Rational design of micro-RNA-like bifunctional siRNAs targeting HIV and the HIV coreceptor CCR5.

Small-interfering RNAs (siRNAs) and micro-RNAs (miRNAs) are distinguished by their modes of action. SiRNAs serve as guides for sequence-specific cleavage of complementary mRNAs and the targets can be in coding or noncoding regions of the target transcripts. MiRNAs inhibit translation via partially complementary base-pairing to 3' untranslated regions (UTRs) and are generally ineffective when targeting coding regions of a transcript. In this study, we deliberately designed siRNAs that simultaneously direct cleavage and translational suppression of HIV RNAs, or cleavage of the mRNA encoding the HIV coreceptor CCR5 and suppression of translation of HIV. These bifunctional siRNAs trigger inhibition of HIV infection and replication in cell culture. The design principles have wide applications throughout the genome, as about 90% of genes harbor sites that make the design of bifunctional siRNAs possible.

MicroRNA-directed transcriptional gene silencing in mammalian cells.

MicroRNAs (miRNAs) regulate gene expression at the posttranscriptional level in the cytoplasm, but recent findings suggest additional roles for miRNAs in the nucleus. To address whether miRNAs might transcriptionally silence gene expression, we searched for miRNA target sites proximal to known gene transcription start sites in the human genome. One conserved miRNA, miR-320, is encoded within the promoter region of the cell cycle gene POLR3D in the antisense orientation. We provide evidence of a cis-regulatory role for miR-320 in transcriptional silencing of POLR3D expression. miR-320 directs the association of RNA interference (RNAi) protein Argonaute-1 (AGO1), Polycomb group (PcG) component EZH2, and tri-methyl histone H3 lysine 27 (H3K27me3) with the POLR3D promoter. Our results suggest the existence of an epigenetic mechanism of miRNA-directed transcriptional gene silencing (TGS) in mammalian cells.

Distance constraints between microRNA target sites dictate efficacy and cooperativity.

MicroRNAs (miRNAs) have the potential to regulate the expression of thousands of genes, but the mechanisms that determine whether a gene is targeted or not are poorly understood. We studied the genomic distribution of distances between pairs of identical miRNA seeds and found a propensity for moderate distances greater than about 13 nt between seed starts. Experimental data show that optimal down-regulation is obtained when two seed sites are separated by between 13 and 35 nt. By analyzing the distance between seed sites of endogenous miRNAs and transfected small interfering RNAs (siRNAs), we also find that cooperative targeting of sites with a separation in the optimal range can explain some of the siRNA off-target effects that have been reported in the literature.

Meta-analysis of breast cancer microarray studies in conjunction with conserved cis-elements suggest patterns for coordinate regulation.

BACKGROUND: Gene expression measurements from breast cancer (BrCa) tumors are established clinical predictive tools to identify tumor subtypes, identify patients showing poor/good prognosis, and identify patients likely to have disease recurrence. However, diverse breast cancer datasets in conjunction with diagnostic clinical arrays show little overlap in the sets of genes identified. One approach to identify a set of consistently dysregulated candidate genes in these tumors is to employ meta-analysis of multiple independent microarray datasets. This allows one to compare expression data from a diverse collection of breast tumor array datasets generated on either cDNA or oligonucleotide arrays. RESULTS: We gathered expression data from 9 published microarray studies examining estrogen receptor positive (ER+) and estrogen receptor negative (ER-) BrCa tumor cases from the Oncomine database. We performed a meta-analysis and identified genes that were universally up or down regulated with respect to ER+ versus ER- tumor status. We surveyed both the proximal promoter and 3' untranslated regions (3'UTR) of our top-ranking genes in each expression group to test whether common sequence elements may contribute to the observed expression patterns. Utilizing a combination of known transcription factor binding sites (TFBS), evolutionarily conserved mammalian promoter and 3'UTR motifs, and microRNA (miRNA) seed sequences, we identified numerous motifs that were disproportionately represented between the two gene classes suggesting a common regulatory network for the observed gene expression patterns. CONCLUSION: Some of the genes we identified distinguish key transcripts previously seen in array studies, while others are newly defined. Many of the genes identified as overexpressed in ER- tumors were previously identified as expression markers for neoplastic transformation in multiple human cancers. Moreover, our motif analysis identified a collection of specific cis-acting target sites which may collectively play a role in the differential gene expression patterns observed in ER+ versus ER- breast cancer tumors. Importantly, the gene sets and associated DNA motifs provide a starting point with which to explore the mechanistic basis for the observed expression patterns in breast tumors.

