Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses

A Metabolomics-Guided Exploration of the Phytochemical Constituents of Vernonia fastigiata with the Aid of Pressurized Hot Water Extraction and Liquid Chromatography-Mass Spectrometry

Vernonia fastigiata is a multi-purpose nutraceutical plant with interesting biological properties. However, very little is known about its phytochemical composition and, thus the need for its phytochemical characterization. In the current study, an environmentally friendly method, pressurized hot water extraction (PHWE), was used to extract metabolites from the leaves of V. fastigiata at various temperatures (50 °C, 100 °C, 150 °C and 200 °C). Ultra-high performance liquid chromatography-quadrupole time of flight mass spectrometry (UHPLC-qTOF-MS) analysis in combination with chemometric methods, particularly principal component analysis (PCA) and liquid/gas chromatography mass spectrometry (XCMS) cloud plots, were used to descriptively visualize the data and identify significant metabolites extracted at various temperatures. A total of 25 different metabolites, including hydroxycinnamic acid derivatives, clovamide, deoxy-clovamide and flavonoids, were noted for the first time in this plant. Overall, an increase in extraction temperature resulted in an increase in metabolite extraction during PHWE. This study is the first scientific report on the phytochemical composition of V. fastigiata, providing insight into the components of the chemo-diversity of this important plant.

NOREVA: normalization and evaluation of MS-based metabolomics data

Diverse forms of unwanted signal variations in mass spectrometry-based metabolomics data adversely affect the accuracies of metabolic profiling. A variety of normalization methods have been developed for addressing this problem. However, their performances vary greatly and depend heavily on the nature of the studied data. Moreover, given the complexity of the actual data, it is not feasible to assess the performance of methods by single criterion. We therefore developed NOREVA to enable performance evaluation of various normalization methods from multiple perspectives. NOREVA integrated five well-established criteria (each with a distinct underlying theory) to ensure more comprehensive evaluation than any single criterion. It provided the most complete set of the available normalization methods, with unique features of removing overall unwanted variations based on quality control metabolites and allowing quality control samples based correction sequentially followed by data normalization. The originality of NOREVA and the reliability of its algorithms were extensively validated by case studies on five benchmark datasets. In sum, NOREVA is distinguished for its capability of identifying the well performed normalization method by taking multiple criteria into consideration and can be an indispensable complement to other available tools. NOREVA can be freely accessed at http://server.idrb.cqu.edu.cn/noreva/.

Metabolomic Studies in Drosophila

Metabolomic analysis provides a powerful new tool for studies of Drosophila physiology. This approach allows investigators to detect thousands of chemical compounds in a single sample, representing the combined contributions of gene expression, enzyme activity, and environmental context. Metabolomics has been used for a wide range of studies in Drosophila, often providing new insights into gene function and metabolic state that could not be obtained using any other approach. In this review, we survey the uses of metabolomic analysis since its entry into the field. We also cover the major methods used for metabolomic studies in Drosophila and highlight new directions for future research.

Furanoterpene Diversity and Variability in the Marine Sponge Spongia officinalis, from Untargeted LC-MS/MS Metabolomic Profiling to Furanolactam Derivatives

The Mediterranean marine sponge Spongia officinalis has been reported as a rich source of secondary metabolites and also as a bioindicator of water quality given its capacity to concentrate trace metals. In this study, we evaluated the chemical diversity within 30 S. officinalis samples collected over three years at two sites differentially impacted by anthropogenic pollutants located near Marseille (South of France). Untargeted liquid chromatography—mass spectrometry (LC–MS) metabolomic profiling (C18 LC, ESI-Q-TOF MS) combined with XCMS Online data processing and multivariate statistical analysis revealed 297 peaks assigned to at least 86 compounds. The spatio-temporal metabolite variability was mainly attributed to variations in relative content of furanoterpene derivatives. This family was further characterized through LC–MS/MS analyses in positive and negative ion modes combined with molecular networking, together with a comprehensive NMR study of isolated representatives such as demethylfurospongin-4 and furospongin-1. The MS/MS and NMR spectroscopic data led to the identification of a new furanosesterterpene, furofficin (2), as well as two derivatives with a glycinyl lactam moiety, spongialactam A (12a) and B (12b). This study illustrates the potential of untargeted LC–MS metabolomics and molecular networking to discover new natural compounds even in an extensively studied organism such as S. officinalis. It also highlights the effect of anthropogenic pollution on the chemical profiles within the sponge.

