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Improved Performance of Anti-miRNA Oligonucleotides Using a Novel Non-Nucleotide Modifier.

ABSTRACT: Anti-microRNA oligonucleotides (AMOs) are steric blocking antisense reagents that inhibit microRNA (miRNA) function by hybridizing and repressing the activity of a mature miRNA. First generation AMOs employed 2'-O-Methyl RNA nucleotides (2'OMe) with phosphorothioate (PS) internucleotide linkages positioned at both ends to block exonuclease attack. Second generation AMOs improved potency through the use of chemical modifications that increase binding affinity to the target, such as locked nucleic acid (LNA) residues. However, this strategy can reduce specificity as high binding affinity compounds can bind to and suppress function of related sequences even if one or more mismatches are present. Further, unnatural modified nucleic acid residues can have toxic side effects. In the present study, a variety of non-nucleotide modifiers were screened for utility in steric blocking antisense applications. A novel compound, N,N-diethyl-4-(4-nitronaphthalen-1-ylazo)-phenylamine ("ZEN"), was discovered that increased binding affinity and blocked exonuclease degradation when placed at or near each end of a single-stranded oligonucleotide. This new modification was combined with the 2'OMe RNA backbone to make ZEN-AMOs. The new ZEN-AMOs have high potency and can effectively inhibit miRNA function in vitro at low nanomolar concentrations, show high specificity, and have low toxicity in cell culture.Molecular Therapy-Nucleic Acids (2013) 2, e117; doi:10.1038/mtna.2013.46; published online 27 August 2013.

SUBMITTER: Lennox KA

PROVIDER: S-EPMC3759741 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Improved Performance of Anti-miRNA Oligonucleotides Using a Novel Non-Nucleotide Modifier.

Lennox Kim A KA Owczarzy Richard R Thomas Derek M DM Walder Joseph A JA Behlke Mark A MA

Molecular therapy. Nucleic acids 20130827

Anti-microRNA oligonucleotides (AMOs) are steric blocking antisense reagents that inhibit microRNA (miRNA) function by hybridizing and repressing the activity of a mature miRNA. First generation AMOs employed 2'-O-Methyl RNA nucleotides (2'OMe) with phosphorothioate (PS) internucleotide linkages positioned at both ends to block exonuclease attack. Second generation AMOs improved potency through the use of chemical modifications that increase binding affinity to the target, such as locked nucleic ...[more]

PMID: 23982190

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Project description:BACKGROUND: Many SNP discrimination strategies employ natural restriction endonucleases to discriminate between allelic states. However, SNPs are often not associated with a restriction site and therefore, a number of attempts have been made to generate sequence-adaptable restriction endonucleases. In this study, a simple, sequence-adaptable SNP discrimination mechanism between a 'wild-type' and 'mutant' template is demonstrated. This model differs from other artificial restriction endonuclease models as cis- rather than trans-orientated regions of single stranded DNA were generated and cleaved, and therefore, overcomes potential issues of either inefficient or non-specific binding when only a single variant is targeted. RESULTS: A series of mismatch 'bubbles' that spanned 0-5-bp surrounding a point mutation was generated and analysed for sensitivity to S1 nuclease. In this model, generation of oligonucleotide-mediated ssDNA mismatch 'bubbles' in the presence of S1 nuclease resulted in the selective degradation of the mutant template while maintaining wild-type template integrity. Increasing the size of the mismatch increased the rate of mutant sequence degradation, until a threshold above which discrimination was lost and the wild-type sequence was degraded. This level of fine discrimination was possible due to the development of a novel high-resolution melting curve assay to empirically determine changes in Tm (~5.0°C per base-pair mismatch) and to optimise annealing conditions (~18.38°C below Tm) of the mismatched oligonucleotide sets. CONCLUSIONS: The in vitro 'cleavage bubble' model presented is sequence-adaptable as determined by the binding oligonucleotide, and hence, has the potential to be tailored to discriminate between any two or more SNPs. Furthermore, the demonstrated fluorometric assay has broad application potential, offering a rapid, sensitive and high-throughput means to determine Tm and annealing rates as an alternative to conventional hybridisation detection strategies.

Project description:BackgroundA large body of evidence shows that miRNA regulates the expression of its target genes at post-transcriptional level and the dysregulation of miRNA is related to many complex human diseases. Accurately discovering disease-related miRNAs is conductive to the exploring of the pathogenesis and treatment of diseases. However, because of the limitation of time-consuming and expensive experimental methods, predicting miRNA-disease associations by computational models has become a more economical and effective mean.ResultsInspired by the work of predecessors, we proposed an improved computational model based on random forest (RF) for identifying miRNA-disease associations (IRFMDA). First, the integrated similarity of diseases and the integrated similarity of miRNAs were calculated by combining the semantic similarity and Gaussian interaction profile kernel (GIPK) similarity of diseases, the functional similarity and GIPK similarity of miRNAs, respectively. Then, the integrated similarity of diseases and the integrated similarity of miRNAs were combined to represent each miRNA-disease relationship pair. Next, the miRNA-disease relationship pairs contained in the HMDD (v2.0) database were considered positive samples, and the randomly constructed miRNA-disease relationship pairs not included in HMDD (v2.0) were considered negative samples. Next, the feature selection based on the variable importance score of RF was performed to choose more useful features to represent samples to optimize the model's ability of inferring miRNA-disease associations. Finally, a RF regression model was trained on reduced sample space to score the unknown miRNA-disease associations. The AUCs of IRFMDA under local leave-one-out cross-validation (LOOCV), global LOOCV and 5-fold cross-validation achieved 0.8728, 0.9398 and 0.9363, which were better than several excellent models for predicting miRNA-disease associations. Moreover, case studies on oesophageal cancer, lymphoma and lung cancer showed that 94 (oesophageal cancer), 98 (lymphoma) and 100 (lung cancer) of the top 100 disease-associated miRNAs predicted by IRFMDA were supported by the experimental data in the dbDEMC (v2.0) database.ConclusionsCross-validation and case studies demonstrated that IRFMDA is an excellent miRNA-disease association prediction model, and can provide guidance and help for experimental studies on the regulatory mechanism of miRNAs in complex human diseases in the future.

Dataset Information

Improved Performance of Anti-miRNA Oligonucleotides Using a Novel Non-Nucleotide Modifier.

Publications

Improved Performance of Anti-miRNA Oligonucleotides Using a Novel Non-Nucleotide Modifier.

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