DNA-PK-Dependent RPA2 Hyperphosphorylation Facilitates DNA Repair and Suppresses Sister Chromatid Exchange

Hyperphosphorylation of RPA2 at serine 4 and serine 8 (S4, S8) has been used as a marker for activation of the DNA damage response. What types of DNA lesions cause RPA2 hyperphosphorylation, which kinase(s) are responsible for them, and what is the biological outcome of these phosphorylations, however, have not been fully investigated. In this study we demonstrate that RPA2 hyperphosphorylation occurs primarily in response to genotoxic stresses that cause high levels of DNA double-strand breaks (DSBs) and that the DNA-dependent protein kinase complex (DNA-PK) is responsible for the modifications in vivo. Alteration of S4, S8 of RPA2 to alanines, which prevent phosphorylations at these sites, caused increased mitotic entry with concomitant increases in RAD51 foci and homologous recombination. Taken together, our results demonstrate that RPA2 hyperphosphorylation by DNA-PK in response to DSBs blocks unscheduled homologous recombination and delays mitotic entry. This pathway thus permits cells to repair DNA damage properly and increase cell viability

The hnRNP family: insights into their role in health and disease

Heterogeneous nuclear ribonucleoproteins (hnRNPs) represent a large family of RNA-binding proteins (RBPs) that contribute to multiple aspects of nucleic acid metabolism including alternative splicing, mRNA stabilization, and transcriptional and translational regulation. Many hnRNPs share general features, but differ in domain composition and functional properties. This review will discuss the current knowledge about the different hnRNP family members, focusing on their structural and functional divergence. Additionally, we will highlight their involvement in neurodegenerative diseases and cancer, and the potential to develop RNA-based therapies

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given