Antioxidant Activity of the Lignins Derived from Fluidized-Bed Fast Pyrolysis

A challenge in recent years has been the rational use of forest and agriculture residues for the production of bio-fuel, biochemical, and other bioproducts. In this study, potentially useful compounds from pyrolytic lignins were identified by HPLC-MS/MS and untargeted metabolomics. The metabolites identified were 2-(4-allyl-2-methoxyphenoxy)-1-(4-hydroxy-3-methoxyphenyl)-1-propanol, benzyl benzoate, fisetinidol, phenyllactic acid, 2-phenylpropionic acid, 6,3′-dimethoxyflavone, and vanillin. The 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity (DPPH), trolox equivalent antioxidant capacity (TEAC), and total phenolics content (TPC) per gram of pyrolytic lignin ranged from 14 to 503 mg ascorbic acid equivalents, 35 to 277 mg trolox equivalents, and 0.42 to 50 mg gallic acid equivalents, respectively. A very significant correlation was observed between the DPPH and TPC (r = 0.8663, p ≤ 0.0001), TEAC and TPC (r = 0.8044, p ≤ 0.0001), and DPPH and TEAC (r = 0.8851, p ≤ 0.0001). The polyphenolic compounds in the pyrolytic lignins which are responsible for radical scavenging activity and antioxidant properties can be readily profiled with HPLC-MS/MS combined with untargeted metabolomics. The results also suggest that DPPH, TEAC, and TPC assays are suitable methods for the measurement of antioxidant activity in a variety of pyrolytic lignins. These data show that the pyrolytic lignins can be considered as promising sources of natural antioxidants and value-added chemicals.

Chemotyping the temporal volatile organic compounds of an invasive fungus to the United States, Raffaelea lauricola

Volatile organic compounds (VOCs) in the headspace of the fungus Raffaelea lauricola have been monitored and identified over a twenty-eight day growth period. R. lauricola is an invasive and phytopathogenic fungus that was first identified in the United States in the mid-2000s. It is believed to be spread by a host beetle, Xyleborus glabratus, and is detrimental both to wild members of the Lauraceae family and to commercial avocado groves particularly in the Southeastern region of the country. The fungus causes the fatal laurel wilt disease, a result of the host tree shutting down its vascular system in order to halt the spread of the fungus. The current study identified the VOCs present in the headspace of R. lauricola over the initial growth stage using headspace solid phase microextracion-gas chromatography-mass spectrometry (HS-SPME-GC-MS). Results revealed the VOC dynamics of the fungus in culture, indicating that the initial growth period of the fungus may coincide with potential responses from the host trees that may recognize and respond to the pathogen when the fungal VOCs are produced as a result of primary metabolic processes. As fungal growth progresses past initial growth phases, the predominant compounds seen in the odor profile are hydrocarbons and terpenes, produced from secondary metabolic processes. The odor profile pattern for the twenty-eight day growth period did change with the stages of growth. Based on the information learned from this pilot study, a discussion is presented of possible host tree reactions to R. lauricola and implications for future experiments.

Untargeted metabolomics analysis reveals dynamic changes in azelaic acid- and salicylic acid derivatives in LPS-treated Nicotiana tabacum cells

To counteract biotic stress factors, plants employ multilayered defense mechanisms responsive to pathogen-derived elicitor molecules, and regulated by different phytohormones and signaling molecules. Here, lipopolysaccharide (LPS), a microbe-associated molecular pattern (MAMP) molecule, was used to induce defense responses in Nicotiana tabacum cell suspensions. Intracellular metabolites were extracted with methanol and analyzed using a liquid chromatography-mass spectrometry (UHPLC-qTOF-MS/MS) platform. The generated data were processed and examined with multivariate and univariate statistical tools. The results show time-dependent dynamic changes and accumulation of glycosylated signaling molecules, specifically those of azelaic acid, salicylic acid and methyl-salicylate as contributors to the altered metabolomic state in LPS-treated cells.

Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites

Covering: 2000 to 2016The labor-intensive process of microbial natural product discovery is contingent upon identifying discrete secondary metabolites of interest within complex biological extracts, which contain inventories of all extractable small molecules produced by an organism or consortium. Historically, compound isolation prioritization has been driven by observed biological activity and/or relative metabolite abundance and followed by dereplication via accurate mass analysis. Decades of discovery using variants of these methods has generated the natural pharmacopeia but also contributes to recent high rediscovery rates. However, genomic sequencing reveals substantial untapped potential in previously mined organisms, and can provide useful prescience of potentially new secondary metabolites that ultimately enables isolation. Recently, advances in comparative metabolomics analyses have been coupled to secondary metabolic predictions to accelerate bioactivity and abundance-independent discovery work flows. In this review we will discuss the various analytical and computational techniques that enable MS-based metabolomic applications to natural product discovery and discuss the future prospects for comparative metabolomics in natural product discovery.

Data Streaming for Metabolomics: Accelerating Data Processing and Analysis from Days to Minutes

The speed and throughput of analytical platforms has been a driving force in recent years in the "omics" technologies and while great strides have been accomplished in both chromatography and mass spectrometry, data analysis times have not benefited at the same pace. Even though personal computers have become more powerful, data transfer times still represent a bottleneck in data processing because of the increasingly complex data files and studies with a greater number of samples. To meet the demand of analyzing hundreds to thousands of samples within a given experiment, we have developed a data streaming platform, XCMS Stream, which capitalizes on the acquisition time to compress and stream recently acquired data files to data processing servers, mimicking just-in-time production strategies from the manufacturing industry. The utility of this XCMS Online-based technology is demonstrated here in the analysis of T cell metabolism and other large-scale metabolomic studies. A large scale example on a 1000 sample data set demonstrated a 10 000-fold time savings, reducing data analysis time from days to minutes. Further, XCMS Stream has the capability to increase the efficiency of downstream biochemical dependent data acquisition (BDDA) analysis by initiating data conversion and data processing on subsets of data acquired, expanding its application beyond data transfer to smart preliminary data decision-making prior to full acquisition.

