Hepatocellular Nuclear Factor 4 alpha (HNF-4 alpha) activation by saRNA rescues dyslipidemia and promotes favorable metabolic profile in a high fat diet (HFD) fed rat model.

67th Annual Meeting of the American-Association-for-the-Study-of-Liver-Diseases (AASLD), Boston, MA, NOV 11-15, 2016International audienceno abstrac

An accurate and interpretable model for siRNA efficacy prediction

BACKGROUND: The use of exogenous small interfering RNAs (siRNAs) for gene silencing has quickly become a widespread molecular tool providing a powerful means for gene functional study and new drug target identification. Although considerable progress has been made recently in understanding how the RNAi pathway mediates gene silencing, the design of potent siRNAs remains challenging. RESULTS: We propose a simple linear model combining basic features of siRNA sequences for siRNA efficacy prediction. Trained and tested on a large dataset of siRNA sequences made recently available, it performs as well as more complex state-of-the-art models in terms of potency prediction accuracy, with the advantage of being directly interpretable. The analysis of this linear model allows us to detect and quantify the effect of nucleotide preferences at particular positions, including previously known and new observations. We also detect and quantify a strong propensity of potent siRNAs to contain short asymmetric motifs in their sequence, and show that, surprisingly, these motifs alone contain at least as much relevant information for potency prediction as the nucleotide preferences for particular positions. CONCLUSION: The model proposed for prediction of siRNA potency is as accurate as a state-of-the-art nonlinear model and is easily interpretable in terms of biological features. It is freely available on the web a

An efficient algorithm for systematic analysis of nucleotide strings suitable for siRNA design

<p>Abstract</p> <p>Background</p> <p>The "off-target" silencing effect hinders the development of siRNA-based therapeutic and research applications. Existing solutions for finding possible locations of siRNA seats within a large database of genes are either too slow, miss a portion of the targets, or are simply not designed to handle a very large number of queries. We propose a new approach that reduces the computational time as compared to existing techniques.</p> <p>Findings</p> <p>The proposed method employs tree-based storage in a form of a modified truncated suffix tree to sort all possible short string substrings within given set of strings (i.e. transcriptome). Using the new algorithm, we pre-computed a list of the best siRNA locations within each human gene ("siRNA seats"). siRNAs designed to reside within siRNA seats are less likely to hybridize off-target. These siRNA seats could be used as an input for the traditional "set-of-rules" type of siRNA designing software. The list of siRNA seats is available through a publicly available database located at <url>http://web.cos.gmu.edu/~gmanyam/siRNA_db/search.php</url></p> <p>Conclusions</p> <p>In attempt to perform top-down prediction of the human siRNA with minimized off-target hybridization, we developed an efficient algorithm that employs suffix tree based storage of the substrings. Applications of this approach are not limited to optimal siRNA design, but can also be useful for other tasks involving selection of the characteristic strings specific to individual genes. These strings could then be used as siRNA seats, as specific probes for gene expression studies by oligonucleotide-based microarrays, for the design of molecular beacon probes for Real-Time PCR and, generally, any type of PCR primers.</p

Ab initio identification of human microRNAs based on structure motifs

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are short, non-coding RNA molecules that are directly involved in post-transcriptional regulation of gene expression. The mature miRNA sequence binds to more or less specific target sites on the mRNA. Both their small size and sequence specificity make the detection of completely new miRNAs a challenging task. This cannot be based on sequence information alone, but requires structure information about the miRNA precursor. Unlike comparative genomics approaches, <it>ab initio </it>approaches are able to discover species-specific miRNAs without known sequence homology.</p> <p>Results</p> <p>MiRPred is a novel method for <it>ab initio </it>prediction of miRNAs by genome scanning that only relies on (predicted) secondary structure to distinguish miRNA precursors from other similar-sized segments of the human genome. We apply a machine learning technique, called linear genetic programming, to develop special classifier programs which include multiple regular expressions (motifs) matched against the secondary structure sequence. Special attention is paid to scanning issues. The classifiers are trained on fixed-length sequences as these occur when shifting a window in regular steps over a genome region. Various statistical and empirical evidence is collected to validate the correctness of and increase confidence in the predicted structures. Among other things, we propose a new criterion to select miRNA candidates with a higher stability of folding that is based on the number of matching windows around their genome location. An ensemble of 16 motif-based classifiers achieves 99.9 percent specificity with sensitivity remaining on an acceptable high level when requiring all classifiers to agree on a positive decision. A low false positive rate is considered more important than a low false negative rate, when searching larger genome regions for unknown miRNAs. 117 new miRNAs have been predicted close to known miRNAs on human chromosome 19. All candidate structures match the free energy distribution of miRNA precursors which is significantly shifted towards lower free energies. We employed a human EST library and found that around 75 percent of the candidate sequences are likely to be transcribed, with around 35 percent located in introns.</p> <p>Conclusion</p> <p>Our motif finding method is at least competitive to state-of-the-art feature-based methods for <it>ab initio </it>miRNA discovery. In doing so, it requires less previous knowledge about miRNA precursor structures while programs and motifs allow a more straightforward interpretation and extraction of the acquired knowledge.</p

