Metabolomic characterization of experimental ovarian cancer ascitic fluid

Introduction

Malignant ascites (MA) is a major cause of morbidity that occurs in 37% of ovarian cancer patients. The accumulation of MA in the peritoneal cavity due to cancer results in debilitating symptoms and extremely poor quality of life. There is an urgent unmet need to expand the understanding of MA to design effective treatment strategies, and to improve MA diagnosis.

Objective

Our purpose here is to contribute to a better characterization of MA metabolic composition in ovarian cancer.

Method

We determined the metabolic composition of ascitic fluids resulting from orthotopic growth of two ovarian cancer cell lines, the mouse ID8- vascular endothelial growth factor (VEGF)-Defb29 cell line and the human OVCAR3 cell line using high-resolution ¹H MRS. ID8-VEGF-Defb29 tumors induce large volumes of ascites, while OVCAR3 tumors induce ascites less frequently and at smaller volumes. To better understand the factors driving the metabolic composition of the fluid, we characterized the metabolism of these ovarian cancer cells in culture by analyzing cell lysates and conditioned culture media with ¹H NMR.

Results

Distinct metabolite patterns were detected in ascitic fluid collected from OVCAR3 and ID8-VEGF-Defb29 tumor bearing mice that were not reflected in the corresponding cell culture or conditioned medium.

Conclusion

High-resolution ¹H NMR metabolic markers of MA can be used to improve characterization and diagnosis of MA. Metabolic characterization of MA can provide new insights into how MA fluid supports cancer cell growth and resistance to treatment, and has the potential to identify metabolic targeting strategies to reduce or eliminate the formation of MA.

     

Metabolomic characterization of human prostate cancer bone metastases reveals increased levels of cholesterol

Background

Metastasis to the bone is one clinically important features of prostate cancer (PCa). Current diagnostic methods cannot predict metastatic PCa at a curable stage of the disease. Identification of metabolic pathways involved in the growth of bone metastases therefore has the potential to improve PCa prognostication as well as therapy.

Methodology/Principal Findings

Metabolomics was applied for the study of PCa bone metastases (n = 20) in comparison with corresponding normal bone (n = 14), and furthermore of malignant (n = 13) and benign (n = 17) prostate tissue and corresponding plasma samples obtained from patients with (n = 15) and without (n = 13) diagnosed metastases and from men with benign prostate disease (n = 30). This was done using gas chromatography-mass spectrometry for sample characterization, and chemometric bioinformatics for data analysis. Results were verified in a separate test set including metastatic and normal bone tissue from patients with other cancers (n = 7). Significant differences were found between PCa bone metastases, bone metastases of other cancers, and normal bone. Furthermore, we identified metabolites in primary tumor tissue and in plasma which were significantly associated with metastatic disease. Among the metabolites in PCa bone metastases especially cholesterol was noted. In a test set the mean cholesterol level in PCa bone metastases was 127.30 mg/g as compared to 81.06 and 35.85 mg/g in bone metastases of different origin and normal bone, respectively (P = 0.0002 and 0.001). Immunohistochemical staining of PCa bone metastases showed intense staining of the low density lipoprotein receptor and variable levels of the scavenger receptor class B type 1 and 3-hydroxy-3-methylglutaryl-coenzyme reductase in tumor epithelial cells, indicating possibilities for influx and de novo synthesis of cholesterol.

Conclusions/Significance

We have identified metabolites associated with PCa metastasis and specifically identified high levels of cholesterol in PCa bone metastases. Based on our findings and the previous literature, this makes cholesterol a possible therapeutic target for advanced PCa.     

Metabolomic prediction of endometrial cancer

Introduction

Endometrial cancer (EC) is associated with metabolic disturbances including obesity, diabetes and metabolic syndrome. Identifying metabolite biomarkers for EC detection has a crucial role in reducing morbidity and mortality.

Objective

To determine whether metabolomic based biomarkers can detect EC overall and early-stage EC.

Methods

We performed NMR and mass spectrometry based metabolomic analyses of serum in EC cases versus controls. A total of 46 early-stage (FIGO stages I–II) and 10 late-stage (FIGO stages III–IV) EC cases constituted the study group. A total of 60 unaffected control samples were used. Patients and controls were divided randomly into a discovery group (n?=?69) and an independent validation group (n?=?47). Predictive algorithms based on biomarkers and demographic characteristics were generated using logistic regression analysis.

