Abstract

AbstractAblation of the mRNA of a targeted protein by the use of antisense DNA and RNA provides degrees of freedom not available in many other strategies to suppress or eliminate gene products (1–3). Numerous examples exist demonstrating the utility of the antisense DNA/RNA strategy for study of signaling (4–18). In the case of ODNs, preparation of reagents requires no additional skill other than knowing the commercial supplier for ODN synthesis and purification (19). Expression of antisense RNA requires a scientific facility exhibiting simple techniques of molecular biology and can be accomplished by a variety of approaches, including constitutive expression by a strong promoter; this latter approach requires no regulation and assumes functional compatibility with the targeted cells (4,9,11,12). Promoters that can be “induced” afford an additional capability; expression of antisense RNA being turned “on” and again “off” in response to molecular signals provide approaches to RNA induction or suppression. The inducibility of antisense RNA is of particular utility in the suppression of mRNAs that encode proteins necessary for viability in cells or in the whole animal. Traditional “knockout” of genes by homologous recombination that are crucial targets leads to lethality in the transgenic mice system and consequently no viable pups. Inducible antisense RNA transgenes are maintained “silently” in utero and later can be turned “on” at birth or thereafter, permitting production of viable transgenic pups. In the “technical knockouts” (TKOs) rendered by inducible antisense RNA vectors, the additional time and expense of breeding to homozygosity in traditional knockouts is avoided, the output of antisense RNA product from a single transgene being sufficient to silence the mRNA for most protein targets.KeywordsAntisense SequenceAntisense ODNsTarget Gene ProductInhibitory AdenylylcyclasePEPCK GeneThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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