Cancer CRC: A Comprehensive Cancer Core Transcriptional Regulatory Circuit Resource and Analysis Platform.

A core transcriptional regulatory circuit (CRC) is a group of interconnected auto-regulating transcription factors (TFs) that form loops and can be identified by super-enhancers (SEs). Studies have indicated that CRCs play an important role in defining cellular identity and determining cellular fate. Additionally, core TFs in CRCs are regulators of cell-type-specific transcriptional regulation. However, a global view of CRC properties across various cancer types has not been generated. Thus, we integrated paired cancer ATAC-seq and H3K27ac ChIP-seq data for specific cell lines to develop the Cancer CRC (http://bio.liclab.net/Cancer_crc/index.html). This platform documented 94,108 cancer CRCs, including 325 core TFs. The cancer CRC also provided the "SE active core TFs analysis" and "TF enrichment analysis" tools to identify potentially key TFs in cancer. In addition, we performed a comprehensive analysis of core TFs in various cancer types to reveal conserved and cancer-specific TFs.

A review of clay based photocatalysts: Role of phyllosilicate mineral in interfacial assembly, microstructure control and performance regulation.

Over the past decades, inspired by the outstanding properties of clay minerals such as abundance, low-cost, environmental benignity, high stability, and regularly arranged silica-alumina framework, researchers put much efforts on the interface assembly and surface modification of natural minerals with bare photocatalysts, i.e. TiO2, g-C3N4, ZnO, MoS2, etc. The clay-based hybrid photocatalysts have resulted in a rich database for their tailor-designed microstructures, characterizations, and environmental-related applications. Therefore, in this study, we took a brief introduction of three representative minerals, i.e. kaolinite, montmorillonite and rectorite, and discussed their basic merits in photocatalysis applications. After that, we summarized the recent advances in construction of stable visible-light driven photocatalysts based on these minerals. The structure-activity relationships between the properties of clay types, pore structure, distribution/dispersion and light absorption, carrier separation efficiency as well as redox performance were illustrated in detail. Such representative information would provide theoretical basis and scientific support for the application of clay based photocatalysts. Finally, we pointed out the major challenges and future directions at the end of this review. Undoubtedly, control and preparation of novel photocatalysts based on clays will continue to witness many breakthroughs in the arena of solar-driven technologies.

MPINet: metabolite pathway identification via coupling of global metabolite network structure and metabolomic profile.

High-throughput metabolomics technology, such as gas chromatography mass spectrometry, allows the analysis of hundreds of metabolites. Understanding that these metabolites dominate the study condition from biological pathway perspective is still a significant challenge. Pathway identification is an invaluable aid to address this issue and, thus, is urgently needed. In this study, we developed a network-based metabolite pathway identification method, MPINet, which considers the global importance of metabolites and the unique character of metabolomic profile. Through integrating the global metabolite functional network structure and the character of metabolomic profile, MPINet provides a more accurate metabolomic pathway analysis. This integrative strategy simultaneously captures the global nonequivalence of metabolites in a pathway and the bias from metabolomic experimental technology. We then applied MPINet to four different types of metabolite datasets. In the analysis of metastatic prostate cancer dataset, we demonstrated the effectiveness of MPINet. With the analysis of the two type 2 diabetes datasets, we show that MPINet has the potentiality for identifying novel pathways related with disease and is reliable for analyzing metabolomic data. Finally, we extensively applied MPINet to identify drug sensitivity related pathways. These results suggest MPINet's effectiveness and reliability for analyzing metabolomic data across multiple different application fields.