Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry

A quantitative atlas of mitotic phosphorylation

The eukaryotic cell division cycle is characterized by a sequence of orderly and highly regulated events resulting in the duplication and separation of all cellular material into two newly formed daughter cells. Protein phosphorylation by cyclin-dependent kinases (CDKs) drives this cycle. To gain further insight into how phosphorylation regulates the cell cycle, we sought to identify proteins whose phosphorylation is cell cycle regulated. Using stable isotope labeling along with a two-step strategy for phosphopeptide enrichment and high mass accuracy mass spectrometry, we examined protein phosphorylation in a human cell line arrested in the G(1) and mitotic phases of the cell cycle. We report the identification of >14,000 different phosphorylation events, more than half of which, to our knowledge, have not been described in the literature, along with relative quantitative data for the majority of these sites. We observed >1,000 proteins with increased phosphorylation in mitosis including many known cell cycle regulators. The majority of sites on regulated phosphopeptides lie in [S/T]P motifs, the minimum required sequence for CDKs, suggesting that many of the proteins may be CDK substrates. Analysis of non-proline site-containing phosphopeptides identified two unique motifs that suggest there are at least two undiscovered mitotic kinases.

Peptizer, a tool for assessing false positive peptide identifications and manually validating selected results

False positive peptide identifications are a major concern in the field of peptidecentric, mass spectrometry-driven gel-free proteomics. They occur in regions where the score distributions of true positives and true negatives overlap. Removal of these false positive identifications necessarily involves a trade-off between sensitivity and specificity. Existing postprocessing tools typically rely on a fixed or semifixed set of assumptions in their attempts to optimize both the sensitivity and the specificity of peptide and protein identification using MS/MS spectra. Because of the expanding diversity in available proteomics technologies, however, these postprocessing tools often struggle to adapt to emerging technology-specific peculiarity. Here we present a novel tool named Peptizer that solves this adaptability issue by making use of pluggable assumptions. This research-oriented postprocessing tool also includes a graphical user interface to perform efficient manual validation of suspect identifications for optimal sensitivity recovery. Peptizer is open source software under the Apache2 license and is written in Java.

Widespread changes in protein synthesis induced by microRNAs

Animal microRNAs (miRNAs) regulate gene expression by inhibiting translation and/or by inducing degradation of target messenger RNAs. It is unknown how much translational control is exerted by miRNAs on a genome-wide scale. We used a new proteomic approach to measure changes in synthesis of several thousand proteins in response to miRNA transfection or endogenous miRNA knockdown. In parallel, we quantified mRNA levels using microarrays. Here we show that a single miRNA can repress the production of hundreds of proteins, but that this repression is typically relatively mild. A number of known features of the miRNA-binding site such as the seed sequence also govern repression of human protein synthesis, and we report additional target sequence characteristics. We demonstrate that, in addition to downregulating mRNA levels, miRNAs also directly repress translation of hundreds of genes. Finally, our data suggest that a miRNA can, by direct or indirect effects, tune protein synthesis from thousands of genes.

Targeted peptidecentric proteomics reveals caspase-7 as a substrate of the caspase-1 inflammasomes

The aspartate-specific cysteine protease caspase-1 is activated by the inflammasomes and is responsible for the proteolytic maturation of the cytokines IL-1 beta and IL-18 during infection and inflammation. To discover new caspase-1 substrates, we made use of a proteome-wide gel-free differential peptide sorting methodology that allows unambiguous localization of the processing site in addition to identification of the substrate. Of the 1022 proteins that were identified, 20 were found to be specifically cleaved after Asp in the setup incubated with recombinant caspase-1. Interestingly, caspase-7 emerged as one of the identified caspase-1 substrates. Moreover half of the other identified cleavage events occurred at sites closely resembling the consensus caspase-7 recognition sequence DEVD, suggesting caspase-1-mediated activation of endogenous caspase-7 in this setup. Consistently recombinant caspase-1 cleaved caspase-7 at the canonical activation sites Asp(23) and Asp(198), and recombinant caspase-7 processed a subset of the identified substrates. In vivo, caspase-7 activation was observed in conditions known to induce activation of caspase-1, including Salmonella infection and microbial stimuli combined with ATP. Interestingly Salmonella- and lipopolysaccharide + ATP-induced activation of caspase-7 was abolished in macrophages deficient in caspase-1, the pattern recognition receptors Ipaf and Cryopyrin, and the inflammasome adaptor ASC, demonstrating an upstream role for the caspase-1 inflammasomes in caspase-7 activation in vivo. In contrast, caspase-1 and the inflammasomes were not required for caspase-3 activation. In conclusion, we identified 20 new substrates activated downstream of caspase-1 and validated caspase-1-mediated caspase-7 activation in vitro and in knock-out macrophages. These results demonstrate for the first time the existence of a nucleotide binding and oligomerization domain-like receptor/caspase-1/caspase-7 cascade and the existence of distinct activation mechanisms for caspase-3 and -7 in response to microbial stimuli and bacterial infection.

Optimizing sample handling for urinary proteomics

Interrogation of the urinary proteome for clinically useful biomarkers of disease will require normalization of methods for protein extraction and sample handling. Variations in collection methods and other procedures may introduce significant discrepancies in qualitative and quantitative measurements. Here we demonstrate that the method of protein extraction, length of handling at room temperature, and repetitive freeze-thaw cycles do not seem to alter the urinary proteome at either the protein or peptide level in a manner that degrades information obtainable by mass spectrometry.

In-depth analysis of tandem mass spectrometry data from disparate instrument types

Mass spectrometric analyses of protein digests produce large numbers of fragmentation spectra that are not identified by routine database searching strategies. Some of these spectra could be identified by development of improved search engines. However, many of these spectra represent fragmentation of peptide components bearing modifications that are not routinely considered in database searches. Here we present new software within Protein Prospector that allows comprehensive analysis of data sets by analyzing the data at increasing levels of depth. Analysis of published data sets is presented to illustrate that the software is not biased to any instrument types. The results show that these data sets contain many modified peptides. As well as searching for known modification types, Protein Prospector permits the detection and identification of unexpected or novel modifications by searching for any mass shift within a user-specified mass range to any chosen amino acid(s). Several modifications never previously reported in proteomics data were identified in these standard data sets using this mass modification searching approach.

