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Saudi Journal of Kidney Diseases and Transplantation
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Table of Contents   
ORIGINAL ARTICLE  
Year : 2013  |  Volume : 24  |  Issue : 3  |  Page : 480-486
Transcription factor activity profile of acute rejection after kidney transplantation


1 Kidney Transplantation and Hemo Purification Center, 181 Hospital of Guilin, Guangxi Province, China
2 The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Guangdong Province, China

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Date of Web Publication24-Apr-2013
 

   Abstract 

Transcription factors (TFs) play a central role in regulating gene expression and in providing an interconnecting regulatory between related pathway elements. Currently, the wide­spread use of kidney transplantation to treat end-stage renal disease has evolved rapidly since the initial successful transplantations from both cadaveric and living donors. However, acute rejection is still a strong risk factor for chronic rejection in recipients of renal grafts. To investigate the possible mechanisms, we describe a comparison between TF' activity profile of acute rejection and controls. Through TF assay analysis and electrophoretic mobility shaft assay confirmation, we identified the activities of TFs in acute rejection after kidney transplantation. From a total of 345 screened TFs, 99 activity-differential TFs were found, of which 95 showed increased activity and four showed decreased activity. Our data indicate that TFs may be potentially involved in the pathogenesis of acute rejection, and can help to prevent, diagnose and treat acute rejection after kidney transplantation. The TF array methods could simplify the assay of multiple TFs and may facilitate high-throughout profiling of large numbers of TFs.

How to cite this article:
Sui W, Lin H, Dai Y, Chen J, Huang H. Transcription factor activity profile of acute rejection after kidney transplantation. Saudi J Kidney Dis Transpl 2013;24:480-6

How to cite this URL:
Sui W, Lin H, Dai Y, Chen J, Huang H. Transcription factor activity profile of acute rejection after kidney transplantation. Saudi J Kidney Dis Transpl [serial online] 2013 [cited 2019 Oct 19];24:480-6. Available from: http://www.sjkdt.org/text.asp?2013/24/3/480/111014

   Introduction Top


Acute rejection remains a problem in kidney transplantation. [1] At present, acute rejection can only be reliably diagnosed by histological ana­lysis of biopsy samples. [2] Clinical management of renal transplant patients would be improved if some novel biomarker for acute rejection could be found. To search for these biomarkers, methods such as mRNA measurements, urine flow cytometry and measurements of alloreactive peripheral blood lymphocytes have been attempted. [3] For example, deposition of C4d in the peritubular capillaries of renal allografts has been reported to be a sensitive marker of acute rejection. [4] Extensive studies have been carried out in searching for biomarkers in the urinary proteome of kidney allograft rejection. [5],[6],[7],[8],[9],[10],[11] However, investigators have not been able to definitively differentiate patients with rejection from stable transplant patients by using urine biomarkers, [12] and search for novel methods are still needed in this area.

As a group of crucial components in the regu­latory networks, transcription factors (TFs) re­gulate the initial step of gene expression and transcription. Analysis of the human genome sequence predicts that there may be as many as 1850 human TFs. [13] The regulatory mechanism of human gene transcription is coordinated and interactive, such that the same TF protein may activate several different genes under different conditions. Products of many cancer-related genes such as C-Myc, p53, E2F1, etc. are them­selves TFs. Unique TFs have been identified in association with cancer. [14] These studies repre­sent the beginning links between TFs and hu­man disease. Further investigations are likely to reveal the involvement of additional TFs and their targets in simple and complex gen­etic diseases.

Acute rejection is the most important risk factor for developing chronic renal allograft rejection. [15],[16] Search for early biomarkers of rejection to supplement renal allograft biopsy remains a challenge.

The aim of our study was to compare the levels of TF activity in kidney biopsies of acute rejection after kidney transplantation bet­ween the normal samples and try to reveal the relation between TFs and acute rejection after transplantation.


   Materials and Methods Top


We studied biopsies of three patients with acute rejections (Banff 97 IA, IB). Renal biop­sies were performed by clinical indication with ultrasound observation using the BIOPTY ins­trument. The samples included renal cortex obtained from renal resection operation. All patients' diagnoses of acute rejection were confirmed by histology tests. The three samples of the control group included renal cortex ob­tained from resection operation of renal tumor and represented normal tissue structures away from the tumor tissue, and checked by light microscope.

The transplanted kidneys were from patients' relatives. Written informed consents were obtained from all subjects or their guardians. Use of biopsy material for further studies beyond routine diagnosis was approved by the local ethics committee. This study abides by the Helsinki Declaration on ethical principles for medical research involving human subjects.

