- Email: [email protected]
Use of cDNA microarrays to probe and understand the toxicological consequences of altered gene expression
Toxicology Letters 112 – 113 (2000) 473 – 477 www.elsevier.com/locate/toxlet
Use of cDNA microarrays to probe and understand the toxicological consequences of altered gene expression William D. Pennie * AstraZeneca Central Toxicology Laboratory, Alderley Park, Cheshire SK10 4TJ, UK
Abstract Genomic sciences offer the ability to measure quantitative modulation of transcription in cells and tissues under a wide variety of conditions. We have developed a series of custom cDNA microarrays specifically to investigate toxicity processes. Around 600 marker genes for toxicity were selected and representative cDNA clones were obtained, amplified and purified by polymerase chain reaction (PCR), before being immobilised on nylon membranes. A detailed database on biochemical function, role in disease and allelic variation has been assembled for each gene. Applications in our laboratory include mechanistic investigation of a number of toxic endpoints such as hepatotoxicity and endocrine disruption. © 2000 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Genomics; Gene regulation; In vitro testing; Hepatotoxicity; Endocrine disruption
1. Introduction The recent advent of genomics sciences offers the potential to revolutionise many areas of investigative biology, including toxicology. Sophisticated technical developments together with large scale cloning and sequencing enterprises in both the industrial and academic sectors, have converged to allow the construction of ‘DNA chips’. These chips (or microarrays) allow quantitative measurement of the transcriptional activity of, potentially, thousands of genes in a biological sample. The applications of microarrays in toxicology, toxicogenomics, have a huge potential to impact on our current mechanistic understanding * Tel.: +44-1625-515-438; fax: +44-1625-590-249. E-mail address: [email protected] (W.D. Pennie)
of toxic processes and our ability to characterise compounds with the potential for adverse health effects.
2. Microarrays; practicalities The construction of microarrays is based on immobilising DNA sequences (which may be oligonucleotides or polymerase chain reaction (PCR) amplified cDNA) on a solid support which can then be hybridised with a labelled RNA probe (normally a cDNA copy). Following quantification, the extent of hybridisation to each individual sequence on the array can be determined by phosphorimager analysis (or similar methodologies), and relative changes in gene expression between two or more biological samples can be measured
0378-4274/00/$ - see front matter © 2000 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0 3 7 8 - 4 2 7 4 ( 9 9 ) 0 0 1 9 9 - X
474
W.D. Pennie / Toxicology Letters 112–113 (2000) 473–477
(see Brown and Botstein, 1999). The microarray is normally generated on either glass slides or nylon membranes (Bowtell, 1999) and can encompass sequences selected to investigate specific endpoints or pathways, or to include genes representing a wide range of biological processes. Normally a number of ‘housekeeping’ genes, whose expression is expected to be stable under a wide range of conditions, is included to assist normalisation of data. By these approaches, changes in the activity of all the genes represented on the array can be measured simultaneously in a single experiment. The combination of large scale genome sequencing efforts and microarrays mean that it is now possible to measure the activity of the entire gene complement of yeast (DeRisi et al., 1997). It is conceivable that as closure is reached in the human genome sequencing programme, measuring the activity of the entire human genome will become possible within the next decade. Despite their obvious strengths, microarrays are only capable of measuring changes in gene expression at the mRNA level and therefore cannot confirm changes in levels of the functional protein encoded by the gene. In addition, protein modifications such as phosphorylation, which may have profound influence on protein function, will not be directly measured by microarray analysis. Nevertheless, it is reasonable to assume that events such as phosphorylation will give rise to downstream alterations in the transcription of other target genes, facilitating identification of the pathways being regulated. Protein function may also be impacted by mutations or polymorphisms in the gene generating the transcript, which similarly will be missed by microarray if the overall level of gene expression is unaltered. Genomics applications to help define these polymorphic responses, pharmacogenetics, are also under development.
