1. Top of page
  2. Abstract
  6. Acknowledgements

Ischemia-reperfusion injury (IRI) causes up to 10% of early liver failures in humans and can lead to a higher incidence of acute and chronic rejection. So far, very few studies have investigated wide gene expression profiles associated with the IRI process. The discovery of novel genes activated by IRI might lead to the identification of potential target genes for the prevention or treatment of the injury. In our study, we compared gene expression levels in reperfused livers (RL group) vs. the basal values before retrieval from the donor (basal liver [BL] group) using oligonucleotide array technology. We examined 10 biopsies from 5 livers, analyzing approximately 33,000 genes represented on the Affymetrix HG-U133APlus 2.0 oligonucleotide arrays (Affymetrix, Santa Clara, CA). About 13,000 individual genes were considered expressed in at least 1 condition. A total of 795 genes whose expression is significantly modified by ischemia-reperfusion in human liver transplantation were identified in this study. Some of them are likely to be completely activated by IRI, as they are not expressed in basal livers. The supervised gene expression analysis revealed that at least 12% of the genes involved in the apoptotic process, 12.5% of the genes involved in inflammatory processes, and 22.5% of the genes encoding for heat shock proteins are differentially expressed in RL samples vs. BL samples. Furthermore, IRI induces the upregulation of some genes' coding for adhesion molecules and integrins. In conclusion, we have identified a relevant amount of early genes regulated in the human liver after 7–9 hours of cold ischemia and 2 hours from reperfusion, many of them not having been described before in this process. Their analyses may help us to better understand the pathophysiology of IRI and to characterize potential target genes for the prevention or treatment of the liver injury in order to increase the number of patients that successfully undergo transplantation. Liver Transpl 13:99–113, 2007. © 2006 AASLD.

Orthotopic liver transplantation has become an effective therapeutic approach for end-stage liver diseases. Advances in surgical procedures and immunosuppression protocols have considerably improved patient survival after liver transplantation. However, ischemia-reperfusion injury (IRI), an antigen-independent component of the insult to the liver, represents a major problem affecting the outcome of liver transplantation. IRI causes up to 10% of early liver failures and can lead to a higher incidence of acute and chronic rejection.1 Moreover, with the increasing donor shortage, more functionally “suboptimal or “marginal” livers are being used. Such livers are more susceptible to the damage caused by IRI compared with normal livers.2

Liver IRI is mediated by several processes that lead to hepatocellular damage, which is triggered when the liver is transiently deprived of oxygen during the organ procurement for transplantation and later reoxygenated during reperfusion. These destructive effects arise mostly from acute generation of reactive oxygen species subsequent to reoxygenation that inflict direct tissue injury even though different mechanisms mediate the early, intermediate, and late phase of hepatic IRI. Ischemia activates Kupffer cells, which are the main sources of vascular reactive oxygen species formation during the initial reperfusion period.3–5 The consequent production of free radicals, cytokines, and chemokines by Kupffer cells principally mediates the middle phase of reperfusion injury for up to 6 hours after restoration of the blood flow.6 In addition to the molecular mechanism of direct vascular and parenchymal injury (inactivation of antiproteases and direct cytotoxic effects), reactive oxygen species can promote reperfusion injury through stimulation of the transcription factors nuclear factor [NF]-kB and activator protein-1 (AP-1).7, 8 The postischemic oxidant stress can enhance the expression of genes such as tumor necrosis factor α, inducible nitric oxide synthase, heme oxygenase-1 (HO-1), CXC chemokines, and adhesion molecules.9

The whole process involved in IRI is also regulated by a great number of transcription factors, through many signaling pathways. Gene expression variations following the IRI process have been investigated mainly in rat liver. To our knowledge, the wide gene expression profile associated with this process in human cadaveric liver transplantation has never been investigated in depth. Only 1 paper describing the gene expression profiling of acute liver stress during living donor liver transplantation has been published.10

The aim of the present study was to identify, in the human transplanted liver, genes that modulate their expression level in response to IRI early, using a larger-scale technology. By oligonucleotide microarray analysis of 33,000 known genes, we have identified more than 700 genes whose expression is significantly regulated by ischemia-reperfusion during transplantation of human cadaveric livers. Similarities and differences with previous studies are discussed.

Expression patterns of genes involved in IRI and comparison to the expression profile in basal conditions can provide insight into the changes in gene expression associated with cellular dysfunction and concomitant regulatory pathways underlying ischemia and reperfusion, suggesting better therapeutic protocols for both prevention and treatment of the injury.


  1. Top of page
  2. Abstract
  6. Acknowledgements

Experimental Design

In the first 4 months of 2005, 16 OLTs were performed at the Transplantation Center of the Cardarelli Hospital in Naples, Italy. We examined 10 biopsies from 5 livers from cadaveric donors. Two biopsies were collected from each liver: 1 biopsy before explantation from the donor (basal liver [BL] group), as the first step, immediately after opening, before ice was applied, and 1 biopsy about 2–3 hours after liver reperfusion in the recipient organism (reperfused livers [RL] group).

Livers were retrieved from donors classified as “standard” and “not standard,” according to the criteria of the “Italian National Transplantation Center.” Of 5 donors, 4 were “standard”; only LT 10 was “not standard” because of age (83 years). In particular, the donors' ages ranged from 38 to 83 years. There was no hypotension, and steatosis was always less than 15%. Mean cold ischemia time was 7 hours, mean warm ischemia time was 45 minutes and mean hospitalization in the intensive care unit was 5 days. Age, recipient gender, and liver ischemia times are shown in Table 1 together with donor and graft information.

Table 1. Data of Liver Donors and Recipients and Liver Ischemia Time
Donor and Graft CharacteristicsRecipient CharacteristicsLiver Ischemia Time
Case CodeAge/GenderSteatosisALT/ASTUI/LAge/GenderPathologyCold Ischemia TimeWarm Ischemia Time
  1. Abbreviations: LT, liver transplantation; ALT, alanine aminotransferase; AST, aspartate aminotransferase; F, female; M, male; HBV, hepatitis B virus; HCV, hepatitis C virus.

LT 848/F<10%47/3155/MCirrhosis HBV8 h 50 min60 min
LT 1083/F<10%107/3334/MCirrhosis HBV8 h 15 min40 min
LT 1540/M15%89/7553/MCirrhosis HCV6 h 50 min40 min
LT 1738/M<10%41/7848/FCirrhosis HCV7 h 30 min50 min
LT 1858/F15%116/10747/FCirrhosis HBV6 h 50 min45 min

BL and RL groups were compared by evaluating gene expression before and after liver ischemia-reperfusion. Ten microarray hybridizations were performed using 5 biological replicates per condition.

Sample Collection and RNA Extraction

Liver biopsies for BL samples were collected in different hospitals, whereas biopsies for RL samples were collected at the Transplantation Center of the Cardarelli Hospital. All biopsies were obtained with informed consent given according to protocols approved by the Institutional Ethics Committee. Biopsies were instantly submerged in the RNAlater solution (Quiagen, Courtaboeuf, France) and then frozen in liquid nitrogen and stored at −135°C until RNA extraction.

