Retention index thresholds for compound matching in GC-MS metabolite profiling

The generation of retention index (RI) libraries is an expensive and time-consuming effort. Procedures for the transfer of RI properties between chromatography variants are, therefore, highly relevant for a shared use. The precision of RI determination and accuracy of RI transfer between 8 method variants employing 5%-phenyl-95%-dimethylpolysiloxane capillary columns was investigated using a series of 9 n-alkanes (C(10)-C(36)). The precision of the RI determination of 13 exemplary fatty acid methyl esters (C(8) ME-C(30) ME) was 0.22-0.33 standard deviation (S.D.) expressed in RI units in low complexity samples. In the presence of complex biological matrices this precision may deteriorate to 0.75-1.11. Application of the previously proposed Kováts, van den Dool or 3rd-5th order polynomial regression algorithms resulted in similar precision of RI calculation. For transfer of empirical van den Dool-RI properties between the chromatography variants 3rd order regression was found to represent the minimal necessary assumption. The range of typical regression coefficients was r(2)=0.9988-0.9998 and accuracy of RI prediction between chromatography variants varied between 5.1 and 19.8 (0.29-0.69%) S.D. of residual RI error, RI(predicted)-RI(determined) (n>64). Accuracy of prediction was enhanced when subsets of chemically similar compound classes were used for regression, for example organic acids and sugars exhibited 0.78 (n=29) and 3.74 (n=37) S.D. of residual RI error, respectively. In conclusion, we suggest use of percent RI error rather than absolute RI units for the definition of matching thresholds. Thresholds of 0.5-1.0% may apply to most transfers between chromatography variants. These thresholds will not solve all matching ambiguities in complex samples. Therefore, we recommend co-analysis of reference substances with each GC-MS profiling experiment. Composition of these defined reference mixtures may best approximate or mimic the quantitative and qualitative composition of the biological matrix under investigation.     

Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach

Biomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated. Up to now, the prediction of phenotypes in plants was based on growing plants and assaying the organs of interest in a time intensive process. For the first time, we demonstrate in this study the application of metabolomics to predict agronomic important phenotypes of a crop plant that was grown in different environments. Our procedure consists of established techniques to screen untargeted for a large amount of metabolites in parallel, in combination with machine learning methods. By using this combination of metabolomics and biomathematical tools metabolites were identified that can be used as biomarkers to improve the prediction of traits. The predictive metabolites can be selected and used subsequently to develop fast, targeted and low‐cost diagnostic biomarker assays that can be implemented in breeding programs or quality assessment analysis. The identified metabolic biomarkers allow for the prediction of crop product quality. Furthermore, marker‐assisted selection can benefit from the discovery of metabolic biomarkers when other molecular markers come to its limitation. The described marker selection method was developed for potato tubers, but is generally applicable to any crop and trait as it functions independently of genomic information.     

Decision tree supported substructure prediction of metabolites from GC-MS profiles

Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at . All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities.     

The ABC Transporter ABCG1 Is Required for Suberin Formation in Potato Tuber Periderm

The lipid biopolymer suberin plays a major role as a barrier both at plant-environment interfaces and in internal tissues, restricting water and nutrient transport. In potato (Solanum tuberosum), tuber integrity is dependent on suberized periderm. Using microarray analyses, we identified ABCG1, encoding an ABC transporter, as a gene responsive to the pathogen-associated molecular pattern Pep-13. Further analyses revealed that ABCG1 is expressed in roots and tuber periderm, as well as in wounded leaves. Transgenic ABCG1-RNAi potato plants with downregulated expression of ABCG1 display major alterations in both root and tuber morphology, whereas the aerial part of the ABCG1-RNAi plants appear normal. The tuber periderm and root exodermis show reduced suberin staining and disorganized cell layers. Metabolite analyses revealed reduction of esterified suberin components and hyperaccumulation of putative suberin precursors in the tuber periderm of RNA interference plants, suggesting that ABCG1 is required for the export of suberin components.     

