nature biotechnology • VOLUME 21 • MAY 2003 • www.nature.com/naturebiotechnology CORRESPONDENCE 492 A genome-wide view of antisense To the editor: The ability to study gene expression on a genomic scale is allowing a deeper under- standing of specificity of antisense inhibi- tion. Although concerns about nonse- quence specific antisense effects have been raised 1 , few genome-wide studies have sys- tematically investigated these effects. We draw readers’ attention to one example from our laboratory where antisense treat- ment has clearly been shown to alter expression of genes that appear to have no relationship to the intended antisense-tar- geted gene. Antisense inhibition of gene expression relies on the simple rules of Watson-Crick base pairing. A synthetic small single- stranded oligonucleotide (generally a 13–25-mer) that is complementary to a specific gene is introduced into cells, binds with mRNA, and inhibits translation. Hybridization of the antisense oligonu- cleotide with the target mRNA can physi- cally block the translation machinery or activate RNase H cleavage at the RNA–DNA duplex site 2–4 . Targeting gene expression at the RNA level allows protein production to be turned off, even if RNA is abundant. When the protein product of translation is important for cell growth and/or viability, antisense inhibition of gene expression can produce a lethal phe- notype. Because a particular 15- to 17-mer sequence has been estimated to occur only once in the entire human genome 1 , theo- retically, antisense inhibition of gene expression should be exquisitely specific. High-density cDNA microarrays enable parallel analysis of the expression of thou- sands of genes in a single hybridization for complex biological systems 5 . We have pre- viously examined genomic effects of anti- sense inhibition of protein kinase A RIα expression in tumor cells using cDNA microarrays 6 . Using in vivo tumor models of PC3M human prostate carcinoma grown in nude mice, the specificity of anti- sense effects on gene expression signatures was critically assessed using three oligonu- cleotides that differed in sequence or chemical modification: an immunostimu- latory phosphorothioate oligonucleotide directed against human RIα, a second-gen- eration immunoinhibitory RNA-DNA mixed-backbone oligonucleotide, and a non-immunostimulatory phosphoroth- ioate oligonucleotide targeted to mouse RIα (this oligonu- cleotide cross-hybridizes with human RIα). Antisense treatment was found to affect one cluster, or signature, of genes involved in proliferation and another involved in differentiation (Fig. 1) 6 . These expression sig- natures were quiescent and unaltered in the livers of anti- sense-treated animals, indicat- ing that distinct cAMP signal- ing pathways regulate growth for normal and cancer cells. A careful analysis of the microarray data reveals that the antisense modulates many genes that appear to have a ten- uous or no relationship with the targeted gene (RIα) 6 . A few examples include remarkable upregulation of genes encod- ing Cdc42, RAP1A, and cytoskeleton regulatory proteins, and marked downregulation of genes coding for MAP kinase 5, collagen type 4, catalase, and M-phase inducer phosphatase 2. While recent clinical success with Genta Pharmaceuticals’ (Berkeley Heights, NJ) Genasense against BCL-2 demonstrates the promise of antisense as an adjunct to more conventional chemotherapeutics, our results demonstrate that downregulating a specific protein will likely have unforeseen conse- quences in multiple cellular signaling path- ways, at least with phosphorothioate oligonucleotides. It is therefore crucial to examine the antisense effect at the genomic level rather than at the level of a single target gene. Our results indicate that microarray studies can facilitate the study of oligonu- cleotide pharmacokinetics, sequence speci- ficity, non-sequence-specific effects, and toxicity. Further adoption of this technology will facilitate development of nucleic acid medicines with higher target specificity and minimized side effects. Yoon S. Cho-Chung, Cellular Biochemistry Section, Basic Research Laboratory, National Cancer Institute, Bethesda, MD and Kevin G. Becker, DNA Array Unit, National Institute on Aging, Baltimore, MD (chochung@helix.nih. gov). 1. Stein, C. & Cheng, Y.-A. in Principles and Practice of Oncology. DeVita, V., Rosenberg, S., Hellman, S. (eds.) 3059–3074 (Lippincott, New York; 1997). 2. Agrawal, S. Trends Biotechnol. 14, 376–387 (1996). 3. Bennett, C.F. Biochem. Pharmacol. 55, 9–19 (1998). 4. Crooke, S. (ed.). Antisense Research and Application. (Springer, New York; 1998). 5. Schena, M., Shalon, D., Davis, R.W. & Brown, P.O. Science 270, 467–470 (1995). 6. Cho, Y.S. et al. Proc. Natl .Acad. Sci. USA 98, 9819–9823 (2001). Figure 1. Molecular portrait of the prostate carcinoma reverted phenotype (reproduced from ref. 6). cDNA microarray analysis of the RIα antisense-induced expression profile shows up- and downregulation of coordinately expressed gene clusters that produce the molecular portrait of a reverted tumor-cell phenotype. These studies also reveal that antisense modulates many genes that appear to have little relationship with the targeted gene (RIα). Delivering zinc fingers To the editor: In his News & Views on advances in the use of zinc-fingers as DNA binding modules in artificial transcription factors in the March issue (Nat. Biotechnol. 21, 242–243, 2003), Ansari mentions “the loftier goal of using artificial transcription factors as therapeu- tic agents.” In this context, he states several challenges, including the need to evade the immune system, delivery, and the ability to regulate their function based upon intra- and extra-cellular signals. For the delivery issue, transgenes encod- ing chimeric transcription factors can be transferred via retroviral gene therapy, although this method suffers from the Rafael Rangel-Aldao, Polar Technology Center, Empresas Polar, Los Cortijos de Lourdes, Caracas, Venezuela (rafael.rangel@empresas-polar.com) 1. Kitano, H. Science 295, 1662–1664 (2002). 2. Lee, T.I. et al. Science 298, 799–804 (2002). 3. Milo, R. et al. Science 298, 824–827 (2002). 4. Barabási, A.-L. & Albert, R. Science 286. 509–512 (1999). 5. Luscombe, N.M. et al. Genome Biol. 3, 8, Research 0040.1 (2002). 6. Koonin, E.V. et al. Nature 420, 218–223 (2002). 7. Jeong, H. et al. 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