metaXCMS: second-order analysis of untargeted metabolomics data

Changes in urinary metabolomic profile show the effectiveness of a nutritional intervention in children 6-12 years old: The ALINFA study

Diet plays an essential role in health and disease. Therefore, its determination is an important component of many investigations. The aim of the study was to evaluate the effect of a nutritional intervention on the urinary metabolome in children aged 6-12 years. Also, it was intended to identify biomarkers of diet quality and dietary intake. A 2-month, randomized, controlled, parallel trial was conducted in Spanish children. The analyses focused on the ALINFA group, which followed a full-fixed meal plan including healthy products, ready-to-eat meals, and healthy recipes. Diet quality was assessed by the KIDMED index and dietary intake by a food frequency questionnaire. Untargeted metabolomic analysis on urine samples was carried out, and multivariate analyses were performed for pattern recognition and characteristic metabolite identification. PLS-DA and Volcano plot analyses were performed to identify the discriminating metabolites of this group. 12 putative metabolites were found to be the most relevant to this intervention. Most of them were products derived from protein and amino acid metabolism (N-Ribosylhistidine, indolacrylic acid, and peptides) and lipid metabolism (3-oxo-2-pentylcyclopentane-1-hexanoic acid methyl, Suberoyl-L-carnitine, and 7-Dehydrodichapetalin E). All these metabolites decreased after the intervention, which was mainly associated with a decrease in the consumption of fatty meat and total fat, especially saturated fat. In turn, N-Ribosylhistidine and Suberoyl-L-carnitine were negatively associated with diet quality, as well as able to predict the change in KIDMED index. In conclusion, the changes observed in urinary metabolome demonstrate the effectiveness of the ALINFA nutritional intervention.

Optimal transport for automatic alignment of untargeted metabolomic data

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline

To standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

Guided analysis of ambient ionization mass spectrometry data with the MQ_Assistant

RATIONALE

Ambient ionization mass spectrometry (AIMS) delivers realistic data from samples in their native state. In addition, AIMS methods reduce time and costs for sample preparation and have less environmental impact. However, AIMS data are often complex and require substantial processing before interpretation.

METHODS

We developed an interactive R script for guided mass spectrometry (MS) data processing. The "MQ_Assistant" is based on MALDIquant, a popular R package for MS data processing. In each step, the user can try and preview the effect of chosen parameters before deciding on the values with the best result and proceeding to the next stage. The outcome of the MQ_Assistant is a feature matrix that can be further analyzed in R and statistics tools such as MetaboAnalyst.

RESULTS

Using 360 AIMS example spectra, we demonstrate the step-by-step processing for creating a feature matrix. In addition, we show how to visualize the results of three biological replicates of a plant-microbe interaction between Arabidopsis and Trichoderma as a heatmap using R and upload them to MetaboAnalyst. The final parameter set can be saved for reuse in MALDIquant workflows of similar data.

CONCLUSIONS

The MQ_Assistant helps novices and experienced users to develop workflows for (AI)MS data processing. The interactive procedure supports the quick finding of appropriate settings. These parameters can be exported and reused in future projects. The stepwise operation with visual feedback also suggests the use of the MQ_Assistant in education.

The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC-TOF MS metabolome samples

Metabolomics by gas chromatography/mass spectrometry (GC/MS) provides a standardized and reliable platform for understanding small molecule biology. Since 2005, the West Coast Metabolomics Center at the University of California at Davis has collated GC/MS metabolomics data from over 156,000 samples and 2000 studies into the standardized BinBase database. We believe that the observations from these samples will provide meaningful insight to biologists and that our data treatment and webtool will provide insight to others who seek to standardize disparate metabolomics studies. We here developed an easy-to-use query interface, BinDiscover, to enable intuitive, rapid hypothesis generation for biologists based on these metabolomic samples. BinDiscover creates observation summaries and graphics across a broad range of species, organs, diseases, and compounds. Throughout the components of BinDiscover, we emphasize the use of ontologies to aggregate large groups of samples based on the proximity of their metadata within these ontologies. This adjacency allows for the simultaneous exploration of entire categories such as "rodents", "digestive tract", or "amino acids". The ontologies are particularly relevant for BinDiscover's ontologically grouped differential analysis, which, like other components of BinDiscover, creates clear graphs and summary statistics across compounds and biological metadata. We exemplify BinDiscover's extensive applicability in three showcases across biological domains.

Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples

Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.

mTORC1 and mTORC2 Complexes Regulate the Untargeted Metabolomics and Amino Acid Metabolites Profile through Mitochondrial Bioenergetic Functions in Pancreatic Beta Cells

Background: Pancreatic beta cells regulate bioenergetics efficiency and secret insulin in response to glucose and nutrient availability. The mechanistic Target of Rapamycin (mTOR) network orchestrates pancreatic progenitor cell growth and metabolism by nucleating two complexes, mTORC1 and mTORC2. Objective: To determine the impact of mTORC1/mTORC2 inhibition on amino acid metabolism in mouse pancreatic beta cells (Beta-TC-6 cells, ATCC-CRL-11506) using high-resolution metabolomics (HRM) and live-mitochondrial functions. Methods: Pancreatic beta TC-6 cells were incubated for 24 h with either: RapaLink-1 (RL); Torin-2 (T); rapamycin (R); metformin (M); a combination of RapaLink-1 and metformin (RLM); Torin-2 and metformin (TM); compared to the control. We applied high-resolution mass spectrometry (HRMS) LC-MS/MS untargeted metabolomics to compare the twenty natural amino acid profiles to the control. In addition, we quantified the bioenergetics dynamics and cellular metabolism by live-cell imaging and the MitoStress Test XF24 (Agilent, Seahorse). The real-time, live-cell approach simultaneously measures the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) to determine cellular respiration and metabolism. Statistical significance was assessed using ANOVA on Ranks and post-hoc Welch t-Tests. Results: RapaLink-1, Torin-2, and rapamycin decreased L-aspartate levels compared to the control (p = 0.006). Metformin alone did not affect L-aspartate levels. However, L-asparagine levels decreased with all treatment groups compared to the control (p = 0.03). On the contrary, L-glutamate and glycine levels were reduced only by mTORC1/mTORC2 inhibitors RapaLink-1 and Torin-2, but not by rapamycin or metformin. The metabolic activity network model predicted that L-aspartate and AMP interact within the same activity network. Live-cell bioenergetics revealed that ATP production was significantly reduced in RapaLink-1 (122.23 ± 33.19), Torin-2 (72.37 ± 17.33) treated cells, compared to rapamycin (250.45 ± 9.41) and the vehicle control (274.23 ± 38.17), p < 0.01. However, non-mitochondrial oxygen consumption was not statistically different between RapaLink-1 (67.17 ± 3.52), Torin-2 (55.93 ± 8.76), or rapamycin (80.01 ± 4.36, p = 0.006). Conclusions: Dual mTORC1/mTORC2 inhibition by RapaLink-1 and Torin-2 differentially altered the amino acid profile and decreased mitochondrial respiration compared to rapamycin treatment which only blocks the FRB domain on mTOR. Third-generation mTOR inhibitors may alter the mitochondrial dynamics and reveal a bioenergetics profile that could be targeted to reduce mitochondrial stress.

Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography–mass spectrometry (LC–MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC–MS metabolomics experiments or batches using only the features’ RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.

metabCombiner: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets

LC-HRMS experiments detect thousands of compounds, with only a small fraction of them identified in most studies. Traditional data processing pipelines contain an alignment step to assemble the measurements of overlapping features across samples into a unified table. However, data sets acquired under nonidentical conditions are not amenable to this process, mostly due to significant alterations in chromatographic retention times. Alignment of features between disparately acquired LC-MS metabolomics data could aid collaborative compound identification efforts and enable meta-analyses of expanded data sets. Here, we describe metabCombiner, a new computational pipeline for matching known and unknown features in a pair of untargeted LC-MS data sets and concatenating their abundances into a combined table of intersecting feature measurements. metabCombiner groups features by mass-to-charge (m/z) values to generate a search space of possible feature pair alignments, fits a spline through a set of selected retention time ordered pairs, and ranks alignments by m/z, mapped retention time, and relative abundance similarity. We evaluated this workflow on a pair of plasma metabolomics data sets acquired with different gradient elution methods, achieving a mean absolute retention time prediction error of roughly 0.06 min and a weighted per-compound matching accuracy of approximately 90%. We further demonstrate the utility of this method by comprehensively mapping features in urine and muscle metabolomics data sets acquired from different laboratories. metabCombiner has the potential to bridge the gap between otherwise incompatible metabolomics data sets and is available as an R package at https://github.com/hhabra/metabCombiner and Bioconductor.

Systems biology approaches to study lipidomes in health and disease

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.

