N6-Methyladenosines Modulate A-to-I RNA Editing

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人A-to-I RNA编辑事件对外显子剪接增强子潜在影响的生物信息学分析

目的:研究人A-to-I RNA编辑事件对外显子剪接增强子(ESE)的潜在影响。方法:搜集文献报道的人A-to-I RNA编辑位点,并筛选包含有A-to-I RNA编辑位点的ESE,分析人A-to-I RNA编辑前后单碱基变化对ESE的潜在影响。结果:3640个A-to-I RNA编辑位点可能使其所在的ESE功能发生潜在改变;A-to-I RNA编辑事件对不同类型ESE的潜在影响不同。结论:A-to-I RNA编辑事件可能潜在影响ESE的功能,对ESE的潜在影响为量的调节,而非质的改变。     

全组织提取物检测A-to-I RNA编辑酶活性

目的:建立一种简便、快速、可靠的检测A-to-IRNA编辑酶活性的方法。方法与结果:一步法制备的C57BL/6小鼠十种组织的全组织提取物,在各提取物中检测到A-to-IRNA编辑酶的非特异性编辑活性,不同组织中A-to-IRNA编辑酶活性的强度依次为脑>肺>胸腺>脾>淋巴结>肝>肾>睾丸>心脏>骨骼肌,编辑活性与加入反应体系中的蛋白量成正相关。结论:一步法制备的全组织提取物可用于检测A-to-IRNA编辑酶活性,该方法操作简便、可控、省时。     

Reduced levels of protein recoding by A-to-I RNA editing in Alzheimer's disease

Adenosine to inosine (A-to-I) RNA editing, catalyzed by the ADAR enzyme family, acts on dsRNA structures within pre-mRNA molecules. Editing of the coding part of the mRNA may lead to recoding, amino acid substitution in the resulting protein, possibly modifying its biochemical and biophysical properties. Altered RNA editing patterns have been observed in various neurological pathologies. Here, we present a comprehensive study of recoding by RNA editing in Alzheimer''s disease (AD), the most common cause of irreversible dementia. We have used a targeted resequencing approach supplemented by a microfluidic-based high-throughput PCR coupled with next-generation sequencing to accurately quantify A-to-I RNA editing levels in a preselected set of target sites, mostly located within the coding sequence of synaptic genes. Overall, editing levels decreased in AD patients’ brain tissues, mainly in the hippocampus and to a lesser degree in the temporal and frontal lobes. Differential RNA editing levels were observed in 35 target sites within 22 genes. These results may shed light on a possible association between the neurodegenerative processes typical for AD and deficient RNA editing.     

RNA编辑功能和RNA干扰

RNA编辑被认为是生命体一种新的基因加工与修饰现象,是指DNA转录成RNA后除RNA剪切外的其他加工过程,以核苷酸的删除、插入或替换等方式改变遗传信息,揭示生物进化过程中基因修饰和调控的另一个重要途径,是对中心法则的重要补充.而RNAi是一种由dsRNA介导的,在转录水平、转录后水平和翻译水平上阻断基因表达的基因调节途径.着重介绍 RNA编辑功能、RNA编辑与RNA干扰关系.     

RFMirTarget: Predicting Human MicroRNA Target Genes with a Random Forest Classifier

MicroRNAs are key regulators of eukaryotic gene expression whose fundamental role has already been identified in many cell pathways. The correct identification of miRNAs targets is still a major challenge in bioinformatics and has motivated the development of several computational methods to overcome inherent limitations of experimental analysis. Indeed, the best results reported so far in terms of specificity and sensitivity are associated to machine learning-based methods for microRNA-target prediction. Following this trend, in the current paper we discuss and explore a microRNA-target prediction method based on a random forest classifier, namely RFMirTarget. Despite its well-known robustness regarding general classifying tasks, to the best of our knowledge, random forest have not been deeply explored for the specific context of predicting microRNAs targets. Our framework first analyzes alignments between candidate microRNA-target pairs and extracts a set of structural, thermodynamics, alignment, seed and position-based features, upon which classification is performed. Experiments have shown that RFMirTarget outperforms several well-known classifiers with statistical significance, and that its performance is not impaired by the class imbalance problem or features correlation. Moreover, comparing it against other algorithms for microRNA target prediction using independent test data sets from TarBase and starBase, we observe a very promising performance, with higher sensitivity in relation to other methods. Finally, tests performed with RFMirTarget show the benefits of feature selection even for a classifier with embedded feature importance analysis, and the consistency between relevant features identified and important biological properties for effective microRNA-target gene alignment.     

Feature Selection and the Class Imbalance Problem in Predicting Protein Function from Sequence

When the standard approach to predict protein function by sequence homology fails, other alternative methods can be used that require only the amino acid sequence for predicting function. One such approach uses machine learning to predict protein function directly from amino acid sequence features. However, there are two issues to consider before successful functional prediction can take place: identifying discriminatory features, and overcoming the challenge of a large imbalance in the training data. We show that by applying feature subset selection followed by undersampling of the majority class, significantly better support vector machine (SVM) classifiers are generated compared with standard machine learning approaches. As well as revealing that the features selected could have the potential to advance our understanding of the relationship between sequence and function, we also show that undersampling to produce fully balanced data significantly improves performance. The best discriminating ability is achieved using SVMs together with feature selection and full undersampling; this approach strongly outperforms other competitive learning algorithms. We conclude that this combined approach can generate powerful machine learning classifiers for predicting protein function directly from sequence.     

A-to-I RNA editing modulates the pharmacology of neuronal ion channels and receptors

The regulation of neuronal excitability is complex, as ion channels and neurotransmitter receptors are underlying a large variety of modulating effects. Alterations in the expression patterns of receptors or channel subunits as well as differential splicing contribute to the regulation of neuronal excitability. RNA editing is another and more recently explored mechanism to increase protein diversity, as the genomic recoding leads to new gene products with novel functional and pharmacological properties. In humans A-to-I RNA editing targets several neuronal receptors and channels, including GluR2/5/6 subunits, the Kv1.1 channel, and the 5-HT_2C receptor. Our review summarizes that RNA editing of these proteins does not only change protein function, but also the pharmacology and presumably the drug therapy in human diseases.     

The dsRBP and Inactive Editor ADR-1 Utilizes dsRNA Binding to Regulate A-to-I RNA Editing across the C. elegans Transcriptome

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