Case study: use of a library of antisense inhibitors for gene functionalization and drug target validation

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Case study: use of a library of antisense inhibitors for gene functionalization and drug target validation Eric G. Marcusson,Thomas M.Vincent, Kumar L. Hari, MingYi Chiang and Nicholas M. Dean The genomics revolution of the past decade has allowed the sequences of all human genes to be determined. The challenge for the future is to establish the functions of all of these genes. Antisense technology is a powerful tool for gene functionalization.This review describes a system used to test antisense oligonucleotides to over 2000 genes in multiple cellbased assays.These assays were designed to help assign gene function.The data generated from these assays is subsequently added to a searchable database that allows potential drug targets to be identified.

Eric G. Marcusson Thomas M.Vincent Kumar L. Hari MingYi Chiang Nicholas M. Dean Isis Pharmaceuticals 2292 Faraday Avenue Carlsbad, CA 92008, USA

▼ Historically, pharmaceutical companies have dealt with enormous challenges in the development of new drugs for common diseases. In research and development, much of this struggle has been due to a gap between knowledge of disease pathology and the identification and roles of causative genes. An additional challenge lies in the selection of gene targets that would have the best chance at making it through the long journey from the bench to the clinic. However, within the past decade the collection and annotation of the genomic sequences of multiple organisms has been accruing [1–4], with one of the most significant being the sequencing of the human genome [5,6]. With the help of the internet, microarray technology and rapidly expanding database collections, researchers can easily access and analyze sequences, expression profiles and protein interaction maps to begin the task of converting masses of data into biological knowledge [7,8]. The promise of having the ability to establish general principles and make predictions in biological systems drives the current efforts in bioinformatics [9]. In fact, many have hopes that the future of computational biology will

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allow researchers to do tests in silico or use bioinformatic algorithms to identify drug targets from sequence and expression array data [10,11]. At present, although these collections of data have promoted an explosive increase in potential drug targets, we are a long way from being able to make accurate predictions of biological activity from such data. What is needed now is a rapid way to determine the function of thousands of genes in the context of a whole biological system. This will allow us to bridge the gap between genomic and functional data. High-throughput functional assays allow one to perturb a dynamic system at the cellular level and consequently gain a better understanding of how a biological system functions [12,13]. Because potential drug targets can be more rapidly identified, drug target validation or defining the role of a gene in promoting or maintaining a disease state [14] has become a major rate-limiting step in the development of therapeutics. In this review, we will outline one method for bridging this gap through the use of a modified antisense oligonucleotide (ASO) library in cell culture to generate a human gene function database.

Antisense oligonucleotides for gene functionalization Many methods are currently used to aid in determining gene function. These include, but are not limited to, expression libraries [15,16], dominant-negative mutants [17,18] and gene manipulation in model organisms [19,20]. However, some of the most common techniques used in gene functionalization revolve around antisense technology. With the discovery by

