Strategy for Studying the Liver Secretome on the Organ Level Yang Zhang, Yan Wang, Wei Sun, Lulu Jia, Sucan Ma, and Youhe Gao* National Key Laboratory of Medical Molecular Biology, Department of Physiology and Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, Beijing 100005, China Received November 18, 2009
Secretome study presents new possibilities for understanding liver secretory function in a comprehensive and exploratory way. Perfusates from isolated perfused rat liver are good targets for liver secretome study on the organ level. There are two major concerns in this type of study, cytosolic and blood contaminations in the perfusates. Therefore, the perfusion conditions were carefully controlled and alanine aminotransferase levels in the perfusates were monitored as indicators of liver integrity and cytosolic contamination. The protein pattern of perfusate was significantly different from cell lysate, which showed low cytosolic contamination. The amount of immunoglobulins in the perfusates identified by both Western blot and MS/MS indicated low serum contamination. In total, 357 secretory protein candidates were identified by the Enrichment Index method or N-terminal signal peptide prediction. Secretory proteins annotated by Swiss-Prot were 5-fold enriched in the perfusates and around 10-fold enriched in the portion identified by the Enrichment Index method. Some cytokines, secretory proteins from liver interstitial cells, and components of the liver microenvironment were found in the perfusates, highlighting the advantages of studying the liver secretome on the organ level. The strategy can be used in physiology research and biomarker discovery for diseases in the liver as well as other organs. Keywords: isolated perfused rat liver • secretome • enrichment index • signal peptide prediction
Introduction One of the major functions of the liver is to produce the main components of plasma proteins in the blood, except for gamma-globulin (immunoglobulin).1–4 These proteins participate in many vital biological processes, such as hemostasis, microbial defense and substance transport. Secretome studies present new possibilities for understanding liver secretory function in a comprehensive and exploratory way. Zwickl and his colleagues5 developed a method of identifying liver secretome by culturing human liver tissue and cells in medium containing isotope-labeled amino acids. Unfortunately, not many hepatic secretory proteins were identified in this study. Higa6 observed a secretome change in the HepG2 cell line infected by dengue virus. Roelofsen7 used SELDI-TOF to observe changes induced by copper in the medium of HepG2 cell cultures, but without protein identification. These studies were all limited to the cellular or tissue level. However, protein expression differs between cultured and in vivo conditions. Secretory proteins harvested in cell culture medium rarely match well with those obtained in vivo.8 Isolated perfused rat liver (IPRL) was first introduced by Claude Bernard in 1855. It has been a widely employed physiological model to study liver functions.9,10 Miller11 used * To whom correspondence should be addressed. Youhe Gao, National Key Laboratory of Medical Molecular Biology, Department of Physiology and Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing 100005, China. E-mail:
[email protected]. Tel: 86-10-6529-6407. Fax: 86-10-6521-2284.
1894 Journal of Proteome Research 2010, 9, 1894–1901 Published on Web 02/11/2010
this model to show that most plasma proteins were synthesized and secreted by the liver, providing some of the earliest evidence directly supporting the view that plasma proteins mainly come from the liver. IPRL can retain its organ integrity for as long as 3 to 4 h under suitable perfusion conditions.10,12 So the perfusates collected during the time window will contain the proteins secreted into the bloodstream by the liver on the organ level. These secretory proteins would more faithfully reflect true secretion conditions in the body, as they were obtained on the organ level when cell-cell communications and cell-matrix connections remain intact.9 In our work presented herein, IPRL was combined with mass spectrometry to study the liver secretome on the organ level. There are two major concerns in this type of study, cytosolic and blood contaminations in the perfusates. Therefore, the perfusion conditions were carefully controlled and alanine aminotransferase levels in the perfusates were monitored as indicators of liver integrity and cytosolic contamination. The amount of immunoglobulins in the perfusates was used as an indicator of blood contamination. The Enrichment Index method and N-terminal signal peptide prediction were then used to identify secretory proteins in the perfusates.
Experimental Procedures The overall experimental strategy is shown in Figure 1. Isolated Rat Liver Perfusion. Adult male Sprague-Dawley rats weighing 200-250 g were used in the experiments. The surgical procedure of liver perfusion was similar to that 10.1021/pr901057k
2010 American Chemical Society
Strategy for Studying the Liver Secretome on the Organ Level
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from the liver tissues were also pooled, similar to the perfusates, resulting in four pooled samples: serum mixtures A and B and liver cytosolic extract mixtures A and B.
Figure 1. Overall experimental strategy. (a) Alanine aminotransferase (ALT) level in the collected perfusate was measured to evaluate the liver structural integrity and the cytosolic contamination, and perfusates with a relatively low ALT level (e7) were chosen (Supporting Information, Table 1). Then the difference of SDS-PAGE patterns and IgG levels in the perfusate, cytosol and serum were used to evaluate the contaminations in the perfusates. The perfusates with low contamination were used for analysis.
