Interpretation Of Metabolomics Data Research Articles

Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer.

Simple SummaryCancer, but also its treatment, can lead to a reprogramming of cellular metabolism. These changes are observable in metabolite abundances, which can be unbiasedly measured via mass spectrometry metabolomics. However, even when the metabolome changes strongly, a (mechanistic) interpretation is difficult as metabolite levels do not necessarily directly correspond to pathway activities. Here we measure the changes of the cellular metabolome in colorectal cancer cell lines sensitive and resistant to the ruthenium-based drug BOLD-100/KP1339 and the platinum-based drug oxaliplatin. We map these changes onto a cancer-specific genome-scale metabolic model, which allows us not only to compute intracellular flux distributions, but also to disentangle drug-specific effects from growth differences from differences in metabolic adaptations due to resistance. Specifically, we find that resistance to BOLD-100/KP1339 induces more extensive reprogramming than oxaliplatin, especially with respect to fatty acid and amino acid metabolism.Background: Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge. Methods: Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively. Results: Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways. Conclusions: Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.

Deep learning meets metabolomics: a methodological perspective.

Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.

Using Genome-Scale Metabolic Networks for Analysis, Visualization, and Integration of Targeted Metabolomics Data.

Interpretation of metabolomics data in the context of biological pathways is important to gain knowledge about underlying metabolic processes. In this chapter we present methodsto analyze genome-scale models (GSMs) and metabolomics data together. This includes reading and mining of GSMs using the SBTab format to retrieve information on genes, reactions, and metabolites. Furthermore, the chapter showcases the generation of metabolic pathway maps using the Escher tool, which can be used for data visualization. Lastly, approaches to constrain flux balance analysis (FBA) by metabolomics data are presented.

Retention Index Prediction Using Quantitative Structure-Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics.

Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure-retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was carried out on the 34 resulting groups to eliminate false positives. Partial least squares (PLS) regression combined with a Genetic algorithm (GA) was applied to construct the linear QSRR models based on a variety of VolSurf+ molecular descriptors. A novel dual-filtering approach, which combines Tanimoto similarity (TS) searching as the primary filter and retention index (RI) similarity clustering as the secondary filter, was utilized to select compounds in training sets to derive the QSRR models yielding R2 of 0.8512 and an average root mean square error in prediction (RMSEP) of 8.45%. With a retention index filter expressed as ±2 standard deviations (SD) of the error, representative compounds were predicted with >91% accuracy, and for 53% of the groups (18/34), at least one false positive compound could be eliminated. The proposed strategy can thus narrow down the number of false positives to be assessed in nontargeted metabolomics.

Network Marker Selection for Untargeted LC-MS Metabolomics Data.

Untargeted metabolomics using high-resolution liquid chromatography-mass spectrometry (LC-MS) is becoming one of the major areas of high-throughput biology. Functional analysis, that is, analyzing the data based on metabolic pathways or the genome-scale metabolic network, is critical in feature selection and interpretation of metabolomics data. One of the main challenges in the functional analyses is the lack of the feature identity in the LC-MS data itself. By matching mass-to-charge ratio (m/z) values of the features to theoretical values derived from known metabolites, some features can be matched to one or more known metabolites. When multiple matchings occur, in most cases only one of the matchings can be true. At the same time, some known metabolites are missing in the measurements. Current network/pathway analysis methods ignore the uncertainty in metabolite identification and the missing observations, which could lead to errors in the selection of significant subnetworks/pathways. In this paper, we propose a flexible network feature selection framework that combines metabolomics data with the genome-scale metabolic network. The method adopts a sequential feature screening procedure and machine learning-based criteria to select important subnetworks and identify the optimal feature matching simultaneously. Simulation studies show that the proposed method has a much higher sensitivity than the commonly used maximal matching approach. For demonstration, we apply the method on a cohort of healthy subjects to detect subnetworks associated with the body mass index (BMI). The method identifies several subnetworks that are supported by the current literature, as well as detects some subnetworks with plausible new functional implications. The R code is available at http://web1.sph.emory.edu/users/tyu8/MSS.

