mtDNA Variation in Caste Populations of Andhra Pradesh, India.

Various anthropological analyses have documented extensive regional variation among populations on the subcontinent of India using morphological, protein, blood group, and nuclear DNA polymorphisms. These patterns are the product of complex population structure (genetic drift, gene flow) and a population history noted for numerous branching events. As a result, the interpretation of relationships among caste populations of South India and between Indians and continental populations remains controversial. The Hindu caste system is a general model of genetic differentiation among endogamous populations stratified by social forces (e.g., religion and occupation). The mitochondrial DNA (mtDNA) molecule has unique properties that facilitate the exploration of population structure. We analyzed 36 Hindu men born in Andhra Pradesh who were unrelated matrilineally through at least 3 generations and who represent 4 caste populations: Brahmin (9), Yadava (10), Kapu (7), and Relli (10). Individuals from Africa (36), Asia (36), and Europe (36) were sampled for comparison. A 200-base-pair segment of hypervariable segment 2 (HVS2) of the mtDNA control region was sequenced in all individuals. In the Indian castes 25 distinct haplotypes are identified. Aside from the Cambridge reference sequence, only two haplotypes are shared between caste populations. Middle castes form a highly supported cluster in a neighbor-joining network. Mean nucleotide diversity within each caste is 0.015, 0.012, 0.011, and 0.012 for the Brahmin, Yadava, Kapu, and Relli, respectively. mtDNA variation is highly structured between castes (GST = 0.17; p < 0.002). The effects of social structure on mtDNA variation are much greater than those on variation measured by traditional markers. Explanations for this discordance inelude (1) the higher resolving power of mtDNA, (2) sex-dependent gene flow, (3) differences in male and female effective population sizes, and (4) elements of the kinship structure. Thirty distinct haplotypes are found in Africans, 17 in Asians, and 13 in Europeans. Mean nucleotide diversity is 0.019, 0.014, 0.009, and 0.007 for Africans, Indians, Asians, and Europeans, respectively. These populations are highly structured geographically (GST = 0.15;p < 0.001). The caste populations of Andhra Pradesh cluster more often with Africans than with Asians or Europeans. This is suggestive of admixture with African populations.We would like to thank T. Jenkins, H. Soodyall, P. Nute, and J. Kidd for providing DNA samples and S. Austin, A. Comuzzie, R. Duggirala, R. Feldman, K. Lum, A. Rogers, and S. Watkins for technical advice, critical comments, and thoughtful discussion. This work was supported in part by the National Science Foundation through grant NSF-DBS-9211255, the Clinical Research Center at the University of Utah through grant NIH RR-00064, and the Technology Access Center of the Utah Human Genome Project

Reconstructing the reproductive mode of an Ediacaran macro-organism.

Enigmatic macrofossils of late Ediacaran age (580-541 million years ago) provide the oldest known record of diverse complex organisms on Earth, lying between the microbially dominated ecosystems of the Proterozoic and the Cambrian emergence of the modern biosphere. Among the oldest and most enigmatic of these macrofossils are the Rangeomorpha, a group characterized by modular, self-similar branching and a sessile benthic habit. Localized occurrences of large in situ fossilized rangeomorph populations allow fundamental aspects of their biology to be resolved using spatial point process techniques. Here we use such techniques to identify recurrent clustering patterns in the rangeomorph Fractofusus, revealing a complex life history of multigenerational, stolon-like asexual reproduction, interspersed with dispersal by waterborne propagules. Ecologically, such a habit would have allowed both for the rapid colonization of a localized area and for transport to new, previously uncolonized areas. The capacity of Fractofusus to derive adult morphology by two distinct reproductive modes documents the sophistication of its underlying developmental biology.This work has been supported by the Natural Environment Research Council [grant numbers NE/I005927/1 to C.G.K., NE/J5000045/1 to J.J.M., NE/L011409/1 to A.G.L. and NE/G523539/1 to E.G.M.], and a Henslow Junior Research Fellowship from Cambridge Philosophical Society to A.G.L.This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/nature1464

Cancer Cell Invasion Is Enhanced by Applied Mechanical Stimulation

Metastatic cells migrate from the site of the primary tumor, through the stroma, into the blood and lymphatic vessels, finally colonizing various other tissues to form secondary tumors. Numerous studies have been done to identify the stimuli that drive the metastatic cascade. This has led to the identification of multiple biochemical signals that promote metastasis. However, information on the role of mechanical factors in cancer metastasis has been limited to the affect of compliance. Interestingly, the tumor microenvironment is rich in many cell types including highly contractile cells that are responsible for extensive remodeling and production of the dense extracellular matrix surrounding the cancerous tissue. We hypothesize that the mechanical forces produced by remodeling activities of cells in the tumor microenvironment contribute to the invasion efficiency of metastatic cells. We have discovered a significant difference in the extent of invasion in mechanically stimulated verses non-stimulated cell culture environments. Furthermore, this mechanically enhanced invasion is dependent upon substrate protein composition, and influenced by topography. Finally, we have found that the protein cofilin is needed to sense the mechanical stimuli that enhances invasion. We conclude that other types of mechanical signals in the tumor microenvironment, besides the rigidity, can enhance the invasive abilities of cancer cells in vitro. We further propose that in vivo, non-cancerous cells located within the tumor micro-environment may be capable of providing the necessary mechanical stimulus during the remodeling of the extracellular matrix surrounding the tumor