previously reported.13 The perfusion conditions were in accordance with the standards proposed by Bessems.9 The isolated rat liver was allowed to equilibrate and wash out the residual blood in the liver for 30 min after connection to a perfusion apparatus in nonrecirculation mode. For the next 60 min, the mode was changed to recirculation for perfusate collection. The perfusate volume obtained in recirculation mode was 150 mL. Recirculation mode is defined as the infusion of the outlet perfusate into the liver again. Before the surgical procedure was initiated, 1.5 mL of blood was collected from the same rat. At the end of the experiment, the perfused rat liver was stored at -80 °C for future sample preparation. Protein Sample Preparation. Alanine aminotransferase (ALT) level in the collected perfusate was measured to evaluate the liver structural integrity and the cytosolic contamination, and perfusates with a relatively low ALT level (e7) were chosen (Supporting Information, Table 1). Finally, 12 sets of perfusate, blood and perfused rat liver from 12 individual rats were chosen and divided into two groups, A and B, each containing six sets. Perfusate. All procedures were performed at 4 °C unless otherwise specified. The samples were first centrifuged at 2500 g for 15 min. The supernatants were then transferred into fresh centrifuge tubes and centrifuged at 12000 g for 20 min to remove all remaining cells and most cell debris. The supernatants were precipitated overnight with 3 volumes of -20 °C acetone, followed by centrifugation at 14 000× g for 30 min. The pellets were resolubilized in 25 mM ammonium bicarbonate and subjected to quantitation by the Bradford method.14 Equal amounts of perfusate proteins derived from six individual rats in the same group were mixed, resulting in two pooled perfusate samples, perfusate mixtures A and B. Serum and Perfused Rat Liver Tissue. The procedure for harvesting serum protein was described previously.15 The perfused rat livers were snap-frozen with liquid nitrogen, and then ground into a powder with a pestle. The liver tissue powder was suspended in 40 mM Tris buffer on ice, followed by centrifugation at 12 000× g for 30 min at 4 °C. The supernatants were collected and quantitated by the Bradford method. The serum protein samples and the protein extracts
To reduce the differences in sample preparation and to better simulate the serum and intracellular protein contaminants in the perfusates, the serum mixtures and liver cytosolic extract mixtures were diluted in 150 mL of Krebs/Henseleit-bicarbonate buffer to a final concentration of around 0.02 µg/µL, and then the diluted mixtures were subjected to the same sample preparation procedure as perfusates. Mass Spectrometry Analysis. All six samples of the mixtures were reduced, alkylated and trypsin-digested as described previously.16 The tryptic peptides were desalted by solid-phase extraction (Oasis column, Waters, Inc.) and dried by vacuum evaporation. The dried peptides were resuspended in an aqueous solution containing 0.1% formic acid. In each run, 25 µg of tryptic peptides was loaded onto a 0.32 × 100 mm Polysulfethyl A (5 µm, 300 Å, PolyLC, Inc.) strong cation exchange column and separated into six fractions using the following elution steps: 12.5 mM, 25 mM, 50 mM, 75 mM, 125 mM and 1 M ammonium acetate. Each SCX fraction was loaded in-line onto a peptide trap and then resolved by a 0.1 × 150 mm Magic C18AQ reverse phase column (Michrom Bioresources, Inc.) using an Agilent 1200 HPLC system (Agilent, Inc.). Separation of the peptides was performed at 500 nL/min and was coupled to online analysis by tandem mass spectrometry using an LTQ XL ion trap mass spectrometer (Thermo Finnigan, Inc.) equipped with a Michrom nanospray ion source (Michrom Bioresources, Inc.). The elution gradient for the reverse column changed from 95% mobile phase A (2% acetonitrile, 0.1% formic acid, 97.9% water) to 40% mobile phase B (10% water, 0.1% formic acid, 89.9% acetonitrile) for 210 min. Elution peptide ions were detected in a survey scan from 400-2000 amu (1 µ scan) followed by seven data-dependent MS/MS scans (1 µ scan, isolation width of 2 m/z, 35% normalized collision energy, dynamic exclusion for 1 min). Four technical replicate analyses were performed for each protein mixture. Database Searching. All MS/MS spectra were searched using the SEQUEST algorithm-based Bioworks 3.3.1 (Thermo Finnigan, Inc.) against the rat IPI 3.49 protein sequence database (40131 entries). Search parameters were set as follows: precursor mass tolerance, ( 2.0 amu; fragment mass tolerance, (1.0 amu; tryptic cleavages at only lysine or arginine with up to two missed cleavage sites allowed; a static modification of +57 amu on cysteine. The search results were further processed by the Trans-Proteomic Pipeline (TPP) (Institute for Systems Biology). The SEQUEST results were validated by PeptideProphet,17 which also calculates the probability of peptide identification. ProteinProphet18 was applied to assign the peptides into proteins and calculate the probability of protein identification. The probability of protein identification was calculated based in part on the peptide probability and the SEQUEST Xcorr score.18 Only protein identifications with a probability >0.9 were considered for further analysis, as this cutoff resulted in a calculated False Discovery Rate (FDR) of around 1%. All of the peptides derived from four technical replicate runs were merged and assigned to proteins by ProteinProphet. The proteins generated by ProteinProphet and filtered with the same cutoff were considered to be the list of proteins identified in each protein mixture. Spectral Counting. Spectral counts were used to estimate the protein amount in each sample. To quantify changes in spectral counts between two samples, the Rsc algorithm19,20 Journal of Proteome Research • Vol. 9, No. 4, 2010 1895
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was applied to avoid discontinuity when a protein showed zero spectral count in one of the samples. Rsc ) log2[(n2+f)/(n1+f)] + log2[(t1-n1+f)/(t2-n2 + f)] “Where, for each protein, Rsc is the log2 ratio of abundance between samples 1 and 2; n1 and n2 are spectral counts for the protein in samples 1 and 2, respectively; t1 and t2 are total numbers of spectra over all proteins in the two samples; and f is a correction factor set to 0.5.. .”21 The Rsc values between replicate runs were also calculated in order to account for technical variation.22,23 Those proteins from two samples with Rsc values beyond the variation were considered differential. The FDR for identification of differential proteins was estimated by the proportion of duplicate pairs outside the technical variation (Supporting Information, Figure 1). Signal Peptide Prediction. SignalP 3.024 (http://www.cbs.dtu.dk/services/SignalP/) was used to predict the N-terminal signal peptide contained in classically secreted proteins. Two algorithms in SignalP, including Neural Networks and Hidden Markov Models, were employed to predict the signal peptide.24,25 We considered the proteins positively predicted by both algorithms to be secreted.