Metabolomic Analysis of Schizosaccharomyces pombe: Sample Preparation, Detection, and Data Interpretation.

Metabolomics is a modern field of chemical biology that strives to simultaneously quantify hundreds of cellular metabolites. Techniques for metabolomic analysis in Schizosaccharomyces pombe have only recently been developed. Here we introduce methods that provide a complete workflow for metabolomic analysis in S. pombe Based on available literature, we estimate the yeast metabolome to comprise on the order of several thousand different metabolites. We discuss the feasibility of extraction and detection of such a large number of metabolites, and the influences of various parameters on the results. Among the parameters addressed are cell cultivation conditions, metabolite extraction techniques, and detection and quantification methods. Further, we provide recommendations on data management and data processing for metabolomic experiments, and describe possible pitfalls regarding the interpretation of metabolomic data. Finally, we briefly discuss potential future developments of this technique.

1D-¹H-nuclear magnetic resonance metabolomics reveals age-related changes in metabolites associated with experimental venous thrombosis.

Age is a significant risk factor for the development of venous thrombosis (VT), but the mechanism(s) that underlie this risk remain(s) undefined and poorly understood. Aging is known to adversely influence inflammation and affect metabolism. Untargeted metabolomics permits an agnostic assessment of the physiological landscape and lends insight into the mechanistic underpinnings of clinical phenotypes. The objective of this exploratory study was to test the feasibility of a metabolomics approach for identifying potential metabolic mechanisms of age-related VT. We subjected whole blood samples collected from young and old nonthrombosed controls and VT mice 2 days after thrombus induction using the electrolytic inferior vena cava, to a methanol:chloroform extraction and assayed the resulting aqueous fractions using 1D-(1)H- nuclear magnetic resonance. Normalized mouse metabolite data were compared across groups using analysis of variance (ANOVA) with Holm-Sidak post-testing. In addition, associations between metabolite concentrations and parameters of thrombosis such as thrombus and vein wall weights, and markers of inflammation, vein wall P- and E-selectin levels, were assessed using linear regression. The relatedness of the found significant metabolites was visually assessed using a bioinformatics tool, Metscape, which generates compound-reaction-enzyme-gene networks to aid in the interpretation of metabolomics data. Old mice with VT had a greater mean vein wall weight compared with young mice with VT (P < .05). Clot weight differences between old and young mice followed the same trend as vein wall weight (0.011 ± 0.04 g vs 0.008 ± 0.003 g; P = not significant). Glutamine (ANOVA, P < .01), proline (ANOVA, P < .01), and phenylalanine (ANOVA, P < .05) levels were increased in old VT mice compared with age-matched controls and young VT mice. Betaine and/or trimethylamine N-oxide levels were increased in aged mice compared with young animals. Vein wall weight was strongly associated with glutamine (P < .05), and phenylalanine (P < .01) concentrations and there was a trend toward an association with proline (P = .09) concentration. Vein wall P-selectin, but not E-selectin levels, were increased in old VT mice and were associated with the three found metabolites of age-related VT. Collectively, with the addition of glutamate, these metabolites form a single compound-reaction-enzyme-gene network that was generated by Metscape. We used 1D-(1)H-nuclear magnetic resonance-metabolite profiling to identify, for the first time, in an experimental model, three potential metabolites, glutamine, phenylalanine, and proline, associated with age-related VT. These metabolites are metabolically related and their levels are associated with vein wall weight and P-selectin concentrations. In aggregate, these findings provide a "roadmap" of pathways that could be interrogated in future studies, which could include provocation of the glutamine, phenylalanine, and proline pathways in the vein wall. This study introduces metabolomics as a new approach to furthering knowledge about the mechanisms of age-related VT.

MRS-based Metabolomics in Cancer Research.

