Solutions for Low and High Accuracy Mass Spectrometric Data Matching: A Data-Driven Annotation Strategy in Nontargeted Metabolomics

Forcisi, Sara; Moritz, Franco; Lucio, Marianna; Lehmann, Rainer; Stefan, Norbert; Schmitt‐Kopplin, Philippe

doi:10.1021/acs.analchem.5b02049

Cited by 40 publications

(46 citation statements)

References 24 publications

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“…It was shown recently (Forcisi et al, 2015) that the network-based molecular formula assignment of Tziotis et al (2011), which is usually used on highresolution MS instruments, also is applicable to mass spectra with lower resolution. Furthermore, Moritz et al (2017) demonstrated how MS data can be used to link metabolomes to genotypes via MDBs.…”

Section: Mdea Of Lc-ms Data and Mapping On Transcriptomic Datamentioning

confidence: 99%

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Mycorrhiza-Triggered Transcriptomic and Metabolomic Networks Impinge on Herbivore Fitness

et al. 2018

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Symbioses between plants and mycorrhizal fungi are ubiquitous in ecosystems and strengthen the plants' defense against aboveground herbivores. Here, we studied the underlying regulatory networks and biochemical mechanisms in leaves induced by ectomycorrhizae that modify herbivore interactions. Feeding damage and oviposition by the widespread poplar leaf beetle were reduced on the ectomycorrhizal hybrid poplar × Integration of transcriptomics, metabolomics, and volatile emission patterns via mass difference networks demonstrated changes in nitrogen allocation in the leaves of mycorrhizal poplars, down-regulation of phenolic pathways, and up-regulation of defensive systems, including protease inhibitors, chitinases, and aldoxime biosynthesis. Ectomycorrhizae had a systemic influence on jasmonate-related signaling transcripts. Our results suggest that ectomycorrhizae prime wounding responses and shift resources from constitutive phenol-based to specialized protective compounds. Consequently, symbiosis with ectomycorrhizal fungi enabled poplars to respond to leaf beetle feeding with a more effective arsenal of defense mechanisms compared with nonmycorrhizal poplars, thus demonstrating the importance of belowground plant-microbe associations in mitigating aboveground biotic stress.

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Section: Mdea Of Lc-ms Data and Mapping On Transcriptomic Datamentioning

confidence: 99%

“…In the next step, the low-resolution (2)LC-MS data were annotated following the procedure described by Forcisi et al (2015). To further improve the accuracy of these annotations, they were matched with the FT-ICR-MS annotations.…”

Section: Mdea Of Lc-ms Data and Mapping On Transcriptomic Datamentioning

confidence: 99%

Mycorrhiza-Triggered Transcriptomic and Metabolomic Networks Impinge on Herbivore Fitness

et al. 2018

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“…The approach by Weber and Viant () has the advantage that each MDB (rpair) can be associated to a set of enzymes, which – knowing some compositional and structural properties of the detected features – can be narrowed down to more specific enzyme sets. These can be targeted in future proteomics and transcriptomics experiments as well as on ultra‐high‐performance LC time‐of‐flight MS (UHPLC‐ToF‐MS) data (Forcisi et al ., ).…”

Section: Resultsmentioning

confidence: 97%

“…() generalized molecular formula propagation through MDiNs as a means of database‐independent molecular formula assignment for UHR‐MS features. The given method was applied on a multitude of analytical matrices (Müller et al ., ; Walker et al ., ; Zhang et al ., ; Forcisi et al ., ; Moritz et al ., ; Witting et al ., ), and extensions to low‐resolution mass spectrometry have been described (Forcisi et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Characterization of poplar metabotypes via mass difference enrichment analysis

Moritz

Kaling

Schnitzler

et al. 2017

Plant Cell & Environment

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Instrumentation technology for metabolomics has advanced drastically in recent years in terms of sensitivity and specificity. Despite these technical advances, data analytical strategies are still in their infancy in comparison with other 'omics'. Plants are known to possess an immense diversity of secondary metabolites. Typically, more than 70% of metabolomics data are not amenable to systems biological interpretation because of poor database coverage. Here, we propose a new general strategy for mass-spectrometry-based metabolomics that incorporates all exact mass features with known sum formulas into the evaluation and interpretation of metabolomics studies. We extend the use of mass differences, commonly used for feature annotation, by redefining them as variables that reflect the remaining 'omic' domains. The strategy uses exact mass difference network analyses exemplified for the metabolomic description of two grey poplar (Populus × canescens) genotypes that differ in their capability to emit isoprene. This strategy established a direct connection between the metabotype and the non-isoprene-emitting phenotype, as mass differences pertaining to prenylation reactions were over-represented in non-isoprene-emitting poplars. Not only was the analysis of mass differences able to grasp the known chemical biology of poplar, but it also improved the interpretability of yet unknown biochemical relationships.

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“…Non-target analysis is the determination of analytes a priori unknown to a chemist before analyzing samples [9][10][11]. This analysis has a special place in metabolomics [12][13][14][15], food authentication [16], environment analysis [4,17], etc. Generally, non-target analysis is carried out using chromatographic and electrophoretic techniques with MS detection where mass spectral libraries are required.…”

Section: Introductionmentioning

confidence: 99%

Strategy for non-target ionic analysis by capillary electrophoresis with ultraviolet detection

2016

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A strategy for non-target analysis of samples with unknown composition by capillary electrophoresis (CE) with ultraviolet (UV) detection is suggested. The strategy is based on the preliminary identification of analytes and further optimization of the conditions for their separation using the developed computational tool set ElphoSeparation. It is shown that, in order to record electrophoretic peaks with the mobilities from the maximum to minimum possible values, the positive and negative voltage polarity and hydrodynamic pressure should be used. To choose the optimal separation conditions, dynamic maps of electrophoretic separation (DMES) are suggested. DMES is a bar chart with theoretical resolutions of adjacent peaks presented in ascending order of the migration time. The resolution is calculated as the division of the difference of the effective electrophoretic mobilities of adjacent analytes by their average peak width in terms of electrophoretic mobility. The suggested strategy is tested by the example of the analysis of herbal medicine (Holosas) on the basis of rose hips. The approach should be used to analyze samples with not very complex composition, such as environmental water and precipitation samples, process liquors, some vegetable extracts, biological fluids, food, and other samples for the determination of widespread compounds capable of forming ionic species. For samples with complex composition, the approach used together with other techniques may produce advantageous information due to specificity of the method, particularly it can be useful for determination of compounds suffering from low volatility or thermal stability, and for analysis of samples with difficult matrices. Graphical Abstract The scheme of performing the non-target ionic analysis by capillary electrophoresis with ultraviolet detection.

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Solutions for Low and High Accuracy Mass Spectrometric Data Matching: A Data-Driven Annotation Strategy in Nontargeted Metabolomics

Cited by 40 publications

References 24 publications

Mycorrhiza-Triggered Transcriptomic and Metabolomic Networks Impinge on Herbivore Fitness

Mycorrhiza-Triggered Transcriptomic and Metabolomic Networks Impinge on Herbivore Fitness

Characterization of poplar metabotypes via mass difference enrichment analysis

Strategy for non-target ionic analysis by capillary electrophoresis with ultraviolet detection

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