Perturbations in the Primary Metabolism of Tomato and Arabidopsis thaliana Plants Infected with the Soil-Borne Fungus Verticillium dahliae

The hemibiotrophic soil-borne fungus Verticillium dahliae is a major pathogen of a number of economically important crop species. Here, the metabolic response of both tomato and Arabidopsis thaliana to V. dahliae infection was analysed by first using non-targeted GC-MS profiling. The leaf content of both major cell wall components glucuronic acid and xylose was reduced in the presence of the pathogen in tomato but enhanced in A. thaliana. The leaf content of the two tricarboxylic acid cycle intermediates fumaric acid and succinic acid was increased in the leaf of both species, reflecting a likely higher demand for reducing equivalents required for defence responses. A prominent group of affected compounds was amino acids and based on the targeted analysis in the root, it was shown that the level of 12 and four free amino acids was enhanced by the infection in, respectively, tomato and A. thaliana, with leucine and histidine being represented in both host species. The leaf content of six free amino acids was reduced in the leaf tissue of diseased A. thaliana plants, while that of two free amino acids was raised in the tomato plants. This study emphasizes the role of primary plant metabolites in adaptive responses when the fungus has colonized the plant.     

The Lon Protease Is Essential for Full Virulence in Pseudomonas aeruginosa

Pseudomonas aeruginosa PAO1 lon mutants are supersusceptible to ciprofloxacin, and exhibit a defect in cell division and in virulence-related properties, such as swarming, twitching and biofilm formation, despite the fact that the Lon protease is not a traditional regulator. Here we set out to investigate the influence of a lon mutation in a series of infection models. It was demonstrated that the lon mutant had a defect in cytotoxicity towards epithelial cells, was less virulent in an amoeba model as well as a mouse acute lung infection model, and impacted on in vivo survival in a rat model of chronic infection. Using qRT-PCR it was demonstrated that the lon mutation led to a down-regulation of Type III secretion genes. The Lon protease also influenced motility and biofilm formation in a mucin-rich environment. Thus alterations in several virulence-related processes in vitro in a lon mutant were reflected by defective virulence in vivo.     

Annotating unknown components from GC/EI-MS-based metabolite profiling experiments using GC/APCI(+)-QTOFMS

GC/EI-MS-based metabolite profiling of derivatized polar fractions of crude plant extracts typically reveals several hundred components. Thereof, only up to one half can be identified using mass spectral and retention index libraries, the rest remains unknown. In the present work, the utility of GC/APCI(+)-QTOFMS for the annotation of unknown components was explored. Hence, EI and APCI(+) mass spectra of ~100 known components were extracted from GC/EI-QMS and GC/APCI(+)-QTOFMS profiles obtained from a methoximated and trimethylsilylated root extract of Arabidopsis thaliana. Based on this reference set, adduct and fragment ion formation under APCI(+) conditions was examined and the calculation of elemental compositions evaluated. During these studies, most of the components formed dominating protonated molecular ions. Despite the high mass accuracy (|Δm| ≤ 3 mDa) and isotopic pattern accuracy (mSigma ≤ 30) the determination of a component’s unique native elemental composition requires additional information, namely the number of trimethylsilyl and methoxime moieties as well as the analysis of corresponding collision-induced dissociation (CID) mass spectra. After all, the reference set was used to develop a strategy for the pairwise assignment of EI and APCI(+) mass spectra. Proceeding from these findings, the annotation of unidentified components detected by GC/EI-QMS using GC/APCI(+)-QTOFMS and corresponding deuterated derivatization reagents was attempted. For a total of 25 unknown components, pairs of EI and APCI(+) mass spectra were compiled and elemental compositions determined. Integrative interpretation of EI and CID mass spectra resulted in 14 structural hypotheses, of which seven were confirmed using authenticated standards.     

Extraction of natural products from bark of <Emphasis Type="Italic">Betula pendula</Emphasis> using ionic liquids

Introduction

Aqueous–methanol mixtures have successfully been applied to extract a broad range of metabolites from plant tissue. However, a certain amount of material remains insoluble.

Objectives

To enlarge the metabolic compendium, two ionic liquids were selected to extract the methanol insoluble part of trunk from Betula pendula.

Methods

The extracted compounds were analyzed by LC/MS and GC/MS.

Results

The results show that 1-butyl-3-methylimidazolium acetate (IL-Ac) predominantly resulted in fatty acids, whereas 1-ethyl-3-methylimidazolium tosylate (IL-Tos) mostly yielded phenolic structures. Interestingly, bark yielded more ionic liquid soluble metabolites compared to interior wood.

Conclusion

From this one can conclude that the application of ionic liquids may expand the metabolic snapshot.