Current Metabolomics (v.4, #2)

Meet Our Editors by Ying-Yong Zhao, Fariba Assadi-Porter (83-85).

Advancing Metabolomics Research and Biomarker Application with Nanotechnology by Cornelius J.F. Taute, Jeremie Z. Lindeque (86-96).
Background: Advances in preparatory and analytical technology have greatly contributed to the maturing of metabolomics research over the past decade. Despite this, specific limitations in sample preparation and analysis remain unresolved within the field. More importantly, post-metabolomic application or commercialization of discovered biomarkers is also scarce because of the lack of simplified analytical techniques to measure these markers in the clinic or industry.
Focus: Nanotechnology can address many of these limitations and provide innovative solutions for metabolomics sample preparation and analysis through compound entrapment technology and nanoparticle-assisted ionization. Moreover, the creative development of biosensors can advance the application and/or commercialization of discovered biomarkers. This innovative use of nanotechnology in metabolomics research and biomarker application is the focus of this paper.
Prospect: The use of existing nanotechnology in analytics is reviewed and inspirational new concepts to improve metabolomics research are discussed as a means to encourage researchers of both fields to collaborate and innovate.

PCA as a Practical Indicator of OPLS-DA Model Reliability by Bradley Worley, Robert Powers (97-103).
Background: Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) are powerful statistical modeling tools that provide insights into separations between experimental groups based on high-dimensional spectral measurements from NMR, MS or other analytical instrumentation. However, when used without validation, these tools may lead investigators to statistically unreliable conclusions. This danger is especially real for Partial Least Squares (PLS) and OPLS, which aggressively force separations between experimental groups. As a result, OPLS-DA is often used as an alternative method when PCA fails to expose group separation, but this practice is highly dangerous. Without rigorous validation, OPLS-DA can easily yield statistically unreliable group separation.
Methods: A Monte Carlo analysis of PCA group separations and OPLS-DA cross-validation metrics was performed on NMR datasets with statistically significant separations in scores-space. A linearly increasing amount of Gaussian noise was added to each data matrix followed by the construction and validation of PCA and OPLS-DA models.
Results: With increasing added noise, the PCA scores-space distance between groups rapidly decreased and the OPLS-DA cross-validation statistics simultaneously deteriorated. A decrease in correlation between the estimated loadings (added noise) and the true (original) loadings was also observed. While the validity of the OPLS-DA model diminished with increasing added noise, the group separation in scoresspace remained basically unaffected.
Conclusion: Supported by the results of Monte Carlo analyses of PCA group separations and OPLS-DA cross-validation metrics, we provide practical guidelines and cross-validatory recommendations for reliable inference from PCA and OPLS-DA models.

Metabolomics Reveals Hyperlipidemic Biomarkers and Antihyperlipidemic Effect of Poria cocos by Hua Chen, Lin Chen, Dan-Dan Tang, Dan-Qian Chen, Hua Miao, Ying-Yong Zhao, Shuang-Cheng Ma (104-115).
Background: Hyperlipidemia has been highlighted to be one of the most prominent global health threats. Poria cocos (PC), a well-known traditional Chinese medicine, is used for treating hyperlipidemia in China. To evaluate its therapeutic function on hyperlipidemia, urinary metabolomics was performed.
Method: Diet-induced hyperlipidemic rat model was produced by high fat food, and then the ethanol extract of PC (250 mg/kg) was used to treat hyperlipidemic rats for 6 weeks. Urine samples were analysed using ultra performance liquid chromatographyhigh definition mass spectrometry coupled with partial least squares-discriminant analysis. Box plots, fold changes, one-way analysis of variance, Mann-Whitney U-test, false discovery rate correction, heatmap display and receiver operating characteristic analysis were employed for further analysis of the identified metabolites. Additionally, visualization of metabolic pathways was conducted by ingenuity pathway analysis and Metscape.
Results: Eighteen metabolites were identified including propionylcarnitine, arginine, trimethyltridecanoic acid, methylhippuric acid, aminoadipic acid, citric acid, etc. The metabolites arginine, aminoadipic acid and citric acid were screened as significant biomarkers by various statistical analysis and receiver operating characteristic curves. The results of quantitative enrichment analysis algorithm and cytoscape indicated that thirty-eight metabolic pathways were perturbed by diet-induced hyperlipidemia. The abnormal levels of these metabolites in model group indicated diet-induced hyperlipidemia mainly disturbed amino acid metabolism, tricarboxylic acid cycle, fatty acid metabolism and nucleic acid metabolism. However, PC partially ameliorated these abnormal metabolisms.
Conclusion: PC positively regulated the perturbed metabolisms induced by hyperlipidemia and metabolomics was proven to be suitable for characterizing antihyperlipidemic effect of PC.