3. Applications of toxicogenomics Microarray applications in toxicology can be broadly classified into two groups; mechanistic or investigative research and predictive toxicology. Where a mechanistic understanding of a toxic compound or toxicity process is required, clearly
a major concern is that the experimental system recapitulates the biology of the process as closely as possible. In all likelihood the toxicity endpoint has been previously characterised, and an appropriate model system, either in vivo or using appropriate primary or established cell lines, can be utilised. The rewards of such studies may be an improved understanding of toxic mechanisms which are difficult to test for using short term or in vitro assays. For example non-genotoxic carcinogenesis is assessed normally using a long term rodent cancer bioassay (Chhabra et al., 1990). It is possible however, that microarray analyses might help characterise the genes involved in the development of this phenotype. Indeed, in excess of 300 altered gene expression events have been identified in rodent hepatocytes following their exposure to the non-genotoxic carcinogen phenobarbital, by both microarray and gel-based expression technologies (Rodi et al., 1999). Clearly many mechanistic endpoints could be explored using this technique and the combinatorial approaches such as using toxicogenomics profiling on knockout (or transgenic) mice of toxicology relevance (Ryffel, 1997) may provide even more powerful insights into the role of specific genes in toxicological processes. In predictive toxicology the hypothesis is that a specific group or class of compounds (grouped by toxic endpoint, mechanism, structure, target organ etc.) can induce unique mRNA expression profiles, or signature patterns. Therefore, using microarray technology to analyse genome-wide patterns of mRNA expression in vitro it should be possible to predict gene expression patterns associated with specific groups of compounds with the same toxic or biological endpoints. As the database of reference compounds grows for an individual toxic mechanism it may be possible develop miniarrays customised for specific toxic endpoint detection, based on pattern recognition of the transcript profiles of a reference set. Clearly the throughput requirements of such an approach will necessitate employing in vitro cell culture systems. While cultured cell systems have many practical advantages there are two major drawbacks to consider; availability of suitable cell lines (although for non-mechanistic studies generic cell
W.D. Pennie / Toxicology Letters 112–113 (2000) 473–477
lines may still be useful) and metabolism of the chemical may be required to produce the active species. If such an approach were to be successful a major application may be the ability to detect potential adverse health effects early in the development process of new pharmaceuticals or agrochemicals, potentially even before enough of the compound is available for more traditional in vitro or in vivo studies.
4. Custom toxicology microarray construction; ‘ToxBlot’ The Central Toxicology Laboratory at AstraZeneca, in collaboration with our colleagues in AstraZeneca Pharmaceuticals, have developed a programme of in house custom microarray construction; ToxBlot. A custom approach allows us control over the choice of genes on the array, allowing gene lists to be focused on areas of particular interest to mechanistic or investigative toxicology research (see Table 1). Once cDNA sets have been amplified, purified and verified the unit cost of array manufacture is very small, removing the issue of array cost from experimental design. Table 1 Example of gene categories represented on ‘ToxBlot’ Acetyl CoA pathway Basic transcription factors Bcl/Bax family Caspases CDC/CDKs Cell adhesion Cell surface receptors Chemokines Cyclins CYP family Drug metabolism
Extracellular matrix GABA receptors/ GABA transport GST Heat shock proteins Histones Heat shock proteins/stress Histones Immediate early genes Interleukins Ion channels Liver acute phase
Markers for GI tract physiology Matrix metalloproteins Metalloproteinases Neurotransmitters/ metabolism Neurotrophic factors and receptors Oxidative stress Peptide hormones Steroid receptors Steroid regulated Steroidogenesis/ aromatase Thyroid hyperplasia
475
For the construction of the ToxBlot arrays, around 2000 sequences of human and murine origin were identified as potential diagnostic markers for various toxicity processes. In house cDNA libraries, of both proprietary and public domain origin, were used as the source material for array construction; each PCR product was individually analysed by electrophoresis to determine success of amplification, purity and estimate of yield, and sequence verification of clones is performed where appropriate. We have immobilised the sequences on nylon membranes, each containing 2400 cDNAs spanning around 600 genes of either human or murine origin. In order to assess reproducibility, each gene being profiled is represented by four individual spots on each array; two non overlapping cDNAs for each gene (in duplicate) wherever possible. To assist in the interpretation of differential gene expression results, we have assembled a database of background ‘literature’ for each gene on the array including biochemical/enzymatic function, tissue distribution and known allelic variation. In this regard, public access databases such as the ‘GeneCards’ system developed at the Weizmann Institute (Rebhan et al., 1997) or the Kyoto Encyclopaedia of Genes and Genomes (KEGG) databases (http://www.genome.ad.jp/kegg/) can act as useful sources of supplementary information on genes of interest. These custom arrays are now in wide use in our laboratories and some example applications are outlined below.
