In silico fragmentation for computer assisted identification of metabolite mass spectra

Wolf, Sebastian I.; Schmidt, Stephan; Müller‐Hannemann, Matthias; Neumann, Steffen

doi:10.1186/1471-2105-11-148

Cited by 607 publications

(573 citation statements)

References 11 publications

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“…Fragment ion spectra were recorded in scanning mode by high-resolution TOF MS. Peaks with intensity of at least 10% of the highest non-saturated peak intensity in the MS/MS spectra were extracted and matched with compounds in the PubChem database using MetFrag 47 . A top rank of the target compound among all matching compounds in the database was considered as further evidence for the initially proposed annotation based on accurate mass.…”

Section: Metabolomicsmentioning

confidence: 99%

Ubiquinone accumulation improves osmotic-stress tolerance in Escherichia coli

2014

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Bacteria are thought to cope with fluctuating environmental solute concentrations primarily by adjusting the osmolality of their cytoplasm. To obtain insights into underlying metabolic adaptions, we analyzed the global metabolic response of Escherichia coli to sustained hyperosmotic stress using non-targeted mass spectrometry. We observed that 52% of 1,071 detected metabolites, including known osmoprotectants, changed abundance with increasing salt challenge. Unexpectedly, unsupervised data analysis revealed a substantial increase of most intermediates in the ubiquinone-8 (Q8) biosynthesis pathway and a 110-fold accumulation of Q8 itself, as confirmed by quantitative lipidomics. We then demonstrate that Q8 is necessary for acute and sustained osmotic stress tolerance using Q8 mutants and tolerance rescue through feeding non-respiratory Q8 analogues. Finally, in vitro experiments with artificial liposomes reveal mechanical membrane stabilization as a principal mechanism of Q8-mediated osmoprotection. Thus, we find that besides regulating intracellular osmolality, E. coli enhances its cytoplasmic membrane stability to withstand osmotic stress. AbstractBacteria are thought to cope with fluctuating environmental solute concentrations primarily by adjusting the osmolality of their cytoplasm. To obtain insights into underlying metabolic adaptions, we analyzed the global metabolic response of Escherichia coli to sustained hyperosmotic stress using non-targeted mass spectrometry. We observed that 52% of 1,071 detected metabolites, including known osmoprotectants, changed abundance with increasing salt challenge. Unexpectedly, unsupervised data analysis revealed a substantial increase of most intermediates in the ubiquinone-8 (Q8) biosynthesis pathway and a 110-fold accumulation of Q8 itself, as confirmed by quantitative lipidomics. We then demonstrate that Q8 is necessary for acute and sustained osmotic stress tolerance using Q8 mutants and tolerance rescue through feeding non-respiratory Q8 analogues.Finally, in vitro experiments with artificial liposomes reveal mechanical membrane stabilization as a principal mechanism of Q8-mediated osmoprotection. Thus, we find that besides regulating intracellular osmolality, E. coli enhances its cytoplasmic membrane stability to withstand osmotic stress.

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Section: Metabolomicsmentioning

confidence: 99%

Ubiquinone accumulation improves osmotic-stress tolerance in Escherichia coli

2014

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“…9) Our approach involved the use of the benchmark dataset reported by Hill et al as well as Wolf et al and Ridder et al In order to compare the results, we generated a merged product ion spectrum and retrieved candidates using PubChem in a manner similar to a previous report. 8) e results of the annotation are shown in Tables 1, S2-S5. Although we could not compare each result precisely, because the number of retrieved candidates was di erent, our protocol showed better results than previous reports in terms of average, median, and third quartile rank.…”

Section: Structure Assignment and Ranking Using A Benchmark Datasetmentioning

confidence: 99%

“…6) Wolf et al developed the MetFrag protocol based on an annotation by comparing with computational fragmentation and scoring with the bond dissociation energy of the cleaved bond. 8) Ridder et al developed an annotation protocol referred to as MAGMa for MS n analysis using computational assignment and scoring based on a penalty value. 9) Our approach involved the use of the benchmark dataset reported by Hill et al as well as Wolf et al and Ridder et al In order to compare the results, we generated a merged product ion spectrum and retrieved candidates using PubChem in a manner similar to a previous report.…”

Section: Structure Assignment and Ranking Using A Benchmark Datasetmentioning

confidence: 99%

“…A computational approach can overcome these problems by virtue of its ability to provide an automatic interpretation. [6][7][8][9] We previously reported on an automated annotation system for interpreting the nontargeted analysis of a multistage CID (MS n ) spectrum, followed by the retrieval of candidates from a compound database. 10) We were able to successfully annotate 20 components that showed contribution to tea quality, such as ca eine, catechins, and a series of organic acid esters.…”