Liver Transplantation Procedure

For liver retrieval, the inferior vena cava (IVC), suprahepatic vena cava, portal vein, and bile duct were isolated in the donor. The aorta was dissected at the origin of the celiac axis, and the celiac artery dissected from its origin to junction with the hepatic artery. After intravenous administration of 200 IU of heparin, the liver was flushed via a cannula placed in the aorta with cold Celsior preservation solution (Imtix Sangstat, Lyon, France),11 and some ice was placed in the donor abdomen for local cooling. The liver was excised and separated from the diaphragm, the right adrenal gland was placed in a basin with cold Celsior preservation solution for preparation of IVC and portal vein cuffs and then stored in a sealed container with Celsior preservation solution at 4°C for about 7–9 hours before implantation.

Total ischemia time was considered as the window time from devascularization in the donor and portal reperfusion in the recipient. It includes a long period of cold ischemia at 4°C (cold ischemia time), applied intentionally to reduce metabolic activities, and a short period of 40-60 minutes (warm ischemia time) at increasing variable temperature, while performing the vascular reconstruction. Portal circulation was restored by connecting the retrohepatic venae cavae with a continuous suture and performing a termino-terminal anastomosis with the 2 portal veins. The arterial flow and the bile duct were reconstituted with an end-to-end anastomosis.12

After surgery, alanine aminotransferase and aspartate aminotransferase enzymes and bilirubin were evaluated every day. The aspartate aminotransferase level ranged from 243 to 648 on the first day and from 23 to 53 on the fifth day. The alanine aminotransferase ranged from 239 to 424 on the first day and from 44 to 153 on the fifth day. Overall, maximum raise of bilirubin was 5.3 mg/dL (second patient on the third day post-OLT). No rejection episode was observed.

One-year follow-up showed good liver function in all recipients, and no major adverse event was noticed in the clinical course.

Array Processing

All the experiments were performed with Affymetrix HG-U133A Plus 2.0 oligonucleotide arrays (Affymetrix, Santa Clara, CA). Total RNA from each sample was extracted using Trizol reagent (Gibco/BRL Life Technologies, Inc., Gaithersburg, MD) and used to prepare biotinylated target complementary RNA, according to the Affymetrix protocols.13 Quality and amount of starting RNA was confirmed using the Agilent 2100 bioanalyzer (Agilent Technologies, Palo Alto, CA).

Ten micrograms of high-quality total RNA were used to generate first-strand complementary DNA using Affymetrix GeneChip T7- oligo(dT) promoter primer; the second-strand synthesis was performed using the Superscript Choice Kit (Invitrogen Life Technologies, Breda, The Netherlands). In vitro transcription and labeling of complementary RNA was performed with biotinylated UTP and CTP using the Affymetrix IVT labeling Kit. The target complementary RNA generated from each sample was fragmented, washed, and stained according to the instructions provided by Affymetrix. Briefly, spike controls were added to 10-μg fragmented complementary RNA before overnight hybridization. Arrays were then washed and stained with streptavidin-phycoerythrin, before being scanned on an Affymetrix GeneChip scanner 3000.14

Microarray Data Acquisition and Analysis

The Affymetrix Software Microarray Suite (version 5.0) was used to assign to each probe set an “average difference” value corresponding to the expression level of the gene it represented. To make comparisons across different chips, data sets on each chip were scaled to a targeted total fluorescence of 500.

GeneSpring software, version 7.3 (Silicon Genetics, Redwood City, CA), was used for data mining. Raw expression data per gene were normalized to median. Normalized data were log-transformed.

Expression data were prefiltered to reduce noise and so discard “unreliable” genes by using the Cross-Gene error model.15 Furthermore, genes were considered reliable when called present in at least 2 out of 10 samples and with a raw signal higher than 10.0. Data were excluded with standard deviation >0.3 within the control group.

Differentially expressed genes were grouped according to Gene Ontology (GO) consortium classification for biological processes and molecular functions.16 Using the GOTree Machine,17 GO categories have been identified with significantly enriched gene numbers in the gene lists when compared to the Affymetrix chip gene set, used as a reference list.18, 19

Real-Time Quantitative Polymerase Chain Reaction

Expression values of 14 genes from the 5 liver samples were checked by real-time quantitative polymerase chain reaction (PCR). The same batch of total RNA was used both for primary gene expression and validation experiments.

Complementary DNA was synthesized with random hexamer primers starting from 1.5 μg of total RNA using the reverse transcription protocol (Taqman Reverse Transcription, Applied Biosystems, Applera, Monza, Italy). Real-time PCR was performed using SYBR Green I Master Mix (Applied Biosystems, Applera) on the DNA Engine Opticon 2 System (MJ Research, Boston, MA) according to the manufacturer's protocols. Reactions were performed in 20 μL total volume with 0.2 μmol/L primers; nucleotides, Taq DNA polymerase, and buffer were included in the SYBR Green I Master mix. PCR reactions were performed in triplicate. The primers (Primm Biotech Products and Services, Milan, Italy) used for amplification are listed in Table 2. Primer pairs were designed using the Primer 3 software20 to obtain amplicons ranging from 100 to 150 base pair, and specifically designed to span introns or cross intron/exon boundaries. Data normalization was performed using hydroxymethyl-bilane synthase as housekeeping gene.21

Table 2. Primer Pairs Used for Real-Time Quantitative PCR
Probe SetGene NameAcc. number ENSEMBLLeft PrimerRight Primer

Experiments were performed twice. The amplification protocol was as follows: 1 cycle of 10 minutes at 95°C, 43 cycles of 95°C for 15 seconds, 56°C for 20 seconds, 72°C for 20 seconds, plus an extension at 72°C for 3 minutes. The relative expression value was calculated with the formula 2−ΔΔct.


Data from different groups were compared using Student's t test, with Benjamini and Hochberg false discovery rate as multiple testing correction. Statistical significance was established at P < 0.01 during the unsupervised analysis and P < 0.05 during the supervised analysis. Genes were considered differentially expressed with a fold change >1.5 between the 2 conditions. Gene sets were considered enriched with a P < 0.01 when compared to the reference gene list.


  1. Top of page
  2. Abstract
  6. Acknowledgements

Approximately 54,000 probe sets represented on the Affymetrix chip corresponding to about 33,000 human genes were analyzed. The Affymetrix Microarray Suite (version 5.0) software was used to identify presence calls and to quantify gene expression (see “Materials and Methods”). Affymetrix software assigns to each probe set an absolute call (present, absent, or marginal) that represents a qualitative indication of gene transcription within each sample. According to this flag, the total number of genes expressed in adult liver was determined first. About 14,000 probe sets, corresponding to about 9,500 genes, were never called present in our liver samples; 18,000 probe sets, corresponding to about 13,000 individual genes, were called present in 1 of the 2 conditions: basal and reperfused liver. Among them, about 1,300 genes were not expressed in the BL samples; they are likely to be induced by the ischemia-reperfusion injury. On the other hand, approximately 200 genes expressed in the basal samples were no more expressed in all the RL samples.

