Rapid identification of cell-specific, internalizing RNA aptamers with bioinformatics analyses of a cell-based aptamer selection.

BACKGROUND: The broad applicability of RNA aptamers as cell-specific delivery tools for therapeutic reagents depends on the ability to identify aptamer sequences that selectively access the cytoplasm of distinct cell types. Towards this end, we have developed a novel approach that combines a cell-based selection method (cell-internalization SELEX) with high-throughput sequencing (HTS) and bioinformatics analyses to rapidly identify cell-specific, internalization-competent RNA aptamers. METHODOLOGY/PRINCIPAL FINDINGS: We demonstrate the utility of this approach by enriching for RNA aptamers capable of selective internalization into vascular smooth muscle cells (VSMCs). Several rounds of positive (VSMCs) and negative (endothelial cells; ECs) selection were performed to enrich for aptamer sequences that preferentially internalize into VSMCs. To identify candidate RNA aptamer sequences, HTS data from each round of selection were analyzed using bioinformatics methods: (1) metrics of selection enrichment; and (2) pairwise comparisons of sequence and structural similarity, termed edit and tree distance, respectively. Correlation analyses of experimentally validated aptamers or rounds revealed that the best cell-specific, internalizing aptamers are enriched as a result of the negative selection step performed against ECs. CONCLUSIONS AND SIGNIFICANCE: We describe a novel approach that combines cell-internalization SELEX with HTS and bioinformatics analysis to identify cell-specific, cell-internalizing RNA aptamers. Our data highlight the importance of performing a pre-clear step against a non-target cell in order to select for cell-specific aptamers. We expect the extended use of this approach to enable the identification of aptamers to a multitude of different cell types, thereby facilitating the broad development of targeted cell therapies.

RNA aptamer-based functional ligands of the neurotrophin receptor, TrkB.

Many cell surface signaling receptors, such as the neurotrophin receptor, TrkB, have emerged as potential therapeutic targets for diverse diseases. Reduced activation of TrkB in particular is thought to contribute to neurodegenerative diseases. Unfortunately, development of therapeutic reagents that selectively activate particular cell surface receptors such as TrkB has proven challenging. Like many cell surface signaling receptors, TrkB is internalized upon activation; in this proof-of-concept study, we exploited this fact to isolate a pool of nuclease-stabilized RNA aptamers enriched for TrkB agonists. One of the selected aptamers, C4-3, was characterized with recombinant protein-binding assays, cell-based signaling and functional assays, and, in vivo in a seizure model in mice. C4-3 binds the extracellular domain of TrkB with high affinity (K(D) ∼2 nM) and exhibits potent TrkB partial agonistic activity and neuroprotective effects in cultured cortical neurons. In mice, C4-3 activates TrkB upon infusion into the hippocampus; systemic administration of C4-3 potentiates kainic acid-induced seizure development. We conclude that C4-3 is a potentially useful therapeutic agent for neurodegenerative diseases in which reduced TrkB activation has been implicated. We anticipate that the cell-based aptamer selection approach used here will be broadly applicable to the identification of aptamer-based agonists for a variety of cell-surface signaling receptors.

An X chromosome microRNA cluster in the marsupial species Monodelphis domestica.

MicroRNAs (miRNAs) are an important class of posttranscriptional gene expression regulators. In the course of mapping novel marsupial-specific miRNAs in the genome of the gray short-tailed opossum, Monodelphis domestica, we encountered a cluster of 39 actual and potential miRNAs spanning 102 kb of the X chromosome. Analysis of the cluster revealed that 37 of the 39 miRNAs are predicted to form thermodynamically stable hairpins, and at least 3 members have been directly cloned from M. domestica tissues. The sequence characteristics of these miRNAs suggest that they all descended from a single common ancestor. Further, 2 distinct families appear to have diversified from the ancestral sequence through different duplication mechanisms: one through a series of simple tandem duplications and the other through a recurrent transposon-mediated duplication process.

Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs.

BACKGROUND: Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models. PRINCIPAL FINDINGS: Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation. CONCLUSIONS: The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques.

Marsupial-specific microRNAs evolved from marsupial-specific transposable elements.