Reliable prediction of Drosha processing sites improves microRNA gene prediction.

MOTIVATION: Mature microRNAs (miRNAs) are processed from long hairpin transcripts. Even though it is only the first of several steps, the initial Drosha processing defines the mature product and is characteristic for all miRNA genes. Methods that can separate between true and false processing sites are therefore essential to miRNA gene discovery. RESULTS: We present a classifier that predicts 5' Drosha processing sites in hairpins that are candidate miRNAs. The classifier, called Microprocessor SVM, correctly predicts the processing site for 50% of known human 5' miRNAs, and 90% of its predictions are within two nucleotides of the true site. Another classifier that is trained on the output from the Microprocessor SVM outperforms existing methods for prediction of unconserved miRNAs. Reanalysis of characteristics and supporting evidence for a set of newly annotated miRNAs shows that some miRNAs may be misannotated. This suggests that expressed hairpins should not be annotated as miRNAs until they are verified to be Drosha and Dicer substrates. AVAILABILITY: The classifiers are publicly available at https://demo1.interagon.com/miRNA/

Robust machine learning algorithms predict microRNA genes and targets.

MicroRNAs (miRNA) are nonprotein coding RNAs with the potential to regulate the gene expression of thousands of protein coding genes. Current estimates suggest the number of miRNA genes may be twice of what is currently known, and the mechanisms governing miRNA targeting remain elusive. Machine learning algorithms can be used to create classifiers that capture the characteristics of verified examples to determine whether genomic hairpins are similar to verified miRNA genes or if message 3'UTRs possess known target characteristics. Algorithms can never replace biological verifications, but should always be used to guide experimental design. This chapter focuses on potential problems that must be addressed when machine learning is used and follows a practical approach to demonstrate how support vector machines and genetic programming can predict miRNA genes and targets.

Predicting non-coding RNA genes in Escherichia coli with boosted genetic programming.

Several methods exist for predicting non-coding RNA (ncRNA) genes in Escherichia coli (E.coli). In addition to about sixty known ncRNA genes excluding tRNAs and rRNAs, various methods have predicted more than thousand ncRNA genes, but only 95 of these candidates were confirmed by more than one study. Here, we introduce a new method that uses automatic discovery of sequence patterns to predict ncRNA genes. The method predicts 135 novel candidates. In addition, the method predicts 152 genes that overlap with predictions in the literature. We test sixteen predictions experimentally, and show that twelve of these are actual ncRNA transcripts. Six of the twelve verified candidates were novel predictions. The relatively high confirmation rate indicates that many of the untested novel predictions are also ncRNAs, and we therefore speculate that E.coli contains more ncRNA genes than previously estimated.

Conserved microRNA characteristics in mammals.

Short hairpin RNAs (shRNAs) and short interfering RNAs (siRNAs) probably enter different stages of the microRNA (miRNA) pathway for depletion of mRNA and suppression of protein translation. Primary and secondary structural characteristics that are shared between endogenous primary miRNA transcripts (pri-miRNAs) may contribute toward efficient biogenesis and potent silencing. This study investigates known miRNA transcripts for characteristics that are conserved between miRNAs and that distinguish them from random hairpins with similar lengths. The primary structure is conserved, as demonstrated by a significant presence or absence of certain bases at specific positions in the miRNA precursors and their flanking regions. The secondary structure is also conserved between miRNAs, as internal loops and bulges commonly appear in specific positions in the miRNA stem. The conservation of base-pairing continues past the mature duplex and 13 bases into the primary stem, with no detectable conservation of secondary structure beyond this region. Based on these observations, we have designed a hairpin construct that incorporates the most important characteristics present in endogenous miRNAs. Preliminary experiments suggest that this construct may rescue the efficacy of shRNA triggers that cannot be used with a miR-30-based hairpin, and vice versa.

Epigenetics and microRNAs.

MicroRNAs (miRNAs) regulate protein-coding genes post transcriptionally in higher eukaryotes. Argonaute proteins are important in miRNA regulation and are also implicated in epigenetic mechanisms such as histone modifications and DNA methylation. Here, we review miRNA regulation and outline its connections to epigenetics.