Raw expression data were normalized and prefiltered to eliminate unreliable data (see “Materials and Methods”) before performing gene expression differential analysis. Then 28,000 probe sets, corresponding to about 16,000 genes expressed in at least 1 sample, were selected.

Power analysis performed using PowerAtlas software (University of Alabama at Birmingham, Birmingham, AL)22 demonstrated that at the threshold 0.01 at which we had chosen to discriminate differentially expressed genes, a sample size of 5 biological replicates per condition corresponded to a true positive probability >92% (Fig. 1).

thumbnail image

Figure 1. Probabilities of true positive (PTP) differential expressed genes calculated using PowerAtlas Software. The PTP is the expected proportion of genes declared significantly differentially expressed between 2 conditions that are truly differentially expressed between the 2 populations. The graphic shows PTP in the comparison between RL and BL samples across a variety of sample sizes and significance levels. At the significance level cutoff 0.01 (blue line) that we have chosen to discriminate differentially expressed genes, a sample size of 5 biological replicates per condition corresponds to a true positive probability >92%. Red, green, black and yellow lines indicate, respectively, the significance level cut-off of 0.1, 0.05, 0.001 and 1.0E-4 for Student's t test with Benjamini and Hochberg false discovery rate as multiple testing correction.

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Unsupervised Gene Expression Analysis

The early gene expression profile of reperfused livers (RL group) was compared to the basal values (BL group). Based on Student's t test with Benjamini and Hochberg false discovery rate multiple correction, 420 genes were differentially expressed between the 2 groups with a fold change >2 and P < 0.01. Of them, 12 genes were downregulated and 408 genes were upregulated in RL samples. About 375 more genes were differentially expressed; 65 genes were downregulated in RL and 310 were upregulated with a fold change ranging between 1.5 and 2; therefore the total number of differentially expressed genes, with very high statistical significance, was 795 (Fig. 2). Table 3 reports the 50 most upregulated genes in RL group. The downregulated genes are listed in Table 4. The number of downregulated genes may be underestimated because Affymetrix Microarray Suite 5.0 software may assign the flag absent to a gene either when it is really not expressed or when its expression rate is not reliable. Genes with weak expression often give unreliable results, so they may be discarded from the analysis.

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Figure 2. Expression profile of 1,063 probe sets differentially expressed in RL vs. BL samples with P < 0.01. Expression values (normalized log transformed data) of 1,063 probe sets, corresponding to 718 genes upregulated and 77 genes downregulated in reperfused livers, are plotted both per patient and per condition. Basal values are plotted on the left side and values after reperfusion are plotted on the right side. Red lines indicate that genes are upregulated in the RL group vs. the control group, and white lines indicate the downregulated genes. Genes were considered differentially expressed by comparing the mean expression values of RL samples with that of BL samples using Student's t test with Benjamini and Hochberg false discovery rate, with a P value <0.01, after filtering expression values for noise and unreliable data. TF, liver transplantation; RL, reperfused liver; BL, basal liver.

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Table 3. List of the 50 Most Upregulated Genes in the RL Group vs. the BL Group, With a P < 0.01 according to the Benjamini and Hochberg False Discovery Rate Test
Probe SetFold ChangeP ValueCommon nameGenbankDescription
  1. NOTE: Probe sets are sorted according to descending fold change. Standard deviation of fold changes per gene was always <2.0.

  2. Abbreviations: cDNA, complementary DNA; mRNA, messenger RNA.

202768_at266.34.56E-05FOSBNM_006732FBJ murine osteosarcoma viral oncogene homolog B
205476_at77.20.00113CCL20NM_004591Chemokine (C-C motif) ligand 20
215078_at39.540.00125SOD2AL050388Superoxide dismutase 2, mitochondrial
36711_at38.040.000133MAFFAL021977Human DNA sequence from clone CTA-447C4
241716_at37.330.00137HSPD1BF965447Heat shock 60kDa protein 1 (chaperonin)
209189_at33.10.00428FOSBC004490v-fos FBJ murine osteosarcoma viral oncogene homolog
204575_s_at26.880.000381MMP19U38321Matrix metalloproteinase 19
230494_at24.040.000393SLC20A1AI671885Solute carrier family 20 (phosphate transporter), member 1
202581_at22.80.000278HSPA1BNM_005346Heat shock 70kDa protein 1B
202859_x_at22.720.00126IL8NM_000584Interleukin 8
202672_s_at21.720.000285ATF3NM_001674Activating transcription factor 3
202627_s_at21.220.00171SERPINE1AL574210Serine (or cysteine) proteinase inhibitor, clade E member 1
200800_s_at19.470.000304HSPA1ANM_005345Heat shock 70kDa protein 1A
202014_at18.970.000399PPP1R15ANM_014330Protein phosphatase 1, regulatory (inhibitor) subunit 15A
218723_s_at18.690.00278RGC32NM_014059Response gene to complement 32
202643_s_at17.910.000465TNFAIP3AI738896Tumor necrosis factor, alpha-induced protein 3
239818_x_at17.30.00277TRIB1AA576947Phosphoprotein regulated by mitogenic pathways
1554333_at16.730.00171DNAJA4BC031044DnaJ (Hsp40) homolog, subfamily A, member 4
244753_at14.660.00151ACTN4BF000430Actinin, alpha 4
204621_s_at14.330.00163NR4A2AI935096Nuclear receptor subfamily 4, group A, member 2
208744_x_at13.560.000521HSPH1BG403660Heat shock 105kDa/110kDa protein 1
201324_at13.480.000287EMP1NM_001423Epithelial membrane protein 1
203821_at12.560.00361DTRNM_001945Diphtheria toxin receptor
201464_x_at12.160.000245JUNBG491844v-jun sarcoma virus 17 oncogene homolog (avian)
212533_at11.920.00156WEE1X62048WEE1 homolog (S. pombe)
221841_s_at11.790.000372KLF4BF514079Kruppel-like factor 4 (gut)
202340_x_at11.580.00118NR4A1NM_002135Nuclear receptor subfamily 4, group A, member 1
202497_x_at11.30.00185SLC2A3AI631159Solute carrier family 2 member 3
206115_at10.840.00387EGR3NM_004430Bridging integrator 3
208891_at10.620.00064DUSP6BC003143Dual specificity phosphatase 6
204103_at10.510.000304CCL4NM_002984Chemokine (C-C motif) ligand 4
242726_at10.360.00403ACSL3BF221850Acyl-CoA synthetase long-chain family member 3
226553_at10.090.00215TMPRSS2AI660243Transmembrane protease, serine 2
217911_s_at9.640.000225BAG3NM_004281BCL2-associated athanogene 3
205214_at9.5620.00373STK17BNM_004226Serine/threonine kinase 17b (apoptosis-inducing)
242963_at9.50.00148MGC26963AI160370CDNA FLJ41298 fis, clone BRAMY2040478
242736_at9.4450.00207SORBS1AI377221Sorbin and SH3 domain containing 1
232017_at9.3320.000443TJP2AK025185Homo sapiens cDNA: FLJ21532 fis, clone COL06049.
242727_at9.2940.00064ARL8BG032269ADP-ribosylation factor-like 8
221031_s_at9.2570.000234DKFZP434F0318NM_030817Hypothetical protein DKFZp434F0318
209803_s_at9.180.0012PHLDA2AF001294Pleckstrin homology-like domain, family A, member 2
208960_s_at9.0880.00389COPEBBE675435Core promoter element binding protein
202464_s_at9.0520.000268PFKFB3NM_0045666-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3
206432_at8.7980.00217HAS2NM_005328Hyaluronan synthase 2
205114_s_at8.4050.00223CCL3NM_002983Chemokine (C-C motif) ligand 3
205681_at8.1860.00284BCL2A1NM_004049BCL2-related protein A1
203751_x_at8.0620.000465JUNDAI762296Jun D proto-oncogene
204094_s_at7.8730.000972KIAA0669NM_014779Homo sapiens KIAA0669 gene product (KIAA0669), mRNA
Table 4. List of All the Downregulated Genes in the RL Samples vs. BL Samples With a Fold Change >1.5 and P < 0.01 When Compared With the Benjamini and Hochberg False Discovery Rate
Probe SetFold ChangeP ValueCommon nameGenbankDescription
  1. NOTE: Standard deviation of fold changes per gene was always <0.2.

