Seungyoon Nam

MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression

MicroRNAs (miRNAs) are small (19–24 nt), nonprotein-coding nucleic acids that regulate specific ‘target’ gene products via hybridization to mRNA transcripts, resulting in translational blockade or transcript degradation. Although miRNAs have been implicated in numerous developmental and adult diseases, their specific impact on biological pathways and cellular phenotypes, in addition to miRNA gene promoter regulation, remain largely unknown. To improve and facilitate research of miRNA functions and regulation, we have developed MMIA (microRNA and mRNA integrated analysis), a versatile and user-friendly web server. By incorporating three commonly used and accurate miRNA prediction algorithms, TargetScan, PITA and PicTar, MMIA integrates miRNA and mRNA expression data with predicted miRNA target information for analyzing miRNA-associated phenotypes and biological functions by gene set analysis, in addition to analysis of miRNA primary transcript gene promoters. To assign biological relevance to the integrated miRNA/mRNA profiles, MMIA uses exhaustive human genome coverage, including classification into various disease-associated genes as well as conventional canonical pathways and Gene Ontology. In summary, this novel web server (cancer.informatics.indiana.edu/mmia) will provide life science researchers with a valuable tool for the study of the biological (and pathological) causes and effects of the expression of this class of interesting protein regulators.

miRGator: an integrated system for functional annotation of microRNAs

MicroRNAs (miRNAs) constitute an important class of regulators that are involved in various cellular and disease processes. However, the functional significance of each miRNA is mostly unknown due to the difficulty in identifying target genes and the lack of genome-wide expression data combining miRNAs, mRNAs and proteins. We introduce a novel database, miRGator, that integrates the target prediction, functional analysis, gene expression data and genome annotation. MiRNA function is inferred from the list of target genes predicted by miRanda, PicTar and TargetScanS programs. Statistical enrichment test of target genes in each term is performed for gene ontology, pathway and disease annotations. Associated terms may provide valuable insights for the function of each miRNA. For the expression analysis, miRGator integrates public expression data of miRNA with those of mRNA and protein. Expression correlation between miRNA and target mRNA/proteins is evaluated and their expression patterns can be readily compared. Our web implementation supports diverse query types including miRNA name, gene symbol, gene ontology, pathway and disease terms. Interfaces for exploring common targets or regulatory miRNAs and for profiling compendium expression data have been developed as well. Currently, miRGator, available at: http://genome.ewha.ac.kr/miRGator/, supports the human and mouse genomes.

EGFR signaling in breast cancer: bad to the bone.

The epidermal growth factor receptor (EGFR) is a member of the ErbB family of receptor tyrosine kinases. This family includes EGFR/ErbB1/HER1, ErbB2/HER2/Neu ErbB3/HER3, and ErbB4/HER4. For many years it was believed that EGFR plays a minor role in the development and progression of breast malignancies. However, recent findings have led investigators to revisit these beliefs. Here we will review these findings and propose roles that EGFR may play in breast malignancies. In particular, we will discuss the potential roles that EGFR may play in triple-negative tumors, resistance to endocrine therapies, maintenance of stem-like tumor cells, and bone metastasis. Thus, we will propose the contexts in which EGFR may be a therapeutic target.

Clinical Validity of the Lung Cancer Biomarkers Identified by Bioinformatics Analysis of Public Expression Data

Identification of molecular markers often leads to important clinical applications such as early diagnosis, prognosis, and drug targeting. Lung cancer, the leading cause of cancer-related deaths, still lacks reliable molecular markers. We have combined the bioinformatics analysis of the public gene expression data and clinical validation to identify biomarker genes for non-small-cell lung cancer. The serial analysis of gene expression and the expressed sequence tag data were meta-analyzed to produce a list of the differentially expressed genes in lung cancer. Through careful inspection of the predicted genes, we selected 20 genes for experimental validation using semiquantitative reverse transcriptase-PCR. The microdissected clinical specimens used in the study consisted of three groups: lung tissues from benign diseases and the paired (cancer and pathologic normal) tissues from non-small-cell lung cancer patients. After extensive statistical analyses, seven genes (CBLC, CYP24A1, ALDH3A1, AKR1B10, S100P, PLUNC, and LOC147166) were identified as potential diagnostic markers. Quantitative real-time PCR was carried out to additionally assess the value of the seven identified genes leading to the confirmation of at least two genes (CBLC and CYP24A1) as highly probable novel biomarkers. The gene properties of the identified markers, especially their relationship to lung cancer and cell signaling pathway regulation, further suggest their potential value as drug targets as well.