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RESEARCH FOCUS therapeutic agent. As a first step, we developed a method that allowed us 476 500 to rapidly identify highly potent anti400 sense inhibitors to specific genes. This 307 was initiated by performing a genomics 300 search to identify areas in a transcript 221 216 that had enough divergence from any 200 164 147 other transcript to permit the design of 95 78 100 65 67 a specific antisense inhibitor. For each 46 30 30 17 9 gene, ~40 different antisense inhibitors 0 that had been designed to interact with the target mRNA anywhere from the 5′ untranslated region (UTR) through to Signal the 3′ UTR were synthesized. The most transducer Enzyme Transporter effective antisense inhibitors for that gene were then identified by transfectTranscription Enzyme ing the ASOs into cells and determinregulator regulator ing which inhibitors best reduced the Function: known or inferred level of the target mRNA through the Drug Discovery Today: TARGETS use of real-time RT–PCR [26]. By using this method, we were able identify, with Figure 1. Targets for the antisense inhibitors that can be found in our library; gene classification is according to their Gene Ontology (GO) annotation. Because some of the targets of inhibitors in confidence, highly potent antisense our library are completely uncharacterized genes, not all of the genes were classified by GO. inhibitors to our gene of interest in as Therefore, the total number depicted here is smaller than the number of inhibitors in the library. little as one week. The most effective Abbreviation: GPCR, G-protein-coupled receptor. ASO for each gene was placed in our library of antisense inhibitors, which would subsequently be used for functional screening. Zamecnik and Stephenson [21] that an oligonucleotide An important decision faced early on was the selection complementary in sequence to the mRNA coding for a proof genes to target with ASOs and represent in our library. tein could lead to the destruction of that mRNA, antisense Validated inhibitors to some genes already existed before technology was born. Today, it is practiced in many forms starting the project. These genes had been selected because [e.g. ribozymes, antisense expression constructs, ASOs and of the possibility that they would have an impact on human most recently small interfering (si)RNA], but they all have disease and because they were of interest to us in our own as an underlying mechanism the requirement that an therapeutic programs. We classified these genes on the oligonucleotide hybridizes to a specific mRNA following basis of Gene Ontology (http://www.geneontology.org/) the rules of Watson–Crick base pairing [22–25]. By one records [27], and ‘Locus Type’ documented in LocusLink mechanism or another, this hybridization blocks the func(http://www.ncbi.nlm.nih.gov/LocusLink/). The gene pool tion of that mRNA and leads to the absence of the protein was then enriched by including genes that fall into the folfrom the cell. The phenotype of the cell or organism that is lowing criteria: genes considered ‘druggable’ by the phardeficient in expression of the targeted gene can then be demaceutical industry (e.g. enzymes, kinases and receptors), termined. In this way, a function can be ascribed to the gene of interest. Furthermore, only the sequence of a gene genes of unknown function, genes with possible disease of interest is necessary to design a specific inhibitor. This association based on genome-wide gene expression profilmakes antisense a very rapid and efficient method of gene ing of normal versus disease-state samples, and gene funcfunctionalization because it can be used against hypothetical tionalization studies from other organisms. A schematic proteins and genes of unknown function. representation of the different gene classes in our library as represented by their Gene Ontology or LocusLink annotations is illustrated in Figure 1. The library currently has inHuman gene function database hibitors to 2258 different genes. Because many of the genes To take advantage of the efficiency of gene functionalizawith inhibitors in our library are completely uncharactertion using ASOs, we built a high-throughput system for acquiring and analyzing data to aid in identifying drug targets ized, they do not have annotations in publicly available that have the promise of producing a specific and effective genomics databases. Therefore, the number of genes in all

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RESEARCH FOCUS of the classes shown in Figure 1 (1968) is smaller than the number of genes with inhibitors in our library (2258). A software package developed by Isis’ in-house bioinformatics group allowed us to track the progress of the inhibitors through the assays and to store and analyze the accumulated data.

Cell-based functional assays In an attempt to identify new drug targets, we focused on four different therapeutic areas (oncology, metabolic disease, inflammation and angiogenesis) and developed cellbased assays that could provide readouts pertinent to these disease areas. The requirements for these assays were that they use human cells, be amenable to high-throughput screening and provide data that would be valuable in determining drug targets in one of the four targeted therapeutic areas. The assays that we designed yielded a total of 37 different primary phenotypic or gene expression endpoints. All of these assays were run in 96-well plates, except for the cell cycle progression assay, which was run in 24-well plates. To ensure that the assays were generating relevant data, each assay was first validated by using small molecule and antisense inhibitors for genes that were predicted to cause significant effects on the basis of previously published data. Multiple negative-control ASOs were also run through the assays to be sure that the endpoints were not non-specifically affected by simply having any antisense oligonucleotide transfected into the cells. For this project we used second-generation modified oligonucleotides that use a RNaseH-dependent mechanism [28]. These are 20-base chimeric oligonucleotides, in which the five bases on each end are modified at the 2′ position of the sugar with a methoxyethyl group. This modification both increases the affinity of the oligonucleotide and increases its affinity for its target. The central portion of the ASO is left unmodified at the 2′ position of the sugar to allow for activation of RNaseH. With the use of these second-generation modified ASOs, the antisense effect can last for up to 72 hours in rapidly dividing cells and even longer in quiescent cells in vitro. These oligonucleotides also have better pharmacodynamic and pharmacokinetic properties and therefore have the advantage of working well in animal models [29,30]. Inhibitors from the library were transfected into triplicate wells that were randomly dispersed throughout a 96well plate. Positive- and negative-control antisense inhibitors were also included on every plate that was assayed. Most of the assays were performed 48 hours after transfection of the oligonucleotides. Because the oligonucleotides work at the mRNA level, this timing even allowed for proteins with long half-lives to be reduced. However, those rare proteins

