Bioinformatics approaches in clinical proteomics

Protein expression profiling is increasingly being used to discover, validate and characterize biomarkers that can potentially be used for diagnostic purposes and to aid in pharmaceutical development. Correct analysis of data obtained from these experiments requires an understanding of the underlying analytic procedures used to obtain the data, statistical principles underlying high-dimensional data and clinical statistical tools used to determine the utility of the interpreted data. This review summarizes each of these steps, with the goal of providing the nonstatistician proteomics researcher with a working understanding of the various approaches that may be used by statisticians. Emphasis is placed on the process of mining high-dimensional data to identify a specific set of biomarkers that may be used in a diagnostic or other assay setting.     

Bioinformatics approaches in clinical proteomics

Protein expression profiling is increasingly being used to discover, validate and characterize biomarkers that can potentially be used for diagnostic purposes and to aid in pharmaceutical development. Correct analysis of data obtained from these experiments requires an understanding of the underlying analytic procedures used to obtain the data, statistical principles underlying high-dimensional data and clinical statistical tools used to determine the utility of the interpreted data. This review summarizes each of these steps, with the goal of providing the nonstatistician proteomics researcher with a working understanding of the various approaches that may be used by statisticians. Emphasis is placed on the process of mining high-dimensional data to identify a specific set of biomarkers that may be used in a diagnostic or other assay setting.     

Extent of modifications in human proteome samples and their effect on dynamic range of analysis in shotgun proteomics

The complexity of the human proteome, already enormous at the organism level, increases further in the course of the proteome analysis due to in vitro sample evolution. Most of in vitro alterations can also occur in vivo as post-translational modifications. These two types of modifications can only be distinguished a posteriori but not in the process of analysis, thus rendering necessary the analysis of every molecule in the sample. With the new software tool ModifiComb applied to MS/MS data, the extent of modifications was measured in tryptic mixtures representing the full proteome of human cells. The estimated level of 8-12 modified peptides per each unmodified tryptic peptide present at >or=1% level is approaching one modification per amino acid on average. This is a higher modification rate than was previously thought, posing an additional challenge to analytical techniques. The solution to the problem is seen in improving sample preparation routines, introducing dynamic range-adjusted thresholds for database searches, using more specific MS/MS analysis using high mass accuracy and complementary fragmentation techniques, and revealing peptide families with identification of additional proteins only by unfamiliar peptides. Extensive protein separation prior to analysis reduces the requirements on speed and dynamic range of a tandem mass spectrometer and can be a viable alternative to the shotgun approach.     

Advances in shotgun proteomics and the analysis of membrane proteomes

The emergence of shotgun proteomics has facilitated the numerous biological discoveries made by proteomic studies. However, comprehensive proteomic analysis remains challenging and shotgun proteomics is a continually changing field. This review details the recent developments in shotgun proteomics and describes emerging technologies that will influence shotgun proteomics going forward. In addition, proteomic studies of integral membrane proteins remain challenging due to the hydrophobic nature in integral membrane proteins and their general low abundance levels. However, there have been many strategies developed for enriching, isolating and separating membrane proteins for proteomic analysis that have moved this field forward. In summary, while shotgun proteomics is a widely used and mature technology, the continued pace of improvements in mass spectrometry and proteomic technology and methods indicate that future studies will have an even greater impact on biological discovery.     

Temporal analysis of xylose fermentation by Scheffersomyces stipitis using shotgun proteomics

Proteomics and fermentation technology have begun to integrate to investigate fermentation organisms in bioprocess development. This is the first shotgun proteomics study employed to monitor the proteomes of Scheffersomyces stipitis during xylose fermentation under oxygen limitation. We identified 958 nonredundant proteins and observed highly similar proteomes from exponential to early stationary phases. In analyzing the temporal proteome, we identified unique expression patterns in biological processes and metabolic pathways, including alternative respiration salicylhydroxamic acid (SHAM) pathway, activation of glyoxylate cycle, expression of galactose enzymes, and secondary zinc-containing alcohol dehydrogenase and O-glycosyl hydrolases. We identified the expression of a putative, high-affinity xylose sugar transporter Xut1p, but low-affinity xylose transporters were absent. Throughout cell growth, housekeeping processes included oxidative phosphorylation, glycolysis, nonoxidative branch of the pentose phosphate pathway, gluconeogenesis, biosynthesis of amino acids and aminoacyl total RNA (tRNA), protein synthesis and proteolysis, fatty acid metabolism, and cell division. This study emphasized qualitative analysis and demonstrated that shotgun proteomics is capable of monitoring S. stipitis fermentation and identifying physiological states, such as nutrient deficiency.     

