The altered human serum metabolome induced by a marathon

Introduction

Endurance races have been associated with a substantial amount of adverse effects which could lead to chronic disease and long-term performance impairment. However, little is known about the holistic metabolic changes occurring within the serum metabolome of athletes after the completion of a marathon.

Objectives

Considering this, the aim of this study was to better characterize the acute metabolic changes induced by a marathon.

Methods

Using an untargeted two dimensional gas chromatography time-of-flight mass spectrometry metabolomics approach, pre- and post-marathon serum samples of 31 athletes were analyzed and compared to identify those metabolites varying the most after the marathon perturbation.

Results

Principle component analysis of the comparative groups indicated natural differentiation due to variation in the total metabolite profiles. Elevated concentrations of carbohydrates, fatty acids, tricarboxylic acid cycle intermediates, ketones and reduced concentrations of amino acids indicated a metabolic shift between various fuel substrate systems. Additionally, elevated odd-chain fatty acids and α-hydroxy acids indicated the utilization of α-oxidation and autophagy as alternative energy-producing mechanisms. Adaptations in gut microbe-associated markers were also observed and correlated with the metabolic flexibility of the athlete.

Conclusion

From these results it is evident that a marathon places immense strain on the energy-producing pathways of the athlete, leading to extensive protein degradation, oxidative stress, mammalian target of rapamycin complex 1 inhibition and autophagy. A better understanding of this metabolic shift could provide new insights for optimizing athletic performance, developing more efficient nutrition regimens and identify strategies to improve recovery.

     

Extraction of natural products from bark of <Emphasis Type="Italic">Betula pendula</Emphasis> using ionic liquids

Introduction

Aqueous–methanol mixtures have successfully been applied to extract a broad range of metabolites from plant tissue. However, a certain amount of material remains insoluble.

Objectives

To enlarge the metabolic compendium, two ionic liquids were selected to extract the methanol insoluble part of trunk from Betula pendula.

Methods

The extracted compounds were analyzed by LC/MS and GC/MS.

Results

The results show that 1-butyl-3-methylimidazolium acetate (IL-Ac) predominantly resulted in fatty acids, whereas 1-ethyl-3-methylimidazolium tosylate (IL-Tos) mostly yielded phenolic structures. Interestingly, bark yielded more ionic liquid soluble metabolites compared to interior wood.

Conclusion

From this one can conclude that the application of ionic liquids may expand the metabolic snapshot.

     

Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

Background

The growing field of metabolomics has opened up new opportunities for prediction of type 2 diabetes (T2D) going beyond the classical biochemistry assays.

Objectives

We aimed to identify markers from different pathways which represent early metabolic changes and test their predictive performance for T2D, as compared to the performance of traditional risk factors (TRF).

Methods

We analyzed 2776 participants from the Erasmus Rucphen Family study from which 1571 disease free individuals were followed up to 14-years. The targeted metabolomics measurements at baseline were performed by three different platforms using either nuclear magnetic resonance spectroscopy or mass spectrometry. We selected 24 T2D markers by using Least Absolute Shrinkage and Selection operator (LASSO) regression and tested their association to incidence of disease during follow-up.

Results

The 24 markers i.e. high-density, low-density and very low-density lipoprotein sub-fractions, certain triglycerides, amino acids, and small intermediate compounds predicted future T2D with an area under the curve (AUC) of 0.81. The performance of the metabolic markers compared to glucose was significantly higher among the young (age?<?50 years) (0.86 vs. 0.77, p-value <0.0001), the female (0.88 vs. 0.84, p-value =0.009), and the lean (BMI?<?25 kg/m²) (0.85 vs. 0.80, p-value =0.003). The full model with fasting glucose, TRFs, and metabolic markers yielded the best prediction model (AUC?=?0.89).

Conclusions

Our novel prediction model increases the long-term prediction performance in combination with classical measurements, brings a higher resolution over the complexity of the lipoprotein component, increasing the specificity for individuals in the low risk group.

     

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.

     

Untargeted and stable isotope-assisted metabolomic analysis of MDA-MB-231 cells under hypoxia

Introduction

Hypoxia commonly occurs in cancers and is highly related with the occurrence, development and metastasis of cancer. Treatment of triple negative breast cancer remains challenge. Knowledge about the metabolic status of triple negative breast cancer cell lines in hypoxia is valuable for the understanding of molecular mechanisms of this tumor subtype to develop effective therapeutics.

Objectives

Comprehensively characterize the metabolic profiles of triple negative breast cancer cell line MDA-MB-231 in normoxia and hypoxia and the pathways involved in metabolic changes in hypoxia.

