Global Protein Expression Profiling of Zebrafish Organs Based on in

Mar 10, 2014 - Identified proteins were subjected to BLAST searches and Gene Ontology classification to improve annotation of zebrafish proteins and o...
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Global Protein Expression Profiling of Zebrafish Organs Based on in Vivo Incorporation of Stable Isotopes Hendrik Nolte, Anne Konzer,† Aaron Ruhs, Benno Jungblut, Thomas Braun, and Marcus Krüger* Max Planck Institute for Heart and Lung Research, Parkstr. 1, 61231 Bad Nauheim, Germany S Supporting Information *

ABSTRACT: The zebrafish has become a widely used model organism employed for developmental studies, live cell imaging, and genetic screens. High-resolution transcriptional profiles of different developmental and adult stages of the fish and of its various organs were generated, which are readily accessible via the ZFIN database. In contrast, quantitative proteomic studies of zebrafish organs are still in their infancy. Here, we used the SILAC (stable isotope labeling by amino acids in cell culture) zebrafish as a “spike-in” reference to generate a protein atlas of nine organs including gills, brain, heart, muscle, liver, spleen, skin, swim bladder, and testis. Single-shot 4 h LC gradients coupled to a QuadrupoleOrbitrap (QExactive) instrument allowed identification of over 5000 proteins in less than 5 days, of which more than 70% were quantified in triplicate. Identified proteins were subjected to BLAST searches and Gene Ontology classification to improve annotation of zebrafish proteins and obtain insights into potential functions. Comparison to mouse tissue proteome data sets revealed differences and similarities in the protein composition of zebrafish versus mouse organs. We reason that the data set will be helpful for the proteomic characterization of zebrafish organs and identification of tissue-specific proteins that might serve as biomarkers. Our approach provides a complementary view into the biochemistry of zebrafish models and will assist large-scale protein quantification in zebrafish disease models. KEYWORDS: SILAC zebrafish, quantitative tissue proteomics

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INTRODUCTION Genetic screens and gene knockdown/inactivation via morpholino/TALEN technologies allow analysis of gene functions in living zebrafish.1 Moreover, the zebrafish has emerged as a versatile model organism to study organ development and in vivo live cell imaging.2 So far, a broad spectrum of transcript expression profiles based on antisense in situ hybridizations, microarray experiments, and deep sequencing has been generated and is available in the zebrafish model organism database ZFIN.3 Although the database provides detailed insights into transcript levels during development and adult stages, a systematic analysis of protein levels has not been accomplished so far. In addition, recent Proteo-genomic studies revealed clear differences of transcript and protein levels in a number of biological systems indicating that the mRNA levels do not necessarily represent protein abundances.4 Thus, a better understanding of the relationship of transcriptomes and proteomes will help to increase the relevance of zebrafish as a human disease model. Furthermore, improved knowledge of protein levels will be important to better understand the physiology of zebrafish models under regular and diseased conditions. During the past decade, the development of high-performance liquid chromatography (LC) systems, electrospray ionization (ESI), and mass spectrometers (MS) enabled detection of © 2014 American Chemical Society

thousands of proteins in complex biological systems, including cell culture samples and living organisms.5 The rapid technical progress in the proteomic field, including reduction of costs and increased scan speed, enables comprehensive and accurate protein quantification and paves the way for improved functional insights. For example, the combination of 2D gels and fluorescence gel electrophoresis (DIGE) improves the detection and quantification of altered protein spots. A recent study investigated the consequences of low oxygen in zebrafish skeletal muscle cells and identified changes in the concentration of several metabolic enzymes during hypoxia based on 2D-DIGE and Maldi-TOF/TOF instrumentation.6 Similarly, a study on BACE1-deficient zebrafish brain tissue, an Alzheimer’s disease model, used a label free approach and quantified 4500 proteins.7 In another study, a chemical dimethyl labeling approach in combination with phosphopeptide enrichment was used to quantify consequences of morpholino-mediated knockdown of the kinase Fyn/Yes in zebrafish embryos.8 Previously Abramsson et al. performed a proteomic analysis of seven adult zebrafish organs and identified ∼1400 proteins.9 Metabolic labeling with stable isotopes (SILAC, stable isotope labeling of amino acids in cell culture) represents another Received: January 10, 2014 Published: March 10, 2014 2162

