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Abstract

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES

The diagnosis of acute cellular rejection (ACR) requires liver biopsy with its attendant expense and risk. Our first aim was to prospectively determine in an exploratory analysis whether there is a serum proteome signature associated with histologically confirmed ACR. Our second aim was to use simpler and faster enzyme-linked immunosorbent assay (ELISA)-based assays for proteins identified as differentially abundant in the proteomic analysis to identify patients with ACR in a separate validation cohort. We used sequential high-abundance protein depletion and isobaric tag for relative and absolute quantitation liquid chromatography–tandem mass spectrometry to characterize the serum proteome in serum samples of patients with or without ACR. Seven of the 41 proteins identified as differentially abundant [serum amyloid A, complement component 4 (C4), fibrinogen, complement component 1q (C1q), complement component 3, heat shock protein 60 (HSP60), and HSP70] could be measured with ELISA-based assays in a validation cohort consisting of patients with ACR (n = 25) and patients without ACR (n = 21). The mean alanine aminotransferase (ALT) levels in patients with ACR and in patients without ACR were 198 ± 27 and 153 ± 34 U/L, respectively. Among the 7 proteins for which ELISA assays were available, C4 and C1q were both independent predictors of ACR. C4 had the greatest predictivity for differentiating patients with or without ACR. A C4 level ≤ 0.31 g/L had a sensitivity of 97%, a specificity of 62%, a positive predictive value of 74%, and a negative predictive value of 94%. A C4 level ≤ 0.31 g/L and an ALT level ≥ 70 IU/mL together had a sensitivity of 96%, a specificity of 81%, a positive predictive value of 86%, and a negative predictive value of 94%. In summary, in this exploratory analysis, serum C4 and ALT levels were highly predictive of ACR in liver transplant recipients. Confirmation in a prospective, larger, and diverse population is needed. Liver Transpl 17:723-732, 2011. © 2011 AASLD.

The rate of survival 1 year after liver transplantation has increased from approximately 30% in the 1970s to greater than 80% today.1-3 Acute cellular rejection (ACR) most commonly occurs in the early posttransplant period (usually within 6 weeks).4 The majority of these episodes (approximately 85%) are resolved with an antirejection treatment (typically pulses of systemic corticosteroids).4 Late ACR (occurring more than 6 months after transplantation) occurs in another 11% to 16% of liver transplant recipients.4 Early ACR is strongly predictive of subsequent death for recipients infected with hepatitis C (>40% of liver transplant recipients in the United States).4, 5 However, late graft survival (graft survival beyond 1 year post-transplantation) is significantly lower, regardless of the original liver disease, among patients who experience late acute rejection.4 Thus, ACR is still a common and important complication of liver transplantation and occurs in approximately 30% of recipients.6, 7

In contrast to many aspects of clinical transplantation, the algorithm for diagnosing ACR has not changed since the advent of clinical transplantation in the 1960s. The diagnosis of ACR requires evidence of graft dysfunction (eg, elevated aminotransferases), and this is typically followed by confirmation by allograft biopsy. Liver biopsy, which has been considered the best means of diagnosing ACR,8 entails expense, risk, time, and inconvenience. In addition, the interpretation of liver biopsy samples can be challenging. The classic triad of a mixed portal tract inflammatory infiltrate, endotheliitis, and bile duct injury is not always present.9, 10 Furthermore, endotheliitis has been reported in up to 60% of patients with a hepatitis C infection.11 Sampling error is another consideration in the interpretation of liver biopsy samples.12, 13

