Wen-Ching Chan

Epigenetic regulation of miR-34b and miR-129 expression in gastric cancer.

MicroRNAs (miRNAs) are small noncoding RNAs that play fundamental roles in diverse biological and pathological processes by targeting the expression of specific genes. Here, we identified 38 methylation‐associated miRNAs, the expression of which could be epigenetically restored by cotreatment with 5‐aza‐2′‐deoxycytidine and trichostatin A. Among these 38 miRNAs, we further analyzed miR‐34b, miR‐127‐3p, miR‐129‐3p and miR‐409 because CpG islands are predicted adjacent to them. The methylation‐silenced expression of these miRNAs could be reactivated in gastric cancer cells by treatment with demethylating drugs in a time‐dependent manner. Analysis of the methylation status of these miRNAs showed that the upstream CpG‐rich regions of mir‐34b and mir‐129‐2 are frequently methylated in gastric cancer tissues compared to adjacent normal tissues, and their methylation status correlated inversely with their expression patterns. The expression of miR‐34b and miR‐129‐3p was downregulated by DNA hypermethylation in primary gastric cancers, and the low expression was associated with poor clinicopathological features. In summary, our study shows that tumor‐specific methylation silences miR‐34b and miR‐129 in gastric cancer cells.

Aberrant hypermethylation of miR-9 genes in gastric cancer

Carcinogenesis of the stomach involves multiple steps including genetic mutation or epigenetic alteration of tumor suppressor genes or oncogenes. Recently, tumor suppressive miRNAs have been shown to be deregulated by aberrant hypermethylation during gastric cancer progression. In this study, we demonstrate that three independent genetic loci encoding for miR-9 (miR-9-1, miR-9-2 and miR-9-3) are simultaneously modified by DNA methylation in gastric cancer cells. Methylation-mediated silencing of these three miR-9 genes can be reactivated in gastric cancer cells through 5-Aza-dC treatment. Subsequent analysis of the expression levels of miR-9 showed that it was significantly down-regulated in gastric cancers compared with adjacent normal tissues (P value < 0.005). A similar tendency toward a tumor-specific DNA methylation pattern was shown for miR-9-1, miR-9-2 and miR-9-3 in 72 primary human gastric cancer specimens. Ectopic expression of miR-9 inhibited cell proliferation, migration and invasion, suggesting its tumor suppressive potential in gastric cancer progression.

Discovery and characterization of medaka miRNA genes by next generation sequencing platform

BackgroundMicroRNAs (miRNAs) are endogenous non-protein-coding RNA genes which exist in a wide variety of organisms, including animals, plants, virus and even unicellular organisms. Medaka (Oryzias latipes) is a useful model organism among vertebrate animals. However, no medaka miRNAs have been investigated systematically. It is beneficial to conduct a genome-wide miRNA discovery study using the next generation sequencing (NGS) technology, which has emerged as a powerful sequencing tool for high-throughput analysis.ResultsIn this study, we adopted ABI SOLiD platform to generate small RNA sequence reads from medaka tissues, followed by mapping these sequence reads back to medaka genome. The mapped genomic loci were considered as candidate miRNAs and further processed by a support vector machine (SVM) classifier. As result, we identified 599 novel medaka pre-miRNAs, many of which were found to encode more than one isomiRs. Besides, additional minor miRNAs (also called miRNA star) can be also detected with the improvement of sequencing depth. These quantifiable isomiRs and minor miRNAs enable us to further characterize medaka miRNA genes in many aspects. First of all, many medaka candidate pre-miRNAs position close to each other, forming many miRNA clusters, some of which are also conserved across other vertebrate animals. Secondly, during miRNA maturation, there is an arm selection preference of mature miRNAs within precursors. We observed the differences on arm selection preference between our candidate pre-miRNAs and their orthologous ones. We classified these differences into three categories based on the distribution of NGS reads. Finally, we also investigated the relationship between conservation status and expression level of miRNA genes. We concluded that the evolutionally conserved miRNAs were usually the most abundant ones.ConclusionsMedaka is a widely used model animal and usually involved in many biomedical studies, including the ones on development biology. Identifying and characterizing medaka miRNA genes would benefit the studies using medaka as a model organism.

Identification of homologous microRNAs in 56 animal genomes.