4.1. Example 1; characterising disruption of endocrine signalling Concern continues in both the scientific community and among the general public over reports that environmental compounds (of both natural and synthetic origin) may have the potential to affect estrogen receptor function, which may impact the reproductive health of animals and humans. Many of the current in vitro screens for this endpoint fail to accommodate the complex mode of action of the estrogen receptor, measuring the activation of a single estrogen responsive gene. Recent research from our laboratory and others
476
W.D. Pennie / Toxicology Letters 112–113 (2000) 473–477
has indicated that the estrogen response element (ERE) on different estrogen-regulated genes may profoundly influence the gene’s responsiveness to estrogen receptor activation (Pennie et al., 1998), indicating that measuring the activity of a single estrogen responsive gene may be misleading. The application of transcript profiling to the biology of the estrogen receptor should help identify pathways of gene regulation involved in estrogen action and may lead to more sophisticated in vitro assays for estrogen receptor activity. For example, the combination of suppression subtractive hybridisation method for comparing differentially regulated genes (Diatchenko et al., 1999) and cDNA construction has recently been employed to characterise differential gene expression in estrogen receptor positive and negative breast carcinoma cell lines (Yang et al., 1999b). Our own laboratory has been employing custom microarrays to attempt to characterise the patterns of gene expression changes that take place in cells exposed to natural (e.g. estradiol), synthetic (e.g. diethylstilbestrol) and phytoestrogens (e.g. genestein). This microarray encompasses many pathways involved in normal endocrine function together with a diverse range of non-endocrine related genes. By the treatment of appropriate cell lines (e.g. T47D, MCF7), with the known estrogens at multiple doses and at various time-points, a pattern of consistent expression changes begins to emerge that we are hopeful may assist is in characterising the endocrine disrupting activity of novel compounds, without employing animal assays such as the uterotrophic assay.
cytotoxicity for cultured hepatocytes (such as the human hepatoma cell line HepG2) has been assessed. Well characterised compounds include ethanol (Neuman et al., 1993), paracetamol (Nicod et al., 1997), hydrogen peroxide (Yang et al., 1999a) and carbon tetrachloride (Dai and Cederbaum, 1995). These compounds cause necrosis in liver (and cytotoxicity in HepG2 cells), induce periportal hepatocyte proliferation, elevate specific enzymes (e.g. cypP450 2E1) and cause similar changes in oxidative stress and lipid peroxidation. We have chosen to profile the gene expression changes induced in HepG2 cells by these four compounds. When attempting to identify consistent gene expression pattern changes across diverse compounds or treatments it is important to standardise treatments on the basis of easily measured parameters. (e.g. enzyme activity, toxicity, DNA replication, cell division, apoptosis etc.) to assist design of dose range and timecourses for each compound. By measuring LDH release over a dose range, we have selected appropriate ‘high’ (50% LDH release) and ‘low’ (25% LDH release) dose points for each compound. We have then profiled gene expression changes induced by each compound at both dose points following a fixed time of exposure. Microarray analysis reveals genes which are up or downregulated consistently between different compound treatments and dose ranges, allowing us the potential to identify patterns of gene changes diagnostic for liver cytotoxicity.