Dose-Response Metabolomics To Understand Biochemical Mechanisms and Off-Target Drug Effects with the TOXcms Software

Small-molecule drugs and toxicants commonly interact with more than a single protein target, each of which may have unique effects on cellular phenotype. Although untargeted metabolomics is often applied to understand the mode of action of these chemicals, simple pairwise comparisons of treated and untreated samples are insufficient to resolve the effects of disrupting two or more independent protein targets. Here, we introduce a workflow for dose-response metabolomics to evaluate chemicals that potentially affect multiple proteins with different potencies. Our approach relies on treating samples with various concentrations of compound prior to analysis with mass spectrometry-based metabolomics. Data are then processed with software we developed called TOXcms, which statistically evaluates dose-response trends for each metabolomic signal according to user-defined tolerances and subsequently groups those that follow the same pattern. Although TOXcms was built upon the XCMS framework, it is compatible with any metabolomic data-processing software. Additionally, to enable correlation of dose responses beyond those that can be measured by metabolomics, TOXcms also accepts data from respirometry, cell death assays, other omic platforms, etc. In this work, we primarily focus on applying dose-response metabolomics to find off-target effects of drugs. Using metformin and etomoxir as examples, we demonstrate that each group of dose-response patterns identified by TOXcms signifies a metabolic response to a different protein target with a unique drug binding affinity. TOXcms is freely available on our laboratory website at http://pattilab.wustl.edu/software/toxcms .

Software Supporting a Workflow of Quantitative Dynamic Flux Maps Estimation in Central Metabolism from SIRM Experimental Data

Stable isotope-resolved metabolomics (SIRM), based on the analysis of biological samples from living cells incubated with artificial isotope enriched substrates, enables mapping the rates of biochemical reactions (metabolic fluxes). We developed software supporting a workflow of analysis of SIRM data obtained with mass spectrometry (MS). The evaluation of fluxes starting from raw MS recordings requires at least three steps of computer support: first, extraction of mass spectra of metabolites of interest, then correction of the spectra for natural isotope abundance, and finally, evaluation of fluxes by simulation of the corrected spectra using a corresponding mathematical model. A kinetic model based on ordinary differential equations (ODEs) for isotopomers of metabolites of the corresponding biochemical network supports the final part of the analysis, which provides a dynamic flux map.

Lymphatic Cannulation for Lymph Sampling and Molecular Delivery

Unlike the blood, the interstitial fluid and the deriving lymph are directly bathing the cellular layer of each organ. As such, composition analysis of the lymphatic fluid can provide more precise biochemical and cellular information on an organ's health and be a valuable resource for biomarker discovery. In this study, we describe a protocol for cannulation of mouse and rat lymphatic collectors that is suitable for the following: the "omic" sampling of pre- and postnodal lymph, collected from different anatomical districts; the phenotyping of immune cells circulating between parenchymal organs and draining lymph nodes; injection of known amounts of molecules for quantitative immunological studies of nodal trafficking and/or clearance; and monitoring an organ's biochemical omic changes in pathological conditions. Our data indicate that probing the lymphatic fluid can provide an accurate snapshot of an organ's physiology/pathology, making it an ideal target for liquid biopsy.

Serum Metabolomics Reveals That Gut Microbiome Perturbation Mediates Metabolic Disruption Induced by Arsenic Exposure in Mice

Arsenic contamination in drinking water has been a worldwide health concern for decades. In addition to being a well-recognized carcinogen, arsenic exposure has also been linked to diabetes, neurological effects, and cardiovascular diseases. Recently, increasing evidence has indicated that gut microbiome is an important risk factor in modulating the development of diseases. We aim to investigate the role of gut microbiome perturbation in arsenic-induced diseases by coupling a mass-spectrometry-based metabolomics approach and an animal model with altered gut microbiome induced by bacterial infection. Serum metabolic profiling has revealed that gut microbiome perturbation and arsenic exposure induced the dramatic changes of numerous metabolite pathways, including fatty acid metabolism, phospholipids, sphingolipids, cholesterols, and tryptophan metabolism, which were not or were less disrupted when the gut microbiome stayed normal. In summary, this study suggests that gut microbiome perturbation can exacerbate or cause metabolic disorders induced by arsenic exposure.

Automatic data analysis workflow for ultra-high performance liquid chromatography-high resolution mass spectrometry-based metabolomics

Data analysis for ultra-performance liquid chromatography high-resolution mass spectrometry-based metabolomics is a challenging task. The present work provides an automatic data analysis workflow (AntDAS2) by developing three novel algorithms, as follows: (i) a density-based ion clustering algorithm is designed for extracted-ion chromatogram extraction from high-resolution mass spectrometry; (ii) a new maximal value-based peak detection method is proposed with the aid of automatic baseline correction and instrumental noise estimation; and (iii) the strategy that clusters high-resolution m/z peaks to simultaneously align multiple components by a modified dynamic programing is designed to efficiently correct time-shift problem across samples. Standard compounds and complex datasets are used to study the performance of AntDAS2. AntDAS2 is better than several state-of-the-art methods, namely, XCMS Online, Mzmine2, and MS-DIAL, to identify underlying components and improve pattern recognition capability. Meanwhile, AntDAS2 is more efficient than XCMS Online and Mzmine2. A MATLAB GUI of AntDAS2 is designed for convenient analysis and is available at the following webpage: http://software.tobaccodb.org/software/antdas2.

Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online

Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LC)-mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5-10 min, depending on user experience; data processing typically takes 1-3 h, and data analysis takes ∼30 min.

Chemocoding as an identification tool where morphological- and DNA-based methods fall short: Inga as a case study

The need for species identification and taxonomic discovery has led to the development of innovative technologies for large-scale plant identification. DNA barcoding has been useful, but fails to distinguish among many species in species-rich plant genera, particularly in tropical regions. Here, we show that chemical fingerprinting, or 'chemocoding', has great potential for plant identification in challenging tropical biomes. Using untargeted metabolomics in combination with multivariate analysis, we constructed species-level fingerprints, which we define as chemocoding. We evaluated the utility of chemocoding with species that were defined morphologically and subject to next-generation DNA sequencing in the diverse and recently radiated neotropical genus Inga (Leguminosae), both at single study sites and across broad geographic scales. Our results show that chemocoding is a robust method for distinguishing morphologically similar species at a single site and for identifying widespread species across continental-scale ranges. Given that species are the fundamental unit of analysis for conservation and biodiversity research, the development of accurate identification methods is essential. We suggest that chemocoding will be a valuable additional source of data for a quick identification of plants, especially for groups where other methods fall short.

Identification and Validation of Sensory-Active Compounds from Data-Driven Research: A Flavoromics Approach

In this study, highly predictive LC-MS features (retention time_ m/ z) derived from untargeted chemical fingerprinting-multivariate analysis (MVA) previously used to model flavor changes in citrus fruits related to aging (freshness) were further isolated and analyzed for sensory impact, followed by structural elucidation. The top 10 statistical features from two MVA approaches, partial least-squares data analysis (PLS-DA) and Random Forrest (RF), were purified to approximately 70% via multidimensional liquid chromatography-mass-directed fractionation to screen for sensory activity. When added to a 'fresh' orange flavor model system, 50-60% of the isolates were reported to cause a sensory change. From the subset of the actives identified, two compounds were selected, on the basis of statistical relevance, that were further purified to >97% for identification (MS, NMR) and for sensory descriptive analysis (DA). The compounds were identified as nomilin glucoside and a novel ionone glucoside. DA evaluation in the recombination orange model indicated both compounds statistically suppressed the perceived intensity of the "orange character" attribute, whereas the novel ionone glycoside also decreased the intensity of the floral character while increasing the green bean attribute intensity.

Identification of Aging-Associated Food Quality Changes in Citrus Products Using Untargeted Chemical Profiling

Chemometric techniques have seen wide application in biological and medical sciences, but they are still developing in the food sciences. This study illustrated the use of untargeted LC/MS chemometric methods to identify features (retention time_m/z) associated with food quality changes as products age (freshness). Extracts of three citrus fruit varietals aged over four time points that corresponded to noted changes in sensory attributes were chemically profiled and modeled by two discriminatory multivariate statistical techniques, projection partial least-squares discrimant analysis (PLS-DA) and machine learning random forest (RF). Age-associated compounds across the citrus platform were identified. Varietal was treated as a nuisance variable to emphasize aging chemistry, and further variable selection using age-related piecewise model generation and meta filtering to emphasize features associated with general aging chemistry common to all the citrus extracts. The identified features were further replicated in a validation study to illustrate the validity and persistence of these markers for applications in citrus food platforms.

Lipidomics in biomedical research-practical considerations

Lipids have many central physiological roles including as structural components of cell membranes, energy storage sources and intermediates in signaling pathways. Lipid-related disturbances are known to underlie many diseases and their co-morbidities. The emergence of lipidomics has empowered researchers to study lipid metabolism at the cellular as well as physiological levels at a greater depth than was previously possible. The key challenges ahead in the field of lipidomics in medical research lie in the development of experimental protocols and in silico techniques needed to study lipidomes at the systems level. Clinical questions where lipidomics may have an impact in healthcare settings also need to be identified, both from the health outcomes and health economics perspectives. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.

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.