Theoretical Model for Cellular Shapes Driven by Protrusive and Adhesive Forces

The forces that arise from the actin cytoskeleton play a crucial role in determining the cell shape. These include protrusive forces due to actin polymerization and adhesion to the external matrix. We present here a theoretical model for the cellular shapes resulting from the feedback between the membrane shape and the forces acting on the membrane, mediated by curvature-sensitive membrane complexes of a convex shape. In previous theoretical studies we have investigated the regimes of linear instability where spontaneous formation of cellular protrusions is initiated. Here we calculate the evolution of a two dimensional cell contour beyond the linear regime and determine the final steady-state shapes arising within the model. We find that shapes driven by adhesion or by actin polymerization (lamellipodia) have very different morphologies, as observed in cells. Furthermore, we find that as the strength of the protrusive forces diminish, the system approaches a stabilization of a periodic pattern of protrusions. This result can provide an explanation for a number of puzzling experimental observations regarding cellular shape dependence on the properties of the extra-cellular matrix

Automated segmentation of multifocal basal ganglia T2*-weighted MRI hypointensities

AbstractMultifocal basal ganglia T2*-weighted (T2*w) hypointensities, which are believed to arise mainly from vascular mineralization, were recently proposed as a novel MRI biomarker for small vessel disease and ageing. These T2*w hypointensities are typically segmented semi-automatically, which is time consuming, associated with a high intra-rater variability and low inter-rater agreement. To address these limitations, we developed a fully automated, unsupervised segmentation method for basal ganglia T2*w hypointensities. This method requires conventional, co-registered T2*w and T1-weighted (T1w) volumes, as well as region-of-interest (ROI) masks for the basal ganglia and adjacent internal capsule generated automatically from T1w MRI. The basal ganglia T2*w hypointensities were then segmented with thresholds derived with an adaptive outlier detection method from respective bivariate T2*w/T1w intensity distributions in each ROI. Artefacts were reduced by filtering connected components in the initial masks based on their standardised T2*w intensity variance. The segmentation method was validated using a custom-built phantom containing mineral deposit models, i.e. gel beads doped with 3 different contrast agents in 7 different concentrations, as well as with MRI data from 98 community-dwelling older subjects in their seventies with a wide range of basal ganglia T2*w hypointensities. The method produced basal ganglia T2*w hypointensity masks that were in substantial volumetric and spatial agreement with those generated by an experienced rater (Jaccard index=0.62±0.40). These promising results suggest that this method may have use in automatic segmentation of basal ganglia T2*w hypointensities in studies of small vessel disease and ageing

Stochastic blockmodeling of the modules and core of the caenorhabditis elegans connectome

Recently, there has been much interest in the community structure or mesoscale organization of complex networks. This structure is characterised either as a set of sparsely inter-connected modules or as a highly connected core with a sparsely connected periphery. However, it is often difficult to disambiguate these two types of mesoscale structure or, indeed, to summarise the full network in terms of the relationships between its mesoscale constituents. Here, we estimate a community structure with a stochastic blockmodel approach, the Erdős-Rényi Mixture Model, and compare it to the much more widely used deterministic methods, such as the Louvain and Spectral algorithms. We used the Caenorhabditis elegans (C. elegans) nervous system (connectome) as a model system in which biological knowledge about each node or neuron can be used to validate the functional relevance of the communities obtained. The deterministic algorithms derived communities with 4–5 modules, defined by sparse inter-connectivity between all modules. In contrast, the stochastic Erdős-Rényi Mixture Model estimated a community with 9 blocks or groups which comprised a similar set of modules but also included a clearly defined core, made of 2 small groups. We show that the “core-in-modules” decomposition of the worm brain network, estimated by the Erdős-Rényi Mixture Model, is more compatible with prior biological knowledge about the C. elegans nervous system than the purely modular decomposition defined deterministically. We also show that the blockmodel can be used both to generate stochastic realisations (simulations) of the biological connectome, and to compress network into a small number of super-nodes and their connectivity. We expect that the Erdős-Rényi Mixture Model may be useful for investigating the complex community structures in other (nervous) systems