Qin, Y.
Citation
Qin, Y. (2011, October 18). Cell adhesion signalling in acute renal failure. Retrieved from https://hdl.handle.net/1887/17953
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A transcriptomics analysis of ATP depletion-mediated stress responses in primary rat proximal tubular epithelial cells
Yu Qin1, John H. Meerman1, Leo S. Price1, Bob van de Water1
1Division of Toxicology, Leiden/Amsterdam Center for Drug Research, Leiden University, The Netherlands
Abstract
ATP depletion is the central biochemical event during renal ischemic injury. We investigated the genes that are involved in ATP depletion-mediated stress responses during ischemic injury and regeneration of kidney epithelial cells. A detailed transcriptomics study was performed using oligonucleotide microarray analysis of mRNA samples from primary rat proximal tubular epithelial cells which had been subjected to metabolic inhibition (MI)- simulated hypoxia followed by a recovery period. Gene clusters by hierarchical clustering analysis could be distinguished between control and MI conditions, and between the different recovery times following MI. Numerous genes were identified that were significantly up- and down-regulated in response to MI followed by 8 h of recovery, with considerable overlap between 20 and 40 min of MI. Gene ontology analysis revealed enrichment of genes involved in receptor and transporter signalling, cellular metabolism, immune response, cytoskeleton organization and development. Further analysis of genes with expression changes ≥ 2 fold identified transmembrane transport, tissue development, cellular metabolism and homeostasis-associated processes, suggesting tissue remodelling and regeneration after ATP depletion-induced hypoxic renal injury. This study has revealed candidate pathways and target genes for further mechanistic investigations to understand the compensatory programs in renal proximal tubular cells after ATP depletion.
Manuscript in preparation
Introduction
Ischemia/reperfusion (I/R) injury is one of the main causes of acute renal failure - the principal target being the proximal tubular epithelial cells (PTECs) of the kidney. Ischemic injury-associated rapid depletion of cellular ATP levels directly affects the metabolic competence of PTECs and results in disruption of the actin cytoskeleton network and as a consequence cell adhesions, leading to cell detachment and death 1-4. ATP depletion may cause cell death through the necrotic or apoptotic pathway, depending on the severity of the injury 5, 6. Surviving PTECs upon brief ischemic injury can recover during the reperfusion/recovery phase and form an epithelial layer again, contributing to the regeneration of the kidney tissue 7, 8.
The RNA/cDNA microarray is a powerful tool for examining genome-wide gene expression changes associated with chemical toxicity 9, 10, drug resistance 11, 12 and disease progression 13-16. By using microarray analysis in both in vivo and in vitro models, pathways and genes have been identified that are associated with pathological processes and clinical outcome of renal I/R injury. Apoptosis may represent an important mechanism for the loss of tubular cells at the early stage, and several apoptosis-associated molecules were found to be involved in early apoptotic response to ischemic renal injury 17-20. The genes involved in the regenerative process during the recovery from ischemic injury are principally related to proinflammatory, remodeling and vasoactive activities 21, 22. Inflammatory pathways and genes are also of pivotal importance for I/R injury during renal transplantation 23-25, as well as extra-renal organ injury 26, 27. Furthermore, several antioxidant genes, in particular the transcription factor NF-E2-related factor-2 (Nrf2), were found to be significantly up- regulated upon reoxygenation following hypoxia and nephrotoxin-induced oxidative stress.
Thus the Nrf2 pathway and related genes are involved in renal epithelial adaptation to oxidative stress and contribute to ischemic preconditioning that is protective against subsequent I/R injury 28, 29. However, the cellular stress responses that are directly and exclusively associated with ATP depletion as well as the signalling components that initiate and control these events have not been systematically investigated.
To study the cellular consequences of ATP depletion, reproducible treatment regimens are necessary. While in vivo renal ischemic/reperfusion models are most relevant, it is difficult to separate the ATP depletion from other biochemical perturbations, e.g. oxidative stress and inflammatory components. A well-established in vitro approach to directly inhibit ATP production is the metabolic inhibition (MI) model by simultaneously using a combination of an inhibitor of the complex III of the respiratory chain, antimycin A (AA) and a glycolysis inhibitor, 2-deoxyglucose (DOG) 30-32. The concentration and exposure time of the inhibitors determine the extent of chemical-induced hypoxia which is reminiscent of the ATP depletion during in vivo ischemic injury. Short period (usually less than 1 h) of MI generates sub-lethal injury to cells, which is a good in vitro model to study cellular changes in PTECs during ischemic renal injury and recovery. Of various cell types of the kidney, PTECs show the strongest transcriptional responses to hypoxia and have been well characterized 33, 34, thus suitable for an in vitro ATP depletion model.
In this study, a microarray analysis was performed on primary rat PTECs which were subjected to 20 or 40 min of MI followed by 8 or 24 h of recovery. Genes which were differentially regulated by mild, sub-lethal MI are predicted to be associated with stress
response pathways involved in recovery from injury, while genes that were differentially regulated by severe, lethal MI are predicted to be associated with the induction of cell death pathways. Using this approach, several injury response, homeostasis and development- associated pathways and genes were identified using GO analysis.
Methods Materials
Dulbecco’s modified Eagles medium (DMEM)/Ham’s F12, DMEM glucose-free medium, PBS, cholera toxin, insulin, and penicillin/streptomycin/amphotericin B were from Invitrogen (Carlsbad, CA). Fetal bovine serum was from Invitrogen (Grand Island, NY). Collagen (type I, rat tail) and epidermal growth factor (EGF) were from Upstate Biotechnology (Lake Placid, NY).