Results and Discussion 1. Control of Cytosolic and Serum Protein Contaminations in the Perfusates. To control cytosolic protein contamination from liver injury, the perfusion conditions were controlled and the period of recirculation perfusion was shortened to one hour, much shorter than the 2-4 h used in many studies.10,27,28 To reduce the amount of serum in the perfusates, a washout time of 30 min was used in this experiment, compared to the usual 10-15 min.26–28 After perfusate selection (Supporting Information, Table 1), 12 sets of perfusate, blood and perfused rat liver from 12 individual rats were divided into two groups, A and B, each containing six sets. 1.1. Low Levels of High-Abundance Cytosolic Proteins in Perfusates Indicated Low Cytosolic Contamination. Figure 2a shows the SDS-PAGE patterns, which were significantly different in the perfusate and liver cytosolic extract. Many highabundance cytosolic proteins (black arrows) were not present in the perfusate, which indicated low cytosolic contamination. Since we selected the perfusates with ALT level less than 7 before, the level could be used to some extent as a reference for future study (Figure 1 and Supporting Information, Table 1). 1.2. Low IgG Levels in Perfusates Indicated Low Serum Contamination. Immunoglobulins are high-abundance proteins, but they are not secreted by the liver,1–4 so the amount of immunoglobulins in perfusates can reflect the level of serum contamination. Therefore, IgG was chosen as an indicator of serum contamination in the perfusates. The IgG level, as shown in Figure 2b, indicated that serum contamination was still present, but at a very low level. In addition, spectral counts of IgG in the perfusates were remarkably lower than in the serum. Only five spectra (redundant peptides) from IgG were identified in perfusate mixture B compared to 978 spectra and 752 spectra in serum mixtures A and B, respectively (see Results Section 2 for details). The following factors may also help to explain low cytosolic and serum contaminations. First, the liver has low-resistance 1896
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Figure 2. Cytosolic and Serum contaminations were low in the perfusates. (a) Ten micrograms each of the perfusate and cytosol mixture from group A was loaded onto the gel. The gel was stained with Coomassie Brilliant Blue. Substantial differences were shown in the perfusate and cytosol. Many high-abundance cytosolic proteins (black arrows) were not shown in the perfusate, which indicated low cytosolic contamination. (b) Ten micrograms each of the perfusate, cytosol and serum mixture from group A was loaded and blotted with anti-IgG antibody. IgG is a good indicator of serum contamination in the perfusates. The small amount of IgG in the perfusate indicated a very low level of serum contamination.
sinuses as its terminal vessels. Second, a large amount of proteins are secreted per hour,11 which suppresses the contaminations in the perfusates. 2. Proteomic Profiling of the Perfusates from IPRL. Online SCX-RPLC-MS/MS analysis was performed to identify the proteins in the perfusates. In perfusate mixtures A and B, 792
Strategy for Studying the Liver Secretome on the Organ Level and 841 proteins were identified respectively, with probabilities >0.9 calculated by ProteinProphet. All of the proteins identified in both mixtures were merged. The proteins identified by a single unique peptide were excluded, resulting in a final total of 886 unique proteins (Supporting Information, Table 2). The protein IgG was included in the list. Five spectra (redundant peptides) from IgG were identified in perfusate mixture B. However, a total of 978 spectra and 752 spectra were identified in serum mixtures A and B, respectively. These results also indicated a low level of blood contamination in the perfusates, consistent with the Western blot results shown above. In addition to IgG, seven other proteins found in the perfusate mixtures had significantly lower abundances than in the serum mixtures (protein spectral weights in perfusate mixtures were less than one-fifth of the spectral weights in serum mixtures; these proteins are denoted by a red font color in Supporting Information, Table 2). Since they were much more abundant in the serum than in the perfusate, they could possibly be due to blood contamination. To be conservative, these eight proteins were removed from the analysis. Therefore, a total of 878 proteins in perfusate mixtures A and B were identified (Supporting Information, Table 3). 3. Perfusate Secretory Proteins Identified by the Enrichment Index Method and Signal Peptide Prediction. Cell lysis is almost inevitable in secretome research29,30 the cell lysis rate in the serum-free culture was reported to be 0.32-1.84%.29 Two methods were then used to distinguish the secretory proteins from the contaminating cytosolic proteins. Computational predictions of N-terminal signal peptides are widely used to identify classically secreted proteins,29–33 and SignalP is a routinely used software program.25 In all 878 proteins identified in the perfusates, 283 proteins were predicted by SignalP to contain a signal peptide. These proteins were considered as being classically secreted by the liver. Secretory proteins can be considered as proteins enriched in secretory protein samples (serum-free culture medium or perfusates) when compared to the cytosolic protein samples.34–36 An Enrichment Index (EI) method was developed in our lab.36 The EI quantification method36 is based on spectral counting, which can be used to estimate the amount of protein in each sample. The Rsc value19,20 is the log2 ratio of abundance between two samples and can be calculated between replicate runs to assess technical variation.22,23 Less than 5% of the proteins had Rsc values >3.3 (Supplement 2, Figure 1). Then an Rsc value of 3.3 was used as the threshold to define if the protein was enriched in the perfusate mixtures. There were a total of 219 proteins enriched in both perfusate mixtures A and B compared to the liver cytosolic extract mixtures. The quantity of these proteins in the perfusate could not be explained by contamination of cytosolic proteins from liver cell damage. These proteins were very likely to be liver secretory proteins, especially if the very low level of blood contamination was ignored (Supporting Information, Table 3). The EI method was also used to identify proteins that were enriched in comparison to the serum mixtures. In total, 263 proteins were enriched when compared to the serum. Of these, 106 proteins were enriched when compared to both the serum and cytosolic proteins, and 88 proteins having a signal peptide were enriched compared to the serum (Figure 3). Finally, 57 proteins were enriched compared to the contaminant proteins identified by the EI method and also were predicted to contain a signal peptide. These 57 proteins were considered to be highconfidence liver secretory proteins (Figure 3).