Use of Plasma Metabolomics to Identify Diagnostic Biomarkers for Early Stage Epithelial Ovarian Cancer

The early detection of ovarian carcinoma is difficult due to the absence of recognizable physical symptoms and a lack of sensitive screening methods. The currently available biomarkers (such as CA125 and HE4) are insufficiently reliable to distinguish early stage (I/II) epithelial ovarian cancer (EOC) patients from normal individuals because they possess a relatively poor sensitivity and specificity. To evaluate the application of metabolomics to biomarker discovery in the early stages of epithelial ovarian cancer (EOC), plasma samples from 21 early stage EOC patients and 31 healthy controls were analyzed with ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC/Q-Tof/MS) in conjunction with multivariate statistical analysis. Eighteen metabolites, including lysophospholipids, 2-piperidone and MG (18:2), were found to be disturbed in early stage EOC with satisfactory diagnostic accuracy (AUC=0.920). These biomarkers were specifically validated in the EOC nude mouse model, and five of the biomarkers (lysophospholipids, adrenoyl ethanolamide et al.) were highly suspected of being associated with EOC because they were differentially expressed with the same tendency in the EOC nude mice versus normal controls. In conclusion, the selected metabolic biomarkers have considerable utility and significant potential for diagnosing early ovarian cancer and investigating its underlying mechanisms.

New Technologies for Studying Biofilms

Bacteria have traditionally been studied as single-cell organisms. In laboratory settings, aerobic bacteria are usually cultured in aerated flasks, where the cells are considered essentially homogenous. However, in many natural environments, bacteria and other microorganisms grow in mixed communities, often associated with surfaces. Biofilms are comprised of surface-associated microorganisms, their extracellular matrix material, and environmental chemicals that have adsorbed to the bacteria or their matrix material. While this definition of a biofilm is fairly simple, biofilms are complex and dynamic. Our understanding of the activities of individual biofilm cells and whole biofilm systems has developed rapidly, due in part to advances in molecular, analytical, and imaging tools and the miniaturization of tools designed to characterize biofilms at the enzyme level, cellular level, and systems level.

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.

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.

An evolving computational platform for biological mass spectrometry: workflows, statistics and data mining with MASSyPup64

In biological mass spectrometry, crude instrumental data need to be converted into meaningful theoretical models. Several data processing and data evaluation steps are required to come to the final results. These operations are often difficult to reproduce, because of too specific computing platforms. This effect, known as 'workflow decay', can be diminished by using a standardized informatic infrastructure. Thus, we compiled an integrated platform, which contains ready-to-use tools and workflows for mass spectrometry data analysis. Apart from general unit operations, such as peak picking and identification of proteins and metabolites, we put a strong emphasis on the statistical validation of results and Data Mining. MASSyPup64 includes e.g., the OpenMS/TOPPAS framework, the Trans-Proteomic-Pipeline programs, the ProteoWizard tools, X!Tandem, Comet and SpiderMass. The statistical computing language R is installed with packages for MS data analyses, such as XCMS/metaXCMS and MetabR. The R package Rattle provides a user-friendly access to multiple Data Mining methods. Further, we added the non-conventional spreadsheet program teapot for editing large data sets and a command line tool for transposing large matrices. Individual programs, console commands and modules can be integrated using the Workflow Management System (WMS) taverna. We explain the useful combination of the tools by practical examples: (1) A workflow for protein identification and validation, with subsequent Association Analysis of peptides, (2) Cluster analysis and Data Mining in targeted Metabolomics, and (3) Raw data processing, Data Mining and identification of metabolites in untargeted Metabolomics. Association Analyses reveal relationships between variables across different sample sets. We present its application for finding co-occurring peptides, which can be used for target proteomics, the discovery of alternative biomarkers and protein-protein interactions. Data Mining derived models displayed a higher robustness and accuracy for classifying sample groups in targeted Metabolomics than cluster analyses. Random Forest models do not only provide predictive models, which can be deployed for new data sets, but also the variable importance. We demonstrate that the later is especially useful for tracking down significant signals and affected pathways in untargeted Metabolomics. Thus, Random Forest modeling supports the unbiased search for relevant biological features in Metabolomics. Our results clearly manifest the importance of Data Mining methods to disclose non-obvious information in biological mass spectrometry . The application of a Workflow Management System and the integration of all required programs and data in a consistent platform makes the presented data analyses strategies reproducible for non-expert users. The simple remastering process and the Open Source licenses of MASSyPup64 (http://www.bioprocess.org/massypup/) enable the continuous improvement of the system.

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.

