Expanding the design horizon of antisense oligonucleotides with alpha-L-LNA

Oligonucleotides containing Locked Nucleic Acids (LNA) to various extents and at various positions were evaluated for antisense activity, RNase H recruitment, nuclease stability and thermal affinity. In this work, two different diastereoisomers of LNA were studied: the beta-D-LNA and the alpha-L-LNA (abbreviated as beta-D-LNA and alpha-L-LNA). Our findings show that the best antisense activity with 16mer gapmers containing beta-D-LNA (oligonucleotides containing consecutive segments of LNA and DNA with a central DNA stretch flanked by two LNA segments, LNA-DNA-LNA) is found with gap sizes between 7 and 10 nt. The optimal gap size is motif-dependent, and requires the right balance between gap size and affinity. Compared to beta-D-LNA, alpha-L-LNA shows superior stability against a 3'-exonuclease. The design possibilities of alpha-L-LNA were explored for different gapmers and other designs, collectively called chimeras. The placement of alpha-L-LNA in the junctions or in the flanks resulted in potent antisense oligonucleotides. Moreover, different chimeras with an alternate composition of DNA, alpha-L-LNA and beta-D-LNA were evaluated in terms of antisense activity and RNase H recruitment. Chimeras with an interrupted DNA stretch with alpha-L-LNA still recruit RNase H and show good levels of antisense activity, while the same design with beta-D-LNA results in a drop in antisense potency. Our findings indicate that alpha-L-LNA is a powerful and versatile nucleotide analogue for designing potent antisense oligonucleotides.     

Electroporation enhances c-myc antisense oligodeoxynucleotide efficacy.

Obtaining high transfection efficiencies and achieving appropriate intracellular concentrations and localization are two of the most important barriers to the implementation of gene targeted therapy. The efficiency of endogenous uptake of oligodeoxynucleotides (ODNs) varies from cell type to cell type and may be a limiting factor of antisense efficacy. The use of electroporation to obtain high intracellular concentrations of a synthetic ODN in essentially 100% of viable cells is described. It is also shown that the transfected ODNs initially localize to the nucleus and remain there for at least 48 hours. The cellular trafficking of electroporated ODNs is shown to be an energy dependent process. Targeting of the c-myc proto-oncogene of U937 cells by electroporation of phosphorothioate-modified ODNs results in rapid and specific suppression of this gene at ODN concentrations much lower than would otherwise be required. This technique appears to be applicable to a variety of cell types and may represent a powerful new investigate tool as well as a promising approach to the ex vivo treatment of hematologic disorders.     

Molecular biology of fruit ripening and its manipulation with antisense genes

Considerable progress in tomato molecular biology has been made over the past five years. At least 19 different mRNAs which increase in amount during tomato fruit ripening have been cloned and genes for enzymes involved in cell wall degradation (polygalacturonase and pectinesterase) and ethylene synthesis (ACC synthase) have been identified by conventional procedures. Transgenic plants have been used to identify regions of DNA flanking fruit-specific, ripening-related and ethylene-regulated genes and trans-acting factors which bind to these promoters have also been identified.Antisense genes expressed in transgenic plants have proved to be highly effective for inhibiting the specific expression of ripening-related genes. These experiments have changed our understanding of how softening occurs in tomato fruit. Antisense techniques have also been used to identify genes encoding enzymes for carotenoid biosynthesis (phytoene synthase) and ethylene biosynthesis (the ethylene-forming enzyme). The altered characteristics of fruit transformed with specific antisense genes, such as retarded ripening and resistance to splitting, may prove to be of value to fruit growers, processors and ultimately the consumer.     

Mutations in chicory FEH genes are statistically associated with enhanced resistance to post-harvest inulin depolymerization

Key message

Nucleotidic polymorphisms were identified in fructan exohydrolases genes which are statistically associated with enhanced susceptibility to post-harvest inulin depolymerization.