Quantitative proteomics as a new piece of the systems biology puzzle

The definition of the role of each gene product in its cellular context is of outstanding importance in the post-genomics era. Recent technological innovations have driven research in proteomics from single protein characterization to global approaches, aiming to achieve a comprehensive qualitative and quantitative description of complex molecular mechanisms. In this review, we discuss the methodology of quantitative proteomics as it applies to the analysis of complex biological model systems. A special attention will be given to model systems that are suitable for functional genomic studies, where the potential of quantitative proteomics can be effectively demonstrated.

Clinical proteomics in neurodegenerative disorders

Neurodegenerative disorders are characterized by neuronal impairment that eventually leads to neuronal death. In spite of the brain's known capacity for regeneration, lost neurons are difficult to replace. Therefore, drugs aimed at inhibiting neurodegenerative processes are likely to be most effective if the treatment is initiated as early as possible. However, clinical manifestations in early disease stages are often numerous, subtle and difficult to diagnose. This is where biomarkers that specifically reflect onset of pathology, directly or indirectly, may have a profound impact on diagnosis making in the future. A triplet of biomarkers for Alzheimer's disease (AD), total and hyperphosphorylated tau and the 42 amino acid isoform of beta-amyloid, has already been established for early detection of AD before the onset of dementia. However, more biomarkers are needed both for AD and for other neurodegenerative disorders, such as Parkinson's disease, frontotemporal dementia and amyotrophic lateral sclerosis. This review provides an update on recent advances in clinical neuroproteomics, a biomarker discovery field that has expanded immensely during the last decade, and gives an overview of the most commonly used techniques and the major clinically relevant findings these techniques have lead to.

A multidimensional chromatography technology for in-depth phosphoproteome analysis

Protein phosphorylation is a post-translational modification widely used to regulate cellular responses. Recent studies showed that global phosphorylation analysis could be used to study signaling pathways and to identify targets of protein kinases in cells. A key objective of global phosphorylation analysis is to obtain an in-depth mapping of low abundance protein phosphorylation in cells; this necessitates the use of suitable separation techniques because of the complexity of the phosphoproteome. Here we developed a multidimensional chromatography technology, combining IMAC, hydrophilic interaction chromatography, and reverse phase LC, for phosphopeptide purification and fractionation. Its application to the yeast Saccharomyces cerevisiae after DNA damage led to the identification of 8764 unique phosphopeptides from 2278 phosphoproteins using tandem MS. Analysis of two low abundance proteins, Rad9 and Mrc1, revealed that approximately 50% of their phosphorylation was identified via this global phosphorylation analysis. Thus, this technology is suited for in-depth phosphoproteome studies.

Monte carlo simulation-based algorithms for analysis of shotgun proteomic data

Two new statistical models based on Monte Carlo Simulation (MCS) have been developed to score peptide matches in shotgun proteomic data and incorporated in a database search program, MassMatrix (www.massmatrix.net). The first model evaluates peptide matches based on the total abundance of matched peaks in the experimental spectra. The second model evaluates amino acid residue tags within MS/MS spectra. The two models provide complementary scores for peptide matches that result in higher confidence in peptide identification when significant scores are returned from both models. The MCS-based models use a variance reduction technique that improves estimation precision. Due to the high computational expense of MCS-based models, peptide matches were prefiltered by other statistical models before further evaluation by the MCS-based models. Receiver operating characteristic analysis of the data sets confirmed that MCS-based models improved the overall performance of the MassMatrix search software, especially for low-mass accuracy data sets.

Phosphoproteomic analysis of human brain by calcium phosphate precipitation and mass spectrometry

Alzheimer's disease (AD), the most common form of dementia, is manifested in the brain by the aggregation of amyloid plaques and neurofibrillary tangles. The tangles are primarily composed of microtubule-associated protein tau that is aberrantly hyperphosphorylated, suggesting that deregulated phosphorylation may contribute to AD pathogenesis. However, systematic analysis of the phosphoproteome in AD brain tissues has not been reported. We used calcium phosphate precipitation to analyze an AD postmortem brain, followed by liquid chromatography-tandem mass spectrometry. The protein sample was first resolved by one-dimensional polyacrylamide gel electrophoresis and subjected to gel excision and in-gel digestion. Phosphopeptides in the resulting peptide mixtures were enriched in a single step of calcium phosphate precipitation, and then analyzed by the LC-MS/MS approach. After database search, stringent filtering, and manual validation of neutral loss in the MS/MS spectra, a total of 466 phosphorylation sites on 185 proteins including tau were identified. A majority of sites were not described previously. This study demonstrates the feasibility of combining calcium phosphate precipitation with mass spectrometry for phosphoproteome analysis of postmortem human brain tissue.

Proteome analysis of non-model plants: a challenging but powerful approach

Biological research has focused in the past on model organisms and most of the functional genomics studies in the field of plant sciences are still performed on model species or species that are characterized to a great extent. However, numerous non-model plants are essential as food, feed, or energy resource. Some features and processes are unique to these plant species or families and cannot be approached via a model plant. The power of all proteomic and transcriptomic methods, that is, high-throughput identification of candidate gene products, tends to be lost in non-model species due to the lack of genomic information or due to the sequence divergence to a related model organism. Nevertheless, a proteomics approach has a great potential to study non-model species. This work reviews non-model plants from a proteomic angle and provides an outline of the problems encountered when initiating the proteome analysis of a non-model organism. The review tackles problems associated with (i) sample preparation, (ii) the analysis and interpretation of a complex data set, (iii) the protein identification via MS, and (iv) data management and integration. We will illustrate the power of 2DE for non-model plants in combination with multivariate data analysis and MS/MS identification and will evaluate possible alternatives.

Proteomic analysis of Vibrio cholerae in human stool

An effective vaccine for Vibrio cholerae is not yet available for use in the developing world, where the burden of cholera disease is highest. Characterizing the proteins that are expressed by V. cholerae in the human host environment may provide insight into the pathogenesis of cholera and assist with the development of an improved vaccine. We analyzed the V. cholerae proteins present in the stools of 32 patients with clinical cholera. The V. cholerae outer membrane porin, OmpU, was identified in all of the human stool samples, and many V. cholerae proteins were repeatedly identified in separate patient samples. The majority of V. cholerae proteins identified in human stool are involved in protein synthesis and energy metabolism. A number of proteins involved in the pathogenesis of cholera, including the A and B subunits of cholera toxin and the toxin-coregulated pilus, were identified in human stool. In a subset of stool specimens, we also assessed which in vivo expressed V. cholerae proteins were recognized uniquely by convalescent-phase as opposed to acute-phase serum from cholera patients. We identified a number of these in vivo expressed proteins as immunogenic during human infection. To our knowledge, this is the first characterization of the proteome of a pathogenic bacteria recovered from a natural host.

