Biomarkers and expression profiles for toxicology

Information

  • Patent Application
  • 20040005547
  • Publication Number
    20040005547
  • Date Filed
    March 14, 2003
    21 years ago
  • Date Published
    January 08, 2004
    21 years ago
Abstract
The present invention is based on the determination of the global changes in gene expression in tissues or cells exposed to known toxins, in particular hepatotoxins, as compared to unexposed tissues or cells as well as the identification of individual genes that are differentially expressed upon toxin exposure. The invention includes methods of predicting at least one toxic effect of a compound, predicting the progression of a toxic effect of a compound, and predicting the hepatoxicity of a compound. Also provided are methods of predicting the mechanism of toxicity of a compound. In a further aspect, the invention provides probes comprising sequences that specifically hybridize to genes in Table 3 as well as solid supports comprising at least two of the said probes.
Description


BACKGROUND OF THE INVENTION

[0001] The present invention relates to toxicogenomic methods useful in the development of safe drugs. More specifically, the present invention relates to methods for the prediction of a toxic effect, especially hepatotoxicity, in animal models or cell cultures. Furthermore, expression profiles characteristic of different mechanisms of hepatoxicity as well as specific markers for hepatoxicity are provided.


[0002] The gene expression pattern governs cellular development and physiology, and is affected by pathological situations, including disease and the response to a toxic insult. Bearing this in mind, it becomes clear that the study of gene and protein expression in preclinical safety experiments will help toxicologists to better understand the effects of chemical exposure on mammalian physiology. On the one hand, the identification of a certain number of modulated genes and/or proteins after exposure to a toxicant will lead to the identification of novel predictive and more sensitive biomarkers which might replace the ones currently used. The knowledge regarding marker genes for particular mechanisms of toxicity, together with the rapidly growing understanding of the structure of the human genome will form the basis for the identification of new biomarkers. These markers may allow the prediction of toxic liabilities, the differentiation of species-specific responses and the identification of responder and non-responder populations. Gene expression analysis is an extremely powerful tool for the detection of new, specific and sensitive markers for given mechanisms of toxicity (Fielden, M. R., and Zacharewski, T. R. (2001). Challenges and limitations of gene expression profiling in mechanistic and predictive toxicology. Toxicol Sci 60, 6-10). These markers should provide additional endpoints for inclusion into early animal studies, thus minimising the time, the cost and the number of animals needed to identify the toxic potential of a compound in development. Also, this will lead to the development of relevant screening assays in vivo and/or in vitro. The understanding of the molecular mechanisms underlying toxicity will also provide more insight into species-specific response to drugs and should immensely increase the predictability of potential risk accumulation for drug-combinations or drug-disease interactions. Moreover, chemically induced changes in gene expression are likely to occur at exposures to chemicals below those that induce an adverse toxicological outcome. As drug-induced liver toxicity is a major issue for health care and drug development, great interest lies in hepatotoxins. Currently, the predictivity of gene and protein expression for toxicity is a generally accepted assumption supported by some published results, but substantially more data are needed to prove the validity of this hypothesis (Waring, J. F., Ciurlionis, R., Jolly, R. A., Heindel, M., and Ulrich, R. G. (2001). Microarray analysis of hepatotoxins in vitro reveals a correlation between gene expression profiles and mechanisms of toxicity. Toxicol Lett 120, 359-68; Bulera, S. J., Eddy, S. M., Ferguson, E., Jatkoe, T. A., Reindel, J. F., Bleavins, M. R., and De La Iglesia, F. A. (2001). RNA expression in the early characterization of hepatotoxicants in Wistar rats by high-density DNA microarrays. Hepatology 33, 1239-58; Bartosiewicz M. J., Jenkins, D., Penn, S., Emery, J., and Buckpitt, A. (2001). Unique gene expression patterns in liver and kidney associated with exposure to chemical toxicants. J Pharmnacol Exp Ther 297, 895-905). A widespread approach to validate these new tools is the use of model compounds in animal models to produce expression profiles which are expected to be characteristic for the compound under examination. Model compounds that have been used for gene expression profiling in the liver include WY-14,643, phentobarbital, clofibrate, ethanol and acetaminophen. The majority of the published results confirm the regulation of genes previously identified and add a large number of genes modulated by the test compound. Thus microarrays showed the induction of cytochromes (CYP2B and CYP3A) as well as of genes related to apoptosis and DNA-repair by phentobarbital (Carver, M. P., and Clancy, B. (2000). Transcriptional profiling of phenobarbital (PB) hepatotoxicity in the mouse. Toxicological Sciences 54, 383). Similarly, studies on the peroxisome proliferator WY-14,643 showed the induction of CYP4A, GST and acyl-CoA hydroxylase, as well as of genes associated with oxidative damage, with cell proliferation and with apoptosis (Carfagna, M. A., Baker, T. K., Wilding, L. A., Neeb, L. A., Torres, S., Ryan, T. P., and Gelbert, L. M. (2000). Effects of a peroxisome proliferator (WY-14,643) on hepatocyte transcription using microarray technology. Toxicological Sciences 54, 383). Ruepp and co-workers investigated gene expression changes after treating mice with acetaminophen, and found that genes such as metallothioneins, c-fos, glutathione peroxidase and proteasome-related-genes were induced (Ruepp, S., Tonge, R. P., Wallis, N. T., Davison, M. D., Orton, T. C., and Pognan, F. (2000). Genomic and proteomic investigations of acetaminophen (APAP) toxicity in mouse liver in vivo. Toxicological Sciences 54, 384). Similar results were also presented by Suter et al. (Suter, L., Boelsterli, U. A., Winter, M., Crameri, F., Gasser, R., Bedoucha, M., deVera, C., and Albertini, S. (2000). Toxicogenomics: Correlation of acetaminophen-induced hepatotoxicity with gene expression using DNA microarrays. Toxicological Sciences 54, 383) and by Reilly et al. (Reilly, T. P., Bourdi, M., Brady, J. N., Pise-Masison, C. A., Radonovich, M. F., George, J. W., and Pohl, L. R. (2001). Expression profiling of acetaminophen liver toxicity in mice using microarray technology. Biochem Biophys Res Commun 282, 321-8). So far, expression profiles and toxicity markers were only provided for specific model compounds in the prior art. Therefore) the technical problem underlying the present invention was to provide for gene expression profiles and toxicity markers, which are characteristic not only for a specific toxic compound, but for a specific mechanism of toxicity and which are reproducible.


[0003] As can be seen, there is a need for methods for the prediction of toxic effects of a compound, for the prediction of the mechanism of toxicity of a compound, especially for the prediction of hepatotoxicity, by using reproducible gene expression profiles caused by known toxic compounds) gene expression profiles characteristic of a mechanism of hepatoxicity, and specific marker genes.



SUMMARY OF THE INVENTION

[0004] The present invention is based on the determination of the global changes in gene expression in tissues or cells exposed to known toxins, in particular hepatotoxins, as compared to unexposed tissues or cells as well as the identification of individual genes that are differentially expressed upon toxin exposure.


[0005] The invention includes methods of predicting at least one toxic effect of a compound, predicting the progression of a toxic effect of a compound, and predicting the hepatoxicity of a compound. Also provided are methods of predicting the mechanism of toxicity of a compound. In a further aspect, the invention provides probes comprising sequences that specifically hybridize to genes in Table 3 as well as solid supports comprising at least two of the said probes, and primers for specific amplification of the genes of Table 3. The prediction of toxic effects comprises the steps of a) generating a database with the expression of marker genes elicited by known toxic compounds in animal models or cell culture systems, b) obtaining a biological sample from the model systems; c) obtaining a gene expression profile characteristic of a given toxicity mechanism and/or detecting and/or measuring the expression of (a) specific marker gene(s) d) comparing the expression profile and/or expression of specific marker gene(s) with the database of step a).


[0006] More specifically, in one aspect of the present invention, a method of predicting at least one toxic effect of a compound, comprises detecting the level of expression of one or more genes from Table 3 in a tissue or cell sample exposed to the compound; wherein differential expression of the one or more genes from Table 3 is indicative of at least one toxic effect.


[0007] In another aspect of the present invention, a method of predicting at least one toxic effect of a compound comprises (a) detecting the level of expression of one or more genes from Table 3 in a tissue or cell sample exposed to the compound; and (b) comparing the level of expression of the one or more genes to their level of expression in a control tissue or cell sample, wherein differential expression of the one or more genes in Table 3 is indicative of at least one toxic effect.


[0008] In yet another aspect of the present invention, a method of predicting the progression of a toxic effect of a compound comprises detecting the level of expression in a tissue or cell sample exposed to the compound of one or more genes from Table 3, wherein differential expression of the one or more genes in Table 3 is indicative of toxicity progression.


[0009] In a further aspect of the present invention, a method of predicting the mechanism of toxicity of a compound comprises detecting the level of expression in a tissue or cell sample exposed to the compound of one or more genes from Table 3, wherein differential expression of the one or more genes in Table 3 is associated with a specific mechanism of toxicity.


[0010] In still a further aspect of the present invention, a method of predicting at least one toxic effect of a compound comprises detecting the level of expression of one of the genes selected from Table 4 in a tissue or cell sample exposed to the compound, wherein differential expression of the gene selected from Table 4 is indicative of at least one toxic effect.


[0011] In still a further aspect of the present invention, a set of nucleic acid primers have primers that specifically amplify at least two of the genes from Table 3.


[0012] In still a further aspect of the present invention, a set of nucleic acid probes have probes that comprise sequences which hybridize to at least a specific number of the genes from Table 3. While not being limited thereto, the specific number of genes may be at least 2 genes from Table 3, at least 5 genes from Table 3, and at least 10 genes from Table 3.


[0013] In still a further aspect of the present invention, a solid support comprises at least two probes, wherein each of the probes comprises a sequence that specifically hybridizes to a gene in Table 3.


[0014] In still a further aspect of the present invention, a computer system comprises a database containing DNA sequence information and expression information of at least two of the genes from Table 3 from tissue or cells exposed to a hepatotoxin, and a user interface.


[0015] In still a further aspect of the present invention, a computer system for predicting at least one toxic effect of a compound comprises a processor and a memory coupled to the processor; wherein the memory stores a first set of data including the level of expression of one or more genes from Table 3 in a tissue or cell sample exposed to the compound, and the memory stores a second set of data including the level of expression of the one or more genes from Table 3 in a control tissue or cell sample; and the processor compares the first set of data with the second set of data to predict the at least one toxic effect of the compound.


[0016] In yet a further aspect of the present invention, a kit comprises 1) at least one solid support having at least two probes, wherein each of the probes comprises a sequence that specifically hybridizes to a gene in Table 3, and 2)gene expression information for the said genes.


[0017] These and other features, aspects and advantages of the present invention will become better understood with reference to the following drawings, description and claims.







BRIEF DESCRIPTION OF THE DRAWINGS

[0018]
FIG. 1: Effect of CCl4 on PEG-3 (progression elevated gene-3) expression and circulating ALT (alanine aminotransferase) levels. Expression levels are expressed in arbitrary units. Circulating ALT-levels are expressed in μkat/ml. For each dose group (Control, Low dose=0.25 ml/kg and High dose=2 ml/kg) the values obtained for each of the 5 animals are represented.


[0019]
FIG. 2: Effect of HDZ on PEG-3 expression. The median expression level of the gene PEG-3 for 10 control animals, 10 low-dose animals (10 mg/kg) and 10 high dose animals (60 mg/kg) are represented. Expression levels are expressed in arbitrary units. An expression of below 100 is considered as non-detectable.


[0020]
FIG. 3: Effect of NFT on PEG-3 expression and on circulating ALT (alanine aminotransferase) levels. The median expression level of the gene PEG-3 for 10 control animals, 10 low-dose animals (5 mg/kg), 10 mid-dose animals (20 mg/kg) and 10 high dose animals (60 mg/kg) are represented. Expression levels are expressed in arbitrary units. An expression of below 100 is considered non-detectable


[0021]
FIG. 4: Effect of Thioacetamide and Thioacetamide-S-oxide on PEG-3 expression at 3 different time points by in vitro exposure of hepatocytes. Rat primary hepatocytes (in monolayer cultures) were exposed to 0, 30, 100 and 300 μM Thioacetamide-S-Oxide and to 3 and 10 mM Thioacetamide. Expression level of PEG-3 were at 2, 6 and 24 hs after exposure. For each dose and time point triplicate samples were analyzed. The expression level of PEG-3 in each experimental condition is displayed in FIG. 4A through C, where each line represents the expression level for each replicate. Expression levels are expressed in arbitrary units. In addition, cytotoxicity (LDH-release) of Thioacetamide-S-oxide at several doses and time points is represented in FIG. 4D.


[0022]
FIG. 5: Effect of in vitro exposure of hepatocytes to Thioacetamide (TAA) and Thioacetamide-S-oxide (TSO) on the expression levels of PEG-3 and on some co-regulated genes as determined by cluster analysis. Rat primary hepatocytes (in monolayer cultures) were exposed to 0, 30, 100 and 300 1M Thioacetamide-S-Oxide and to 3 and 10 mM Thioacetamide and analyzed at 3 different time points. Each line represents a single gene; the intensity of the grey colour is proportional to the expression level.


[0023]
FIG. 6: Effect of in vitro exposure to Glucokinase activators on the expression levels of PEG-3, GADD-45 and GADD-153 and on mitochondrial beta-oxidation. FIG. 6A represents the induction of PEG-3, GADD-45 and GADD-153 with increasing doses of Ro 28-2310 at 6 hours. FIG. 6B shows the inhibition of beta-oxidation by 3 glucokinase activators.


[0024]
FIG. 7: Recursive Feature Elimination (RFE) for generation of support vector machines (SVMs) for the direct acting group (see Example 9).


[0025]
FIG. 8: Classification of Amineptine as a steatotic compound. The bigger a positive discriminant value is, the better is the data fit into a specific class defined by the respective SVM. A negative discriminant value means that data do not fit into a compound class.


[0026]
FIG. 9: Classification of 1,2-Dichlorobenzene as a direct acting compound. The bigger a positive discriminant value is, the better is the data fit into a specific class defined by the respective SVM. A negative discriminant value means that data do not fit into a compound class.


[0027]
FIG. 10: Differentiation of toxic and non-toxic compounds using RT-PCR


[0028]
FIG. 11: Western-Blots of liver extracts with antibody specific for CYP2B. Two representative animals from each treatment group were analyzed. a: lane 1: MW-markers; lanes 2 and 3: control (6H); lanes 4 and 5: Ro 65-7199 (6H); lanes 6 and 7: Ro 66-0074 (6H); lanes 8 and 9: controls (24H); lanes 10 and 11: Ro 65-7199 (24H); lanes 12 and 13: Ro 66-0074 (24H). b: lane 1: MW-markers; lanes 2 and 3: controls (7 Days); lanes 4 and 5 30 mg/kg/d Ro 65-7199 (7 Days); lanes 6 and 7:100 mg/kg/d Ro 65-7199 (7 Days); lanes 8 and 9: 400 mg/kg/d Ro 65-7199 (7 Days); lane 11: Positive control (phenobarbital induced rat microsomal extract). Inlet bargraphs represent the densitometric quantification of each gel.







[0029] In the present invention it was found that marker genes are differentially expressed in tissues obtained after exposure of non-human animals, e.g. rats, to model toxic compounds at doses and/or time points in which these compounds did not elicit the conventionally measured response of elevation in plasma liver enzymes. It was also observed that this elevation of particular marker genes was evident not only after in vivo exposure of the test animals but also in vitro in primary hepatic cell cultures exposed to a similar group of hepatotoxins. Furthermore, the regulation of a group of genes by several compounds with a similar mechanism of toxicity provides a characteristic gene expression profile or “fingerprint” for said mechanism of toxicity.


[0030] In the present study, compounds with well characterized toxicity as Chlorpromazine, Cyclosporine A, Erythromycin, Glibenclamide, Lithocholic acid, Ro 48-5695 (Endothelin receptor antagonist), Dexamethasone, 1,2-Dichlorobenzene, Aflatoxin B1, Bromobenzene, Carbon tetrachloride, Diclofenac, Hydrazine, Nitrofurantoin, Thioacetamide, Concanavaline A, Tacrine, Tempium (Lazabemide), Tolcapone (Tasmar), 1,4-Dichlorobenzene, Amineptin, Amiodarone, Doxycycline, Ro 28-1674 (glucokinase activator), Ro 28-1675 (glucokinase activator), Ro 65-7199 (5-HT6 receptor antagonist), Tetracycline, Dinitrophenol, Cyproterone Acetate, Phenobarbital, Clofibrate, Acetaminophen, Thioacetamide-S-Oxide, Perhexiline, Methapyrilene were selected as known hepatotoxins. These compounds are widely known to cause hepatic injury in animals and/or in man, as described in “Toxicology of the liver, 2nd. Ed, Ed. By G. L. Plaa and W. R. Hewitt, Target organ toxicology series, 1997. A brief summary of the known effects of these compounds is listed below.


[0031] Carbon tetrachloride (CCl4), bromobenzene and 1,2-dichlorobenzene are halogenated, highly reactive compounds leading to toxicity in the liver in rodents and in man (Brondeau MT, Bonnet P, Guenier JP, De Ceaurriz J. (1983). Short-term inhalation test for evaluating industrial hepatotoxicants in rats. Toxicol Lett. 19, 139-46; Rikans, L. E. (1989). Influence of aging on chemically induced hepatotoxicity: role of age-related changes in metabolism. Drug Metab Rev 20, 87-110). For CC14, several studies have shown that the toxicity is mediated by its metabolic product, the highly reactive trichloromethyl free radical (Stoyanovsky, D. A., and Cederbaum, A. I. (1999). Metabolism of carbon tetrachloride to trichloromethyl radical: An ESR and HPLC-EC study. Chem Res Toxicol 12, 730-6). This radical leads to lipid peroxidation and can react with cellular proteins and with DNA (Castro, G. D., Diaz Gomez, M. I., and Castro, J. A. (1997). DNA bases attack by reactive metabolites produced during carbon tetrachloride biotransformation and promotion of liver microsomal lipid peroxidation. Res Commun Mol Pathol Pharmacol 95, 253-8). Secondary liver injury following the administration of these halogenated compounds is believed to be caused by inflammatory processes originating from products of activated Kupffer cells (Edwards, M. J., Keller, B. J., Kauffman, F. C., and Thurman, R. G. (1993). The involvement of Kupffer cells in carbon tetrachloride toxicity. Toxicol Appl Pharmacol 119, 275-9). Thus, the observed toxicity is due to direct action of the free radicals and to indirect action mediated by cytokines such as TNF alpha (DeCicco, L. A., Rikans, L. E., Tutor, C. G., and Hornbrook, K. R. (1998). Typical lesions produced by these compounds a few hours after a single administration are centrilobular cell degeneration and necrosis accompanied by lipid peroxidation, followed by hepatic regeneration starting 48 hours after administration. Elevation of serum enzyme activities is seen as a result of of the hepatocellular necrosis (e.g. AST, ALT, SDH).


[0032] Hydrazine and hydrazine derivatives are among the early drugs reported to cause damage to the liver. Thioacetamide and its metabolite Thioacetamide-S-oxide are also known to cause liver injury, histopathological examination showed necrotic hepatocytes around the central vein with infiltration of macrophages, neutrophils and eosinophils. Thus biochemical and histologic and clinical features indicate hepatocellular injury, with parenchimal degeneration and necrosis (Dashti, Jeppsson, Hagerstrand, Hultberg, Srinivas, Abdulla, Joelsson and Bengmark (1987). Early biochemical and histological changes in rats exposed to a single injection of thioacetamide. Pharmacol Toxicol 3, 171-4.; Albano, Goria-Gatti, Clot, Jannone and Tomasi (1993). Possible role of free radical intermediates in hepatotoxicity of hydrazine derivatives. Toxicol Ind Health 3, 529-38.).


