Computational Target Prediction Research Articles

Enhanced protein production by microRNA-30 family in CHO cells is mediated by the modulation of the ubiquitin pathway

Functional genomics represent a valuable approach to improve culture performance of Chinese hamster ovary (CHO) cell lines for biopharmaceutical manufacturing. Recent advances in applied microRNA (miRNAs) research suggest that these small non-coding RNAs are critical for the regulation of cell phenotypes in CHO cells. However, the notion that individual miRNAs usually control the expression of hundreds of different genes makes miRNA target identification highly complex. We have recently reported that the entire miR-30 family enhances recombinant protein production in CHO cells. To better understand the pro-productive effects of this miRNA family, we set out to identify their downstream target genes in CHO cells. Computational target prediction combined with a comprehensive functional validation enabled the discovery of a set of twenty putative target genes for all productivity enhancing miR-30 family members. We demonstrate that all miR-30 isoforms contribute to the regulation of the ubiquitin pathway in CHO cells by directly targeting the ubiquitin E3 ligase S-phase kinase-associated protein 2 (Skp2). Finally, we provide several lines of evidence that miR-30-mediated modulation of the ubiquitin pathway may enhance recombinant protein expression in CHO cells. In summary, this study supports the importance of non-coding RNAs, especially of miRNAs, in the context of cell line engineering.

Abstract 200: Effects of H. pylori infection in regulation of miRNAs-associated with DNA mismatch repair

Abstract It is well known that genetic and epigenetic inactivation of DNA mismatch repair (MMR) genes might leads to mutations in cancer-related genes and to cancer development. In gastric carcinogenesis, it has been shown that the infection by H. pylori, the major causative agent of gastric cancer, induces DNA damage and mutation and/or inhibits MMR DNA repair. Recently, it has been reported that miRNAs (miRs) have an important role in regulating genomic stability and MMR DNA repair. Thus, the aim of this work was to identify miRs regulating MMR pathway in H. pylori-associated gastric carcinogenesis To evaluate the effects of H. pylori on miRs releated with MMR, AGS gastric carcinoma cell line and the H. pylori strains 26695, and P12 were used in co-culture experiments for 4, 8 and 12 h. As an initial screening, the AGS cells were co-cultured with H. pylori strain 26695 and subjected to Cancer PathwayFinder miRNA PCR Array (Qiagen) to determine the expression profile of 84 miRs. The expression levels of EXO1, MLH1, MSH2, MSH3, MSH6, POLD3, PMS1, and PMS2 were assessed by real-time PCR. Computational prediction of putative interaction of selected miRs to 3′UTR of MMR mRNAs were obtained from TargetScan, mirDB and MetaCore. The target validation was evaluated using mimics/inhibitors and luciferase gene reporter assays. The results from the Cancer PathwayFinder PCR miRNA Array showded that H. pylori infection (strain 26695) modulates the expression of 23% (19/84) of the miRs after 4 and 12 h of infection, of which 89% were up-regulated and 11% down-regulated. Our data show that H. pylori infection significantly down-regulates the expression of all selected genes. The data generated from computational miR target prediction enriched with the gene expression fold induction indicated that 4 miRs (miR-150-5p, miR-155-5p, miR-3163, and miR-4775) modulated by H. pylori has at least one MMR target gene. Regarding the target validation, our results indicated that MLH1 and MSH2, are regulated by miR-150-5p. The miR-155-5p is involved in down-regulation of MLH1, MSH2, MSH3 and POLD3 expression. MSH3 is regulated by both miR-3163 and miR-4775. Briefly, this study shows that H. pylori impairs MMR DNA repair pathway thought a modulation of miR expression. Citation Format: Juliana C. Santos, Mitsue Brianti, Victor de Almeida, Ander Matheu, Marcelo L. Ribeiro. Effects of H. pylori infection in regulation of miRNAs-associated with DNA mismatch repair. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 200. doi:10.1158/1538-7445.AM2015-200

Loss of MicroRNA‐101 Promotes Epithelial to Mesenchymal Transition in Hepatocytes