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SDS by washing with urea, proteins were alkylated with 50 mM iodoacetamide (Sigma-Aldrich) and digested with the endopeptidase Lys-C (Wako) in Tris buffer (pH 8.5). Eluted peptides were purified by stop and go extraction (STAGE) tips.21

approach to monitor changes in protein concentrations very accurately.10 Incubation of SILAC amino acids (mainly lysine and arginine) for ∼5 cell doublings results in a complete replacement of natural amino acids with labeled versions in cell culture. After mixing the nonlabeled “light” and labeled “heavy” cell populations, the SILAC peak intensities after mass spectrometric measurements can be used for relative protein quantification. Although this method was initially devised for cell culture, the SILAC approach was recently extended to completely label almost all model organisms, such as yeast, worms, flies, rodents, and zebrafish.11−15 So far, living animals are labeled with a diet containing the amino acid 13C6 lysine (Lys-6). Complete incorporation of Lys-6 in the proteome is achieved after 1−2 generations. Due to the complexity and heterogeneity of tissues from living animals, SILAC animals are typically used as a spike-in reference to nonlabeled control and nonlabeled experimental condition.16−18 In a recent study, a combination of the super SILAC approach and the SILAC mouse was used to perform tissue-wide protein quantification. In this experiment, 27 labeled organs and one embryonic stage were mixed and used as a spiked-in labeled reference to monitor protein changes between different nonlabeled organs. Besides the detection of proteins with a tissue-specific expression profile, the catalog revealed that tissue specificity is already manifested in the most abundant proteins.19 Here we used organs from SILAC zebrafish as an internal standard to quantify protein abundances in nine nonlabeled zebrafish organs. We show that the single-shot approach allows extensive coverage of organ-specific protein expression. We determined relative abundances of more than 5000 proteins in less than a week’s instrument time, which allowed us to set up an atlas of tissue-specific expression profiles in zebrafish. Moreover, several novel proteins with no apparent orthologous in mammalians were detected.

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Ultra-High-Pressure Liquid Chromatography and Mass Spectrometry

Peptides were separated using a binary buffer system of A (0.1% (v/v) formic acid in H20) and B (0.1% (v/v) formic acid in 80% acetonitrile) on an Easy nanoflow HPLC system (Thermo Fisher Scientific, Odense Denmark). We applied a linear gradient from 7 to 35% B in 220 min followed by 95% B for 10 min and then reequilibration to 5% B for 10 min on a 50 cm column (75 μm ID) packed in-house with 1.9 μm diameter C18 resin. To control column temperature, we used a custom-made column oven at 40 °C. The UHPLC was coupled via a nanoelectrospray ionization source (Thermo Fisher Scientific, Bremen, Germany) to the quadrupole-based mass spectrometer QExactive (Thermo Scientific, Bremen, Germany). MS spectra were acquired using 3e6 as AGC target at a resolution of 70 000 (200 m/z) in a mass range of 350−1650 m/z. A maximum injection time of 60 ms was used for ion accumulation. MS/MS events were measured in a data-dependent mode for the 10 most abundant peaks (Top10 method) in the high mass accuracy Orbitrap after HCD (Higher energy C-Trap Dissociation) fragmentation at 25 collision energy in a 100−1650 m/z mass range. The resolution was set to 17 500 at 200 m/z combined with an injection time of 60 ms. Data Analysis

All 27 raw files were processed using MaxQuant (1.3.7.4) and the implemented Andromeda search engine. For protein assignment, ESI-MS/MS fragmentation spectra were correlated with the Uniprot zebrafish database (Danio rerio, 40 368 entries 2012), including a list of common contaminants. Searches were performed with Lys-C digestion specificity allowing two missed cleavages and a mass tolerance of 4.5 ppm for MS and 6 ppm for MS/MS spectra. Carbamidomethyl at cysteine residues was set as a fixed modification and oxidation at methionine, and acetylation at the N-terminus were defined as variable modifications. The minimal peptide length was set to seven amino acids, and the false discovery rate for proteins and peptides to 1%. Only SILAC ratios with a minimum of two counts from unique peptides were used for further analysis. The statistical environment R was used for graphics and statistical calculations on the data set. Prior to analysis, the data set was filtered to remove contaminants and reverse entries. Labeling efficiency of the zebrafish was determined to be ∼97%.12 As a conservative cutoff, we excluded proteins having a logarithmic ratio above log2 95/5 (heavy/light). SILAC ratios above 95% are most likely only represented by the heavy standard. The SILAC ratios were normalized manually with respect to a median of 1:1. Protein BLAST searches were carried out using the stand alone PC tool (BLAST 2.2.28+).22 All Uniprot identifiers of the zebrafish data set were used, and results were extracted by the zebrafish ID importing the best hits for the proteome analysis. Each row contains the ID (Uniprot human and mouse), Bit Score, and the expected value (e-value) for filtering (Supplementary Table 1). Default settings were used for BLAST searches against the human, mouse and Xenopus Uniprot databases providing accurate annotation information. Notably, e-values and results are highly influenced by the database size and settings (for example gap penalty, scoring matrix). For Gene Ontology enrichment analysis, we used an e-value of 0.0001 as a cutoff.