[0033] Cyclosporine A (CsA) is an immunosupressant that has been reported to induce cholestasis in transplanted patients. Several mechanisms have been proposed to explain this toxic manifestation: hepatotoxicity, competition for bilirary excretion, inhibition of bilirubin excretion, inhibition of the synthesis of bile acids, etc. (Le Thai, Dumont, Michel, Erlinger and Houssin (1988). Cholestatic effect of cyclosporine in the rat. An inhibition of bile acid secretion. Transplantation 4, 510-2.). In spite of the liver being one of the main target organs for CsA-induced toxicity, the kidney is also a target organ for toxicity. Nevertheless, nephrotoxicity seems to be the consequence of chronic exposure to the drug. Using animal studies (rats), it has been shown that the bile flow is significantly reduced after chronic (3 weeks) or acute (single dose) administration of CsA. This decrease in BA-flow is reflected by an increase in plasma bile acids and plasma bilirubin. No histopathological findings accompany this effect (Stone, Warty, Dindzans and Van Thiel (1988). The mechanism of cyclosporine-induced cholestasis in the rat. Transplant Proc 3 Suppl 3, 841-4, Roman, Monte, Esteller and Jimenez (1989). Cholestasis in the rat by means of intravenous administration of cyclosporine vehicle, Cremophor EL. Transplantation 4, 554-8).


[0034] Tacrine is a compound for the treatment of Alzheimer's disease in man. In treated patients, it shows hepatotoxicity with an incidence of 40-50%. In rodents, tacrine elicited hepatic toxicity manifested as pericentral necrosis and fatty changes, accompanied by an increase in circulating liver enzymes (Monteith and Theiss (1996). Comparison of tacrine-induced cytotoxicity in primary cultures of rat, mouse, monkey, dog, rabbit, and human hepatocytes. Drug Chem Toxicol 1-2, 59-70; Stachlewitz, Arteel, Raleigh, Connor, Mason and Thurman (1997). Development and characterization of a new model of tacrine-induced hepatotoxicity: role of the sympathetic nervous system and hypoxia-reoxygenation. J Pharmacol Exp Ther 3, 1591-9).


[0035] Concanavaline A is a model compound used for studying the role of liver-associated T cells in acute hepatitis produced in rats. Concanavalin A produces a severe hepatitis, which can be assessed by serum biochemistry showing increased interleukines (IL-6 and TNF-alpha), as well as alanine aminotransferase (ALT) (Mizuhara, O'Neill, Seki, Ogawa, Kusunoki, Otsuka, Satoh, Niwa, Senoh and Fujiwara (1994). T cell activation-associated hepatic injury: mediation by tumor necrosis factors and protection by interleukin 6. J Exp Med 5, 1529-37).


[0036] Chlorpromazine has a clear and well-studied profile of producing liver injury in man. It is the most extensively studied neuroleptic and the hepytic injury that produces is hepatocanalicular cholestasis. Up to 1% of the treated patients develop jaundice. Some studies have shown that chlorpromazine inhibits Na+-K+-ATPase cation pumping in intact cells, therefore contributing to the chlorpromazine-induced cholestasis in animals and humans. (Van Dyke and Scharschmidt (1987). Effects of chlorpromazine on Na+-K+-ATPase pumping and solute transport in rat hepatocytes. Am J Physiol 5 Pt 1, G613-21.). Lithocholic acid is one of the bile acids transported into the bile canaliculi. An increase in the concentration of lithocholic acid causes intrahepatic cholestasis (Shefer, Zaki and Salen (1983). Early morphologic and enzymatic changes in livers of rats treated with chenodeoxycholic and ursodeoxycholic acids. Hepatology 2, 201-8).


[0037] Erythromycine have been incriminated as the cause of cholestatic liver injury. The pattern of injury is usually hepatocanalicular cholestasis. In rare casis, erythromycin can also lead to liver necrosis.(Gaeta, Utili, Adinolfi, Abernathy and Giusti (1985). Characterization of the effects of erythromycin estolate and erythromycin base on the excretory function of the isolated rat liver. Toxicol Appl Pharmacol 2, 185-92.). Glibenclamide has been associated with reversible cholestasis in clinical case studies (Del-Val, Garrigues, Ponce and Benages (1991). Glibenclamide-induced cholestasis. J Hepatol 3, 375).


[0038] Dinitrophenol is a widely used model compound for mitochondrial uncoupling. Dosing of animals with this compound leads to increased mitochondrial respiration, decreased ATP-levels and increase in body temperature (Okuda, Lee, Kumar and Chance (1992). Comparison of the effect of a mitochondrial uncoupler, 2,4-dinitrophenol and adrenaline on oxygen radical production in the isolated perfused rat liver. Acta Physiol Scand 2, 159-68).


[0039] Dexamethasone is a known glucocorticoid which is used in many experimental models to induce the activity of cytochromes P450 in the liver and in hepatocyte cultures (Kocarek and Reddy (1998). Negative regulation by dexamethasone of fluvastatin-inducible CYP2B expression in primary cultures of rat hepatocytes: role of CYP3A. Biochem Pharmacol 9, 1435-43). Other drugs that are usually related with hepatomegaly and/or peroxisome proliferation and are known inducers of some cytochromes P450 in the liver and in hepatocyte cell cultures are phenobarbital, cyproterone acetate and fibrates such as clofibrate (Menegazzi, Carcereri-De Prati, Suzuki, Shinozuka, Pibiri, Piga, Columbano and Ledda-Columbano (1997). Liver cell proliferation induced by nafenopin and cyproterone acetate is not associated with increases in activation of transcription factors NF-kappaB and AP-1 or with expression of tumor necrosis factor alpha. Hepatology 3, 585-92.; Kietzmann, Hirsch-Ernst, Kahl and Jungermann (1999). Mimicry in primary rat hepatocyte cultures of the in vivo perivenous induction by phenobarbital of cytochrome P-450 2B1 mRNA: role of epidermal growth factor and perivenous oxygen tension. Mol Pharmacol 1, 46-53; Diez-Fernandez, Sanz, Alvarez, Wolf and Cascales (1998). The effect of non-genotoxic carcinogens, phenobarbital and clofibrate, on the relationship between reactive oxygen species, antioxidant enzyme expression and apoptosis. Carcinogenesis 10, 1715-22).


[0040] Acetaminophen is a widely used analgesic and antipyretic drug that causes acute liver damage upon overdosis. This drug is often missused for suicidal purposses. If overdosed, the hepatic glutathione pool becomes depleted and the metabolic activation of the compound leads to a highly reactive metabolite. This metabolite can bind to DNA and proteins in the cell, leading to hepatocellular necrosis (Tarloff, Khairallah, Cohen and Goldstein (1996). Sex- and age-dependent acetaminophen hepato- and nephrotoxicity in Sprague-Dawley rats: role of tissue accumulation, nonprotein sulffiydryl depletion, and covalent binding. Ftindanm Appl Toxicol 1, 13-22; Cohen and Khairallah (1997). Selective protein arylation and acetaminophen-induced hepatotoxicity. Drug Metab Rev 1-2, 59-77; Fountoulakis, Berndt, Boelsterli, Crameri, Winter, Albertini and Suter (2000). Two-dimensional database of mouse liver proteins: changes in hepatic protein levels following treatment with acetaminophen or its nontoxic regioisomer 3-acetamidophenol. Electrophoresis 11, 2148-61).


[0041] Methapyrilene is an antihistamin drug that causes acute periportal hepatotoxicity in rats, but also exerts a variety of toxic effects in the liver. Apparently, CYP2C11 is responsible for the suicide substrate bioactivation of methapyrilene and the acute toxicologic outcome largely relied upon an abundance of detoxifying enzymes present in the liver Another potentially very significant effect of MP is that it induces a large increase in hepatic cell proliferation coupled with mitochondrial proliferation. In addition, some results suggest that methapyrilene hydrochloride is a DNA damaging agent (Althaus, Lawrence, Sattler and Pitot (1982). DNA damage induced by the antihistaminic drug methapyrilene hydrochloride. Mutat Res 3-6, 213-8; Ratra, Cottrell and Powell (1998). Effects of induction and inhibition of cytochromes P450 on the hepatotoxicity of methapyrilene. Toxicol Sci 1, 185-96).


[0042] Tetracyclines and Doxycyclines lead to dose-dependent hepatic injury. The hepatotoxicity of tetracyclines is well known. The characteristic lesion is microvesicular steatosis which poor prognosis, resembling Reye's syndrome. The underlying mechanism of toxicity seems to be the inhibition of mitochondrial beta oxidation together with an inhibition of the transport of lipids from the liver (Hopf, Bocker and Estler (1985). Comparative effects of tetracycline and doxycycline on liver function of young adult and old mice. Arch Int Pharmacodyn Ther 1, 157-68; Lienart, Morissens, Jacobs and Ducobu (1992). Doxycycline and hepatotoxicity. Acta Clin Belg 3, 205-8).


[0043] Diclofenac is a widely used NSAID (non-steroid anti-inflammatory drug). Several cases related hepatic injury, sometimes with fatal outcome, with the administration of this compound. The The pattern of injury is usually hepatocellular with acute necrosis. The mechanism by which diclofenac elicits this effect is unknown, but some speculations have been made regarding metabolic idiosyncracy. Also, diclofenac can bind irreversibly to hepatic proteins via its acyl glucuronide metabolite; these protein adducts could be involved in the pathogenesis of diclofenac-associated liver damage (Kretz-Rommel and Boelsterli (1994). Mechanism of covalent adduct formation of diclofenac to rat hepatic microsomal proteins. Retention of the glucuronic acid moiety in the adduct. Drug Metab Dispos 6, 956-61).


[0044] Nitrofurantoin is an antimicrobial widely used in the treatment of urinary tract infection which is known to cause acute and chronic liver injury. The injury can be either cholestatic or hepatocellular, and the underlying mechanism seems to be immunologic idiosyncrasy (Villa, Carugo and Guaitani (1992). No evidence of intracellular oxidative stress during ischemia-reperfusion damage in rat liver in vivo. Toxicol Lett 2-3, 283-90; Tacchini, Fusar-Poli and Bernelli-Zazzera (2002). Activation of transcription factors by drugs inducing oxidative stress in rat liver. Biochem Pharmacol 2, 139-148).


[0045] Aflatoxin B1 is a contaminant in food, source: Aspergillus flavus and Aspergillus parasiticus. Aflatoxin induces also ROS production, lipid peroxidation and 8-OhdG formation in DNA. It reacts also with various liver and blood plasma proteins, particularly with serum albumin. Acutelly, it leads to liver necrosis, given chronically shows a carcinogenic effect (Liu, Yang, Lee, Shen, Ang and Ong (1999). Effect of Salvia miltiorrhiza on aflatoxin BI-induced oxidative stress in cultured rat hepatocytes. Free Radic Res 6, 559-68; Barton, Hill, Yee, Barton, Ganey and Roth (2000). Bacterial lipopolysaccharide exposure augments aflatoxin B(1)-induced liver injury. Toxicol Sci 2, 444-52).


[0046] Amineptine, amiodarone and perhexiline are drugs known to cause microvesicular steatosis through the inhibition of mitochondrial beta oxidation (Le Dinh, Freneaux, Labbe, Letteron, Degott, Geneve, Berson, Larrey and Pessayre (1988). Amineptine, a tricyclic antidepressant, inhibits the mitochondrial oxidation of fatty acids and produces microvesicular steatosis of the liver in mice. J Pharmacol Exp Ther 2, 745-50; Bach, Schultz, Cohen, Squire, Gordon, Thung and Schaffner (1989). Amiodarone hepatotoxicity: progression from steatosis to cirrhosis. Mt Sinai J Med 4, 293-6; Deschamps, DeBeco, Fisch, Fromenty, Guillouzo and Pessayre (1994). Inhibition by perhexiline of oxidative phosphorylation and the beta-oxidation of fatty acids: possible role in pseudoalcoholic liver lesions. Hepatology 4, 948-61; Fromenty and Pessayre (1997). Impaired mitochondrial function in microvesicular steatosis. Effects of drugs, ethanol, hormones and cytokines. J Hepatol Suppl 2, 43-53).


[0047] In the present invention it was found that the modulation of gene expression by several compounds that show a similar hepatotoxicity defines a characteristic profile which is expected to be similar for further compounds that elicit the same type of toxicity. Thus, these profiles can be used for the prediction of the toxic potential of unknown compounds. Said characteristic profiles (or “fingerprints) for classes of hepatotoxins are defined in Table 3.


[0048] Accordingly, the present invention relates to a method of predicting at least one toxic effect of a compound, comprising detecting the level of expression of one or more genes from Table 3 in a tissue or cell sample exposed to the compound, wherein differential expression of the genes in Table 3 is indicative of at least one toxic effect.


[0049] The present invention moreover provides a method of predicting at least one toxic effect of a compound, comprising:


[0050] (a) detecting the level of expression of one or more genes from Table 3 in a tissue or cell sample exposed to the compound;


[0051] (b) comparing the level of expression of the genes to their level of expression in a control tissue or cell sample, wherein differential expression of the genes in Table 3 is indicative of at least one toxic effect.


[0052] In a further embodiment, the present invention relates to a method of predicting the progression of a toxic effect of a compound, comprising detecting the level of expression in a tissue or cell sample exposed to the compound of one or more genes from Table 3, wherein differential expression of the genes in Table 3 is indicative of toxicity progression.


[0053] As defined in the present invention, a toxic effect includes any adverse effect on the physiological status of a cell or an organism. The effect includes changes at the molecular or cellular level. A preferred toxic effect is hepatotoxicity, which includes pathologies comprising among others liver necrosis, hepatitis, fatty liver and cholestasis.


[0054] The progression of a toxic effect is defined as the histological, functional or physiological manifestation with time of a toxic injury that can be detected by measuring the gene expression levels found after initial exposure of an animal or cell to a drug, drug candidate, toxin, pollutant etc.


[0055] In general, a method to predict a toxic effect of a compound or a composition of compounds comprises the steps of exposing a model animal or a cell culture to the compound or composition of compounds, detecting or measuring the differential expression (mRNA, protein-content, etc) of one or more genes from Table 3 in a biological sample of said model animal or said cell culture compared to a control, and comparing the determined differential expression to the differential expression disclosed in Table 3.


[0056] In the context of the present invention, the term “expression level” comprises, inter alia, the gene expression levels defined as RNA-levels, i.e. the amount or quality of RNA, mRNA, and the corresponding cDNA-levels; and the protein expression levels.


[0057] The term “differential gene expression” in accordance with this invention relates to the up- or down-regualtion of genes in tissues or cells derived from treated animals/cell cultures in comparison to control animals/cell cultures. These genes, which are differentially expressed, are also refered to as marker genes. Furthermore, it is envisaged that said comparison is carried out in a computer-assisted fashion. Said comparison may also comprise the analysis in high-throughput screens.


[0058] Most preferably, an increase or decrease of the expression level in (a) marker gene(s) as listed in Table 3 and as detected by the inventive method is indicative of hepatotoxic liability. It is also preferred that in addition to the said marker genes, the gene expression profile as depicted in Table 3 will also be analyzed in order to categorize hepatotoxic liability of the test compound(s).


[0059] It is also envisaged that the method of the invention comprises the comparison of differentially expressed marker genes, i.e. marker genes which are up or downregulated in tissues, cells, body fluids etc, from biological samples after exposure to model compounds (as exemplified in Tables 1 and 2), with markers which are not changed, i.e. which are not diagnostic for hepatotoxicity. Such unchanged marker genes comprise, inter alia, the ribosomal RNA control as employed in the appended examples, as well as house-keeping genes (N° 10 as depicted in Table 4).


[0060] The detection and/or measurement of the expression levels of the genes from Table 3 according to the methods of the present invention may comprise the detection of an increase, decrease and/or the absence of a specific nucleic acid molecule, for example mRNA or cDNA.


[0061] Methods for the detection/measurement of mRNA and or cDNA levels are well known in the art and comprise methods as described in the appended examples, but are not limited to microarray- and PCR-technology.


[0062] In addition, protein expression levels from marker genes as listed in Table 4 and of some genes in Table 3 can also be assessed. Methods for the detection/measurement of protein levels are well known in the art and include, but are not limited to Western-blot, two-dimensional electrophoresis, ELISA, RIA, immunohistochemistry, etc.


[0063] Additional assay formats may be used to monitor the induced change of the expression level of a gene identified in Table 3. For instance, mRNA expression may be monitored directly by hybridization of probes to the nucleic acids of the invention. Cell lines are exposed to the agent to be tested under appropriate conditions and time and total RNA or mRNA is isolated by standard procedures such as those disclosed in Sambrook et al (Molecular Cloning: A Laboratory 30 Manual, 2nd Ed. Cold Spring Harbor Laboratory Press, 1989).


[0064] Any assay format to detect gene expression may be used. For example, traditional Northern blotting, dot or slot blot, nuclease protection, primer directed amplification, RT-PCR, semi- or quantitative PCR, branched-chain DNA and differential display methods may be used for detecting gene expression levels. Those methods are useful for some embodiments of the invention. In cases where smaller numbers of genes are detected, amplification-based assays may be most efficient. Methods and assays of the invention, however, may be most efficiently designed with hybridization-based methods for detecting the expression of a large number of genes. Any hybridization assay format may be used, including solution-based and solid support-based assay formats.


[0065] In another assay format, cell lines that contain reporter gene fusions between the open reading frame and/or the transcriptional regulatory regions of a gene in Table 3 and any assayable fusion partner may be prepared. Numerous assayable fusion partners are known and readily available including the firefly luciferase gene and the gene encoding chloramphenicol acetyltransferase (Alam et al. (1990) Anal. Biochem. 188, 245-254). Cell lines containing the reporter gene fusions are then exposed to the compound to be tested under appropriate conditions and time. Differential expression of the reporter gene between samples exposed to the compound and control samples identifies compounds which modulate the expression of the nucleic acid.


[0066] Preferably in the method of the present invention, the expression of at least one gene as listed in Table 3 is detected/measured. Yet, it is also envisaged that the expression of at least two, at least three, at least five, at least ten, at least twenty, at least thirty, at least forty, at least fifty, at least one hundred genes as listed in Table 3 are detected/measured. Moreover, it is envisaged that the expression of nearly all genes from Table 3 or of all genes from Table 3 is detected. It is furthermore envisaged that specific patterns of differentially expressed marker genes as depicted in Table 3 are detected, measured and/or compared.


[0067] The above mentioned animal model to be employed in the methods of the present invention and comprising and/or expressing a maker gene as defined herein is a non-human animal, preferably a mammal, most preferably mice, rats, sheep, calves, dogs, monkeys or apes. Most preferred are rodent models such as rats and mice. The animal model also comprises non-human transgenic animals, which preferably express at least one toxicity marker gene as disclosed in Table 3.


[0068] Yet it is also envisaged that non-human transgenic animals be produced which do not express marker genes as disclosed in Table 3 or which over-express said marker genes.


[0069] Transgenic non-human animals comprising and/or expressing the up-regulated marker genes of the present invention or, in contrast which comprise silenced or less efficient versions of down-regulated marker genes for hepatotoxicity, as well as cells derived thereof, are useful models for studying hepatotoxicity mechanisms.


[0070] Accordingly, said transgenic animal model may be transfected or transformed with the vector comprising a nucleic acid molecule coding for a marker gene as disclosed in Table 3. Said animal model may therefore be genetically modified with a nucleic acid molecule encoding such a marker gene or with a vector comprising such a nucleic acid molecule. The term “genetically modified” means that the animal model comprises in addition to its natural genome a nucleic acid molecule or vector as defined herein and coding for a toxicity marker of Table 3 or at least a fragment thereof. Said additional genetic material may be introduced into the animal model or into one of its predecessors/parents. The nucleic acid molecule or vector may be present in the genetically modified animal model or cell either as an independent molecule outside the genome, preferably as a molecule which is capable of replication, or it may be stably integrated into the genome of the animal model or cell thereof.


[0071] As mentioned herein above, the method of the present invention may also employ a cell culture. Preferred are cultures of primary animal cells or cell lines. Suitable animal cells are, for instance, primary mammalian hepatocytes; insect cells, vertebrate cells, preferably mammalian cell lines, such as e.g. CHO, HeLa, NIH3T3 or MOLT-4. Further suitable cell lines known in the art are obtainable from cell line depositories, like the American Type Culture Collection (ATCC). Most preferred are primary hepatocyte cultures or hepatic cell lines comprising rodent or human primary hepatocyte cultures including monolayer, sandwich cultures and slices cultures; as well as rodent cell lines such as BRL3, NRL clone9, and human cell lines such as HepG2 cells.


