Phylogeny and evolution of the major intrinsic protein family

BACKGROUND INFORMATION: MIPs (major intrinsic proteins) form channels across biological membranes that control recruitment of water and small solutes such as glycerol and urea in all living organisms. Because of their widespread occurrence and large number, MIPs are a sound model system to understand evolutionary mechanisms underlying the generation of protein structural and functional diversity. With the recent increase in genomic projects, there is a considerable increase in the quantity and taxonomic range of MIPs in molecular databases. RESULTS: In the present study, I compiled more than 450 non-redundant amino acid sequences of MIPs from NCBI databases. Phylogenetic analyses using Bayesian inference reconstructed a statistically robust tree that allowed the classification of members of the family into two main evolutionary groups, the GLPs (glycerol-uptake facilitators or aquaglyceroporins) and the water transport channels or AQPs (aquaporins). Separate phylogenetic analyses of each of the MIP subfamilies were performed to determine the main groups of orthology. In addition, comparative sequence analyses were conducted to identify conserved signatures in the MIP molecule. CONCLUSIONS: The earliest and major gene duplication event in the history of the MIP family led to its main functional split into GLPs and AQPs. GLPs show typically one single copy in microbes (eubacteria, archaea and fungi), up to four paralogues in vertebrates and they are absent from plants. AQPs are usually single in microbes and show their greatest numbers and diversity in angiosperms and vertebrates. Functional recruitment of NOD26-like intrinsic proteins to glycerol transport due to the absence of GLPs in plants was highly supported. Acquisition of other MIP functions such as permeability to ammonia, arsenite or CO2 is restricted to particular MIP paralogues. Up to eight fairly conserved boxes were inferred in the primary sequence of the MIP molecule. All of them mapped on to one side of the channel except the conserved glycine residues from helices 2 and 5 that were found in the opposite side.     

Generalized spatial structural equation models

It is common in public health research to have high-dimensional, multivariate, spatially referenced data representing summaries of geographic regions. Often, it is desirable to examine relationships among these variables both within and across regions. An existing modeling technique called spatial factor analysis has been used and assumes that a common spatial factor underlies all the variables and causes them to be related to one another. An extension of this technique considers that there may be more than one underlying factor, and that relationships among the underlying latent variables are of primary interest. However, due to the complicated nature of the covariance structure of this type of data, existing methods are not satisfactory. We thus propose a generalized spatial structural equation model. In the first level of the model, we assume that the observed variables are related to particular underlying factors. In the second level of the model, we use the structural equation method to model the relationship among the underlying factors and use parametric spatial distributions on the covariance structure of the underlying factors. We apply the model to county-level cancer mortality and census summary data for Minnesota, including socioeconomic status and access to public utilities.     

Investigating the evolutionary history of the Pacific Northwest mesic forest ecosystem: hypothesis testing within a comparative phylogeographic framework

We examine the evolution of mesic forest ecosystems in the Pacific Northwest of North America using a statistical phylogeography approach in four animal and two plant lineages. Three a priori hypotheses, which explain the disjunction in the mesic forest ecosystem with either recent dispersal or ancient vicariance, are tested with phylogenetic and coalescent methods. We find strong support in three amphibian lineages (Ascaphus spp., and Dicampton spp., and Plethodon vandykei and P. idahoensis) for deep divergence between coastal and inland populations, as predicted by the ancient vicariance hypothesis. Unlike the amphibians, the disjunction in other Pacific Northwest lineages is likely due to recent dispersal along a northern route. Topological and population divergence tests support the northern dispersal hypothesis in the water vole (Microtus richardsoni) and northern dispersal has some support in both the dusky willow (Salix melanopsis) and whitebark pine (Pinus albicaulis). These analyses demonstrate that genetic data sampled from across an ecosystem can provide insight into the evolution of ecological communities and suggest that the advantages of a statistical phylogeographic approach are most pronounced in comparisons across multiple taxa in a particular ecosystem. Genetic patterns in organisms as diverse as willows and salamanders can be used to test general regional hypotheses, providing a consistent metric for comparison among members of an ecosystem with disparate life-history traits.     

