David A. Stephens

Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans.

Identifying transplant recipients in whom immunological tolerance is established or is developing would allow an individually tailored approach to their posttransplantation management. In this study, we aimed to develop reliable and reproducible in vitro assays capable of detecting tolerance in renal transplant recipients. Several biomarkers and bioassays were screened on a training set that included 11 operationally tolerant renal transplant recipients, recipient groups following different immunosuppressive regimes, recipients undergoing chronic rejection, and healthy controls. Highly predictive assays were repeated on an independent test set that included 24 tolerant renal transplant recipients. Tolerant patients displayed an expansion of peripheral blood B and NK lymphocytes, fewer activated CD4+ T cells, a lack of donor-specific antibodies, donor-specific hyporesponsiveness of CD4+ T cells, and a high ratio of forkhead box P3 to alpha-1,2-mannosidase gene expression. Microarray analysis further revealed in tolerant recipients a bias toward differential expression of B cell-related genes and their associated molecular pathways. By combining these indices of tolerance as a cross-platform biomarker signature, we were able to identify tolerant recipients in both the training set and the test set. This study provides an immunological profile of the tolerant state that, with further validation, should inform and shape drug-weaning protocols in renal transplant recipients.

Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modeling

In the past ten years there has been a dramatic increase of in terest in the Bayesian analysis of finite mixture models. This is primarily because of the emergence of Markov chain Monte Carlo (MCMC) methods. While MCMC provides a convenient way to draw inference from compli cated statistical models, there are many, perhaps underappreciated, problems associated with the MCMC analysis of mixtures. The problems are mainly caused by the nonidentifiability of the components under symmetric priors, which leads to so-called label switching in the MCMC output. This means that ergodic averages of component specific quantities will be identical and thus useless for inference. We review the solutions to the label switching problem, such as artificial identifiability constraints, relabelling algorithms and label invariant loss functions. We also review various MCMC sampling schemes that have been suggested for mixture models and discuss posterior sensitivity to prior specification.

A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes: An Application of Bayesian Hierarchical Clustering of Curves

Malaria represents one of the major worldwide challenges to public health. A recent breakthrough in the study of the disease follows the annotation of the genome of the malaria parasite Plasmodium falciparum and the mosquito vector (an organism that spreads an infectious disease)Anopheles. Of particular interest is the molecular biology underlying the immune response system of Anopheles, which actively fights against Plasmodium infection. This article reports a statistical analysis of gene expression time profiles from mosquitoes that have been infected with a bacterial agent. Specifically, we introduce a Bayesian model-based hierarchical clustering algorithm for curve data to investigate mechanisms of regulation in the genes concerned; that is, we aim to cluster genes having similar expression profiles. Genes displaying similar, interesting profiles can then be highlighted for further investigation by the experimenter. We show how our approach reveals structure within the data not captured by other approaches. One of the most pertinent features of the data is the sample size, which records the expression levels of 2,771 genes at 6 time points. Additionally, the time points are unequally spaced, and there is expected nonstationary behavior in the gene profiles. We demonstrate our approach to be readily implementable under these conditions, and highlight some crucial computational savings that can be made in the context of a fully Bayesian analysis.

Bayesian Retrospective Multiple- changepoint Identification

Changepoint identification is important in many data analysis problems, such as industrial control and medical diagnosis–given a data sequence, we wish to make inference about the location of one or more points of the sequence at which there is a change in the model or parameters driving the system. For long data sequences, however, analysis (especially in the multiple‐changepoint case) can become computationally prohibitive, and for complex non‐linear models analytical and conventional numerical techniques are infeasible. We discuss the use of a sampling‐based technique, the Gibbs sampler, in multiple‐changepoint problems and demonstrate how it can be used to reduce the computational load involved considerably. Also, often it is reasonable to presume that the data model itself is continuous with respect to time, i.e. continuous at the changepoints. This necessitates a continuous parameter representation of the changepoint problem, which also leads to computational difficulties. We demonstrate how inferences can be made readily in such problems by using the Gibbs sampler. We study three examples: A simple discrete two‐changepoint problem based on a binomial data model; a continuous switching linear regression problem; a continuous, non‐linear, multiple‐changepoint problem.

A role for arcuate cocaine and amphetamine-regulated transcript in hyperphagia, thermogenesis, and cold adaptation.

We have recently shown that injection of the hypothalamic peptide cocaine and amphetamine regulated transcript (CART) into discrete hypothalamic nuclei stimulates food intake. This stimulation was particularly marked in the arcuate nucleus. Here we show that twice daily intra‐arcuate injection of 0.2 nmole CART peptide for 7 days was associated with a 60% higher daytime food intake, an 85% higher thermogenic response to the β3 agonist BRL 35135, and a 60% increase in brown adipose tissue UCP‐1 mRNA. In a separate study, using stereotactically targeted gene transfer, a CART transgene was delivered by using polyethylenimine to the arcuate nucleus of adult rats. Food intake was increased significantly during ad libitum feeding and following periods of food withdrawal and food restriction in CART over‐expressing animals. CART over‐expressing animals lost 12% more weight than controls following a 24‐h fast. Brown adipose tissue uncoupling protein‐1 (UCP‐1) mRNA levels (collected Day 25) were 80% higher in CART over‐expressing animals. Finally, by using quantitative in situ hybridization, we found that chronic cold exposure (20 days at 4oC) increased arcuate nucleus CART mRNA by 124%. Together with the orexigenic and thermogenic effects of CART, this finding suggests a role for arcuate nucleus CART in cold adaptation.

Objectively monitored patching regimens for treatment of amblyopia: randomised trial.

