Gene Expression Profiling in a Model of - ACS Publications - American

Chem. Res. Toxicol. 2005, 18, 1193-1202

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Chemical Profiles Gene Expression Profiling in a Model of D-Penicillamine-Induced Autoimmunity in the Brown Norway Rat: Predictive Value of Early Signs of Danger Be´atrice Se´guin,† Paul C. Boutros,‡ Xujian Li,† Allan B. Okey,‡ and Jack P. Uetrecht*,†,‡ Department of Pharmaceutical Sciences, Faculty of Pharmacy, and Department of Pharmacology, Faculty of Medicine, University of Toronto, Ontario M5S 2S2, Canada Received February 14, 2005

Idiosyncratic drug reactions (IDRs) cause significant morbidity and mortality. In an animal model of IDRs, 50-80% of Brown Norway rats exposed to D-penicillamine develop an autoimmune syndrome after several weeks of treatment. The symptoms of the IDR are similar to that observed in humans who take D-penicillamine. The mechanism of this reaction is unknown, and no effective biomarkers have been identified to predict susceptibility. We postulate that cell stress caused by drugs is required to initiate the response. We used a highthroughput approach to identify factors that might represent danger signals by profiling hepatic gene expression 6 h after dosing with D-penicillamine (150 mg/kg). Our results show that the drug-treated animals cluster into two distinct groups. One group exhibits substantial expression changes relative to control animals. The most significantly altered transcripts have a role in stress, energy metabolism, acute phase response, and inflammation. We used quantitative reverse transcriptase polymerase chain reaction to measure transcript levels in liver biopsies of 33 rats and found that resistant animals cluster together. This “resistant” cluster of animals contains 87.5% (7/8) resistant animals but only 48% (12/25) “sensitive” animals. This separation is statistically significant at the p ) 0.01 level.

Introduction Drug discovery is an expensive, time-consuming process (1). Despite the high attrition rate of compounds entering clinical trials and the precautions taken to eliminate troublesome candidates, an approved drug may still give rise to unforeseeable idiosyncratic drug reactions (IDRs)1 in some individuals once it is marketed. The consequence often is the withdrawal of the drug from the market (e.g., tienilic acid). It is currently impossible to accurately predict which drugs will be associated with a significant incidence of IDRs or which patients will be affected. If we could understand the mechanism of IDRs or identify biomarkers that predicted the risk that a drug would be associated with IDRs, it would markedly facilitate drug development. * To whom correspondence should be addressed. Tel: 416-978-8939. Fax: 416-978-8911. E-mail: [email protected]. † Faculty of Pharmacy. ‡ Faculty of Medicine. 1 Abbreviations: IDR, idiosyncratic drug reaction; BN, Brown Norway; SAM, significance analysis of microarrays; qRT-PCR, quantitative reverse transcriptase polymerase chain reaction; znf 354A/ kid-1, zinc finger protein 354A/kidney ischemia development 1; gck, glucokinase; pcscl, peroxisomal calcium-dependent solute carrier-like protein; sgk, serum glucocorticoid-regulated kinase; pde4b, phosphodiesterase 4B; usp2, ubiquitin specific protease 2; lcl2, lipocalin 2; egr1, early growth response 1; cinc/gro, cytokine-induced neutrophil chemoattractant.

IDRs (or type B reactions) are defined as adverse drug reactions that do not involve known pharmacological properties of a drug and do not occur in most patients at any dose (2). One of the distinctive features of IDRs is that there is almost always a delay between the start of drug therapy and the onset of the reaction (3). This is one of the main reasons why it is believed that many IDRs are immune-mediated; however, the underlying mechanism of IDRs remains unclear. It has been suggested that the development of an IDR is associated with the induction of a danger signal (4, 5). The “danger hypothesis” proposes that the immune system is not concerned with distinguishing self from nonself, but rather, it responds to “danger” and potential destruction. It is conceivable that drugs or reactive metabolites of drugs may cause cell stress or damage (i.e., danger) and lead to stimulation of immunomodulatory signals leading to an IDR. To study the role of danger in the context of IDRs, we used an animal model of D-penicillamineinduced autoimmunity in the BN rat. This is similar to an IDR that is seen in some patients who suffer from rheumatoid arthritis or Wilson’s disease and are treated with D-penicillamine (6). We proposed that if the danger hypothesis is true, one would expect specific gene expression of danger signals in animals that develop the IDR. This reaction is idiosyncratic in the BN rat in that it occurs only in this strain and only in 50-80% of these

10.1021/tx050040m CCC: $30.25 © 2005 American Chemical Society Published on Web 07/01/2005

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animals despite their being highly inbred and syngeneic. The penicillamine model has unique advantages for testing this hypothesis because it is idiosyncratic in both humans and rats. Moreover, not all animals in a syngeneic strain develop the syndrome; therefore, genetic contributions are minimized, and any differences observed between animals that develop the syndrome and those that do not are likely to be related to the mechanism of the syndrome. Our objective was therefore to identify factors that might be danger signals using liver gene expression profiling to compare animals 6 h after receiving 150 mg/ kg of D-penicillamine (n ) 8) or vehicle (n ) 4). This time point was based on preliminary pilot studies where the most significant gene expression changes in the liver occurred at 6 h postdosage [data set publicly available through deposition at the Gene Expression Omnibus (GEO) at NCBI].

