Untargeted metabolomics reveals a mild impact of remote ischemic conditioning on the plasma metabolome and α-hydroxybutyrate as a possible cardioprotective factor and biomarker of tissue ischemia

Introduction

Remote ischemic conditioning (RIC) is a maneuver by which short non-lethal ischemic events are applied on distant organs or limbs to reduce ischemia and reperfusion injuries caused by e.g. myocardial infarct. Although intensively investigated, the specific mechanism of this protective phenomenon remains incompletely understood and in particular, knowledge on the role of small metabolites is scarce.

Objectives

In this study, we aimed to study perturbations in the plasma metabolome following RIC and gain insight into metabolic changes by the intervention as well as to identify potential novel cardio-protective metabolites.

Methods

Blood plasma samples from ten healthy males were collected prior to and after RIC and tested for bioactivity in a HL-1 based cellular model of ischemia–reperfusion damage. Following this, the plasma was analyzed using untargeted LC-qTOF-MS and regulated metabolites were identified using univariate and multivariate statistical analysis. Results were finally verified in a second plasma study from the same group of volunteers and by testing a metabolite ester in the HL-1 cell model.

Results

The analysis revealed a moderate impact on the plasma metabolome following RIC. One metabolite, α-hydroxybutyrate (AHB) however, stood out as highly significantly upregulated after RIC. AHB might be a novel and more sensitive plasma-biomarker of transient tissue ischemia than lactate. Importantly, it was also found that a cell permeable AHB precursor protects cardiomyocytes from ischemia–reperfusion damage.

Conclusion

Untargeted metabolomics analysis of plasma following RIC has led to insight into metabolism during RIC and revealed a possible novel metabolite of relevance to ischemic-reperfusion damage.

     

High-mass-resolution MALDI mass spectrometry imaging reveals detailed spatial distribution of metabolites and lipids in roots of barley seedlings in response to salinity stress

Introduction

Mass spectrometry imaging (MSI) is a technology that enables the visualization of the spatial distribution of hundreds to thousands of metabolites in the same tissue section simultaneously. Roots are below-ground plant organs that anchor plants to the soil, take up water and nutrients, and sense and respond to external stresses. Physiological responses to salinity are multifaceted and have predominantly been studied using whole plant tissues that cannot resolve plant salinity responses spatially.

Objectives

This study aimed to use a comprehensive approach to study the spatial distribution and profiles of metabolites, and to quantify the changes in the elemental content in young developing barley seminal roots before and after salinity stress.

Methods

Here, we used a combination of liquid chromatography–mass spectrometry (LC–MS), inductively coupled plasma mass spectrometry (ICP–MS), and matrix-assisted laser desorption/ionization (MALDI–MSI) platforms to profile and analyze the spatial distribution of ions, metabolites and lipids across three anatomically different barley root zones before and after a short-term salinity stress (150 mM NaCl).

Results

We localized, visualized and discriminated compounds in fine detail along longitudinal root sections and compared ion, metabolite, and lipid composition before and after salt stress. Large changes in the phosphatidylcholine (PC) profiles were observed as a response to salt stress with PC 34:n showing an overall reduction in salt treated roots. ICP–MS analysis quantified changes in the elemental content of roots with increases of Na⁺ and decreases of K⁺ content.

Conclusion

Our results established the suitability of combining three mass spectrometry platforms to analyze and map ionic and metabolic responses to salinity stress in plant roots and to elucidate tolerance mechanisms in response to abiotic stress, such as salinity stress.

     

Ethnicity influences gut metabolites and microbiota of the tribes of Assam,India

Introduction

The human gut microbes and their metabolites are involved in multiple host metabolic pathways. Dysbiosis in the gut microbiota and altered metabolite profiles were reported in diseased state. In a region like Assam, where 12.4% of the populations are tribal population, evaluating the influence of ethnicity on gut microbiota and metabolites has become important to further differentiate it from the diseased state.

Objective

To study the influence of ethnicity on fecal metabolite profile and their association with the gut microbiota composition.