ATP-binding Cassette Subfamily C Member 5 (ABCC5) Functions as an Efflux Transporter of Glutamate Conjugates and Analogs

The ubiquitous efflux transporter ABCC5 (ATP-binding cassette subfamily C member 5) is present at high levels in the blood-brain barrier, neurons, and glia, but its in vivo substrates and function are not known. Using untargeted metabolomic screens, we show that Abcc5(-/-) mice accumulate endogenous glutamate conjugates in several tissues, but brain in particular. The abundant neurotransmitter N-acetylaspartylglutamate was 2.4-fold higher in Abcc5(-/-) brain. The metabolites that accumulated in Abcc5(-/-) tissues were depleted in cultured cells that overexpressed human ABCC5. In a vesicular membrane transport assay, ABCC5 also transported exogenous glutamate analogs, like the classic excitotoxic neurotoxins kainic acid, domoic acid, and NMDA; the therapeutic glutamate analog ZJ43; and, as previously shown, the anti-cancer drug methotrexate. Glutamate conjugates and analogs are of physiological relevance because they can affect the function of glutamate, the principal excitatory neurotransmitter in the brain. After CO2 asphyxiation, several immediate early genes were expressed at lower levels in Abcc5(-/-) brains than in wild type brains, suggesting altered glutamate signaling. Our results show that ABCC5 is a general glutamate conjugate and analog transporter that affects the disposition of endogenous metabolites, toxins, and drugs.

Preprocessing of raw metabonomic data

Recent advances in metabolic profiling techniques allow global profiling of metabolites in cells, tissues, or organisms, using a wide range of analytical techniques such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). The raw data acquired from these instruments are abundant with technical and structural complexity, which makes it statistically difficult to extract meaningful information. Preprocessing involves various computational procedures where data from the instruments (gas chromatography (GC)/liquid chromatography (LC)-MS, NMR spectra) are converted into a usable form for further analysis and biological interpretation. This chapter covers the common data preprocessing techniques used in metabonomics and is primarily focused on baseline correction, normalization, scaling, peak alignment, detection, and quantification. Recent years have witnessed development of several software tools for data preprocessing, and an overview of the frequently used tools in data preprocessing pipeline is covered.

Investigations into the Use of a Protein Sensor Assay for Metabolite Analysis

Rapid and definitive classification of biological samples has application in industrial, agricultural, and clinical settings. Considerable effort has been given to analytical methods to address such applications over the past 50 years, with the majority of successful solutions focusing on a single molecular target. However, in many cases, a single or even a few features are insufficient for accurate characterization or classification. Serum albumin (SA) proteins are a class of cargo-carrying proteins in blood that have evolved to transport a wide variety of metabolites and peptides in mammals. These proteins have up to seven binding sites which communicate allosterically to orchestrate a complex pick-up and delivery system involving a large number of different molecules at any time. The ability of SA proteins to bind multiple molecular species in a sophisticated manner inspired the development of assays to differentiate complex biological solutions. The combination of SA and high-resolution liquid chromatography mass spectrometry (LC-MS) is showing exciting promise as a protein sensor assay (PSA) for classification of complex biological samples. In this study, the PSA has been applied to cells undergoing and recovering from mild oxidative stress. Analysis using traditional LC-MS-based metabolomics failed to differentiate samples into treatment or temporal groups, whereas samples first treated with the PSA were cleanly classified into both correct treatment and temporal groups. The success of the PSA could be attributed to selective binding of metabolites, leading to a reduction in sample complexity and a general reduction in chemical noise. Metabolites important to successful sample classification were often enriched by 100-fold or more yet displayed a wide range of affinities for SA. The end result of PSA treatment is better classification of samples with a reduction in the number of features seen overall. Together, these results demonstrate how the use of a protein-based assay before LC-MS analysis can greatly improve separation and lead to more accurate and successful tracking of the metabolic state in an organism, suggesting potential application in a wide range of fields.

MET-XAlign: a metabolite cross-alignment tool for LC/MS-based comparative metabolomics

Liquid chromatography/mass spectrometry (LC/MS) metabolite profiling has been widely used in comparative metabolomics studies; however, LC/MS-based comparative metabolomics currently faces several critical challenges. One of the greatest challenges is how to effectively align metabolites across different LC/MS profiles; a single metabolite can give rise to multiple peak features, and the grouped peak features that can be used to construct a spectrum pattern of single metabolite can vary greatly between biochemical experiments and even between instrument runs. Another major challenge is that the observed retention time for a single metabolite can also be significantly affected by experimental conditions. To overcome these two key challenges, we present a novel metabolite-based alignment approach entitled MET-XAlign to align metabolites across LC/MS metabolomics profiles. MET-XAlign takes the deduced molecular mass and estimated compound retention time information that can be extracted by our previously published tool, MET-COFEA, and aligns metabolites based on this information. We demonstrate that MET-XAlign is able to cross-align metabolite compounds, either known or unknown, in LC/MS profiles not only across different samples but also across different biological experiments and different electrospray ionization modes. Therefore, our proposed metabolite-based cross-alignment approach is a great step forward and its implementation, MET-XAlign, is a very useful tool in LC/MS-based comparative metabolomics. MET-XAlign has been successfully implemented with core algorithm coding in C++, making it very efficient, and visualization interface coding in the Microsoft.NET Framework. The MET-XAlign software along with demonstrative data is freely available at http://bioinfo.noble.org/manuscript-support/met-xalign/ .

