From correlation to causation: analysis of metabolomics data using systems biology approaches

Introduction

Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

Objectives

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

Methods

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

Results

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

Conclusions

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

Normalization techniques for PARAFAC modeling of urine metabolomic data

Introduction

One of the body fluids often used in metabolomics studies is urine. The concentrations of metabolites in urine are affected by hydration status of an individual, resulting in dilution differences. This requires therefore normalization of the data to correct for such differences. Two normalization techniques are commonly applied to urine samples prior to their further statistical analysis. First, AUC normalization aims to normalize a group of signals with peaks by standardizing the area under the curve (AUC) within a sample to the median, mean or any other proper representation of the amount of dilution. The second approach uses specific end-product metabolites such as creatinine and all intensities within a sample are expressed relative to the creatinine intensity.

Objectives

Another way of looking at urine metabolomics data is by realizing that the ratios between peak intensities are the information-carrying features. This opens up possibilities to use another class of data analysis techniques designed to deal with such ratios: compositional data analysis. The aim of this paper is to develop PARAFAC modeling of three-way urine metabolomics data in the context of compositional data analysis and compare this with standard normalization techniques.

Methods

In the compositional data analysis approach, special coordinate systems are defined to deal with the ratio problem. In essence, it comes down to using other distance measures than the Euclidian Distance that is used in the conventional analysis of metabolomic data.

Results

We illustrate using this type of approach in combination with three-way methods (i.e. PARAFAC) of a longitudinal urine metabolomics study and two simulations. In both cases, the advantage of the compositional approach is established in terms of improved interpretability of the scores and loadings of the PARAFAC model.

Conclusion

For urine metabolomics studies, we advocate the use of compositional data analysis approaches. They are easy to use, well established and proof to give reliable results.

     

NormalizeMets: assessing,selecting and implementing statistical methods for normalizing metabolomics data

Introduction

In metabolomics studies, unwanted variation inevitably arises from various sources. Normalization, that is the removal of unwanted variation, is an essential step in the statistical analysis of metabolomics data. However, metabolomics normalization is often considered an imprecise science due to the diverse sources of variation and the availability of a number of alternative strategies that may be implemented.

Objectives

We highlight the need for comparative evaluation of different normalization methods and present software strategies to help ease this task for both data-oriented and biological researchers.

Methods

We present NormalizeMets—a joint graphical user interface within the familiar Microsoft Excel and freely-available R software for comparative evaluation of different normalization methods. The NormalizeMets R package along with the vignette describing the workflow can be downloaded from https://cran.r-project.org/web/packages/NormalizeMets/. The Excel Interface and the Excel user guide are available on https://metabolomicstats.github.io/ExNormalizeMets.

Results

NormalizeMets allows for comparative evaluation of normalization methods using criteria that depend on the given dataset and the ultimate research question. Hence it guides researchers to assess, select and implement a suitable normalization method using either the familiar Microsoft Excel and/or freely-available R software. In addition, the package can be used for visualisation of metabolomics data using interactive graphical displays and to obtain end statistical results for clustering, classification, biomarker identification adjusting for confounding variables, and correlation analysis.

Conclusion

NormalizeMets is designed for comparative evaluation of normalization methods, and can also be used to obtain end statistical results. The use of freely-available R software offers an attractive proposition for programming-oriented researchers, and the Excel interface offers a familiar alternative to most biological researchers. The package handles the data locally in the user’s own computer allowing for reproducible code to be stored locally.

     

Pathway-based analyses

Background

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation andpenalties for multiple testing.

Methods

The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge.

Results

Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data.

Conclusions

The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.