[0072] Cells or cell lines used in the method of the present invention may be transfected or transformed with a vector comprising a nucleic acid molecule coding for a marker gene as disclosed in Table 3. Said cell or cell line may therefore be genetically modified with a nucleic acid molecule encoding such a marker gene or with a vector comprising such a nucleic acid molecule. The term “genetically modified” means that the cell comprises in addition to its natural genome a nucleic acid molecule or vector as defined herein and coding for a toxicity marker of Table 3 or at least a fragment thereof. The nucleic acid molecule or vector may be present in the genetically modified cell either as an independent molecule outside the genome, preferably as a molecule which is capable of replication, or it may be stably integrated into the genome of the cell.


[0073] In accordance with the present invention, the term “biological sample” or “sample” as employed herein means a sample which comprises material wherein said differential expression of marker genes may be measured and may be obtained. “Samples” may be tissue samples derived from tissues of non-human animals, as well as cell samples, derived from cells of non-human animals or from cell cultures. For animal experimentation, biological samples comprise target organ tissues obtained after necropsy or biopsy and body fluids, such as blood or urine. For possible clinical use of the markers, particular preferred samples comprise body fluids, like blood, sera, plasma, urine, synovial fluid, spinal fluid, cerebrospinal fluid, semen or lymph, as well as body tissues obtained by biopsy. Particularly documented in the appended examples are rat liver tissues and primary hepatocyte cultures. Peripheral blood samples were also obtained to analyze circulating liver enzymes.


[0074] The cell population that is exposed to the compound or composition may be exposed in vitro or in vivo. For instance, cultured or freshly isolated hepatocytes, in particular rat hepatocytes, may be exposed to the compound under standard laboratory and cell culture conditions. In another assay format, in vivo exposure may be accomplished by administration of the compound to a living animal, for instance a laboratory rat. Procedures for designing and conducting toxicity tests in in vitro and in vivo systems are well known, and are described in many texts on the subject, such as Loomis et al. (Loomis's Esstentials of Toxicology, 4th Ed. Academic Press, New York, 1996; Echobichon, The Basics of Toxicity Testing, CRC Press, Boca Raton, 1992; Frazier, editor, In Vitro Toxicity Testing, Marcel Dekker, New York, 1992) and the like. In in vitro toxicity testing, two groups of test organisms are usually employed: One group serves as a control and the other group receives the test compound in a single dose (for acute toxicity tests) or a regimen of doses (for prolonged or chronic toxicity tests). Since in some cases, the extraction of tissue as called for in the methods of the invention requires sacrificing the test animal, both the control group and the group receiving the compound must be large enough to permit removal of animals for sampling tissues, if it is desired to observe the dynamics of gene expression through the duration of an experiment. In setting up a toxicity study, extensive guidance is provided in the literature for selecting the appropriate test organism for the compound being tested, route of administration, dose ranges, and the like. Water or physiological saline (0.9% NaCl in water) is the solute of choice for the test compound since these solvents permit administration by a variety of routes. When this is not possible because of solubility limitations, vegetable oils such as corn oil or organic solvents such as propylene glycol may be used.


[0075] A method of predicting the mechanism of toxicity of a compound comprising detecting the level of expression in a tissue or cell sample exposed to the compound of one or more genes from Table 3 is also provided, wherein differential expression of the genes in Table 3 is associated with a specific mechanism of toxicity.


[0076] By “mechanism of toxicity” it is meant the measurable manifestation of the toxic event, regarding target organ, time of onset, underlying molecular mechanism (i.e. DNA-damage, formation of protein adduct, etc) histopathological and biochemical findings such as circulating liver enzymes. Gene expression profiles can also be characteristic of a toxicity mechanism.


[0077] Different mechanisms of toxicity are known for hepatotoxins. Direct acting compounds are those compounds that cause damage to macromolecules, in particular proteins and lipids by directly interacting with them. This interaction could occur through the test compound itself or, more commonly, through a highly reactive metabolite thereof. Histological manifestations of these class of hepatoxicity include hepatocellular necrosis, lipid peroxidation and elevation of circulating levels of enzymes of hepatic origin such as ALT (alanine aminotransferase). Inflammation can also be observed due to the activation of the hepatic Kupffer cells. Steatotic compounds are those that cause an accummulation of fat in the liver. There are tvo types of steatosis: macrovesicular steatosis and microvesicular steatosis. All the test compounds used in this invention belong to the latter type. Characteristic of microvesicular steatosis is the accumulation of small lipid vesicles in the hepatocytes (so-called fatty liver), which usually lead to accute liver failure. The underlying molecular mechanisms are thought to be an inhibition of mitochondrial beta oxidation (due to mitochondrial damage) and/or an inhibition of the export of fatty acids from the hepatocyte. Compounds leading to cholestasis impair the bile flow, causing the clinical manifestation of jaundice. Intrahepatic cholestasis involves usually the inhibition of the bile acid transporters in the hepatocytes, leading to an accummulation of bile acids. Increased bile acids are responsible for slight hepatocyte injury, little inflammation and the elevation of circulating alkaline phosphatase (G. L. Plaa and W. R. Hewitt Ed. “Toxicology of the liver, 2nd Ed., Target organ toxicology series, 1997; Fromenty and Pessayre (1995). Inhibition of mitochondrial beta-oxidation as a mechanism of hepatotoxicity. Pharmacol Ther 1, 101-54; Jaeschke, Gores, Cederbaum, Hinson, Pessayre and Lemasters (2002). Mechanisms of hepatotoxicity. Toxicol Sci 2, 166-76).


[0078] Detection of toxic potential as identified and/or obtained by the methods of the present invention are particularly useful in the development of new drugs in terms of safety.


[0079] Moreover, a method of predicting at least one toxic effect of a compound, comprising detecting the level of expression of progression elevated gene 3 (PEG-3) or Translocon associated protein (TRAP) from Table 4 in a tissue or cell sample exposed to the compound is provided, wherein differential expression of PEG-3 and TRAP is indicative of at least one toxic effect. The preferred toxic effect of the compound in the present method is hepatotoxicity.


[0080] PEG-3 belongs to the family of GADD-45 and GADD-153, which are genes up-regulated upon DNA-damage. While GADD-genes are known stress-inducible markers that lead to a cell cycle arrest (Seth A, Giunta S, Franceschil C, Kola I, Venanzoni MC (1999). Regulation of the human stress response gene GADD153 expression: role of ETS1 and FLI-1 gene products. Cell Death Differ 6(9), 902-7; Tchounwou PB, Wilson BA, Ishaque AB, Schneider J. Atrazine potentiation of arsenic trioxide-induced cytotoxicity and gene expression in human liver carcinoma cells (HepG2). Mol Cell Biochem. 222, 49-59; Tchounwou PB, Ishaque AB, Schneider J (2001). Cytotoxicity and transcriptional activation of stress genes in human liver carcinoma cells (HepG2) exposed to cadmium chloride. Mol Cell Biochem. 222, 21-8; Tchounwou PB, Wilson BA, Ishaque AB, Schneider J (2001). Transcriptional activation of stress genes and cytotoxicity in human liver carcinoma cells (HepG2) exposed to 2,4,6-trinitrotoluene, 2,4-dinitrotoluene, and 2,6-dinitrotoluene. Environ Toxicol. 16, 209-16.; Zhan Q, Fan S, Smith ML, Bae I, Yu K, Alamo I Jr, O'Connor PM, Fornace AJ Jr (1996). Abrogation of p53 function affects gadd gene responses to DNA base-damaging agents and starvation. DNA Cell Biol 15, 805-15), PEG-3 is involved in progression (Park JS, Qiao L, Su ZZ, Hinman D, Willoughby K, McKinstry R, Yacoub A, Duigou GJ, Young CS, Grant S, Hagan MP, Ellis E, Fisher PB, Dent P (2001). Ionizing radiation modulates vascular endothelial growth factor (VEGF) expression through multiple mitogen activated protein kinase dependent pathways. Oncogene 20, 3266-80.; Su ZZ, Goldstein NI, Jiang H, Wang Minn., Duigou GJ, Young CS, Fisher PB (1999). PEG-3, a nontransforming cancer progression gene, is a positive regulator of cancer aggressiveness and angiogenesis. Proc Natl Acad Sci U S A. 96, 15115-20; Su Z, Shi Y, Friedman R, Qiao L, McKinstry R, Hinman D, Dent P, Fisher PB (2001). PEA3 sites within the progression elevated gene-3 (PEG-3) promoter and mitogen-activated protein kinase contribute to differentialPEG-3 expression in Ha-ras and v-raf oncogene transformed rat embryo cells. Nucleic Acids Res 29, 1661-71; Su, Z. Z., Shi, Y., and Fisher, P. B. (1997). Subtraction hybridization identifies a transformation progression associated gene PEG-3 with sequence homology to a growth arrest and DNA damage-inducible gene. Proc Natl Acad Sci U S A 94, 9125-30). The results of the present invention show that the up-regulation of PEG-3 seems to be triggered earlier than that of GADDs, so that it is a possible early marker for cell damage.


[0081] TRAP proteins are part of a complex whose function is to bind Ca2+ to the membrane of the endoplasmic reticulum (ER) and regulate thereby the retention of ER resident proteins (Hartmann E, Gorlich D, Kostka S, Otto A, Kraft R, Knespel S, Burger E, Rapoport TA, Prehn S (1993). A tetrameric complex of membrane proteins in the endoplasmic reticulum. Eur J Biochem. 214, 375-81).


[0082] Compounds used in the method of the present invention may be unknown compounds or compounds which are known to elicit a toxic effect in an organism.


[0083] Compounds in accordance with the method of the present invention include, inter alia, peptides, proteins, nucleic acids including DNA, RNA, RNAi, PNA, ribozymes, antibodies, small organic compounds, small molecules, ligands, and the like.


[0084] The compounds whose toxic effect is to be predicted with the method(s) of the present invention do not only comprise single, isolated compounds. It is also envisaged that mixtures of compounds are screened with the method of the present invention. It is also possible to employ natural products and extracts, like, inter alia, cellular extracts from prokaryotic or eukaryotic cells or organisms.


[0085] In addition, the compound identified by the inventive method as having low toxic effect can be employed as a lead compound to achieve modified site of action, spectrum of activity and/or organ specificity, and/or improved potency, and/or decreased toxicity (improved therapeutic index), and/or decreased side effects, and/or modified onset of therapeutic action, duration of effect, and/or modified pharmakinetic parameters (resorption, distribution, metabolism and excretion), and/or modified physico-chemical parameters (solubility, hygroscopicity, color, taste, odor, stability, state), and/or improved general specificity, organ/tissue specificity, and/or optimized application form and route, and may be modified by esterification of carboxyl groups, or esterification of hydroxyl groups with carbon acids, or esterification of hydroxyl groups to, e.g. phosphates, pyrophosphates or sulfates or hemi succinates, or formation of pharmaceutically acceptable salts, or formation of pharmaceutically acceptable complexes, or synthesis of pharmacologically active polymers, or introduction of hydrophylic moieties, or introduction/exchange of substituents on aromates or side chains, change of substituent pattern, or modification by introduction of isosteric or bioisosteric moieties, or synthesis of homologous compounds, or introduction of branched side chains, or conversion of alkyl substituents to cyclic analogues, or derivatisation of hydroxyl group to ketales, acetales, or N-acetylation to amides, phenylcarbamates, or synthesis of Mannich bases, imines, or transformation of ketones or aldehydes to Schiffs bases, oximes, acetales, ketales, enolesters, oxazolidines, thiozolidines or combinations thereof.


[0086] In another embodiment, the present invention provides for a set of nucleic acid primers, wherein the primers specifically amplify at least two of the genes from Table 3. The set of nucleic acid primers may also specifically amplify at least 5, at least 10, at least 20, at least 30 of the genes from Table 3. The set of nucleic acid primers may also specifically amplify nearly all or all of the genes from Table 3.


[0087] Moreover, the present invention provides for a set of nucleic acid probes, wherein the probes comprise sequences which hybridize to at least two of the genes from Table 3. The set of nucleic acid probes may comprise sequences which hybridize to at least 5, at least 10, at least 20, at least 30 of the genes from Table 3. The set of nucleic acid probes may also comprise sequences which hybridize to nearly all or all of the genes from Table 3.


[0088] In a further embodiment, the set of probes may be attached to a solid support. A solid support comprising at least two probes, wherein each of the probes comprises a sequence that specifically hybridizes to a gene in Table 3 is also provided. The solid support may also comprise at least 5 probes, at least 10, at least 20, at least 30 probes. The solid support may also comprise all or nearly all probes, wherein each of the probes comprises a sequence that specifically hybridizes to a gene in Table 3.


[0089] Solid supports containing oligonucleotide or cDNA probes for differentially expressed genes of the invention can be filters, polyvinyl chloride dishes, particles, beads, microparticles or silicon or glass based chips, etc. Such chips, wafers and hybridization methods are widely available, for example, those disclosed in WO95/11755. Any solid surface to which a nucleotide sequence can be bound, either directly or indirectly, either covalently or non-covalently, can be used. A preferred solid support is a DNA chip. These contain a particular probe in a predetermined location on the chip. Each predetermined location may contain more than one molecule of the probe, but each molecule within the predetermined location has an identical sequence. Such predetermined locations are termed features. There may be, for example, from 2, 10, 100, 1000 to 10000, 100000 or 400000 of such features on a single solid support. The solid support, or the area within which the probes are attached may be on the order of about a square centimeter.


[0090] Probes corresponding to the genes of Table 3 may be attached to single or multiple solid support structures, e.g., the probes may be attached to a single chip or to multiple chips to comprise a chip set. Probe arrays for expression monitoring can be made and used according to any techniques known in the art (see for example, Lockhart et al., Nat. Biotechnol. (1996) 14, 1675-1680; McGall et al., Proc. Nat. Acad. Sci. USA (1996) 93, 13555-60). Such probe arrays may contain at least two or more probes that are complementary to or hybridize to two or more of the genes described in Table 3. For instance, such arrays may contain probes that are complementary or hybridize to at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 50, 70, 100 or more of the genes described herein. Preferred arrays contain probes for all or nearly all of the genes listed in Table 3. In a preferred embodiment, arrays are constructed that contain probes to detect all or nearly all of the genes of Table 3 on a single solid support substrate, such as a chip. The sequences of the expression marker genes of Table 3 are available in public databases and their GenBank Accession Number is provided (see www.rzcbi.nlm.nih.gov/). These sequences may be used in the methods of the invention or may be used to produce the probes and arrays of the invention. As described above, in addition to the sequences of the GenBank Accessions Numbers disclosed in Table 3, sequences such as naturally occurring variant or polymorphic sequences may be used in the methods and compositions of the invention. For instance, expression levels of various allelic or homologous forms of a gene disclosed in the Table 3 may be assayed. Any and all nucleotide variations that do not alter the functional activity of a gene listed in Table 3, including all naturally occurring allelic variants of the genes herein disclosed, may be used in the methods and to make the compositions (e.g., arrays) of the invention.


[0091] Probes based on the sequences of the genes described above may be prepared by any commonly available method. “Probe” refers to a hybridizable nucleotide sequence that can be attached to a solid support or used in a liquid form. As used herein a “probe” is defined as a nucleic acid sequence, capable of binding to a target nucleic acid of complementary sequence through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation. As used herein, a probe may include natural (i.e., A, G, U, C, or T) or modified bases (7-deazaguanosine, inosine, etc.). In addition, the bases in probes may be joined by a linkage other than a phosphodiester bond, so long as it does not interfere with hybridization. Thus, probes may be peptide nucleic acids in which the constituent bases are joined by peptide bonds rather than phosphodiester linkages. Said probes are specific oligonucleotides or cDNA-fragments. Oligonucleotide probes or cDNAs for screening or assaying a tissue or cell sample are preferably of sufficient length to specifically hybridize only to appropriate, complementary genes or transcripts. Typically the oligonucleotide probes will be at least 10, 12, 14, 16, 18, 20 or 25 nucleotides in length. In some cases, longer probes of at least 30, 40, or 50 nucleotides will be desirable. Typically, the cDNA probes will be between 300 and 1000 nucleotides in length. As used herein, oligonucleotide sequences that are complementary to one or more of the genes described in Table 3 refer to probes that are capable of hybridizing under stringent conditions to at least part of the nucleotide sequences of said genes. Such hybridizable probes will typically exhibit at least about 75% sequence identity at the nucleotide level to said genes, preferably about 80% or 85% sequence identity or more preferably about 90% or 95% or more sequence identity to said genes. The phrase “hybridizing specifically to” refers to the binding, duplexing, or hybridizing of a molecule substantially to or only to a particular nucleotide sequence or sequences under stringent conditions when that sequence is present in a complex mixture (e.g., total cellular) DNA or RNA. Assays and methods of the invention may utilize available formats to simultaneously screen at least 2, preferably about tens to thousends different nucleic acid hybridizations. The terms “background” or “background signal intensity” refer to hybridization signals resulting from non-specific binding, or other interactions, between the labeled target nucleic acids and components of the oligonucleotide array (e.g., the probes, control probes, the array substrate, etc.). Background signals may also be produced by intrinsic fluorescence of the array components themselves. A single background signal can be calculated for the entire array, or a different background signal may be calculated for each target nucleic acid. One of skill in the art will appreciate that where the probes to a particular gene hybridize well and thus appear to be specifically binding to a target sequence, they should not be used in a background signal calculation. Background can also be calculated as the average signal intensity produced by regions of the array that lack any probes at all.


[0092] One of skill in the art will appreciate that an enormous number of array designs are suitable for the practice of this invention. The array will typically include a number of test probes, at least 2, preferably tens to thousends that specifically hybridize to the sequences of interest. Probes may be produced from any region of the identified genes. In instances where the gene reference in the Tables is an EST, probes may be designed from that sequence or from other regions of the corresponding full-length transcript that may be available in any of the sequence databases, such as those herein described. Any available software may be used to produce specific probe sequences, including, for instance, software available from Applied Biosystems (Primer Express). The said probes may be attached to the solid support by a variety of methods, including among others synthesis onto the glass and spotting of a specified amount of cDNA onto the support. In addition to test probes that bind the target nucleic acid(s) of interest, the arrays can contain a number of control probes. The control probes may fall into three categories referred to herein as 1) normalization controls; 2) expression level controls; and 3) unspecific binding controls. Normalization controls are probes that are complementary to labeled reference oligonucleotides or other nucleic acid sequences that are added to the nucleic acid sample to be screened. The signals obtained from the normalization controls after hybridization provide a control for variations in hybridization conditions, label intensity, “reading” efficiency and other factors that may cause the signal of a perfect hybridization to vary between arrays. Signals read from all other probes in the array may be divided by the signal (e.g., fluorescence intensity) from the control probes thereby normalizing the measurements. Virtually any probe may serve as a normalization control. However, it is recognized that hybridization efficiency varies with base composition and probe length. Preferred normalization probes are selected to reflect the average length of the other probes present in the array. Expression level controls are probes that hybridize specifically with constitutively expressed genes in the biological sample. Virtually any constitutively expressed gene provides a suitable target for expression level controls. Typically expression level control probes have sequences complementary to subsequences of constitutively expressed “housekeeping genes” including, but not limited to the actin gene, the transferrin receptor gene, the GAPDH gene, and the like. Unspecific binding controls can be but are not limited to DNA from other species (i.e. Hering Sperm DNA) that should not hybridize with the target sequences or mismatched sequences. Mismatched sequences are oligonucleotide probes or other nucleic acid probes identical to their corresponding test or control probes except for the presence of one or more mismatched bases. A mismatched base is a base selected so that it is not complementary to the corresponding base in the target sequence to which the probe would otherwise specifically hybridize. One or more mismatches are selected such that under appropriate hybridization conditions (e.g. stringent conditions) the test or control probe would be expected to hybridize with its target sequence, but the mismatch probe would not hybridize (or would hybridize to a significantly lesser extent). Unspecific binding controls thus provide a control for non-specific binding or cross hybridization to a nucleic acid in the sample other than the target to which the probe is directed.


