We describe the application of a peptide retention time reversed phase liquid chromatography (RPLC) prediction model previously reported (Petritis et al. Anal. Chem. 2003, 75, 1039) for improved peptide identification. The model uses peptide sequence information to generate a theoretical (predicted) elution time that can be compared with the observed elution time. Using data from a set of known proteins, the retention time parameter was incorporated into a discriminant function for use with tandem mass spectrometry (MS/MS) data analyzed with the peptide/protein identification program SEQUEST. For singly charged ions, the number of confident identifications increased by 12% when the elution time metric is included compared to when mass spectral data is the sole source of information in the context of a Drosophila melanogaster database. A 3-4% improvement was obtained for doubly and triply charged ions for the same biological system. Application to the larger Rattus norvegicus (rat) and human proteome databases resulted in an 8-9% overall increase in the number of confident identifications, when both the discriminant function and elution time are used. The effect of adding "runner-up" hits (peptide matches that are not the highest scoring for a spectra) from SEQUEST is also explored, and we find that the number of confident identifications is further increased by 1% when these hits are also considered. Finally, application of the discriminant functions derived in this work with approximately 2.2 million spectra from over three hundred LC-MS/MS analyses of peptides from human plasma protein resulted in a 16% increase in confident peptide identifications (9022 vs 7779) using elution time information. Further improvements from the use of elution time information can be expected as both the experimental control of elution time reproducibility and the predictive capability are improved.

Correlations between the dimensions of a 2-D separation create trend lines that depend on structural or chemical characteristics of the compound class and thus facilitate classification of unknowns. This broadly applies to conventional ion mobility spectrometry (IMS)/mass spectrometry (MS), where the major biomolecular classes (e.g., lipids, peptides, nucleotides) occupy different trend line domains. However, strong correlation between the IMS and MS separations for ions of same charge has impeded finer distinctions. Differential IMS (or FAIMS) is generally less correlated to MS and thus could separate those domains better. We report the first observation of chemical class separation by trend lines using FAIMS, here for lipids. For lipids, FAIMS is indeed more independent of MS than conventional IMS, and subclasses (such as phospho-, glycero-, or sphingolipids) form distinct, often non-overlapping domains. Even finer categories with different functional groups or degrees of unsaturation are often separated. As expected, resolution improves in He-rich gases: at 70% He, glycerolipid isomers with different fatty acid positions can be resolved. These results open the door for application of FAIMS to lipids, particularly in shotgun lipidomics and targeted analyses of bioactive lipids.

BACKGROUND

High-throughput methods for obtaining global measurements of transcript and protein levels in biological samples has provided a large amount of data for identification of 'target' genes and proteins of interest. These targets may be mediators of functional processes involved in disease and therefore represent key points of control for viruses and bacterial pathogens. Genes and proteins that are the most highly differentially regulated are generally considered to be the most important. We present topological analysis of co-abundance networks as an alternative to differential regulation for confident identification of target proteins from two related global proteomics studies of hepatitis C virus (HCV) infection.

RESULTS

We analyzed global proteomics data sets from a cell culture study of HCV infection and from a clinical study of liver biopsies from HCV-positive patients. Using lists of proteins known to be interaction partners with pathogen proteins we show that the most differentially regulated proteins in both data sets are indeed enriched in pathogen interactors. We then use these data sets to generate co-abundance networks that link proteins based on similar abundance patterns in time or across patients. Analysis of these co-abundance networks using a variety of network topology measures revealed that both degree and betweenness could be used to identify pathogen interactors with better accuracy than differential regulation alone, though betweenness provides the best discrimination. We found that though overall differential regulation was not correlated between the cell culture and liver biopsy data, network topology was conserved to an extent. Finally, we identified a set of proteins that has high betweenness topology in both networks including a protein that we have recently shown to be essential for HCV replication in cell culture.

