Article pubs.acs.org/jpr
Assessment of Global Proteome Dynamics in Carp: A Model for Investigating Environmental Stress Mary K. Doherty,† Matthew A. G. Owen,‡,¶ Simon J. Davies,‡ Iain S. Young,∥,§ and Phillip D. Whitfield*,∥,† †
Proteome Analysis Facility, University of the Highlands and Islands, Inverness, IV2 3JH, United Kingdom Fish Nutrition and Health Research Group, School of Biological and Biomedical Sciences, University of Plymouth, Plymouth, PL4 8AA, United Kingdom § Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, United Kingdom ‡
S Supporting Information *
ABSTRACT: Fish have to respond to a range of natural and man-made environmental stressors, which can lead to molecular changes within their tissues. Many studies focused on environmental stress in fish have examined the change in protein abundance or mRNA level. However, it is well-known that there is a disconnect between mRNA and protein expression. In order to bridge this gap, protein turnover must also be considered. We have developed an experimental strategy to determine the synthesis rates of individual proteins in the tissues of fish on a proteome-wide scale. This approach has been applied to the common carp (Cyprinus carpio), a key model species for investigating environmentally induced physiological plasticity. We have calculated the rates of protein synthesis for over a thousand individual proteins from the skeletal muscle and liver of carp. The median synthesis rate of proteins from liver was higher than that of skeletal muscle. The analysis further revealed that the same protein can have a different rate of synthesis depending on the tissue type. Our strategy permits a full investigation of proteome dynamics in fish and will have relevance to the fields of integrative biology and ecotoxicology. KEYWORDS: common carp, liver, muscle, protein turnover
■
INTRODUCTION Fish are exposed to multiple natural and man-made stressors in the environment. The temperature, pH and oxygen concentration of an aquatic environment can change on a daily and seasonal basis eliciting adaptive responses from fish species. In addition, intensive industrial and agricultural development, together with increasing urbanization of coastal areas, is having a substantial impact through the release of pollutants and synthetic chemicals. Such expansion has led to increased contamination of aquatic environments. There is interest in exploring the responses of fish to a changing environment. The common carp (Cyprinus carpio) has been widely employed to study the effects of chemicals,1 herbicides,2 pharmaceuticals3 and heavy metals.4 Furthermore, the common carp has a well-developed capacity to maintain physiological homeostasis under severe environmental stress. © 2013 American Chemical Society
As a result, it has become a key species with which to investigate the mechanisms underpinning phenotypic change.5,6 Proteomic strategies are increasingly being used to study the effects of environmental stressors in fish.7−10 The proteome is in constant flux, and although studies relating the transcriptome and proteome dominate large-scale functional genomics, correlations between mRNA and protein expression are often poor.11−13 To bridge this gap, it is not enough to measure the steady state levels of proteins, rather protein turnover needs to be considered. In recent years, proteomics strategies have been developed to measure the synthesis and degradation rates of proteins in yeast,14 bacteria,15,16 algae,17 cultured human cells,18 Special Issue: Agricultural and Environmental Proteomics Received: June 30, 2013 Published: September 18, 2013 5246
dx.doi.org/10.1021/pr4006475 | J. Proteome Res. 2013, 12, 5246−5252
Journal of Proteome Research
Article
a flow rate of 400 nL/min using an ACN/water gradient; 1% ACN for 1 min, followed by 0−62.5% ACN over 21 min, 62.5− 85% ACN for 1.5 min, 85% ACN for 2 min and 1% ACN for 15 min. MS spectra were collected using data-dependent acquisition in the range m/z 300−2000 using a precursor ion resolution of 30 000, following which individual precursor ions (top 5) were automatically fragmented using collision induced dissociation (CID) with a relative collision energy of 35%. Dynamic exclusion was enabled with a repeat count of 2, repeat duration of 30 s and exclusion duration of 180 s. Data were analyzed using MaxQUANT v1.1.1.3624 with the Andromeda search engine25 against the Danio rerio IPI database v3.67. The initial search parameters allowed for a single trypsin missed cleavage, carbamidomethyl modification of cysteine residues, oxidation of methionine, acetylation of N-terminal peptides, a precursor mass tolerance of 10 ppm, a fragment mass tolerance of ±0.5 Da, and a FDR of 0.01. An additional parameter, coded as a pseudo-post-translational modification was included to search for peptides containing [2H7]-leucine. For proteins that were described as “novel” or “hypothetical”, a BLAST search was performed in order to ascertain the identity of homologous proteins.