MicroRNAs (miRNAs) are endogenous non-protein-coding RNAs of approximately 22 nucleotides. Thousands of miRNA genes have been identified (computationally and/or experimentally) in a variety of organisms, which suggests that miRNA genes have been widely shared and distributed among species. Here, we used unique miRNA sequence patterns to scan the genome sequences of 56 bilaterian animal species for locating candidate miRNAs first. The regions centered surrounding these candidate miRNAs were then extracted for folding and calculating the features of their secondary structure. Using a support vector machine (SVM) as a classifier combined with these features, we identified an additional 13,091 orthologous or paralogous candidate pre-miRNAs, as well as their corresponding candidate mature miRNAs. Stem-loop RT-PCR and deep sequencing methods were used to experimentally validate the prediction results in human, medaka and rabbit. Our prediction pipeline allows the rapid and effective discovery of homologous miRNAs in a large number of genomes.

Aberrant expression of miR-196a in gastric cancers and correlation with recurrence

MicroRNAs (miRNAs) are short noncoding RNAs (˜22 nt) that play important roles in the pathogenesis of human diseases by negatively regulating gene expression. Here, we examined the relationship between miR‐196a and gastric cancer. By the analysis of 72 gastric cancer samples, we found that the expression level of miR‐196a microRNA significantly increased in primary gastric cancer tissues versus adjacent normal tissues. In addition, extracellular miR‐196a detected in conditioned medium was strongly correlated with its cellular expression status and increased circulating miR‐196a in patient serum was associated with gastric cancer disease status and relapse. Furthermore, ectopic expression of miR‐196a microRNA promoted the epithelial‐mesenchymal transition and migration/invasion capabilities of transfected cells, suggesting its oncogenic potential in gastric cancer progression. Altogether, our data demonstrate that miR‐196a exerts an oncogenic role in gastric cancer and miR‐196a may be a novel biomarker for detecting gastric cancer and for monitoring disease recurrence.

Transcriptional regulation of miR-196b by ETS2 in gastric cancer cells

E26 transformation-specific sequence (ETS)-2 is a transcriptional modulator located on chromosome 21, alterations in its expression have been implicated with a reduced incidence of solid tumors in Down syndrome patients. MicroRNAs (miRNAs) are thought to participate in diverse biological functions; however, the regulation of miRNAs is not well characterized. Recently, we reported that miR-196b is highly expressed in gastric cancers. Herein, we demonstrate that miR-196b expression was significantly repressed by ETS2 during gastric cancer oncogenesis. We demonstrate that knockdown of endogenous ETS2 expression increases miR-196b expression. A genomic region between −751 and −824 bp upstream of the miR-196b transcriptional start site was found to be critical for the repression activity. This putative regulatory promoter region contains three potential ETS2-binding motifs. Mutations within the ETS2 binding sites blocked the repression activity of ETS2. Furthermore, knockdown of ETS2 or overexpression of miR-196b significantly induced migration and invasion in gastric cancer cells. In addition, alterations in ETS2 and miR-196b expression in gastric cancer cell lines affected the expression of epithelial–mesenchymal transition-related genes. The levels of vimentin, matrix metalloproteinase (MMP)-2 and MMP9 were drastically induced, but levels of E-cadherin were decreased in shETS2- or miR-196b-transfected cells. Our data indicate that ETS2 plays a key role in controlling the expression of miR-196b, and miR-196b may mediate the tumor suppressor effects of ETS2. We demonstrated that miR-196b was transcriptionally regulated by ETS2 and there was an inverse expression profile between miR-196b and ETS2 in clinical samples. This finding could be beneficial for the development of effective cancer diagnostic and alternative therapeutic strategies.

MetaMirClust: Discovery of miRNA cluster patterns using a data-mining approach

Recent genome-wide surveys on ncRNA have revealed that a substantial fraction of miRNA genes is likely to form clusters. However, the evolutionary and biological function implications of clustered miRNAs are still elusive. After identifying clustered miRNA genes under different maximum inter-miRNA distances (MIDs), this study intended to reveal evolution conservation patterns among these clustered miRNA genes in metazoan species using a computation algorithm. As examples, a total of 15-35% of known and predicted miRNA genes in nine selected species constitute clusters under the MIDs ranging from 1kb to 50kb. Intriguingly, 33 out of 37 metazoan miRNA clusters in 56 metazoan genomes are co-conserved with their up/down-stream adjacent protein-coding genes. Meanwhile, a co-expression pattern of miR-1 and miR-133a in the mir-133-1 cluster has been experimentally demonstrated. Therefore, the MetaMirClust database provides a useful bioinformatic resource for biologists to facilitate the advanced interrogations on the composition of miRNA clusters and their evolution patterns.

