The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks

Gene conversion in human rearranged immunoglobulin genes

Over the past 20 years, many DNA sequences have been published suggesting that all or part of the V<sub>H</sub> segment of a rearranged immunoglobulin gene may be replaced in vivo. Two different mechanisms appear to be operating. One of these is very similar to primary V(D)J recombination, involving the RAG proteins acting upon recombination signal sequences, and this has recently been proven to occur. Other sequences, many of which show partial V<sub>H</sub> replacements with no addition of untemplated nucleotides at the V<sub>H</sub>–V<sub>H</sub> joint, have been proposed to occur by an unusual RAG-mediated recombination with the formation of hybrid (coding-to-signal) joints. These appear to occur in cells already undergoing somatic hypermutation in which, some authors are convinced, RAG genes are silenced. We recently proposed that the latter type of V<sub>H</sub> replacement might occur by homologous recombination initiated by the activity of AID (activation-induced cytidine deaminase), which is essential for somatic hypermutation and gene conversion. The latter has been observed in other species, but not in human Ig genes, so far. In this paper, we present a new analysis of sequences published as examples of the second type of rearrangement. This not only shows that AID recognition motifs occur in recombination regions but also that some sequences show replacement of central sections by a sequence from another gene, similar to gene conversion in the immunoglobulin genes of other species. These observations support the proposal that this type of rearrangement is likely to be AID-mediated rather than RAG-mediated and is consistent with gene conversion

Age-Related Comparisons of Evolution of the Inflammatory Response After Intracerebral Hemorrhage in Rats

In the hours to days after intracerebral hemorrhage (ICH), there is an inflammatory response within the brain characterized by the infiltration of peripheral neutrophils and macrophages and the activation of brain-resident microglia and astrocytes. Despite the strong correlation of aging and ICH incidence, and increasing information about cellular responses, little is known about the temporal- and age-related molecular responses of the brain after ICH. Here, we monitored a panel of 27 genes at 6 h and 1, 3, and 7 days after ICH was induced by injecting collagenase into the striatum of young adult and aged rats. Several molecules (CR3, TLR2, TLR4, IL-1β, TNFα, iNOS, IL-6) were selected to reflect the classical activation of innate immune cells (macrophages, microglia) and the potential to exacerbate inflammation and damage brain cells. Most of the others are associated with the resolution of innate inflammation, alternative pathways of macrophage/microglial activation, and the repair phase after acute injury (TGFβ, IL-1ra, IL-1r2, IL-4, IL-13, IL-4Rα, IL-13Rα1, IL-13Rα2, MRC1, ARG1, CD163, CCL22). In young animals, the up-regulation of 26 in 27 genes (not IL-4) was detected within the first week. Differences in timing or levels between young and aged animals were detected for 18 of 27 genes examined (TLR2, GFAP, IL-1β, IL-1ra, IL-1r2, iNOS, IL-6, TGFβ, MMP9, MMP12, IL-13, IL-4Rα, IL-13Rα1, IL-13Rα2, MRC1, ARG1, CD163, CCL22), with a generally less pronounced or delayed inflammatory response in the aged animals. Importantly, within this complex response to experimental ICH, the induction of pro-inflammatory, potentially harmful mediators often coincided with resolving and beneficial molecules

Analyses of the Microbial Diversity across the Human Microbiome

Analysis of human body microbial diversity is fundamental to understanding community structure, biology and ecology. The National Institutes of Health Human Microbiome Project (HMP) has provided an unprecedented opportunity to examine microbial diversity within and across body habitats and individuals through pyrosequencing-based profiling of 16 S rRNA gene sequences (16 S) from habits of the oral, skin, distal gut, and vaginal body regions from over 200 healthy individuals enabling the application of statistical techniques. In this study, two approaches were applied to elucidate the nature and extent of human microbiome diversity. First, bootstrap and parametric curve fitting techniques were evaluated to estimate the maximum number of unique taxa, Smax, and taxa discovery rate for habitats across individuals. Next, our results demonstrated that the variation of diversity within low abundant taxa across habitats and individuals was not sufficiently quantified with standard ecological diversity indices. This impact from low abundant taxa motivated us to introduce a novel rank-based diversity measure, the Tail statistic, (“τ”), based on the standard deviation of the rank abundance curve if made symmetric by reflection around the most abundant taxon. Due to τ’s greater sensitivity to low abundant taxa, its application to diversity estimation of taxonomic units using taxonomic dependent and independent methods revealed a greater range of values recovered between individuals versus body habitats, and different patterns of diversity within habitats. The greatest range of τ values within and across individuals was found in stool, which also exhibited the most undiscovered taxa. Oral and skin habitats revealed variable diversity patterns, while vaginal habitats were consistently the least diverse. Collectively, these results demonstrate the importance, and motivate the introduction, of several visualization and analysis methods tuned specifically for next-generation sequence data, further revealing that low abundant taxa serve as an important reservoir of genetic diversity in the human microbiome

Characterization of the Fecal Microbiome from Non-Human Wild Primates Reveals Species Specific Microbial Communities

