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Human tissues are known to exhibit interindividual variability, but a deeper understanding of the different factors affecting protein expression is necessary to further apply this knowledge. Our goal was to explore the proteomic variability between individuals as well as between healthy and diseased samples, and to test the efficacy of machine learning classifiers. In order to investigate whether disparate proteomics data sets may be combined, we performed a retrospective analysis of proteomics data from 9 different human tissues. These data sets represent several different sample prep methods, mass spectrometry instruments, and tissue health. Using these data, we examined interindividual and intertissue variability in peptide expression, and analyzed the methods required to build accurate tissue classifiers. We also evaluated the limits of tissue classification by downsampling the peptide data to simulate situations where less data is available, such as clinical biopsies, laser capture microdissection or potentially single-cell proteomics. Our findings reveal the strong potential for utilizing proteomics data to build robust tissue classifiers, which has many prospective clinical applications for evaluating the applicability of model clinical systems.

We report on the effectiveness of CID, HCD, and ETD for LC-FT MS/MS analysis of peptides using a tandem linear ion trap-Orbitrap mass spectrometer. A range of software tools and analysis parameters were employed to explore the use of CID, HCD, and ETD to identify peptides isolated from human blood plasma without the use of specific “enzyme rules”. In the evaluation of an FDR-controlled SEQUEST scoring method, the use of accurate masses for fragments increased the numbers of identified peptides (by ~50%) compared to the use of conventional low accuracy fragment mass information, and CID provided the largest contribution to the identified peptide datasets compared to HCD and ETD. The FDR-controlled Mascot scoring method provided significantly fewer peptide identifications than with SEQUEST (by 1.3–2.3 fold) at the same confidence levels, and CID, HCD, and ETD provided similar contributions to identified peptides. Evaluation of de novo sequencing and the UStags method for more intense fragment ions revealed that HCD afforded more sequence consecutive residues (e.g., ≥7 amino acids) than either CID or ETD. Both the FDR-controlled SEQUEST and Mascot scoring methods provided peptide datasets that were affected by the decoy database and mass tolerances applied (e.g., the identical peptides between the datasets could be limited to ~70%), while the UStags method provided the most consistent peptide datasets (>90% overlap) with extremely low (near zero) numbers of false positive identifications. The m/z ranges in which CID, HCD, and ETD contributed the largest number of peptide identifications were substantially overlapping. This work suggests that the three peptide ion fragmentation methods are complementary, and that maximizing the number of peptide identifications benefits significantly from a careful match with the informatics tools and methods applied. These results also suggest that the decoy strategy may inaccurately estimate identification FDRs.

Efficient deconstruction of cellulosic biomass to fermentable sugars for fuel and chemical production is accomplished by a complex mixture of cellulases, hemicellulases, and accessory enzymes (e.g., >50 extracellular proteins). Cellulolytic enzyme mixtures, produced industrially mostly using fungi like Trichoderma reesei, are poorly characterized in terms of their protein composition and its correlation to hydrolytic activity on cellulosic biomass. The secretomes of commercial glycosyl hydrolase-producing microbes was explored using a proteomics approach with high-throughput quantification using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Here, we show that proteomics-based spectral counting approach is a reasonably accurate and rapid analytical technique that can be used to determine protein composition of complex glycosyl hydrolase mixtures that also correlates with the specific activity of individual enzymes present within the mixture. For example, a strong linear correlation was seen between Avicelase activity and total cellobiohydrolase content. Reliable, quantitative and cheaper analytical methods that provide insight into the cellulosic biomass degrading fungal and bacterial secretomes would lead to further improvements toward commercialization of plant biomass-derived fuels and chemicals.

Herein we describe a strategy for degradomic-peptidomic analyses. The human blood peptidome was isolated through application of AC/SEC, which enriched its components by >300-fold. The isolated peptidome components were separated by long column HRLC providing a peak capacity of approximately 300 for species having MWs of up to 20 kDa. The separated species were identified by the FT MS/MS-UStags sequencing method. We identified >200 peptidome components that originated from 29 protein substrates from the blood plasma of a single healthy person. The peptidome peptides identified had MWs range of 0.5-14 kDa and identifications were achieved with extremely low (near zero) false discovery rates through searching the IPI human protein database (approximately 70,000 entries). Some of the peptidome peptides identified have mutations and modifications such as acetylation, acetylhexosamine, amidation, cysteinylation, didehydro, oxidation, and pyro-glu. The capabilities described enable the global analysis of the peptidome peptides to identify degradome targets such as degradome proteases, proteases inhibitors, and other relevant substrates, the cleavage specificities for the degradation of individual substrates, as well as a potential basis for using the various extents of substrate degradation for diagnostic purposes.