Proteomic profile of dormant Trichophyton rubrum conidia

Background

Trichophyton rubrum is the most common dermatophyte causing fungal skin infections in humans. Asexual sporulation is an important means of propagation for T. rubrum, and conidia produced by this way are thought to be the primary cause of human infections. Despite their importance in pathogenesis, the conidia of T. rubrum remain understudied. We intend to intensively investigate the proteome of dormant T. rubrum conidia to characterize its molecular and cellular features and to enhance the development of novel therapeutic strategies.

Results

The proteome of T. rubrum conidia was analyzed by combining shotgun proteomics with sample prefractionation and multiple enzyme digestion. In total, 1026 proteins were identified. All identified proteins were compared to those in the NCBI non-redundant protein database, the eukaryotic orthologous groups database, and the gene ontology database to obtain functional annotation information. Functional classification revealed that the identified proteins covered nearly all major biological processes. Some proteins were spore specific and related to the survival and dispersal of T. rubrum conidia, and many proteins were important to conidial germination and response to environmental conditions.

Conclusion

Our results suggest that the proteome of T. rubrum conidia is considerably complex, and that the maintenance of conidial dormancy is an intricate and elaborate process. This data set provides the first global framework for the dormant T. rubrum conidia proteome and is a stepping stone on the way to further study of the molecular mechanisms of T. rubrum conidial germination and the maintenance of conidial dormancy.

Linear discriminant analysis-based estimation of the false discovery rate for phosphopeptide identifications

The development of liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has made it possible to characterize phosphopeptides in an increasingly large-scale and high-throughput fashion. However, extracting confident phosphopeptide identifications from the resulting large data sets 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 reanalyze 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 peptide scores. A linear discriminant analysis is then performed to determine how to optimally combine peptide scores (in this case, from 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. The Phosphopeptide FDR Estimator software is freely available for download at http://ncrr.pnl.gov/software/.

On the benefits of acquiring peptide fragment ions at high measured mass accuracy

The advantages and disadvantages of acquiring tandem mass spectra by collision-induced dissociation (CID) of peptides in linear ion trap Fourier-transform hybrid instruments are described. These instruments offer the possibility to transfer fragment ions from the linear ion trap to the FT-based analyzer for analysis with both high resolution and high mass accuracy. In addition, performing CID during the transfer of ions from the linear ion trap (LTQ) to the FT analyzer is also possible in instruments containing an additional collision cell (i.e., the "C-trap" in the LTQ-Orbitrap), resulting in tandem mass spectra over the full m/z range and not limited by the ejection q value of the LTQ. Our results show that these scan modes have lower duty cycles than tandem mass spectra acquired in the LTQ with nominal mass resolution, and typically result in fewer peptide identifications during data-dependent analysis of complex samples. However, the higher measured mass accuracy and resolution provides more specificity and hence provides a lower false positive ratio for the same number of true positives during database search of peptide tandem mass spectra. In addition, the search for modified and unexpected peptides is greatly facilitated with this data acquisition mode. It is therefore concluded that acquisition of tandem mass spectral data with high measured mass accuracy and resolution is a competitive alternative to "classical" data acquisition strategies, especially in situations of complex searches from large databases, searches for modified peptides, or for peptides resulting from unspecific cleavages.

Identification and characterization of sulfolobus solfataricus P2 proteome using multidimensional liquid phase protein separations

We have identified and characterized the proteome of Sulfolobus solfataricus P2 using multidimensional liquid phase protein separations. Multidimensional liquid phase chromatography was performed using ion exchange chromatography in the first dimension, followed by reverse-phase chromatography using 500 microm i.d. poly(styrene-divinylbenzene) monoliths in the second dimension to separate soluble protein lysates from S. solfataricus. The 2DLC protein separations from S. solfataricus protein lysates enabled the generation of a 2D liquid phase map analogous to the traditional 2DE map. Following separation of the proteins in the second dimension, fractions were collected, digested in solution using trypsin and analyzed using mass spectrometry. These approaches offer significant reductions in labor intensity and the overall time taken to analyze the proteome in comparison to 2DE, taking advantage of automation and fraction collection associated with this approach. Furthermore, following proteomic analysis using 2DLC, the data obtained was compared to previous 2DE and shotgun proteomic studies of a soluble protein lysate from S. solfataricus. In comparison to 2DE, the results show an overall increase in proteome coverage. Moreover, 2DLC showed increased coverage of a number of protein subsets including acidic, basic, low abundance and small molecular weight proteins in comparison to 2DE. In comparison to shotgun studies, an increase in proteome coverage was also observed. Furthermore, 187 unique proteins were identified using 2DLC, demonstrating this methodology as an alternative approach for proteomic studies or in combination with 2DE and shotgun workflows for global proteomics.

The influence of sample preparation and replicate analyses on HeLa Cell phosphoproteome coverage

Ongoing optimization of proteomic methodologies seeks to improve both the coverage and confidence of protein identifications. The optimization of sample preparation, inclusion of technical replicates (repeated instrumental analysis of the same sample), and biological replicates (multiple individual samples) are crucial in proteomic studies to avoid the pitfalls associated with single point analysis and under-sampling. Phosphopeptides were isolated from HeLa cells and analyzed by nano-reversed phase liquid chromatography electrospray ionization tandem mass spectrometry (nano-RP-LC-MS/MS). We observed that a detergent-based protein extraction approach, followed with additional steps for nucleic acid removal, provided a simple alternative to the broadly used Trizol extraction. The evaluation of four technical replicates demonstrated measurement reproducibility with low percent variance in peptide responses at approximately 3%, where additional peptide identifications were made with each added technical replicate. The inclusion of six technical replicates for moderately complex protein extracts (approximately 4000 uniquely identified peptides per data set) affords the optimal collection of peptide information.

OMSSAGUI: An open-source user interface component to configure and run the OMSSA search engine

We here present a user-friendly and extremely lightweight tool that can serve as a stand-alone front-end for the Open MS Search Algorithm (OMSSA) search engine, or that can directly be used as part of an informatics processing pipeline for MS driven proteomics. The OMSSA graphical user interface (OMSSAGUI) tool is written in Java, and is supported on Windows, Linux, and OSX platforms. It is an open source under the Apache 2 license and can be downloaded from http://code.google.com/p/mass-spec-gui/.