Primary cell isolation and culture
Primary proximal tubular epithelial cells (PTEC) were isolated from male Wistar rats (200- 250 g) by collagenase H (Sigma-Aldrich) perfusion and separated by density centrifugation using Nycodenz (Sigma-Aldrich) as described previously 35. Cells were cultured in collagen- coated 6-well plates in Dulbecco’s modified Eagle’s medium (DMEM)/Ham’s F12 containing 1% (v/v) fetal bovine serum, 0.5 mg/ml bovine serum albumin, 10 μg/ml insulin, 10 ng/ml epidermal growth factor, 10 ng/ml cholera toxin, and 1% (v/v) penicillin/streptomycin/amphotericin B. Primary rat PTECs were maintained at 37°C in a humidified atmosphere of 95% air/5% CO2 and the medium was refreshed every other day.
Cells were used for experiments after they had reached confluence 6 to 9 days after plating.
Table 1. Experimental set-up for microarrays.
Group Annotation MI period Recovery period Samples
1 0min 8hr 0 min 8 h 1, 2, 3
2 20min 8hr 20 min 8 h 4, 5, 6
3 40min 8hr 40 min 8 h 7, 8, 9
4 0min 24hr 0 min 24 h 10, 11, 12
5 20min 24hr 20 min 24 h 13, 14, 15
6 40min 24hr 40 min 24 h 16, 17, 18
Experimental set-up
Confluent monolayers of primary rat PTECs in collagen-coated 6-well plates were washed with PBS once. Metabolic inhibition (MI) 30, 31 was accomplished by inhibiting simultaneously the mitochondrial respiration and the glycolytic pathway with 10 µM antimycinA and 10 mM 2-deoxyglucose, respectively in DMEM glucose-free medium with 10% FBS and 1% BSA. At the end of MI, cells were washed twice with PBS and allowed to recover in complete culture medium. The MI-induced cytotoxicity was determined by measuring lactate dehydrogenase (LDH) released into the culture medium.
For microarray experiments, 6 groups were selected to represent different MI and recovery (short and long) conditions with appropriate controls. The experiment was
performed on three separate occasions using separate isolations and purifications of primary rat PTECs. Groups and samples are described in Table 1.
Determination of LDH release
Necrotic cell death was monitored by the release of LDH from cells into the culture medium as described previously 35. The percentage cell death was calculated from the amount of LDH release caused by treatment with toxicants relative to the amount to that released by 0.1%
(w/v) Triton X-100, i.e., 100% release.
RNA isolation, microarray platform, and microarray experiments
All transcriptome data were generated from triplicates of cell treatment conditions. Total mRNA was isolated independently using Trizol reagent (Invitrogen, Carlsbad, CA) followed by cleanup with RNeasy Mini kit (Qiagen). Samples were used for the synthesis of digoxigenin-labeled cRNA, which was hybridized to Applied Biosystems Rat Genome Survey oligo-microarrays (Applied Biosystems, Foster City, California, USA) at the Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain. Microarrays contain probes representing a complete, annotated and manually curated set of approximately 27,000 rat genes from both public and Celera databases. The hybridized arrays were scanned using the Applied Biosystems 1700 Chemiluminescent Microarray Analyzer to produce images that were further processed and quantified.
Data processing, gene clustering and class comparison
After spot identification and background corrections, spot filters were used to exclude the spot if the Spot Flag contains numeric values outside the range 0-32822. After conversion of the raw signals to expression values, a further filtering step was applied based on a signal-to noise (S/N) ratio of ≥3, representing 99% confidence that the measurement is real. Any gene under the condition that percent of data missing or filtered out exceeds 85 % was also excluded. Among 26857 transcripts available on the microarray, 17289 transcripts (64.4%) passed filtering criteria and underwent further analysis.
Subsequently, a median-normalization step (array median centering and gene median centering) was performed by comparing a gene-by-gene difference between each array and the reference array (the array whose overall log-intensity is the median of entire array), and subtracting the median difference from the log-intensities on that array, so that the gene-by- gene difference between the normalized array and the reference array is 0.
Principal components analysis (PCA) was performed for all the samples after median normalization to evaluate the similarities among the triplicates in the same group. In this analysis, plotted data points represent expression profiles of individual samples. Increased similarity among gene expression profiles is represented as closer proximity. The PCA is composed of 18 expression profiles and is helpful to identify potential outliers in our evaluation study. The PCA analysis showed that the majority of the experiment samples from each group clustered together except for one sample (sample 12) from group 4 (0min 24 hr) and one sample (sample 13) from group 5 (20min 24hr) which were scattered outside the clusters of their respective groups (Figure 1A). Thus these two gene expression profiles were considered as outliers, and removed from further analysis. Average linkage clustering was
performed by correlation (centered) similarity metric, using hierarchical clustering based on euclidean distance and complete linkage (Figure 1B).
A probe-to-gene annotation (release version RGD 11-02-2011) from Celera (Rockville, MD, USA) was used for gene annotation. BRB-ArrayTools Version 4.1.0 (http://linus.nci.nih.gov/BRB-ArrayTools.html) was used for statistical analysis of the data.
To discover global gene expression patterns, class comparisons among different groups were applied. The test statistics used were random variance F-tests 36, with p-values less than 0.001 considered significant. Corrections for multiple testing were made by calculating the false discovery rates according to Benjamini and Hochberg 37 and only genes with a FDR<0.05 were included in the final lists for significantly differentially expressed genes. To determine contrasts within classes, pair-wise comparisons were performed with T-tests at p<0.001.
