Heterogenous Turnover of Sperm and Seminal Vesicle Proteins in the Mouse Revealed by Dynamic Metabolic Labeling
2012; Elsevier BV; Volume: 11; Issue: 6 Linguagem: Inglês
10.1074/mcp.m111.014993
ISSN1535-9484
AutoresAmy J. Claydon, Steven A. Ramm, Andrea Pennington, Jane L. Hurst, Paula Stockley, Robert J. Beynon,
Tópico(s)Genetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
ResumoPlasticity in ejaculate composition is predicted as an adaptive response to the evolutionary selective pressure of sperm competition. However, to respond rapidly to local competitive conditions requires dynamic modulation in the production of functionally relevant ejaculate proteins. Here we combine metabolic labeling of proteins with proteomics to explore the opportunity for such modulation within mammalian ejaculates. We assessed the rate at which proteins are synthesized and incorporated in the seminal vesicles of male house mice (Mus musculus domesticus), where major seminal fluid proteins with potential roles in sperm competition are produced. We compared rates of protein turnover in the seminal vesicle with those during spermatogenesis, the timing of which is well known in mice. The subjects were fed a diet containing deuterated valine ([2H8]valine) for up to 35 days, and the incorporation of dietary-labeled amino acid into seminal vesicle- or sperm-specific proteins was assessed by liquid chromatography-mass spectrometry of samples recovered from the seminal vesicle lumen and cauda epididymis, respectively. Analyses of epididymal contents were consistent with the known duration of spermatogenesis and sperm maturation in this species and in addition revealed evidence for a subset of epididymal proteins subject to rapid turnover. For seminal vesicle proteins, incorporation of the stable isotope was evident from day 2 of labeling, reaching a plateau of labeling by day 24. Hence, even in the absence of copulation, the seminal vesicle proteins and certain epididymal proteins demonstrate considerable turnover, a response that is consonant with the capacity to rapidly modulate protein production. These techniques can now be used to assess the extent of phenotypic plasticity in mammalian ejaculate production and allocation according to social and environmental cues of sperm competition. Plasticity in ejaculate composition is predicted as an adaptive response to the evolutionary selective pressure of sperm competition. However, to respond rapidly to local competitive conditions requires dynamic modulation in the production of functionally relevant ejaculate proteins. Here we combine metabolic labeling of proteins with proteomics to explore the opportunity for such modulation within mammalian ejaculates. We assessed the rate at which proteins are synthesized and incorporated in the seminal vesicles of male house mice (Mus musculus domesticus), where major seminal fluid proteins with potential roles in sperm competition are produced. We compared rates of protein turnover in the seminal vesicle with those during spermatogenesis, the timing of which is well known in mice. The subjects were fed a diet containing deuterated valine ([2H8]valine) for up to 35 days, and the incorporation of dietary-labeled amino acid into seminal vesicle- or sperm-specific proteins was assessed by liquid chromatography-mass spectrometry of samples recovered from the seminal vesicle lumen and cauda epididymis, respectively. Analyses of epididymal contents were consistent with the known duration of spermatogenesis and sperm maturation in this species and in addition revealed evidence for a subset of epididymal proteins subject to rapid turnover. For seminal vesicle proteins, incorporation of the stable isotope was evident from day 2 of labeling, reaching a plateau of labeling by day 24. Hence, even in the absence of copulation, the seminal vesicle proteins and certain epididymal proteins demonstrate considerable turnover, a response that is consonant with the capacity to rapidly modulate protein production. These techniques can now be used to assess the extent of phenotypic plasticity in mammalian ejaculate production and allocation according to social