Geographic constraints on social network groups

Social groups are fundamental building blocks of human societies. While our social interactions have always been constrained by geography, it has been impossible, due to practical difficulties, to evaluate the nature of this restriction on social group structure. We construct a social network of individuals whose most frequent geographical locations are also known. We also classify the individuals into groups according to a community detection algorithm. We study the variation of geographical span for social groups of varying sizes, and explore the relationship between topological positions and geographic positions of their members. We find that small social groups are geographically very tight, but become much more clumped when the group size exceeds about 30 members. Also, we find no correlation between the topological positions and geographic positions of individuals within network communities. These results suggest that spreading processes face distinct structural and spatial constraints.Comment: 10 pages, 5 figure

ElliPro: a new structure-based tool for the prediction of antibody epitopes

<p>Abstract</p> <p>Background</p> <p>Reliable prediction of antibody, or B-cell, epitopes remains challenging yet highly desirable for the design of vaccines and immunodiagnostics. A correlation between antigenicity, solvent accessibility, and flexibility in proteins was demonstrated. Subsequently, Thornton and colleagues proposed a method for identifying continuous epitopes in the protein regions protruding from the protein's globular surface. The aim of this work was to implement that method as a web-tool and evaluate its performance on discontinuous epitopes known from the structures of antibody-protein complexes.</p> <p>Results</p> <p>Here we present ElliPro, a web-tool that implements Thornton's method and, together with a residue clustering algorithm, the MODELLER program and the Jmol viewer, allows the prediction and visualization of antibody epitopes in a given protein sequence or structure. ElliPro has been tested on a benchmark dataset of discontinuous epitopes inferred from 3D structures of antibody-protein complexes. In comparison with six other structure-based methods that can be used for epitope prediction, ElliPro performed the best and gave an AUC value of 0.732, when the most significant prediction was considered for each protein. Since the rank of the best prediction was at most in the top three for more than 70% of proteins and never exceeded five, ElliPro is considered a useful research tool for identifying antibody epitopes in protein antigens. ElliPro is available at <url>http://tools.immuneepitope.org/tools/ElliPro</url>.</p> <p>Conclusion</p> <p>The results from ElliPro suggest that further research on antibody epitopes considering more features that discriminate epitopes from non-epitopes may further improve predictions. As ElliPro is based on the geometrical properties of protein structure and does not require training, it might be more generally applied for predicting different types of protein-protein interactions.</p

Laboratory prediction of the requirement for renal replacement in acute falciparum malaria

BACKGROUND: Acute renal failure is a common complication of severe malaria in adults, and without renal replacement therapy (RRT), it carries a poor prognosis. Even when RRT is available, delaying its initiation may increase mortality. Earlier identification of patients who will need RRT may improve outcomes. METHOD: Prospectively collected data from two intervention studies in adults with severe malaria were analysed focusing on laboratory features on presentation and their association with a later requirement for RRT. In particular, laboratory indices of acute tubular necrosis (ATN) and acute kidney injury (AKI) that are used in other settings were examined. RESULTS: Data from 163 patients were available for analysis. Whether or not the patients should have received RRT (a retrospective assessment determined by three independent reviewers) was used as the reference. Forty-three (26.4%) patients met criteria for dialysis, but only 19 (44.2%) were able to receive this intervention due to the limited availability of RRT. Patients with impaired renal function on admission (creatinine clearance < 60 ml/min) (n = 84) had their laboratory indices of ATN/AKI analysed. The plasma creatinine level had the greatest area under the ROC curve (AUC): 0.83 (95% confidence interval 0.74-0.92), significantly better than the AUCs for, urinary sodium level, the urea to creatinine ratio (UCR), the fractional excretion of urea (FeUN) and the urinary neutrophil gelatinase-associated lipocalcin (NGAL) level. The AUC for plasma creatinine was also greater than the AUC for blood urea nitrogen level, the fractional excretion of sodium (FeNa), the renal failure index (RFI), the urinary osmolality, the urine to plasma creatinine ratio (UPCR) and the creatinine clearance, although the difference for these variables did not reach statistical significance. CONCLUSIONS: In adult patients with severe malaria and impaired renal function on admission, none of the evaluated laboratory indices was superior to the plasma creatinine level when used to predict a later requirement for renal replacement therapy

