In silico evidence of the relationship between miRNAs and siRNAs

Both short interfering RNAs (siRNAs) and microRNAs (miRNAs) mediate the repression of specific sequences of mRNA through the RNA interference pathway. In the last years several experiments have supported the hypothesis that siRNAs and miRNAs may be functionally interchangeable, at least in cultured cells. In this work we verify that this hypothesis is also supported by a computational evidence. We show that a method specifically trained to predict the activity of the exogenous siRNAs assigns a high silencing level to experimentally determined human miRNAs. This result not only supports the idea of siRNAs and miRNAs equivalence but indicates that it is possible to use computational tools developed using synthetic small interference RNAs to investigate endogenous miRNAs.Comment: 8 pages, 2 figure

Integrated siRNA design based on surveying of features associated with high RNAi effectiveness

BACKGROUND: Short interfering RNAs have allowed the development of clean and easily regulated methods for disruption of gene expression. However, while these methods continue to grow in popularity, designing effective siRNA experiments can be challenging. The various existing siRNA design guidelines suffer from two problems: they differ considerably from each other, and they produce high levels of false-positive predictions when tested on data of independent origins. RESULTS: Using a distinctly large set of siRNA efficacy data assembled from a vast diversity of origins (the siRecords data, containing records of 3,277 siRNA experiments targeting 1,518 genes, derived from 1,417 independent studies), we conducted extensive analyses of all known features that have been implicated in increasing RNAi effectiveness. A number of features having positive impacts on siRNA efficacy were identified. By performing quantitative analyses on cooperative effects among these features, then applying a disjunctive rule merging (DRM) algorithm, we developed a bundle of siRNA design rule sets with the false positive problem well curbed. A comparison with 15 online siRNA design tools indicated that some of the rule sets we developed surpassed all of these design tools commonly used in siRNA design practice in positive predictive values (PPVs). CONCLUSION: The availability of the large and diverse siRNA dataset from siRecords and the approach we describe in this report have allowed the development of highly effective and generally applicable siRNA design rule sets. Together with ever improving RNAi lab techniques, these design rule sets are expected to make siRNAs a more useful tool for molecular genetics, functional genomics, and drug discovery studies

Reconsideration of In-Silico siRNA Design Based on Feature Selection: A Cross-Platform Data Integration Perspective

RNA interference via exogenous short interference RNAs (siRNA) is increasingly more widely employed as a tool in gene function studies, drug target discovery and disease treatment. Currently there is a strong need for rational siRNA design to achieve more reliable and specific gene silencing; and to keep up with the increasing needs for a wider range of applications. While progress has been made in the ability to design siRNAs with specific targets, we are clearly at an infancy stage towards achieving rational design of siRNAs with high efficacy. Among the many obstacles to overcome, lack of general understanding of what sequence features of siRNAs may affect their silencing efficacy and of large-scale homogeneous data needed to carry out such association analyses represents two challenges. To address these issues, we investigated a feature-selection based in-silico siRNA design from a novel cross-platform data integration perspective. An integration analysis of 4,482 siRNAs from ten meta-datasets was conducted for ranking siRNA features, according to their possible importance to the silencing efficacy of siRNAs across heterogeneous data sources. Our ranking analysis revealed for the first time the most relevant features based on cross-platform experiments, which compares favorably with the traditional in-silico siRNA feature screening based on the small samples of individual platform data. We believe that our feature ranking analysis can offer more creditable suggestions to help improving the design of siRNA with specific silencing targets. Data and scripts are available at http://csbl.bmb.uga.edu/publications/materials/qiliu/siRNA.html

Criteria for effective design, construction, and gene knockdown by shRNA vectors

BACKGROUND: RNA interference (RNAi) technology is a powerful methodology recently developed for the specific knockdown of targeted genes. RNAi is most commonly achieved either transiently by transfection of small interfering (si) RNA oligonucleotides, or stably using short hairpin (sh) RNA expressed from a DNA vector or virus. Much controversy has surrounded the development of rules for the design of effective siRNA oligonucleotides; and whether these rules apply to shRNA is not well characterized. RESULTS: To determine whether published algorithms for siRNA oligonucleotide design apply to shRNA, we constructed 27 shRNAs from 11 human genes expressed stably using retroviral vectors. We demonstrate an efficient method for preparing wild-type and mutant control shRNA vectors simultaneously using oligonucleotide hybrids. We show that sequencing through shRNA vectors can be problematic due to the intrinsic secondary structure of the hairpin, and we determine a strategy for effective sequencing by using a combination of modified BigDye chemistries and DNA relaxing agents. The efficacy of knockdown for the 27 shRNA vectors was evaluated against six published algorithms for siRNA oligonucleotide design. Our results show that none of the scoring algorithms can explain a significant percentage of variance in shRNA knockdown efficacy as assessed by linear regression analysis or ROC curve analysis. Application of a modification based on the stability of the 6 central bases of each shRNA provides fair-to-good predictions of knockdown efficacy for three of the algorithms. Analysis of an independent set of data from 38 shRNAs pooled from previous publications confirms these findings. CONCLUSION: The use of mixed oligonucleotide pairs provides a time and cost efficient method of producing wild type and mutant control shRNA vectors. The addition to sequencing reactions of a combination of mixed dITP/dGTP chemistries and DNA relaxing agents enables read through the intrinsic secondary structure of problematic shRNA vectors. Six published algorithms for siRNA oligonucleotide design that were tested in this study show little or no efficacy at predicting shRNA knockdown outcome. However, application of a modification based on the central shRNA stability should provide a useful improvement to the design of effective shRNA vectors

Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

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.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.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

Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs

Most metazoan microRNAs (miRNAs) target many genes for repression, but the nematode lsy-6 miRNA is much less proficient. Here we show that the low proficiency of lsy-6 can be recapitulated in HeLa cells and that miR-23, a mammalian miRNA, also has low proficiency in these cells. Reporter results and array data indicate two properties of these miRNAs that impart low proficiency: their weak predicted seed-pairing stability (SPS) and their high target-site abundance (TA). These two properties also explain differential propensities of small interfering RNAs (siRNAs) to repress unintended targets. Using these insights, we expand the TargetScan tool for quantitatively predicting miRNA regulation (and siRNA off-targeting) to model differential miRNA (and siRNA) proficiencies, thereby improving prediction performance. We propose that siRNAs designed to have both weaker SPS and higher TA will have fewer off-targets without compromised on-target activity.National Institutes of Health (U.S.) (grant GM067031)Seoul National University (Research Settlement Fund)Howard Hughes Medical Institute (Investigator

The role of intra-tumoural heterogeneity in resistance to neoadjuvant chemotherapy in breast cancer

Breast cancer is a heterogeneous disease and accumulating evidence suggests that treatment failure may be driven by intra-tumour heterogeneity (ITH). Utilising the current protocol for neoadjuvant (pre-surgery) chemotherapy (NAC) provides the opportunity to study molecular genetic changes between pre- and post-therapy by assessing pre-therapy biopsies and post-therapy surgical resections. Whole exome sequencing was performed on matched pre- and post-treatment cancer cells from 6 patients with oestrogen receptor positive breast cancers that showed partial responses to the chemotherapeutic combination epirubicin/cyclophosphamide. Data analysis was performed to determine differences in genetic aberrations between pre- and post-NAC, and in particular to identify evidence of consistent selection by therapy of aberrations that therefore may define chemotherapy resistance or sensitivity. There were extensive differences in the range of genetic aberrations between pre- and post-NAC. 48 genes were identified for further study based on evidence of mutations conferring a selective advantage or disadvantage during chemotherapeutic response. The relevance of these was screened using siRNA knock-down and assessment of response to epirubicin using cell viability assays in vitro. Two genes were taken forward. Potential loss-of-function mutations in MUC17 were selected against during therapy in patients, and in accordance with this MUC17 knock-down was associated with increased sensitivity in vitro. Potential loss-of-function mutations in PCNX1 were selected for during therapy in patients, and in accordance with this PCNX1 knock-down was associated with resistance. Further work was performed to investigate mechanisms by which these genes modify chemotherapy response, by examining drug loading and ABC transporter expression levels. Data indicate that both genes impact on drug loading, potentially through modulating ABC transporter expression. Also, MUC17 or PCNX1 protein levels were tested as prognostic and predictive markers for breast cancer clinical outcomes using tissue taken from cohorts of patients who received adjuvant chemotherapy or neoadjuvant chemotherapy. Kaplan-Meier survival analyses revealed that low MUC17 expression after neoadjuvant chemotherapy was significantly associated with longer disease free survival, which was in agreement with the selection of MUC17 mutations seen after therapy in the initial patient group, and with the in vitro siRNA findings concerning drug sensitivity. I concluded that MUC17 and PCNX1 are potential markers of response to chemotherapy in breast cancer, and that therapeutic modulation of their activities could enhance chemotherapy responses

Wet-dry-wet drug screen leads to the synthesis of TS1, a novel compound reversing lung fibrosis through inhibition of myofibroblast differentiation

Therapies halting the progression of fibrosis are ineffective and limited. Activated myofibroblasts are emerging as important targets in the progression of fibrotic diseases. Previously, we performed a high-throughput screen on lung fibroblasts and subsequently demonstrated that the inhibition of myofibroblast activation is able to prevent lung fibrosis in bleomycin-treated mice. High-throughput screens are an ideal method of repurposing drugs, yet they contain an intrinsic limitation, which is the size of the library itself. Here, we exploited the data from our “wet” screen and used “dry” machine learning analysis to virtually screen millions of compounds, identifying novel anti-fibrotic hits which target myofibroblast differentiation, many of which were structurally related to dopamine. We synthesized and validated several compounds ex vivo (“wet”) and confirmed that both dopamine and its derivative TS1 are powerful inhibitors of myofibroblast activation. We further used RNAi-mediated knock-down and demonstrated that both molecules act through the dopamine receptor 3 and exert their anti-fibrotic effect by inhibiting the canonical transforming growth factor β pathway. Furthermore, molecular modelling confirmed the capability of TS1 to bind both human and mouse dopamine receptor 3. The anti-fibrotic effect on human cells was confirmed using primary fibroblasts from idiopathic pulmonary fibrosis patients. Finally, TS1 prevented and reversed disease progression in a murine model of lung fibrosis. Both our interdisciplinary approach and our novel compound TS1 are promising tools for understanding and combating lung fibrosis