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Analysis of the Zebrafish Proteome during Embryonic Development*

  • Margaret B. Lucitt
    Affiliations
    Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104

    Department of Pharmacology, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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  • Thomas S. Price
    Affiliations
    Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104

    Department of Pharmacology, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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  • Angel Pizarro
    Affiliations
    Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104

    Department of Pharmacology, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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  • Weichen Wu
    Affiliations
    Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104

    Department of Pharmacology, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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  • Anastasia K. Yocum
    Affiliations
    Department of Pharmacology, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104

    Centers for Cancer Pharmacology and Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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  • Christoph Seiler
    Affiliations
    Department of Medicine, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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  • Michael A. Pack
    Affiliations
    Department of Medicine, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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  • Ian A. Blair
    Footnotes
    Affiliations
    Department of Pharmacology, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104

    Centers for Cancer Pharmacology and Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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  • Garret A. FitzGerald
    Correspondence
    The McNeil Professor in Translational Medicine and Therapeutics. To whom correspondence may be addressed: Inst. for Translational Medicine and Therapeutics, University of Pennsylvania, 153 Johnson Pavilion, 3620 Hamilton Walk, Philadelphia, PA 19104-6084. Tel.: 215-898-1184
    Affiliations
    Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104

    Department of Pharmacology, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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  • Tilo Grosser
    Correspondence
    To whom correspondence may be addressed: Inst. for Translational Medicine and Therapeutics, University of Pennsylvania, 153 Johnson Pavilion, 3620 Hamilton Walk, Philadelphia, PA 19104-6084. Tel.: 215-898-1184
    Affiliations
    Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104

    Department of Pharmacology, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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  • Author Footnotes
    * This work was supported, in whole or in part, by National Institutes of Health Grant HL 62250 (to G. A. F.). This work was also supported by the American Heart Association (National Scientist Development Grant 0430148N to T. G.) and the Higher Education Authority of Ireland (to M. B. L.). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
    The on-line version of this article (available at http://www.mcponline.org) contains supplemental material.
    ** The A. N. Richards Professor of Pharmacology.
      The model organism zebrafish (Danio rerio) is particularly amenable to studies deciphering regulatory genetic networks in vertebrate development, biology, and pharmacology. Unraveling the functional dynamics of such networks requires precise quantitation of protein expression during organismal growth, which is incrementally challenging with progressive complexity of the systems. In an approach toward such quantitative studies of dynamic network behavior, we applied mass spectrometric methodology and rigorous statistical analysis to create comprehensive, high quality profiles of proteins expressed at two stages of zebrafish development. Proteins of embryos 72 and 120 h postfertilization (hpf) were isolated and analyzed both by two-dimensional (2D) LC followed by ESI-MS/MS and by 2D PAGE followed by MALDI-TOF/TOF protein identification. We detected 1384 proteins from 327,906 peptide sequence identifications at 72 and 120 hpf with false identification rates of less than 1% using 2D LC-ESI-MS/MS. These included only ∼30% of proteins that were identified by 2D PAGE-MALDI-TOF/TOF. Roughly 10% of all detected proteins were derived from hypothetical or predicted gene models or were entirely unannotated. Comparison of proteins expression by 2D DIGE revealed that proteins involved in energy production and transcription/translation were relatively more abundant at 72 hpf consistent with faster synthesis of cellular proteins during organismal growth at this time compared with 120 hpf. The data are accessible in a database that links protein identifications to existing resources including the Zebrafish Information Network database. This new resource should facilitate the selection of candidate proteins for targeted quantitation and refine systematic genetic network analysis in vertebrate development and biology.
      Embryonic development is governed by highly coordinated changes in the expression of large protein sets. Resolving the programs controlling these changes at the molecular level can provide important insights into the principles of human biology and disease. The zebrafish (Danio rerio) is an attractive vertebrate model organism for studies into the molecular mechanisms of development (
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      ). The data are accessible in a fully searchable database (see supplemental data for the URL database link under Instructions for Downloading) that links protein identifications to existing resources including the Zebrafish Information Network (ZFIN)
      The abbreviations used are: ZFIN, Zebrafish Information Network; EBP, Empirical Bayes Protein Identifier; GO, Gene Ontology; hpf, hours postfertilization; HSP, heat shock protein; IPI, International Protein Index; 2D, two-dimensional; PMF, peptide mass fingerprint; ACTH, adrenocorticotropic hormone; BLAST, Basic Local Alignment Search Tool.
      1The abbreviations used are: ZFIN, Zebrafish Information Network; EBP, Empirical Bayes Protein Identifier; GO, Gene Ontology; hpf, hours postfertilization; HSP, heat shock protein; IPI, International Protein Index; 2D, two-dimensional; PMF, peptide mass fingerprint; ACTH, adrenocorticotropic hormone; BLAST, Basic Local Alignment Search Tool.
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      EXPERIMENTAL PROCEDURES

       Sample Preparation—

      Methods for breeding and raising zebrafish embryos were followed as described previously (
      • Westerfield M.
      ). Embryos were obtained from natural matings of wild-type tail long fin fish. Embryos from four independent matings were collected 72 and 120 h postfertilization (hpf), snap frozen in liquid nitrogen, and stored at −80 °C. Sample preparation, protein digestion, and chromatographic separation were performed as described previously (
      • Price T.S.
      • Lucitt M.B.
      • Wu W.
      • Austin D.J.
      • Pizarro A.
      • Yocum A.K.
      • Blair I.A.
      • FitzGerald G.A.
      • Grosser T.
      EBP, a program for protein identification using multiple tandem mass spectrometry datasets.
      ). Briefly, five tubes of 20–30 embryos were lysed (7 m urea, 2 m thiourea, 4% CHAPS (all Amersham Biosciences), 100 mm DTT (Bio-Rad), Phosphatase Inhibitor Mixture 11 (Sigma), Complete protease inhibitor (Roche Applied Science)) and homogenized (TissueLyser, Qiagen; 2 × 3 min, 30 Hz), and proteins were precipitated and resuspended in 0.2% (w/v) RapiGest™ SF (Waters Co., Milford, MA) in 50 mm ammonium bicarbonate (Sigma). Samples containing 2.0 mg of protein were reduced (5 mm DTT, Bio-Rad), alkylated (5 mm iodoacetamide; Bio-Rad), and trypsin-proteolyzed using a 1:20 (w/w) enzyme-to-protein ratio (Promega, Madison, WI) at 37 °C for 16 h. Prior to strong cation exchange chromatography the pH was lowered to 3.

