Jin-Wu Nam

Molecular Basis for the Recognition of Primary microRNAs by the Drosha-DGCR8 Complex

The Drosha-DGCR8 complex initiates microRNA maturation by precise cleavage of the stem loops that are embedded in primary transcripts (pri-miRNAs). Here we propose a model for this process that is based upon evidence from both computational and biochemical analyses. A typical metazoan pri-miRNA consists of a stem of approximately 33 bp, with a terminal loop and flanking segments. The terminal loop is unessential, whereas the flanking ssRNA segments are critical for processing. The cleavage site is determined mainly by the distance (approximately 11 bp) from the stem-ssRNA junction. Purified DGCR8, but not Drosha, interacts with pri-miRNAs both directly and specifically, and the flanking ssRNA segments are vital for this binding to occur. Thus, DGCR8 may function as the molecular anchor that measures the distance from the dsRNA-ssRNA junction. Our current study thus facilitates the prediction of novel microRNAs and will assist in the rational design of small hairpin RNAs for RNA interference.

miR-29 miRNAs activate p53 by targeting p85 alpha and CDC42.

The tumor suppressor p53 is central to many cellular stress responses. Although numerous protein factors that control p53 have been identified, the role of microRNAs (miRNAs) in regulating p53 remains unexplored. In a screen for miRNAs that modulate p53 activity, we find that miR-29 family members (miR-29a, miR-29b and miR-29c) upregulate p53 levels and induce apoptosis in a p53-dependent manner. We further find that miR-29 family members directly suppress p85α (the regulatory subunit of PI3 kinase) and CDC42 (a Rho family GTPase), both of which negatively regulate p53. Our findings provide new insights into the role of miRNAs in the p53 pathway.

Conserved MicroRNA miR-8/miR-200 and Its Target USH/FOG2 Control Growth by Regulating PI3K

How body size is determined is a long-standing question in biology, yet its regulatory mechanisms remain largely unknown. Here, we find that a conserved microRNA miR-8 and its target, USH, regulate body size in Drosophila. miR-8 null flies are smaller in size and defective in insulin signaling in fat body that is the fly counterpart of liver and adipose tissue. Fat body-specific expression and clonal analyses reveal that miR-8 activates PI3K, thereby promoting fat cell growth cell-autonomously and enhancing organismal growth non-cell-autonomously. Comparative analyses identify USH and its human homolog, FOG2, as the targets of fly miR-8 and human miR-200, respectively. USH/FOG2 inhibits PI3K activity, suppressing cell growth in both flies and humans. FOG2 directly binds to p85alpha, the regulatory subunit of PI3K, and interferes with the formation of a PI3K complex. Our study identifies two novel regulators of insulin signaling, miR-8/miR-200 and USH/FOG2, and suggests their roles in adolescent growth, aging, and cancer.

Discovery of microRNA–mRNA modules via population-based probabilistic learning

MOTIVATION MicroRNAs (miRNAs) and mRNAs constitute an important part of gene regulatory networks, influencing diverse biological phenomena. Elucidating closely related miRNAs and mRNAs can be an essential first step towards the discovery of their combinatorial effects on different cellular states. Here, we propose a probabilistic learning method to identify synergistic miRNAs involving regulation of their condition-specific target genes (mRNAs) from multiple information sources, i.e. computationally predicted target genes of miRNAs and their respective expression profiles. RESULTS We used data sets consisting of miRNA-target gene binding information and expression profiles of miRNAs and mRNAs on human cancer samples. Our method allowed us to detect functionally correlated miRNA-mRNA modules involved in specific biological processes from multiple data sources by using a balanced fitness function and efficient searching over multiple populations. The proposed algorithm found two miRNA-mRNA modules, highly correlated with respect to their expression and biological function. Moreover, the mRNAs included in the same module showed much higher correlations when the related miRNAs were highly expressed, demonstrating our methods ability for finding coherent miRNA-mRNA modules. Most members of these modules have been reported to be closely related with cancer. Consequently, our method can provide a primary source of miRNA and target sets presumed to constitute closely related parts of gene regulatory pathways.

ProMiR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs

ProMiR is a web-based service for the prediction of potential microRNAs (miRNAs) in a query sequence of 60–150 nt, using a probabilistic colearning model. Identification of miRNAs requires a computational method to predict clustered and nonclustered, conserved and nonconserved miRNAs in various species. Here we present an improved version of ProMiR for identifying new clusters near known or unknown miRNAs. This new version, ProMiR II, integrates additional evidence, such as free energy data, G/C ratio, conservation score and entropy of candidate sequences, for more controllable prediction of miRNAs in mouse and human genomes. It also provides a wider range of services, e.g. the prediction of miRNA genes in long nonrelated sequences such as viral genomes. Importantly, we have validated this method using several case studies. All data used in ProMiR II are structured in the MySQL database for efficient analysis. The ProMiR II web server is available at .

