Hydroponic isotope labeling of entire plants (HILEP) for quantitative plant proteomics

Quantitative analysis by mass spectrometry (MS) is a major challenge in proteomics as the correlation between analyte concentration and signal intensity is often poor due to varying ionisation efficiencies in the presence of molecular competitors. However, relative quantitation methods that utilise differential stable isotope labelling and mass spectrometric detection are available. Many drawbacks inherent to chemical labelling methods (ICAT, iTRAQ) can be overcome by metabolic labelling with amino acids containing stable isotopes (e.g. 13C and/or 15N) in methods such as Stable Isotope Labelling with Amino acids in Cell culture (SILAC). SILAC has also been used for labelling of proteins in plant cell cultures (1) but is not suitable for whole plant labelling. Plants are usually autotrophic (fixing carbon from atmospheric CO2) and, thus, labelling with carbon isotopes becomes impractical. In addition, SILAC is expensive. Recently, Arabidopsis cell cultures were labelled with 15N in a medium containing nitrate as sole nitrogen source. This was shown to be suitable for quantifying proteins and nitrogen-containing metabolites from this cell culture (2,3). Labelling whole plants, however, offers the advantage of studying quantitatively the response to stimulation or disease of a whole multicellular organism or multi-organism systems at the molecular level. Furthermore, plant metabolism enables the use of inexpensive labelling media without introducing additional stress to the organism. And finally, hydroponics is ideal to undertake metabolic labelling under extremely well-controlled conditions. We demonstrate the suitability of metabolic 15N hydroponic isotope labelling of entire plants (HILEP) for relative quantitative proteomic analysis by mass spectrometry. To evaluate this methodology, Arabidopsis plants were grown hydroponically in 14N and 15N media and subjected to oxidative stress.

Multiplexed quantitative proteomics using differential metabolic 15N-labeling

There are several advantages of using metabolic labeling in quantitative proteomics. The early pooling of samples compared to post-labeling methods eliminates errors from different sample processing, protein extraction and enzymatic digestion. Metabolic labeling is also highly efficient and relatively inexpensive compared to commercial labeling reagents. However, methods for multiplexed quantitation in the MS-domain (or ‘non-isobaric’ methods), suffer from signal dilution at higher degrees of multiplexing, as the MS/MS signal for peptide identification is lower given the same amount of peptide loaded onto the column or injected into the mass spectrometer. This may partly be overcome by mixing the samples at non-uniform ratios, for instance by increasing the fraction of unlabeled proteins. We have developed an algorithm for arbitrary degrees of nonisobaric multiplexing for relative protein abundance measurements. We have used metabolic labeling with different levels of 15N, but the algorithm is in principle applicable to any isotope or combination of isotopes. Ion trap mass spectrometers are fast and suitable for LC-MS/MS and peptide identification. However, they cannot resolve overlapping isotopic envelopes from different peptides, which makes them less suitable for MS-based quantitation. Fourier-transform ion cyclotron resonance (FTICR) mass spectrometry is less suitable for LC-MS/MS, but provides the resolving power required to resolve overlapping isotopic envelopes. We therefore combined ion trap LC-MS/MS for peptide identification with FTICR LC-MS for quantitation using chromatographic alignment. We applied the method in a heat shock study in a plant model system (A. thaliana) and compared the results with gene expression data from similar experiments in literature.

Effects of sulphur nutrition during potato cultivation on the formation of acrylamide and aroma compounds during cooking

Lack of sulphur nutrition during potato cultivation has been shown to have profound effects on tuber composition, affecting in particular the concentrations of free asparagine, other amino acids and sugars. This is important because free asparagine and sugars react at high temperatures to form acrylamide, a suspect carcinogen. Free amino acids and sugars also form a variety of other compounds associated with colour and flavour. In this study the volatile aroma compounds formed in potato flour heated at 180 °C for 20 min were compared for three varieties of potato grown, with and without sulphur fertiliser. Approximately 50 compounds were quantified in the headspace extracts of the heated flour, of which over 40 were affected by sulphur fertilisation and/or variety. Many of the 41 compounds found at higher concentrations in the sulphur-deficient flour were Strecker aldehydes and compounds formed from their condensation, whereas only one compound, benzaldehyde, behaved in the same way as did acrylamide and was found at higher concentrations in the sulphur-sufficient flour. The reasons for these effects are discussed.

Glomus intraradices and Gigaspora margarita arbuscular mycorrhizal associations differentially affect nitrogen and potassium nutrition of Plantago lanceolata in a low fertility dune soil

Two controlled microcosm experiments aimed at a critical re-assessment of the contributions of divergent arbuscular mycorrhizal (AM) fungi to plant mineral nutrition were established that specifically targeted Plantago lanceolata–Glomus intraradices (B.B/E) and –Gigaspora margarita (BEG 34) symbioses developed in a native, nutrient limited, coastal dune soil. Plant tissue nitrogen (N), phosphorus (P) and potassium (K) status as well as plant growth parameters and levels of mycorrhizal colonization were assessed at harvest. In addition to the general well-established mycorrhizal facilitation of P uptake, the study was able to demonstrate a G. intraradices-specific contribution to improved plant nitrogen and potassium nutrition. In the two respective experiments, G. intraradices-inoculated plants had 27.8% and 40.8% more total N and 55.8% and 23.3% more total K when compared to Gi. margarita inoculated counterparts. Dissimilar overall contribution of the two isolates to plant nutrition was identified in AM-genus specific differences in plant tissue N:P:K ratios. G. intraradices inoculated and non-mycorrhizal plants generally exhibited N:P:K ratios indicative of P limitation whereas for Gi.margarita mycorrhizal plants, corresponding ratios strongly implied either N or K limitation. The study provides further evidence highlighting AM functional biodiversity in respect to plant nutrient limitation experienced by mycorrhizal P. lanceolata in an ecologically relevant soil system.

Fully automated software solution for protein quantitation by global metabolic labeling with stable isotopes

Metabolic stable isotope labeling is increasingly employed for accurate protein (and metabolite) quantitation using mass spectrometry (MS). It provides sample-specific isotopologues that can be used to facilitate comparative analysis of two or more samples. Stable Isotope Labeling by Amino acids in Cell culture (SILAC) has been used for almost a decade in proteomic research and analytical software solutions have been established that provide an easy and integrated workflow for elucidating sample abundance ratios for most MS data formats. While SILAC is a discrete labeling method using specific amino acids, global metabolic stable isotope labeling using isotopes such as (15)N labels the entire element content of the sample, i.e. for (15)N the entire peptide backbone in addition to all nitrogen-containing side chains. Although global metabolic labeling can deliver advantages with regard to isotope incorporation and costs, the requirements for data analysis are more demanding because, for instance for polypeptides, the mass difference introduced by the label depends on the amino acid composition. Consequently, there has been less progress on the automation of the data processing and mining steps for this type of protein quantitation. Here, we present a new integrated software solution for the quantitative analysis of protein expression in differential samples and show the benefits of high-resolution MS data in quantitative proteomic analyses.