and environmental cues of sperm competition. Plasticity in the size and composition of ejaculates is predicted as an adaptive response to the evolutionary selective pressure of sperm competition (1Parker G.A. Pizzari T. Sperm competition and ejaculate economics.Biol. Rev. Camb. Philos. Soc. 2010; 85: 897-934Crossref PubMed Google Scholar). That is, males should adjust both the overall and relative amount of ejaculate proteins produced to maximize fertilization success in competition with rivals when females mate promiscuously. Adaptive plasticity in sperm production according to perceived levels of sperm competition is now relatively well documented in diverse animal taxa (2Gage M.J. Continuous variation in reproductive strategy as an adaptive response to population density in the moth plodia interpunctella.Proc. Biol. 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In such cases, males must engage in sperm competition to secure fertilizations, with theory predicting an increased sperm allocation per ejaculate (7Parker G.A. Sperm competition and the evolution of ejaculates: Towards a theory base.Sperm Comp. Sexual Selection. 1998; 3: 54Google Scholar, 8Wedell N. Gage M.J. Parker G.A. Sperm competition, male prudence and sperm-limited females.Trends Ecol. Evol. 2002; 17: 313-320Abstract Full Text Full Text PDF Scopus (948) Google Scholar). Whether similar adjustments in the absolute and relative production of seminal fluid proteins might also be implemented according to competitive conditions remains largely unknown (but see Refs. 9Fedorka K.M. Winterhalter W.E. Ware B. Perceived sperm competition intensity influences seminal fluid protein production prior to courtship and mating.Evolution. 2011; 65: 584-590Crossref PubMed Scopus (69) Google Scholar, 10Lemaître J.F. Ramm S.A. Hurst J.L. Stockley P. Social cues of sperm competition influence accessory reproductive gland size in a promiscuous mammal.Proc. Biol. Sci. 2011; 278: 1171-1176Crossref PubMed Scopus (52) Google Scholar, 11Wigby S. Sirot L.K. Linklater J.R. Buehner N. Calboli F.C. Bretman A. Wolfner M.F. Chapman T. Seminal fluid protein allocation and male reproductive success.Curr. Biol. 2009; 19: 751-757Abstract Full Text Full Text PDF PubMed Scopus (263) Google Scholar). However, a growing theoretical literature on this issue predicts that such a plasticity could be adaptive (1Parker G.A. Pizzari T. Sperm competition and ejaculate economics.Biol. Rev. Camb. Philos. Soc. 2010; 85: 897-934Crossref PubMed Google Scholar, 12Cameron E. Day T. Rowe L. Sperm competition and the evolution of ejaculate composition.Am. Nat. 2007; 169: E158-E172Crossref PubMed Scopus (89) Google Scholar), and seminal fluid-mediated effects are now known to be critical to various aspects of sexual selection and sexual conflict (13Avila F.W. Sirot L.K. LaFlamme B.A. Rubinstein C.D. Wolfner M.F. Insect seminal fluid proteins: Identification and function.Annu. Rev. Entomol. 2011; 56: 21-40Crossref PubMed Scopus (610) Google Scholar, 14Poiani A. Complexity of seminal fluid: A review.Behav. Ecol. Sociobiol. 2006; 60: 289-310Crossref Scopus (379) Google Scholar). Seminal fluid in mammals and other taxa has multiple functions consistent with an influential role in postcopulatory sexual selection, including sperm transport, maintenance of sperm viability, and enhancement of sperm motility and capacitation (13Avila F.W. Sirot L.K. LaFlamme B.A. Rubinstein C.D. Wolfner M.F. Insect seminal fluid proteins: Identification and function.Annu. Rev. Entomol. 2011; 56: 21-40Crossref PubMed Scopus (610) Google Scholar, 14Poiani A. Complexity of seminal fluid: A review.Behav. Ecol. Sociobiol. 