Multiple imputation for model checking: completed-data plots with missing and latent data

In problems with missing or latent data, a standard approach is to first impute the unobserved data, then perform all statistical analyses on the completed dataset--corresponding to the observed data and imputed unobserved data--using standard procedures for complete-data inference. Here, we extend this approach to model checking by demonstrating the advantages of the use of completed-data model diagnostics on imputed completed datasets. The approach is set in the theoretical framework of Bayesian posterior predictive checks (but, as with missing-data imputation, our methods of missing-data model checking can also be interpreted as "predictive inference" in a non-Bayesian context). We consider the graphical diagnostics within this framework. Advantages of the completed-data approach include: (1) One can often check model fit in terms of quantities that are of key substantive interest in a natural way, which is not always possible using observed data alone. (2) In problems with missing data, checks may be devised that do not require to model the missingness or inclusion mechanism; the latter is useful for the analysis of ignorable but unknown data collection mechanisms, such as are often assumed in the analysis of sample surveys and observational studies. (3) In many problems with latent data, it is possible to check qualitative features of the model (for example, independence of two variables) that can be naturally formalized with the help of the latent data. We illustrate with several applied examples.     

A class of Bayesian shared gamma frailty models with multivariate failure time data

For multivariate failure time data, we propose a new class of shared gamma frailty models by imposing the Box-Cox transformation on the hazard function, and the product of the baseline hazard and the frailty. This novel class of models allows for a very broad range of shapes and relationships between the hazard and baseline hazard functions. It includes the well-known Cox gamma frailty model and a new additive gamma frailty model as two special cases. Due to the nonnegative hazard constraint, this shared gamma frailty model is computationally challenging in the Bayesian paradigm. The joint priors are constructed through a conditional-marginal specification, in which the conditional distribution is univariate, and it absorbs the nonlinear parameter constraints. The marginal part of the prior specification is free of constraints. The prior distributions allow us to easily compute the full conditionals needed for Gibbs sampling, while incorporating the constraints. This class of shared gamma frailty models is illustrated with a real dataset.     

Bayesian adaptive biased-coin designs for clinical trials with normal responses

Adaptive designs are used in phase III clinical trials for skewing the allocation pattern toward the better treatments. We use optimum design theory to derive a skewed Bayesian biased-coin procedure for sequential designs with continuous responses. The skewed designs are used to provide adaptive designs, the performance of which is studied numerically and theoretically. Important properties are loss and the proportion of allocation to the better treatment.     

Modeling association among demographic parameters in analysis of open population capture-recapture data

We present a hierarchical extension of the Cormack-Jolly-Seber (CJS) model for open population capture-recapture data. In addition to recaptures of marked animals, we model first captures of animals and losses on capture. The parameter set includes capture probabilities, survival rates, and birth rates. The survival rates and birth rates are treated as a random sample from a bivariate distribution, thus the model explicitly incorporates correlation in these demographic rates. A key feature of the model is that the likelihood function, which includes a CJS model factor, is expressed entirely in terms of identifiable parameters; losses on capture can be factored out of the model. Since the computational complexity of classical likelihood methods is prohibitive, we use Markov chain Monte Carlo in a Bayesian analysis. We describe an efficient candidate-generation scheme for Metropolis-Hastings sampling of CJS models and extensions. The procedure is illustrated using mark-recapture data for the moth Gonodontis bidentata.     

Bias adjustment in Bayesian estimation of bird nest age-specific survival rates

The populations of many North American landbirds are showing signs of declining. Gathering information on breeding productivity allows early detection of unhealthy populations and helps develop good habitat-management practices. In this paper, we study the performance of the Bayesian model (He, 2003, Biometrics 59, 962-973) for age-specific nest survival rates with irregular visits. We find that the estimates are satisfactory except for the age-one survival rate. Usually the more days skipped between two visits, the more serious the underestimation of the age-one survival rate. We investigated the problem and developed three approaches to adjust for the underestimation bias. The simulation results show that the three approaches can significantly improve the estimation of the age-one survival rate.     

Estimating immunoregulatory gene networks in human herpesvirus type 6-infected T cells

The immune response to viral infection involves complex network of dynamic gene and protein interactions. We present here the dynamic gene network of the host immune response during human herpesvirus type 6 (HHV-6) infection in an adult T-cell leukemia cell line. Using a pathway-focused oligonucleotide DNA microarray, we found a possible association between chemokine genes regulating Th1/Th2 balance and genes regulating T-cell proliferation during HHV-6B infection. Gene network analysis using an integrated comprehensive workbench, VoyaGene, revealed that a gene encoding a TEC-family kinase, ITK, might be a putative modulator in the host immune response against HHV-6B infection. We conclude that Th2-dominated inflammatory reaction in host cells may play an important role in HHV-6B-infected T cells, thereby suggesting the possibility that ITK might be a therapeutic target in diseases related to dysregulation of Th1/Th2 balance. This study describes a novel approach to find genes related with the complex host-virus interaction using microarray data employing the Bayesian statistical framework.