Histological analysis

Biopsy material was immediately fixed in 10% phosphate-buffered paraformaldehyde and stored at 4°C. After fixation, biopsies dehy­drated through ascending ethanol series were embedded in EPON 812. Serial semithin sec­tions (0.5-μm thick) were cut on a Reichert Ultracut Emicrotome. Resin was removed by treatment of sections with sodium methoxide prior to rehydration and immune-staining pre­viously described.

Preparation of renal tissue samples

Renal cortex pieces (<0.3 mm × 0.3 mm × 0.3 mm) obtained after nephrectomy was immediately washed with 0.9% NaCl (RNase-free) and quickly dipped in RNase inhibitor (Epicentre, USA) according to the manufac­turer's instructions. After being stored at 4°C overnight, the depressors were removed from the biopsies and stored at -80°C for further tests.

Nuclear extracts and protein concentration

Nuclear extracts were prepared using the Nuclear Extract Kit (Panomics Nuclear Extrac­tion Kit, Cat.) as per the manufacturer's ins­tructions. The concentration of nuclear protein was determined with the bicinchoninic acid (BCA) protein assay reagent kit (Pierce, Rock­ford, IL, USA) to normalize for the amounts of protein within each experiment.

Transcription factor array method (TranSignal TM protein/DNA array)

Preparation of arrays and probe mix incubation

TF array analysis (Panomics Transcription Factor Pathfinder Array TranSignal Kit, Red­wood City, CA, USA) was used to profile the activities of 345 TFs. In this section, we pre­pared the array membranes for use. We prepared pre-treatment buffers I and II and warmed the hybridization buffer to 42°C in a water bath. Then, we allowed the DNA probes to bind TFs from the + nuclear extract. We made sure to dilute the solutions/buffers.

Isolating TF-bound probes

We isolated the protein-bound probes from the non-bound probes. All centrifuge steps were carried out on a regular bench-top centri­fuge at 7000 rpm at 4°C, unless otherwise stated.

Hybridization

Before hybridization, we warmed the hybridi­zation buffer to 42°C in a water bath. We de­natured the eluted probe by heating it at 95°C for 3 min and quickly chilled it in ice for 2 min. Then, we added the eluted probe to each centrifuge tube and hybridized at 42°C over­night. We decanted the hybridization mixture from each hybridization tube and washed each membrane.

Detection

We did not allow the membrane to dry during the detection. Using forceps, we carefully re­moved each membrane from its hybridization tube and transfered it to a clean container con­taining 20 mL of 1X blocking buffer; each membrane needed its own container. We blocked the membrane by incubating it at room temperature with the 1X blocking buffer for 15 min with gentle shaking. We then dil­uted 20 μL of Streptavidin-HRP and conjugated it 1:500 with the 1X blocking buffer from each membrane's container. We continued shaking the membrane for 15 min at room temperature and then we decanted the diluted Streptavidin-HRP solution. Afterwards, we washed each membrane three times at room temperature with 20 mL of 1X wash buffer, each for 8 min. We added 20 mL of 1X detection buffer to each membrane and incubated them at room temperature for 5 min. Then, we overlaid each blot with 2.5 mL of working substrate solu­tion. Then, we pipeted 2.5 mL of the mixed substrate solution onto each membrane and incubated them at room temperature for 5 min. We removed, afterwards, the excess substrate by gently applying pressure over the top sheet. Using a paper towel, we removed the excess substrate that might be remaining on the sur­face of these sheets. Finally, we exposed the membranes.

Final analysis

We adjusted the exposure time such that the majority of the spots had equal signal intensity and obtained an electronic image of our blot. Then, we analyzed the intensity of each spot using software with this ability. The signal intensities for each spot were analyzed and calculated by the Image Quant 5.0 (Amersham Pharmacia Biotech Ltd.) and Array vision 6.0 (Imaging Research Ltd.). Signal intensities for each spot were scanned and calculated by sub­tracting local background (based on the me­dian intensity of the area surrounding each spot) from the total intensities. An average value of the two spot replicates of each TF was generated after data transformation (to convert any negative value to 0.01); normalization was performed by using a per-chip 50 th percentile method that normalizes each chip on its me­dian, allowing comparison among chips. To highlight TFs that characterize each group, a per-gene on median normalization was per­formed, which normalizes the expression of every TF on its median among samples. We saved data in an Excel spreadsheet and calcu­lated the ratio of the data collected from the images. Any spots with two-fold increase or decrease were considered significant and were confirmed by an electrophoretic mobility shift assay (EMSA).

