Literature DB >> 9737971

Tetranucleotide GGGA motif in primary RNA transcripts. Novel target site for antisense design.

G C Tu1, Q N Cao, F Zhou, Y Israel.   

Abstract

Selecting effective antisense target sites on a given mRNA molecule constitutes a major problem in antisense therapeutics. By trial-and-error, only 1 in 18 (6%) of antisense oligonucleotides designed to target the primary RNA transcript of tumor necrosis factor-alpha (TNF-alpha) strongly inhibited TNF-alpha synthesis. Subsequent studies showed that the area in RNA targeted by antisense oligonucleotides could be moved effectively 10-15 bases in either direction from the original area. We observed that only molecules that incorporated a tetranucleotide motif TCCC (complementary to GGGA on RNA) yielded potent antisense oligonucleotides against TNF-alpha. A comprehensive literature survey showed that this motif is unwittingly present in 48% of the most potent antisense oligonucleotides reported in the literature. This finding was prospectively used to predict the sequences of additional antisense oligonucleotides for the rat TNF-alpha primary RNA transcript. Over 50% of antisense constructs (13 of 22) containing the TCCC motif were found to effectively inhibit TNF-alpha synthesis. Marked reductions in mRNA were also observed. This motif was found to be most effective when targeting introns in the primary RNA transcript, suggesting a nuclear localization for the antisense action. Predicting target sites based on the presence of this motif in primary RNA transcripts should be of value in the development on new antisense pharmacotherapy.

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Year:  1998        PMID: 9737971     DOI: 10.1074/jbc.273.39.25125

Source DB:  PubMed          Journal:  J Biol Chem        ISSN: 0021-9258            Impact factor:   5.157


  18 in total

1.  Predicting oligonucleotide affinity to nucleic acid targets.

Authors:  D H Mathews; M E Burkard; S M Freier; J R Wyatt; D H Turner
Journal:  RNA       Date:  1999-11       Impact factor: 4.942

2.  The experimental use of antisense oligonucleotides: a guide for the perplexed.

Authors:  C A Stein
Journal:  J Clin Invest       Date:  2001-09       Impact factor: 14.808

3.  Antisense oligonucleotides selected by hybridisation to scanning arrays are effective reagents in vivo.

Authors:  M Sohail; H Hochegger; A Klotzbücher; R L Guellec; T Hunt; E M Southern
Journal:  Nucleic Acids Res       Date:  2001-05-15       Impact factor: 16.971

4.  Artificial neural network prediction of antisense oligodeoxynucleotide activity.

Authors:  Michael C Giddings; Atul A Shah; Sue Freier; John F Atkins; Raymond F Gesteland; Olga V Matveeva
Journal:  Nucleic Acids Res       Date:  2002-10-01       Impact factor: 16.971

5.  Effects of RNA secondary structure on cellular antisense activity.

Authors:  T A Vickers; J R Wyatt; S M Freier
Journal:  Nucleic Acids Res       Date:  2000-03-15       Impact factor: 16.971

6.  RNase H sequence preferences influence antisense oligonucleotide efficiency.

Authors:  Lukasz J Kielpinski; Peter H Hagedorn; Morten Lindow; Jeppe Vinther
Journal:  Nucleic Acids Res       Date:  2017-12-15       Impact factor: 16.971

7.  Identification of sequence motifs in oligonucleotides whose presence is correlated with antisense activity.

Authors:  O V Matveeva; A D Tsodikov; M Giddings; S M Freier; J R Wyatt; A N Spiridonov; S A Shabalina; R F Gesteland; J F Atkins
Journal:  Nucleic Acids Res       Date:  2000-08-01       Impact factor: 16.971

8.  Inhibition of human telomerase in immortal human cells leads to progressive telomere shortening and cell death.

Authors:  B Herbert; A E Pitts; S I Baker; S E Hamilton; W E Wright; J W Shay; D R Corey
Journal:  Proc Natl Acad Sci U S A       Date:  1999-12-07       Impact factor: 11.205

9.  Determination of optimal sites of antisense oligonucleotide cleavage within TNFalpha mRNA.

Authors:  B H Lloyd; R V Giles; D G Spiller; J Grzybowski; D M Tidd; D R Sibson
Journal:  Nucleic Acids Res       Date:  2001-09-01       Impact factor: 16.971

10.  Antisense DNA parameters derived from next-nearest-neighbor analysis of experimental data.

Authors:  Donald M Gray; Carla W Gray; Byong-Hoon Yoo; Tzu-Fang Lou
Journal:  BMC Bioinformatics       Date:  2010-05-14       Impact factor: 3.169

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