13C Metabolomics: NMR and IROA for Unknown Identification by Chaevien S. Clendinen, Gregory S. Stupp, Bing Wang, Timothy J. Garrett, Arthur S. Edison (116-120).
Background: Isotopic Ratio Outlier Analysis (IROA) is an untargeted metabolomics method that uses stable isotopic labeling and LC-HRMS for identification and relative quantification of metabolites in a biological sample under varying experimental conditions.
Objective: We demonstrate a method using high-sensitivity 13C NMR to identify an unknown metabolite isolated from fractionated material from an IROA LC-HRMS experiment.
Methods: IROA samples from the nematode Caenorhabditis elegans were fractionated using LC-HRMS using 5 repeated injections and collecting 30 sec fractions. These were concentrated and analyzed by 13C NMR.
Results: We isotopically labeled samples of C. elegans and collected 2 adjacent LC fractions. By HRMS, one contained at least 2 known metabolites, phenylalanine and inosine, and the other contained tryptophan and an unknown feature with a monoisotopic mass of m/z 380.0742 [M+H]+. With NMR, we were able to easily verify the known compounds, and we then identified the spin system networks responsible for the unknown resonances. After searching the BMRB database and comparing the molecular formula from LC-HRMS, we determined that the fragments were a modified anthranilate and a glucose modified by a phosphate. We then performed quantum chemical NMR chemical shift calculations to determine the most likely isomer, which was 3'-O-phospho-β-D-glucopyranosyl-anthranilate. This compound had previously been found in the same organism, validating our approach.
Conclusion: We were able to dereplicate previously known metabolites and identify a metabolite that was not in databases by matching resonances to NMR databases and using chemical shift calculations to determine the correct isomer. This approach is efficient and can be used to identify unknown compounds of interest using the same material used for IROA.

Background: Targeted urinary metabolic profiling of patients with type 2 diabetes mellitus (DM) and diabetic nephropathy (DN) along with healthy control was conducted with urine being a non-invasive biofluid and ideal for the biomarker studies related to kidney disorders.
Objective: Other markers of risk for DN in combination with microalbuminuria are needed for optimal clinical management. The purpose of this study was to propose new urinary metabolic markers involved in the pathophysiology the DN.
Method: Liquid chromatography coupled mass spectrometry (LC-MS) method was developed using diamond hydride column for 43 diverse polarities of metabolites. Multiple reaction monitoring (MRM) was performed using optimized ionization modes for the 43 studied metabolites. Diabetics (n=26), diabetic nephropathy (n=27) patients, and healthy controls (27) participated in the study and chemometric analysis was performed using Graphpad and noncommercial software “Multibase excel add in” to identify significant metabolites. Pathway analysis is performed using free software metaboanalyst.
Results: Screening of metabolites with the Bonferroni multiple comparison test was performed, which suggested significantly higher urinary excretion of hippurate in the DN patients as compared to DM and the control group. Significant positive correlation was found between hippurate and albumin across all the three groups. Partial least square discriminant analysis (PLS-DA) was performed for DN and DM groups and showed a good separation between DM patients and DN patients. Further, the pathway analysis suggested the involvement of arginine and proline metabolism in the pathogenesis of DN.
Conclusion: This study suggests potential use of new LC-MS method for targeted analysis of metabolites covering different pathways and proposes hippurate potential urinary marker metabolites for discrimination of DM and DN groups.