Methods

In this study, we determined the untargeted fecal metabolites from five ethnic groups of Assam (Tai-Aiton, Bodo, Karbi, Tea-tribe and Tai-Phake) using GC–MS and compared them among the tribes for common and unique metabolites. Metabolites of microbial origin were related with the available metagenomic data on gut bacterial profiles of the same ethnic groups and functional analysis were carried out based on HMDB.

Results

The core fecal metabolite profile of the Tea-tribe contained aniline, benzoate and acetaldehyde. PLS-DA based on the metabolites suggested that the individuals grouped based on their ethnicity. PCA plot of the data on bacterial abundance at the level of genus indicated clustering of individuals based on ethnicity. Positive correlations were observed between propionic acid and the genus Clostridium (R?=?0.43 and p?=?0.03), butyric acid and the genus Lactobacillus (R?=?0.45 and p?=?0.024), acetic acid and the genus Bacteroides (R?=?0.63 and p?=?0.001) and methane and the genus Escherichia (R?=?0.58 and p?=?0.002).

Conclusion

Results of this study indicated that ethnicity influences both gut bacterial profile and their metabolites.

     

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.

     

Comparative metabolomics of the interaction between rice and the brown planthopper

Introduction

The interactions between plants and insect herbivores are complex and multifaceted. Rice and its specialist insect pest the brown planthopper (BPH), Nilaparvata lugens Stål (Hemiptera: Delphacidae) constitute an ideal system for studying plant–insect interactions.

Objectives

Combined metabolomics analyses of rice plant and BPH were conducted to understand the mechanism of host rice plant defense and BPH insect response.

Methods

Metabolite dynamics in rice leaf sheath and BPH honeydew was investigated using the gas chromatography–mass spectrometry (GC–MS) method. The GC–MS data were analyzed by principal component analysis and partial least squares-discriminant analysis.

Results

Twenty-six metabolites were detected in the leaf sheath extracts. Rice leaf sheath metabolomics analysis results show that BPH feeding induces distinct changes in the metabolite profiles of YHY15 and TN1 plants. These results suggest that BPH infestation enhance fatty acid oxidation, the glyoxylate cycle, gluconeogenesis and the GABA shunt in TN1 plants, and glycolysis and the shikimate pathway in YHY15. We propose that the BPH15 gene mediates a resistance reaction that increases the synthesis of secondary metabolites through the shikimate pathway. Thirty-three metabolites were identified in BPH honeydew. Honeydew metabolomics analysis results show that when BPH insects were fed on resistant YHY15 plants, most of the amino acids in honeydew were significantly decreased compared to those of BPH fed on TN1 plants. Based on metabolomics results, we propose that BPH feeding on resistant YHY15 plants would enhance amino acid absorption. At the same time, urea was significantly increased in BPH fed on YHY15.

Conclusion

Metabolomics study is valuable in understanding the complex and multifaceted interaction between plants and insect herbivores and provide essential clue for development of novel control BPH strategies.

     

A review of metabolism-associated biomarkers in lung cancer diagnosis and treatment

Introduction

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection has proven essential to extend survival. Genomic and proteomic advances have provided impetus to the effort dedicated to detect and diagnose the disease at an earlier stage. Recently, the study of metabolites associated with tumor formation and progression has inaugurated the era of cancer metabolomics to aid in this effort.

Objectives

This review summarizes recent work regarding novel metabolites with the potential to serve as biomarkers for early lung tumor detection, evaluation of disease progression, and prediction of patient outcomes.

Method

We compare the metabolite profiling of cancer patients with that of healthy individuals, and the metabolites identified in tissue and biofluid samples and their usefulness as lung cancer biomarkers. We discuss metabolite alterations in tumor versus paired non-tumor lung tissues, as well as metabolite alterations in different stages of lung cancers and their usefulness as indicators of disease progression and overall survival. We evaluate metabolite dysregulation in different types of lung cancers, and those associated with lung cancer versus other lung diseases. We also examine metabolite differences between lung cancer patients and smokers/risk-factor individuals.

Result

Although an extensive list of metabolites has been evaluated to distinguish between these cases, refinement of methods is further required for adequate patient diagnosis and treatment.

