- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Journal of Biomedicine and Biotechnology
Volume 2011 (2011), Article ID 248613, 12 pages
Relative Quantification of Several Plasma Proteins during Liver Transplantation Surgery
1HUSLAB, Helsinki University Central Hospital, 00290 Helsinki, Finland
2Transplantation Laboratory and Infection Biology Research Program, Haartman Institute, University of Helsinki, 00290 Helsinki, Finland
3Transplantation and Liver Surgery Clinic, Helsinki University Central Hospital, 00290 Helsinki, Finland
Received 7 July 2011; Revised 26 August 2011; Accepted 18 September 2011
Academic Editor: P. J. Oefner
Copyright © 2011 Ville Parviainen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Plasma proteome is widely used in studying changes occurring in human body during disease or other disturbances. Immunological methods are commonly used in such studies. In recent years, mass spectrometry has gained popularity in high-throughput analysis of plasma proteins. In this study, we tested whether mass spectrometry and iTRAQ-based protein quantification might be used in proteomic analysis of human plasma during liver transplantation surgery to characterize changes in protein abundances occurring during early graft reperfusion. We sampled blood from systemic circulation as well as blood entering and exiting the liver. After immunodepletion of six high-abundant plasma proteins, trypsin digestion, iTRAQ labeling, and cation-exchange fractionation, the peptides were analyzed by reverse phase nano-LC-MS/MS. In total, 72 proteins were identified of which 31 could be quantified in all patient specimens collected. Of these 31 proteins, ten, mostly medium-to-high abundance plasma proteins with a concentration range of 50–2000 mg/L, displayed relative abundance change of more than 10%. The changes in protein abundance observed in this study allow further research on the role of several proteins in ischemia-reperfusion injury during liver transplantation and possibly in other surgery.
Blood plasma is one of the most complex biological fluids in humans. It contains hundreds of physiological circulatory proteins and hundreds to thousands of condition-specific proteins that change in concentration in response to pathophysiological changes in the body . This richness of proteins and its accessibility makes plasma one of the most commonly used sources for medical diagnostics [2–4]. Currently used routine immunodiagnostic tests are able to identify and quantify numerous plasma biomarkers with great precision and sensitivity. The problem with modern immunodiagnostic methods is that they are based on antibody interactions with the proteins being investigated, requiring a separate test for each biomarker, thus increasing the workload and costs in medical healthcare. To overcome the limitations of single ELISA assay, several chip-based antibody microarray methods have been developed [5–7]. In antibody microarrays the antibodies are attached to the surface of the assay chip and the sample is flushed over the chip allowing the proteins to bind to the antibodies on the chip. After washes the bound proteins can be detected with either direct labeling  or indirect sandwich type immunoassays . The main benefit of antibody microarrays is the sensitivity and precision of traditional antibody assays combined with the speed and efficiency of analyzing numerous proteins simultaneously. The disadvantage is the requirement for specific antibodies targeted to the desired proteins or against each posttranslationally modified version of the protein. In addition, the dynamic range of antibody microarrays is generally similar to normal ELISA-type assays, ranging three to four orders of magnitude [6, 10, 11] complicating the analysis of complex, high dynamic range samples such as plasma. Consequently, the current antibody arrays are most suitable for targeted analysis of disease or condition-specific biomarkers [7, 12–14] rather than examining changes in the whole proteome or discovering novel biomarkers.
Advances in the field of mass spectrometry (MS) have enabled an alternative way of investigating cellular and plasma protein biomarkers [15–17]. Current MS machinery and bioinformatics software are rapidly approaching the sensitivity and dynamic range limits of immunodiagnostic tests [18, 19]. The main benefit of the MS-based assay is the ability to characterize tens to hundreds of proteins simultaneously  without the need to target any specific posttranslational modification, subproteome, or disease. Further, parallel analysis of several proteins in a single MS run greatly reduces the time and costs of diagnostic analyses. The drawback on using plasma in MS-based assays is the high dynamic range of protein abundances . Albumin, the most abundant protein in plasma, constitutes ca. 60% of the entire amount of proteins in plasma at approximate concentration of 50 mg/mL. On the other hand, cytokines are found in plasma at the level of picograms per milliliter or lower. Such variability in protein abundances is a problem for modern mass spectrometers with a dynamic range of 103–104 . In this study we examined the applicability of iTRAQ peptide labeling technology  to characterize relative changes in the levels of several plasma proteins during liver transplantation (Figure 1).
The success of liver transplantation is significantly influenced by both preservation and reperfusion injury. During procurement, liver is disconnected from circulation, resulting in interrupted oxygen supply to cells. Ischemia induces several physiological effects such as activation of apoptosis and necrosis pathways . Upon graft implantation, the cells suffer from reperfusion injury. Reactive oxygen species are generated after the sudden increase in cellular oxygen levels leading to DNA, plasma membrane, and protein damage . Further, inflammation and coagulation cascades become activated damaging both the graft and other end-organs. Characterization of protein abundance changes during liver transplantation could be extremely helpful in patient prognosis and care. In this pilot study we characterized several plasma proteins that change in abundance during graft reperfusion in liver transplantation using iTRAQ peptide labeling technology.