Interrogation of rabbit miRNAs and their isomiRs

Rabbit (Oryctolagus cuniculus) is the only lagomorph animal of which the genome has been sequenced. Establishing a rabbit miRNA resource will benefit subsequent functional genomic studies in mammals. We have generated small RNA sequence reads with SOLiD and Solexa platforms to identify rabbit miRNAs, where we identified 464 pre-miRNAs and 886 mature miRNAs. The brain and heart miRNA libraries were used for further in-depth analysis of isomiR distributions. There are several intriguing findings. First, several rabbit pre-miRNAs form highly conserved clusters. Second, there is a preference in selecting one strand as mature miRNA, resulting in an arm selection preference. Third, we analyzed the isomiR expression and validated the expression of isomiR types in different rabbit tissues. Moreover, we further performed additional small RNA libraries and defined miRNAs differentially expressed between brain and heart. We conclude also that isomiR distribution profiles could vary between brain and heart tissues.

UMARS: Un-MAppable Reads Solution

BackgroundUn-MAppable Reads Solution (UMARS) is a user-friendly web service focusing on retrieving valuable information from sequence reads that cannot be mapped back to reference genomes. Recently, next-generation sequencing (NGS) technology has emerged as a powerful tool for generating high-throughput sequencing data and has been applied to many kinds of biological research. In a typical analysis, adaptor-trimmed NGS reads were first mapped back to reference sequences, including genomes or transcripts. However, a fraction of NGS reads failed to be mapped back to the reference sequences. Such un-mappable reads are usually imputed to sequencing errors and discarded without further consideration.MethodsWe are investigating possible biological relevance and possible sources of un-mappable reads. Therefore, we developed UMARS to scan for virus genomic fragments or exon-exon junctions of novel alternative splicing isoforms from un-mappable reads. For mapping un-mappable reads, we first collected viral genomes and sequences of exon-exon junctions. Then, we constructed UMARS pipeline as an automatic alignment interface.ResultsBy demonstrating the results of two UMARS alignment cases, we show the applicability of UMARS. We first showed that the expected EBV genomic fragments can be detected by UMARS. Second, we also detected exon-exon junctions from un-mappable reads. Further experimental validation also ensured the authenticity of the UMARS pipeline. The UMARS service is freely available to the academic community and can be accessed via http://musk.ibms.sinica.edu.tw/UMARS/.ConclusionsIn this study, we have shown that some un-mappable reads are not caused by sequencing errors. They can originate from viral infection or transcript splicing. Our UMARS pipeline provides another way to examine and recycle the un-mappable reads that are commonly discarded as garbage.

Learning to predict expression efficacy of vectors in recombinant protein production

BackgroundRecombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is to fuse a target protein into different vectors in Escherichia coli (E. coli). However, the production efficacy of different vectors varies for different target proteins. Trial-and-error is still the common practice to find out the efficacy of a vector for a given target protein. Previous studies are limited in that they assumed that proteins would be over-expressed and focused only on the solubility of expressed proteins. In fact, many pairings of vectors and proteins result in no expression.ResultsIn this study, we applied machine learning to train prediction models to predict whether a pairing of vector-protein will express or not express in E. coli. For expressed cases, the models further predict whether the expressed proteins would be soluble. We collected a set of real cases from the clients of our recombinant protein production core facility, where six different vectors were designed and studied. This set of cases is used in both training and evaluation of our models. We evaluate three different models based on the support vector machines (SVM) and their ensembles. Unlike many previous works, these models consider the sequence of the target protein as well as the sequence of the whole fusion vector as the features. We show that a model that classifies a case into one of the three classes (no expression, inclusion body and soluble) outperforms a model that considers the nested structure of the three classes, while a model that can take advantage of the hierarchical structure of the three classes performs slight worse but comparably to the best model. Meanwhile, compared to previous works, we show that the prediction accuracy of our best method still performs the best. Lastly, we briefly present two methods to use the trained model in the design of the recombinant protein production systems to improve the chance of high soluble protein production.ConclusionIn this paper, we show that a machine learning approach to the prediction of the efficacy of a vector for a target protein in a recombinant protein production system is promising and may compliment traditional knowledge-driven study of the efficacy. We will release our program to share with other labs in the public domain when this paper is published.