Jan Hummel

PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plant-specific phosphorylation site predictor

The PhosPhAt database provides a resource consolidating our current knowledge of mass spectrometry-based identified phosphorylation sites in Arabidopsis and combines it with phosphorylation site prediction specifically trained on experimentally identified Arabidopsis phosphorylation motifs. The database currently contains 1187 unique tryptic peptide sequences encompassing 1053 Arabidopsis proteins. Among the characterized phosphorylation sites, there are over 1000 with unambiguous site assignments, and nearly 500 for which the precise phosphorylation site could not be determined. The database is searchable by protein accession number, physical peptide characteristics, as well as by experimental conditions (tissue sampled, phosphopeptide enrichment method). For each protein, a phosphorylation site overview is presented in tabular form with detailed information on each identified phosphopeptide. We have utilized a set of 802 experimentally validated serine phosphorylation sites to develop a method for prediction of serine phosphorylation (pSer) in Arabidopsis. An analysis of the current annotated Arabidopsis proteome yielded in 27 782 predicted phosphoserine sites distributed across 17 035 proteins. These prediction results are summarized graphically in the database together with the experimental phosphorylation sites in a whole sequence context. The Arabidopsis Protein Phosphorylation Site Database (PhosPhAt) provides a valuable resource to the plant science community and can be accessed through the following link http://phosphat.mpimp-golm.mpg.de

Elemental formula annotation of polar and lipophilic metabolites using 13C, 15N and 34S isotope labelling, in combination with high-resolution mass spectrometry

The unbiased and comprehensive analysis of metabolites in any organism presents a major challenge if proper peak annotation and unambiguous assignment of the biological origin of the peaks are required. Here we provide a comprehensive multi-isotope labelling-based strategy using fully labelled (13) C, (15) N and (34) S plant tissues, in combination with a fractionated metabolite extraction protocol. The extraction procedure allows for the simultaneous extraction of polar, semi-polar and hydrophobic metabolites, as well as for the extraction of proteins and starch. After labelling and extraction, the metabolites and lipids were analysed using a high-resolution mass spectrometer providing accurate MS and all-ion fragmentation data, providing an unambiguous readout for every detectable isotope-labelled peak. The isotope labelling assisted peak annotation process employed can be applied in either an automated database-dependent or a database-independent analysis of the plant polar metabolome and lipidome. As a proof of concept, the developed methods and technologies were applied and validated using Arabidopsis thaliana leaf and root extracts. Along with a large repository of assigned elemental compositions, which is provided, we show, using selected examples, the accuracy and reliability of the developed workflow.

13C Isotope-Labeled Metabolomes Allowing for Improved Compound Annotation and Relative Quantification in Liquid Chromatography-Mass Spectrometry-based Metabolomic Research

Metabolomics is rapidly becoming an integral part of many life science studies ranging from disease diagnostics to systems biology. However, a number of problems such as the discrimination of biological from non-biological signals, efficient compound annotation, and reliable quantification are still not satisfactorily solved in untargeted LC-MS-based metabolomics research. Extending our previous work on direct infusion-based metabolomics, we here describe a (13)C isotope labeling strategy in combination with an Ultra Performance Liquid Chromatography Fourier Transform Ion Cyclotron Resonance Mass Spectrometry-based approach (UPLC-FTICR MS) which provides a technological platform offering solutions to a number of the above-mentioned problems. We further demonstrate that the use of a fully labeled metabolome is not only beneficial for high end mass spectrometers, such as that used in this study but also provides a considerable improvement to every other mass spectrometry-based metabolomic platform.

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.

High-Resolution Direct Infusion-Based Mass Spectrometry in Combination with Whole 13C Metabolome Isotope Labeling Allows Unambiguous Assignment of Chemical Sum Formulas

A new strategy for direct infusion-based metabolite analysis employing a combination of high-resolution mass spectrometry and (13)C-isotope labeling of entire metabolomes is described. Differentially isotope labeled metabolite extracts from otherwise identically grown reference plants were prepared and infused into a Fourier transform ion cyclotron resonance mass spectrometer. The derived accurate mass lists from each extract were searched, using an in-house-developed database search tool, against a number of comprehensive metabolite databases. Comparison of the retrieved chemical formulas from both, the (12)C and (13)C samples, leads to two major advantages compared to nonisotope-based metabolite fingerprinting: first, removal of background contaminations from the result list, due to the (12)C/(13)C peak pairing principle and therefore positive identification of compounds of true biological origin; second, elimination of ambiguity in chemical formula assignment due to the same principle, leading to the clear association of one measured mass to only one chemical formula. Applying this combination of strategies to metabolite extracts of the model plant Arabidopsis thaliana therefore resulted in the reproducible identification of more than 1000 unambiguous chemical sum formulas of biological origin of which more than 80% have not been associated to Arabidopsis before.