Figure 1. PCA and hierarchical clustering analysis. (A) PCA and (B) hierarchical clustering of gene expression profiles show a principal distinction between control and MI- treated samples and between the recovery periods (8 and 24 h) after MI. Two outliers (sample 12 and 13) determined in the PCA analysis were excluded from hierarchical clustering analysis.
Identification of significant transcriptional changes
At the point of microarray hybridization, samples were divided into 2 different batches for hybridization: samples 1-8 into batch I and samples 9-18 into batch II. We anticipated that this was likely to result in artefactual differences between samples from the two different hybridization batches. This is supported by the fact that sample 9 (40min 8hr from hybridization batch II) is separated from other 8 hr samples from hybridization batch I in the hierarchical clustering (Figure 1B).
We performed 3-group comparisons for the same recovery period. The comparison within the 8-h recovery groups revealed numerous genes which were differentially expressed after 20 or 40 min of MI. However, the comparison within the 24-h recovery groups showed few significantly regulated genes under the same cut-off condition. To avoid any batch effect from different hybridizations, we performed comparison on all the 8-h recovery samples from the same batch and 517 transcripts were identified which were differentially expressed.
Gene ontology (GO) analysis
A GO analysis was performed for significantly differentially regulated genes between different treatment groups to identify enriched GO classes. Only GO classes and parent classes with at least 5 observations in the selected subset and with an ‘Observed vs.
Expected’ ratio of at least 2 were picked up.
We also performed group testing, i.e. testing whether predefined lists of genes that locate in the same subcellular locations (cellular components), directly perform the same activities (molecular functions), or contributes to the same biological processes, are significantly changed as a group in a microarray dataset. Several different tests were used: LS/KS permutation test finds gene sets which have more genes differentially expressed among the phenotype classes than expected by chance; Efron-Tibshirani's test uses 'maxmean' statistics to identify gene sets differentially expressed; Goeman's Global test finds gene sets which are associated with the phenotype classes. The testing was performed by the combination of these tests and as implemented in the BRB-ArrayTools Version 4.1.0. By grouping together genes before testing, the number of tests is decreased and the severity of the correction for multiple testing is less. Genes were grouped together based on function derived from GO categories. Corrected p-values of less than 0.005 were used as an empirical cutoff for retrieving altered gene sets.
Results
Cytotoxicity in response to MI and recovery
To determine MI-induced cytotoxicity, confluent primary rat PTECs were treated with antimycin A/deoxyglucose for different periods up to 120 min followed by recovery for 24 h (Figure 2A). The LDH release into the supernatant was measured, which displayed a MI time-dependent increase. To further determine recovery-related cytotoxicity, primary rat PTECs treated with different periods of MI solution were switched to complete medium and allowed to recover for different periods up to 30 h (Figure 2B). The result of supernatant LDH leakage showed a recovery time-dependent LDH increase for each MI condition, and confirmed the MI time-dependent cytotoxicity. Treatment with MI solution for 15-30 min led to a moderate induction of LDH during recovery, which increase pronouncedly at 5 h of
recovery, reaching 50% of total after 24 h. Therefore, we selected 20 min and 40 min of MI and 8 h and 24 h of recovery to generate microarray samples (Table 1).
Principal components analysis (PCA) and hierarchical clustering
Two main clusters of samples were established in PCA analysis: one cluster containing the control (0 min of MI) and the other cluster containing the 20 and 40 min of MI samples (Figure 1A). Using the annotations from the recovery time also resulted in 2 clusters: 8 h and 24 h of recovery.
We also analyzed sample clustering in response to different MI and recovery times by hierarchical clustering (Figure 1B). A principal distinction was observed between control and MI-treated samples and between 8 and 24 h of recovery after MI.
Figure 2. Time-dependent cytotoxicity after MI and recovery. LDH release in culture medium was measured for cytotoxicity after different MI periods followed by (A) 24 h of recovery and (B) different recovery periods up to 30 h.
Figure 3. Venn diagrams of differentially- expressed transcripts with fold change ≥ 2 in response to MI followed by 8 h of recovery.
Venn diagrams of (A) all 166 significantly regulated transcripts, (B) 95 up-regulated transcripts and (C) 71 down-regulated transcripts. The diagrams show overlaps of transcripts after 20 and 40 min of MI.
Gene expression changes following MI and recovery
Although significant cytotoxicity was observed after 24 h of recovery, few genes were found differentially expressed at this time point (only 95 transcripts identified after 40 min of MI compared to control), suggesting that gene transcriptional response occurs early and transiently upon the hypoxic injury. We therefore focussed on the 8-h recovery samples.
Among 517 transcripts that were identified differentially expressed (p ≤ 0.001 and FDR ≤ 0.05), there were 166 transcripts significantly regulated with fold change ≥ 2 in expression.
Figure 3 shows the Venn diagrams of these 166 transcripts following different periods of MI.
Some genes were predominantly regulated after 20 min of MI (Table 2). Such proteins may be important in mediating stress signalling and adaptation to sub-lethal injury thus protecting cells from subsequent injury. For instance, among the genes those were up- regulated after mild hypoxic injury, Mapk8ip3 mediates the activation of JNK that is responsive to cellular stress, Eps8 mediates actin cytoskeleton reorganization, and Mt1a mediates detoxification and responses to oxidative stress. Meanwhile, Hao2 and Hsd3b5 which normally maintain mitochondria function and mediate oxidation-reduction processes were down-regulated. Rhob, which belongs to the Rho family of small GTPases and mediates transformed cell apoptosis, adhesion and differentiation was also down-regulated after 20 min of MI.