The need for a noninvasive method for the diagnosis of ACR has been highlighted by major societies and the National Institutes of Health. Noninvasive approaches have been made difficult by the lack of a serum (or urinary) protein signature for ACR. Attempts to describe the serum (or urinary) proteome have been limited by the inability to accurately quantify low-abundance proteins, which constitute the great majority of serum proteins. Recent advances in protein separation, including the depletion of highly abundant proteins14 and high-throughput mass spectrometry,14 have facilitated the detailed characterization of complex biological samples such as serum.15, 16 We explored the possibility of a serum signature for ACR through a prospective study conducted in 2 phases. In the first phase, we used a combination of high-abundance protein depletion and isobaric tag for relative and absolute quantitation (iTRAQ) mass spectrometry to characterize the serum proteomic signature of ACR. In the second phase, proteins identified in the first phase as differentially abundant in the serum proteome were validated in a separate set of patients with the more clinically applicable enzyme-linked immunosorbent assay (ELISA) method of protein quantification.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES

First Phase: Proteomic Analysis

Study Participants

Two groups of subjects were studied:

  • 1
    ACR group (n = 8): This group comprised 8 liver transplant recipients with a hepatitis C virus (HCV) infection who had Banff criteria for ACR on protocol day 7 liver biopsy samples (patients with HCV were chosen to provide a potential source of necroinflammation other than ACR in both groups).
  • 2
    Non-ACR group (control; n = 8): This group comprised 8 liver transplant recipients with an HCV infection who had biochemical profiles similar to those of the ACR group but did not have cholangitis or endotheliitis histologically. Recipients in the control group did not receive treatment for ACR at any point.

Both the ACR and control (ACR-negative) groups were matched for age and gender. For all subjects in the ACR group, ACR was resolved histologically and biochemically with a single course of methyl prednisolone therapy. Subjects in the control group did not require treatment for ACR at any time and subsequently had allograft histology demonstrating persistent and progressive histological changes consistent with the recurrence of HCV infection. Whole blood samples (10 mL) were collected in glass tubes without an additive (10-mL BD Vacutainer tubes, Becton Dickinson, Franklin Lakes, NJ) and were allowed to clot at room temperature for 40 minutes. Serum was separated by centrifugation at 1500 rpm for 15 minutes. Aliquots of serum (1 mL) were taken and stored at −80 C until they were ready for use. The time from collection to frozen storage was no more than 30 minutes. The investigators participating in the study were blinded with respect to the sample collection, and the samples contained no identifying features that would make it possible to identify the subjects. The study was approved by the institutional review board of the Mayo Foundation.

Depletion of Highly Abundant Serum Proteins

Serum samples were processed with a 4.6 × 50 mm multiple-affinity removal column (Agilent Technologies, Palo Alto, CA) that selectively removed albumin, immunoglobulin G, immunoglobulin A, antitrypsin, transferrin, and haptoglobin from the serum samples; this column was attached to an EZChrom Elite high-performance liquid chromatograph (Hitachi High Technologies America, San Jose, CA). The samples were processed according to the manufacturer's instructions.

Sample Preparation for Multidimensional Liquid Chromatography–Tandem Mass Spectrometry (LC-MS/MS) Analysis

Each cleaned and iTRAQ-labeled sample was fractionated into 10 fractions on a strong cation-exchange column (300 μm × 5 cm Biox SCX, Dionex, Sunnyvale, CA) with an offline capillary liquid chromatography system (1100 series, Agilent, Wilmington, DE). The LC-MS/MS analysis of the peptides in each fraction was performed on an API QSTAR XL quadrupole time-of-flight mass spectrometer (Applied Biosystems, Framingham, MA) configured with a Protana nanospray ion source (Proxeon, Denmark) and with an UltiMate Nano liquid chromatography system (Dionex). The peptides were separated on an Agilent Zorbax C18 microbore column (100 μm × 150 mm) with a gradient from 5% to 60% buffer B over 120 minutes (buffer A was 0.1% formic acid, 98% water, and 2% acetonitrile; buffer B was 0.1% formic acid, 2% water, and 98% acetonitrile). The tandem mass spectrometry (MS/MS) data were obtained with the information-dependent acquisition mode of Analyst QS software. This consisted of a 1.5-second survey scan from 350 to 1600 m/z and a switch to 2.0-second fragmentation scans for the 3 most intense ions from the survey. These ions were then excluded from repetition for 45 seconds. The applied collision energy was varied automatically with respect to the precursor m/z value and charge state.