Materials and Methods Animals. Male Brown Norway (BN) rats weighing 150170 g were purchased from Charles River (Montreal, QC). All animals were housed in plastic cages (two/cage) with wooden chips as bedding in a 12:12 h light:dark cycle at 22 °C. Rats were given access to standard rat chow (Agribrands, Purina Canada, Strathroy, ON) and ad libitum access to tap water for 1 week (acclimatization period) before starting experiments. Chemicals, Kits, and Solutions. D-Penicillamine was purchased from Richman Chemical Inc. (Lower Gwynedd, PA). RNeasy kits were purchased from Qiagen Inc. (Mississauga, ON). RAE230A arrays were purchased from Affymetrix (Santa Clara, CA). Anti-cytokine-induced neutrophil chemoattractant (cinc) and anti-lipocalin 2 (lcl2) antibodies were purchased from Cedarlane (Hornby, ON). Purified recombinant lcl2 was a gift from Jonathan Barasch (Columbia University, New York, NY). OmniScript reverse transcriptase was purchased from Qiagen. Quantitative PCR reagents were purchased from Roche (Montreal, QC). D-Penicillamine Treatment. For expression profiling, rats were given water (vehicle, n ) 4) or 1 mL of D-penicillamine dissolved in tap water in a single dose of 150 mg/kg by oral gavage (n ) 8). For time-course studies involving sampling of liver biopsies, rats were given a single dose of 150 mg/kg by oral gavage and liver biopsies were taken under anesthesia 6 h postdosage. From the second day onward, rats were given free access to D-penicillamine dissolved in tap water and received 150 mg/kg/day as previously described (7). For time-course studies involving peripheral blood sampling, rats were given a single dose of 150 mg/kg by oral gavage and blood samples were taken from the tail vein 6 and 10 h postdosage. From the second day onward, rats were given free access to D-penicillamine in drinking water. Rats were monitored until they developed signs of D-penicillamine-induced autoimmunity [severe dermatitis, weight loss, and arthritis, (8)] at which point they were sacrificed. During chronic D-penicillamine treatment, the D-penicillamine in water was prepared fresh every 3rd day because, using mass spectrometry, we found that d-penicillamine is oxidized to the disulfide over time, which is not readily reversible (9); this has the potential to reduce the incidence of illness. All animal procedures were performed according to the University of Toronto guidelines for the care and use of laboratory animals. Sample and Tissue Collection. For expression profiling, rats were given D-penicillamine and sacrificed 6 h postdosage by asphyxiation with CO2. Liver tissue was snap frozen in liquid nitrogen and stored at -80 °C until use. For liver biopsies, rats were given inhaled anesthetics (5% isoflurane) and surgery was performed at the Division of Comparative Medicine at the University of Toronto. Liver biopsies (70 mg) from 40 rats were excised using the guillotine method: A loop suture was placed around the protruding margin of a liver lobe and pulled tight

Se´ guin et al. to crush through the parenchyma before tying the ligature. With a sharp blade, the hepatic tissue was removed approximately 5 mm distal to the ligature, snap frozen in liquid nitrogen, and stored at -80 °C until use. RNA Preparation. Total RNA was isolated from frozen liver tissues of individual rats using Qiagen RNeasy mini kits as described by the manufacturer. RNA concentrations and purity were determined spectrophotometrically. RNA quality was further assessed by capillary electrophoresis using the Agilent Bioanalyzer. We used 10 µg of total RNA from each individual animal for microarray analysis (vehicle controls n ) 4, D-penicillamine treated n ) 8). Aliquots of RNA were saved for subsequent quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) confirmation of the gene expression changes initially detected by microarrays. Microarray Protocol. RNA processing was performed exactly as described by the microarray manufacturer (Affymetrix) at the microarray facility, at The Centre for Applied Genomics, Hospital for Sick Children (Toronto, ON) as outlined in ref 10 using RAE230A chips. Microarray Data Analysis. Microarray data were preprocessed with the RMA method (11). This method involves sequential application of a probe specific background correction based on an additive noise model (12), quantile-based normalization (13), and ProbeSet summarization with a robust fitting procedure (Median Polish). We first tested the hypothesis that the treated (n ) 8) and control (n ) 4) animals differed from one another in a systematic fashion using a permutation-based modified t-test (14). To test the hypothesis that the treated group of animals was heterogeneous and contained subpopulations, we investigated the correlation between treatment effect and measurement fidelity. The treatment effect for a gene was defined as the difference between mean normalized signal intensity for treated animals less the mean normalized signal intensity for control animals. Measurement fidelity was represented by the standard deviation of the normalized signal intensities for treated animals. Pattern recognition (clustering) (15) used agglomerative hierarchical clustering with complete linkage across sets of transcripts with low measurement fidelity (high interanimal variability). Clustering was performed in the R statistical language (v 1.8.0). qRT-PCR. D-Penicillamine-induced changes in gene expression (of relevant transcripts) detected by microarray were confirmed using reverse transcription and qRT-PCR. RNA (1-2 µg) from individual rat samples was reverse transcribed at 37 °C for 1 h using oligo(dT12) (Roche) or oligo(dT16) (Qiagen). After enzyme inactivation (95 °C, 5 min), cDNA was amplified using gene specific primers (Table 1) Faststart Taq DNA Polymerase and SYBR green (Roche) in a Light Cycler instrument. qRT-PCR conditions were identical for all genes: 95° for 10 min; 40 cycles in four steps: 95° for 20 s, 56° for 5 s, and 72° for 15 s. At the end of amplification cycles, melting temperature analysis was carried out (20/s) up to 95°. Amplification, data acquisition, and analysis were carried out by LightCycler instrument using LightCycler (v 5.3.2) (Roche). Validation of Array-Derived Gene Classifier. For each of the nine gene transcripts identified in the microarray study, we performed a one-tailed p-test between the resistant (n ) 8) and sensitive (n ) 25) groups under the assumption of heteroscedasticity between the two classes. Unsupervised pattern recognition techniques were used on the validation data with the three genes found to be significantly differentially expressed between the two groups. This validation was performed using two entirely different algorithms: agglomerative hierarchical clustering using R statistical language (v 2.0.0) and k-means clustering with k)2 (16). To test if the enrichment of resistant animals in one cluster relative to the other was nonrandom, we used the hypergeometric test (17).