Results

A total of 181 metabolites were evaluated. Extensive changes in metabolite levels were noted in the EC versus the control group. The combination of C14:2, phosphatidylcholine with acyl-alkyl residue sum C38:1 (PCae C38:1) and 3-hydroxybutyric acid had an area under the receiver operating characteristics curve (AUC) (95% CI)?=?0.826 (0.706–0.946) and a sensitivity?=?82.6%, and specificity?=?70.8% for EC overall. For early EC prediction: BMI, C14:2 and PC ae C40:1 had an AUC (95% CI)?=?0.819 (0.689–0.95) and a sensitivity?=?72.2% and specificity?=?79.2% in the validation group.

Conclusions

EC is characterized by significant perturbations in important cellular metabolites. Metabolites accurately detected early-stage EC cases and EC overall which could lead to the development of non-invasive biomarkers for earlier detection of EC and for monitoring disease recurrence.

     

Type 2 diabetes mellitus, hypertension, dyslipidemia and obesity: A systematic comparison of their impact on cognition

Vascular risk factors, such as type 2 diabetes mellitus, hypertension, dyslipidemia and obesity, have been associated with an increased risk of cognitive dysfunction, particularly in the elderly. The aim of this systematic review was to compare these risk factors with regard to the nature and magnitude of the associated cognitive decrements. Cross-sectional and longitudinal studies that assessed cognitive functioning in non-demented persons in relation to diabetes/impaired glucose metabolism (k=36), hypertension (k=24), dyslipidemia (k=7) and obesity (k=6) and that adjusted or matched for age, gender and education were included. When possible, effect sizes (Cohen's d) were computed per cognitive domain. Diabetes and hypertension were clearly associated with cognitive decrements; the results for obesity and dyslipidemia were less consistent. Effect sizes were moderate (median~-0.3) for all risk factors. Decline was found in all cognitive domains, although the effects on cognitive speed, mental flexibility and memory were most consistent. Methodological aspects of included studies and implications of these findings are discussed.     

Metabolomic Profile of the Genus Inula

Plants have a long history as therapeutics in the treatment of human diseases and have been used as source of medicines for ages. Searching for new biologically active natural products, many plants and herbs are screened for natural products with pharmacological activities. In this field, the genus Inula, which comprises more than 100 species, several of them being used in traditional medicine, is very important, especially due to the finding that several of the isolated pure secondary metabolites proved to possess important biological activities. Inula species have been reported as rich sources of sesquiterpene lactones, including eudesmanes, germacranes, guaianes, and dimeric structures, and since 2006 ca. 400 secondary metabolites, including more than 100 new natural products, some of them with relevant pharmacological activities, have been identified. Herein, we critically compile and update the information regarding the types of secondary metabolites found in the genus Inula and the progress in their isolation.     

Metabolomic investigations of anaerobic hydrocarbon-impacted environments

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Highlights► This review describes metabolomic studies of anaerobic hydrocarbon-impacted sites. ► The discovery of new pathways has provided new metabolic and molecular biomarkers. ► Assessment of in situ biodegradation is enhanced via integrative approaches.     

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.

     

Metabolomic analysis of the interaction between plants and herbivores

Insect herbivores by necessity have to deal with a large arsenal of plant defence metabolites. The levels of defence compounds may be increased by insect damage. These induced plant responses may also affect the metabolism and performance of successive insect herbivores. As the chemical nature of induced responses is largely unknown, global metabolomic analyses are a valuable tool to gain more insight into the metabolites possibly involved in such interactions. This study analyzed the interaction between feral cabbage (Brassica oleracea) and small cabbage white caterpillars (Pieris rapae) and how previous attacks to the plant affect the caterpillar metabolism. Because plants may be induced by shoot and root herbivory, we compared shoot and root induction by treating the plants on either plant part with jasmonic acid. Extracts of the plants and the caterpillars were chemically analysed using Ultra Performance Liquid Chromatography/Time of Flight Mass Spectrometry (UPLCT/MS). The study revealed that the levels of three structurally related coumaroylquinic acids were elevated in plants treated on the shoot. The levels of these compounds in plants and caterpillars were highly correlated: these compounds were defined as the ‘metabolic interface’. The role of these metabolites could only be discovered using simultaneous analysis of the plant and caterpillar metabolomes. We conclude that a metabolomics approach is useful in discovering unexpected bioactive compounds involved in ecological interactions between plants and their herbivores and higher trophic levels.     