PATHOME: an algorithm for accurately detecting differentially expressed subpathways

The translation of high-throughput gene expression data into biologically meaningful information remains a bottleneck. We developed a novel computational algorithm, PATHOME, for detecting differentially expressed biological pathways. This algorithm employs straightforward statistical tests to evaluate the significance of differential expression patterns along subpathways. Applying it to gene expression data sets of gastric cancer (GC), we compared its performance with those of other leading programs. Based on a literature-driven reference set, PATHOME showed greater consistency in identifying known cancer-related pathways. For the WNT pathway uniquely identified by PATHOME, we validated its involvement in gastric carcinogenesis through experimental perturbation of both cell lines and animal models. We identified HNF4α-WNT5A regulation in the cross-talk between the AMPK metabolic pathway and the WNT signaling pathway, and further identified WNT5A as a potential therapeutic target for GC. We have demonstrated PATHOME to be a powerful tool, with improved sensitivity for identifying disease-related dysregulated pathways.

Identification of direct regulatory targets of the transcription factor Sox10 based on function and conservation

BackgroundSox10, a member of the Sry-related HMG-Box gene family, is a critical transcription factor for several important cell lineages, most notably the neural crest stem cells and the derivative peripheral glial cells and melanocytes. Thus far, only a handful of direct target genes are known for this transcription factor limiting our understanding of the biological network it governs.ResultsWe describe identification of multiple direct regulatory target genes of Sox10 through a procedure based on function and conservation. By combining RNA interference technique and DNA microarray technology, we have identified a set of genes that show significant down-regulation upon introduction of Sox10 specific siRNA into Schwannoma cells. Subsequent comparative genomics analyses led to potential binding sites for Sox10 protein conserved across several mammalian species within the genomic region proximal to these genes. Multiple sites belonging to 4 different genes (proteolipid protein, Sox10, extracellular superoxide dismutase, and pleiotrophin) were shown to directly interact with Sox10 by chromatin immunoprecipitation assay. We further confirmed the direct regulation through the identified cis-element for one of the genes, extracellular superoxide dismutase, using electrophoretic mobility shift assay and reporter assay.ConclusionIn sum, the process of combining differential expression profiling and comparative genomics successfully led to further defining the role of Sox10, a critical transcription factor for the development of peripheral glia. Our strategy utilizing relatively accessible techniques and tools should be applicable to studying the function of other transcription factors.

Detection of PIWI and piRNAs in the mitochondria of mammalian cancer cells.

Piwi-interacting RNAs (piRNAs) are 26-31 nt small noncoding RNAs that are processed from their longer precursor transcripts by Piwi proteins. Localization of Piwi and piRNA has been reported mostly in nucleus and cytoplasm of higher eukaryotes germ-line cells, where it is believed that known piRNA sequences are located in repeat regions of nuclear genome in germ-line cells. However, localization of PIWI and piRNA in mammalian somatic cell mitochondria yet remains largely unknown. We identified 29 piRNA sequence alignments from various regions of the human mitochondrial genome. Twelve out 29 piRNA sequences matched stem-loop fragment sequences of seven distinct tRNAs. We observed their actual expression in mitochondria subcellular fractions by inspecting mitochondrial-specific small RNA-Seq datasets. Of interest, the majority of the 29 piRNAs overlapped with multiple longer transcripts (expressed sequence tags) that are unique to the human mitochondrial genome. The presence of mature piRNAs in mitochondria was detected by qRT-PCR of mitochondrial subcellular RNAs. Further validation showed detection of Piwi by colocalization using anti-Piwil1 and mitochondria organelle-specific protein antibodies.

