Jonas Grossmann

Genome-Scale Proteomics Reveals Arabidopsis thaliana Gene Models and Proteome Dynamics

We have assembled a proteome map for Arabidopsis thaliana from high-density, organ-specific proteome catalogs that we generated for different organs, developmental stages, and undifferentiated cultured cells. We matched 86,456 unique peptides to 13,029 proteins and provide expression evidence for 57 gene models that are not represented in the TAIR7 protein database. Analysis of the proteome identified organ-specific biomarkers and allowed us to compile an organ-specific set of proteotypic peptides for 4105 proteins to facilitate targeted quantitative proteomics surveys. Quantitative information for the identified proteins was used to establish correlations between transcript and protein accumulation in different plant organs. The Arabidopsis proteome map provides information about genome activity and proteome assembly and is available as a resource for plant systems biology.

Large-Scale Arabidopsis Phosphoproteome Profiling Reveals Novel Chloroplast Kinase Substrates and Phosphorylation Networks

We have characterized the phosphoproteome of Arabidopsis (Arabidopsis thaliana) seedlings using high-accuracy mass spectrometry and report the identification of 1,429 phosphoproteins and 3,029 unique phosphopeptides. Among these, 174 proteins were chloroplast phosphoproteins. Motif-X (motif extractor) analysis of the phosphorylation sites in chloroplast proteins identified four significantly enriched kinase motifs, which include casein kinase II (CKII) and proline-directed kinase motifs, as well as two new motifs at the carboxyl terminus of ribosomal proteins. Using the phosphorylation motifs as a footprint for the activity of a specific kinase class, we connected the phosphoproteins with their putative kinases and constructed a chloroplast CKII phosphorylation network. The network topology suggests that CKII is a central regulator of different chloroplast functions. To provide insights into the dynamic regulation of protein phosphorylation, we analyzed the phosphoproteome at the end of day and end of night. The results revealed only minor changes in chloroplast kinase activities and phosphorylation site utilization. A notable exception was ATP synthase β-subunit, which is found phosphorylated at CKII phosphorylation sites preferentially in the dark. We propose that ATP synthase is regulated in cooperation with 14-3-3 proteins by CKII-mediated phosphorylation of ATP synthase β-subunit in the dark.

Dynamic Spectrum Quality Assessment and Iterative Computational Analysis of Shotgun Proteomic Data Toward More Efficient Identification of Post-translational Modifications, Sequence Polymorphisms, and Novel Peptides

In mass spectrometry-based proteomics, frequently hundreds of thousands of MS/MS spectra are collected in a single experiment. Of these, a relatively small fraction is confidently assigned to peptide sequences, whereas the majority of the spectra are not further analyzed. Spectra are not assigned to peptides for diverse reasons. These include deficiencies of the scoring schemes implemented in the database search tools, sequence variations (e.g. single nucleotide polymorphisms) or omissions in the database searched, post-translational or chemical modifications of the peptide analyzed, or the observation of sequences that are not anticipated from the genomic sequence (e.g. splice forms, somatic rearrangement, and processed proteins). To increase the amount of information that can be extracted from proteomic MS/MS datasets we developed a robust method that detects high quality spectra within the fraction of spectra unassigned by conventional sequence database searching and computes a quality score for each spectrum. We also demonstrate that iterative search strategies applied to such detected unassigned high quality spectra significantly increase the number of spectra that can be assigned from datasets and that biologically interesting new insights can be gained from existing data.

A Fast SEQUEST Cross Correlation Algorithm

The SEQUEST program was the first and remains one of the most widely used tools for assigning a peptide sequence within a database to a tandem mass spectrum. The cross correlation score is the primary score function implemented within SEQUEST and it is this score that makes the tool particularly sensitive. Unfortunately, this score is computationally expensive to calculate, and thus, to make the score manageable, SEQUEST uses a less sensitive but fast preliminary score and restricts the cross correlation to just the top 500 peptides returned by the preliminary score. Classically, the cross correlation score has been calculated using Fast Fourier Transforms (FFT) to generate the full correlation function. We describe an alternate method of calculating the cross correlation score that does not require FFTs and can be computed efficiently in a fraction of the time. The fast calculation allows all candidate peptides to be scored by the cross correlation function, potentially mitigating the need for the preliminary score, and enables an E-value significance calculation based on the cross correlation score distribution calculated on all candidate peptide sequences obtained from a sequence database.