Generalized method for probability-based peptide and protein identification from tandem mass spectrometry data and sequence database searching

Tandem mass spectrometry-based proteomics is currently in great demand of computational methods that facilitate the elimination of likely false positives in peptide and protein identification. In the last few years, a number of new peptide identification programs have been described, but scores or other significance measures reported by these programs cannot always be directly translated into an easy to interpret error rate measurement such as the false discovery rate. In this work we used generalized lambda distributions to model frequency distributions of database search scores computed by MASCOT, X!TANDEM with k-score plug-in, OMSSA, and InsPecT. From these distributions, we could successfully estimate p values and false discovery rates with high accuracy. From the set of peptide assignments reported by any of these engines, we also defined a generic protein scoring scheme that enabled accurate estimation of protein-level p values by simulation of random score distributions that was also found to yield good estimates of protein-level false discovery rate. The performance of these methods was evaluated by searching four freely available data sets ranging from 40,000 to 285,000 MS/MS spectra.

Robust and sensitive iTRAQ quantification on an LTQ Orbitrap mass spectrometer

Isobaric stable isotope tagging reagents such as tandem mass tags or isobaric tags for relative and absolute quantification enable multiplexed quantification of peptides via reporter ion signals in the low mass range of tandem mass spectra. Until recently, the poor recovery of low mass fragments observed in tandem mass spectra acquired on ion trap mass spectrometers precluded the use of these reagents on this widely available instrument platform. The Pulsed Q Dissociation (PQD) technique allows negotiating this limitation but suffers from poor fragmentation efficiency, which has raised doubts in the community as to its practical utility. Here we show that by carefully optimizing instrument parameters such as collision energy, activation Q, delay time, ion isolation width, number of microscans, and number of trapped ions, low m/z fragment ion intensities can be generated that enable accurate peptide quantification at the 100 amol level. Side by side comparison of PQD on an LTQ Orbitrap with CID on a five-year old Q-Tof Ultima using complex protein digests shows that whereas precision of quantification of 10-15% can be achieved by both approaches, PQD quantifies twice as many proteins. PQD on an LTQ Orbitrap also outperforms "higher energy collision induced dissociation" on the same instrument using the recently introduced octapole collision cell in terms of lower limit of quantification. Finally, we demonstrate the significant analytical potential of iTRAQ quantification using PQD on an LTQ Orbitrap by quantitatively measuring the kinase interaction profile of the small molecule drug imatinib in K-562 cells. This article gives practical guidance for the implementation of PQD, discusses its merits, and for the first time, compares its performance to higher energy collision-induced dissociation.

Comprehensive analysis of the mouse renal cortex using two-dimensional HPLC - tandem mass spectrometry

Background

Proteomic methodologies increasingly have been applied to the kidney to map the renal cortical proteome and to identify global changes in renal proteins induced by diseases such as diabetes. While progress has been made in establishing a renal cortical proteome using 1-D or 2-DE and mass spectrometry, the number of proteins definitively identified by mass spectrometry has remained surprisingly small. Low coverage of the renal cortical proteome as well as our interest in diabetes-induced changes in proteins found in the renal cortex prompted us to perform an in-depth proteomic analysis of mouse renal cortical tissue.

Results

We report a large scale analysis of mouse renal cortical proteome using SCX prefractionation strategy combined with HPLC – tandem mass spectrometry. High-confidence identification of ~2,000 proteins, including cytoplasmic, nuclear, plasma membrane, extracellular and unknown/unclassified proteins, was obtained by separating tryptic peptides of renal cortical proteins into 60 fractions by SCX prior to LC-MS/MS. The identified proteins represented the renal cortical proteome with no discernible bias due to protein physicochemical properties, subcellular distribution, biological processes, or molecular function. The highest ranked molecular functions were characteristic of tubular epithelium, and included binding, catalytic activity, transporter activity, structural molecule activity, and carrier activity. Comparison of this renal cortical proteome with published human urinary proteomes demonstrated enrichment of renal extracellular, plasma membrane, and lysosomal proteins in the urine, with a lack of intracellular proteins. Comparison of the most abundant proteins based on normalized spectral abundance factor (NSAF) in this dataset versus a published glomerular proteome indicated enrichment of mitochondrial proteins in the former and cytoskeletal proteins in the latter.

Conclusion

A whole tissue extract of the mouse kidney cortex was analyzed by an unbiased proteomic approach, yielding a dataset of ~2,000 unique proteins identified with strict criteria to ensure a high level of confidence in protein identification. As a result of extracting all proteins from the renal cortex, we identified an exceptionally wide range of renal proteins in terms of pI, MW, hydrophobicity, abundance, and subcellular location. Many of these proteins, such as low-abundance proteins, membrane proteins and proteins with extreme values in pI or MW are traditionally under-represented in 2-DE-based proteomic analysis.

Complementary analysis of the vegetative membrane proteome of the human pathogen Staphylococcus aureus

The Gram-positive bacterium Staphylococcus aureus is a serious human pathogen causing a wide variety of diseases, and its increasing resistance toward all available antibiotics makes its further investigation absolutely essential. We examined the membrane proteome of exponentially growing cells of S. aureus COL because this subproteome plays a major role in the virulence of the bacterium in its host. In general, an analysis of membrane proteins is impeded by their hydrophobic nature as well as by a high abundance of many cytosolic proteins. The implementation of three different technologies, one-dimensional gel-LC, two-dimensional LC, and a membrane shaving approach combined with MS/MS analyses, enabled an identification of 271 integral and 86 peripheral membrane proteins from exponentially growing cells. In particular, the latter approach that combined membrane shaving with a subsequent chymotrypsin digest of integral membrane domains of proteins greatly facilitated the detection of hydrophobic peptides derived from membrane-spanning segments (713 peptides, 60% of all peptides) and therefore yielded almost exclusively highly hydrophobic integral membrane proteins (96.7%). A comparison of the various methods disclosed the one-dimensional gel-LC and the shaving approach to be highly complementary techniques. A combination of them will reveal a most comprehensive view on membrane proteomes.