Metabolomic profiling of brain from infants who died from Sudden Infant Death Syndrome reveals novel predictive biomarkers

OBJECTIVE

Sudden Infant Death Syndrome (SIDS) is defined as the sudden death of an infant <1 year of age that cannot be explained following a thorough investigation. Currently, no reliable clinical biomarkers are available for the prediction of infants who will die of SIDS.

STUDY DESIGN

This study aimed to profile the medulla oblongata from postmortem human brain from SIDS victims (n=16) and compare their profiles with that of age-matched controls (n=7).

RESULTS

Using LC-Orbitrap-MS, we detected 12 710 features in electrospray ionization positive (ESI+) mode and 8243 in ESI- mode from polar extracts of brain. Five features acquired in ESI+ mode produced a predictive model for SIDS with an area under the receiver operating characteristic curve (AUC) of 1 (confidence interval (CI): 0.995-1) and a predictive power of 97.4%. Three biomarkers acquired in ESI- mode produced a predictive model with an AUC of 0.866 (CI: 0.767-0.942) and a predictive power of 77.6%. We confidently identified 5 of these features (l-(+)-ergothioneine, nicotinic acid, succinic acid, adenosine monophosphate and azelaic acid) and putatively identify another 4 out of the 15 in total.

CONCLUSIONS

This study underscores the potential value of metabolomics for studying SIDS. Further characterization of the metabolome of postmortem SIDS brains could lead to the identification of potential antemortem biomarkers for novel prevention strategies for SIDS.

An untargeted metabolomics approach to determine component differences and variation in their in vivo distribution between Kuqin and Ziqin, two commercial specifications of Scutellaria Radix

Kuqin (KQ) and Ziqin (ZQ), derived from the roots of Scutellaria baicalensis Georgi, are two important commercial specifications of Scutellariae Radix (SR, termed Huang qin in Chinese).

Freeze-dried strawberry and blueberry attenuates diet-induced obesity and insulin resistance in rats by inhibiting adipogenesis and lipogenesis

Freeze-dried strawberry/blueberry powder might be useful for treatment/prevention of obesity-related diseases as it improves weight, fat and glucose-related biomarkers.

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

In untargeted metabolomics analysis, several factors (e.g., unwanted experimental & biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.

Biotransformation of 2,4-dinitroanisole by a fungal Penicillium sp

Insensitive munitions explosives are new formulations that are less prone to unintended detonation compared to traditional explosives. While these formulations have safety benefits, the individual constituents, such as 2,4-dinitroanisole (DNAN), have an unknown ecosystem fate with potentially toxic impacts to flora and fauna exposed to DNAN and/or its metabolites. Fungi may be useful in remediation and have been shown to degrade traditional nitroaromatic explosives, such as 2,4,6-trinitrotoluene and 2,4-dinitrotoluene, that are structurally similar to DNAN. In this study, a fungal Penicillium sp., isolated from willow trees and designated strain KH1, was shown to degrade DNAN in solution within 14 days. Stable-isotope labeled DNAN and an untargeted metabolomics approach were used to discover 13 novel transformation products. Penicillium sp. KH1 produced DNAN metabolites resulting from ortho- and para-nitroreduction, demethylation, acetylation, hydroxylation, malonylation, and sulfation. Incubations with intermediate metabolites such as 2-amino-4-nitroanisole and 4-amino-2-nitroanisole as the primary substrates confirmed putative metabolite isomerism and pathways. No ring-cleavage products were observed, consistent with other reports that mineralization of DNAN is an uncommon metabolic outcome. The production of metabolites with unknown persistence and toxicity suggests further study will be needed to implement remediation with Penicillium sp. KH1. To our knowledge, this is the first report on the biotransformation of DNAN by a fungus.

Untargeted Metabolomics Strategies-Challenges and Emerging Directions

Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes and, as such, the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS-based untargeted metabolomics studies-specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described. Graphical Abstract ᅟ.

Mass Spectrometry-Based Visualization of Molecules Associated with Human Habitats

The cars we drive, the homes we live in, the restaurants we visit, and the laboratories and offices we work in are all a part of the modern human habitat. Remarkably, little is known about the diversity of chemicals present in these environments and to what degree molecules from our bodies influence the built environment that surrounds us and vice versa. We therefore set out to visualize the chemical diversity of five built human habitats together with their occupants, to provide a snapshot of the various molecules to which humans are exposed on a daily basis. The molecular inventory was obtained through untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of samples from each human habitat and from the people that occupy those habitats. Mapping MS-derived data onto 3D models of the environments showed that frequently touched surfaces, such as handles (e.g., door, bicycle), resemble the molecular fingerprint of the human skin more closely than other surfaces that are less frequently in direct contact with humans (e.g., wall, bicycle frame). Approximately 50% of the MS/MS spectra detected were shared between people and the environment. Personal care products, plasticizers, cleaning supplies, food, food additives, and even medications that were found to be a part of the human habitat. The annotations indicate that significant transfer of chemicals takes place between us and our built environment. The workflows applied here will lay the foundation for future studies of molecular distributions in medical, forensic, architectural, space exploration, and environmental applications.

