A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

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.

     

rDolphin: a GUI R package for proficient automatic profiling of 1D <Superscript>1</Superscript>H-NMR spectra of study datasets

Introduction

Adoption of automatic profiling tools for ¹H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices.

Objectives

To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process.

Methods

rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality.

Results

The information and quality achieved in two public datasets of complex matrices are maximized.

Conclusion

rDolphin is an open-source R package (http://github.com/danielcanueto/rDolphin) able to provide the best balance between accuracy, reproducibility and ease of use.

     

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.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.

     

Crop metabolomics: from diagnostics to assisted breeding

Background

Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.

Aim of Review

We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.

Key Scientific Concepts of Review

Translational metabolomics applied to crop breeding programs.

     

MiteFinderII: a novel tool to identify miniature inverted-repeat transposable elements hidden in eukaryotic genomes

Background

Miniature inverted-repeat transposable element (MITE) is a type of class II non-autonomous transposable element playing a crucial role in the process of evolution in biology. There is an urgent need to develop bioinformatics tools to effectively identify MITEs on a whole genome-wide scale. However, most of currently existing tools suffer from low ability to deal with large eukaryotic genomes.

Methods

In this paper, we proposed a novel tool MiteFinderII, which was adapted from our previous algorithm MiteFinder, to efficiently detect MITEs from genomics sequences. It has six major steps: (1) build K-mer Index and search for inverted repeats; (2) filtration of inverted repeats with low complexity; (3) merger of inverted repeats; (4) filtration of candidates with low score; (5) selection of final MITE sequences; (6) selection of representative sequences.

Results

To test the performance, MiteFinderII and three other existing algorithms were applied to identify MITEs on the whole genome of oryza sativa. Results suggest that MiteFinderII outperforms existing popular tools in terms of both specificity and recall. Additionally, it is much faster and more memory-efficient than other tools in the detection.

Conclusion

MiteFinderII is an accurate and effective tool to detect MITEs hidden in eukaryotic genomes. The source code is freely accessible at the website: https://github.com/screamer/miteFinder.

     

Metabolomics meets functional assays: coupling LC–MS and microfluidic cell-based receptor-ligand analyses

Introduction

Metabolomics has become a valuable tool in many research areas. However, generating metabolomics-based biochemical profiles without any related bioactivity is only of indirect value in understanding a biological process. Therefore, metabolomics research could greatly benefit from tools that directly determine the bioactivity of the detected compounds.

Objective

We aimed to combine LC–MS metabolomics with a cell based receptor assay. This combination could increase the understanding of biological processes and may provide novel opportunities for functional metabolomics.

Methods

We developed a flow through biosensor with human cells expressing both the TRPV1, a calcium ion channel which responds to capsaicin, and the fluorescent intracellular calcium ion reporter, YC3.6. We have analysed three contrasting Capsicum varieties. Two were selected with contrasting degrees of spiciness for characterization by HPLC coupled to high mass resolution MS. Subsequently, the biosensor was then used to link individual pepper compounds with TRPV1 activity.

Results

Among the compounds in the crude pepper fruit extracts, we confirmed capsaicin and also identified both nordihydrocapsaicin and dihydrocapsaicin as true agonists of the TRPV1 receptor. Furthermore, the biosensor was able to detect receptor activity in extracts of both Capsicum fruits as well as a commercial product. Sensitivity of the biosensor to this commercial product was similar to the sensory threshold of a human sensory panel.

Conclusion

Our results demonstrate that the TRPV1 biosensor is suitable for detecting bioactive metabolites. Novel opportunities may lie in the development of a continuous functional assay, where the biosensor is directly coupled to the LC–MS.

     

NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics

Introduction

Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks.

Objectives

The objective of NMRProcFlow is to assist the expert in this task in the best way without requirement of programming skills.

Methods

NMRProcFlow was developed to be a graphical and interactive 1D NMR (¹H & ¹³C) spectra processing tool.

Results

NMRProcFlow (http://nmrprocflow.org), dedicated to metabolic fingerprinting and targeted metabolomics, covers all spectra processing steps including baseline correction, chemical shift calibration and alignment.

Conclusion

Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses.

     

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.

     

Monitoring spatial and temporal metabolic dynamics of woody poplar root under mechanical stress conditions by NMR-based metabolomics

Introduction

Molecular factors are differentially observed in various bent sectors of poplar (Populus nigra) woody taproots. Responses to stress are modulated by a complex interplay among different hormones and signal transduction pathways. In recent years, metabolomics has been recognized as a powerful tool to characterize metabolic network regulation, and it has been widely applied to investigate plant responses to biotic and abiotic stresses.

Objectives

In this paper we used metabolomics to understand if long term-bending stress induces a “spatial” and a “temporal” metabolic reprogramming in woody poplar roots.

Methods

By NMR spectroscopy and statistical analysis we investigated the unstressed and three portions of stressed root (above-bent, bent, and below-bent) sectors collected at 12 (T₀), 13 (T₁) and 14 (T₂) months after stress induction.

Results

The data indicate a clear between-class separation of control and stressed regions, based on the metabolites regulation, during both spatial and temporal changes. We found that taproots, as a consequence of the stress, try to restore homeostasis and normal metabolic fluxes thorough the synthesis and/or accumulation of specific compounds related to mechanical forces distribution along the bent taproot.

Conclusion

The data demonstrate that the impact of mechanical stress on plant biology can efficiently be studied by NMR-based metabolomics.

     

Intensive determination of storage condition effects on human plasma metabolomics

Introduction

Human plasma metabolomics offer powerful tools for understanding disease mechanisms and identifying clinical biomarkers for diagnosis, efficacy prediction and patient stratification. Although storage conditions can affect the reliability of data from metabolites, strict control of these conditions remains challenging, particularly when clinical samples are included from multiple centers. Therefore, it is necessary to consider stability profiles of each analyte.

Objectives

The purpose of this study was to extract unstable metabolites from vast metabolome data and identify factors that cause instability.

Method

Plasma samples were obtained from five healthy volunteers, were stored under ten different conditions of time and temperature and were quantified using leading-edge metabolomics. Instability was evaluated by comparing quantitation values under each storage condition with those obtained after ?80 °C storage.

Result

Stability profiling of the 992 metabolites showed time- and temperature-dependent increases in numbers of significantly changed metabolites. This large volume of data enabled comparisons of unstable metabolites with their related molecules and allowed identification of causative factors, including compound-specific enzymatic activity in plasma and chemical reactivity. Furthermore, these analyses indicated extreme instability of 1-docosahexaenoylglycerol, 1-arachidonoylglycerophosphate, cystine, cysteine and N⁶-methyladenosine.

Conclusion

A large volume of data regarding storage stability was obtained. These data are a contribution to the discovery of biomarker candidates without misselection based on unreliable values and to the establishment of suitable handling procedures for targeted biomarker quantification.

     

Does centrifugation matter? Centrifugal force and spinning time alter the plasma metabolome

Background

Centrifugation is an indispensable procedure for plasma sample preparation, but applied conditions can vary between labs.

Aim

Determine whether routinely used plasma centrifugation protocols (1500×g 10 min; 3000×g 5 min) influence non-targeted metabolomic analyses.

Methods

Nuclear magnetic resonance spectroscopy (NMR) and High Resolution Mass Spectrometry (HRMS) data were evaluated with sparse partial least squares discriminant analyses and compared with cell count measurements.

Results

Besides significant differences in platelet count, we identified substantial alterations in NMR and HRMS data related to the different centrifugation protocols.

Conclusion

Already minor differences in plasma centrifugation can significantly influence metabolomic patterns and potentially bias metabolomics studies.