Structural diversity in binary nanoparticle superlattices

Assembly of small building blocks such as atoms, molecules and nanoparticles into macroscopic structures - that is, 'bottom up' assembly - is a theme that runs through chemistry, biology and material science. Bacteria(1), macromolecules(2) and nanoparticles(3) can self-assemble, generating ordered structures with a precision that challenges current lithographic techniques. The assembly of nanoparticles of two different materials into a binary nanoparticle superlattice (BNSL)(3-7) can provide a general and inexpensive path to a large variety of materials (metamaterials) with precisely controlled chemical composition and tight placement of the components. Maximization of the nanoparticle packing density has been proposed as the driving force for BNSL formation(3,8,9), and only a few BNSL structures have been predicted to be thermodynamically stable. Recently, colloidal crystals with micrometre-scale lattice spacings have been grown from oppositely charged polymethyl methacrylate spheres(10,11). Here we demonstrate formation of more than 15 different BNSL structures, using combinations of semiconducting, metallic and magnetic nanoparticle building blocks. At least ten of these colloidal crystalline structures have not been reported previously. We demonstrate that electrical charges on sterically stabilized nanoparticles determine BNSL stoichiometry; additional contributions from entropic, van der Waals, steric and dipolar forces stabilize the variety of BNSL structures.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62551/1/nature04414.pd

Bioenergetic Consequences of PINK1 Mutations in Parkinson Disease

BACKGROUND: Mutations of the gene for PTEN-induced kinase 1 (PINK1) are a cause of familial Parkinson's disease (PD). PINK1 protein has been localised to mitochondria and PINK1 gene knockout models exhibit abnormal mitochondrial function. The purpose of this study was to determine whether cells derived from PD patients with a range of PINK1 mutations demonstrate similar defects of mitochondrial function, whether the nature and severity of the abnormalities vary between mutations and correlate with clinical features. METHODOLOGY: We investigated mitochondrial bioenergetics in live fibroblasts from PINK1 mutation patients using single cell techniques. We found that fibroblasts from PINK1 mutation patients had significant defects of bioenergetics including reduced mitochondrial membrane potential, altered redox state, a respiratory deficiency that was determined by substrate availability, and enhanced sensitivity to calcium stimulation and associated mitochondrial permeability pore opening. There was an increase in the basal rate of free radical production in the mutant cells. The pattern and severity of abnormality varied between different mutations, and the less severe defects in these cells were associated with later age of onset of PD. CONCLUSIONS: The results provide insight into the molecular pathology of PINK1 mutations in PD and also confirm the critical role of substrate availability in determining the biochemical phenotype--thereby offering the potential for novel therapeutic strategies to circumvent these abnormalities

Human Gene Coexpression Landscape: Confident Network Derived from Tissue Transcriptomic Profiles

This is an open-access article distributed under the terms of the Creative Commons Attribution License.[Background]: Analysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co-transcribed genes. However, most studies done at global >omic> scale are not focused on human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. Moreover, the technical noise present in genome-wide expression microarrays is another well reported problem that many times is not addressed with robust statistical methods, and the estimation of errors in the data is not provided. [Methodology/Principal Findings]: Human genome-wide expression data from a controlled set of normal-healthy tissues is used to build a confident human gene coexpression network avoiding both pathological and technical noise. To achieve this we describe a new method that combines several statistical and computational strategies: robust normalization and expression signal calculation; correlation coefficients obtained by parametric and non-parametric methods; random cross-validations; and estimation of the statistical accuracy and coverage of the data. All these methods provide a series of coexpression datasets where the level of error is measured and can be tuned. To define the errors, the rates of true positives are calculated by assignment to biological pathways. The results provide a confident human gene coexpression network that includes 3327 gene-nodes and 15841 coexpression-links and a comparative analysis shows good improvement over previously published datasets. Further functional analysis of a subset core network, validated by two independent methods, shows coherent biological modules that share common transcription factors. The network reveals a map of coexpression clusters organized in well defined functional constellations. Two major regions in this network correspond to genes involved in nuclear and mitochondrial metabolism and investigations on their functional assignment indicate that more than 60% are house-keeping and essential genes. The network displays new non-described gene associations and it allows the placement in a functional context of some unknown non-assigned genes based on their interactions with known gene families. [Conclusions/Significance]: The identification of stable and reliable human gene to gene coexpression networks is essential to unravel the interactions and functional correlations between human genes at an omic scale. This work contributes to this aim, and we are making available for the scientific community the validated human gene coexpression networks obtained, to allow further analyses on the network or on some specific gene associations. The data are available free online at http://bioinfow.dep.usal.es/coexpression/. © 2008 Prieto et al.Funding and grant support was provided by the Ministery of Health, Spanish Government (ISCiii-FIS, MSyC; Project reference PI061153) and by the Ministery of Education, Castilla-Leon Local Government (JCyL; Project reference CSI03A06).Peer Reviewe