with extremely long half-lives (i.e. >48 hours) would not have had time to be reduced by more than 50% and could therefore be a potential source of false-negatives. To verify the positive biological activity generated by the antisense inhibitors, ‘hit’ criteria were defined. For most assays, these criteria were 50% above or below the value obtained with the negative-control oligonucleotide; however, activity criteria for certain assays did vary based upon the strength of activity of the positive oligonucleotide controls. Inhibitors whose activities met these criteria were subject to a data confirmation process. This process consisted of repeating the primary assay in the respective therapeutic area, confirming that the target gene was expressed in the appropriate cell type and verifying that the ASO reduced the mRNA of the target gene. On the basis of these confirmation criteria, we evaluated the ‘hit-rate’ for each assay. Of particular note is that the hit-rate reflects the percentage of inhibitors going through the confirmation process and does not necessarily indicate the percentage of genes that have biological relevance to any given assay or therapeutic area. Our intention was to set these criteria as guidelines to streamline our assays. Inhibitors that gave interesting results in the primary assays were funneled into secondary assays for the appropriate therapeutic area. These secondary phenotypic assays were designed to provide additional information to validate the genes as potential drug targets in that therapeutic area. Those cells that had been treated with inhibitors and retained an interesting profile after reviewing the data from the primary and secondary assays were analyzed using Affymetrix gene expression arrays. Particularly interesting data produced using these assays could trigger the identification of an inhibitor for that gene in a model organism. In this way, we can move forward to investigate what effects that inhibition of the target gene has in an appropriate in vivo model of human disease. A schematic diagram of the process of design, synthesis, lead identification and the flow through the functional assays for an antisense inhibitor is shown in Figure 2.

Mining targets from the database Presently, antisense inhibitors to more than 1800 genes have been examined in at least some of the primary assays. A large number of these have also been investigated in our secondary assays; there are more than 50 000 datapoints from both the primary and secondary assays in the database. We have also investigated the global effects on gene expression caused by ASOs to 14 different targets using affymetrix gene chips. These experiments expanded the database by several hundred thousand datapoints. With the enormous amount of data in this database, it is essential

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RESEARCH FOCUS Mining by therapeutic area Identify target gene sequence

Mining data by an individual therapeutic area has identified targets that Design and synthesize antisense inhibitors yield both specific and robust effects for a given indication, particularly Cell culture screening when compared with the positive control. The inhibition of Jagged 2 [31,32], Lead inhibitor of target gene a ligand for the Notch 1 receptor and a positive control for the oncology asCell based primary and secondary assays says, produced apoptotic effects in the Significant Yes No three cell lines used in the oncology effects? section of the database, as measured by Invalidated Functional genomics Identification of lead two methods: a caspase activity assay target validated target antisense inhibitor to and a cell cycle progression assay. animal gene Inhibition of this target also caused significant effects in assays of all of Testing in animal models the other therapeutic areas (E.G. Marcusson et al., unpublished). This Significant Yes No effects? indicates that Jagged 2 plays important Lead antisense inhibitor Invalidated roles in multiple cellular processes and becomes drug candidate target might not make a very specific drug for clinical trials target. Nevertheless, using the Jagged 2 Drug Discovery Today: TARGETS data from within the oncology section Figure 2. Schematic representation of the flow of a target through the functionalization process. as a benchmark, we identified a target The process starts with the identification of a target of interest and, depending on the data, can involved in cell cycle progression, the lead all the way through in vivo testing. inhibition of which produced equally robust apoptotic effects in the caspase activity and cell cycle assays for all three cell lines. to have tools to aid in the mining of targets from the dataMoreover, inhibiting this target appeared to cause oncolbase. We have written a proprietary data mining software ogy-specific effects because activity was not observed program that helps in this process. There are several differin assays for angiogenesis, inflammation or metabolism ent ways the software can be used to identify interesting (E.G. Marcusson et al., unpublished). targets. If there is a particular signaling pathway that is of interest, the names of several genes from the pathway can Mining by pathway be added and the data for all of these genes extracted. In Mitogen-activated protein kinase (MAPK) signaling casthis way, the specificity of a particular pathway can be decades coordinate cellular responses to a variety of stimuli, termined for a given therapeutic area and the particular including inflammatory cytokines and environmental links in the pathway that might make the best drug targets stresses [33]. Using data from the angiogenesis area, we can be predicted. Another potential way to look at the were able to examine more than 40 components within database is by therapeutic area. It is possible to look at the the four major groupings of MAPK cascades. First, the data database as a whole and find inhibitors to genes that have specific for MAPK pathway members was identified using strong effects in only the assays of one therapeutic area. Gene Ontology-based classifications that were programSuch genes make particularly good drug targets because inmatically linked to targets in the database. Our in-house hibiting them can lead to very specific effects. A more sodata-mining package was used to recover MAPK targets phisticated way to mine for potential drug targets is to look associated with the reduced tube-forming capacity of for inhibitors of genes that match the activity profile of human umbilical vein endothelial cells (HUVECs). ASOs our positive-control oligonucleotides or small molecules. targeting one MAPK member caused specific reductions in Most of our positive controls hit validated drug targets (e.g. tube formation (40% relative to negative controls) and inintegrin β3, Eg5 and PPARγ); therefore, inhibitors that have tegrin β3 expression (39% relative to negative controls). In the same effects are probably good drug targets. Examples addition, multiple ASOs for this target were tested in an of these different methods of database mining are given in vitro migration assay and found to reduce the ability of below.