CONCLUSIONS

The results presented show that the network topology of protein co-abundance networks can be used to identify proteins important for viral replication. These proteins represent targets for further experimental investigation that will provide biological insight and potentially could be exploited for novel therapeutic approaches to combat HCV infection.

Approaches to separation and characterization of ions based on their mobilities in gases date back to the 1960s. Conventional ion mobility spectrometry (IMS) measures the absolute mobility, and field asymmetric waveform IMS (FAIMS) exploits the difference between mobilities at high and low electric fields. However, in all previous IMS and FAIMS experiments ions experienced an essentially free rotation; thus the separation was based on the orientationally averaged cross-sections Omega(avg) between ions and buffer gas molecules. Virtually all large ions are permanent electric dipoles that will be oriented by a sufficiently strong electric field. Under typical FAIMS conditions this will occur for dipole moments >400 D, found for many macroions including most proteins above approximately 30 kDa. Mobilities of aligned dipoles depend on directional cross-sections Omega(dir) (rather than Omega(avg)), which should have a major effect on FAIMS separation parameters. Here we report the FAIMS behavior of electrospray-ionization-generated ions for 10 proteins up to approximately 70 kDa. Those above 29 kDa exhibit a strong increase of mobility at high field, which is consistent with predicted ion dipole alignment. This effect expands the useful FAIMS separation power by an order of magnitude, allowing separation of up to approximately 10(2) distinct protein conformers and potentially revealing information about Omega(dir) and ion dipole moment that is of utility for structural characterization. Possible approaches to extending dipole alignment to smaller ions are discussed.

Mass spectrometry-based proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. Though recent years have seen a tremendous improvement in instrument performance and the computational tools used, significant challenges remain, and there are many opportunities for statisticians to make important contributions. In the most widely used "bottom-up" approach to proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and analyzed using a mass spectrometer. The two fundamental challenges in the analysis of bottom-up MS-based proteomics are: (1) Identifying the proteins that are present in a sample, and (2) Quantifying the abundance levels of the identified proteins. Both of these challenges require knowledge of the biological and technological context that gives rise to observed data, as well as the application of sound statistical principles for estimation and inference. We present an overview of bottom-up proteomics and outline the key statistical issues that arise in protein identification and quantification.

Microbial glycoside hydrolases play a dominant role in the biochemical conversion of cellulosic biomass to high-value biofuels. Anaerobic cellulolytic bacteria are capable of producing multicomplex catalytic subunits containing cell-adherent cellulases, hemicellulases, xylanases, and other glycoside hydrolases to facilitate the degradation of highly recalcitrant cellulose and other related plant cell wall polysaccharides. Clostridium thermocellum is a cellulosome-producing bacterium that couples rapid reproduction rates to highly efficient degradation of crystalline cellulose. Herein, we have developed and applied a suite of difluoromethylphenyl aglycone, N-halogenated glycosylamine, and 2-deoxy-2-fluoroglycoside activity-based protein profiling (ABPP) probes to the direct labeling of the C. thermocellum cellulosomal secretome. These activity-based probes (ABPs) were synthesized with alkynes to harness the utility and multimodal possibilities of click chemistry and to increase enzyme active site inclusion for liquid chromatography-mass spectrometry (LC-MS) analysis. We directly analyzed ABP-labeled and unlabeled global MS data, revealing ABP selectivity for glycoside hydrolase (GH) enzymes, in addition to a large collection of integral cellulosome-containing proteins. By identifying reactivity and selectivity profiles for each ABP, we demonstrate our ability to widely profile the functional cellulose-degrading machinery of the bacterium. Derivatization of the ABPs, including reactive groups, acetylation of the glycoside binding groups, and mono- and disaccharide binding groups, resulted in considerable variability in protein labeling. Our probe suite is applicable to aerobic and anaerobic microbial cellulose-degrading systems and facilitates a greater understanding of the organismal role associated with biofuel development.