Protein Identification, Quantification, and Data Analysis

We performed protein identification by searching MS/MS spectra against the CDS FASTA database (Applied Biosystems), and we performed quantification with ProQuant software (Applied Biosystems). The data were further analyzed with ProGroup (Applied Biosystems); this functionality provides an important second stage of protein identification analysis. The results of the quantification were normalized with the overall ratio obtained for all tagged peptide pairs in the sample.17 A difference of 2 standard deviations (ie, an approximately 1.2-fold difference in abundance) was considered to be significant; the confidence limit was >90% with a simple Gaussian approximation.17, 18 This approximation would, therefore, apply to normalized expression levels >1.2 or, in reciprocal form, <0.8.

Second Phase: ELISA Validation

For the validation of the results obtained in the proteomic analysis, sera were collected from 2 groups of patients who underwent liver transplantation at the same center and were not included in the first phase of experiments:

  • 1
    ACR group (n = 25): This group comprised liver transplant recipients who met the Banff criteria for ACR according to protocol day 7 liver biopsy samples.
  • 2
    Non-ACR group (control; n = 21): This group comprised transplant recipients who had biochemical profiles similar to those of the ACR group but did not have histological evidence of rejection. Recipients in the control group did not receive treatment for ACR at any point.

Patients who had an equivocal diagnosis or more than 1 diagnosis on liver biopsy (eg, ACR and perfusion injury), for whom day 7 liver biopsy samples were unavailable, or who underwent multiple organ transplantation were excluded.

ELISA

Forty-one proteins were identified as differentially abundant in the sera of patients with ACR in the proteomic analysis. Commercial ELISA assays were available for 7 of these proteins: serum amyloid A (SAA), complement component 4 (C4), fibrinogen, complement component 1q (C1q), complement component 3 (C3), heat shock protein 60 (HSP60), and HSP70. These 7 proteins were then measured in the sera of patients with ACR (n = 25) and in the sera of patients with no ACR (n = 21). The abundance of each of the proteins measured by ELISA was calculated with a spline algorithm. The optical density was measured within 30 minutes at 450 nm.

Serum SAA levels were determined with a 2-step ELISA (Abazyme LLC, Needham, MA) that used a monoclonal antibody specific for human SAA. A C4 ELISA kit was obtained from Assaypro (St. Charles, MO). In this assay, the C4 in the samples consisted of biotinylated C4 sandwiched by an immobilized antibody and a streptavidin-peroxidase conjugate. The fibrinogen concentration was determined with a 2-step ELISA from Alpco Diagnostics (Salem, NH). C1q was obtained from R&D Systems (Minneapolis, MN). A C3 ELISA kit from Abnova (Taiwan) was used. The standard or sample (25 μL) was added to each well in duplicate. An HSP60 ELISA kit from Assay Designs, Inc. (Ann Arbor, MI), was used. The standard, samples, and control (100 μL) were added in duplicate to the respective wells. An Hsp70 ELISA kit from Assay Designs was used. The standard, samples, and control (100 μL) were added in duplicate to the respective wells. All assays were performed according to the manufacturers' suggested protocols.

Statistical Analysis

The Wilcoxon rank-sum test was used to compare the means of the protein concentrations in the 2 groups (ACR and non-ACR). A simple logistic regression was used to test individual variables that might predict ACR. Variables that were significant predictors of ACR on the simple logistic regression were entered into a multiple logistic regression model. P < 0.05 was considered significant. Statistical analysis was performed with JMP 8.0.1 software (SAS Institute, Inc., Cary, NC).

RESULTS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES

Proteomic Analysis Results

The demographic and clinical characteristics of patients in the ACR and non-ACR groups are summarized in Table 1.

