Spatial organization of nucleotide excision repair proteins after UV-induced DNA damage in the human cell nucleus.

Nucleotide excision repair (NER) is an evolutionary conserved DNA repair system that is essential for the removal of UV-induced DNA damage. In this study we investigated how NER is compartmentalized in the interphase nucleus of human cells at the ultrastructural level by using electron microscopy in combination with immunogold labeling. We analyzed the role of two nuclear compartments: condensed chromatin domains and the perichromatin region. The latter contains transcriptionally active and partly decondensed chromatin at the surface of condensed chromatin domains. We studied the distribution of the damage-recognition protein XPC and of XPA, which is a central component of the chromatin-associated NER complex. Both XPC and XPA rapidly accumulate in the perichromatin region after UV irradiation, whereas only XPC is also moderately enriched in condensed chromatin domains. These observations suggest that DNA damage is detected by XPC throughout condensed chromatin domains, whereas DNA-repair complexes seem preferentially assembled in the perichromatin region. We propose that UV-damaged DNA inside condensed chromatin domains is relocated to the perichromatin region, similar to what has been shown for DNA replication. In support of this, we provide evidence that UV-damaged chromatin domains undergo expansion, which might facilitate the translocation process. Our results offer novel insight into the dynamic spatial organization of DNA repair in the human cell nucleus.

The influence of environmental spatial structure on the life history traits and diversity of species in a metacommunity

Several models have been proposed to understand how so many species can coexist in ecosystems. Despite evidence showing that natural habitats are often patchy and fragmented, these models rarely take into account environmental spatial structure. In this study we investigated the influence of spatial structure in habitat and disturbance regime upon species' traits and species' coexistence in a metacommunity. We used a population-based model to simulate competing species in spatially explicit landscapes. The species traits we focused on were dispersal ability, competitiveness, reproductive investment and survival rate. Communities were characterized by their species richness and by the four life-history traits averaged over all the surviving species. Our results show that spatial structure and disturbance have a strong influence on the equilibrium life-history traits within a metacommunity. In the absence of disturbance, spatially structured landscapes favour species investing more in reproduction, but less in dispersal and survival. However, this influence is strongly dependent on the disturbance rate, pointing to an important interaction between spatial structure and disturbance. This interaction also plays a role in species coexistence. While spatial structure tends to reduce diversity in the absence of disturbance, the tendency is reversed when disturbance occurs. In conclusion, the spatial structure of communities is an important determinant of their diversity and characteristic traits. These traits are likely to influence important ecological properties such as resistance to invasion or response to climate change, which in turn will determine the fate of ecosystems facing the current global ecological crisis.

Regulation of peptidase activity in a three-dimensional aggregate model of brain tumor vasculature.

The abnormal vascular system of brain cancers inappropriately expresses membrane proteins, including proteolytic enzymes, ultimately resulting in blood extravasation. The production of inflammatory mediators, such as cytokines and nitric oxide, and tumor hypoxia have been implicated in these effects. We have previously shown that the activity of aminopeptidase A is increased in the abnormal vascular system of human and rat brain tumors. To study the mechanisms regulating the activities of peptidases in cerebral vasculature in brain tumors, we have developed a three-dimensional model of differentiated rat brain cells in aggregate cultures in which rat brain microvessels were incorporated. The secretion of interleukin-6 (IL-6) in the culture medium of aggregates was used as an indicator of inflammatory activation. Addition to these aggregates of C6 glioma cell medium (C6-CM) conditioned under hypoxic or normoxic conditions or serum mimicked tumor-dependent hypoxia or conditions of dysfunction of brain tumor vasculature. Hypoxic and normoxic C6-CM, but not serum, regulated peptidase activity in aggregates, and in particular it increased the activity of aminopeptidase A determined using histoenzymography. Serum, but not C6-CM, increased IL-6 production, but did not increase aminopeptidase A activity in aggregates. Thus soluble glioma-derived factors, but not serum-derived factors, induce dysfunctions of cerebral vasculature by directly regulating the activity of peptidases, not involving inflammatory activation. Tumor hypoxia is not necessary to modulate peptidase activity.