METHODS AND MATERIALS

Dissection of Zebrafish Organs

We used the local zebrafish strain “Bad Nauheim (BNA)”, which was maintained under standard laboratory conditions at 28 °C. For dissection of organs, three nonlabeled zebrafish and one SILAC zebrafish were anaesthetized in water containing 0.1% ethyl-3-aminobenzoat-methan-sulfonat (Tricaine, Sigma-Aldrich). The body wall was incised at the abdomen and cut until the operculum to expose internal organs. Heart, gills, liver, spleen, swim bladder, and the genital tract were dissected individually, followed by brain, skeletal muscle, and skin. All organs were washed several times in ice-cold PBS and frozen in liquid nitrogen. Sample Preparation and Protein Digestion

Tissues were homogenized in SDS lysis buffer containing 4% SDS in 100 mM Tris buffer (pH 7.6). Homogenates were boiled at 95 °C for 5 min prior to sonication for DNA sharing. Crude lysates were cleared by centrifugation at 16 000g for 10 min, and the protein concentrations were determined by DC protein assay (Biorad). To generate a SILAC protein standard, equal amounts of all Lys-6 labeled tissues from the SILAC zebrafish were combined and mixed with corresponding nonlabeled tissues (10 μg of labeled SILAC standard + 10 μg of nonlabeled tissue).12 Samples were digested according to the FASP protocol.20 In brief, proteins were reduced with 100 mM dithiothreitol (DTT, Sigma-Aldrich) and transferred to a centrifugal filter unit with a molecular weight cutoff of 30 kDa (Millipore). After removal of 2163

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Figure 1. General concept of experimental procedure. (A) Proteomic workflow using the single-shot approach and the FASP in solution digestion. The SILAC mixture contains equal amounts of protein extracts from nine organs: bladder, brain, gills, liver, muscle, skin, spleen, heart, and testis. The SILAC mixture from an unlabeled fish (WT) was spiked into extracts from each single organ, after which samples were subjected to in-solution digestion and measured in triplicate using a UHPLC−QExactive mass spectrometer setup. The schematic spectra describe the principal quantification by a super SILAC approach. (B) Determination of ratios between two organs based on the SILAC mixture (red). Color code corresponds to schematic spectra from A. (C) Correlation between biological replicates of the investigated zebrafish heart as indicated by a high Pearson correlation coefficient of 0.97 to 0.98 for logarithmized raw protein ratios. (D) Boxplot of log2 normalized SILAC ratios for several organs.

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RESULTS

standard with each nonlabeled organ in triplicate. Samples were digested in solution with the protease LysC and analyzed with LC-MS/MS on a hybrid quadrupole Orbitrap instrument (QExactive). We measured 27 samples each with a gradient of 4 h and acquired 2 467 778 tandem mass spectra resulting in 22 824 unique peptides. The average mass error of precursor ions of identified peptides was found to be 1.53 ppm. Proteins were identified with the MaxQuant software tool combined with the Andromeda search engine. In total, we

SILAC Quantification of Nine Zebrafish Organs

To perform a global protein analysis, we isolated nine different tissues from nonlabeled wild-type zebrafish (strain BNA, n = 3) as well as from SILAC labeled zebrafish (Figure 1A). After protein extraction, we mixed equal amounts of all nine SILAC organs in order to generate a universal pool of SILAC-labeled proteins. Next, we combined equal amounts of the SILAC 2164

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Figure 2. Profiling of zebrafish organs and selected MS spectra of SILAC pairs. (A) Number of quantified proteins in the investigated organs. (B) Protein ranking from the highest to the lowest intensity. (C) The selected peptide from Aconitase 2 shows the highest expression in heart brain and skeletal muscle. Color code of marked MS spectra correlates with Supplementary Table 1. The red circle indicates the labeled SILAC peptide. Peptide scores are based on MaxQuant Andromeda Score for the corresponding MS/MS spectra.