       Two-dimensional Chromatography and Mass Spectrometry—

      Off-line fractionation and LC-MS/MS were performed as described previously (
      • Price T.S.
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      • Austin D.J.
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      • Blair I.A.
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      • Grosser T.
      EBP, a program for protein identification using multiple tandem mass spectrometry datasets.
      ). The sample was loaded onto a PolySulfoethyl A column (100 mm × 4.6 mm, 5 μm, 300 Å; PolyLC, Columbia, MD) at a flow rate of 0.2 ml/min with mobile phase A (10 mm ammonium formate, 25% acetonitrile, pH 3). A linear gradient for 80 min was run to 100% mobile phase B (500 mm ammonium formate, 25% acetonitrile, pH 6.8). Sixty 1.6-min fractions were collected with a Foxy Jr. (Dionex, Sunnyvale, CA) automated fraction collector. Fractions with low peptide concentration were combined to yield a total of 40 fractions, which were lyophilized and stored at −80 °C. Lyophilized peptides were reconstituted with 0.1% formic acid, 5% acetonitrile for reversed phase liquid chromatography onto a Thermo Finnigan liquid ion trap mass spectrometer (Thermo Finnigan, San Jose, CA) using electron spray ionization. Each fraction was injected onto a Vydac C18 column (Everest 150 mm × 1 mm inner diameter, 300 Å, 5 μm; Bodmann, Aston, PA) with mobile phase A (0.1% formic acid, 0.01% trichloroacetic acid in water). A gradient (30 μl/min) was run over 180 min from 3 to 70% mobile phase B (0.1% formic acid, 0.01% trichloroacetic acid in acetonitrile). Nitrogen was used as the sheath (75 p.s.i.) and auxiliary (10 units) gas with the heated capillary at 180 °C. The mass spectrometer was operated in a data-dependent MS/MS mode (m/z 300–2000) in which the top seven ions were subjected to fragmentation at 27% normalized CID energy. Dynamic mass exclusion was enabled with a repeat count of 2 every 45 s for a list size of 250.

       Analysis of LC-MS/MS Data—

      The data sets of each sample were searched against the International Protein Index (IPI) D. rerio protein sequence database (version 3.07; number of protein sequences, 45,388; number of amino acid residues, 23,104,717) for peptide sequences using two independent algorithms, SEQUEST 3.1 (ThermoFinnigan, San Jose, CA) and MASCOT 2.1.04 (Matrix Sciences, Boston, MA). Raw mass spectra were converted to DTA peak lists using BioWorks Browser 3.2 (ThermoFinnigan) with the following parameter settings: peptide mass range, 300–5000 Da; threshold, 10; precursor mass, ±1.4 Da; group scan, 1; minimum group count, 1; minimum ion count, 15. Searches specified that peptides should have a maximum of two internal tryptic cleavage sites with methionine oxidation and cysteine carbamidomethylation as possible modifications. SEQUEST searches specified that peptides should possess at least one tryptic terminus and used a peptide mass tolerance of ±1.4 Da and a fragment ion tolerance of 0. MASCOT searches specified tryptic digestion and used a peptide mass tolerance of ±1.5 Da and a fragment ion tolerance of ±0.1 Da. The search results were converted into pepXML format. Peptide identification probabilities for both SEQUEST and MASCOT searches were calculated by executing PeptideProphet as implemented in the Trans-Proteomic Pipeline version 2.8 (Institute for Systems Biology, Seattle, WA) (
      • Keller A.
      • Nesvizhskii A.I.
      • Kolker E.
      • Aebersold R.
      Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.
      ). SEQUEST results were processed using the “-Ol” tag, which uses ΔCn* values unchanged.
      Results from both searches (SEQUEST and MASCOT) and all biological replicates were combined in a single statistical analysis of protein expression per developmental stage using Empirical Bayes Protein Identifier (EBP) 1.0 as described previously (
      • Price T.S.
      • Lucitt M.B.
      • Wu W.
      • Austin D.J.
      • Pizarro A.
      • Yocum A.K.
      • Blair I.A.
      • FitzGerald G.A.
      • Grosser T.
      EBP, a program for protein identification using multiple tandem mass spectrometry datasets.
      ). Briefly EBP estimates both sensitivity and false identification rate and has been validated empirically for analysis of zebrafish liquid ion trap mass spectrometry data using a reversed/forward sequence database search approach (
      • Price T.S.
      • Lucitt M.B.
      • Wu W.
      • Austin D.J.
      • Pizarro A.
      • Yocum A.K.
      • Blair I.A.
      • FitzGerald G.A.
      • Grosser T.
      EBP, a program for protein identification using multiple tandem mass spectrometry datasets.
      ). EBP combines the probabilities of correct peptide identification across multiple peptide searches using a function that returns the maximum probability from consensus identifications and penalizes non-consensual identifications. Different charge states of the same peptide are treated as a single identification. The statistical model parameters include protein length, estimated protein abundance, the size of the search database, and the number of peptide sequence identifications in the data set. For each protein in the database, an expression probability is estimated using an “expectation-maximization” algorithm. Replicates are integrated by simultaneously estimating multiple sets of model parameters. Peptides whose sequence matches multiple proteins are integrated in the analysis using “Occam's razor,” a principle by which the smallest set of probable proteins is chosen that is sufficient to explain the peptide sequence identifications. When proteins cannot be reliably distinguished by unique peptides, they are reported as a protein group. EBP analyses were run on combined SEQUEST and MASCOT results and biological replicates for each developmental stage using its default settings except for the calculation of the number of trypsin digests per protein, which specified peptides with at least one tryptic terminus. The default settings specify that only peptide identifications with probabilities greater than 0.5 are used in the calculation of protein identification probabilities. This is equivalent to using only those spectra for which SEQUEST and MASCOT reached consensus for the most likely peptide identification. Only proteins with expression probabilities corresponding to a false identification rate of less than 0.01 (1%) were reported. This was equivalent to expression probabilities of p > 0.77 and p > 0.87 in the given data sets, 72 and 120 hpf (Fig. 1). Spectra of protein identifications that met these criteria but were based on a single unique peptide were manually inspected. Thirty-eight such protein identifications were excluded after inspection. All single unique peptide identifications that remained in the data set are summarized in supplemental Table 3 as specified by the MCP data submission guidelines. Graphical representations of the corresponding annotated MS/MS spectra were extracted from the Trans-Proteomic Pipeline (Institute for Systems Biology, Seattle, WA) and the EBP plug-in using the Ruby 1.8.5 scripting language. Thus, result files were parsed for the peptide sequence with the highest PeptideProphet probability score for each charge state. Up to three such spectra were extracted if multiple spectra shared the highest probability value. HTML pages were created to display the resulting spectra images and hyperlinks included in supplemental Table 3 that open these pages. These pages and the table are included in the compressed (zipped) file (Lucitt_Supplemental_Table_3.zip) that installs the table and correct subfolder structure for viewing the linked MS/MS spectra when unpacked.
      Figure thumbnail gr1
      Fig. 1Estimated sensitivity and error of protein identification by 2D LC-MS/MS.a, integrated analysis of peptide sequence information obtained by MASCOT and SEQUEST searches of three replicate 72-hpf samples resulted in the identification of 1112 unique proteins with an estimated (est.) rate of less than 1% incorrectly identified proteins at an expression probability of p > 0.77 (dashed blue line). b, the analysis of the 120-hpf samples resulted in 867 unique protein identifications with an estimated error rate of less than 1% at an expression probability of p > 0.87 (dashed blue line).