  2. Abbreviations: mRNA, messenger RNA; cDNA, complementary DNA.

222518_at0.6610.00776ARFGEF2BF525399ADP-ribosylation factor guanine nucleotide-exchange factor 2
200614_at0.6580.00373CLTCNM_004859Clathrin, heavy polypeptide (Hc)
1555815_a_at0.6570.00576L3MBTL2AL136564l(3)mbt-like 2 (Drosophila)
206313_at0.6510.0033HLA-DOANM_002119Major histocompatibility complex, class II, DO alpha
217914_at0.6460.00925TPCN1NM_017901Two pore segment channel 1
235471_at0.6450.00811C10orf72BE8584647g29d12.x1 NCI_CGAP_Brn23
225910_at0.6440.00418LOC284019BF514723Helicase with zinc finger domain
226093_at0.6430.00723DCP1BAW204088Decapping enzyme hDcp1b
226510_at0.6420.00703C14orf125BF435286Chromosome 14 open reading frame 125
235303_at0.6410.00555RG9MTD3AV728846RNA (guanine-9-) methyltransferase domain containing 3
1566956_at0.640.00985OR7E104PAL137719Olfactory receptor, family 7, subfamily E, member 104 pseudogene
242895_x_at0.6360.00317RNF39R63837Ring finger protein 39
205752_s_at0.6350.00519GSTM5NM_000851Glutathione S-transferase M5
209316_s_at0.6350.00286HBS1LBC001465HBS1-like (S. cerevisiae)
221696_s_at0.6350.00894STYK1AF251059Protein kinase STYK1
227502_at0.6340.00304FLJ10842AV649579LCHN protein
216885_s_at0.6340.00819H326AK026481Homo sapiens cDNA: FLJ22828 fis, clone KAIA4051
221063_x_at0.6320.00568RNF123NM_022064Ring finger protein 123
223483_at0.6270.00737DELGEFBC000707Deafness locus associated putative guanine nucleotide exchange
230214_at0.620.00334MRVI1AL044056Murine retrovirus integration site 1 homolog
1557189_at0.6160.00691DNASE1AW468509Deoxyribonuclease I
1557953_at0.6150.00712ZNF36BG761185602718526F1 NIH_MGC_49
225499_at0.6140.0075KIAA1272AW296194Chromosome 20 open reading frame 74
1556464_a_at0.6140.0039LOC257407AF086098Hypothetical protein LOC257407
202607_at0.6140.00983NDST1AL526632AL526632 Homo sapiens NEUROBLASTOMA COT 25-NORMAL
224686_x_at0.6110.0093LOC474170AA045233FLJ34306 protein/// Hypothetical LOC388397
221871_s_at0.610.00644TFGBF057492TRK-fused gene
202288_at0.6090.00801FRAP1U88966FK506 binding protein 12-rapamycin associated protein 1
201620_at0.6090.00738MBTPS1NM_003791Membrane-bound transcription factor protease, site 1
217902_s_at0.6070.00571HERC2NM_004667Hect domain and RLD 2
219320_at0.6040.00532MYOHD1NM_025109Myosin head domain containing 1
223760_s_at0.6040.00311RAB7AF119891Predicted protein of HQ2706; Homo sapiens PRO2706 mRNA
49452_at0.6020.00243LOC283445AI057637Acetyl-coenzyme A carboxylase beta
212880_at0.60.00894WDR7AB011113WD repeat domain 7
202204_s_at0.5960.00489AMFRAF124145Autocrine motility factor receptor
1552279_a_at0.5930.000972MGC9564AK074161Sterile alpha and TIR motif containing 1
204691_x_at0.590.00323PLA2G6NM_003560Phospholipase A2, group VI (cytosolic, calcium-independent)
219245_s_at0.5890.00311FLJ13491AI309636Hypothetical protein FLJ13491
223351_at0.5890.00137HLC-8N21028yx46f01.s1 Soares melanocyte 2NbHM
1565641_at0.5780.00321C16orf45BE503823Chromosome 16 open reading frame 45
205601_s_at0.5770.00492HOXB5NM_002147Homeo box B5
229223_at0.5760.00784NFATC3AI038402Nuclear factor of activated T-cells, cytoplasmic, calcineurin-dep
226450_at0.5740.00874INSRAV703054Insulin receptor
242759_at0.5730.00762ZFP64AI821726Zinc finger protein 64 homolog (mouse)
228456_s_at0.5720.00283LOC149832AU151357Clone IMAGE:5729395, mRNA
225509_at0.570.00516LOC56757AI862477Hypothetical protein FLJ11526
218873_at0.5680.00514FLJ20203NM_017710Synonyms: FLJ12923, FLJ23040, KIAA1606, DKFZp761I241
217225_x_at0.5660.00284LOC283820AL512687Hypothetical protein LOC283820
200637_s_at0.5660.00285PTPRFAI762627Protein tyrosine phosphatase, receptor type, F
205783_at0.5640.00995KLK13NM_015596Kallikrein 13
1566646_at0.5630.00982LOC149086AK057562Hypothetical protein LOC149086
1560198_at0.5540.00278C14orf70AV701600AV701600 ADB Homo sapiens cDNA clone ADBCWF03 5′
225065_x_at0.5480.00206MGC40157AI826279wk33e07.x1 NCI_CGAP_Pr22 3′
211973_at0.5480.00137NUDT3AW341200Nudix (nucleoside diphosphate linked moiety X)-type motif 3
214955_at0.5430.00243TMPRSS6AI912086Transmembrane serine protease 6
225115_at0.5410.00679HIPK2BF529628Homeodomain interacting protein kinase 2
228688_at0.5380.00838PSMD7AA843726Transcribed sequence with strong similarity prf:2111281A
239586_at0.5340.00568BJ-TSA-9AA085776Hypothetical protein MGC14128
225504_at0.5340.00698FLJ21616AW294031Hypothetical protein FLJ21616
230888_at0.5190.00264HSPC049AW300278HSPC049 protein
236153_at0.5170.00223SUHW4BF447323Suppressor of hairy wing homolog 4 (Drosophila)
227319_at0.510.00308C16orf44AI693862Chromosome 16 open reading frame 44
160020_at0.5030.00506MMP14Z48481Matrix metalloproteinase 14 (membrane-inserted)
228287_at0.4770.00172ING5BG054893Inhibitor of growth family, member 5
231304_at0.4710.00307PPP3R2AI936596Glutamate receptor, ionotropic, N-methyl-D-aspartate 3A
200688_at0.4630.00342SF3B3D13642Splicing factor 3b, subunit 3, 130kDa
218019_s_at0.4590.00304C21orf97NM_021941Chromosome 21 open reading frame 97
1552568_at0.450.00114TMEM7NM_031440Transmembrane protein 7
224653_at0.4440.000245EIF4EBP2U88989Eukaryotic translation initiation factor 4E binding protein 2
200617_at0.3920.003KIAA0152NM_014730Go_component: integral to membrane [goid 0016021]
226495_at0.3790.00349KIAA1271BE727883601564286F1 NIH_MGC_20.
200784_s_at0.3410.000225LRP1BF304759Low density lipoprotein-related protein 1