ChimerDB—a knowledgebase for fusion sequences

Chromosome translocation and gene fusion are frequent events in the human genome and are often the cause of many types of tumor. ChimerDB is the database of fusion sequences encompassing bioinformatics analysis of mRNA and expressed sequence tag (EST) sequences in the GenBank, manual collection of literature data and integration with other known database such as OMIM. Our bioinformatics analysis identifies the fusion transcripts that have non-overlapping alignments at multiple genomic loci. Fusion events at exon–exon borders are selected to filter out the cloning artifacts in cDNA library preparation. The result is classified into two groups—genuine chromosome translocation and fusion between neighboring genes owing to intergenic splicing. We also integrated manually collected literature and OMIM data for chromosome translocation as an aid to assess the validity of each fusion event. The database is available at for human, mouse and rat genomes.

Pathway-Based Evaluation in Early Onset Colorectal Cancer Suggests Focal Adhesion and Immunosuppression along with Epithelial-Mesenchymal Transition

Colorectal cancer (CRC) has one of the highest incidences among all cancers. The majority of CRCs are sporadic cancers that occur in individuals without family histories of CRC or inherited mutations. Unfortunately, whole-genome expression studies of sporadic CRCs are limited. A recent study used microarray techniques to identify a predictor gene set indicative of susceptibility to early-onset CRC. However, the molecular mechanisms of the predictor gene set were not fully investigated in the previous study. To understand the functional roles of the predictor gene set, in the present study we applied a subpathway-based statistical model to the microarray data from the previous study and identified mechanisms that are reasonably associated with the predictor gene set. Interestingly, significant subpathways belonging to 2 KEGG pathways (focal adhesion; natural killer cell-mediated cytotoxicity) were found to be involved in the early-onset CRC patients. We also showed that the 2 pathways were functionally involved in the predictor gene set using a text-mining technique. Entry of a single member of the predictor gene set triggered a focal adhesion pathway, which confers anti-apoptosis in the early-onset CRC patients. Furthermore, intensive inspection of the predictor gene set in terms of the 2 pathways suggested that some entries of the predictor gene set were implicated in immunosuppression along with epithelial-mesenchymal transition (EMT) in the early-onset CRC patients. In addition, we compared our subpathway-based statistical model with a gene set-based statistical model, MIT Gene Set Enrichment Analysis (GSEA). Our method showed better performance than GSEA in the sense that our method was more consistent with a well-known cancer-related pathway set. Thus, the biological suggestion generated by our subpathway-based approach seems quite reasonable and warrants a further experimental study on early-onset CRC in terms of dedifferentiation or differentiation, which is underscored in EMT and immunosuppression.

HNF4α is a therapeutic target that links AMPK to WNT signalling in early-stage gastric cancer

Background Worldwide, gastric cancer (GC) is the fourth most common malignancy and the most common cancer in East Asia. Development of targeted therapies for this disease has focused on a few known oncogenes but has had limited effects. Objective To determine oncogenic mechanisms and novel therapeutic targets specific for GC by identifying commonly dysregulated genes from the tumours of both Asian-Pacific and Caucasian patients. Methods We generated transcriptomic profiles of 22 Caucasian GC tumours and their matched non-cancerous samples and performed an integrative analysis across different GC gene expression datasets. We examined the inhibition of commonly overexpressed oncogenes and their constituent signalling pathways by RNAi and/or pharmacological inhibition. Results Hepatocyte nuclear factor-4α (HNF4α) upregulation was a key signalling event in gastric tumours from both Caucasian and Asian patients, and HNF4α antagonism was antineoplastic. Perturbation experiments in GC tumour cell lines and xenograft models further demonstrated that HNF4α is downregulated by AMPKα signalling and the AMPK agonist metformin; blockade of HNF4α activity resulted in cyclin downregulation, cell cycle arrest and tumour growth inhibition. HNF4α also regulated WNT signalling through its target gene WNT5A, a potential prognostic marker of diffuse type gastric tumours. Conclusions Our results indicate that HNF4α is a targetable oncoprotein in GC, is regulated by AMPK signalling through AMPKα and resides upstream of WNT signalling. HNF4α may regulate ‘metabolic switch’ characteristic of a general malignant phenotype and its target WNT5A has potential prognostic values. The AMPKα-HNF4α-WNT5A signalling cascade represents a potentially targetable pathway for drug development.