Metabolomics is a relatively new technique that is gaining importance very rapidly. MRS-based metabolomics, in particular, is becoming a useful tool in the study of body fluids, tissue biopsies and whole organisms. Advances in analytical techniques and data analysis methods have opened a new opportunity for such technology to contribute in the field of diagnostics. In the MRS approach to the diagnosis of disease, it is important that the analysis utilizes all the essential information in the spectra, is robust, and is non-subjective. Although some of the data analytic methods widely used in chemical and biological sciences are sketched, a more extensive discussion is given of a 5-stage Statistical Classification Strategy. This proposes powerful feature selection methods, based on, for example, genetic algorithms and novel projection techniques. The applications of MRS-based metabolomics in breast cancer, prostate cancer, colorectal cancer, pancreatic cancer, hepatobiliary cancers, gastric cancer, and brain cancer have been reviewed. While the majority of these applications relate to body fluids and tissue biopsies, some in vivo applications have also been included. It should be emphasized that the number of subjects studied must be sufficiently large to ensure a robust diagnostic classification. Before MRS-based metabolomics can become a widely used clinical tool, however, certain challenges need to be overcome. These include manufacturing user-friendly commercial instruments with all the essential features, and educating physicians and medical technologists in the acquisition, analysis, and interpretation of metabolomics data.

Abstract ED04-03: Interpretation of metabolomic data: Challenges and approaches

Abstract Revealing biologically relevant information from metabolomic experiments typically involves multiple data analysis steps, which are performed on different abstraction levels: primary data analysis (peak detection, metabolite identification, etc.), data preprocessing (normalization, outlier detection, etc.), biostatistic analysis (hypothesis testing, clustering, etc.), and data integration for putting the results into a larger biological context. For many of these tasks, a large pool of well-established methods exists and in various aspects we can borrow from experience on other omics technologies. However, specificities of metabolomic data such as the usually incomplete coverage of the metabolic space also require novel concepts for data analysis and interpretation. After a brief overview over typical challenges in metabolomic data interpretation, this lecture will introduce two examples for such concepts. First, the analysis of metabolite ratios as proxies for reaction constants will be discussed. Second, the reconstruction of in vivo metabolic networks from metabolomic data and the application of these networks for data integration and interpretation will be shown. Citation Format: Gabi Kastenmüller, Jan Krumsiek, Christian Gieger, Karsten Suhre. Interpretation of metabolomic data: Challenges and approaches. [abstract]. In: Proceedings of the Twelfth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2013 Oct 27-30; National Harbor, MD. Philadelphia (PA): AACR; Can Prev Res 2013;6(11 Suppl): Abstract nr ED04-03.

Impact of geographical region on urinary metabolomic and plasma fatty acid profiles in subjects with the metabolic syndrome across Europe: the LIPGENE study

The application of metabolomics in multi-centre studies is increasing. The aim of the present study was to assess the effects of geographical location on the metabolic profiles of individuals with the metabolic syndrome. Blood and urine samples were collected from 219 adults from seven European centres participating in the LIPGENE project (Diet, genomics and the metabolic syndrome: an integrated nutrition, agro-food, social and economic analysis). Nutrient intakes, BMI, waist:hip ratio, blood pressure, and plasma glucose, insulin and blood lipid levels were assessed. Plasma fatty acid levels and urine were assessed using a metabolomic technique. The separation of three European geographical groups (NW, northwest; NE, northeast; SW, southwest) was identified using partial least-squares discriminant analysis models for urine (R² X: 0·33, Q²: 0·39) and plasma fatty acid (R² X: 0·32, Q²: 0·60) data. The NW group was characterised by higher levels of urinary hippurate and N-methylnicotinate. The NE group was characterised by higher levels of urinary creatine and citrate and plasma EPA (20 : 5 n-3). The SW group was characterised by higher levels of urinary trimethylamine oxide and lower levels of plasma EPA. The indicators of metabolic health appeared to be consistent across the groups. The SW group had higher intakes of total fat and MUFA compared with both the NW and NE groups (P≤ 0·001). The NE group had higher intakes of fibre and n-3 and n-6 fatty acids compared with both the NW and SW groups (all P< 0·001). It is likely that differences in dietary intakes contributed to the separation of the three groups. Evaluation of geographical factors including diet should be considered in the interpretation of metabolomic data from multi-centre studies.