EMSA verification of TF array results

DNA-binding activity of selected TFs was analyzed by EMSA as described earlier. EMSA was performed using an EMSA chemiluminescence kit (LightShift; Pierce) according to the manufacturer's protocols. For gel shift assays, the nuclear extracts were incubated with indi­vidual biotin-labeled probes in binding buffer for 30 min at 15°C. The probe sequences were as follows: AP-1:5'-CGCTTGATGACTCAGCCGGAA-3', 5'-TTCCGGCTGAGTCATCAAGCG-3'; Pbx1: 5'-CGAATTGATTGATGCACTAATTGGAG-3', 5'-CTCCAATTAGTGCATCAATCAATT-CG-3 '; MEF-2:5 '-CTCTCTGCTTATTTAGAA-CCTAGTC-3', 5'-GACTAGGTTCTAAATAA-GCAGAGAG-3'; which were used to study their DNA-binding activity. Briefly, 15 μg of the nuclear extract prepared was incubated with 16 fmol 32 P-r-ATP-end-labeled consensus oligo-nucleotides for 20 min at 37°C. The incubation mixture included 2-3 μg of poly dI·dC in a binding buffer. The DNA-protein complex thus formed was separated from free oligonucleotide on 7.5% native polyacryla­mide gel. The gel was dried and exposed to an X-ray film, and the radioactive bands were visualized.


   Results Top


The quantification of the protein samples in the acute rejection and normal control groups were checked using the BCA protein assay kit [Table 1] respectively. The protein quantifica­tion results confirmed the good quality of the total protein isolated.
Table 1: Protein quantification using the BCA protein assay kit.

Click here to view


To study the expression of TFs, we employed a newly developed protein-DNA array techno­logy. This array is a high-throughout, DNA-based system that facilitates profiling of the activities of multiple TFs in one assay. Array membranes were treated with nuclear extracts isolated from control and AR. After norma­lization of the raw data, 345 TFs were detected in both groups, with 99 of them differentially expressed by TF array in which 95 TFs were up-regulated and four TFs were down-regu­lated in the acute rejection group compared with the normal control group (part highlighted in boxes in [Figure 1], while there were 246 TFs without significant different expression levels. These data provide the first evidence that the activities of various transcription fac­tors are differentially regulated in the acute rejection and normal control groups. These re­sults clearly indicated a role for the TFs in AR signaling mechanisms.
Figure 1:

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Because the protein-DNA array is a high-throughout method, the results require veri­fication by a secondary assay. We performed an EMSA to confirm the nuclear extract array data. We selected AP-1, Pbx1 and MEF-2 for confirmation because of their apparent signi­ficance. After electrophoresis and membrane transfer, both complexes and probes were iden­tified with streptavidin-horseradish peroxidase and substrate. The array data were consistent with the EMSA verification results for the acute rejection/normal control ratio of AP-1, Pbx1 and MEF-2 in the array test were 14.7642, 18.1152 and 32.4040 correspondingly, while in the EMSA test they were 13.1427, 17.8523 and 30.5439 [Figure 1]B, which was close. These EMSA results demonstrated that the protein-DNA array is an effective method for identifying TFs whose DNA-binding activities had changed.


   Discussion Top


In this study, we isolated and analyzed TFs in the kidney allograft tissue of transplanted pa­tients with acute rejection and normal controls using TF array analysis. The study identified 99 TFs in the acute rejection samples, of which 95 TFs were up-regulated and four TFs were down-regulated.

To date, the exact mechanisms involved in acute rejection after solid organ transplantation are not completely understood. Researchers have performed a lot on the studies of acute rejection after renal transplantation from the aspects of DNA, mRNA and protein, expect to illustrate the mechanism of acute rejection and find ideal biomarkers for diagnosis and precaution of acute rejection. [5],[6],[7],[8],[9],[10],[11] Moreover, urine and serum have been used in the search for biomarkers, but we are still far away from success.

TFs play critical roles in the development and function of the immune system. [17],[18],[19],[20] The link between TFs and human diseases has been proven. Therefore, we apply TF array to ana­lyze the relationship between TFs and acute rejection, which has not been studied before.

The aim of our study was to reveal the rela­tionship between TFs and acute renal rejection to the public, expecting to draw other research groups' attention to this area. Function research on TFs in acute renal rejection is in our next plan. We identified the 99 TFs differentially expressed in acute rejection after renal transplantation whose expression profiling may provide a useful clue for the pathophysiology research of acute rejection. AP-1 is a kind of nuclear TF. It can regulate target genes and plays an important role in the cell's normal growth and malignant transformation. It has a close rela­tionship with the liver disease and is increa­singly involved in other diseases. Pbx1 may play a part in steroid biosynthesis and sex differentiation. Recent studies have shown that it may be involved in HOXA1 protein-induced cell enriching and apoptosis, leading to nerve cell differentiation impairment. MEF-2 is a specific TF, and is fascinating due to its involve­ment in diverse functions of gene regulation and a multiplicity of regulatory mechanisms. The apparent ability of MEF-2 is to control the transcription of genes in muscle differentiation. The main functions are to mediate differentia­tion during the development of skeletal, car­diac and smooth muscle. Our study indicates that TFs such as AP-1, Pbx1 and MEF-2 are potential diagnosis biomarkers and probable factors involved in the pathogenesis of acute rejection. Further investigation is needed to cla­rify the roles of the identified TFs. Our work of TFs may lead to finding novel methods to diagnose, prevent and treat acute renal rejec­tion and to provide a novel research method for rejection of other solid organ transplantation.


