Automatic metabolite annotation in complex LC-MS(n ≥ 2) data using MAGMa

Poster presented at the Analytical Tools for Cutting-edge Metabolomics meeting in London, 30 April 201

Characterization of the natural variation in Arabidopsis thaliana metabolome by the analysis of metabolic distance

Metabolite fingerprinting is widely used to unravel the chemical characteristics of biological samples. Multivariate data analysis and other statistical tools are subsequently used to analyze and visualize the plasticity of the metabolome and/or the relationship between those samples. However, there are limitations to these approaches for example because of the multi-dimensionality of the data that makes interpretation of the data obtained from untargeted analysis almost impossible for an average human being. These limitations make the biological information that is of prime importance in untargeted studies be partially exploited. Even in the case of full exploitation, current methods for relationship elucidation focus mainly on between groups variation and differences. Therefore, a measure that is capable of exploiting both between- and within-group biological variation would be of great value. Here, we examined the natural variation in the metabolome of nine Arabidopsis thaliana accessions grown under various environmental conditions and established a measure for the metabolic distance between accessions and across environments. This data analysis approach shows that there is just a minor correlation between genetic and metabolic diversity of the nine accessions. On the other hand, it delivers so far in Arabidopsis unexplored chemical information and is shown to be biologically relevant for resistance studies

Testing a global standard for quantifying species recovery and assessing conservation impact.

Recognizing the imperative to evaluate species recovery and conservation impact, in 2012 the International Union for Conservation of Nature (IUCN) called for development of a "Green List of Species" (now the IUCN Green Status of Species). A draft Green Status framework for assessing species' progress toward recovery, published in 2018, proposed 2 separate but interlinked components: a standardized method (i.e., measurement against benchmarks of species' viability, functionality, and preimpact distribution) to determine current species recovery status (herein species recovery score) and application of that method to estimate past and potential future impacts of conservation based on 4 metrics (conservation legacy, conservation dependence, conservation gain, and recovery potential). We tested the framework with 181 species representing diverse taxa, life histories, biomes, and IUCN Red List categories (extinction risk). Based on the observed distribution of species' recovery scores, we propose the following species recovery categories: fully recovered, slightly depleted, moderately depleted, largely depleted, critically depleted, extinct in the wild, and indeterminate. Fifty-nine percent of tested species were considered largely or critically depleted. Although there was a negative relationship between extinction risk and species recovery score, variation was considerable. Some species in lower risk categories were assessed as farther from recovery than those at higher risk. This emphasizes that species recovery is conceptually different from extinction risk and reinforces the utility of the IUCN Green Status of Species to more fully understand species conservation status. Although extinction risk did not predict conservation legacy, conservation dependence, or conservation gain, it was positively correlated with recovery potential. Only 1.7% of tested species were categorized as zero across all 4 of these conservation impact metrics, indicating that conservation has, or will, play a role in improving or maintaining species status for the vast majority of these species. Based on our results, we devised an updated assessment framework that introduces the option of using a dynamic baseline to assess future impacts of conservation over the short term to avoid misleading results which were generated in a small number of cases, and redefines short term as 10 years to better align with conservation planning. These changes are reflected in the IUCN Green Status of Species Standard

Depth of dormancy in tomato seeds is related to the progression of the cell cycle prior to its induction

Cell cycle activities are initiated following imbibition of non-dormant seeds. However, it is not known whether cell cycle related events other than DNA replication also remain suppressed in imbibed dormant seeds. The objective of this study was to demonstrate that the transitions between the non-dormant and dormant (both primary and secondary) states are reflected in cell cycle events, such as DNA replication and the changing patterns of the microtubular cytoskeleton involved in the processes of growth and development. The present studies were conducted on seeds from tomato (Lycopersicon esculentum cv. Moneymaker) that possessed primary dormancy or were manipulated to attain secondary dormancy. In addition, a non-dormant abscisic acid (ABA)-deficient mutant, sitw, was used. DNA replication, as measured by flow cytometry, and -tubulin accumulation, analysed by immunoblotting, were compared with immunocytological studies of active DNA synthesis and microtubular cytoskeleton formation. It is shown that the depth of dormancy, which distinguishes primary and secondary dormancy, may depend on the progression of the cell cycle prior to the induction of dormancy

Plant Metabolomics: The Missing Link in Functional Genomics Strategies

After the establishment of technologies for high-throughput DNA sequencing (genomics), gene expression analysis (transcriptomics), and protein analysis (proteomics), the remaining functional genomics challenge is that of metabolomics. Metabolomics is the term coined for essentially comprehensive, nonbiased, high-throughput analyses of complex metabolite mixtures typical of plant extracts. This potentially holistic approach to metabolome analysis is driven primarily by recent advances in mass spectrometry (MS) technology and by the goals of functional genomics research. Achieving the broadest overview of metabolic composition is very complex and entails establishing a multifaceted, fully integrated strategy for optimal sample extraction, metabolite separation/detection/identification, automated data gathering/handling/analysis, and, ultimately, quantification. Both analytical and computational developments are essential to achieve this goal. The First International Congress on Plant Metabolomics was held in Wageningen, The Netherlands, in April 2002, with the primary goal of bringing together those players who are already active in this field and those who soon plan to be. In so doing, opportunities are created for collaboration, overlap can be avoided, and joint strategies can be determined to meet the metabolomics challenge. Indexed abstracts from the oral and poster presentations at the meeting are now accessible at www. metabolomics.nl, and this site will continue to be used as an aid to information exchange and enhanced collaboration. Although microbes may prove to be the richest overall source of metabolites, plants are the source of the most complex individual mixtures. Mariet van der Werf (TNO-Food, Zeist, The Netherlands) reported that it has been predicted that bacterial genomes already sequenced can support the biosynthesis of just a few hundred metabolites (e.g., 580 for Bacillus subtilis and 800 for Escherichia coli), but for individual plants, this value is likely to be in the tens of thousands. This metabolic richness comes not just from the number of genes present (20,000 to 50,000) but also from multiple substrate specificities for many enzymes (Aharoni et al., 2000), subcellular compartmentation, and the occurrence of nonenzymic reactions. Approximately 50,000 different compounds have been elucidated in plants (De Luca and St. Pierre, 2000), and it is predicted that the final figure for the plant kingdom will approach or even exceed 200,000 (Pichersky and Gang, 2000; Fiehn, 2001, 2002). Thus, metabolomics represents a considerable challenge for plant scientists. Metabolomics research will prove an invaluable tool for generating information of use in many fields. For functional genomics strategies, potentially fast-track methods exploiting metabolomics analyses of tagged lines or known mutants are likely to prove invaluable (Motoko Awazuhara, Chiba University, Japan [Arabidopsis]; Andy Pereira, Plant Research International, Wageningen, The Netherlands; and Jon Lightner, Exelixis Plant Sciences, Portland, OR [Arabidopsis and tomato]). Metabolomics information not only will assist in the establishment of a deeper understanding of the complex interactive nature of plant metabolic networks and their responses to environmental and genetic change but also will provide unique insights into the fundamental nature of plant phenotypes in relation to development, physiology, tissue identity, resistance, biodiversity, etc. Some key conference presentations are described below. This report is organized according to categories representing the fields of greatest importance to the successful establishment of plant metabolomics as a technology complementary to those for gene expression and protein profiling already in existence