Volatility in mRNA secondary structure as a design principle for antisense

Designing effective antisense sequences is a formidable problem. A method for predicting efficacious antisense holds the potential to provide fundamental insight into this biophysical process. More practically, such an understanding increases the chance of successful antisense design as well as saving considerable time, money and labor. The secondary structure of an mRNA molecule is believed to be in a constant state of flux, sampling several different suboptimal states. We hypothesized that particularly volatile regions might provide better accessibility for antisense targeting. A computational framework, GenAVERT was developed to evaluate this hypothesis. GenAVERT used UNAFold and RNAforester to generate and compare the predicted suboptimal structures of mRNA sequences. Subsequent analysis revealed regions that were particularly volatile in terms of intramolecular hydrogen bonding, and thus potentially superior antisense targets due to their high accessibility. Several mRNA sequences with known natural antisense target sites as well as artificial antisense target sites were evaluated. Upon comparison, antisense sequences predicted based upon the volatility hypothesis closely matched those of the naturally occurring antisense, as well as those artificial target sites that provided efficient down-regulation. These results suggest that this strategy may provide a powerful new approach to antisense design.

[1]  Terminal inverted repeats of prokaryotic transposable element IS186 which can generate duplications of variable length at an identical target sequence. , 1986, Gene.

[2]  L. Poulsen,et al.  A family of genes encoding a cell‐killing function may be conserved in all Gram‐negative bacteria , 1989, Molecular microbiology.

[3]  J. Erickson,et al.  Transcriptional regulation of the heat shock regulatory gene rpoH in Escherichia coli: involvement of a novel catabolite-sensitive promoter , 1990, Journal of bacteriology.

[4]  L. Poulsen,et al.  The gef gene from Escherichia coli is regulated at the level of translation , 1991, Molecular microbiology.

[5]  D. J. Petersen.  Characterization of the acetone production pathway genes from Clostridium acetobutylicum ATCC 824 , 1991 .

[6]  P. Dürre,et al.  mRNA analysis of the adc gene region of Clostridium acetobutylicum during the shift to solventogenesis , 1992, Journal of bacteriology.

[7]  K. Gerdes,et al.  Mechanism of post-segregational killing by the hok/sok system of plasmid R1. Sok antisense RNA regulates hok gene expression indirectly through the overlapping mok gene. , 1992, Journal of molecular biology.

[8]  E. Papoutsakis,et al.  Sequence and arrangement of two genes of the butyrate-synthesis pathway of Clostridium acetobutylicum ATCC 824. , 1993, Gene.

[9]  Walter Fontana,et al.  Fast folding and comparison of RNA secondary structures , 1994 .

[10]  K. Gerdes,et al.  Mechanism of post-segregational killing by hok-homologue pnd of plasmid R483: two translational control elements in the pnd mRNA. , 1995, Journal of molecular biology.

[11]  A. Gultyaev,et al.  Antisense RNA-regulated programmed cell death. , 1997, Annual review of genetics.

[12]  Robert Giegerich,et al.  RNA Movies: Visualizing RNA secondary structure spaces , 1997, German Conference on Bioinformatics.

[13]  J. SantaLucia,et al.  A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[14]  S. P. Walton,et al.  Prediction of antisense oligonucleotide binding affinity to a structured RNA target. , 1999, Biotechnology and bioengineering.

[15]  Eleftherios T. Papoutsakis,et al.  Antisense RNA Strategies for Metabolic Engineering of Clostridium acetobutylicum , 1999, Applied and Environmental Microbiology.

[16]  K. Gerdes,et al.  Multiple hok genes on the chromosome of Escherichia coli , 1999, Molecular microbiology.

[17]  G Sczakiel,et al.  A theoretical approach to select effective antisense oligodeoxyribonucleotides at high statistical probability. , 1999, Nucleic acids research.