4.2. Example 2; hepatocyte cytotoxicity
5. Future issues
The characterisation of cytotoxicity in cultured human hepatocytes provides an ideal model for grouping similarly acting compounds on the basis of gene expression data. Many liver cell models retain liver-specific characteristics in culture and have proved useful in toxicology assays including cytotoxicity and genotoxicity assays, drug – drug interactions, kinetic studies and mechanistic studies on the biochemical effects of various toxicants (Guillouzo, 1998). A number of ‘gold standard’ hepatotoxicants have been investigated where the
The management of the vast amount of data which will be generated by array technologies is a formidable task. Approaches such as statistical clustering have been used to group patterns of genes from microarray analyses. Analysing temporal patterns of expression in this way has assisted approaches such as characterising serum-responsiveness and wound repair (Iyer et al., 1999) and has been successful in separating cancerous tissue from normal tissues and cell lines on the basis of microarray data (Alon et al.,
W.D. Pennie / Toxicology Letters 112–113 (2000) 473–477
1999). Clearly this approach will have useful applications in sorting array data on different compounds with a similar toxic endpoint, facilitating the identification of diagnostic patterns of gene expression for these endpoints. In turn, these approaches may enhance prediction of the toxicity of novel compounds. The building of ‘reference data sets’ by comparison of microarray output across different laboratories will require consistency in data analysis and format if it is to be feasible. A number of resources, both academic and commercial, exist for such purposes (for review see Bassett et al., 1999). For example, The National Human Genome Research Institute is developing such a system, ArrayDB, for the storage, retrieval and analysis of array data along with information on 15 000 genes linked to public domain sequence and pathway information databases (Ermolaeva et al., 1998). Clearly, a combination of the building of large reference data sets together with sophisticated computing technologies, which facilitate analysis of the intrinsic patterns and relationships in large-scale gene expression data, will be required to allow genomics technologies to realise their full potential.
References Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J., 1999. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. USA 96, 6745–6750. Bassett, D.E. Jr., Eisen, M.B., Boguski, M.S., 1999. Gene expression informatics: it’s all in your mine. Nat. Genet. (1 Supplement) 21, 51 – 55. Bowtell, D.D., 1999. Options available-from start to finish-for obtaining expression data by microarray. Nat. Genet. (1 Supplement) 21, 25 – 32. Brown, P.O., Botstein, D., 1999. Exploring the new world of the genome with DNA microarrays. Nat. Genet. (1 Supplement) 21, 33 – 37. Chhabra, R.S., Huff, J.E., Schwetz, B.S., Selkirk, J., 1990. An overview of prechronic and chronic toxicity/carcinogenicity experimental study designs and criteria used by the National Toxicology Program. Environ. Health Perspect. 86, 313 – 321. .
477
Dai, Y., Cederbaum, A.I., 1995. Inactivation and degradation of human cytochrome P4502E1 by CCl4 in a transfected HepG2 cell line. J. Pharmacol. Exp. Ther. 275, 1614 – 1622. DeRisi, J.L., Iyer, V.R., Brown, P.O., 1997. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680 – 686. Diatchenko, L., Lukyanov, S., Lau, Y.F., Siebert, P.D., 1999. Suppression subtractive hybridization: a versatile method for identifying differentially expressed genes. Methods Enzymol. 303, 349 – 380. Ermolaeva, O., Rastogi, M., Pruitt, K.D., Schuler, G.D., Bittner, M.L., Chen, Y., Simon, R., Meltzer, P., Trent, J.M., Boguski, M.S., 1998. Data management and analysis for gene expression arrays. Nat. Genet. 