Chemical dereplication of marine actinomycetes by liquid chromatography-high resolution mass spectrometry profiling and statistical analysis

Discovery of novel bioactive metabolites from marine bacteria is becoming increasingly challenging, and the development of novel approaches to improve the efficiency of early steps in the microbial drug discovery process is therefore of interest. For example, current protocols for the taxonomic dereplication of microbial strains generally use molecular tools which do not take into consideration the ability of these selected bacteria to produce secondary metabolites. As the identification of novel chemical entities is one of the key elements driving drug discovery programs, this study reports a novel methodology to dereplicate microbial strains by a metabolomics approach using liquid chromatography-high resolution mass spectrometry (LC-HRMS). In order to process large and complex three dimensional LC-HRMS datasets, the reported method uses a bucketing and presence-absence standardization strategy in addition to statistical analysis tools including principal component analysis (PCA) and cluster analysis. From a closely related group of Streptomyces isolated from geographically varied environments, we demonstrated that grouping bacteria according to the chemical diversity of produced metabolites is reproducible and provides greatly improved resolution for the discrimination of microbial strains compared to current molecular dereplication techniques. Importantly, this method provides the ability to identify putative novel chemical entities as natural product discovery leads.

Meclizine inhibits mitochondrial respiration through direct targeting of cytosolic phosphoethanolamine metabolism

We recently identified meclizine, an over-the-counter drug, as an inhibitor of mitochondrial respiration. Curiously, meclizine blunted respiration in intact cells but not in isolated mitochondria, suggesting an unorthodox mechanism. Using a metabolic profiling approach, we now show that treatment with meclizine leads to a sharp elevation of cellular phosphoethanolamine, an intermediate in the ethanolamine branch of the Kennedy pathway of phosphatidylethanolamine biosynthesis. Metabolic labeling and in vitro enzyme assays confirmed direct inhibition of the cytosolic enzyme CTP:phosphoethanolamine cytidylyltransferase (PCYT2). Inhibition of PCYT2 by meclizine led to rapid accumulation of its substrate, phosphoethanolamine, which is itself an inhibitor of mitochondrial respiration. Our work identifies the first pharmacologic inhibitor of the Kennedy pathway, demonstrates that its biosynthetic intermediate is an endogenous inhibitor of respiration, and provides key mechanistic insights that may facilitate repurposing meclizine for disorders of energy metabolism.

Depletion of casein kinase I leads to a NAD(P)(+)/NAD(P)H balance-dependent metabolic adaptation as determined by NMR spectroscopy-metabolomic profile in Kluyveromyces lactis

BACKGROUND

In the Crabtree-negative Kluyveromyces lactis yeast the rag8 mutant is one of nineteen complementation groups constituting the fermentative-deficient model equivalent to the Saccharomyces cerevisiae respiratory petite mutants. These mutants display pleiotropic defects in membrane fatty acids and/or cell walls, osmo-sensitivity and the inability to grow under strictly anaerobic conditions (Rag(-) phenotype). RAG8 is an essential gene coding for the casein kinase I, an evolutionary conserved activity involved in a wide range of cellular processes coordinating morphogenesis and glycolytic flux with glucose/oxygen sensing.

METHODS

A metabolomic approach was performed by NMR spectroscopy to investigate how the broad physiological roles of Rag8, taken as a model for all rag mutants, coordinate cellular responses.

RESULTS

Statistical analysis of metabolomic data showed a significant increase in the level of metabolites in reactions directly involved in the reoxidation of the NAD(P)H in rag8 mutant samples with respect to the wild type ones. We also observed an increased de novo synthesis of nicotinamide adenine dinucleotide. On the contrary, the production of metabolites in pathways leading to the reduction of the cofactors was reduced.

CONCLUSIONS

The changes in metabolite levels in rag8 showed a metabolic adaptation that is determined by the intracellular NAD(P)(+)/NAD(P)H redox balance state.

GENERAL SIGNIFICANCE

The inadequate glycolytic flux of the mutant leads to a reduced/asymmetric distribution of acetyl-CoA to the different cellular compartments with loss of the fatty acid dynamic respiratory/fermentative adaptive balance response.

Metabolic expressivity of human genetic variants: NMR metabotyping of MEN1 pathogenic mutants

Functional consequences of mutations in predisposition genes for familial cancer syndromes remain often elusive, especially when the corresponding gene products play pleiotropic functions and interact with numerous partners. Understanding the consequences of these genetic alterations requires access to their functional effects at the phenotypic level. Nuclear magnetic resonance (NMR) has emerged as a promising functional genomics probe, through its ability to monitor the consequences of genetic variations at the biochemical level. Here, we determine by NMR the metabolic perturbations associated with different disease-related mutations in the MEN1 gene, responsible for the multiple endocrine neoplasia syndrome, type 1 (MEN1), an example of hereditary cancer. The MEN1 gene encodes the Menin protein. Based on a cellular model that allows exogenous overexpression of either the wild type (WT) Menin protein or disease-related variant forms, we evaluate the feasibility of using metabolic profiles to discriminate cells with WT versus variant Menin overexpression. High-resolution magic angle spinning (HRMAS) NMR of whole cells allows to determine the metabolic features associated with overexpression of WT Menin as compared to the one of six different missense variants observed in MEN1 patients. We then identify several statistically significant individual metabolites associated with the metabolic signature of pathogenic versus WT variants. Whether such a metabolic phenotyping approach using cell lines could be exploited as a functional test in a human genetic cancer syndrome is further discussed.

Coefficient of Variation, Signal-to-Noise Ratio, and Effects of Normalization in Validation of Biomarkers from NMR-based Metabonomics Studies

A primary goal of metabonomics research is biomarker discovery for human diseases based on differences in metabolic profiles between healthy and diseased patient populations. One of the most significant challenges in biomarker discovery is validation, which implicitly depends on the coefficient of variation (CV) associated with the measurement technique. This paper investigates how the CV of metabolite resonances measured by nuclear magnetic resonance spectroscopy (NMR) depends on signal-to-noise ratio (SNR) and normalization method. CVs were calculated for NMR resonance peaks in a series of NMR spectra of five synthetic urine samples collected over an eight-month period. An inverse correlation was detected between SNR and CV for all normalization methods. Small peaks with SNR<15 tended to have larger CVs (15-30%) compared to peaks with the highest SNR>150, which typically had smaller CVs (5-10%). The inverse relationship between CV and SNR roughly obeyed a log₁₀ dependence. Quotient normalization (QN) tended to produce smaller CVs for smaller peaks, but larger CVs for the strongest peaks in the data, compared to no normalization, normalization to total intensity (NTI) or normalization to an internal standard (NIS). Consequently, quotient normalization appears optimal for validating low concentration metabolites. NTI or NIS appear superior to QN for samples that have very small variation in total signal intensity. While the inverse relationship between CV and log₁₀(SNR) did not strictly hold for all metabolites, weaker concentration metabolites will likely require more rigorous validation as potential biomarkers since they tend to have poorer reproducibility.

1H-NMR metabolic profiling of cerebrospinal fluid in patients with complex regional pain syndrome-related dystonia

In complex regional pain syndrome (CRPS)-related dystonia, compelling evidence points to the involvement of the central nervous system, but the underpinning pathobiology is still unclear. Thus, to enable a hypothesis-free, unbiased view of the problem and to obtain new insight into the pathobiology of dystonia in CRPS, we applied an exploratory metabolomics analysis of cerebrospinal fluid (CSF) of patients with CRPS-related dystonia. (1)H-NMR spectroscopy in combination with multivariate modeling were used to investigate metabolic profiles of a total of 105 CSF samples collected from patients with CRPS-related dystonia and controls. We found a significantly different metabolic profile of CSF in CRPS patients compared to controls. The differences were already reflected in the first two principal components of the principal component analysis model, which is an indication that the variance associated with CRPS is stronger than variance caused by such classical confounders as gender, age, or individual differences. A supervised analysis generated a strong model pinpointing the most important metabolites contributed to the metabolic signature of patients with CRPS-related dystonia. From the set of identified discriminators, the most relevant metabolites were 2-keto-isovalerate, glucose, glutamine, and lactate, which all showed increased concentrations, and urea, which showed decreased concentration in CRPS subjects. Our findings point at a catabolic state in chronic CRPS patients with dystonia that is likely associated with inflammation.

