Computational Plant Biochemistry

Mass spectrometry (MS) has become the analytical method of choice in plant metabolomics. Nevertheless, metabolite annotation remains a major challenge and implies the integration of structural searches in compound libraries with biological knowledge inferred from metabolite regulation studies. Here we propose a novel integrative approach to process and exploit the rich structural information contained in in-source fragmentation patterns of high-resolution LC–MS profiles. In this approach, a correlation matrix is first calculated from individual mass features extracted by xcms processing. Mass feature co-regulation patterns corresponding to metabolite in-source fragmentation are then detected and assembled into compound spectra using the R package CAMERA and processed for in silico fragment-based structure elucidation using MetFrag. We validate the performance of this approach for the rapid annotation of the twelve largest compound spectra, including four O-acyl sugars and six 17-hydroxygeranyllinalool diterpene glycosides in metabolic profiles of insect-attacked Nicotiana attenuata leaves. Additionally, we demonstrate the power of refining MetFrag metabolite annotations based on co-regulation patterns between known and unknown compounds in the correlation matrix and proposed structural annotations on two previously un-characterized O-acyl sugars. In summary, this novel approach facilitates compound annotation from in-source fragmentation patterns using correlation between intensities of mass features of one or several metabolites. Additionally, this analysis provides further support that insect herbivory activates major metabolic reconfigurations in N. attenuata leaves.

Metabolomics has advanced significantly in the past 10 years with important developments related to hardware, software and methodologies and an increasing complexity of applications. In discovery-based investigations, applying untargeted analytical methods, thousands of metabolites can be detected with no or limited prior knowledge of the metabolite composition of samples. In these cases, metabolite identification is required following data acquisition and processing. Currently, the process of metabolite identification in untargeted metabolomic studies is a significant bottleneck in deriving biological knowledge from metabolomic studies. In this review we highlight the different traditional and emerging tools and strategies applied to identify subsets of metabolites detected in untargeted metabolomic studies applying various mass spectrometry platforms. We indicate the workflows which are routinely applied and highlight the current limitations which need to be overcome to provide efficient, accurate and robust identification of metabolites in untargeted metabolomic studies. These workflows apply to the identification of metabolites, for which the structure can be assigned based on entries in databases, and for those which are not yet stored in databases and which require a de novo structure elucidation.

Besides a plethora of formal ontologies, the requirement for simple data annotation has led to an increased use of so called controlled vocabularies (CV) in multiple omics communities. We analyze two of those CVs from an ontological viewpoint, highlight typical modelling errors and propose more adequate solutions. Discovered errors are discussed in the light of the OOPS ontology pitfall framework and the OBO Foundry naming conventions. As a result the CVs could be improved and the OOPS catalogue could be amended and expanded with new, previously missing error categories. In an outlook we discuss potential reasons for the error prevalence and analyse what criticism is justified for CV semantics and what `errors' are more valid for formal ontologies rather than CVs. We conclude that although many design principles valid for description logics ontologies are not relevant for semantically flat CVs and in turn there is a need for CV-best-practices that are not appropriate for description logics ontologies, there is room for improvement in the analysed CVs. The scope difference between CVs and formal semantics however should affect policy providers, which should narrow down the scope of their policies, i.e. by stating for each policy the expressivity regime for which it is valid.

Previous chapters have introduced protocols and examples for high‐throughput metabolomics experiments. Metabolite identification is an important step in these experiments, bridging the metabolomics experiment, metabolite profiling, and the biological interpretation of the results. The elemental composition of the individual metabolites is the most basic information that can be calculated already from the mass spectrometry (MS) profiling data. For a more thorough identification, the “interesting” peaks are subjected to MS2, or even higher‐order MSn measurements. Such spectra carry rich structural hints, revealing building blocks of the unknown compound, or allowing comparison with databases of reference spectra. This chapter describes a general strategy to identify metabolites, and proceeds through the steps of the identification for two example compounds, first calculating elemental compositions, performing in silico identification without reference spectra, and finally spectral library lookup.

