Searching the coding region for microRNA targets

Finding microRNA targets in the coding region is difficult due to the overwhelming signal encoding the amino acid sequence. Here, we introduce an algorithm (called PACCMIT-CDS) that finds potential microRNA targets within coding sequences by searching for conserved motifs that are complementary to the microRNA seed region and also overrepresented in comparison with a background model preserving both codon usage and amino acid sequence. Precision and sensitivity of PACCMIT-CDS are evaluated using PAR-CLIP and proteomics data sets. Thanks to the properly constructed background, the new algorithm achieves a lower rate of false positives and better ranking of predictions than do currently available algorithms, which were designed to find microRNA targets within 3′ UTRs.     

Computational identification of sweet wormwood (Artemisia annua) microRNA and their mRNA targets

Despite its efficacy against malaria, the relatively low yield (0.01%-0.8%) of artemisinin in Artemisia annua is a serious limitation to the commercialization of the drug. A better understanding of the biosynthetic pathway of artemisinin and its regulation by both exogenous and endogenous factors is essential to improve artemisinin yield. Increasing evidence has shown that microRNAs (miRNAs) play multiple roles in various biological processes. In this study, we used previously known miRNAs from Arabidopsis and rice against expressed sequence tag (EST) database of A. annua to search for potential miRNAs and their targets in A. annua. A total of six potential miRNAs were predicted, which belong to the miR414 and miR1310 families. Furthermore, eight potential target genes were identified in this species. Among them, seven genes encode proteins that play important roles in ar- temisinin biosynthesis, including HMG-CoA reductase (HMGR), amorpha-4,11-diene synthase (ADS), farnesyl pyrophosphate synthase (FPS) and cytochrome P450. In addition, a gene coding for putative AINTEGUMENTA, which is involved in signal transduction and development, was also predicted as one of the targets. This is the first in silico study to indicate that miRNAs target genes encoding enzymes involved in artemisinin biosynthesis, which may help to understand the miRNA-mediated regulation of artemisinin biosynthesis in A. annua.     

Identification of miR414 and expression analysis of conserved miRNAs from Stevia rebaudiana

MicroRNAs (miRNAs) usually contain 19-24 nucleotides and have been identified as important eukaryotic gene regulators. Applications of various computational approaches have simplified the task by predicting miRNAs from available sequence data sources. In this study, we identified a conserved miR414 from a computational analysis of EST sequence data available from Stevia rebaudiana. In addition, we also identified six conserved miRNAs namely miR169, miR319, miR414, miR164, miR167 and miR398 using stem-loop RT-PCR analysis. Hence, miR414 was commonly identified using both methods. The expression analysis of these miRNAs documented their roles in growth and development of Stevia. Furthermore, the detected miRNAs were found to target genes involved in plant growth, development, metabolism and signal transduction. This is the first study reporting these conserved miRNAs and their expression in Stevia.     

Target discovery and validation in the post-genomic era

The recent publication of the human genome sequence provides an opportunity both to combat diseases that are presently considered as pharmaceutically intractable and also to improve current therapies for many common human diseases. The identification of every human gene by ongoing bioinformatic efforts has the potential, when combined with functional genomic approaches, to pinpoint the molecular basis of every human disease, and to discover appropriate intervention points. This exciting prospect is directly relevant to the successful development of effective therapeutics because the past record of drug discovery suggests that 30%–40% of experimental drugs fail because an inappropriate biological target was pursued. The major impact of genomic information may therefore be to reduce this biological failure rate by earlier definition of drug targets related to disease susceptibility or progression. This paper briefly reviews some of the approaches that can be used to identify biologically relevant drug targets.     

miRDB: a microRNA target prediction and functional annotation database with a wiki interface

MicroRNAs (miRNAs) are short noncoding RNAs that are involved in the regulation of thousands of gene targets. Recent studies indicate that miRNAs are likely to be master regulators of many important biological processes. Due to their functional importance, miRNAs are under intense study at present, and many studies have been published in recent years on miRNA functional characterization. The rapid accumulation of miRNA knowledge makes it challenging to properly organize and present miRNA function data. Although several miRNA functional databases have been developed recently, this remains a major bioinformatics challenge to miRNA research community. Here, we describe a new online database system, miRDB, on miRNA target prediction and functional annotation. Flexible web search interface was developed for the retrieval of target prediction results, which were generated with a new bioinformatics algorithm we developed recently. Unlike most other miRNA databases, miRNA functional annotations in miRDB are presented with a primary focus on mature miRNAs, which are the functional carriers of miRNA-mediated gene expression regulation. In addition, a wiki editing interface was established to allow anyone with Internet access to make contributions on miRNA functional annotation. This is a new attempt to develop an interactive community-annotated miRNA functional catalog. All data stored in miRDB are freely accessible at http://mirdb.org.     

玉米纹枯病抗性相关miRNA的鉴定与功能分析

MicroRNA (miRNA)是一类内源性非编码小分子RNA,通过指导剪切或者抑制翻译等方式调节植物基因的表达,参与调控植物的生长发育,并在多种非生物与生物胁迫响应中发挥重要作用. 但目前关于玉米纹枯病抗性相关miRNA表达调节与功能尚不十分清楚. 本研究结合直接克隆法与生物信息学分析,鉴定玉米纹枯病抗性相关9个新的玉米miRNA和已知的zma-miR168a、zma-miR168a*;WMD 3软件进行靶基因预测显示,共获得靶基因总数34个,靶基因功能主要涉及玉米的抗氧化胁迫机制、自身反馈调节、转录调控途径、抗病相关代谢途径以及毒物转运外排等调控过程;实时定量PCR检测miRNA显示,耐感纹枯病材料R15和Ye478叶片和叶鞘中共有9个miRNA受纹枯病感染诱导发生特异性差异表达. 本研究结果提示,玉米纹枯病抗性相关 miRNA介导的玉米对纹枯病诱导产生可能的抗病途径构成了玉米抗纹枯病侵染复杂的防御机制.     