Motif-specific sampling of phosphoproteomes

Phosphoproteomics, the targeted study of a subfraction of the proteome which is modified by phosphorylation, has become an indispensable tool to study cell signaling dynamics. We described a methodology that linked phosphoproteome and proteome analysis based on Ba2+ binding properties of amino acids. This technology selected motif-specific phosphopeptides independent of the system under analysis. MudPIT (Multidimensional Identification Technology) identified 1037 precipitated phosphopeptides from as little as 250 microg of proteins. To extend coverage of the phosphoproteome, we sampled the nuclear extract of HeLa cells with three values of Ba2+ ions molarity. The presence of more than 70% of identified phosphoproteins was further substantiated by their nonmodified peptides. Upon isoproterenol stimulation of HEK cells, we identified an increasing number of phosphoproteins from MAPK cascades and AKAP signaling hubs. We quantified changes in both protein and phosphorylation levels of 197 phosphoproteins including a critical kinase, MAPK1. Integration of differential phosphorylation of MAPK1 with knowledge bases constructed modules that correlated well with its role as node in cross-talk of canonical pathways.

Comparison of Mascot and X!Tandem performance for low and high accuracy mass spectrometry and the development of an adjusted Mascot threshold

It is a major challenge to develop effective sequence database search algorithms to translate molecular weight and fragment mass information obtained from tandem mass spectrometry into high quality peptide and protein assignments. We investigated the peptide identification performance of Mascot and X!Tandem for mass tolerance settings common for low and high accuracy mass spectrometry. We demonstrated that sensitivity and specificity of peptide identification can vary substantially for different mass tolerance settings, but this effect was more significant for Mascot. We present an adjusted Mascot threshold, which allows the user to freely select the best trade-off between sensitivity and specificity. The adjusted Mascot threshold was compared with the default Mascot and X!Tandem scoring thresholds and shown to be more sensitive at the same false discovery rates for both low and high accuracy mass spectrometry data.

Improved recovery of proteome-informative, protein N-terminal peptides by combined fractional diagonal chromatography (COFRADIC)

We previously described a proteome-wide, peptide-centric procedure for sorting protein N-terminal peptides and used these peptides as readouts for protease degradome and xenoproteome studies. This procedure is part of a repertoire of gel-free techniques known as COmbined FRActional DIagonal Chromatography (COFRADIC) and highly enriches for alpha-amino-blocked peptides, including alpha-amino-acetylated protein N-terminal peptides. Here, we introduce two additional steps that significantly increase the fraction of such proteome-informative, N-terminal peptides: strong cation exchange (SCX) segregation of alpha-amino-blocked and alpha-amino-free peptides and an enzymatic step liberating pyroglutamyl peptides for 2,4,6-trinitrobenzenesulphonic acid (TNBS) modification and thus COFRADIC sorting. The SCX step reduces the complexity of the analyte mixture by enriching N-terminal peptides and depleting alpha-amino-free internal peptides as well as proline-starting peptides prior to COFRADIC. The action of pyroglutamyl aminopeptidases prior to the first COFRADIC peptide separation results in greatly diminishing numbers of contaminating pyroglutamyl peptides in peptide maps. We further show that now close to 95% of all COFRADIC-sorted peptides are alpha-amino-acetylated and, using the same amount of starting material, our novel procedure leads to an increased number of protein identifications.

The Glc7 phosphatase subunit of the cleavage and polyadenylation factor is essential for transcription termination on snoRNA genes

Glc7, the yeast protein phosphatase 1, is a component of the cleavage and polyadenylation factor (CPF). Here we show that downregulation of Glc7, or its dissociation from CPF in the absence of CPF subunits Ref2 or Swd2, results in similar snoRNA termination defects. Overexpressing a C-terminal fragment of Sen1, a superfamily I helicase required for snoRNA termination, suppresses the growth and termination defects associated with loss of Swd2 or Ref2, but not Glc7. Suppression by Sen1 requires nuclear localization and direct interaction with Glc7, which can dephosphorylate Sen1 in vitro. The suppressing fragment, and in a similar manner full-length Sen1, copurifies with the snoRNA termination factors Nrd1 and Nab3, suggesting loss of Glc7 from CPF can be compensated by recruiting Glc7 to Nrd1-Nab3 through Sen1. Swd2 is also a subunit of the Set1c histone H3K4 methyltransferase complex and is required for its stability and optimal methyltransferase activity.

Characterization of the vitreous proteome in diabetes without diabetic retinopathy and diabetes with proliferative diabetic retinopathy

An understanding of the diabetes-induced alterations in vitreous protein composition in the absence and in the presence of proliferative diabetic retinopathy (PDR) may provide insights into factors and mechanisms responsible for this disease. We have performed a comprehensive proteomic analysis and comparison of vitreous samples from individuals with diabetes but without diabetic retinopathy (noDR) or with PDR and nondiabetic individuals (NDM). Using preparative one-dimensional SDS-PAGE and nano-LC/MS/MS of 17 independent vitreous samples, we identified 252 proteins from human vitreous. Fifty-six proteins were differentially abundant in noDR and PDR vitreous compared with NDM vitreous, including 32 proteins increased and 10 proteins decreased in PDR vitreous compared with NDM vitreous. Comparison of noDR and PDR groups revealed increased levels of angiotensinogen and decreased levels of calsyntenin-1, interphotoreceptor retinoid-binding protein, and neuroserpin in PDR vitreous. Biological pathway analysis revealed that vitreous contains 30 proteins associated with the kallikrein-kinin, coagulation, and complement systems. Five of them (complement C3, complement factor I, prothrombin, alpha-1-antitrypsin, and antithrombin III) were increased in PDR vitreous compared with NDM vitreous. Factor XII was detected in PDR vitreous but not observed in either NDM or noDR vitreous. PDR vitreous also had increased levels of peroxiredoxin-1 and decreased levels of extracellular superoxide dismutase, compared with noDR or NDM vitreous. These data provide an in depth analysis of the human vitreous proteome and reveal protein alterations that are associated with PDR.