[0093] Cell or tissue samples may be exposed to the test compound in vitro or in vivo. When cultured cells or tissues are used, appropriate mammalian liver extracts may also be added with the test agent to evaluate compound s that may require biotransformation to exhibit toxicity. In a preferred format, primary isolates of animal or human hepatocytes which already express the appropriate complement of drug-metabolizing enzymes may be exposed to the test compound without the addition of mammalian liver extracts. The genes which are assayed according to the present invention are typically in the form of mRNA or reverse transcribed mRNA. The genes may be cloned or not. The genes may be amplified or not. The cloning and/or amplification do not appear to bias the representation of genes within a population. In some assays, it may be preferable, however, to use polyA+RNA as a source, as it can be used with less processing steps. As is apparent to one of ordinary skill in the art, nucleic acid samples used in the methods and assays of the invention may be prepared by any available method or process. Methods of isolating total mRNA are well known to those of skill in the art. For example, methods of isolation and purification of nucleic acids are described in detail in Chapter 3 of Laboratory Techniques in Biochemistry and Molecular Biology: Hybridization With Nucleic Acid Probes, Part I Theory and Nucleic Acid Preparation, P. Tijssen, Ed., Elsevier, N. Y. (1993). Such samples include RNA samples, but also include cDNA synthesized from a mRNA sample isolated from a cell or tissue of interest. Such samples also include DNA amplified from the cDNA, and RNA transcribed from the amplified DNA. One of skill in the art would appreciate that it is desirable to inhibit or destroy RNase present in homogenates before homogenates are used. Biological samples may be of any biological tissue or fluid or cells from any organism as well as cells raised in vitro, such as cell lines and tissue culture cells. Frequently the sample will be a tissue or cell sample that has been exposed to a compound, agent, drug, pharmaceutical composition, potential environmental pollutant or other composition, In some formats, the sample will be a “clinical sample” which is a sample derived from a patient. Typical clinical samples include, but are not limited to, sputum, blood, blood-cells (e.g. white cells), tissue or fine needle biopsy samples, urine, peritoneal fluid, and pleural fluid, or cells therefrom. Biological samples may also include sections of tissues, such as frozen sections or formalin fixed sections taken for histological purposes.


[0094] Nucleic acid hybridization simply involves contacting a probe and target nucleic acid under conditions where the probe and its complementary target can form stable hybrid duplexes through complementary base pairing (See WO99/32660). The nucleic acids that do not form hybrid duplexes are then washed away leaving the hybridized nucleic acids to be detected, typically through detection of an attached detectable label. It is generally recognized that nucleic acids are denatured by increasing the temperature or decreasing the salt concentration of the buffer containing the nucleic acids. The term “stringent conditions” refers to conditions under which a probe will hybridize to its target sequence, but with only insubstantial hybridization to other sequences. Stringent conditions are sequence-dependent and will be different in different circumstances. Longer sequences hybridize specifically at higher temperatures. Generally, stringent conditions are selected to be about 5° C. lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength and pH. Stringent conditions may also be achieved with the addition of destabilizing agents such as formamide. Under low stringency conditions (e.g. low temperature and/or high salt) hybrid duplexes (e.g. DNA:DNA, RNA:RNA, or RNA:DNA) will form even where the annealed sequences are not perfectly complementary. Thus, specificity of hybridization is reduced at lower stringency. Conversely, at higher stringency (e.g. higher temperature or lower salt) successful hybridization tolerates fewer mismatches. One of skill in the art will appreciate that hybridization conditions may be selected to provide any degree of stringency. In a preferred embodiment, hybridization is performed at low stringency, to ensure hybridization and then subsequent washes are performed at higher stringency to eliminate mismatched hybrid duplexes. Successive washes may be performed at increasingly higher stringency until a desired level of hybridization specificity is obtained. Stringency can also be increased by addition of agents such as formamide. Hybridization specificity may be evaluated by comparison of hybridization to the test probes with hybridization to the various controls that can be present (e.g., expression level control, normalization control, mismatch controls, etc.). In general, there is a tradeoff between hybridization specificity (stringency) and signal intensity. Thus, in a preferred embodiment, the wash is performed at the highest stringency that produces consistent results and that provides a signal intensity greater than approximately 10% of the background intensity. Thus, in a preferred embodiment, the hybridized array may be washed at successively higher stringency solutions and read between each wash. Analysis of the data sets thus produced will reveal a wash stringency above which the hybridization pattern is not appreciably altered and which provides adequate signal for the particular probes of interest.


[0095] The hybridized nucleic acids are typically detected by detecting one or more labels attached to the sample nucleic acids. The labels may be incorporated by any of a number of means well known to those of skill in the art (see WO99/32660).


[0096] The present invention includes databases containing DNA sequence information as well as gene expression information from tissue or cells exposed to various standard toxins, such as those herein described (see Tables 1-2). The Toxicogenomics database is supported by in-house developed software (RACE-R, F. Hoffmann-La Roche AG, Basle, Switzerland) which allows the storage, analysis and comparison of absolute (intensity) and relative (fold-induction) gene expression data obtained by a variety of methods such as the aforementioned Affymetrix high density arrays, low density spotted arrays, PCR, etc. This database allows also for the incorporation of additional data such as sample description, biochemical parameters, histological evaluation, etc. Additional databases may also contain information associated with a given DNA sequence or tissue sample such as descriptive information about the gene associated with the sequence information (see Table 3), or descriptive information concerning the clinical status of the tissue sample, or the animal from which the sample was derived. The database may allow the use of algorithms (i.e. Toxicology Model Matcher, F. Hoffmann-La Roche AG, Basle, Switzerland) for the extensive comparison of gene expression profiles between known or unknown test compounds and compounds which are already in the database as listed in Tables 1 and 2. Methods for the configuration and construction of such databases are widely available, for instance, see U.S. Pat. No. 5,953,727. The databases of the invention may be linked to an outside or external database such as GenBank (www.ncbi.nlm.nih.gov/entrez.index.html); KEGG 25 (www.genome.adjp/kegg); SPAD (www.grt.kyushu-u.acjp/spad/index.html); HUGO (www.gene.ucl.ac.uk/hugo); Swiss-Prot (www.expasy.ch.sprot); Prosite (www. expasy. ch/tools/scnpsitl. h&d); OMIM (www. ncbi.nlm.nih.gov/omim); GDB (www.gdb.org); and GeneCard (bioinformatics.weizmann.ac.il/cards). In a preferred embodiment, as described in Tables 3, 7 and 8, the external database is GenBank and the associated databases maintained by the National Center for Biotechnology Information (NCBI) (www.ncbi.nlm.nih.gov). Any appropriate computer platform may be used to perform the necessary comparisons between sequence information, gene expression information and any other information in the database or information provided as an input. For example, a large number of computer workstations are available from a variety of manufacturers. Client/server environments, database servers and networks are also widely available and appropriate platforms for the databases of the invention. The databases of the invention may be used to determine the cell type or tissue in which a given gene is expressed and to allow determination of the abundance or expression level of a given gene in a particular tissue or cell. The databases of the invention may also be used to present information identifying the expression level in a tissue or cell of a set of genes comprising one or more of the genes in Table 3, comprising the step of comparing the expression level of at least one gene in Table 3 in a cell or tissue exposed to a test compound to the level of expression of the gene in the database. Such methods may be used to predict the toxic potential of a given compound by comparing the level of expression of a gene or genes in Table 3 from a tissue or cell sample exposed to the test compound to the expression levels found in a control tissue or cell samples exposed to a standard toxin or hepatotoxin such as those herein described.


[0097] The gene expression data generated by the methods of the present invention may be analysed by various methods known in the art, including but not limited to hierarchical clustering, self-organizing maps and support vector machines. Support Vector Machines (SVMs), a class of supervised learning algorithms originally introduced by Vapnik and co-workers, have already been shown to perform well in multiple areas of biological analysis (Boser, B. E., Guyon, I. M., Vapnik, V. N. (1992) A training algorithm for optimal margin classifiers. In Proceedings of the 4th Annual International Conference on Computational Learning Theory, ACM Press, Pittsburgh, Pa., 144-152; Vapnik, V. N. (1998) Statistical Learning Theory. Wiley, New York; Scholkopf, B., Guyon, I. M., Weston, J. (2002) Statistical Learning and Kernel Methods in Bioinformatics. In Proceedings NATO Advanced Studies Inst. on Artificial Intelligence and Heuristics Methods for Bioinformatics, San Miniato, Italy October 1-11).


[0098] Given a set of training examples, SVMs are able to recognize informative patterns in the input data and generalize on previously unseen data. Trivial solutions, which overfit the training data, are avoided by minimizing the bound on the expected generalization error. In contrast to unsupervised methods like hierarchical clustering and self-organizing maps, the SVM approach takes advantage of prior knowledge in the form of class labels attached to the training examples. The extraordinary robustness with respect to sparse and noisy data makes SVMs the tool of choice in a growing number of applications. They are particularly well suited to analyze microarray expression data because of their ability to handle situations where the number of features (genes) is very large compared to the number of training patterns (chip replicates). It has been demonstrated in several studies that SVMs typically tend to outperform other classification techniques in this field (Brown, M. P. S., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C. W., Furey, T. S., Ares, M., Haussler, D. (2000) Knowledge-based analysis of microarray gene expression data by using support vector machines. PNAS 97, 262-267; Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., Haussler, D. (2000) Support Vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906-914; Yeang, C., Ramaswamy, S., Tamayo, P., Mukherjee, S., Rifkin, R. M., Angelo, M., Reich, M., Lander, E., Mesirov, J., Golub, T. (2001) Molecular classification of multiple tumor types. Bioinformatics 17, 316-322). In addition, the method proved effective in discovering informative features such as genes which are especially relevant for the classification and therefore might be critically important for the biological processes under investigation (Guyon, I. M., Weston, J., Barnhill, S., Vapnik, V. N. (2002) Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning 46, 389-442).


[0099] The SVM approach can be used to generate classifiers for discrimination of a specific toxicant class from all other classes, but also to generate discriminators to distinguish between a specific toxicant and controls can be defined. Alternatively classifiers for discrimination of toxic and non-toxic compounds can be constructed. These classifiers are useful to predict toxicity as well as for identification of a specific toxicity mechanism.


[0100] Recursive feature elimination (RFE) allows identifying genes that contribute to the greatest extent to classification. In each iteration, a certain fraction of genes is removed from the training procedure, selected by the corresponding weights in the decision function. The least important genes are omitted for the next iteration. During this process, quality parameters of the resulting SVM classifiers are monitored. The final choice of a best subset of genes is made on the basis of classification accuracy, model simplicity and gene count. This method makes no orthogonality assumptions about gene expression levels but implicitly takes into account correlation between the single gene expression measurements. It results in a minimized set of predictive genes by effectively removing noise and redundancy from the set of all genes on the chip. The support vector mechanism, where borderline (and not ‘typical’) training patterns play a crucial role in classifications, was shown to assist in the feature elimination process by preventing genes that are irrelevant for classification but nevertheless differentially expressed in the majority of chip samples from gaining predominant influence (Guyon, I. M., Weston, J., Barnhill, S., Vapnik, V. N. (2002) Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning 46, 389-442). Using RFE a small subset of genes is selected. This subset can subsequently be used as a diagnostic biomarker set to predict toxicity and/or the mechanism of toxicity.


[0101] The present invention therefore also provides a computer system comprising a database containing DNA sequence information and expression information of at least two of the genes from Table 3 from tissue or cells exposed to a hepatotoxin, and a user interface.


[0102] The invention further includes kits combining, in different combinations, nucleic acid primers for the amplification of the genes of Table 3, solid supports with attached probes, reagents for use with the solid supports, protein reagents encoded by the genes of Table 3, signal detection and array-processing instruments, gene expression databases and analysis and database management software described above. The kits may be used, for example, to predict or model the toxic response of a test compound, to monitor the progression of hepatic disease states, to identify genes that show promise as new drug targets and to screen known and newly designed drugs as discussed above.


[0103] The databases packaged with the kits are a compilation of expression patterns from human or laboratory animal genes and gene fragments (corresponding to the genes of Table 3). In particular, the database software and packaged information include the expression results of Table 3 that can be used to predict toxicity of a test compound. In another format, database and software information may be provided in a remote electronic format, such as a website, the address of which may be packaged in the kit.


[0104] The invention is now described by reference to the following examples and figures which are merely illustrative and are not to be construed as a limitation of scope of the present invention.


EXAMPLES

[0105] Commercially available reagents referred to in the examples were used according to manufacturer's instructions unless otherwise indicated.



Example 1


Hepatotoxicity Assay with Non-Human Animals

[0106] All animals received human care as specified by Swiss law and in accordance with the “Guide for the care and use of laboratory animals” published by the NIH. Male Wistar rats (generally 5 animals/dose-group) were purchased from BRL (Futllingsdorf, Switzerland) and housed individually. Treated animals were dosed either orally, intraperitoneally, or intravenously with several doses of test compounds (Table 1). The test compounds were categorized according to their toxic manifestation in the rat liver. Control animals received the same volume of vehicle as placebo. Necropsy was performed 6 or 24 hours after a single administration and liver samples from the left medial lobe were placed immediately in RNALater (Ambion, Tex., USA) for RNA extraction and gene expression analysis. Samples in RNALater were stored at −20° C. until further processing. Additional liver samples were snap-frozen in liquid nitrogen for measurement of intrahepatic lipids and/or proteins.



Example 2


Hepatocyte Cell Culture Assay Toxicity

[0107] Hepatocytes were isolated from adult male Wistar rats by two-step collagenase liver perfusion previously described (Goldlin C. R., Boelsterli U. A. (1991). Reactive oxygen species and non-peroxidative mechanisms of cocaine-induced cytotoxicity in rat hepatocyte cultures. Toxicology 69, 79-91). Briefly, the rats were anaesthetized with sodium pentobarbital (120 mg/kg, i.p.). The perfusate tubing was inserted via the portal vein, then the v. cava caudalis was cut, and the perfusion was started. The liver was first perfused for 5 min with a preperfusing solution consisting of calcium-free, EGTA (0.5 mM)-supplemented, HEPES (20 mM)-buffered Hank's balanced salt solution (5.36 mM KCl, 0.44 mM KH2PO4, 137 mM NaCl, 4.2 mM NaHCO3, 0.34 mM Na2HPO4, 5.55 mM D-glucose). This was followed by a 12-min perfusion with NaHCO3 (25 mM)-supplemented Hank's solution containing bovine CaCl2 (5 mM), and collagenase (0.2 U/ml). Flow rate was maintained at 28 ml/min and all solutions were kept at 37° C. After in situ perfusion the liver was excised and the liver capsule was mechanically disrupted. The cells were suspended in William's Medium E without phenol red (WME, Sigma Chemie, Buchs, Switzerland) and filtered through a set of tissue sieves (30-, 50-, and 80-mesh). Dead cells were removed by a sedimentation step (1×g, for 15 min at 4° C.) followed by a Percoll (Sigma) centrifugation step and an additional centriftigation in WME (50 g, 3 min). Hepatocyte viability was assessed by trypan blue exclusion and typically lied between 85% and 95%. The cells were seeded into collagen-coated 6-well Falcon Primaria® plates at a density of 9×105 cells/well in 2 ml WME supplemented with 10% fetal calf serum (BioConcept, Allschwil, Switzerland), penicillin (100 U/ml, Sigma Chemie, Buchs, Switzerland), streptomycin (0.1 mg/ml, Sigma Chemie, Buchs, Switzerland), insulin (100 nM, Sigma Chemie, Buchs, Switzerland), and dexamethasone (100 nM). After an attachment period of 3 hrs, the medium was replaced by 1.5 ml/well serum-free WME, supplemented with antibiotics and hormones, and incubated overnight at 37° C. in an atmosphere of 5% C02/95% air. Cells were then incubated with the test compounds or vehicle (Table 2) and harvested for RNA extraction at 6 or 24 hours.



Example 3


Measurement of Circulating and Hepatic Enzymes

[0108] In the hepatotoxicity assay with non-human animals as described in Example 1, circulating enzymes of hepatic origin, as well as the hepatic lipid content were assessed. Blood samples for clinical chemistry were obtained shortly before sacrifice. The enzymatic activities of aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH) and 5-nucleotidase (5-ND) were measured in serum samples. Liver lipids were extracted using liver homogenates as described by Freneaux et al (Freneaux, E., Labbe, G., Letteron, P., The Le, D., Degott, C., Geneve, J., Larrey, D., and Pessayre, D. (1988). Inhibition of the mitochondrial oxidation of fatty acids by tetracycline in mice and in man: possible role in microvesicular steatosis induced by this antibiotic. Hepatology 8, 1056-62) and the contents of triglycerides, phospholipids and total lipids were measured. Automated analysis was performed using commercially available test kits (Roche Diagnostics, Mannheim, Germany) on a Cobas Fara autoanalyzer (Roche, Basel, Switzerland).



Example 4


RNA Sample Preparation

[0109] RNA isolation from hepatocytes was typically performed by resuspending approximately 3 Mio. Cells/1.2 mL RNAzol (Tel-Test Inc., TX, USA). For RNA isolation from liver tissue, a portion of tissue of approximately 100 mg was transferred to a tube containing 1.2 ml RNAzol. Cells or tissue in RNAzol were disrupted in FastPrep tubes for 20 seconds in a Savant homogenizer (Bio101, Buena Vista, Calif., U.S.A.). Total RNA was isolated according to the manufacturer's instruction and quantified by measuring the optical density at 280 nm. The quality of RNA was assessed with gel electrophoresis.



Example 5


Synthesis and Hybridization of cRNA

[0110] Double stranded cDNA was synthesized from 20 μg of total RNA using a cDNA Synthesis System (Roche Diagnostics, Mannheim, Germany) with the oligo(dT)24 T7prom)65 primer. The MEGAScript T7 kit (Ambion, Austin, Tex., U.S.A.) was used to transcribe the cDNA into cRNA in the presence of Biotin-11-CTP and Biotin-16-UTP (Enzo, Farmingdale, N. Y., U.S.A.) according to the instructions supplied with the kit. After purification with the RNeasy kit (Qiagen, Hilden, Germany) integrity of the cRNA was checked using gel electrophoresis. 10-15 μg fragmented cRNA were used for hybridization to the RG-U34A array (Affymetrix GeneChip® array, Santa Clara, Calif.). The oligonucleotide array used in the present study contains probe sets for over 5000 rat genes. Hybridization and staining were performed basically as described previously (Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL (1996). Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol. 14, 1675-80.; de Saizieu A, Certa U, Warrington J, Gray C, Keck W, Mous J. (1998). Bacterial transcript imaging by hybridization of total RNA to oligonucleotide arrays. Nat Biotechnol. 16, 45-8). Arrays were scanned with a confocal laser scanner (Hewlett-Packard, Palo Alto, Calif., USA).



Example 6


Expression Analysis

[0111] After hybridization and scanning, the expression level of each gene was calculated by subtracting the fluorescence intensity of the mismatch probes from the match signal (=average difference) using the GENECHIP 3.1 software or its up-dated versions MAS 4.0 and MAS 5.0 (Affymetrix). Gene expression data were further analyzed with an in-house-developed data analysis tool (RACE-A, Hoffmann-La Roche, Basel, Switzerland). Data sets of treated versus time and vehicle matched control were generated for each treatment (treated vs. controls) and compared. Analyzed data were stored in a toxicogenomics database (RACE-R, Hoffmann-La Roche, Basel, Switzerland). For the gene expression analysis, difference of means, fold-induction, statistical significance (Students' t-test) were applied for querying the data base. Likewise, increase in circulating liver enzymes (ALT, AST, ALP, etc.) were analyzed. A linear regression was performed between gene expression levels for each gene and circulating enzyme levels. An example of correlation between gene expression and circulating ALT levels is given in FIG. 1.



Example 7


Detection of Profiles and Specific Marker Genes

[0112] Gene expression changes common to a number of compounds belonging to the same hepatotoxicity mechanism were considered profiles typical for this mechanism. The profiles were determined after the in vivo exposure of adult male Wistar rats to 18 compounds, from which 6 were steatotic, 6 were direct acting and 6 were cholestatic. For each tested compound, one or several independent experiments were performed. The results are depicted in Table 3, showing that 680 differentially expressed genes were identified in vivo: 30 of them were only regulated in livers after exposure to steatotic compounds; 18 were regulated after exposure to cholestatic compounds; and 559 after exposure to direct acting compounds. 11 genes showed regulation by all types of tested toxic compounds and are probably related to cellular stress. Others show regulation by two types of toxicity mechanisms.