identified over 5000 proteins with a false discovery rate of 1%, of which 3577 (72%) were quantified. All peptides and protein groups are listed in (Supplementary Table 1). The data set represents a comprehensive proteomic catalog that allows

profiling of protein levels in liver, spleen, gills, heart, skeletal muscle, testis, skin, swim bladder, and brain. Because the SILAC zebrafish was used as an internal protein standard, we obtained a direct comparison of all nonlabeled tissues by dividing one 2165

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Figure 3. Comparison of the 10 most abundant proteins between mouse and fish organs. (A) Relative intensities of the 10 most abundant proteins in skeletal muscle of mouse and fish. (B) SILAC ratio heat map. Green boxes indicate increased SILAC ratios against the SILAC-labeled standard, whereas red boxes show depletion against the SILAC-labeled standard. The values in the table represent log2 SILAC ratios. (C) Brain tissue of mouse and zebrafish. (D) Liver tissue of mouse and zebrafish. (E) Relative intensities of the 10 most abundant proteins in skin, testis, swim bladder, and gills. Red arrow and boxes show log2 SILAC ratios of selected candidates.

proteins in skeletal muscle but ∼2400 proteins in gill tissue (Figure 2A). A more detailed comparison indicated that 634 (18%) proteins were shared by all organs examined. The relatively low number of overlapping proteins is most likely due to differences in protein expression levels between highly specialized tissues. A representative peptide for the aconitase-2 (aco2) shows the typical SILAC ratios between the nonlabeled and labeled peak with a mass difference of 6 Da in each of the measured organs (Figure 2C). Most aquatic animals have developed gills to improve efficiency of gas exchange. Moreover, gills are also important for exchanger of ions, acids, and nitrogenous wastes. We detected the highest number of proteins in gills, which showed a relatively equal distribution of protein intensities compared to skeletal muscle (Figure 2A). Half of the mass of gill proteins was

SILAC ratio (heavy-standard/tissue_1) by another (heavystandard/tissue_2) (Figure 1B). This approach allowed us to eliminate potential artifacts arising from variations in cellular composition, protein expression, and metabolic function. To estimate the reproducibility of our SILAC approach, we performed a biological triplicate using zebrafish hearts. The analysis revealed a Pearson coefficient over 0.97 for all replicates, indicating that the SILAC approach in fish and the subsequent single-shot gradient is sufficient to accurately quantify proteins of complex organs (Figure 1C,D). In addition, a 4 h gradient without any separation step prior to mass spectrometry analysis economizes measurement time. The number of proteins identified after filtering with respect to unique peptides and normalized ratios revealed clear differences between investigated organs. For example, we found ∼1300 2166

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represented by more than 90 proteins. Myosin heavy chain b, which reflected ∼3% of the total protein mass showed the highest expression level (Figure 3E). Notably, among the 10 most abundant proteins in gills, we detected several blood proteins, including apolipoprotein, hemoglobin, and serotransferrin, which is most likely caused by the high amount of blood in this organ.