birds19 and rodents.20−22 We recently established these methods in fish, focusing on the turnover of a small group of closely related parvalbumin isoforms.23 This involved the dietary administration of a stable isotope-labeled amino acid to common carp together with the mass spectrometric analysis of signature peptides. In the current study we have extended our work and describe an approach to compare the dynamics of protein turnover in skeletal muscle and liver of common carp. The developed methodology offers the potential to explore gene−protein relationships in fish species, which will have relevance within the fields of integrative biology and environmental toxicology.
■
MATERIALS AND METHODS
Production of [2H7]-L-Leucine Labeled Tissues in Fish
Full experimental details have been described previously.23 In brief, common carp (average weight = 19 g/fish) were maintained in the University of Liverpool aquarium at 25 ± 0.5 °C (at pH 6.5−7.0) on a 16 h light:8 h dark photoperiod throughout the study. For turnover experiments carp were fed on an experimental diet, in which 50% of the L-leucine in the diet (that proportion added as crystalline amino acid) was replaced with [2H7]-L-leucine (98% purity) (Cambridge Isotope Laboratories, Andover, MA, USA). Fish (n = 4) were sampled at t = 0, 1, 2, 3, 4, 5, and 7 weeks. The feeding regimen was carried out under license granted by the U.K. Animal (Scientific Procedures) Act, 1986. Fish were killed in accordance with U.K. Home Office Schedule One regulations. Skeletal muscle (approximately 300−400 mg) and liver (approximately 100−200 mg) samples were subsequently prepared for proteomic analysis.
Calculation of Protein Synthesis Rates
The relative isotope abundance (RIA) of the precursor pool of both skeletal muscle and liver was determined using peptides containing two leucine residues from a range of different proteins. The identity and scan number of dileucine peptides was obtained using MaxQUANT; however, it is not possible to automatically calculate the intensity of multilabeled peptides using this approach. This was performed by manual inspection of the data. Once RIA of the precursor pool is known, this is then used to deconvolute the peptide ion intensity from monoleucine peptides to that which is contributed by preexisting “old” protein and that which is newly synthesized. This relative partition of intensity is plotted over time, allowing the rates of synthesis of each protein to be calculated. The theoretical derivation of the formulas used for RIA, and synthesis determination has been described previously.19,23 From these data, the appearance of labeled peptide was analyzed by nonlinear curve fitting to derive the rate of incorporation of label from the protein pool.
1-D SDS-PAGE
The soluble proteins from skeletal muscle and liver were separated by 1-D SDS-PAGE using a Mini-Protean Tetra system (Bio-Rad Laboratories, Ltd., Hemel Hempstead, U.K.). Samples were electrophoresed at a constant potential of 200 V through a 12.5% w/v polyacrylamide resolving gel with a 4% w/ v stacking gel. Samples were incubated at 95 °C for 5 min in a reducing buffer (125 mM Tris-HCl; 140 mM SDS; 20% v/v glycerol; 200 mM DTT and 30 mM bromophenol blue) prior to loading. Gels were stained with Colloidal Coomassie Blue (Bio-Rad).
Functional Annotation of Proteins
To assign a functional category to each of the proteins, GoRetriever (www.agbase.msstate.edu/) was used.26 This classifies proteins according to cellular location, class, function and biological process. GoSlim Viewer was then used to obtain a profile of the proteins identified in both tissue types. The analysis was performed for both the total identified proteins from carp skeletal muscle and liver and also those for which synthesis rates were determined. Enrichment of functional classes of proteins was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 bioinformatic resource (http://david.abcc.ncifcrf.gov) as was pathway enrichment analysis.27 The identified proteins were compared to a background derived from the annotated zebrafish reference proteome.
