Metabolic diversity in a collection of wild and cultivated Brassica rapa subspecies.

Brassica rapa (B. rapa) and its subspecies contain many bioactive metabolites that are important for plant defense and human health. This study aimed at investigating the metabolite composition and variation among a large collection of B. rapa genotypes, including subspecies and their accessions. Metabolite profiling of leaves of 102 B. rapa genotypes was performed using ultra-performance liquid chromatography coupled with a photodiode array detector and quadrupole time-of-flight mass spectrometry (UPLC-PDA-QTOF-MS/MS). In total, 346 metabolites belonging to different chemical classes were tentatively identified; 36 out of them were assigned with high confidence using authentic standards and 184 were those reported in B. rapa leaves for the first time. The accumulation and variation of metabolites among genotypes were characterized and compared to their phylogenetic distance. We found 47 metabolites, mostly representing anthocyanins, flavonols, and hydroxycinnamic acid derivatives that displayed a significant correlation to the phylogenetic relatedness and determined four major phylometabolic branches; 1) Chinese cabbage, 2) yellow sarson and rapid cycling, 3) the mizuna-komatsuna-turnip-caitai; and 4) a mixed cluster. These metabolites denote the selective pressure on the metabolic network during B. rapa breeding. We present a unique study that combines metabolite profiling data with phylogenetic analysis in a large collection of B. rapa subspecies. We showed how selective breeding utilizes the biochemical potential of wild B. rapa leading to highly diverse metabolic phenotypes. Our work provides the basis for further studies on B. rapa metabolism and nutritional traits improvement.

The metabolic and proteomic repertoires of periderm tissue in skin of the reticulated Sikkim cucumber fruit.

Suberized and/or lignified (i.e. lignosuberized) periderm tissue appears often on surface of fleshy fruit skin by mechanical damage caused following environmental cues or developmental programs. The mechanisms underlying lignosuberization remain largely unknown to date. Here, we combined an assortment of microscopical techniques with an integrative multi-omics approach comprising proteomics, metabolomics and lipidomics to identify novel molecular components involved in fruit skin lignosuberization. We chose to investigate the corky Sikkim cucumber (Cucumis sativus var. sikkimensis) fruit. During development, the skin of this unique species undergoes massive cracking and is coated with a thick corky layer, making it an excellent model system for revealing fundamental cellular machineries involved in fruit skin lignosuberization. The large-scale data generated provides a significant source for the field of skin periderm tissue formation in fleshy fruit and suberin metabolism.

Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease.

Complex diseases, such as coronary artery disease (CAD), are often multifactorial, caused by multiple underlying pathological mechanisms. Here, to study the multifactorial nature of CAD, we performed comprehensive clinical and multi-omic profiling, including serum metabolomics and gut microbiome data, for 199 patients with acute coronary syndrome (ACS) recruited from two major Israeli hospitals, and validated these results in a geographically distinct cohort. ACS patients had distinct serum metabolome and gut microbial signatures as compared with control individuals, and were depleted in a previously unknown bacterial species of the Clostridiaceae family. This bacterial species was associated with levels of multiple circulating metabolites in control individuals, several of which have previously been linked to an increased risk of CAD. Metabolic deviations in ACS patients were found to be person specific with respect to their potential genetic or environmental origin, and to correlate with clinical parameters and cardiovascular outcomes. Moreover, metabolic aberrations in ACS patients linked to microbiome and diet were also observed to a lesser extent in control individuals with metabolic impairment, suggesting the involvement of these aberrations in earlier dysmetabolic phases preceding clinically overt CAD. Finally, a metabolomics-based model of body mass index (BMI) trained on the non-ACS cohort predicted higher-than-actual BMI when applied to ACS patients, and the excess BMI predictions independently correlated with both diabetes mellitus (DM) and CAD severity, as defined by the number of vessels involved. These results highlight the utility of the serum metabolome in understanding the basis of risk-factor heterogeneity in CAD.

Viral infection of algal blooms leaves a unique metabolic footprint on the dissolved organic matter in the ocean.

Algal blooms are hotspots of primary production in the ocean, forming the basis of the marine food web and fueling the dissolved organic matter (DOM) pool. Viruses are key players in controlling algal demise, thereby diverting biomass from higher trophic levels to the DOM pool, a process termed the "viral shunt." To decode the metabolic footprint of the viral shunt in the environment, we induced a bloom of Emiliania huxleyi and followed its succession using untargeted exometabolomics. We show that bloom succession induces dynamic changes in the exometabolic landscape. We found a set of chlorine-iodine-containing metabolites that were induced by viral infection and released during bloom demise. These metabolites were further detected in virus-infected oceanic E. huxleyi blooms. Therefore, we propose that halogenation with both chlorine and iodine is a distinct hallmark of the virus-induced DOM of E. huxleyi, providing insights into the metabolic consequences of the viral shunt.