Table 2. Up- and down-regulated transcripts with fold change ≥ 2 after 20 min of MI compared to control (0 min of MI) followed by 8 h of recovery.
Probe ID Gene symbol FC Description
Up-regulated
21812744 RGD1306119 2.72 similar to transcriptional regulating protein 132 22268091 Nav2 2.59 neuron navigator 2
22170383 Mt1a 2.40 metallothionein 1a
22309763 Nr6a1 2.30 nuclear receptor subfamily 6, group A, member 1
21577344 2.23
22005887 Fam129a 2.21 family with sequence similarity 129, member A 21348639 Qser1 2.16 glutamine and serine rich 1
22233903 Ipo7 2.12 importin 7
22048828 2.10
20721657 Sel1l 2.05 sel-1 suppressor of lin-12-like (C. elegans)
21878350 Kctd17 2.05 potassium channel tetramerisation domain containing 17 21600261 Eps8 2.04 epidermal growth factor receptor pathway substrate 8 21005625 Mapk8ip3 2.01 mitogen-activated protein kinase 8 interacting protein 3 22059960 Xpo1 2.00 exportin 1, CRM1 homolog (yeast)
Down-regualted
21901261 Hao2 0.46 hydroxyacid oxidase 2 (long chain)
22319933 Hsd3b5 0.47 hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 5
21661159 Stmn4 0.49 stathmin-like 4
22001741 0.49
21275762 Rhob 0.50 ras homolog gene family, member B
Genes that were predominantly regulated after 40 min of MI may be involved in mediating resistance to cell death and regeneration from the injury or involved in the cell death response itself (Table 3). Genes that mediate stress resistance and anti-apoptotic activity, including Hells, Hspb1 and Nrg 1, were up-regulated. Another set of up-regulated genes were more involved in the maintenance or rebuilding of tubular cell morphology and function, including Lin7c and Tspan5 that contribute to polarization and cell adhesions, as well as Acadsb, Nipal4 and Slc25a36 that maintain mitochondrial metabolic processes.
Furthermore, several cAMP signalling-associated genes were found also up-regulated, including Gpr126, Akap12 and Gpr137b. On the other hand, genes related to the epithelial phenotype, cell differentiation and adhesion of proximal tubular cells were down-regulated.
These included Colla2, Itga11, Myl9, Tgfb3, Ephb1 and Ltbp2, suggesting a morphology change in response to 40 min of MI.
Table 3. Up- and down-regulated transcripts with fold change ≥ 2 after 40 min of MI compared to control (0 min of MI) followed by 8 h of recovery.
Probe ID Gene symbol FC Description
Up-regulated
20904805 Gpr126 3.26 G protein-coupled receptor 126
21684370 3.12
20727695 Dnaja4 3.05 DnaJ (Hsp40) homolog, subfamily A, member 4 21466308 Acadsb 2.48 acyl-Coenzyme A dehydrogenase, short/branched chain 20990323 Hells 2.29 helicase, lymphoid specific
21382371 Ptpn11 2.28 protein tyrosine phosphatase, non-receptor type 11 20753769 Hspb1 2.27 heat shock protein 1
21584564 Psat1 2.25 phosphoserine aminotransferase 1 21142307 Nrip3 2.24 nuclear receptor interacting protein 3 21420770 Sdcbp2 2.19 syndecan binding protein (syntenin) 2
21090942 2.16
22167201 Noc3l 2.15 nucleolar complex associated 3 homolog (S. cerevisiae) 21250423 Cth 2.13 cystathionase (cystathionine gamma-lyase)
21932447 Lin7c 2.12 lin-7 homolog C (C. elegans) 22395924 Iqcb1 2.12 IQ motif containing B1
21943006 F2rl1 2.06 coagulation factor II (thrombin) receptor-like 1 20717612 Tspan5 2.06 tetraspanin 5
22395968 Nipal4 2.04 NIPA-like domain containing 4 21676505 Akap12 2.04 A kinase (PRKA) anchor protein 12 21951725 Nrg1 2.03 neuregulin 1
21232474 Itpk1 2.02 inositol 1,3,4-triphosphate 5/6 kinase 22288020 Gpr137b 2.00 G protein-coupled receptor 137B 20984947 Slc25a36 2.00 solute carrier family 25, member 36
Down-regulated
22026257 Col1a2 0.37 collagen, type I, alpha 2 21208312 Itga11 0.41 integrin, alpha 11 22197072 Plcl1 0.41 phospholipase C-like 1
22169981 Chsy3 0.41 chondroitin sulfate synthase 3
21420385 0.43
21187823 Vps13c 0.43 vacuolar protein sorting 13 homolog C (S. cerevisiae) 21115921 Fggy 0.45 FGGY carbohydrate kinase domain containing
21285233 0.46
21864603 0.46
20805519 Sub1 0.46 SUB1 homolog (S. cerevisiae)
21599792 0.47
21316862 Myl9 0.47 myosin, light chain 9, regulatory 20813345 Pdzrn3 0.47 PDZ domain containing RING finger 3 22021619 Ifitm1 0.48 interferon induced transmembrane protein 1
22112651 0.49
22267648 Large 0.49 like-glycosyltransferase 22110808 Tgfb3 0.49 transforming growth factor, beta 3 20721131 Plcb4 0.49 phospholipase C, beta 4
21599791 0.50
22301861 Ephb1 0.50 Eph receptor B1
20824802 St3gal5 0.50 ST3 beta-galactoside alpha-2,3-sialyltransferase 5 21043131 Ltbp2 0.50 latent transforming growth factor beta binding protein 2
Considerable overlap of the significantly regulated transcripts was observed between 20 and 40 min of MI (Table 4). The most significantly up-regulated gene among these overlapping transcripts was a protein tyrosine phosphatase Ptprr, which was up-regulated 6- and 8-fold after 20 and 40 min of MI, respectively. The gene product is an inactivator of MAPKs such as MAPK1, MAPK3 and MAPK14. The most significantly down-regulated gene was the Tyro protein tyrosine kinase binding protein Tyrobp, with 5-fold down- regulation after both 20 and 40 min of MI. This protein associates with activating receptors of CD300 family and results in cellular activation of CD300 to regulate leukocyte functions.