2006; 60: 289-310Crossref Scopus (379) Google Scholar, 15Pitnick S. Wolfner M.F. Suarez S.S. Ejaculate-female and sperm-female interactions: Biology: An Evolutionary Perspective. Academic, Press2009: 247-304Google Scholar). Seminal fluid also contains signaling agents that influence female reproductive physiology and immune responses, with consequences for female-mediated effects on sperm transport, survival, and competence (16Robertson S.A. Seminal fluid signaling in the female reproductive tract: Lessons from rodents and pigs.J. Anim. Sci. 2007; 85: E36-E44Crossref PubMed Scopus (210) Google Scholar). In rodents and other mammals, seminal fluid proteins in the ejaculate become cross-linked soon after ejaculation to form a copulatory plug, reducing the probability of female remating and/or enhancing sperm retention (17Ramm S.A. McDonald L. Hurst J.L. Beynon R.J. Stockley P. Comparative proteomics reveals evidence for evolutionary diversification of rodent seminal fluid and its functional significance in sperm competition.Mol. Biol. Evol. 2009; 26: 189-198Crossref PubMed Scopus (91) Google Scholar) or transport (18Ramm S.A. Parker G.A. Stockley P. Sperm competition and the evolution of male reproductive anatomy in rodents.Proc. Biol. Sci. 2005; 272: 949-955Crossref PubMed Scopus (160) Google Scholar). Comparative studies of rodents reveal that a major component of the copulatory plug, SVS2, is evolving rapidly and divergently between species, with changes in molecular mass consistent with selection under sperm competition (6Ramm S.A. Stockley P. Adaptive plasticity of mammalian sperm production in response to social experience.Proc. Biol. Sci. 2009; 276: 745-751PubMed Google Scholar, 19Ramm S.A. Oliver P.L. Ponting C.P. Stockley P. Emes R.D. Sexual selection and the adaptive evolution of mammalian ejaculate proteins.Mol. Biol. Evol. 2008; 25: 207-219Crossref PubMed Scopus (101) Google Scholar). Additional proteins secreted by rodent seminal vesicles also have properties consistent with a role in sperm competition. For example, SVS4 has immune-modulating and anti-inflammatory properties in the female reproductive tract (20Ialenti A. Santagada V. Caliendo G. Severino B. Fiorino F. Maffia P. Ianaro A. Morelli F. Di Micco B. Cartenì M. Stiuso P. Metafora V. Metafora S. Synthesis of novel anti-inflammatory peptides derived from the amino-acid sequence of the bioactive protein SV-IV.Eur. J. Biochem. 2001; 268: 3399-3406Crossref PubMed Scopus (24) Google Scholar), potentially mediating sexual conflicts over sperm survival (21Greeff J.M. Parker G.A. Spermicide by females: What should males do?.Proc. Biol. Sci. 2000; 267: 1759-1763Crossref PubMed Scopus (36) Google Scholar); SVS2 and Ceacam10 enhance sperm motility (22Li S.H. Lee R.K. Hsiao Y.L. Chen Y.H. Demonstration of a glycoprotein derived from the ceacam10 gene in mouse seminal vesicle secretions.Biol. Reprod. 2005; 73: 546-553Crossref PubMed Scopus (20) Google Scholar, 23Luo C.W. Lin H.J. Chen Y.H. A novel heat-labile phospholipid-binding protein, SVS VII, in mouse seminal vesicle as a sperm motility enhancer.J. Biol. Chem. 2001; 276: 6913-6921Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar), whereas SVS1 and SVS3 may act as additional plug components, because they also cross-link in the presence of prostate-derived transglutaminases (24Tseng H.C. Lin H.J. Sudhakar Gandhi P.S. Wang C.Y. Chen Y.H. Purification and identification of transglutaminase from mouse coagulating gland and its cross-linking activity among seminal vesicle secretion proteins.J. Chromatogr. B. 2008; 876: 198-202Crossref PubMed Scopus (13) Google Scholar). Given the likely significance of these major seminal fluid proteins in the context of sperm competition, we might therefore expect selection to favor plasticity in their production, so that ejaculate composition can be matched adaptively to competitive conditions. A key parameter in the plasticity of protein expression is the rate of protein turnover or replacement (25Beynon R.J. Pratt J.M. Strategies for measuring dynamics: The temporal component of proteomics.Methods Biochem. Anal. 2006; 49: 15-25PubMed Google Scholar). A protein that changes in abundance quickly must have a high turnover rate to allow rapid and bidirectional adjustment of the protein pool. For intracellular proteins, the rate of replacement is synonymous with the rate of turnover, mediated by intracellular processes. For extracellular secreted proteins, however, after synthesis, proteins are secreted from the cell to the extracellular space, defining irreversible the loss of the protein from the cell. Thus, for proteins such as the seminal vesicle