Chi-Ying F. Huang

miRTarBase: a database curates experimentally validated microRNA–target interactions

MicroRNAs (miRNAs), i.e. small non-coding RNA molecules (∼22 nt), can bind to one or more target sites on a gene transcript to negatively regulate protein expression, subsequently controlling many cellular mechanisms. A current and curated collection of miRNA–target interactions (MTIs) with experimental support is essential to thoroughly elucidating miRNA functions under different conditions and in different species. As a database, miRTarBase has accumulated more than 3500 MTIs by manually surveying pertinent literature after data mining of the text systematically to filter research articles related to functional studies of miRNAs. Generally, the collected MTIs are validated experimentally by reporter assays, western blot, or microarray experiments with overexpression or knockdown of miRNAs. miRTarBase curates 3576 experimentally verified MTIs between 657 miRNAs and 2297 target genes among 17 species. miRTarBase contains the largest amount of validated MTIs by comparing with other similar, previously developed databases. The MTIs collected in the miRTarBase can also provide a large amount of positive samples to develop computational methods capable of identifying miRNA–target interactions. miRTarBase is now available on http://miRTarBase.mbc.nctu.edu.tw/, and is updated frequently by continuously surveying research articles.

Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme

BackgroundThe development of microarrays permits us to monitor transcriptomes on a genome-wide scale. To validate microarray measurements, quantitative-real time-reverse transcription PCR (Q-RT-PCR) is one of the most robust and commonly used approaches. The new challenge in gene quantification analysis is how to explicitly incorporate statistical estimation in such studies. In the realm of statistical analysis, the various available methods of the probe level normalization for microarray analysis may result in distinctly different target selections and variation in the scores for the correlation between microarray and Q-RT-PCR. Moreover, it remains a major challenge to identify a proper internal control for Q-RT-PCR when confirming microarray measurements.ResultsSixty-six Affymetrix microarray slides using lung adenocarcinoma tissue RNAs were analyzed by a statistical re-sampling method in order to detect genes with minimal variation in gene expression. By this approach, we identified DDX5 as a novel internal control for Q-RT-PCR. Twenty-three genes, which were differentially expressed between adjacent normal and tumor samples, were selected and analyzed using 24 paired lung adenocarcinoma samples by Q-RT-PCR using two internal controls, DDX5 and GAPDH. The percentage correlation between Q-RT-PCR and microarray were 70% and 48% by using DDX5 and GAPDH as internal controls, respectively.ConclusionTogether, these quantification strategies for Q-RT-PCR data processing procedure, which focused on minimal variation, ought to significantly facilitate internal control evaluation and selection for Q-RT-PCR when corroborating microarray data.

A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae

MOTIVATION The explosion of microarray studies has promised to shed light on the temporal expression patterns of thousands of genes simultaneously. However, available methods are far from adequate in efficiently extracting useful information to aid in a greater understanding of transcriptional regulatory network. Biological systems have been modeled as dynamic systems for a long history, such as genetic networks and cell regulatory network. This study evaluated if the stochastic differential equation (SDE), which is prominent for modeling dynamic diffusion process originating from the irregular Brownian motion, can be applied in modeling the transcriptional regulatory network in Saccharomyces cerevisiae. RESULTS To model the time-continuous gene-expression datasets, a model of SDE is applied to depict irregular patterns. Our goal is to fit a generalized linear model by combining putative regulators to estimate the transcriptional pattern of a target gene. Goodness-of-fit is evaluated by log-likelihood and Akaike Information Criterion. Moreover, estimations of the contribution of regulators and inference of transcriptional pattern are implemented by statistical approaches. Our SDE model is basic but the test results agree well with the observed dynamic expression patterns. It implies that advanced SDE model might be perfectly suited to portray transcriptional regulatory networks. AVAILABILITY The R code is available on request. CONTACT cykao@csie.ntu.edu.tw SUPPLEMENTARY INFORMATION http://www.csie.ntu.edu.tw/~b89x035/yeast/

POINT: a database for the prediction of protein--protein interactions based on the orthologous interactome

One possible path towards understanding the biological function of a target protein is through the discovery of how it interfaces within protein-protein interaction networks. The goal of this study was to create a virtual protein-protein interaction model using the concepts of orthologous conservation (or interologs) to elucidate the interacting networks of a particular target protein. POINT (the prediction of interactome database) is a functional database for the prediction of the human protein-protein interactome based on available orthologous interactome datasets. POINT integrates several publicly accessible databases, with emphasis placed on the extraction of a large quantity of mouse, fruit fly, worm and yeast protein-protein interactions datasets from the Database of Interacting Proteins (DIP), followed by conversion of them into a predicted human interactome. In addition, protein-protein interactions require both temporal synchronicity and precise spatial proximity. POINT therefore also incorporates correlated mRNA expression clusters obtained from cell cycle microarray databases and subcellular localization from Gene Ontology to further pinpoint the likelihood of biological relevance of each predicted interacting sets of protein partners.