The DNA damage response likely includes a global phosphorylation signaling cascade process for sensing the damaged DNA condition and coordinating responses to cope with and repair the perturbed cellular state. We utilized a label-free liquid chromatography-mass spectrometry approach to evaluate changes in protein phosphorylation associated with PP5 activity during the DNA damage response. Biological replicate analyses of bleomycin-treated HeLa cells expressing either WT-PP5 or mutant inactive PP5 lead to the identification of six potential target proteins of PP5 action. Four of these putative targets have been previously reported to be involved in DNA damage responses. Using phospho-site specific antibodies, we confirmed that phosphorylation of one target, ribosomal protein S6, was selectively decreased in cells overexpressing catalytically inactive PP5. Our findings also suggest that PP5 may play a role in controlling translation and in regulating substrates for proline-directed kinases, such as MAP kinases and cyclin-dependent protein kinases that are involved in response to DNA damage.

Reversible protein phosphorylation is a central cellular regulatory mechanism in modulating protein activity and propagating signals within cellular pathways and networks. Development of more effective methods for the simultaneous identification of phosphorylation sites and quantification of temporal changes in protein phosphorylation could provide important insights into molecular signaling mechanisms in various cellular processes. Here we present an integrated quantitative phosphoproteomics approach and its application for comparative analysis of Cos-7 cells in response to lysophosphatidic acid (LPA) gradient stimulation. The approach combines trypsin-catalyzed (16)O/ (18)O labeling plus (16)O/ (18)O-methanol esterification for quantitation, a macro-immobilized metal-ion affinity chromatography trap for phosphopeptide enrichment, and LC-MS/MS analysis. LC separation and MS/MS are followed by neutral loss-dependent MS/MS/MS for phosphopeptide identification using a linear ion trap (LTQ)-FT mass spectrometer. A variety of phosphorylated proteins were identified and quantified including receptors, kinases, proteins associated with small GTPases, and cytoskeleton proteins. A number of hypothetical proteins were also identified as differentially expressed followed by LPA stimulation, and we have shown evidence of pseudopodia subcellular localization of one of these candidate proteins. These results demonstrate the efficiency of this quantitative phosphoproteomics approach and its application for rapid discovery of phosphorylation events associated with LPA gradient sensing and cell chemotaxis.

Elevated levels of protein tyrosine nitration have been found in various neurodegenerative diseases and age-related pathologies. Until recently, however, the lack of an efficient enrichment method has prevented the analysis of this important low-level protein modification. We have developed a method that specifically enriches nitrotyrosine-containing peptides so that both nitrotyrosine peptides and specific nitration sites can be unambiguously identified with LC-MS/MS. The procedure consists of the derivatization of nitrotyrosine into free sulfhydryl groups followed by high efficiency enrichment of sulfhydryl-containing peptides with thiopropyl sepharose beads. The derivatization process includes: (1) acetylation with acetic anhydride to block all primary amines, (2) reduction of nitrotyrosine to aminotyrosine, (3) derivatization of aminotyrosine with N-Succinimidyl S-Acetylthioacetate (SATA), and (4) deprotection of S-acetyl on SATA to form free sulfhydryl groups. The high specificity of this method is demonstrated by the contrasting percentage of nitrotyrosine-derivatized peptides in the identified tandem mass spectra between enriched and unenriched samples. Global analysis of unenriched in vitro nitrated human histone H1.2, bovine serum albumin (BSA), and mouse brain homogenate samples had 9%, 9%, and 5.9% of identified nitrotyrosine-containing peptides, while the enriched samples had 91% , 62%, and 35%, respectively. Duplicate LC-MS/MS analyses of the enriched mouse brain homogenate identified 150 unique nitrated peptides covering 102 proteins with an estimated 3.3% false discovery rate.