SILAC ratio of 0.1 against the SILAC standard showed no enrichment compared to other tissues (Figure 3B). Conversely, heart-specific proteins including myosin, light polypeptide 7, regulatory (Myl7) showed an average log2 SILAC ratio of ∼3 indicated by the green (high SILAC ratio) color code. Proteins which were downregulated compared to the heavy standard are labeled with an orange to red color (Figure 3B and Supplementary Table 1). Protein Profiling of Brain Tissue. Next, we compared the protein expression pattern of mouse and fish brains. Among the highly expressed brain proteins were myelin binding protein, spectrin α chain, nonerythrocytic (Spna2), mitochondrial ATP synthase β, and several others proteins, which were highly expressed in both species. Two proteins which were exclusively enriched in fish brains were Flj13639 and ependymin. A BLAST search against human sequences unraveled that the human dehydrogenase/reductase SDR family member 12 (Dhrs12) is highly similar to zebrafish Flj13639 with an e-value of 6 × 10 −140 (Figure 3C, Supplementary Table 1). Short-chain dehydrogenases/reductases (SDRs) have multiple functions including processing of molecules such as hormones, prostaglandins, retinoids, and lipids. The Dhrs12 protein is mainly membrane-associated and processes retinoids and steroids.24 The highest expression of Dhrs12 was found in human lung and liver tissue. The zebrafish Flj13639 protein was detected in myelin-containing neurons and oligodendrocytes in fish brain.25 The glycoprotein ependymin was also detected with high intensities in fish brain, whereas the mouse homologue, mammalian ependymin-related protein 1 (Epdr1), was only weakly expressed in the brain (Supplementary Table 1). The differential expression of fish ependymin and mouse Epdr1 corresponds to previous findings, (i.e., involvement of fish ependymin in long-term memory and neuronal regeneration versus a role of the mammalian counterpart in hematopoietic progenitor cells).26 Differences in Protein Expression Profiles Indicate Distinct Metabolic Functions of Mouse and Fish Livers. Next, we compared expression of proteins in mouse and fish livers. Similar expression levels were found for several metabolic enzymes, including adenosylhomocysteinase (Ahcy) and betaine-homocysteine (S)-methyltransferase 1 (Bhmt1). In contrast, the carbamoyl phosphate synthetase I (Cps1), a mitochondrial enzyme involved in the urea cycle was highly abundant in mouse liver but absent in zebrafish liver. Zebrafish are ammonotelic organisms, which secrete their ammonia directly into the surrounding water and do not produce urea. Interestingly, several enzymes related to the ornithine-urea cycles were differentially expressed in brain, liver, and gills of zebrafish. For example, we detected a tissue-specific expression of the glutamine synthetase (Gs) and glutaminase b (Glsb) in brain tissue, whereas ornithine aminotransferase, glutamate dehydrogenase, and uricase were mainly expressed in liver and spleen. Another regulator of the nitrogen flux is the argininosuccinate synthase (ASS), catalyzing synthesis of argininosuccinate from citrulline and aspartate, which was detected mainly in muscle and gills. Recent studies indicated that ammonia/NH3 is actively exported via specific transporters, including rhesus glycoproteins, Na+/H+ exchanger, and proton ATPases,27 which corresponded well to the high expression of the ammonium transporter Rhesus blood group, B glycoprotein (Rhbg) and the Rhesus blood group-associated glycoprotein (Rhag) in gills (Figure 3 E).

Protein Profiling of Zebrafish and Mouse Tissues

The global comparison to the mouse atlas19 resulted in a Pearson correlation of SILAC ratios between 0.32 in spleen to 0.59 in skeletal muscle (Supplementary Figure 1A). The weak correlation of mouse and fish spleen is probably caused by substantial biological differences of the lymphatic system between both species. Conversely, the best correlation was observed for sarcomeric proteins of the heart and skeletal muscle reflecting similar physiology and architecture of the contractile apparatus between both species (Supplementary Figure 1B, D). Skeletal Muscle Tissue Shows a Very High Dynamic Range of Protein Concentrations. A limiting factor for the detection of peptides in complex samples is the dynamic range of protein abundance that can be analyzed by the MS instrumentation, usually 5 orders of magnitude. An example for a complex tissue with a huge dynamic range of protein concentrations is skeletal muscle. Here, the amount of abundant sarcomeric proteins such as myosin is so high that it prevents detection of proteins with lower expression, including transcription factors. In zebrafish skeletal muscle, we found very high peak intensities in the total ion chromatogram for αtropomyosin, muscle creatine kinase a, myosin heavy chain b, and parvalbumin 4 (mouse ortholog: parvalbumin α) (Figure 3 A, Supplementary Table 1). Conversely, proteins like the sarcolemma-associated protein a, dysferlin limb girdle muscular dystrophy 2B, si:ch211-195m20.1 (mouse: striated musclespecific Ser/Thr-protein kinase), and unc-45 homologue B showed the lowest intensities among all detected skeletal muscle proteins (Supplementary Table 1). The overwhelming concentration of sarcomeric proteins in muscle tissue, which defines the dynamic range of the MS analysis, makes it difficult to identify low expressed proteins. To visualize this effect, we calculated the relative amount of each protein compared to all quantified proteins based on the accumulated intensity of all labeled proteins in one organ (Figure 2B). In skeletal muscle, the first 10 most intense proteins represented ∼50% of the whole muscle specific proteome. Notably, in mouse, the most abundant skeletal muscle protein is the calcium-binding protein parvalbumin α, which represents ∼10% of the protein mass.19 In zebrafish, we detected three (parvalbumin 2, 3, 4) out of nine different parvalbumin isoforms.23 The combined intensity of all three detected isoforms represented ∼9% of the protein mass in zebrafish skeletal muscle (Figure 3A). Abundant Expression of Slow Ventricular MyosinHeavy-Chain-Like Myosin in Zebrafish Heart Tissue. The most abundant protein found in zebrafish heart was vmhcl (mouse gene name: myosin-7). Vmhcl is a slow twitch type I myosin that is mainly expressed in ventricles of the heart, where it constitutes the main component of the contractile apparatus. Another abundant protein in mouse and fish hearts is the ironand oxygen-binding protein myoglobin (Mb), which facilitates transport of oxygen within muscles. We assume that detection of high levels of the serum protein Apolipoprotein (Apoa1b) is mostly due to blood contaminations, because the Apoa1b log2 2167