Epithelial-to-mesenchymal transition (EMT) has been implicated in embryonic development and various pathological events. However, the involvement of microRNA in the process of EMT remains to be fully defined in hepatocyte. ZEB1 is a well-known transcriptional repressor of E-cadherin and plays a major role in triggering EMT during organ fibrosis and cancer cell metastasis. Computational microRNA target predictions detect a conserved sequence matching to miR-101 in the 3'UTR of ZEB1 mRNA. Our results confirm that miR-101 suppresses ZEB1 expression by targeting the predicted site of ZEB1 3'UTR. Subsequent investigations show that miR-101 is significantly downregulated in the cultured hepatocytes undergoing EMT and in the hepatocytes isolated from fibrotic liver. Along with the loss of miR-101, the ZEB1 expression increases simultaneously in hepatocytes. In addition, miR-101 levels in HCC cell lines are negatively associated with the ZEB1 productions and the metastatic potentials of tumor cells. Mechanistically, we demonstrate that miR-101 significantly inhibits the TGF-β1-induced EMT in hepatocytes, whereas inhibition of miR-101 promotes the EMT process as indicated by the changes of morphology, cell migration, and the expression profiles of EMT markers. In the fibrotic liver, ectopic expression of miR-101 can significantly downregulate ZEB1 in the hepatocyte and thereby reduces the mesenchymal marker expression. Moreover, miR-101 significantly inhibits the proliferation and migration of HCC cell. Our results demonstrate that miR-101 regulates HCC cell phenotype by upregulating the epithelial marker genes and suppressing the mesenchymal ones.

Circulating microRNAs in obese and lean heart failure patients: A case–control study with computational target prediction analysis

AimsMicroRNAs (miRs) regulate processes involved in both cardiac remodeling and obesity. We investigated if the expression of selected miRs in patients with heart failure (HF) is influenced by the presence of obesity. MethodsIn this case–control study, we compared plasma levels of miR-21, -130b, -221, -423-5p, and the -221/-130b ratio in 57 age- and gender-matched subjects: 40 HF patients (20 obese HF and 20 lean HF) and 17 lean healthy controls. Body composition was estimated by bioelectrical impedance analysis. MiRs were measured by quantitative reverse transcription-PCR. Bioinformatics analysis was performed based on miRs findings to predict their putative targets and investigate their biological function. ResultsHF was associated with increased miR-423-5p levels in both lean and obese patients (P<0.05 vs. controls) without differences between HF groups. MiR-130b levels were reduced in obese HF patients compared with HF lean (P=0.036) and controls (P=0.025). MiR-221 levels were non-significantly increased in obese HF patients. MiR-21 levels were not different among the groups. MiR-221/-130b ratio was increased in obese HF patients, and was positively associated with body fat percentage (r=0.43; P=0.002), body mass index (r=0.44; P=0.002), and waist circumference (r=0.40; P=0.020). Computational prediction of target genes followed by functional enrichment analysis indicated a relevant role of miR-130b and miR-221 in modulating the expression of genes associated to cardiovascular and endocrine diseases, and suggested their influence in important signaling mechanisms and in numerous processes related to the circulatory and endocrine systems. ConclusionsIn HF patients, the presence of obesity is associated with a differential expression of selected miRs and the miR-221/-130b ratio had significant correlations with adiposity parameters. Computational target prediction analysis identified several interrelated pathways targeted by miR-130b and miR-221 with a known relationship with endocrine and cardiovascular diseases, representing potential mechanisms to be further validated.

Circulating microRNA Signatures in Rodent Models of Pain.

MicroRNAs (miRNAs) remain stable in circulation and have been identified as potential biomarkers for a variety of conditions. We report miRNA changes in blood from multiple rodent models of pain, including spinal nerve ligation and spared nerve injury models of neuropathic pain; a complete Freund's adjuvant (CFA) model of inflammatory pain; and a chemotherapy-induced model of pain using the histone deacetylase inhibitor JNJ-26481585. The effect of celecoxib, a cyclooxygenase-2-selective nonsteroidal anti-inflammatory drug, was investigated in the CFA model as proof of principle for assessing the utility of circulating miRNAs as biomarkers in determining treatment response. Each study resulted in a unique miRNA expression profile. Despite differences in miRNAs identified from various models, computational target prediction and functional enrichment have identified biological pathways common among different models. The Wnt signaling pathway was affected in all models, suggesting a crucial role for this pathway in the pathogenesis of pain. Our studies demonstrate the utility of circulating miRNAs as pain biomarkers and suggest the potential for rigorous forward and reversetranslational approaches. Evaluating alterations in miRNA fingerprints under different pain conditions and after administering therapeutic agents may be beneficial in evaluating clinical trial outcomes, predicting treatment response, and developing correlational outcomes between preclinical and human studies.

