Bioinformatics can boost metabolomics research

Metabolome profiling in the study of aging processes

Aging of a living organism is closely related to systemic metabolic changes. But due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, this problem is solved using one of the main approaches of metabolomics — untargeted metabolome profiling. The purpose of this publication is to systematize the results of metabolomic studies based on such profiling, both in animal models and in humans.

Metabolome Profiling in Aging Studies

Organism aging is closely related to systemic metabolic changes. However, due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, scientists are trying to solve this problem using one of the main approaches of metabolomics-untargeted metabolome profiling. The purpose of this publication is to review metabolomic studies based on such profiling, both in animal models and in humans. This review describes metabolites that vary significantly across age groups and include carbohydrates, amino acids, carnitines, biogenic amines, and lipids. Metabolic pathways associated with the aging process are also shown, including those associated with amino acid, lipid, and energy metabolism. The presented data reveal the mechanisms of aging and can be used as a basis for monitoring biological age and predicting age-related diseases in the early stages of their development.

Structure similarity and molecular networking analysis for the discovery of polyphenol biotransformation products of gut microbes

Intestinal bacteria can metabolize polyphenols into highly bioavailable derivatives, which provide potential health-promoting effects to the host. However, the metabolic pathways and related products in this process are still largely unclear. Polyphenols are generally characterized by the presence of many phenolic structural units, which makes it possible to explore correlations among compounds based on similar molecular networks. In this study, we developed a standard-oriented/database-assisted molecular networking (SODA-MN) method for iterative compound annotation analysis to explore the metabolic profiles of polyphenol-rich black raspberry extract metabolized by representative gut bacteria. Starting from a group of polyphenol metabolites, the SODA-MN method predicted the possible polyphenol derivatives by adding or deducting common biotransformation groups and iterative annotating of structure similarity based on fragmentation spectra. Our results showed that 48 polyphenol derivatives in the first round of analysis alone (fragmentation match≥5, spec score >0.5) can be annotated, which were associated with 13 detected polyphenol standards that served as seed compounds. Meanwhile, this method was applied to a time-course study to show the time-dependent changes of polyphenols metabolized by a mix of gut bacteria. In addition, the metabolic capabilities of polyphenols among four representative gut bacteria were compared via our newly developed method and differential polyphenol metabolites were detected. In summary, the SODA-MN method provides a new approach for the annotation of unknown compounds by structure similarity and molecular networking analysis. Our analysis results could provide identification of key polyphenol derivatives that may contribute to the mechanistic investigations of their functions and assist our understanding of how polyphenols and gut bacteria interact to promote human health.

Review of the recent developments in metabolomics-based phytochemical research

Phytochemicals are important bioactive components present in natural products. Although the health benefits of many food products are well-known and accepted as a common knowledge, the identity of the main bioactive molecules and the mechanism by which they interact in the body of human are often unknown. It was only in the last 30 years when the field of metabolomics had matured that the identification of such molecules with bioactivity has been made possible through the development of instruments to separate and computational techniques to characterize complex samples. This in turn has enabled in vitro studies to quantify the biological activity of the respective phytochemical either in mice models or in humans. In this review, the importance of key dietary phytochemicals such as phenolic acids, flavonoids, carotenoids, resveratrol, curcumin, and capsaicinoids are discussed together with their potential functions for human health. Untargeted metabolomics, in particular, liquid chromatography mass spectrometry, is the most used method to isolate, identify and profile bioactive compounds in the study of phytochemicals in foods. The application of metabolomics in drug discovery is a common practice nowadays and has boosted the drug and/or supplement manufacturing sector.HighlightsPhytochemicals are beneficial compounds for human healthPhytochemicals are plant-based bioactive and obtainable from natural productsUntargeted metabolomics has boosted the discovery of phytochemicals from foodTargeted metabolomics is key in the authentication and screening of phytochemicalsMetabolomics of phytochemicals is reshaping the road to drug and supplement manufacture.