   Disclosure Top


The authors have declared that no competing interests exist.

 
   References Top

1.Heeger PS, Hricik D. Immune monitoring in kidney transplant recipients revisited. J Am Soc Nephrol 2002;13:288-90.  Back to cited text no. 1
    
2.Kolb LG, Velosa JA, Bergstralh EJ, Offord KP. Percutaneous renal allograft biopsy. A comparison of two needle types and analysis of risk factors. Transplantation 1994;57:1742-6.  Back to cited text no. 2
    
3.Gonzalez-Buitrago JM. Urinary proteomics. Clin Chim Acta 2007;375:49-56.  Back to cited text no. 3
    
4.Kim DJ, Lee S, Kim SJ, et al. Effects of hypoxia-inducible factor-1alpha expression and C4d deposition in implantation biopsies on renal allograft. Transplant Proc 2008;40:2145-6.  Back to cited text no. 4
    
5.Sawitzki B, Bushell A, Steger U, et al. Identi­fication of gene markers for the prediction of allograft rejection or permanent acceptance. Am J Transplant 2007;7:1091-102.  Back to cited text no. 5
    
6.Nazeer K, Arthur JM, Barber K, Swartz M, Budisavljevic MN. Detection and characteri­zation of glomerulonephritis associated pro­teins using a proteomics approach. J Am Soc Nephrol 2003;14(Suppl):408A.  Back to cited text no. 6
    
7.Tomosugi N, Yamaya H, Sato K, et al. Serum proteome profiles in acute renal allograft rejection by ProteinChip analysis. J Am Soc Nephrol 2003;14(Suppl):431A.  Back to cited text no. 7
    
8.Clarke W, Silverman B, Zhang Z, Chan DW, Klein AS, Molmenti EP. Characterization of renal allograft rejection by urinary proteomic analysis. Ann Surg 2003;237:660-5.  Back to cited text no. 8
    
9.O'Riordan E, Orlova TN, Mei JJ, et al. Bio-informatic analysis of the urine proteome of acute allograft rejection. J Am Soc Nephrol 2004;15:3240-8.  Back to cited text no. 9
    
10.Schaub S, Rush D, Wilkins J, et al. Proteomic-based detection of urine proteins associated with acute renal allograft rejection. J Am Soc Nephrol 2004;15:219-27.  Back to cited text no. 10
    
11.Schaub S, Wilkins JA, Antonovici M, et al. Proteomic-based identification of cleaved uri­nary β2-microglobulin as a potential marker of acute tubular injury in renal allograft. Am J Transplant 2005;5:729-38.  Back to cited text no. 11
    
12.Clarke W. Proteomic research in renal trans­plantation. Ther Drug Monit 2006;28:19-22.  Back to cited text no. 12
    
13.Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. Science 2001;291:1304-52.  Back to cited text no. 13
    
14.Mees C, Nemunaitis J, Senzer N. Transcription factors: Their potential as targets for an indivi­dualized therapeutic approach to cancer. Cancer Gene Ther 2009;16:103-12.  Back to cited text no. 14
    
15.Kamoun M. Cellular and molecular parameters in human allo rejection. Clin Biochem 2001; 34:29-34.  Back to cited text no. 15
    
16.Ramanathan V, Goral S, Helderman JH. Renal transplantation. S Nephrol 2001;21:213-9.  Back to cited text no. 16
    
17.Matthias P, Rolink AG. Transcriptional net­works in developing and mature B cells. Nat Rev Immunol 2005;5:497-508.  Back to cited text no. 17
    
18.Macian F. NFAT proteins: Key regulators of T-cell development and function. Nat Rev Immunol 2005;5:472-84.  Back to cited text no. 18
    
19.Takayanagi H. Mechanistic insight into osteoclast differentiation in osteoimmunology. J Mol Med 2005;83:170-9.  Back to cited text no. 19
    
20.Kawai T, Akira S. TLR signaling. Cell Death Differ 2006;13:816-25.  Back to cited text no. 20
    

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Correspondence Address:
Yong Dai
The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Guangdong Province 518020
China
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DOI: 10.4103/1319-2442.111014

PMID: 23640618

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