Identification of pathways associated with invasive behavior by ovarian cancer cells using multidimensional protein identification technology (MudPIT)

Proteomic profiling has emerged as a useful tool for identifying tissue alterations in disease states including malignant transformation. The aim of this study was to reveal expression profiles associated with the highly motile/invasive ovarian cancer cell phenotype. Six ovarian cancer cell lines were subjected to proteomic characterization using multidimensional protein identification technology (MudPIT), and evaluated for their motile/invasive behavior, so that these parameters could be compared. Within whole cell extracts of the ovarian cancer cells, MudPIT identified proteins that mapped to 2245 unique genes. Western blot analysis for selected proteins confirmed the expression profiles revealed by MudPIT, demonstrating the fidelity of this high-throughput analysis. Unsupervised cluster analysis partitioned the cell lines in a manner that reflected their motile/invasive capacity. A comparison of protein expression profiles between cell lines of high (group 1) versus low (group 2) motile/invasive capacity revealed 300 proteins that were differentially expressed, of which 196 proteins were significantly upregulated in group 1. Protein network and KEGG pathway analysis indicated a functional interplay between proteins up-regulated in group 1 cells, with increased expression of several key members of the actin cytoskeleton, extracellular matrix (ECM) and focal adhesion pathways. These proteomic expression profiles can be utilized to distinguish highly motile, aggressive ovarian cancer cells from lesser invasive ones, and could prove to be essential in the development of more effective strategies that target pivotal cell signaling pathways used by cancer cells during local invasion and distant metastasis.

Proteomics based on peptide fractionation by SDS-free PAGE

Here we demonstrate the usefulness of peptide fractionation by SDS-free polyacrylamide gel electrophoresis and its applicability to proteomics studies. In the absence of SDS, the driving force for the electrophoretic migration toward the anode is supplied by negatively charged acidic amino acid residues and other residues as phosphate, sulfate and sialic acid, while the resulting mobility depends on both the charge and the molecular mass of the peptides. A straightforward method was achieved for SDS-PAGE of proteins, enzyme digestion, peptide transfer and fractionation by SDS-free PAGE, which was named dual-fractionation polyacrylamide gel electrophoresis (DF-PAGE). This method increases the number of identified proteins 2.5-fold with respect to the proteins identified after direct analysis, and more than 80% of assigned peptides were found in unique SDS-free gel slices. A vast majority of identified peptides (93%) have p I values below 7.0, and 7% have p I values between 7.0 and 7.35. Peptide digests that were derived from complex protein mixtures were in consequence simplified as peptides that are positively charged are not recovered in the present conditions. The analysis of a membrane protein extract from Neisseria meningitidis by this approach allowed the identification of 97 proteins, including low-abundance components.

SALAMI (Spectrum ALignments using high Accuracy Mass and hIgh sensitivity data): how to make the best out of hybrid MS/MS data

We present a software algorithm that combines ion trap and orbitrap product ion spectra acquired in parallel. The hybrid product ion spectra identify more peptides than when using two separate searches for the orbitrap and LTQ data. The program extracts the high-accuracy mass data from the Orbitrap mass analyzer and combines it with the high-sensitivity data analyzed in the LTQ linear ion trap; the m/z values of the high-confidence fragment ions are corrected to orbitrap mass accuracies and the fragment ion intensities are amplified. This approach utilizes the parallel spectrum measurement capabilities of the LTQ-Orbitrap. We present our approach to handling this type of hybrid data, explain our alignment program, and discuss the advantages of the chosen methodology.

Alignment of LC-MS images, with applications to biomarker discovery and protein identification

LC-MS-based approaches have gained considerable interest for the analysis of complex peptide or protein mixtures, due to their potential for full automation and high sampling rates. Advances in resolution and accuracy of modern mass spectrometers allow new analytical LC-MS-based applications, such as biomarker discovery and cross-sample protein identification. Many of these applications compare multiple LC-MS experiments, each of which can be represented as a 2-D image. In this article, we survey current approaches to LC-MS image alignment. LC-MS image alignment corrects for experimental variations in the chromatography and represents a computational key technology for the comparison of LC-MS experiments. It is a required processing step for its two major applications: biomarker discovery and protein identification. Along with descriptions of the computational analysis approaches, we discuss their relative merits and potential pitfalls.

Taking aim at shotgun phosphoproteomics

Shotgun phosphoproteomics employs liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) to analyze phosphopeptides from complex protein mixtures, allowing detection and quantification of phosphorylation events on a global scale. Within the past few years, this powerful technique has been used to uncover novel phosphorylation sites as well as explore changes in protein phosphorylation during cellular signaling in various systems. This review presents a general overview of current phosphoproteomic methodologies, including summaries of various approaches to phosphopeptide enrichment, alternative MS fragmentation strategies, and the latest software for analysis of phosphopeptide data sets.

Two-dimensional strong cation exchange/porous layer open tubular/mass spectrometry for ultratrace proteomic analysis using a 10 microm id poly(styrene- divinylbenzene) porous layer open tubular column with an on-line triphasic trapping column

This study expands the capabilities for ultratrace proteomic analysis of our previous work by incorporating on-line sample desalting using a triphasic (RP/strong cation exchange (SCX)/micro-SPE) trapping column connected to a 3.2 m x 10 microm id poly(styrene-divinylbenzene) (PS-DVB) porous layer open tubular (PLOT) column. To minimize extra sample handling steps, C18 RP packing was incorporated in the capillary tubing upstream of the SCX column for the on-line desalting. For the micro-SPE column, a 50 microm id PS-DVB monolithic column was positioned downstream of the SCX column. High-performance separation was achieved on the PLOT column at a mobile phase flow rate of 20 nL/min. The sensitivity and high resolution capability of the new multidimensional platform was evaluated using an in-gel tryptic digested sample of a cervical cancer (SiHa) cell line. For the injected amount of 1200 cells ( approximately 500 ng), over 2700 peptides covering greater than 850 unique proteins were identified from the triphasic SCX/PLOT/MS analysis of a single SDS gel section (>40 kDa). The 2-D LC/MS platform demonstrated good separation performance, such that more than 85% of the identified peptides were detected from only one salt fraction. In a triplicate analysis of the above >40 kDa gel section, 4497 peptides and 1209 unique proteins were identified when applying stringent filtering criteria, with a false-positive rate of 2.4%. When all three SDS-PAGE gel sections of the lysed SiHa cells were analyzed, 5047 peptides and 1857 unique proteins (false-positive rate 1.8%), including cancer-related proteins such as MAP kinases, were identified.

Phosphoproteome analysis of Drosophila melanogaster embryos

Protein phosphorylation is a key regulatory event in most cellular processes and development. Mass spectrometry-based proteomics provides a framework for the large-scale identification and characterization of phosphorylation sites. Here, we used a well-established phosphopeptide enrichment and identification strategy including the combination of strong cation exchange chromatography, immobilized metal affinity chromatography, and high-accuracy mass spectrometry instrumentation to study phosphorylation in developing Drosophila embryos. In total, 13,720 different phosphorylation sites were discovered from 2702 proteins with an estimated false-discovery rate (FDR) of 0.63% at the peptide level. Because of the large size of the data set, both novel and known phosphorylation motifs were extracted using the Motif-X algorithm, including those representative of potential ordered phosphorylation events.