Conclusion

We conclude that with technological advancement, metabolomics may be able to replace more invasive and costly diagnostic procedures while also providing the means to more effectively tailor treatment to patient-specific tumors.

     

Targeted and global pharmacometabolomics in everolimus-based immunosuppression: association of co-medication and lysophosphatidylcholines with dose requirement

Introduction

The immunosuppressive therapy with everolimus (ERL) after heart transplantation is characterized by a narrow therapeutic window and a substantial variability in dose requirement. Factors explaining this variability are largely unknown.

Objectives

Our aim was to evaluate factors affecting ERL metabolism and to identify novel metabolites associated with the individual ERL dose requirement to elucidate mechanisms underlying ERL dose response variability.

Method

We used liquid chromatography coupled with mass spectrometry for quantification of ERL metabolites in 41 heart transplant patients and evaluated the effect of clinical and genetic factors on ERL pharmacokinetics. Non-targeted plasma metabolic profiling by ultra-performance liquid chromatography and high resolution quadrupole-time-of-flight mass spectrometry was used to identify novel metabolites associated with ERL dose requirement.

Results

The determination of ERL metabolites revealed differences in metabolite patterns that were independent from clinical or genetic factors. Whereas higher ERL dose requirement was associated with co-administration of sodium-mycophenolic acid and the CYP3A5 expressor genotype, lower dose was required for patients receiving vitamin K antagonists. Global metabolic profiling revealed several novel metabolites associated with ERL dose requirement. One of them was identified as lysophosphatidylcholine (lysoPC) (16:0/0:0). Subsequent targeted analysis revealed that high levels of several lysoPCs were significantly associated with higher ERL dose requirement.

Conclusion

For the first time, this study describes distinct ERL metabolite patterns in heart transplant patients and detected potentially new drug–drug interactions. The global metabolic profiling facilitated the discovery of novel metabolites associated with ERL dose requirement that might represent new clinically valuable biomarkers to guide ERL therapy.

     

A metabolomics guided exploration of marine natural product chemical space

Introduction

Natural products from culture collections have enormous impact in advancing discovery programs for metabolites of biotechnological importance. These discovery efforts rely on the metabolomic characterization of strain collections.

Objective

Many emerging approaches compare metabolomic profiles of such collections, but few enable the analysis and prioritization of thousands of samples from diverse organisms while delivering chemistry specific read outs.

Method

In this work we utilize untargeted LC–MS/MS based metabolomics together with molecular networking to inventory the chemistries associated with 1000 marine microorganisms.

Result

This approach annotated 76 molecular families (a spectral match rate of 28 %), including clinically and biotechnologically important molecules such as valinomycin, actinomycin D, and desferrioxamine E. Targeting a molecular family produced primarily by one microorganism led to the isolation and structure elucidation of two new molecules designated maridric acids A and B.

Conclusion

Molecular networking guided exploration of large culture collections allows for rapid dereplication of know molecules and can highlight producers of uniques metabolites. These methods, together with large culture collections and growing databases, allow for data driven strain prioritization with a focus on novel chemistries.

     

Comprehensive metabolite profiling of onion bulbs (<Emphasis Type="Italic">Allium cepa</Emphasis>) using liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry

Introduction

Onion (Allium cepa) represents one of the most important horticultural crops and is used as food, spice and medicinal plant almost worldwide. Onion bulbs accumulate a broad range of primary and secondary metabolites which impact nutritional, sensory and technological properties.

Objectives

To complement existing analytical methods targeting individual compound classes this work aimed at the development and validation of an analytical workflow for comprehensive metabolite profiling of onion bulbs.

Method

Metabolite profiling was performed by liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (LC/ESI-QTOFMS). For annotation of metabolites accurate mass tandem mass spectrometry experiments were carried out.