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Figure 4. Overview of metabolic pathways and protein abundance. (A) Schematic view of glycolysis and pentose phosphate pathways. Log2 SILAC ratios of liver (L), skeletal muscle (SM), heart (H), and brain (B) are color-coded from green (high SILAC ratios) to red (low SILAC ratios). (B) Enzymes participating in formation of glucose-6-phosphate from glycogen (glycogenolysis).

Pattern of Glycolytic Enzymes in Liver, Muscle, Heart, and Brain

In conclusion, proteomic profiling of different tissues provided interesting insights into regulation of physiological processes in fish organs and provided evidence that SILAC ratios between the labeled standard and nonlabeled organs can be used to measure tissue-specific protein expression.

The breakdown and formation of carbohydrates is one the most important pathways to provide energy in living animals. Metabolization of glucose by glycolysis is controlled by 10 2168

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Figure 5. Gene Ontology analysis. (A) Bar plot of Gene Ontology terms for each class: biological process (BP), molecular function (MF), and cellular compartment (CC). (B) Twelve clusters obtained by K-means clustering. Enriched GO terms are indicated next to the panels.

Comparison to Human GO Terms Extended the Catalog of Zebrafish GO Terms

enzymes and results in the synthesis of pyruvate. Conversely, organs like the liver and kidney also synthesis glucose using the gluconeogenesis pathway. To further analyze differences in metabolic processes in liver, muscle, heart, and brain, we had a specific look at enzymes of the glycolytic and gluconeogenic pathway (Figure 4). Certain tissues showed increased expression of distinct isoenzymes catalyzing different steps of glycolysis. For example, the hexokinase domain containing protein 1 (gene name hkdc1)28 was highly expressed in liver tissue, whereas the hexokinase 1 (hk1) was mainly found in the brain. As expected, three enzymes, which are crucial for gluconeogenesis such as pyruvate kinase, phosphoenolpyruvate carboxykinase, and fructose 1, 6- bisphosphatase were enriched in liver compared to muscle and brain. However, glucose 6phosphatase, which dephosphorylates glucose-6-phosphate to glucose, was not detected. Our data set allows quantification of a broad range of different isoforms of metabolic enzymes and will help to assign tissue specific enzymatic functions under regular and pathophysiological conditions.

Gene Ontology is an important tool to obtain information about gene product attributes (e.g., cellular localization and function of gene products) allowing identification of clusters of genes with a common function in distinct data sets. Here, we analyzed our data set with respect to several global gene ontology terms including biological process (BP), molecular function (MF), and cellular component (CC) based on human orthologues of zebrafish genes, which provides GO terms for ∼70% of zebrafish genes.29 We used a BLAST search approach against human sequences to bypass the poor GO-term annotation of the zebrafish database22 To find human orthologs, we performed a BLAST search against the human Uniprot database (April 2013) annotated with human Gene Ontology (GO) terms. By using an e-value cutoff of 0.0001, we identified more than 2000 annotated GO terms that were used for enrichment analysis. In contrast, usage of zebrafish Uniprot identifiers yielded only ∼600 GO terms (Figure 5A). Next, we performed a K-means 2169

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Figure 6. Detailed comparison of heart and skeletal muscle tissue. (A) RESA analysis with seven summary figures. Boxplot with additional marks at 90th and 10th percentile are shown. Red boxes indicate the first and third quartile. Orange lines represent means, black lines represent the medians. Smaller black lines indicate maximum and minimum. (B) Scatter plot of log2 ratios for heart and skeletal muscle. Gray dashed line represents the slope of one and intercept of 0. Proteins with a fold change >5 are color coded in orange. Proteins which were exclusively found in skeletal muscle or in heart are shown in the left and right panel, respectively. Ratios are color coded as indicated in Supplementary Table 1.

protein ubiquitination, indicating that these general cell functions are equally represented in all investigated organs. Taken together, homology searches and the usage of human identifiers of the Swissprot/Uniprot database helped us to increase the number of Gene Ontology annotation and offered a more comprehensive bioinformatics analysis of differentially expressed proteins. In addition, the GO-term analysis allowed us to identify metabolic activities between different tissues, which should be helpful for further unbiased proteomics screens of zebrafish models.