A miR-200b/200c/429-Binding Site Polymorphism in the 3′ Untranslated Region of the AP-2α Gene Is Associated with Cisplatin Resistance

The transcription factor AP-2α functions as a tumor suppressor by regulating various genes that are involved in cell proliferation and apoptosis. Chemotherapeutic drugs including cisplatin induce post-transcriptionally endogenous AP-2α, which contributes to chemosensitivity by enhancing therapy-induced apoptosis. microRNAs (miRNAs) miR-200b, miR-200c and miR-429 (miR-200b/200c/429) are up-regulated in endometrial and esophageal cancers, and their overexpression correlates with resistance to cisplatin treatment. Using computational programs, we predicted that the 3′ untranslated region (UTR) of AP-2α gene contains a potential miRNA response element (MRE) for the miR-200b/200c/429 family, and the single nucleotide polymorphism (SNP) site rs1045385 (A or C allele) resided within the predicted MRE. Luciferase assays and Western blot analysis demonstrated that the miR-200b/200c/429 family recognized the MRE in the 3′ UTR of AP-2α gene and negatively regulated the expression of endogenous AP-2α proteins. SNP rs1045385 A>C variation enhanced AP-2α expression by disrupting the binding of the miR-200b/200c/429 family to the 3′ UTR of AP-2α. The effects of the two polymorphic variants on cisplatin sensitivity were determined by clonogenic assay. The overexpression of AP-2α with mutant 3′ UTR (C allele) in the endometrial cancer cell line HEC-1A, which has high levels of endogenous miR-200b/200c/429 and low levels of AP-2α protein, significantly increased cisplatin sensitivity, but overexpression of A allele of AP-2α has no significant effects, compared with mock transfection. We concluded that miR-200b/200c/429 induced cisplatin resistance by repressing AP-2α expression in endometrial cancer cells. The SNP (rs1045385) A>C variation decreased the binding of miR-200b/200c/429 to the 3′ UTR of AP-2α, which upregulated AP-2α protein expression and increased cisplatin sensitivity. Our results suggest that SNP (rs1045385) may be a potential prognostic marker for cisplatin treatment

MTL-CEBPA, a Small Activating RNA Therapeutic Upregulating C/EBP-α, in Patients with Advanced Liver Cancer: A First-in-Human, Multicenter, Open-Label, Phase I Trial.

PURPOSE: Transcription factor C/EBP-α (CCAAT/enhancer-binding protein alpha) acts as a master regulator of hepatic and myeloid functions and multiple oncogenic processes. MTL-CEBPA is a first-in-class small activating RNA oligonucleotide drug that upregulates C/EBP-α. PATIENTS AND METHODS: We conducted a phase I, open-label, dose-escalation trial of MTL-CEBPA in adults with advanced hepatocellular carcinoma (HCC) with cirrhosis, or resulting from nonalcoholic steatohepatitis or with liver metastases. Patients received intravenous MTL-CEBPA once a week for 3 weeks followed by a rest period of 1 week per treatment cycle in the dose-escalation phase (3+3 design). RESULTS: Thirty-eight participants have been treated across six dose levels (28-160 mg/m2) and three dosing schedules. Thirty-four patients were evaluable for safety endpoints at 28 days. MTL-CEBPA treatment-related adverse events were not associated with dose, and no maximum dose was reached across the three schedules evaluated. Grade 3 treatment-related adverse events occurred in nine (24%) patients. In 24 patients with HCC evaluable for efficacy, an objective tumor response was achieved in one patient [4%; partial response (PR) for over 2 years] and stable disease (SD) in 12 (50%). After discontinuation of MTL-CEBPA, seven patients were treated with tyrosine kinase inhibitors (TKIs); three patients had a complete response with one further PR and two with SD. CONCLUSIONS: MTL-CEBPA is the first saRNA in clinical trials and demonstrates an acceptable safety profile and potential synergistic efficacy with TKIs in HCC. These encouraging phase I data validate targeting of C/EBP-α and have prompted MTL-CEBPA + sorafenib combination studies in HCC

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

miREE: miRNA recognition elements ensemble

Abstract Background Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction. The Ab-Initio module leverages upon a genetic algorithmic approach to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties. Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. As a result the prediction takes into account information regarding both miRNA-target structural stability and accessibility. Results The proposed method significantly improves the state-of-the-art prediction tools in terms of accuracy with a better balance between specificity and sensitivity, as demonstrated by the experiments conducted on several large datasets across different species. miREE achieves this result by tackling two of the main challenges of current prediction tools: (1) The reduced number of false positives for the Ab-Initio part thanks to the integration of a machine learning module (2) the specificity of the machine learning part, obtained through an innovative technique for rich and representative negative records generation. The validation was conducted on experimental datasets where the miRNA:mRNA interactions had been obtained through (1) direct validation where even the binding site is provided, or through (2) indirect validation, based on gene expression variations obtained from high-throughput experiments where the specific interaction is not validated in detail and consequently the specific binding site is not provided. Conclusions The coupling of two parts: a sensitive Ab-Initio module and a selective machine learning part capable of recognizing the false positives, leads to an improved balance between sensitivity and specificity. miREE obtains a reasonable trade-off between filtering false positives and identifying targets. miREE tool is available online at http://didattica-online.polito.it/eda/miREE/</p