20 (1), 19 – 23. Guillouzo, A., 1998. Liver cell models in, in vitro toxicology. Environ. Health Perspect. 106 (Suppl 2), 511 – 532. Iyer, V.R., Eisen, M.B., Ross, D.T., Schuler, G., Moore, T., Lee, J.C.F., Trent, J.M., Staudt, L.M., Hudson, J. Jr., Boguski, M.S., Lashkari, D., Shalon, D., Botstein, D., Brown, P.O., 1999. The transcriptional program in the response of human fibroblasts to serum. Science 283, 83 – 87. Neuman, M.G., Koren, G., Tiribelli, C., 1993. In vitro assessment of the ethanol-induced hepatotoxicity on HepG2 cell line. Biochem. Biophys. Res. Commun. 197, 932 – 941. Nicod, L., Viollon, C., Regnier, A., Jacqueson, A., Richert, L., 1997. Rifampicin and isoniazid increase acetaminophen and isoniazid cytotoxicity in human HepG2 hepatoma cells. Hum. Exp. Toxicol. 16, 28 – 34. Pennie, W.D., Aldridge, T.C., Brooks, A.N., 1998. Differential activation by xenoestrogens of ER alpha and ER beta when linked to different response elements. J. Endocrinol. 158, R11 – 14. Rebhan, M., Chalifa-Caspi, V., Prilusky, J., Lancet, D., 1997. GeneCards: encyclopedia for genes, proteins and diseases. Weizmann Institute of Science, Bioinformatics Unit and Genome Center (Rehovot, Israel), World Wide Web URL: http://bioinfo.weizmann.ac.il/cards Rodi, C.P., Bunch, R.T., Curtiss, S.W., Kier, L.D., Cabonce, M.A., Davila, J.C., Mitchell, M.D., Alden, C.L., Morris, D.L., 1999. Revolution through genomics in investigative and discovery toxicology. Toxicol. Pathol. 27, 107 – 110. Ryffel, B., 1997. Impact of knockout mice in toxicology. Crit. Rev. Toxicol. 27, 135 – 154. Yang, C.F., Shen, H.M., Ong, C.N., 1999a. Protective effect of ebselen against hydrogen peroxide-induced cytotoxicity and DNA damage in HepG2 cells. Biochem. Pharmacol. 57, 273 – 279. Yang, G.P., Ross, D.T., Kuang, W.W., Brown, P.O., Weigel, R.J., 1999b. Combining SSH and cDNA microarrays for rapid identification of differentially expressed genes. Nucleic Acids Res. 27, 1517 – 1523.
Use of cDNA microarrays to probe and understand the toxicological consequences of altered gene expression William D. Pennie * AstraZeneca Central Toxicology Laboratory, Alderley Park, Cheshire SK10 4TJ, UK
Abstract Genomic sciences offer the ability to measure quantitative modulation of transcription in cells and tissues under a wide variety of conditions. We have developed a series of custom cDNA microarrays specifically to investigate toxicity processes. Around 600 marker genes for toxicity were selected and representative cDNA clones were obtained, amplified and purified by polymerase chain reaction (PCR), before being immobilised on nylon membranes. A detailed database on biochemical function, role in disease and allelic variation has been assembled for each gene. Applications in our laboratory include mechanistic investigation of a number of toxic endpoints such as hepatotoxicity and endocrine disruption. © 2000 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Genomics; Gene regulation; In vitro testing; Hepatotoxicity; Endocrine disruption
1. Introduction The recent advent of genomics sciences offers the potential to revolutionise many areas of investigative biology, including toxicology. Sophisticated technical developments together with large scale cloning and sequencing enterprises in both the industrial and academic sectors, have converged to allow the construction of ‘DNA chips’. These chips (or microarrays) allow quantitative measurement of the transcriptional activity of, potentially, thousands of genes in a biological sample. The applications of microarrays in toxicology, toxicogenomics, have a huge potential to impact on our current mechanistic understanding * Tel.: +44-1625-515-438; fax: +44-1625-590-249. E-mail address: [email protected] (W.D. Pennie)
of toxic processes and our ability to characterise compounds with the potential for adverse health effects.