Table 1. Demographic and Clinical Characteristics of the Proteomic Analysis Patients
CharacteristicACRNon-ACR
  1. NOTE: The data are presented as means and standard deviations unless otherwise noted. None of the differences were statistically significant.

Age (years)44.0 ± 1.547.8 ± 2.8
Male/female (n/n)4/44/4
Total bilirubin (mg/dL)6.3 ± 1.94.2 ± 1.2
Alkaline phosphatase (U/L)771 ± 150328 ± 56
Aspartate aminotransferase (U/L)181 ± 58183 ± 118
ALT (U/L)316 ± 66260 ± 74

Across all serum samples, 2801 proteins were analyzed, and 41 of these proteins were found to be significantly differentially abundant in the sera of all patients with ACR. Of these 41 proteins, 28 were up-regulated and 13 were down-regulated in the ACR patients in comparison with the non-ACR patients (Tables 2 and 3). Standardized ELISA assays were available for 7 of the 41 differentially abundant proteins: SAA, C4, fibrinogen, C1q, C3, HSP60, and HSP70.

Table 2. Proteins Increased in ACR Patients Versus Non-ACR Patients
ProteinFold Increase
Ubiquitin-conjugating enzyme E26.28 ± 0.35
HSP605.83 ± 0.27
NFAT14.87 ± 0.26
Ubiquitin4.19 ± 0.34
HSP703.42 ± 0.12
Zinc finger protein 1352.88 ± 0.18
C1q2.54 ± 0.23
NFAT2B2.51 ± 0.16
FK-506–binding protein 10 precursor2.35 ± 0.11
HSPC0782.21 ± 0.23
Uridine diphosphoglucuronate glucose pyrophosphorylase 22.17 ± 0.22
C32.04 ± 0.48
Alpha-fibrinogen precursor2.04 ± 0.48
Sulfated glycoprotein 22.00 ± 0.32
SAA11.99 ± 0.21
Glyceraldehyde 3-phosphate dehydrogenase1.94 ± 0.14
C4A1.90 ± 0.32
C4B1.87 ± 0.32
Proapolipoprotein AI protein1.85 ± 0.11
Retinol-binding protein1.72 ± 0.26
A chain A crystal structure of a serpin:protease complex1.69 ± 0.11
Leucine-rich alpha-2-glycoprotein1.55 ± 0.21
Zinc alpha-2-glycoprotein precursor1.55 ± 0.21
Retinol-binding protein 4 gene product1.52 ± 0.26
Myeloid cell surface antigen CD33 precursor1.51 ± 0.05
Alpha-2-glycoprotein 1, zinc1.51 ± 0.05
FK-506–binding protein 101.48 ± 0.07
Alpha-1-microglobulin/bikunin protein precursor1.45 ± 0.36
Table 3. Proteins Decreased in ACR Patients Versus Non-ACR Patients
ProteinFold Decrease
Human apolipoprotein CI0.80 ± 0.02
Nuclear protein0.72 ± 0.11
Zinc alpha-2-glycoprotein0.63 ± 0.12
Apolipoprotein B1000.46 ± 0.09
Apolipoprotein H0.46 ± 0.03
Serine (or cysteine) proteinase inhibitor0.36 ± 0.21
Ribosomal protein L150.31 ± 0.02
Apolipoprotein D0.26 ± 0.32
Adenylate kinase 70.26 ± 0.02
Plasma protease C1 inhibitor precursor0.23 ± 0.11
Beta-2-glycoprotein I precursor0.21 ± 0.04
IGFBP0.17 ± 0.06
Ribonucleoprotein autoantigen 60-kDa subunit0.12 ± 0.04

Validation Results

The demographic and clinical characteristics of the validation group are summarized in Table 4. Quantitative ELISA results for C4 and C1q are shown in Figs. 1 and 2. The means for both C4 and C1q were significantly different between the ACR and non-ACR groups (P = 0.0001 and P = 0.0183, respectively). Using a simple logistic regression model for the individual proteins, we found both C4 (P = 0.0024) and C1q (P = 0.0341) to be predictive for ACR. When these 2 proteins were entered into a multiple logistic regression model, both were independent predictors of ACR (P = 0.0015 for C4 and P = 0.0092 for C1q; Table 5). None of the other measured proteins improved the predictivity.