From protein catalogues towards targeted proteomics approaches in cereal grains

Due to their importance for human nutrition, the protein content of cereal grains has been a subject of intense study for over a century and cereal grains were not surprisingly one of the earliest subjects for 2D-gel-based proteome analysis. Over the last two decades, countless cereal grain proteomes, mostly derived using 2D-gel based technologies, have been described and hundreds of proteins identified. However, very little is still known about post-translational modifications, subcellular proteomes, and protein-protein interactions in cereal grains. Development of techniques for improved extraction, separation and identification of proteins and peptides is facilitating functional proteomics and analysis of sub-proteomes from small amounts of starting material, such as seed tissues. The combination of proteomics with structural and functional analysis is increasingly applied to target subsets of proteins. These “next-generation” proteomics studies will vastly increase our depth of knowledge about the processes controlling cereal grain development, nutritional and processing characteristics.     

Quantitative analysis of human centrosome architecture by targeted proteomics and fluorescence imaging

Centrioles are essential for the formation of centrosomes and cilia. While numerical and/or structural centrosomes aberrations are implicated in cancer, mutations in centriolar and centrosomal proteins are genetically linked to ciliopathies, microcephaly, and dwarfism. The evolutionarily conserved mechanisms underlying centrosome biogenesis are centered on a set of key proteins, including Plk4, Sas‐6, and STIL, whose exact levels are critical to ensure accurate reproduction of centrioles during cell cycle progression. However, neither the intracellular levels of centrosomal proteins nor their stoichiometry within centrosomes is presently known. Here, we have used two complementary approaches, targeted proteomics and EGFP‐tagging of centrosomal proteins at endogenous loci, to measure protein abundance in cultured human cells and purified centrosomes. Our results provide a first assessment of the absolute and relative amounts of major components of the human centrosome. Specifically, they predict that human centriolar cartwheels comprise up to 16 stacked hubs and 1 molecule of STIL for every dimer of Sas‐6. This type of quantitative information will help guide future studies of the molecular basis of centrosome assembly and function.     

Altered proteolytic events in experimental autoimmune encephalomyelitis discovered by iTRAQ shotgun proteomics analysis of spinal cord

Background  

Abnormal activation of protease activities during experimental autoimmune encephalomyelitis (EAE) in rats, a rodent model of multiple sclerosis, have been implicated in either the direct destruction of myelin components or the intracellular signal transduction pathways that lead to lymphocyte infiltration, oligodendrocyte destruction, neuronal dysfunctions and axonal degeneration. The identification of changes in regulated proteolytic events during EAE is crucial for uncovering activated proteases that may underline the pathological features such as inflammation and demyelination. We searched for either non-tryptic or semi-tryptic peptides from a previous shotgun proteomics study using isobaric tags for relative and absolute quantification (iTRAQ) to compare the proteomes of normal and EAE rat lumbar spinal cords.     

Protein probabilities in shotgun proteomics: evaluating different estimation methods using a semi-random sampling model

The calculation of protein probabilities is one of the most intractable problems in large-scale proteomic research. Current available estimating methods, for example, ProteinProphet, PROT_PROBE, Poisson model and two-peptide hits, employ different models trying to resolve this problem. Until now, no efficient method is used for comparative evaluation of the above methods in large-scale datasets. In order to evaluate these various methods, we developed a semi-random sampling model to simulate large-scale proteomic data. In this model, the identified peptides were sampled from the designed proteins and their cross-correlation scores were simulated according to the results from reverse database searching. The simulated result of 18 control proteins was consistent with the experimental one, demonstrating the efficiency of our model. According to the simulated results of human liver sample, ProteinProphet returned slightly higher probabilities and lower specificity than real cases. PROT_PROBE was a more efficient method with higher specificity. Predicted results from a Poisson model roughly coincide with real datasets, and the method of two-peptide hits seems solid but imprecise. However, the probabilities of identified proteins are strongly correlated with several experimental factors including spectra number, database size and protein abundance distribution.     