2. Microarrays; practicalities The construction of microarrays is based on immobilising DNA sequences (which may be oligonucleotides or polymerase chain reaction (PCR) amplified cDNA) on a solid support which can then be hybridised with a labelled RNA probe (normally a cDNA copy). Following quantification, the extent of hybridisation to each individual sequence on the array can be determined by phosphorimager analysis (or similar methodologies), and relative changes in gene expression between two or more biological samples can be measured
0378-4274/00/$ - see front matter © 2000 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0 3 7 8 - 4 2 7 4 ( 9 9 ) 0 0 1 9 9 - X
474
W.D. Pennie / Toxicology Letters 112–113 (2000) 473–477
(see Brown and Botstein, 1999). The microarray is normally generated on either glass slides or nylon membranes (Bowtell, 1999) and can encompass sequences selected to investigate specific endpoints or pathways, or to include genes representing a wide range of biological processes. Normally a number of ‘housekeeping’ genes, whose expression is expected to be stable under a wide range of conditions, is included to assist normalisation of data. By these approaches, changes in the activity of all the genes represented on the array can be measured simultaneously in a single experiment. The combination of large scale genome sequencing efforts and microarrays mean that it is now possible to measure the activity of the entire gene complement of yeast (DeRisi et al., 1997). It is conceivable that as closure is reached in the human genome sequencing programme, measuring the activity of the entire human genome will become possible within the next decade. Despite their obvious strengths, microarrays are only capable of measuring changes in gene expression at the mRNA level and therefore cannot confirm changes in levels of the functional protein encoded by the gene. In addition, protein modifications such as phosphorylation, which may have profound influence on protein function, will not be directly measured by microarray analysis. Nevertheless, it is reasonable to assume that events such as phosphorylation will give rise to downstream alterations in the transcription of other target genes, facilitating identification of the pathways being regulated. Protein function may also be impacted by mutations or polymorphisms in the gene generating the transcript, which similarly will be missed by microarray if the overall level of gene expression is unaltered. Genomics applications to help define these polymorphic responses, pharmacogenetics, are also under development.
3. Applications of toxicogenomics Microarray applications in toxicology can be broadly classified into two groups; mechanistic or investigative research and predictive toxicology. Where a mechanistic understanding of a toxic compound or toxicity process is required, clearly
a major concern is that the experimental system recapitulates the biology of the process as closely as possible. In all likelihood the toxicity endpoint has been previously characterised, and an appropriate model system, either in vivo or using appropriate primary or established cell lines, can be utilised. The rewards of such studies may be an improved understanding of toxic mechanisms which are difficult to test for using short term or in vitro assays. For example non-genotoxic carcinogenesis is assessed normally using a long term rodent cancer bioassay (Chhabra et al., 1990). It is possible however, that microarray analyses might help characterise the genes involved in the development of this phenotype. Indeed, in excess of 300 altered gene expression events have been identified in rodent hepatocytes following their exposure to the non-genotoxic carcinogen phenobarbital, by both microarray and gel-based expression technologies (Rodi et al., 1999). Clearly many mechanistic endpoints could be explored using this technique and the combinatorial approaches such as using toxicogenomics profiling on knockout (or transgenic) mice of toxicology relevance (Ryffel, 1997) may provide even more powerful insights into the role of specific genes in toxicological processes. In predictive toxicology the hypothesis is that a specific group or class of compounds (grouped by toxic endpoint, mechanism, structure, target organ etc.) can induce unique mRNA expression profiles, or signature patterns. Therefore, using microarray technology to analyse genome-wide patterns of mRNA expression in vitro it should be possible to predict gene expression patterns associated with specific groups of compounds with the same toxic or biological endpoints. As the database of reference compounds grows for an individual toxic mechanism it may be possible develop miniarrays customised for specific toxic endpoint detection, based on pattern recognition of the transcript profiles of a reference set. Clearly the throughput requirements of such an approach will necessitate employing in vitro cell culture systems. While cultured cell systems have many practical advantages there are two major drawbacks to consider; availability of suitable cell lines (although for non-mechanistic studies generic cell
W.D. Pennie / Toxicology Letters 112–113 (2000) 473–477
lines may still be useful) and metabolism of the chemical may be required to produce the active species. If such an approach were to be successful a major application may be the ability to detect potential adverse health effects early in the development process of new pharmaceuticals or agrochemicals, potentially even before enough of the compound is available for more traditional in vitro or in vivo studies.