The proapoptotic genes Tp53inp1, Ngef, Trib3, Bmp2 were up-regulated, while the antiapoptotic genes Aif1 and Angptl4 were down-regulated. Changes in the oxidative state could be responsible for downstream renal cell death 38. Metallothioneines (MTs) are small cysteine-rich metal-binding proteins which have been suggested to play a role in the detoxification of toxic metals and protection against oxidative stress 39-41 and are induced as part of a general response to oxidative stress via the Nrf2 transcription factor 28, 29. In addition to the moderate up-regulation of Mt1a after 20 min of MI, another metallothionine, Mt3, showed a strongly enhanced expression in response to oxidative stress after both 20 and 40 min of MI. Furthermore, several heat shock proteins were significantly up-regulated in response to MI as expected, and mostly prominent after 40 min of MI.
Growth factors mediate survival responses in many cell types 42. Increased gene expression levels of the growth-signalling factors Igfbp1 (insulin-like growth factor binding protein 1), Vgf (VGF nerve growth factor inducible) and Eps8 (epidermal growth factor receptor pathway substrate 8) suggested a cellular survival program in which the cells attempted to be protected from hypoxia-related cell damage. In contrast, expression levels of Fgfr3 (fibroblast growth factor receptor 3), Tgfbi (transforming growth factor, beta induced), Tgfb3 (transforming growth factor, beta 3) and Ltbp2 (latent transforming growth factor beta
binding protein 2) were down-regulated, indicating reduced survival signalling that might contribute to the apoptotic response.
Several genes in the contractile machinery (cytoskeleton and cell adhesion clusters) were down-regulated, including Tuba8 (tubulin, alpha 8), Rhob (ras homolog gene family, member B), Col1a2 (collagen, type I, alpha2), Itga11 (integrin, alpha11), Myl9 (myosin, light chain 9, regulatory).
Genes with obvious renal-associated functions, such as transmembrane transporters of ions and water, were also up-regulated. Genes coding solute carrier family members include sodium/myoinositol cotransporter Slc5a3 (solute carrier family 5, member 3), cationic amino acid transporter Slc7a5 (family 7, member 5), monocarboxylic acid transporter Slc16a1 (family 16, member 1), phosphate transporter Slc20a1 (family 20, member 1), and mitochondrial carrier Slc25a36 (family 25, member 36). The up-regulation of these genes after MI may contribute to restoring the cellular ion/water homeostasis, thus are most likely involved in tissue remodeling and recovery.
Table 4. Up- and down-regulated transcripts with a fold change ≥ 2 after both 20 and 40 min of MI compared to control (0 min of MI) followed by 8 h of recovery.
Fold change Probe ID Gene symbol
20min 40min Description
Up-regulated
21983525 Ptprr 6.31 7.86 protein tyrosine phosphatase, receptor type, R
21398132 5.62 2.85
22361658 Scel 4.41 4.14 sciellin
21226588 Emp1 4.18 5.71 epithelial membrane protein 1
21391288 3.96 4.06
22029340 Mt3 3.92 3.90 metallothionein 3 22210564 Hspa1a 3.78 6.63 heat shock protein 1A 22227799 Rragd 3.73 3.31 Ras-related GTP binding D 22357696 Hspa1a 3.67 6.05 heat shock protein 1A
22070557 Hmga1 3.49 4.20 high mobility group AT-hook 1 22017213 Mlf1 3.45 5.77 myeloid leukemia factor 1
22385531 3.41 4.09
21645003 Leprel1 3.29 2.78 leprecan-like 1
21860958 Tp53inp1 3.29 3.36 tumor protein p53 inducible nuclear protein 1 20764810 Emp2 3.27 3.27 epithelial membrane protein 2
21654269 Tgm1 3.17 3.33 transglutaminase 1, K polypeptide 21739265 Lgals3 3.13 3.05 lectin, galactoside-binding, soluble, 3
21686944 Tiparp 3.12 2.81 TCDD-inducible poly(ADP-ribose) polymerase 20848336 Klf15 3.09 2.66 Kruppel-like factor 15
22180789 Slc5a3 3.03 3.37 solute carrier family 5 (sodium/myo-inositol cotransporter), member 3
20749852 Klf4 2.97 2.93 Kruppel-like factor 4 (gut)
22115127 Igfbp1 2.95 2.46 insulin-like growth factor binding protein 1 22030722 Abcb1a 2.94 2.89 ATP-binding cassette, sub-family B
(MDR/TAP), member 1A
22366006 2.94 2.79
21617076 Xkr6 2.74 2.64 XK, Kell blood group complex subunit-related family, member 6
22340292 Glb1l2 2.70 3.11 galactosidase, beta 1-like 2
21665127 Tiparp 2.70 2.59 TCDD-inducible poly(ADP-ribose) polymerase 21331194 G0s2 2.67 3.58 G0/G1switch 2
21331086 Ipmk 2.60 2.41 inositol polyphosphate multikinase 21170650 Pcdh21 2.59 2.53 protocadherin 21
22144545 Rpp25 2.59 2.16 ribonuclease P 25 subunit (human)
21248175 Ngef 2.59 2.59 neuronal guanine nucleotide exchange factor 21832208 Hsph1 2.48 4.12 heat shock 105kDa/110kDa protein 1
21591825 Artn 2.40 2.27 artemin
22229141 Dcbld2 2.40 3.04 discoidin, CUB and LCCL domain containing 2 20893189 Gmfb 2.37 2.13 glia maturation factor, beta
22163042 Trib3 2.35 2.53 tribbles homolog 3 (Drosophila) 20990598 Zfp217 2.35 2.19 zinc finger protein 217
21646514 B3galnt1 2.32 2.24 beta-1,3-N-acetylgalactosaminyltransferase 1 21982118 Il33 2.31 2.62 interleukin 33
21036620 2.27 2.29
21743176 Bmp2 2.25 2.33 bone morphogenetic protein 2 20905105 Timd2 2.19 3.08 T-cell immunoglobulin and mucin domain
containing 2
21952815 Slc7a5 2.18 2.59 solute carrier family 7 (cationic amino acid transporter, y+ system), member 5 21184793 Slc16a1 2.17 2.32 solute carrier family 16, member 1
(monocarboxylic acid transporter 1) 21728398 Vgf 2.15 2.18 VGF nerve growth factor inducible 21102639 Odc1 2.15 2.50 ornithine decarboxylase 1