Analysis of wild tomato introgression lines elucidates the genetic basis of transcriptome and metabolome variation underlying fruit traits and pathogen response.

Wild tomato species represent a rich gene pool for numerous desirable traits lost during domestication. Here, we exploited an introgression population representing wild desert-adapted species and a domesticated cultivar to establish the genetic basis of gene expression and chemical variation accompanying the transfer of wild-species-associated fruit traits. Transcriptome and metabolome analysis of 580 lines coupled to pathogen sensitivity assays resulted in the identification of genomic loci associated with levels of hundreds of transcripts and metabolites. These associations occurred in hotspots representing coordinated perturbation of metabolic pathways and ripening-related processes. Here, we identify components of the Solanum alkaloid pathway, as well as genes and metabolites involved in pathogen defense and linking fungal resistance with changes in the fruit ripening regulatory network. Our results outline a framework for understanding metabolism and pathogen resistance during tomato fruit ripening and provide insights into key fruit quality traits.

In plaque-mass spectrometry imaging of a bloom-forming alga during viral infection reveals a metabolic shift towards odd-chain fatty acid lipids.

Tapping into the metabolic crosstalk between a host and its virus can reveal unique strategies employed during infection. Viral infection is a dynamic process that generates an evolving metabolic landscape. Gaining a continuous view into the infection process is highly challenging and is limited by current metabolomics approaches, which typically measure the average of the entire population at various stages of infection. Here, we took an innovative approach to study the metabolic basis of host-virus interactions between the bloom-forming alga Emiliania huxleyi and its specific virus. We combined a classical method in virology, the plaque assay, with advanced mass spectrometry imaging (MSI), an approach we termed 'in plaque-MSI'. Taking advantage of the spatial characteristics of the plaque, we mapped the metabolic landscape induced during infection in a high spatiotemporal resolution, unfolding the infection process in a continuous manner. Further unsupervised spatially aware clustering, combined with known lipid biomarkers, revealed a systematic metabolic shift during infection towards lipids containing the odd-chain fatty acid pentadecanoic acid (C15:0). Applying 'in plaque-MSI' may facilitate the discovery of bioactive compounds that mediate the chemical arms race of host-virus interactions in diverse model systems.

A Complete Pipeline for Generating a High-Resolution LC-MS-Based Reference Mass Spectra Library.

Databases containing mass spectrometry (MS) spectral data (i.e., MS reference libraries) are currently the most reliable and widely accepted approach to annotate unknown features in MS-based metabolomics. While for gas chromatography (GC)-MS data, a strategy for collecting, storing, and comparing to raw data has been established, this is not the case for liquid chromatography (LC)-MS data. Here, we present our approach for high-throughput data collection and automated MS reference library generation, as applied recently in the WEIZMASS library of plant metabolites. Methodologies to experimentally generate pools of chemical standards and computationally convert them into a unique source of reference data are detailed.

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.

The WEIZMASS spectral library for high-confidence metabolite identification.

Annotation of metabolites is an essential, yet problematic, aspect of mass spectrometry (MS)-based metabolomics assays. The current repertoire of definitive annotations of metabolite spectra in public MS databases is limited and suffers from lack of chemical and taxonomic diversity. Furthermore, the heterogeneity of the data prevents the development of universally applicable metabolite annotation tools. Here we present a combined experimental and computational platform to advance this key issue in metabolomics. WEIZMASS is a unique reference metabolite spectral library developed from high-resolution MS data acquired from a structurally diverse set of 3,540 plant metabolites. We also present MatchWeiz, a multi-module strategy using a probabilistic approach to match library and experimental data. This strategy allows efficient and high-confidence identification of dozens of metabolites in model and exotic plants, including metabolites not previously reported in plants or found in few plant species to date.

How to Choose the Suitable Template for Homology Modelling of GPCRs: 5-HT7 Receptor as a Test Case.