211 Mirna-34a Reduces Neointima fommation through Inhibiting Smooth Muscle Cell Proliferation and Migration

<h3>Background/objective</h3> We have recently reported that microRNA-34a (miR-34a) regulates vascular smooth muscle cell (VSMC) differentiation from stem cells <i>in vitro</i> and <i>in vivo</i>. However, little is known about the functional involvements of miR-34a in VSMC functions and vessel injury-induced neointima formation. In the current study, we aimed to establish the causal role of miR-34a and its target genes in VSMC proliferation, migration and neointima lesion formation. <h3>Methods and results</h3> miR-34a was significantly down-regulated in VSMCs upon various pathological stimuli, and in injured arteries. miR-34a over-expression in serum-starved VSMCs significantly inhibited VSMC proliferation and migration, while knockdown of miR-34a dramatically promoted VSMC proliferation and migration, respectively. Notch homolog 1 (Notch1), a well-reported regulator in VSMC functions and arterial remodelling, was predicted as one of the top targets of miR-34a by using several computational miRNA target prediction tools, and was negatively regulated by miR-34a in VSMCs. Luciferase assay showed miR-34a substantially repressed wild type Notch1-3’-UTR-luciferase activity in VSMCs, but not mutant Notch1-3’-UTR-luciferease reporter, confirming the Notch1 is the functional target of miR-34a in VSMCs. Data from co-transfection experiments also revealed that miR-34a inhibited VSMC proliferation and migration through modulating Notch gene expression levels. Importantly, miR-34a perivascular overexpression significantly reduced Notch1 expression levels, decreased VSMC proliferation, and inhibited neointima formation in wire-injured femoral arteries. <h3>Conclusions/implications</h3> Our data have demonstrated that miR-34a is an important regulator in VSMC functions and neointima hyperplasia, suggesting its potential therapeutic application for vascular diseases.

Deep sequencing of cardiac microRNA-mRNA interactomes in clinical and experimental cardiomyopathy.

MicroRNAs are a family of short (~21 nucleotide) noncoding RNAs that serve key roles in cellular growth and differentiation and the response of the heart to stress stimuli. As the sequence-specific recognition element of RNA-induced silencing complexes (RISCs), microRNAs bind mRNAs and prevent their translation via mechanisms that may include transcript degradation and/or prevention of ribosome binding. Short microRNA sequences and the ability of microRNAs to bind to mRNA sites having only partial/imperfect sequence complementarity complicate purely computational analyses of microRNA-mRNA interactomes. Furthermore, computational microRNA target prediction programs typically ignore biological context, and therefore the principal determinants of microRNA-mRNA binding: the presence and quantity of each. To address these deficiencies we describe an empirical method, developed via studies of stressed and failing hearts, to determine disease-induced changes in microRNAs, mRNAs, and the mRNAs targeted to the RISC, without cross-linking mRNAs to RISC proteins. Deep sequencing methods are used to determine RNA abundances, delivering unbiased, quantitative RNA data limited only by their annotation in the genome of interest. We describe the laboratory bench steps required to perform these experiments, experimental design strategies to achieve an appropriate number of sequencing reads per biological replicate, and computer-based processing tools and procedures to convert large raw sequencing data files into gene expression measures useful for differential expression analyses.

MiR-301a Mediates the Effect of IL-6 on the AKT/GSK Pathway and Hepatic Glycogenesis by Regulating PTEN Expression