The metabolomic window into hepatobiliary disease

The emergent discipline of metabolomics has attracted considerable research effort in hepatology. Here we review the metabolomic data for non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), cirrhosis, hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), alcoholic liver disease (ALD), hepatitis B and C, cholecystitis, cholestasis, liver transplantation, and acute hepatotoxicity in animal models. A metabolomic window has permitted a view into the changing biochemistry occurring in the transitional phases between a healthy liver and hepatocellular carcinoma or cholangiocarcinoma. Whether provoked by obesity and diabetes, alcohol use or oncogenic viruses, the liver develops a core metabolomic phenotype (CMP) that involves dysregulation of bile acid and phospholipid homeostasis. The CMP commences at the transition between the healthy liver (Phase 0) and NAFLD/NASH, ALD or viral hepatitis (Phase 1). This CMP is maintained in the presence or absence of cirrhosis (Phase 2) and whether or not either HCC or CCA (Phase 3) develops. Inflammatory signalling in the liver triggers the appearance of the CMP. Many other metabolomic markers distinguish between Phases 0, 1, 2 and 3. A metabolic remodelling in HCC has been described but metabolomic data from all four Phases demonstrate that the Warburg shift from mitochondrial respiration to cytosolic glycolysis foreshadows HCC and may occur as early as Phase 1. The metabolic remodelling also involves an upregulation of fatty acid β-oxidation, also beginning in Phase 1. The storage of triglycerides in fatty liver provides high energy-yielding substrates for Phases 2 and 3 of liver pathology. The metabolomic window into hepatobiliary disease sheds new light on the systems pathology of the liver.

Development of a ¹³C-optimized 1.5-mm high temperature superconducting NMR probe

We report a 1.5-mm NMR probe based on high temperature superconductors operating at 14.1T optimized for (13)C detection. The probe has a total sample volume of about 35 microliters (μL) with an active volume of 20 μL and provides exceptional mass sensitivity for (13)C detection. The probe also has excellent (1)H sensitivity and employs a (2)H lock; (15)N irradiation capability can be added in the future. The coils are cooled to about 20K using a standard Agilent cryogenic refrigeration system, and the sample temperature is regulated near room temperature. The coil design considerations are discussed in detail. This probe is ideal for directly detected (13)C NMR experiments for natural products chemistry and metabolomics applications, for which 35 μL is an optimal sample volume. The outstanding (13)C sensitivity of this probe allowed us to directly determine the (13)C connectivity on 1.1mg of natural abundance histidine using an INADEQUATE experiment. We demonstrated the utility of this probe for (13)C-based metabolomics using a synthetic mixture of common natural abundance metabolites whose concentrations ranged from 1 to 5mM (40-200 nmol).

Metabolomic analysis of isonitrosoacetophenone-induced perturbations in phenolic metabolism of Nicotiana tabacum cells

Plants have developed biochemical and molecular responses to adapt to different stress environments. One of the characteristics of the multi-component defence response is the production of defence-related metabolites. Plant defences can be triggered by various stimuli, including synthetic or naturally occurring molecules, especially those derived from pathogens. In the current study, Nicotiana tabacum cell suspensions were treated with isonitrosoacetophenone (INAP), a subcomponent of a plant-derived stress metabolite with anti-fungal and anti-oxidant properties, in order to investigate the effect thereof on cellular metabolism. Subsequent metabolomic-based analyses were employed to evaluate changes in the metabolome. UPLC-MS in conjunction with multivariate data analyses was found to be an appropriate approach to study the effect of chemical inducers like INAP on plant metabolism in this model system. Principal component analysis (PCA) indicated that INAP is capable of inducing time-dependent metabolic perturbations in the cultured cells. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) revealed metabolites of which the levels are affected by INAP, and eight of these were tentatively annotated from the mass spectral data and online databases. These metabolites are known in the context of plant stress- and defence responses and include benzoic- or cinnamic acid derivatives that are either glycosylated or quinilated as well as flavonoid derivatives. The results indicate that INAP affects the shikimate-, phenylpropanoid- and flavonoid pathways, the products of which may subsequently lead to an anti-oxidant environment in vivo.

Exploring the effect of dentition, dental decay and familiality on oral health using metabolomics

As a proof of principle, we used an untargeted global metabolic profiling of saliva to understand the biochemical processes associated with dental decay, dentition (primary and secondary tooth eruption) and familiality in a sample of 25 sibling pairs. Pairs were selected to represent four different combinations of dentition and tooth health: (1) both siblings with primary teeth and no decay (n=5); (2) both siblings with primary teeth and discordant for dental decay (n=6); (3) both siblings with primary teeth and dental decay (n=4); and (4) one sibling with primary teeth the other with mixed dentition and both with no dental decay (n=10). There was a strong effect of sibship on the metabolite profiles identified; this may reflect the effects of common genes, environment and behaviors, and shared oral microbial communities. Nested in the familial effects were associations of metabolite profile with dentition and with dental decay. Using three different analyses (Euclidean distance, hierarchical clustering and PCA using selected biochemicals) metabolite profiles of saliva from children with decayed teeth were more similar than the metabolite profiles of saliva from children with healthy (sound) teeth. Larger studies that include host behaviors, environmental factors, oral microbiota composition and structure, and host genetic predisposition are required to identify biomarkers for decay, and to estimate the relative contribution of host factors and oral microbes on risk of dental decay.

Metabolomics evaluation of the effects of green tea extract on acetaminophen-induced hepatotoxicity in mice

Green tea has been purported to have beneficial health effects including protective effects against oxidative stress. Acetaminophen (APAP) is a widely used analgesic drug that can cause acute liver injury in overdose situations. These studies explored the effects of green tea extract (GTE) on APAP-induced hepatotoxicity in liver tissue extracts using ultra performance liquid chromatography/quadrupole time-of-flight mass spectrometry and nuclear magnetic resonance spectroscopy. Mice were orally administered GTE, APAP or GTE and APAP under three scenarios. APAP alone caused a high degree of hepatocyte necrosis associated with increases in serum transaminases and alterations in multiple metabolic pathways. The time of GTE oral administration relative to APAP either protected against or potentiated the APAP-induced hepatotoxicity. Dose dependent decreases in histopathology scores and serum transaminases were noted when GTE was administered prior to APAP; whereas, the opposite occurred when GTE was administered after APAP. Similarly, metabolites altered by APAP alone were less changed when GTE was given prior to APAP. Significantly altered pathways included fatty acid metabolism, glycerophospholipid metabolism, glutathione metabolism, and energy pathways. These studies demonstrate the complex interaction between GTE and APAP and the need to employ novel analytical strategies to understand the effects of dietary supplements on pharmaceutical compounds.

Exploring the potentialities of comprehensive two-dimensional gas chromatography coupled to time of flight mass spectrometry to distinguish bivalve species: Comparison of two clam species (Venerupis decussata and Venerupis philippinarum)

Metabolomics represents an emerging topic that can be valuable in the knowledge of organism responses to different stimuli. Metabolomic studies of bivalves may reveal the constraints they are subjected to, and may help clarifying the functions most affected and the tolerance mechanisms triggered. In response to this approach, two-dimensional gas chromatography-time of flight mass spectrometry (GC×GC-ToFMS) combined with headspace solid phase microextraction (HS-SPME) was applied, for the first time to our knowledge, to the untargeted and comprehensive study of the volatile composition of clam species. Firstly, experimental parameters that influence the SPME extraction efficiency were evaluated: sample preparation mode, sample volume and SPME fiber coating. Taking into account the results from the optimization step, the metabolomic profiles were performed using 1 ml of clam soft tissues homogenized with N2 and diluted in deionised water (1:2, w/v), using the PDMS/DVB fiber coating. From a total of more than 200 compounds detected per sample, 63 were tentatively identified and distributed over the chemical families of hydrocarbons, ketones, aldehydes, alcohols, and terpenoids, which seems to arise from clams own metabolism, and/or from their activity as filter-feeders. The potential of this methodology to discriminate close related species was explored by comparing the volatile profiles of Venerupis philippinarum and Venerupis decussata from Ria de Aveiro, two clams belonging to the same genus. Both species experience different environment conditions, and physiological and biochemical characteristics, which may explain the observed differentiation between their metabolic profiles. To our knowledge, this is the most detailed information available so far about clam volatile composition, which represents a valuable data for future advanced studies in the ecology, toxicology and physiology of bivalves based on clams fingerprinting.

