A lymphoma plasma membrane-associated protein with ankyrin-like properties.

In this study we have used several complementary techniques to isolate and characterize a 72-kD polypeptide that is tightly associated with a major mouse T-lymphoma membrane glycoprotein, gp 85 (a wheat germ agglutinin-binding protein), in a 16 S complex. These two proteins do not separate in the presence of high salt but can be dissociated by treatment with 2 M urea. Further analysis indicates that the 72-kD protein has ankyrin-like properties based on the following criteria: (a) it cross-reacts with specific antibodies raised against erythrocyte and brain ankyrin; (b) it displays a peptide mapping pattern and a pI (between 6.5 and 6.8) similar to that of the 72-kD proteolytic fragment of erythrocyte ankyrin; (c) it competes with erythrocyte ghost membranes (spectrin-depleted preparations) for spectrin binding; and (d) it binds to purified spectrin and fodrin molecules. Most importantly, in intact lymphoma cells this ankyrin-like protein is localized directly underneath the plasma membrane and is found to be preferentially accumulated beneath receptor cap structures as well as associated with a membrane-cytoskeleton complex preparation. It is proposed that the ankyrin-like 72-kD protein may play an important role in linking certain surface glycoprotein(s) to fodrin which, in turn, binds to actin filaments required for lymphocyte cap formation

Fully Bayesian tests of neutrality using genealogical summary statistics

<p>Abstract</p> <p>Background</p> <p>Many data summary statistics have been developed to detect departures from neutral expectations of evolutionary models. However questions about the neutrality of the evolution of genetic loci within natural populations remain difficult to assess. One critical cause of this difficulty is that most methods for testing neutrality make simplifying assumptions simultaneously about the mutational model and the population size model. Consequentially, rejecting the null hypothesis of neutrality under these methods could result from violations of either or both assumptions, making interpretation troublesome.</p> <p>Results</p> <p>Here we harness posterior predictive simulation to exploit summary statistics of both the data and model parameters to test the goodness-of-fit of standard models of evolution. We apply the method to test the selective neutrality of molecular evolution in non-recombining gene genealogies and we demonstrate the utility of our method on four real data sets, identifying significant departures of neutrality in human influenza A virus, even after controlling for variation in population size.</p> <p>Conclusion</p> <p>Importantly, by employing a full model-based Bayesian analysis, our method separates the effects of demography from the effects of selection. The method also allows multiple summary statistics to be used in concert, thus potentially increasing sensitivity. Furthermore, our method remains useful in situations where analytical expectations and variances of summary statistics are not available. This aspect has great potential for the analysis of temporally spaced data, an expanding area previously ignored for limited availability of theory and methods.</p

Transition probabilities for general birth-death processes with applications in ecology, genetics, and evolution

A birth-death process is a continuous-time Markov chain that counts the number of particles in a system over time. In the general process with $n$ current particles, a new particle is born with instantaneous rate $\lambda_n$ and a particle dies with instantaneous rate $\mu_n$. Currently no robust and efficient method exists to evaluate the finite-time transition probabilities in a general birth-death process with arbitrary birth and death rates. In this paper, we first revisit the theory of continued fractions to obtain expressions for the Laplace transforms of these transition probabilities and make explicit an important derivation connecting transition probabilities and continued fractions. We then develop an efficient algorithm for computing these probabilities that analyzes the error associated with approximations in the method. We demonstrate that this error-controlled method agrees with known solutions and outperforms previous approaches to computing these probabilities. Finally, we apply our novel method to several important problems in ecology, evolution, and genetics

Polychromatic immunophenotypic characterization of T cell profiles among HIV-infected patients experiencing immune reconstitution inflammatory syndrome (IRIS)