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Figure 1. Serum C4 levels in the ACR and non-ACR groups. A scatter plot of individual C4 values is shown. The mean C4 levels were significantly lower among patients with ACR (P = 0.0001). With a C4 cutoff value of ≤0.31 g/L, C4 had a sensitivity of 97%, a specificity of 62%, a positive predictive value of 74%, and a negative predictive value of 94% in identifying patients with ACR.

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thumbnail image

Figure 2. Serum C1q levels in the ACR and non-ACR groups. A scatter plot of individual C1q values is shown. The mean C1q levels were significantly lower among patients with ACR (P = 0.0183).

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Table 4. Demographic and Clinical Characteristics of the Patients in the Validation Groups
CharacteristicACR (n = 25)Non-ACR (n = 21)
  • *

    Medians and ranges are presented (P = 0.143).

  • Some patients had more than one diagnosis.

Age (years)*52 (19-72)57 (40-70)
Male/female (n/n)13/1213/8
Liver disease (n)  
 HCV32
 Alcohol44
 Nonalcoholic steatohepatitis03
 Primary sclerosing cholangitis53
 Primary biliary cirrhosis22
 Autoimmune hepatitis20
 Cholangiocarcinoma26
 Hepatocellular carcinoma23
 Acute fulminant hepatitis30
 Wilson's disease10
 Amyloidosis20
 Oxalosis10
 Hepatic artery thrombosis21
 Alpha-1-antitrypsin deficiency01
 Cryptogenic01
Table 5. Multiple Logistic Regression for C4 and C1q
TermEstimateStandard Errorχ2P for χ2
  • *

    Statistically significant.

Intercept14.63067914.70108999.690.0019*
C4 (g/L)−18.2421275.73013810.130.0015*
C1q (g/L)−0.28106480.10788816.790.0092*

With a cutoff value of ≤0.31 g/L, C4 had a sensitivity of 97%, a specificity of 62%, a positive predictive value of 74%, and a negative predictive value of 94%. We found that the addition of an elevated alanine aminotransferase (ALT) level improved the specificity of C4 without a significant change in the sensitivity. A C4 level ≤ 0.31 g/L and an ALT level ≥ 70 IU/mL (2 times the upper limit of normal) together had a sensitivity of 96%, a specificity of 81%, a positive predictive value of 86%, and a negative predictive value of 94%. The area under the receiver operating characteristic curve was 0.88. C1q was also significantly predictive of ACR. C1q levels were, however, less sensitive than C4 levels (56% versus 97%) and more specific (86% versus 62%). The combination of these 2 markers (C4 and C1q) significantly lowered the sensitivity of C4.

DISCUSSION

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES

The first important finding of this exploratory study is that ACR is associated with the differential abundance of a distinct set of proteins that can be measured in the serum proteome when or before ACR is suspected or is apparent clinically. Only 41 (0.14%) of the identified serum proteins were differentially abundant in patients with ACR versus controls in the first (proteomic) phase of experiments. Because we removed high-abundance serum proteins, which typically are proteins whose function depends on their presence in serum, our analysis was able to detect proteins that were present in serum on a transient basis (eg, because of cell destruction or secretion), such as cytokines, receptor ligands, and hormones, which rely on serum for transportation to cells at anatomically remote sites. The proteins that we found to be differentially abundant in ACR are diverse in their known functions; they include transcription factors, stress response, protein degradation, lipid metabolism, complement activation and immune function, cell adhesion, growth factors, and signal transduction. The identification of a distinct serum proteomic signature for ACR, which includes proteins not previously associated with ACR, raises the possibility of new diagnostic and therapeutic approaches to the management of immunosuppression in liver transplant patients and perhaps in other organ transplant recipients. The second important finding of our experiments is that in our exploratory analysis of a validation cohort, patients with ACR could be identified rapidly with inexpensive ELISA measurements of the C4 component of complement.