In-Gel Trypsin Digestion
Gel bands were excised, and the proteins were subjected to ingel tryptic digestion and peptide extraction. Each gel lane was sliced into 24 slices, and each slice was placed in distilled deionized water (50 μL). The water was then removed, and the gel piece was treated with destain solution (50 μL of ACN/100 mM ammonium bicarbonate 1:1 v/v). The protocol included a second cycle of reduction and alkylation. The gel slice was dehydrated in ACN. Trypsin (Roche) (0.2 μg/μL in 50 mM acetic acid) was added at a ratio of protein:trypsin 50:1, and the digestion was allowed to proceed overnight at 37 °C. LC−MS/MS Analysis of Peptides
Peptide analysis was performed in positive ion mode using a Thermo LTQ-Orbitrap XL LC−MSn mass spectrometer equipped with a nanospray source and coupled to a Waters nanoAcquity UPLC system. The samples were initially desalted and concentrated on a BEH C18 trapping column (Waters, Manchester, U.K.). The peptides were then separated on a BEH C18 nanocolumn (1.7 μm, 75 μm × 250 mm, Waters) at
■
RESULTS AND DISCUSSION
Protein Identifications in Carp Skeletal Muscle and Liver
1-D SDS-PAGE revealed a large dynamic range in the expression of soluble muscle and liver proteins, which was 5247
dx.doi.org/10.1021/pr4006475 | J. Proteome Res. 2013, 12, 5246−5252
Journal of Proteome Research
Article
consistent with previous findings.7,28,29 The two tissue preparations were very different in the profile of their protein complements. Over 1950 proteins were identified in skeletal muscle and 871 from liver; from these identified proteins, 1415 muscle and 615 liver proteins were confidently assigned with more than one peptide and were taken forward for the remaining analyses (Supporting Information Table S1). The most predominant biological process in skeletal muscle was structure development, followed by nitrogen compound metabolic processes and biosynthetic processes (Figure 1). Enrichment analysis (Supporting Information Table S2) found 51 clusters of proteins in terms of biological process, of which 10 were enriched (p < 0.05) compared to the complete Danio rerio database. Those clusters with the greatest enrichment included proteins involved in glucose and protein catabolism along with a variety of other metabolic processes. The most common molecular function was kinase activity. The data analysis (25 clusters of which 14 were significantly enriched) also indicated a specialization of the muscle, reflecting the known function of muscle as demonstrated in carp and other fish species. The proteins were mainly located in the cytoplasm of the cell although a number of nuclear, ribosomal and mitochondrial proteins were also detected. In particular, there was enrichment (12 clusters, 7 significantly enriched) observed for ribosomal and sarcomeric proteins along with nucleasomal proteins and those involved in kinesin complexes. In liver, the most common biological process observed was small molecule metabolism, followed by protein transport and catabolic processes. Metabolism also featured strongly in the enrichment analysis (38 clusters, 15 significantly enriched) along with cellular homeostasis, response to hypoxia and protein polymerization. The over-riding molecular function of the proteins identified in the liver was found to be oxidoreductase activity with peptidase, isomerase and ligase activity all represented. This reflects the role of the liver as a detoxification and processing organ and was also observed in the enrichment analysis (40 clusters, 14 significantly enriched). Metabolic Labeling of Carp and Calculation of RIA
Changes in protein concentration are mediated by the opposing processes of protein synthesis and degradation. Studies predicated on transcript analysis invariably focus on expected changes in protein synthesis rates but often neglect the equally important contribution of protein degradation in determining the concentration of mature protein in a cell. Therefore, it is important that we can understand and calculate the contribution of protein dynamics to the function of an organism. In this study we have used metabolic labeling approaches to determine protein synthesis rates in fish. Carp were administered a standard diet in which a crystalline, stable isotope-labeled amino acid [2H7]-L-leucine had been added at an amount equivalent to that found in unlabeled form in the diet. If the labeled and unlabeled amino acids are digested and absorbed equally into the protein precursor pool, we would expect the RIA of labeled leucine available for protein synthesis to be 50%. Mass isotopomer distribution analysis (MIDA)30,31 can be used to determine this value experimentally from individual proteins. MIDA assesses the combinatorial probabilities of amino acid assembly during protein synthesis. When using a metabolic labeling approach, the resulting proteins (and peptides) are present as a mixture of distinct species that
Figure 1. Characterization of the proteomes of carp skeletal muscle and liver. Proteins were extracted from carp skeletal muscle (white bars) and liver (black bars) and separated by 1-D SDS-PAGE. The profile of each tissue was assessed in terms of (A) biological processes, (B) cellular location and (C) molecular function.