Methods

Differences in metabolic profiles affected pathways of MDA-MB-231 cells in normoxia and hypoxia were characterized using GC–MS based untargeted and stable isotope assisted metabolomic techniques.

Results

Thirty-three metabolites were significantly changed in hypoxia and nine pathways were involved. Hypoxia increased glycolysis, inhibited TCA cycle, pentose phosphate pathway and pyruvate carboxylation, while increased glutaminolysis in MDA-MB-231 cells.

Conclusion

The current results provide metabolic differences of MDA-MB-231 cells in normoxia and hypoxia conditions as well as the involved metabolic pathways, demonstrating the power of combined use of untargeted and stable isotope-assisted metabolomic methods in comprehensive metabolomic analysis.

     

How the chemical features of molecules may have addressed the settlement of metabolic steps

Introduction

While the evolutionary adaptation of enzymes to their own substrates is a well assessed and rationalized field, how molecules have been originally selected in order to initiate and assemble convenient metabolic pathways is a fascinating, but still debated argument.

Objectives

Aim of the present study is to give a rationale for the preferential selection of specific molecules to generate metabolic pathways.

Methods

The comparison of structural features of molecules, through an inductive methodological approach, offer a reading key to cautiously propose a determining factor for their metabolic recruitment.

Results

Starting with some commonplaces occurring in the structural representation of relevant carbohydrates, such as glucose, fructose and ribose, arguments are presented in associating stable structural determinants of these molecules and their peculiar occurrence in metabolic pathways.

Conclusions

Among other possible factors, the reliability of the structural asset of a molecule may be relevant or its selection among structurally and, a priori, functionally similar molecules.

     

Analysis of the free fatty acid metabolome in the plasma of patients with systemic lupus erythematosus and fever

Introduction

Systemic lupus erythematosus (SLE) is a multifactorial autoimmune disease with heterogeneous clinical manifestations mediated by immune dysregulation.

Objectives

We aimed to analyze the metabolomic differences in free fatty acids (FFAs) between patients with SLE and healthy controls (HCs).

Methods

In this study, the levels of 24 FFAs, as their tert-butyldimethylsilyl derivatives, in the plasma of 41 patients with SLE and 41 HCs, were investigated using gas chromatography with mass spectrometry in selected-ion monitoring mode.

Results

The results showed that patients with SLE and HCs had significantly different levels of 13 of the 24 FFAs. The levels of myristic, palmitoleic, oleic, and eicosenoic acids were significantly higher, whereas the levels of caproic, caprylic, linoleic, stearic, arachidonic, eicosanoic, behenic, lignoceric, and hexacosanoic acids were significantly lower in patients with SLE, than in the HCs. In the partial-correlation analysis of the FFA profiles and markers of disease activity of SLE, several metabolic markers correlated with SLE disease activity.

Conclusions

Our results provide a comprehensive understanding of the relationship between FFAs and markers of SLE disease activity. Thus, this approach has promising potential for the discovery of metabolic biomarkers of SLE.

     

Identifying diseases-related metabolites using random walk

Background

Metabolites disrupted by abnormal state of human body are deemed as the effect of diseases. In comparison with the cause of diseases like genes, these markers are easier to be captured for the prevention and diagnosis of metabolic diseases. Currently, a large number of metabolic markers of diseases need to be explored, which drive us to do this work.

Methods

The existing metabolite-disease associations were extracted from Human Metabolome Database (HMDB) using a text mining tool NCBO annotator as priori knowledge. Next we calculated the similarity of a pair-wise metabolites based on the similarity of disease sets of them. Then, all the similarities of metabolite pairs were utilized for constructing a weighted metabolite association network (WMAN). Subsequently, the network was utilized for predicting novel metabolic markers of diseases using random walk.

Results

Totally, 604 metabolites and 228 diseases were extracted from HMDB. From 604 metabolites, 453 metabolites are selected to construct the WMAN, where each metabolite is deemed as a node, and the similarity of two metabolites as the weight of the edge linking them. The performance of the network is validated using the leave one out method. As a result, the high area under the receiver operating characteristic curve (AUC) (0.7048) is achieved. The further case studies for identifying novel metabolites of diabetes mellitus were validated in the recent studies.

Conclusion

In this paper, we presented a novel method for prioritizing metabolite-disease pairs. The superior performance validates its reliability for exploring novel metabolic markers of diseases.

     

A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles

Introduction

It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.

Objectives

This work simplifies this process.

Methods

A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.

Results

The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.

Conclusion

This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.