     

Illuminating the dark metabolome to advance the molecular characterisation of biological systems

Background

The latest version of the Human Metabolome Database (v4.0) lists 114,100 individual entries. Typically, however, metabolomics studies identify only around 100 compounds and many features identified in mass spectra are listed only as ‘unknown compounds’. The lack of ability to detect all metabolites present, and fully identify all metabolites detected (the dark metabolome) means that, despite the great contribution of metabolomics to a range of areas in the last decade, a significant amount of useful information from publically funded studies is being lost or unused each year. This loss of data limits our potential gain in knowledge and understanding of important research areas such as cell biology, environmental pollution, plant science, food chemistry and health and biomedical research. Metabolomics therefore needs to develop new tools and methods for metabolite identification to advance as a field.

Aim of review

In this critical review, some potential issues with metabolite identification are identified and discussed. New and novel emerging technologies and tools which may contribute to expanding the number of compounds identified in metabolomics studies (thus illuminating the dark metabolome) are reviewed. The aim is to stimulate debate and research in the molecular characterisation of biological systems to drive forward metabolomic research.

Key scientific concepts of review

The work specifically discusses dynamic nuclear polarisation nuclear magnetic resonance spectroscopy (DNP-NMR), non-proton NMR active nuclei, two-dimensional liquid chromatography (2DLC) and Raman spectroscopy (RS). It is suggested that developing new methods for metabolomics with these techniques could lead to advances in the field and better characterisation of biological systems.

     

Untargeted metabolomics unravels functionalities of phosphorylation sites in <Emphasis Type="Italic">Saccharomyces cerevisiae</Emphasis>

Background

Coordinated through a complex network of kinases and phosphatases, protein phosphorylation regulates essentially all cellular processes in eukaryotes. Recent advances in proteomics enable detection of thousands of phosphorylation sites (phosphosites) in single experiments. However, functionality of the vast majority of these sites remains unclear and we lack suitable approaches to evaluate functional relevance at a pace that matches their detection.

Results

Here, we assess functionality of 26 phosphosites by introducing phosphodeletion and phosphomimic mutations in 25 metabolic enzymes and regulators from the TOR and HOG signaling pathway in Saccharomyces cerevisiae by phenotypic analysis and untargeted metabolomics. We show that metabolomics largely outperforms growth analysis and recovers 10 out of the 13 previously characterized phosphosites and suggests functionality for several novel sites, including S79 on the TOR regulatory protein Tip41. We analyze metabolic profiles to identify consequences underlying regulatory phosphorylation events and detecting glycerol metabolism to have a so far unknown influence on arginine metabolism via phosphoregulation of the glycerol dehydrogenases. Further, we also find S508 in the MAPKK Pbs2 as a potential link for cross-talking between HOG signaling and the cell wall integrity pathway.

Conclusions

We demonstrate that metabolic profiles can be exploited for gaining insight into regulatory consequences and biological roles of phosphosites. Altogether, untargeted metabolomics is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of phosphosites.

     

Inferring domain-domain interactions from protein-protein interactions in the complex network conformation

Background

As protein domains are functional and structural units of proteins, a large proportion of protein-protein interactions (PPIs) are achieved by domain-domain interactions (DDIs), many computational efforts have been made to identify DDIs from experimental PPIs since high throughput technologies have produced a large number of PPIs for different species. These methods can be separated into two categories: deterministic and probabilistic. In deterministic methods, parsimony assumption has been utilized. Parsimony principle has been widely used in computational biology as the evolution of the nature is considered as a continuous optimization process. In the context of identifying DDIs, parsimony methods try to find a minimal set of DDIs that can explain the observed PPIs. This category of methods are promising since they can be formulated and solved easily. Besides, researches have shown that they can detect specific DDIs, which is often hard for many probabilistic methods. We notice that existing methods just view PPI networks as simply assembled by single interactions, but there is now ample evidence that PPI networks should be considered in a global (systematic) point of view for it exhibits general properties of complex networks, such as 'scale-free' and 'small-world'.

Results

In this work, we integrate this global point of view into the parsimony-based model. Particularly, prior knowledge is extracted from these global properties by plausible reasoning and then taken as input. We investigate the role of the added information extensively through numerical experiments. Results show that the proposed method has improved performance, which confirms the biological meanings of the extracted prior knowledge.

