractice and fraud, and various physical and chemical analytical techniques have been employed for this purpose. Besides wet chemistry, these include chromatography, isotopic ratio mass spectrometry, optical spectroscopy and nuclear magnetic resonance (NMR) spectroscopy, which have been applied in recent years in combination with chemometric approaches. For many years, 2H NMR spectroscopy was the method of choice and achieved official recognition in the detection of sugar addition to grape products. Recently, 1H NMR spectroscopy, a simpler and faster method (in terms of sample preparation), has gathered more and more attention in wine analysis, even if it still lacks official recognition. This technique makes targeted quantitative determination of wine ingredients and non-targeted detection of the metabolomic fingerprint of a wine sample possible. This review summarizes the possibilities and limitations of 1H NMR spectroscopy in analytical wine authentication, by reviewing its applications as reported in the literature. Examples of commercial and open source solutions combining NMR spectroscopy and chemometrics are also examined herein, together with its opportunities of becoming an official method.openSolovyev, P.; Fauhl-Hassek, C.; Riedl, J.; Esslinger, S.; Bontempo, L.; Camin, F.Solovyev, P.; Fauhl-Hassek, C.; Riedl, J.; Esslinger, S.; Bontempo, L.; Camin, F

Medications Activating Tubular Fatty Acid Oxidation Enhance the Protective Effects of Roux-en-Y Gastric Bypass Surgery in a Rat Model of Early Diabetic Kidney Disease

Background: Roux-en-Y gastric bypass surgery (RYGB) improves biochemical and histological parameters of diabetic kidney disease (DKD). Targeted adjunct medical therapy may enhance renoprotection following RYGB. Methods: The effects of RYGB and RYGB plus fenofibrate, metformin, ramipril, and rosuvastatin (RYGB-FMRR) on metabolic control and histological and ultrastructural indices of glomerular and proximal tubular injury were compared in the Zucker Diabetic Sprague Dawley (ZDSD) rat model of DKD. Renal cortical transcriptomic (RNA-sequencing) and urinary metabolomic (1H-NMR spectroscopy) responses were profiled and integrated. Transcripts were assigned to kidney cell types through in silico deconvolution in kidney single-nucleus RNA-sequencing and microdissected tubular epithelial cell proteomics datasets. Medication-specific transcriptomic responses following RYGB-FMRR were explored using a network pharmacology approach. Omic correlates of improvements in structural and ultrastructural indices of renal injury were defined using a molecular morphometric approach. Results: RYGB-FMRR was superior to RYGB alone with respect to metabolic control, albuminuria, and histological and ultrastructural indices of glomerular injury. RYGB-FMRR reversed DKD-associated changes in mitochondrial morphology in the proximal tubule to a greater extent than RYGB. Attenuation of transcriptomic pathway level activation of pro-fibrotic responses was greater after RYGB-FMRR than RYGB. Fenofibrate was found to be the principal medication effector of gene expression changes following RYGB-FMRR, which led to the transcriptional induction of PPARα-regulated genes that are predominantly expressed in the proximal tubule and which regulate peroxisomal and mitochondrial fatty acid oxidation (FAO). After omics integration, expression of these FAO transcripts positively correlated with urinary levels of PPARα-regulated nicotinamide metabolites and negatively correlated with urinary tricarboxylic acid (TCA) cycle intermediates. Changes in FAO transcripts and nicotinamide and TCA cycle metabolites following RYGB-FMRR correlated strongly with improvements in glomerular and proximal tubular injury. Conclusions: Integrative multi-omic analyses point to PPARα-stimulated FAO in the proximal tubule as a dominant effector of treatment response to combined surgical and medical therapy in experimental DKD. Synergism between RYGB and pharmacological stimulation of FAO represents a promising combinatorial approach to the treatment of DKD in the setting of obesity.Health Research BoardHealth Service ExecutiveScience Foundation IrelandUniversity College DublinWellcome TrustSwedish Medical Research CouncilEuropean Foundation for the Study of Diabetes/Boehringer Ingelheim European Diabetes Research ProgrammeHealth and Social Care, Research and Development Division, Northern Irelan

The long-term culture of human fibroblasts reveals a spectroscopic signature of senescence

Aging is a complex process which leads to progressive loss of fitness/capability/ability, increasing susceptibility to disease and, ultimately, death. Regardless of the organism, there are some features common to aging, namely, the loss of proteostasis and cell senescence. Mammalian cell lines have been used as models to study the aging process, in particular, cell senescence. Thus, the aim of this study was to characterize the senescence-associated metabolic profile of a long-term culture of human fibroblasts using Fourier Transform Infrared and Nuclear Magnetic Resonance spectroscopy. We sub-cultivated fibroblasts from a newborn donor from passage 4 to passage 17 and the results showed deep changes in the spectroscopic profile of cells over time. Late passage cells were characterized by a decrease in the length of fatty acid chains, triglycerides and cholesterol and an increase in lipid unsaturation. We also found an increase in the content of intermolecular β-sheets, possibly indicating an increase in protein aggregation levels in cells of later passages. Metabolic profiling by NMR showed increased levels of extracellular lactate, phosphocholine and glycine in cells at later passages. This study suggests that spectroscopy approaches can be successfully used to study changes concomitant with cell senescence and validate the use of human fibroblasts as a model to monitor the aging process.publishe