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Mining by profile

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HUVECs to migrate across a fibronectincoated membrane (E.G. Marcusson et al., unpublished). These effects were specific to angiogenesis and secondary assays revealed that they were not associated with increased caspase activity in HUVECs. On the basis of these data, we focused our analyses towards other members within the same MAPK cascade and found that the inhibition of an upstream MAPKK and a downstream transcription factor also blocked tube formation in HUVECs. Given the quantity of data available within the database and associations with multiple gene classification and pathway annotations, numerous cellular pathways can be dissected using this approach.

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A more sophisticated way to mine for Figure 3. Comparative activity profile for an antisense inhibitor of a specific regulator of potential drug targets is to look for translation in a selected group of the phenotypic assays.The profile closely matches that of the positive control for the adipocyte differentiation assays, known as peroxisome proliferatorinhibitors of genes that match the acactivated receptor γ.Abbreviation: adip., adipocyte; aP2, adipocyte lipid binding protein 2; GLUT4, tivity profile of our positive-control glucose transporter 4; HSL, hormone-sensitive lipase; MMP, matrix metalloprotease; PPARγ, oligonucleotides or small molecules. peroxisome proliferator-activated receptor-γ;TEM, tumor endothelial marker. For example, ASO inhibitors for 1683 genes were tested in the human occurred upon treating cells with an ASO that targets a adipocyte triglyceride production assay. Significant activity translational regulator. The antisense-mediated inhibition in the assay was defined as triglyceride production levels of of this regulator decreased adipocyte triglyceride producless than 50% and greater than 150% of the negative-contion greater than 50% relative to negative controls. In adtrol ASO-treated cells. These cutoffs yielded hit-rates of dition, the expression of two of the four marker genes for 14.8% (250 of 1683) for decreased production and 2.5% adipogenesis [GLUT4 and hormone-sensitive lipase (HSL)] (42 of 1683) for increased production. To further characterwas markedly reduced (to 52% and 41%, respectively) relaize the active inhibitors, we compared their profiles across tive to the negative control (Figure 3). Leptin secretion was all database assays to an ASO that targets a master regulator mildly reduced, but the percentage decrease fell outside of of adipocyte differentiation, known as peroxisome prolifthe activity criteria for this assay. Importantly, caspase activity erator activated receptor gamma (PPARγ) [34,35]. The PPARγ ASO served as the positive control for the adipocyte levels were not significantly increased in adipocytes treated assays and was included in triplicate on every 96-well plate with the ASO that targeted the translational regulator, sugtested. The antisense activity profile across all therapeutic gesting that the phenotypes observed upon downregulation areas for PPARγ showed strong and specific phenotypes in of the regulator did not result from cell death (Figure 3). the metabolism assays (Figure 3). The involvement of this translational regulator in adipocyte Eighty-seven genes showed profiles similar to PPARγ, as differentiation appears to be a novel function for this gene. measured by the percentage decrease in triglyceride production (less than 40%, relative to the negative antisense In vivo testing control) and percentage decrease in glucose transporter 4 One of the greatest advantages of our chemically modified (GLUT4) expression (less than 50%, relative to the negaRNaseH-dependent ASOs is the almost seamless transition tive antisense control). Of these, ten were associated with from in vitro assays to in vivo studies. The RNaseH mechaincreased caspase activity in preadipocytes, indicating that nism of antisense has been used successfully for in vivo the inhibition of these targets might cause cell death. Specific studies far more often than other mechanisms of antisense, effects on multiple markers of adipocyte differentiation such as siRNA or ribozymes [36–40]. To date, we have used