This article explores consensus structure elucidation on the basis of GC/EI-MS, structure generation, and calculated properties for unknown compounds. Candidate structures were generated using the molecular formula and substructure information obtained from GC/EI-MS spectra. Calculated properties were then used to score candidates according to a consensus approach, rather than filtering or exclusion. Two mass spectral match calculations (MOLGEN-MS and MetFrag), retention behavior (Lee retention index/boiling point correlation, NIST Kovat’s retention index), octanol–water partitioning behavior (log Kow), and finally steric energy calculations were used to select candidates. A simple consensus scoring function was developed and tested on two unknown spectra detected in a mutagenic subfraction of a water sample from the Elbe River using GC/EI-MS. The top candidates proposed using the consensus scoring technique were purchased and confirmed analytically using GC/EI-MS and LC/MS/MS. Although the compounds identified were not responsible for the sample mutagenicity, the structure-generation-based identification for GC/EI-MS using calculated properties and consensus scoring was demonstrated to be applicable to real-world unknowns and suggests that the development of a similar strategy for multidimensional high-resolution MS could improve the outcomes of environmental and metabolomics studies.

To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open 'data commoning' culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared 'Investigation-Study-Assay' framework to support that vision.

Liquid chromatography coupled to mass spectrometry is routinely used for metabolomics experiments. In contrast to the fairly routine and automated data acquisition steps, subsequent compound annotation and identification require extensive manual analysis and thus form a major bottleneck in data interpretation. Here we present CAMERA, a Bioconductor package integrating algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data. To evaluate the algorithms, we compared the annotation of CAMERA against a manually defined annotation for a mixture of known compounds spiked into a complex matrix at different concentrations. CAMERA successfully extracted accurate masses for 89.7% and 90.3% of the annotatable compounds in positive and negative ion modes, respectively. Furthermore, we present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. We demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics experiments, where the application of CAMERA drastically reduced the amount of manual analysis.

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Targeted proteomics via selected reaction monitoring is a powerful mass spectrometric technique affording higher dynamic range, increased specificity and lower limits of detection than other shotgun mass spectrometry methods when applied to proteome analyses. However, it involves selective measurement of predetermined analytes, which requires more preparation in the form of selecting appropriate signatures for the proteins and peptides that are to be targeted. There is a growing number of software programs and resources for selecting optimal transitions and the instrument settings used for the detection and quantification of the targeted peptides, but the exchange of this information is hindered by a lack of a standard format. We have developed a new standardized format, called TraML, for encoding transition lists and associated metadata. In addition to introducing the TraML format, we demonstrate several implementations across the community, and provide semantic validators, extensive documentation, and multiple example instances to demonstrate correctly written documents. Widespread use of TraML will facilitate the exchange of transitions, reduce time spent handling incompatible list formats, increase the reusability of previously optimized transitions, and thus accelerate the widespread adoption of targeted proteomics via selected reaction monitoring.

Mass spectrometry is an important analytical technology for the identification of metabolites and small compounds by their exact mass. But dozens or hundreds of different compounds may have a similar mass or even the same molecule formula. Further elucidation requires tandem mass spectrometry, which provides the masses of compound fragments, but in silico fragmentation programs require substantial computational resources if applied to large numbers of candidate structures.We present and evaluate an approach to obtain candidates from a relational database which contains 28 million compounds from PubChem.A training phase associates tandem-MS peaks with corresponding fragment structures. For the candidate search, the peaks in a query spectrum are translated to fragment structures, and the candidates are retrieved and sorted by the number of matching fragment structures. In the cross validation the evaluation of the relative ranking positions (RRP) using different sizes of training sets confirms that a larger coverage of training data improves the average RRP from 0.65 to 0.72. Our approach allows downstream algorithms to process candidates in order of importance.