Background/Aims: IL-6 has been implicated in the pathogenesis of insulin resistance. MiR-301a plays an important role in various biological and pathological processes, including cellular development and differentiation, inflammation, apoptosis and cancer. However, whether miR-301a mediates IL-6-induced insulin resistance in hepatocytes remains unknown. Methods: The activation of AKT/GSK pathway and the level of glycogenesis were examed in NCTC 1469 cells transfected miR-301a mimics and inhibitor. Using computational miRNA target prediction database, PTEN was a target of miR-301a. The effect of miR-301a on PTEN expression was evaluated using Luciferase assay and western blot. A PTEN-specific siRNA was used to further determine the effect of PTEN on IL-6-induced insulin resistance. Results: In vivo and in vitro treatment with IL-6 was led to down-regulation of miR-301a, accompanied by impairment of theAKT/GSK pathway and glycogenesis. Importantly, over-expression of miR-301a rescued IL-6-induced decreased activation of the AKT/GSK pathway and hepatic glycogenesis. In contrast, down-regulation of miR-301a induced impaired phosphorylation of AKT and GSK, accompanied by reduced glycogenesis in hepatocytes. Moreover, our results indicate that suppression of PTEN, a target of miR-301a, diminished the effect of IL-6 on the AKT/GSK pathway and hepatic glycogenesis. Conclusion: We present novel evidence of the contribution of miR-301a to IL-6-induced insulin resistance by direct regulation of PTEN expression.

MiR-639 regulates transforming growth factor beta-induced epithelial-mesenchymal transition in human tongue cancer cells by targeting FOXC1.

Epithelial-to-mesenchymal transition (EMT) is implicated in embryonic development and various pathological events. Transforming growth factor beta (TGFβ) has been reported to induce EMT in tumor cells, which is a critical step in the process of metastasis leading to cancer spreading and treatment failure. However, the involvement of microRNA during the EMT process in tongue squamous cell carcinoma (TSCC) remains to be determined. To address this question, TSCC cell lines SCC9 and CAL27 were treated with human recombinant TGFβ1 for 48 h. miRNA microarray illustrated that miR-639 was significantly downregulated in TGFβ-treated SCC9 cells. Ectopic expression of miR-639 with miRNA mimics effectively blocked TGFβ-induced EMT in SCC9 and CAL27 cells, but inhibition of miR-639 in SCC9 and CAL27 cells with antisense oligonucleotides induced EMT. Computational microRNA target predictions detected a conserved sequence matching to the seed region of miR-639 in the 3′-UTR of FOXC1 mRNA. Luciferase reporter assays revealed that miR-639 targets FOXC1. Ectopic expression of FOXC1 induces EMT in TSCC cells. Silencing FOXC1 expression blocked TGFβ-induced EMT in SCC9 cells. Clinically, reduced miR-639 expression was associated with metastasis in TSCC and poor patient survival. The data from the present study suggest that reduced expression of miR-639 underscores the mechanism of TGFβ-induced EMT in TSCC by targeting FOXC1 and may serve as therapeutic targets in the process of metastasis.

RNA expression profiling of human iPSC-derived cardiomyocytes in a cardiac hypertrophy model.

Cardiac hypertrophy is an independent risk factor for cardiovascular disease and heart failure. There is increasing evidence that microRNAs (miRNAs) play an important role in the regulation of messenger RNA (mRNA) and the pathogenesis of various cardiovascular diseases. However, the ability to comprehensively study cardiac hypertrophy on a gene regulatory level is impacted by the limited availability of human cardiomyocytes. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) offer the opportunity for disease modeling. Here we utilize a previously established in vitro model of cardiac hypertrophy to interrogate the regulatory mechanism associated with the cardiac disease process. We perform miRNA sequencing and mRNA expression analysis on endothelin 1 (ET-1) stimulated hiPSC-CMs to describe associated RNA expression profiles. MicroRNA sequencing revealed over 250 known and 34 predicted novel miRNAs to be differentially expressed between ET-1 stimulated and unstimulated control hiPSC-CMs. Messenger RNA expression analysis identified 731 probe sets with significant differential expression. Computational target prediction on significant differentially expressed miRNAs and mRNAs identified nearly 2000 target pairs. A principal component analysis approach comparing the in vitro data with human myocardial biopsies detected overlapping expression changes between the in vitro samples and myocardial biopsies with Left Ventricular Hypertrophy. These results provide further insights into the complex RNA regulatory mechanism associated with cardiac hypertrophy.

MiR-CATCH: microRNA capture affinity technology.

Several experimental methods exist to explore the microRNA (miRNA) regulome. These methods almost exclusively focus on multiple targets bound to a single, or perhaps a few miRNAs of interest. Here, we describe a microRNA capture affinity technology (miR-CATCH) which uses an affinity capture oligonucleotide to co-purify a single target messenger RNA (mRNA) together with all its endogenously bound miRNAs. This bench-top method is similar to RNA immunoprecipitation (RIP) and provides an experimental alternative to computational miRNA target prediction.