Abstract Objective To immunophenotype CD4+ and CD8+ T cell sub-populations in HIV-associated immune reconstitution inflammatory syndrome (IRIS). Design Nested case-control immunological study. Methods ART-naïve HIV-infected patients were prospectively observed for IRIS during the first 6 months of ART. Twenty-two IRIS cases and 22 ART-duration matched controls were sampled for T cell immunophenotyping. Results IRIS cases demonstrated significantly lower CD4 cell counts compared to controls (baseline: 79 versus 142, p = 0.02; enrollment: 183 versus 263, p = 0.05, respectively) with no differences in HIV RNA levels. Within CD4+T cells, cases exhibited more of an effector memory phenotype compared to controls (40.8 versus 27.0%, p = 0.20), while controls trended towards a central memory phenotype (43.8 versus 30.8%, p = 0.07). Within CD8+ T cells, controls exhibited more central memory (13.9 versus 7.81%, p = 0.01, respectively) and effector (13.2 versus 8.8%, p = 0.04, respectively) phenotypes compared to cases, whereas cases demonstrated more terminal effectors than controls (28.8 versus 15.1%, p = 0.05). Cases demonstrated increased activation of CD8+ T cell effector memory, terminal effector, and effector subsets than controls (p = 0.04, 0.02, and 0.02, respectively). Conclusion CD4+ and CD8+ T cell subset maturational phenotypes were heterogeneous among IRIS cases and controls. However, IRIS cases demonstrated significant increases in activation of CD8+ T cell effector subpopulations

An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration

While statisticians are well-accustomed to performing exploratory analysis in the modeling stage of an analysis, the notion of conducting preliminary general-purpose exploratory analysis in the Monte Carlo stage (or more generally, the model-fitting stage) of an analysis is an area which we feel deserves much further attention. Towards this aim, this paper proposes a general-purpose algorithm for automatic density exploration. The proposed exploration algorithm combines and expands upon components from various adaptive Markov chain Monte Carlo methods, with the Wang-Landau algorithm at its heart. Additionally, the algorithm is run on interacting parallel chains -- a feature which both decreases computational cost as well as stabilizes the algorithm, improving its ability to explore the density. Performance is studied in several applications. Through a Bayesian variable selection example, the authors demonstrate the convergence gains obtained with interacting chains. The ability of the algorithm's adaptive proposal to induce mode-jumping is illustrated through a trimodal density and a Bayesian mixture modeling application. Lastly, through a 2D Ising model, the authors demonstrate the ability of the algorithm to overcome the high correlations encountered in spatial models.Comment: 33 pages, 20 figures (the supplementary materials are included as appendices

Bayesian modeling of recombination events in bacterial populations

Background: We consider the discovery of recombinant segments jointly with their origins within multilocus DNA sequences from bacteria representing heterogeneous populations of fairly closely related species. The currently available methods for recombination detection capable of probabilistic characterization of uncertainty have a limited applicability in practice as the number of strains in a data set increases. Results: We introduce a Bayesian spatial structural model representing the continuum of origins over sites within the observed sequences, including a probabilistic characterization of uncertainty related to the origin of any particular site. To enable a statistically accurate and practically feasible approach to the analysis of large-scale data sets representing a single genus, we have developed a novel software tool (BRAT, Bayesian Recombination Tracker) implementing the model and the corresponding learning algorithm, which is capable of identifying the posterior optimal structure and to estimate the marginal posterior probabilities of putative origins over the sites. Conclusion: A multitude of challenging simulation scenarios and an analysis of real data from seven housekeeping genes of 120 strains of genus Burkholderia are used to illustrate the possibilities offered by our approach. The software is freely available for download at URL http://web.abo.fi/fak/ mnf//mate/jc/software/brat.html

Spatial Dynamics of Human-Origin H1 Influenza A Virus in North American Swine

The emergence and rapid global spread of the swine-origin H1N1/09 pandemic influenza A virus in humans underscores the importance of swine populations as reservoirs for genetically diverse influenza viruses with the potential to infect humans. However, despite their significance for animal and human health, relatively little is known about the phylogeography of swine influenza viruses in the United States. This study utilizes an expansive data set of hemagglutinin (HA1) sequences (n = 1516) from swine influenza viruses collected in North America during the period 2003–2010. With these data we investigate the spatial dissemination of a novel influenza virus of the H1 subtype that was introduced into the North American swine population via two separate human-to-swine transmission events around 2003. Bayesian phylogeographic analysis reveals that the spatial dissemination of this influenza virus in the US swine population follows long-distance swine movements from the Southern US to the Midwest, a corn-rich commercial center that imports millions of swine annually. Hence, multiple genetically diverse influenza viruses are introduced and co-circulate in the Midwest, providing the opportunity for genomic reassortment. Overall, the Midwest serves primarily as an ecological sink for swine influenza in the US, with sources of virus genetic diversity instead located in the Southeast (mainly North Carolina) and South-central (mainly Oklahoma) regions. Understanding the importance of long-distance pig transportation in the evolution and spatial dissemination of the influenza virus in swine may inform future strategies for the surveillance and control of influenza, and perhaps other swine pathogens