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RESEARCH FOCUS the data from the database to select eleven genes that are potentially interesting drug targets and we have begun testing ASOs to these genes in animal models of human disease. Many more targets have shown interesting preliminary results but are still under consideration, and because of the discontinuous process of testing ASOs in a large number of assays we have not obtained a full dataset for many of them. Of the eleven interesting drug targets for which we have a full dataset, ASO inhibitors to six of these genes have been tested in animal models of human disease. The majority of these inhibitors have shown significant effects in at least one animal model. These inhibitors are being more thoroughly characterized in animal models and tested in preliminary toxicology assays. If the results of these tests are positive, not only will the genes be validated drug targets, but also the antisense inhibitors themselves will serve as potential drug candidates for clinical trials.

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Concluding remarks The human gene function database has provided a way to rapidly identify potential new drug targets. Of equal importance, our screening has invalidated the vast majority of targets, allowing us to focus our limited resources for in vivo testing on genes that are more likely to produce a drug candidate. To date, we have tested ASO inhibitors to ~1800 genes using at least a portion of our collection of assays. The seamless transfer of an ASO from target validation tool to human therapeutic is one of the greatest efficiencies in using antisense molecules. This duality makes antisense inhibition a very powerful and efficient tool in the drug discovery process.

References 1 Fleischmann, R.D. et al. (1995) Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 269, 496–512 2 Goffeau, A. et al. (1996) Life with 6000 genes. Science 274, 546–567 3 Consortium (1998) Genome sequence of the nematode C. elegans: a platform for investigating biology. Science 282, 2012–2018 4 Adams, M.D. et al. (2000) The genome sequence of Drosophila melanogaster. Science 287, 2185–2195 5 Lander, E.S. et al. (2001) Initial sequencing and analysis of the human genome. Nature 409, 860–921 6 Venter, J.C. et al. (2001) The sequence of the human genome. Science 291, 1304–1351 7 Katsuma, S. and Tsujimoto, G. (2001) Genome medicine promised by microarray technology. Expert Rev. Mol. Diagn. 1, 377–382 8 Sanseau, P. (2001) Impact of human genome sequencing for in silico target discovery. Drug Discov. Today 6, 316–323 9 Kanehisa, M. and Bork, P. (2003) Bioinformatics in the post-sequence era. Nat. Genet. 33 (Suppl.), 305–310 10 Frazier, M.E. et al. (2003) Realizing the potential of the genome revolution: the genomes to life program. Science 300, 290–293 11 Chanda, S.K. and Caldwell, J.S. (2003) Fulfilling the promise: drug discovery in the post-genomic era. Drug Discov. Today 8, 168–174 12 Johnston, P.A. (2002) Cellular platforms for HTS: three case studies. Drug Discov. Today 7, 353–363 13 Horrocks, C. et al. (2003) Human cell systems for drug discovery. Curr. Opin. Drug Discov. Devel. 6, 570–575 14 Dean, N.M. (2001) Functional genomics and target validation