Concurrent profiling of polar metabolites and lipids in human plasma using HILIC-FTMS

Blood plasma is the most popularly used sample matrix for metabolite profiling studies, which aim to achieve global metabolite profiling and biomarker discovery. However, most of the current studies on plasma metabolite profiling focused on either the polar metabolites or lipids. In this study, a comprehensive analysis approach based on HILIC-FTMS was developed to concurrently examine polar metabolites and lipids. The HILIC-FTMS method was developed using mixed standards of polar metabolites and lipids, the separation efficiency of which is better in HILIC mode than in C5 and C18 reversed phase (RP) chromatography. This method exhibits good reproducibility in retention times (CVs < 3.43%) and high mass accuracy (<3.5 ppm). In addition, we found MeOH/ACN/Acetone (1:1:1, v/v/v) as extraction cocktail could achieve desirable gathering of demanded extracts from plasma samples. We further integrated the MeOH/ACN/Acetone extraction with the HILIC-FTMS method for metabolite profiling and smoking-related biomarker discovery in human plasma samples. Heavy smokers could be successfully distinguished from non smokers by univariate and multivariate statistical analysis of the profiling data, and 62 biomarkers for cigarette smoke were found. These results indicate that our concurrent analysis approach could be potentially used for clinical biomarker discovery, metabolite-based diagnosis, etc.

A hybrid polyketide-nonribosomal peptide in nematodes that promotes larval survival

Polyketides and nonribosomal peptides are two important types of natural products that are produced by many species of bacteria and fungi but are exceedingly rare in metazoans. Here, we elucidate the structure of a hybrid polyketide-nonribosomal peptide from Caenorhabditis elegans that is produced in the canal-associated neurons (CANs) and promotes survival during starvation-induced larval arrest. Our results uncover a novel mechanism by which animals respond to nutrient fluctuations to extend survival.

Stimulatory Effects of Acibenzolar-S-Methyl on Chlorogenic Acids Biosynthesis in Centella asiatica Cells

Centella asiatica is a perrenial herb that grows in tropical regions with numerous medicinal properties mostly attributed to the presence of pentacyclic triterpenoids. Interestingly, this plant also possess a significant amount of phenylpropanoid-derived chlorogenic acids (CGAs) that have recently been reported to confer neuroprotective properties. In a biotechnological attempt to increase the biosynthesis of CGA-derivatives in cultured Centella cells, acibenzolar-S-methyl was applied as a xenobiotic inducer in combination with quinic acid and shikimic acid as precursor molecules. Applying a semi-targeted metabolomics-based approach, time and concentration studies were undertaken to evaluate the effect of the manipulation on cellular metabolism leading to CGA production. Phytochemical extracts were prepared using methanol and analyzed using a UHPLC-qTOF-MS platform. Data was processed and analyzed using multivariate data models. A total of four CGA-derivatives, annotated as trans-5-feruloylquinic acid, 3,5 di-caffeoylquinic acid, 3,5-O-dicaffeoyl-4-O-malonylquinic acid (irbic acid) and 3-caffeoyl, 5-feruloylquinic acid, were found to be upregulated by the acibenzolar-S-methyl treatment. To the best of our knowledge, this is the first report on the induction of CGA derivatives in this species. Contrary to expectations, the effects of precursor molecules on the levels of the CGAs were insignificant. However, a total of 16 metabolites, including CGA derivatives, were up-regulated by precursor treatment. Therefore, this study shows potential to biotechnologically manipulate C. asiatica cells to increase the production of these health beneficial CGAs.

Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism.

The metabolomic signature of hematologic malignancies

The ongoing accumulation of knowledge raises hopes that understanding tumor metabolism will provide new ways for predicting, diagnosing, and even treating cancers. Some metabolic biomarkers are at present routinely utilized to diagnose cancer and metabolic alterations of tumors are being confirmed as therapeutic targets. The growing utilization of metabolomics in clinical research may rapidly turn it into one of the most potent instruments used to detect and fight tumor. In fact, while the current state and trends of high throughput metabolomics profiling focus on the purpose of discovering biomarkers and hunting for metabolic mechanism, a prospective direction, namely reprogramming metabolomics, highlights the way to use metabolomics approach for the aim of treatment of disease by way of reconstruction of disturbed metabolic pathways. In this review, we present an ample summary of the current clinical appliances of metabolomics in hematological malignancies.

