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.

     

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.

     

Metabolic risks of neonates at birth following in utero exposure to HIV-ART: the amino acid profile of cord blood

Introduction

Untargeted metabolomics of cord blood indicated that antiretroviral therapy to HIV-infected mothers (HIV-ART) did not compromise the exposed neonates with regard to the stress of neonatal hypoglycaemia at birth. However, identified biomarkers reflected stress in their energy metabolism, raising concern over developmental risks in some newborns exposed to ART.

Objectives

This study addresses the concern over HIV-ART-induced metabolic perturbations by expanding the metabolomics study to the amino acid profiles in cord blood collected at birth from newborns either exposed or unexposed to HIV-ART in utero.

Methods

Amino acid profiles derived from liquid chromatographic triple quadruple spectra of cord blood from neonates exposed and unexposed to HIV-ART (cohort 1) were investigated using a metabolomics approach. Amino acid data, generated by ultra performance liquid chromatography–tandem mass spectrometry from similar cases (cohort 2), were included for comparison.

Results

Multivariate and supporting statistics indicated differentiation between the exposed and unexposed neonates in both cohorts, caused by a general decrease or downregulation of amino acid concentrations in the cord blood samples from the exposed cases. Specifically, significant upregulation of aspartic acid in both cohorts and downregulation of arginine, and of threonine, tryptophan and lysine in cohorts 1 and 2, respectively, were observed.

Conclusions

The benefits of ART for HIV-infected pregnant women are well established. However, the amino acid profile of cord blood, obtained from the two independent cohorts, adds to observed metabolic risks of in utero HIV-ART-exposed newborns. These risks could potentially have adverse consequences for the future health of some exposed infants.

     

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.

     

Effect of gut microbiota on host whole metabolome

Introduction

Recent advances in microbiome research have revealed the diverse participation of gut microbiota in a number of diseases. Bacteria-specific endogenous small molecules are produced in the gut, are transported throughout the whole body by circulation, and play key roles in disease establishment. However, the factors and mechanisms underlying these microbial influences largely remain unknown.

Objectives

The purpose of this study was to use metabolomics to better understand the influence of microbiota on host physiology.

Methods

Germ-free mice (GF) were orally administered with the feces of specific pathogen-free (SPF) mice and were maintained in a vinyl isolator for 4 weeks for establishing the so-called ExGF mice. Comparative metabolomics was performed on luminal contents, feces, urine, plasma, and tissues of GF and ExGF mice.

Results

The metabolomics profile of 1716 compounds showed marked difference between GF and ExGF for each matrix. Intestinal differences clearly showed the contribution of microbiota to host digestive activities. In addition, colonic metabolomics revealed the efficient conversion of primary to secondary metabolites by microbiota. Furthermore, metabolomics of tissues and excrements demonstrated the effect of microbiota on the accumulation of metabolites in tissues and during excretion. These effects included known bacterial effects (such as bile acids and amino acids) as well as novel ones, including a drastic decrease of sphingolipids in the host.

Conclusion

The diverse effects of microbiota on different sites of the host metabolome were revealed and novel influences on host physiology were demonstrated. These findings should contribute to a deeper understanding of the influence of gut microbiota on disease states and aid in the development of effective intervention strategies.

     

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.

     

Minimal variation of the plasma lipidome after delayed processing of neonatal cord blood

Background

Cord blood lipids are potential disease biomarkers. We aimed to determine if their concentrations were affected by delayed blood processing.

Method

Refrigerated cord blood from six healthy newborns was centrifuged every 12 h for 4 days. Plasma lipids were analysed by liquid chromatography/mass spectroscopy.

Results

Of 262 lipids identified, only eight varied significantly over time. These comprised three dihexosylceramides, two phosphatidylserines and two phosphatidylethanolamines whose relative concentrations increased and one sphingomyelin that decreased.

Conclusion

Delay in separation of plasma from refrigerated cord blood has minimal effect overall on the plasma lipidome.