Considering the list of 408 genes upregulated in RL group with a fold change >2 and P < 0.01, 74 GO categories for molecular process were relatively enriched when compared with the complete Affymetrix chip gene set, with a ratio >2 between observed and expected values. Seven categories were relatively enriched in the list of 77 downregulated genes. Table 5 reports the observed and the expected gene numbers in these lists.

Table 5. Gene Ontology Categories for Biological Process Relatively Highly Enriched When Compared With the Complete Affymetrix Chip Gene Set, in the List of 408 Upregulated Genes and 77 Downregulated Genes (Fold Change >2 and P < 0.01)
Upregulated Genes
GO CategoriesObservedExpectedRatioSignificance
  1. NOTE: The observed and the expected gene numbers in these lists are reported. Data are ordered per observed/expected ratio.

  2. Abbreviations: MAPK, mitogen activated protein kinase; JAK-STAT, janus kinase-signal transducers and activators of the transcriptors; MAPKKK, mitogen activated protein kinase cascade.

Circadian rhythm40.4010.000.000524
Response to protein stimulus101.198.400.000000
Response to unfolded protein101.198.400.000000
Positive regulation of cell differentiation40.508.000.001368
Actin filament organization40.586.900.002425
Negative regulation of transcription from RNA polymerase II promoter111.756.290.000001
Rhythmic process50.806.250.001045
Positive regulation of development40.725.560.005244
Blood vessel morphogenesis91.705.290.000044
Vasculature development91.705.290.000044
Blood vessel development91.705.290.000044
Regulation of cell adhesion50.985.100.002749
Regulation of MAPK activity71.385.070.000412
JAK-STAT cascade40.805.000.007696
Negative regulation of apoptosis173.554.790.000000
Negative regulation of programmed cell death173.584.750.000000
Cell cycle arrest81.704.710.000270
Negative regulation of transcription\. DNA-dependent132.814.630.000004
Regulation of kinase activity143.314.230.000005
Regulation of protein kinase activity143.314.230.000005
Regulation of transferase activity143.344.190.000006
Positive regulation of protein kinase activity61.464.110.003209
Positive regulation of transferase activity61.484.050.003516
Negative regulation of signal transduction61.513.970.003844
Negative regulation of transcription154.163.610.000018
Regulation of transcription from RNA polymerase II promoter246.923.470.000000
Regulation of apoptosis308.723.440.000000
Regulation of programmed cell death308.773.420.000000
Negative regulation of nucleobase\. nucleoside\. nucleotide and nucleic acid metabolism154.403.410.000034
Negative regulation of cellular metabolism174.983.410.000010
Locomotory behavior113.263.370.000416
Negative regulation of cellular physiological process5316.043.300.000000
Programmed cell death4313.333.230.000000
Cell death4514.023.210.000000
Regulation of cell growth92.813.200.001981
Negative regulation of progression through cell cycle134.063.200.000212
Negative regulation of physiological process5316.673.180.000000
Negative regulation of cellular process5617.783.150.000000
Transcription from RNA polymerase II promoter3812.143.130.000000
Negative regulation of cell proliferation123.903.080.000531
Cell growth113.603.060.000971
Regulation of cell size113.603.060.000971
Negative regulation of biological process5819.223.020.000000
MAPKKK cascade72.333.000.008717
Induction of apoptosis113.742.940.001305
Induction of programmed cell death113.742.940.001305
Regulation of growth93.072.930.003657
Response to chemical stimulus258.612.900.000002
Negative regulation of metabolism175.882.890.000086
Regulation of enzyme activity175.992.840.000107
Inflammatory response155.332.810.000296
Regulation of cell proliferation207.452.680.000058
Positive regulation of cell proliferation93.372.670.006613
Positive regulation of apoptosis114.162.640.003060
Positive regulation of programmed cell death114.192.630.003213
Response to abiotic stimulus2610.102.570.000009
Cell motility155.882.550.000840
Localization of cell155.882.550.000840
Protein kinase cascade187.082.540.000268
Regulation of signal transduction135.412.400.003071
Response to wounding239.672.380.000107
Regulation of progression through cell cycle2611.422.280.000078
Regulation of cell cycle2611.452.270.000081
Cell proliferation2913.362.170.000070
Cellular morphogenesis146.572.130.006355
Downregulated Genes
GO categoriesObservedExpectedRatioSignificance
Cellular catabolism71.674.190.00120548
Cellular protein metabolism179.151.860.00492348
Cellular macromolecule metabolism179.321.820.00598653
Biopolymer metabolism168.841.810.00874002
Protein metabolism1810.011.800.00517154
Macromolecule metabolism2313.841.660.00304722
Primary metabolism3222.771.410.00318918

More than 700 genes expressed in adult liver are likely to not be affected by the ischemia-reperfusion process, because their expression is significantly homogeneous (fold change <1.1) in the 2 conditions.

Supervised Gene Expression Analysis

Next, the analysis addressed genes involved in pathways already investigated in the physiopathology of IRI. The genes involved in the apoptotic process were investigated in order to identify genes whose expression is modulated during ischemia-reperfusion. The HG-U133A Plus 2.0 chip contains about 1,000 probe sets, corresponding to about 600 genes, related to apoptosis. Seventy-two apoptosis related genes may be considered early genes in the IRI process, as they are regulated after 2 hours of reperfusion with a fold change >1.5 and P < 0.05. Three of them are downregulated. The complete list of these genes is reported in Table 6.

Table 6. Apoptosis-Related Genes Differentially Expressed in RL Samples vs. BL Samples With a Fold Change >1.5 and P < 0.05
Probe SetP ValueFold ChangeCommon NameDescription
  1. Abbreviations: cDNA, complementary DNA; mRNA, messenger RNA.