Metabolic drift in the aging nervous system is reflected in human cerebrospinal fluid

Chronic diseases affecting the central nervous system (CNS) like Alzheimer's or Parkinson's disease typically develop with advanced chronological age. Yet, aging at the metabolic level has been explored only sporadically in humans using biofluids in close proximity to the CNS such as the cerebrospinal fluid (CSF). We have used an untargeted liquid chromatography high-resolution mass spectrometry (LC-HRMS) based metabolomics approach to measure the levels of metabolites in the CSF of non-neurological control subjects in the age of 20 up to 74. Using a random forest-based feature selection strategy, we extracted 69 features that were strongly related to age (p_age < 0.001, r_age = 0.762, R²_{Boruta age} = 0.764). Combining an in-house library of known substances with in silico chemical classification and functional semantic annotation we successfully assigned putative annotations to 59 out of the 69 CSF metabolites. We found alterations in metabolites related to the Cytochrome P450 system, perturbations in the tryptophan and kynurenine pathways, metabolites associated with cellular energy (NAD+, ADP), mitochondrial and ribosomal metabolisms, neurological dysfunction, and an increase of adverse microbial metabolites. Taken together our results point at a key role for metabolites found in CSF related to the Cytochrome P450 system as most often associated with metabolic aging.

Comparison of Targeted and Untargeted Approaches in Breath Analysis for the Discrimination of Lung Cancer from Benign Pulmonary Diseases and Healthy Persons

The aim of the present study was to compare the efficiency of targeted and untargeted breath analysis in the discrimination of lung cancer (Ca+) patients from healthy people (HC) and patients with benign pulmonary diseases (Ca−). Exhaled breath samples from 49 Ca+ patients, 36 Ca− patients and 52 healthy controls (HC) were analyzed by an SPME–GC–MS method. Untargeted treatment of the acquired data was performed with the use of the web-based platform XCMS Online combined with manual reprocessing of raw chromatographic data. Machine learning methods were applied to estimate the efficiency of breath analysis in the classification of the participants. Results: Untargeted analysis revealed 29 informative VOCs, from which 17 were identified by mass spectra and retention time/retention index evaluation. The untargeted analysis yielded slightly better results in discriminating Ca+ patients from HC (accuracy: 91.0%, AUC: 0.96 and accuracy 89.1%, AUC: 0.97 for untargeted and targeted analysis, respectively) but significantly improved the efficiency of discrimination between Ca+ and Ca− patients, increasing the accuracy of the classification from 52.9 to 75.3% and the AUC from 0.55 to 0.82. Conclusions: The untargeted breath analysis through the inclusion and utilization of newly identified compounds that were not considered in targeted analysis allowed the discrimination of the Ca+ from Ca− patients, which was not achieved by the targeted approach.

Drug-Induced Lipid Remodeling in Leishmania Parasites

Leishmania parasites efficiently develop resistance against several types of drugs including antimonials, the primary antileishmanial drug historically implemented. The resistance to antimonials is considered to be a major risk factor for effective leishmaniasis treatment. To detect biomarkers/biopatterns for the differentiation of antimony-resistant Leishmania strains, we employed untargeted global mass spectrometry to identify intracellular lipids present in antimony sensitive and resistant parasites before and after antimony exposure. The lipidomic profiles effectively differentiated the sensitive and resistant phenotypes growing with and without antimony pressure. Resistant phenotypes were characterized by significant downregulation of phosphatidylcholines, sphingolipid decrease, and lysophosphatidylcholine increase, while sensitive phenotypes were characterized by the upregulation of triglycerides with long-chain fatty acids and a tendency toward the phosphatidylethanolamine decrease. Our findings suggest that the changes in lipid composition in antimony-resistant parasites contribute to the physiological response conducted to combat the oxidative stress unbalance caused by the drug. We have identified several lipids as potential biomarkers associated with the drug resistance.

