S. Patrick Walton

Prediction of antisense oligonucleotide binding affinity to a structured RNA target

Antisense oligonucleotides, which act through the pairing of complementary bases to an RNA target sequence, are showing great promise in research and clinical applications. However, the selection of effective antisense oligonucleotides has proven more difficult than initially presumed. We developed a prediction algorithm to identify those sequences with the highest predicted binding affinity for their target mRNA based on a thermodynamic cycle that accounts for the energetics of structural alterations in both the target mRNA and the oligonucleotide. The model was used to predict the binding affinity of antisense oligonucleotides complementary to the rabbit beta-globin (RBG) and mouse tumor necrosis factor-alpha (TNFalpha) mRNAs, for which large experimental datasets were available. Of the top ten candidates identified by the algorithm for the RBG mRNA, six were the most strongly binding sequences determined from an experimental assay. The prediction for the TNFalpha mRNA also identified high affinity sequences with approximately 60% accuracy. Computational prediction of antisense efficacy is more cost-efficient and faster than in vitro or in vivo selection and can potentially speed the development of sequences for both research and clinical applications.

Designing highly active siRNAs for therapeutic applications

The discovery of RNA interference (RNAi) generated considerable interest in developing short interfering RNAs (siRNAs) for understanding basic biology and as the active agents in a new variety of therapeutics. Early studies showed that selecting an active siRNA was not as straightforward as simply picking a sequence on the target mRNA and synthesizing the siRNA complementary to that sequence. As interest in applying RNAi has increased, the methods for identifying active siRNA sequences have evolved from focusing on the simplicity of synthesis and purification, to identifying preferred target sequences and secondary structures, to predicting the thermodynamic stability of the siRNA. As more specific details of the RNAi mechanism have been defined, these have been incorporated into more complex siRNA selection algorithms, increasing the reliability of selecting active siRNAs against a single target. Ultimately, design of the best siRNA therapeutics will require design of the siRNA itself, in addition to design of the vehicle and other components necessary for it to function in vivo. In this minireview, we summarize the evolution of siRNA selection techniques with a particular focus on one issue of current importance to the field, how best to identify those siRNA sequences likely to have high activity. Approaches to designing active siRNAs through chemical and structural modifications will also be highlighted. As the understanding of how to control the activity and specificity of siRNAs improves, the potential utility of siRNAs as human therapeutics will concomitantly grow.

Thermodynamic and Kinetic Characterization of Antisense Oligodeoxynucleotide Binding to a Structured mRNA

Antisense oligonucleotides act as exogenous inhibitors of gene expression by binding to a complementary sequence on the target mRNA, preventing translation into protein. Antisense technology is being applied successfully as a research tool and as a molecular therapeutic. However, a quantitative understanding of binding energetics between short oligonucleotides and longer mRNA targets is lacking, and selecting a high-affinity antisense oligonucleotide sequence from the many possibilities complementary to a particular RNA is a critical step in designing an effective antisense inhibitor. Here, we report measurements of the thermodynamics and kinetics of hybridization for a number of oligodeoxynucleotides (ODNs) complementary to the rabbit beta-globin (RBG) mRNA using a binding assay that facilitates rapid separation of bound from free species in solution. A wide range of equilibrium dissociation constants were observed, and association rate constants within the measurable range correlated strongly with binding affinity. In addition, a significant correlation was observed of measured binding affinities with binding affinity values predicted using a thermodynamic model involving DNA and RNA unfolding, ODN hybridization, and RNA restructuring to a final free energy minimum. In contrast to the behavior observed for hybridization of short strands, the association rate constant increased with temperature, suggesting that the kinetics of association are related to disrupting the native structure of the target RNA. The rate of cleavage of the RBG mRNA in the presence of ribonuclease H and ODNs of varying association kinetics displayed apparent first-order kinetics, with the rate constant exhibiting binding-limited behavior at low association rates and reaction-limited behavior at higher rates. Implications for the rational design of effective antisense reagents are discussed.