201466_s_at0.0074821.83JUNv-jun sarcoma virus 17 oncogene homolog (avian)
202014_at0.0006318.97PPP1R15AProtein phosphatase 1, regulatory (inhibitor) subunit 15A
202643_s_at0.00078317.91TNFAIP3Tumor necrosis factor, alpha-induced protein 3
208891_at0.0010310.62DUSP6Dual specificity phosphatase 6
217911_s_at0.0001719.64BAG3BCL2-associated athanogene 3
205214_at0.008279.562STK17BSerine/threonine kinase 17b (apoptosis-inducing)
205681_at0.006498.186BCL2A1BCL2-related protein A1
217997_at0.001917.719PHLDA1Pleckstrin homology-like domain, family A, member 1
202431_s_at0.00287.607MYCv-myc myelocytomatosis viral oncogene homolog (avian)
39402_at0.01527.392IL1BInterleukin 1, beta
200798_x_at0.0001716.484MCL1Myeloid cell leukemia sequence 1 (BCL2-related)
210655_s_at0.02565.604FOXO3AForkhead box O3A
201502_s_at0.0001715.571NFKBIANuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha
218368_s_at0.005145.342TNFRSF12ATumor necrosis factor receptor superfamily, member 12A
207574_s_at0.0001715.143GADD45BGrowth arrest and DNA-damage-inducible, beta
201631_s_at0.005954.78IER3Immediate early response 3
203725_at0.00114.63GADD45AGrowth arrest and DNA-damage-inducible, alpha
203120_at0.003354.59TP53BP2Tumor protein p53 binding protein, 2
221009_s_at0.00284.434ANGPTL4Angiopoietin-like 4
202284_s_at0.001334.383CDKN1ACyclin-dependent kinase inhibitor 1A (p21, Cip1)
236402_at0.04243.776BRAFv-raf murine sarcoma viral oncogene homolog B1
209308_s_at0.0008523.523BNIP2BCL2/adenovirus E1B 19kDa interacting protein 2
227345_at0.001493.514TNFRSF10DTumor necrosis factor receptor superfamily, member 10d, decoy with truncated death domain
204121_at0.01613.323GADD45GGrowth arrest and DNA-damage-inducible, gamma
226525_at0.04093.283STK17Byz03f02.s1 Soares_multiple_sclerosis_2NbHMSP Homo sapiens
238509_at0.01563.271CUL1cullin 1
202724_s_at0.005143.053FOXO1AForkhead box O1A (rhabdomyosarcoma)
243664_at0.01422.942TXNLThioredoxin-like, 32kDa
208309_s_at0.02972.778MALT1Mucosa associated lymphoid tissue lymphoma translocation gene 1
209545_s_at0.001782.771RIPK2Receptor-interacting serine-threonine kinase 2
239629_at0.00422.735CFLARCASP8 and FADD-like apoptosis regulator
231775_at0.02542.7TNFRSF10Azd33e01.r1 Soares_fetal_heart_NbHH19W Homo sapiens cDNA clone IMAGE:342456 5′ similar to contains Alu repetitive element;, mRNA sequence.
227558_at0.03762.668CBX4Chromobox homolog 4 (Pc class homolog, Drosophila)
206665_s_at0.04092.63BCL2L1BCL2-like 1
213596_at0.005142.608CASP4Caspase 4, apoptosis-related cysteine protease
230499_at0.001782.544BIRC3Baculoviral IAP repeat-containing 3
205263_at0.01912.497BCL10B-cell CLL/lymphoma 10
211919_s_at0.008242.485CXCR4Chemokine (C-X-C motif) receptor 4
210405_x_at0.005762.411TNFRSF10BTumor necrosis factor receptor superfamily, member 10b
209239_at0.001192.37NFKB1Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (p105)
202693_s_at0.02822.333STK17ASerine/threonine kinase 17a (apoptosis-inducing)
222728_s_at0.02972.332MGC5306NB4 apoptosis/ differentiation related protein; Homo sapiens PNAS-104
205192_at0.02222.33MAP3K14Mitogen-activated protein kinase kinase kinase 14
229519_at0.005142.264FXR1Fragile X mental retardation, autosomal homolog 1
201739_at0.01612.258SGKSerum/glucocorticoid regulated kinase
202076_at0.01312.177BIRC2Baculoviral IAP repeat-containing 2
218088_s_at0.005142.167RRAGCRas-related GTP binding C
204005_s_at0.03822.028PAWRPRKC, apoptosis, WT1, regulator
201375_s_at0.02342.014PPP2CBProtein phosphatase 2 (formerly 2A), catalytic subunit, beta isoform
208652_at0.007562.012PPP2CAProtein phosphatase 2 (formerly 2A), catalytic subunit, alpha isoform
225434_at0.01312.003DEDD2Death effector domain containing 2
214499_s_at0.006541.925BCLAF1BCL2-associated transcription factor 1
200608_s_at0.0411.902RAD21RAD21 homolog (S. pombe)
222985_at0.01341.879YWHAGTyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein
231809_x_at0.01911.867PDCD7EST365840 MAGE resequences, MAGC Homo sapiens cDNA, mRNA sequence.
209941_at0.0251.821RIPK1Receptor (TNFRSF)-interacting serine-threonine kinase 1
210792_x_at0.02991.816SIVACD27-binding (Siva) protein
200071_at0.0251.801SMNDC1Survival motor neuron domain containing 1
208905_at0.008191.69CYCSCytochrome c, somatic
202886_s_at0.01461.596PPP2R1BProtein phosphatase 2 (formerly 2A), regulatory subunit A (PR 65), beta isoform
222158_s_at0.02781.585PNAS-4CGI-146 protein
212373_at0.0251.582FEM1Bfem-1 homolog b (C. elegans)
213026_at0.02251.561APG12LAPG12 autophagy 12-like (S. cerevisiae)
221478_at0.02971.558BNIP3LBCL2/adenovirus E1B 19kDa interacting protein 3-like
204780_s_at0.04091.552TNFRSF6Tumor necrosis factor receptor superfamily, member 6
201149_s_at0.01421.528TIMP3Tissue inhibitor of metalloproteinase 3 (Sorsby fundus dystrophy, pseudoinflammatory)
210101_x_at0.03161.507SH3GLB1SH3-domain GRB2-like endophilin B1
212401_s_at0.007481.505CDC2L1Cell division cycle 2-like 2
1553178_a_at0.02970.639SSTR3Somatostatin receptor 3
205488_at0.0240.632GZMAGranzyme A (granzyme 1, cytotoxic T-lymphocyte-associated serine esterase 3)
210685_s_at0.006490.564UBE4BUbiquitination factor E4B (UFD2 homolog, yeast)

Genes related to the inflammatory response and heat shock genes were also analyzed. Spotted on the HG133 Plus 2.0 chip are 195 probe sets related to inflammatory response. They correspond to 132 genes, 17 of which are differentially expressed, upregulated or downregulated, and mostly chemokines CC and CXC and interleukins 1 and 8 (Table 7). Other interleukins are not significantly regulated after IRI.