[18]  W. Bentley,et al.  Antisense Downregulation of ς32 as a Transient Metabolic Controller in Escherichia coli: Effects on Yield of Active Organophosphorus Hydrolase , 2000, Applied and Environmental Microbiology.

[19]  L. Argaman,et al.  fhlA repression by OxyS RNA: kissing complex formation at two sites results in a stable antisense-target RNA complex. , 2000, Journal of molecular biology.

[20]  A D Tsodikov,et al.  Identification of sequence motifs in oligonucleotides whose presence is correlated with antisense activity. , 2000, Nucleic acids research.

[21]  H. Huthoff,et al.  Two alternating structures of the HIV-1 leader RNA. , 2001, RNA.

[22]  Hirotada Mori,et al.  Molecular characterization of long direct repeat (LDR) sequences expressing a stable mRNA encoding for a 35‐amino‐acid cell‐killing peptide and a cis‐encoded small antisense RNA in Escherichia coli , 2002, Molecular microbiology.

[23]  C. M. Roth,et al.  Antisense technology in molecular and cellular bioengineering. , 2003, Current opinion in biotechnology.

[24]  C. Lawrence,et al.  A statistical sampling algorithm for RNA secondary structure prediction. , 2003, Nucleic acids research.

[25]  E. Papoutsakis,et al.  Design of Antisense RNA Constructs for Downregulation of the Acetone Formation Pathway of Clostridium acetobutylicum , 2003, Journal of bacteriology.

[26]  Robert Giegerich,et al.  Abstract shapes of RNA. , 2004, Nucleic acids research.

[27]  Isidore Rigoutsos,et al.  The web server of IBM's Bioinformatics and Pattern Discovery group: 2004 update , 2004, Nucleic Acids Res..

[28]  Robert Giegerich,et al.  Pure multiple RNA secondary structure alignments: a progressive profile approach , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[29]  Ye Ding,et al.  Sfold web server for statistical folding and rational design of nucleic acids , 2004, Nucleic Acids Res..

[30]  C. Lawrence,et al.  RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble. , 2005, RNA.

[31]  Xiaochen Bo,et al.  TargetFinder: a software for antisense oligonucleotide target site selection based on MAST and secondary structures of target mRNA. , 2005, Bioinformatics.

[32]  M. Höchsmann,et al.  The tree alignment model : algorithms, implementations and applications for the analysis of RNA secondary structures , 2005 .

[33]  M. Gleave,et al.  A phase I pharmacokinetic and pharmacodynamic study of OGX-011, a 2'-methoxyethyl antisense oligonucleotide to clusterin, in patients with localized prostate cancer. , 2005, Journal of the National Cancer Institute.

[34]  J. Vogel,et al.  An antisense RNA inhibits translation by competing with standby ribosomes. , 2007, Molecular cell.

[35]  E. Wagner,et al.  RNA antitoxins. , 2007, Current opinion in microbiology.

[36]  Michael Zuker,et al.  UNAFold: software for nucleic acid folding and hybridization. , 2008, Methods in molecular biology.

[37]  Ignacio Tinoco,et al.  Following translation by single ribosomes one codon at a time , 2008, Nature.

[38]  C. Bennett,et al.  RNA targeting therapeutics: molecular mechanisms of antisense oligonucleotides as a therapeutic platform. , 2010, Annual review of pharmacology and toxicology.

[39]  Rolf Backofen,et al.  Freiburg RNA Tools: a web server integrating IntaRNA, ExpaRNA and LocARNA , 2010, Nucleic Acids Res..

[40]  Mark A. Ragan,et al.  Quantitative Prediction of miRNA-mRNA Interaction Based on Equilibrium Concentrations , 2011, PLoS Comput. Biol..

[41]  Peter F. Stadler,et al.  RNApredator: fast accessibility-based prediction of sRNA targets , 2011, Nucleic Acids Res..

[42]  Peter D. Karp,et al.  EcoCyc: a comprehensive database of Escherichia coli biology , 2010, Nucleic Acids Res..