4. Custom toxicology microarray construction; ‘ToxBlot’ The Central Toxicology Laboratory at AstraZeneca, in collaboration with our colleagues in AstraZeneca Pharmaceuticals, have developed a programme of in house custom microarray construction; ToxBlot. A custom approach allows us control over the choice of genes on the array, allowing gene lists to be focused on areas of particular interest to mechanistic or investigative toxicology research (see Table 1). Once cDNA sets have been amplified, purified and verified the unit cost of array manufacture is very small, removing the issue of array cost from experimental design. Table 1 Example of gene categories represented on ‘ToxBlot’ Acetyl CoA pathway Basic transcription factors Bcl/Bax family Caspases CDC/CDKs Cell adhesion Cell surface receptors Chemokines Cyclins CYP family Drug metabolism
Extracellular matrix GABA receptors/ GABA transport GST Heat shock proteins Histones Heat shock proteins/stress Histones Immediate early genes Interleukins Ion channels Liver acute phase
Markers for GI tract physiology Matrix metalloproteins Metalloproteinases Neurotransmitters/ metabolism Neurotrophic factors and receptors Oxidative stress Peptide hormones Steroid receptors Steroid regulated Steroidogenesis/ aromatase Thyroid hyperplasia
475
For the construction of the ToxBlot arrays, around 2000 sequences of human and murine origin were identified as potential diagnostic markers for various toxicity processes. In house cDNA libraries, of both proprietary and public domain origin, were used as the source material for array construction; each PCR product was individually analysed by electrophoresis to determine success of amplification, purity and estimate of yield, and sequence verification of clones is performed where appropriate. We have immobilised the sequences on nylon membranes, each containing 2400 cDNAs spanning around 600 genes of either human or murine origin. In order to assess reproducibility, each gene being profiled is represented by four individual spots on each array; two non overlapping cDNAs for each gene (in duplicate) wherever possible. To assist in the interpretation of differential gene expression results, we have assembled a database of background ‘literature’ for each gene on the array including biochemical/enzymatic function, tissue distribution and known allelic variation. In this regard, public access databases such as the ‘GeneCards’ system developed at the Weizmann Institute (Rebhan et al., 1997) or the Kyoto Encyclopaedia of Genes and Genomes (KEGG) databases (http://www.genome.ad.jp/kegg/) can act as useful sources of supplementary information on genes of interest. These custom arrays are now in wide use in our laboratories and some example applications are outlined below.
4.1. Example 1; characterising disruption of endocrine signalling Concern continues in both the scientific community and among the general public over reports that environmental compounds (of both natural and synthetic origin) may have the potential to affect estrogen receptor function, which may impact the reproductive health of animals and humans. Many of the current in vitro screens for this endpoint fail to accommodate the complex mode of action of the estrogen receptor, measuring the activation of a single estrogen responsive gene. Recent research from our laboratory and others
476
W.D. Pennie / Toxicology Letters 112–113 (2000) 473–477
has indicated that the estrogen response element (ERE) on different estrogen-regulated genes may profoundly influence the gene’s responsiveness to estrogen receptor activation (Pennie et al., 1998), indicating that measuring the activity of a single estrogen responsive gene may be misleading. The application of transcript profiling to the biology of the estrogen receptor should help identify pathways of gene regulation involved in estrogen action and may lead to more sophisticated in vitro assays for estrogen receptor activity. For example, the combination of suppression subtractive hybridisation method for comparing differentially regulated genes (Diatchenko et al., 1999) and cDNA construction has recently been employed to characterise differential gene expression in estrogen receptor positive and negative breast carcinoma cell lines (Yang et al., 1999b). Our own laboratory has been employing custom microarrays to attempt to characterise the patterns of gene expression changes that take place in cells exposed to natural (e.g. estradiol), synthetic (e.g. diethylstilbestrol) and phytoestrogens (e.g. genestein). This microarray encompasses many pathways involved in normal endocrine function together with a diverse range of non-endocrine related genes. By the treatment of appropriate cell lines (e.g. T47D, MCF7), with the known estrogens at multiple doses and at various time-points, a pattern of consistent expression changes begins to emerge that we are hopeful may assist is in characterising the endocrine disrupting activity of novel compounds, without employing animal assays such as the uterotrophic assay.