       Two-dimensional Gel Electrophoresis—

      Two biological replicate samples of each developmental stage were analyzed. Proteins were resuspended in 30 mm Tris, pH 8.5, 7 m urea, 2 m thiourea, 4% CHAPS (GE Healthcare) for differential display using the DIGE technology (GE Healthcare) and in 7 m urea, 2 m thiourea, 4% CHAPS, 40 mm DTT for preparative gels. Fifty micrograms of protein from 72- and 120-hpf embryos were labeled with 400 pmol of Cy3 and Cy5 DIGE fluorophores (GE Healthcare), respectively. Reverse labeling was used to normalize for label differences. An internal control containing 25 μg of protein from both 72- and 120-hpf embryos was labeled with 400 pmol of Cy2 and included in each analytical gel. Samples were rehydrated into 24-cm, pH range 3–10, non-linear IPG strips (GE Healthcare) for 14 h at 30 V. A step and hold isoelectric focusing was completed as follows: 500 V for 500 Vhrs, 1000 V for 1000 Vhrs, 3000 V for 6000 Vhrs, 5000 V for 10,000 Vhrs, and 8000 V for 70,000 Vhrs. The cysteine sulfhydryl groups were reduced and carbamidomethylated (50 mm DTT for 15 min, 240 mm iodoacetamide for 15 min room temperature). IPG strips were then placed on top of an 8–16% precast polyacrylamide gel (Jule, Inc., Milford, CT). SDS-PAGE (Ethan DALT Twelve, GE Healthcare) was carried out at 2 watts/gel for 30 min followed by 4 watts/gel until the bromphenol dye front ran off the gel. Preparative gels contained 500 μg of unlabeled protein for identification of differentially regulated protein spots. These samples were dialyzed against 7 m urea, 2 m thiourea for 2 h prior to tow-dimensional (2D) PAGE, and gels were stained with Novex Colloidal Blue stain (Invitrogen). DIGE gels were imaged (Typhoon 9410, GE Healthcare) using excitation/emission wavelengths of 488/520 nm for Cy2, 532/580 nm for Cy3, and 633/670 nm for Cy5. The Colloidal Blue-stained gels were imaged using a wavelength of 633 nm.

       Image Analysis—

      Images were analyzed with Progenesis PG200 software (Nonlinear Dynamics, Newcastle, UK) according to the manufacturer's instructions applying a “cross-stain analysis” on the DIGE gels. Thus, two multiplex groups (groups of images derived from the same gel) were defined as follows: multiplex group one: gel 1, 72-hpf Cy3, 120-hpf Cy5, and internal control Cy2; multiplex group two: gel 2, 72-hpf Cy5, 120-hpf Cy3, and internal control Cy2). Three replicate groups were defined as replicate 1 (72-hpf Cy3 from gel 1 and 72-hpf Cy5 from gel 2), replicate 2 (120-hpf Cy5 from gel 1 and 120-hpf Cy3 from gel 2), and replicate 3 (internal standard labeled with Cy2 from gels 1 and 2). A reference gel was automatically selected by the software using the default settings and is based on an internal standard image. The maximum number of gels in which a spot was allowed absent within the replicate group parameters was selected to be 0. Spot detection, background subtraction, warping, matching, and normalization were all set at the default settings of the software. Where possible, unmatched spots were edited on each multiplex group based on a three-dimensional view of the spot, afterward normalization was restored, and the reference gels were updated. Differences in average normalized volume between 72 and 120 hpf of 3-fold or more were considered for protein identification.