Proteome-wide identification of proteins and their modifications with decreased ambiguities and improved false discovery rates using unique sequence tags

Identifying proteins and their modification states and with known levels of confidence remains as a significant challenge for proteomics. Random or decoy peptide databases are increasingly being used to estimate the false discovery rate (FDR), e.g., from liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses of tryptic digests. We show that this approach can significantly underestimate the FDR and describe an approach for more confident protein identifications that uses unique partial sequences derived from a combination of database searching and amino acid residue sequencing using high-accuracy MS/MS data. Applied to a Saccharomyces cerevisiae tryptic digest, the approach provided 3 132 confident peptide identifications ( approximately 5% modified in some fashion), covering 575 proteins with an estimated zero FDR. The conventional approach provided 3 359 peptide identifications and 656 proteins with 0.3% FDR based upon a decoy database analysis. However, the present approach revealed approximately 5% of the 3 359 identifications to be incorrect and many more as potentially ambiguous (e.g., due to not considering certain amino acid substitutions and modifications). In addition, 677 peptides and 39 proteins were identified that had been missed by conventional analysis, including nontryptic peptides, peptides with a variety of expected/unexpected chemical modifications, known/unknown post-translational modifications, single nucleotide polymorphisms or gene encoding errors, and multiple modifications of individual peptides.

Automatic validation of phosphopeptide identifications by the MS2/MS3 target-decoy search strategy

Manual checking is commonly employed to validate the phosphopeptide identifications from database searching of tandem mass spectra. It is very time-consuming and labor intensive as the number of phosphopeptide identifications increases greatly. In this study, a simple automatic validation approach was developed for phosphopeptide identification by combining consecutive stage mass spectrometry data and the target-decoy database searching strategy. Only phosphopeptides identified from both MS2 and its corresponding MS3 were accepted for further filtering, which greatly improved the reliability in phosphopeptide identification. Before database searching, the spectra were validated for charge state and neutral loss peak intensity, and then the invalid MS2/MS3 spectra were removed, which greatly reduced the database searching time. It was found that the sensitivity was significantly improved in MS2/MS3 strategy as the number of identified phosphopeptides was 2.5 times that obtained by the conventional filter-based MS2 approach. Because of the use of the target-decoy database, the false-discovery rate (FDR) of the identified phosphopeptides could be easily determined, and it was demonstrated that the determined FDR can precisely reflect the actual FDR without any manual validation stage.

Systematic determination of ion score cutoffs based on calculated false positive rates: application for identifying ubiquitinated proteins by tandem mass spectrometry

We report a simple approach for determining ion score cutoffs that permit the confident identification of ubiquitinated proteins by tandem mass spectrometry (MS/MS). Initial experiments involving the analysis of gel bands containing multi-Ubiquitin chains with quadrupole time-of-flight and quadrupole ion trap mass spectrometers revealed that standard ion score cutoffs used for database searching were not sufficiently stringent. We also found that false positive and false negative rates (FPR and FNR) varied significantly depending on the cutoff scores used and that appropriate cutoffs could only be determined following a systematic evaluation of false positive rates. When standard cutoff scores were used for the analysis of complex mixtures of ubiquitinated proteins, unacceptably high FPR were observed. Finally, we found that FPR for ubiquitinated proteins are affected by the size of the protein database that is searched. These observations may be applicable for the study of other post-translational modifications.

Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics

Data processing is a central and critical component of a successful proteomics experiment, and is often the most time-consuming step. There have been considerable advances in the field of proteomics informatics in the past 5 years, spurred mainly by free and open-source software tools. Along with the gains afforded by new software, the benefits of making raw data and processed results freely available to the community in data repositories are finally in evidence. In this review, we provide an overview of the general analysis approaches, software tools, and repositories that are enabling successful proteomics research via tandem mass spectrometry.

iTRAQ-facilitated proteomic analysis of human prostate cancer cells identifies proteins associated with progression

The unpredictable behavior of prostate cancer presents a major clinical challenge during patient management. In order to gain an insight into the molecular mechanisms associated with prostate cancer progression, we employed the shot-gun proteomic approach of isobaric tags for relative and absolute quantitation (iTRAQ), followed by 2D-LC-MS/MS, using the poorly metastatic LNCaP cell line and its highly metastatic variant LNCaP-LN3 cell line as a model. A total number of 280 unique proteins were identified (> or =95% confidence), and relative expression data was obtained for 176 of these. Ten proteins were found to be significantly up-regulated (> or =1.50 fold), while 4 proteins were significantly down-regulated (> or = -1.50 fold), in LNCaP-LN3 cells. Differential expression of brain creatine kinase (CKBB), soluble catechol-O-methyltransferase (S-COMT), tumor rejection antigen (gp96), and glucose regulated protein, 78 kDa (grp78), was confirmed by Western blotting or independent 2D-PAGE analysis. Additionally, iTRAQ analysis identified absence of the lactate dehydrogenase-B (LDH-B) subunit in LNCaP-LN3 cells, confirming our published data. The clinical relevance of gp96 was assessed by immunohistochemistry using prostate tissues from benign ( n = 95), malignant ( n = 66), and metastatic cases ( n = 3). Benign epithelium showed absent/weak gp96 expression in the basal cells, in contrast to the moderate/strong expression seen in malignant epithelium. Furthermore, there was a statistically significant difference in the intensity of gp96 expression between benign and malignant cases ( p < 0.0005, Mann-Whitney U). Our study is the first to report the application of iTRAQ technology and its potential for the global proteomic profiling of prostate cancer cells, including the identification of absent protein expression.