     

A systematic review of metabolomics biomarkers for Bisphenol A exposure

Introduction

Bisphenol A (BPA), 2,2-bis(4-hydroxyphenyl) propane, a common industrial chemical which has extremely huge production worldwide, is ubiquitous in the environment. Human have high risk of exposing to BPA and the health problems caused by BPA exposure have aroused public concern. However, the biomarkers for BPA exposure are lacking. As a rapidly developing subject, metabolomics has accumulated a large amount of valuable data in various fields. The secondary application of published metabolomics data could be a very promising field for generating novel biomarkers whilst further understanding of toxicity mechanisms.

Objectives

To summarize the published literature on the use of metabolomics as a tool to study BPA exposure and provide a systematic perspectives of current research on biomarkers screening of BPA exposure.

Methods

We conducted a systematic search of MEDLINE (PubMed) up to the end of June 25, 2017 with the key term combinations of ‘metabolomics’, ‘metabonomics’, ‘mass spectrometry’, ‘nuclear magnetic spectroscopy’, ‘metabolic profiling’ and ‘amino acid profile’ combined with ‘BPA exposure’. Additional articles were identified through searching the reference lists from included studies.

Results

This systematic review included 15 articles. Intermediates of glycolysis, Krebs cycle, β oxidation of long chain fatty acids, pentose phosphate pathway, nucleoside metabolism, branched chain amino acid metabolism, aromatic amino acids metabolism, sulfur-containing amino acids metabolism were significantly changed after BPA exposure, suggesting BPA had a highly complex toxic effects on organism which was consistent with existing studies. The biomarkers most consistently associated with BPA exposure were lactate and choline.

Conclusion

Existing metabolomics studies of BPA exposure present heterogeneous findings regarding metabolite profile characteristics. We need more evidence from target metabolomics and epidemiological studies to further examine the reliability of these biomarkers which link to low, environmentally relevant, exposure of BPA in human body.

     

Removing the bottlenecks of cell culture metabolomics: fast normalization procedure,correlation of metabolites to cell number,and impact of the cell harvesting method

Introduction

Although cultured cells are nowadays regularly analyzed by metabolomics technologies, some issues in study setup and data processing are still not resolved to complete satisfaction: a suitable harvesting method for adherent cells, a fast and robust method for data normalization, and the proof that metabolite levels can be normalized to cell number.

Objectives

We intended to develop a fast method for normalization of cell culture metabolomics samples, to analyze how metabolite levels correlate with cell numbers, and to elucidate the impact of the kind of harvesting on measured metabolite profiles.

Methods

We cultured four different human cell lines and used them to develop a fluorescence-based method for DNA quantification. Further, we assessed the correlation between metabolite levels and cell numbers and focused on the impact of the harvesting method (scraping or trypsinization) on the metabolite profile.

Results

We developed a fast, sensitive and robust fluorescence-based method for DNA quantification showing excellent linear correlation between fluorescence intensities and cell numbers for all cell lines. Furthermore, 82–97 % of the measured intracellular metabolites displayed linear correlation between metabolite concentrations and cell numbers. We observed differences in amino acids, biogenic amines, and lipid levels between trypsinized and scraped cells.

Conclusion

We offer a fast, robust, and validated normalization method for cell culture metabolomics samples and demonstrate the eligibility of the normalization of metabolomics data to the cell number. We show a cell line and metabolite-specific impact of the harvesting method on metabolite concentrations.

     

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.

     

Italian cohort of patients affected by inflammatory bowel disease is characterised by variation in glycerophospholipid,free fatty acids and amino acid levels

Background

Inflammatory bowel disease is a group of pathologies characterised by chronic inflammation of the intestine and an unclear aetiology. Its main manifestations are Crohn’s disease and ulcerative colitis. Currently, biopsies are the most used diagnostic tests for these diseases and metabolomics could represent a less invasive approach to identify biomarkers of disease presence and progression.

Objectives

The lipid and the polar metabolite profile of plasma samples of patients affected by inflammatory bowel disease have been compared with healthy individuals with the aim to find their metabolomic differences. Also, a selected sub-set of samples was analysed following solid phase extraction to further characterise differences between pathological samples.

Methods

A total of 200 plasma samples were analysed using drift tube ion mobility coupled with time of flight mass spectrometry and liquid chromatography for the lipid metabolite profile analysis, while liquid chromatography coupled with triple quadrupole mass spectrometry was used for the polar metabolite profile analysis.