Table 7. Inflammatory Response Genes Differentially Expressed in RL Samples vs. BL Samples With a Fold Change >1.5 and P < 0.05
Probe setP ValueFold ChangeCommon NameDescription
203140_at0.001042.283BCL6B-cell CLL/lymphoma 6 (zinc finger protein 51)
207655_s_at0.02181.811BLNKB-cell linker
205476_at0.0021177.2CCL20Chemokine (C-C motif) ligand 20
205114_s_at0.005348.405CCL3Chemokine (C-C motif) ligand 3
204103_at0.00086610.51CCL4Chemokine (C-C motif) ligand 4
209774_x_at0.001612.194CXCL2Chemokine (C-X-C motif) ligand 2
206336_at0.02672.481CXCL6Chemokine (C-X-C motif) ligand 6 (granulocyte chemotactic protein 2)
211919_s_at0.01142.485CXCR4Chemokine (C-X-C motif) receptor 4
221664_s_at0.04251.782F11RF11 receptor
209189_at0.012733.1FOSv-fos FBJ murine osteosarcoma viral oncogene homolog
205119_s_at0.002953.477FPR1Formyl peptide receptor 1
39402_at0.01817.392IL1BInterleukin 1, beta
207008_at0.01273.103IL8RBInterleukin 8 receptor, beta
206157_at0.044.588PTX3Pentaxin-related gene, rapidly induced by IL-1 beta
209545_s_at0.002112.771RIPK2Receptor-interacting serine-threonine kinase 2
204924_at0.02121.897TLR2Toll-like receptor 2
202859_x_at0.000240.8IL8Interleukin 8

For heat shock genes, 261 probe sets that correspond to 180 genes have been tested. Twenty-six genes are differentially expressed between BL and RL samples with a fold change >1.5 and P < 0.05. They are all upregulated except for the heat shock protein HSPC049 gene, which that is 2-fold downregulated in RL samples. The list of these genes is reported in Table 8.

Table 8. Heat Shock Proteins Differentially Expressed in RL Samples vs. BL Samples With a Fold Change >1.5 and P < 0.05
Probe SetP ValueFold ChangeCommon NameDescription
202581_at0.000422.8HSPA1BHeat shock 70kDa protein 1B
200800_s_at0.000419.47HSPA1AHeat shock 70kDa protein 1A
208744_x_at0.000613.56HSPH1Heat shock 105kDa/110kDa protein 1
207714_s_at0.018310.67SERPINH1CBP1, CBP2; collagen-binding protein 1
1554334_a_at0.00069.787DNAJA4DnaJ (Hsp40) homolog, subfamily A, member 4
200666_s_at0.00047.331DNAJB1DnaJ (Hsp40) homolog, subfamily B, member 1
213418_at0.01157.044HSPA6Heat shock 70kDa protein 6 (HSP70B')
203811_s_at0.00424.073DNAJB4DnaJ (Hsp40) homolog, subfamily B, member 4
219343_at0.00523.626CDC37L1Hsp90-associating relative of Cdc37
201841_s_at0.01513.247HSPB1Heat shock 27kDa protein 1
210211_s_at0.01512.589HSPCAHeat shock 90kDa protein 1, alpha
213330_s_at0.03722.518STIP1Stress-induced-phosphoprotein 1 (Hsp70/Hsp90-organizprotein)
200881_s_at0.01512.252DNAJA1DnaJ (Hsp40) homolog, subfamily A, member 1
218936_s_at0.03252.209HSPC128HSPC128 protein
208811_s_at0.01192.165DNAJB6DnaJ (Hsp40) homolog, subfamily B, member 6
214328_s_at0.01842.133HSPCATranscribed sequences
200064_at0.01422.127HSPCBHeat shock 90kDa protein 1, beta
202843_at0.03252.117DNAJB9DnaJ (Hsp40) homolog, subfamily B, member 9
223486_at0.03722.007HSPC135HSPC135 protein
200806_s_at0.03251.93HSPD1Transcribed sequence with strong similarity to protein pir:A32800
205133_s_at0.03721.833HSPE1Heat shock 10kDa protein 1 (chaperonin 10)
223271_s_at0.03031.772HSPC129Hypothetical protein HSPC129
208815_x_at0.01341.629HSPA4Heat shock 70kDa protein 4
218728_s_at0.03681.542HSPC163HSPC163 protein
230888_at0.00550.519HSPC049HSPC049 protein
203665_at0.01230.512HMOX1Heme oxygenase (decycling) 1, HO-1

Quantitative real-time PCR analysis on 14 genes was carried out to verify the results of microarray analysis. The genes were chosen as follows: 10 genes upregulated in RL group, 2 downregulated genes, and 2 normoregulated genes. The results confirm the microarray data for these genes (Table 9). Correlation rates between quantitative real-time PCR and microarray data were satisfactory for all the tested genes (r > 0.85) except for the homeobox B5 gene, which is downregulated in RL according to microarray data and normoregulated according to real-time PCR.

Table 9. Comparison Between Quantitative Real-time PCR Analysis and Microarray Data on 14 Genes
Probe SetGene DescriptionRL/BL MicroarrayRL/BLqPCR
  1. Abbreviation: qPCR, quantitative PCR.

202076_atBaculoviral IAP repeat-containing 2 (BIRC2)2.22.0
230499_atBaculoviral IAP repeat-containing 3 (BIRC3)2.511.8
201236_s_atBTG family, member 2 (BTG2)2.46.0
227558_atChromobox homolog 4 (Pc class homolog, Drosophila) (CBX4)2.72.5
206432_atHyaluronan synthase 2 (HAS2)8.834.2
205601_s_atHomeobox B5 (HOXB5)0.61.1
215485_s_atIntercellular adhesion molecule 1 (CD54), human rhinovirus receptor (ICAM1)2.011.0
228287_atInhibitor of growth family, member 5 (ING5)0.50.6
202068_s_atLow density lipoprotein receptor (familial hypercholesterolemia) (LDLR)8.711.3
205192_atMitogen-activated protein kinase kinase kinase 14 (MAP3K14)2.33.3
209239_atNuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (p105) (NFKB1)2.32.0
202464_s_at6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3)9.05.6
200688_atSplicing factor 3b, subunit 3, 130kDa (SF3B3)0.50.8
225721_atSynaptopodin 2 (SYNPO2)0.81.2


  1. Top of page
  2. Abstract
  6. Acknowledgements

A total of 795 genes whose expression is significantly modified by ischemia-reperfusion in human liver have been identified during transplantation of livers from cadaveric donors. All of them are early-responding genes, as their expression is significantly altered within 3 hours after reperfusion. The analysis of the enriched GO categories for biological processes in the list of upregulated genes showed a very significant presence of genes involved in signal transduction, intracellular signaling cascade, Janus kinase-signal transducers and activators of the transcriptors (JAK-STAT) and mitogen activated protein kinase (MAPK) activity, apoptosis induction and regulation, cell death, and cell growth. Many genes included in the group of significantly upregulated genes are likely to be completely activated by IRI, as these genes are not expressed at all in the basal samples.