       Protein Identification from 2D Gels—

      A total of 164 spots considered differentially regulated based on the above criteria and 379 spots that were considered not to be differentially regulated were excised for identification by MALDI-TOF/TOF. Spots of interest were robotically excised into 96-well plates using an Ettan Spot Picker (GE Healthcare). Gel plugs were washed with 100 μl of Milli-Q water for 15 min and three times with 100 μl of 25 mm ammonium bicarbonate, 50% ACN for 30 min while Vortex mixing. Plugs were then dehydrated in 100% ACN for 10 min and allowed to air dry. This was followed by reduction with 10 mm DTT in 50 mm ammonium bicarbonate at 60 °C for 30 min followed by alkylation with 100 mm iodoacetamide in 50 mm ammonium bicarbonate for 45 min at room temperature in the dark. Wash steps as mentioned above were repeated, and gel plugs were dehydrated with 100% ACN. Twenty micrograms of sequencing grade modified trypsin (Promega) was solubilized in 40 mm ammonium bicarbonate, 5% ACN to a concentration of 20 ng/μl. Ten microliters of the trypsin solution was added to each plug and allowed to rehydrate the gel plugs on ice for 30 min and then incubated at 37 °C overnight. Digestion buffer was removed to a new 96-well plate, and 50 μl of 1% TFA in 50% ACN was added to the gel plugs and sonicated for 30 min. This extract was removed and combined with the digestion buffer and dried in a SpeedVac concentrator (Jouan, RC1022, Thermo Savant, Milford, MA) for 45 min. Peptides were then resuspended in 15 μl of 0.5% TFA in Milli-Q water. Peptides were solid phase-extracted (Millipore reverse phase ZipTipC18) according to the manufacturer's instructions. Samples were eluted into a 96-well plate with 4 μl of a 0.1% TFA, 50% ACN solution.
      One microliter of the eluate was premixed with 2 μl of α-cyano-4-hydroxycinnamic acid matrix (3 mg/ml in 10 mm ammonium phosphate, 50% acetonitrile, 0.1% TFA) and spotted in duplicate on a MALDI target plate (Opti-TOF® 192-well insert, Applied Biosystems, Foster City, CA). MALDI-TOF MS and tandem TOF/TOF MS were performed on a Voyager 4700 instrument (Applied Biosystems). Thus, two peptide mass fingerprint (PMF) spectra per gel spot were generated from separate MALDI plate wells. The spectra were acquired in the reflector mode by averaging 3000 laser shots per spectrum (mass range, 800–4000 Da; focus mass, 2000 Da). Spectra were smoothed (Gaussian filter width, 9; target resolution at 1300 m/z, 20,000) for internal calibration to trypsin autolytic peptides (m/z 842.510, 1045.564, 1940.935, 2211.105, 2239.136, 2299.179, and 2807.300) and only peaks that exceeded a signal-to-noise ratio of 100 (local noise window, 200 m/z) and a half-maximal width of 2.9 bins were considered. A minimum of two monoisotopic trypsin peaks were required to calibrate each spectrum to a mass accuracy within 20 ppm. Failure to meet these criteria resulted in the application of the external plate calibration that was performed prior to each run and required matching of six standard peptide ion masses (m/z 904.468, 1296.685, 1570.677, 2093.087, 2465.199, and 3657.929) from six calibration spots (4700 Mass Standard kit, catalog number 4333604, Applied Biosystems). The laser power for PMF acquisition was adjusted to produce an average intensity of ∼7000 for the m/z 2093.087 standard ion (ACTH-(1–17)) across the six calibration spots prior to each run. Data-dependent MS/MS analyses, using PSD on one replicate PMF spectra set and CID on the other replicate, was performed on the 15 most abundant peptide ions (excluding trypsin autolysis ions) to generate amino acid sequence information. MS/MS spectra were integrated over 3000 laser shots in the 1-kV positive ion mode with the metastable suppressor turned on. Air at the medium gas pressure setting (1.25 × 10−6 torr) was used as the collision gas in the CID on mode. An internal calibration of MS/MS spectra was attempted on at least two ions of the immonium ion series and the y1 ions of arginine, lysine, and histidine (m/z: Arg immonium, 70.066, 87.081, 100.088, and 112.088; Arg y1, 175.119; Lys immonium, 84.081, 101.108, and 129.103; Lys y1, 147.113; His immonium, 110.072 and 138.067) or reverted to the external calibration, which was performed prior to each PSD or CID run on four fragmentation ions of Glu1-fibrinopeptide B (m/z: precursor, 1570.677; y1, 175.120; y4, 480.257; y6, 684.347; y9, 1056.475). The laser intensity for the MS/MS spectra acquisition was adjusted to an intensity of ∼4000 of the y9 ion (m/z 1056.475) prior to each run.
      The Global Proteome Server (GPS) Explorer 3.5 build 321 software (Applied Biosystems) was used to extract peaks from raw spectra using the following settings: MS peak filtering: mass range, 800–4000 Da; minimum signal-to-noise ratio, 10; peak density filter, 50 peaks/200 Da; maximum number of peaks, 65; MS/MS peak filtering: mass range, 60–20 Da below precursor mass; minimum signal-to-noise ratio, 10; peak density filter, 50 peaks/200 Da; maximum number of peaks, 65. A combined MS peptide fingerprint and MS/MS peptide sequencing search was performed against the IPI D. rerio version 3.07 database (number of protein sequences, 45,388; number of amino acid residues, 23,104,717) using the MASCOT 2.1.04 search algorithm. These searches specified trypsin as the digestion enzyme and allowed for carbamidomethylation of cysteine, partial oxidation of methionine residues (all variable modifications), and one missed trypsin cleavage. The monoisotopic precursor ion tolerance was set to 50 ppm, and the MS/MS ion tolerance was set to 0.05 Da. The output was limited to the 10 best hits. MS/MS peptide spectra with a minimum ion score confidence interval ≥95% were accepted; this was equivalent to a median ion score cutoff of ∼27 in the data set. Protein identifications were accepted with a statistically significant MASCOT protein search score ≥65 that corresponded to an error probability of p < 0.01 in our data set. All possible protein identifications from replicate analyses that met the above criteria were reported for each gel spot. However, the protein identification with the highest score was selected in the case of redundant protein identifications.
      The raw mass spectra were exported to mzXML using the PzMsXML script (Nathan Edwards, University of Maryland Center for Bioinformatics and Computational Biology, College Park, MD). Annotated PMF spectra were produced by combining the spectra file formats for raw and processed peaks, mzXML and Mascot generic format, respectively. Peak annotations and modification information for identified peptides was extracted from the result summary table (supplemental Table 4). The Ruby scripting language was used to parse these files, send the spectra and annotation information to the R statistical tool (The R Project for Statistical Computing) for plotting, and creation of the HTML result pages. Annotated spectra for the tandem mass spectrometry experiments were obtained by transforming the dynamic MASCOT Web pages into static content using Ruby and saved locally to the drive. Hyperlinks to both PMF and MS/MS pages are included in supplemental Table 4. These pages and the table are included in a compressed (zipped) file (Lucitt_Supplemental_Table_4.zip) that installs the table and correct subfolder structure for viewing the linked spectra when unpacked.

       Protein Classification—

      Proteins were classified using the Gene Ontology (GO) functional annotations for cellular component, molecular function, and biological process (
      • Ashburner M.
      • Ball C.A.
      • Blake J.A.
      • Botstein D.
      • Butler H.
      • Cherry J.M.
      • Davis A.P.
      • Dolinski K.
      • Dwight S.S.
      • Eppig J.T.
      • Harris M.A.
      • Hill D.P.
      • Issel-Tarver L.
      • Kasarskis A.
      • Lewis S.
      • Matese J.C.
      • Richardson J.E.
      • Ringwald M.
      • Rubin G.M.
      • Sherlock G.
      Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.
      ). Annotation categories were taken from level three in the GO trees. GO enrichment analysis was conducted by calculating for each category the probability that the number of annotations in the protein list could have arisen by chance, assuming an underlying hypergeometric distribution (
      • Draghici S.
      • Khatri P.
      • Martins R.P.
      • Ostermeier G.C.
      • Krawetz S.A.
      Global functional profiling of gene expression.
      ). Pathway analysis (Ingenuity Systems, Redwood City, CA) was used to search for enrichment of proteins in canonical and metabolic signaling pathways. IPI protein sequences were BLAST searched against the RefSeq human and mouse protein sequence databases, and BLAST results were used for mapping in the Ingenuity Systems pathway database.

       Zebrafish Proteomics Database—

      A database was constructed parsing IPI records, GenBank™ records, the NCBI taxonomy, and GO ontology into a BioSQL relational database schema. The schema was extended to include the experimental result data and key word searching capabilities as well as optimized for the Web application. The web site itself was constructed using the Ruby on Rails web application framework. The zebrafish proteomics database can be accessed on line (see supplemental data for the URL database link under Instructions for Downloading).