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Figure 3. Proteins found to be enriched by the EI method or predicted by SignalP to contain an N-terminal signal peptide. Of the proteins identified in the perfusates, 283 proteins (32.2%) were predicted by SignalP to contain an N-terminal signal peptide. Additionally, 219 proteins (24.9%) were found by the EI method to be enriched when compared to the liver cytosolic extracts, and 263 proteins (30.0%) were enriched when compared to the serum.
4. Secretory Proteins Were Enriched in Perfusates. SwissProt is a high-quality, manually annotated and reviewed protein database that includes protein subcellular location information. In total, 423 perfusate proteins had annotations in the database. Of these 423 proteins, 25.5% (108/423) were annotated as secreted by Swiss-Prot. For proteins that were enriched compared to the cytosol as determined by the EI method, 62.8% (71/113) were annotated as secreted. However, only around 5% of the proteins from the rat37 or mouse38,39 liver tissue proteome were annotated as secreted in the database (Figure 4). These results showed that liver secretory proteins were enriched 5-fold in the perfusates, and were enriched approximately 10-fold in the EI-enriched portion. Therefore, a higher proportion of liver secretory proteins exist in the perfusates than in the liver tissue proteome.37–39 The degree of enrichment for secretory proteins in the perfusates wassimilartoseveralpreviousstudiesonthecellularsecretome33,40 in which the percentages of secretory proteins were 20-24.4%. In the EI-enriched portion, the percentage of proteins annotated as secreted was higher than in all of the perfusate proteins and the values from previous reports,33,40 indicating that the EI method was successful for the determination of secretory proteins. 5. Comparison between the Methods of EI and Signal Peptide Prediction. UniprotKB is a high-quality protein database (http://www.uniprot.org/), which includes Swiss-Prot (manually annotated and reviewed) and TrEMBL (automatically annotated). It contains subcellular location information extracted from literature and computational analysis, which was based on sequence similarity research against annotated proteins,41 rather than from N-terminal signal peptide prediction. And the proteins annotated as secreted in this database were used as reference in previous secretome studies.24,37,42–44 So we used UniprotKB as an independent reference for comparison between the methods of EI and signal peptide prediction. We identified 219 enriched proteins by the EI method and determined 283 proteins containing a signal peptide predicted by SignalP, leading to a final total of 357 possible secretory proteins in the perfusates (Supporting Information, Table 4). Each protein in the secretory protein list was manually searched Journal of Proteome Research • Vol. 9, No. 4, 2010 1897
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Figure 4. Secretory proteins were enriched in perfusates and in the EI enriched portion. The secretory proportions in all perfusate proteins [All proteins in Perfusates] and the enriched portion compared to liver cytosolic extracts [Enriched compared to Cytosol] were significantly higher than in the rat or mouse liver tissue proteome [Rat/Mouse liver tissue]. (Number of proteins annotated by Swiss-Prot in each group). Keywords used in searching Swiss-Prot (Jan. 2009, http://www.uniprot.org/): “location: secreted/extracellular; liver; organism: Rattus norvegicus/ Mus musculus; Reviewed: yes.”