[0113] In addition to the defined profiles, gene expression levels and their correlation (linear regression) with the circulating liver enzymes were used to select genes whose expression varied across the samples. These genes were chosen as possible toxicity markers and selected with less stringent filtering criteria. Candidate marker genes were categorized as follows:


[0114] a) differentially expressed genes (up-regulated in animals showing elevated enzymes in comparison to the matched controls, 2-fold change, p<0.05), b) differentially expressed genes (down-regulated in animals showing elevated enzymes in comparison to the matched controls, 2-fold change, p<0.05); c) genes that fulfilled the criteria in a) but that additionally showed an up-regulation at doses and/or time points at which no elevation of circulating enzymes could be detected; d) genes that fulfilled the criteria in b) but that additionally showed an down-regulation at doses and/or time points at which no elevation of circulating enzymes could be detected. Some of these marker genes are listed in Table 4. Among them, PEG-3 (progression elevated gene 3, # 1 in the said Table 4) and TRAP (translocon-associated protein, #9 in Table 4) showed very good characteristics as a possible early predictor in vivo (Michael Fountoulakis, Maria-Cristina de Vera, Flavio Crameri, Franziska Boess, Rodolfo Gasser, Silvio Albertini, Laura Suter: “Modulation of gene and protein expression by carbon tetrachloride in the rat liver”, Toxicol and Appl. Pharmacol. Aug. 15, 2002;183(1):71-80) as well as in vitro. This is represented in FIGS. 1 through 4.



Example 8


Data Validation for Selected Genes

[0115] The regulation of the mRNA levels of several candidate genes (Table 4) was verified with quantitative RT-PCR. Specific primers for these genes have been designed in order to evaluate the expression with RT-PCR using SybrGreen assay (Table 6). For each performed RT-PCR reaction, the specificity of the assay was evaluated with dissociation curve software (Applied Biosystems), as well as by assessment of the size of the product using either gel electrophoresis or Agilent Bioanalyzer. The obtained results are described in this example and generally confirmed the results obtained using GeneChips (analysis performed with microarray suite version MAS 4.0 or MAS 5.0).


[0116] Western-blot analysis was performed on 10 micrograms total liver protein following standard laboratory procedures. Proteins transferred onto a nitrocellulose membrane were incubated with the first antibody (Anti-cytochrome p450 2B 1, raised in goat, purchased at GenTest, Mass.); followed by an incubation with the second antibody (donkey anti-sheep/Goat Immunoglobulin Horseradish Peroxidase Conjugated, from Chemikon). Chemoluminiscence was quantified by densitometry in a Multimage Light Cabinet (Alpha Inotech Corporation, San Leandro, Calif., USA) using Lumilight (Roche Diagnostics AG, Rotkreuz, Switzerland) solution.


[0117] Real-time PCR is a truly quantitative method, while genechip-analysis is only semi-quantitative for transcriptional expression studies. In a first step, the induction of the mRNA levels of 9 candidate genes (Table 5 and FIG. 10) was verified with quantitative PCR. The results obtained with both methods showed that the expression of these genes would allow the differentiation of a steatotic compound from a pharmacological analogue that does not show steatotic potential. These genes could be possible diagnostic and predictive molecular markers for different mechanisms of hepatic toxicity. As an internal control, the house-keeping gene glycernine-aldehyde-phosphate-dehydrogenase (GAPDH) was also analyzed.


[0118] 5-HT6 Receptor Antagonists


[0119] RT-PCR results confirmed that the two 5-HT6 receptor antagonists could be distinguished using the expression levels of few marker genes (FIG. 10). Note that for some of the selected genes, a slight induction with the non-toxic compound Ro 66-0074 was also observed. However, this induction was minor when compared to the larger effect elicited by the toxic compound Ro 65-7199 so that differentiation of both compounds remains possible.


[0120] In addition, Western blots were performed using specific antibodies against the cytochrome P450 CYP2B family in order to evaluate if the clear induction of messenger RNA also led to an increase in the hepatic protein levels. The results of the protein levels of CYP2B closely paralleled the amounts of mRNA at 24 hours and at 7 days after Ro 65-7199 treatment. However, the induction of CYP2B could not be detected 6 hours after administration of Ro 65-7199, in spite of the increased levels of messenger RNA. This is due to the time lag between protein and mRNA induction (FIG. 11).


[0121] Direct Acting Compounds Regulate the GADD-Family


[0122] Further experiments with RT-PCR confirmed results regarding the induction of genes from the GADD family, namely GADD-45, GADD-153 and PEG-3 by direct acting compounds (Hydrazine, Thioacetamide, 1,2-dichlorobenzene) (Table 9). The induction of these genes correlates with the histopathological findings in a time related fashion: While PEG-3 seems to be an early marker, GADD-45 and GADD-143 appear regulated at later time points, when the tissue damage is obvious by conventional endpoints. These results are in line with literature and confirm the assumption that PEG-3 is an early marker for hepatic damage.


[0123] Induction of EGR1 by Tolcapone (Tasmar) and Dinitrophenol


[0124] Tolcapone (Tasmar) is a human hepatotoxin with no known toxicity in the rat. In this experiment, a slight induction of EGR1 was detected after exposure of rats to a high dose of Tolcapone (300 mg/kg) and dinitrophenol (10 and 30 mg/kg). The induction was slight and showed high inter-individual variability but experiments with RT-PCR confirmed these results (Table 10).


[0125] EGR1 (early growth response gene 1, EMBL_ro:rnngf1a) is a transcription factor that is also known by synonyms such as Zif268, NGF1-A, Krox24, TIS8. Its name derives of the kinetics of its induction, since it is a primary transcribed signal: the protein can be induced within minutes of a stimulus and then decays within hours (Khachigian, L. M. and T. Collins, Inducible expression of Egr-1-dependent genes. A paradigm of transcriptional activation in vascular endothelium. Circ Res, 1997. 81(4): p. 457-61; Yan, S.F., et al., Egr-1: is it always immediate and early? J Clin Invest, 2000. 105(5): p. 553-4). Nevertheless, maintained high expression of EGR1 has been described in atherosclerotic tissue and in connection to cell death in Alzheimer's disease, establishing a relationship between EGR1 overexpression and chronic conditions (McCaffrey, T. A., et al., High-level expression of Egr-1 and Egr-1-inducible genes in mouse and human atherosclerosis. J Clin Invest, 2000. 105(5): p. 653-62). Its function under normal conditions is still unclear, since EGR1-null mice display normal phenotype with exception of infertility in females (Lee, S. L., et al., Luteinizing hormone deficiency and female infertility in mice lacking the transcription factor NGFI-A (Egr-1). Science, 1996.273(5279): p. 1219-21). Thus, the physiological role of EGR1 might only become manifest upon environmental challenge. This gene has been found overexpressed in several pathologic conditions, including exposure to ionising radiation, prostate cancer and hypoxia (Weichselbaum, R. R., et al., Radiation-induced tumour necrosis factor-alpha expression: clinical application of transcriptional and physical targeting of gene therapy. Lancet Oncol, 2002.3(11): p. 665-71). The up-regulation of EGR1 after hypoxia leads to vascular and perivascular tissue damage. In particular in lung, EGR1 induction leads an increase of Tissue Factor (TF) and to deposition of fibrin in the lung vasculature (Yan, S. F., et al., Egr-1, a master switch coordinating upregulation of divergent gene families underlying ischemic stress. Nat Med, 2000.6(12): p. 1355-61). EGR1-deficient mice show significantly reduced prostate tumor formation and significantly less pulmonary vascular permeability and therefore better survival after ischemic injury (Abdulkadir, S. A., et al., Impaired prostate tumorigenesis in Egr1-deficient mice. Nat Med, 2001.7(1): p.101-7; Ten, V. S. and D. J. Pinsky, Endothelial response to hypoxia: physiologic adaptation and pathologic dysfunction. Curr Opin Crit Care, 2002.8(3): p.242-50).


[0126] The literature reports suggest a possible involvement of EGR1 as an early signal to injury that triggers subsequent tissue damage. In particular in the liver, a link between mitochondrial uncoupling (as caused by dinitrophenol) and induction of EGR1 was established. Also, tolcapone (Tasmar) has been described as having mitochondrial uncoupling properties in vitro. Thus, it is suggested that the induction of EGR1 in rats exposed to tolcapone (Tasmar) might lead to hepatic tissue injury if the physiological environment (i.e. existing disease or genetic background) is appropriate. This would explain the low incidence of human hepatotoxicity caused by tolcapone (Tasmar).



Example 9


Expression Data Analysis with Support Vector Machines

[0127] Adult male Wistar rats were dosed in vivo and the resulting expression profile in liver was determined. SVMs were built for the discrimination between 3 different classes of hepatotoxicants, non-toxic substances and controls. The training set consisted of 180 gene expression profiles from individual animals treated with direct acting, cholestatic, steatotic, non-toxic compounds and corresponding vehicle-dosed controls. As a first step the chips were rescaled to a median value of 0 and a standard deviation of 1. Subsequently chips were presented to a linear kernel SVM (for classifier training the SVM software package from William Stafford Noble, Department of Computer Science, Columbia University, New York was used. Procedures for the Recursive Feature Elimination (RFE), automation of the whole training cycles and further data analysis were developed in house, using the PERL language.) Single binary classifiers for each category of chips were obtained by training one group against all others (‘One-vs-All’ training method). Multi-class classification for a given test chip was then carried out by combining the outputs of all binary classifiers. A leave-one-out cross validation procedure was applied to assess the quality of the trained machines with respect to the training data. This method consists of removing one sample from the training set, building a classifier on the basis of the remaining data and then testing on the withheld example. By removing all replicates of one compound from the training data, followed by classifying these chips with the resulting decision function, the individual contribution of the given compound for a successful classifier could be examined. RFE was used to investigate the relationship between the number of genes for generating the classifiers, the resulting prediction accuracy, cross-validation errors and the number of used support vectors. The first iteration reduced the gene count to a multiple of 2. In each subsequent iteration the gene count was halved until 32 genes remained. Afterwards only one gene per iteration was removed. An example of a RFE for the direct acting class is shown in FIG. 7.


[0128] Based on classification accuracy, model simplicity and gene count a SVM was selected for each class. As can be seen the SVM for discrimination of direct acting compounds from all others was based on 6 genes. The corresponding gene numbers for the other SVMs were:


[0129] Steatotic (6 genes), cholestatic (21 genes), non-toxic (19 genes) and controls (46 genes).


[0130] Compounds not present in the initial training set were subsequently classified based on their expression profiles. Expression profiles for individual animals were classified using the 5 previously generated support vector machines. The successful identification of amineptine as a steatotic compound is depicted in FIG. 8 and the identification of 1,2-Dichlorobenzene as a direct acting compound is shown in FIG. 9. The bigger a positive value is, the better is the data fit into a specific class defined by the respective SVM. A negative discriminant value means that data do not fit into a compound class.


[0131] The above-described method was used to find class-specific genes that allow discrimination of a class from all other classes (class-discriminating genes, Table 7).


[0132] The same approach was also used to find toxicant specific genes for each of the categories. Using RFE SVMs for the discrimination of direct acting compounds from controls were generated. Based on classification accuracy, model simplicity and gene count one SVM was selected for discrimination of the direct acting group from the control group. This classifier was based on 14 genes (specific genes for the direct acting group, Table 8).


[0133] The same procedure was repeated for the steatotic and the cholestatic class. The classifier for the cholestatic group contained 34 genes and the classifier for the steatotic group contained 3 genes (Table 8). The genes required to separate a class from all other classes or just from controls can therefore be different.
1TABLE 1Hepato-TargettoxicityCompoundDose levelsorganMechanismChlorpromazine15mg/kgLiverCholestaticCyclosporine A5, 15 and 30mg/kgLiverCholestaticErythromycin734mg/kgLiverCholestaticGlibenclamide2.5 and 25mg/kgLiverCholestaticLithocholic acid60 and 120μmol/kgLiverCholestaticRo 48-5695 (ETA)25mg/kgLiverCholestaticDexamethasone0.6mg/kgLiverCyp inducer/prolif1,2-Dichloro-1.5 and 4.5mmol/kgLiverDirectbenzeneActingAflatoxin B11 and 4mg/kgLiverDirectActingBromobenzene1 and 3mmol/kgLiverDirectActingCarbon0.25 and 2ml/kgLiverDirecttetrachlorideActingDiclofenac10, 30, 100mg/kgLiverDirectActingHydrazine10, 60, 90mg/kgLiverDirectActingNitrofurantoin5, 20, 60mg/kgLiverDirectActingThioacetamide2, 10, 50mg/kgLiverDirectActingConcanavaline A0.1, 20mg/kgLiverHepatitis/InfammationTacrine5, 15 and 35mg/kgLiverHumanHepatotoxTempium20 and 1000mg/kgLiverHuman(Lazabemide)hepatotoxTolcapone300mg/kgLiverHuman(Tasmar)hepatotox1,4-Dichloro-4.5mmol/kgLiverNon toxicbenzeneAmineptin125, 250, 500μmol/kgLiverSteatoticAmiodarone50, 100, 600mg/kgLiverSteatoticDoxycycline5, 20, 40mg/kgLiverSteatoticRo 28-1674 (GKA)250mg/kgLiverSteatoticRo 28-1675 (GKA)100mg/kgLiverSteatoticRo 65-7199 (5HT6)30, 100, 400mg/kgLiverSteatoticTetracycline125, 200, 250μmol/kgLiverSteatoticDinitrophenol10 and 30mg/kgLiverUncouplingRo 48-5695: Pyridin-2-ylcarbamic acid 2-[6-(5-isopropyl-pyridin-2-ylsulfonylamino)-5-(2-methoxy-phenoxy)-2-morpholin-4-yl-pyrimidin-4-yloxyl-ethyl ester; Ro 28-1674: 3-Cyclopentyl-2[S]-(4-methanesulfonyl-phenyl)-N-thiazol-2-yl-propionamide; Ro 28-1675: 3-Cyclopentyl-2[R]-(4-methanesulfonyl-phenyl)-N-thiazol-2-yl-propionamide.


[0134]

2








TABLE 2










Hepa-





tocyte
Hepato-



Test
culture
toxicity


Compound
Concentrations
System
Mechanism



















Chlorpromazine
10, 30 100
μM
Monolayer
Cholestatic


Cyclosporine A
0.5 and 5
μM
Monolayer
Cholestatic


Erythromycin
100 and 300
μM
Monolayer
Cholestatic


Lithocholic acid
10 and 30
μM
Monolayer
Cholestatic


Ro 48-5695
20 and 60
μM
Monolayer
Cholestatic


(ETA)


Cyproterone
5 and 25
μM
Monolayer
Cyp inducer/


Acetate



prolif


Phenobarbital
200 and 2000
μM
Monolayer
Cyp inducer/






prolif


Clofibrate
100 and 1000
μM
Monolayer
Cyp inducer/






prolif


Acetaminophen
1000, 2500 and 5000
μM
Monolayer
Direct






Acting


Acetaminophen
1000, 2500
μM
Sandwich
Direct






Acting


Bromobenzene
1000 and 2000
μM
Monolayer
Direct






Acting


Carbon
3000 and 5000
μM
Monolayer
Direct


tetrachloride



Acting


Hydrazine
8000 and 16000
μM
Monolayer
Direct






Acting


Methapyrilene
20
μM
Sandwich
Direct






Acting


Methapyrilene
100, 300 and 1000
μM
Monolayer
Direct






Acting


Nitrofurantoin
20, 100 and 200
μM
Monolayer
Direct






Acting


Thioacetamide
3000 and 10000
μM
Monolayer
Direct






Acting


Thioacetamide-S-
30, 100 and 300
μM
Monolayer
Direct


Oxide



Acting


Amineptin
500, 1000 and 1500
μM
Monolayer
Steatotic


Amiodarone
30, 70, 100 and 300
μM
Monolayer
Steatotic


Doxycycline
100, 500 and 1000
μM
Monolayer
Steatotic


Perhexiline
3, 10 and 30
μM
Monolayer
Steatotic


Ro 28-1674
19 and 75
μM
Monolayer
Steatotic


(GKA)



Ro 28-1675
19 and 75
μM
Monolayer
Steatotic


(GKA)


Ro 65-7199
20 and 100
μM
Monolayer
Steatotic


(5HT)


Tetracycline
100 and 500
μM
Monolayer
Steatotic










[0135]

3





TABLE 3










Gene identifiers are given as the Affymetrix ID from the Affymetrix GeneChip ®


RG-U34A. The accession numbers refer to GenBank and for each type of


hepatotoxicity, the direction of the gene regulation is indicated (1 for up-regulation, −1


for down-regulation). Blank cells indicate the lack of regulation under the used analysis


criteria.