Breathomics: Review of Sample Collection and Analysis, Data Modeling and Clinical Applications

Metabolomics research is rapidly gaining momentum in disease diagnosis, on top of other Omics technologies. Breathomics, as a branch of metabolomics is developing in various frontiers, for early and noninvasive monitoring of disease. This review starts with a brief introduction to metabolomics and breathomics. A number of important technical issues in exhaled breath collection and factors affecting the sampling procedures are presented. We review the recent progress in metabolomics approaches and a summary of their applications on the respiratory and non-respiratory diseases investigated by breath analysis. Recent reports on breathomics studies retrieved from Scopus and Pubmed were reviewed in this work. We conclude that analyzing breath metabolites (both volatile and nonvolatile) is valuable in disease diagnoses, and therefore believe that breathomics will turn into a promising noninvasive discipline in biomarker discovery and early disease detection in personalized medicine. The problem of wide variations in the reported metabolite concentrations from breathomics studies should be tackled by developing more accurate analytical methods and sophisticated numerical analytical alogorithms.

Potential for Novel Biomarkers in Diabetes-Associated Chronic Kidney Disease: Epigenome, Metabolome, and Gut Microbiome

Diabetes-associated chronic kidney disease is a pandemic issue. Despite the global increase in the number of individuals with this chronic condition together with increasing morbidity and mortality, there are currently only limited therapeutic options to slow disease progression. One of the reasons for this is that the current-day "gold standard" biomarkers lack adequate sensitivity and specificity to detect early diabetic chronic kidney disease (CKD). This review focuses on the rapidly evolving areas of epigenetics, metabolomics, and the gut microbiome as potential sources of novel biomarkers in diabetes-associated CKD and discusses their relevance to clinical practice. However, it also highlights the problems associated with many studies within these three areas-namely, the lack of adequately powered longitudinal studies, and the lack of reproducibility of results which impede biomarker development and clinical validation in this complex and susceptible population.

[Ten years of the Russian metabolomics: history of development and achievements]

Metabolomics is one of the omics sciences, the technologies of which are widely used today in many life sciences. Its application influenced the discovery of new biomarkers of diseases, the description of biochemical processes occurring in many organisms, laid the basis for a new generation of clinical laboratory diagnostics. The purpose of this review is to show how metabolomics is represented in the studies of Russian scientists, to demonstrate the main directions and achievements of the Russian science in this field. The review also highlights the history of metabolomics, existing problems and the place of Russian metabolomics in their solution.

Computational Oncology in the Multi-Omics Era: State of the Art

Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.

The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR

The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR infrastructure. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) ‘Training & Education’, coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 300 training courses were carried out with about 6,000 participants and these courses received recommendation rates of almost 90% (status as of July 2020). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics.

Bioinformatics for Marine Products: An Overview of Resources, Bottlenecks, and Perspectives

The sea represents a major source of biodiversity. It exhibits many different ecosystems in a huge variety of environmental conditions where marine organisms have evolved with extensive diversification of structures and functions, making the marine environment a treasure trove of molecules with potential for biotechnological applications and innovation in many different areas. Rapid progress of the omics sciences has revealed novel opportunities to advance the knowledge of biological systems, paving the way for an unprecedented revolution in the field and expanding marine research from model organisms to an increasing number of marine species. Multi-level approaches based on molecular investigations at genomic, metagenomic, transcriptomic, metatranscriptomic, proteomic, and metabolomic levels are essential to discover marine resources and further explore key molecular processes involved in their production and action. As a consequence, omics approaches, accompanied by the associated bioinformatic resources and computational tools for molecular analyses and modeling, are boosting the rapid advancement of biotechnologies. In this review, we provide an overview of the most relevant bioinformatic resources and major approaches, highlighting perspectives and bottlenecks for an appropriate exploitation of these opportunities for biotechnology applications from marine resources.