Effect of amino acid supplementation on titer and glycosylation distribution in hybridoma cell cultures-Systems biology-based interpretation using genome-scale metabolic flux balance model and multivariate data analysis

Genome-scale flux balance analysis (FBA) is a powerful systems biology tool to characterize intracellular reaction fluxes during cell cultures. FBA estimates intracellular reaction rates by optimizing an objective function, subject to the constraints of a metabolic model and media uptake/excretion rates. A dynamic extension to FBA, dynamic flux balance analysis (DFBA), can calculate intracellular reaction fluxes as they change during cell cultures. In a previous study by Read et al. (2013), a series of informed amino acid supplementation experiments were performed on twelve parallel murine hybridoma cell cultures, and this data was leveraged for further analysis (Read et al., Biotechnol Prog. 2013;29:745-753). In order to understand the effects of media changes on the model murine hybridoma cell line, a systems biology approach is applied in the current study. Dynamic flux balance analysis was performed using a genome-scale mouse metabolic model, and multivariate data analysis was used for interpretation. The calculated reaction fluxes were examined using partial least squares and partial least squares discriminant analysis. The results indicate media supplementation increases product yield because it raises nutrient levels extending the growth phase, and the increased cell density allows for greater culture performance. At the same time, the directed supplementation does not change the overall metabolism of the cells. This supports the conclusion that product quality, as measured by glycoform assays, remains unchanged because the metabolism remains in a similar state. Additionally, the DFBA shows that metabolic state varies more at the beginning of the culture but less by the middle of the growth phase, possibly due to stress on the cells during inoculation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1163-1173, 2016.

Training in metabolomics research. II. Processing and statistical analysis of metabolomics data, metabolite identification, pathway analysis, applications of metabolomics and its future

Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. This second part of a comprehensive description of the methods of metabolomics focuses on data analysis, emerging methods in metabolomics and the future of this discipline. Copyright © 2016 John Wiley & Sons, Ltd.

Exhaled Molecular Fingerprinting in Diagnosis and Monitoring: Validating Volatile Promises

Medical diagnosis and phenotyping increasingly incorporate information from complex biological samples. This has promoted the development and clinical application of non-invasive metabolomics in exhaled air (breathomics). In respiratory medicine, expired volatile organic compounds (VOCs) are associated with inflammatory, oxidative, microbial, and neoplastic processes. After recent proof of concept studies demonstrating moderate to good diagnostic accuracies, the latest efforts in breathomics are focused on optimization of sensor technologies and analytical algorithms, as well as on independent validation of clinical classification and prediction. Current research strategies are revealing the underlying pathophysiological pathways as well as clinically-acceptable levels of diagnostic accuracy. Implementing recent guidelines on validating molecular signatures in medicine will enhance the clinical potential of breathomics and the development of point-of-care technologies.

A simple multi-scale Gaussian smoothing-based strategy for automatic chromatographic peak extraction

Peak detection is a critical step in chromatographic data analysis. In the present work, we developed a multi-scale Gaussian smoothing-based strategy for accurate peak extraction. The strategy consisted of three stages: background drift correction, peak detection, and peak filtration. Background drift correction was implemented using a moving window strategy. The new peak detection method is a variant of the system used by the well-known MassSpecWavelet, i.e., chromatographic peaks are found at local maximum values under various smoothing window scales. Therefore, peaks can be detected through the ridge lines of maximum values under these window scales, and signals that are monotonously increased/decreased around the peak position could be treated as part of the peak. Instrumental noise was estimated after peak elimination, and a peak filtration strategy was performed to remove peaks with signal-to-noise ratios smaller than 3. The performance of our method was evaluated using two complex datasets. These datasets include essential oil samples for quality control obtained from gas chromatography and tobacco plant samples for metabolic profiling analysis obtained from gas chromatography coupled with mass spectrometry. Results confirmed the reasonability of the developed method.

Global Isotope Metabolomics Reveals Adaptive Strategies for Nitrogen Assimilation

Nitrogen cycling is a microbial metabolic process essential for global ecological/agricultural balance. To investigate the link between the well-established ammonium and the alternative nitrate assimilation metabolic pathways, global isotope metabolomics was employed to examine three nitrate reducing bacteria using (15)NO3 as a nitrogen source. In contrast to a control (Pseudomonas stutzeri RCH2), the results show that two of the isolates from Oak Ridge, Tennessee (Pseudomonas N2A2 and N2E2) utilize nitrate and ammonia for assimilation concurrently with differential labeling observed across multiple classes of metabolites including amino acids and nucleotides. The data reveal that the N2A2 and N2E2 strains conserve nitrogen-containing metabolites, indicating that the nitrate assimilation pathway is a conservation mechanism for the assimilation of nitrogen. Co-utilization of nitrate and ammonia is likely an adaption to manage higher levels of nitrite since the denitrification pathways utilized by the N2A2 and N2E2 strains from the Oak Ridge site are predisposed to the accumulation of the toxic nitrite. The use of global isotope metabolomics allowed for this adaptive strategy to be investigated, which would otherwise not have been possible to decipher.

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.

GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity

Breast cancers possess fundamentally altered metabolism that fuels their pathogenicity. While many metabolic drivers of breast cancers have been identified, the metabolic pathways that mediate breast cancer malignancy and poor prognosis are less well understood. Here, we used a reactivity-based chemoproteomic platform to profile metabolic enzymes that are enriched in breast cancer cell types linked to poor prognosis, including triple-negative breast cancer (TNBC) cells and breast cancer cells that have undergone an epithelial-mesenchymal transition-like state of heightened malignancy. We identified glutathione S-transferase Pi 1 (GSTP1) as a novel TNBC target that controls cancer pathogenicity by regulating glycolytic and lipid metabolism, energetics, and oncogenic signaling pathways through a protein interaction that activates glyceraldehyde-3-phosphate dehydrogenase activity. We show that genetic or pharmacological inactivation of GSTP1 impairs cell survival and tumorigenesis in TNBC cells. We put forth GSTP1 inhibitors as a novel therapeutic strategy for combatting TNBCs through impairing key cancer metabolism and signaling pathways.

Metabolomics: beyond biomarkers and towards mechanisms

Metabolomics, which is the profiling of metabolites in biofluids, cells and tissues, is routinely applied as a tool for biomarker discovery. Owing to innovative developments in informatics and analytical technologies, and the integration of orthogonal biological approaches, it is now possible to expand metabolomic analyses to understand the systems-level effects of metabolites. Moreover, because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases.

Phenylpropanoid Defences in Nicotiana tabacum Cells: Overlapping Metabolomes Indicate Common Aspects to Priming Responses Induced by Lipopolysaccharides, Chitosan and Flagellin-22

Plants have evolved both constitutive and inducible defence strategies to cope with different biotic stimuli and stresses. Exposure of a plant to a challenging stress can lead to a primed state that allows it to launch a more rapid and stronger defence. Here we applied a metabolomic approach to study and compare the responses induced in Nicotiana tabacum cells by microbe-associated molecular pattern (MAMP) molecules, namely lipopolysaccharides (LPS), chitosan (CHT) and flagellin-22 (FLG22). Early response metabolites, extracted with methanol, were analysed by UHPLC-MS/MS. Using multivariate statistical tools the metabolic profiles induced by these elicitors were analysed. In the metabolic fingerprint of these agents a total of 19 cinnamic acid derivatives conjugated to quinic acids (chlorogenic acids), shikimic acid, tyramine, polyamines or glucose were found as discriminant biomarkers. In addition, treatment with the phytohormones salicylic acid (SA), methyljasmonic acid (MJ) and abscisic acid (ABA) resulted in differentially-induced phenylpropanoid pathway metabolites. The results indicate that the phenylpropanoid pathway is activated by these elicitors while hydroxycinnamic acid derivatives are commonly associated with the metabolic response to the MAMPs, and that the activated responses are modulated by both SA and MJ, with ABA not playing a role.

Enabling Metabolomics Based Biomarker Discovery Studies Using Molecular Phenotyping of Exosome-Like Vesicles

Identification of sensitive and specific biomarkers with clinical and translational utility will require smart experimental strategies that would augment expanding the breadth and depth of molecular measurements within the constraints of currently available technologies. Exosomes represent an information rich matrix to discern novel disease mechanisms that are thought to contribute to pathologies such as dementia and cancer. Although proteomics and transcriptomic studies have been reported using Exosomes-Like Vesicles (ELVs) from different sources, exosomal metabolome characterization and its modulation in health and disease remains to be elucidated. Here we describe methodologies for UPLC-ESI-MS based small molecule profiling of ELVs from human plasma and cell culture media. In this study, we present evidence that indeed ELVs carry a rich metabolome that could not only augment the discovery of low abundance biomarkers but may also help explain the molecular basis of disease progression. This approach could be easily translated to other studies seeking to develop predictive biomarkers that can subsequently be used with simplified targeted approaches.

Mass Spectrometry in Plant-omics

Plant-omics is rapidly becoming an important field of study in the scientific community due to the urgent need to address many of the most important questions facing humanity today with regard to agriculture, medicine, biofuels, environmental decontamination, ecological sustainability, etc. High-performance mass spectrometry is a dominant tool for interrogating the metabolomes, peptidomes, and proteomes of a diversity of plant species under various conditions, revealing key insights into the functions and mechanisms of plant biochemistry.

A UDP-glucosyltransferase functions in both acylphloroglucinol glucoside and anthocyanin biosynthesis in strawberry (Fragaria × ananassa)

Physiologically active acylphloroglucinol (APG) glucosides were recently found in strawberry (Fragaria sp.) fruit. Although the formation of the APG aglycones has been clarified, little is known about APG glycosylation in plants. In this study we functionally characterized ripening-related glucosyltransferase genes in Fragaria by comprehensive biochemical analyses of the encoded proteins and by a RNA interference (RNAi) approach in vivo. The allelic proteins UGT71K3a/b catalyzed the glucosylation of diverse hydroxycoumarins, naphthols and flavonoids as well as phloroglucinols, enzymatically synthesized APG aglycones and pelargonidin. Total enzymatic synthesis of APG glucosides was achieved by co-incubation of recombinant dual functional chalcone/valerophenone synthase and UGT71K3 proteins with essential coenzyme A esters and UDP-glucose. An APG glucoside was identified in strawberry fruit which has not yet been reported in other plants. Suppression of UGT71K3 activity in transient RNAi-silenced fruits led to a loss of pigmentation and a substantial decrease of the levels of various APG glucosides and an anthocyanin. Metabolite analyses of transgenic fruits confirmed UGT71K3 as a UDP-glucose:APG glucosyltransferase in planta. These results provide the foundation for the breeding of fruits with improved health benefits and for the biotechnological production of bioactive natural products.