Quantitative structure–retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: Endogenous metabolites and banned compounds

Quantitative structure–retention relationship (QSRR) is a technique capable of improving the identification of analytes by predicting their retention time on a liquid chromatography column (LC) and/or their properties. This approach is particularly useful when LC is coupled with a high-resolution mass spectrometry (HRMS) platform. The main aim of the present study was to develop and describe appropriate QSRR models that provide usable predictive capability, allowing false positive identification to be removed during the interpretation of metabolomics data, while additionally increasing confidence of experimental results in doping control area. For this purpose, a dataset consisting of 146 drugs, metabolites and banned compounds from World Anti-Doping Agency (WADA) lists, was used. A QSRR study was carried out separately on high quality retention data determined by reversed-phase (RP-LC–HRMS) and hydrophilic interaction chromatography (HILIC-LC–HRMS) systems, employing a single protocol for each system. Multiple linear regression (MLR) was applied to construct the linear QSRR models based on a variety of theoretical molecular descriptors. The regression equations included a set of three descriptors for each model: ALogP, BELe6, R2p and ALogP2, FDI, BLTA96, were used in the analysis of reversed-phase and HILIC column models, respectively. Statistically significant QSRR models (squared correlation coefficient for model fitting, R2=0.95 for RP and R2=0.84 for HILIC) indicate a strong correlation between retention time and the molecular descriptors. An evaluation of the best correlation models, performed by validation of each model using three tests (leave-one-out, leave-many-out, external tests), demonstrated the reliability of the models. This paper provides a practical and effective method for analytical chemists working with LC/HRMS platforms to improve predictive confidence of studies that seek to identify small molecules.

Muma, An R Package for Metabolomics Univariate and Multivariate Statistical Analysis

Metabolomics, similarly to other high-throughput “-omics” techniques, generates large arrays of data, whose analysis and interpretation can be difficult and not always straightforward. Several software for the detailed metabolomics statistical analysis are available, however there is a lack of simple protocols guiding the user through a standard statistical analysis of the data. Herein we present “muma”, an R package providing a simple step-wise pipeline for metabolomics univariate and multivariate statistical analyses. Based on published statistical algorithms and techniques, muma provides user-friendly tools for the whole process of data analysis, ranging from data imputation and preprocessing, to dataset exploration, to data interpretation through unsupervised/supervised multivariate and/or univariate techniques. Of note, specific tools and graphics aiding the explanation of statistical outcomes have been developed. Finally, a section dedicated to metabolomics data interpretation has been implemented, providing specific techniques for molecular assignments and biochemical interpretation of metabolic patterns. muma is a free, user-friendly and versatile tool suite tailored to assist the user in the interpretation of metabolomics data in the identification of biomarkers and in the analysis of metabolic patterns.

Raphael Bastos Mareschi Aggio: Bioanalysis Young Investigator

Raphael Aggio is about to complete his PhD studies in the next few months and already has seven published works in the field of metabolomics. Raphael has been my best PhD student so far, he is hard working and dedicated with a very creative mind. Raphael is one of those few hybrid professionals capable of working in two or more fields very comfortably (e.g., biology, chemistry, mathematics and informatics). Owing to his exceptional performance as a young investigator and PhD student, and the success of his brilliantly created, very powerful bioinformatics tools for assisting the analysis and interpretation of metabolomics data - the impact of this in the field of metabolomics evident by the numerous contacts received from different research groups around the world enquiring about the methods - I highly recommend Raphael for the Bioanalysis Young Investigator Award.