Blood samples were drawn simultaneously from portal and hepatic veins and from radial artery. Following immunodepletion of the six high-abundant human plasma proteins, the plasma proteome was digested to peptides with trypsin. The digests were then labeled with iTRAQ 8plex mass tags. The iTRAQ technology is based on small amine-reactive isobaric mass tags that can be attached to the N-terminus and lysine residues of digested peptides. The tag consists of a reporter ion used for quantification and a balancer group balancing the reporter masses of each iTRAQ tag to equal mass. During collision-induced fragmentation within the mass spectrometer the peptide bound mass tag is fragmented, along with the peptide backbone, to produce a balancer group and a reporter ion of known mass (113, 114, 115, 116, 117, 118, 119, and 121 daltons in iTRAQ 8plex system). Each reporter ion produces a distinct MS fragment where the intensity of the reporter ion fragment corresponds to the amount of the peptide. The relative abundances of the peptides, and ultimately proteins, which are derived from different iTRAQ labeling pools (i.e., different samples), can be deduced from these spectra.
Samples were fractionated by means of strong cation-exchange and reverse phase chromatography and finally analyzed by quadrupole time-of-flight mass spectrometry. Protein identification and quantification was performed using the Mascot search engine. We were able to identify 72 different proteins and to quantify 31 of those proteins in all patient specimens.
2. Materials and Methods
2.1. Patients and Sampling
Three male patients undergoing liver transplantation surgery for primary sclerosing cholangitis (PSC) in the transplantation and liver surgery clinic of the surgical hospital participated in this study (Table 1). Patients with PSC were recruited to ensure similar surgical technique and comparable cold ischemia and anhepatic times and perioperative bleeding. The patients were transplanted within a time period of two months.
The study was approved by the Ethics Committee of the Department of Surgery, Hospital District of Helsinki and Uusimaa (DNr. 33/13/03/02/08). All grafts were obtained from brain-dead donors and were preserved with University of Wisconsin solution. The classic surgical technique of transplantation was used without bypass or piggyback. In brief, the patient’s native liver is removed after clamping of supra- and infrahepatic inferior caval vein. Then the portal vein and hepatic artery are clamped. The suprahepatic caval anastomosis is completed first. Just before completion of portal anastomosis, the liver is flushed via the portal vein with 1000 mL of Ringer’s solution with the suprahepatic caval vein clamped, followed by rinse with 400 mL of portal venous blood. After graft rinsing, the suprahepatic caval and portal veins are declamped, followed by completion of the infrahepatic caval anastomosis and declamping. Finally, the hepatic arterial anastomosis is completed.
Three separate 10 mL blood specimens were taken from each patient during early graft reperfusion. A sample representing blood flow from the liver was drawn from hepatic vein during graft flushing with portal blood before permanent connection to circulation. Simultaneously, a sample representing blood entering the liver was drawn from portal vein. To monitor changes in systemic circulation, an additional sample was drawn from radial artery. Samples from hepatic and portal veins were drawn by puncture directly from the vessel whereas the sample from radial artery was drawn from an indwelling arterial cannula. All samples were drawn into sterile syringes and transferred immediately to sodium citrate anticoagulated tubes (Vacutainer Systems Europe, Becton Dickinson, Plymouth, UK) and placed on ice until further processing. Within 30 minutes, plasma was separated by centrifugation at 2000 g for 10 minutes at +4°C and aliquotted to 150 μL fractions. The aliquots were then stored at −80°C until analysis. Samples from each patient were analyzed at the same time, approximately 6 months after sampling. The entire protocol for each patient was performed twice from depletion to MS analysis.
2.2. Plasma Depletion
To pellet the precipitated material, 450 μL of plasma were thawed on ice and centrifuged at 16 100 g at +4°C for 5 minutes. The supernatant was diluted 1 : 5 with Agilent Multiple Affinity Removal Buffer A and proteinase inhibitor was added to the samples (complete EDTA free mini, Roche Diagnostics GmbH, Mannheim, Germany). The samples were filtered with 0.22 μm syringe filters (low protein binding Durapore, Millipore Corporation, Billerica, MA). Protein concentrations measurements were done from all samples using BCA protein concentration measurement kit (Pierce, Rockford, IL). In order to maintain the original concentration ratios throughout the processing of the samples, the volumes were kept equal in all samples, 455 μL of diluted plasma from each sample were diluted to 2 mL with Agilent Buffer A. Six high-abundant plasma proteins, including albumin, IgG, IgA, transferrin, haptoglobin, and antitrypsin, were depleted using Agilent Multiple Affinity Removal LC Column-Human 6 (Agilent Technologies, Palo Alto, CA) using Waters 666 pump and 600S controller HPLC system coupled to Waters 2487 Dual wavelength absorbance detector (Waters Corporation, Milford, MA). The injection volume was 200 μL, 1.75 mL of flow thru fraction containing low abundance proteins was collected. Injection and collection was performed three times to monitor the reproducibility of the depletion (representative chromatograms of repeatability and sample-to-sample comparison are shown in Supplemental File 4 in supplementary Material available online at doi: 10.1155/2011/248613). A control sample was prepared by pooling and then aliquotting the arterial, hepatic, and portal vein samples from one patient. The control sample was used in iTRAQ quantification as a benchmark to which all three patient samples were compared in order to acquire the quantification values. All samples were then stored at −80°C.