Abstract

Industrial chicory (Cichorium intybus L.) root is the main commercial source of inulin, a linear fructose polymer used as dietary fiber. Post-harvest, inulin is depolymerized into fructose which drastically increases processing cost. To identify genetic variations associated with enhanced susceptibility to post-harvest inulin depolymerization and related free sugars content increase, we used a candidate-gene approach focused on inulin and sucrose synthesis and degradation genes, all members of the family 32 of glycoside hydrolases (GH32). Polymorphism in these genes was first investigated by carrying out EcoTILLING on two groups of chicory breeding lines exhibiting contrasted response to post-harvest inulin depolymerization. This allowed the identification of polymorphisms significantly associated with depolymerization in three fructan exohydrolase genes (FEH). This association was confirmed on a wider panel of 116 unrelated families in which the FEH polymorphism explained 35 % of the post-harvest variance for inulin content, 36 % of variance for sucrose content, 18 % for inulin degree of polymerization, 23 % for free fructose content and 22 % for free glucose content. These polymorphisms were associated with significant post-harvest changes of inulin content, inulin chain length and free sugars content.     

GOstat: find statistically overrepresented Gene Ontologies within a group of genes

SUMMARY: Modern experimental techniques, as for example DNA microarrays, as a result usually produce a long list of genes, which are potentially interesting in the analyzed process. In order to gain biological understanding from this type of data, it is necessary to analyze the functional annotations of all genes in this list. The Gene-Ontology (GO) database provides a useful tool to annotate and analyze the functions of a large number of genes. Here, we introduce a tool that utilizes this information to obtain an understanding of which annotations are typical for the analyzed list of genes. This program automatically obtains the GO annotations from a database and generates statistics of which annotations are overrepresented in the analyzed list of genes. This results in a list of GO terms sorted by their specificity. AVAILABILITY: Our program GOstat is accessible via the Internet at http://gostat.wehi.edu.au     

Modular genes with metazoan-specific domains have increased tissue specificity

We have systematically examined the domain composition across a comprehensive set of tissue-specific, midrange and housekeeping genes as defined by their mode of expression in 52 normal mouse tissues. We show a definite correlation between the number of domains and the degree of tissue specificity. This trend is further supported by a novel analysis involving the time of origin of each domain. Genes containing metazoan-specific domains are more prevalent in signal transduction and cell-communication pathways, and are depleted in primary metabolism. Our analyses suggest that highly modular gene products have been recruited for tissue-specific functions that are required in complex organisms.     

Profiled support vector machines for antisense oligonucleotide efficacy prediction

Background  

This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1) feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE), and (2) AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to different parts of the training data to focus the training on the most important regions.     

Enhancing antisense efficacy with multimers and multi-targeting oligonucleotides (MTOs) using cleavable linkers

The in vivo potency of antisense oligonucleotides (ASO) has been significantly increased by reducing their length to 8–15 nucleotides and by the incorporation of high affinity RNA binders such as 2′, 4′-bridged nucleic acids (also known as locked nucleic acid or LNA, and 2′,4′-constrained ethyl [cET]). We now report the development of a novel ASO design in which such short ASO monomers to one or more targets are co-synthesized as homo- or heterodimers or multimers via phosphodiester linkers that are stable in plasma, but cleaved inside cells, releasing the active ASO monomers. Compared to current ASOs, these multimers and multi-targeting oligonucleotides (MTOs) provide increased plasma protein binding and biodistribution to liver, and increased in vivo efficacy against single or multiple targets with a single construct. In vivo, MTOs synthesized in both RNase H-activating and steric-blocking oligonucleotide designs provide ≈4–5-fold increased potency and ≈2-fold increased efficacy, suggesting broad therapeutic applications.     

Predicting the efficacy of short oligonucleotides in antisense and RNAi experiments with boosted genetic programming

MOTIVATION: Both small interfering RNAs (siRNAs) and antisense oligonucleotides can selectively block gene expression. Although the two methods rely on different cellular mechanisms, these methods share the common property that not all oligonucleotides (oligos) are equally effective. That is, if mRNA target sites are picked at random, many of the antisense or siRNA oligos will not be effective. Algorithms that can reliably predict the efficacy of candidate oligos can greatly reduce the cost of knockdown experiments, but previous attempts to predict the efficacy of antisense oligos have had limited success. Machine learning has not previously been used to predict siRNA efficacy. RESULTS: We develop a genetic programming based prediction system that shows promising results on both antisense and siRNA efficacy prediction. We train and evaluate our system on a previously published database of antisense efficacies and our own database of siRNA efficacies collected from the literature. The best models gave an overall correlation between predicted and observed efficacy of 0.46 on both antisense and siRNA data. As a comparison, the best correlations of support vector machine classifiers trained on the same data were 0.40 and 0.30, respectively.