contain both labeled and unlabeled amino acids. The relative proportion of each isotopic species can be quantified using mass spectrometry19 and deconvoluted using MIDA to determine the RIA of the precursor pool. In order to calculate the precursor pool RIA in the carp we analyzed the labeling pattern of dileucine peptides from both skeletal muscle and liver (Figure 2). In both cases, the RIA rapidly plateaued reaching values of 0.51 ± 0.01 (muscle) and 0.42 ± 0.004 (liver). This shows that all of the labeled amino acid is effectively and efficiently entering the protein precursor 5248
dx.doi.org/10.1021/pr4006475 | J. Proteome Res. 2013, 12, 5246−5252
Journal of Proteome Research
Article
Figure 2. Incorporation of [2H7]-L-leucine into muscle and liver proteins and calculation of precursor RIA. The incorporation of the stable isotope-labeled leucine into the proteins of skeletal muscle and liver was monitored by mass spectrometry. Data was obtained from multiple fish (n = 4) over seven time-points using multiple peptides.
pool and is available for the synthesis of new proteins. Indeed, the calculated RIA for muscle indicates that the amount of labeled leucine may be marginally higher than 50% in the diet, although this is within the bounds of experimental error. The RIA value is similar between the tissues, although that of liver is lower. This may reflect higher recycling of amino acids in liver but highlights the requirement to experimentally calculate the amount of labeled amino acid available for protein synthesis in the tissue of interest. Rates of Synthesis of Carp Muscle and Liver Proteins
The rate of protein synthesis for over 580 muscle proteins was determined (Figure 3, Supporting Information Table S3). The
Figure 4. Global analysis of protein synthesis rates in carp tissues. The change in RIA for each protein was assessed over the labeling period. The first order rate constants of approximately 580 proteins from (A) skeletal muscle and 450 proteins from (B) liver were determined.
Similarly, the synthesis rates of 442 proteins from carp liver were determined. Again, the gene ontology profiles were similar to the global profile, although there was greater parity between the molecular function categorization, with a slight reduction in the percentage of proteins ascribed to oxidoreductase activity. The synthesis rates of liver proteins spanned 1.9 × 10−3 d−1 to 36.25 d−1 (mean = 0.16 d−1, median = 0.049 d−1), which correlates to doubling times of 27 min to 76 days. The median protein doubling time for muscle was 27 days compared to 14 days for liver (Figure 5). This correlates with other studies that have shown that muscle is a more stable, slow turnover tissue, whereas the metabolic rate of liver is faster in animal species.21,22 If it is assumed that the concentration of each protein does not change over time, it would be possible to calculate from these data the individual rates of protein degradation. However, we do not believe that this assumption would hold true for each protein, and therefore only the rates of protein synthesis have been presented. These rates in both tissues cover a wide range, with the replacement of unlabeled leucine with the deuterated form of the amino acid in some proteins occurring almost instantaneously, whereas label incorporation in many proteins is slow and indicative of the growth rate of the fish. This reflects the dynamic nature of the proteome in a cell or tissue and reinforces the need to measure
Figure 3. The rates of protein synthesis in skeletal muscle and liver. The amount of newly synthesized protein was measured at each timepoint for individual proteins. The rate of synthesis for each protein was determined by fitting the data to a single exponential growth curve.