Objectives To compare visual outcome in response to two prescribed rates of occlusion (six hours a day and 12 hours a day). Design Unmasked randomised trial. Setting Research clinics in two London hospitals. Participants 97 children with a confirmed diagnosis of amblyopia associated with strabismus, anisometropia, or both. Interventions: 18 week period of wearing glasses (refractive adaptation) followed by occlusion prescribed (“patching”) for six or 12 hours a day. Main outcome measures Visual acuity measured by logMAR letter recognition; objectively monitored rate of occlusion (hours a day). Results The mean age of children at study entry was 5.6 (SD 1.5) years. Ninety were eligible for occlusion but 10 dropped out in this phase, leaving 80 children who were randomised to a prescribed dose rate of six (n=40) or 12 (n=40) hours a day. The mean change in visual acuity of the amblyopic eye was not significantly different (P=0.64) between the two groups (0.26 (95% confidence interval 0.21 to 0.31) log units in six hour group; 0.24 (0.19 to 0.29) log units in 12 hour group). The mean dose rates (hours a day) actually received, however, were also not significantly different (4.2 (3.7 to 4.7) in six hour group v 6.2 (5.1 to 7.3) in 12 hour group; P=0.06). The visual outcome was similar for those children who received three to six hours a day or more than six to 12 hours a day, but significantly better than that in children who received less than three hours a day. Children aged under 4 required significantly less occlusion than older children. Visual outcome was not influenced by type of amblyopia. Conclusions Substantial (six hours a day) and maximal (12 hours a day) prescribed occlusion results in similar visual outcome. On average, the occlusion dose received in the maximal group was only 50% more than in the substantial group and in both groups was much less than that prescribed. Younger children required the least occlusion. Trials registration Clinical Trials NCT00274664.

BAYESIAN ANALYSIS OF ERRORS-IN-VARIABLES REGRESSION MODELS

SUMMARY Use of errors-in-variables models is appropriate in many practical experimental problems. However, inference based on such models is by no means straightforward. In previous analyses, simplifying assumptions have been made in order to ease this intractability, but assumptions of this nature are unfortunate and restrictive. In this paper, we analyse errors-in-variables models in full generality under a Bayesian formulation. In order to compute the necessary posterior distributions, we utilize various computational techniques. Two specific non-linear errors-in-variables regression examples are considered; the first is a re-analysed Berkson-type model, and the second is a classical errors-in-variables model. Our analyses are compared and contrasted with those presented elsewhere in the literature.

Transmission Clustering Drives the Onward Spread of the HIV Epidemic Among Men Who Have Sex With Men in Quebec

Phylodynamic analysis and epidemiologic data identified 3 patterns of spread of primary human immunodeficiency virus type 1 infection (PHI) among men who have sex with men (2001-2009): 420 unique PHIs, 102 small clusters (2-4 PHIs per cluster, n = 280), and 46 large clusters (5-31 PHIs per cluster, n = 450). Large clusters disproportionately increased from 25.2% of PHIs in 2005 to 39.1% in 2009 (χ(2) = 33.9, P < .001). Scalar expansion of large clusters over 11 months (interquartile range, 3.5-25.5 months) correlated with cluster membership size (r(2) = 0.174, F = 4.424, P = .047). PHI cohort data revealed variations in social networks and risk behaviors among the 3 groups, suggesting the need for tailored prevention measures.

Bayesian analysis of quantitative trait locus data using reversible jump Markov chain Monte Carlo

The advent of molecular markers has created a great potential for the understanding of quantitative inheritance in plants as well as in animals. Taking the newly available data into account, biometric models have been constructed for the mapping of quantitative trait loci (QTLs). In current approaches, the lack of knowledge on the number and location of the most important QTLs contributing to a trait is a major problem. In this paper, we utilize reversible jump Markov chain Monte Carlo methodology (Green, 1995, Biometrika 82, 711-732) in order to compute the posterior quantities required for fully Bayesian inference. It yields posterior densities not only for the parameters, given the number of QTL, but also for the number of QTL itself. As an example, the algorithm is applied to simulated data according to a standard design in plant breeding.

Reductions in intestinal Clostridiales precede the development of nosocomial Clostridium difficile infection

BackgroundAntimicrobial use is thought to suppress the intestinal microbiota, thereby impairing colonization resistance and allowing Clostridium difficile to infect the gut. Additional risk factors such as proton-pump inhibitors may also alter the intestinal microbiota and predispose patients to Clostridium difficile infection (CDI). This comparative metagenomic study investigates the relationship between epidemiologic exposures, intestinal bacterial populations and subsequent development of CDI in hospitalized patients. We performed a nested case–control study including 25 CDI cases and 25 matched controls. Fecal specimens collected prior to disease onset were evaluated by 16S rRNA gene amplification and pyrosequencing to determine the composition of the intestinal microbiota during the at-risk period.ResultsThe diversity of the intestinal microbiota was significantly reduced prior to an episode of CDI. Sequences corresponding to the phylum Bacteroidetes and to the families Bacteroidaceae and Clostridiales Incertae Sedis XI were depleted in CDI patients compared to controls, whereas sequences corresponding to the family Enterococcaceae were enriched. In multivariable analyses, cephalosporin and fluoroquinolone use, as well as a decrease in the abundance of Clostridiales Incertae Sedis XI were significantly and independently associated with CDI development.ConclusionsThis study shows that a reduction in the abundance of a specific bacterial family - Clostridiales Incertae Sedis XI - is associated with risk of nosocomial CDI and may represent a target for novel strategies to prevent this life-threatening infection.