Metabolomic profiling and antioxidant activity of some Acacia species

Metabolomic profiling of different parts (leaves, flowers and pods) of Acacia species (Acacia nilotica, Acacia seyal and Acacia laeta) was evaluated. The multivariate data analyses such as principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were used to differentiate the distribution of plant metabolites among different species or different organs of the same species. A.nilotica was characterized with a high content of saponins and A.seyal was characterized with high contents of proteins, phenolics, flavonoids and anthocyanins. A.laeta had a higher content of carbohydrates than A. nilotica and A. seyal. On the basis of these results, total antioxidant capacity, DPPH free radical scavenging activity and reducing power of the methanolic extracts of studied parts were evaluated. A.nilotica and A.seyal extracts showed less inhibitory concentration 50 (IC₅₀) compared to A.laeta extracts which means that these two species have the strongest radical scavenging activity whereas A. laeta extracts have the lowest radical scavenging activity. A positive correlation between saponins and flavonoids with total antioxidant capacity and DPPH radical scavenging activity was observed. Based on these results, the potentiality of these plants as antioxidants was discussed.     

Urinary Metabolomic Signatures in Obstructive Sleep Apnea through Targeted Metabolomic Analysis: A Pilot Study

Introduction

Obstructive sleep apnea (OSA) is very common sleep problem, and it is associated with serious morbidities such as cardiovascular diseases and metabolic diseases. Overnight polysomnography (PSG) is the gold standard test for OSA, but it is expensive and requires specific facilities and equipment. Thus, novel screening methods are needed for effective diagnosis and follow-up in OSA.

Objectives

The aims of the study were to investigate the urinary metabolic signatures and identify potential urine markers for OSA using a mass spectrometry (MS)-based assay for targeted metabolomics.

Methods

Urine samples were collected from 48 male subjects who visited a sleep clinic for suspicious OSA. All underwent overnight in-laboratory polysomnography. The Biocrates AbsoluteIDQ p180 kit was used for targeted metabolomics.

Results

Among the 86 metabolites quantified, three acylcarnitines, one biogenic amine, two glycerophospholipids, and two sphingomyelins were differently expressed in OSA patients [apnea-hypopnea index (AHI) ≥5] compared with control groups (AHI <5 and/or simple snoring with no other sleep disorders). Additional partial correlation and multivariate logistic regression analysis revealed that long-chain acylcarnitine C14:1, symmetric dimethylarginine, and sphingomyelin C18:1 might be potential biomarkers for OSA. Receiver operating characteristic analysis showed favorable predictive properties of these metabolites. Furthermore, a combination of the metabolites exceeding cutoff values yielded further improved sensitivity or specificity.

Conclusions

MS-based targeted metabolomics identified specific classes of urinary metabolites that were up-regulated in OSA patients. Further assessments in large populations are required to clarify the screening values of these metabolite markers.

     

Metabolomic analysis using optimized NMR and statistical methods

NMR-based metabolomics requires robust automated methodologies, and the accuracy of NMR-based metabolomics data is greatly influenced by the reproducibility of data acquisition and processing methods. Effective water resonance signal suppression and reproducible spectral phasing and baseline traces across series of related samples are crucial for statistical analysis. We assess robustness, repeatability, sensitivity, selectivity, and practicality of commonly used solvent peak suppression methods in the NMR analysis of biofluids with respect to the automated processing of the NMR spectra and the impact of pulse sequence and data processing methods on the sensitivity of pattern recognition and statistical analysis of the metabolite profiles. We introduce two modifications to the excitation sculpting pulse sequence whereby the excitation solvent suppression pulse cascade is preceded by low-power water resonance presaturation pulses during the relaxation delay. Our analysis indicates that combining water presaturation with excitation sculpting water suppression delivers the most reproducible and information-rich NMR spectra of biofluids.     

