A structural perspective of human RNA polymerase III

RNA polymerase III transcription and cancer: A tale of two RPC7 subunits

RNA polymerase III composition is shaped by the mutually exclusive incorporation of two paralogous subunits, RPC7α and RPC7β, encoded by genes POLR3G and POLR3GL in vertebrates. The expression of POLR3G and POLR3GL is spatiotemporally regulated during development, and multiple reports point to RPC7α-enhanced Pol III activity patterns, indicating that Pol III identity may underly dynamic Pol III transcription patterns observed in higher eukaryotes. In cancer, upregulation of POLR3G, but not POLR3GL, is associated with poor survival outcomes among patients, suggesting differences between RPC7α and RPC7β further influence disease progression and may translate into future biomarkers and therapeutic strategies. Here, we outline our current understanding of Pol III identity and transcription and reexamine the distinct protein characteristics of Pol III subunits RPC7α and RPC7β. Drawing on both structural and genomic studies, we discuss differences between RPC7α and RPC7β and the potential mechanisms by which Pol III identity may establish differential activities during development and disease.

iPro-WAEL: a comprehensive and robust framework for identifying promoters in multiple species

Promoters are consensus DNA sequences located near the transcription start sites and they play an important role in transcription initiation. Due to their importance in biological processes, the identification of promoters is significantly important for characterizing the expression of the genes. Numerous computational methods have been proposed to predict promoters. However, it is difficult for these methods to achieve satisfactory performance in multiple species. In this study, we propose a novel weighted average ensemble learning model, termed iPro-WAEL, for identifying promoters in multiple species, including Human, Mouse, E.coli, Arabidopsis, B.amyloliquefaciens, B.subtilis and R.capsulatus. Extensive benchmarking experiments illustrate that iPro-WAEL has optimal performance and is superior to the current methods in promoter prediction. The experimental results also demonstrate a satisfactory prediction ability of iPro-WAEL on cross-cell lines, promoters annotated by other methods and distinguishing between promoters and enhancers. Moreover, we identify the most important transcription factor binding site (TFBS) motif in promoter regions to facilitate the study of identifying important motifs in the promoter regions. The source code of iPro-WAEL is freely available at https://github.com/HaoWuLab-Bioinformatics/iPro-WAEL.