Christopher M. Rose

Rapid phosphoproteomic and transcriptomic changes in the rhizobia-legume symbiosis

Symbiotic associations between legumes and rhizobia usually commence with the perception of bacterial lipochitooligosaccharides, known as Nod factors (NF), which triggers rapid cellular and molecular responses in host plants. We report here deep untargeted tandem mass spectrometry-based measurements of rapid NF-induced changes in the phosphorylation status of 13,506 phosphosites in 7739 proteins from the model legume Medicago truncatula. To place these phosphorylation changes within a biological context, quantitative phosphoproteomic and RNA measurements in wild-type plants were compared with those observed in mutants, one defective in NF perception (nfp) and one defective in downstream signal transduction events (dmi3). Our study quantified the early phosphorylation and transcription dynamics that are specifically associated with NF-signaling, confirmed a dmi3-mediated feedback loop in the pathway, and suggested “cryptic” NF-signaling pathways, some of them being also involved in the response to symbiotic arbuscular mycorrhizal fungi.

NeuCode Labels for Relative Protein Quantification

We describe a synthesis strategy for the preparation of lysine isotopologues that differ in mass by as little as 6 mDa. We demonstrate that incorporation of these molecules into the proteomes of actively growing cells does not affect cellular proliferation, and we discuss how to use the embedded mass signatures (neutron encoding (NeuCode)) for multiplexed proteome quantification by means of high-resolution mass spectrometry. NeuCode SILAC amalgamates the quantitative accuracy of SILAC with the multiplexing of isobaric tags and, in doing so, offers up new opportunities for biological investigation. We applied NeuCode SILAC to examine the relationship between transcript and protein levels in yeast cells responding to environmental stress. Finally, we monitored the time-resolved responses of five signaling mutants in a single 18-plex experiment.

Neutron encoded labeling for peptide identification.

Metabolic labeling of cells using heavy amino acids is most commonly used for relative quantitation; however, partner mass shifts also detail the number of heavy amino acids contained within the precursor species. Here, we use a recently developed metabolic labeling technique, NeuCode (neutron encoding) stable isotope labeling with amino acids in cell culture (SILAC), which produces precursor partners spaced ~40 mDa apart to enable amino acid counting. We implement large scale counting of amino acids through a program, Amino Acid Counter, which determines the most likely combination of amino acids within a precursor based on NeuCode SILAC partner spacing and filters candidate peptide sequences during a database search using this information. Counting the number of lysine residues for precursors selected for MS/MS decreases the median number of candidate sequences from 44 to 14 as compared to an accurate mass search alone (20 ppm). Furthermore, the ability to co-isolate and fragment NeuCode SILAC partners enables counting of lysines in product ions, and when the information is used, the median number of candidates is reduced to 7. We then demonstrate counting leucine in addition to lysine results in a 6-fold decrease in search space, 43 to 7, when compared to an accurate mass search. We use this scheme to analyze a nanoLC-MS/MS experiment and demonstrate that accurate mass plus lysine and leucine counting reduces the number of candidate sequences to one for ~20% of all precursors selected, demonstrating an ability to identify precursors without MS/MS analysis.

Instant spectral assignment for advanced decision tree-driven mass spectrometry

We have developed and implemented a sequence identification algorithm (inSeq) that processes tandem mass spectra in real-time using the mass spectrometer’s (MS) onboard processors. The inSeq algorithm relies on accurate mass tandem MS data for swift spectral matching with high accuracy. The instant spectral processing technology takes ∼16 ms to execute and provides information to enable autonomous, real-time decision making by the MS system. Using inSeq and its advanced decision tree logic, we demonstrate (i) real-time prediction of peptide elution windows en masse (∼3 min width, 3,000 targets), (ii) significant improvement of quantitative precision and accuracy (~3x boost in detected protein differences), and (iii) boosted rates of posttranslation modification site localization (90% agreement in real-time vs. offline localization rate and an approximate 25% gain in localized sites). The decision tree logic enabled by inSeq promises to circumvent problems with the conventional data-dependent acquisition paradigm and provides a direct route to streamlined and expedient targeted protein analysis.