The small regulatory RNA SyR1/PsrR1 controls photosynthetic functions in cyanobacteria.

Little is known so far about RNA regulators of photosynthesis in plants, algae, or cyanobacteria. The small RNA PsrR1 (formerly SyR1) has been discovered in Synechocystis sp PCC 6803 and appears to be widely conserved within the cyanobacterial phylum. Expression of PsrR1 is induced shortly after a shift from moderate to high-light conditions. Artificial overexpression of PsrR1 led to a bleaching phenotype under moderate light growth conditions. Advanced computational target prediction suggested that several photosynthesis-related mRNAs could be controlled by PsrR1, a finding supported by the results of transcriptome profiling experiments upon pulsed overexpression of this small RNA in Synechocystis sp PCC 6803. We confirmed the interaction between PsrR1 and the ribosome binding regions of the psaL, psaJ, chlN, and cpcA mRNAs by mutational analysis in a heterologous reporter system. Focusing on psaL as a specific target, we show that the psaL mRNA is processed by RNase E only in the presence of PsrR1. Furthermore, we provide evidence for a posttranscriptional regulation of psaL by PsrR1 in the wild type at various environmental conditions and analyzed the consequences of PsrR1-based regulation on photosystem I. In summary, computational and experimental data consistently establish the small RNA PsrR1 as a regulatory factor controlling photosynthetic functions.

Identification and validation of miRNA target sites within nontraditional miRNA targets.

miRNAs are endogenous small RNAs that regulate gene expression through recognition of complementary RNA sequences. While miRNAs have generally been understood to repress gene expression posttranscriptionally through recognition of 3'-untranslated regions (3'-UTRs) of mRNA transcripts, they have the potential to target additional classes of RNAs. Understanding the expanding pool of potential miRNA targets has been hindered by the lack of tools for predicting target sites within these RNAs. Here, the principles for developing computational algorithms for identifying putative miRNA target sites outside of mRNA are discussed. Laboratory techniques for validating computational miRNA target predictions are described.

Benchmark data sets for structure-based computational target prediction.

Structure-based computational target prediction methods identify potential targets for a bioactive compound. Methods based on protein-ligand docking so far face many challenges, where the greatest probably is the ranking of true targets in a large data set of protein structures. Currently, no standard data sets for evaluation exist, rendering comparison and demonstration of improvements of methods cumbersome. Therefore, we propose two data sets and evaluation strategies for a meaningful evaluation of new target prediction methods, i.e., a small data set consisting of three target classes for detailed proof-of-concept and selectivity studies and a large data set consisting of 7992 protein structures and 72 drug-like ligands allowing statistical evaluation with performance metrics on a drug-like chemical space. Both data sets are built from openly available resources, and any information needed to perform the described experiments is reported. We describe the composition of the data sets, the setup of screening experiments, and the evaluation strategy. Performance metrics capable to measure the early recognition of enrichments like AUC, BEDROC, and NSLR are proposed. We apply a sequence-based target prediction method to the large data set to analyze its content of nontrivial evaluation cases. The proposed data sets are used for method evaluation of our new inverse screening method iRAISE. The small data set reveals the method's capability and limitations to selectively distinguish between rather similar protein structures. The large data set simulates real target identification scenarios. iRAISE achieves in 55% excellent or good enrichment a median AUC of 0.67 and RMSDs below 2.0 Å for 74% and was able to predict the first true target in 59 out of 72 cases in the top 2% of the protein data set of about 8000 structures.

Plant microRNA-target interaction identification model based on the integration of prediction tools and support vector machine.

BackgroundConfident identification of microRNA-target interactions is significant for studying the function of microRNA (miRNA). Although some computational miRNA target prediction methods have been proposed for plants, results of various methods tend to be inconsistent and usually lead to more false positive. To address these issues, we developed an integrated model for identifying plant miRNA–target interactions.ResultsThree online miRNA target prediction toolkits and machine learning algorithms were integrated to identify and analyze Arabidopsis thaliana miRNA-target interactions. Principle component analysis (PCA) feature extraction and self-training technology were introduced to improve the performance. Results showed that the proposed model outperformed the previously existing methods. The results were validated by using degradome sequencing supported Arabidopsis thaliana miRNA-target interactions. The proposed model constructed on Arabidopsis thaliana was run over Oryza sativa and Vitis vinifera to demonstrate that our model is effective for other plant species.ConclusionsThe integrated model of online predictors and local PCA-SVM classifier gained credible and high quality miRNA-target interactions. The supervised learning algorithm of PCA-SVM classifier was employed in plant miRNA target identification for the first time. Its performance can be substantially improved if more experimentally proved training samples are provided.