2.3. Protein Precipitation, Digestion, and iTRAQ Labeling
Portal, hepatic, and arterial blood samples from two patients were analyzed simultaneously with the control sample. 150 μL of the low abundance protein fraction, containing an average protein mass of 51.75 μg in arterial sample, 51.05 μg in portal sample, and 40.4 μg in hepatic sample, was precipitated by adding 100% trichloroacetic acid to a final concentration of 25% (v/v) and incubated on ice for 30 minutes with periodic vortexing. Precipitated proteins were pelleted by centrifugation (16 100 g, +4°C, 5 minutes) using a table top centrifuge. The protein pellet was washed once with 500 μL of −20°C acetone with 0.1 M hydrochloric acid and centrifugated (16 100 g, +4°C, 5 minutes), and the wash was repeated using −20°C acetone.
Digestion was performed by the iTRAQ peptide labeling protocol by AB Sciex (Concord, Canada). All used buffers were from iTRAQ Reagent 8plex Buffer Kit from AB Sciex. First, the protein samples were dissolved into 50 μL of iTRAQ dissolution buffer. Two microliters of iTRAQ denaturant were added to the samples and mixed vigorously to solubilize the protein pellet. Then 2 μL of reducing reagent were added and samples were incubated at +60°C for 1 hour. After reduction, 1 μL of cysteine blocking reagent was added followed by incubation at room temperature for 10 minutes. Two micrograms of trypsin (Promega sequencing grade modified trypsin, Promega, Madison, WI) were added to the samples, which were then incubated at +37°C for approximately 16 hours.
iTRAQ reagents were dissolved in 50 μL of isopropanol and transferred to the digested peptide samples. The labeling reaction was performed at room temperature for 2 hours before quenching by 10 μL of 1 M ammonium bicarbonate. Quenching was allowed to progress at room temperature for 30 minutes. After labeling, the samples were pooled into a new sample tube (the full iTRAQ labeling scheme is given in Supplemental File 4).
2.4. Peptide Fractionation and Mass Spectrometry
To reduce the complexity of peptide samples for MS, a fractionation step was performed with a cation-exchange cartridge system (AB Sciex). The cartridge was washed and conditioned according to the Manufacturer’s instructions. The pooled sample was evaporated to below 30 μL, diluted to 1 mL of cation-exchange load buffer and pH was adjusted to 2.5–3.5 with HCl. The sample was slowly injected into the cartridge and washed with 1 mL of cation-exchange load buffer. Elution of peptides was performed by increasing the concentration of KCl. Eleven 500 μL elution fractions were collected (5 mM, 10 mM, 15 mM, 25 mM, 35 mM, 50 mM, 75 mM, 100 mM, 150 mM, 200 mM, and 350 mM KCl in 10 mM KH2HPO4, pH = 3, with 25% acetonitrile). All fractions were dried in a SpeedVac vacuum concentrator (Thermo Savant, Holbrook, NY) and solubilized to 100 μL of MS phase A buffer (5% acetonitrile with 0.1% formic acid). Fractions were stored at −20°C until mass spectrometric analysis.
On-line reverse phase separation was performed using Waters Symmetry300, C18, 5 μm NanoEase Trap Column coupled to an LC Packings-Dionex Acclaim PepMap C18 column, 75 μm × 250 mm (LC Packings/Dionex, Amsterdam, The Netherlands) on a Waters-Micromass nanoLC CapLC (passive stream-split) coupled to a Q-TOF Ultima Global mass spectrometer. Eluent A contained 0.1% formic acid in 5% acetonitrile, eluent B 0.1% formic acid in 95% acetonitrile. The flow rate during the LC-run was set to 300–400 nL/minute depending on the amount of organic solvent, with a gradient ranging from 95% to 5% of eluent A. MS data was acquired for 90 minutes in positive ion mode by data-dependent acquisition collecting the precursor MS and six MSMS spectra during one duty cycle. The threshold for switching to MSMS was set to ten counts per second for each individual ion and 5 counts per second for switching back to MS scan. The scan time was 2.4 seconds with a 0.1 second interscan delay. The scan range for MS scan was set to 300–2000 m/z and 50–2500 for MSMS. No lock-mass correction was used (Exact Waters-Micromass nanoLC CapLC and Q-TOF Ultima Global mass spectrometer settings and an example chromatogram of MS run are shown in Supplemental File 4).
2.5. Protein Identification and iTRAQ Quantification
Each MS run was performed twice to gather more data and to compensate for the stochastic nature of the data-dependent acquisition. The peak lists were calculated using Mascot Distiller Software (version 126.96.36.199, Matrix Science, London, UK) and the data from each KCl elution fraction was merged. Peptides were identified with Mascot search engine (version 2.2.04, Matrix Science) against SwissProt database (version 57.7) with Homo sapiens as the specified organism. The parameters for search were trypsin digested peptides with maximum of two missed cleavages, iTRAQ-8plex on lysine and N-terminus as well as methylthio-cysteine as fixed modifications, and oxidation of methionine as variable modification. Default Mascot iTRAQ correction factors were used to compensate for the iTRAQ reagent isotope impurities. Peptide tolerances for the search were ±0.2 Da for parent and ±0.1 Da for fragment ions resulting in false discovery rate of less than 1.4% when searched against false decoy database. The significance threshold for peptides was set to “Identity” and probability value of 0.05. For peptide identification, a Mascot peptide score of more than 20 was required and “Require bold red” designation was used for protein identification.