Proteome and metabolome profiling of wild-type and YCA1-knock-out yeast cells during acetic acid-induced programmed cell death

Caspase proteases are responsible for the regulated disassembly of the cell into apoptotic bodies during mammalian apoptosis. Structural homologues of the caspase family (called metacaspases) are involved in programmed cell death in single-cell eukaryotes, yet the molecular mechanisms that contribute to death are currently undefined. Recent evidence revealed that a programmed cell death process is induced by acetic acid (AA-PCD) in Saccharomyces cerevisiae both in the presence and absence of metacaspase encoding gene YCA1. Here, we report an unexpected role for the yeast metacaspase in protein quality and metabolite control. By using an "omics" approach, we focused our attention on proteins and metabolites differentially modulated en route to AA-PCD either in wild type or YCA1-lacking cells. Quantitative proteomic and metabolomic analyses of wild type and Δyca1 cells identified significant alterations in carbohydrate catabolism, lipid metabolism, proteolysis and stress-response, highlighting the main roles of metacaspase in AA-PCD. Finally, deletion of YCA1 led to AA-PCD pathway through the activation of ceramides, whereas in the presence of the gene yeast cells underwent an AA-PCD pathway characterized by the shift of the main glycolytic pathway to the pentose phosphate pathway and a proteolytic mechanism to cope with oxidative stress.

SIGNIFICANCE

The yeast metacaspase regulates both proteolytic activities through the ubiquitin-proteasome system and ceramide metabolism as revealed by proteome and metabolome profiling of YCA1-knock-out cells during acetic-acid induced programmed cell death.

Collision-Induced Dissociation Mass Spectrometry: A Powerful Tool for Natural Product Structure Elucidation

Mass spectrometry is a powerful tool in natural product structure elucidation, but our ability to directly correlate fragmentation spectra to these structures lags far behind similar efforts in peptide sequencing and proteomics. Often, manual data interpretation is required and our knowledge of the expected fragmentation patterns for many scaffolds is limited, further complicating analysis. Here, we summarize advances in natural product structure elucidation based upon the application of collision induced dissociation fragmentation mechanisms.

A chemometric-assisted method based on gas chromatography-mass spectrometry for metabolic profiling analysis

An automatic and efficient data analysis method for comprehensive metabolic profiling analysis is urgently required. In this study, a new chemometric-assisted method for metabolic profiling analysis (CAMMPA) was developed to discover potentially valuable metabolites automatically and efficiently. The proposed method mainly consists of three stages. First, automatic chromatographic peak detection is performed based on the total ion chromatograms of samples to extract chromatographic peaks that can be accurately quantified. Second, a novel peak-shift alignment technique based on peak detection results is implemented to resolve time-shift problems across samples. Consequently, aligned results, including aligned chromatograms, and peak area tables, among others, can be successfully obtained. Third, statistical analysis using results from unsupervised and supervised classification results, together with ANOVA and partial least square-discriminate analysis, is performed to extract potential metabolites. To demonstrate the proposed technique, a complex GC-MS metabolic profiling dataset was measured to identify potential metabolites in tobacco plants of different growth stages as well as different plant tissues after maturation. Results indicated that the efficiency of the routine metabolic profiling analysis procedure can be significantly improved and potential metabolites can be accurately identified with the aid of CAMMPA.

Multiple reaction monitoring-ion pair finder: a systematic approach to transform nontargeted mode to pseudotargeted mode for metabolomics study based on liquid chromatography-mass spectrometry

Pseudotargeted metabolic profiling is a novel strategy combining the advantages of both targeted and untargeted methods. The strategy obtains metabolites and their product ions from quadrupole time-of-flight (Q-TOF) MS by information-dependent acquisition (IDA) and then picks targeted ion pairs and measures them on a triple-quadrupole MS by multiple reaction monitoring (MRM). The picking of ion pairs from thousands of candidates is the most time-consuming step of the pseudotargeted strategy. Herein, a systematic and automated approach and software (MRM-Ion Pair Finder) were developed to acquire characteristic MRM ion pairs by precursor ions alignment, MS(2) spectrum extraction and reduction, characteristic product ion selection, and ion fusion. To test the reliability of the approach, a mixture of 15 metabolite standards was first analyzed; the representative ion pairs were correctly picked out. Then, pooled serum samples were further studied, and the results were confirmed by the manual selection. Finally, a comparison with a commercial peak alignment software was performed, and a good characteristic ion coverage of metabolites was obtained. As a proof of concept, the proposed approach was applied to a metabolomics study of liver cancer; 854 metabolite ion pairs were defined in the positive ion mode from serum. Our approach provides a high throughput method which is reliable to acquire MRM ion pairs for pseudotargeted metabolomics with improved metabolite coverage and facilitate more reliable biomarkers discoveries.