Results Drug-Treated Animals Behave Heterogeneously. When we applied the significance analysis of microarray

Danger in a Model of Idiosyncratic Drug Reactions

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Table 1. Primer Sequences for qRT-PCR Confirmation transcript name usp2 kid-1 gck sgk pcscl pde4b lcn2 egr1 cinc gapdh

forward primer

reverse primer

5′-CCCAATGATGTGGTGAGCC-3′ 5′-TGTGGCTGTGCTCTTTACTC-3′ 5′-ACAAGGGCATCCTCCTCAAT-3′ 5′′-ATCGAGCACAATGGGACAA-3′ 5′-TGGATAAGAATGGGACAATG-3′ 5′-GGTACTTCATGCCGCCTTCC-3′ 5′-TCCAGAAAAGAAAGACAAAGCC-3′ 5′-AGCGAACAACCCTACGAGC-3′ 5′-GCACCCAAACCGAAGTCATAG-3′ 5′-CCATCACCATCTTCCAGGAG-3′

5′-CAATCCGACTGTCTTCCCT-3′ 5′-CTCTATGGGACTTTGGGCTA-3′ 5′-CATCCACCATCCGGTCATAC-3′ 5′-ATCAAAGTGCCGAAGGTCA-3′ 5′-AACAAGGCGTTTCATCTGC-3′ 5′-GCGACCTCTTGTTCGGTGCT-3′ 5′-GGTGGGAACAGAGAAAACGAT-3′ 5′-GGAACCTGGAAACCACCCT-3′ 5′-CAGAAGCCAGCGTTCACCAGA-3′ 5′-CCTGCTTCACCACCTTCTTG-3′

Figure 1. SAM plot. We utilized a modified t-test, SAM (14), to detect differentially expressed transcripts in the microarray study. The SAM algorithm did not detect any transcripts as significantly differentially regulated between the group treated with D-penicillamine and the group of control animals. Differential expression would appear as significant nonlinearity in the SAM plot.

(SAM) algorithm (Figure 1) to our normalized microarray data, we did not detect any transcripts significantly differentially regulated between D-penicillamine-treated (n ) 8) and control (n ) 4) animals. We confirmed that no differences existed between treated and control groups by performing direct t-tests (using a Bonferroni correction for multiple testing) as well as by using a simple thresholding approach. Neither method detected any statistically significant differential expression. There were two possible explanations: (i) Either the treatment group was homogeneous and indistinguishable from the control group, or (ii) the treatment group was heterogeneous and included differentially affected subpopulations of animals. Given the historical knowledge that only 5080% of treated animals develop the IDR, it would not be surprising if the response to D-penicillamine was heterogeneous. We tested this hypothesis and looked for subpopulations by investigating the correlation between treatment effect and measurement fidelity. A Pearson correlation coefficient (R2) between the standard deviation (SD) and the absolute value of treatment effect was 0.54, representing a correlation (R) of approximately 0.75 (Figure 2). This result strongly suggested that the treated group of animals was comprised of multiple subpopulations. To rule out the possibility of a generalized, pathwayindependent disturbance, we investigated subsets of the data. We selected the 1000 ProbeSets (out of 15923 in total) that displayed the highest coefficients of variance

Figure 2. Pearson correlation between treatment effect and measurement fidelity. Measurement fidelity was considered as the standard deviation (SD) of the normalized signal intensities for the treated animals (n ) 8). A Pearson correlation coefficient (R2) between the SD and the absolute value of treatment effect was 0.54, representing a correlation of R ) 0.75.