Section: Introductionmentioning

confidence: 99%

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Method for the Compound Annotation of Conjugates in Nontargeted Metabolomics Using Accurate Mass Spectrometry, Multistage Product Ion Spectra and Compound Database Searching

et al. 2015

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Owing to biotransformation, xenobiotics are o en found in conjugated form in biological samples such as urine and plasma. Liquid chromatography coupled with accurate mass spectrometry with multistage collision-induced dissociation provides spectral information concerning these metabolites in complex materials. Unfortunately, compound databases typically do not contain a su cient number of records for such conjugates. We report here on the development of a novel protocol, referred to as ChemProphet, to annotate compounds, including conjugates, using compound databases such as PubChem and ChemSpider.e annotation of conjugates involves three steps: 1. Recognition of the type and number of conjugates in the sample; 2. Compound search and annotation of the deconjugated form; and 3. In silico evaluation of the candidate conjugate. ChemProphet assigns a spectrum to each candidate by automatically exploring the substructures corresponding to the observed product ion spectrum. When nished, it annotates the candidates assigning a rank for each candidate based on the calculated score that ranks its relative likelihood. We assessed our protocol by annotating a benchmark dataset by including the product ion spectra for 102 compounds, annotating the commercially available standard for quercetin 3-glucuronide, and by conducting a model experiment using urine from mice that had been administered a green tea extract. e results show that by using the ChemProphet approach, it is possible to annotate not only the deconjugated molecules but also the conjugated molecules using an automatic interpretation method based on deconjugation that involves multistage collision-induced dissociation and in silico calculated conjugation.Please cite this article as: Mass Spectrom (Tokyo) 2015; 4(1): A0036

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“…Further identification using MetFrag (http://msbi. ipb-halle.de/MetFrag/) was performed which provided in silico fragmentation (Wolf et al, 2010) for confirmation of the structural identity. The unsupervised multivariate statistics, principle component analysis (PCA), was performed using the ProfileAnalysis 2.0 software (BrukerDaltonics, Germany).…”

Section: Data Analysis and Multivariate Statisticsmentioning

confidence: 99%

METABOLOMICS UNRAVEL DIFFERENCES BETWEEN CAMEROON Dura AND DELI Dura OIL PALM (Elaeis guineensis Jacq.) GENETIC BACKGROUNDS AGAINST BASAL STEM ROT

Nurazah¹,

Idris²,

Kushairi³

et al. 2017

JOPR

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Metabolomics is emerging as a powerful tool for screening of metabolites in both phenotyping and diagnostic analyses in plants. Metabolomics coupled with multivariate statistical analysis has provided a fast and unbiased comparative investigation of the metabolite composition in oil palm root. Basal stem rot (BSR) caused by the white rot fungus, Ganoderma boninense is a common (MPOB, 2016). However, the palm is susceptible to various pests and diseases, the most serious in South-east Asia being basal stem rot (BSR) by the white rot fungus, Ganoderma boninense (Cooper et al., 2011;Paterson et al., 2009; Idris et al., 2004a). Integrated control using chemical and biological means has been tried against the disease (Sundram, 2013;Sundram et al., 2008;Susanto et al., 2005; Idris et al., 2004b) but with at most limited success. Resistance to BSR is only partial (Idris et al., 2004a) and the disease management is complicated by the presence of different species of Ganoderma with various degrees of aggressiveness with the dominant species varying in different localities (Wong et al., 2012).The disease triangle -the concept of a disease caused by the trinity of a pathogen attacking a DOI: https://doi.org/10.21894/jopr.2017DOI: https://doi.org/10.21894/jopr. .2902 227

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In silico fragmentation for computer assisted identification of metabolite mass spectra

Cited by 607 publications

References 11 publications

Ubiquinone accumulation improves osmotic-stress tolerance in Escherichia coli

Ubiquinone accumulation improves osmotic-stress tolerance in Escherichia coli

Method for the Compound Annotation of Conjugates in Nontargeted Metabolomics Using Accurate Mass Spectrometry, Multistage Product Ion Spectra and Compound Database Searching

METABOLOMICS UNRAVEL DIFFERENCES BETWEEN CAMEROON Dura AND DELI Dura OIL PALM (Elaeis guineensis Jacq.) GENETIC BACKGROUNDS AGAINST BASAL STEM ROT

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