Impact of target mRNA structure on siRNA silencing efficiency: A large‐scale study

The selection of active siRNAs is generally based on identifying siRNAs with certain sequence and structural properties. However, the efficiency of RNA interference has also been shown to depend on the structure of the target mRNA, primarily through studies using exogenous transcripts with well‐defined secondary structures in the vicinity of the target sequence. While these studies provide a means for examining the impact of target sequence and structure independently, the predicted secondary structures for these transcripts are often not reflective of structures that form in full‐length, native mRNAs where interactions can occur between relatively remote segments of the mRNAs. Here, using a combination of experimental results and analysis of a large dataset, we demonstrate that the accessibility of certain local target structures on the mRNA is an important determinant in the gene silencing ability of siRNAs. siRNAs targeting the enhanced green fluorescent protein were chosen using a minimal siRNA selection algorithm followed by classification based on the predicted minimum free energy structures of the target transcripts. Transfection into HeLa and HepG2 cells revealed that siRNAs targeting regions of the mRNA predicted to have unpaired 5′‐ and 3′‐ends resulted in greater gene silencing than regions predicted to have other types of secondary structure. These results were confirmed by analysis of gene silencing data from previously published siRNAs, which showed that mRNA target regions unpaired at either the 5′‐end or 3′‐end were silenced, on average, ∼10% more strongly than target regions unpaired in the center or primarily paired throughout. We found this effect to be independent of the structure of the siRNA guide strand. Taken together, these results suggest minimal requirements for nucleation of hybridization between the siRNA guide strand and mRNA and that both mRNA and guide strand structure should be considered when choosing candidate siRNAs. Biotechnol. Bioeng. 2008;100: 744–755.

Rational selection and quantitative evaluation of antisense oligonucleotides.

Antisense oligonucleotides are an attractive therapeutic option to modulate specific gene expression. However, not all antisense oligonucleotides are effective in inhibiting gene expression, and currently very few methods exist for selecting the few effective ones from all candidate oligonucleotides. The lack of quantitative methods to rapidly assess the efficacy of antisense oligonucleotides also contributes to the difficulty of discovering potent and specific antisense oligonucleotides. We have previously reported the development of a prediction algorithm for identifying high affinity antisense oligonucleotides based on mRNA-oligonucleotide hybridization. In this study, we report the antisense activity of these rationally selected oligonucleotides against three model target mRNAs (human lactate dehydrogenase A and B and rat gp130) in cell culture. The effectiveness of oligonucleotides was evaluated by a kinetic PCR technique, which allows quantitative evaluation of mRNA levels and thus provides a measure of antisense-mediated decreases in target mRNA, as occurs through RNase H recruitment. Antisense oligonucleotides that were predicted to have high affinity for their target proved effective in almost all cases, including tests against three different targets in two cell types with phosphodiester and phosphorothioate oligonucleotide chemistries. This approach may aid the development of antisense oligonucleotides for a variety of applications.

Development of a dual-aptamer-based multiplex protein biosensor.

Parallel biosensors for proteins are becoming more essential for the thorough and systematic investigation of complex biological processes. These tools also enable improved clinical diagnoses relative to single-protein analyses due to their greater information content. If implemented correctly, affinity-based techniques can provide unique advantages in terms of sensitivity and flexibility. Aptamers are increasingly being used as the affinity reagents of choice for protein biosensing applications. Here, we describe the development and characterization of an aptamer-based method for parallel protein analyses that relies on recognition of the target protein by two unique aptamers targeting different epitopes on the protein. Our results show that the technique achieved simultaneous and quantitative detection of thrombin and platelet-derived growth factor-BB (PDGF-BB) with high specificity both in buffered solutions and in serum samples.