Preterm birth is a global health issue impacting millions of mothers and babies. However, the etiology of preterm birth is not clearly understood. Our recent finding that premature decidual senescence with terminal differentiation is a cause of preterm birth in mice with uterine Trp53 deletion, encoding p53 protein, led us to explore other potential factors that are related to preterm birth. Using proteomics approaches, here, we show that 183 candidate proteins show significant changes in deciduae with Trp53 deletion as compared with normal deciduae. Functional categorization of these proteins unveiled new pathways that are influenced by p53. In particular, down-regulation of a cluster of antioxidant enzymes in p53-deficient deciduae suggests that increased oxidative stress could be one cause of preterm birth in mice harboring uterine deletion of Trp53.

The human urinary proteome provides an assessment of kidney injury with specific biomarkers for different kidney injury phenotypes. In an effort to fully map and decipher changes in the urine proteome and peptidome after kidney transplantation, renal allograft biopsy matched urine samples were collected from 396 kidney transplant recipients. Centralized and blinded histology data from paired graft biopsies was used to classify urine samples into diagnostic categories of acute rejection, chronic allograft nephropathy, BK virus nephritis, and stable graft. A total of 245 urine samples were analyzed by liquid chromatography-mass spectrometry using isobaric Tags for Relative and Absolute Quantitation (iTRAQ) reagents. From a group of over 900 proteins identified in transplant injury, a set of 131 peptides were assessed by selected reaction monitoring for their significance in accurately segregating organ injury causation and pathology in an independent cohort of 151 urine samples. Ultimately, a minimal set of 35 proteins were identified for their ability to segregate the 3 major transplant injury clinical groups, comprising the final panel of 11 urinary peptides for acute rejection (93% area under the curve [AUC]), 12 urinary peptides for chronic allograft nephropathy (99% AUC), and 12 urinary peptides for BK virus nephritis (83% AUC). Thus, urinary proteome discovery and targeted validation can identify urine protein panels for rapid and noninvasive differentiation of different causes of kidney transplant injury, without the requirement of an invasive biopsy.

BACKGROUND

MultiAlign is a free software tool that aligns multiple liquid chromatography-mass spectrometry datasets to one another by clustering mass and chromatographic elution features across datasets. Applicable to both label-free proteomics and metabolomics comparative analyses, the software can be operated in several modes. For example, clustered features can be matched to a reference database to identify analytes, used to generate abundance profiles, linked to tandem mass spectra based on parent precursor masses, and culled for targeted liquid chromatography-tandem mass spectrometric analysis. MultiAlign is also capable of tandem mass spectral clustering to describe proteome structure and find similarity in subsequent sample runs.

RESULTS

MultiAlign was applied to two large proteomics datasets obtained from liquid chromatography-mass spectrometry analyses of environmental samples. Peptides in the datasets for a microbial community that had a known metagenome were identified by matching mass and elution time features to those in an established reference peptide database. Results compared favorably with those obtained using existing tools such as VIPER, but with the added benefit of being able to trace clusters of peptides across conditions to existing tandem mass spectra. MultiAlign was further applied to detect clusters across experimental samples derived from a reactor biomass community for which no metagenome was available. Several clusters were culled for further analysis to explore changes in the community structure. Lastly, MultiAlign was applied to liquid chromatography-mass spectrometry-based datasets obtained from a previously published study of wild type and mitochondrial fatty acid oxidation enzyme knockdown mutants of human hepatocarcinoma to demonstrate its utility for analyzing metabolomics datasets.

CONCLUSIONS

MultiAlign is an efficient software package for finding similar analytes across multiple liquid chromatography-mass spectrometry feature maps, as demonstrated here for both proteomics and metabolomics experiments. The software is particularly useful for proteomic studies where little or no genomic context is known, such as with environmental proteomics.