Results

On the basis of LC/ESI-QTOFMS and two chromatographic methods an analytical workflow was developed which facilitates profiling of polar and semi-polar onion metabolites including fructooligosaccharides, proteinogenic amino acids, peptides, S-substituted cysteine conjugates, flavonoids and saponins. To minimize enzymatic conversion of S-alk(en)ylcysteine sulfoxides, a sample preparation and extraction protocol for fresh onions was developed comprising cryohomogenization and a low-temperature quenching step. A total of 123 metabolites were annotated and characterized by chromatographic and tandem mass spectral data. For validation, recovery rates and matrix effects were determined for 15 model compounds. Repeatability and linearity were assessed for more than 80 endogenous metabolites.

Conclusion

As exemplarily demonstrated by comparative metabolic analysis of six onion cultivars the established analytical workflow in combination with targeted and non-targeted data analysis strategies can be successfully applied for comprehensive metabolite profiling of onion bulbs.

     

Postprandial metabolite profiles associated with type 2 diabetes clearly stratify individuals with impaired fasting glucose

Introduction

Fasting metabolite profiles have been shown to distinguish type 2 diabetes (T2D) patients from normal glucose tolerance (NGT) individuals.

Objectives

We investigated whether, besides fasting metabolite profiles, postprandial metabolite profiles associated with T2D can stratify individuals with impaired fasting glucose (IFG) by their similarities to T2D.

Methods

Three groups of individuals (age 45–65 years) without any history of IFG or T2D were selected from the Netherlands Epidemiology of Obesity study and stratified by baseline fasting glucose concentrations (NGT (n?=?176), IFG (n?=?186), T2D (n?=?171)). 163 metabolites were measured under fasting and postprandial states (150 min after a meal challenge). Metabolite profiles specific for a high risk of T2D were identified by LASSO regression for fasting and postprandial states. The selected profiles were utilised to stratify IFG group into high (T2D probability?≥?0.7) and low (T2D probability?≤?0.5) risk subgroups. The stratification performances were compared with clinically relevant metabolic traits.

Results

Two metabolite profiles specific for T2D (n_fasting = 12 metabolites, n_postprandial = 4 metabolites) were identified, with all four postprandial metabolites also being identified in the fasting state. Stratified by the postprandial profile, the high-risk subgroup of IFG individuals (n?=?72) showed similar glucose concentrations to the low-risk subgroup (n?=?57), yet a higher BMI (difference: 3.3 kg/m² (95% CI 1.7–5.0)) and postprandial insulin concentrations (21.5 mU/L (95% CI 1.8–41.2)).

Conclusion

Postprandial metabolites identified T2D patients as good as fasting metabolites and exhibited enhanced signals for IFG stratification, which offers a proof of concept that metabolomics research should not focus on the fasting state alone.

     

A survey of plants and plant products traditionally used in livestock health management in Buuri district,Meru County,Kenya

Background

Up till now, nomadic communities in Africa have been the primary focus of ethnoveterinary research. Although mainly arable and/or mixed arable/pastoral farmers, Ameru of central Kenya are known to have a rich history of ethnoveterinary knowledge. Their collective and accumulative ethnoveterinary knowledge (EVK) is likely to be just as rich and worth documenting. The aim of the study was to document and analyse the ethnoveterinary knowledge of the Ameru.

Methods

Non-alienating, dialogic, participatory action research (PAR) and participatory rural appraisal (PRA) approaches involving 21 women and men aged between 50 and 79 years old were utilized. A combination of snowball and purposive sampling methods were used to select 21 key respondents. The methods comprised a set of triangulation approach needed in EVK for non-experimental validation of ethnoknowledge of the Ameru.

Results

A total of 48 plant species distributed in 26 families were documented with details of diseases/ill-health conditions, parts of plants used and form of preparation and administration methods applied to different animal groups. Of these families, Fabaceae had the highest number of species (16.67%), followed by Solanaceae (12.5%), Asteraceae and Euphorbiacea (each comprising 8.33%), Lamiaceae (6.25%), Apocynaceae and Boraginaceae (each comprising 4.17%), while the rest of the 19 families, each was represented by a single plant species. About 30 livestock diseases/ill-health conditions were described, each treated by at least one of the 48 plant species. Most prevalent diseases/ill-health conditions included: - anaplasmosis, diarrhea, East Coast fever, pneumonia, helminthiasis, general weakness and skin diseases involving wounds caused by ectoparasites.