Using a direct miRNA cloning strategy we previously identified fourteen marsupial- or species-specific microRNAs in the marsupial species Monodelphis domestica. In the present study we examined each of the pre-miRNAs and their flanking sequences and demonstrate that half of these miRNAs evolved from marsupial-specific transposable elements. These findings reinforce the view that transposable elements are a previously unappreciated source of new, lineage-specific microRNAs.

IDT SciTools: a suite for analysis and design of nucleic acid oligomers.

DNA and RNA oligomers are used in a myriad of diverse biological and biochemical experiments. These oligonucleotides are designed to have unique biophysical, chemical and hybridization properties. We have created an integrated set of bioinformatics tools that predict the properties of native and chemically modified nucleic acids and assist in their design. Researchers can select PCR primers, probes and antisense oligonucleotides, find the most suitable sequences for RNA interference, calculate stable secondary structures, and evaluate the potential for two sequences to interact. The latest, most accurate thermodynamic algorithms and models are implemented. This free software is available at http://www.idtdna.com/SciTools/SciTools.aspx.

Identification of sequence motifs significantly associated with antisense activity.

BACKGROUND: Predicting the suppression activity of antisense oligonucleotide sequences is the main goal of the rational design of nucleic acids. To create an effective predictive model, it is important to know what properties of an oligonucleotide sequence associate significantly with antisense activity. Also, for the model to be efficient we must know what properties do not associate significantly and can be omitted from the model. This paper will discuss the results of a randomization procedure to find motifs that associate significantly with either high or low antisense suppression activity, analysis of their properties, as well as the results of support vector machine modelling using these significant motifs as features. RESULTS: We discovered 155 motifs that associate significantly with high antisense suppression activity and 202 motifs that associate significantly with low suppression activity. The motifs range in length from 2 to 5 bases, contain several motifs that have been previously discovered as associating highly with antisense activity, and have thermodynamic properties consistent with previous work associating thermodynamic properties of sequences with their antisense activity. Statistical analysis revealed no correlation between a motif's position within an antisense sequence and that sequences antisense activity. Also, many significant motifs existed as subwords of other significant motifs. Support vector regression experiments indicated that the feature set of significant motifs increased correlation compared to all possible motifs as well as several subsets of the significant motifs. CONCLUSION: The thermodynamic properties of the significantly associated motifs support existing data correlating the thermodynamic properties of the antisense oligonucleotide with antisense efficiency, reinforcing our hypothesis that antisense suppression is strongly associated with probe/target thermodynamics, as there are no enzymatic mediators to speed the process along like the RNA Induced Silencing Complex (RISC) in RNAi. The independence of motif position and antisense activity also allows us to bypass consideration of this feature in the modelling process, promoting model efficiency and reducing the chance of overfitting when predicting antisense activity. The increase in SVR correlation with significant features compared to nearest-neighbour features indicates that thermodynamics alone is likely not the only factor in determining antisense efficiency.

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features.

BACKGROUND: RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities. RESULTS: Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. CONCLUSION: The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at the following site: ftp://scitoolsftp.idtdna.com/SEQ2SVM/.

Design of active small interfering RNAs.

Small interfering RNAs (siRNAs) have become the experimental tool of choice to suppress gene expression in a wide variety of organisms. Site selection and optimization does not appear to be as difficult for siRNAs as would be expected from historical experience with antisense oligonucleotides. Nevertheless, not all sites within a target gene perform equally. Significant progress has been made in defining sequence features that contribute to siRNA potency, and a variety of computational tools are available from academic and commercial sources to assist with siRNA design. Potential siRNA sequences should be screened for homology to other genes within the target organism's transcriptome to minimize cross-hybridization and inadvertent knockdown of unrelated genes via off-target effects. In addition to rational design criteria, chemical modification of the RNA can improve function by improving stability, reducing the potential for off-target effects and avoiding stimulation of the innate immune system.

Nox2-containing NADPH oxidase and Akt activation play a key role in angiotensin II-induced cardiomyocyte hypertrophy.