(CV ) SD/mean) among the eight treated animals. We termed this subset CV1000 and refer to differently sized subsets with similar nomenclature below. We then calculated all 66 distinct pairwise Pearson correlation coefficients. These correlations clustered in a tight range from 0.92 to 0.98, indicating that the inherent variability in this population could not be detected at this level. We then considered progressively smaller subsets of data,

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Figure 3. Representation of the expression profile of the CV25 (25 genes with significant coefficient of variance) data on a selforganizing heat map after hierarchical clustering. The vertical axis represents transcript identity, and the horizontal axis represents the identity of the animal (treated rats were labeled as T1-T8, and control rats were labeled as C1-C4). Four of the treated samples (T3, T4, T5, and T8) cluster together into one clade (clade 1), while the remaining four treated samples comingle with the four control samples in a second clade (clade 2). There is no clear separation between the four control samples and the four treated samples in clade 2: The groups appear indistinguishable.

finally determining that the CV100 set of most variable ProbeSets was maximally informative. Examination of the Pearson correlations of this set of data strongly indicated the presence of at least two but no more than three distinct subpopulations of treated animals. Drug-Treated Animals Segregate into Two Clusters of Gene Expression Patterns. To visualize the distinct populations, within the drug-treated group, we employed a pattern recognition technique (18) of agglomerative two-dimensional hierarchical clustering. Clustering was performed on the CV25, CV50, CV100, and CV1000 sets. The results of the clustering were broadly comparable in all data sets. In particular, the separation of treated animals into two subgroups remained consistent across all data sets. We present the CV25 clustering results because this subset is the smallest one capable of directly indicating both sample specific and gene specific patterns in the data set. The CV25 clustergram is shown in Figure 3. From the horizontal dendogram, we can determine the sample-wise clustering patterns. Four of the treated samples (T3, T4, T5, and T8) clustered together into one clade (clade 1), while the remaining four treated samples comingled with the four control samples in a second clade (clade 2). There is no clear separation between the four control samples and the four treated samples in clade 2: The groups appear indistinguishable. There are two groups of transcripts that show significant up-regulation in clade 1 animals. These transcripts appear as blocks of red, one in the top left of the heat

map (cluster A, indicated by arrow) and the other in the bottom left of the heat map (cluster B, indicated by arrow). These two subclusters display a tight coexpression within group patterns, which strongly suggests some form of underlying coregulation (19). By contrast, the sets of coexpressed transcripts up-regulated in the combined clade 2 show significant sample-to-sample variability with no clear pattern being evident. Changes in Gene Expression in Clade 1 Appear To Be Danger Signals. After a single dose of Dpenicillamine (150 mg/kg, gavage), four of the treated samples (T3, T4, T5, and T8) clustered together into clade 1. These four animals were characterized by two clusters of transcripts that exhibit up-regulated expression relative to clade 2 animals. The transcripts (12 in total, three of which are ESTs that represent products of genes of unknown identity and function; Table 2) induced in clusters A and B appear to encode stress proteins, acute phase proteins, and energy metabolism proteins. qRTPCR was performed, using RNA from each rat liver, to confirm the microarray expression profiles. We found that, except for phosphodiesterase B, expression changes determined by microarray analysis correlated with changes observed by qRT-PCR (Table 2). Four out of six transcripts in cluster A exhibit changes above 1.1 log2 units (i.e., 21.1 or 2.14-fold as usually expressed; for example, a change in expression of -3 log2 units indicates an 8-fold down-regulation relative to control animals while a change of +2 log2 units indicates a 4-fold



Table 2. List of Transcripts Displaying Differential Regulation in Four (T3, T4, T5, and T8) of the Treated BN Rats Who Received D-Penicillaminea change in log2 units qRT-PCR

Entrez gene ID

1.1 1.3 1.4 0.8 1.2 1.4

N/A N/A 2.0 1.4 1.6 1.3

N/A N/A 24522 24385 246771 29517

0.4 1.3 1.2 -1.0 0.4 0.2

-2.9 2.3 2 -1.9 0.6 N/A

24626 115771 170496 24330 81503 N/A

microarray

gene symbol

ProbeSet nos.

gene name

cluster A N/A N/A Znf354a Gck Slc25a25 Skg

1372966_at 1374932_at 1368877_at 1387312_at 1371754_at 1367802_at

hypothetical protein transcribed sequences kid-1 gck pcscl sgk

cluster B Pde4b Usp2 Lcn2 Egr1 Cxcl1 N/A

1374157_at 1387703_at 1387011_at 1368321_at 1387316_at 1376387_at

pde4b usp2 lcn2 egr1 cinc transcribed sequences

a Numbers represent changes in expression in log2 spaces relative to control detected by microarray and qRT-PCR (a change in of -3 log2 units indicates an 8-fold down-regulation relative to control animals while a change of +2 log2 units indicates a 4-fold up-regulation in expression relative to control).