proteins, newly synthesized proteins could accumulate in the seminal vesicle lumen prior to ejaculation and, if these proteins were only removed slowly or upon ejaculation, might not be capable of a rapid change in composition. If the seminal vesicle proteins were retained within the lumen of the gland for an extended period, the opportunities for modulation of protein composition would be limited, and opportunities for adjustment of the composition of the seminal vesicle protein complement would be largely through selective replenishment, postejaculation. If, however, there was a significant irreversible loss of protein from the extracellular pool (even in the absence of ejaculation), the seminal vesicle proteins would be dominated by newly synthesized proteins. In this more dynamic scenario, the composition of the ejaculate could be modulated by the animal in time frames compatible with competitive interactions, prior to copulation. To assess the potential for plasticity in protein expression within mammalian ejaculates, we examined the rate of metabolic labeling of ejaculate components, achieved by the addition of stable isotope-labeled amino acid in the diet. The rate at which the dietary amino acid is incorporated into the proteins is readily assessed by analysis of peptides, derived from the proteins, by proteomics. For seminal vesicle secretions, proteins that are rapidly synthesized (either by virtue of pool expansion or through rapid turnover) would incorporate label rapidly. By contrast, proteins that are synthesized slowly or retained in the seminal vesicle would not appear to be labeled to the same extent within the same time frame. In this study, we monitor the incorporation of a stable isotope-labeled dietary amino acid into seminal vesicle and sperm proteins. Because the timing of protein expression during spermatogenesis is well known in the mouse (26Good J.M. Nachman M.W. Rates of protein evolution are positively correlated with developmental timing of expression during mouse spermatogenesis.Mol. Biol. Evol. 2005; 22: 1044-1052Crossref PubMed Scopus (81) Google Scholar, 27Oakberg E.F. Duration of spermatogenesis in the mouse and timing of stages of the cycle of the seminiferous epithelium.Am. J. Anat. 1956; 99: 507-516Crossref PubMed Scopus (696) Google Scholar), examining sperm proteins in addition to seminal vesicle proteins serves to validate our labeling and proteomics workflows and additionally enables us to gauge the relative speed with which the production of seminal fluid proteins could potentially be modulated to alter ejaculate composition. We find that sperm labeling is consistent with known patterns of protein expression during spermatogenesis and that seminal fluid labeling is consistent with rapid turnover of this secretion and thus indicates the potential for rapid dynamic adjustments in ejaculate composition. Subject males (n = 10), aged 11–14 months, were from a colony of wild house mice that had been outbred for six or fewer generations in captivity and originally derived from local populations in Cheshire. Male mice were housed individually in polypropylene cages and given free access to normal laboratory feed. At the start of the experiment, this was substituted for the same diet, based on the 5002 certified rodent diet (pellet form) but supplemented with crystalline [2H8]valine (prepared by International Product Supplies, London) at a quantity equal to the natural valine content of the diet (1.05% (w/w), predominantly protein-bound). At the indicated times (2 days, 1 week, 2 weeks, 3.5 weeks, and 5 weeks) throughout the dietary labeling period, pairs of animals were killed humanely, and the contents of the seminal vesicle (SV) 1The abbreviations used are:SVseminal vesicleMUPmajor urinary proteinRIArelative isotope abundanceLDHClactate dehydrogenase CHheavyLlight. and cauda epididymis (predominantly sperm) were recovered for proteomics analysis. Following dissection, seminal vesicle secretions were expelled from the lumen of the left seminal vesicle. To recover mature sperm, the left cauda epididymis was placed in 20 μl of ammonium bicarbonate buffer, and the surface