Angiotensin II (ANG II) has profound effects on the development and progression of pathological cardiac hypertrophy; however, the intracellular signaling mechanisms are not fully understood. In this study, we used genetic tools to test the hypothesis that increased formation of superoxide (O2-*) radicals from a Rac1-regulated Nox2-containing NADPH oxidase is a key upstream mediator of ANG II-induced activation of serine-threonine kinase Akt, and that this signaling cascade plays a crucial role in ANG II-dependent cardiomyocyte hypertrophy. ANG II caused a significant time-dependent increase in Rac1 activation and O2-* production in primary neonatal rat cardiomyocytes, and these responses were abolished by adenoviral (Ad)-mediated expression of a dominant-negative Rac1 (AdN17Rac1) or cytoplasmic Cu/ZnSOD (AdCu/ZnSOD). Moreover, both AdN17Rac1 and AdCu/ZnSOD significantly attenuated ANG II-stimulated increases in cardiomyocyte size. Quantitative real-time PCR analysis demonstrated that Nox2 is the homolog expressed at highest levels in primary neonatal cardiomyocytes, and small interference RNA (siRNA) directed against it selectively decreased Nox2 expression by >95% and abolished both ANG II-induced O2-* generation and cardiomyocyte hypertrophy. Finally, ANG II caused a time-dependent increase in Akt activity via activation of AT(1) receptors, and this response was abolished by Ad-mediated expression of cytosolic human O2-* dismutase (AdCu/ZnSOD). Furthermore, pretreatment of cardiomyocytes with dominant-negative Akt (AdDNAkt) abolished ANG II-induced cellular hypertrophy. These findings suggest that O2-* generated by a Nox2-containing NADPH oxidase is a central mediator of ANG II-induced Akt activation and cardiomyocyte hypertrophy, and that dysregulation of this signaling cascade may play an important role in cardiac hypertrophy.

Generation of an oligonucleotide array for analysis of gene expression in Chlamydomonas reinhardtii.

The availability of genome sequences makes it possible to develop microarrays that can be used for profiling gene expression over developmental time, as organisms respond to environmental challenges, and for comparison between wild-type and mutant strains under various conditions. The desired characteristics of microarrays (intense signals, hybridization specificity and extensive coverage of the transcriptome) were not fully met by the previous Chlamydomonas reinhardtii microarray: probes derived from cDNA sequences (approximately 300 bp) were prone to some nonspecific cross-hybridization and coverage of the transcriptome was only approximately 20%. The near completion of the C. reinhardtii nuclear genome sequence and the availability of extensive cDNA information have made it feasible to improve upon these aspects. After developing a protocol for selecting a high-quality unigene set representing all known expressed sequences, oligonucleotides were designed and a microarray with approximately 10,000 unique array elements (approximately 70 bp) covering 87% of the known transcriptome was developed. This microarray will enable researchers to generate a global view of gene expression in C. reinhardtii. Furthermore, the detailed description of the protocol for selecting a unigene set and the design of oligonucleotides may be of interest for laboratories interested in developing microarrays for organisms whose genome sequences are not yet completed (but are nearing completion).

Population genetics of duplicated disease-defense genes, hm1 and hm2, in maize (Zea mays ssp. mays L.) and its wild ancestor (Zea mays ssp. parviglumis).

Plant defense genes are subject to nonneutral evolutionary dynamics. Here we investigate the evolutionary dynamics of the duplicated defense genes hm1 and hm2 in maize and its wild ancestor Zea mays ssp. parviglumis. Both genes have been shown to confer resistance to the fungal pathogen Cochliobolus carbonum race 1, but the effectiveness of resistance differs between loci. The genes also display different population histories. The hm1 locus has the highest nucleotide diversity of any gene yet sampled in the wild ancestor of maize, and it contains a large number of indel polymorphisms. There is no evidence, however, that high diversity in hm1 is a product of nonneutral evolution. In contrast, hm2 has very low nucleotide diversity in the wild ancestor of maize. The distribution of hm2 polymorphic sites is consistent with nonneutral evolution, as indicated by Tajima's D and other neutrality tests. In addition, one hm2 haplotype is more frequent than expected under the equilibrium neutral model, suggesting hitchhiking selection. Both defense genes retain >80% of the level of genetic variation in maize relative to the wild ancestor, and this level is similar to other maize genes that were not subject to artificial selection during domestication.