calculated synthesis rates ranged from 5 × 10−4 ± 1 × 10−5 d−1 to 1.49 ± 0.5 d−1 (mean = 0.056 d−1, median = 0.026 d−1), relating to doubling times from just half a day to 822 days (Figure 4). The characterized proteins covered the mass range 6 to 278 kDa and were found to have multiple intracellular roles. These were similar to those seen for the global profile, indicating that a representative cohort of proteins were being analyzed. 5249
dx.doi.org/10.1021/pr4006475 | J. Proteome Res. 2013, 12, 5246−5252
Journal of Proteome Research
Article
KEGG pathway analysis showed that a number of pathways were represented in those proteins that were synthesized at a faster rate than the mean. These pathways included glycolysis (9.7 fold enrichment, p = 1.86 × 10−8) and proteasome (4.6 fold enrichment, p = 0.02). Other pathways were observed (e.g., RNA degradation, insulin signaling pathway, MAPK signaling pathway and regulation of actin cytoskeleton) but were not significantly enriched in this protein cohort. Three pathways that were enriched in muscle proteins were synthesized at a slower rate than the mean. Glycolysis was again represented (4.4 fold enrichment, p = 0.005) although this is likely to be indicative of the large amount of glycolytic proteins present in muscle. Ribosomal proteins were also significantly enriched (16.2 fold enrichment, p < 0.005), reflecting perhaps the stable nature of these important proteins, or the fact that the muscle was not in a state of rapid growth. Similarly in liver, the glycolysis pathway was found to be enriched in those proteins synthesized faster than the mean (6 fold enrichment, p = 0.026). Propanoate metabolism (8.9 fold enrichment, p = 0.042) was also significantly enriched. Other pathways including proteasome, ribosome and smooth muscle contraction were observed but were not significantly enriched. Where the rates of synthesis of liver proteins were slower than the mean the ribosome was significantly enriched (9.5 fold
Figure 5. Distribution of protein synthesis rates. The mean protein synthesis rates from both skeletal muscle and liver were determined. There was a difference between the two tissues, with liver exhibiting a higher average rate of synthesis (0.16 d−1) compared to muscle (0.056 d−1).
protein synthesis (and degradation) rates in order to fully understand the biology of the system.
Figure 6. Comparison of common proteins. A subset of proteins (n = 136) were identified that were common between the carp tissue types. (A) There were five proteins where the rate of synthesis in skeletal muscle (white bars) was more than double that of the same proteins in liver (hatched bars). (B) Conversely, there were 36 proteins in liver with a 2-fold higher rate of synthesis compared to the same protein in skeletal muscle. 5250
dx.doi.org/10.1021/pr4006475 | J. Proteome Res. 2013, 12, 5246−5252
Journal of Proteome Research
Article
enrichment, p < 0.005). Other pathways (glycolysis, endocytosis) were represented but not found to be significantly enriched.
carp. These tissues are routinely used to examine physical and chemical stressors in fish. Conventional proteomic approaches provide a “snapshot” at given point in time but do not address the dynamics of the proteome or the mechanism of change. Therefore, it is important to consider protein turnover in order to relate changes in protein expression to transcript data. Our strategy provides additional scope with which to explore gene− protein relationships in fish species.