Metabolite Profiling and Chemometric Study for Varietal Difference in Piper betle L. Leaf by Swagata Karak, Plaban Bhattacharya, Ashis Nandy, Achintya Saha, Bratati De (129-140).
Background: Piper betle L. leaves, largely used as masticatory, differ in aroma and taste with varieties due to the presence of diverse volatile oil and other constituents.
Objective: The present study was aimed to profile polar and non-polar metabolites present in eight local varieties to explore varietal variation with the help of chemometric studies.
Methods: The leaves, crushed with liquid nitrogen, were extracted with hexane (for analysis of non-polar metabolites) or methanol (for analysis of polar and some nonpolar metabolites). All metabolites were analyzed by GC-MS.
Results: 42 Non-polar constituents belonging to terpenoid and phenylpropanoid groups were identified. A total of 75 other metabolites belonging to groups like amino acids, organic acids, fatty acids, phenols, sugar alcohols, sugars were found to be present in the methanol extract. Nonpolar metabolites showed seasonal variation in all the varieties. The varieties could also be segregated into different clusters, when analyzed by PCA and PLS-DA, on the basis of the different constituents. The contributory constituents for varietal variation were identified.
Conclusion: It was observed that the varieties Meetha and Chhaanchi were distinctly different from the other varieties when analyzed on the basis of different metabolites.

Background: Metabolomics aims to characterize the metabolic phenotype and metabolic pathways utilized by microorganisms or other cellular systems. A crucial component to metabolomics research as it applies to microbial metabolism is the development of robust and reproducible methods for extraction of intracellular metabolites. The goal is to extract all metabolites in a non-biased and consistent manner; however, most methods used thus far are targeted to specific metabolite classes and use harsh conditions that may contribute to metabolite degradation. Metabolite extraction methodologies need to be optimized for each microorganism of interest due to different cellular characteristics contributing to lysis resistance.
Methods: Three cell pellet wash solutions were compared for the potential to influence intracellular metabolite leakage of P. aeruginosa. We also compared four different extraction methods using (i) methanol:chloroform (2:1); (ii) 50% methanol; (iii) 100% methanol; or (iv) 100% water to extract intracellular metabolites from P. aeruginosa planktonic and biofilm cultures.
Results: Intracellular metabolite extraction efficiency was found to be dependent on the extraction method and varies between microbial modes of growth. Methods using the 60% methanol wash produced the greatest amount of intracellular material leakage. Quantification of intracellular metabolites via 1H NMR showed that extraction protocols using 100% water or 50% methanol achieved the greatest extraction efficiencies, while addition of sonication to facilitate cell lysis to the 50% methanol extraction method resulted in at least a two-fold increase in signal intensities for approximately half of the metabolites identified. Phosphate buffered saline (PBS) was determined to be the most appropriate wash solution, yielding little intracellular metabolite leakage from cells.
Conclusion: We determined that washing in 1X PBS and extracting intracellular metabolites with 50% methanol is the most appropriate metabolite extraction protocol because (a) leakage is minimal; (b) a broad range of metabolites present at sufficiently high concentrations is detectable by NMR; and (c) this method proved suitable for metabolite extraction of both planktonic and biofilm P. aeruginosa cultures.

Plant Volatilome Resources by Biswapriya B. Misra (148-150).
Background: In the current -omics era plant volatilome has enormous potential in enhancing our understanding of plant growth, defense, and productivity.
Focus: Plant volatiles pose enormous challenges in their identification and functional assignment due their chemical complexities and associated analytical challenges.
Prospects: However, there have been efforts to develop databases, softwares, and webservers to allow accurate identification of these volatiles. In this highlight, I summarize the past and current efforts in such endeavors.