21926550 Chac1 2.14 2.46 ChaC, cation transport regulator homolog 1 (E.
coli)
21333892 2.12 2.30
22136783 Slc20a1 2.09 2.00 solute carrier family 20 (phosphate transporter), member 1
22425577 Arid5b 2.09 2.08 AT rich interactive domain 5B (Mrf1 like) 21145926 Bcar3 2.07 2.26 breast cancer anti-estrogen resistance 3 21785384 Cars 2.07 2.38 cysteinyl-tRNA synthetase
21349481 Zfand2a 2.06 2.90 zinc finger, AN1-type domain 2A 21511321 Ypel4 2.06 2.17 yippee-like 4 (Drosophila) 22156235 Atf5 2.05 2.29 activating transcription factor 5 22103609 Loxl4 2.02 2.40 lysyl oxidase-like 4
21264336 Pdpn 2.00 2.14 podoplanin
Down-regulated
21067289 Tyrobp 0.20 0.18 Tyro protein tyrosine kinase binding protein 21861133 Pf4 0.22 0.09 platelet factor 4
21312781 Aif1 0.23 0.15 allograft inflammatory factor 1 21405412 Txnip 0.24 0.26 thioredoxin interacting protein
22357996 C1qc 0.24 0.13 complement component 1, q subcomponent, C chain
21136368 RT1-Da 0.25 0.18 RT1 class II, locus Da 22006457 Rfx8 0.27 0.29 regulatory factor X 8 21056523 Arrdc4 0.27 0.20 arrestin domain containing 4 20885726 RT1-Ba 0.28 0.18 RT1 class II, locus Ba 22001662 Ctss 0.29 cathepsin S 22178267 Lyz2 0.29 0.16 lysozyme 2
22417170 Nppc 0.30 0.22 natriuretic peptide precursor C
21644783 C1qa 0.31 0.19 complement component 1, q subcomponent, A chain
21761328 C1qb 0.31 0.26 complement component 1, q subcomponent, B chain
21515889 Angptl4 0.32 0.31 angiopoietin-like 4
20908978 0.33 0.33
21142883 Dhrs3 0.34 0.18 dehydrogenase/reductase (SDR family) member 3 22256251 Fgfr3 0.34 0.44 fibroblast growth factor receptor 3
20824132 Ankrd1 0.36 0.38 ankyrin repeat domain 1 (cardiac muscle) 21537743 Cd74 0.37 0.24 Cd74 molecule, major histocompatibility
complex, class II invariant chain
21805005 0.38
21350627 RT1-Bb 0.39 0.40 RT1 class II, locus Bb
22408631 Cnn1 0.39 0.37 calponin 1, basic, smooth muscle
22286173 0.40 0.38
21526742 Lpxn 0.40 0.44 leupaxin
21357205 0.40 0.32
20789084 Ly86 0.41 lymphocyte antigen 86
21981282 Lims2 0.41 0.30 LIM and senescent cell antigen like domains 2 21816975 Rtn1 0.41 0.49 reticulon 1
21299129 Ctsz 0.45 0.34 cathepsin Z
21512130 Ifi35 0.45 0.50 interferon-induced protein 35 20989169 Tspan18 0.45 0.30 tetraspanin 18
22176226 Golga4 0.46 0.32 golgi autoantigen, golgin subfamily a, 4 21102044 Tuba8 0.47 0.47 tubulin, alpha 8
21261216 Tmem178 0.47 0.34 transmembrane protein 178 20886623 Sytl2 0.47 0.46 synaptotagmin-like 2
21591235 0.48 0.46
21712253 Megf6 0.48 0.35 multiple EGF-like-domains 6
20879740 0.48 0.44
20976288 Antxr1 0.48 0.29 anthrax toxin receptor 1
21911962 Tgfbi 0.48 0.35 transforming growth factor, beta induced 20990022 Lsp1 0.49 0.45 lymphocyte-specific protein 1
21136318 0.49 0.44
21869330 RGD1311756 0.49 0.39 similar to hypothetical protein FLJ20950
Identification of ATP depletion-specific biological processes during hypoxic renal injury A GO ‘Observed vs. Expected’ analysis based on 517 differentially expressed genes revealed the enriched localization of these genes in several cellular compartments, such as leading edge membrane (Observed/Expected ratio = 6.5), filopodium (4.53), actin filament (2.75) and basement membrane (2.3). The molecular function of these genes were enriched in growth factor binding such as VEGFR binding (17.19), IGF binding (5.8) and TGF-β receptor binding (5.61), and also in actin filament binding (3.5). Not surprisingly, most genes were enriched in the biological processes associated with hypoxic stress and inflammation such as cellular response to glucose starvation (14.6) and antigen processing and presentation of peptide or polysaccharide antigen via MHC class II (8.57). The whole list of GO classes and parent classes with at least 5 observations and with an 'Observed vs. Expected' ratio of at least 2 is given in Supplementary Table S1.