Toll-Like Receptor Ligands Induce Human T Cell Activation and Death, a Model for HIV Pathogenesis

Background: Recently, heightened systemic translocation of microbial products was found in persons with chronic HIV infection and this was linked to immune activation and CD4 + T cell homeostasis. Methodology: We examined here the effects of microbial Toll-like receptor (TLR) ligands on T cell activation in vitro. Conclusions/Findings: We show that exposure to TLR ligands results in activation of memory and effector CD4 + and CD8 + T cells. After exposure to each of 8 different ligands that activate TLRs 2, 3, 4, 5, 7, 8, and 9, CD8 + T cells are activated and gain expression of the C type lectin CD69 that may promote their retention in lymphoid tissues. In contrast, CD4 + T cells rarely increase CD69 expression but instead enter cell cycle. Despite activation and cell cycle entry, CD4 + T cells divide poorly and instead, disproportionately undergo activation-induced cell death. Systemic exposure to TLR agonists may therefore increase immune activation, effector cell sequestration in lymphoid tissues and T cell turnover. These events may contribute to the pathogenesis of immune dysfunction and CD4+ T cell losses in chronic infection with the human immunodeficiency virus

Cell-selective labeling using amino acid precursors for proteomic studies of multicellular environments.

We report a technique to selectively and continuously label the proteomes of individual cell types in coculture, named cell type-specific labeling using amino acid precursors (CTAP). Through transgenic expression of exogenous amino acid biosynthesis enzymes, vertebrate cells overcome their dependence on supplemented essential amino acids and can be selectively labeled through metabolic incorporation of amino acids produced from heavy isotope-labeled precursors. When testing CTAP in several human and mouse cell lines, we could differentially label the proteomes of distinct cell populations in coculture and determine the relative expression of proteins by quantitative mass spectrometry. In addition, using CTAP we identified the cell of origin of extracellular proteins secreted from cells in coculture. We believe that this method, which allows linking of proteins to their cell source, will be useful in studies of cell-cell communication and potentially for discovery of biomarkers

Eukaryotic Evolutionary Transitions Are Associated with Extreme Codon Bias in Functionally-Related Proteins

Codon bias in the genome of an organism influences its phenome by changing the speed and efficiency of mRNA translation and hence protein abundance. We hypothesized that differences in codon bias, either between-species differences in orthologous genes, or within-species differences between genes, may play an evolutionary role. To explore this hypothesis, we compared the genome-wide codon bias in six species that occupy vital positions in the Eukaryotic Tree of Life. We acquired the entire protein coding sequences for these organisms, computed the codon bias for all genes in each organism and explored the output for relationships between codon bias and protein function, both within- and between-lineages. We discovered five notable coordinated patterns, with extreme codon bias most pronounced in traits considered highly characteristic of a given lineage. Firstly, the Homo sapiens genome had stronger codon bias for DNA-binding transcription factors than the Saccharomyces cerevisiae genome, whereas the opposite was true for ribosomal proteins – perhaps underscoring transcriptional regulation in the origin of complexity. Secondly, both mammalian species examined possessed extreme codon bias in genes relating to hair – a tissue unique to mammals. Thirdly, Arabidopsis thaliana showed extreme codon bias in genes implicated in cell wall formation and chloroplast function – which are unique to plants. Fourthly, Gallus gallus possessed strong codon bias in a subset of genes encoding mitochondrial proteins – perhaps reflecting the enhanced bioenergetic efficiency in birds that co-evolved with flight. And lastly, the G. gallus genome had extreme codon bias for the Ciliary Neurotrophic Factor – which may help to explain their spontaneous recovery from deafness. We propose that extreme codon bias in groups of genes that encode functionally related proteins has a pathway-level energetic explanation