up-regulation in expression relative to control), and two out of six transcripts exhibit changes above 1.1 log2 units (log2 space) in cluster B. This level of change in expression is biologically significant considering the early time point (6 h) at which the liver was sampled. The function of each altered gene in clusters A and B was examined from gene annotations and the literature and further assessed for their potential as biomarkers or predictors of D-penicillamine-induced autoimmunity. Correlating Disease to Changes in Gene Expression. In the hepatic expression profiling experiment, we sacrificed the rats at the 6 h time point to obtain liver samples and thus could not know if the four animals in clade 1 (T3, T4, T5, and T8) would have gone on to develop D-penicillamine-induced autoimmunity or if the four treated animals in clade 2 (T1, T2, T6 and T7) would have remained resistant. To test this hypothesis, we performed three additional studies in parallel: rechallenge with drug to see if the same changes would be observed as on initial exposure; measuring protein levels of cinc and lcl2; and hepatic biopsy and time-course measurement of transcripts originally identified. In the first of these studies, expression of the nine putative danger signals 6 h after rechallenge with drug in rats that were previously exposed to D-penicillamine for 8 weeks was measured using using qRT-PCR. We did not observe any significant changes as compared to vehicle animals so it appears that the response is different on rechallenge. In the second study, we tried to measure protein levels. Of the nine gene products identified, only lcl2 and cinc are released into the plasma at levels that might allow us to detect them; however, we were unable to detect these proteins at the two time points that we tested despite being able to detect a positive control in a Western blot. In the third, we tested changes in mRNA expression from liver biopsies taken 6 h after the first dose of D-penicillamine in 40 rats (n ) 7 controls; n ) 33 D-penicillamine-treated). Following excision of the liver biopsy, rats were allowed to recover and we recorded any animal that developed signs of D-penicillamine-induced autoimmunity. We also observed control animals for any signs of illness that might arise to due to the surgery

Figure 4. Incidence of illness of BN rats subjected to liver biopsies under anesthesia. BN rats treated with D-penicillamine (n ) 33) displayed a typical time to onset and incidence of illness. None of the control animals (n ) 7) developed any infections or other signs of poor health in response to the surgery itself.

itself. We found that none of the control animals developed any signs of infection or illness (Figure 4). We determined mRNA changes in expressions (relative to control animals, log2 space) in each animal for each of the nine transcripts originally identified in the microarray experiment. Animals were categorized as either sick or nonsick. To test if the full set or subsets of the nine gene transcripts would discriminate between resistant and susceptible animals, we analyzed the qRT-PCR data obtained from the time-course study. Of the 33 rats treated with D-penicillamine in this validation study, 24% (eight rats) were found to be resistant to D-penicillamineinduced IDRs. The expression levels of each gene-rat combination are found in Table 3. For each gene, we performed a one-tailed p test between the resistant and the sensitive collectives under the assumption of heteroscedasticity between the two classes (p values indicated at the bottom of Table 3). Three transcripts, kid-1, sgk, and cinc, were confirmed to be significantly differentially expressed between the two groups at the p ) 0.25 level. Because the initial classifier (Figure 3) was based on unsupervised pattern recognition techniques, it is a group-based classifier, not a gene specific one. As a result, unsupervised pattern recognition techniques were used on the validation data with the three genes found to be significantly differentially expressed between the two groups. This validation was performed using two different clustering algorithms: agglomerative hierarchical clus-

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Table 3. Liver Sampling 6 h Postdosage with D-Penicillaminea animal kid-1 pcscl R1 R2 R3 R4 R5 R6 R7 R8 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 P value

0.2 -0.6 -1.3 2.7 0.3 -1.5 -1.9 0.6 0.0 1.0 -2.0 1.3 -1.8 0.1 -1.7 -1.1 -0.7 0.7 1.3 -0.2 -0.2 3.0 -0.7 -0.6 3.4 0.8 -0.5 -2.0 -1.0 0.8 3.1 1.9 1.1 0.25

0.3 -1.0 0.6 -1.2 0.2 -1.4 -3.8 0.2 -2.0 -2.6 -1.0 -1.5 -2.1 -1.1 -3.3 -1.3 -2.3 -1.4 -0.2 -0.2 1.3 0.5 0.9 -1.2 0.6 -0.2 1.1 -0.9 -1.3 0.6 -0.1 -0.4 -3.2 0.44

gck

sgk

-1.4 -0.3 -1.1 -2.4 0.1 -0.2 -2.1 -0.3 -1.2 -0.1 -2.2 0.6 -2.1 0.7 -1.4 -1.7 0.9 -1.7 -0.1 -4.7 -0.5 -1.7 -1.2 0.2 0.7 0.8 0.7 -1.6 -1.3 -2.7 -1.7 -2.0 0.1 0.47

1.3 1.0 2.7 -1.1 1.6 -0.2 -1.4 1.3 -0.8 -1.0 -0.2 -0.7 0.4 0.1 -2.1 -0.8 -1.0 -1.2 0.2 -0.6 0.6 0.7 0.9 0.7 0.8 -1.0 2.9 0.2 -0.5 0.2 -0.1 0.1 -1.0 0.09

pde4b usp2 0.0 1.1 1.2 0.7 0.1 -0.3 -0.4 0.1 0.3 0.4 0.3 0.5 0.7 0.5 -0.4 0.1 0.7 0.7 -1.0 0.3 0.7 0.0 -0.3 0.6 0.7 0.6 -0.4 0.3 0.5 0.5 0.0 0.1 0.1 0.41