Design of siRNA Therapeutics from the Molecular Scale

While protein-based therapeutics is well-established in the market, development of nucleic acid therapeutics has lagged. Short interfering RNAs (siRNAs) represent an exciting new direction for the pharmaceutical industry. These small, chemically synthesized RNAs can knock down the expression of target genes through the use of a native eukaryotic pathway called RNA interference (RNAi). Though siRNAs are routinely used in research studies of eukaryotic biological processes, transitioning the technology to the clinic has proven challenging. Early efforts to design an siRNA therapeutic have demonstrated the difficulties in generating a highly-active siRNA with good specificity and a delivery vehicle that can protect the siRNA as it is transported to a specific tissue. In this review article, we discuss design considerations for siRNA therapeutics, identifying criteria for choosing therapeutic targets, producing highly-active siRNA sequences, and designing an optimized delivery vehicle. Taken together, these design considerations provide logical guidelines for generating novel siRNA therapeutics.

In vitro binding of single-stranded RNA by human Dicer

While Dicer alone has been shown to form stable complexes with double‐stranded RNAs and short interfering RNAs, its interactions with single‐stranded RNAs (ssRNAs) have not been characterized. Here, we show that recombinant human Dicer alone can bind 21‐nt ssRNAs in vitro, independent of their sequence and structure. We also demonstrate that Dicer binds ssRNAs having a 5′‐phosphate with greater affinity versus those with a 5′‐hydroxyl. In addition, 3′‐biotinylated ssRNAs are bound by Dicer with lower affinity than 3′‐hydroxyl ssRNAs. The stability of ssRNA–Dicer complexes was found to depend on divalent cations. Together, our results suggest a role for the PAZ domain of Dicer in binding ssRNAs and may indicate roles for Dicer in cellular function beyond those currently known.

Confocal microscopy for the analysis of siRNA delivery by polymeric nanoparticles

Clinical applications of genetic therapies, including delivery of short, interfering RNAs (siRNAs) for RNA interference (RNAi), are limited due to the difficulty of delivering nucleic acids to specific cells of interest while at the same time minimizing toxicity and immunogenicity. The use of cationic polymers to deliver nucleic acid therapeutics has the potential to address these complex issues but is currently limited by low‐delivery efficiencies. Although cell culture studies have shown that some polymers can be used to deliver siRNAs and achieve silencing, it is still not clear what physical or chemical properties are needed to ensure that the polymers form active polymer‐siRNA complexes. In this study, we used multicolor fluorescence confocal microscopy to analyze the cellular uptake of siRNAs delivered by novel propargyl glycolide polymeric nanoparticles (NPs). Delivery by these vehicles was compared with delivery by linear polyethyleneimine (LPEI) and Lipofectamine 2000 (LF2K), which are both known as effective delivery vehicles for siRNAs. Our results showed that when LF2K and LPEI were used, large quantities of siRNA were delivered rapidly, presumably overwhelming the basal levels of mRNA to initiate silencing. In contrast, our novel polymeric NPs showed delivery of siRNAs but at concentrations that were initially too low to achieve silencing. Nonetheless, the exceptionally low cytotoxicity of our NPs, and the simplicity with which they can be modified, makes them good candidates for further study to optimize their delivery profiles and, in turn, achieve efficient silencing. Microsc. Res. Tech. 73:878–885, 2010.

Latest developments in experimental and computational approaches to characterize protein–lipid interactions

Understanding the functional roles of all the molecules in cells is an ultimate goal of modern biology. An important facet is to understand the functional contributions from intermolecular interactions, both within a class of molecules (e.g. protein–protein) or between classes (e.g. protein‐DNA). While the technologies for analyzing protein–protein and protein–DNA interactions are well established, the field of protein–lipid interactions is still relatively nascent. Here, we review the current status of the experimental and computational approaches for detecting and analyzing protein–lipid interactions. Experimental technologies fall into two principal categories, namely solution‐based and array‐based methods. Computational methods include large–scale data‐driven analyses and predictions/dynamic simulations based on prior knowledge of experimentally identified interactions. Advances in the experimental technologies have led to improved computational analyses and vice versa, thereby furthering our understanding of protein–lipid interactions and their importance in biological systems.