Yi-An Chen

TargetMine, an Integrated Data Warehouse for Candidate Gene Prioritisation and Target Discovery

Prioritising candidate genes for further experimental characterisation is a non-trivial challenge in drug discovery and biomedical research in general. An integrated approach that combines results from multiple data types is best suited for optimal target selection. We developed TargetMine, a data warehouse for efficient target prioritisation. TargetMine utilises the InterMine framework, with new data models such as protein-DNA interactions integrated in a novel way. It enables complicated searches that are difficult to perform with existing tools and it also offers integration of custom annotations and in-house experimental data. We proposed an objective protocol for target prioritisation using TargetMine and set up a benchmarking procedure to evaluate its performance. The results show that the protocol can identify known disease-associated genes with high precision and coverage. A demonstration version of TargetMine is available at http://targetmine.nibio.go.jp/.

Targeting BIG3–PHB2 interaction to overcome tamoxifen resistance in breast cancer cells

The acquisition of endocrine resistance is a common obstacle in endocrine therapy of patients with oestrogen receptor-α (ERα)-positive breast tumours. We previously demonstrated that the BIG3–PHB2 complex has a crucial role in the modulation of oestrogen/ERα signalling in breast cancer cells. Here we report a cell-permeable peptide inhibitor, called ERAP, that regulates multiple ERα-signalling pathways associated with tamoxifen resistance in breast cancer cells by inhibiting the interaction between BIG3 and PHB2. Intrinsic PHB2 released from BIG3 by ERAP directly binds to both nuclear- and membrane-associated ERα, which leads to the inhibition of multiple ERα-signalling pathways, including genomic and non-genomic ERα activation and ERα phosphorylation, and the growth of ERα-positive breast cancer cells both in vitro and in vivo. More importantly, ERAP treatment suppresses tamoxifen resistance and enhances tamoxifen responsiveness in ERα-positive breast cancer cells. These findings suggest inhibiting the interaction between BIG3 and PHB2 may be a new therapeutic strategy for the treatment of luminal-type breast cancer.

Inhibitory Roles of Signal Transducer and Activator of Transcription 3 in Antitumor Immunity during Carcinogen-Induced Lung Tumorigenesis

Stat3 mediates a complex spectrum of cellular responses, including inflammation, cell proliferation, and apoptosis. Although evidence exists in support of a positive role for Stat3 in cancer, its role has remained somewhat controversial because of insufficient study of how its genetic deletion may affect carcinogenesis in various tissues. In this study, we show using epithelium-specific knockout mice (Stat3(Δ/Δ)) that Stat3 blunts rather than supports antitumor immunity in carcinogen-induced lung tumorigenesis. Although Stat3(Δ/Δ) mice did not show any lung defects in terms of proliferation, apoptosis, or angiogenesis, they exhibited reduced urethane-induced tumorigenesis and increased antitumor inflammation and natural killer (NK) cell immunity. Comparative microarray analysis revealed an increase in Stat3(Δ/Δ) tumors in proinflammatory chemokine production and a decrease in MHC class I antigen expression associated with NK cell recognition. Consistent with these findings, human non-small cell lung cancer (NSCLC) cells in which Stat3 was silenced displayed an enhancement of proinflammatory chemokine production, reduced expression of MHC class I antigen, and increased susceptibility to NK cell-mediated cytotoxicity. In addition, supernatants from Stat3-silenced NSCLC cells promoted monocyte migration. Collectively, our findings argue that Stat3 exerts an inhibitory effect on antitumor NK cell immunity in the setting of carcinogen-induced tumorigenesis.

An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework

Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format. Database URL: http://targetmine.mizuguchilab.org

Proteomic Analysis of Hepatitis C Virus (HCV) Core Protein Transfection and Host Regulator PA28γ Knockout in HCV Pathogenesis: A Network-Based Study

Hepatitis C virus (HCV) causes chronic liver disease worldwide. HCV Core protein (Core) forms the viral capsid and is crucial for HCV pathogenesis and HCV-induced hepatocellular carcinoma, through its interaction with the host factor proteasome activator PA28γ. Here, using BD-PowerBlot high-throughput Western array, we attempt to further investigate HCV pathogenesis by comparing the protein levels in liver samples from Core-transgenic mice with or without the knockout of PA28γ expression (abbreviated PA28γ(-/-)CoreTG and CoreTG, respectively) against the wild-type (WT). The differentially expressed proteins integrated into the human interactome were shown to participate in compact and well-connected cellular networks. Functional analysis of the interaction networks using a newly developed data warehouse system highlighted cellular pathways associated with vesicular transport, immune system, cellular adhesion, and cell growth and death among others that were prominently influenced by Core and PA28γ in HCV infection. Follow-up assays with in vitro HCV cell culture systems validated VTI1A, a vesicular transport associated factor, which was upregulated in CoreTG but not in PA28γ(-/-)CoreTG, as a novel regulator of HCV release but not replication. Our analysis provided novel insights into the Core-PA28γ interplay in HCV pathogenesis and identified potential targets for better anti-HCV therapy and potentially novel biomarkers of HCV infection.

Integrated pathway clusters with coherent biological themes for target prioritisation.