After peptide identification, an exclusion list was constructed from the identified peptides. This exclusion list was used in subsequent identical MS run to search for unidentified peptides. The resulting peak lists were again merged and searched. A second exclusion list was created from these results and combined with the first list. The fractions were then similarly analyzed with MS for the third time. The benefit of using exclusion lists is clearly illustrated in Figure 2 representing the total distinct peptides that were identified in first iTRAQ experiment. The first exclusion MS run identified 97 and the second 38 additional novel peptides. On the average, the first exclusion run increased the distinct peptide count by 28% and the second run by 10%. All peak lists, from the no-exclusion to two exclusion runs were finally combined and searched with the same search parameters.
Protein identification and quantification was performed with Mascot search engine using MudPIT scoring for protein identification. Normalization was done using summed peptide intensities and automatic outlier removal. This corrects for the possible dilution effect of rinsing the graft with Ringer solution before flushing with portal blood. This normalization step is based on the assumption that the overall plasma protein concentration does not change in the liver. Protein ratios were calculated as median ratio of each patient sample to control sample with at least three peptides required for quantification, (full combined Mascot results are given in Supplemental Files 1–3).
The quantification results were copied to Microsoft Office Excel (Microsoft Corporation, Redmond, WA) and differences between samples were calculated as percent changes. Change across the hepatic circulation (portal to hepatic venous change) was calculated as hepatic vein quantification value subtracted by the portal vein value and divided by the original hepatic vein value. Similarly, change from systemic circulation to portal venous blood was calculated as portal vein quantification values subtracted from arterial value and divided by the original portal value. To calculate the change between arterial and hepatic venous samples, the hepatic vein values were subtracted from arterial value and then divided by hepatic vein value. Finally the resulting values were multiplied by 100 to gain percentage changes.
All quantification values of patient samples and repeat runs were then averaged. Due to the small number of patients and quality of the data, a significance threshold of 10% was used to filter the results.
The blood samples were obtained from patients undergoing liver transplantation surgery during the early phase of reperfusion. Samples were drawn from the portal vein (blood entering the liver), hepatic vein (blood exiting the liver), and the radial artery (systemic circulation).
After sampling blood was centrifuged to yield plasma. The protein concentrations were measured before high abundance plasma protein depletion. Generally the portal venous and systemic arterial samples had equal concentrations, 76.4 mg/mL in portal venous and 74.5 mg/mL in arterial blood, while the hepatic venous samples had a markedly lower concentration, an average of 53.6 mg/mL. The dilution is probably due to surgical procedures as the liver is first rinsed with Ringer solution before flushing with portal blood and subsequent sampling of this effluent blood from hepatic vein. To reduce the complexity of the samples by removing the highly abundant albumin and five other high-abundant proteins (IgG, IgA, transferrin, haptoglobin, and antitrypsin), a small aliquot of each sample was subjected to Agilent Multiaffinity Removal column. This depletion step reduced the protein content of the samples by 65 to 75%.
After depletion, trypsin digestion, iTRAQ labeling, extensive peptide fractionation, and mass spectrometer runs, the data files of each patient and repeat runs were combined. This overall list was then searched against Swiss-Prot human database and 72 proteins were identified with good confidence (Table 2. A complete list of all identifications and quantification is found in Supplemental Files 1–3).
Most of the identified proteins (53 of 72) are found on top of the 150 most abundant proteins in plasma (HUPO Plasma Proteome Project 3020 confirmed identifications ). This demonstrates the limitations in sensitivity and dynamic range of the mass spectrometer used in this study. Of the 72 identified proteins, 31 could be quantified in all patient specimens and repeat runs (Figure 3).
As the identified proteins belong to the medium-to-high abundance plasma protein category and major changes in protein abundance are improbable, we decided to consider significant only those changes where the average change of all MS runs was more than 10%.
For changes within the liver from portal to hepatic venous blood, eight proteins changed more than 10%. Five of these were reduced in concentration: histidine-rich glycoprotein (HRG, Swiss-Prot:P04196), antithrombin-III (SERPINC, Swiss-Prot:P01008), fibrinogen alpha chain (FGA, Swiss-Prot:P02671), apolipoprotein E (APOE Swiss-Prot: P02649), and beta-2-glycoprotein 1 (APOH Swiss-Prot: P02749)) and three had increased across the liver: alpha-1-acid glycoprotein 1 (ORM1 Swiss-Prot: P02763), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4 Swiss-Prot: Q14624), and alpha-2-macroglobulin (A2M Swiss-Prot: P01023). The differences in plasma proteome in systemic arterial to hepatic venous sample were similar to the portal to hepatic venous change: HRG, SERPINC, and FGA had lower and ORM1, ITIH4, and A2M higher concentrations within the liver. Additionally two other proteins, serum paraoxonase/arylesterase 1 (PON1 Swiss-Prot: P27169) and angiotensinogen (AGT Swiss-Prot: P01019) increased in concentration. The changes observed in the systemic arterial to portal venous blood were not as extensive as portal to hepatic vein changes. Only two proteins, PON1 and AGT, increased more than 10%.