Characterization and Diagnostic Value of Amino Acid Side Chain Neutral Losses Following Electron-Transfer Dissociation

Using a large set of high mass accuracy and resolution ETD tandem mass spectra, we characterized ETD-induced neutral losses. From these data we deduced the chemical formula for 20 of these losses. Many of them have been previously observed in electron-capture dissociation (ECD) spectra, such as losses of the side chains of arginine, aspartic acid, glutamic acid, glutamine, asparagine, leucine, histidine, and carbamidomethylated cysteine residues. With this information, we examined the diagnostic value of these amino acid-specific losses. Among 1285 peptide–spectrum matches, 92.5% have agreement between neutral loss-derived peptide amino acid composition and the peptide sequences. Moreover, we show that peptides can be uniquely identified by using only the accurate precursor mass and amino acid composition based on neutral losses; the median number of sequence candidates from an accurate mass query is reduced from 21 to 8 by adding side chain loss information. Besides increasing confidence in peptide identification, our findings suggest the potential use of these diagnostic losses in ETD spectra to improve false discovery rate estimation and to enhance the performance of scoring functions in database search algorithms.

The Negative Mode Proteome with Activated Ion Negative Electron Transfer Dissociation (AI-NETD)

The field of proteomics almost uniformly relies on peptide cation analysis, leading to an underrepresentation of acidic portions of proteomes, including relevant acidic posttranslational modifications. Despite the many benefits negative mode proteomics can offer, peptide anion analysis remains in its infancy due mainly to challenges with high-pH reversed-phase separations and a lack of robust fragmentation methods suitable for peptide anion characterization. Here, we report the first implementation of activated ion negative electron transfer dissociation (AI-NETD) on the chromatographic timescale, generating 7,601 unique peptide identifications from Saccharomyces cerevisiae in single-shot nLC-MS/MS analyses of tryptic peptides—a greater than 5-fold increase over previous results with NETD alone. These improvements translate to identification of 1,106 proteins, making this work the first negative mode study to identify more than 1,000 proteins in any system. We then compare the performance of AI-NETD for analysis of peptides generated by five proteases (trypsin, LysC, GluC, chymotrypsin, and AspN) for negative mode analyses, identifying as many as 5,356 peptides (1,045 proteins) with LysC and 4,213 peptides (857 proteins) with GluC in yeast—characterizing 1,359 proteins in total. Finally, we present the first deep-sequencing approach for negative mode proteomics, leveraging offline low-pH reversed-phase fractionation prior to online high-pH separations and peptide fragmentation with AI-NETD. With this platform, we identified 3,467 proteins in yeast with trypsin alone and characterized a total of 3,730 proteins using multiple proteases, or nearly 83% of the expressed yeast proteome. This work represents the most extensive negative mode proteomics study to date, establishing AI-NETD as a robust tool for large-scale peptide anion characterization and making the negative mode approach a more viable platform for future proteomic studies.

Neutron-encoded mass signatures for quantitative top-down proteomics.

The ability to acquire highly accurate quantitative data is an increasingly important part of any proteomics experiment, whether shotgun or top-down approaches are used. We recently developed a quantitation strategy for peptides based on neutron encoding, or NeuCode SILAC, which uses closely spaced heavy isotope-labeled amino acids and high-resolution mass spectrometry to provide quantitative data. We reasoned that the strategy would also be applicable to intact proteins and could enable robust, multiplexed quantitation for top-down experiments. We used yeast lysate labeled with either 13C615N2-lysine or 2H8-lysine, isotopologues of lysine that are spaced 36 mDa apart. Proteins having such close spacing cannot be distinguished during a medium resolution scan, but upon acquiring a high-resolution scan, the two forms of the protein with each amino acid are resolved and the quantitative information revealed. An additional benefit NeuCode SILAC provides for top down is that the spacing of the isotope peaks indicates the number of lysines present in the protein, information that aids in identification. We used NeuCode SILAC to quantify several hundred isotope distributions, manually identify and quantify proteins from 1:1, 3:1, and 5:1 mixed ratios, and demonstrate MS2-based quantitation using ETD.

