An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics’ sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the ‘Cancer’ and ‘Gene’ sections. The ‘Survival’ panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, ‘Survival Analysis’ in ‘Customized-analysis,’ allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics’ sophisticated information, and also constructed a Summary panel in the ‘Cancer’ and ‘Gene’ sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.

Background: Hypoxia induces the epithelial-mesenchymal transition, EMT, to promote cancer metastasis. In addition to transcriptional regulation mediated by hypoxia-inducible factors, HIFs, other epigenetic mechanisms of gene regulation, such as histone modifications and DNA methylation, are utilized under hypoxia. However, whether DNA demethylation mediated by TET1, a DNA dioxygenase converting 5-methylcytosine, 5mC, into 5-hydroxymethylcytosine, 5hmC, plays a role in hypoxia-induced EMT is largely unknown. Results: We show that TET1 regulates hypoxia-responsive gene expression. Hypoxia/HIF-2α regulates the expression of TET1. Knockdown of TET1 mitigates hypoxia-induced EMT. RNA sequencing and 5hmC sequencing identified the set of TET1-regulated genes. Cholesterol metabolic process genes are among the genes that showed high prevalence and statistical significance. We characterize one of the genes, INSIG1 (insulin induced gene 1), to confirm its expression and the 5hmC levels in its promoter. Knockdown of INSIG1 also mitigates hypoxia-induced EMT. Finally, TET1 is shown to be a transcriptional co-activator that interacts with HIF-1α and HIF-2α to enhance their transactivation activity independent of its enzymatic activity. TET1 acts as a co-activator to further enhance the expression of INSIG1 together with HIF-2α. We define the domain in HIF-1α that interacts with TET1 and map the domain in TET1 that confers transactivation to a 200 amino acid region that contains a CXXC domain. The TET1 catalytically inactive mutant is capable of rescuing hypoxia-induced EMT in TET1 knockdown cells. Conclusions: These findings demonstrate that TET1 serves as a transcription co-activator to regulate hypoxia-responsive gene expression and EMT, in addition to its role in demethylating 5mC.

We previously presented DriverDB, a database that incorporates ∼6000 cases of exome-seq data, in addition to annotation databases and published bioinformatics algorithms dedicated to driver gene/mutation identification. The database provides two points of view, ‘Cancer’ and ‘Gene’, to help researchers visualize the relationships between cancers and driver genes/mutations. In the updated DriverDBv2 database (http://ngs.ym.edu.tw/driverdb) presented herein, we incorporated >9500 cancer-related RNA-seq datasets and >7000 more exome-seq datasets from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and published papers. Seven additional computational algorithms (meaning that the updated database contains 15 in total), which were developed for driver gene identification, are incorporated into our analysis pipeline, and the results are provided in the ‘Cancer’ section. Furthermore, there are two main new features, ‘Expression’ and ‘Hotspot’, in the ‘Gene’ section. ‘Expression’ displays two expression profiles of a gene in terms of sample types and mutation types, respectively. ‘Hotspot’ indicates the hotspot mutation regions of a gene according to the results provided by four bioinformatics tools. A new function, ‘Gene Set’, allows users to investigate the relationships among mutations, expression levels and clinical data for a set of genes, a specific dataset and clinical features.

Inflammation has been found to be associated with many neurodegenerative diseases, including Parkinson’s and dementia. Attenuation of microglia-induced inflammation is a strategy that impedes the progression of neurodegenerative diseases. We used lipopolysaccharide (LPS) to simulate murine microglia cells (BV2 cells) as an experimental model to mimic the inflammatory environment in the brain. In addition, we examined the anti-inflammatory ability of corylin, a main compound isolated from Psoralea corylifolia L. that is commonly used in Chinese herbal medicine. The production of nitric oxide (NO) by LPS-activated BV2 cells was measured using Griess reaction. The secretion of proinflammatory cytokines including tumor necrosis factor (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6) by LPS-activated BV2 cells was analyzed using enzyme-linked immunosorbent assay (ELISA). The expression of inducible NO synthase (iNOS), cyclooxygenase-2 (COX-2), nucleotide-binding oligomerization domain-like receptor containing pyrin domain 3 (NLRP3), apoptosis-associated speck-like protein containing a caspase-activation and recruitment domain (ASC), caspase-1, IL-1β and mitogen-activated protein kinases (MAPKs) in LPS-activated BV2 cells was examined by Western blot. Our experimental results demonstrated that corylin suppressed the production of NO and proinflammatory cytokines by LPS-activated BV2 cells. In addition, corylin inhibited the expression of iNOS and COX-2, attenuated the phosphorylation of ERK, JNK and p38, decreased the expression of NLRP3 and ASC, and repressed the activation of caspase-1 and IL-1β by LPS-activated BV2 cells. Our results indicate the anti-inflammatory effects of corylin acted through attenuating LPS-induced inflammation and inhibiting the activation of NLRP3 inflammasome in LPS-activated BV2 cells. These results suggest that corylin might have potential in treating brain inflammation and attenuating the progression of neurodegeneration diseases.

Current clinical challenges of prostate cancer management are to restrict tumor growth and prohibit metastasis. AICAR (5-aminoimidazole-4-carbox-amide-1-β-d-ribofuranoside), an AMP-activated protein kinase (AMPK) agonist, has demonstrated antitumor activities for several types of cancers. However, the activity of AICAR on the cell growth and metastasis of prostate cancer has not been extensively studied. Herein we examine the effects of AICAR on the cell growth and metastasis of prostate cancer cells. Cell growth was performed by MTT assay and soft agar assay; cell apoptosis was examined by Annexin V/propidium iodide (PI) staining and poly ADP ribose polymerase (PARP) cleavage western blot, while cell migration and invasion were evaluated by wound-healing assay and transwell assay respectively. Epithelial–mesenchymal transition (EMT)-related protein expression and AMPK/mTOR-dependent signaling axis were analyzed by western blot. In addition, we also tested the effect of AICAR on the chemosensitivity to docetaxel using MTT assay. Our results indicated that AICAR inhibits cell growth in prostate cancer cells, but not in non-cancerous prostate cells. In addition, our results demonstrated that AICAR induces apoptosis, attenuates transforming growth factor (TGF)-β-induced cell migration, invasion and EMT-related protein expression, and enhances the chemosensitivity to docetaxel in prostate cancer cells through regulating the AMPK/mTOR-dependent pathway. These findings support AICAR as a potential therapeutic agent for the treatment of prostate cancer.

With the continuing rise of lipidomic studies, there is an urgent need for a useful and comprehensive tool to facilitate lipidomic data analysis. The most important features making lipids different from general metabolites are their various characteristics, including their lipid classes, double bonds, chain lengths, etc. Based on these characteristics, lipid species can be classified into different categories and, more interestingly, exert specific biological functions in a group. In an effort to simplify lipidomic analysis workflows and enhance the exploration of lipid characteristics, we have developed a highly flexible and user-friendly web server called LipidSig. It consists of five sections, namely, Profiling, Differential Expression, Correlation, Network and Machine Learning, and evaluates lipid effects on cellular or disease phenotypes. One of the specialties of LipidSig is the conversion between lipid species and characteristics according to a user-defined characteristics table. This function allows for efficient data mining for both individual lipids and subgroups of characteristics. To expand the server's practical utility, we also provide analyses focusing on fatty acid properties and multiple characteristics. In summary, LipidSig is expected to help users identify significant lipid-related features and to advance the field of lipid biology. The LipidSig webserver is freely available at http://chenglab.cmu.edu.tw/lipidsig