Results

Variations in the lipid profile between inflammatory bowel disease and healthy individuals were highlighted. Phosphatidylcholines, lyso-phosphatidylcholines and fatty acids were significantly changed among pathological samples suggesting changes in phospholipase A₂ and arachidonic acid metabolic pathways. Variations in the levels of cholesteryl esters and glycerophospholipids were also found. Furthermore, a decrease in amino acids levels suggests mucosal damage in inflammatory bowel disease.

Conclusions

Given good statistical results and predictive power of the model produced in our study, metabolomics can be considered as a valid tool to investigate inflammatory bowel disease.

     

Serum metabolites associated with feed efficiency in black angus steers

Introduction

Improving feed utilization in cattle is required to reduce input costs, increase production, and ultimately improve sustainability of the beef cattle industry. Characterizing metabolic differences between efficient and non-efficient animals will allow stakeholders to identify more efficient cattle during backgrounding.

Objectives

This study used an untargeted metabolomics approach to determine differences in serum metabolites between animals of low and high residual feed intake.

Methods

Residual feed intake was determined for 50 purebred Angus steers and 29 steers were selected for the study steers based on low versus high feed efficiency. Blood samples were collected from steers and analyzed using untargeted metabolomics via mass spectrometry. Metabolite data was analyzed using Metaboanalyst, visualized using orthogonal partial least squares discriminant analysis, and p-values derived from permutation testing. Non-esterified fatty acids, urea nitrogen, and glucose were measured using commercially available calorimetric assay kits. Differences in metabolites measured were grouped by residual feed intake was measured using one-way analysis of variance in SAS 9.4.

Results

Four metabolites were found to be associated with differences in feed efficiency. No differences were found in other serum metabolites, including serum urea nitrogen, non-esterified fatty acids, and glucose.

Conclusions

Four metabolites that differed between low and high residual feed intake have important functions related to nutrient utilization, among other functions, in cattle. This information will allow identification of more efficient steers during backgrounding.

     

Diagnosis of post-surgical fine-needle aspiration biopsies of thyroid lesions with indeterminate cytology using HRMAS NMR-based metabolomics

Introduction

Ultrasound examination coupled with fine-needle aspiration (FNA) cytology is the gold standard for the diagnosis of thyroid cancer. However, about 10–40% of these analyses cannot be conclusive on the malignancy of the lesions and lead to surgery. The cytological indeterminate FNA biopsies are mainly constituted of follicular—patterned lesions, which are benign in 80% of the cases.

Objectives

The development of a FNAB classification approach based on the metabolic phenotype of the lesions, complementary to cytology and other molecular tests in order to limit the number of patients undergoing unnecessary thyroidectomy.

Methods

We explored the potential of a NMR-based metabolomics approach to improve the quality of the diagnosis from FNABs, using thyroid tissues collected post-surgically.

Results

The NMR-detected metabolites were used to produce a robust OPLSDA model to discriminate between benign and malignant tumours. Malignancy was correlated with amino acids such as tyrosine, serine, alanine, leucine and phenylalanine and anti-correlated with myo-inositol, scyllo-inositol and citrate. Diagnosis accuracy was of 84.8% when only indeterminate lesions were considered.

Conclusion

These results on model FNAB indicate that there is a clear interest in exploring the possibility to export NMR metabolomics to pre-surgical diagnostics.

     

Metabolomic profiling in follicular fluid of patients with infertility-related deep endometriosis

Introduction

Endometriosis is an estrogen-dependent gynecological disease that causes infertility, and potential metabolomic biomarkers related to ovarian endometriosis and poor outcomes after assisted reproductive treatments are still lacking.

Objectives

The present study analyzed the metabolomic profiling of follicular fluid samples from 40 patients undergoing in vitro fertilization.

Methods

The follicular fluid samples were classified as controls (n = 22) and endometriosis patients (n = 18). The samples were submitted to Bligh and Dyer protocol followed by metabolomics analysis by ultra-performance liquid chromatography mass spectrometry. Clinical data was assessed by Students’ T-test and metabolomics data was analyzed by multivariate statistics by MetaboAnalyst 3.0 to obtain intrinsic characteristics that allowed for groups discrimination. The Receiver Operating Characteristic curve was carried out for the proposed biomarkers, aiming to determine their specificity and sensitivity, as a set and individually.