Spermatozoa are highly specialized cells that, when mature, are capable of navigating the female reproductive tract and fertilizing an oocyte. The sperm cell is thought to be largely quiescent in terms of transcriptional and translational activity. As a result, once it has left the male reproductive tract, the sperm cell is essentially operating with a static population of proteins. It therefore is theoretically possible to understand the protein networks contained in a sperm cell and to deduce its cellular function capabilities. To this end, we performed a proteomic analysis of mouse sperm isolated from the cauda epididymis and confidently identified 2850 proteins, which to our knowledge is the most comprehensive sperm proteome for any species reported to date. These proteins comprise many complete cellular pathways, including those for energy production via glycolysis, beta-oxidation and oxidative phosphorylation, protein folding and transport, and cell signaling systems. This proteome should prove a useful tool for assembly and testing of protein networks important for sperm function.

While the use of detergents is necessary for a variety of protein isolation preparation protocols, they are not compatible with mass spectral analysis due to ion suppression and adduct formation. This manuscript describes optimization of detergent removal, using commercially available SDS depletion spin columns containing an affinity resin, providing for both increased protein recovery and thorough SDS removal. Ion mobility spectrometry coupled with mass spectrometry (IMS-MS) allowed for a concurrent analysis of both analyte and detergent. In the case of both proteins and peptides, higher detergent concentrations than previously reported provided an increase of sample recovery; however there was a limit as SDS was detected by IMS-MS at higher levels of SDS indicating incomplete detergent depletion. The results also suggest that optimal conditions for SDS removal are dependent on the sample concentration. Overall, this study provides a useful guide for proteomic studies where SDS is required for efficient sample preparation.

Integrated top-down bottom-up proteomics combined with on-line digestion has great potential to improve the characterization of protein isoforms in biological systems and is amendable to high throughput proteomics experiments. Bottom-up proteomics ultimately provides the peptide sequences derived from the tandem MS analyses of peptides after the proteome has been digested. Top-down proteomics conversely entails the MS analyses of intact proteins for more effective characterization of genetic variations and/or post-translational modifications. Herein, we describe recent efforts toward efficient integration of bottom-up and top-down LC-MS-based proteomics strategies. Since most proteomics separations utilize acidic conditions, we exploited the compatibility of pepsin (where the optimal digestion conditions are at low pH) for integration into bottom-up and top-down proteomics work flows. Pressure-enhanced pepsin digestions were successfully performed and characterized with several standard proteins in either an off-line mode using a Barocycler or an on-line mode using a modified high pressure LC system referred to as a fast on-line digestion system (FOLDS). FOLDS was tested using pepsin and a whole microbial proteome, and the results were compared against traditional trypsin digestions on the same platform. Additionally, FOLDS was integrated with a RePlay configuration to demonstrate an ultrarapid integrated bottom-up top-down proteomics strategy using a standard mixture of proteins and a monkey pox virus proteome.

Identifying useful markers of cancer can be problematic due to limited amounts of sample. Some samples such as nipple aspirate fluid (NAF) or early-stage tumors are inherently small. Other samples such as serum are collected in larger volumes but archives of these samples are very valuable and only small amounts of each sample may be available for a single study. Also, given the diverse nature of cancer and the inherent variability in individual protein levels, it seems likely that the best approach to screen for cancer will be to determine the profile of a battery of proteins. As a result, a major challenge in identifying protein markers of disease is the ability to screen many proteins using very small amounts of sample. In this review, we outline some technological advances in proteomics that greatly advance this capability. Specifically, we propose a strategy for identifying markers of breast cancer in NAF that utilizes mass spectrometry (MS) to simultaneously screen hundreds or thousands of proteins in each sample. The best potential markers identified by the MS analysis can then be extensively characterized using an ELISA microarray assay. Because the microarray analysis is quantitative and large numbers of samples can be efficiently analyzed, this approach offers the ability to rapidly assess a battery of selected proteins in a manner that is directly relevant to traditional clinical assays.