Toxicity in mice expressing short hairpin RNAs gives new insight into RNAi.

Short hairpin RNAs can provide stable gene silencing via RNA interference. Recent studies have shown toxicity in vivo that appears to be related to saturation of the endogenous microRNA pathway. Will these findings limit the therapeutic use of such hairpins?

Chemical modifications rescue off-target effects of RNAi.

RNAi's specificity has been questioned for some time. Three recent papers show that off-target effects should be considered normal, but one paper also provides insight on how chemical modifications of siRNAs may overcome the problem.

Expressing short hairpin RNAs in vivo.

Promoter-based expression of short hairpin RNAs (shRNAs) may in principle provide stable silencing of genes in any tissue. As for all approaches that require transgene expression, safe delivery is the biggest obstacle, but toxicity can also occur via expression of the sequence itself. Innate immunity mechanisms can be triggered by expressed hairpin RNAs, critical cellular factors can be saturated, and genes other than the intended target can be silenced. Nevertheless, shRNAs constitute a valuable tool for in vivo research and have great therapeutic potential if the challenges with delivery and side effects are appropriately addressed.

Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms.

We present a new microRNA target prediction algorithm called TargetBoost, and show that the algorithm is stable and identifies more true targets than do existing algorithms. TargetBoost uses machine learning on a set of validated microRNA targets in lower organisms to create weighted sequence motifs that capture the binding characteristics between microRNAs and their targets. Existing algorithms require candidates to have (1) near-perfect complementarity between microRNAs' 5' end and their targets; (2) relatively high thermodynamic duplex stability; (3) multiple target sites in the target's 3' UTR; and (4) evolutionary conservation of the target between species. Most algorithms use one of the two first requirements in a seeding step, and use the three others as filters to improve the method's specificity. The initial seeding step determines an algorithm's sensitivity and also influences its specificity. As all algorithms may add filters to increase the specificity, we propose that methods should be compared before such filtering. We show that TargetBoost's weighted sequence motif approach is favorable to using both the duplex stability and the sequence complementarity steps. (TargetBoost is available as a Web tool from http://www.interagon.com/demo/.).

A comparison of siRNA efficacy predictors.

Short interfering RNA (siRNA) efficacy prediction algorithms aim to increase the probability of selecting target sites that are applicable for gene silencing by RNA interference. Many algorithms have been published recently, and they base their predictions on such different features as duplex stability, sequence characteristics, mRNA secondary structure, and target site uniqueness. We compare the performance of the algorithms on a collection of publicly available siRNAs. First, we show that our regularized genetic programming algorithm GPboost appears to have a higher and more stable performance than other algorithms on the collected datasets. Second, several algorithms gave close to random classification on unseen data, and only GPboost and three other algorithms have a reasonably high and stable performance on all parts of the dataset. Third, the results indicate that the siRNAs' sequence is sufficient input to siRNA efficacy algorithms, and that other features that have been suggested to be important may be indirectly captured by the sequence.

Many commonly used siRNAs risk off-target activity.

Using small interfering RNA (siRNA) to induce sequence specific gene silencing is fast becoming a standard tool in functional genomics. As siRNAs in some cases tolerate mismatches with the mRNA target, knockdown of genes other than the intended target could make results difficult to interpret. In an investigation of 359 published siRNA sequences, we have found that about 75% of them have a risk of eliciting non-specific effects. A possible cause for this is the popular BLAST search engine, which is inappropriate for such short oligos as siRNAs. Furthermore, we used new special purpose hardware to do a transcriptome-wide screening of all possible siRNAs, and show that many unique siRNAs exist per target even if several mismatches are allowed. Hence, we argue that the risk of off-target effects is unnecessary and should be avoided in future siRNA design.

Designing effective siRNAs with off-target control.

Successful gene silencing by RNA interference requires a potent and specific depletion of the target mRNA. Target candidates must be chosen so that their corresponding short interfering RNAs are likely to be effective against that target and unlikely to accidentally silence other transcripts due to sequence similarity. We show that both effective and unique targets exist in mouse, fruit fly, and worm, and present a new design tool that enables users to make the trade-off between efficacy and uniqueness. The tool lists all targets with partial sequence similarity to the primary target to highlight candidates for negative controls.