Nucleostemin deletion reveals an essential mechanism that maintains the genomic stability of stem and progenitor cells

Stem and progenitor cells maintain a robust DNA replication program during the tissue expansion phase of embryogenesis. The unique mechanism that protects them from the increased risk of replication-induced DNA damage, and hence permits self-renewal, remains unclear. To determine whether the genome integrity of stem/progenitor cells is safeguarded by mechanisms involving molecules beyond the core DNA repair machinery, we created a nucleostemin (a stem and cancer cell-enriched protein) conditional-null allele and showed that neural-specific knockout of nucleostemin predisposes embryos to spontaneous DNA damage that leads to severe brain defects in vivo. In cultured neural stem cells, depletion of nucleostemin triggers replication-dependent DNA damage and perturbs self-renewal, whereas overexpression of nucleostemin shows a protective effect against hydroxyurea-induced DNA damage. Mechanistic studies performed in mouse embryonic fibroblast cells showed that loss of nucleostemin triggers DNA damage and growth arrest independently of the p53 status or rRNA synthesis. Instead, nucleostemin is directly recruited to DNA damage sites and regulates the recruitment of the core repair protein, RAD51, to hydroxyurea-induced foci. This work establishes the primary function of nucleostemin in maintaining the genomic stability of actively dividing stem/progenitor cells by promoting the recruitment of RAD51 to stalled replication-induced DNA damage foci

Adaption of Seasonal H1N1 Influenza Virus in Mice

The experimental infection of a mouse lung with influenza A virus has proven to be an invaluable model for studying the mechanisms of viral adaptation and virulence. The mouse adaption of human influenza A virus can result in mutations in the HA and other proteins, which is associated with increased virulence in mouse lungs. In this study, a mouse-adapted seasonal H1N1 virus was obtained through serial lung-to-lung passages and had significantly increased virulence and pathogenicity in mice. Genetic analysis indicated that the increased virulence of the mouse-adapted virus was attributed to incremental acquisition of three mutations in the HA protein (T89I, N125T, and D221G). However, the mouse adaption of influenza A virus did not change the specificity and affinity of receptor binding and the pH-dependent membrane fusion of HA, as well as the in vitro replication in MDCK cells. Notably, infection with the mouse adapted virus induced severe lymphopenia and modulated cytokine and chemokine responses in mice. Apparently, mouse adaption of human influenza A virus may change the ability to replicate in mouse lungs, which induces strong immune responses and inflammation in mice. Therefore, our findings may provide new insights into understanding the mechanisms underlying the mouse adaption and pathogenicity of highly virulent influenza viruses

Genomic Polymorphism of the Pandemic A (H1N1) Influenza Viruses Correlates with Viral Replication, Virulence, and Pathogenicity In Vitro and In Vivo

The novel pandemic A (H1N1) virus was first identified in Mexico in April 2009 and quickly spread worldwide. Like all influenzas, the H1N1 strain-specific properties of replication, virulence, and pathogenicity are a result of the particular genomic sequence and concerted expression of multiple genes. Thus, specific mutations may support increased virulence and may be useful as biomarkers of potential threat to human health. We performed comparative genomic analysis of ten strains of the 2009 pandemic A (H1N1) influenza viruses to determine whether genotypes associated with clinical phenotypes, which ranged from mild to severe illness and up to lethal. Virus replication capacity was tested for each strain in vitro using cultured epithelial cells, while virulence and pathogenicity were investigated in vivo using the BALB/c mouse model. The results indicated that A/Sichuan/1/2009 strain had significantly higher replication ability and virulence than the other strains, and five unique non-synonymous mutations were identified in important gene-encoding sequences. These mutations led to amino acid substitutions in HA (L32I), PA (A343T), PB1 (K353R and T566A), and PB2 (T471M), and may be critical molecular determinants for replication, virulence, and pathogenicity. Our results suggested that the replication capacity in vitro and virulence in vivo of the 2009 pandemic A (H1N1) viruses were not associated with the clinical phenotypes. This study offers new insights into the transmission and evolution of the 2009 pandemic A (H1N1) virus

Animal Models for Tuberculosis in Translational and Precision Medicine

Tuberculosis (TB) is a health threat to the global population. Anti-TB drugs and vaccines are key approaches for TB prevention and control. TB animal models are basic tools for developing biomarkers of diagnosis, drugs for therapy, vaccines for prevention and researching pathogenic mechanisms for identification of targets; thus, they serve as the cornerstone of comparative medicine, translational medicine, and precision medicine. In this review, we discuss the current use of TB animal models and their problems, as well as offering perspectives on the future of these models

Auto-Weighted Structured Graph-Based Regression Method for Heterogeneous Change Detection

Change detection using heterogeneous remote sensing images is an increasingly interesting and very challenging topic. To make the heterogeneous images comparable, some graph-based methods have been proposed, which first construct a graph for the image to capture the structure information and then use the graph to obtain the structural changes between images. Nonetheless, previous graph-based change detection approaches are insufficient in representing and exploiting the image structure. To address these issues, in this paper, we propose an auto-weighted structured graph (AWSG)-based regression method for heterogeneous change detection, which mainly consists of two processes: learning the AWSG to capture the image structure and using the AWSG to perform structure regression to detect changes. In the graph learning process, a self-conducted weighting strategy is employed to make the graph more robust, and the local and global structure information are combined to make the graph more informative. In the structure regression process, we transform one image to the domain of the other image by using the learned AWSG, where the high-order neighbor information hidden in the graph is exploited to obtain a better regression image and change image. Experimental results and comparisons on four real datasets with seven state-of-the-art methods demonstrate the effectiveness of the proposed approach

Auto-Weighted Structured Graph-Based Regression Method for Heterogeneous Change Detection

Change detection using heterogeneous remote sensing images is an increasingly interesting and very challenging topic. To make the heterogeneous images comparable, some graph-based methods have been proposed, which first construct a graph for the image to capture the structure information and then use the graph to obtain the structural changes between images. Nonetheless, previous graph-based change detection approaches are insufficient in representing and exploiting the image structure. To address these issues, in this paper, we propose an auto-weighted structured graph (AWSG)-based regression method for heterogeneous change detection, which mainly consists of two processes: learning the AWSG to capture the image structure and using the AWSG to perform structure regression to detect changes. In the graph learning process, a self-conducted weighting strategy is employed to make the graph more robust, and the local and global structure information are combined to make the graph more informative. In the structure regression process, we transform one image to the domain of the other image by using the learned AWSG, where the high-order neighbor information hidden in the graph is exploited to obtain a better regression image and change image. Experimental results and comparisons on four real datasets with seven state-of-the-art methods demonstrate the effectiveness of the proposed approach