against the human, mouse and rat UniprotKB databases and the subcellular location annotation was used to determine whether it was secreted. 5.1. EI Method Was Complementary to Signal Peptide Prediction. The EI and signal peptide prediction methods are based on different principles and produced unique results, so the two methods are complementary. As shown in Figure 3, only 145 proteins of total 357 were considered secreted by both methods, partially because the EI method could also discover nonclassically secreted proteins. In the EI-enriched portion, 29 proteins without a signal peptide were annotated as secretory or membrane proteins in UniprotKB (“EI-SignalP” in Figure 5). Since nonclassically secreted proteins may not to be well annotated by UniprotKB, the actually number of verified nonclassically secreted proteins might be underestimated. In the portion identified as secreted by both methods (“SignalP∩EI” in Figure 5), as many as 82.1% were annotated as secreted and 11.7% were annotated as membrane proteins, which showed that 93.8% proteins were within the scope of the secretome. This specificity was higher than in the part determined by SignalP (“SignalP” in Figure 5), revealing that the EI method could reduce the FDR for signal peptide prediction. Some proteins containing the signal peptide are transferred to the lysosome or are retained in the endoplasmic reticulum instead of being secreted, which can lead to the FDR. Of the 283 secretory proteins predicted by SignalP, 8.5% were annotated as in the lysosome or endoplasmic reticulum, while the percentage was 3.4% in the part identified by both methods (“SignalP∩EI” in Figure 5). Therefore, the two methods can be used together to identify secretory proteins in different situations. For example, in secretory biomarker research when the candidates should be 1898
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Zhang et al. as specific as possible due to the expensive validation cost, the overlapping part (Figure 3) identified by both methods would be preferable. By contrast, with the purpose for understanding liver secretory function, comprehensiveness should be the first priority. Liver secretory proteins determined through both classical and nonclassical pathways should be included. Therefore, the combined part (Figure 3) identified by both methods would be preferable, although the FDR risk would be higher (Figure 5). 5.2. False Negatives of the EI Method. In the portion predicted as secreted by SignalP without EI-enriched (“SignalPEI” in Figure 5), 57.2% were annotated as secreted, which were the false negatives of EI method. These false negatives could be attributed to two reasons. First, the EI method considered secretory proteins as those having higher concentrations in the perfusates than in the liver cytosolic extracts. However, for some secretory proteins, the majority of the synthesized proteins was retained within the cells. Since they were not enriched in the perfusates, they were not selected by the EI method. The second was that in spectral counting quantitation, the differential threshold had to be set at a high level to reduce the FDR. In this study, only proteins that had approximately 10-fold higher spectral weights in the perfusate than in the cytosol (Rsc > 3.3) and were enriched in both perfusate mixtures, were finally considered as being truly secreted in the perfusates. Therefore, the EI method was less efficient for detecting low-abundance secretory proteins in the perfusates. For example, C-X-C motif chemokine 10 was only detected and enriched in perfusate mixture B, and thus was not selected in final result. Therefore, more precise quantitation methods can help to improve the EI method. 6. Secretory Proteins in the Perfusates. 6.1 Many Well-Known Liver Secretory Proteins Identified in Perfusates. Many well-known liver secretory proteins45 (Supporting Information, Table 5) were identified in the perfusates, including albumin, various coagulation factors, complements, carrier proteins and apolipoproteins. Some secretory proteins without a signal peptide were identified by the EI method, while some proteins not enriched were identified by SignalP. These results also highlighted that the two methods were complementary. In addition, most proteins formerly thought to be secreted by the liver were not enriched compared to serum (Supporting Information, Table 5). This result was also caused by the false negatives of the EI method, as the levels of some liver secretory proteins in the serum were the result of long-term accumulation, while they might not accumulate for long enough in the perfusate. 6.2. High-Confidence Liver Secretory Proteins Contained in the Perfusates. In total, 57 proteins were identified as highconfidence liver secretory proteins by the EI method and signal peptide prediction (Figure 3 and Supporting Information, Table 6). Among these proteins, 37 proteins (65%) were annotated as secreted by UniprotKB and 12 (21%) were annotated as membrane proteins. Not surprisingly, nearly all of proteins listed in Supporting Information, Table 6 are produced by the liver. Many well-known liver secretory proteins are found in the table, such as retinol-binding protein, insulin-like growth factor and its binding proteins, fibrinogens, beta-2-microglobulin and alpha-2 antiplasmin. Six proteins are annotated as intracellular by UniprotKB. Two proteins do not have subcellular location information until now. These eight proteins might be new liver secretory proteins at least in perfusion conditions.
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Figure 5. Comparison of EI and SignalP methods. SignalP is a bioinformatics tool to predict N-terminal signal peptides. SignalP: secretory proteins identified by signal peptide prediction; EI: secretory proteins identified by EI method; SignalP∪EI: secretory proteins identified by EI method or SignalP; SignalP∩EI: secretory proteins identified by both EI method and SignalP; SignalP-EI: secretory proteins identified by SignalP without being enriched compared to the cytosol; EI-SignalP: secretory proteins identified by EI method without containing a signal peptide. Lyso/E.R.: proteins annotated in the lysosome or endoplasmic reticulum; Other: proteins annotated in other subcelluar locations.
6.3. Low Overlap with Previous Hepatic Secretome Studies. The previous studies on the liver secretome5,6 identified 136 possible secretory proteins, but only 41 of those proteins were identified in our research. The low overlap rate could be attributed to various factors, including organism difference (all previous data were from human) and disease effects (one data set6 was from infected hepatic cells). These differences can be further explained by the following reasons. (1) The secretory proteins identified in this research were on the organ level. They differ from secretory proteins harvested in cell-culture medium and may more faithfully reflect true secretion conditions in the body. (2) As the structural integrity of the living liver was maintained, perfusates included proteins secreted from interstitial cells. We detected proteins from Kupffer cells, blood vessels and connective tissues in the liver, which otherwise would not be included in hepatic cell culture. Many extracellular matrix proteins, such as procollagens I, VI, XVI, XVIII, fibronectin 1, selenoprotein P, alpha-2-HS-glycoprotein, syndecan and lumican, were detected in the perfusates. They are the main components of liver cell microenvironments. (3) Since there were huge numbers of cells, which are unparalleled by any cell culture, the detection of low-abundance proteins was much easier. A few cytokines, including C-X-C motif chemokines 6, 9, and 10, growthregulated alpha protein, fibroblast growth factor 21, and transforming growth factor, were identified in the perfusates. (4) Nonclassically secreted proteins can be discovered by the EI method. Of the proteins enriched when compared to both the serum and cytosol (Figure 3 and Supporting Information, Table 4 for details), 49 have no signal peptide. Of those 49 proteins, 8 were annotated as secreted by UniprotKB. For example, purine nucleoside phosphorylase was found in the perfusates with 3370 spectra. This enzyme is involved in purine nucleoside metabolism and was recently discovered to be present in rat cerebrospinal fluid.46 Adenosine deaminase was another protein in this group that participates in nucleoside metabolism and the stress response, and was found to play its role extracellularly.47 (5) Perfusion conditions may cause
injuries like ischemia, which might explain the presence of related proteins such as xanthine oxidase and creatine kinase. Perfusion may also impose shear stress on the vascular endothelium which may explain the presence of several circulating proteins that were anchored on the endothelium.