A Kernel Method for MicroRNA Target Prediction Using Sensible Data and Position-Based Features

MicroRNAs (miRNAs) are small endogenous RNAs of ~ 22nt that act as direct post-transcriptional regulators in animals and plants. MicroRNAs generally perform a function by binding to the complementary site on the 3’ untranslated region of its target gene and especially the 8mers on the 5’ part of miRNA seems important as a seed. Computational methods for miRNA target prediction have been focusing on this seed region, but recent researches revealed that the specificity of the seed region may be sharply decreased even by a point mutation. In this paper, we present a kernel method for miRNA target prediction in animals, which improves the prediction performance with biologically sensible data and position-based features reflecting the way of miRNA: mRNA pairing mechanism. In building a training dataset, we choose experimentally verified data only to improve the quality of dataset by excluding randomly synthesized one and consequently to make the result of learning valid. We use sensitivity, specificity, and area under ROC curve as performance measures of our algorithm and compare the results of various dataset configurations. The overall results were 92.1% in sensitivity, 83.3% in specificity, and 0.931 in area under ROC curve. With position-based features, an increase of 3.3% in sensitivity and 1.6% in specificity were observed.

Two-step genetic programming for optimization of RNA common-structure

We present an algorithm for identifying putative non-coding RNA (ncRNA) using an RCSG (RNA Common-Structural Grammar) and show the effectiveness of the algorithm. The algorithm consists of two steps: structure learning step and sequence learning step. Both steps are based on genetic programming. Generally, genetic programming has been applied to learning programs automatically, reconstructing networks, and predicting protein secondary structures. In this study, we use genetic programming to optimize structural grammars. The structural grammars can be formulated as rules of tree structure including function variables. They can be learned by genetic programming. We have defined the rules on how structure definition grammars can be encoded into function trees. The performance of the algorithm is demonstrated by the results obtained from the experiments with RCSG of tRNA and 5S small RNA.

Identification and characterization of small RNAs from vernalizedArabidopsis thaliana

MicroRNAs (miRNAs) and small interfering RNAs (siRNAs) are two major classes of small non-coding RNAs with important roles in the regulation of gene expression, such as mRNA degradation and translational repression, heterochromatin formation, genome defense against transposons and viruses in eukaryotes. MiRNA- and siRNA-directed processes have emerged as a regulatory mechanism for growth and development in both animals and plants. To identify small RNAs that might be involved in vernalization, a process accelerating flowering brought on by a long period of cold, we generated a library of small RNAs from Arabidopsis that had been subject to vernalization. From the analysis of the library, 277 small RNAs were identified. They were distributed throughout all the five chromosomes. While the vast majority of small RNA genes locate on intergenic regions, others locate on repeat-rich regions, centromeric regions, transposon-related genes, and protein-coding genes. Five of them were mapped to convergent overlapping gene pairs. Two-hundred and forty of them were novel endogenous small RNAs that have not been cloned yet from plants grown under normal conditions and other environmental stresses. Seven putative miRNAs were up- or down-regulated by vernalization. In conclusion, many small RNAs were identified from vernalized Arabidopsis and some of these identified small RNAs may play roles in plant responses to vernalization.

Computational methods for identification of human microRNA precursors

MicroRNA (miRNA), one of non-coding RNAs (ncRNAs), regulates gene expression directly by arresting the messenger RNA (mRNA) translation, which is important for identifying putative miRNAs. In this study, we suggest a searching procedure for human miRNA precursors using genetic programming that automatically learn common structures of miRNAs from a set of known miRNA precursors. Our method consists of three-steps. At first, for each miRNA precursor, we adopted genetic programming techniques to optimize the RNA Common-Structural Grammar (RCSG) of populations until certain fitness is achieved. In this step, the specificity and the sensitivity of a RCSG for the training data set were used as the fitness criteria. Next, for each optimized RCSG, we collected candidates of matching miRNA precursors with the corresponding grammar from genome databases. Finally, we selected miRNA precursors over a threshold (= 365) of scoring model from the candidates. This step would reduce false positives in the candidates. To validate the effectiveness of our miRNA method, we evaluated the learned RCSG and the scoring model with test data. Here, we obtained satisfactory results, with high specificity (= 51/64) and proper sensitivity (= 51/82) using human miRNA precursors as a test data set.

Dinucleotide step parameterization of pre-miRNAs using multi-objective evolutionary algorithms

MicroRNAs (miRNAs) form a large functional family of small noncoding RNAs and play an important role as posttranscriptional regulators, by repressing the translation of mRNAs. Recently, the processing mechanism of miRNAs has been reported to involve Drosha/DGCR8 complex and Dicer, however, the exact mechanism and molecular principle are still unknown. We thus have tried to understand the related phenomena in terms of the tertiary structure of pre-miRNA. Unfortunately, the tertiary structure of RNA double helix has not been studied sufficiently compared to that of DNA double helix. The tertiary structure of pre-miRNA double helix is determined by 15 types of dinucleotide step (d-step) parameters for three classes of angles, i.e., twist, roll, and tilt. In this study, we estimate the 45 d-step parameters (15 types by 3 classes) using an evolutionary algorithm, under several assumptions inferred from the literature. Considering the trade-off among the four objective functions in our study, we deployed a multi-objective evolutionary algorithm, NSGA-II, to the search for a nondominant set of parameters. The performance of our method was evaluated on a separate test dataset. Our study provides a novel approach to understanding the processing mechanism of pre-miRNAs with respect to their tertiary structure and would be helpful for developing a comprehensible prediction method for pre-miRNA and mature miRNA structures.