     

Investigation of species and environmental effects on rhubarb roots metabolome using <Superscript>1</Superscript>H NMR combined with high performance thin layer chromatography

Introduction

The pharmacological activities of medicinal plants are reported to be due to a wide range of metabolites, therein, the concentrations of which are greatly affected by many genetic and/or environmental factors. In this context, a metabolomics approach has been applied to reveal these relationships. The investigation of such complex networks that involve the correlation between multiple biotic and abiotic factors and the metabolome, requires the input of information acquired by more than one analytical platform. Thus, development of new metabolomics techniques or hyphenations is continuously needed.

Objectives

Feasibility of high performance thin-layer chromatography (HPTLC) were investigated as a supplementary tool for medicinal plants metabolomics supporting ¹H nuclear magnetic resonance (¹H NMR) spectroscopy.

Method

The overall metabolic difference of plant material collected from two species (Rheum palmatum and Rheum tanguticum) in different geographical locations and altitudes were analyzed by ¹H NMR- and HPTLC-based metabolic profiling. Both NMR and HPTLC data were submitted to multivariate data analysis including principal component analysis and orthogonal partial least square analysis.

Results

The NMR and HPTLC profiles showed that while chemical variations of rhubarb are in some degree affected by all the factors tested in this study, the most influential factor was altitude of growth. The metabolites responsible for altitude differentiation were chrysophanol, emodin and sennoside A, whereas aloe emodin, catechin, and rhein were the key species-specific markers.

Conclusion

These results demonstrated the potential of HTPLC as a supporting tool for metabolomics due to its high profiling capacity of targeted metabolic groups and preparative capability.

     

Metabolomics analysis of liver reveals profile disruption in bovines upon steroid treatment

Introduction

The surveillance of illegal anabolic practices in bovine meat production is necessary to guarantee consumers’ health. Screening strategies based on the recognition of indirect biological effects are considered by the community as promising tools to overcome some limitations of classical analytical methods and might therefore concur to ensure safer food for the consumer.

Objectives

The present work aims at characterizing the metabolic profile induced in liver by administration of anabolic steroids, and at identifying potential disturbances in the hepatic metabolism.

Methods

A total of 32 liver samples, 16 from untreated bulls and 16 from bulls treated with an ear implant (Revalor-XS^®) containing trenbolone acetate (200 mg) and β-estradiol (40 mg), were analyzed following a LC–MS-based metabolomic analysis combining RP and HILIC chromatographic separations. Different multivariate statistical tools were applied to the datasets to select common metabolites that may be considered as potential markers based on their significant changes in concentrations after administration of sexual steroids.

Results

Eight candidate markers were identified. Moreover, a subset of four markers was also validated by a different laboratory that performed the same analysis using an independent instrumental and elaboration platform, confirming the robustness of the results achieved.

Conclusion

This study was performed mimicking experimental conditions that may be used during a potential misuse practice. It is promising in the objective of setting up an analytical strategy to highlight sexual steroids abuse in livestock animals.

     

TarMet: a reactive GUI tool for efficient and confident quantification of MS based targeted metabolic and stable isotope tracer analysis

Introduction

Untargeted and targeted analyses are two classes of metabolic study. Both strategies have been advanced by high resolution mass spectrometers coupled with chromatography, which have the advantages of high mass sensitivity and accuracy. State-of-art methods for mass spectrometric data sets do not always quantify metabolites of interest in a targeted assay efficiently and accurately.

Objectives

TarMet can quantify targeted metabolites as well as their isotopologues through a reactive and user-friendly graphical user interface.

Methods

TarMet accepts vendor-neutral data files (NetCDF, mzXML and mzML) as inputs. Then it extracts ion chromatograms, detects peak position and bounds and confirms the metabolites via the isotope patterns. It can integrate peak areas for all isotopologues automatically.

Results

TarMet detects more isotopologues and quantify them better than state-of-art methods, and it can process isotope tracer assay well.

Conclusion

TarMet is a better tool for targeted metabolic and stable isotope tracer analyses.

     

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.

     

Predicting ovarian cancer recurrence by plasma metabolic profiles before and after surgery

Background

Previous metabolomic studies have revealed that plasma metabolic signatures may predict epithelial ovarian cancer (EOC) recurrence. However, few studies have performed metabolic profiling of pre- and post-operative specimens to investigate EOC prognostic biomarkers.

Objective

The aims of our study were to compare the predictive performance of pre- and post-operative specimens and to create a better model for recurrence by combining biomarkers from both metabolic signatures.

Methods

Thirty-five paired plasma samples were collected from 35 EOC patients before and after surgery. The patients were followed-up until December, 2016 to obtain recurrence information. Metabolomics using rapid resolution liquid chromatography–mass spectrometry was performed to identify metabolic signatures related to EOC recurrence. The support vector machine model was employed to predict EOC recurrence using identified biomarkers.