Comparison of Proteins Common to Skeletal Muscle and Liver
In carp there were 136 proteins that were identified in both muscle and liver. Of these proteins, 95 had similar synthesis rates in both tissues (less than 2-fold difference). There were five proteins where the rate of synthesis observed in muscle was more than 2-fold greater than in liver (Figure 6A). These were dynein heavy chain 1 (muscle = 0.121 d−1 ± 0.02; liver = 0.03268 d−1 ± 0.0048), G protein-coupled receptor kinase 6 (muscle = 0.016 d−1 ± 0.01; liver = 0.0056 d−1 ± 0.001), fumarate hydratase (muscle = 0.113 d−1 ± 0.03; liver = 0.047 d−1 ± 0.0034), scaffold attachment factor B (muscle = 0.046 d−1 ± 0.01; liver = 0.0206 d−1 ± 0.002) and reticulon 4-l (muscle = 0.1019 d−1 ± 0.01; liver = 0.0526 d−1 ± 0.0001). Dynein is a molecular motor protein that helps transport vesicles and also plays a role in the positioning the mitotic spindles for cell division. G protein-coupled receptor kinase 6 is involved in the phosphorylation of proteins and fumarate hydratase is a mitochondrial protein involved in the TCA cycle. Scaffold attachment factor B is a DNA-binding protein and is believed to be involved in transcription of proteins and may inhibit cell proliferation. Reticulon-4-1, also known as Nogo-α, is an ER protein that is linked to vesicle and pore formation. Like GPRK6, reticulon-4-1 is primarily expressed in the brain but has also been found in skeletal muscle of zebrafish.32 Gene ontology analysis did not reveal enrichment of any clusters relating to biological process, molecular function or cellular component. A further 36 proteins had rates of synthesis in liver that was more than 2-fold greater than in muscle (Figure 6B). The majority of these proteins were cytoplasmic, with four mitochondrial, one endoplasmic reticulum and five ribosomal proteins. The proteins were mainly involved in enzyme activity (oxidoreductase, ligase, isomerase) but also transmembrane transport. Ten of these proteins are involved in protein synthesis and folding (3 ribosomal proteins, 4 heat-shock proteins, prolyl isomerase, elongation factor 1a, TCTP), which may be a reflection of the higher median protein synthesis rates observed for liver. Furthermore, synthesis rates of proteins related to carbohydrate metabolism and energy regulation are also elevated in liver compared to muscle in addition to a number of proteins related to amino acid and methylation/ folate metabolism. Gene ontology analysis showed significant (p < 0.05) enrichment of five biological processes, specifically glycolysis (36-fold), cellular metabolic processes (2.2-fold), carboxylic acid metabolic process (5.7-fold), nucleobase, nucleoside and nucleotide metabolic processes (6.45-fold) and protein folding (12.5-fold). The only significantly enriched molecular function in this cohort of proteins was nucleotide binding (2.3-fold enrichment) with no significant enrichment of specific cellular components. It is not clear why these specific proteins showed differences between the tissue types, but this would be an interesting area for further investigation, particularly if alterations in these rates were observed following application of an environmental stress to the fish.
■
ASSOCIATED CONTENT
S Supporting Information *
Identified skeletal muscle and liver proteins in carp are provided in Table S1. The enrichment analysis is detailed in Table S2. The calculated rates of protein synthesis are also provided in Table S3. This material is available free of charge via the Internet at http://pubs.acs.org.
■
AUTHOR INFORMATION
Corresponding Author
*E-mail: phil.whitfi
[email protected]. Tel: +44-1463-279-577. Fax: +44-1463-711-245. Present Address ¶
Current address: Skretting Aquaculture Research Centre, Sjøhagen 3, 4016 Stavanger, Norway.
Author Contributions ∥
I. S. Young and P. D. Whitfield are both principal investigators of this study. Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS The work was supported by the Biotechnology and Biological Sciences Research Council (Grant: BB/D012848/1 to I.S.Y. and P.D.W.), European Regional Development Fund, Highlands and Islands Enterprise and Scottish Funding Council. We thank Mr. Gregor Govan for assistance with the fish husbandry.