      RESULTS

       Zebrafish Embryonic Protein Expression Profiles—

      We performed 2D LC-MS/MS experiments to generate an accurate repository of proteins reliably detectable in zebrafish embryos at 72 and 120 hpf. Replicate samples for each embryonic developmental stage were analyzed. The acquired raw mass spectra data sets for each replicate were matched to peptide sequences in the IPI D. rerio database using the search engines MASCOT and SEQUEST. This generated 327,906 possible peptide identifications. Peptide sequence identifications generated from both search algorithms for each replicate were combined in an integrated analysis using the EBP algorithm for protein assignment to augment sensitivity and error control (
      • Price T.S.
      • Lucitt M.B.
      • Wu W.
      • Austin D.J.
      • Pizarro A.
      • Yocum A.K.
      • Blair I.A.
      • FitzGerald G.A.
      • Grosser T.
      EBP, a program for protein identification using multiple tandem mass spectrometry datasets.
      ).
      This approach identified 1112 unique proteins at 72 hpf and 867 unique proteins at 120 hpf with false identification rates of less then 1% and sensitivities of 91.7 and 88.2% (Fig. 1 and supplemental Tables 1, 2, and 3). An additional 45 proteins at 72 hpf and 31 proteins at 120 hpf were as likely to be expressed but were indistinguishable from homologous proteins based on the peptide evidence. Eighty-six percent of the identified proteins at 72 hpf and 82% at 120 hpf were based on gene models derived from transcript or protein sequences specific for the zebrafish genome (Ensembl Assembly Zv7, April 2007 (
      • Flicek P.
      • Aken B.L.
      • Beal K.
      • Ballester B.
      • Caccamo M.
      • Chen Y.
      • Clarke L.
      • Coates G.
      • Cunningham F.
      • Cutts T.
      • Down T.
      • Dyer S.C.
      • Eyre T.
      • Fitzgerald S.
      • Fernandez-Banet J.
      • Graf S.
      • Haider S.
      • Hammond M.
      • Holland R.
      • Howe K.L.
      • Howe K.
      • Johnson N.
      • Jenkinson A.
      • Kahari A.
      • Keefe D.
      • Kokocinski F.
      • Kulesha E.
      • Lawson D.
      • Longden I.
      • Megy K.
      • Meidl P.
      • Overduin B.
      • Parker A.
      • Pritchard B.
      • Prlic A.
      • Rice S.
      • Rios D.
      • Schuster M.
      • Sealy I.
      • Slater G.
      • Smedley D.
      • Spudich G.
      • Trevanion S.
      • Vilella A.J.
      • Vogel J.
      • White S.
      • Wood M.
      • Birney E.
      • Cox T.
      • Curwen V.
      • Durbin R.
      • Fernandez-Suarez X.M.
      • Herrero J.
      • Hubbard T.J.
      • Kasprzyk A.
      • Proctor G.
      • Smith J.
      • Ureta-Vidal A.
      • Searle S.
      Ensembl 2008.
      )). Hypothetical proteins or proteins predicted by comparison with other genomes constituted 13% of the detected proteins at 72 hpf and 17% at 120 hpf.
      The separation of proteins at the peptide level by 2D LC-MS/MS may preclude discrimination of homologous proteins, such as distinct isoforms or modified forms of a protein. Gel-based proteomics techniques allow more readily the distinction of similar proteins based on their migration pattern in the electrical field. Thus, we ran protein samples from both developmental stages on 2D gels. In total, 348 unique proteins at 72 hpf and 317 unique proteins at 120 hpf were identified from 2D gels using MALDI-TOF/TOF tandem mass spectrometry with an error probability of less than 0.01 (Fig. 2 and supplemental Table 4). Approximately 85% of the detected proteins were annotated at the highest level of quality, and 15% were hypothetical or predicted proteins with similarity to sequences of other species (Ensembl Assembly Zv7, April 2007 (
      • Flicek P.
      • Aken B.L.
      • Beal K.
      • Ballester B.
      • Caccamo M.
      • Chen Y.
      • Clarke L.
      • Coates G.
      • Cunningham F.
      • Cutts T.
      • Down T.
      • Dyer S.C.
      • Eyre T.
      • Fitzgerald S.
      • Fernandez-Banet J.
      • Graf S.
      • Haider S.
      • Hammond M.
      • Holland R.
      • Howe K.L.
      • Howe K.
      • Johnson N.
      • Jenkinson A.
      • Kahari A.
      • Keefe D.
      • Kokocinski F.
      • Kulesha E.
      • Lawson D.
      • Longden I.
      • Megy K.
      • Meidl P.
      • Overduin B.
      • Parker A.
      • Pritchard B.
      • Prlic A.
      • Rice S.
      • Rios D.
      • Schuster M.
      • Sealy I.
      • Slater G.
      • Smedley D.
      • Spudich G.
      • Trevanion S.
      • Vilella A.J.
      • Vogel J.
      • White S.
      • Wood M.
      • Birney E.
      • Cox T.
      • Curwen V.
      • Durbin R.
      • Fernandez-Suarez X.M.
      • Herrero J.
      • Hubbard T.J.
      • Kasprzyk A.
      • Proctor G.
      • Smith J.
      • Ureta-Vidal A.
      • Searle S.
      Ensembl 2008.
      )).
      Figure thumbnail gr2
      Fig. 22D PAGE reference images of 72- and 120-hpf protein lysates. Annotated 2D gels for 72-hpf (a) and 120-hpf (b) zebrafish embryo proteins based on DIGE expression differences are shown.
      Proteins with high quality annotations identified by either method included structural proteins (e.g. myosin, tubulin, actin, annexin, lamin B2, matrilin 4 precursor, septin 6, cofilin 2, and cytokeratin), heat shock proteins (e.g. HSP 70-kDa protein 5, HSP 8, HSP 9B, and HSC 70), molecular chaperone proteins (e.g. chaperonin containing TCP1 subunit 2, calreticulin-like protein, chaperone protein GP96, and retinoblastoma-binding protein 4), cell cycle proteins (e.g. cell division cycle gene CDC48 and prohibitin), and multiple forms of the yolk protein vitellogenin. Annotated proteins involved in organ functions included those specific to kidney (e.g. intraflagellar protein IFT81), skeletal muscle (e.g. creatine kinase), liver (e.g. basic fatty acid-binding protein and 6-phosphofructokinase), central nervous system (e.g. synaptosome-associated protein and brain-type fatty acid-binding protein), heart (e.g. ATPase 2A), and lens proteins (crystallin γN2 and βB1). Proteins regulating developmental processes included proteins such as β-catenins 1 and 2, staufen homolog 2, and kelch-like 1.
      About 50% of all identified proteins were detected at both embryonic stages (Fig. 3). Interestingly a large fraction of proteins were exclusively identified by 2D PAGE (248 and 231 at 72 and 120 hpf, respectively) but not by 2D LC-MS/MS. Only a total of 97 proteins at 72 hpf and 86 at 120 hpf were detected by both 2D PAGE and 2D LC-MS/MS at either stage (Fig. 3). Thus, ∼70% of the proteins identified on gels were not detected by 2D LC-MS/MS. A potential explanation for this discrepancy is that the gel separation method may favor proteins that are not well digested in solution.
      Figure thumbnail gr3
      Fig. 3Comparison of proteins identified by 2D LC-MS/MS and from 2D PAGE.a and b, Venn diagram illustration of the overlap between proteins identified by 2D LC-MS/MS (a) or from 2D PAGE (b) at 72 and 120 hpf. c and d, overlap between 2D LC-MS/MS- and 2D PAGE-identified proteins at 72 hpf (c) and at 120 hpf (d).