Conclusion The isolated perfused rat liver model combined with mass spectrometry provided a strategy for liver secretome study on the organ level. Some cytokines, secretory proteins from liver interstitial cells and components of liver microenvironment were found in the perfusates, which showed the advantages of studying the liver secretome on the organ level. The strategy can be used in physiology research and biomarkers discovery for diseases in the liver as well as other organs.
Acknowledgment. This work was supported by grants from National Natural Science Foundation (30725009, 30870502), Research Grant for Public Interest from the Ministry of Public Health (20082007), Beijing Natural Science Foundation (5072037), Research Fund for the Doctoral Program of Higher Education (20070023021,20070023071). We thank Shuzhen Wu (Core Instrument Facility, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/ School of Basic Medicine, Peking Union Medical College) for protein sample preparation and MS analysis. Note Added after ASAP Publication. This paper was published on the Web on March 5, 2010, with an error in the caption for Figure 5. The corrected version was reposted on March 11, 2010. Supporting Information Available: Table 1 lists general characteristics of the chosen perfusates. Table 2 and 3 lists all proteins identified in perfusates. Table 4 lists all possible secretory proteins identified in perfusates. Table 5 lists the proteins in perfusates routinely considered to be secreted by the liver. Table 6 lists high-confidence liver secretory proteins Journal of Proteome Research • Vol. 9, No. 4, 2010 1899
research articles contained in perfusates. Figure 1 shows Frequency versus Rsc values between replicate runs. This material is available free of charge via the Internet at http://pubs.acs.org.
References (1) Linke, T.; Doraiswamy, S.; Harrison, E. H. Rat plasma proteomics: effects of abundant protein depletion on proteomic analysis. J. Chromatogr., B.: Analyt. Technol. Biomed. Life Sci. 2007, 849 (12), 273–81. (2) Zhang, Q.; Menon, R.; Deutsch, E. W.; Pitteri, S. J.; Faca, V. M.; Wang, H.; Newcomb, L. F.; Depinho, R. A.; Bardeesy, N.; Dinulescu, D.; Hung, K. E.; Kucherlapati, R.; Jacks, T.; Politi, K.; Aebersold, R.; Omenn, G. S.; States, D. J.; Hanash, S. M. A mouse plasma peptide atlas as a resource for disease proteomics. Genome Biol. 2008, 9 (6), R93. (3) Miller, L. L.; Bale, W. F. Synthesis of all plasma protein fractions except gamma globulins by the liver; the use of zone electrophoresis and lysine-epsilon-C14 to define the plasma proteins synthesized by the isolated perfused liver. J. Exp. Med. 1954, 99 (2), 125–32. (4) Miller, L. L.; Bly, C. G.; Bale, W. F. Plasma and tissue proteins produced by non-hepatic rat organs as studied with lysine-epsilonC14; gamma globulins the chief plasma protein fraction produced by non-hepatic tissues. J. Exp. Med. 1954, 99 (2), 133–53. (5) Zwickl, H.; Traxler, E.; Staettner, S.; Parzefall, W.; Grasl-Kraupp, B.; Karner, J.; Schulte-Hermann, R.; Gerner, C. A novel technique to specifically analyze the secretome of cells and tissues. Electrophoresis 2005, 26 (14), 2779–85. (6) Higa, L. M.; Caruso, M. B.; Canellas, F.; Soares, M. R.; OliveiraCarvalho, A. L.; Chapeaurouge, D. A.; Almeida, P. M.; Perales, J.; Zingali, R. B.; Da Poian, A. T. Secretome of HepG2 cells infected with dengue virus: implications for pathogenesis. Biochim. Biophys. Acta 2008, 1784 (11), 1607–16. (7) Roelofsen, H.; Balgobind, R.; Vonk, R. J. Proteomic analyzes of copper metabolism in an in vitro model of Wilson disease using surface enhanced laser desorption/ionization-time of flight-mass spectrometry. J. Cell. Biochem. 2004, 93 (4), 732–40. (8) Schelfhout, V. R.; Coene, E. D.; Delaey, B.; Waeytens, A. A.; De Rycke, L.; Deleu, M.; De Potter, C. R. The role of heregulin-alpha as a motility factor and amphiregulin as a growth factor in wound healing. J. Pathol. 