Proteomic and metabolomic analysis reveal gender-specific responses of mussel Mytilus galloprovincialis to 2,2',4,4'-tetrabromodiphenyl ether (BDE 47)

Polybrominated diphenyl ethers (PBDEs) are a class of brominated flame-retardants (BFRs) that are widely used in industrial products and have posed potential risk on the coastal environment of the Laizhou Bay in China. They are of great concern due to their toxicities, such as hepatotoxicity, carcinogenecity, neurotoxicity, immunotoxicity and endocrine disrupting effects in animals. In this work, we focused on the gender-specific responses of BDE 47 in mussel Mytilus galloprovincialis using a combined proteomic and metabolomic approach. Metabolic responses indicated that BDE 47 mainly caused disturbance in energy metabolism in male mussel gills. For female mussel samples, disruption in both osmotic regulation and energy metabolism was found in terms of differential metabolic profiles. Proteomic responses revealed that BDE 47 induced cell apoptosis and reduced reactive oxygen species (ROS) production in both male and female mussels, disturbance in protein homeostasis in male mussels as well as disturbance in female mussel proteolysis based on the differential proteomic biomarkers. Overall, these results confirmed the gender-specific responses in mussels to BDE 47 exposures. This work demonstrated that an integrated metabolomic and proteomic approach could provide an important insight into the toxicological effects of environmental pollutant to organisms.

Metabolomics in cancer biomarker discovery: current trends and future perspectives

Cancer is one of the most devastating human diseases that causes a vast number of mortalities worldwide each year. Cancer research is one of the largest fields in the life sciences and despite many astounding breakthroughs and contributions over the past few decades, there is still a considerable amount to unveil on the function of cancer. It is well known that cancer metabolism differs from that of normal tissue and an important hypothesis published in the 1950s by Otto Warburg proposed that cancer cells rely on anaerobic metabolism as the source for energy, even under physiological oxygen levels. Following this, cancer central carbon metabolism has been researched extensively and beyond respiration, cancer has been found to involve a wide range of metabolic processes, and many more are still to be unveiled. Studying cancer through metabolomics could reveal new biomarkers for cancer that could be useful for its future prognosis, diagnosis and therapy. Metabolomics is becoming an increasingly popular tool in the life sciences since it is a relatively fast and accurate technique that can be applied with either a particular focus or in a global manner to reveal new knowledge about biological systems. There have been many examples of its application to reveal potential biomarkers in different cancers that have employed a range of different analytical platforms. In this review, approaches in metabolomics that have been employed in cancer biomarker discovery are discussed and some of the most noteworthy research in the field is highlighted.

Distinct metabolic differences between various human cancer and primary cells

Recent years have seen resurging interest in cancer cell metabolism and the role of secreted cancer metabolites in modulating the tumor stroma. Using a combination of nontargeted and targeted LC and GC-MS methods, the exometabolomes of three leukemia, two melanoma, three renal cell carcinoma, two colorectal adenocarcinoma, four hepatocellular carcinoma, three breast cancer, two bladder carcinoma, and one glioblastoma cell line, as well as five primary cultures of human melanocytes, hepatocytes, monocytes, CD4 and CD8 lymphocytes, that had been all cultivated under identical conditions, were investigated. Unsupervised affinity propagation clustering of the metabolic footprints yielded five distinct clusters that grouped the investigated cell cultures mainly according to the tissue of origin. A common expected feature of all neoplastic cells was high lactate production. Extracellular arginine and nicotinamide were major discriminants between normal and neoplastic hepatocytes. Further, significant differences in the assimilation of di- and tripeptides were observed. This finding appears to underscore the importance of peptides for meeting the increased bioenergetic and biosynthetic demands of many cancers.

The role of metabolomics in neonatal and pediatric laboratory medicine

Metabolomics consists of the quantitative analysis of a large number of low molecular mass metabolites involving substrates or products in metabolic pathways existing in all living systems. The analysis of the metabolic profile detectable in a human biological fluid allows to instantly identify changes in the composition of endogenous and exogenous metabolites caused by the interaction between specific physiopathological states, gene expression, and environment. In pediatrics and neonatology, metabolomics offers new encouraging perspectives for the improvement of critically ill patient outcome, for the early recognition of metabolic profiles associated with the development of diseases in the adult life, and for delivery of individualized medicine. In this view, nutrimetabolomics, based on the recognition of specific cluster of metabolites associated with nutrition and pharmacometabolomics, based on the capacity to personalize drug therapy by analyzing metabolic modifications due to therapeutic treatment may open new frontiers in the prevention and in the treatment of pediatric and neonatal diseases. This review summarizes the most relevant results published in the literature on the application of metabolomics in pediatric and neonatal clinical settings. However, there is the urgent need to standardize physiological and preanalytical variables, analytical methods, data processing, and result presentation, before establishing the definitive clinical value of results.

Plasma and urine metabolic fingerprinting of type 1 diabetic children

Type 1 diabetes mellitus is one of the most common chronic disorders of childhood. The metabolic control is lost due to the lack of insulin, which is the main treatment for the disease. Nevertheless, long-term complications appear even under good glycemic control. Metabolomics, an emerging strategy, can help in diagnosis, prognosis, and monitoring of metabolic disorders. The objective of the present study was to investigate the alterations in plasma (by LC-MS) and urine (CE-MS) of type 1 diabetic children that were under insulin treatment and good glycemic control. Even without remarkable biochemical differences between the two groups (diabetic and control) except for glucose level and glycosilated hemoglobin, metabolomic tools were able to capture subtle metabolic differences. The main changes in plasma were associated to lipidic metabolism (nonesterified fatty acids, lysophospholipids, and other derivatives of fatty acids), and some markers of the differential activity of the gut microflora were also found (bile acids, p-cresol sulfate). In urine, changes associated to protein and amino acid metabolism were found (amino acids, their metabolites and derivatives), and among them one advanced glycation end product (carboxyethylarginine) and one early glycation end product (fructosamine) were excreted in higher proportion in the diabetic group.

Serum and tissue metabolomics of head and neck cancer

BACKGROUND

No reliable clinical markers to diagnose early stage-disease and predict its prognosis have yet been found for squamous cell carcinoma of the head and neck (HNSCC).

MATERIALS AND METHODS

In the present study, the metabolomic analysis of serum and tissue samples obtained from patients with HNSCC was performed using gas chromatography/mass spectrometry.

RESULTS

In serum, levels of several metabolites related to the glycolytic pathway, such as glucose, were higher in patients with HNSCC, and the levels of several amino acids were lower. In contrast to sera, the levels of many metabolites related to the glycolytic pathway appeared to be lower in tumor tissues of HNSCC than in non-tumorous tissues, and the levels of several amino acids, such as valine, thyrosine, serine and methionine, were higher.

CONCLUSION

Our results demonstrate that changes in metabolite patterns are useful in assessing the clinical characteristics of HNSCC, and will hopefully lead to the establishment of novel diagnostic tools.

Targeted metabolic profiling of hepatocellular carcinoma and hepatitis C using LC-MS/MS

Hepatitis C virus (HCV) infection of the liver is a global health problem and a major risk factor for the development of hepatocellular carcinoma (HCC). Sensitive methods are needed for the improved and earlier detection of HCC, which would provide better therapy options. Metabolic profiling of the high-risk population (HCV patients) and those with HCC provides insights into the process of liver carcinogenesis and possible biomarkers for earlier cancer detection. Seventy-three blood metabolites were quantitatively profiled in HCC (n = 30) and cirrhotic HCV (n = 22) patients using a targeted approach based on LC-MS/MS. Sixteen of 73 targeted metabolites differed significantly (p < 0.05) and their levels varied up to a factor of 3.3 between HCC and HCV. Four of these 16 metabolites (methionine, 5-hydroxymethyl-2'-deoxyuridine, N2,N2-dimethylguanosine, and uric acid) that showed the lowest p values were used to develop and internally validate a classification model using partial least squares discriminant analysis. The model exhibited high classification accuracy for distinguishing the two groups with sensitivity, specificity, and area under the receiver operating characteristic curve of 97%, 95%, and 0.98, respectively. A number of perturbed metabolic pathways, including amino acid, purine, and nucleotide metabolism, were identified based on the 16 biomarker candidates. These results provide a promising methodology to distinguish cirrhotic HCV patients, who are at high risk to develop HCC, from those who have already progressed to HCC. The results also provide insights into the altered metabolism between HCC and HCV.