MRP1 mediates folate transport and antifolate sensitivity in Plasmodium falciparum

Multidrug resistance-associated proteins (MRP) of Plasmodium falciparum have been associated with altered drug sensitivity. Knowledge on MRP substrate specificity is indispensible for the characterization of resistance mechanisms and identifying its physiological roles. An untargeted metabolomics approach detected decreased folate concentrations in red blood cells infected with schizont stage parasites lacking expression of MRP1. Furthermore, a tenfold decrease in sensitivity toward the folate analog methotrexate was detected for parasites lacking MRP1. PfMRP1 is involved in the export of folate from parasites into red blood cells and is therefore a relevant factor for efficient malaria treatment through the folate pathway.

Untargeted metabolomic analysis using LC-TOF/MS and LC-MS/MS for revealing metabolic alterations linked to alcohol-induced hepatic steatosis in rat serum and plasma

Untargeted LC-MS metabolomics to screen differential metabolites in rat serum and plasma, and reveal metabolic alterations linked to AHS.

Pure ion chromatogram extraction via optimal k-means clustering

A new method called KPIC is proposed for extracting pure ion chromatogram from raw LC-MS data accurately, which is based on optimalk-means clustering. And KPIC can reduces the number of split signals and provide higher quality chromatographic peaks.

Profiling of Altered Metabolomic States in Nicotiana tabacum Cells Induced by Priming Agents

Metabolomics has developed into a valuable tool for advancing our understanding of plant metabolism. Plant innate immune defenses can be activated and enhanced so that, subsequent to being pre-sensitized, plants are able to launch a stronger and faster defense response upon exposure to pathogenic microorganisms, a phenomenon known as priming. Here, three contrasting chemical activators, namely acibenzolar-S-methyl, azelaic acid and riboflavin, were used to induce a primed state in Nicotiana tabacum cells. Identified biomarkers were then compared to responses induced by three phytohormones-abscisic acid, methyljasmonate, and salicylic acid. Altered metabolomes were studied using a metabolite fingerprinting approach based on liquid chromatography and mass spectrometry. Multivariate data models indicated that these inducers cause time-dependent metabolic perturbations in the cultured cells and revealed biomarkers of which the levels are affected by these agents. A total of 34 metabolites were annotated from the mass spectral data and online databases. Venn diagrams were used to identify common biomarkers as well as those unique to a specific agent. Results implicate 20 cinnamic acid derivatives conjugated to (i) quinic acid (chlorogenic acids), (ii) tyramine, (iii) polyamines, or (iv) glucose as discriminatory biomarkers of priming in tobacco cells. Functional roles for most of these metabolites in plant defense responses could thus be proposed. Metabolites induced by the activators belong to the early phenylpropanoid pathway, which indicates that different stimuli can activate similar pathways but with different metabolite fingerprints. Possible linkages to phytohormone-dependent pathways at a metabolomic level were indicated in the case of cells treated with salicylic acid and methyljasmonate. The results contribute to a better understanding of the priming phenomenon and advance our knowledge of cinnamic acid derivatives as versatile defense metabolites.

Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening

Background

Breath volatile organic compounds (VOCs) have been reported as biomarkers of lung cancer, but it is not known if biomarkers identified in one group can identify disease in a separate independent cohort. Also, it is not known if combining breath biomarkers with chest CT has the potential to improve the sensitivity and specificity of lung cancer screening.

Methods

Model-building phase (unblinded): Breath VOCs were analyzed with gas chromatography mass spectrometry in 82 asymptomatic smokers having screening chest CT, 84 symptomatic high-risk subjects with a tissue diagnosis, 100 without a tissue diagnosis, and 35 healthy subjects. Multiple Monte Carlo simulations identified breath VOC mass ions with greater than random diagnostic accuracy for lung cancer, and these were combined in a multivariate predictive algorithm. Model-testing phase (blinded validation): We analyzed breath VOCs in an independent cohort of similar subjects (n = 70, 51, 75 and 19 respectively). The algorithm predicted discriminant function (DF) values in blinded replicate breath VOC samples analyzed independently at two laboratories (A and B). Outcome modeling: We modeled the expected effects of combining breath biomarkers with chest CT on the sensitivity and specificity of lung cancer screening.