Bioanalysis Young Investigator: Richard A Scheltema

Supervisor’s supporting comments I would like to nominate Richard A Scheltema for the Bioanalysis Young Investigator Award 2011, for the work presented in his PhD thesis ‘Surfing the Metabolome’. Richard has independently developed a computationally elegant and creative approach for dealing with the complex datasets generated by the new generation of high-resolution MS equipment. The novel methodologies developed in Richard’s thesis are collected in mzMatch, a pipeline of tools for the analysis and interpretation of metabolomics data. There is currently no other comparable tool for this increasingly important type of bioanalytical data. Richard’s key bioinformatics results were not only published in a number of important methodological papers, but are also available as open-source software on a community website ( http://mzmatch.sourceforge.net ). Richard’s research has been very favorably received in the broader bioanalytics community: metabolomics researchers realize that his software is setting a new standard for the analysis of their complex datasets, exceeding available tools in reliability and quality of the results. The mzMatch tools developed by Richard are a central component of the analysis software of the Netherlands Proteomics-Bioinformatics Interface and are currently piloted for standardized use by the Dutch Metabolomics Centre. Currently, Richard is working as a postdoctoral researcher in the group of Matthias Mann (Munich), where he is applying his innovative ideas to the bioanalytics challenges faced in proteomics.

Is the biological interpretation of metabolomic data misled by the extraction method performed?

Is the biological interpretation of metabolomic data misled by the extraction method performed?

Metabolic Network Fluxes in Heterotrophic Arabidopsis Cells: Stability of the Flux Distribution under Different Oxygenation Conditions

Steady-state labeling experiments with [1-(13)C]Glc were used to measure multiple metabolic fluxes through the pathways of central metabolism in a heterotrophic cell suspension culture of Arabidopsis (Arabidopsis thaliana). The protocol was based on in silico modeling to establish the optimal labeled precursor, validation of the isotopic and metabolic steady state, extensive nuclear magnetic resonance analysis of the redistribution of label into soluble metabolites, starch, and protein, and a comprehensive set of biomass measurements. Following a simple modification of the cell culture procedure, cells were grown at two oxygen concentrations, and flux maps of central metabolism were constructed on the basis of replicated experiments and rigorous statistical analysis. Increased growth rate at the higher O(2) concentration was associated with an increase in fluxes throughout the network, and this was achieved without any significant change in relative fluxes despite differences in the metabolite profile of organic acids, amino acids, and carbohydrates. The balance between biosynthesis and respiration within the tricarboxylic acid cycle was unchanged, with 38% +/- 5% of carbon entering used for biosynthesis under standard O(2) conditions and 33% +/- 2% under elevated O(2). These results add to the emerging picture of the stability of the central metabolic network and its capacity to respond to physiological perturbations with the minimum of rearrangement. The lack of correlation between the change in metabolite profile, which implied significant disruption of the metabolic network following the alteration in the oxygen supply, and the unchanging flux distribution highlights a potential difficulty in the interpretation of metabolomic data.

HiRes—a tool for comprehensive assessment and interpretation of metabolomic data

The increasing role of metabolomics in system biology is driving the development of tools for comprehensive analysis of high-resolution NMR spectral datasets. This task is quite challenging since unlike the datasets resulting from other 'omics', a substantial preprocessing of the data is needed to allow successful identification of spectral patterns associated with relevant biological variability. HiRes is a unique stand-alone software tool that combines standard NMR spectral processing functionalities with techniques for multi-spectral dataset analysis, such as principal component analysis and non-negative matrix factorization. In addition, HiRes contains extensive abilities for data cleansing, such as baseline correction, solvent peak suppression, removal of frequency shifts owing to experimental conditions as well as auxiliary information management. Integration of these components together with multivariate analytical procedures makes HiRes very capable of addressing the challenges for assessment and interpretation of large metabolomic datasets, greatly simplifying this otherwise lengthy and difficult process and assuring optimal information retrieval. HiRes is freely available for research purposes at http://hatch.cpmc.columbia.edu/highresmrs.html