Protein Tpr is required for establishing nuclear pore-associated zones of heterochromatin exclusion

Amassments of heterochromatin in somatic cells occur in close contact with the nuclear envelope (NE) but are gapped by channel- and cone-like zones that appear largely free of heterochromatin and associated with the nuclear pore complexes (NPCs). To identify proteins involved in forming such heterochromatin exclusion zones (HEZs), we used a cell culture model in which chromatin condensation induced by poliovirus (PV) infection revealed HEZs resembling those in normal tissue cells. HEZ occurrence depended on the NPC-associated protein Tpr and its large coiled coil-forming domain. RNAi-mediated loss of Tpr allowed condensing chromatin to occur all along the NE's nuclear surface, resulting in HEZs no longer being established and NPCs covered by heterochromatin. These results assign a central function to Tpr as a determinant of perinuclear organization, with a direct role in forming a morphologically distinct nuclear sub-compartment and delimiting heterochromatin distribution

Molecular mechanistic associations of human diseases

<p>Abstract</p> <p>Background</p> <p>The study of relationships between human diseases provides new possibilities for biomedical research. Recent achievements on human genetic diseases have stimulated interest to derive methods to identify disease associations in order to gain further insight into the network of human diseases and to predict disease genes.</p> <p>Results</p> <p>Using about 10000 manually collected causal disease/gene associations, we developed a statistical approach to infer meaningful associations between human morbidities. The derived method clustered cardiometabolic and endocrine disorders, immune system-related diseases, solid tissue neoplasms and neurodegenerative pathologies into prominent disease groups. Analysis of biological functions confirmed characteristic features of corresponding disease clusters. Inference of disease associations was further employed as a starting point for prediction of disease genes. Efforts were made to underpin the validity of results by relevant literature evidence. Interestingly, many inferred disease relationships correspond to known clinical associations and comorbidities, and several predicted disease genes were subjects of therapeutic target research.</p> <p>Conclusions</p> <p>Causal molecular mechanisms present a unifying principle to derive methods for disease classification, analysis of clinical disorder associations, and prediction of disease genes. According to the definition of causal disease genes applied in this study, these results are not restricted to genetic disease/gene relationships. This may be particularly useful for the study of long-term or chronic illnesses, where pathological derangement due to environmental or as part of sequel conditions is of importance and may not be fully explained by genetic background.</p

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system