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24 25 26

27 28

29

30 31 32

33

34 35

36

37 38

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approaches using antisense oligonucleotide technology. Curr. Opin. Biotechnol. 12, 622–625 Lorens, J.B. et al. (2001) The use of retroviruses as pharmaceutical tools for target discovery and validation in the field of functional genomics. Curr. Opin. Biotechnol. 12, 613–621 Nuttall, M.E. (2001) Drug discovery and target validation. Cells Tissues Organs 169, 265–271 Bradham, C.A. et al. (2001) Dominant-negative TAK1 induces c-Myc and G(0) exit in liver. Am. J. Physiol. Gastrointest. Liver Physiol. 281, G1279–G1289 Curnock, A.P. and Ward, S.G. (2003) Development and characterisation of tetracycline-regulated phosphoinositide 3-kinase mutants: assessing the role of multiple phosphoinositide 3-kinases in chemokine signaling. J. Immunol. Methods 273, 29–41 Inamdar, M.S. (2001) Functional genomics the old-fashioned way: chemical mutagenesis in mice. Bioessays 23, 116–120 Kulkarni, A.B. et al. (2002) Function of cytokines within the TGF-beta superfamily as determined from transgenic and gene knockout studies in mice. Curr. Mol. Med. 2, 303–327 Zamecnik, P.C. and Stephenson, M.L. (1978) Inhibition of Rous sarcoma virus replication and cell transformation by a specific oligodeoxynucleotide. Proc. Natl. Acad. Sci. U. S. A. 75, 280–284 Bennett, C.F. (2002) Efficiency of antisense oligonucleotide drug discovery. Antisense Nucleic Acid Drug Dev. 12, 215–224 Takagi, Y. et al. (2002) Mechanism of action of hammerhead ribozymes and their applications in vivo: rapid identification of functional genes in the post-genome era by novel hybrid ribozyme libraries. Biochem. Soc. Trans. 30, 1145–1149 Shi, Y. (2003) Mammalian RNAi for the masses. Trends Genet. 19, 9–12 Vacek, M. et al. (2003) Antisense-mediated redirection of mRNA splicing. Cell. Mol. Life Sci. 60, 825–833 Bennett, C.F. and Cowsert, L.M. (1999) Application of antisense oligonucleotides for gene functionalization and target validation. Curr. Opin. Mol. Ther. 1, 359–371 Harris, M.A. et al. (2004) The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 32, D258–D261 Dean, N.M. and Griffey, R.H. (1997) Identification and characterization of second-generation antisense oligonucleotides. Antisense Nucleic Acid Drug Dev. 7, 229–233 Monia, B.P. (1997) First- and second-generation antisense oligonucleotide inhibitors targeted against human c-raf kinase. Ciba Found. Symp. 209, 107–119 Butler, M. et al. (2002) Specific inhibition of PTEN expression reverses hyperglycemia in diabetic mice. Diabetes 51, 1028–1034 Jang, M.S. et al. (2000) Notch signaling as a target in multimodality cancer therapy. Curr. Opin. Mol. Ther. 2, 55–65 Zlobin, A. et al. (2000) Toward the rational design of cell fate modifiers: notch signaling as a target for novel biopharmaceuticals. Curr. Pharm. Biotechnol. 1, 83–106 Kyriakis, J.M. and Avruch, J. (2001) Mammalian mitogen-activated protein kinase signal transduction pathways activated by stress and inflammation. Physiol. Rev. 81, 807–869 Picard, F. and Auwerx, J. (2002) PPAR(gamma) and glucose homeostasis. Annu. Rev. Nutr. 22, 167–197 Lee, C.H. et al. (2003) Minireview: lipid metabolism, metabolic diseases, and peroxisome proliferator-activated receptors. Endocrinology 144, 2201–2207 Dean, N.M. and McKay, R. (1994) Inhibition of protein kinase C-alpha expression in mice after systemic administration of phosphorothioate antisense oligodeoxynucleotides. Proc. Natl. Acad. Sci. U. S. A. 91, 11762–11766 Nyce, J.W. and Metzger, W.J. (1997) DNA antisense therapy for asthma in an animal model. Nature 385, 721–725 Skorski, T. et al. (1994) Suppression of Philadelphia1 leukemia cell growth in mice by BCR-ABL antisense oligodeoxynucleotide. Proc. Natl. Acad. Sci. U. S. A. 91, 4504–4508 Webb, A. et al. (1997) BCL-2 antisense therapy in patients with nonHodgkin lymphoma. Lancet 349, 1137–1141 Yao, Z. et al. (1996) In vivo inhibition of hepatitis B viral gene expression by antisense phosphorothioate oligodeoxynucleotides in athymic nude mice. J. Viral Hepat. 3, 19–22