G protein-coupled receptors (GPCRs) are a super-family of membrane proteins that attract great pharmaceutical interest due to their involvement in almost every physiological activity, including extracellular stimuli, neurotransmission, and hormone regulation. Currently, structural information on many GPCRs is mainly obtained by the techniques of computer modelling in general and by homology modelling in particular. Based on a quantitative analysis of eighteen antagonist-bound, resolved structures of rhodopsin family "A" receptors - also used as templates to build 153 homology models - it was concluded that a higher sequence identity between two receptors does not guarantee a lower RMSD between their structures, especially when their pair-wise sequence identity (within trans-membrane domain and/or in binding pocket) lies between 25 % and 40 %. This study suggests that we should consider all template receptors having a sequence identity ≤50 % with the query receptor. In fact, most of the GPCRs, compared to the currently available resolved structures of GPCRs, fall within this range and lack a correlation between structure and sequence. When testing suitability for structure-based drug design, it was found that choosing as a template the most similar resolved protein, based on sequence resemblance only, led to unsound results in many cases. Molecular docking analyses were carried out, and enrichment factors as well as attrition rates were utilized as criteria for assessing suitability for structure-based drug design.

MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments.

Due to their sensitivity and speed, mass-spectrometry based analytical technologies are widely used to in metabolomics to characterize biological phenomena. To address issues like metadata organization, quality assessment, data processing, data storage, and, finally, submission to public repositories, bioinformatic pipelines of a non-interactive nature are often employed, complementing the interactive software used for initial inspection and visualization of the data. These pipelines often are created as open-source software allowing the complete and exhaustive documentation of each step, ensuring the reproducibility of the analysis of extensive and often expensive experiments. In this paper, we will review the major steps which constitute such a data processing pipeline, discussing them in the context of an open-source software for untargeted MS-based metabolomics experiments recently developed at our institute. The software has been developed by integrating our metaMS R package with a user-friendly web-based application written in Grails. MetaMS takes care of data pre-processing and annotation, while the interface deals with the creation of the sample lists, the organization of the data storage, and the generation of survey plots for quality assessment. Experimental and biological metadata are stored in the ISA-Tab format making the proposed pipeline fully integrated with the Metabolights framework.

Constructing a mass measurement error surface to improve automatic annotations in liquid chromatography/mass spectrometry based metabolomics.

RATIONALE: Estimation of mass measurement accuracy is an elementary step in the application of mass spectroscopy (MS) data towards metabolite annotations and has been addressed several times in the past. However, the reproducibility of mass measurements over a diverse set of analytes and in variable operating conditions, which are common in high-throughput metabolomics studies, has, to the best of our knowledge, not been addressed so far. METHODS: A method to automatically extract mass measurement errors from a large data set of measurements made on a quadrupole time-of-flight (QTOF) MS instrument has been developed. The size of the data processed in this study has enabled us to use a statistical data driven approach to build a model which reliably predicts the confidence interval of the absolute mass measurement error based on individual ion peak conditions in a fast, high-throughput manner. RESULTS: We show that our model predictions are reproducible in external datasets generated in similar, but not identical conditions, and have demonstrated the advantage of our approach over the common practice of fixed mass measurement error limits. CONCLUSIONS: Outlined is an approach which can promote a more rational use of MS technology by automatically evaluating the absolute mass measurement error based on the individual peak conditions. The immediate application of our method is integration in high-throughput peak annotation pipelines for database searches.

Evaluation of peak picking quality in LC-MS metabolomics data.

The output of LC-MS metabolomics experiments consists of mass-peak intensities identified through a peak-picking/alignment procedure. Besides imperfections in biological samples and instrumentation, data accuracy is highly dependent on the applied algorithms and their parameters. Consequently, quality control (QC) is essential for further data analysis. Here, we present a QC approach that is based on discrepancies between replicate samples. First, the quantile normalization of per-sample log-signal distributions is applied to each group of biologically homogeneous samples. Next, the overall quality of each replicate group is characterized by the Z-transformed correlation coefficients between samples. This general QC allows a tuning of the procedure's parameters which minimizes the inter-replicate discrepancies in the generated output. Subsequently, an in-depth QC measure detects local neighborhoods on a template of aligned chromatograms that are enriched by divergences between intensity profiles of replicate samples. These neighborhoods are determined through a segmentation algorithm. The retention time (RT)-m/z positions of the neighborhoods with local divergences are indicative of either: incorrect alignment of chromatographic features, technical problems in the chromatograms, or to a true biological discrepancy between replicates for particular metabolites. We expect this method to aid in the accurate analysis of metabolomics data and in the development of new peak-picking/alignment procedures.