Coupled global and targeted proteomics of human embryonic stem cells during induced differentiation

Elucidating the complex combinations of growth factors and signaling molecules that maintain pluripotency or, alternatively, promote the controlled differentiation of human embryonic stem cells (hESCs) has important implications for the fundamental understanding of human development, devising cell replacement therapies, and cancer cell biology. hESCs are commonly grown on irradiated mouse embryonic fibroblasts (MEFs) or in conditioned medium from MEFs. These culture conditions interfere with many experimental conclusions and limit the ability to perform conclusive proteomics studies. The current investigation avoided the use of MEFs or MEF-conditioned medium for hESC culture, allowing global proteomics analysis without these confounding conditions, and elucidated neural cell-specific signaling pathways involved in noggin-induced hESC differentiation. Based on these analyses, we propose the following early markers of hESC neural differentiation: collapsin response mediator proteins 2 and 4 and the nuclear autoantigenic sperm protein as a marker of pluripotent hESCs. We then developed a directed mass spectrometry assay using multiple reaction monitoring (MRM) to identify and quantify these markers and in addition the epidermal ectoderm marker cytokeratin-8. Analysis of global proteomics, quantitative RT-PCR, and MRM data led to testing the isoform interference hypothesis where redundant peptides dilute quantification measurements of homologous proteins. These results show that targeted MRM analysis on non-redundant peptides provides more exact quantification of homologous proteins. This study describes the facile transition from discovery proteomics to targeted MRM analysis and allowed us to identify and verify several potential biomarkers for hESCs during noggin-induced neural and BMP4-induced epidermal ectoderm differentiation.

Properties of average score distributions of SEQUEST: the probability ratio method

High throughput identification of peptides in databases from tandem mass spectrometry data is a key technique in modern proteomics. Common approaches to interpret large scale peptide identification results are based on the statistical analysis of average score distributions, which are constructed from the set of best scores produced by large collections of MS/MS spectra by using searching engines such as SEQUEST. Other approaches calculate individual peptide identification probabilities on the basis of theoretical models or from single-spectrum score distributions constructed by the set of scores produced by each MS/MS spectrum. In this work, we study the mathematical properties of average SEQUEST score distributions by introducing the concept of spectrum quality and expressing these average distributions as compositions of single-spectrum distributions. We predict and demonstrate in the practice that average score distributions are dominated by the quality distribution in the spectra collection, except in the low probability region, where it is possible to predict the dependence of average probability on database size. Our analysis leads to a novel indicator, the probability ratio, which takes optimally into account the statistical information provided by the first and second best scores. The probability ratio is a non-parametric and robust indicator that makes spectra classification according to parameters such as charge state unnecessary and allows a peptide identification performance, on the basis of false discovery rates, that is better than that obtained by other empirical statistical approaches. The probability ratio also compares favorably with statistical probability indicators obtained by the construction of single-spectrum SEQUEST score distributions. These results make the robustness, conceptual simplicity, and ease of automation of the probability ratio algorithm a very attractive alternative to determine peptide identification confidences and error rates in high throughput experiments.

Postexperiment monoisotopic mass filtering and refinement (PE-MMR) of tandem mass spectrometric data increases accuracy of peptide identification in LC/MS/MS

Methods for treating MS/MS data to achieve accurate peptide identification are currently the subject of much research activity. In this study we describe a new method for filtering MS/MS data and refining precursor masses that provides highly accurate analyses of massive sets of proteomics data. This method, coined "postexperiment monoisotopic mass filtering and refinement" (PE-MMR), consists of several data processing steps: 1) generation of lists of all monoisotopic masses observed in a whole LC/MS experiment, 2) clusterization of monoisotopic masses of a peptide into unique mass classes (UMCs) based on their masses and LC elution times, 3) matching the precursor masses of the MS/MS data to a representative mass of a UMC, and 4) filtration of the MS/MS data based on the presence of corresponding monoisotopic masses and refinement of the precursor ion masses by the UMC mass. PE-MMR increases the throughput of proteomics data analysis, by efficiently removing "garbage" MS/MS data prior to database searching, and improves the mass measurement accuracies (i.e. 0.05 +/- 1.49 ppm for yeast data (from 4.46 +/- 2.81 ppm) and 0.03 +/- 3.41 ppm for glycopeptide data (from 4.8 +/- 7.4 ppm)) for an increased number of identified peptides. In proteomics analyses of glycopeptide-enriched samples, PE-MMR processing greatly reduces the degree of false glycopeptide identification by correctly assigning the monoisotopic masses for the precursor ions prior to database searching. By applying this technique to analyses of proteome samples of varying complexities, we demonstrate herein that PE-MMR is an effective and accurate method for treating massive sets of proteomics data.

Quantitative shotgun proteomics of enriched heterocysts from Nostoc sp. PCC 7120 using 8-plex isobaric peptide tags

The filamentous cyanobacterium Nostoc sp. strain PCC 7120 is capable of fixing atmospheric nitrogen. The labile nature of the core process requires the terminal differentiation of vegetative cells to form heterocysts, specialized cells with altered cellular and metabolic infrastructure to mediate the N2-fixing process. We present an investigation targeting the cellular proteomic expression of the heterocysts compared to vegetative cells of a population cultured under N2-fixing conditions. New 8-plex iTRAQ reagents were used on enriched replicate heterocyst and vegetative cells, and replicate N2-fixing and non-N2-fixing filaments to achieve accurate measurements. With this approach, we successfully identified 506 proteins, where 402 had confident quantifications. Observations provided by purified heterocyst analysis enabled the elucidation of the dominant metabolic processes between the respective cell types, while emphasis on the filaments enabled an overall comparison. The level of analysis provided by this investigation presents various tools and knowledge that are important for future development of cyanobacterial biohydrogen production.

Comprehensive proteomic analysis of bovine spermatozoa of varying fertility rates and identification of biomarkers associated with fertility

BACKGROUND

Male infertility is a major problem for mammalian reproduction. However, molecular details including the underlying mechanisms of male fertility are still not known. A thorough understanding of these mechanisms is essential for obtaining consistently high reproductive efficiency and to ensure lower cost and time-loss by breeder.

RESULTS

Using high and low fertility bull spermatozoa, here we employed differential detergent fractionation multidimensional protein identification technology (DDF-Mud PIT) and identified 125 putative biomarkers of fertility. We next used quantitative Systems Biology modeling and canonical protein interaction pathways and networks to show that high fertility spermatozoa differ from low fertility spermatozoa in four main ways. Compared to sperm from low fertility bulls, sperm from high fertility bulls have higher expression of proteins involved in: energy metabolism, cell communication, spermatogenesis, and cell motility. Our data also suggests a hypothesis that low fertility sperm DNA integrity may be compromised because cell cycle: G2/M DNA damage checkpoint regulation was most significant signaling pathway identified in low fertility spermatozoa.

CONCLUSION

This is the first comprehensive description of the bovine spermatozoa proteome. Comparative proteomic analysis of high fertility and low fertility bulls, in the context of protein interaction networks identified putative molecular markers associated with high fertility phenotype.