       Differential Protein Expression during Development—

      Zebrafish embryonic development between the hatching period (72 hpf) and the larval stage (120 hpf) is characterized by rapid maturation of primal organs to form a viable organism. We assessed relative protein expression changes during this period by DIGE. Protein lysates of both stages were differentially fluorescently labeled, and an internal control was included to which protein spot intensities of both stages were normalized. A total of 148 of 789 resolved protein spots were in excess of 3-fold more abundant at 120 hpf than at 72 hpf. The expression intensity of 236 spots was below a third of that observed at 72 hpf. Sixty-one spots with a more than 3-fold increase and 103 spots with a more than 3-fold decrease in relative expression at 120 versus 72 hpf were identified with an error probability of less then 0.05 (Table I and supplemental Table 4).
      Table IProtein spot identifications with 3-fold change or more in relative expression at 120 versus 72 hpf
      Spot
      Gel spot number (also reference to supplemental Table 4).
      Accession
      IPI accession number.
      Protein nameScore
      MASCOT protein score.
      Molecular weightpIID Spec.
      Mass spectrum ID (as reference to view the annotated mass spectrum in supplemental Table 4).
      Pep. count (n)
      Number of unique peptides matched to mass peaks.
      Unmtch. (n)
      Number of unmatched mass peaks.
      Cov.
      Sequence coverage in percent.
      -Fold change
      -Fold change of normalized spot volume between embryonic stage (120 vs. 72 hpf).
      %
      1699IPI00503469Actin152419475.2259011614024.1
      2270IPI00551966Actin, α1, skeletal muscle90419555.236078543110.8
      2124IPI00482295Actin, cytoplasmic 174417395.35806711810.3
      1709IPI00503469Actin101419475.226017542310
      2071IPI00503469Actin134419475.225931257368.5
      2068IPI00503469Actin135419475.225871056357
      1752IPI00503469Actin115419475.225711064385
      2001IPI00503469Actin103419475.22606953324.7
      2315IPI00503469Actin90419475.22568764214.6
      2298IPI00503469Actin85419475.22608752214.4
      2096IPI00503469Actin113419475.22564769274.2
      1466IPI00503469Actin219419475.225691260413.3
      2049IPI00503469Actin85419475.22577966323.1
      1510IPI00551966Actin, α1, skeletal muscle103419555.236386330−4.4
      1392IPI00503469Actin89419475.2261395833−13.7
      1379IPI00503469Actin102419475.2261195029−16.8
      1393IPI00503469Actin222419475.22636124944−19.2
      1919IPI00483287Fast skeletal myosin h. chain 394518135.4958313502927.3
      1713IPI00497758Fast myosin heavy chain 4742220675.545661240914.5
      1861IPI00483287Fast skeletal myosin h. chain 391518135.4957213633111.6
      1834IPI00483287Fast skeletal myosin h. chain 388518135.496031458317
      1766IPI00497758Fast myosin heavy chain 4762220675.545822055116.4
      1803IPI00509014α-Tropomyosin226327204.75921858475.3
      1822IPI00497758Fast myosin heavy chain 4652220675.546101857123.7
      1605IPI00509014α-Tropomyosin241327204.71172051493.3
      1338IPI00483287Fast skeletal myosin h. chain 379518135.49638135829−3.2
      1519IPI00497758Fast myosin heavy chain 41652220675.54674192310−3.3
      1975IPI00499941Fast skeletal myosin l. chain 1a138209184.63668105461−3.4
      1467IPI00497758Fast myosin heavy chain 41392220675.54657213310−3.4
      1495IPI00483287Fast skeletal myosin h. chain 3105518135.49666144130−3.7
      1595IPI00483287Fast skeletal myosin h. chain 374518135.4966792319−4.8
      1530IPI00509014α-Tropomyosin272327204.781215353−4.8
      1885IPI00509014α-Tropomyosin79327204.7663115731−6.3
      1414IPI00497758Fast myosin heavy chain 4712220675.5464014236−6.6
      1491IPI00497758Fast myosin heavy chain 4922220675.54664202810−7.6
      1235PI00483287Fast skeletal myosin h. chain 3117518135.49622154032−8.3
      2158IPI00488085Myosin light chain 2216188534.68654124569−8.6
      1531IPI00497758Fast myosin heavy chain 4922220675.5465916267−9
      1109IPI00497758Fast myosin heavy chain 41002220675.54649274317−11.1
      1029IPI00497758Fast myosin heavy chain 41312220675.54615224810−11.2
      1392IPI00483287Fast skeletal myosin h. chain 3184518135.4961376123−13.7
      1133IPI00483287Fast skeletal myosin h. chain 3103518135.49643155231−14.2
      1104IPI00497758Fast myosin heavy chain 4792220675.54629225214−15.4
      1405IPI00500057Myosin, heavy polypeptide 2662217425.55626104531−24.5
      2022IPI00607465Vg1 protein276364099.2346116136−44.4
      2026IPI00607465Vg1 protein179364099.2355116137−82.4
      2028IPI00607465Vg1 protein264364099.2367126137−40.5
      2040IPI00607465Vg1 protein225364099.2360116135−7.3
      2046IPI00607465Vg1 protein161364099.2378105732−6.7
      1539IPI00496717Vitellogenin 1681494528.6864115010−10.6
      1666IPI00513217Vitellogenin 1882111897575340−16.1
      1695IPI00513217Vitellogenin 1652111893485452−5.2
      1712IPI00513217Vitellogenin 11302111893675639−14.9
      1734IPI00513217Vitellogenin 1842111895065932−192.6