Although there are, as far as we are aware, no previously published proteomic analyses of serum or tissue of liver transplant recipients with ACR, urine proteomic analyses of renal transplant recipients with acute rejection19-21 identified urinary beta-2-microglobulin as a potential biomarker for ACR.22 The abundance of alpha-B-crystallin and tropomyosin is increased in serum during ACR in heart transplant recipients.23 A direct comparison of our results is thus difficult, and this probably reflects the difficulty in measuring the abundance of low-concentration serum proteins. A single mass spectrum is limited to a dynamic range <104-5. Serum protein, however, has a dynamic range >1010; this ranges from >4.5 g/dL for albumin to 1-10 pg/mL for cytokines. Because liquid chromatography–mass spectrometry analysis has absolute detection limits in the attomolar-zeptomolar range,24, 25 the sensitivity of serum proteomic analysis is limited not by the lower limits of detection of the instrument but instead by the presence of the high-abundance proteins. We applied a high-abundance protein depletion method13 to circumvent this limitation. We selectively removed high-abundance serum proteins with high-performance liquid chromatography, which removed approximately 94% of total serum proteins. All the differentially abundant proteins in our analysis were thus present in only small amounts in serum. In contrast to previous proteomic analyses of serum, we employed the iTRAQ peptide label; this eliminates the dependence on relatively nonabundant cysteine-containing peptides intrinsic to isotope-coded affinity tag–based methods,26 yields labeled peptides that are identical in mass, and also produces strong, diagnostic, low-mass MS/MS signature ions.27, 28 This difference in the labeling strategy allows the tagging of most tryptic peptides, simplifies the analysis, and increases the analytical precision.17 Although we are certain that there are still many ultralow-abundance proteins that we were not able to differentially quantify, our protein depletion/iTRAQ approach yielded considerable novel information.

The diverse functions of the proteins found to be differentially abundant in ACR patients suggest a complex basis for the serum protein signature. A comprehensive review of the biological function of the 28 proteins that were differentially abundant in ACR is beyond the scope of this article. The overall pattern of differentially abundant proteins suggests, however, that patients with ACR have increased immune activation in comparison with HCV-infected controls without ACR. Reported peripheral blood markers of immune activation include interleukin-2 (IL-2),29, 30 soluble IL-2R,29, 31 IL-6,31, 32 IL-7,33 IL-8,32 interferon-γ,29 soluble intercellular cell adhesion molecule 1,34 and soluble major histocompatibility complex antigens.34 None of these were identified as differentially abundant in our analysis. This does not imply that the levels of these immune activation markers were not elevated in the ACR patients; rather, their levels were not measurably different from those seen in the sera of our control group (liver transplant recipients infected with HCV). In contrast, the mediators of immune activation that we found to be differentially overabundant in the ACR group were as follows: the T cell transcription factor nuclear factor of activated T cells 1 (NFAT1), HSP60, HSP70, C1q, C3, C4, and CD33. The differential abundance of these proteins merits individual consideration.