Medicago PhosphoProtein Database: a repository for Medicago truncatula phosphoprotein data

The ability of legume crops to fix atmospheric nitrogen via a symbiotic association with soil rhizobia makes them an essential component of many agricultural systems. Initiation of this symbiosis requires protein phosphorylation-mediated signaling in response to rhizobial signals named Nod factors. Medicago truncatula (Medicago) is the model system for studying legume biology, making the study of its phosphoproteome essential. Here, we describe the Medicago PhosphoProtein Database (MPPD; http://phospho.medicago.wisc.edu), a repository built to house phosphoprotein, phosphopeptide, and phosphosite data specific to Medicago. Currently, the MPPD holds 3,457 unique phosphopeptides that contain 3,404 non-redundant sites of phosphorylation on 829 proteins. Through the web-based interface, users are allowed to browse identified proteins or search for proteins of interest. Furthermore, we allow users to conduct BLAST searches of the database using both peptide sequences and phosphorylation motifs as queries. The data contained within the database are available for download to be investigated at the user’s discretion. The MPPD will be updated continually with novel phosphoprotein and phosphopeptide identifications, with the intent of constructing an unparalleled compendium of large-scale Medicago phosphorylation data.

Activated Ion ETD Performed in a Modified Collision Cell on a Hybrid QLT-Oribtrap Mass Spectrometer

AbstractWe describe the implementation and characterization of activated ion electron transfer dissociation (AI-ETD) on a hybrid QLT-Orbitrap mass spectrometer. AI-ETD was performed using a collision cell that was modified to enable ETD reactions, in addition to normal collisional activation. The instrument manifold was modified to enable irradiation of ions along the axis of this modified cell with IR photons from a CO2 laser. Laser power settings were optimized for both charge (z) and mass to charge (m/z) and the instrument control firmware was updated to allow for automated adjustments to the level of irradiation. This implementation of AI-ETD yielded 1.6-fold more unique identifications than ETD in an nLC-MS/MS analysis of tryptic yeast peptides. Furthermore, we investigated the application of AI-ETD on large scale analysis of phosphopeptides, where laser power aids ETD, but can produce b- and y-type ions because of the phosphoryl moiety’s high IR adsorption. nLC-MS/MS analysis of phosphopeptides derived from human embryonic stem cells using AI-ETD yielded 2.4-fold more unique identifications than ETD alone, demonstrating a promising advance in ETD sequencing of PTM containing peptides.n Figureᅟ

A Proteogenomic Survey of the Medicago truncatula Genome

Peptide sequencing by computational assignment of tandem mass spectra to a database of putative protein sequences provides an independent approach to confirming or refuting protein predictions based on large-scale DNA and RNA sequencing efforts. This use of mass spectrometrically-derived sequence data for testing and refining predicted gene models has been termed proteogenomics. We report herein the application of proteogenomic methodology to a database of 10.9 million tandem mass spectra collected over a period of two years from proteolytically generated peptides isolated from the model legume Medicago truncatula. These spectra were searched against a database of predicted M. truncatula protein sequences generated from public databases, in silico gene model predictions, and a whole-genome six-frame translation. This search identified 78,647 distinct peptide sequences, and a comparison with the publicly available proteome from the recently published M. truncatula genome supported translation of 9,843 existing gene models and identified 1,568 novel peptides suggesting corrections or additions to the current annotations. Each supporting and novel peptide was independently validated using mRNA-derived deep sequencing coverage and an overall correlation of 93% between the two data types was observed. We have additionally highlighted examples of several aspects of structural annotation for which tandem MS provides unique evidence not easily obtainable through typical DNA or RNA sequencing. Proteogenomic analysis is a valuable and unique source of information for the structural annotation of genomes and should be included in such efforts to ensure that the genome models used by biologists mirror as accurately as possible what is present in the cell.