0.8 -1.4 -1.2 1.4 0.0 -0.7 -1.7 -0.5 -0.9 0.3 -1.8 0.3 -0.7 0.1 -1.5 -1.2 -1.0 0.2 -0.2 1.2 -0.6 2.3 -0.9 -0.3 1.8 -0.5 0.2 -1.9 -0.8 -0.2 1.4 0.4 -0.4 0.31

lcl2

egr1

cinc

1.5 2.0 -0.1 0.1 -0.2 -0.5 0.0 -0.4 0.2 1.5 -1.0 0.3 1.0 0.1 -0.6 2.1 0.3 3.4 0.2 -0.4 1.6 0.7 1.9 0.2 0.1 -0.8 -0.7 -0.6 -0.5 -0.5 2.4 3.3 -0.3 0.27

0.2 0.0 0.4 1.1 0.8 -2.0 -2.2 -0.7 -0.2 1.0 -0.8 -1.0 -1.0 -1.7 -2.0 0.1 -0.6 -1.0 -1.1 0.0 0.3 0.9 0.0 -0.4 1.4 0.2 -2.1 -1.0 0.7 0.5 1.5 -0.4 0.6 0.45

3.6 -0.5 -0.9 1.2 0.9 2.3 1.7 0.8 2.8 2.2 -1.0 1.7 -0.2 0.4 -1.5 3.0 0.1 0.5 0.1 -1.4 1.7 1.0 0.8 0.1 2.3 0.9 -0.2 -0.1 1.3 0.1 2.9 1.0 0.1 0.25

a Expression levels of transcripts originally identified in microarray (change relative to controls in log2 space determined by qRTPCR). Resistant, R1-R8; susceptible, S1-S25.

Table 4. Characterization of Validation Groupsa

resistant animals susceptible animals kid-1 expression sgk expression cinc expression

group 1

group 2

7/8 12/25 low high high

1/8 13/25 high low low

a Both hierarchical (agglomerative) and k-means (k ) 2) clustering of RT-PCR results separated the data into two identical groups. A summary of the properties of the two groups of animals is shown here. The two groups have statistically different prognostic outcomes, with group 1 animals being more resistant than group 2 animals (p < 0.01) to D-PA-induced IDRs. Interestingly, while the expression profiles of cinc and sgk appear correlated, both genes are required to give optimal separation of resistant and sensitive animals.

tering (R statistical language v 2.0.0) and k-means clustering with k ) 2. Furthermore, the k-means clustering was repeated 1000 times with different initialization vectors to ensure that the optimal minimum was reached, and the same minimum was found in 965 of the thousand trials. Both clustering algorithms identified two groups of animals with distinctive expression profiles (Table 4). The first group included 7/8 resistant animals (87.5%) and 12/25 sensitive animals (48%). This group of animals was characterized by high expression levels of cinc and sgk but low levels of kid-1. The second group, by contrast, contained only one resistant animal (12.5%) and was characterized by low levels of cinc and sgk and higher

Figure 5. Representation of the expression profiles obtained from the liver biopsy time-course study on a self-organizing heat map after K-means clustering. The horizontal axis represents animal identity (rats resistant to D-penicillamine-induced autoimmunity were labeled R1-R8, and susceptible rats were labeled S1-S25), and the vertical axis represents the transcript identity (kid-1, sgk, or cinc). The enrichment of resistant animals was found to be significantly nonrandom at the p ) 0.01 level with a hypergeometric test. Data on gene expression were obtained by qRT-PCR.

levels of kid-1. The k-means-derived profile is displayed in Figure 5. To test if the enrichment of resistant animals in the first cluster was a random occurrence, we performed a hypergeometric test. The enrichment of resistant animals was found to be statistically significantly at the p ) 0.01 level.

Discussion We found significant differences in expression of putative danger signals in roughly half of drug-treated animals and from previous experiments we know that roughly half of treated animals develop autoimmunity. We further tested if these putative danger signals predicted susceptibility to D-penicillamine-induced autoimmunity and found that these expression profiles were broadly but not completely predictive of IDR sensitivity. The ability of these expression profiles detected at 6 h to broadly separate “resistant” animals from “sensitive” animals is consistent with the idea that the “decision” to respond/not respond to drug appears to be made early after drug treatment. This hypothesis is based on previous findings from our laboratory: Pretreating BN rats with one single dose of misoprostol, a prostaglandin analogue (t1/2 ∼ 15 min), 1 day prior to initiating D-penicillamine treatment, has long-lasting effects and completely prevents the onset of D-penicillamine-induced autoimmunity in BN rats (8).