BACKGROUND: Host-associated microbes comprise an integral part of animal digestive systems and these interactions have a long evolutionary history. It has been hypothesized that the gastrointestinal microbiome of humans and other non-human primates may have played significant roles in host evolution by facilitating a range of dietary adaptations. We have undertaken a comparative sequencing survey of the gastrointestinal microbiomes of several non-human primate species, with the goal of better understanding how these microbiomes relate to the evolution of non-human primate diversity. Here we present a comparative analysis of gastrointestinal microbial communities from three different species of Old World wild monkeys. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed fecal samples from three different wild non-human primate species (black-and-white colobus [Colubus guereza], red colobus [Piliocolobus tephrosceles], and red-tailed guenon [Cercopithecus ascanius]). Three samples from each species were subjected to small subunit rRNA tag pyrosequencing. Firmicutes comprised the vast majority of the phyla in each sample. Other phyla represented were Bacterioidetes, Proteobacteria, Spirochaetes, Actinobacteria, Verrucomicrobia, Lentisphaerae, Tenericutes, Planctomycetes, Fibrobacateres, and TM7. Bray-Curtis similarity analysis of these microbiomes indicated that microbial community composition within the same primate species are more similar to each other than to those of different primate species. Comparison of fecal microbiota from non-human primates with microbiota of human stool samples obtained in previous studies revealed that the gut microbiota of these primates are distinct and reflect host phylogeny. CONCLUSION/SIGNIFICANCE: Our analysis provides evidence that the fecal microbiomes of wild primates co-vary with their hosts, and that this is manifested in higher intraspecies similarity among wild primate species, perhaps reflecting species specificity of the microbiome in addition to dietary influences. These results contribute to the limited body of primate microbiome studies and provide a framework for comparative microbiome analysis between human and non-human primates as well as a comparative evolutionary understanding of the human microbiome

Prevalence of self-reported constipation in adults from the general population

OBJECTIVE To estimate the prevalence of self-reported constipation and associated factors in the general population of a Brazilian city. METHOD Secondary analysis of an epidemiological study, population-based, cross-sectional study, about bowel habits of Brazilian population. A total of 2,162 individuals were interviewed using two instruments: sociodemographic data and the adapted and validated Brazilian version of the "Bowel Function in the Community" tool. RESULTS There was a prevalence of 25.2% for the self-reported constipation, 37.2% among women and 10.2% among men. Stroke and old age were associated with constipation in the three statistical models used. CONCLUSION The prevalence found showed to be similar to the findings in the literature, although some associated factors obtained here have never been investigated

Rapid and Accurate Multiple Testing Correction and Power Estimation for Millions of Correlated Markers

With the development of high-throughput sequencing and genotyping technologies, the number of markers collected in genetic association studies is growing rapidly, increasing the importance of methods for correcting for multiple hypothesis testing. The permutation test is widely considered the gold standard for accurate multiple testing correction, but it is often computationally impractical for these large datasets. Recently, several studies proposed efficient alternative approaches to the permutation test based on the multivariate normal distribution (MVN). However, they cannot accurately correct for multiple testing in genome-wide association studies for two reasons. First, these methods require partitioning of the genome into many disjoint blocks and ignore all correlations between markers from different blocks. Second, the true null distribution of the test statistic often fails to follow the asymptotic distribution at the tails of the distribution. We propose an accurate and efficient method for multiple testing correction in genome-wide association studies—SLIDE. Our method accounts for all correlation within a sliding window and corrects for the departure of the true null distribution of the statistic from the asymptotic distribution. In simulations using the Wellcome Trust Case Control Consortium data, the error rate of SLIDE's corrected p-values is more than 20 times smaller than the error rate of the previous MVN-based methods' corrected p-values, while SLIDE is orders of magnitude faster than the permutation test and other competing methods. We also extend the MVN framework to the problem of estimating the statistical power of an association study with correlated markers and propose an efficient and accurate power estimation method SLIP. SLIP and SLIDE are available at http://slide.cs.ucla.edu

Validation of Skeletal Muscle cis-Regulatory Module Predictions Reveals Nucleotide Composition Bias in Functional Enhancers

We performed a genome-wide scan for muscle-specific cis-regulatory modules (CRMs) using three computational prediction programs. Based on the predictions, 339 candidate CRMs were tested in cell culture with NIH3T3 fibroblasts and C2C12 myoblasts for capacity to direct selective reporter gene expression to differentiated C2C12 myotubes. A subset of 19 CRMs validated as functional in the assay. The rate of predictive success reveals striking limitations of computational regulatory sequence analysis methods for CRM discovery. Motif-based methods performed no better than predictions based only on sequence conservation. Analysis of the properties of the functional sequences relative to inactive sequences identifies nucleotide sequence composition can be an important characteristic to incorporate in future methods for improved predictive specificity. Muscle-related TFBSs predicted within the functional sequences display greater sequence conservation than non-TFBS flanking regions. Comparison with recent MyoD and histone modification ChIP-Seq data supports the validity of the functional regions