Chemical Diversity and Classification of Secondary Metabolites in Nine Bryophyte Species

The central aim in ecometabolomics and chemical ecology is to pinpoint chemical features that explain molecular functioning. The greatest challenge is the identification of compounds due to the lack of constitutive reference spectra, the large number of completely unknown compounds, and bioinformatic methods to analyze the big data. In this study we present an interdisciplinary methodological framework that extends ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) and the automated in silico classification of fragment peaks into compound classes. We synthesize findings from a prior study that explored the influence of seasonal variations on the chemodiversity of secondary metabolites in nine bryophyte species. Here we reuse and extend the representative dataset with DDA-MS data. Hierarchical clustering, heatmaps, dbRDA, and ANOVA with post-hoc Tukey HSD were used to determine relationships of the study factors species, seasons, and ecological characteristics. The tested bryophytes showed species-specific metabolic responses to seasonal variations (50% vs. 5% of explained variation). Marchantia polymorpha, Plagiomnium undulatum, and Polytrichum strictum were biochemically most diverse and unique. Flavonoids and sesquiterpenoids were upregulated in all bryophytes in the growing seasons. We identified ecological functioning of compound classes indicating light protection (flavonoids), biotic and pathogen interactions (sesquiterpenoids, flavonoids), low temperature and desiccation tolerance (glycosides, sesquiterpenoids, anthocyanins, lactones), and moss growth supporting anatomic structures (few methoxyphenols and cinnamic acids as part of proto-lignin constituents). The reusable bioinformatic framework of this study can differentiate species based on automated compound classification. Our study allows detailed insights into the ecological roles of biochemical constituents of bryophytes with regard to seasonal variations. We demonstrate that compound classification can be improved with adding constitutive reference spectra to existing spectral libraries. We also show that generalization on compound classes improves our understanding of molecular ecological functioning and can be used to generate new research hypotheses.

Comprehensive metabolic profile analysis of the root bark of different species of tree peonies (Paeonia Sect. Moutan)

Tree peonies (Paeonia Sect. Moutan) are well-known for their medicinal and ornamental uses but most wild species in the Moutan section are endangered. The comprehensive metabolomics evaluation of tree peonies is essential to distinguish different species and to identify undescribed compounds, thereby elucidating the diversity of their metabolites and discovering potential active ingredients. In this study, the metabolome variations of root barks of nine species and their varieties collected from one botanical garden after years of localization were systematically investigated. A digital database of specialized metabolites was established to improve feature identification or annotation and various bio- and cheminformatics tools were employed to analyse and visualize the profiled metabolomic data. As a result, 384 compounds were identified or annotated, including various monoterpene glycosides, flavonoids, phenols, terpenoids and steroids, tannins, stilbenes and others. All samples were clearly divided into two subsections: Vaginatae and Delavayanae. The distribution and abundance of metabolites were also analysed and discussed in order to find potential biomarkers in different wild tree peonies.

Soil management type differentially modulates the metabolomic profile of olive xylem sap

In conventional olive growing, frequent soil tillage strongly reduces the complexity and diversity of the agro-ecosystem. Here, a metabolomic analysis was carried out on the xylem sap (XS) of olive plants (Olea europaea L.) from a grove located in Southern Italy (Basilicata region). The orchard has been divided in two plots that have been managed for 18 years with two different systems: a) 'sustainable management' (S_mng), with no-tillage, fertigation and internal C-inputs (spontaneous weeds and pruning residues), and b) an adjacent rainfed 'conventional management' (C_mng), that included soil tillage and mineral fertilization. The XS was extracted from olive shoots in two sampling times (ST1: May; ST2: October) using a Sholander pressure chamber, and its metabolome analyzed by ultra-high performance liquid chromatography (UHPLC) coupled to a hybrid quadrupole-time-of-flight mass spectrometer (QTOF-MS). The discriminating compounds were 94 at ST1 and 119 at ST2, and 35 of them were in common between the two sampling times. The majority of the discriminating metabolites (73 on 94 at ST1, and 109 on 119 at ST2) were found at higher concentration in the XS of S_mng plants, compared to that of C_mng ones. Most of the discriminating metabolites found in XS (about 80%, both at ST1 and ST2) were involved in plant secondary metabolism, mainly for plant chemical defense, growth regulation and signal transduction. The most prevailing class of compounds included terpenoids, phytohormones, alkaloids, sterols/steroids, retinols/retinoids, tocopherols and carotenoids. For the first time, we have demonstrated that the XS of a tree crop significantly responds to a shift of soil management. Generally, the plants of the S_mng plot showed an up-regulated secondary metabolism. The results of our study encourage the use of a set of sustainable agricultural practices in a productive orchard, in order to enhance plant physiological status, increase yield quantity/quality, safeguard the environment and ameliorate human health.