Quantifying the weight of fingerprint evidence through the spatial relationship, directions and types of minutiae observed on fingermarks

This paper presents a statistical model for the quantification of the weight of fingerprint evidence. Contrarily to previous models (generative and score-based models), our model proposes to estimate the probability distributions of spatial relationships, directions and types of minutiae observed on fingerprints for any given fingermark. Our model is relying on an AFIS algorithm provided by 3M Cogent and on a dataset of more than 4,000,000 fingerprints to represent a sample from a relevant population of potential sources. The performance of our model was tested using several hundreds of minutiae configurations observed on a set of 565 fingermarks. In particular, the effects of various sub-populations of fingers (i.e., finger number, finger general pattern) on the expected evidential value of our test configurations were investigated. The performance of our model indicates that the spatial relationship between minutiae carries more evidential weight than their type or direction. Our results also indicate that the AFIS component of our model directly enables us to assign weight to fingerprint evidence without the need for the additional layer of complex statistical modeling involved by the estimation of the probability distributions of fingerprint features. In fact, it seems that the AFIS component is more sensitive to the sub-population effects than the other components of the model. Overall, the data generated during this research project contributes to support the idea that fingerprint evidence is a valuable forensic tool for the identification of individuals.

ConnectomeViewer - Multi-Modal Multi-Level Network Visualization and Analysis.

Introduction: The field of Connectomic research is growing rapidly, resulting from methodological advances in structural neuroimaging on many spatial scales. Especially progress in Diffusion MRI data acquisition and processing made available macroscopic structural connectivity maps in vivo through Connectome Mapping Pipelines (Hagmann et al, 2008) into so-called Connectomes (Hagmann 2005, Sporns et al, 2005). They exhibit both spatial and topological information that constrain functional imaging studies and are relevant in their interpretation. The need for a special-purpose software tool for both clinical researchers and neuroscientists to support investigations of such connectome data has grown. Methods: We developed the ConnectomeViewer, a powerful, extensible software tool for visualization and analysis in connectomic research. It uses the novel defined container-like Connectome File Format, specifying networks (GraphML), surfaces (Gifti), volumes (Nifti), track data (TrackVis) and metadata. Usage of Python as programming language allows it to by cross-platform and have access to a multitude of scientific libraries. Results: Using a flexible plugin architecture, it is possible to enhance functionality for specific purposes easily. Following features are already implemented: * Ready usage of libraries, e.g. for complex network analysis (NetworkX) and data plotting (Matplotlib). More brain connectivity measures will be implemented in a future release (Rubinov et al, 2009). * 3D View of networks with node positioning based on corresponding ROI surface patch. Other layouts possible. * Picking functionality to select nodes, select edges, get more node information (ConnectomeWiki), toggle surface representations * Interactive thresholding and modality selection of edge properties using filters * Arbitrary metadata can be stored for networks, thereby allowing e.g. group-based analysis or meta-analysis. * Python Shell for scripting. Application data is exposed and can be modified or used for further post-processing. * Visualization pipelines using filters and modules can be composed with Mayavi (Ramachandran et al, 2008). * Interface to TrackVis to visualize track data. Selected nodes are converted to ROIs for fiber filtering The Connectome Mapping Pipeline (Hagmann et al, 2008) processed 20 healthy subjects into an average Connectome dataset. The Figures show the ConnectomeViewer user interface using this dataset. Connections are shown that occur in all 20 subjects. The dataset is freely available from the homepage (connectomeviewer.org). Conclusions: The ConnectomeViewer is a cross-platform, open-source software tool that provides extensive visualization and analysis capabilities for connectomic research. It has a modular architecture, integrates relevant datatypes and is completely scriptable. Visit www.connectomics.org to get involved as user or developer.