Using plasma depletion, mass spectrometry based protein identification and iTRAQ labeling of peptides, we were able to identify 72 different, mostly medium to high abundance plasma proteins in blood samples taken from patients undergoing liver transplantation. This bias of medium-to-high proteins is not unexpected as the depletion column removes only six of the most abundant proteins. Additionally the sensitivity of the used mass spectrometer limits the dynamic detection limit to the high concentration plasma proteins. For this reason major changes in the detected proteins are unlikely. Of the 72 identified proteins 31 were quantified in all patients. Of these 31 proteins, ten proteins changed more than the 10% confidence limit. We then compared the changes in protein abundances from portal venous to hepatic venous blood, systemic arterial to hepatic venous blood, and systemic arterial to portal venous blood. The changes in portal to hepatic venous blood and systemic arterial to hepatic venous blood were quite similar. The observed changes in arterial to portal blood are quite small as only two proteins were shown to differ.
4.1. Sample Characteristics
We used several methods to reduce the inherent complexity of the plasma sample. Six high-abundant proteins were removed from plasma using the Agilent Multiple Affinity Removal system reducing the protein content of the samples by more than 65%. The depletion allows the identification of more proteins in the sample  by reducing the dynamic range and by increasing the amount of medium-to-low abundance proteins that can be labeled and injected to MS. Even though depletion reduces the complexity and dynamic range of the analysis, it also removes those plasma proteins that bind to albumin, immunoglobulins, or other high abundance proteins . This carrier effect may cause some differences in quantification results especially with low abundance proteins. This issue was not considered significant in this study as the quantified proteins belong to the medium-to-high abundance plasma protein category.
After trypsin digestion and iTRAQ labeling the peptides were purified and fractionated offline using strong cation exchange cartridge followed by online separation with reverse phase chromatography coupled to MS. Such two-dimensional fractionation setup is extremely important in iTRAQ-based peptide quantification . First, the complexity is reduced allowing a more detailed MS analysis as the mass spectrometer can only analyze a certain amount of ions during one duty cycle. Second, the prefractionation decreases the chances of false peptide quantification due to simultaneous fragmentation of coeluting peptides with equal mass that originate from different proteins. Third, two fractionation steps allow for a greater amount of sample to be loaded onto the column, thus increasing the chance of detecting lower abundance proteins. The drawback of several fractionation steps is the cost in time and possible sample loss during fractionation.
Despite its proven reliability, iTRAQ-based protein quantification has some inherent problems that may affect the results. Different protein isoforms, various glycosylation, and other posttranslational modifications may cause discrepancy in peptide-based quantification. These issues are fundamental in plasma proteomics, but they are not discussed in here. It should be noted that the physiological status of the recipient and donor as well as medication and care before and during surgery may also affect general liver functions.
Protein degradation during sample collection, handling, and storage is of major concern when quantifying plasma proteins [29–31]. Protein half-lives in normal circulation are controlled by production by liver or other tissues, clearance through various organs, or degradation by circulatory proteases . After sampling, the degradation is continued by plasma proteases as well as unspecific proteolysis of labile proteins through physicochemical fragmentation. Therefore, the speed of sample handling, length of storage and temperature, and as simultaneous sample analysis as possible, are crucial factors in quantifying plasma proteome samples . Although blood samples were immediately kept at +4°C after sampling, long-term stored at −80°C [29, 33], and kept on ice with proteinase inhibitors added prior to depletion, we cannot rule out the effect of nonspecific protein degradation during sample handling or storage on the final results. However, in our opinion the effect of such degradation is moderate as our experiment identified mainly medium-to-high abundance proteins. It has been shown that sample storage temperature and time do not have a profound effect on the low-molecular-weight proteome  or the high-abundant proteins .
Further, we assume that while each protein species has a unique half-life or degradation time, it is constant between samples (e.g., albumin in arterial sample degrades at the same rate as in portal or hepatic venous samples). Even though the proteins are constantly degraded, affecting the absolute abundances of proteins in samples, the relative abundances of each protein species between the samples remains more or less the same. As all three samples from one patient were analyzed always at the same time using the same protocol, the effect of protein degradation on relative iTRAQ quantification is likely minor.
We observed a major reduction in overall protein concentrations in hepatic venous samples as compared to those taken from radial artery and portal vein. This significant reduction of almost 30% was considered to be caused by rinsing the graft with Ringer solution before rinsing with portal blood and subsequent sampling. This dilution effect is compensated by the normalization step in Mascot iTRAQ quantification method.
4.2. Changes in the Hepatic Circulation
As the hepatic venous samples contain the first blood rinsed through the transplanted liver, the changes in protein abundances between portal and hepatic venous blood reflect the severity of reperfusion injury. At this point, hepatic arterial blood flow is not yet reconstructed. Therefore the hepatic venous protein content is not influenced by the systemic arterial proteome. In this study five proteins were found to decrease across the hepatic circulation. These were histidine-rich glycoprotein (HRG), antithrombin-III (SERPINC1), fibrinogen alpha chain (FGA), apolipoprotein E (APOE), and beta-2-glycoprotein 1 (APOH).