Metabolomic responses triggered by arbuscular mycorrhiza enhance tolerance to water stress in wheat cultivars

Under global climate change forecasts, the pressure of environmental stressors (and in particular drought) on crop productivity is expected to rise and challenge further global food security. The application of beneficial microorganisms may represent an environment friendly tool to secure improved crop performance and yield stability. Accordingly, this current study aimed at elucidating the metabolomic responses triggered by mycorrhizal (Funneliformis mosseae) inoculation of durum (Triticum durum Desf.; cv. 'Mongibello') and bread wheat cultivars (Triticum aestivum L.; cv. 'Chinese Spring') under full irrigation and water deficit regimes. Metabolomics indicated a similar regulation of secondary metabolism in both bread and durum wheat cultivars following water limiting conditions. Nonetheless, a mycorrhizal fungi (AMF) x cultivar interaction could be observed, with the bread wheat cultivar being more affected by arbuscular colonization under water limiting conditions. Discriminant compounds could be mostly related to sugars and lipids, both being positively modulated by AMF colonization under water stress. Moreover, a regulation of metabolites related to oxidative stress and a tuning of crosstalk between phytohormones were also evidenced. Among the latter, the stimulation of the brassinosteroids biosynthetic pathway was particularly evident in inoculated wheat roots, supporting the hypothesis of their involvement in enhancing plant response to water stress and modulation of oxidative stress conditions. This study proposes new insights on the modulation of the tripartite interaction plant-AMF-environmental stress.

A Tool to Encourage Minimum Reporting Guideline Uptake for Data Analysis in Metabolomics

Despite the proposal of minimum reporting guidelines for metabolomics over a decade ago, reporting on the data analysis step in metabolomics studies has been shown to be unclear and incomplete. Major omissions and a lack of logical flow render the data analysis' sections in metabolomics studies impossible to follow, and therefore replicate or even imitate. Here, we propose possible reasons why the original reporting guidelines have had poor adherence and present an approach to improve their uptake. We present in this paper an R markdown reporting template file that guides the production of text and generates workflow diagrams based on user input. This R Markdown template contains, as an example in this instance, a set of minimum information requirements specifically for the data pre-treatment and data analysis section of biomarker discovery metabolomics studies, (gleaned directly from the original proposed guidelines by Goodacre at al). These minimum requirements are presented in the format of a questionnaire checklist in an R markdown template file. The R Markdown reporting template proposed here can be presented as a starting point to encourage the data analysis section of a metabolomics manuscript to have a more logical presentation and to contain enough information to be understandable and reusable. The idea is that these guidelines would be open to user feedback, modification and updating by the metabolomics community via GitHub.