A screening pattern recognition method finds new and divergent targets for drugs and natural products.

Computational target prediction methods using chemical descriptors have been applied exhaustively in drug discovery to elucidate the mechanisms-of-action (MOAs) of small molecules. To predict truly novel and unexpected small molecule-target interactions, compounds must be compared by means other than their chemical structure alone. Here we investigated predictions made by a method, HTS fingerprints (HTSFPs), that matches patterns of activities in experimental screens. Over 1,400 drugs and 1,300 natural products (NPs) were screened in more than 200 diverse assays, creating encodable activity patterns. The comparison of these activity patterns to an MOA-annotated reference panel led to the prediction of 5,281 and 2,798 previously unknown targets for the NP and drug sets, respectively. Intriguingly, there was limited overlap among the targets predicted; the drugs were more biased toward membrane receptors and the NPs toward soluble enzymes, consistent with the idea that they represent unexplored pharmacologies. Importantly, HTSFPs inferred targets that were beyond the prediction capabilities of standard chemical descriptors, especially for NPs but also for the more explored drug set. Of 65 drug-target predictions that we tested in vitro, 48 (73.8%) were confirmed with AC50 values ranging from 38 nM to 29 μM. Among these interactions was the inhibition of cyclooxygenases 1 and 2 by the HIV protease inhibitor Tipranavir. These newly discovered targets that are phylogenetically and phylochemically distant to the primary target provide an explanation for spontaneous bleeding events observed for patients treated with this drug, a physiological effect that was previously difficult to reconcile with the drug's known MOA.

Facing the Challenges of Structure-Based Target Prediction by Inverse Virtual Screening

Computational target prediction for bioactive compounds is a promising field in assessing off-target effects. Structure-based methods not only predict off-targets, but, simultaneously, binding modes, which are essential for understanding the mode of action and rationally designing selective compounds. Here, we highlight the current open challenges of computational target prediction methods based on protein structures and show why inverse screening rather than sequential pairwise protein-ligand docking methods are needed. A new inverse screening method based on triangle descriptors is introduced: iRAISE (inverse Rapid Index-based Screening Engine). A Scoring Cascade considering the reference ligand as well as the ligand and active site coverage is applied to overcome interprotein scoring noise of common protein-ligand scoring functions. Furthermore, a statistical evaluation of a score cutoff for each individual protein pocket is used. The ranking and binding mode prediction capabilities are evaluated on different datasets and compared to inverse docking and pharmacophore-based methods. On the Astex Diverse Set, iRAISE ranks more than 35% of the targets to the first position and predicts more than 80% of the binding modes with a root-mean-square deviation (RMSD) accuracy of <2.0 Å. With a median computing time of 5 s per protein, large amounts of protein structures can be screened rapidly. On a test set with 7915 protein structures and 117 query ligands, iRAISE predicts the first true positive in a ranked list among the top eight ranks (median), i.e., among 0.28% of the targets.

Identifying microRNA targets in different gene regions.

BackgroundCurrently available microRNA (miRNA) target prediction algorithms require the presence of a conserved seed match to the 5' end of the miRNA and limit the target sites to the 3' untranslated regions of mRNAs. However, it has been noted that these requirements may be too stringent, leading to a substantial number of missing targets.ResultsWe have developed TargetS, a novel computational approach for predicting miRNA targets with the target sites located along entire gene sequences, which permits finding additional targets that are not located in the 3' un-translated regions. Our model is based on both canonical seed matching and non-canonical seed pairing, which discovers targets that allow one bit GU wobble. It does not rely on evolutionary conservation, so it allows the detection of species-specific miRNA-mRNA interactions and makes it suitable for analyzing un-conserved gene sequences. To test the performance of our approach, we have imported the widely used benchmark dataset revealing fold-changes in protein production corresponding to each of the five selected microRNAs. Compared to well-known miRNA target prediction tools, including TargetScanS, PicTar and MicroT_CDS, our method yields the highest sensitivity, while achieving a comparable level of accuracy. Human miRNA target predictions using our computational approach are available online at http://liubioinfolab.org/targetS/mirna.htmlConclusionsA simple but powerful computational miRNA target prediction method is developed that is solely based on canonical and non-canonical seed matches without requiring evolutionary conservation of the target sites. Our method also expands the target search space to different gene regions, rather than limiting to 3'UTR only. This improves the sensitivity of miRNA target identification, while achieving a comparable accuracy with existing methods.