Direct


Acc
SEQ


Affymetrix ID
Cholestatic
Acting
Steatotic
Profile
Number
ID NO
















AF023087_s_at
1
1
1
Unspecific
AF023087
1


D11445exon#1-
1
1
1
Unspecific
D11445
2


4_s_at


L25785_at
−1
−1
−1
Unspecific
L25785
3


M18416_at
1
1
1
Unspecific
M18416
4


M58634_at
1
1
1
Unspecific
M58634
5


M60921_g_at
1
1
1
Unspecific
M60921
6


rc_AA891041_at
1
1
1
Unspecific
AA891041
7


rc_AA893485_at
−1
−1
−1
Unspecific
AA893485
8


rc_AI137856_s_at
1
1
1
Unspecific
AI137856
9


rc_AI172293_at
−1
−1
−1
Unspecific
AI172293
10


U75397UTR#1_s_at
1
1
1
Unspecific
U75397
11


AF003835_at

−1
1
Steatotic/
AF003835
12






Direct Acting


AF014503_at

1
1
Steatotic/
AF014503
13






Direct Acting


AF079864_at

−1
−1
Steatotic/
AF079864
14






Direct Acting


D14989_f_at

−1
−1
Steatotic/
D14989
15






Direct Acting


D17370_at

−1
−1
Steatotic/
D17370
16






Direct Acting


D17370_g_at

−1
−1
Steatotic/
D17370
17






Direct Acting


D44495_s_at

1
1
Steatotic/
D44495
18






Direct Acting


E01524cds_s_at

1
1
Steatotic/
E01524
19






Direct Acting


J02585_at

−1
−1
Steatotic/
J02585
20






Direct Acting


L16764_s_at

1
1
Steatotic/
L16764
21






Direct Acting


L16995_at

−1
−1
Steatotic/
L16995
22






Direct Acting


M15481_at

−1
−1
Steatotic/
M15481
23






Direct Acting


M21208mRNA_s_at

1
1
Steatotic/
M21208
24






Direct Acting


M23572_at

−1
−1
Steatotic/
M23572
25






Direct Acting


rc_AA799766_at

1
1
Steatotic/
AA799766
26






Direct Acting


rc_AA800224_at

1
−1
Steatotic/
AA800224
27






Direct Acting


rc_AA891713_at

1
1
Steatotic/
AA891713
28






Direct Acting


rc_AA892775_at

1
1
Steatotic/
AA892775
29






Direct Acting


rc_AA946503_at

1
1
Steatotic/
AA946503
30






Direct Acting


rc_AI145931_at

−1
−1
Steatotic/
AI145931
31






Direct Acting


rc_AI169327_g_at

1
1
Steatotic/
AI169327
32






Direct Acting


rc_AI176546_at

1
1
Steatotic/
AI176546
33






Direct Acting


rc_AI177004_s_at

−1
−1
Steatotic/
AI177004
34






Direct Acting


rc_AI639391_at

−1
−1
Steatotic/
AI639391
35






Direct Acting


X05684_at

−1
−1
Steatotic/
X05684
36






Direct Acting


X52625_at

−1
−1
Steatotic/
X52625
37






Direct Acting


X91234_at

−1
−1
Steatotic/
X91234
38






Direct Acting


AA848218_at


1
Steatotic
AA848218
39


AB010635_s_at


1
Steatotic
AB010635
40


AF022136_at


−1
Steatotic
AF022136
41


AF087839mRNA#1_s_at


1
Steatotic
AF087839
42


K02814_at


1
Steatotic
K02814
43


L09647_at


−1
Steatotic
L09647
44


L32132_at


1
Steatotic
L32132
45


L36460mRNA_at


1
Steatotic
L36460
46


M10068mRNA_s_at


1
Steatotic
M10068
47


M14369exon#2_at


1
Steatotic
M14369
48


M23566exon_s_at


1
Steatotic
M23566
49


M35300_f_at


1
Steatotic
M35300
50


rc_AA892522_at


−1
Steatotic
AA892522
51


rc_AA894316_at


−1
Steatotic
AA894316
52


rc_AA900582_at


1
Steatotic
AA900582
53


rc_AI044985_at


−1
Steatotic
AI044985
54


rc_AI175764_s_at


−1
Steatotic
AI175764
55


rc_AI176351_s_at


1
Steatotic
AI176351
56


rc_AI230256_at


−1
Steatotic
AI230256
57


rc_AI639108_at


−1
Steatotic
AI639108
58


rc_H31144_g_at


1
Steatotic
H31144
59


S81478_s_at


−1
Steatotic
S81478
60


U02553cds_s_at


−1
Steatotic
U02553
61


U08214_s_at


1
Steatotic
U08214
62


U35345_s_at


1
Steatotic
U35345
63


U48220_at


−1
Steatotic
U48220
64


U88630_at


1
Steatotic
U88630
65


X07648cds_g_at


1
Steatotic
X07648
66


X62952_at


1
Steatotic
X62952
67


X91810_at


1
Steatotic
X91810
68


AA799276_at

1

Direct Acting
AA799276
69


AB002086_g_at

1

Direct Acting
AB002086
70


AB004096_at

−1

Direct Acting
AB004096
71


AB009636_at

−1

Direct Acting
AB009636
72


AB010466_s_at

1

Direct Acting
AB010466
73


AB010963_s_at

−1

Direct Acting
AB010963
74


AB012230_g_at

−1

Direct Acting
AB012230
75


AB014722_g_at

1

Direct Acting
AB014722
76


AB015433_s_at

1

Direct Acting
AB015433
77


AB016536_s_at

1

Direct Acting
AB016536
78


AB017188_at

1

Direct Acting
AB017188
79


AB020504_at

−1

Direct Acting
AB020504
80


AF001417_s_at

1

Direct Acting
AF001417
81


AF013144_at

1

Direct Acting
AF013144
82


AF017637_at

−1

Direct Acting
AF017637
83


AF020618_at

1

Direct Acting
AF020618
84


AF021935_at

1

Direct Acting
AF021935
85


AF025308_f_at

1

Direct Acting
AF025308
86


AF029240_g_at

−1

Direct Acting
AF029240
87


AF029310_at

1

Direct Acting
AF029310
88


AF030086UTR#1_at

−1

Direct Acting
AF030086
89


AF030087UTR#1_at

1

Direct Acting
AF030087
90


AF030087UTR#1_g_at

1

Direct Acting
AF030087
91


AF036335_at

1

Direct Acting
AF036335
92


AF037072_at

−1

Direct Acting
AF037072
93


AF039890mRNA_s_at

−1

Direct Acting
AF039890
94


AF041066_at

−1

Direct Acting
AF041066
95


AF044574_at

−1

Direct Acting
AF044574
96


AF045464_s_at

1

Direct Acting
AF045464
97


AF05661UTR#1_at

1

Direct Acting
AF050661
98


AF054618_s_at

1

Direct Acting
AF054618
99


AF058791_at

1

Direct Acting
AF058791
100


AF061443_at

−1

Direct Acting
AF061443
101


AF062594_g_at

1

Direct Acting
AF062594
102


AF062741_g_at

−1

Direct Acting
AF062741
103


AF063447_at

1

Direct Acting
AF063447
104


AF067650_at

1

Direct Acting
AF067650
105


AF069782_at

1

Direct Acting
AF069782
106


AF080507_at

−1

Direct Acting
AF080507
107


AF080507_g_at

−1

Direct Acting
AF080507
108


AF082124_s_at

1

Direct Acting
AF082124
109


AF084186_s_at

1

Direct Acting
AF084186
110


AF087037_at

1

Direct Acting
AF087037
111


AJ011607_g_at

−1

Direct Acting
AJ011607
112


AJ012603UTR#1_at

1

Direct Acting
AJ012603
113


AJ222724_at

1

Direct Acting
AJ222724
114


AJ224120_at

1

Direct Acting
AJ224120
115


D00636cds_s_at

−1

Direct Acting
D00636
116


D00636Poly_A_Site#1_s_at

−1

Direct Acting
D00636
117


D00698_s_at

−1

Direct Acting
D00698
118


D10354_s_at

1

Direct Acting
D10354
119


D10587_g_at

1

Direct Acting
D10587
120


D10756_g_at

1

Direct Acting
D10756
121


D12769_at

1

Direct Acting
D12769
122


D13122_f_at

1

Direct Acting
D13122
123


D13623_at

1

Direct Acting
D13623
124


D13623_g_at

1

Direct Acting
D13623
125


D13667cds_s_at

1

Direct Acting
D13667
126


D13907_at

1

Direct Acting
D13907
127


D13978_s_at

1

Direct Acting
D13978
128


D14014_at

1

Direct Acting
D14014
129


D14425_s_at

1

Direct Acting
D14425
130


D14564cds_s_at

−1

Direct Acting
D14564
131


D14987_f_at

−1

Direct Acting
D14987
132


D21800_g_at

1

Direct Acting
D21800
133


D25224_at

1

Direct Acting
D25224
134


D25224_g_at

1

Direct Acting
D25224
135


D26564_at

1

Direct Acting
D26564
136


D28557_s_at

1

Direct Acting
D28557
137


D28560_at

−1

Direct Acting
D28560
138


D28560_g_at

−1

Direct Acting
D28560
139


D30649mRNA_s_at

−1

Direct Acting
D30649
140


D30666_at

−1

Direct Acting
D30666
141


D30804_at

1

Direct Acting
D30804
142


D30804_g_at

1

Direct Acting
D30804
143


D31662exon#4_s_at

−1

Direct Acting
D31662
144


D31874_at

1

Direct Acting
D31874
145


D38061exon_s_at

1

Direct Acting
D38061
146


D38062exon_s_at

1

Direct Acting
D38062
147


D38381_s_at

−1

Direct Acting
D38381
148


D38468_s_at

1

Direct Acting
D38468
149


D43964_at

−1

Direct Acting
D43964
150


D45247_at

1

Direct Acting
D45247
151


D50694_at

1

Direct Acting
D50694
152


D63704_at

−1

Direct Acting
D63704
153


D63704_g_at

−1

Direct Acting
D63704
154


D82928_at

1

Direct Acting
D82928
155


D85435_at

1

Direct Acting
D85435
156


D85435_g_at

1

Direct Acting
D85435
157


D87839_g_at

−1

Direct Acting
D87839
158


D87991_at

1

Direct Acting
D87991
159


D88034_at

1

Direct Acting
D88034
160


D88890_at

1

Direct Acting
D88890
161


D89069_f_at

1

Direct Acting
D89069
162


D89514_at

1

Direct Acting
D89514
163


D89983_at

1

Direct Acting
D89983
164


D90109_at

−1

Direct Acting
D90109
165


D90265_s_at

1

Direct Acting
D90265
166


E12286cds_at

−1

Direct Acting
E12286
167


E12625cds_at

−1

Direct Acting
E12625
168


J02589mRNA#2_at

−1

Direct Acting
J02589
169


J02646_at

1

Direct Acting
J02646
170


J02679_s_at

1

Direct Acting
J02679
171


J02962_at

1

Direct Acting
J02962
172


J03179_g_at

1

Direct Acting
J03179
173


J03572_i_at

1

Direct Acting
J03572
174


J03969_at

1

Direct Acting
J03969
175


J04187_at

−1

Direct Acting
J04187
176


J04791_s_at

1

Direct Acting
J04791
177


J04943_at

1

Direct Acting
J04943
178


J05035_g_at

−1

Direct Acting
J05035
179


J05122_at

1

Direct Acting
J05122
180


J05166_at

1

Direct Acting
J05166
181


J05210_at

−1

Direct Acting
J05210
182


J05210_g_at

−1

Direct Acting
J05210
183


K01934mRNA#2_at

−1

Direct Acting
K01934
184


K03045cds_r_at

1

Direct Acting
K03045
185


K03249_at

−1

Direct Acting
K03249
186


L01267_at

1

Direct Acting
L01267
187


L03294_g_at

1

Direct Acting
L03294
188


L07114_at

−1

Direct Acting
L07114
189


L07407_at

1

Direct Acting
L07407
190


L08505_at

1

Direct Acting
L08505
191


L12025_at

1

Direct Acting
L12025
192


L12382_at

−1

Direct Acting
L12382
193


L12383_at

1

Direct Acting
L12383
194


L13235UTR#1_f_at

−1

Direct Acting
L13235
195


L13600_at

1

Direct Acting
L13600
196


L13635_s_at

1

Direct Acting
L13635
197


L17127_g_at

1

Direct Acting
L17127
198


L19031_at

−1

Direct Acting
L19031
199


L19931_at

1

Direct Acting
L19931
200


L19998_at

−1

Direct Acting
L19998
201


L20900_at

1

Direct Acting
L20900
202


L22294_at

−1

Direct Acting
L22294
203


L22339_at

−1

Direct Acting
L22339
204


L22339_g_at

−1

Direct Acting
L22339
205


L23148_g_at

1

Direct Acting
L23148
206


L24207_r_at

−1

Direct Acting
L24207
207


L27075_g_at

−1

Direct Acting
L27075
208


L27843_s_at

1

Direct Acting
L27843
209


L32591mRNA_at

1

Direct Acting
L32591
210


L32591mRNA_g_at

1

Direct Acting
L32591
211


L32601_s_at

−1

Direct Acting
L32601
212


L34049_g_at

−1

Direct Acting
L34049
213


L38482_g_at

1

Direct Acting
L38482
214


L38615_g_at

1

Direct Acting
L38615
215


L41275cds_s_at

1

Direct Acting
L41275
216


L41685mRNA_at

1

Direct Acting
L41685
217


M11266_at

−1

Direct Acting
M11266
218


M11942_s_at

1

Direct Acting
M11942
219


M12156_at

1

Direct Acting
M12156
220


M12919mRNA#2_at

1

Direct Acting
M12919
221


M12919mRNA#2_g

1

Direct Acting
M12919
222


at


M13100cds#3_f_at

−1

Direct Acting
M13100
223


M13100cds#4_f_at

−1

Direct Acting
M13100
224


M13962mRNA#2_at

−1

Direct Acting
M13962
225


M14972_i_at

1

Direct Acting
M14972
226


M15883_g_at

1

Direct Acting
M15883
227


M18363cds_s_at

−1

Direct Acting
M18363
228


M21842_at

−1

Direct Acting
M21842
229


M22359mRNA_s_at

−1

Direct Acting
M22359
230


M22360_s_at

−1

Direct Acting
M22360
231


M23601_at

−1

Direct Acting
M23601
232


M24067_at

1

Direct Acting
M24067
233


M24604_at

1

Direct Acting
M24604
234


M24604_g_at

1

Direct Acting
M24604
235


M25157mRNA_i_at

−1

Direct Acting
M25157
236


M25490_at

−1

Direct Acting
M25490
237


M25804_at

1

Direct Acting
M25804
238


M25804_g_at

1

Direct Acting
M25804
239


M27158cds_at

1

Direct Acting
M27158
240


M27207mRNA_s_at

−1

Direct Acting
M27207
241


M29249cds_at

1

Direct Acting
M29249
242


M31837_at

−1

Direct Acting
M31837
243


M32062_at

1

Direct Acting
M32062
244


M32062_g_at

1

Direct Acting
M32062
245


M33962_at

1

Direct Acting
M33962
246


M36151cds_s_at

1

Direct Acting
M36151
247


M37828_at

−1

Direct Acting
M37828
248


M55015cds_s_at

1

Direct Acting
M55015
249


M57728_at

1

Direct Acting
M57728
250


M58041_s_at

−1

Direct Acting
M58041
251


M59460mRNA#2_at

−1

Direct Acting
M59460
252


M60103_at

−1

Direct Acting
M60103
253


M61219_s_at

1

Direct Acting
M61219
254


M63282_at

1

Direct Acting
M63282
255


M64795_f_at

1

Direct Acting
M64795
256


M64862_at

−1

Direct Acting
M64862
257


M69246_at

−1

Direct Acting
M69246
258


M73808mRNA_at

1

Direct Acting
M73808
259


M75168_at

1

Direct Acting
M75168
260


M76767_s_at

−1

Direct Acting
M76767
261


M77245_at

1

Direct Acting
M77245
262


M77479_at

−1

Direct Acting
M77479
263


M81183Exon_UTR_g_at

−1

Direct Acting
M81183
264


M81855_at

1

Direct Acting
M81855
265


M81920_at

1

Direct Acting
M81920
266


M83675_at

−1

Direct Acting
M83675
267


M84719_at

−1

Direct Acting
M84719
268


M89945mRNA_at

−1

Direct Acting
M89945
269


M89945mRNA_g_at

−1

Direct Acting
M89945
270


M91466_at

−1

Direct Acting
M91466
271


M91652complete_seq_at

−1

Direct Acting
M91652
272


M91652complete_seq_g_at

−1

Direct Acting
M91652
273


M93297cds_at

−1

Direct Acting
M93297
274


M93401_at

−1

Direct Acting
M93401
275


M94043_at

−1

Direct Acting
M94043
276


M94555_at

1

Direct Acting
M94555
277


M95591_at

−1

Direct Acting
M95591
278


M95591_g_at

−1

Direct Acting
M95591
279


M96674_at

−1

Direct Acting
M96674
280


rc_AA686164_at

1

Direct Acting
AA686164
281


rc_AA799418_at

1

Direct Acting
AA799418
282


rc_AA799479_at

1

Direct Acting
AA799479
283


rc_AA799481_at

1

Direct Acting
AA799481
284


rc_AA799508_at

1

Direct Acting
AA799508
285


rc_AA799531_at

1

Direct Acting
AA799531
286


rc_AA799531_g_at

1

Direct Acting
AA799531
287


rc_AA799560_at

−1

Direct Acting
AA799560
288


rc_AA799672_s_at

1

Direct Acting
AA799672
289


rc_AA799735_at

1

Direct Acting
AA799735
290


rc_AA799788_s_at

1

Direct Acting
AA799788
291


rc_AA799814_at

1

Direct Acting
AA799814
292


rc_AA799893_g_at

1

Direct Acting
AA799893
293


rc_AA799997_at

−1

Direct Acting
AA799997
294


rc_AA800017_at

1

Direct Acting
AA800017
295


rc_AA800169_at

1

Direct Acting
AA800169
296


rc_AA800179_at

1

Direct Acting
AA800179
297


rc_AA800218_at

1

Direct Acting
AA800218
298


rc_AA800456_at

−1

Direct Acting
AA800456
299


rc_AA800738_at

1

Direct Acting
AA800738
300


rc_AA800739_at

1

Direct Acting
AA800739
301


rc_AA800750_f_at

−1

Direct Acting
AA800750
302


rc_AA800753_at

1

Direct Acting
AA800753
303


rc_AA800797_at

−1

Direct Acting
AA800797
304


rc_AA800912_g_at

1

Direct Acting
AA800912
305


rc_AA817846_at

−1

Direct Acting
AA817846
306


rc_AA817854_s_at

−1

Direct Acting
AA817854
307


rc_AA817987_f_at

−1

Direct Acting
AA817987
308


rc_AA818072_s_at

1

Direct Acting
AA818072
309


rc_AA818122_f_at

−1

Direct Acting
AA818122
310


rc_AA818951_at

1

Direct Acting
AA818951
311


rc_AA819776_f_at

1

Direct Acting
AA819776
312


rc_AA849722_at

1

Direct Acting
AA849722
313


rc_AA852004_s_at

−1

Direct Acting
AA852004
314


rc_AA858879_at

1

Direct Acting
AA858879
315


rc_AA859648_at

1

Direct Acting
AA859648
316


rc_AA859652_at

1

Direct Acting
AA859652
317


rc_AA859663_at

−1

Direct Acting
AA859663
318


rc_AA859680_at

1

Direct Acting
AA859680
319


rc_AA859680_g_at

1

Direct Acting
AA859680
320


rc_AA859722_at

1

Direct Acting
AA859722
321


rc_AA859980_at

−1

Direct Acting
AA859980
322


rc_AA859980_g_at

−1

Direct Acting
AA859980
323


rc_AA860030_s_at

1

Direct Acting
AA860030
324


rc_AA866264_s_at

−1

Direct Acting
AA866264
325


rc_AA866426_at

−1

Direct Acting
AA866426
326


rc_AA874791_at

1

Direct Acting
AA874791
327


rc_AA874802_s_at

−1

Direct Acting
AA874802
328


rc_AA874889_g_at

1

Direct Acting
AA874889
329


rc_AA875054_at

1

Direct Acting
AA875054
330


rc_AA875126_g_at

1

Direct Acting
AA875126
331


rc_AA875205_at

1

Direct Acting
AA875205
332


rc_AA875205_g_at

1

Direct Acting
AA875205
333


rc_AA875511_at

−1

Direct Acting
AA875511
334


rc_AA875531_s_at

−1

Direct Acting
AA875531
335


rc_AA875537_at

1

Direct Acting
AA875537
336


rc_AA875563_at

1

Direct Acting
AA875563
337


rc_AA875620_g_at

1

Direct Acting
AA875620
338


rc_AA891226_s_at

1

Direct Acting
AA891226
339


rc_AA891553_at

1

Direct Acting
AA891553
340


rc_AA891689_at

1

Direct Acting
AA891689
341


rc_AA891689_g_at

1

Direct Acting
AA891689
342


rc_AA891739_at

−1

Direct Acting
AA891739
343


rc_AA891785_at

1

Direct Acting
AA891785
344


rc_AA891790_at

1

Direct Acting
AA891790
345


rc_AA891829_at

1

Direct Acting
AA891829
346


rc_AA891838_at

1

Direct Acting
AA891838
347


rc_AA891998_i_at

1

Direct Acting
AA891998
348


rc_AA892006_at

1

Direct Acting
AA892006
349


rc_AA892010_g_at

1

Direct Acting
AA892010
350


rc_AA892014_r_at

1

Direct Acting
AA892014
351


rc_AA892027_at

−1

Direct Acting
AA892027
352


rc_AA892053_at

1

Direct Acting
AA892053
353


rc_AA892120_at

1

Direct Acting
AA892120
354


rc_AA892154_g_at

−1

Direct Acting
AA892154
355


rc_AA892248_g_at

−1

Direct Acting
AA892248
356


rc_AA892251_at

−1

Direct Acting
AA892251
357


rc_AA892333_at

1

Direct Acting
AA892333
358


rc_AA892367_i_at

1

Direct Acting
AA892367
359


rc_AA892378_at

1

Direct Acting
AA892378
360


rc_AA892500_at

−1

Direct Acting
AA892500
361


rc_AA892562_at

1

Direct Acting
AA892562
362


rc_AA892562_g_at

1

Direct Acting
AA892562
363


rc_AA892582_s_at

1

Direct Acting
AA892582
364


rc_AA892598_at

1

Direct Acting
AA892598
365


rc_AA892598_g_at

1

Direct Acting
AA892598
366


rc_AA892602_at

1

Direct Acting
AA892602
367


rc_AA892680_at

1

Direct Acting
AA892680
368


rc_AA892799_i_at

1

Direct Acting
AA892799
369


rc_AA892799_r_at