Results

Global metabolomic profiles distinguished recurrent from non-recurrent EOC using both pre- and post-operative plasma. Ten common significant biomarkers, hydroxyphenyllactic acid, uric acid, creatinine, lysine, 3-(3,5-diiodo-4-hydroxyphenyl) lactate, phosphohydroxypyruvic acid, carnitine, coproporphyrinogen, l-beta-aspartyl-l-glutamic acid and 24,25-hydroxyvitamin D3, were identified as predictive biomarkers for EOC recurrence. The area under the receiver operating characteristic (AUC) values in pre- and post-operative plasma were 0.815 and 0.909, respectively; the AUC value after combining the two sets reached 0.964.

Conclusion

Plasma metabolomic analysis could be used to predict EOC recurrence. While post-operative biomarkers have a predictive advantage over pre-operative biomarkers, combining pre- and post-operative biomarkers showed the best predictive performance and has great potential for predicting recurrent EOC.

     

Metabolic response of porcine colon explants to in vitro infection by <Emphasis Type="Italic">Brachyspira hyodysenteriae</Emphasis>: a leap into disease pathophysiology

Introduction

Swine dysentery caused by Brachyspira hyodysenteriae is a production limiting disease in pig farming. Currently antimicrobial therapy is the only treatment and control method available.

Objective

The aim of this study was to characterize the metabolic response of porcine colon explants to infection by B. hyodysenteriae.

Methods

Porcine colon explants exposed to B. hyodysenteriae were analyzed for histopathological, metabolic and pro-inflammatory gene expression changes.

Results

Significant epithelial necrosis, increased levels of l-citrulline and IL-1α were observed on explants infected with B. hyodysenteriae.

Conclusions

The spirochete induces necrosis in vitro likely through an inflammatory process mediated by IL-1α and NO.

     

Oxylipin profiling of human plasma reflects the renal dysfunction in uremic patients

Introduction

Nearly all the enzymes that mediate the metabolism of polyunsaturated fatty acids (PUFAs) are present in the kidney. However, the correlation of renal dysfunction with PUFAs metabolism in uremic patients remains unknown.

Objectives

To test whether the alterations in the metabolism of PUFAs reflect the renal dysfunction in uremic patients.

Methods

LC–MS/MS-based oxylipin profiling was conducted for the plasma samples from the uremic patients and controls. The data were analyzed by principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). The receiver operating characteristic (ROC) curves and the correlation of the estimated glomerular filtration rate (eGFR) with the key markers were evaluated. Furthermore, qPCR analysis of the whole blood cells was conducted to investigate the possible mechanisms. In addition, a 2nd cohort was used to validate the findings from the 1st cohort.

Results

The plasma oxylipin profile distinguished the uremic patients from the controls successfully by using both PCA and OPLS-DA models. 5,6-Dihydroxyeicosatrienoic acid (5,6-DHET), 5-hydroxyeicosatetraenoic acid (5-HETE), 9(10)-epoxyoctadecamonoenoic acid [9(10)-EpOME] and 12(13)-EpOME were identified as the key markers to discriminate the patients from controls. The excellent predictive performance of these four markers was validated by ROC analysis. The eGFR significantly correlated with plasma levels of 5,6-DHET and 5-HETE positively but with plasma 9(10)-EpOME and 12(13)-EpOME negatively. The changes of these markers may account for the inactivation of cytochrome P450 2C18, 2C19, microsome epoxide hydrolase (EPHX1), and 5-lipoxygenase in the patients.

Conclusion

The alterations in plasma metabolic profile reflect the renal dysfunction in the uremic patients.

     

Assembly of seed-associated microbial communities within and across successive plant generations

Background and aims

Seeds are involved in the transmission of microorganisms from one plant generation to another and consequently may act as the initial inoculum source for the plant microbiota. In this work, we assessed the structure and composition of the seed microbiota of radish (Raphanus sativus) across three successive plant generations.

Methods

Structure of seed microbial communities were estimated on individual plants through amplification and sequencing of genes that are markers of taxonomic diversity for bacteria (gyrB) and fungi (ITS1). The relative contribution of dispersal and ecological drift in inter-individual fluctuations were estimated with a neutral community model.

Results

Seed microbial communities of radish display a low heritability across plant generations. Fluctuations in microbial community profiles were related to changes in community membership and composition across plant generations, but also to variation between individual plants. Ecological drift was an important driver of the structure of seed bacterial communities, while dispersal was involved in the assembly of the fungal fraction of the seed microbiota.

Conclusions

These results provide a first glimpse of the governing processes driving the assembly of the seed microbiota.

     

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.