Results

Unblinded model-building phase. The algorithm identified lung cancer with sensitivity 74.0%, specificity 70.7% and C-statistic 0.78. Blinded model-testing phase: The algorithm identified lung cancer at Laboratory A with sensitivity 68.0%, specificity 68.4%, C-statistic 0.71; and at Laboratory B with sensitivity 70.1%, specificity 68.0%, C-statistic 0.70, with linear correlation between replicates (r = 0.88). In a projected outcome model, breath biomarkers increased the sensitivity, specificity, and positive and negative predictive values of chest CT for lung cancer when the tests were combined in series or parallel.

Conclusions

Breath VOC mass ion biomarkers identified lung cancer in a separate independent cohort, in a blinded replicated study. Combining breath biomarkers with chest CT could potentially improve the sensitivity and specificity of lung cancer screening.

Trial Registration

ClinicalTrials.gov NCT00639067

A roadmap for the XCMS family of software solutions in metabolomics

Global profiling of metabolites in biological samples by liquid chromatography/mass spectrometry results in datasets too large to evaluate manually. Fortunately, a variety of software programs are now available to automate the data analysis. Selection of the appropriate processing solution is dependent upon experimental design. Most metabolomic studies a decade ago had a relatively simple experimental design in which the intensities of compounds were compared between only two sample groups. More recently, however, increasingly sophisticated applications have been pursued. Examples include comparing compound intensities between multiple sample groups and unbiasedly tracking the fate of specific isotopic labels. The latter types of applications have necessitated the development of new software programs, which have introduced additional functionalities that facilitate data analysis. The objective of this review is to provide an overview of the freely available bioinformatic solutions that are either based upon or are compatible with the algorithms in XCMS, which we broadly refer to here as the 'XCMS family' of software. These include CAMERA, credentialing, Warpgroup, metaXCMS, X(13)CMS, and XCMS Online. Together, these informatic technologies can accommodate most cutting-edge metabolomic applications and offer unique advantages when compared to the original XCMS program.

Functional Characterization and Substrate Promiscuity of UGT71 Glycosyltransferases from Strawberry (Fragaria × ananassa)

Glycosylation determines the complexity and diversity of plant natural products. To characterize fruit ripening-related UDP-dependent glycosyltransferases (UGTs) functionally in strawberry, we mined the publicly available Fragaria vesca genome sequence and found 199 putative UGT genes. Candidate UGTs whose expression levels were strongly up-regulated during fruit ripening were cloned from F.×ananassa and six were successfully expressed in Escherichia coli and biochemically characterized. UGT75T1 showed very strict substrate specificity and glucosylated only galangin out of 33 compounds. The other recombinant enzymes exhibited broad substrate tolerance, accepting numerous flavonoids, hydroxycoumarins, naphthols and the plant hormone, (+)-S-abscisic acid (ABA). UGT71W2 showed the highest activity towards 1-naphthol, while UGT71A33, UGT71A34a/b and UGT71A35 preferred 3-hydroxycoumarin and formed 3- and 7-O-glucosides as well as a diglucoside from flavonols. Screening of a strawberry physiological aglycone library identified kaempferol, quercetin, ABA and three unknown natural compounds as putative in planta substrates of UGT71A33, UGT71A34a and UGT71W2. Metabolite analyses of RNA interference (RNAi)-mediated silenced fruits demonstrated that UGT71W2 contributes to the glycosylation of flavonols, xenobiotics and, to a minor extent, of ABA, in planta. The study showed that both specialist and generalist UGTs were expressed during strawberry fruit ripening and the latter were probably not restricted to only one function in plants.

Acylphloroglucinol Biosynthesis in Strawberry Fruit

Phenolics have health-promoting properties and are a major group of metabolites in fruit crops. Through reverse genetic analysis of the functions of four ripening-related genes in the octoploid strawberry (Fragaria × ananassa), we discovered four acylphloroglucinol (APG)-glucosides as native Fragaria spp. fruit metabolites whose levels were differently regulated in the transgenic fruits. The biosynthesis of the APG aglycones was investigated by examination of the enzymatic properties of three recombinant Fragaria vesca chalcone synthase (FvCHS) proteins. CHS is involved in anthocyanin biosynthesis during ripening. The F. vesca enzymes readily catalyzed the condensation of two intermediates in branched-chain amino acid metabolism, isovaleryl-Coenzyme A (CoA) and isobutyryl-CoA, with three molecules of malonyl-CoA to form phlorisovalerophenone and phlorisobutyrophenone, respectively, and formed naringenin chalcone when 4-coumaroyl-CoA was used as starter molecule. Isovaleryl-CoA was the preferred starter substrate of FvCHS2-1. Suppression of CHS activity in both transient and stable CHS-silenced fruit resulted in a substantial decrease of APG glucosides and anthocyanins and enhanced levels of volatiles derived from branched-chain amino acids. The proposed APG pathway was confirmed by feeding isotopically labeled amino acids. Thus, Fragaria spp. plants have the capacity to synthesize pharmaceutically important APGs using dual functional CHS/(phloriso)valerophenone synthases that are expressed during fruit ripening. Duplication and adaptive evolution of CHS is the most probable scenario and might be generally applicable to other plants. The results highlight that important promiscuous gene function may be missed when annotation relies solely on in silico analysis.