Functional analysis of proteins and protein species using shotgun proteomics and linear mathematics

Covalent post-translational modification of proteins is the primary modulator of protein function in the cell. It greatly expands the functional potential of the proteome compared to the genome. In the past few years shotgun proteomics-based research, where the proteome is digested into peptides prior to mass spectrometric analysis has been prolific in this area. It has determined the kinetics of tens of thousands of sites of covalent modification on an equally large number of proteins under various biological conditions and uncovered a transiently active regulatory network that extends into diverse branches of cellular physiology. In this review, we discuss this work in light of the concept of protein speciation, which emphasizes the entire post-translationally modified molecule and its interactions and not just the modification site as the functional entity. Sometimes, particularly when considering complex multisite modification, all of the modified molecular species involved in the investigated condition, the protein species must be completely resolved for full understanding. We present a mathematical technique that delivers a good approximation for shotgun proteomics data.     

Detecting differential and correlated protein expression in label-free shotgun proteomics

Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris label-free LC-MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests: Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC-MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics.     

Advances in mass spectrometry-based cancer research and analysis: from cancer proteomics to clinical diagnostics

Introduction: The last 20 years have seen significant improvements in the analytical capabilities of biological mass spectrometry (MS). Studies using advanced MS have resulted in new insights into cell biology and the etiology of diseases as well as its use in clinical applications.

Areas covered: This review discusses recent developments in MS-based technologies and their cancer-related applications with a focus on proteomics. It also discusses the issues around translating the research findings to the clinic and provides an outline of where the field is moving.

Expert commentary: Proteomics has been problematic to adapt for the clinical setting. However, MS-based techniques continue to demonstrate potential in novel clinical uses beyond classical cancer proteomics.     

Strategic shotgun proteomics approach for efficient construction of an expression map of targeted protein families in hepatoma cell lines

An expression map of the most abundant proteins in human hepatoma HepG2 cells was established by a combination of complementary shotgun proteomics approaches. Two-dimensional liquid chromatography (LC)-nano electrospray ionization (ESI) tandem mass spectrometry (MS/MS) as well as one-dimensional LC-matrix-assisted laser desorption/ionization MS/MS were evaluated and shown that additional separation introduced at the peptide level was not as efficient as simple prefractionation of protein extracts in extending the range and total number of proteins identified. Direct LC-nanoESI MS/MS analyses of peptides from total solubilized fraction and the excised gel bands from one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis fractionated insolubilized fraction afforded the best combination in efficient construction of a nonredundant cell map. Compiling data from multiple variations of rapid shotgun proteomics analyses is nonetheless useful to increase sequence coverage and confidence of hits especially for those proteins identified primarily by a single or two peptide matches. While the returned hit score in general reflects the abundance of the respective proteins, it is not a reliable index for differential expression. Using another closely related hepatoma Hep3B as a comparative basis, 16 proteins with more than two-fold difference in expression level as defined by spot intensity in two-dimensional gel electrophoresis analysis were identified which notably include members of the heat shock protein (Hsp) and heterogeneous nuclear ribonucleoprotein (hnRPN) families. The observed higher expression level of hnRNP A2/B1 and Hsp90 in Hep3B led to a search for reported functional roles mediated in concert by both these multifunctional cellular chaperones. In agreement with the proposed model for telomerase and telomere bound proteins in promoting their interactions, data was obtained which demonstrated that the expression proteomics data could be correlated with longer telomeric length in tumorigenic Hep3B. This biological significance constitutes the basis for further delineation of the dynamic interactions and modifications of the two protein families and demonstrated how proteomic and biological investigation could be mutually substantiated in a productive cycle of hypothesis and pattern driven research.     

Comparative secretome analysis of human follicular dermal papilla cells and fibroblasts using shotgun proteomics

The dermal papilla cells (DPCs) of hair follicles are known to secrete paracrine factors for follicular cells. Shotgun proteomic analysis was performed to compare the expression profiles of the secretomes of human DPCs and dermal fibroblasts (DFs). In this study, the proteins secreted by DPCs and matched DFs were analyzed by 1DE/LTQ FTICR MS/MS, semi-quantitatively determined using emPAI mole percent values and then characterized using protein interaction network analysis. Among the 1,271 and 1,188 proteins identified in DFs and DPCs, respectively, 1,529 were further analyzed using the Ingenuity Pathway Analysis tool. We identified 28 DPC-specific extracellular matrix proteins including transporters (ECM1, A2M), enzymes (LOX, PON2), and peptidases (C3, C1R). The biochemically- validated DPC-specific proteins included thrombospondin 1 (THBS1), an insulin-like growth factor binding protein3 (IGFBP3), and, of particular interest, an integrin beta1 subunit (ITGB1) as a key network core protein. Using the shotgun proteomic technique and network analysis, we selected ITGB1, IGFBP3, and THBS1 as being possible hair-growth modulating protein biomarkers.     