We compared our data with data previously obtained by Borozan et al.10 during living donor transplantation. The authors analyzed 19,000 complementary DNA clones, identifying a set of genes whose expression is impaired in acute but not in chronic liver stress. They demonstrated that 2 hours after reperfusion of transplanted liver, 125 genes were at least 2-fold upregulated and 106 were downregulated with P < 0.01 when compared with basal gene expression. Even though experimental conditions are different, as we considered livers from cadaveric donors instead of living donors and our ischemia times were far longer, our data showed 90% agreement (95/125) with the list of upregulated genes. In particular, when we considered the list of 25 most dysregulated genes that the authors confirmed by real-time PCR, the agreement was quite complete for the upregulated genes. The genes downregulated after reperfusion showed a similar fold change but with poor significance in our data (Table 10). We hypothesize that the expression of many downregulated genes was already impaired in basal samples at BL, as we began our analysis with cadaveric livers.

Table 10. Comparison Between Borozan Data and Our Microarray Data
Probe SetFold Change, Borozan et al.Fold Change, Our DataP Value, Our DataCommon NameGenBank Acc.
  1. Abbreviations: ID, identification; Acc., accession number.

Upregulated Genes
Downregulated Genes

The supervised gene expression analysis revealed that 12% of the genes involved in the apoptotic process are differentially expressed in RL samples vs. BL samples. They are mostly upregulated except for the somatostatin receptor 3, the granzyme A, and the ubiquitination factor E4B, which are downregulated after reperfusion. The 72 upregulated genes are subdivided as follows: 41 proapoptotic genes, 31 antiapoptotic genes.

The 12.5% of the genes involved in the inflammatory process were induced by IRI. The most upregulated genes in this category encoded for chemotactic agents like C-C chemokines cystein cistein ligand (CCL) CCL4, and CCL20. Interleukin (IL) 1 and IL-8 were more than 5-fold upregulated. These results agree with the published data in mouse lung ischemia-reperfusion previously reported by Yamane.23 IL-1 is a strong inducer of hepatic chemokine synthesis.24 CXC chemokines recruit neutrophils into the reperfused liver due to their strong chemotactic activity. There is a strong activation of IL-6 gene in human liver. This result confirms the previous data obtained in lung experiments. The IL-6 receptor and the IL-6 signal transducer are deeply upregulated. IL-6 is an antiinflammatory citokine, as it can downregulate the tumor necrosis factorα. Its expression in liver is completely induced by IRI, as IL-6 is not expressed at all in the basal samples.

The activation of genes encoding for heat shock protein (HSP) is also in agreement with previously reported data.25 About 22.5% of the heat shock genes were differentially expressed in the 2 tested conditions. The most upregulated genes were HSP70, HSP105 and HSP90. HSPs are mostly molecular chaperons. They have already been used to evaluate IRI in rat liver.26

HSP induction can reduce the nuclear binding of proinflammatory transcription factors27 and increase the antioxidant power of cells.28 HSP and HO-1 are considered to be key factors in protection because of their preconditioning mechanisms such as ischemic and heat shock preconditioning.29 HO-1 was about 2-fold upregulated in samples after reperfusion. HO-1 degrades heme into carbon monoxide, iron, and biliverdin and is ubiquitously distributed in mammalian tissues. HO-1 is strongly and rapidly induced by a variety of stimuli and agents that cause oxidative stress under pathological conditions. Increased HO-1 expression has been proposed to reduce inducible nitric oxide synthase activity through accelerated degradation of heme thereby exerting a protective effect in animal models of inflammation.

The comparison between these data and data from literature confirms the activation of the adhesion molecule intercellular adhesion molecule (ICAM1). Other adhesion molecules induced by IRI in our liver samples were chemotactic factors such as interleukin-8 (IL-8) and CCL4 and angiogenic inducers such as the cystein-rich angiogenic inducer 61 (CYR61) and the tumor necrosis factor receptor 12A and integrins such as beta-1 integrins. It is known that interaction between integrins and other adhesion molecules is responsible for the process of extravasation of neutrophils, a crucial prerequisite for hepatocyte damage.30 Inhibiting adhesion molecules production should be helpful in reducing leukocyte adhesion to vasal endothelium and therefore cell injury consequent to this event.

Pathway analysis of all of the differential expressed genes, performed using the Pathway Express software (Department of Computer Science, Wayne State University, Detroit, MI), revealed a possible orderly sequence of gene activation in the dysregulated pathways. Cytokine-cytokine receptor interaction pathway was one of the most altered pathways, as 38 out of 229 genes represented on the chip are upregulated in RL samples, including IL-8 (22-fold upregulated) and chemokine (C-C motif) ligand 3 and 4 and IL-6 and -1 (more than 7-fold upregulated). This pathway regulates the focal adhesion pathway (dysregulated with 28/155 upregulated genes) that in turn may influence 3 other pathways: WNT signaling, apoptosis, and mitogen activated protein kinase (MAPK) signaling pathways (all dysregulated with 23/128, 21/92, and 52/232 upregulated genes in RL, respectively). These 3 pathways cooperate to induce cyclin D2 expression, regulating the cell cycle pathway and therefore promoting the liver regeneration process. Cyclin D2, indeed, is more than 2-fold upregulated in RL samples. In this manner, quiescent hepatocytes are induced to reenter the cell cycle, also stimulated by IL-6. Induction of the liver regeneration process is demonstrated the upregulation of MYC, JUND, JUNB, FOS, and FOSB genes, already identified during an early G1 phase of cell cycle in the regenerating liver.31 These genes are highly expressed in RL samples together with other genes upregulated in G1 such as DUSP6 and the cyclin dependent kinase inhibitor p21 (CDKN1A). MYC, FOS, JUND, and FOSB were not expressed in our basal samples. FOSB was very highly upregulated in RL samples. Other genes activated during late cell cycle phases, such as cyclin D1 and genes involved in DNA replication and mitotic checkpoint control were not yet upregulated in our samples 2–3 hours after reperfusion.

In conclusion we have identified a huge amount of early human genes regulated in surgical phases of liver transplantation. Some of these genes are involved in liver injury and cell death, while other genes play a pivotal role in potential anti-inflammatory, regenerating, and protective mechanisms. The present study focused on the molecular mechanisms of liver injury. Obviously it is possible to hypothesize that there are other mechanisms that influence protein levels, despite gene expression behavior. The discovery of novel genes activated by IRI might lead to identification of potential target genes for the prevention or treatment of the liver injury. In that case we would consider it very helpful to extend the analysis to a proteomic level. The expression rate of the 50 most upregulated genes (Table 3) could potentially be used to check liver conditions according to specific therapeutic protocols.


  1. Top of page
  2. Abstract
  6. Acknowledgements

The authors thank Myriam Alcalay, Simone Minardi (FIRC Institute of Molecular Oncology Foundation, Milan), and Pasquale De Luca (Genetic Engineering Center, Naples) for bioinformatic assistance.


  1. Top of page
  2. Abstract
  6. Acknowledgements