■
REFERENCES
(1) Bervoets, L.; Van Campenhout, K.; Reynders, H.; Knapen, D.; Covaci, A.; Blust, R. Bioaccumulation of micropollutants and biomarker responses in caged carp (Cyprinus carpio). Ecotoxicol. Environ. Saf. 2009, 72 (3), 720−728. (2) Chang, L. W.; Toth, G. P.; Gordon, D. A.; Graham, D. W.; Meier, J. R.; Knapp, C. W.; et al. Responses of molecular indicators of exposure in mesocosms: common carp (Cyprinus carpio) exposed to the herbicides alachlor and atrazine. Environ. Toxicol. Chem. 2005, 24 (1), 190−197. (3) Stepanova, S.; Praskova, E.; Chromcova, L.; Plhalova, L.; Prokes, M.; Blahova, J.; et al. The effects of diclofenac on early life stages of common carp (Cyprinus carpio). Environ. Toxicol. Pharmacol. 2013, 35 (3), 454−460. (4) Eyckmans, M.; Blust, R.; De Boeck, G. Subcellular differences in handling Cu excess in three freshwater fish species contributes greatly to their differences in sensitivity to Cu. Aquat. Toxicol. 2012, 118−119 97−107. (5) Johnston, I. A.; Temple, G. K. Thermal plasticity of skeletal muscle phenotype in ectothermic vertebrates and its significance for locomotory behaviour. J. Exp. Biol. 2002, 205 (Pt 15), 2305−2322. (6) Cossins, A.; Fraser, J.; Hughes, M.; Gracey, A. Post-genomic approaches to understanding the mechanisms of environmentally induced phenotypic plasticity. J. Exp. Biol. 2006, 209 (Pt 12), 2328− 2336.
■
CONCLUSIONS In this study we have determined the synthesis rates of hundreds of proteins from skeletal muscle and liver in common 5251
dx.doi.org/10.1021/pr4006475 | J. Proteome Res. 2013, 12, 5246−5252
Journal of Proteome Research
Article
(7) McLean, L.; Young, I. S.; Doherty, M. K.; Robertson, D. H.; Cossins, A. R.; Gracey, A. Y.; et al. Global cooling: cold acclimation and the expression of soluble proteins in carp skeletal muscle. Proteomics 2007, 7 (15), 2667−2681. (8) Tomanek, L. Environmental proteomics: changes in the proteome of marine organisms in response to environmental stress, pollutants, infection, symbiosis, and development. Annu. Rev. Mar. Sci. 2011, 3373−399. (9) Eyckmans, M.; Benoot, D.; Van Raemdonck, G. A. A.; Zegels, G.; Van Ostade, X. W. M.; Witters, E.; et al. Comparative proteomics of copper exposure and toxicity in rainbow trout, common carp and gibel carp. Comp. Biochem. Physiol., Part D: Genomics Proteomics 2012, 7 (2), 220−232. (10) Ralston-Hooper, K. J.; Turner, M. E.; Soderblom, E. J.; Villeneuve, D.; Ankley, G. T.; Moseley, M. A.; et al. Application of a label-free, gel-free quantitative proteomics method for ecotoxicological studies of small fish species. Environ. Sci. Technol. 2013, 47 (2), 1091− 1100. (11) Chen, G.; Gharib, T. G.; Huang, C. C.; Taylor, J. M.; Misek, D. E.; Kardia, S. L.; et al. Discordant protein and mRNA expression in lung adenocarcinomas. Mol. Cell. Proteomics 2002, 1 (4), 304−313. (12) Ghazalpour, A.; Bennett, B.; Petyuk, V. A.; Orozco, L.; Hagopian, R.; Mungrue, I. N.; et al. Comparative analysis of proteome and transcriptome variation in mouse. PLoS Genet. 2011, 7 (6), e1001393. (13) Martyniuk, C. J.; Popesku, J. T.; Chown, B.; Denslow, N. D.; Trudeau, V. L. Quantitative proteomics in teleost fish: insights and challenges for neuroendocrine and neurotoxicology research. Gen. Comp. Endrocrinol. 