Inhibition of CUTIN DEFICIENT 2 Causes Defects in Cuticle Function and Structure and Metabolite Changes in Tomato Fruit

Tomato (Solanum lycopersicum) fruit cuticle has been extensively studied due to its effect on the biochemical and physiological properties of the fruit. To date, several tomato mutants defective in proper cuticle formation have been identified. To gain insight into tomato cuticle formation, we investigated one such mutant, sticky peel/light green (pe lg). We verified the responsible gene by fine mapping and obtained the same conclusion as a previous report. To elucidate the pleiotropic effects of cuticle deficiency caused by the cd2 mutation, CD2 suppression lines were constructed. As found in the pe lg mutant, the suppression lines showed enhanced water permeability and aberrant leaf and fruit cuticles. Water use efficiency of the suppression line was lower than that of the wild type. However, photosynthetic ability was not affected in the suppression line. Since these phenotypes are related to altered deposition of wax and cutin, other lipidic metabolites might be changed, too. To confirm this hypothesis, we conducted metabolite profiling. The metabolite profiling revealed that not only lipid but also sugar, flavonoid and glycoalkaloid metabolites in fruit were changed in the cd2 mutant. These results indicate that CD2 is essential both for normal cutin and wax deposition and for proper accumulation of specific metabolites in tomato fruit.

Global gas chromatography/time-of-flight mass spectrometry (GC/TOFMS)-based metabonomic profiling of lyophilized human feces

Gas chromatography mass spectrometry (GC/MS)-based fecal metabonomics represents a powerful systems biology approach for elucidating metabolic biomarkers of lower gastrointestinal tract (GIT) diseases. Unlike metabolic profiling of fecal water, the profiling of complete fecal material remains under-explored. Here, a gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) method was developed and validated for the global metabonomic profiling of human feces. Fecal and fecal water metabotypes were also profiled and compared. Additionally, the unclear influence of blood in stool on the fecal metabotype was investigated unprecedentedly. Eighty milligram of lyophilized feces was ultrasonicated with 1mL of methanol:water (8:2) for 30min, followed by centrifugation, drying of supernatant, oximation and trimethylsilylation for 45min. Lyophilized feces demonstrated a more comprehensive metabolic coverage than fecal water, based on the number of chromatographic peaks. Principal component analysis (PCA) indicated occult blood (1mgHb/g feces) exerted a negligible effect on the fecal metabotype. Conversely, a unique metabotype related to feces spiked with gross blood (100mgHb/g feces) was revealed (PCA, R(2)X=0.837, Q(2)=0.794), confirming the potential confounding effect of gross GIT bleeding on the fecal metabotype. This pertinent finding highlights the importance of prudent interpretation of fecal metabonomic data, particularly in GIT diseases where bleeding is prevalent.

Is aristolochic acid nephropathy a widespread problem in developing countries? A case study of Aristolochia indica L. in Bangladesh using an ethnobotanical-phytochemical approach

ETHNOPHARMACOLOGICAL RELEVANCE

Species of Aristolochia are associated with aristolochic acid nephropathy (AAN), a renal interstitial fibrosis and upper urinary tract cancer (UUC). Aristolochic acid nephropathy has been reported in ten countries but its true incidence is unknown and most likely underestimated. By combining an ethnobotanical and phytochemical approach we provide evidence for the risk of AAN occurring in Bangladesh. More specifically, we assess the intra-specific variation of aristolochic acid analogues in medicinally used Aristolochia indica samples from Bangladesh.

MATERIALS AND METHODS

Ethnobotanical information was collected from 16 kavirajes (traditional healers) in different study locations in Bangladesh. Plant samples were obtained from native habitats, botanical gardens, herbal markets and pharmaceutical companies. The samples were extracted using 70% methanol and were analysed using LC-DAD-MS and (1)H-NMR.

RESULTS

Roots as well as leaves are commonly used for symptoms such as snake bites and sexual problems. Among the informants knowledge about toxicity or side effects is very limited and Aristolochia indica is often administered in very high doses. Replacement of Aristolochia indica with other medicinal plants such as Rauvolfia serpentina (L.) Benth. ex Kurz was common. Aristolochia indica samples contained a variety of aristolochic acid analogues such as aristolochic acid I, aristolochic acid II, cepharadione A and related compounds.

CONCLUSIONS

AAN cases are likely to occur in Bangladesh and more awareness needs to be raised about the health risks associated with the use of Aristolochia indica and other species of Aristolochia as herbal medicines.

Integration of transcriptomics and metabonomics: improving diagnostics, biomarker identification and phenotyping in ulcerative colitis

A systems biology approach to multi-faceted diseases has provided an opportunity to establish a holistic understanding of the processes at play. Thus, the current study merges transcriptomics and metabonomics data in order to improve diagnostics, biomarker identification and to explore the possibilities of a molecular phenotyping of ulcerative colitis (UC) patients. Biopsies were obtained from the descending colon of 43 UC patients (22 active UC and 21 quiescent UC) and 15 controls. Genome-wide gene expression analyses were performed using Affymetrix GeneChip Human Genome U133 Plus 2.0. Metabolic profiles were generated using 1H Nuclear magnetic resonance spectroscopy (Bruker 600 MHz, Bruker BioSpin, Rheinstetten, Germany). Data were analyzed with the use of orthogonal-projection to latent structure-discriminant analysis and a multivariate logistic regression model fitted by lasso. Prediction performance was evaluated using nested Monte Carlo cross-validation. The prediction performance of the merged data sets and that of relative small (<20 variables) multivariate biomarker panels suggest that it is possible to discriminate between active UC, quiescent UC, and controls; between patients with or without steroid dependency, as well as between early or late disease onset. Consequently, this study demonstrates that the novel approach of integrating metabonomics and transcriptomics combines the better of the two worlds, and provides us with clinical applicable candidate biomarker panels. These combined panels improve diagnostics and more importantly also the molecular phenotyping in UC and provide insight into the pathophysiological processes at play, making optimized and personalized medication a possibility.

Quantitative structure-retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: endogenous metabolites and banned compounds

Quantitative structure-retention relationship (QSRR) is a technique capable of improving the identification of analytes by predicting their retention time on a liquid chromatography column (LC) and/or their properties. This approach is particularly useful when LC is coupled with a high-resolution mass spectrometry (HRMS) platform. The main aim of the present study was to develop and describe appropriate QSRR models that provide usable predictive capability, allowing false positive identification to be removed during the interpretation of metabolomics data, while additionally increasing confidence of experimental results in doping control area. For this purpose, a dataset consisting of 146 drugs, metabolites and banned compounds from World Anti-Doping Agency (WADA) lists, was used. A QSRR study was carried out separately on high quality retention data determined by reversed-phase (RP-LC-HRMS) and hydrophilic interaction chromatography (HILIC-LC-HRMS) systems, employing a single protocol for each system. Multiple linear regression (MLR) was applied to construct the linear QSRR models based on a variety of theoretical molecular descriptors. The regression equations included a set of three descriptors for each model: ALogP, BELe6, R2p and ALogP(2), FDI, BLTA96, were used in the analysis of reversed-phase and HILIC column models, respectively. Statistically significant QSRR models (squared correlation coefficient for model fitting, R(2)=0.95 for RP and R(2)=0.84 for HILIC) indicate a strong correlation between retention time and the molecular descriptors. An evaluation of the best correlation models, performed by validation of each model using three tests (leave-one-out, leave-many-out, external tests), demonstrated the reliability of the models. This paper provides a practical and effective method for analytical chemists working with LC/HRMS platforms to improve predictive confidence of studies that seek to identify small molecules.

