Sequence composition influences eIF4E responsiveness.

<p>Top row: median fold change of four groups of sequences corresponding to the four possible nucleotides at each position in the alignment around a) cap region (nt 1–20), b) start region (positions −19…20 with position 1 being the first nt of the coding region), c) stop region (positions −19…20 with position 1 being the first nt of the 3′UTR). Blue: A, red: C, green: G, black: U. Bottom row: Negative decadic logarithm of the Kruskal-Wallis test p-value as a function of the sequence position. The statistical test is applied at each alignment column to the fold change values of the four groups mention above. Note that because the Kruskal-Wallis test is not defined for completely conserved alignment columns, the start and stop codon regions are skipped (Figures a)–c)). The eIF4E overexpression data set consisting of 11387 mRNAs was used to generate the plots (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004868#s2" target="_blank">Materials and Methods</a>).</p

<i>In Silico</i> Design and Enzymatic Synthesis of Functional RNA Nanoparticles

ConspectusThe use of RNAs as scaffolds for biomedical applications has several advantages compared with other existing nanomaterials. These include (i) programmability, (ii) precise control over folding and self-assembly, (iii) natural functionalities as exemplified by ribozymes, riboswitches, RNAi, editing, splicing, and inherent translation and transcription control mechanisms, (iv) biocompatibility, (v) relatively low immune response, and (vi) relatively low cost and ease of production. We have tapped into several of these properties and functionalities to construct RNA-based functional nanoparticles (RNA NPs). In several cases, the structural core and the functional components of the NPs are inherent in the same construct. This permits control over the spatial disposition of the components, intracellular availability, and precise stoichiometry.To enable the generation of RNA NPs, a pipeline is being developed. On one end, it encompasses the rational design and various computational schemes that promote design of the RNA-based nanoconstructs, ultimately producing a set of sequences consisting of RNA or RNA–DNA hybrids, which can assemble into the designed construct. On the other end of the pipeline is an experimental component, which takes the produced sequences and uses them to initialize and characterize their proper assembly and then test the resulting RNA NPs for their function and delivery in cell culture and animal models. An important aspect of this pipeline is the feedback that constantly occurs between the computational and the experimental parts, which synergizes the refinement of both the algorithmic methodologies and the experimental protocols. The utility of this approach is depicted by the several examples described in this Account (nanocubes, nanorings, and RNA–DNA hybrids). Of particular interest, from the computational viewpoint, is that in most cases, first a three-dimensional representation of the assembly is produced, and only then are algorithms applied to generate the sequences that will assemble into the designated three-dimensional construct. This is opposite to the usual practice of predicting RNA structures from a given sequence, that is, the RNA folding problem. To be considered is the generation of sequences that upon assembly have the proper intra- or interstrand interactions (or both). Of particular interest from the experimental point of view is the determination and characterization of the proper thermodynamic, kinetic, functionality, and delivery protocols. Assembly of RNA NPs from individual single-stranded RNAs can be accomplished by one-pot techniques under the proper thermal and buffer conditions or, potentially more interestingly, by the use of various RNA polymerases that can promote the formation of RNA NPs cotransciptionally from specifically designed DNA templates.Also of importance is the delivery of the RNA NPs to the cells of interest <i>in vitro</i> or <i>in vivo</i>. Nonmodified RNAs rapidly degrade in blood serum and have difficulties crossing biological membranes due to their negative charge. These problems can be overcome by using, for example, polycationic lipid-based carriers. Our work involves the use of bolaamphiphiles, which are amphipathic compounds with positively charged hydrophilic head groups at each end connected by a hydrophobic chain. We have correlated results from molecular dynamics computations with various experiments to understand the characteristics of such delivery agents

A few upregulated mRNAs show positive selection for miRNA binding sites.

<p>List of miRNAs with positive selection (accumulation of binding sites) among 40 highly eIF4E upregulated mRNAs (fold change greater 4.0) and 1200 nonregulated mRNAs (fold change between 1.05 and 1.0/1.05). See caption of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004868#pone-0004868-t005" target="_blank">Table 5</a> for an explanation of table columns.</p

Probability of base pairing is greater for upregulated mRNAs at the regions just upstream of the start codon and flanking the stop codon.

<p>Secondary structure profiles of mRNA regions. Red, black, green: average secondary structure probability for upregulated, un-regulated or downregulated mRNAs respectively; blue: p-value of the Wilcoxon-Mann-Whitney two-sample rank sum test using logarithmic scale shown on the right y-axis. a) start codon (positions −19…20 with position 1 being the first nt of the coding region), b) stop codon (positions −19…20 with position 1 being the first nt of the 3′UTR).The positions of start and stop codon are indicated in red. The used subsets of the eIF4E overexpression data consists of 1835 upregulated mRNAs, 679 downregulated mRNAs and 3814 nonregulated mRNAs (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004868#s2" target="_blank">Material and Methods</a>).</p

G+C content shows correlation with polysome shift for total mRNA, coding and 3′UTR but not for 5′UTR sequence.

<p>Table shows G+C content of mRNA (total mRNA or 5′UTR, coding, 3′UTR) as a function of fold change. P-values and correlation coefficients are computed according to the Spearman correlation for the eIF4E overexpression data set (11387 mRNAs) and the AKT activation data set (7496 mRNAs).</p

The support vector machine shows high correlation for combinations of total length with 3′UTR length and/or G+C content.

<p>Result of support vector machine. Shown is the Spearman correlation coefficient as well as the Matthews correlation coefficient of the predicted fold change versus the actual fold change using different feature combinations. LT: total length; L3: length of 3′UTR region; GC: G+C content.</p

Support vector machine classifier effectively predicts fold change.

<p>Log-log plot of the eIF4E dataset fold change plotted with the corresponding support vector machine classifier results. The used eIF4E overexpression dataset consists of 4000 mRNAs for training and 5629 mRNAs for testing the classifier (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004868#s2" target="_blank">Materials and Methods</a>).</p

Confusion matrix of support vector machine for combination of total length with 3′UTR length and G+C content.

<p>Results of the support vector machine (corresponding to first row in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004868#pone-0004868-t003" target="_blank">Table 3</a>) applied as a two-class predictor. The used eIF4E overexpression dataset consists of 4000 mRNAs for training and 5629 mRNAs for testing the classifier (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004868#s2" target="_blank">Materials and Methods</a>).</p

Length correlates with fold shift for total, coding and 3′UTR but not 5′UTR of target mRNAs.

<p>Table of correlations between length of mRNA (total mRNA, 5′UTR, coding, 3′UTR) and the eIF4E fold change for the eIF4E dataset. P-values and correlation coefficient are computed according to the Spearman correlation for the eIF4E overexpression data set (11387 mRNAs) and the AKT activation data set (7496 mRNAs).</p

Highly upregulated mRNAs show mostly negative selection (site avoidance) for miRNA binding sites.

<p>List of miRNAs with negative selection (avoidance of binding sites) among 40 highly eIF4E upregulated mRNAs (fold change greater 4.0) and 1200 nonregulated mRNAs (fold change between 1.05 and 1.0/1.05). upreg⁺: number of upregulated mRNAs that show positive selection with respect to the specified miRNA, upreg⁻: number of upregulated mRNAs with negative selection, nonreg⁺: number of nonregulated mRNAs with positive selection of miRNA-target binding sites; nonreg⁻: number of nonregulated mRNAs with negative selection. P-values are computed according using a Fisher exact test, all entries with a p-value smaller than 0.02 are listed. Ratio: (upreg⁺/(upreg⁺+upreg⁻))/(nonreg⁺/(nonreg⁺+nonreg⁻)).</p