Results

From the metabolomic analysis, 20 ion masses were selected as potential biomarkers from principal component analysis, which showed that all biomarkers were more abundant in the endometriosis group when compared to controls. Tentative attribution was performed by lipid maps database, demonstrating that these potential biomarkers correspond to fatty acids, carnitines, monoacylglycerols, lysophosphatidic acids, lysophosphatidylglycerols, diacylglycerols, lysophosphatidylcholines, phosphatidylserine, lysophosphatidylinositols and Phosphatidic Acid.

Conclusion

The use of mass spectrometry-based metabolomics allowed for the identification of effective biomarkers for ovarian endometriosis, which may contribute for a better comprehension of the disease and how it affects the ovary, as well as assisting in the development of accessory tools for endometriosis diagnosis and infertility management.

     

Metabolic profiling of isolated mitochondria and cytoplasm reveals compartment-specific metabolic responses

Introduction

Subcellular compartmentalization enables eukaryotic cells to carry out different reactions at the same time, resulting in different metabolite pools in the subcellular compartments. Thus, mutations affecting the mitochondrial energy metabolism could cause different metabolic alterations in mitochondria compared to the cytoplasm. Given that the metabolite pool in the cytosol is larger than that of other subcellular compartments, metabolic profiling of total cells could miss these compartment-specific metabolic alterations.

Objectives

To reveal compartment-specific metabolic differences, mitochondria and the cytoplasmic fraction of baker’s yeast Saccharomyces cerevisiae were isolated and subjected to metabolic profiling.

Methods

Mitochondria were isolated through differential centrifugation and were analyzed together with the remaining cytoplasm by gas chromatography–mass spectrometry (GC–MS) based metabolic profiling.

Results

Seventy-two metabolites were identified, of which eight were found exclusively in mitochondria and sixteen exclusively in the cytoplasm. Based on the metabolic signature of mitochondria and of the cytoplasm, mutants of the succinate dehydrogenase (respiratory chain complex II) and of the F_OF₁-ATP-synthase (complex V) can be discriminated in both compartments by principal component analysis from wild-type and each other. These mitochondrial oxidative phosphorylation machinery mutants altered not only citric acid cycle related metabolites but also amino acids, fatty acids, purine and pyrimidine intermediates and others.

Conclusion

By applying metabolomics to isolated mitochondria and the corresponding cytoplasm, compartment-specific metabolic signatures can be identified. This subcellular metabolomics analysis is a powerful tool to study the molecular mechanism of compartment-specific metabolic homeostasis in response to mutations affecting the mitochondrial metabolism.

     

A metabolomics approach to uncover effects of different exercise modalities in type 1 diabetes

Introduction

Exercise-associated metabolism in type 1 diabetes (T1D) remains under-studied due to the complex interplay between exogenous insulin, counter-regulatory hormones and insulin-sensitivity.

Objective

To identify the metabolic differences induced by two exercise modalities in T1D using ultra high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC–HRMS) based metabolomics.

Methods

Twelve T1D adults performed intermittent high-intensity (IHE) and continuous-moderate-intensity (CONT) exercise. Serum samples were analysed by UHPLC–HRMS.

Results

Metabolic profiling of IHE and CONT highlighted exercise-induced changes in purine and acylcarnitine metabolism.

Conclusion

IHE may increase beta-oxidation through higher ATP-turnover. UHPLC–HRMS based metabolomics as a data-driven approach without an a priori hypothesis may help uncover distinctive metabolic effects during exercise in T1D.Clinical trial registration number is www.clinicaltrials.gov: NCT02068638.

     

Synthesis and physicochemical characterization of the one-carbon carrier 10-formyltetrahydrofolate; a reference standard for metabolomics

Introduction

Metabolomics analysis depends on the identification and validation of specific metabolites. This task is significantly hampered by the absence of well-characterized reference standards. The one-carbon carrier 10-formyltetrahydrofolate acts as a donor of formyl groups in anabolism, where it is a substrate in formyltransferase reactions in purine biosynthesis. It has been reported as an unstable substance and is currently unavailable as a reference standard for metabolomics analysis.