Identification of a novel cell cycle regulated gene, HURP, overexpressed in human hepatocellular carcinoma

An analytic strategy was followed to identify putative regulatory genes during the development of human hepatocellular carcinoma (HCC). This strategy employed a bioinformatics analysis that used a database search to identify genes, which are differentially expressed in human HCC and are also under cell cycle regulation. A novel cell cycle regulated gene (HURP) that is overexpressed in HCC was identified. Full-length cDNAs encoding the human and mouse HURP genes were isolated. They share 72 and 61% identity at the nucleotide level and amino-acid level, respectively. Endogenous levels of HURP mRNA were found to be tightly regulated during cell cycle progression as illustrated by its elevated expression in the G2/M phase of synchronized HeLa cells and in regenerating mouse liver after partial hepatectomy. Immunofluorescence studies revealed that hepatoma up-regulated protein (HURP) localizes to the spindle poles during mitosis. Overexpression of HURP in 293T cells resulted in an enhanced cell growth at low serum levels and at polyhema-based, anchorage-independent growth assay. Taken together, these results strongly suggest that HURP is a potential novel cell cycle regulator that may play a role in the carcinogenesis of human cancer cells.

Phosphorylation and stabilization of HURP by Aurora-A: Implication of HURP as a transforming target of Aurora-A

ABSTRACT Aurora-A, a mitotic serine/threonine kinase with oncogene characteristics, has recently drawn intense attention because of its association with the development of human cancers and its relationship with mitotic progression. Using the gene expression profiles of Aurora-A as a template to search for and compare transcriptome expression profiles in publicly accessible microarray data sets, we identified HURP (encodes hepatoma upregulated protein) as one of the best Aurora-A-correlated genes. Empirical validation indicates that HURP has several characteristics in common with Aurora-A. These two genes have similar expression patterns in hepatocellular carcinoma, liver regeneration after partial hepatectomy, and cell cycle progression and across a variety of tissues and cell lines. Moreover, Aurora-A phosphorylated HURP in vitro and in vivo. Ectopic expression of either the catalytically inactive form of Aurora-A or the HURP-4P mutant, in which the Aurora-A phosphorylation sites were replaced with Ala, resulted in HURP instability and complex disassembly. In addition, HURP-wild-type stable transfectants were capable of growing in low-serum environments whereas HURP-4P grew poorly under low-serum conditions and failed to proliferate. These studies together support the view that the ability to integrate evidence derived from microarray studies into biochemical analyses may ultimately augment our predictive power when analyzing the potential role of poorly characterized proteins. While this combined approach was simply an initial attempt to answer a range of complex biological questions, our findings do suggest that HURP is a potential oncogenic target of Aurora-A.

Simple, realistic models of complex biological processes: Positive feedback and bistability in a cell fate switch and a cell cycle oscillator

Here we review some of our work over the last decade on Xenopus oocyte maturation, a cell fate switch, and the Xenopus embryonic cell cycle, a highly dynamical process. Our approach has been to start with wiring diagrams for the regulatory networks that underpin the processes; carry out quantitative experiments to describe the response functions for individual legs of the networks; and then construct simple analytical models based on chemical kinetic theory and the graphical rate‐balance formalism. These studies support the view that the all‐or‐none, irreversible nature of oocyte maturation arises from a saddle–node bifurcation in the regulatory system that drives the process, and that the clock‐like oscillations of the embryo are built upon a hysteretic switch with two saddle–node bifurcations. We believe that this type of reductionistic systems biology holds great promise for understanding complicated biochemical processes in simpler terms.

Trifluoperazine, an Antipsychotic Agent, Inhibits Cancer Stem Cell Growth and Overcomes Drug Resistance of Lung Cancer