To further investigate the pathways and biological processes that were significantly changed after MI, analysis of differential expression of gene sets (based on GO analysis) was performed on 166 transcripts that were differently expressed with fold change ≥ 2. Different tests were used to identify significantly expressed gene sets among these 166 transcripts (p
≤0.005): LS/KS permutation test found 3 significant gene sets; Efron-Tibshirani's maxmean test found 3 significant gene sets (under 200 permutations); Goeman's Global test found 29 significant gene sets. In total 34 out of 420 investigated gene sets passed the 0.005 significance threshold among at least one test. From these 34 gene sets, 8 are Cellular Component (CC) categories; 10 are Molecular Function (MF) categories; 16 are Biological Process (BP) categories. The gene sets as well as relevant genes are shown in Table 5. The 166 strongly regulated transcripts were mostly associated with membrane systems and extracellular matrix, and contributed to transmembrane tranporter activity, hydrolase activity and transcription repressor activity. The enrichment in tissue development processes may suggest a regeneration process due to the hypoxic injury. In addition, transmembrane transport, cellular metabolism and homeostasis-associated processes were also significantly altered, suggesting tissue remodelling response to ATP depletion.
Table 5. Gene sets with significant alterations in response to MI followed by 8 h of recovery as determine by GO analysis on 166 transcripts with fold change ≥ 2.
GO category * GO term Genes
Cellular Component
GO:0005624 membrane fraction Rhob, Plcb4, Hsd3b5, Eps8, Lin7c GO:0005626 insoluble fraction Rhob, Hspb1, Plcb4, Hsd3b5, Eps8,
Lin7c
GO:0045202 synapse Mt3, Plcb4, Eps8, Nrg1, Lin7c GO:0044456 synapse part Mt3, Plcb4, Eps8, Lin7c
GO:0031012 extracellular matrix Lgals3, Angptl4, Tgfbi, Tgfb3, Col1a2, Nav2
GO:0005578 proteinaceous extracellular matrix Lgals3, Angptl4, Tgfbi, Col1a2, Nav2 GO:0012505 endomembrane system Sel1l, B3galnt1, Mapk8ip3, Hsd3b5,
Xpo1, Vgf, St3gal5
GO:0031090 organelle membrane Lgals3, Sel1l, Rhob, B3galnt1, Mapk8ip3, Hsd3b5, Vgf, St3gal5
Molecular Function
GO:0017111 nucleoside-triphosphatase activity
GO:0016818
hydrolase activity, acting on acid anhydrides, in phosphorus- containing anhydrides
GO:0016817 hydrolase activity, acting on acid anhydrides
GO:0016462 pyrophosphatase activity
Abcb1a, Tuba8, Hells, Nav2
GO:0022892 substrate-specific transporter activity Slc5a3, Slc16a1, Slc7a5, Slc20a1, Xpo1, Pdpn
GO:0022891 substrate-specific transmembrane
transporter activity Slc5a3, Slc16a1, Slc7a5, Slc20a1, Pdpn GO:0005215 transporter activity Abcb1a, Slc5a3, Slc16a1, Slc7a5,
Slc20a1, Xpo1, Pdpn GO:0022857 transmembrane transporter activity
GO:0022804 active transmembrane transporter activity
Abcb1a, Slc5a3, Slc16a1, Slc7a5, Slc20a1, Pdpn
GO:0016564 transcription repressor activity Ankrd1, Klf4, Bmp2, Zfp217, Trib3
Biological Process
GO:0019725 cellular homeostasis Mt3, Large, Mt1a, Fggy, Nrg1 GO:0035295 tube development Bmp2, Ipmk, Mapk8ip3, Tgfb3, Pdpn GO:0060284 regulation of cell development Mt3, Atf5, Bmp2, Mapk8ip3, Tgfb3 GO:0055085 transmembrane transport Abcb1a, Slc5a3, Slc16a1, Slc7a5, Pdpn GO:0009791 post-embryonic development Tiparp, Klf4, Mapk8ip3, Nppc, Arid5b GO:0001655 urogenital system development Tiparp, Odc1, Bmp2, Hells, Arid5b GO:0072001 renal system development
GO:0001822 kidney development Tiparp, Odc1, Bmp2, Arid5b
GO:0045935
positive regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process
Klf4, Klf15, Bmp2, Tgfb3, Nppc
GO:0010557 positive regulation of macromolecule
biosynthetic process Klf4, Klf15, Bmp2, Fam129a, Tgfb3 GO:0010035 response to inorganic substance Txnip, Mt3, Abcb1a, Mt1a
GO:0001944 vasculature development Angptl4, Tiparp, Rhob, Pf4, Colla2, Pdpn GO:0001568 blood vessel development Angptl4, Tiparp, Rhob, Pf4, Colla2 GO:0048514 blood vessel morphogenesis Angptl4, Tiparp, Rhob, Pf4
GO:0008544 epidermis development
GO:0007398 ectoderm development Txnip, Tgm1, Klf4, Scel, Col1a2
* Tests used to find significant gene sets are: LS/KS permutation test, Efron-Tibshirani's GSA maxmean test, Goeman's global test. The selection of significant gene sets is based on p value ≤ 0.005 among at least one test.