Estrategias para el análisis de datos metabolómicos dirigidos al diagnóstico clínico

La metabolómica es un área de investigación emergente y puede ser considerada, a nivel bioquímico, como el final de la cascada “ómica” (genómica → transcriptómica →proteómica → metabolómica) ya que los cambios en el metaboloma constituyen la última respuesta del organismo a alteraciones genéticas, químicas, ambientales, etc. En ese sentido, el metaboloma está muy ligado al fenotipo y puede constituir una herramienta sumamente útil para diagnosticar enfermedades y evaluar el efecto de los tratamientos. Los metabolitos son los productos finales de todos los procesos que se producen en las células, y las concentraciones de metabolitos en los procesos patológicos reflejan la adaptación de los sistemas biológicos a las alteraciones bioquímicas características de cada enfermedad. La firma metabólica de un paciente, generalmente obtenida de forma no-invasiva a partir del análisis de biofluidos, contiene información relacionada con el genotipo, pero también con otros factores como la progresión de la enfermedad o la respuesta a los tratamientos. Esto explica por qué la metabolómica está atrayendo tanto interés para la identificación de biomarcadores de valor diagnóstico y para el seguimiento de pacientes de distintas patologías. En particular, los procesos oncológicos, que implican la desregulación de múltiples vías bioquímicas, son excelentes candidatos para la realización de estudios metabolómicos. La variabilidad en la fisiopatología de la enfermedad, junto con las diferencias individuales en la respuesta a los tratamientos, constituye la base de los esfuerzos por personalizar este tipo de terapias. Para la obtención de información biológica relevante y llevar a cabo con éxito un estudio metabolómico es necesario establecer una metodología experimental adecuada que debe incluir un buen diseño experimental, una adecuada selección y almacenamiento de las muestras, así como una adecuada técnica analítica y un correcto tratamiento e interpretación de los datos. Este estudio se divide en tres partes que intentan abordar los elementos adecuados y necesarios para una correcta aproximación experimental en un estudio de metabolómica enfocado a la búsqueda de nuevos biomarcadores de utilidad clínica. En la primera parte se evalúa la estabilidad de las muestras durante los procesos de la fase preanalítica para así poder identificar posibles biomarcadores de calidad de las muestras. La heterogeneidad de los procedimientos de muestreo y almacenamiento puede introducir una variabilidad significativa en la composición molecular de las muestras biológicas y, en consecuencia, interferir en el resultado experimental o afectar a su reproducibilidad. Tanto el suero como el plasma son biofluidos usados ampliamente como matrices biológicas en la investigación biomédica para identificar biomarcadores clínicamente relevantes. En este contexto es importante conocer la viabilidad de las muestras y las condiciones específicas que son necesarias para el uso de esta tecnología tanto para la investigación como para la práctica clínica diaria. La Resoncia Magnética Nuclear (RMN) de protón (1H-RMN) es una técnica no-invasiva que tiene multitud de aplicaciones en el análisis de sistemas biológicos y puede resultar extremadamente útil. Una de las principales limitaciones en el análisis metabolómico es la ausencia de metodologías estandarizadas que permitan desarrollar estudios en profundidad y fácilmente reproducibles en otros laboratorios. La segunda parte del proyecto analiza las diferentes estrategias y herramientas que se emplean en el análisis de los perfiles metabolómicos. Cómo desde un biofluido, usando como plataforma la 1H-RMN, podemos obtener información global de las señales correspondientes a los perfiles metabolómicos presentes en la muestra (metabolomic fingerprinting), pudiéndose identificar diferencias y/o similitudes entre los individuos a través de un análisis conjunto y multivariante de estas señales. En este contexto, se plantean dos aplicaciones prácticas dirigidas a la búsqueda de nuevos biomarcadores. Por un lado, ser realizó un estudio para evaluar la variabilidad biología y la búsqueda de biomarcadores en muestras de orina de pacientes con Cáncer de Próstata frente a individuos con Hiperplasia Benigna de Próstata. Por otro lado, se realizó un estudio de validación de biomarcadores de suero en pacientes Cáncer de Pulmón No Microcítico (CPNM). En este contexto, tras la realización de un estudio previo en nuestro laboratorio de un conjunto de metabolitos útiles en el diagnóstico precoz de CPNM, se realizó un estudio de validación de dichos metabolitos con un conjunto de muestras independiente