−1

Direct Acting
AA892799
370


rc_AA892828_at

−1

Direct Acting
AA892828
371


rc_AA892828_g_at

−1

Direct Acting
AA892828
372


rc_AA892832_at

−1

Direct Acting
AA892832
373


rc_AA892855_at

−1

Direct Acting
AA892855
374


rc_AA892861_at

−1

Direct Acting
AA892861
375


rc_AA892950_at

1

Direct Acting
AA892950
376


rc_AA892986_at

−1

Direct Acting
AA892986
377


rc_AA893032_at

−1

Direct Acting
AA893032
378


rc_AA893199_at

1

Direct Acting
AA893199
379


rc_AA893235_at

1

Direct Acting
AA893235
380


rc_AA893239_at

−1

Direct Acting
AA893239
381


rc_AA893242_g_at

−1

Direct Acting
AA893242
382


rc_AA893280_at

1

Direct Acting
AA893280
383


rc_AA893325_at

−1

Direct Acting
AA893325
384


rc_AA893366_at

−1

Direct Acting
AA893366
385


rc_AA893384_g_at

−1

Direct Acting
AA893384
386


rc_AA893471_s_at

−1

Direct Acting
AA893471
387


rc_AA893495_at

−1

Direct Acting
AA893495
388


rc_AA893517_at

1

Direct Acting
AA893517
389


rc_AA893532_at

1

Direct Acting
AA893532
390


rc_AA893562_at

1

Direct Acting
AA893562
391


rc_AA893584_at

1

Direct Acting
AA893584
392


rc_AA893690_at

1

Direct Acting
AA893690
393


rc_AA893770_g_at

1

Direct Acting
AA893770
394


rc_AA894027_at

−1

Direct Acting
AA894027
395


rc_AA894086_g_at

1

Direct Acting
AA894086
396


rc_AA894258_at

−1

Direct Acting
AA894258
397


rc_AA894298_s_at

1

Direct Acting
AA894298
398


rc_AA900476_at

−1

Direct Acting
AA900476
399


rc_AA924267_s_at

1

Direct Acting
AA924267
400


rc_AA924289_s_at

−1

Direct Acting
AA924289
401


rc_AA924326_s_at

1

Direct Acting
AA924326
402


rc_AA926193_at

−1

Direct Acting
AA926193
403


rc_AA944156_s_at

1

Direct Acting
AA944156
404


rc_AA944397_at

1

Direct Acting
AA944397
405


rc_AA945082_at

1

Direct Acting
AA945082
406


rc_AA945867_at

1

Direct Acting
AA945867
407


rc_AA946532_at

−1

Direct Acting
AA946532
408


rc_AA956958_at

1

Direct Acting
AA956958
409


rc_AA963449_s_at

−1

Direct Acting
AA963449
410


rc_AA963839_s_at

−1

Direct Acting
AA963839
411


rc_AA965147_at

1

Direct Acting
AA965147
412


rc_AA997614_s_at

−1

Direct Acting
AA997614
413


rc_AI008074_s_at

1

Direct Acting
AI008074
414


rc_AI008131_s_at

1

Direct Acting
AI008131
415


rc_AI009338_at

−1

Direct Acting
AI009338
416


rc_AI009806_at

1

Direct Acting
AI009806
417


rc_AI011998_at

1

Direct Acting
AI011998
418


rc_AI012595_at

1

Direct Acting
AI012595
419


rc_AI012604_at

1

Direct Acting
AI012604
420


rc_AI013513_at

1

Direct Acting
AI013513
421


rc_AI014091_at

−1

Direct Acting
AI014091
422


rc_AI014163_at

1

Direct Acting
AI014163
423


rc_AI031019_g_at

1

Direct Acting
AI031019
424


rc_AI044900_s_at

−1

Direct Acting
AI044900
425


rc_AI044985_g_at

−1

Direct Acting
AI044985
426


rc_AI045395_at

−1

Direct Acting
AI045395
427


rc_AI070295_at

1

Direct Acting
AI070295
428


rc_AI070295_g_at

1

Direct Acting
AI070295
429


rc_AI102103_g_at

1

Direct Acting
AI102103
430


rc_AI105348_f_at

1

Direct Acting
AI105348
431


rc_AI105348_i_at

1

Direct Acting
AI105348
432


rc_AI111401_s_at

1

Direct Acting
AI111401
433


rc_AI137790_at

1

Direct Acting
AI137790
434


rc_AI169695_f_at

−1

Direct Acting
AI169695
435


rc_AI169735_g_at

−1

Direct Acting
AI169735
436


rc_AI170608_at

1

Direct Acting
AI170608
437


rc_AI171966_at

1

Direct Acting
AI171966
438


rc_AI172476_at

1

Direct Acting
AI172476
439


rc_AI175486_at

1

Direct Acting
AI175486
440


rc_AI175959_at

1

Direct Acting
AI175959
441


rc_AI176488_at

−1

Direct Acting
AI176488
442


rc_AI176595_s_at

1

Direct Acting
AI176595
443


rc_AI177161_at

−1

Direct Acting
AI177161
444


rc_AI177161_g_at

−1

Direct Acting
AI177161
445


rc_AI177986_at

1

Direct Acting
AI177986
446


rc_AI178135_at

1

Direct Acting
AI178135
447


rc_AI178828_i_at

1

Direct Acting
AI178828
448


rc_AI179610_at

1

Direct Acting
AI179610
449


rc_AI180442_at

−1

Direct Acting
AI180442
450


rc_AI228738_s_at

1

Direct Acting
AI228738
451


rc_AI229637_at

1

Direct Acting
AI229637
452


rc_AI230260_s_at

1

Direct Acting
AI230260
453


rc_AI230294_at

−1

Direct Acting
AI230294
454


rc_AI230614_s_at

1

Direct Acting
AI230614
455


rc_AI230712_at

1

Direct Acting
AI230712
456


rc_AI231007_at

1

Direct Acting
AI231007
457


rc_AI231807_g_at

1

Direct Acting
AI231807
458


rc_AI232783_s_at

−1

Direct Acting
AI232783
459


rc_AI234604_s_at

1

Direct Acting
AI234604
460


rc_AI235631_at

1

Direct Acting
AI235631
461


rc_AI235890_s_at

−1

Direct Acting
AI235890
462


rc_AI236597_at

1

Direct Acting
AI236597
463


rc_AI236601_at

1

Direct Acting
AI236601
464


rc_AI237535_s_at

1

Direct Acting
AI237535
465


rc_AI638948_at

−1

Direct Acting
AI638948
466


rc_AI638966_r_at

−1

Direct Acting
AI638966
467


rc_AI639008_at

1

Direct Acting
AI639008
468


rc_AI639029_s_at

1

Direct Acting
AI639029
469


rc_AI639067_at

−1

Direct Acting
AI639067
470


rc_AI639167_at

1

Direct Acting
AI639167
471


rc_AI639185_s_at

−1

Direct Acting
AI639185
472


rc_AI639393_at

1

Direct Acting
AI639393
473


rc_AI639488_at

1

Direct Acting
AI639488
474


rc_AI639518_g_at

1

Direct Acting
AI639518
475


rc_H31287_g_at

1

Direct Acting
H31287
476


rc_H31351_at

1

Direct Acting
H31351
477


rc_H31722_at

1

Direct Acting
H31722
478


rc_H31976_at

1

Direct Acting
H31976
479


rc_H31982_at

1

Direct Acting
H31982
480


rc_H33426_at

−1

Direct Acting
H33426
481


rc_H33426_g_at

−1

Direct Acting
H33426
482


rc_H33491_at

−1

Direct Acting
H33491
483


S46785_at

−1

Direct Acting
S46785
484


S46785_g_at

−1

Direct Acting
S46785
485


S55224_s_at

1

Direct Acting
S55224
486


S61868_g_at

1

Direct Acting
S61868
487


S66024_at

1

Direct Acting
S66024
488


S69874_s_at

1

Direct Acting
S69874
489


S71021_s_at

1

Direct Acting
S71021
490


S72506_s_at

1

Direct Acting
S72506
491


S76054_s_at

1

Direct Acting
S76054
492


S76489_s_at

−1

Direct Acting
S76489
493


S79213_at

1

Direct Acting
S79213
494


S79820_at

1

Direct Acting
S79820
495


S80456_s_at

1

Direct Acting
S80456
496


S82820mRNA_s_at

1

Direct Acting
S82820
497


S85184_at

1

Direct Acting
S85184
498


S85184_g_at

1

Direct Acting
S85184
499


U01146_s_at

1

Direct Acting
U01146
500


U01344_at

−1

Direct Acting
U01344
501


U03390_at

1

Direct Acting
U03390
502


U05014_g_at

1

Direct Acting
U05014
503


U05784_s_at

1

Direct Acting
U05784
504


U07201_at

1

Direct Acting
U07201
505


U08141_at

−1

Direct Acting
U08141
506


U12268_at

−1

Direct Acting
U12268
507


U14746_at

1

Direct Acting
U14746
508


U17035_s_at

1

Direct Acting
U17035
509


U17697_s_at

−1

Direct Acting
U17697
510


U18729_at

1

Direct Acting
U18729
511


U21101_at

−1

Direct Acting
U21101
512


U21719mRNA_s_at

1

Direct Acting
U21719
513


U21871_at

1

Direct Acting
U21871
514


U24174_at

1

Direct Acting
U24174
515


U28504_at

−1

Direct Acting
U28504
516


U29873_at

−1

Direct Acting
U29873
517


U30186_at

1

Direct Acting
U30186
518


U31777_g_at

1

Direct Acting
U31777
519


U31866_at

−1

Direct Acting
U31866
520


U33500_g_at

1

Direct Acting
U33500
521


U33541cds_at

−1

Direct Acting
U33541
522


U36482_g_at

−1

Direct Acting
U36482
523


U38253_at

1

Direct Acting
U38253
524


U38253_g_at

1

Direct Acting
U38253
525


U40004_s_at

−1

Direct Acting
U40004
526


U44948_at

1

Direct Acting
U44948
527


U50412_at

−1

Direct Acting
U50412
528


U52530_s_at

−1

Direct Acting
U52530
529


U53873cds_at

−1

Direct Acting
U53873
530


U55815_at

1

Direct Acting
U55815
531


U60416_at

1

Direct Acting
U60416
532


U60882_at

1

Direct Acting
U60882
533


U63923_at

1

Direct Acting
U63923
534


U64705cds_f_at

1

Direct Acting
U64705
535


U66322_at

−1

Direct Acting
U66322
536


U67915_at

−1

Direct Acting
U67915
537


U68168_at

−1

Direct Acting
U68168
538


U72349_at

1

Direct Acting
U72349
539


U73174_at

1

Direct Acting
U73174
540


U75210_s_at

−1

Direct Acting
U75210
541


U75405UTR#1_f_at

−1

Direct Acting
U75405
542


U75917_at

1

Direct Acting
U75917
543


U76714_at

1

Direct Acting
U76714
544


U77918_at

1

Direct Acting
U77918
545


U83896_at

1

Direct Acting
U83896
546


U84410_s_at

−1

Direct Acting
U84410
547


U88036_at

−1

Direct Acting
U88036
548


U91561_g_at

1

Direct Acting
U91561
549


U96490_at

1

Direct Acting
U96490
550


V01225mRNA_s_at

−1

Direct Acting
V01225
551


V01274_at

−1

Direct Acting
V01274
552


X02610_at

1

Direct Acting
X02610
553


X02741_s_at

1

Direct Acting
X02741
554


X04069_at

−1

Direct Acting
X04069
555


X04267_at

1

Direct Acting
X04267
556


X05137_at

−1

Direct Acting
X05137
557


X05472cds#1_s_at

−1

Direct Acting
X05472
558


X06423_g_at

1

Direct Acting
X06423
559


X06801cds_f_at

1

Direct Acting
X06801
560


X07259cds_s_at

1

Direct Acting
X07259
561


X07551cds_s_at

1

Direct Acting
X07551
562


X07686cds_s_at

−1

Direct Acting
X07686
563


X07944exon#1-

1

Direct Acting
X07944
564


12_s_at


X08056cds_s_at

−1

Direct Acting
X08056
565


X12367cds_s_at

−1

Direct Acting
X12367
566


X13044_at

1

Direct Acting
X13044
567


X13058_at

1

Direct Acting
X13058
568


X13527cds_s_at

−1

Direct Acting
X13527
569


X14181cds_s_at

1

Direct Acting
X14181
570


X14254cds_g_at

1

Direct Acting
X14254
571


X15580complete_seq_s_at

−1

Direct Acting
X15580
572


X16038exon_s_at

1

Direct Acting
X16038
573


X16043cds_at

1

Direct Acting
X16043
574


X16044cds_s_at

1

Direct Acting
X16044
575


X16554_at

1

Direct Acting
X16554
576


X17053mRNA_s_at

1

Direct Acting
X17053
577


X52619_at

1

Direct Acting
X52619
578


X52815cds_f_at

1

Direct Acting
X52815
579


X53581cds#3_f_at

−1

Direct Acting
X53581
580


X53588_at

−1

Direct Acting
X53588
581


X55286_at

1

Direct Acting
X55286
582


X57432cds_s_at

1

Direct Acting
X57432
583


X57523_at

1

Direct Acting
X57523
584


X57523_g_at

1

Direct Acting
X57523
585


X58465mRNA_at

1

Direct Acting
X58465
586


X58465mRNA_g_at

1

Direct Acting
X58465
587


X59859_i_at

1

Direct Acting
X59859
588


X60212_i_at

1

Direct Acting
X60212
589


X60769mRNA_at

1

Direct Acting
X60769
590


X61296cds#2_f_at

−1

Direct Acting
X61296
591


X62086mRNA_s_at

−1

Direct Acting
X62086
592


X62145cds_at

1

Direct Acting
X62145
593


X62295cds_s_at

−1

Direct Acting
X62295
594


X62875mRNA_g_at

1

Direct Acting
X62875
595


X64052cds_f_at

−1

Direct Acting
X64052
596


X66870_at

1

Direct Acting
X66870
597


X67788_at

1

Direct Acting
X67788
598


X69903_at

−1

Direct Acting
X69903
599


X70369_s_at

−1

Direct Acting
X70369
600


X70871_at

1

Direct Acting
X70871
601


X74565cds_g_at

1

Direct Acting
X74565
602


X76453_at

−1

Direct Acting
X76453
603


X77235_at

1

Direct Acting
X77235
604


X77932_at

−1

Direct Acting
X77932
605


X77934cds_at

−1

Direct Acting
X77934
606


X78327_at

1

Direct Acting
X78327
607


X78997_at

1

Direct Acting
X78997
608


X79081mRNA_f_at

−1

Direct Acting
X79081
609


X81448cds_at

1

Direct Acting
X81448
610


X84210complete_seq_s_at

−1

Direct Acting
X84210
611


X89225cds_s_at

1

Direct Acting
X89225
612


X95189_at

−1

Direct Acting
X95189
613


X95986mRNA#1_f_at

1

Direct Acting
X95986
614


X97772_at

1

Direct Acting
X97772
615


X97772_g_at

1

Direct Acting
X97772
616


Y00396mRNA_at

1

Direct Acting
Y00396
617


Y00396mRNA_g_at

1

Direct Acting
Y00396
618


Y08355cds#2_at

1

Direct Acting
Y08355
619


Y09333_at

1

Direct Acting
Y09333
620


Y09365cds_s_at

1

Direct Acting
Y09365
621


Y12635_at

1

Direct Acting
Y12635
622


Y14933mRNA_s_at

1

Direct Acting
Y14933
623


Y17295cds_s_at

1

Direct Acting
Y17295
624


Z36944cds_at

1

Direct Acting
Z36944
625


Z83757mRNA_at

−1

Direct Acting
Z83757
626


Z83757mRNA_g_at

−1

Direct Acting
Z83757
627


J03863_at
1

1
Cholestatic/
J03863
628






Steatotic


J05460_s_at
1

−1
Cholestatic/
J05460
629






Steatotic


X13119cds_s_at
1

1
Cholestatic/
X13119
630






Steatotic


AF020618_g_at
1
1

Cholestatic/
AF020618
631






Direct Acting


AF039832_at
1
1

Cholestatic/
AF039832
632






Direct Acting


AF086624_s_at
1
1

Cholestatic/
AF086624
633






Direct Acting


AF089825_at
−1
−1

Cholestatic/
AF089825
634






Direct Acting


D12769_g_at
1
1

Cholestatic/
D12769
635






Direct Acting


D37920_at
−1
−1

Cholestatic/
D37920
636






Direct Acting


D86580_at
1
−1

Cholestatic/
D86580
637






Direct Acting


J02722cds_at
1
1

Cholestatic/
J02722
638






Direct Acting


J04171_at
1
1

Cholestatic/
J04171
639






Direct Acting


K03041mRNA_s_at
1
−1

Cholestatic/
K03041
640






Direct Acting


L37333_s_at
1
−1

Cholestatic/
L37333
641






Direct Acting


M57507_at
−1
−1

Cholestatic/
M57507
642






Direct Acting


M60921_at
1
1

Cholestatic/
M60921
643






Direct Acting


M96548_at
1
1

Cholestatic/
M96548
644






Direct Acting


rc_AA799861_g_at
−1
1

Cholestatic/
AA799861
645






Direct Acting


rc_AA800678_g_at
−1
−1

Cholestatic/
AA800678
646






Direct Acting


rc_AA891944_at
−1
−1

Cholestatic/
AA891944
647






Direct Acting


rc_AA900505_at
1
1

Cholestatic/
AA900505
648






Direct Acting


rc_AI009098_at
−1
1

Cholestatic/
AI009098
649






Direct Acting


rc_AI112173_at
1
1

Cholestatic/
AI112173
650






Direct Acting


rc_H31707_at
−1
1

Cholestatic/
H31707
651






Direct Acting


S61868_at
1
1

Cholestatic/
S61868
652






Direct Acting


U14005exon#1_s_at
−1
−1

Cholestatic/
U14005
653






Direct Acting


U42627_at
1
−1

Cholestatic/
U42627
654






Direct Acting


X07266cds_s_at
1
1

Cholestatic/
X07266
655






Direct Acting


X63594cds_at
1
1

Cholestatic/
X63594
656






Direct Acting


X96437mRNA_g_at
1
1

Cholestatic/
X96437
657






Direct Acting


AF000942_at
−1


Cholestatic
AF000942
658


AF075382_at
1


Cholestatic
AF075382
659


D00403_g_at
1


Cholestatic
D00403
660


J03865mRNA_f_at
1


Cholestatic
J03865
661


K03243mRNA_s_at
1


Cholestatic
K03243
662


L13619_at
1


Cholestatic
L13619
663


L13619_g_at
1


Cholestatic
L13619
664


M11794cds#2_f_at
−1
1
1
Cholestatic
M11794
665


M33962_g_at
1


Cholestatic
M33962
666


M63122_at
−1
1
1
Cholestatic
M63122
667


rc_AA685221_at
−1


Cholestatic
AA685221
668


rc_AA800613_at
1


Cholestatic
AA800613
669


rc_AA866383_at
1


Cholestatic
AA866383
670


rc_AA893192_at
1


Cholestatic
AA893192
671


rc_AA893602_at
−1


Cholestatic
AA893602
672


rc_AA946108_at
1
−1
−1
Cholestatic
AA946108
673


rc_AI102562_at
−1
1
1
Cholestatic
AI102562
674


rc_AI176456_at
−1
1
1
Cholestatic
AI176456
675


rc_AI176662_s_at
1


Cholestatic
AI176662
676


rc_AI639141_at
1


Cholestatic
AI639141
677


rc_H31118_at
1


Cholestatic
H31118
678


U15211_g_at
−1


Cholestatic
U15211
679


X63594cds_g_at
1


Cholestatic
X63594
680










[0136]

4





TABLE 4










Candidate Marker Genes












#
Name
Affymetrix IDs
Acc. Numbers
Comment
SEQ ID NO















1
PEG-3
AF020618_at;
AF020618
Early cell stress
  84;




AF020618_g_at

marker
631


2
GADD45
L32591mRNA_at;
L32591;
Stress marker
 210;




L32591mRNA_g_at;
RNGADD45X

211




rc_AI070295_at;




rc_AI070295_g_at


3
GADD153
U30186_at
U30186
Stress marker
518


4
PC3 (BTG2)
M60921_at;
M60921;
Stress marker
643




M60921_g_at;


 6




rc_AA944156_s_at
AA944156

404


5
PC4 (IFR1)
rc_AI014163_at
AI014163
Stress marker
423


6
CYP2b2
M13234cds_f_at;
M13234;
Induced by
741




J00728cds_f_at
J00728
some steatotic






compounds


7
AH-
AF082125_s_at;
AF082125;
Induced by
109



Receptor
AF082124_s_at
AF082124
some steatotic






compounds


8
IGFBP−1
M58634_at
M58634
Stress marker
 5


9
TRAP
Z14030_at
Z14030
Induced by
860






some direct






acting






compounds


10
GAPDH
M17701_s_at
P04797
House-keeping






gene


11
Amyloid_A4
X07648cds_at
X07648
Induced by
 66






some steatotic






compounds


12
Glutathione
U73174_g_at
U73174

540



reductase


13
Carboxyl
AB010635_s_at
AB010635
Induced by
 40



esterase


some steatotic






compounds


14
CYP3A1
D13912_s_at
D13912
Induced by
861






some steatotic






compounds


15
CYP9B
L00320cds_f_at
L00320
Induced by
793






some steatotic






compounds


16
UDP-
M13506_at
RNUD2A10;
Induced by
862



glucuronosy

M35086;
some steatotic



ltransferase

J05482
compounds



2B


17
EGR1
AF023087
AF023087;

 1



(Krox24)

M18416;





U7539;





U75398;





AI176662;





RNNGFIA










[0137]

5









TABLE 5










PCR






Validation


Treatment
Toxic
AH-R
PC3 (BTG2)
CYP2B2














Group
manifestation
RT-PCR
Affymetrix
RT-PCR
Affymetrix
RT-PCR
Affymetrix

















Control, 6 H
Control
1.0
1.0
1.0
1.0
1.0
1.0


Ro65-7199,
Steatotic
2.7
8.0
0.6
0.1
31.2
3.2


6 H


Ro66-0074,
Non-toxic
1.7
1.0
0.5
0.4
8.4
−1.2


6 H


Control, 24 H
Control
1.0
1.0
1.0
1.0
1.0
1.0


Ro65-7199,
Steatotic
1.1
4.5
2.7
5.7
15.9
3.4


24 H


Ro66-0074,
Non-toxic
1.2
1.0
0.7
0.6
1.3
1.1


24 H


Control, 7 D
Control
1.0
1.0
1.0
1.0
1.0
1.0


Ro65-7199,
Steatotic
2.0
2.8
0.0
0.7
3.9
4.1


7 D






Ro66-0074: 4-(2-Bromo-6-pyrrolidin-1-yl-pyridine-4-sulfonyl)-phenylamine




Ro65-7199: (4-Amino-N-(6-bromo-1 H-indol-4-yl)-benzenesulfonamide.