NFAT1, NFAT2, and NFAT4 are induced by calcineurin and transactivate cytokine genes that regulate proliferative responses of T cells.35 The major immunosuppressive action of calcineurin inhibitors is the prevention of NFAT nuclear translocation.36 The relatively increased abundance of NFAT1 in our ACR group may have conferred resistance to calcineurin inhibition. HSPs are a ubiquitously expressed family of molecules. Immune reactivity to HSPs has been implicated in the pathogenesis of ACR. Anti-HSP immune reactivity is thought to be important in transplant rejection responses. Of particular interest is the fact that the proliferation of Hsp60 and Hsp70, both of which were relatively overabundant in ACR patients in our analysis, has been significantly associated with rejection.37, 38 HSP60 and HSP70 gene expression has been reported to be increased in cardiac allografts during ACR.23 C1q, C3, C4A, and C4B were all overexpressed during ACR in our ACR group. T cells, B cells, and antigen-presenting cells (APCs) express complement receptors that respond to stimulation by split complement products, including C4a and C4b. T cell and APC cell surfaces also bear several complement control proteins that recognize covalently bound fragments of C4 that are capable of signal transduction with subsequent T cell and APC activation in ACR.39-41 CD33 was also overexpressed in ACR patients in our experiments. CD33 is a 67-kDa glycoprotein that, although found predominantly on myeloid cells,42, 43 has also been reported on dendritic cells, natural killer cells, and in vitro expanded T cells.44-47 Although there are no reports on the role of CD33+ cells in ACR in liver transplantation, after bone marrow stem cell transplantation, alloresponses against hemopoietic progenitor cells bearing CD33 cause graft rejection.48 Whether the overexpression of CD33 contributed to alloimmunity or was a marker of T cell induction in the patients with ACR in our study cannot be ascertained. Ubiquitin was also greatly overexpressed in the ACR group in our experiments. Ubiquitin is important in regulating the signal transduction and gene expression of a variety of proteins, including transforming growth factor β/SMAD, signal transducer and activator of transcription, Jun, and protein 53.49-52 The impact of lower circulating levels of ubiquitin is hard to predict because the actions of ubiquitin are so diverse and occur intracellularly. We can find no reports of the differential abundance of circulating ubiquitin levels in ACR. However, zinc finger transcription factor Kruppel-like factor 4, a potent negative regulator of cell proliferation, is inhibited by extracellular ubiquitin.53 When they are taken together, the relative overabundance of these proteins in ACR patients in our experiments may provide a novel index of immune activation.

The interpretation of the relative overabundance of zinc finger protein 135 is similarly difficult. Although zinc fingers have diverse effects, including the stimulation of IL-2–independent growth of T cells,54-56 a role of zinc finger protein 135 in alloimmunity has not been described.

Belonging to the immunophilin family of intracellular proteins, 65-kDa FK-506–binding protein (FKBP65) was relatively overabundant in ACR patients along with its precursor protein.57 FKBP65 assists in the cis-trans isomerization of X-proline bonds in newly synthesized proteins and is up-regulated in response to tissue injury.58 The up-regulation of FKBP65 may have been the basis of increased nonhepatic tissue injury in the ACR patients.

Several proteins that were relatively underabundant in the ACR group in our experiments are also of particular interest: insulin-like growth factor binding protein 1 (IGFBP1), alpha-fibrinogen, and adenylate kinase 7. Hepatic IGFBP1 synthesis is under the regulation of mammalian target of rapamycin (mTOR), an important regulator of T cell signaling cascades and T cell responsiveness.59, 60 Lower IGFBP1 levels suggest lower mTOR levels. If lower serum IGFBP1 levels are indicative of lower intracellular mTOR levels, lower serum IGFBP1 levels may be a surrogate of greater baseline immune activation in the ACR group.

It would be reasonable to consider whether the differential abundance of proteins that we observed in the proteomic phase of experiments was simply based on inflammation rather than ACR. We had considered this possibility before we conducted the study, and we chose controls (age- and gender-matched, HCV-infected patients also undergoing protocol liver biopsy on day 7 after liver transplantation) who were matched for their biochemical profiles and degree of necroinflammation. Both the ACR group and the non-ACR group had HCV infections. The levels of immunosuppression were also similar between the study groups by design. Although the parameters of immunosuppression were similar between the groups, it is possible and is indeed likely that the physiological levels of immunosuppression were in fact different between the groups, as evidenced by the development of ACR. This raises the possibility that indices of immune activation may be more predictive of ACR than 12-hour tacrolimus troughs, which do not take into account differences in the baseline innate or adaptive immunity between patients. We did not compare the serum proteomes of patients who had biochemical abnormalities or biopsy abnormalities due to etiologies other than HCV with or without ACR, such as biliary or vascular abnormalities, and our results should not be extrapolated to those conditions. A further limitation of our analysis is that we were able to study the serum proteome and perform ELISA-based quantitation of differentially abundant proteins at only a single time point. A longitudinal analysis may yield different or new information.