Discussion
A transcriptomics analysis was performed in this study to investigate the transcriptional changes in primary rat PTECs subjected to MI and recovery, by using the Rat Genome Survey oligo-microarrays consisting of approximately 27,000 rat genes. Our results demonstrate diverse transcriptional profiles induced by differential MI and recovery periods.
GO analysis revealed highly enriched tissue remodelling and regeneration processes in response to MI and 8 h of recovery. Our study therefore identified candidate genes and pathways that are associated with and may also play an important role in ATP depletion- mediated stress responses in the context of renal ischemic injury and regeneration. This may lead to novel targets and strategies to protect from or promote renal tissue repair following ischemic injury.
Experimental conditions for generating mRNA samples were identified which induced mild and severe hypoxic injury, based on cytotoxicity (Figure 2). After 40 min of MI, significant cell death/cytotoxicity was observed at 24 h of recovery. However, at 8 h of recovery, cell death was not observed, suggesting that at this point, gene expression changes were underway that would eventually implement cell death. Genes associated with resistance to cell death may also be up-regulated at this point via feedback or compensatory mechanism.
We hypothesized that gene profiles from these conditions represent those that define severe hypoxic injury. After 20 min of MI, cells recovered over a period of 24 h with minimal cell death. We therefore hypothesized that gene expression changes at 8 h of recovery were underway that are involved in sub-lethal stress responses and adaptation to the hypoxic stress.
Gene profiles from these conditions represent those that define mild hypoxic injury.
The clustering of gene expression data showed a clear distinction between 8 and 24 h of recovery following MI treatment (Figure 1). This might be associated with different extents of cell death at these two points. Fewer genes were differentially expressed at 24 h after MI, suggesting that an acute recovery period had elapsed. These samples were generated from two different hybridizations and the separated clustering from different recovery periods may also be influenced by different hybridization batches. We performed class comparisons with samples 1-8 from hybridization batch I. This excluded the batch effect from different hybridizations. In total 517 transcripts passed the significance criteria (p ≤ 0.001 and FDR ≤ 0.05) and contributed to diverse gene ontologies (Supplementary Table S1). These results identify several pathways that are involved in key biological processes in hypoxic injury, highlighting the response to metabolic defects, oxidative stress and endoplasmic reticulum (ER) stress.
Genes that were strongly differentially expressed (fold change ≥ 2) were further selected for gene profiling and GO analysis. Genes regulated after 20 min of MI were expected to mediate stress responses that contribute to adaptation or direct survival, thus prevented further cellular injury. For instance, one of the up-regulated genes Mt1a belongs to the metallothioneine family and mediates the detoxification of toxic metals and protection against oxidative stress 39-41. Mt1a is also induced as part of a general response to oxidative stress via the Nrf2 transcription factor 28, 29. Another up-regulated gene Nav2 showed positive regulation of extracellular matrix, which in turn might provide survival signal to protect cells from injury.
In vitro studies with sub-lethal ATP depletion models in cultured PTECs have indicated that I/R-associated disruption of the cytoskeleton and cell adhesion complexes are under the control of the Rho GTPase 1, 31, 43, 44. Indeed, the Rhob gene was down-regulated, which may impact the cytoskeleton and cell adhesion complexes 45. Eps8, which also regulates actin organization and remodelling, was up-regulated after 20 min of MI, suggesting a cytoskeletal alteration in response to mild hypoxic injury 46-48. Furthermore, GO ‘Observed vs. Expected’
analysis also revealed cytoskeleton organization as one of the most significantly altered biological processes.
After 40 min of MI, several cell adhesion-associated genes were down-regulated in response to severe hypoxic injury. These may contribute to cell detachment, which in turn can result in apoptosis 49. However, some antiapoptotic genes were also up-regulated and also genes in charge of cellular metabolism and growth. Other genes which are implicated in cell differentiation were down-regulated, suggesting that they contribute to dedifferentiation upon severe hypoxic. These genes may play a role in survival and subsequent regeneration.
ATP depletion causes dephosphorylation of focal adhesion (FA) proteins as well as a decrease in total tyrosine phosphorylation and F-actin cytoskeleton disruption. During recovery, both FAs and the F-actin cytoskeleton are restored and reorganized. These might be continually regulated by kinases, phosphatases and other signalling events 50-52. Among the significantly regulated genes, the protein tyrosine phosphatase Ptprr was most strongly up- regulated. This gene product is an inactivator of ERK1/2 MAP kinase. Our previous work showed that inhibition of the ERK1/2 activator MEK, with a small molecule inhibitor, protected against renal ischemic injury in rats 4. Up-regulation of PTPRR may represent an adaptive response to reduce MAP kinase signalling and subsequent cell stress. Therefore, activation of PTPRR has therapeutic potential, offering a selective regulation of the pathway.
Further follow-up work is required to validate the differential expression and identify the functional significance of these genes. RNA interference technology will be used to validate selected genes from the genomic analysis. The effects of depletion of gene products will be examined in an in vitro renal hypoxia model similar to that used to generate the original mRNA samples. This will enable us to determine whether these proteins play a functional role in injury or recovery and are thus potential drug targets. Furthermore, an attempt at pharmacological manipulation of these gene products in vivo may offer critical insights into cellular process of clinical ARF and novel leads for targeted therapy.
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