Thermodynamic analysis of protein-ligand interactions in complex biological mixtures using a shotgun proteomics approach

Shotgun proteomics protocols are widely used for the identification and/or quantitation of proteins in complex biological samples. Described here is a shotgun proteomics protocol that can be used to identify the protein targets of biologically relevant ligands in complex protein mixtures. The protocol combines a quantitative proteomics platform with a covalent modification strategy, termed Stability of Proteins from Rates of Oxidation (SPROX), which utilizes the denaturant dependence of hydrogen peroxide-mediated oxidation of methionine side chains in proteins to assess the thermodynamic properties of proteins and protein-ligand complexes. The quantitative proteomics platform involves the use of isobaric mass tags and a methionine-containing peptide enhancement strategy. The protocol is evaluated in a ligand binding experiment designed to identify the proteins in a yeast cell lysate that bind the well-known enzyme cofactor, β-nicotinamide adenine dinucleotide (NAD+). The protocol is also used to investigate the protein targets of resveratrol, a biologically active ligand with less well-understood protein targets. A known protein target of resveratrol, cytosolic aldehyde dehydrogenase, was identified in addition to six other potential new proteins targets including four that are associated with the protein translation machinery, which has previously been implicated as a target of resveratrol.     

Fast and accurate identification of semi-tryptic peptides in shotgun proteomics

MOTIVATION: One of the major problems in shotgun proteomics is the low peptide coverage when analyzing complex protein samples. Identifying more peptides, e.g. non-tryptic peptides, may increase the peptide coverage and improve protein identification and/or quantification that are based on the peptide identification results. Searching for all potential non-tryptic peptides is, however, time consuming for shotgun proteomics data from complex samples, and poses a challenge for a routine data analysis. RESULTS: We hypothesize that non-tryptic peptides are mainly created from the truncation of regular tryptic peptides before separation. We introduce the notion of truncatability of a tryptic peptide, i.e. the probability of the peptide to be identified in its truncated form, and build a predictor to estimate a peptide's truncatability from its sequence. We show that our predictions achieve useful accuracy, with the area under the ROC curve from 76% to 87%, and can be used to filter the sequence database for identifying truncated peptides. After filtering, only a limited number of tryptic peptides with the highest truncatability are retained for non-tryptic peptide searching. By applying this method to identification of semi-tryptic peptides, we show that a significant number of such peptides can be identified within a searching time comparable to that of tryptic peptide identification.     

Large-scale identification by shotgun proteomics of proteins expressed in porcine liver and salivary gland

Protein catalogs containing a large number of proteins expressed in a variety of organs can be powerful tools for stem-cell research, because this requires accurate knowledge about how cells differentiate. Salivary gland progenitor (SGP) cells are somatic stem cells isolated from the salivary gland that can differentiate into hepatic or pancreatic cell lineages. Their differentiation state has been assessed by the expression of major protein markers, but to use these cells in regenerative medicine, it will be necessary to establish additional means of quality assessment. We examined the use of shotgun proteomics for porcine salivary gland (a source of SGP cells) and liver (a destination of differentiated SGP cells) for determining the state of SGP cell differentiation. Protein complexes from each organ were digested into peptides and separated by two-dimensional liquid chromatography involving strong cation-exchange chromatography followed by reversed-phase liquid chromatography. The separated peptides were analyzed by on-line electrospray ionization tandem mass spectrometry using a quadrupole-time of flight mass spectrometer (ESI Q-TOF MS/MS), and the spectra obtained were processed to search peptides against a mammalian database for protein identification. Using this method, we identified 117 proteins in porcine salivary gland and 154 proteins in porcine liver. Of these, 72 and 109 were specific to salivary gland and liver, respectively, and some of these were previously shown to be organ specific. The current study can be utilized in the future as a basis to study the pattern of differentiation in protein expression by stem cells.