2012, 176 (3), 314−320. (14) Pratt, J. M.; Petty, J.; Riba-Garcia, I.; Robertson, D. H.; Gaskell, S. J.; Oliver, S. G.; et al. Dynamics of protein turnover, a missing dimension in proteomics. Mol. Cell. Proteomics 2002, 1 (8), 579−591. (15) Cargile, B. J.; Bundy, J. L.; Grunden, A. M.; Stephenson, J. L., Jr. Synthesis/degradation ratio mass spectrometry for measuring relative dynamic protein turnover. Anal. Chem. 2004, 76 (1), 86−97. (16) Rao, P. K.; Roxas, B. A.; Li, Q. Determination of global protein turnover in stressed mycobacterium cells using hybrid-linear ion trapfourier transform mass spectrometry. Anal. Chem. 2008, 80 (2), 396− 406. (17) Martin, S. F.; Munagapati, V. S.; Salvo-Chirnside, E.; Kerr, L. E.; Le Bihan, T. Proteome turnover in the green alga Ostreococcus tauri by time course 15N metabolic labeling mass spectrometry. J. Proteome Res. 2011, 11 (1), 476−486. (18) Doherty, M. K.; Hammond, D. E.; Clague, M. J.; Gaskell, S. J.; Beynon, R. J. Turnover of the human proteome: determination of protein intracellular stability by dynamic SILAC. J. Proteome Res. 2009, 8 (1), 104−112. (19) Doherty, M. K.; Whitehead, C.; McCormack, H.; Gaskell, S. J.; Beynon, R. J. Proteome dynamics in complex organisms: using stable isotopes to monitor individual protein turnover rates. Proteomics 2005, 5 (2), 522−533. (20) Papageorgopoulos, C.; Caldwell, K.; Shackleton, C.; Schweingrubber, H.; Hellerstein, M. K. Measuring protein synthesis by mass isotopomer distribution analysis (MIDA). Anal. Biochem. 1999, 267 (1), 1−16. (21) Price, J. C.; Guan, S.; Burlingame, A.; Prusiner, S. B.; Ghaemmaghami, S. Analysis of proteome dynamics in the mouse brain. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (32), 14508−14513. (22) Claydon, A. J.; Thom, M. D.; Hurst, J. L.; Beynon, R. J. Protein turnover: measurement of proteome dynamics by whole animal metabolic labelling with stable isotope labelled amino acids. Proteomics 2012, 12 (8), 1194−1206. (23) Doherty, M. K.; Brownridge, P.; Owen, M. A.; Davies, S. J.; Young, I. S.; Whitfield, P. D. A proteomics strategy for determining the synthesis and degradation rates of individual proteins in fish. J. Proteomics 2012, 75 (14), 4471−4477. (24) Cox, J.; Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008, 26 (12), 1367−1372.
(25) Cox, J. R.; Neuhauser, N.; Michalski, A.; Scheltema, R. A.; Olsen, J. V.; Mann, M. Andromeda: A peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 2011, 10 (4), 1794− 1805. (26) McCarthy, F.; Wang, N.; Magee, G. B.; Nanduri, B.; Lawrence, M.; Camon, E.; et al. AgBase: a functional genomics resource for agriculture. BMC Genomics 2006, 7 (1), 229. (27) Huang, D. W.; Sherman, B. T.; Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat. Protoc. 2009, 4 (1), 44−57. (28) Martin, S. A. M.; Cash, P.; Blaney, S.; Houlihan, D. F. Proteome analysis of rainbow trout (Oncorhynchus mykiss) liver proteins during short term starvation. Fish Physiol. Biochem. 2001, 24 (3), 259−270. (29) Ibarz, A.; Martín-Pérez, M.; Blasco, J.; Bellido, D.; de Oliveira, E.; Fernández-Borràs, J. Gilthead sea bream liver proteome altered at low temperatures by oxidative stress. Proteomics 2010, 10 (5), 963− 975. (30) Hellerstein, M. K.; Neese, R. A. Mass isotopomer distribution analysis: a technique for measuring biosynthesis and turnover of polymers. Am. J. Physiol. 1992, 263 (5 Pt 1), E988−1001. (31) Hellerstein, M. K.; Neese, R. A. Mass isotopomer distribution analysis at eight years: theoretical, analytic, and experimental considerations. Am. J. Physiol. 1999, 276 (6 Pt 1), E1146−1170. (32) Chen, Y. C.; Wu, B. K.; Chu, C. Y.; Cheng, C. H.; Han, H. W.; Chen, G. D.; et al. Identification and characterization of alternative promoters of zebrafish Rtn-4/Nogo genes in cultured cells and zebrafish embryos. Nucleic Acids Res. 2010, 38 (14), 4635−4650.
5252
dx.doi.org/10.1021/pr4006475 | J. Proteome Res. 2013, 12, 5246−5252