Analytical methods in untargeted metabolomics: state of the art in 2015

Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.

Robust alignment of chromatograms by statistically analyzing the shifts matrix generated by moving window fast Fourier transform cross-correlation

Retention time shift is one of the most challenging problems during the preprocessing of massive chromatographic datasets. Here, an improved version of the moving window fast Fourier transform cross-correlation algorithm is presented to perform nonlinear and robust alignment of chromatograms by analyzing the shifts matrix generated by moving window procedure. The shifts matrix in retention time can be estimated by fast Fourier transform cross-correlation with a moving window procedure. The refined shift of each scan point can be obtained by calculating the mode of corresponding column of the shifts matrix. This version is simple, but more effective and robust than the previously published moving window fast Fourier transform cross-correlation method. It can handle nonlinear retention time shift robustly if proper window size has been selected. The window size is the only one parameter needed to adjust and optimize. The properties of the proposed method are investigated by comparison with the previous moving window fast Fourier transform cross-correlation and recursive alignment by fast Fourier transform using chromatographic datasets. The pattern recognition results of a gas chromatography mass spectrometry dataset of metabolic syndrome can be improved significantly after preprocessing by this method. Furthermore, the proposed method is available as an open source package at https://github.com/zmzhang/MWFFT2.

Comparison of three generic vancomycin products using liquid chromatography-mass spectrometry and an online tool

PURPOSE

Three different generic vancomycin products were compared using liquid chromatography-mass spectrometry (LC-MS) and open-access metabolomic tools.

METHODS

Single-lot samples of vancomycin hydrochloride from three different manufacturers (Hospira, APP Pharmaceuticals, and Pfizer) were reconstituted and injected into a high-resolution LC-MS system. The mass spectral fingerprints were compared for similarity of nonvancomycin B components using the XCMS Online system through Scripps University. Significance was defined as a p of ≤0.01 and a fold change of ≥1.5. The concentration of vancomycin B in each product was also measured using LC-MS on days 0, 1, 2, 4, 7, 10, and 14.

RESULTS

Qualitative comparisons of the products using the XCMS Online interface indicated the presence of significant differences among the products at the time of reconstitution; however, these variations seemed to converge after 14 days of storage. The concentration profiles of vancomycin B during refrigerated storage did not differ significantly among the three products. XCMS Online analyses revealed that the Pfizer and Hospira products were the most similar to each other.

CONCLUSION

While there were no significant differences found in the concentration of vancomycin B among Pfizer, APP, and Hospira products, there were differences in their initial mass spectral analysis after reconstitution. Liquid chromatography-tandem mass spectrometry profiles of the ions or isotopes present in the three products showed significant differences in impurities such as crystalline degradation product (CDP)-1 and CDP intermediate. After 14 days of refrigerated storage, the differences among the products converged, and fewer distinct features could be detected.

Meta-analysis of global metabolomic data identifies metabolites associated with life-span extension

The manipulation of distinct signaling pathways and transcription factors has been shown to influence life span in a cell-non-autonomous manner in multicellular model organisms such as Caenorhabditis elegans. These data suggest that coordination of whole-organism aging involves endocrine signaling, however, the molecular identities of such signals have not yet been determined and their potential relevance in humans is unknown. Here we describe a novel metabolomic approach to identify molecules directly associated with extended life span in C. elegans that represent candidate compounds for age-related endocrine signals. To identify metabolic perturbations directly linked to longevity, we developed metabolomic software for meta-analysis that enabled intelligent comparisons of multiple different mutants. Simple pairwise comparisons of long-lived glp-1, daf-2, and isp-1 mutants to their respective controls resulted in more than 11,000 dysregulated metabolite features of statistical significance. By using meta-analysis, we were able to reduce this number to six compounds most likely to be associated with life-span extension. Mass spectrometry-based imaging studies suggested that these metabolites might be localized to C. elegans muscle. We extended the metabolomic analysis to humans by comparing quadricep muscle tissue from young and old individuals and found that two of the same compounds associated with longevity in worms were also altered in human muscle with age. These findings provide candidate compounds that may serve as age-related endocrine signals and implicate muscle as a potential tissue regulating their levels in humans.

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

XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, "control" versus "disease" experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.