clustering using the Euclidean distance of all SILAC ratios among the nine different organs. For visualization of calculated K-mean clusters, we exploited the Multiple Array Viewer MeV30 and for identification of enriched GO terms within the clusters, we used the gene ontology tool Gorilla31 (Figure 5B). In heart and skeletal muscle, we detected a cluster of 21 proteins showing significant enrichment (p-value 10 protein quantifications per min. We performed protein GO-term analysis and clustering to validate our quantitative approach resulting in the identification of tissuespecific expression profiles of structural proteins, metabolic enzymes, and signaling molecules. For example, the direct comparison of heart and skeletal muscle revealed clear differences in metabolic properties as well as in Z-disk and Mline structures, which are important for the integration and regulation of the contractile apparatus in heart and skeletal muscle. So far, the zebrafish is mainly used as a model organism to follow early developmental processes of fish larvae by imaging techniques. In recent years, the development of sensitive mRNA detection methods, including whole mount in situ hybridization and microarray technologies provided insights into tissuespecific gene function. However, mRNA and protein levels do not necessarily correlate.4,33 Therefore, quantitative proteomic analysis based on mass spectrometry represents an important approach to examine the cellular composition of proteins, independently of antibody or tracer technologies.19,33,37 Because mass spectrometric instrumentation increased in sensitivity and sample preparation became more efficient in recent years, it is now feasible to perform proteomics with low amounts of zebrafish tissues even with larval hearts or single embryos.12,38 To the best of our knowledge, our data sets provide the first quantitative proteome atlas of nine distinct adult zebrafish organs. The atlas can be used for identification of tissue-specific protein expression and will serve as a reference for pathophysiological conditions and for disease models. In addition, comparison of our data set to mouse protein expression profiles confirmed differences in the biochemical machinery between teleosts and mammals. 2171

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diffusion and specific transporters in gills.41 Recently, it was shown that Rhesus factors (Rh proteins), important blood group determinants in humans, are responsible for ammonium transport across the plasma membrane in gills. We only found a strong expression of the Rhesus protein Rhbg in gill tissue, whereas Rhag was detected in gills, spleen, and skin with similar numbers of peptides. Because gills and spleen are blood-rich tissues, it is possible that the Rhag protein was derived from blood contaminations. However, hemoglobin was equally distributed among all organs and we did not find Rhag in other blood rich tissues, such as the liver, muscle, and heart, suggesting that Rhag is indeed specifically expressed in gills, spleen, and skin (Supplementary Figure 1). The gill is also a crucial organ for ion/fluid homeostasis in fresh water fishes. Recently, transporters such as vesicular proton pumps (V-ATPase), anion exchangers (SLC), Na+/ K+ ATPase transporter (NKA), and Na+/H+ exchangers (NHE) were identified to regulate ammonia and ion homeostasis in freshwater fishes.42 Here, we quantified more than ∼130 transporters and found several specific transporters which were exclusively expressed in gill tissue. The highest SILAC ratio was found for the ATPase atp1a1a.2 (Na+/K+ transporting, alpha 1a polypeptide, tandem duplicate 2), an enzyme which regulates sodium and potassium exchange within the plasma membrane. Another membrane transporter is the Na+/ Cl− cotransporter (NCC) family member solute carrier protein SCL12a10.2 (sodium/ potassium/chloride transporters, member 10, tandem duplicate 2), which was found specifically in gills, in line with a previous RT-PCR expression analysis.43 So far, most of the transporters were only identified at the transcript level and by using antibodies from other species, which is a notoriously cumbersome and unreliable method. In contrast, the accurate SILAC quantification of unique peptides allowed us to distinguish between similar isoforms of highly related transporters. Our method revealed the presence of several ion transporters in gills and provides a starting point for a more detailed analysis of the complex physiology of gills. Future experimental strategies such as the isolation of single cell populations from different organs and reduction of blood contaminations will help to increase the resolution of our analysis. Such approaches will also benefit from screenings for interaction partners based on SILAC quantification and analysis of changes in post translational modifications, including phosphopeptides and acetylated peptides. In conclusion, SILAC-based protein quantification in different tissues allows systematic analysis of protein expression in zebrafish, which will be particularly useful for assessment of various zebrafish mutants. The internal protein standard of thousands of labeled proteins from the SILAC zebrafish mix allows confident protein quantifications. The SILAC-based onepeptide quantification will improve accurate quantification of post-translational modifications. The current protein atlas of nine zebrafish organs provides a valuable resource for the zebrafish community and will help to analyze numerous zebrafish disease models in more detail.

