Anti-Histone H2A (aa 1-100) polyclonal antibody

RecQ-mediated genome instability protein 1 (RMI1) is an important component of the BLM-Topo III alpha-RMI1-RMI2 complex and plays a critical role in maintaining genome stability. However, the cellular functions of RMI1 in response to ionizing radiation (IR) are poorly understood. In this study, we found that RMI1 knockdown led to enhanced radiosensitivity and apoptosis after irradiation. To analyze the effect of RMI1 knockdown on the expression of circular RNAs (circRNAs), we performed high-throughput RNA sequencing on four groups of human embryonic kidney (HEK) 293T cells: control cells and RMI1 knockdown cells with or without IR exposure. A total of 179 and 160 differentially expressed circRNAs (DE-circRNAs) were identified under RMI1 knockdown without and with exposure to IR, respectively. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that these DE-circRNAs were involved in a variety of functions and signal pathways, including histone H3-K36 methylation, nuclear pore organization, mRNA destabilization, the mismatch repair pathway, and the apoptotic signaling pathway. Overall, our results indicate that RMI1 plays a crucial role in the response to IR and, more generally, that circRNAs are important in the regulatory mechanism of the radiation response.

Protein phosphorylation is one of the most important post-translational modifications, and many biological processes are related to phosphorylation, such as DNA repair, transcriptional regulation and signal transduction and, therefore, abnormal regulation of phosphorylation usually causes diseases. If we can accurately predict human phosphorylation sites, this could help to solve human diseases. Therefore, we developed a kinase-specific phosphorylation prediction system, GasPhos, and proposed a new feature selection approach, called Gas, based on the ant colony system and a genetic algorithm and used performance evaluation strategies focused on different kinases to choose the best learning model. Gas uses the mean decrease Gini index (MDGI) as a heuristic value for path selection and adopts binary transformation strategies and new state transition rules. GasPhos can predict phosphorylation sites for six kinases and showed better performance than other phosphorylation prediction tools. The disease-related phosphorylated proteins that were predicted with GasPhos are also discussed. Finally, Gas can be applied to other issues that require feature selection, which could help to improve prediction performance.