Conclusion

The study showed that there was a rich knowledge and ethnopractices for traditional animal healthcare amongst the Ameru. This study therefore provides some groundwork for elucidating the efficacy of some of these plants, plant products and ethnopractices in managing livestock health as further research may lead to discovery of useful ethnopharmaceutical agents applicable in livestock industry.

     

Phytochemical and pharmacological investigation of <Emphasis Type="Italic">Spiraea chamaedryfolia</Emphasis>: a contribution to the chemotaxonomy of <Emphasis Type="Italic">Spiraea</Emphasis> genus

Objective

Diterpene alkaloids are secondary plant metabolites and chemotaxonomical markers with a strong biological activity. These compounds are characteristic for the Ranunculaceae family, while their occurrence in other taxa is rare. Several species of the Spiraea genus (Rosaceae) are examples of this rarity. Screening Spiraea species for alkaloid content is a chemotaxonomical approach to clarify the classification and phylogeny of the genus. Novel pharmacological findings make further investigations of Spiraea diterpene alkaloids promising.

Results

Seven Spiraea species were screened for diterpene alkaloids. Phytochemical and pharmacological investigations were performed on Spiraea chamaedryfolia, the species found to contain diterpene alkaloids. Its alkaloid-rich fractions were found to exert a remarkable xanthine-oxidase inhibitory activity and a moderate antibacterial activity. The alkaloid distribution within the root was clarified by microscopic techniques.

     

Untargeted metabolic profiling of <Emphasis Type="Italic">Eucalyptus</Emphasis> spp. leaf oils using comprehensive two-dimensional gas chromatography with high resolution mass spectrometry: Expanding the metabolic coverage

Introduction

Chromatography with mass spectrometry (MS) is a technique of choice for metabolomic analysis of plant extracts. Single dimension gas chromatography (1DGC) with MS leads to poorly resolved metabolites of complex Eucalyptus spp. leaf oil secondary metabolites and consequently limited metabolic coverage of secondary compounds. Multidimensional chromatography with high resolution MS can contribute to advances in this field.

Objectives

Deeper insight into metabolite composition and variation for Eucalyptus spp. leaf oils through systematic untargeted metabolic profiling using comprehensive two-dimensional GC (GC?×?GC) with high resolution time-of-flight MS (accTOFMS), using generalised processes for metabolite identification.

Methods

GC?×?GC separation used cryogenic modulation, with standard length polar first dimension and short fast analysis non-polar ²D columns. Compound tentative identification incorporated ¹D and ²D retention information, retention indices, mass spectrum matching, and accurate mass MS data. Global metabolic profiles were interpreted through 2D contour plots and chemometric analysis.

Results

Strategies for metabolite screening and identification using GC?×?GC-accTOFMS were proposed. Considerably more components are detected and recognised than for 1DGC. Structured 2D molecular composition chromatographic patterns aid identification. ca. 400 metabolites were detected, 183 compounds were identified or tentatively identified, representing between 50.8–90.0% of the total ion count, comprising various chemical families. PCA revealed discriminating metabolites, allowing chemotaxonomic classification of species.

Conclusion

Expansion of metabolic coverage by using GC?×?GC-accTOFMS, and detailed 2D metabolic fingerprints of E. polybractea, E. citriodora, E. radiata and E. globulus leaf oils were established. This high resolution analytical platform, and identification strategy can be adapted to metabolic analysis of other plant extracts.

Graphical abstract

Phytoconstituents of four Australian eucalypt leaf oils were profiled using high resolution GC?×?GC-accurate mass TOFMS. Two-dimensional plots illustrated significant expansion of metabolic coverage. PCA discriminated metabolites of the eucalypts.

Open image in new window

     

A metabolomic,geographic, and seasonal analysis of the contribution of pollen-derived adenosine to allergic sensitization

Background

Studies on ragweed and birch pollen extracts suggested that the adenosine content is an important factor in allergic sensitization. However, exposure levels from other pollens and considerations of geographic and seasonal factors have not been evaluated.