The most notable reduction of nearly 30% in the concentration was observed with HRG. HRG is a glycosylated plasma protein with histidine and proline rich regions . Produced by the liver, HRG is found in plasma at a relatively high concentration of 100 mg/L and at a much lower concentration in platelets . Although the main function of HRG is not entirely clear, it is suggested to participate in immunoregulation, metal homeostasis, and blood coagulation . HRG interacts with plasminogen, heparin, and thrombospondin [37–39]. Its role in coagulation may explain the reduction in HRG levels in liver transplantation as the coagulation system is activated in the ischemia-reperfusion injury. Alternatively, HRG may be retained in the liver in immunoregulative functions as the graft is recognized as foreign material by the innate immune system.
Additionally, the levels of three other proteins related to coagulation decreased across the hepatic circulation, implying protein consumption within the graft. Antithrombin-III  is an important regulator of coagulation . It inhibits thrombin and several other serine proteases of the clotting cascade . Fibrin, on the other hand, is the main component of fibrin clots. It is formed by cleavage of fibrinopeptides from fibrinogen alpha and beta chains . Another potential coagulation inhibitory protein is beta-2-glycoprotein-1 or apolipoprotein H (APOH). APOH binding to the exposed phospholipids of the damaged tissues may inhibit activation of the coagulation cascade .
Hepatic consumption of these four proteins indicates that blood coagulation is activated immediately during graft reperfusion. Innate immune system recognizes the postischemic liver tissue and beta-2-glycoprotein attaches to the surface of the broken cells. Reperfusion rapidly initiates blood coagulation, leading to fibrinogen conversion to fibrin and fibrin clots, whereas SERPINC1 is consumed in the liver to inhibit excessive thrombin formation. Further, HRG is retained in the liver to promote coagulation or to exert immunoregulative functions. This proposed cascade leads to the observed uptake of these proteins into the liver.
The levels of Apolipoprotein E (APOE), a major component of chylomicrons and other low-density lipoprotein particles, were lower in the hepatic venous samples when compared to those from the portal vein. APOE is produced in a variety of tissues, including the liver. APOE mediates functions of the chylomicron internationalization process, in which the lipids and cholesterol of chylomicrons are transported from intestines to tissues. APOE also mediates the removal of chylomicron remnants in hepatocytes . This detected APOE uptake reflects the chylomicron and APOE clearance from the blood into the liver.
Three proteins were found to increase in concentration across the hepatic circulation. These include alpha-1-acid glycoprotein (ORM1), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), and alpha-2-macroglobulin (A2M). All three are high-abundant plasma proteins produced by the liver, ITHI4, and ORM1 almost exclusively. A2M is a protease inhibitor that inhibits a wide range of proteases including thrombin, plasmin, and kallikrein . ITIH4 and ORM1 both function in acute-phase reactions [47, 48].
The profile of changes in HRG, SERPINC1, FGA, ORM1, ITIH4, and A2M levels was similar when systemic arterial samples were compared to either hepatic or portal venous samples. This suggests that the major changes in these proteins indeed take place in the liver and not in other parts of the body.
Using protein depletion and LC-MS/MS we have analyzed the very early ischemia-reperfusion changes in plasma proteome during liver transplantation surgery. Despite the sensitivity limit imposed by the used mass spectrometer and analysis methods we were able to characterize differences in several plasma proteins. Major changes were observed in proteins involved in coagulation, especially in histidine-rich glycoprotein. Further studies are needed to validate these results. Nevertheless, our data demonstrates that MS-based protein identification and quantification is a feasible option when examining the effects of major surgery or other organ-related shock to plasma proteome.
Conflict of Interests
The authors declare that they have no competing interests.
The authors wish to thank Ms. Niina Tohmola, MS.c., for her help in laboratory work and advice in preparation of this paper.
- C. Gabay and I. Kushner, “Acute-phase proteins and other systemic responses to inflammation,” The New England Journal of Medicine, vol. 340, no. 6, pp. 448–454, 1999.
- N. L. Anderson, “The clinical plasma proteome: a survey of clinical assays for proteins in plasma and serum,” Clinical Chemistry, vol. 56, no. 2, pp. 177–185, 2010.
- N. L. Anderson and N. G. Anderson, “The human plasma proteome: history, character, and diagnostic prospects,” Molecular & Cellular Proteomics, vol. 1, no. 11, pp. 845–867, 2002.
- K. A. Stringer, N. J. Serkova, A. Karnovsky, K. Guire, R. Paine, and T. J. Standiford, “Metabolic consequences of sepsis-induced acute lung injury revealed by plasma 1H-nuclear magnetic resonance quantitative metabolomics and computational analysis,” The American Journal of Physiology—Lung Cellular and Molecular Physiology, vol. 300, no. 1, pp. L4–L11, 2011.
- R. P. Huang, “Detection of multiple proteins in an antibody-based protein microarray system,” Journal of Immunological Methods, vol. 255, no. 1-2, pp. 1–13, 2001.
- S. F. Kingsmore and D. D. Patel, “Multiplexed protein profiling on antibody-based microarrays by rolling circle amplification,” Current Opinion in Biotechnology, vol. 14, no. 1, pp. 74–81, 2003.