Implementation and evaluation of relative and absolute quantification in shotgun proteomics with label-free methods.

Tandem mass spectrometry allows for fast protein identification in a complex sample. As mass spectrometers get faster, more sensitive and more accurate, methods were devised by many academic research groups and commercial suppliers that allow protein research also in quantitative respect. Since label-free methods are an attractive alternative to labeling approaches for proteomics researchers seeking for accurate quantitative results we evaluated several open-source analysis tools in terms of performance on two reference data sets, explicitly generated for this purpose. In this paper we present an implementation, T3PQ (Top 3 Protein Quantification), of the method suggested by Silva and colleagues for LC-MS(E) applications and we demonstrate its applicability to data generated on FT-ICR instruments acquiring in data dependent acquisition (DDA) mode. In order to validate this method and to show its usefulness also for absolute protein quantification, we generated a reference data set of a sample containing four different proteins with known concentrations. Furthermore, we compare three other label-free quantification methods using a complex biological sample spiked with a standard protein in defined concentrations. We evaluate the applicability of these methods and the quality of the results in terms of robustness and dynamic range of the spiked-in protein as well as other proteins also detected in the mixture. We discuss drawbacks of each method individually and consider crucial points for experimental designs. The source code of our implementation is available under the terms of the GNU GPLv3 and can be downloaded from sourceforge (http://fqms.svn.sourceforge.net/svnroot/fqms). A tarball containing the data used for the evaluation is available on the FGCZ web server (http://fgcz-data.uzh.ch/public/T3PQ.tgz).

Comparative phosphoproteome profiling reveals a function of the STN8 kinase in fine-tuning of cyclic electron flow (CEF)

Important aspects of photosynthetic electron transport efficiency in chloroplasts are controlled by protein phosphorylation. Two thylakoid-associated kinases, STN7 and STN8, have distinct roles in short- and long-term photosynthetic acclimation to changes in light quality and quantity. Although some substrates of STN7 and STN8 are known, the complexity of this regulatory kinase system implies that currently unknown substrates connect photosynthetic performance with the regulation of metabolic and regulatory functions. We performed an unbiased phosphoproteome-wide screen with Arabidopsis WT and stn8 mutant plants to identify unique STN8 targets. The phosphorylation status of STN7 was not affected in stn8, indicating that kinases other than STN8 phosphorylate STN7 under standard growth conditions. Among several putative STN8 substrates, PGRL1-A is of particular importance because of its possible role in the modulation of cyclic electron transfer. The STN8 phosphorylation site on PGRL1-A is absent in both monocotyledonous plants and algae. In dicots, spectroscopic measurements with Arabidopsis WT, stn7, stn8, and stn7/stn8 double-mutant plants indicate a STN8-mediated slowing down of the transition from cyclic to linear electron flow at the onset of illumination. This finding suggests a possible link between protein phosphorylation by STN8 and fine-tuning of cyclic electron flow during this critical step of photosynthesis, when the carbon assimilation is not commensurate to the electron flow capacity of the chloroplast.

An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics

We present a computational pipeline for the quantification of peptides and proteins in label-free LC-MS/MS data sets. The pipeline is composed of tools from the OpenMS software framework and is applicable to the processing of large experiments (50+ samples). We describe several enhancements that we have introduced to OpenMS to realize the implementation of this pipeline. They include new algorithms for centroiding of raw data, for feature detection, for the alignment of multiple related measurements, and a new tool for the calculation of peptide and protein abundances. Where possible, we compare the performance of the new algorithms to that of their established counterparts in OpenMS. We validate the pipeline on the basis of two small data sets that provide ground truths for the quantification. There, we also compare our results to those of MaxQuant and Progenesis LC-MS, two popular alternatives for the analysis of label-free data. We then show how our software can be applied to a large heterogeneous data set of 58 LC-MS/MS runs.

