Information
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Patent Application
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20040005547
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Publication Number
20040005547
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Date Filed
March 14, 200321 years ago
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Date Published
January 08, 200421 years ago
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CPC
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US Classifications
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International Classifications
- C12Q001/70
- C12Q001/68
- G01N033/53
- G01N033/567
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.
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.
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.
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).
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.
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).
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.
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.
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).
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 1
|
|
Hepato-
Targettoxicity
CompoundDose levelsorganMechanism
|
|
Chlorpromazine15mg/kgLiverCholestatic
Cyclosporine A5, 15 and 30mg/kgLiverCholestatic
Erythromycin734mg/kgLiverCholestatic
Glibenclamide2.5 and 25mg/kgLiverCholestatic
Lithocholic acid60 and 120μmol/kgLiverCholestatic
Ro 48-5695 (ETA)25mg/kgLiverCholestatic
Dexamethasone0.6mg/kgLiverCyp inducer/
prolif
1,2-Dichloro-1.5 and 4.5mmol/kgLiverDirect
benzeneActing
Aflatoxin B11 and 4mg/kgLiverDirect
Acting
Bromobenzene1 and 3mmol/kgLiverDirect
Acting
Carbon0.25 and 2ml/kgLiverDirect
tetrachlorideActing
Diclofenac10, 30, 100mg/kgLiverDirect
Acting
Hydrazine10, 60, 90mg/kgLiverDirect
Acting
Nitrofurantoin5, 20, 60mg/kgLiverDirect
Acting
Thioacetamide2, 10, 50mg/kgLiverDirect
Acting
Concanavaline A0.1, 20mg/kgLiverHepatitis/
Infammation
Tacrine5, 15 and 35mg/kgLiverHuman
Hepatotox
Tempium20 and 1000mg/kgLiverHuman
(Lazabemide)hepatotox
Tolcapone300mg/kgLiverHuman
(Tasmar)hepatotox
1,4-Dichloro-4.5mmol/kgLiverNon toxic
benzene
Amineptin125, 250, 500μmol/kgLiverSteatotic
Amiodarone50, 100, 600mg/kgLiverSteatotic
Doxycycline5, 20, 40mg/kgLiverSteatotic
Ro 28-1674 (GKA)250mg/kgLiverSteatotic
Ro 28-1675 (GKA)100mg/kgLiverSteatotic
Ro 65-7199 (5HT6)30, 100, 400mg/kgLiverSteatotic
Tetracycline125, 200, 250μmol/kgLiverSteatotic
Dinitrophenol10 and 30mg/kgLiverUncoupling
|
Ro 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 |
|