RATIONALE Cancer stem cell (CSC) theory has drawn much attention, with evidence supporting the contribution of stem cells to tumor initiation, relapse, and therapy resistance. OBJECTIVES To screen drugs that target CSCs to improve the current treatment outcome and overcome drug resistance in patients with lung cancer. METHODS We used publicly available embryonic stem cell and CSC-associated gene signatures to query the Connectivity Map for potential drugs that can, at least in part, reverse the gene expression profile of CSCs. High scores were noted for several phenothiazine-like antipsychotic drugs, including trifluoperazine. We then treated lung CSCs with different EGFR mutation status with trifluoperazine to examine its anti-CSC properties. Lung CSCs resistant to epidermal growth factor receptor-tyrosine kinase inhibitor or cisplatin were treated with trifluoperazine plus gefitinib or trifluoperazine plus cisplatin. Animal models were used for in vivo validation of the anti-CSC effect and synergistic effect of trifluoperazine with gefitinib. MEASUREMENTS AND MAIN RESULTS We demonstrated that trifluoperazine inhibited CSC tumor spheroid formation and down-regulated the expression of CSC markers (CD44/CD133). Trifluoperazine inhibited Wnt/β-catenin signaling in gefitinib-resistant lung cancer spheroids. The combination of trifluoperazine with either gefitinib or cisplatin overcame drug resistance in lung CSCs. Trifluoperazine inhibited the tumor growth and enhanced the inhibitory activity of gefitinib in lung cancer metastatic and orthotopic CSC animal models. CONCLUSIONS Using in silico drug screening by Connectivity Map followed by empirical validations, we repurposed an existing phenothiazine-like antipsychotic drug, trifluoperazine, as a potential anti-CSC agent that could overcome epidermal growth factor receptor-tyrosine kinase inhibitor and chemotherapy resistance.

VEGFA upregulates FLJ10540 and modulates migration and invasion of lung cancer via PI3K/AKT pathway

Background Lung adenocarcinoma is the leading cause of cancer-related deaths among both men and women in the world. Despite recent advances in diagnosis and treatment, the mortality rates with an overall 5-year survival of only 15%. This high mortality is probably attributable to early metastasis. Although several well-known markers correlated with poor/metastasis prognosis in lung adenocarcinoma patients by immunohistochemistry was reported, the molecular mechanisms of lung adenocarcinoma development are still not clear. To explore novel molecular markers and their signaling pathways will be crucial for aiding in treatment of lung adenocarcinoma patients. Methodology/Principal Findings To identify novel lung adenocarcinoma-associated /metastasis genes and to clarify the underlying molecular mechanisms of these targets in lung cancer progression, we created a bioinformatics scheme consisting of integrating three gene expression profile datasets, including pairwise lung adenocarcinoma, secondary metastatic tumors vs. benign tumors, and a series of invasive cell lines. Among the novel targets identified, FLJ10540 was overexpressed in lung cancer tissues and is associated with cell migration and invasion. Furthermore, we employed two co-expression strategies to identify in which pathway FLJ10540 was involved. Lung adenocarcinoma array profiles and tissue microarray IHC staining data showed that FLJ10540 and VEGF-A, as well as FLJ10540 and phospho-AKT exhibit positive correlations, respectively. Stimulation of lung cancer cells with VEGF-A results in an increase in FLJ10540 protein expression and enhances complex formation with PI3K. Treatment with VEGFR2 and PI3K inhibitors affects cell migration and invasion by activating the PI3K/AKT pathway. Moreover, knockdown of FLJ10540 destabilizes formation of the P110-α/P85-α-(PI3K) complex, further supporting the participation of FLJ10540 in the VEGF-A/PI3K/AKT pathway. Conclusions/Significance This finding set the stage for further testing of FLJ10540 as a new therapeutic target for treating lung cancer and may contribute to the development of new therapeutic strategies that are able to block the PI3K/AKT pathway in lung cancer cells.

PhosphoPOINT: a comprehensive human kinase interactome and phospho-protein database

MOTIVATION To fully understand how a protein kinase regulates biological processes, it is imperative to first identify its substrate(s) and interacting protein(s). However, of the 518 known human serine/threonine/tyrosine kinases, 35% of these have known substrates, while 14% of the kinases have identified substrate recognition motifs. In contrast, 85% of the kinases have protein-protein interaction (PPI) datasets, raising the possibility that we might reveal potential kinase-substrate pairs from these PPIs. RESULTS PhosphoPOINT, a comprehensive human kinase interactome and phospho-protein database, is a collection of 4195 phospho-proteins with a total of 15 738 phosphorylation sites. PhosphoPOINT annotates the interactions among kinases, with their down-stream substrates and with interacting (phospho)-proteins to modulate the kinase-substrate pairs. PhosphoPOINT implements various gene expression profiles and Gene Ontology cellular component information to evaluate each kinase and their interacting (phospho)-proteins/substrates. Integration of cSNPs that cause amino acids change with the proteins with the phosphoprotein dataset reveals that 64 phosphorylation sites result in a disease phenotypes when changed; the linked phenotypes include schizophrenia and hypertension. PhosphoPOINT also provides a search function for all phospho-peptides using about 300 known kinase/phosphatase substrate/binding motifs. Altogether, PhosphoPOINT provides robust annotation for kinases, their downstream substrates and their interaction (phospho)-proteins and this should accelerate the functional characterization of kinomemediated signaling. AVAILABILITY PhosphoPOINT can be freely accessed in http://kinase. bioinformatics.tw/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.