Metabolomic analysis and signatures in motor neuron disease

Motor neuron diseases (MND) are a heterogeneous group of disorders that includes amyotrophic lateral sclerosis (ALS) and result in death of motor neurons. These diseases may produce characteristic perturbations of the metabolome, the collection of small-molecules (metabolites) present in a cell, tissue, or organism. To test this hypothesis, we used high performance liquid chromatography followed by electrochemical detection to profile blood plasma from 28 patients with MND and 30 healthy controls. Of 317 metabolites, 50 were elevated in MND patients and more than 70 were decreased (p<0.05). Among the compounds elevated, 12 were associated with the drug Riluzole. In a subsequent study of 19 subjects with MND who were not taking Riluzole and 33 healthy control subjects, six compounds were significantly elevated in MND, while the number of compounds with decreased concentration was similar to study 1. Our data also revealed a distinctive signature of highly correlated metabolites in a set of four patients, three of whom had lower motor neuron (LMN) disease. In both datasets we were able to separate MND patients from controls using multivariate regression techniques. These results suggest that metabolomic studies can be used to ascertain metabolic signatures of disease in a non-invasive fashion. Elucidation of the structures of signature molecules in ALS and other forms of MND should provide insight into aberrant biochemical pathways and may provide diagnostic markers and targets for drug design.Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users. ^†S.R., M.E.C. and M.B. contributed equally to this work. W.R.M. and B.S.K. contributed equally to this work. S.R., M.B., W.R.M., B.S.K., C.B., S.H., P.V., M.F.B., and R.K-D. have financial interests in Metabolon Inc., a company engaged in metabolic profiling. ^††Electronic supporting figures, tables and datasets are available at the Journal’s website.*To whom corrsepondence should be addressed.E-mail: kaddu001@mc.duke.eduCurrent address: Duke University Medical Center, Department of Psychiatry, P.O. Box 3950, Durham, NC 27710.     

Metabolomic profile of hepatitis C virus-infected hepatocytes

Hepatitis C virus (HCV) is capable of disrupting different facets of lipid metabolism and lipids have been shown to play a crucial role in the viral life cycle. The aim of this study was to examine the effect HCV infection has on the hepatocyte metabolome. Huh-7.5 cells were infected using virus produced by the HCV J6/JFH1 cell culture system and cells were harvested 24, 48, and 72-hours following infection. Metabolic profiling was performed using a non-targeted multiple platform methodology combining ultrahigh performance liquid chromatography/tandem mass spectrometry (UHPLC/MS/MS(2)) and gas chromatography/mass spectrometry (GC/MS). There was a significant increase in a number of metabolites involved in nucleotide synthesis and RNA replication during early HCV infection. NAD levels were also significantly increased along with several amino acids. A number of lipid metabolic pathways were disrupted by HCV infection, resulting in an increase in cholesterol and sphingolipid levels, altered phospholipid metabolism and a possible disruption in mitochondrial fatty acid transport. Fluctuations in 5'-methylthioadenosine levels were also noted, along with alterations in the glutathione synthesis pathway. These results highlight a number of previously unreported metabolic interactions and give a more in depth insight into the effect HCV has on host cell biochemical processes.     

Metabolomic investigations of Ricinus communis for cultivar and provenance determination

Eight specimens of six known cultivars of Ricinus communis were investigated for differences in their metabolome that could be used to determine both provenance and cultivar. Seven replicates of three seeds per specimen were subjected to ¹H NMR analysis, with the collected data further investigated using multivariate statistical analysis (OPLS-DA), resulting in class separations according to provenance. Analysis of loadings plots in addition to further chemical analysis of the extracts allowed for the identification of phenylalanine, ricinine, the N-demethyl and O-demethyl analogues of ricinine, and sucrose as important molecular markers for particular cultivars. To test the strength of the model, extracts generated from blinded specimens were used as a prediction set and were correctly classified according to provenance and cultivar.     

Metabolomic Responses of Guard Cells and Mesophyll Cells to Bicarbonate

Anthropogenic CO₂ presently at 400 ppm is expected to reach 550 ppm in 2050, an increment expected to affect plant growth and productivity. Paired stomatal guard cells (GCs) are the gate-way for water, CO₂, and pathogen, while mesophyll cells (MCs) represent the bulk cell-type of green leaves mainly for photosynthesis. We used the two different cell types, i.e., GCs and MCs from canola (Brassica napus) to profile metabolomic changes upon increased CO₂ through supplementation with bicarbonate (HCO₃ ^-). Two metabolomics platforms enabled quantification of 268 metabolites in a time-course study to reveal short-term responses. The HCO₃ ^- responsive metabolomes of the cell types differed in their responsiveness. The MCs demonstrated increased amino acids, phenylpropanoids, redox metabolites, auxins and cytokinins, all of which were decreased in GCs in response to HCO₃ ^-. In addition, the GCs showed differential increases of primary C-metabolites, N-metabolites (e.g., purines and amino acids), and defense-responsive pathways (e.g., alkaloids, phenolics, and flavonoids) as compared to the MCs, indicating differential C/N homeostasis in the cell-types. The metabolomics results provide insights into plant responses and crop productivity under future climatic changes where elevated CO₂ conditions are to take center-stage.