Understanding the Biostimulant Action of Vegetal-Derived Protein Hydrolysates by High-Throughput Plant Phenotyping and Metabolomics: A Case Study on Tomato

Designing and developing new biostimulants is a crucial process which requires an accurate testing of the product effects on the morpho-physiological traits of plants and a deep understanding of the mechanism of action of selected products. Product screening approaches using omics technologies have been found to be more efficient and cost effective in finding new biostimulant substances. A screening protocol based on the use of high-throughput phenotyping platform for screening new vegetal-derived protein hydrolysates (PHs) for biostimulant activity followed by a metabolomic analysis to elucidate the mechanism of the most active PHs has been applied on tomato crop. Eight PHs (A-G, I) derived from enzymatic hydrolysis of seed proteins of Leguminosae and Brassicaceae species were foliarly sprayed twice during the trial. A non-ionic surfactant Triton X-100 at 0.1% was also added to the solutions before spraying. A control treatment foliarly sprayed with distilled water containing 0.1% Triton X-100 was also included. Untreated and PH-treated tomato plants were monitored regularly using high-throughput non-invasive imaging technologies. The phenotyping approach we used is based on automated integrative analysis of photosynthetic performance, growth analysis, and color index analysis. The digital biomass of the plants sprayed with PH was generally increased. In particular, the relative growth rate and the growth performance were significantly improved by PHs A and I, respectively, compared to the untreated control plants. Kinetic chlorophyll fluorescence imaging did not allow to differentiate the photosynthetic performance of treated and untreated plants. Finally, MS-based untargeted metabolomics analysis was performed in order to characterize the functional mechanisms of selected PHs. The treatment modulated the multi-layer regulation process that involved the ethylene precursor and polyamines and affected the ROS-mediated signaling pathways. Although further investigation is needed to strengthen our findings, metabolomic data suggest that treated plants experienced a metabolic reprogramming following the application of the tested biostimulants. Nonetheless, our experimental data highlight the potential for combined use of high-throughput phenotyping and metabolomics to facilitate the screening of new substances with biostimulant properties and to provide a morpho-physiological and metabolomic gateway to the mechanisms underlying PHs action on plants.

CEU Mass Mediator 3.0: A Metabolite Annotation Tool

CEU Mass Mediator (CMM, http://ceumass.eps.uspceu.es ) is an online tool that has evolved from a simple interface to query different metabolomic databases (CMM 1.0) to a tool that unifies the compounds from these databases and, using an expert system with knowledge about the experimental setup and the compounds properties, filters and scores the query results (CMM 2.0). Since this last major revision, CMM has continued to grow, expanding the knowledge base of its expert system and including new services to support researchers in the metabolite annotation and identification process. The information from external databases has been refreshed, and an in-house library with oxidized lipids not present in other sources has been added. This has increased the number of experimental metabolites up 332,665 and the number of predicted metabolites to 681,198. Furthermore, new taxonomy and ontology metadata have been included. CMM has expanded its functionalities with a service for the annotation of oxidized glycerophosphocholines, a service for spectral comparison from MS² data, and a spectral quality-assessment service to determine the reliability of a spectrum for compound identification purposes. To facilitate the collaboration and integration of CMM with external tools and metabolomic platforms, a RESTful API has been created, and it has already been integrated into the HMDB (Human Metabolome Database). This paper will present the novel functionalities incorporated into version 3.0 of CMM.

The metabolomic quest for a biomarker in chronic kidney disease

Chronic kidney disease (CKD) is a growing burden on people and on healthcare for which the diagnostics are niether disease-specific nor indicative of progression. Biomarkers are sought to enable clinicians to offer more appropriate patient-centred treatments, which could come to fruition by using a metabolomics approach. This mini-review highlights the current literature of metabolomics and CKD, and suggests additional factors that need to be considered in this quest for a biomarker, namely the diet and the gut microbiome, for more meaningful advances to be made.

Current Challenges in Plant Eco-Metabolomics

The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant⁻organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.

KniMet: a pipeline for the processing of chromatography-mass spectrometry metabolomics data

INTRODUCTION

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

OBJECTIVES

Merge in the same platform the steps required for metabolomics data processing.

METHODS

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

RESULTS

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

CONCLUSION

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC-MS and LC-MS open profiling data.