Mass spectrometry proteomic analysis is technically challenging and quite slow. The measurement of protein abundance by ELISA, in contrast, is simple, inexpensive, fast, and thus potentially clinically applicable. We selected 7 of the 41 differentially abundant proteins (the ones for which ELISA kits are currently available) to validate the use of ELISA-based protein quantification in a validation cohort of patients. These proteins were SAA, C4, fibrinogen, C1q, C3, HSP60, and HSP70. On the basis of our proteomic analysis, we believe that other proteins may also be valuable in distinguishing ACR from non-ACR patients. Our validation study showed that C4 levels distinguished between ACR patients and non-ACR patients with high sensitivity (97%) but modest specificity (62%). Adding ALT substantially improved the specificity (from 62% to 81%) without significantly changing the sensitivity (from 97% to 96%). Interestingly, the C4 level was low in all but 1 of our 25 ACR patients, although it was among the 28 up-regulated proteins in our proteomic analysis. An explanation for this finding is uncertain. However, the forms of C4 that were found to be highly expressed in serum in our proteomic analysis were not the same as those that we measured in the serum with ELISA. In the proteomic analysis, we measured split products of C4 (C4a and C4b), whereas in the ELISA analysis, we measured intact C4. When the complement cascade is activated, intact C4 is cleaved, and this generates increased split products of C4 and a subsequent decrease in the level of intact C4. C4d, the activated component of C4, is a central factor of the classical complement pathway. Since it was first described by Feucht et al.,61 it has been established as a marker of acute humoral and chronic rejection in kidney transplant patients.62, 63 In the liver, C4d deposition in association with B lymphocytes and plasma cells has been described in acute allograft rejection.64-67 This suggests that the humoral immune response may play a role in acute allograft rejection in the liver.64 Both C4d68 and fibrinogen69 have been described as potential markers for acute rejection after heart transplantation. The high sensitivity of serum C4 in differentiating between ACR and non-ACR patients (sensitivity = 97%, positive predictive value = 74%, and negative predictive value = 94%) suggests that C4 levels may have value as a screening tool for identifying patients who may have ACR. Liver biopsy for patients with C4 levels > 0.31 g/L is highly unlikely to identify ACR. The concentration of C1q was also significantly lower in the ACR group versus the non-ACR group, although it was much less sensitive than C4. Both C4 and C1q were independent predictors of ACR in the multiple logistic regression model. The addition of an elevated ALT level improved the specificity of C4 without a significant change in the sensitivity. A C4 level ≤ 0.31 g/L and an ALT level ≥ 70 IU/mL (2 times the upper limit of normal) together had a sensitivity of 96%, a specificity of 81%, a positive predictive value of 86%, and a negative predictive value of 94%. The area under the receiver operating characteristic curve was 0.88. The relatively small sample size, the single-center nature, and the limitation of study subjects (by design) to recipients with HCV infections all limit the applicability of our results. Our goal was to stimulate further, more comprehensive studies in this important field: the noninvasive diagnosis of rejection.

In conclusion, serum C4 is a relatively noninvasive, inexpensive, easy-to-measure, and quick marker that, in these preliminary studies, accurately identified patients with histological ACR after liver transplantation. These results need to be prospectively validated in a larger and more diverse group of recipients before any recommendations can be made for clinical use. Whether C4 levels have other potential uses (eg, confirming a diagnosis of ACR when liver biopsy is equivocal or monitoring immune activation) should be the subject of further research.

REFERENCES

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES