Genome-wide SNP and haplotype analyses reveal a rich history underlying dog domestication

Advances in genome technology have facilitated a new understanding of the historical and genetic processes crucial to rapid phenotypic evolution under domestication(1,2). To understand the process of dog diversification better, we conducted an extensive genome-wide survey of more than 48,000 single nucleotide polymorphisms in dogs and their wild progenitor, the grey wolf. Here we show that dog breeds share a higher proportion of multi-locus haplotypes unique to grey wolves from the Middle East, indicating that they are a dominant source of genetic diversity for dogs rather than wolves from east Asia, as suggested by mitochondrial DNA sequence data(3). Furthermore, we find a surprising correspondence between genetic and phenotypic/functional breed groupings but there are exceptions that suggest phenotypic diversification depended in part on the repeated crossing of individuals with novel phenotypes. Our results show that Middle Eastern wolves were a critical source of genome diversity, although interbreeding with local wolf populations clearly occurred elsewhere in the early history of specific lineages. More recently, the evolution of modern dog breeds seems to have been an iterative process that drew on a limited genetic toolkit to create remarkable phenotypic diversity.

Bayesian correlated clustering to integrate multiple datasets.

MOTIVATION: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct-but often complementary-information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets. RESULTS: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI's performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques-as well as to non-integrative approaches-demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods.

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/.

Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data

We present a nonparametric Bayesian method for disease subtype discovery in multi-dimensional cancer data. Our method can simultaneously analyse a wide range of data types, allowing for both agreement and disagreement between their underlying clustering structure. It includes feature selection and infers the most likely number of disease subtypes, given the data. We apply the method to 277 glioblastoma samples from The Cancer Genome Atlas, for which there are gene expression, copy number variation, methylation and microRNA data. We identify 8 distinct consensus subtypes and study their prognostic value for death, new tumour events, progression and recurrence. The consensus subtypes are prognostic of tumour recurrence (log-rank p-value of $3.6 \times 10^{-4}$ after correction for multiple hypothesis tests). This is driven principally by the methylation data (log-rank p-value of $2.0 \times 10^{-3}$) but the effect is strengthened by the other 3 data types, demonstrating the value of integrating multiple data types. Of particular note is a subtype of 47 patients characterised by very low levels of methylation. This subtype has very low rates of tumour recurrence and no new events in 10 years of follow up. We also identify a small gene expression subtype of 6 patients that shows particularly poor survival outcomes. Additionally, we note a consensus subtype that showly a highly distinctive data signature and suggest that it is therefore a biologically distinct subtype of glioblastoma. The code is available from https://sites.google.com/site/multipledatafusion/

Making Sadza with Deaf Zimbabwean Women: A Missiological Reorientation of Practical Theological Method toward Self-Theologizing Agency among Subaltern Communities

Missiological calls for self-theologizing among faith communities present the field of practical theology with a challenge to develop methodological approaches that address the complexities of cross-cultural, practical theological research. Although a variety of approaches can be considered critical correlative practical theology, existing methods are often built on assumptions that limit their use in subaltern contexts. This study seeks to address these concerns by analyzing existing theological methodologies with sustained attention to a community of Deaf Zimbabwean women struggling to develop their own agency in relation to child rearing practices. This dilemma serves as an entry point to an examination of the limitations of existing methodologies and a constructive, interdisciplinary theological exploration. The use of theological modeling methodology employs my experience of learning to cook sadza, a staple dish of Zimbabwe, as a guide for analyzing and reorienting practical theological methodology. The study explores a variety of theological approaches from practical theology, mission oriented theologians, theology among Deaf communities, and African women’s theology in relationship to the challenges presented by subaltern communities such as Deaf Zimbabwean women. Analysis reveals that although there is much to commend in these existing methodologies, questions about who does the critical correlation, whose interests are guiding the study, and consideration for the cross-cultural and power dynamics between researchers and faith communities remain problematic for developing self-theologizing agency. Rather than frame a comprehensive methodology, this study proposes three attitudes and guideposts to reorient practical theological researchers who wish to engender self-theologizing agency in subaltern communities. The creativity of enacted theology, the humility of using checks and balances in research methods, and the grace of finding strategies to build bridges of commonality and community offer ways to reorient practical theological methodologies toward the development of self-theologizing agency among subaltern people. This study concludes with discussion of how these guideposts can not only benefit particular work with a community of Deaf Zimbabwean women, but also provide research and theological reflection in other subaltern contexts.

Patterns of de novo allo B cells and antibody formation in chronic cardiac allograft rejection after alemtuzumab treatment.

Even though the etiology of chronic rejection (CR) is multifactorial, donor specific antibody (DSA) is considered to have a causal effect on CR development. Currently the antibody-mediated mechanisms during CR are poorly understood due to lack of proper animal models and tools. In a clinical setting, we previously demonstrated that induction therapy by lymphocyte depletion, using alemtuzumab (anti-human CD52), is associated with an increased incidence of serum alloantibody, C4d deposition and antibody-mediated rejection in human patients. In this study, the effects of T cell depletion in the development of antibody-mediated rejection were examined using human CD52 transgenic (CD52Tg) mice treated with alemtuzumab. Fully mismatched cardiac allografts were transplanted into alemtuzumab treated CD52Tg mice and showed no acute rejection while untreated recipients acutely rejected their grafts. However, approximately half of long-term recipients showed increased degree of vasculopathy, fibrosis and perivascular C3d depositions at posttransplant day 100. The development of CR correlated with DSA and C3d deposition in the graft. Using novel tracking tools to monitor donor-specific B cells, alloreactive B cells were shown to increase in accordance with DSA detection. The current animal model could provide a means of testing strategies to understand mechanisms and developing therapeutic approaches to prevent chronic rejection.

Costimulation blockade alters germinal center responses and prevents antibody-mediated rejection.

De novo donor-specific antibody (DSA) after organ transplantation promotes antibody-mediated rejection (AMR) and causes late graft loss. Previously, we demonstrated that depletion using anti-CD3 immunotoxin combined with tacrolimus and alefacept (AMR regimen) reliably induced early DSA production with AMR in a nonhuman primate kidney transplant model. Five animals were assigned as positive AMR controls, four received additional belatacept and four received additional anti-CD40 mAb (2C10R4). Notably, production of early de novo DSA was completely attenuated with additional belatacept or 2C10R4 treatment. In accordance with this, while positive controls experienced a decrease in peripheral IgM(+) B cells, bela- and 2C10R4-added groups maintained a predominant population of IgM(+) B cells, potentially indicating decreased isotype switching. Central memory T cells (CD4(+) CD28(+) CD95(+)) as well as PD-1(hi) CD4(+) T cells were decreased in both bela-added and 2C10R4-added groups. In analyzing germinal center (GC) reactions in situ, lymph nodes further revealed a reduction of B cell clonal expansion, GC-follicular helper T (Tfh) cells, and IL-21 production inside GCs with additional belatacept or 2C10R4 treatment. Here we provide evidence that belatacept and 2C10R4 selectively suppresses the humoral response via regulating Tfh cells and prevents AMR in this nonhuman primate model.

The Body in Pain: What do people of faith have to say about torture

An examination of why American Protestant churches have a higher likelihood to support torture

Peculiar Motion

A collection of poetry

Rapamycin Interferes With Postdepletion Regulatory T Cell Homeostasis and Enhances DSA Formation Corrected by CTLA4-Ig.

Previously, we demonstrated that alemtuzumab induction with rapamycin as sole maintenance therapy is associated with an increased incidence of humoral rejection in human kidney transplant patients. To investigate the role of rapamycin in posttransplant humoral responses after T cell depletion, fully MHC mismatched hearts were transplanted into hCD52Tg mice, followed by alemtuzumab treatment with or without a short course of rapamycin. While untreated hCD52Tg recipients acutely rejected B6 hearts (n = 12), hCD52Tg recipients treated with alemtuzumab alone or in conjunction with rapamycin showed a lack of acute rejection (MST > 100). However, additional rapamycin showed a reduced beating quality over time and increased incidence of vasculopathy. Furthermore, rapamycin supplementation showed an increased serum donor-specific antibodies (DSA) level compared to alemtuzumab alone at postoperation days 50 and 100. Surprisingly, additional rapamycin treatment significantly reduced CD4(+) CD25(+) FoxP3(+) T reg cell numbers during treatment. On the contrary, ICOS(+) PD-1(+) CD4 follicular helper T cells in the lymph nodes were significantly increased. Interestingly, CTLA4-Ig supplementation in conjunction with rapamycin corrected rapamycin-induced accelerated posttransplant humoral response by directly modulating Tfh cells but not Treg cells. This suggests that rapamycin after T cell depletion could affect Treg cells leading to an increase of Tfh cells and DSA production that can be reversed by CTLA4-Ig.