There is circumstantial evidence to suggest that drugs that can induce cell stress or a danger signal are more likely to cause IDRs. For example, drugs such as halothane and isoniazid cause a transient rise in transaminases in a high percentage of patients; this presumably represents a significant danger signal (20, 21). This suggests that the induction of specific stress-related genes by a drug may predict which drugs will be associated with IDRs or which patients may be at increased risk of an IDR to the drug. One of the issues in performing expression profiling studies lies in the choice of the target organ and the time points at which the changes in response to drug treatment are measured. D-Penicillamine-induced autoimmunity occurs after approximately 3 weeks of chronic treatment with D-penicillamine and is characterized in the rat by the presence of antinuclear antibodies, elevated serum IgE levels, proteinuria, immune complex glomerulonephritis, weight loss, necrotic lesions in the spleen, liver, lungs, and skin (22). The necrotic lesions in the liver are a prominent feature, which is one reason that we chose to study gene expression changes in this organ. Moreover, previously performed pilot studies in the spleen detected few significant changes in gene expression at early time points following drug treatment. It is possible that we failed to detect changes in the spleen because we used total tissue mRNA from the spleen, and thus, gene expression changes in a small group of cells, although biologically significant, were below the threshold of detection. The liver contains a more homogeneous population of cells; we therefore reasoned that we would detect changes more readily in this tissue. The changes in hepatic mRNA expression levels that we detected in our study warrant special attention because they are plausible danger signals and potential biomarkers of D-penicillamine-induced autoimmunity. In cluster A, four of six transcripts represent genes of known function: Zinc finger protein 354A (Znf 345A) is known as kidney ischemia development protein (kid-1), a transcription factor that is expressed in response to injury and toxic stresses (23). Increased expression of kid-1 has been shown to cause fragmentation of the nucleolus, a phenomenon normally seen during cell stress (24) and considered a danger signal (25). Serum glucocorticoidregulated kinase (sgk) plays a role in carbohydrate metabolism but has also been implicated in stress signaling through its induction by glucocorticoids, osmotic changes, cytokines, brain injury, changes in cell volume, chronic viral hepatitis, or DNA-damaging agents (26). Glucokinase (gck) is a carbohydrate-metabolizing enzyme. Peroxisomal calcium-dependent solute carrier-like protein (pcscl) is an energy-metabolism protein with a role in hepatocyte differentiation. In cluster B, five of the six transcripts are genes of known function: Phosphodiesterase 4B (pde4b) is expressed in inflammatory cells such as macrophages (27), neutrophils (28), dentritic cells (29), mast cells, and epithelial cells (30). It is known that increased expression of pde4b can lead to increased TNFR mRNA production, which plays a role in rheumatoid arthritis (30). Ubiquitin is involved in stress responses and in protein degradation pathways (31). The list of functions of ubiquitin specific proteases (usp) is growing as they have been shown to inhibit chromatin-mediated gene silencing (32), alter cell cycle progression, and are involved in receptor tyrosine kinase-mediated signal transduction pathways (33). Lcl2 (lpcl2 also known as


ngal) is a novel acute phase protein expressed during inflammation (34, 35). Lcl2 is associated with pathological conditions, such as osteoarthritis in humans (35). Cytokine-induced neutrophil chemoattractant (cinc, also known as gro) is a member of the interleukin-8 (IL-8) family. It has several stress response and immune response functions. It is a potent inducer of neutrophil chemotaxis in vitro and in vivo (36, 37). Cinc has also been shown to play a role in immune complex-mediated inflammation in the rat model of nephrotoxic nephritis (38). Finally, early growth response 1 (egr1) regulates inducible heparanase gene transcription in activated T cells (39). Therefore, we conclude that clusters A and B contain putative danger signals, although the consistency and tightness of cluster B is not as definite as cluster A. The fact that these putative danger signals were upregulated only in animals T3, T4, T5, and T8 lead us to postulate that these animals would have gone on to develop the disease. However, it also is possible that upregulation of these genes represents an adaptive coping mechanism that helped protect these four animals from D-penicillamine toxicity. The putative danger signals for T1, T2, T6, T7 and C1-C4 (Figure 3) show significant sample-to-sample variability suggesting that the sets of transcripts in the combined clade 2 are more likely to be related to innate biological variability rather than to D-penicillamine-induced autoimmunity. To further understand if the signals detected in T3, T4, T5, and T8 were in fact danger signals and whether their expression predicted which animal would develop D-penicillamine-induced autoimmunity, we had to find another way to probe these genes without sacrificing rats at 6 h postexposure. We hypothesized that changes observed at 6 h postdosage would be observed again on rechallenge with D-penicillamine. In most immune-mediated reactions, the time to onset on rechallenge with the offending agent is shortened. This is observed in the nevirapine-induced skin rash model in the BN rat, thus reminiscent of an amnestic immune response (40). However, in the case of drug-induced autoimmune syndromes, such as propylthiouracil (PTU) and D-penicillamineinduced autoimmunity in the cat (41) and BN rat, respectively, the onset of disease upon rechallenge in previously exposed animals is almost as long as in animals following initial exposure. Therefore, we postulated that the reaction may be characterized by the expression of the same danger signals (reminiscent of a de novo immune response). However, this did not prove to be the case. Another strategy is to measure plasma levels of proteins coded for by the mRNAs for which changes in expression were detected. Of the nine gene products identified, only lcl2 and cinc are released into the plasma at levels that might allow us to detect them; however, we were unable to detect these proteins at the two time points that we tested. Although plasma lcl2 has been detected after a strong stimulus and we were able to detect 20 ng of recombinant lcl2 protein as a positive control, the small increase in lcl2 predicted by the modest changes in mRNA that we observed was likely below our limit of detection. Likewise, in a model of intrapulmonary inflammatory challenge, intravenous injection of bacterial endotoxin produced increases in cinc rat plasma levels to 120 ng/mL (42). If a strong proinflammatory agent, such as bacterial endotoxin, produces only nanogram levels of the chemokine, it is likely that D-penicillamine