cytotoxicity for cultured hepatocytes (such as the human hepatoma cell line HepG2) has been assessed. Well characterised compounds include ethanol (Neuman et al., 1993), paracetamol (Nicod et al., 1997), hydrogen peroxide (Yang et al., 1999a) and carbon tetrachloride (Dai and Cederbaum, 1995). These compounds cause necrosis in liver (and cytotoxicity in HepG2 cells), induce periportal hepatocyte proliferation, elevate specific enzymes (e.g. cypP450 2E1) and cause similar changes in oxidative stress and lipid peroxidation. We have chosen to profile the gene expression changes induced in HepG2 cells by these four compounds. When attempting to identify consistent gene expression pattern changes across diverse compounds or treatments it is important to standardise treatments on the basis of easily measured parameters. (e.g. enzyme activity, toxicity, DNA replication, cell division, apoptosis etc.) to assist design of dose range and timecourses for each compound. By measuring LDH release over a dose range, we have selected appropriate ‘high’ (50% LDH release) and ‘low’ (25% LDH release) dose points for each compound. We have then profiled gene expression changes induced by each compound at both dose points following a fixed time of exposure. Microarray analysis reveals genes which are up or downregulated consistently between different compound treatments and dose ranges, allowing us the potential to identify patterns of gene changes diagnostic for liver cytotoxicity.
4.2. Example 2; hepatocyte cytotoxicity
5. Future issues
The characterisation of cytotoxicity in cultured human hepatocytes provides an ideal model for grouping similarly acting compounds on the basis of gene expression data. Many liver cell models retain liver-specific characteristics in culture and have proved useful in toxicology assays including cytotoxicity and genotoxicity assays, drug – drug interactions, kinetic studies and mechanistic studies on the biochemical effects of various toxicants (Guillouzo, 1998). A number of ‘gold standard’ hepatotoxicants have been investigated where the
The management of the vast amount of data which will be generated by array technologies is a formidable task. Approaches such as statistical clustering have been used to group patterns of genes from microarray analyses. Analysing temporal patterns of expression in this way has assisted approaches such as characterising serum-responsiveness and wound repair (Iyer et al., 1999) and has been successful in separating cancerous tissue from normal tissues and cell lines on the basis of microarray data (Alon et al.,
W.D. Pennie / Toxicology Letters 112–113 (2000) 473–477
1999). Clearly this approach will have useful applications in sorting array data on different compounds with a similar toxic endpoint, facilitating the identification of diagnostic patterns of gene expression for these endpoints. In turn, these approaches may enhance prediction of the toxicity of novel compounds. The building of ‘reference data sets’ by comparison of microarray output across different laboratories will require consistency in data analysis and format if it is to be feasible. A number of resources, both academic and commercial, exist for such purposes (for review see Bassett et al., 1999). For example, The National Human Genome Research Institute is developing such a system, ArrayDB, for the storage, retrieval and analysis of array data along with information on 15 000 genes linked to public domain sequence and pathway information databases (Ermolaeva et al., 1998). Clearly, a combination of the building of large reference data sets together with sophisticated computing technologies, which facilitate analysis of the intrinsic patterns and relationships in large-scale gene expression data, will be required to allow genomics technologies to realise their full potential.
References Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J., 1999. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. USA 96, 6745–6750. Bassett, D.E. Jr., Eisen, M.B., Boguski, M.S., 1999. Gene expression informatics: it’s all in your mine. Nat. Genet. (1 Supplement) 21, 51 – 55. Bowtell, D.D., 1999. Options available-from start to finish-for obtaining expression data by microarray. Nat. Genet. (1 Supplement) 21, 25 – 32. Brown, P.O., Botstein, D., 1999. Exploring the new world of the genome with DNA microarrays. Nat. Genet. (1 Supplement) 21, 33 – 37. Chhabra, R.S., Huff, J.E., Schwetz, B.S., Selkirk, J., 1990. An overview of prechronic and chronic toxicity/carcinogenicity experimental study designs and criteria used by the National Toxicology Program. Environ. Health Perspect. 86, 313 – 321. .
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