Expanding the paradigm of thiol redox in the thermophilic root of life

BACKGROUND

The current paradigm of intracellular redox chemistry maintains that cells establish a reducing environment maintained by a pool of small molecule and protein thiol to protect against oxidative damage. This strategy is conserved in mesophilic organisms from all domains of life, but has been confounded in thermophilic organisms where evidence suggests that intracellular proteins have abundant disulfides.

METHODS

Chemical labeling and 2-dimensional gel electrophoresis were used to capture disulfide bonding in the proteome of the model thermophile Sulfolobus solfataricus. The redox poise of the metabolome was characterized using both chemical labeling and untargeted liquid chromatography mass spectrometry. Gene annotation was undertaken using support vector machine based pattern recognition.

RESULTS

Proteomic analysis indicated the intracellular protein thiol of S. solfataricus was primarily in the disulfide form. Metabolic characterization revealed a lack of reduced small molecule thiol. Glutathione was found primarily in the oxidized state (GSSG), at relatively low concentration. Combined with genetic analysis, this evidence shows that pathways for synthesis of glutathione do exist in the archaeal domain.

CONCLUSIONS

In observed thermophilic organisms, thiol abundance and redox poise suggest that this system is not directly utilized for protection against oxidative damage. Instead, a more oxidized intracellular environment promotes disulfide bonding, a critical adaptation for protein thermostability.

GENERAL SIGNIFICANCE

Based on the placement of thermophilic archaea close to the last universal common ancestor in rRNA phylogenies, we hypothesize that thiol-based redox systems are derived from metabolic pathways originally tasked with promoting protein stability.

Metabolomics approaches for characterizing metabolic interactions between host and its commensal microbes

It is increasingly evident that the gut microbiota is involved in the regulation of multiple mammalian metabolic pathways through a series of interactive host-microbiota metabolic, signaling, and immune-inflammatory axes that physiologically connect the gut, liver, brain, and other organs. Correlation of the metabotypes with the gut microbial profiles derived from culture-independent molecular techniques is increasingly useful for deciphering inherent and intimate host-microbe relationships. Real-time analysis of the small molecule metabolites derived from gut microbial-host co-metabolism is essential for understanding the metabolic functions of the gut microbiome and has tremendous implications for personalized healthcare strategies. Metabolomics, an array of analytical techniques that includes high resolution NMR spectroscopy and chromatography-MS in conjunction with chemometrics and bioinformatics tools, enables characterization of the metabolic footprints of mammalian hosts that correlate with the microbial community in the intestinal tract. The metabolomics approach provides important information of a complete spectrum of metabolites produced from the gut microbial-mammalian co-metabolism and is improving our understanding of the molecular mechanisms underlying multilevel host-microbe interactions. In this review, the interactions of gut microbiota with their host are discussed and some examples of NMR- or MS-based metabolomics applications for characterizing the metabolic footprints of gut microbial-host co-metabolism are described. Advances in the metabolomic analysis of bile acids, short-chain fatty acids, and choline metabolism are also summarized.

Fingerprinting-based metabolomic approach with LC-MS to sleep apnea and hypopnea syndrome: a pilot study

Sleep apnea and hypopnea syndrome (SAHS) is a multicomponent disorder, with associated cardiovascular and metabolic alterations, second in order of frequency among respiratory disorders. Sleep apnea is diagnosed with an overnight sleep test called a polysomnogram, which requires having the patient in hospital. In addition, a more clear classification of patients according to mild and severe presentations would be desirable. The aim of the present study was to assess the relative metabolic changes in SAHS to identify new potential biomarkers for diagnosis, able to evaluate disease severity to establish response to therapeutic interventions and outcomes. For this purpose, metabolic fingerprinting represents a valuable strategy to monitor, in a nontargeted manner, the changes that are at the base of the pathophysiological mechanism of SAHS. Plasma samples of 33 SAHS patients were collected after polysomnography and analyzed with LC coupled to MS (LC-QTOF-MS). After data treatment and statistical analysis, signals differentiating nonsevere and severe patients were detected. Putative identification of 14 statistically significant features was obtained and changes that can be related to the episodes of hypoxia/reoxygenation (inflammation) have been highlighted. Among them, the patterns of variation of platelet activating factor and lysophospholipids, together with some compounds related to differential activity of the gut microflora (bile pigments and pipecolic acid) open new lines of research that will benefit our understanding of the alterations, offering new possibilities for adequate monitoring of the stage of the disease.

Omic-profiling in breast cancer metastasis to bone: implications for mechanisms, biomarkers and treatment

Despite well-recognised advances in breast cancer treatment, there remain substantial numbers of patients who develop metastatic disease, of which up to 70% involves spread to bone, resulting in skeletal complications which have a major negative impact on mortality and quality of life. Bisphosphonates and newer bone-targeted agents have reduced the prevalence of skeletal complications, yet there remains significant unmet clinical need, particularly for the development of more specific therapies for the prevention and treatment of metastatic bone disease, for the prediction of risk of its development in individual patients and for the prediction of response to treatments. Modern 'omic' strategies can potentially make a major contribution to meeting this need. Technological advances in the field of nucleic acid sequencing, mass spectrometry and metabolic profiling have driven progress in genomics, transcriptomics (functional genomics), proteomics and metabolomics. This review appraises the recent application of these approaches to studies of breast cancer metastasis (particularly to bone), with a focus on understanding how omic approaches may lead to new therapeutic options and to novel biomarker molecules or molecular signatures with potential value in clinical practise. The increasingly recognised need for rigorous sample quality control and both pre-clinical and clinical validation to meet the ultimate goals of clinical utility and patient benefit is discussed. Future directions of omic driven research in breast cancer metastasis are considered, in particular micro-RNAs and their role in the post-transcriptional regulation of gene function and the possible role of cancer-stem cells and epigenetic modifications in the development of distant metastases.

Evaluation of the effect of chance correlations on variable selection using Partial Least Squares-Discriminant Analysis

Variable subset selection is often mandatory in high throughput metabolomics and proteomics. However, depending on the variable to sample ratio there is a significant susceptibility of variable selection towards chance correlations. The evaluation of the predictive capabilities of PLSDA models estimated by cross-validation after feature selection provides overly optimistic results if the selection is performed on the entire set and no external validation set is available. In this work, a simulation of the statistical null hypothesis is proposed to test whether the discrimination capability of a PLSDA model after variable selection estimated by cross-validation is statistically higher than that attributed to the presence of chance correlations in the original data set. Statistical significance of PLSDA CV-figures of merit obtained after variable selection is expressed by means of p-values calculated by using a permutation test that included the variable selection step. The reliability of the approach is evaluated using two variable selection methods on experimental and simulated data sets with and without induced class differences. The proposed approach can be considered as a useful tool when no external validation set is available and provides a straightforward way to evaluate differences between variable selection methods.

Metabolomic analyses of faeces reveals malabsorption in cirrhotic patients

BACKGROUND

The study of faeces offers a unique opportunity to observe cooperation between the microbiome and the metabolism of mammalian hosts, an essential element in the study of the human metabolome. In the present study, a global metabolomics approach was used to identify metabolites differentially excreted in the faeces of cirrhotic patients compared to controls.

METHODS

Seventeen cirrhotic patients and 24 healthy individuals were recruited. Faecal metabolites were detected through non-targeted reversed-phase ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry.

RESULTS

A total of 9215 peaks were detected. Using unequal variance t-tests, 2393 peaks were observed with P≤0.05, approximately 74.0% of which were due to decreased faecal metabolite concentrations in liver cirrhosis vs. healthy controls. Integrating multivariate data analyses, we identified six major groups of metabolites. Relative levels of identified metabolites were as follows: strong increase in lysophosphatidylcholines, aromatic amino acids, fatty acids, and acylcarnitines, and a dramatic decrease in bile acids and bile pigments.

CONCLUSION

With severe hepatic injury in patients with liver cirrhosis, malabsorption occurs along with disorders of fatty acid metabolism, potentially due to changes in gut microflora.