Objective

This study compared the metabolite profile of pollen species important for allergic disease, specifically measured the adenosine content, and evaluated exposure to pollen-derived adenosine.

Methods

An NMR metabolomics approach was used to measure metabolite concentrations in 26 pollen extracts. Pollen count data was analyzed from five cities to model exposure.

Results

A principal component analysis of the various metabolites identified by NMR showed that pollen extracts could be differentiated primarily by sugar content: glucose, fructose, sucrose, and myo-inositol. In extracts of 10 mg of pollen/ml, the adenosine was highest for grasses (45 µM) followed by trees (23 µM) and weeds (19 µM). Pollen count data showed that tree pollen was typically 5–10 times the amount of other pollens. At the daily peaks of tree, grass, and weed season the pollen-derived adenosine exposure per day is likely to be only 1.1, 0.11, and 0.12 µg, respectively. Seasonal models of pollen exposure and respiration suggest that it would be a rare event limited to tree pollen season for concentrations of pollen-derived adenosine to approach physiological levels.

Conclusion

Sugar content and other metabolites may be useful in classifying pollens. Unless other factors create localized exposures that are very different from these models, pollen-derived adenosine is unlikely to be a major factor in allergic sensitization.

     

Metabolic fingerprinting analysis of oil palm reveals a set of differentially expressed metabolites in fatal yellowing symptomatic and non-symptomatic plants

Introduction

Oil palm (E. guineensis), the most consumed vegetable oil in the world, is affected by fatal yellowing (FY), a condition that can lead to the plant’s death. Although studies have been performed since the 1980s, including investigations of biotic and abiotic factors, FY’s cause remains unknown and efforts in researches are still necessary.

Objectives

This work aims to investigate the metabolic expression in plants affected by FY using an untargeted metabolomics approach.

Method

Metabolic fingerprinting analysis of oil palm leaves was performed using ultra high liquid chromatography–electrospray ionization–mass spectrometry (UHPLC–ESI–MS). Chemometric analysis, using principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA), was applied to data analysis. Metabolites identification was performed by high resolution mass spectrometry (HRMS), MS/MS experiments and comparison with databases and literature.

Results

Metabolomics analysis based on MS detected more than 50 metabolites in oil palm leaf samples. PCA and PLS-DS analysis provided group segregation and classification of symptomatic and non-symptomatic FY samples, with a great external validation of the results. Nine differentially expressed metabolites were identified as glycerophosphorylcholine, arginine, asparagine, apigenin 6,8-di-C-hexose, tyramine, chlorophyllide, 1,2-dihexanoyl-sn-glycero-3-phosphoethanolamine, proline and malvidin 3-glucoside-5-(6″-malonylglucoside). Metabolic pathways and biological importance of those metabolites were assigned.

Conclusion

Nine metabolites were detected in a higher concentration in non-symptomatic FY plants. Seven are related to stress factors i.e. plant defense and nutrient absorption, which can be affected by the metabolic depression of these compounds. Two of those metabolites (glycerophosphorylcholine and 1,2-dihexanoyl-sn-glycero-3-phosphoethanolamine) are presented as potential biomarkers, since they have no known direct relation to plant stress.

     

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.

     

Metabolomic analysis of tomato seed germination

Introduction

Seed germination is inherently related to seed metabolism, which changes throughout its maturation, desiccation and germination processes. The metabolite content of a seed and its ability to germinate are determined by underlying genetic architecture and environmental effects during development.

Objective

This study aimed to assess an integrative approach to explore genetics modulating seed metabolism in different developmental stages and the link between seed metabolic- and germination traits.

Methods

We have utilized gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) metabolite profiling to characterize tomato seeds during dry and imbibed stages. We describe, for the first time in tomato, the use of a so-called generalized genetical genomics (GGG) model to study the interaction between genetics, environment and seed metabolism using 100 tomato recombinant inbred lines (RILs) derived from a cross between Solanum lycopersicum and Solanum pimpinellifolium.

Results

QTLs were found for over two-thirds of the metabolites within several QTL hotspots. The transition from dry to 6 h imbibed seeds was associated with programmed metabolic switches. Significant correlations varied among individual metabolites and the obtained clusters were significantly enriched for metabolites involved in specific biochemical pathways.