Facing the challenges of computational target prediction.

To which proteins does a compound bind? Is it selective or promiscuous? These are questions which can be answered by inverse virtual screening, which identifies potential targets for a molecule of interest. Experimental screening of a molecule on thousands of targets is costly and elaborate. Contrarily, structure-based computational methods are only limited by the availability of 3D structures, rendering them an important complement to experimental methods. Our inverse screening method XxirT combines triangle descriptor matching [1] with a new ranking approach, considering a reference score for each pocket. A precalculated bitmap encoding of the descriptors and an efficient design of a database for 3D protein structures allows a rapid screening of thousands of protein-ligand complexes with a query compound. Classical protein-ligand scoring functions are not capable of inter-target score comparison, since the absolute values are target-dependent. Therefore, in inverse virtual screening, a major problem is the design of a ranking scheme allowing the comparison of target scores with respect to one query compound [2]. A lack of available data for statistical evaluation of the ranking capability further complicates the task. Data sets mostly contain positive hits, e.g. binding affinities for one molecule to several proteins while lacking negative data points, such as ‘the molecule does not bind to this protein’. As a basis for statistical evaluation of our new inverse screening concept, we introduce a dataset consisting of a ligand set of approved drugs and the scPDB target database [3]. Drugs are well qualified for use in a method evaluation dataset, as they are well tested and had to pass selectivity standard tests to get approved. Therefore, their targets are comparably well-characterized, allowing a classification into true positives and true negatives. This approach is the first that evaluates a structure-based inverse screening method on a systematic statistical test.

MiR-291b-3p Induces Apoptosis in Liver Cell Line NCTC1469 by Reducing the Level of RNA-binding Protein HuR

Background: There is increasing evidence that miRNAs are involved in cellular apoptosis. However, the specific role of miR-291b-3p in apoptosis has not been elucidated. In the present study, we investigated the effect of miR-291b-3p on NCTC1469 cell growth and apoptosis. Methods: Cell viability and apoptosis were examined in NCTC1469 cells transfected with miR-291b-3p mimics, inhibitor miRNA or negative control. Using computational miRNA target prediction databases, HuR was predicted as a target of miR-291b-3p. Luciferase assay, immunofluorescence and western blot were used to further explore the effects of miR-291b-3p on HuR expression. In addition, the effect of HuR on cell apoptosis was evaluated using a HuR-specific siRNA. Results: TNF-α-induced hepatocyte apoptosis was accompanied by enhanced expression of miR-291b-3p, suggesting that miR-291b-3p might contribute to the apoptotic process. Follow-up experiments showed that upregulation of miR-291b-3p decreased cell viability and induced NCTC1469 cell apoptosis. Additionally, similar to the activity of miR-519, which is another member of the same miRNA family, miR-291b-3p suppressed HuR translation through binding to the HuR coding region (CR). We further showed that the downregulation of HuR expression by miR-291b-3p was accompanied by reduced Bcl-2 expression. Moreover, knockdown of HuR also impaired Bcl-2 expression and increased the ratio of Bax/Bcl-2. More significantly, downregulation of miR-291b-3p failed to increase Bcl2 expression in NCTC1469 cells that were co-transfected with siRNA-HuR. Finally, inhibition of miR-291b-3p led to reduced apoptosis, while knockdown of HuR by siRNA promoted apoptosis, even in NCTC1469 cells that were co-transfected with the miR-291b-3p inhibitor. Conclusion: The current data suggested that miR-291b-3p contributed to NCTC1469 cell apoptosis by regulating the expression of HuR, which in turn increased Bcl-2 stability.