Proteomics and metabolomics for mechanistic insights and biomarker discovery in cardiovascular disease

In the last decade, proteomics and metabolomics have contributed substantially to our understanding of cardiovascular diseases. The unbiased assessment of pathophysiological processes without a priori assumptions complements other molecular biology techniques that are currently used in a reductionist approach. In this review, we highlight some of the "omics" methods used to assess protein and metabolite changes in cardiovascular disease. A discrete biological function is very rarely attributed to a single molecule; more often it is the combined input of many proteins. In contrast to the reductionist approach, in which molecules are studied individually, "omics" platforms allow the study of more complex interactions in biological systems. Combining proteomics and metabolomics to quantify changes in metabolites and their corresponding enzymes will advance our understanding of pathophysiological mechanisms and aid the identification of novel biomarkers for cardiovascular disease.

Responses of Mytilus galloprovincialis to bacterial challenges by metabolomics and proteomics

Pathogens can cause diseases and lead to massive mortalities of aquaculture animals and substantial economic loss. In this work, we studied the responses induced by Micrococcus luteus and Vibrio anguillarum in gill of mussel Mytilus galloprovincialis at protein and metabolite levels. Metabolic biomarkers (e.g., amino acids, betaine, ATP) suggested that both M. luteus and V. anguillarum induced disturbances in energy metabolism and osmotic regulation. The unique and some more remarkably altered metabolic biomarkers (threonine, alanine, aspartate, taurine, succinate) demonstrated that V. anguillarum could cause more severe disturbances in osmotic regulation and energy metabolism. Proteomic biomarkers (e.g., goose-type lysozyme 2, matrilin, ependymin-related protein, peptidyl-prolyl cis-trans isomerases) indicated that M. luteus caused immune stress, and disturbances in signaling pathways and protein synthesis. However, V. anguillarum mainly induced oxidative stress and disturbance in energy metabolism in mussel gills indicated by altered procollagen-proline dioxygenase, protein disulfide isomerase, nucleoside diphosphate kinases, electron transfer flavoprotein and glutathione S-transferase. This work confirmed that an integration of proteomics and metabolomics could provide an insightful view into the effects of pathogens to the marine mussel M. galloprovincialis.

Stem cell metabolism in tissue development and aging

Recent advances in metabolomics and computational analysis have deepened our appreciation for the role of specific metabolic pathways in dictating cell fate. Once thought to be a mere consequence of the state of a cell, metabolism is now known to play a pivotal role in dictating whether a cell proliferates, differentiates or remains quiescent. Here, we review recent studies of metabolism in stem cells that have revealed a shift in the balance between glycolysis, mitochondrial oxidative phosphorylation and oxidative stress during the maturation of adult stem cells, and during the reprogramming of somatic cells to pluripotency. These insights promise to inform strategies for the directed differentiation of stem cells and to offer the potential for novel metabolic or pharmacological therapies to enhance regeneration and the treatment of degenerative disease.

Metabolomic profiling of oesophago-gastric cancer: a systematic review

AIMS

This review aims to identify metabolomic biomarkers of oesophago-gastric (OG) cancer in human biological samples, and to discuss the dominant metabolic pathways associated with the observed changes.

METHODS

A systematic review of the literature, up to and including 9th November 2012, was conducted for experimental studies investigating the metabolomic profile of human biological samples from patients with OG cancer compared to a control group. Inclusion criteria for analytical platforms were mass spectrometry or nuclear magnetic resonance spectroscopy. The QUADAS-2 tool was used to assess the quality of the included studies.

RESULTS

Twenty studies met the inclusion criteria and samples utilised for metabolomic analysis included tissue (n = 11), serum (n = 8), urine (n = 1) and gastric content (n = 1). Several metabolites of glycolysis, the tricarboxylic acid cycle, anaerobic respiration and protein/lipid metabolism were found to be significantly different between cancer and control samples. Lactate and fumurate were the most commonly recognised biomarkers of OG cancer related to cellular respiration. Valine, glutamine and glutamate were the most commonly identified amino acid biomarkers. Products of lipid metabolism including saturated and un-saturated free fatty acids, ketones and aldehydes and triacylglycerides were also identified as biomarkers of OG cancer. Unclear risk of bias for patient selection was reported for the majority of studies due to the lack of clarity regarding patient recruitment.

CONCLUSION

The application of metabolomics for biomarker detection in OG cancer presents new opportunities for the purposes of screening and therapeutic monitoring. Future studies should provide clear details of patient selection and develop metabolite assays suitable for progress beyond phase 1 pre-clinical exploratory studies.

The cyanobacterial amino acid β-N-methylamino-l-alanine perturbs the intermediary metabolism in neonatal rats

The neurotoxic amino acid β-N-methylamino-l-alanine (BMAA) is produced by most cyanobacteria. BMAA is considered as a potential health threat because of its putative role in neurodegenerative diseases. We have previously observed cognitive disturbances and morphological brain changes in adult rodents exposed to BMAA during the development. The aim of this study was to characterize changes of major intermediary metabolites in serum following neonatal exposure to BMAA using a non-targeted metabolomic approach. NMR spectroscopy was used to obtain serum metabolic profiles from neonatal rats exposed to BMAA (40, 150, 460mg/kg) or vehicle on postnatal days 9-10. Multivariate data analysis of binned NMR data indicated metabolic pattern differences between the different treatment groups. In particular five metabolites, d-glucose, lactate, 3-hydroxybutyrate, creatine and acetate, were changed in serum of BMAA-treated neonatal rats. These metabolites are associated with changes in energy metabolism and amino acid metabolism. Further statistical analysis disclosed that all the identified serum metabolites in the lowest dose group were significantly (p<0.05) decreased. The neonatal rat model used in this study is so far the only animal model that displays significant biochemical and behavioral effects after a low short-term dose of BMAA. The demonstrated perturbation of intermediary metabolism may contribute to BMAA-induced developmental changes that result in long-term effects on adult brain function.

Cancer cells incorporate and remodel exogenous palmitate into structural and oncogenic signaling lipids

De novo lipogenesis is considered the primary source of fatty acids for lipid synthesis in cancer cells, even in the presence of exogenous fatty acids. Here, we have used an isotopic fatty acid labeling strategy coupled with metabolomic profiling platforms to comprehensively map palmitic acid incorporation into complex lipids in cancer cells. We show that cancer cells and tumors robustly incorporate and remodel exogenous palmitate into structural and oncogenic glycerophospholipids, sphingolipids, and ether lipids. We also find that fatty acid incorporation into oxidative pathways is reduced in aggressive human cancer cells, and instead shunted into pathways for generating structural and signaling lipids. Our results demonstrate that cancer cells do not solely rely on de novo lipogenesis, but also utilize exogenous fatty acids for generating lipids required for proliferation and protumorigenic lipid signaling. This article is part of a special issue entitled Lipid Metabolism in Cancer.

Dynamics of amino acid utilization in Phaeobacter inhibens DSM 17395

Time-resolved utilization of multiple amino acids by Phaeobacter inhibens DSM 17395 was studied during growth with casamino acids. The 15 detected amino acids could be grouped according to depletion rate into four different categories, i.e. from rapid (category I) to nondepletion (category IV). Upon entry into stationary growth phase, amino acids of category I (e.g. glutamate) were (almost) completely depleted, while those of categories II (e.g. leucine) and III (e.g. serine) were further consumed at varying rates and to different extents. Thus, cultures entered stationary growth phase despite the ample presence of organic nutrients, i.e. under nonlimiting conditions. Integrated proteomic and metabolomic analysis identified 1747 proteins and 94 intracellular metabolites. Of these, 180 proteins and 86 metabolites displayed altered abundance levels during growth. Most strikingly, abundance and activity profiles of alanine dehydrogenase concomitantly increased with the onset of enhanced alanine utilization during transition into stationary growth phase. Most enzymes of amino acid and central metabolism, however, displayed unaltered abundances across exponential and stationary growth phases. In contrast, metabolites of the Entner-Doudoroff pathway and gluconeogenesis as well as cellular fatty acids increased markedly in abundance in early stationary growth phase.

PPARβ/δ modulates ethanol-induced hepatic effects by decreasing pyridoxal kinase activity

Because of the significant morbidity and lethality caused by alcoholic liver disease (ALD), there remains a need to elucidate the regulatory mechanisms that can be targeted to prevent and treat ALD. Toward this goal, minimally invasive biomarker discovery represents an outstanding approach for these purposes. The mechanisms underlying ALD include hepatic lipid accumulation. As the peroxisome proliferator-activated receptor-β/δ (PPARβ/δ) has been shown to inhibit steatosis, the present study examined the role of PPARβ/δ in ALD coupling metabolomic, biochemical and molecular biological analyses. Wild-type and Pparβ/δ-null mice were fed either a control or 4% ethanol diet and examined after 4-7 months of treatment. Ethanol fed Pparβ/δ-null mice exhibited steatosis after short-term treatment compared to controls, the latter effect appeared to be due to increased activity of sterol regulatory element binding protein 1c (SREBP1c). The wild-type and Pparβ/δ-null mice fed the control diet showed clear differences in their urinary metabolomic profiles. In particular, metabolites associated with arginine and proline metabolism, and glycerolipid metabolism, were markedly different between genotypes suggesting a constitutive role for PPARβ/δ in the metabolism of these amino acids. Interestingly, urinary excretion of taurine was present in ethanol-fed wild-type mice but markedly lower in similarly treated Pparβ/δ-null mice. Evidence suggests that PPARβ/δ modulates pyridoxal kinase activity by altering Km, consistent with the observed decreased in urinary taurine excretion. These data collectively suggest that PPARβ/δ prevents ethanol-induced hepatic effects by inhibiting hepatic lipogenesis, modulation of amino acid metabolism, and altering pyridoxal kinase activity.

Adaptation of Phaeobacter inhibens DSM 17395 to growth with complex nutrients

Phaeobacter inhibens DSM 17395, a member of the Roseobacter clade, was studied for its adaptive strategies to complex and excess nutrient supply, here mimicked by cultivation with Marine Broth (MB). During growth in process-controlled fermenters, P. inhibens DSM 17395 grew faster (3.6-fold higher μmax ) and reached higher optical densities (2.2-fold) with MB medium, as compared to the reference condition of glucose-containing mineral medium. Apparently, in the presence of MB medium, metabolism was tuned to maximize growth rate at the expense of efficiency. Comprehensive proteomic analysis of cells harvested at ½ ODmax identified 1783 (2D DIGE, membrane and extracellular protein-enriched fractions, shotgun) different proteins (50.5% coverage), 315 (based on 2D DIGE) of which displayed differential abundance profiles. Moreover, 145 different metabolites (intra- and extracellular combined) were identified, almost all of which (140) showed abundance changes. During growth with MB medium, P. inhibens DSM 17395 specifically formed the various proteins required for utilization of phospholipids and several amino acids, as well as for gluconeogenesis. Metabolic tuning on amino acid utilization is also reflected by massive discharge of urea to dispose the cell of excess ammonia. Apparently, P. inhibens DSM 17395 modulated its metabolism to simultaneously utilize diverse substrates from the complex nutrient supply.

Analysis of chloroformate-derivatised amino acids, dipeptides and polyamines by LC-MS/MS

A liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed which, with sample preparation using a commercially available kit, allows rapid quantitation of 39 chloroformate-derivatised amino acids (AAs), polyamines (PAs) and dipeptides (DPs) in complex biological matrices. Lower limits of quantitation (LOQ) were 20-150nM for putrescine, spermine, spermidine, cadaverine, agmatine, and below 5μM for all analytes. Responses were linear for all analytes between 0.5 and 50μM. Quantitative measurements of all 39 metabolites were achieved within a 15min runtime. The method was evaluated with a Pseudomonas aeruginosa cell extract study (n=24) and a larger human urine study (n=308). Batch effects were observed in the urine study and an investigation of instrument and sample stability showed a wave-like pattern in the MS responses. Both the run order and inter-batch variation were successfully corrected by normalising to pooled urine quality control data. Thus, this method should be suitable for diverse biological matrices and for large as well as small sample sets.

Recent advances in the application of metabolomics to Alzheimer's Disease

The pathophysiological changes associated with Alzheimer's Disease (AD) begin decades before the emergence of clinical symptoms. Understanding the early mechanisms associated with AD pathology is, therefore, especially important for identifying disease-modifying therapeutic targets. While the majority of AD clinical trials to date have focused on anti-amyloid-beta (Aβ) treatments, other therapeutic approaches may be necessary. The ability to monitor changes in cellular networks that include both Aβ and non-Aβ pathways is essential to advance our understanding of the etiopathogenesis of AD and subsequent development of cognitive symptoms and dementia. Metabolomics is a powerful tool that detects perturbations in the metabolome, a pool of metabolites that reflects changes downstream of genomic, transcriptomic and proteomic fluctuations, and represents an accurate biochemical profile of the organism in health and disease. The application of metabolomics could help to identify biomarkers for early AD diagnosis, to discover novel therapeutic targets, and to monitor therapeutic response and disease progression. Moreover, given the considerable parallel between mouse and human metabolism, the use of metabolomics provides ready translation of animal research into human studies for accelerated drug design. In this review, we will summarize current progress in the application of metabolomics in both animal models and in humans to further understanding of the mechanisms involved in AD pathogenesis.

Spectral deconvolution for gas chromatography mass spectrometry-based metabolomics: current status and future perspectives

Mass spectrometry coupled to gas chromatography (GC-MS) has been widely applied in the field of metabolomics. Success of this application has benefited greatly from computational workflows that process the complex raw mass spectrometry data and extract the qualitative and quantitative information of metabolites. Among the computational algorithms within a workflow, deconvolution is critical since it reconstructs a pure mass spectrum for each component that the mass spectrometer observes. Based on the pure spectrum, the corresponding component can be eventually identified and quantified. Deconvolution is challenging due to the existence of co-elution. In this review, we focus on progress that has been made in the development of deconvolution algorithms and provide thoughts on future developments that will expand the application of GC-MS in metabolomics.

Lacritin rescues stressed epithelia via rapid forkhead box O3 (FOXO3)-associated autophagy that restores metabolism

Homeostasis is essential for cell survival. However, homeostatic regulation of surface epithelia is poorly understood. The eye surface, lacking the cornified barrier of skin, provides an excellent model. Tears cover the surface of the eye and are deficient in dry eye, the most common eye disease affecting at least 5% of the world's population. Only a tiny fraction of the tear proteome appears to be affected, including lacritin, an epithelium-selective mitogen that promotes basal tearing when topically applied to rabbit eyes. Here we show that homeostasis of cultured corneal epithelia is entirely lacritin-dependent and elucidate the mechanism as a rapid autophagic flux to promptly restore cellular metabolism and mitochondrial fusion in keeping with the short residence time of lacritin on the eye. Accelerated flux appears to be derived from lacritin-stimulated acetylation of FOXO3 as a novel ligand for ATG101 and coupling of stress-acetylated FOXO1 with ATG7 (which remains uncoupled without lacritin) and be sufficient to selectively divert huntingtin mutant Htt103Q aggregates largely without affecting non-aggregated Htt25Q. This is in keeping with stress as a prerequisite for lacritin-stimulated autophagy. Lacritin targets the cell surface proteoglycan syndecan-1 via its C-terminal amino acids Leu(108)-Leu(109)-Phe(112) and is also available in saliva, plasma, and lung lavage. Thus, lacritin may promote epithelial homeostasis widely.

Human liver tissue metabolic profiling research on hepatitis B virus-related hepatocellular carcinoma

AIM: To select characteristic endogenous metabolites in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients and to identify their molecular mechanism and potential clinical value.

METHODS: An ultra performance liquid chromatography and linear trap quadrupole-Orbitrap XL-mass spectrometry platform was used to analyze endogenous metabolites in the homogenate of central tumor tissue, adjacent tissue and distant tissue obtained from 10 HBV-related HCC patients. After pretreatment with Mzmine software, including peak detection, alignment and normalization, the acquired data were treated with Simca-P+software to establish multivariate statistical analysis based on a pattern recognition technique and characteristic metabolites highly correlated with changing trends in metabolic profiling were selected and further identified.

RESULTS: Based on data acquired using Mzmine software, a principal component analysis model (R2X = 66.9%, Q2 = 21.7%) with 6 principal components and an orthogonal partial least squares discriminant analysis model (R2X = 76.5%, R2Y = 93.7%, Q2 = 68.7%) with 2 predicted principal components and 5 orthogonal principal components were established in the three tissue groups. Forty-nine ions were selected, 33 ions passed the 2 related samples nonparametric test (P < 0.05) and 14 of these were further identified as characteristic metabolites that showed significant differences in levels between the central tumor tissue group and distant tumor tissue group, including 9 metabolites (L-phenylalanine, glycerophosphocholine, lysophosphatidylcholines, lysophosphatidylethanolamines and chenodeoxycholic acid glycine conjugate) which had been reported as serum metabolite biomarkers for HCC diagnosis in previous research, and 5 metabolites (beta-sitosterol, quinaldic acid, arachidyl carnitine, tetradecanal, and oleamide) which had not been reported before.

CONCLUSION: Characteristic metabolites and metabolic pathways highly related to HCC pathogenesis and progression are identified through metabolic profiling analysis of HCC tissue homogenates.

Metabolomic approach to human brain spectroscopy identifies associations between clinical features and the frontal lobe metabolome in multiple sclerosis

Proton magnetic resonance spectroscopy ((1)H-MRS) is capable of noninvasively detecting metabolic changes that occur in the brain tissue in vivo. Its clinical utility has been limited so far, however, by analytic methods that focus on independently evaluated metabolites and require prior knowledge about which metabolites to examine. Here, we applied advanced computational methodologies from the field of metabolomics, specifically partial least squares discriminant analysis and orthogonal partial least squares, to in vivo (1)H-MRS from frontal lobe white matter of 27 patients with relapsing-remitting multiple sclerosis (RRMS) and 14 healthy controls. We chose RRMS, a chronic demyelinating disorder of the central nervous system, because its complex pathology and variable disease course make the need for reliable biomarkers of disease progression more pressing. We show that in vivo MRS data, when analyzed by multivariate statistical methods, can provide reliable, distinct profiles of MRS-detectable metabolites in different patient populations. Specifically, we find that brain tissue in RRMS patients deviates significantly in its metabolic profile from that of healthy controls, even though it appears normal by standard MRI techniques. We also identify, using statistical means, the metabolic signatures of certain clinical features common in RRMS, such as disability score, cognitive impairments, and response to stress. This approach to human in vivo MRS data should promote understanding of the specific metabolic changes accompanying disease pathogenesis, and could provide biomarkers of disease progression that would be useful in clinical trials.

Application of metabolomics on diagnosis and treatment of patients with psoriasis in traditional Chinese medicine

Traditional Chinese medicine (TCM) is one of the oldest forms of medical system. With syndrome as the core of diagnosis and therapy in TCM, it has the advantage of collecting macroscopic information of patients for diagnosis. To understand the in vivo mechanism of TCM, a metabolomics approach was used to investigate the global biological characterization of the urine of psoriasis patients with Blood Stasis Syndrome and the therapeutic metabolomics mechanism of the Optimized Yinxieling formula. A total of 41 cases of psoriasis patients with Blood Stasis Syndrome and 19 healthy volunteers were enrolled in this study. Fasting urine samples from patients with consecutive Optimized Yinxieling intake after 0, 4, 8 and 12 weeks and from healthy volunteers were analyzed by Orthogonal Projection on Latent Structures Discriminant Analysis (OPLS-DA), which was utilized for High Performance Liquid Chromatography (HPLC) analysis and temporal metabolic changes identification. For psoriasis group, the scores of PASI of patients decreased after 12 weeks of Optimized Yinxieling treating. The metabolic variations visualized not only in the healthy group and psoriasis group, but also in the psoriasis group before and after Optimized Yinxieling treatment, demonstrated that the metabolic characteristics of the two groups were significantly different. The optimized complex structure of the target proteins from Protein Data Bank was analyzed by software package Discovery Studio. With docking score of original inhibitor and the receptor as the threshold values, two compounds from Chinese medicinal chemical database were predicted to have good interactions with the target proteins. The Metabolomics technique combining molecular docking analysis enhanced our current understanding of the metabolic response to Blood Stasis Syndrome of Psoriasis and the action mechanism of Optimized Yinxieling. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

Granger causality in integrated GC-MS and LC-MS metabolomics data reveals the interface of primary and secondary metabolism

Metabolomics has emerged as a key technique of modern life sciences in recent years. Two major techniques for metabolomics in the last 10 years are gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled to mass spectrometry (LC-MS). Each platform has a specific performance detecting subsets of metabolites. GC-MS in combination with derivatisation has a preference for small polar metabolites covering primary metabolism. In contrast, reversed phase LC-MS covers large hydrophobic metabolites predominant in secondary metabolism. Here, we present an integrative metabolomics platform providing a mean to reveal the interaction of primary and secondary metabolism in plants and other organisms. The strategy combines GC-MS and LC-MS analysis of the same sample, a novel alignment tool MetMAX and a statistical toolbox COVAIN for data integration and linkage of Granger Causality with metabolic modelling. For metabolic modelling we have implemented the combined GC-LC-MS metabolomics data covariance matrix and a stoichiometric matrix of the underlying biochemical reaction network. The changes in biochemical regulation are expressed as differential Jacobian matrices. Applying the Granger causality, a subset of secondary metabolites was detected with significant correlations to primary metabolites such as sugars and amino acids. These metabolic subsets were compiled into a stoichiometric matrix N. Using N the inverse calculation of a differential Jacobian J from metabolomics data was possible. Key points of regulation at the interface of primary and secondary metabolism were identified.

analyzed by an integrative GC-MS and LC-MS metabolomics platform

Potentilla anserina L. (Rosaceae) is known for its beneficial effects of prevention of pre-menstrual syndrome (PMS). For this reason P. anserina is processed into many food supplements and pharmaceutical preparations. Here we analyzed hydroalcoholic reference extracts and compared them with various extracts of different pharmacies using an integrative metabolomics platform comprising GC-MS and LC-MS analysis and software toolboxes for data alignment (MetMAX Beta 1.0) and multivariate statistical analysis (COVAIN 1.0). Multivariate statistics of the integrated GC-MS and LC-MS data showed strong differences between the different plant extract formulations. Different groups of compounds such as chlorogenic acid, kaempferol 3-O-rutinoside, acacetin 7-O-rutinoside, and genistein were reported for the first time in this species. The typical fragmentation pathway of the isoflavone genistein confirmed the identification of this active compound that was present with different abundances in all the extracts analyzed. As a result we have revealed that different extraction procedures from different vendors produce different chemical compositions, e.g. different genistein concentrations. Consequently, the treatment may have different effects. The integrative metabolomics platform provides the highest resolution of the phytochemical composition and a mean to define subtle differences in plant extract formulations.

Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma

Metabolomics as one of the most rapidly growing technologies in the "-omics" field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Formula: see text] We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and-despite all its current limitations-can deliver marker panels with high selectivity even in multi-class settings.

NMRbot: Python scripts enable high-throughput data collection on current Bruker BioSpin NMR spectrometers

To facilitate the high-throughput acquisition of nuclear magnetic resonance (NMR) experimental data on large sets of samples, we have developed a simple and straightforward automated methodology that capitalizes on recent advances in Bruker BioSpin NMR spectrometer hardware and software. Given the daunting challenge for non-NMR experts to collect quality spectra, our goal was to increase user accessibility, provide customized functionality, and improve the consistency and reliability of resultant data. This methodology, NMRbot, is encoded in a set of scripts written in the Python programming language accessible within the Bruker BioSpin TopSpin™ software. NMRbot improves automated data acquisition and offers novel tools for use in optimizing experimental parameters on the fly. This automated procedure has been successfully implemented for investigations in metabolomics, small-molecule library profiling, and protein-ligand titrations on four Bruker BioSpin NMR spectrometers at the National Magnetic Resonance Facility at Madison. The investigators reported benefits from ease of setup, improved spectral quality, convenient customizations, and overall time savings.