2002, 198 (4), 523–33. (9) Bessems, M.; t Hart, N. A.; Tolba, R.; Doorschodt, B. M.; Leuvenink, H. G.; Ploeg, R. J.; Minor, T.; van Gulik, T. M. The isolated perfused rat liver: standardization of a time-honoured model. Lab. Anim. 2006, 40 (3), 236–46. (10) Gores, G. J.; Kost, L. J.; LaRusso, N. F. The isolated perfused rat liver: conceptual and practical considerations. Hepatology 1986, 6 (3), 511–7. (11) Miller, L. L.; Bly, C. G.; Watson, M. L.; Bale, W. F. The dominant role of the liver in plasma protein synthesis; a direct study of the isolated perfused rat liver with the aid of lysine-epsilon-C14. J. Exp. Med. 1951, 94 (5), 431–53. (12) Mischinger, H. J.; Walsh, T. R.; Liu, T.; Rao, P. N.; Rubin, R.; Nakamura, K.; Todo, S.; Starzl, T. E. An improved technique for isolated perfusion of rat livers and an evaluation of perfusates. J. Surg. Res. 1992, 53 (2), 158–65. (13) Corbello Pereira, S. R.; Darronqui, E.; Constantin, J.; da Silva, M. H.; Yamamoto, N. S.; Bracht, A. The urea cycle and related pathways in the liver of Walker-256 tumor-bearing rats. Biochim. Biophys. Acta 2004, 1688 (3), 187–96. (14) Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 1976, 72, 248–54. (15) He, P.; He, H. Z.; Dai, J.; Wang, Y.; Sheng, Q. H.; Zhou, L. P.; Zhang, Z. S.; Sun, Y. L.; Liu, F.; Wang, K.; Zhang, J. S.; Wang, H. X.; Song, Z. M.; Zhang, H. R.; Zeng, R.; Zhao, X. The human plasma proteome: analysis of Chinese serum using shotgun strategy. Proteomics 2005, 5 (13), 3442–53. (16) Sun, W.; Li, F.; Wu, S.; Wang, X.; Zheng, D.; Wang, J.; Gao, Y. Human urine proteome analysis by three separation approaches. Proteomics 2005, 5 (18), 4994–5001. (17) 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. Anal. Chem. 2002, 74 (20), 5383–92. (18) Nesvizhskii, A. I.; Keller, A.; Kolker, E.; Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 2003, 75 (17), 4646–58.
1900
Journal of Proteome Research • Vol. 9, No. 4, 2010
Zhang et al. (19) Beissbarth, T.; Hyde, L.; Smyth, G. K.; Job, C.; Boon, W. M.; Tan, S. S.; Scott, H. S.; Speed, T. P. Statistical modeling of sequencing errors in SAGE libraries. Bioinformatics 2004, 20 (1), i31–9. (20) Rual, J. F.; Venkatesan, K.; Hao, T.; Hirozane-Kishikawa, T.; Dricot, A.; Li, N.; Berriz, G. F.; Gibbons, F. D.; Dreze, M.; Ayivi-Guedehoussou, N.; Klitgord, N.; Simon, C.; Boxem, M.; Milstein, S.; Rosenberg, J.; Goldberg, D. S.; Zhang, L. V.; Wong, S. L.; Franklin, G.; Li, S.; Albala, J. S.; Lim, J.; Fraughton, C.; Llamosas, E.; Cevik, S.; Bex, C.; Lamesch, P.; Sikorski, R. S.; Vandenhaute, J.; Zoghbi, H. Y.; Smolyar, A.; Bosak, S.; Sequerra, R.; Doucette-Stamm, L.; Cusick, M. E.; Hill, D. E.; Roth, F. P.; Vidal, M. Towards a proteomescale map of the human protein-protein interaction network. Nature 2005, 437 (7062), 1173–8. (21) Old, W. M.; Meyer-Arendt, K.; Aveline-Wolf, L.; Pierce, K. G.; Mendoza, A.; Sevinsky, J. R.; Resing, K. A.; Ahn, N. G. Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Mol. Cell. Proteomics 2005, 4 (10), 1487–502. (22) Keene, S. D.; Greco, T. M.; Parastatidis, I.; Lee, S. H.; Hughes, E. G.; Balice-Gordon, R. J.; Speicher, D. W.; Ischiropoulos, H. Mass spectrometric and computational analysis of cytokine-induced alterations in the astrocyte secretome. Proteomics 2009, 9 (3), 768– 82. (23) Wang, Y.; Chen, Y.; Zhang, Y.; Wu, S.; Ma, S.; Hu, S.; Zhang, L.; Shao, C.; Li, M.; Gao, Y. Differential ConA-enriched urinary proteome in rat experimental glomerular diseases. Biochem. Biophys. Res. Commun. 2008, 371 (3), 385–90. (24) Bendtsen, J. D.; Nielsen, H.; von Heijne, G.; Brunak, S. Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol. 2004, 340 (4), 783–95. (25) Emanuelsson, O.; Brunak, S.; von Heijne, G.; Nielsen, H. Locating proteins in the cell using TargetP, SignalP and related tools. Nat. Protoc. 2007, 2 (4), 953–71. (26) Mehvar, R.; Zhang, X. Development and application of an isolated perfused rat liver model to study the stimulation and inhibition of tumor necrosis factor-alpha production ex vivo. Pharm. Res. 2002, 19 (1), 47–53. (27) Hong, Y.; Ramzan, I.; McLachlan, A. J. Hepatobiliary disposition of liposomal amphotericin B in the isolated perfused rat liver. J. Pharm. Sci. 2005, 94 (1), 169–76. (28) Vuppugalla, R.; Mehvar, R. Selective effects of nitric oxide on the disposition of chlorzoxazone and dextromethorphan in isolated perfused rat livers. Drug. Metab. Dispos. 