Identification of combinatorial patterns of post-translational modifications on individual histones in the mouse brain.

Post-translational modifications (PTMs) of proteins are biochemical processes required for cellular functions and signalling that occur in every sub-cellular compartment. Multiple protein PTMs exist, and are established by specific enzymes that can act in basal conditions and upon cellular activity. In the nucleus, histone proteins are subjected to numerous PTMs that together form a histone code that contributes to regulate transcriptional activity and gene expression. Despite their importance however, histone PTMs have remained poorly characterised in most tissues, in particular the brain where they are thought to be required for complex functions such as learning and memory formation. Here, we report the comprehensive identification of histone PTMs, of their combinatorial patterns, and of the rules that govern these patterns in the adult mouse brain. Based on liquid chromatography, electron transfer, and collision-induced dissociation mass spectrometry, we generated a dataset containing a total of 10,646 peptides from H1, H2A, H2B, H3, H4, and variants in the adult brain. 1475 of these peptides carried one or more PTMs, including 141 unique sites and a total of 58 novel sites not described before. We observed that these PTMs are not only classical modifications such as serine/threonine (Ser/Thr) phosphorylation, lysine (Lys) acetylation, and Lys/arginine (Arg) methylation, but also include several atypical modifications such as Ser/Thr acetylation, and Lys butyrylation, crotonylation, and propionylation. Using synthetic peptides, we validated the presence of these atypical novel PTMs in the mouse brain. The application of data-mining algorithms further revealed that histone PTMs occur in specific combinations with different ratios. Overall, the present data newly identify a specific histone code in the mouse brain and reveal its level of complexity, suggesting its potential relevance for higher-order brain functions.

iTRAQ-based analysis of changes in the cassava root proteome reveals pathways associated with post-harvest physiological deterioration.

The short storage life of harvested cassava roots is an important constraint that limits the full potential of cassava as a commercial food crop in developing countries. We investigated the molecular changes during physiological deterioration of cassava root after harvesting using isobaric tags for relative and absolute quantification (iTRAQ) of proteins in soluble and non-soluble fractions prepared during a 96 h post-harvest time course. Combining bioinformatic approaches to reduce information redundancy for unsequenced or partially sequenced plant species, we established a comprehensive proteome map of the cassava root and identified quantitatively regulated proteins. Up-regulation of several key proteins confirmed that physiological deterioration of cassava root after harvesting is an active process, with 67 and 170 proteins, respectively, being up-regulated early and later after harvesting. This included regulated proteins that had not previously been associated with physiological deterioration after harvesting, such as linamarase, glutamic acid-rich protein, hydroxycinnamoyl transferase, glycine-rich RNA binding protein, β-1,3-glucanase, pectin methylesterase, maturase K, dehydroascorbate reductase, allene oxide cyclase, and proteins involved in signal pathways. To confirm the regulation of these proteins, activity assays were performed for selected enzymes. Together, our results show that physiological deterioration after harvesting is a highly regulated complex process involving proteins that are potential candidates for biotechnology approaches to reduce such deterioration.

Semi-supervised LC/MS alignment for differential proteomics

MOTIVATION Mass spectrometry (MS) combined with high-performance liquid chromatography (LC) has received considerable attention for high-throughput analysis of proteomes. Isotopic labeling techniques such as ICAT [5,6] have been successfully applied to derive differential quantitative information for two protein samples, however at the price of significantly increased complexity of the experimental setup. To overcome these limitations, we consider a label-free setting where correspondences between elements of two samples have to be established prior to the comparative analysis. The alignment between samples is achieved by nonlinear robust ridge regression. The correspondence estimates are guided in a semi-supervised fashion by prior information which is derived from sequenced tandem mass spectra. RESULTS The semi-supervised method for finding correspondences was successfully applied to aligning highly complex protein samples, even if they exhibit large variations due to different biological conditions. A large-scale experiment clearly demonstrates that the proposed method bridges the gap between statistical data analysis and label-free quantitative differential proteomics. AVAILABILITY The software will be available on the website http://people.inf.ethz.ch/befische/proteomics.