Objectives

The current study was undertaken to provide the metabolomics community thoroughly characterized 10-formyltetrahydrofolate along with analytical methodology and guidelines for its storage and handling.

Methods

Anaerobic base treatment of 5,10-methenyltetrahydrofolate chloride in the presence of antioxidant was utilized to prepare 10-formyltetrahydrofolate.

Results

Pure 10-formyltetrahydrofolate has been prepared and physicochemically characterized. Conditions toward maintaining the stability of a solution of the dipotassium salt of 10-formyltetrahydrofolate have been determined.

Conclusion

This study describes the facile preparation of pure (>90%) 10-formyltetrahydrofolate, its qualitative physicochemical characterization, as well as conditions to enable its use as a reference standard in physiologic samples.

     

An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies

Introduction

For pediatric diseases like childhood leukemia, a short latency period points to in-utero exposures as potentially important risk factors. Untargeted metabolomics of small molecules in archived newborn dried blood spots (DBS) offers an avenue for discovering early-life exposures that contribute to disease risks.

Objectives

The purpose of this study was to develop a quantitative method for untargeted analysis of archived newborn DBS for use in an epidemiological study (California Childhood Leukemia Study, CCLS).

Methods

Using experimental DBS from the blood of an adult volunteer, we optimized extraction of small molecules and integrated measurement of potassium as a proxy for blood hematocrit. We then applied this extraction method to 4.7-mm punches from 106 control DBS samples from the CCLS. Sample extracts were analyzed with liquid chromatography—high resolution mass spectrometry (LC-HRMS) and an untargeted workflow was used to screen for metabolites that discriminate population characteristics such as sex, ethnicity, and birth weight.

Results

Thousands of small molecules were measured in extracts of archived DBS. Normalizing for potassium levels removed variability related to varying hematocrit across DBS punches. Of the roughly 1000 prevalent small molecules that were tested, multivariate linear regression detected significant associations with ethnicity (three metabolites) and birth weight (15 metabolites) after adjusting for multiple testing.

Conclusions

This untargeted workflow can be used for analysis of small molecules in archived DBS to discover novel biomarkers, to provide insights into the initiation and progression of diseases, and to provide guidance for disease prevention.

     

Metabolic risks at birth of neonates exposed in utero to HIV-antiretroviral therapy relative to unexposed neonates: an NMR metabolomics study of cord blood

Introduction

Antiretroviral therapy (ART) for HIV-infected pregnant women is highly effective in preventing mother-to-child transmission (PMTCT) of the virus, but deleterious metabolic and mitochondrial observations in infants born to HIV-infected women treated with ART during pregnancy are periodically reported.

Objectives

This study addresses the concern of HIV-ART-induced metabolic perturbations through a metabolomics study of cord blood collected during transitional neonatal hypoglycaemia following birth from newborns either exposed or unexposed to fetal HIV-ART.

Methods

Proton magnetic resonance spectra from cord blood of 11 in utero HIV-ART-exposed and 14 unexposed newborns, as well as serum from 8 control infants, generated 114 spectral bins which were used to identify significant metabolites by means of univariate and multivariate statistical analyses.

Results

The metabolite profiles differed significantly between that from the unexposed newborns and that from infants—interpreted to characterize the state of transitional neonatal hypoglycaemia (low glucose and high lactic acid and ketone bodies). Quantitative analysis of potential ATP generation showed no meaningful difference in the global metabolite profiles of HIV-ART-exposed and unexposed neonates, but Volcano plot analysis, affirmed by odds ratios, indicated that exposure to HIV-ART affected the plasma 3-hydroxybutyric acid and hypoxanthine concentrations.

Conclusions

The metabolite profile for transitional neonatal hypoglycaemia indicated that HIV-ART did not compromise the exposed neonates to the energy stress of allostasis experienced at birth. Increased hypoxanthine and 3-hydroxybutyric acid indicates metabolic stress at birth in some of the newborns exposed to HIV-ART and raises a concern about unrecognized prolonged allostasis with potential neurological consequences for these infants.