2006, 34 (7), 1160–6. (29) Wu, H. Y.; Chang, Y. H.; Chang, Y. C.; Liao, P. C. Proteomics analysis of nasopharyngeal carcinoma cell secretome using a hollow fiber culture system and mass spectrometry. J. Proteome Res. 2009, 8 (1), 380–9. (30) Sardana, G.; Jung, K.; Stephan, C.; Diamandis, E. P. Proteomic analysis of conditioned media from the PC3, LNCaP, and 22Rv1 prostate cancer cell lines: discovery and validation of candidate prostate cancer biomarkers. J. Proteome Res. 2008, 7 (8), 3329–38. (31) Alvarez-Llamas, G.; Szalowska, E.; de Vries, M. P.; Weening, D.; Landman, K.; Hoek, A.; Wolffenbuttel, B. H.; Roelofsen, H.; Vonk, R. J. Characterization of the human visceral adipose tissue secretome. Mol. Cell. Proteomics 2007, 6 (4), 589–600. (32) Roelofsen, H.; Dijkstra, M.; Weening, D.; de Vries, M. P.; Hoek, A.; Vonk, R. J. Comparison of isotope-labeled amino acid incorporation rates (CILAIR) provides a quantitative method to study tissue secretomes. Mol. Cell. Proteomics 2009, 8 (2), 316–24. (33) Yoon, J. H.; Yea, K.; Kim, J.; Choi, Y. S.; Park, S.; Lee, H.; Lee, C. S.; Suh, P. G.; Ryu, S. H. Comparative proteomic analysis of the insulin-induced L6 myotube secretome. Proteomics 2009, 9 (1), 51–60. (34) Chevallet, M.; Diemer, H.; Van Dorssealer, A.; Villiers, C.; Rabilloud, T. Toward a better analysis of secreted proteins: the example of the myeloid cells secretome. Proteomics 2007, 7 (11), 1757–70. (35) Jacobs, J. M.; Waters, K. M.; Kathmann, L. E.; Camp, D. G.; Wiley, H. S.; Smith, R. D.; Thrall, B. D. The mammary epithelial cell secretome and its regulation by signal transduction pathways. J. Proteome Res. 2008, 7 (2), 558–69. (36) Chen, Y.; Gu, B.; Wu, S.; Sun, W.; Ma, S.; Liu, Y.; Gao, Y. Using enrichment index for quality control of secretory protein sample and identification of secretory proteins. J. Mass Spectrom. 2009, 44 (3), 397–403. (37) Jiang, X. S.; Zhou, H.; Zhang, L.; Sheng, Q. H.; Li, S. J.; Li, L.; Hao, P.; Li, Y. X.; Xia, Q. C.; Wu, J. R.; Zeng, R. A high-throughput approach for subcellular proteome: identification of rat liver proteins using subcellular fractionation coupled with twodimensional liquid chromatography tandem mass spectrometry and bioinformatic analysis. Mol. Cell. Proteomics 2004, 3 (5), 441– 55.
research articles
Strategy for Studying the Liver Secretome on the Organ Level (38) Foster, L. J.; de Hoog, C. L.; Zhang, Y.; Zhang, Y.; Xie, X.; Mootha, V. K.; Mann, M. A mammalian organelle map by protein correlation profiling. Cell 2006, 125 (1), 187–99. (39) Shi, R.; Kumar, C.; Zougman, A.; Zhang, Y.; Podtelejnikov, A.; Cox, J.; Wisniewski, J. R.; Mann, M. Analysis of the mouse liver proteome using advanced mass spectrometry. J. Proteome Res. 2007, 6 (8), 2963–72. (40) Wu, H. Y.; Chang, Y. H.; Chang, Y. C.; Liao, P. C. Proteomics Analysis of Nasopharyngeal Carcinoma Cell Secretome Using a Hollow Fiber Culture System and Mass Spectrometry. J. Proteome Res. 2009, 8, 380–9. (41) Apweiler, R. Functional information in SWISS-PROT: the basis for large-scale characterisation of protein sequences. Brief Bioinform. 2001, 2 (1), 9–18. (42) Emanuelsson, O.; Nielsen, H.; Brunak, S.; von Heijne, G. Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J. Mol. Biol. 2000, 300 (4), 1005–16. (43) Bendtsen, J. D.; Jensen, L. J.; Blom, N.; Von Heijne, G.; Brunak, S. Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng. Des. Sel. 2004, 17 (4), 349–56.
(44) Jiang, X. S.; Dai, J.; Sheng, Q. H.; Zhang, L.; Xia, Q. C.; Wu, J. R.; Zeng, R. A comparative proteomic strategy for subcellular proteome research: ICAT approach coupled with bioinformatics prediction to ascertain rat liver mitochondrial proteins and indication of mitochondrial localization for catalase. Mol. Cell. Proteomics 2005, 4 (1), 12–34. (45) Boron, W. F.; Boulpaep, E. L. Medical Physiology: A Cellular and Molecular Approach; Saunders: Philadelphia, PA, 2003; p 996. (46) Silva, R. G.; Santos, D. S.; Basso, L. A.; Oses, J. P.; Wofchuk, S.; Portela, L. V.; Souza, D. O. Purine nucleoside phosphorylase activity in rat cerebrospinal fluid. Neurochem. Res. 2004, 29 (10), 1831–5. (47) Correia-de-Sa, P.; Adaes, S.; Timoteo, M. A.; Vieira, C.; MagalhaesCardoso, T.; Nascimento, C.; Duarte-Araujo, M. Fine-tuning modulation of myenteric motoneurons by endogenous adenosine: on the role of secreted adenosine deaminase. Auton. Neurosci. 2006, 12 (6-127), 211–24.
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