      %
      2022IPI00496717Vitellogenin 12401494528.6846145611−44.4
      2026IPI00496717Vitellogenin 11551494528.6855165614−82.4
      2028IPI00496717Vitellogenin 12281494528.6867165512−40.5
      2040IPI00496717Vitellogenin 11921494528.6860155612−7.3
      2046IPI00496717Vitellogenin 11481494528.6878165115−6.7
      1624IPI00507087Muscle creatine kinase75427976.326595527−5.7
      1532IPI00495855l-Lactate dehydrogenase B130360896.4348113936−10.5
      1423IPI00490850Aldolase c193392346.2154165555−34.1
      1322IPI00485952Creatine kinase, mitochondr. 287462656.491993324−24.1
      1231IPI00490877Eno3 protein118474026.220135745−50.3
      1363IPI00508284Ribosomal protein SA74339904.753053630−12.9
      1285IPI00501593RNA-binding protein 4100461056.8118101629−18
      1285IPI00615024RNA binding motif protein 476460896.811781623−18
      1501IPI00496845Eukar. transl. init. fact. 387385725.5373114440−4
      1495IPI00491050Het. nuclear ribonucleoprotein78370875.763575122−3.7
      1819IPI00480889Prohibitin126296665.283796038−5
      1029IPI00498630Chaperonin contain. TCP1 S5125599255.3326175022−11.2
      1064IPI00508003Hspd1 protein90611575.5632123029−10.4
      2062IPI00495773Crystallin, γN2194217305.86660114762−18.6
      1921IPI00502990Crystallin, βB1183267976.4452105846−4
      2002IPI00490966βA4-Crystallin216230136.2540135885−8.4
      1979IPI00504818βA1–2-Crystallin83245216.47796267−6.4
      2478IPI00513361Novel β-type globin111119516.819577077−3.2
      2478IPI00502256Similar to embryonic 1124161516.899586863−3.2
      1531IPI00498781Similar to vertebrate APEX97348745.776896532−9
      2743IPI00510181Ubiquitin ribosomal prot. S27a71179879.6889537309.3
      2743IPI00619743Ubiquitin C117264867.8589537409.3
      2010IPI00483436NADH dehydrogenase66236875.74104856385.3
      a Gel spot number (also reference to supplemental Table 4).
      b IPI accession number.
      c MASCOT protein score.
      d Mass spectrum ID (as reference to view the annotated mass spectrum in supplemental Table 4).
      e Number of unique peptides matched to mass peaks.
      f Number of unmatched mass peaks.
      g Sequence coverage in percent.
      h -Fold change of normalized spot volume between embryonic stage (120 vs. 72 hpf).
      Annotated proteins included primarily structural protein isoforms, which were resolved in multiple spots, such as actin, myosin, tropomyosin, and tubulin isoforms. For example, actin isoforms were resolved in 17 spots (13 increased and four decreased at 120 hpf); myosins were identified in 27 spots (eight increased and 19 decreased at 120 hpf) representing multiple distinct isoforms and post-translationally modified or partially truncated variants. Vitellogenin (IPI00496717 and IPI00607465) was identified in 10 spots, also indicative of the existence of multiple modified variants. As expected, this yolk protein was decreased at 120 hpf as it is consumed for energy and protein production during development.
      Proteins involved in energy production and metabolism (muscle-specific creatine kinase, IPI00507087; l-lactate dehydrogenase B chain, IPI00495855; aldolase c fructose-bisphosphate, IPI00490850; creatine kinase mitochondrial 2, IPI00485952; and enolase 3 protein, IPI00490877) were between 5- and 50-fold less abundant at 120 hpf than at 72 hpf. Transcription/translation proteins (ribosomal protein SA, IPI00508284; RNA binding motif protein 4, IPI00615024; eukaryotic translation initiation factor 3, IPI00496845; and heterogeneous nuclear ribonucleoprotein that binds to nascent RNA polymerase II transcripts and plays a role in both transcript-specific packaging and alternative splicing of pre-mRNAs, IPI00491050) were 4–18-fold more abundant at 72 hpf than at 120 hpf, consistent with the faster synthesis of cellular proteins during organismal growth at the earlier developmental stage. Similarly prohibitin (IPI00480889), chaperonin containing TCP1 subunit 5 (IPI00498630), and heat shock protein Hspd1 (IPI00508003), which are all involved in cell cycle control, were more abundant (5–10-fold) at 72 hpf than at 120 hpf. All four lens proteins (crystallin γN2, IPI00495773; β-crystallin B1, IPI00502990; β-crystallin A4, IPI00490966; and β-crystallin A1–2, IPI00504818) were more prominent (6–19-fold) at the earlier stage relative to total protein, consistent with the relatively larger volume of the eyes in comparison with the whole organism at this stage. Three embryonic proteins, novel β-type globin (IPI00513361), novel protein similar to embryonic 1 (IPI00502256), and novel protein similar to vertebrate apurinic/apyrimidinic endonuclease (APEX) (IPI00498781), were also decreased at 120 hpf (3–9-fold).
      Apart from the structural proteins, several proteins that were more abundant at 120 hpf than at 72 hpf fell into the hypothetical/predicted or unknown categories. Other up-regulated proteins were ubiquitin C (IPI00619743; 9-fold) and ribosomal protein S27a (IPI00510181; 9-fold), which are involved in targeting cellular proteins for degradation (Table I).