- C. Schröder, A. Jacob, S. Tonack et al., “Dual-color proteomic profiling of complex samples with a microarray of 810 cancer-related antibodies,” Molecular and Cellular Proteomics, vol. 9, no. 6, pp. 1271–1280, 2010.
- H. Zhou, K. Bouwman, M. Schotanus et al., “Two-color, rolling-circle amplification on antibody microarrays for sensitive, multiplexed serum-protein measurements,” Genome Biology, vol. 5, no. 4, p. R28, 2004.
- C. C. Wang, R. P. Huang, M. Sommer et al., “Array-based multiplexed screening and quantitation of human cytokines and chemokines,” Journal of Proteome Research, vol. 1, no. 4, pp. 337–343, 2002.
- R. Rimini, J. M. Schwenk, M. Sundberg et al., “Validation of serum protein profiles by a dual antibody array approach,” Journal of Proteomics, vol. 73, no. 2, pp. 252–266, 2009.
- S. F. Kingsmore, “Multiplexed protein measurement: technologies and applications of protein and antibody arrays,” Nature Reviews Drug Discovery, vol. 5, no. 4, pp. 310–320, 2006.
- J. Burke, E. B. Butler, B. S. Teh, and B. B. Haab, “Antibody microarray profiling of human prostate cancer sera: antibody screening and identification of potential biomarkers,” Proteomics, vol. 3, no. 1, pp. 56–63, 2003.
- A. Carlsson, D. M. Wuttge, J. Ingvarsson, et al., “Serum protein profiling of systemic lupus erythematosus and systemic sclerosis using recombinant antibody microarrays,” Molecular & Cellular Proteomics, vol. 10, no. 5, Article ID M110.005033, 2011.
- M. K. Han, Y. H. Oh, J. Kang et al., “Protein profiling in human sera for identification of potential lung cancer biomarkers using antibody microarray,” Proteomics, vol. 9, no. 24, pp. 5544–5552, 2009.
- J. R. Whiteaker, H. Zhang, L. Zhao et al., “Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer,” Journal of Proteome Research, vol. 6, no. 10, pp. 3962–3975, 2007.
- S. M. Hanash, S. J. Pitteri, and V. M. Faca, “Mining the plasma proteome for cancer biomarkers,” Nature, vol. 452, no. 7187, pp. 571–579, 2008.
- T. A. Addona, X. Shi, H. Keshishian, et al., “A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease,” Nature Biotechnology, vol. 29, no. 7, pp. 635–643, 2011.
- A. N. Hoofnagle, J. O. Becker, M. H. Wener, and J. W. Heinecke, “Quantification of thyroglobulin, a low-abundance serum protein, by immunoaffinity peptide enrichment and tandem mass spectrometry,” Clinical Chemistry, vol. 54, no. 11, pp. 1796–1804, 2008.
- V. Kumar, D. R. Barnidge, L. S. Chen et al., “Quantification of serum 1–84 parathyroid hormone in patients with hyperparathyroidism by immunocapture in situ digestion liquid chromatography-tandem mass spectrometry,” Clinical Chemistry, vol. 56, no. 2, pp. 306–313, 2010.
- E. I. Chen, J. Hewel, B. Felding-Habermann, and J. R. Yates, “Large scale protein profiling by combination of protein fractionation and multidimensional protein identification technology (MudPIT),” Molecular and Cellular Proteomics, vol. 5, no. 1, pp. 53–56, 2006.
- M. Gstaiger and R. Aebersold, “Applying mass spectrometry-based proteomics to genetics, genomics and network biology,” Nature Reviews Genetics, vol. 10, no. 9, pp. 617–627, 2009.
- P. L. Ross, Y. N. Huang, J. N. Marchese et al., “Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents,” Molecular and Cellular Proteomics, vol. 3, no. 12, pp. 1154–1169, 2004.
- S. Suzuki and L. H. Toledo-Pereyra, “Interleukin 1 and tumor necrosis factor production as the initial stimulants of liver ischemia and reperfusion injury,” Journal of Surgical Research, vol. 57, no. 2, pp. 253–258, 1994.
- L. B. Becker, “New concepts in reactive oxygen species and cardiovascular reperfusion physiology,” Cardiovascular Research, vol. 61, no. 3, pp. 461–470, 2004.
- G. S. Omenn, D. J. States, M. Adamski et al., “Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database,” Proteomics, vol. 5, no. 13, pp. 3226–3245, 2005.
- K. Björhall, T. Miliotis, and P. Davidsson, “Comparison of different depletion strategies for improved resolution in proteomic analysis of human serum samples,” Proteomics, vol. 5, no. 1, pp. 307–317, 2005.
- N. Zolotarjova, J. Martosella, G. Nicol, J. Bailey, B. E. Boyes, and W. C. Barrett, “Differences among techniques for high-abundant protein depletion,” Proteomics, vol. 5, no. 13, pp. 3304–3313, 2005.
- S. Y. Ow, M. Salim, J. Noirel, C. Evans, and P. C. Wright, “Minimising iTRAQ ratio compression through understanding LC-MS elution dependence and high-resolution HILIC fractionation,” Proteomics, vol. 11, no. 11, pp. 2341–2346, 2011.