MS dissociation methods, including collision induced dissociation (CID), high energy collision dissociation (HCD), and electron transfer dissociation (ETD), can each contribute distinct peptidome identifications using conventional peptide identification methods (Shen et al. J. Proteome Res. 2011), but such samples still pose significant informatics challenges. In this work, we explored utilization of high accuracy fragment ion mass measurements, in this case provided by Fourier transform MS/MS, to improve peptidome peptide data set size and consistency relative to conventional descriptive and probabilistic scoring methods. For example, we identified 20-40% more peptides than SEQUEST, Mascot, and MS_GF scoring methods using high accuracy fragment ion information and the same false discovery rate (FDR) from CID, HCD, and ETD spectra. Identified species covered90% of the collective identifications obtained using various conventional peptide identification methods, which significantly addresses the common issue of different data analysis methods generating different peptide data sets. Choice of peptide dissociation and high-precision measurement-based identification methods presently available for degradomic-peptidomic analyses needs to be based on the coverage and confidence (or specificity) afforded by the method, as well as practical issues (e.g., throughput). By using accurate fragment information,1000 peptidome components can be identified from a single human blood plasma analysis with low peptide-level FDRs (e.g., 0.6%), providing an improved basis for investigating potential disease-related peptidome components.

Disulfide bonds are a form of post-translational modification that often determines protein structure(s) and function(s). In this work, we report a mass spectrometry method for identification of disulfides in degradation products of proteins, specifically endogenous peptides in the human blood plasma peptidome. LC-Fourier transform tandem mass spectrometry (FT MS/MS) was used for acquiring mass spectra that were de novo sequenced and then searched against the IPI human protein database. Through the use of unique sequence tags (UStags), we unambiguously correlated the spectra to specific database proteins. Examination of the UStags' prefix and/or suffix sequences that contain cysteine(s) in conjunction with sequences of the UStags-specified database proteins is shown to enable the unambigious determination of disulfide bonds. Using this method, we identified the intermolecular and intramolecular disulfides in human blood plasma peptidome peptides that have molecular weights of up to approximately 10 kDa.

Human platelets play a key role in homeostasis and thrombosis and have recently emerged as key regulators of inflammation. Platelets stored for transfusion produce pro-thrombotic and pro-inflammatory mediators implicated in adverse transfusion reactions. Correspondingly, these mediators are central players in pathological conditions including cardiovascular disease, the major cause of death in diabetics. In view of this, a mass spectrometry based proteomics study was performed on platelets collected from healthy and type-2 diabetics stored for transfusion. Strikingly, our innovative and sensitive proteomic approach identified 122 proteins that were either up- or down-regulated in type-2 diabetics relative to non-diabetic controls and 117 proteins whose abundances changed during a 5-day storage period. Notably our studies are the first to characterize the proteome of platelets from diabetics before and after storage for transfusion. These identified differences allow us to formulate new hypotheses and experimentation to improve clinical outcomes by targeting “high risk platelets” that render platelet transfusion less effective or even unsafe.

The development of liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has made it possible to measure phosphopeptides on an increasingly large-scale and high-throughput fashion. However, extracting confident phosphopeptide identifications from the resulting large dataset in a similar high-throughput fashion remains difficult, as does rigorously estimating the false discovery rate (FDR) of a set of phosphopeptide identifications. This article describes a data analysis pipeline designed to address these issues. The first step is to re-analyze phosphopeptide identifications that contain ambiguous assignments for the incorporated phosphate(s) to determine the most likely arrangement of the phosphate(s). The next step is to employ an expectation maximization algorithm to estimate the joint distribution of the SEQUEST scores. A linear discriminant analysis is then performed to determine how to optimally combine peptide scores (in this case, SEQUEST) into a discriminant score that possesses the maximum discriminating power. Based on this discriminant score, the p- and q-values for each phosphopeptide identification are calculated, and the phosphopeptide identification FDR is then estimated. This data analysis approach was applied to data from a study of irradiated human skin fibroblasts to provide a robust estimate of FDR for phosphopeptides, and has been coded into a software package that is freely available (http://ncrr.pnl.gov/downloads/data/Du2008_Supplementary_Data.zip).

To identify phosphoproteins regulated by the phosphoprotein phosphatase (PPP) family of S/T phosphatases, we performed a large-scale characterization of changes in protein phosphorylation on extracts from HeLa cells treated with or without calyculin A, a potent PPP enzyme inhibitor. A label-free comparative phosphoproteomics approach using immobilized metal ion affinity chromatography and targeted tandem mass spectrometry was employed to discover and identify signatures based upon distinctive changes in abundance. Overall, 232 proteins were identified as either direct or indirect targets for PPP enzyme regulation. Most of the present identifications represent novel PPP enzyme targets at the level of both phosphorylation site and protein. These include phosphorylation sites within signaling proteins such as p120 Catenin, A Kinase Anchoring Protein 8, JunB, and Type II Phosphatidyl Inositol 4 Kinase. These data can be used to define underlying signaling pathways and events regulated by the PPP family of S/T phosphatases.