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would produce a less marked induction and therefore below the detection limit of the Western blot technique. The advantage of performing liver biopsies to obtain mRNA from tissue was that it offered sensitivity and flexibility: We could test all nine putative danger signals by qRT-PCR. The limitation of this approach was the knowledge that surgery itself might cause enough cell stress or tissue damage to increase the incidence of D-penicillamine-induced autoimmunity. We were reluctant to perform this experiment because we knew that the risk of procainamide-induced agranulocytosis appears to be 10-fold higher in patients whose treatment was started immediately after open heart surgery than in nonsurgical patients (43). We expected that inflammation associated with the surgery would cause all of the animals to develop autoimmunity much like the effect of poly I:C (8, 44). However, we observed a normal time to onset and incidence of illness in the rats that were subjected to the liver biopsy. This finding only reinforces how little we understand of IDR mechanisms. When we tried to correlate changes in gene expression, 6 h after exposure to D-penicillamine, to disease outcome, we found that three genes were significantly differentially expressed between the susceptible and the resistant groups: kid-1, sgk, and cinc. Two separate clustering algorithms separated the animals into the same two groups. Because these two algorithms pursue entirely distinct clustering strategies, the similarity of results generated by the two approaches provides strong validation of the stability of the clustering. The enrichment of resistant animals (7/8 in group 1) was found to be significantly nonrandom, strongly indicating that our classifier is successfully separating rats resistant to D-penicillamine-induced autoimmunity from susceptible rats. However, we obtained an approximately 50% falsepositive “resistant” classification when 12 of the 25 rats that developed the IDR in response to D-penicillamine showed the same pattern of expression as the 7/8 resistant rats. We can only speculate on the roles that kid-1, sgk, and cinc might be playing in the development of autoimmunity. The observation that only one of the resistant animals had a pattern that included an increase in kid-1 suggests that this zfp represents a marker for animals that are most likely to develop autoimmunity, which is consistent with the observation that it is up-regulated by other types of cell injury (29). It is possible that the sgk and cinc expression in the resistant animals reflect a protective effect rather than a “danger signal”. However, expression of sgk and cinc did not appear to be sufficient to prevent autoimmunity in all of the animals in which expression is increased. Although it is unlikely that there was sufficient time for the increase in sgk to be due to increased glucocorticoid levels, the fact that glucocorticoids are immunosuppressive and sgk is up-regulated by glucocorticoids suggests that sgk induction may have protective effects. In contrast, cinc codes for an inflammatory chemokine might be expected to be increased in the animals that will develop autoimmunity rather than those that are resistant; however, cytokines often have effects that appear paradoxical. Kid-1, sgk, and cinc appear to have some role in D-penicillamine-induced autoimmunity; yet, these genes are not 100% predictive. The puzzle is incomplete; there must be important environmental factors and/or other

Se´ guin et al.

genes, possibly due to having only one sampling time and organ, that we missed. Moreover, the current RAE230A rat arrays only contain approximately one-third of all rat genes. To further investigate the role of these three genes, we could cotreat rats with agents that up- or downregulate them independent of D-penicillamine. For example, we could study the effects of adrenocorticosteroid analogues on sgk levels and disease outcome. In the case of cinc, we could study its effects on D-penicillamineinduced autoimmunity by directly injecting varying doses of recombinant cinc. This has been done in a model of rat ischemic and nephrotoxic renal injury (45). Nevertheless, the concept that a group or pattern of transcripts can predict disease where single genes cannot is becoming apparent in such complex diseases as cancer (46, 47); only recently have researchers begun to perform these studies in toxicology (48). This is the first study in which a high-throughput gene expression screen has been performed to identify mechanistic and predictive roles of putative danger signals in an animal model of an IDR. Although IDRs are not usually detected in preclinical animal testing due to the fact that they are just as idiosyncratic in animals as they are in humans, it is likely that reactive metabolites (or chemically reactive drugs such as D-penicillamine) could cause similar stresses in both animals and humans. The potential role of expression-based classifiers in IDRs is highlighted by this study. However, we caution that until more animal studies show that changes in gene expression correlate with the development of an IDR, it would be inappropriate to draw firm conclusions with respect to the feasibility of such screening methods as predictors of a drug’s potential to cause IDRs. However, our experiments do suggest that D-penicillamine leads to up-regulation of putative danger signals very early after exposure. Expression of these signals 3 weeks prior to onset of the IDR suggests that “trigger” events may indeed act early after initiation of drug treatment and affect the “decision” to become sick or not. Although the pattern associated with D-penicillamine was not uniform and further studies are required to determine if this is a common feature of drugs that cause a high incidence of IDRs, such high-throughput gene searches show promise for defining pathways that lead to IDRs and identifying biological responses that predict which individuals are likely to develop such reactions. Both raw and normalized microarray data from this study have been deposited at the Gene Expression Omnibus (GEO) repository at NCBI. The experiment has the accession number GSE2825.

Acknowledgment. J.U. holds a Canada Research Chair in Adverse Drug Reactions. B.S. is a recipient of a doctoral fellowship awarded by the Canadian Institute of Health Research (CIHR). We thank Tom Rushmore and Merck & Co. (West Point, PA) for performing the pilot study on spleen samples. This work was supported by grants from the Canadian Institute for Health Research (CIHR MT9336).

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