Gene Duplications in Zebrafish Might Lead to Generation of Additional Protein Isoforms

The high accuracy of our SILAC-based quantification method allowed detection of eight different parvalbumin isoforms in zebrafish tissues. We also found six different members of the mammalian Myomesin family (also known as Skelemin) with distinct expression profiles between heart and skeletal muscle in zebrafish. BLAST search identified two isoforms (myom1a, mym1b) with a high similarity to the mammalian Myomesin-1 and 4 isoforms, which are more similar to the mammalian Myomesin-2 (similarity ∼50%) (Supplementary Figure 1). The higher number of different isoforms compared to mammals is most likely the consequence of an ancient tetraploidization event that occurred before teleost radiation and hence represents the genomic heritage of all teleosts. Interestingly, our global BLAST search also revealed several unknown proteins, which do not show any obvious homologues in Xenopus, mouse, and human. Among the 44 nonhomologous proteins, nine proteins carried a Pfam domain (Supplementary Table 2). Four candidates are members of the IgG V-set and I-set family, which were found in immunoglobulin light and heavy chains, in T-cell receptors, and adhesion molecules, including VCAM and ICAM. In addition, we found two proteins with a cathepsine inhibitor I29 domain, suggesting a potential function as proteinase inhibitors. Another example is the protein F1QIC8 (gene name: LOC1000004582), which was detected with a restricted expression in brain that showed no striking homology to molecules in mammalian databases. Moreover, the uncharacterized candidate F1R3T1 was also detected in brain and muscle tissue and is annotated as si:dkeyp-77h1.4 in the ZFIN database. In situ hybridization showed a clear expression in the central nervous system and cranial ganglia during embryonic development.39 However, further functional studies in zebrafish will be necessary to characterize the cellular function of those proteins. Our proteomics study defined several novel zebrafish proteins with no obvious homologues in other species and established their tissue-specific expression pattern. In addition, our data set validate available transcript databases, provide definitive proof for the generation of proteins from yet uncharacterized genes and will help to dissect post-transcriptional and post-translational regulatory mechanisms. Metabolic Adaption to the Aquatic Environment

Proteomic analysis of metabolic pathways in zebrafish provided clear evidence for adaptions to the aquatic environment. In mammals, toxic ammonia (NH3/NH4+) is converted into harmless urea and excreted by kidney as a component of urine. The first step of the ornithine-urea cycle that detoxifies ammonia is the conversion from NH4+ + HCO3− to carbamoyl phosphate by carbamoyl phosphate synthetase (CPS I). The next steps are catalyzed by ornithine carbamoyltranferase (OTC1), argininosuccinate synthase 1 (ASS1), argininosuccinate lyase (ASL), and arginase-1 (Arg1). These enzymes are among the most abundant proteins in mouse liver. However, in fish, ammonia is first converted into glutamine by glutamine synthetase (GS) and then converted into carbamoyl phosphate and citrulline by CPS III (ortholog to the mammalien CPS I). Although we were not able to identify CPSIII, OTC1, ASS1, and ASL in liver tissue, we found enriched levels of ASS1 in gill and muscle tissue of the zebrafish. ASS1 most likely provides intermediates for the TCA cycle in these tissues. Notably, activity of ornithine-urea enzymes is generally low or undetectable in ammoniotelic fish.40 Instead, fresh water fishes excrete ammonia into the surrounding water by

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ASSOCIATED CONTENT

S Supporting Information *

Pearson correlation of mouse and zebrafish organs, parvalbumin expression profile in the nine different organs, protein groups file of all 27 raw files obtained by MaxQuant, protein groups file of 44 non-homologous candidates in zebrafish, description of column 2172

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identifiers from Table 1. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Fax: +49(0) 6032705419. Tel: +49(0)60327051760. Present Address †

Uppsala University, Department of Chemistry−BMC, Analytical Chemistry, Husargatan 3, 75237 Uppsala,Sweden. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors wish to thank Sylvia Jeratsch for excellent technical assistance and Eva Bober for reading the manuscript. This work was supported by the Max-Planck-Society, the Excellence Initiative “Cardiopulmonary System”, the University of Giessen-Marburg Lung Center (UGMLC).

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