- A. J. Rai, C. A. Gelfand, B. C. Haywood et al., “HUPO Plasma Proteome Project specimen collection and handling: towards the standardization of parameters for plasma proteome samples,” Proteomics, vol. 5, no. 13, pp. 3262–3277, 2005.
- M. K. Tuck, D. W. Chan, D. Chia et al., “Standard operating procedures for serum and plasma collection: early detection research network consensus statement standard operating procedure integration working group,” Journal of Proteome Research, vol. 8, no. 1, pp. 113–117, 2009.
- A. S. Schrohl, S. Würtz, E. Kohn et al., “Banking of biological fluids for studies of disease-associated protein biomarkers,” Molecular and Cellular Proteomics, vol. 7, no. 10, pp. 2061–2066, 2008.
- L. Anderson and J. Seilhamer, “A comparison of selected mRNA and protein abundances in human liver,” Electrophoresis, vol. 18, no. 3-4, pp. 533–537, 1997.
- S. Y. Hsieh, R. K. Chen, Y. H. Pan, and H. L. Lee, “Systematical evaluation of the effects of sample collection procedures on low-molecular-weight serum/plasma proteome profiling,” Proteomics, vol. 6, no. 10, pp. 3189–3198, 2006.
- T. Koide, D. Foster, S. Yoshitake, and E. W. Davie, “Amino acid sequence of human histidine-rich glycoprotein derived from the nucleotide sequence of its cDNA,” Biochemistry, vol. 25, no. 8, pp. 2220–2225, 1986.
- L. Leung, P. C. Harpel, R. L. Nachman, and E. M. Rabellino, “Histidine-rich-glycoprotein is present in human platelets and is released following thrombin stimulation,” Blood, vol. 62, no. 5, pp. 1016–1021, 1983.
- T. Shigekiyo, H. Yoshida, Y. Kanagawa et al., “Histidine-rich glycoprotein (HRG) Tokushima 2: novel HRG deficiency, molecular and cellular characterization,” Thrombosis and Haemostasis, vol. 84, no. 4, pp. 675–679, 2000.
- H. R. Lijnen, M. Hoylaerts, and D. Collen, “Isolation and characterization of a human plasma protein with affinity for the lysine binding sites in plasminogen. Role in the regulation of fibrinolysis and identification as histidine-rich glycoprotein,” Journal of Biological Chemistry, vol. 255, no. 21, pp. 10214–10222, 1980.
- H. R. Lijnen, M. Hoylaerts, and D. Collen, “Heparin binding properties of human histidine-rich glycoprotein. Mechanism and role in the neutralization of heparin in plasma,” Journal of Biological Chemistry, vol. 258, no. 6, pp. 3803–3808, 1983.
- L. L. Leung, R. L. Nachman, and P. C. Harpel, “Complex formation of platelet thrombospondin with histidine-rich glycoprotein,” Journal of Clinical Investigation, vol. 73, no. 1, pp. 5–12, 1984.
- S. C. Bock, K. L. Wion, G. A. Vehar, and R. M. Lawn, “Cloning and expression of the cDNA for human antithrombin III,” Nucleic Acids Research, vol. 10, no. 24, pp. 8113–8125, 1982.
- D. J. Perry and R. W. Carrell, “Molecular genetics of human antithrombin deficiency,” Human Mutation, vol. 7, no. 1, pp. 7–22, 1996.
- M. R. Downing, J. W. Bloom, and K. G. Mann, “Comparison of the inhibition of thrombin by three plasma protease inhibitors,” Biochemistry, vol. 17, no. 13, pp. 2649–2653, 1978.
- A. Z. Budzynski and J. R. Shainoff, “Fibrinogen and fibrin: biochemistry and pathophysiology,” Critical Reviews in Oncology and Hematology, vol. 6, no. 2, pp. 97–146, 1986.
- R. Schwarzenbacher, K. Zeth, K. Diederichs et al., “Crystal structure of human β2-glycoprotein I: implications for phospholipid binding and the antiphospholipid syndrome,” EMBO Journal, vol. 18, no. 22, pp. 6228–6239, 1999.
- R. W. Mahley, “Apolipoprotein E: cholesterol transport protein with expanding role in cell biology,” Science, vol. 240, no. 4852, pp. 622–630, 1988.
- L. Sottrup-Jensen, P. B. Lonblad, T. M. Stepanik, T. E. Petersen, S. Magnusson, and H. Jornvall, “Primary structure of the 'bait' region for proteinases in alpha 2-macroglobulin. nature of the complex,” FEBS Letters, vol. 127, no. 2, pp. 167–173, 1981.
- M. Piñeiro, M. A. Alava, N. González-Ramón et al., “ITIH4 serum concentration increases during acute-phase processes in human patients and is up-regulated by interleukin-6 in hepatocarcinoma HepG2 cells,” Biochemical and Biophysical Research Communications, vol. 263, no. 1, pp. 224–229, 1999.
- T. Hochepied, F. G. Berger, H. Baumann, and C. Libert, “α1-acid glycoprotein: an acute phase protein with inflammatory and immunomodulating properties,” Cytokine and Growth Factor Reviews, vol. 14, no. 1, pp. 25–34, 2003.