Conclusions

Extensive genetic variation in metabolite abundance was uncovered. Numerous identified genetic regions that coordinate groups of metabolites were detected and these will contain plausible candidate genes. The combined analysis of germination phenotypes and metabolite profiles provides a strong indication for the hypothesis that metabolic composition is related to germination phenotypes and thus to seed performance.

     

High-throughput extraction and quantification method for targeted metabolomics in murine tissues

Introduction

Global metabolomics analyses using body fluids provide valuable results for the understanding and prediction of diseases. However, the mechanism of a disease is often tissue-based and it is advantageous to analyze metabolomic changes directly in the tissue. Metabolomics from tissue samples faces many challenges like tissue collection, homogenization, and metabolite extraction.

Objectives

We aimed to establish a metabolite extraction protocol optimized for tissue metabolite quantification by the targeted metabolomics AbsoluteIDQ? p180 Kit (Biocrates). The extraction method should be non-selective, applicable to different kinds and amounts of tissues, monophasic, reproducible, and amenable to high throughput.

Methods

We quantified metabolites in samples of eleven murine tissues after extraction with three solvents (methanol, phosphate buffer, ethanol/phosphate buffer mixture) in two tissue to solvent ratios and analyzed the extraction yield, ionization efficiency, and reproducibility.

Results

We found methanol and ethanol/phosphate buffer to be superior to phosphate buffer in regard to extraction yield, reproducibility, and ionization efficiency for all metabolites measured. Phosphate buffer, however, outperformed both organic solvents for amino acids and biogenic amines but yielded unsatisfactory results for lipids. The observed matrix effects of tissue extracts were smaller or in a similar range compared to those of human plasma.

Conclusion

We provide for each murine tissue type an optimized high-throughput metabolite extraction protocol, which yields the best results for extraction, reproducibility, and quantification of metabolites in the p180 kit. Although the performance of the extraction protocol was monitored by the p180 kit, the protocol can be applicable to other targeted metabolomics assays.

     

Application of BATMAN and BAYESIL for quantitative <Superscript>1</Superscript>H-NMR based metabolomics of urine: discriminant analysis of lean,obese, and obese-diabetic rats

Introduction

BATMAN and BAYESIL are software tools, which can provide a solution for automated metabolite quantifications based on the proton nuclear magnetic resonance (¹H-NMR) spectral data of bio-fluids. However, their specific application for the quantitative ¹H-NMR based metabolomics of urine has not been investigated.

Objectives

The aim of this study is to evaluate the performance of BATMAN and BAYESIL in the quantitative metabolite analysis of urine based on its ¹H-NMR spectra.

Methods

BATMAN and BAYESIL were used for automated metabolite quantification based on the ¹H-NMR spectra of the urine from the lean, obese and obese-diabetic rat groups. PLS-DA model was used to discriminate the three different groups based on the results from the quantifications.

Results

BATMAN was found to be superior to BAYESIL in identifying and quantifying the metabolites in the urine samples, owing to its flexibility that allows users to define and adjust the relevant signals of the pure standard metabolites in the database in order to fit the signals in the samples, a necessary step since variations and peak shift are natural in most ¹H-NMR spectra. The results of BATMAN also agreed well with that of the manual deconvolution method, which indicated the higher accuracy in metabolite quantification, despite the need of pre-processing and longer processing time than BAYESIL. However, in the case where the problems in baseline correction and peak shift of ¹H-NMR spectra are absent, the use of BAYESIL is more advantageous. Application of quantitative ¹H-NMR based metabolomics of the urine showed that PLS-DA model derived from BATMAN could satisfactorily discriminate the lean, obese, and obese-diabetic rat groups.

Conclusion

Both BATMAN and BAYESIL are useful for the quantitative automation of urine metabolites based on its ¹H-NMR spectra. The results from BATMAN method is superior to BAYESIL but require expertise in spectroscopy and longer computer time. Both methods help in simplifying the interpretation of metabolite status in the VIP analysis.