       Pathway Membership of Detected Proteins—

      We sought to categorize protein identifications using a pathway enrichment analysis based on a database that describes signaling pathways and network relationship (Ingenuity Systems). As this resource does not include D. rerio sequences, we performed a BLASTP search of all protein identifications against the RefSeq human and mouse databases. This resulted in 731 RefSeq cross-references for 120 hpf (83% of total identifications from IPI) and 883 for 72 hpf (80% of total identification from IPI). The network and pathway database contained functional annotations for 461 proteins at 120 hpf (55% of original protein identifications) and 561 proteins at 72 hpf (51% of original identifications). These were analyzed for their membership in collated canonical and metabolic signaling pathways. A total of 163 proteins for each time point, 120 and 72 hpf, were mapped to a canonical signaling pathway. Metabolic pathway information existed for 366 proteins for 120 hpf and 397 proteins for 72 hpf (supplemental Tables 5 and 6).
      The distribution of proteins mapped to the canonical pathways is illustrated in Fig. 4. Pathways with the most contributing proteins were related to calcium, integrin, extracellular signal-regulated kinase (ERK)/mitogen-activated protein kinase, and vascular endothelial growth factor signaling. Proteins associated with morphogenesis such as the WNT/β-catenin pathway were less prominent but present at both 120 and 72 hpf (not shown). Indeed the developmental stages were relatively similar in their functional associations with the notable exception of the calcium signaling pathway, which was detected at 120 hpf (23 proteins) but was absent at 72 hpf.
      Figure thumbnail gr4
      Fig. 4Pathway analysis. The number of proteins associated with canonical signaling pathways as defined by Ingenuity Pathway analysis is shown. Only pathways with five or more protein associations are shown. ERK, extracellular signal-regulated kinase; MAPK, mitogen-activated protein kinase; PI3K, phosphatidylinositol 3-kinase; VEGF, vascular endothelial growth factor; SAPK, stress-associated protein kinase; JNK, c-Jun NH2-terminal kinase; GABA, γ-aminobutyric acid.
      Proteins associated with metabolic signaling pathways are represented in supplemental Fig. 1. Again the numbers of proteins classified as pathway members were similar for both embryo stages. More proteins identified at 72 hpf were grouped into the two metabolic pathways oxidative phosphorylation and ubiquinone biosynthesis than at the 120-hpf stage (12 and eight more proteins, respectively).
      BLASTP analysis of protein sequences identified from 2D PAGE resulted in 235 RefSeq cross-references for 120 hpf (73%) and 262 cross-references for 72 hpf (75%). A total of 86 proteins for 120 hpf (26%) and 96 proteins at 72 hpf (27%) were annotated with canonical and signaling pathway information in the Ingenuity Systems pathway database. Fewer pathways were detected compared with 2D LC-MS/MS identifications. However, the pathway profile was again similar between the stages (Fig. 4 and supplemental Fig. 1).
      A second approach to the functional analysis of the embryonic zebrafish proteins was based on GO annotations. Annotated proteins were categorized into the broad GO classes biological process, molecular function, and cellular component. A graphical representation of these categories for each embryonic stage is shown in Fig. 5 and supplemental Fig. 2 for 72 hpf and supplemental Fig. 3 for 120 hpf. The protein identifications at both 120 and 72 hpf were categorized similarly using GO. Approximately 30% of proteins had GO annotation to cellular metabolism, 13% had GO annotation to transport, 4% had GO annotation to cell organization and biogenesis, and 2% had GO annotation to translation/transcription and signal transduction. About 1% of proteins were annotated with functions relating to morphogenesis, cell differentiation, and development. Structural molecule activity, a category that is often over-represented in proteomics analyses, was associated with 8% of the proteins at 120 hpf and 6% at 72 hpf. Enzyme inhibitor activity, signal transducer activity, and motor activity all had 1% or less associated proteins at both 120 and 72 hpf. Cellular component information was unavailable for 60% of proteins. Association to organelles was ∼20%, association to intracellular localization was 10%, and association to membrane localization was 8% at both stages.
      Figure thumbnail gr5
      Fig. 5Gene Ontology classification. Gene Ontology biological process classification of proteins identified at 72 and 120 hpf from 2D LC-MS/MS and 2D PAGE is shown.
      Some of the GO categories were more frequently represented than expected from a random distribution. Such enrichment of functional groups points to protein classes that are either specifically expressed in embryos of the selected developmental stages or preferentially detected by the selected methodologies. Enriched GO classes are shown in supplemental Tables 7 and 8. GO categories with the most significant enrichment at 72 hpf were “development” (p = 0.009), “cellular metabolism” (p = 0.022), and “cell death” (p = 0.027). Cell differentiation, cell organization, biogenesis, and transport were less significantly enriched (0.05 > p > 0.027). GO categories significantly enriched at 120 hpf were cellular metabolism (p = 0.007), cell death (p = 0.021), and “growth” (p = 0.021). Others enriched with 0.05 > p > 0.025 were development, “transport,” “cell organization,” “biogenesis,” and “signal transduction.” In the GO category “molecular function,” “catalytic activity” (GO:0003824; p = 0.005) and “binding” (GO:0005488; p = 0.018) were most prominent at 120 hpf. Both “intracellular complex” (GO:0005622; p = 0.005 72 hpf) and “protein complex” (GO:0043234; p = 0.028) were most enriched at 72 hpf.

       Zebrafish Proteomics Database—

      A relational database was constructed by combination of IPI, GenBank, the NCBI taxonomy, GO assignments, and the experimental data. A Web application interface was developed for user-friendly ad hoc queries of the sequence annotation as well as perusal of the experimental and data mining results. The zebrafish proteomics database is available on line. The source code and associated database are also available for download at the site (see supplemental data for the URL database link under Instructions for Downloading).

      DISCUSSION

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      ) used for zebrafish, require confirmation of computationally predicted genes by independent evidence and/or manual validation for highest quality annotation. The additional evidence can take the form of experimentally documented transcription within the species (such as expressed sequence tags) or conservation across distant organisms. Indeed computational gene finding increasingly incorporates cross-species homology between closely related genomes to produce improved gene models (
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      ,
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      ,
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      ,
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      ) and allow quantitation of compounds with high specificity and precision (
      • Oe T.
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      ,
      • Anderson L.
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      ). Although the sensitivity of immunoassays for protein quantitation still exceeds most mass spectrometry assays, stable isotope analogs normalize for selective losses of analytes as well as act as carriers for trace amounts of analytes subjected to complex isolation procedures (
      • Oe T.
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      ). The development of such assays, however, requires detailed prior knowledge of (i) which proteins are expressed in the sample and are reliably detectable, (ii) which peptides are uniquely diagnostic for the targeted proteins, and (iii) under which experimental conditions they can be detected (i.e. in which strong cation exchange chromatography fractions do they elute). Our data set provides this information.
      Analysis of known or predicted protein functions within the data set revealed a similar representation of protein classes relevant for cell function at both developmental stages, including proteins related to structure, transcription/translation, cell cycle, nucleotide metabolism, ion transport, carbohydrate, energy, and lipid metabolism. Proteins associated with organ systems such as central nervous system, heart, and skeletal muscle were represented in both stages. Analysis of relative expression changes revealed that proteins involved in energy production, transcription/translation, and cell cycle control were relatively more abundant at 72 hpf, consistent with the faster synthesis of cellular proteins during organismal growth at this time compared with 120 hpf. A large fraction, greater than 50% for both data sets, lacked functional information such as Gene Ontology classifications. More than 40 and 60% had no information relating to “molecular and biological function” and “cellular processes,” respectively. Thus, all protein assignments at both stages were aligned with sequences in the human or mouse RefSeq protein databases. This revealed alignment of 83% at 120 hpf and 80% at 72 hpf. However, these homologous sequences also had poor annotation in Ingenuity Pathway analysis. Thus, many of the identified proteins may represent candidates for the exploration of their protein functions.
      Our large scale proteome analysis of embryonic zebrafish tissue revealed expression of previously uncharacterized proteins and detected developmentally regulated functional protein classes. The data are accessible on line in a fully searchable database that links protein identifications to existing resources including the ZFIN database (
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      Quantitative analysis of amyloid β peptides in cerebrospinal fluid of Alzheimer's disease patients by immunoaffinity purification and stable isotope dilution liquid chromatography/negative electrospray ionization tandem mass spectrometry.
      ) in mutagenesis and chemical screens and may refine systematic genetic network analysis in vertebrate development and biology.

      Supplementary Material

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