Prioritising candidate genes for further experimental characterisation is an essential, yet challenging task in biomedical research. One way of achieving this goal is to identify specific biological themes that are enriched within the gene set of interest to obtain insights into the biological phenomena under study. Biological pathway data have been particularly useful in identifying functional associations of genes and/or gene sets. However, biological pathway information as compiled in varied repositories often differs in scope and content, preventing a more effective and comprehensive characterisation of gene sets. Here we describe a new approach to constructing biologically coherent gene sets from pathway data in major public repositories and employing them for functional analysis of large gene sets. We first revealed significant overlaps in gene content between different pathways and then defined a clustering method based on the shared gene content and the similarity of gene overlap patterns. We established the biological relevance of the constructed pathway clusters using independent quantitative measures and we finally demonstrated the effectiveness of the constructed pathway clusters in comparative functional enrichment analysis of gene sets associated with diverse human diseases gathered from the literature. The pathway clusters and gene mappings have been integrated into the TargetMine data warehouse and are likely to provide a concise, manageable and biologically relevant means of functional analysis of gene sets and to facilitate candidate gene prioritisation.

BIG3 Inhibits the Estrogen-Dependent Nuclear Translocation of PHB2 via Multiple Karyopherin-Alpha Proteins in Breast Cancer Cells

We recently reported that brefeldin A-inhibited guanine nucleotide-exchange protein 3 (BIG3) binds Prohibitin 2 (PHB2) in cytoplasm, thereby causing a loss of function of the PHB2 tumor suppressor in the nuclei of breast cancer cells. However, little is known regarding the mechanism by which BIG3 inhibits the nuclear translocation of PHB2 into breast cancer cells. Here, we report that BIG3 blocks the estrogen (E2)-dependent nuclear import of PHB2 via the karyopherin alpha (KPNA) family in breast cancer cells. We found that overexpressed PHB2 interacted with KPNA1, KPNA5, and KPNA6, thereby leading to the E2-dependent translocation of PHB2 into the nuclei of breast cancer cells. More importantly, knockdown of each endogenous KPNA by siRNA caused a significant inhibition of E2-dependent translocation of PHB2 in BIG3-depleted breast cancer cells, thereby enhancing activation of estrogen receptor alpha (ERα). These data indicated that BIG3 may block the KPNAs (KPNA1, KPNA5, and KPNA6) binding region(s) of PHB2, thereby leading to inhibition of KPNAs-mediated PHB2 nuclear translocation in the presence of E2 in breast cancer cells. Understanding this regulation of PHB2 nuclear import may provide therapeutic strategies for controlling E2/ERα signals in breast cancer cells.

Brefeldin A-inhibited guanine nucleotide-exchange protein 3 (BIG3) is predicted to interact with its partner through an ARM-type α-helical structure

BackgroundBrefeldin A-inhibited guanine nucleotide-exchange protein 3 (BIG3) has been identified recently as a novel regulator of estrogen signalling in breast cancer cells. Despite being a potential target for new breast cancer treatment, its amino acid sequence suggests no association with any well-characterized protein family and provides little clues as to its molecular function. In this paper, we predicted the structure, function and interactions of BIG3 using a range of bioinformatic tools.ResultsHomology search results showed that BIG3 had distinct features from its paralogues, BIG1 and BIG2, with a unique region between the two shared domains, Sec7 and DUF1981. Although BIG3 contains Sec7 domain, the lack of the conserved motif and the critical glutamate residue suggested no potential guaninyl-exchange factor (GEF) activity. Fold recognition tools predicted BIG3 to adopt an α-helical repeat structure similar to that of the armadillo (ARM) family. Using state-of-the-art methods, we predicted interaction sites between BIG3 and its partner PHB2.ConclusionsThe combined results of the structure and interaction prediction led to a novel hypothesis that one of the predicted helices of BIG3 might play an important role in binding to PHB2 and thereby preventing its translocation to the nucleus. This hypothesis has been subsequently verified experimentally.

A-kinase anchoring protein BIG3 coordinates oestrogen signalling in breast cancer cells

Approximately 70% of breast cancer cells express oestrogen receptor alpha (ERα). Previous studies have shown that the Brefeldin A-inhibited guanine nucleotide-exchange protein 3-prohibitin 2 (BIG3-PHB2) complex has a crucial role in these cells. However, it remains unclear how BIG3 regulates the suppressive activity of PHB2. Here we demonstrate that BIG3 functions as an A-kinase anchoring protein that binds protein kinase A (PKA) and the α isoform of the catalytic subunit of protein phosphatase 1 (PP1Cα), thereby dephosphorylating and inactivating PHB2. E2-induced PKA-mediated phosphorylation of BIG3-S305 and -S1208 serves to enhance PP1Cα activity, resulting in E2/ERα signalling activation via PHB2 inactivation due to PHB2-S39 dephosphorylation. Furthermore, an analysis of independent cohorts of ERα-positive breast cancers patients reveal that both BIG3 overexpression and PHB2-S39 dephosphorylation are strongly associated with poor prognosis. This is the first demonstration of the mechanism of E2/ERα signalling activation via the BIG3-PKA-PP1Cα tri-complex in breast cancer cells.

Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism

Abstract DNA-binding proteins (DBPs) perform diverse biological functions ranging from transcription to pathogen sensing. Machine learning methods can not only identify DBPs de novo but also provide insights into their DNA-recognition dynamics. However, it remains unclear whether available methods that can accurately predict DNA-binding sites in known DBPs can also identify novel DBPs. Moreover, sequence information is blind to the cellular- and disease-specific contexts of DBP activities, whereas the under-utilized knowledge from public gene expression data offers great promise. To address these issues, we have developed novel methods for predicting DBPs by integrating sequence and gene expression-derived features and applied them to explore human, mouse and Arabidopsis proteomes. While our sequence-based models outperformed the gene expression-based ones, some proteins with weaker DBP-like sequence features were correctly predicted by gene expression-based features, suggesting that these proteins acquire a tangible DBP functionality in a conducive gene expression environment. Analysis of motif enrichment among the co-expressed genes of top 100 candidates DBPs from hitherto unannotated genes provides further avenues to explore their functional associations.