[0138]

6










TABLE 6








Gene
Acc.

SEQ ID

SEQ ID


Name
Number
Forward Primer
NO
Reverse Primer
NO




















PEG-3
AF020618
GCGGCTCAGATCTTTC
830
AGTGGTCACATCT
831




AAAGC

TCGCTGAGG





GADD45
L32591; ATAACTGTCGGCGTGT
832
ATCCATGTAGCGA
833



RNGADD
ACGAGG

CTTTCCCG



45X





GADD153
U30186
TTTCGCCTTTGAGACA
834
TCACCACTCTGTT
835




GTGTCC

TCCGTTTCC





PC3
M60921;
TTGGCCTAGCCAAGGT
836
ATAGCCCACCCTC
837


(BTG2)
AA944156
AAAAGG

CAAAAACG





CYP2B2
M13234;
TGCTCAAGTACCCCCA
838
CAAATGCCCTTTC
839



J00728
TCTCA

CTGTGGA





AH-
AF082125;
TTCTTTCCACCCCAAT
840
CTGCATGCTTCTG
841


Receptor
AF082124
TCCC

ATGTCTTCG





IGFBP-1
M58634
TTCTTTCCACCCCAAT
842
CTGCATGCTTCTG
843




TCCC

ATGTCTTCG





Amyloid_
X07648
ACACATGGCCAGAGTT
844
TCTTGAATCTCCT
845


A4

GAAGCC

CAGCCACGG





Glutathione
U73174
CATGATCACGTGGATT
846
CAACCCATCACTG
847


reductase

ACGGC

CTTATCCCC





Carboxyl-
AB010635
CAACATGCACCCAGCT
848
AGTCTTGGTCCAG
849


esterase

ATTTCA

AACTGCAGC





CYP3A1
D13912
CTTTCCTTTGTCCTGC
850
TCAATGCTGCCCT
851




ATTCCC

TGTTCTCC





CYP9B
L00320
CAACCCTTGATGACCG
852
CCCCAAGACAAAT
853




CACTA

GTGCTTTC





UDP-
RNUD2A1
GAGCCGTCTTCTGGAT
854
GGTCCCAACGCTG
855


glucuronosyl
0,
CGAGTA

TCTTCTTTT


transferase
M35086;


2B
J05482





EGR1
AF023087;
CAAAGCCAAGCAAACC
856
TCACGATTGCACA
857


(Krox24)
M18416;
AATGG

TGTCCAGC



U7539;



U75398;



AI176662;



RNNGFIA





GAPDH
P04797
CCCAGAACATCATCCC
858
ATGTAGGCCATGA
859




TGCATC

CGTCCACCA










[0139]

7





TABLE 7










The accession numbers refer to GenBank.










Affymetrix ID
Discrimination
Acc Number
SEQ ID NO





J03588_at
direct acting vs all
J03588
681



other classes


M13100cds#2_s_at
direct acting vs all
M13100
682



other classes


rc_AA800054_at
direct acting vs all
AA800054
683



other classes


rc_AI178750_at
direct acting vs all
AI178750
684



other classes


X53581cds#3_f_at
direct acting vs all
X53581
685



other classes


X58465mRNA_g_at
direct acting vs all
X58465
686



other classes


D78308_g_at
steatotic vs all
D78308
687



other classes


K00996mRNA_s_at
steatotic vs all
K00996
688



other classes


M94918mRNA_f_at
steatotic vs all
M94918
689



other classes


rc_AA892888_g_at
steatotic vs all
AA892888
690



other classes


rc_AA946503_at
steatotic vs all
AA946503
691



other classes


U88036_at
steatotic vs all
U88036
692



other classes


AF038870_at
cholestatic vs all
AF038870
693



other classes


AF076183_at
cholestatic vs all
AF076183
694



other classes


D00753_at
cholestatic vs all
D00753
695



other classes


J00738_s_at
cholestatic vs all
J00738
696



other classes


J03588_at
cholestatic vs all
J03588
697



other classes


K01932_f_at
cholestatic vs all
K01932
698



other classes


L27843_s_at
cholestatic vs all
L27843
699



other classes


M11670_at
cholestatic vs all
M11670
700



other classes


M15327_at
cholestatic vs all
M15327
701



other classes


rc_AA799899_i_at
cholestatic vs all
AA799899
702



other classes


rc_AA858673_at
cholestatic vs all
AA858673
703



other classes


rc_AA891220_at
cholestatic vs all
AA891220
704



other classes


rc_AA892333_at
cholestatic vs all
AA892333
705



other classes


rc_AA892775_at
cholestatic vs all
AA892775
706



other classes


rc_AA945143_at
cholestatic vs all
AA945143
707



other classes


rc_AA945321_at
cholestatic vs all
AA945321
708



other classes


rc_AI007820_s_at
cholestatic vs all
AI007820
709



other classes


rc_AI104524_s_at
cholestatic vs all
AI104524
710



other classes


rc_AI228674_s_at
cholestatic vs all
AI228674
711



other classes


rc_AI232087_at
cholestatic vs all
AI232087
712



other classes


X15734_at
cholestatic vs all
X15734
713



other classes


AB008424_s_at
non-toxic vs all
AB008424
714



other classes


AF045464_s_at
non-toxic vs all
AF045464
715



other classes


D78308_at
non-toxic vs all
D78308
716



other classes


J01435cds#8_s_at
non-toxic vs all
J01435
717



other classes


K01932_f_at
non-toxic vs all
K01932
718



other classes


M11794cds#2_f_at
non-toxic vs all
M11794
719



other classes


M13100cds#2_s_at
non-toxic vs all
M13100
720



other classes


M20131cds_s_at
non-toxic vs all
M20131
721



other classes


M64733mRNA_s_at
non-toxic vs all
M64733
722



other classes


rc_AA800054_at
non-toxic vs all
AA800054
723



other classes


rcAA817964_s_at
non-toxic vs all
AA817964
724



other classes


rc_AA945054_s_at
non-toxic vs all
AA945054
725



other classes


rc_AA945169_at
non-toxic vs all
AA945169
726



other classes


rc_AI104679_s_at
non-toxic vs all
AI104679
727



other classes


rc_AI179012_s_at
non-toxic vs all
AI179012
728



other classes


rc_AI236795_s_at
non-toxic vs all
AI236795
729



other classes


S72505_f_at
non-toxic vs all
S72505
730



other classes


X03468_at
non-toxic vs all
X03468
731



other classes


X07467_at
non-toxic vs all
X07467
732



other classes


AB008807_g_at
controls vs all
AB008807
733



other classes


D00362_s_at
controls vs all
D00362
734



other classes


D00913_g_at
controls vs all
D00913
735



other classes


D25224_at
controls vs all
D25224
736



other classes


D25224_g_at
controls vs all
D25224
737



other classes


D43964_at
controls vs all
D43964
738



other classes


E01184cds_s_at
controls vs all
E01184
739



other classes


H32189_s_at
controls vs all
H32189
740



other classes


J00728cds_f_at
controls vs all
J00728
741



other classes


J02596cds_g_at
controls vs all
J02596
742



other classes


L37333_s_at
controls vs all
L37333
743



other classes


M11670_at
controls vs all
M11670
744



other classes


M15481_at
controls vs all
M15481
745



other classes


M20629_s_at
controls vs all
M20629
746



other classes


M28255_s_at
controls vs all
M28255
747



other classes


M31363mRNA_f_at
controls vs all
M31363
748



other classes


M58041_s_at
controls vs all
M58041
749



other classes


M64733mRNA_s_at
controls vs all
M64733
750



other classes


M76767_s_at
controls vs all
M76767
751



other classes


rc_AA800318_at
controls vs all
AA800318
752



other classes


rc_AA858673_at
controls vs all
AA858673
753



other classes


rc_AA860062_g_at
controls vs all
AA860062
754



other classes


rc_AA875107_at
controls vs all
AA875107
755



other classes


rc_AA891774_at
controls vs all
AA891774
756



other classes


rc_AA892775_at
controls vs all
AA892775
757



other classes


rc_AA892888_g_at
controls vs all
AA892888
758



other classes


rc_AA945143_at
controls vs all
AA945143
759



other classes


rc_AA946503_at
controls vs all
AA946503
760



other classes


rc_AI008641_at
controls vs all
AI008641
761



other classes


rc_AI011998_at
controls vs all
AI011998
762



other classes


rc_AI104524_s_at
controls vs all
AI104524
763



other classes


rc_AI136891_at
controls vs all
AI136891
764



other classes


rc_AI169372_g_at
controls vs all
AI169372
765



other classes


rc_AI172017_at
controls vs all
AI172017
766



other classes


rc_AI228674_s_at
controls vs all
AI228674
767



other classes


rc_AI232087_at
controls vs all
AI232087
768



other classes


S61868_g_at
controls vs all
S61868
769



other classes


S72505_f_at
controls vs all
S72505
770



other classes


S76779_s_at
controls vs all
S76779
771



other classes


X15096cds_s_at
controls vs all
X15096
772



other classes


X15512_at
controls vs all
X15512
773



other classes


X56325mRNA_s_at
controls vs all
X56325
774



other classes


X57432cds_s_at
controls vs all
X57432
775



other classes


X74549_at
controls vs all
X74549
776



other classes


X76456cds_at
controls vs all
X76456
777



other classes


X79081mRNA_f_at
controls vs all
X79081
778



other classes










[0140]

8





TABLE 8










The accession numbers refer to GenBank.










Affymetrix ID
Discriminator
Acc Number
SEQ ID NO.





D25224g_at
direct acting vs
D25224
779



controls


E01184cds_s_at
direct acting vs
E01184
780



controls


J02585_at
direct acting vs
J02585
781



controls


J02597cds_s_at
direct acting vs
J02597
782



controls


J03588_at
direct acting vs
J03588
783



controls


L19998_at
direct acting vs
L19998
784



controls


M13100cds#2_s_at
direct acting vs
M13100
785



controls


M94548_at
direct acting vs
M94548
786



controls


rc_AA800054_at
direct acting vs
AA800054
787



controls


rc_AI231807_g_at
direct acting vs
AI231807
788



controls


S76489_s_at
direct acting vs
S76489
789



controls


X53581cds#3_f_at
direct acting vs
X53581
790



controls


X57432cds_s_at
direct acting vs
X57432
791



controls


X58465mRNA_g_at
direct acting vs
X58465
792



controls


L00320cds_f_at
steatotic vs
L00320
793



controls


rc_AA946503_at
steatotic vs
AA946503
794



controls


X56325mRNA_s_at
steatotic vs
X56325
795



controls


AF038870_at
cholestatic vs
AF038870
796



controls


AF076183_at
cholestatic vs
AF076183
797



controls


D89375_s_at
cholestatic vs
D89375
798



controls


J00738_s_at
cholestatic vs
J00738
799



controls


J01435cds#1_s_at
cholestatic vs
J01435
800



controls


J03588_at
cholestatic vs
J03588
801



controls


J03863_at
cholestatic vs
J03863
802



controls


K01932_f_at
cholestatic vs
K01932
803



controls


K01934mRNA#2_at
cholestatic vs
K01934
804



controls


L27843_s_at
cholestatic vs
L27843
805



controls


M10068mRNA_s_at
cholestatic vs
M10068
806



controls


M11670_at
cholestatic vs
M11670
807



controls


M13100cds#3_f_at
cholestatic vs
M13100
808



controls


M14775_s_at
cholestatic vs
M14775
809



controls


M15327_at
cholestatic vs
M15327
810



controls


M20629_s_at
cholestatic vs
M20629
811



controls


M31018_f_at
cholestatic vs
M31018
812



controls


M34331_g_at
cholestatic vs
M34331
813



controls


M57718mRNA_s_at
cholestatic vs
M57718
814



controls


rc_AA800318_at
cholestatic vs
AA800318
815



controls


rc_AA858673_at
cholestatic vs
AA858673
816



controls


rc_AA859372_s_at
cholestatic vs
AA859372
817



controls


rc_AA945143_at
cholestatic vs
AA945143
818



controls


rc_AA945321_at
cholestatic vs
AA945321
819



controls


rc_AI072634_at
cholestatic vs
AI072634
820



controls


rc_AI102562_at
cholestatic vs
AI102562
821



controls


rc_AI104524_s_at
cholestatic vs
AI104524
822



controls


rc_AI105448_at
cholestatic vs
AI105448
823



controls


rc_AI228674_s_at
cholestatic vs
AI228674
824



controls


S76489_s_at
cholestatic vs
S76489
825



controls


X04979_at
cholestatic vs
X04979
826



controls


X15734_at
cholestatic vs
X15734
827



controls


X86561cds#2_at
cholestatic vs
X86561
828



controls


Y07704_at
cholestatic vs
Y07704
829



controls










[0141]

9





TABLE 9










Regualtion of GADD-family genes assessed by RT-PCR.












1









2











Shaded cells represent significant induction (threshold usually 2-fold induction).








[0142]

10





TABLE 10










EGR-1 induction by Tasmar and Dinitrophenol.












3











§: The compounds were administered to the experimental animals three times, every 12 Hours. Animals were sacrificed 3 hours after the last administration. Shaded cells represent significant induction (threshold usually 2-fold induction).








[0143]


Claims
  • 1. A method of predicting at least one toxic effect of a compound, comprising detecting the level of expression of one or more genes from Table 3 in a tissue or cell sample exposed to the compound; wherein differential expression of the one or more genes from Table 3 is indicative of at least one toxic effect.
  • 2. The method according to claim 1, wherein the toxic effect is hepatotoxicity.
  • 3. The method according to claim 1, wherein the hepatotoxicity comprises at least one liver disease pathology selected from the group consisting of hepatitis, liver necrosis, protein adduct formation and fatty liver.
  • 4. The method according to claim 1, wherein the expression levels of at least 2 genes from Table 3 are detected.
  • 5. The method according to claim 1, wherein the expression levels of at least 5 genes from Table 3 are detected.
  • 6. The method according to claim 1, wherein the expression levels of at least 10 genes from Table 3 are detected.
  • 7. The method according to claim 1, wherein the expression levels of nearly all genes from Table 3 are detected.
  • 8. The method according to claim 1, wherein the expression levels of all genes from Table 3 are detected.
  • 9. The method according to claim 1, wherein the level of expression is detected by an amplification, hybridization or reporter gene assay.
  • 10. A method of predicting at least one toxic effect of a compound, comprising: (a) detecting the level of expression of one or more genes from Table 3 in a tissue or cell sample exposed to the compound; (b) comparing the level of expression of the one or more genes to their level of expression in a control tissue or cell sample, wherein differential expression of the one or more genes in Table 3 is indicative of at least one toxic effect.
  • 11. The method according to claim 10, wherein the toxic effect is hepatotoxicity.
  • 12. The method according to claim 10, wherein the hepatotoxicity comprises at least one liver disease pathology selected from the group consisting of hepatitis, liver necrosis, protein adduct formation and fatty liver.
  • 13. The method according to claim 10, wherein the expression levels of at least 2 genes from Table 3 are detected.
  • 14. The method according to claim 10, wherein the expression levels of at least 5 genes from Table 3 are detected.
  • 15. The method according to claim 10, wherein the expression levels of at least 10 genes from Table 3 are detected.
  • 16. The method according to claim 10, wherein the expression levels of nearly all genes from Table 3 are detected.
  • 17. The method according to claim 10, wherein the expression levels of all genes from Table 3 are detected.
  • 18. The method according to claim 10, wherein the level of expression is detected by an amplification, hybridization or reporter gene assay.
  • 19. A method of predicting the progression of a toxic effect of a compound, comprising detecting the level of expression in a tissue or cell sample exposed to the compound of one or more genes from Table 3, wherein differential expression of the one or more genes in Table 3 is indicative of toxicity progression.
  • 20. The method according to claim 19, wherein the toxic effect is hepatotoxicity.
  • 21. The method according to claim 19, wherein the hepatotoxicity comprises at least one liver disease pathology selected from the group consisting of hepatitis, liver necrosis, protein adduct formation and fatty liver.
  • 22. The method according to claim 19, wherein the expression levels of at least 2 genes from Table 3 are detected.
  • 23. The method according to claim 19, wherein the expression levels of at least 5 genes from Table 3 are detected.
  • 24. The method according to claim 19, wherein the expression levels of at least 10 genes from Table 3 are detected.
  • 25. The method according to claim 19, wherein the expression levels of nearly all genes from Table 3 are detected.
  • 26. The method according to claim 19, wherein the expression levels of all genes from Table 3 are detected.
  • 27. The method according to claim 19, wherein the level of expression is detected by an amplification, hybridization or reporter gene assay.
  • 28. A method of predicting the mechanism of toxicity of a compound comprising detecting the level of expression in a tissue or cell sample exposed to the compound of one or more genes from Table 3, wherein differential expression of the one or more genes in Table 3 is associated with a specific mechanism of toxicity.
  • 29. The method according to claim 28, wherein the expression levels of at least 2 genes from Table 3 are detected.
  • 30. The method according to claim 28, wherein the expression levels of at least 5 genes from Table 3 are detected.
  • 31. The method according to claim 28, wherein the expression levels of at least 10 genes from Table 3 are detected.
  • 32. The method according to claim 28, wherein the expression levels of nearly all genes from Table 3 are detected.
  • 33. The method according to claim 28, wherein the expression levels of all genes from Table 3 are detected.
  • 34. The method according to claim 28, wherein the level of expression is detected by an amplification, hybridization or reporter gene assay.
  • 35. A method of predicting at least one toxic effect of a compound, comprising detecting the level of expression of one of the genes selected from Table 4 in a tissue or cell sample exposed to the compound, wherein differential expression of the gene selected from Table 4 is indicative of at least one toxic effect.
  • 36. The method according to claim 35, wherein the gene selected from Table 4 is progression elevated gene 3 or Translocon associated protein.
  • 37. The method according to claim 35, wherein the toxic effect is hepatotoxicity.
  • 38. The method according to claim 35, wherein the level of expression is detected by an amplification, hybridization or reporter gene assay.
  • 39. A set of nucleic acid primers, wherein the primers specifically amplify at least two of the genes from Table 3.
  • 40. A set of nucleic acid probes, wherein the probes comprise sequences which hybridize to at least two of the genes from Table 3.
  • 41. A set of nucleic acid probes, wherein the probes comprise sequences which hybridize to at least 5 of the genes from Table 3.
  • 42. A set of nucleic acid probes, wherein the probes comprise sequences which hybridize to at least 10 of the genes from Table 3.
  • 43. The set of probes according to claim 40, wherein the probes are attached to a solid support.
  • 44. A solid support comprising at least two probes, wherein each of the probes comprises a sequence that specifically hybridizes to a gene in Table 3.
  • 45. A computer system comprising a database containing DNA sequence information and expression information of at least two of the genes from Table 3 from tissue or cells exposed to a hepatotoxin, and a user interface.
  • 46. A computer system for predicting at least one toxic effect of a compound comprising: a processor and a memory coupled to said processor; said memory storing a first set of data including the level of expression of one or more genes from Table 3 in a tissue or cell sample exposed to said compound, and said memory storing a second set of data including the level of expression of the one or more genes from Table 3 in a control tissue or cell sample; and said processor comparing said first set of data with said second set of data to predict said at least one toxic effect of said compound.
  • 47. A kit comprising at least one solid support according to claim 44 and gene expression information for the said genes.
Priority Claims (2)
Number Date Country Kind
02005336.9 Mar 2002 EP
02015657.6 Jul 2002 EP