was pierced several times with a scalpel blade to release its contents. Throughout the labeling period, urine samples were collected as a source of major urinary proteins (MUPs). All of the samples were frozen at −80°C until analysis. seminal vesicle major urinary protein relative isotope abundance lactate dehydrogenase C heavy light. All of the samples (sperm preparations and SV, five different labeling times, two animals per time point) were analyzed as tryptic peptides, resolved by high resolution liquid chromatography (Waters nanoAcquity) prior to tandem mass spectrometry, either using a Synapt G1 (Waters) or LTQ-Orbitrap Velos (Thermo). After determination of protein concentration by the Bradford dye-binding method, 100 μg of protein was reduced, alkylated, and digested. Specifically, protein samples were dispersed in 50 mm ammonium bicarbonate buffer containing 0.05% RapiGestTM (Waters), a proprietary surfactant that enhances proteolysis. The samples were reduced with 3 mm dithiothreitol for 10 min at 60 °C and then alkylated for 30 min at room temperature in the dark with 9 mm iodoacetamide. Finally, trypsin (final concentration, 0.01 μg/μl) was added, and the digestion proceeded for 16 h at 37 °C. At the end of the digestion, the reaction was stopped, and the surfactant was inactivated and precipitated by the addition of trifluoroacetic acid to a final volume of 0.5% (v/v). After incubating for 45 min at 37 °C, the samples were clarified by centrifugation at 13,000 × g for 15 min. The collected urine samples were diluted so that ∼10 μg of protein was loaded onto a 15% one-dimensional SDS-PAGE gel and separated over 1 h. Small plugs of gel were excised from the protein band known to represent MUPs in mouse urine. The gel plugs were destained in 50% ACN, subjected to reduction and alkylation, and then dehydrated with ACN before the addition of 10 μl of 25 mm ammonium bicarbonate, containing 0.01 μg/μl trypsin. After overnight incubation at 37 °C, the resulting peptides extracted from the gel plugs were analyzed by MALDI-TOF MS on an Axima TOF2 (Shimadzu). Valine-containing peptides were identified from known MUP sequences, confirmed by the presence of the correct labeling at the later time points (data not shown). The primary use of the MUP data was to assess the precursor pool labeling trajectory, for which divaline peptides are required. Peak intensities of the labeled (HL/LH and HH) valine peaks were recorded. Where HL/LH refers to a divaline peptide with only one labeled amino acid incorporated and HH defines a divaline peptide where both incorporated valine residues are labeled (see later). For discovery proteomics and for isotope incorporation analysis, virtually all digests were analyzed on a Thermo LTQ-Orbitrap Velos system, the exception being the MUPs, for which isotope incorporation data were obtained by MALDI-TOF analysis of tryptic digests of urinary proteins (MUPs comprise over 99% of mouse urinary protein in the healthy individual). For each digested sample analyzed by LC-MS, 500 ng of protein was injected onto a 75-μm × 150-mm BEH C18 column, and the peptides were resolved over a 90-min linear organic gradient of 3–40% buffer B (0.1% formic acid in acetonitrile). Data acquisition was data-dependent, with the top 20 most intense peptides in each MS scan selected for fragmentation. The raw data collected were processed using default parameters (s/n threshold = 3, minimum peak count = 6) in Proteome Discoverer (Version 1.3, Thermo) and searched against the UniProt Mus database using Mascot (v 2.3.01). The database searched contained 16,367 reviewed Mus (restricted to taxonomy) protein entries from the UniProt database (July 2010). To this, a small number of UniProt unreviewed protein sequences were added that correspond to seminal vesicle proteins previously validated (17Ramm S.A. McDonald L. Hurst J.L. Beynon R.J. Stockley P. Comparative proteomics reveals evidence for evolutionary diversification of rodent seminal fluid and its functional significance in sperm competition.Mol. Biol. Evol. 2009; 26: 189-198Crossref PubMed Scopus (91) Google Scholar). A peptide tolerance of 10 ppm