Ultra Performance Liquid Chromatography and High Resolution Mass Spectrometry for the Analysis of Plant Lipids

Holistic analysis of lipids is becoming increasingly popular in the life sciences. Recently, several interesting, mass spectrometry-based studies have been conducted, especially in plant biology. However, while great advancements have been made we are still far from detecting all the lipids species in an organism. In this study we developed an ultra performance liquid chromatography-based method using a high resolution, accurate mass, mass spectrometer for the comprehensive profiling of more than 260 polar and non-polar Arabidopsis thaliana leaf lipids. The method is fully compatible to the commonly used lipid extraction protocols and provides a viable alternative to the commonly used direct infusion-based shotgun lipidomics approaches. The whole process is described in detail and compared to alternative lipidomic approaches. Next to the developed method we also introduce an in-house developed database search software (GoBioSpace), which allows one to perform targeted or un-targeted lipidomic and metabolomic analysis on mass spectrometric data of every kind.

ProMEX: a mass spectral reference database for proteins and protein phosphorylation sites

BackgroundIn the last decade, techniques were established for the large scale genome-wide analysis of proteins, RNA, and metabolites, and database solutions have been developed to manage the generated data sets. The Golm Metabolome Database for metabolite data (GMD) represents one such effort to make these data broadly available and to interconnect the different molecular levels of a biological system [1]. As data interpretation in the light of already existing data becomes increasingly important, these initiatives are an essential part of current and future systems biology.ResultsA mass spectral library consisting of experimentally derived tryptic peptide product ion spectra was generated based on liquid chromatography coupled to ion trap mass spectrometry (LC-IT-MS). Protein samples derived from Arabidopsis thaliana, Chlamydomonas reinhardii, Medicago truncatula, and Sinorhizobium meliloti were analysed. With currently 4,557 manually validated spectra associated with 4,226 unique peptides from 1,367 proteins, the database serves as a continuously growing reference data set and can be used for protein identification and quantification in uncharacterized biological samples. For peptide identification, several algorithms were implemented based on a recently published study for peptide mass fingerprinting [2] and tested for false positive and negative rates. An algorithm which considers intensity distribution for match correlation scores was found to yield best results. For proof of concept, an LC-IT-MS analysis of a tryptic leaf protein digest was converted to mzData format and searched against the mass spectral library. The utility of the mass spectral library was also tested for the identification of phosphorylated tryptic peptides. We included in vivo phosphorylation sites of Arabidopsis thaliana proteins and the identification performance was found to be improved compared to genome-based search algorithms. Protein identification by ProMEX is linked to other levels of biological organization such as metabolite, pathway, and transcript data. The database is further connected to annotation and classification services via BioMoby.ConclusionThe ProMEX protein/peptide database represents a mass spectral reference library with the capability of matching unknown samples for protein identification. The database allows text searches based on metadata such as experimental information of the samples, mass spectrometric instrument parameters or unique protein identifier like AGI codes. ProMEX integrates proteomics data with other levels of molecular organization including metabolite, pathway, and transcript information and may thus become a useful resource for plant systems biology studies. The ProMEX mass spectral library is available at http://promex.mpimp-golm.mpg.de/.

Discrimination of wine attributes by metabolome analysis.

The chemical composition of any wine sample contains numerous small molecules largely derived from three different sources: the grape berry, the yeast strain used for fermentation, and the containers used for wine making and storage. The combined sum of these small molecules present in the wine, therefore, might account for all wine specific features such as cultivar, vintage, origin, and quality. Still, most wine authentication procedures rely either on subjective human measures or if they are based on measurable features, they include a limited number of compounds. In this study, which is based on an untargeted UPLC-FT-ICR-MS-based approach, we provide data, demonstrating that unbiased and objective analytical chemistry in combination with multivariate statistical methods allows to reproducible classify/distinguish wine attributes like variety, origin, vintage, and quality.

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.

A rapid approach for phenotype-screening and database independent detection of cSNP/protein polymorphism using mass accuracy precursor alignment

The dynamics of a proteome can only be addressed with large‐scale, high‐throughput methods. To cope with the inherent complexity, techniques based on targeted quantification using proteotypic peptides are arising. This is an essential systems biology approach; however, for the exploratory discovery of unexpected markers, nontargeted detection of proteins, and protein modifications is indispensable. We present a rapid label‐free shotgun proteomics approach that extracts relevant phenotype‐specific peptide product ion spectra in an automated workflow without prior identification. These product ion spectra are subsequently sequenced with database search and de novo prediction algorithms. We analyzed six potato tuber cultivars grown on three plots of two geographically separated fields in Germany. For data mining about 1.5 million spectra from 107 analyses were aligned and statistically examined in approximately 1 day. Several cultivar‐specific protein markers were detected. Based on de novo‐sequencing a dominant protein polymorphism not detectable in the available EST‐databases was assigned exclusively to a specific potato cultivar. The approach is applicable to organisms with unsequenced or incomplete genomes and to the automated extraction of relevant mass spectra that potentially cannot be identified by genome/EST‐based search algorithms.