Conclusions

This work provides us some clues for using these properties of complex networks in computational models and to some extent reveals the biological meanings underlying these general network properties.

     

ASICS: an automatic method for identification and quantification of metabolites in complex 1D <Superscript>1</Superscript>H NMR spectra

Introduction

Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.

Objectives

In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.

Methods

A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.

Results

The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.

Conclusion

ASICS is a completely automated procedure to identify and quantify metabolites in ¹H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.

     

Normalization and integration of large-scale metabolomics data using support vector regression

Introduction

Untargeted metabolomics studies for biomarker discovery often have hundreds to thousands of human samples. Data acquisition of large-scale samples has to be divided into several batches and may span from months to as long as several years. The signal drift of metabolites during data acquisition (intra- and inter-batch) is unavoidable and is a major confounding factor for large-scale metabolomics studies.

Objectives

We aim to develop a data normalization method to reduce unwanted variations and integrate multiple batches in large-scale metabolomics studies prior to statistical analyses.

Methods

We developed a machine learning algorithm-based method, support vector regression (SVR), for large-scale metabolomics data normalization and integration. An R package named MetNormalizer was developed and provided for data processing using SVR normalization.

Results

After SVR normalization, the portion of metabolite ion peaks with relative standard deviations (RSDs) less than 30 % increased to more than 90 % of the total peaks, which is much better than other common normalization methods. The reduction of unwanted analytical variations helps to improve the performance of multivariate statistical analyses, both unsupervised and supervised, in terms of classification and prediction accuracy so that subtle metabolic changes in epidemiological studies can be detected.

Conclusion

SVR normalization can effectively remove the unwanted intra- and inter-batch variations, and is much better than other common normalization methods.

     

Navigating freely-available software tools for metabolomics analysis

Introduction

The field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools.

Objectives

To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics.

Methods

The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC–MS, GC–MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for.

Results

A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary.

Conclusion

This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools’ abilities to perform specific data analysis tasks e.g. peak picking.

     

Answering biological questions by analysis of the strawberry metabolome

Background

The qualitative and quantitative analysis of all low molecular weight metabolites within a biological sample, known as the metabolome, provides powerful insights into their roles in biological systems and processes. The study of all the chemical structures, concentrations, and interactions of the thousands of metabolites is called metabolomics. However present state of the art methods and equipment can only analyse a small portion of the numerous, structurally diverse groups of chemical substances found in biological samples, especially with respect to samples of plant origin with their huge diversity of secondary metabolites. Nevertheless, metabolite profiling and fingerprinting techniques have been applied to the analysis of the strawberry metabolome since their early beginnings.

Aim

The application of metabolomics and metabolite profiling approaches within strawberry research was last reviewed in 2011. Here, we aim to summarize the latest results from research of the strawberry metabolome since its last review with a special emphasis on studies that address specific biological questions.

Key scientific concepts

Analysis of strawberry, and other fruits, requires a plethora of analytical methods and approaches encompassing the analysis of primary and secondary metabolites, as well as capturing and quantifying volatile compounds that are related to aroma as well as fruit development, function and plant-to-plant communication. The success and longevity of metabolite and volatile profiling approaches in fruit breeding relies upon the ability of the approach to uncover biologically meaningful insights. The key concepts that must be addressed and are reviewed include: gene function analysis and genotype comparison, analysis of environmental effects and plant protection, screening for bioactive compounds for food and non-food uses, fruit development and physiology as well as fruit sensorial quality. In future, the results will facilitate fruit breeding due to the identification of metabolic QTLs and candidate genes for fruit quality and consumer preference.

     

Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics

Introduction

Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput ‘omics’ technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.

Objectives

We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.

Methods

Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC–TOF–MS). For each batch OPLS-DA^® was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.

Results

A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.

Conclusion

Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.