was set, with an MS/MS tolerance of 0.5 Da and modifications of fixed cysteinyl carbamidomethylation and variable oxidation of methionine. Trypsin was the specified enzyme, allowing for one miscleavage and ions scores greater than 18 were accepted for MS/MS identifications (p < 0.05) as suggested by Mascot. For label-free quantification on the Synapt G1 Q-TOF instrument, a predigested protein standard (rabbit muscle glycogen phosphorylase, UniProt Accession number P00489) was added to the tryptic peptide preparation, such that the final on-column load was 500 ng of digested protein and 50 fmol of protein standard. This permitted label-free quantification of the protein loading using "Hi3," where the intensities of the three most intense unique peptides per protein are compared with those of the protein standard (28Silva J.C. Gorenstein M.V. Li G.Z. Vissers J.P. Geromanos S.J. Absolute quantification of proteins by lcmse: A virtue of parallel ms acquisition.Mol. Cell. Proteomics. 2006; 5: 144-156Abstract Full Text Full Text PDF PubMed Scopus (1144) Google Scholar). The peptides were resolved using a nanoAcquity LC system as above. The data were collected with the mass spectrometer in V-mode via MSE (data-independent acquisition) (29Geromanos S.J. Vissers J.P. Silva J.C. Dorschel C.A. Li G.Z. Gorenstein M.V. Bateman R.H. Langridge J.I. The detection, correlation, and comparison of peptide precursor and product ions from data independent lc-ms with data dependant lc-ms/ms.Proteomics. 2009; 9: 1683-1695Crossref PubMed Scopus (396) Google Scholar), using alternating 1.5 s of low energy and elevated energy scans, with a 0.1-s interscan delay. LC-MS/MS data were processed using ProteinLynx Global Server (version 2.4) software and searched against the same UniProt Mus database. To reduce the protein lists to high confidence data sets, consensus scoring was implemented across the 10 independent biological samples using the Orbitrap data. The high confidence proteins were then examined specifically for recovery of the isotope labeling patterns. For analysis of proteome dynamics, valine-containing peptides derived exclusively from each protein were selected, and the mass spectrum of each peptide, in [2H8]valine labeled and unlabeled form, was isolated and quantified by relative peak intensity. The [2H8]valine is partially transaminated to [2H7]valine (see below); thus, the labeled peptides attributable to the [2H8]valine and [2H7]valine variants were summed to yield the total pool of labeled protein attributable to synthesis de novo. The intensities of the labeled and unlabeled forms were then used to calculate the relative isotope abundance (RIA) for each protein (30Doherty M.K. Whitehead C. McCormack H. Gaskell S.J. Beynon R.J. Proteome dynamics in complex organisms: Using stable isotopes to monitor individual protein turnover rates.Proteomics. 2005; 5: 522-533Crossref PubMed Scopus (135) Google Scholar). In most instances, multiple monovaline peptides were used to build the abundance statistic. As the rate of replacement of each protein was tracked over 35 days, using samples from two mice at each of the five time points, there was a good degree of both biological and technical replication. To calculate the extent of transamination of the labeled valine, the intensities of the overlapping isotope profiles for the mixture of [2H8]valine and [2H7]valine were acquired between M0 (monoisotopic) and M6 (+6 Da). The isotope profile of the peptide was obtained from the MS-Isotope tool (http://prospector.ucsf.edu/prospector/cgi-bin/msform.cgi?form=msisotope) using the amino acid sequence. The optimal combination of the [2H8] and [2H7] profiles was fitted to the experimental data using the nonlinear optimizer within Microsoft Excel (Solver) using the sum of the squares of the residuals between the theoretical and experimental data points as the object function. The only parameter in the optimization was the fraction of [2H7] valine generated by transamination. Optimization was repeated from different starting values of the fractional value, and in all instances, the optimization converged on the same value. A spreadsheet