Experimental identification of microRNA-140 targets by silencing and overexpressing miR-140

MicroRNAs (miRNAs) are short noncoding RNA molecules regulating the expression of mRNAs. Target identification of miRNAs is computationally difficult due to the relatively low homology between miRNAs and their targets. We present here an experimental approach to target identification where the cartilage-specific miR-140 was overexpressed and silenced in cells it is normally expressed in separate experiments. Expression of mRNAs was profiled in both experiments and the intersection of mRNAs repressed by miR-140 overexpression and derepressed by silencing of miR-140 was identified. The intersection contained only 49 genes, although both treatments affected the accumulation of hundreds of mRNAs. These 49 genes showed a very strong enrichment for the miR-140 seed sequence implying that the approach is efficient and specific. Twenty-one of these 49 genes were predicted to be direct targets based on the presence of the seed sequence. Interestingly, none of these were predicted by the published target prediction methods we used. One of the potential target mRNAs, Cxcl12, was experimentally validated by Northern blot analysis and a luciferase reporter assay.     

急性呼吸窘迫综合征中miRNAs的生物标记作用与调控机制

MiRNAs作为非编码单链RNA分子,具有时空特异性和较高的保守性。近年来,许多实验数据证明,miRNA对细胞基因表达、细胞分化和组织发育等过程有着重要的调控作用,特别是在一些疾病的发生与发展中,miRNAs会异常表达且通过某些机制促进或抑制疾病的恶化。急性呼吸窘迫综合征(acute respiratory distress syndrome, ARDS)的发生由多种因素造成,主要临床表现为肺泡-毛细血管损伤。在ARDS发病过程中,一些miRNAs表达异常,且通过调控mRNA转录和表达,参与整个发病过程。在ARDS发病过程中明显上调或下调且具有特异性的miRNA分子可能为ARDS的预前及预后提供新的标志物,同时研究其调控机制也为诊疗提供新靶点。     

Signal peptide prediction based on analysis of experimentally verified cleavage sites

A number of computational tools are available for detecting signal peptides, but their abilities to locate the signal peptide cleavage sites vary significantly and are often less than satisfactory. We characterized a set of 270 secreted recombinant human proteins by automated Edman analysis and used the verified cleavage sites to evaluate the success rate of a number of computational prediction programs. An examination of the frequency of amino acid in the N-terminal region of the data set showed a preference of proline and glutamine but a bias against tyrosine. The data set was compared to the SWISS-PROT database and revealed a high percentage of discrepancies with cleavage site annotations that were computationally generated. The best program for predicting signal sequences was found to be SignalP 2.0-NN with an accuracy of 78.1% for cleavage site recognition. The new data set can be utilized for refining prediction algorithms, and we have built an improved version of profile hidden Markov model for signal peptides based on the new data.     

Determinants of Functional MicroRNA Targeting

MicroRNAs (miRNAs) play cardinal roles in regulating biological pathways and processes, resulting in significant physiological effects. To understand the complex regulatory network of miRNAs, previous studies have utilized massive-scale datasets of miRNA targeting and attempted to computationally predict the functional targets of miRNAs. Many miRNA target prediction tools have been developed and are widely used by scientists from various fields of biology and medicine. Most of these tools consider seed pairing between miRNAs and their mRNA targets and additionally consider other determinants to improve prediction accuracy. However, these tools exhibit limited prediction accuracy and high false positive rates. The utilization of additional determinants, such as RNA modifications and RNA-binding protein binding sites, may further improve miRNA target prediction. In this review, we discuss the determinants of functional miRNA targeting that are currently used in miRNA target prediction and the potentially predictive but unappreciated determinants that may improve prediction accuracy.     

RosettaHoles: Rapid assessment of protein core packing for structure prediction,refinement, design,and validation

We present a novel method called RosettaHoles for visual and quantitative assessment of underpacking in the protein core. RosettaHoles generates a set of spherical cavity balls that fill the empty volume between atoms in the protein interior. For visualization, the cavity balls are aggregated into contiguous overlapping clusters and small cavities are discarded, leaving an uncluttered representation of the unfilled regions of space in a structure. For quantitative analysis, the cavity ball data are used to estimate the probability of observing a given cavity in a high‐resolution crystal structure. RosettaHoles provides excellent discrimination between real and computationally generated structures, is predictive of incorrect regions in models, identifies problematic structures in the Protein Data Bank, and promises to be a useful validation tool for newly solved experimental structures.     

Bioinformatics prediction of miRNAs in the Prunus persica genome with validation of their precise sequences by miR-RACE

We predicted 262 potential MicroRNAs (miRNAs) belonging to 70 miRNA families from the peach (Prunus persica) genome and two specific 5′ and 3′ miRNA rapid amplification of cDNA ends (miR-RACE) PCR reactions and sequence-directed cloning were employed to accurately validate 61 unique P. persica miRNAs (Ppe-miRNAs) sequences belonging to 61 families comprising 97 Ppe-miRNAs. Validation of the termini nucleotides in particular can define the real sequences of the Ppe-miRNAs on peach genome. Comparison between predicted and validated Ppe-miRNAs through alignment revealed that 43 unique orthologous sequences were identical, while the remaining 18 exhibited some divergences at their termini nucleotides. Quantitative real-time polymerase chain reaction (qRT-PCR) was further employed to analyze the expression of all the 61 miRNAs and 10 putative targets of 8 randomly selected Ppe-miRNAs in peach leaves, flowers and fruits at different stages of development, where both the miRNAs and the putative target genes showed tissue-specific expression.