demonstrating the calculation of transamination extent is provided as supplemental File 1. For the derivation of the turnover parameters, the (RIA, t) data were used in the nonlinear curve fitting package pro Fit (version 6.3; www.quantsoft.com) where the labeling trajectories were specified user functions defined in Python. For the simple rise to plateau, the two parameters were the final RIA (Af) and the first order rate constant for pool turnover (k). For the more complex behavior exhibited by some of the sperm proteins, the two parameters were the first order rate constant for pool turnover (k) and a term defining the delay before label could appear in the pool (delay). This modification and the conditional statement in the function had the effect of invoking a delay before appearance of the label in the protein. For this analysis, and to avoid over-parameterization of the analysis, we assumed a final RIA value of 0.5, commensurate with the diet design and experimental observations. View Large Image Figure ViewerDownload Hi-res image Download (PPT) To define the subsets of proteins to be examined in this study, seminal vesicle proteins and sperm proteins were analyzed in a discovery proteomics workflow, although the generation of exhaustive lists of protein identifications was not the intention of this study. In brief, seminal vesicle or sperm samples were analyzed by LC-MS/MS, without any prefractionation, to identify those proteins that were derived from each pool. Those proteins identified by peptides with sufficiently high quality signals to monitor the incorporation of labeled valine were chosen to assess protein dynamics in these samples (see below). We used two mass spectrometric platforms for the discovery phase of the work, an LTQ-Orbitrap Velos and a Synapt G1 Q-TOF. Initial analyses were conducted in a data-dependent acquisition mode (Orbitrap), but for quantitative analyses, we used a data-independent acquisition (MSE on a Synapt Q-TOF instrument). Absolute label-free quantification was achieved using the Hi3 strategy, in which the intensities of the three most abundant peptides are summed and compared with the peptides from a known amount of a standard protein from a heterologous species: in this study, rabbit muscle glycogen phosphorylase (28Silva J.C. Gorenstein M.V. Li G.Z. Vissers J.P. Geromanos S.J. Absolute quantification of proteins by lcmse: A virtue of parallel ms acquisition.Mol. Cell. Proteomics. 2006; 5: 144-156Abstract Full Text Full Text PDF PubMed Scopus (1144) Google Scholar, 31Silva J.C. Denny R. Dorschel C.A. Gorenstein M. Kass I.J. Li G.Z. McKenna T. Nold M.J. Richardson K. Young P. Geromanos S. Quantitative proteomic analysis by accurate mass retention time pairs.Anal. Chem. 2005; 77: 2187-2200Crossref PubMed Scopus (521) Google Scholar). The requirement for at least three high quality peptides for identification and quantification is stringent. The high resolution data obtained on the Orbitrap Velos instrument were used to derive further identifications and as a source of high resolution mass spectral data for label incorporation studies. From the proteome analysis of the two tissue preparations, it was evident that the samples were very different in the profile and abundances of the protein complement (Fig. 1). SDS-PAGE analysis of the seminal vesicle samples yielded a simple protein pattern, comprising no more than 10 visible protein bands, contrasting strongly with the sperm samples that exhibited a complex pattern of bands (Fig. 1b). In the preliminary profiling analyses, greater proteome depth was obtained with the LTQ-Orbitrap Velos instrument. A total of 887 proteins were tentatively identified: 148 in the seminal vesicle preparations and 739 in the sperm samples, although many of these proteins were present at low abundance as evidenced by the low exponentially modified protein abundance index, a form of spectral counting (32Ishihama Y. Oda Y. Tabata T. Sato T. Nagasu T. Rappsilber J. Mann M. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein.Mol. Cell. Proteomics. 2005; 4: 1265-1272Abstract Ful
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