Three-dimensional structure of tropism-switching Bordetella bacteriophage.

Bacteriophage BPP-1, which infects Bordetella species, can switch its specificity by mutations to the ligand-binding surface of its major tropism-determinant protein, Mtd. This targeted mutagenesis results from the activity of a phage-encoded diversity-generating retroelement. Purified Mtd binds its receptor with low affinity, yet BPP-1 binding and infection of Bordettella cells are efficient because of high-avidity binding between phage-associated Mtd and its receptor. Here, using an integrative approach of three-dimensional (3D) structural analyses of the entire phage by cryo-electron tomography and single-prticle cryo-electron microscopy, we provide direct localization of Mtd in the phage and the structural basis of the high-avidity binding of the BPP-1 phage. Our structure shows that each BPP-1 particle has a T = 7 icosahedral head and an unusual tail apparatus consisting of a short central tail "hub," six short tail spikes, and six extended tail fibers. Subtomographic averaging of the tail fiber maps revealed a two-lobed globular structure at the distal end of each long tail fiber. Tomographic reconstructions of immuno-gold-labeled BPP-1 directly localized Mtd to these globular structures. Finally, our icosahedral reconstruction of the BPP-1 head at 7A resolution reveals an HK97-like major capsid protein stabilized by a smaller cementing protein. Our structure represents a unique bacteriophage reconstruction with its tail fibers and ligand-binding domains shown in relation to its tail apparatus. The localization of Mtd at the distal ends of the six tail fibers explains the high avidity binding of Mtd molecules to cell surfaces for initiation of infection.

A comparison of Markov and generalized linear models of hospital mortality

This paper reports a comparison of three modeling strategies for the analysis of hospital mortality in a sample of general medicine inpatients in a Department of Veterans Affairs medical center. Logistic regression, a Markov chain model, and longitudinal logistic regression were evaluated on predictive performance as measured by the c-index and on accuracy of expected numbers of deaths compared to observed. The logistic regression used patient information collected at admission; the Markov model was comprised of two absorbing states for discharge and death and three transient states reflecting increasing severity of illness as measured by laboratory data collected during the hospital stay; longitudinal regression employed Generalized Estimating Equations (GEE) to model covariance structure for the repeated binary outcome. Results showed that the logistic regression predicted hospital mortality as well as the alternative methods but was limited in scope of application. The Markov chain provides insights into how day to day changes of illness severity lead to discharge or death. The longitudinal logistic regression showed that increasing illness trajectory is associated with hospital mortality. The conclusion is reached that for standard applications in modeling hospital mortality, logistic regression is adequate, but for new challenges facing health services research today, alternative methods are equally predictive, practical, and can provide new insights. ^

Characterization of template switching and recombination during Moloney murine leukemia virus replication

Retroviruses uniquely co-package two copies of their genomic RNA within each virion. The two copies are used as templates for synthesis of the proviral DNA during the process of reverse transcription. Two template switches are required to complete retroviral DNA synthesis by the retroviral enzyme, reverse transcriptase. With two RNA genomes present in the virion, reverse transcriptase can make template switches utilizing only one of the RNA templates (intramolecular) or utilizing both RNA templates (intermolecular) during the process of reverse transcription. The results presented in this study show that during a single cycle of Moloney murine leukemia virus replication, both nonrecombinant and recombinant proviruses predominantly underwent intramolecular minus- and plus-strand transfers during the process of reverse transcription. This is the first study to examine the nature of the required template switches occurring during MLV replication and these results support the previous findings for SNV, and the hypothesis that the required template switches are ordered events. This study also determined rates for deletion and a rate of recombination for a single cycle of MLV replication. The rates reported here are comparable to the rates previously reported for both SNV and MLV. ^

Estimating parameters in Markov models for longitudinal studies with missing data or surrogate outcomes

The discrete-time Markov chain is commonly used in describing changes of health states for chronic diseases in a longitudinal study. Statistical inferences on comparing treatment effects or on finding determinants of disease progression usually require estimation of transition probabilities. In many situations when the outcome data have some missing observations or the variable of interest (called a latent variable) can not be measured directly, the estimation of transition probabilities becomes more complicated. In the latter case, a surrogate variable that is easier to access and can gauge the characteristics of the latent one is usually used for data analysis. ^ This dissertation research proposes methods to analyze longitudinal data (1) that have categorical outcome with missing observations or (2) that use complete or incomplete surrogate observations to analyze the categorical latent outcome. For (1), different missing mechanisms were considered for empirical studies using methods that include EM algorithm, Monte Carlo EM and a procedure that is not a data augmentation method. For (2), the hidden Markov model with the forward-backward procedure was applied for parameter estimation. This method was also extended to cover the computation of standard errors. The proposed methods were demonstrated by the Schizophrenia example. The relevance of public health, the strength and limitations, and possible future research were also discussed. ^

Logistic regression with Markov chains as covariates

The tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is an obvious carcinogen for lung cancer. Since CBMN (Cytokinesis-blocked micronucleus) has been found to be extremely sensitive to NNK-induced genetic damage, it is a potential important factor to predict the lung cancer risk. However, the association between lung cancer and NNK-induced genetic damage measured by CBMN assay has not been rigorously examined. ^ This research develops a methodology to model the chromosomal changes under NNK-induced genetic damage in a logistic regression framework in order to predict the occurrence of lung cancer. Since these chromosomal changes were usually not observed very long due to laboratory cost and time, a resampling technique was applied to generate the Markov chain of the normal and the damaged cell for each individual. A joint likelihood between the resampled Markov chains and the logistic regression model including transition probabilities of this chain as covariates was established. The Maximum likelihood estimation was applied to carry on the statistical test for comparison. The ability of this approach to increase discriminating power to predict lung cancer was compared to a baseline "non-genetic" model. ^ Our method offered an option to understand the association between the dynamic cell information and lung cancer. Our study indicated the extent of DNA damage/non-damage using the CBMN assay provides critical information that impacts public health studies of lung cancer risk. This novel statistical method could simultaneously estimate the process of DNA damage/non-damage and its relationship with lung cancer for each individual.^

Longitudinal categorical data analysis: A continuous -time Markov chain approach

In this dissertation, we propose a continuous-time Markov chain model to examine the longitudinal data that have three categories in the outcome variable. The advantage of this model is that it permits a different number of measurements for each subject and the duration between two consecutive time points of measurements can be irregular. Using the maximum likelihood principle, we can estimate the transition probability between two time points. By using the information provided by the independent variables, this model can also estimate the transition probability for each subject. The Monte Carlo simulation method will be used to investigate the goodness of model fitting compared with that obtained from other models. A public health example will be used to demonstrate the application of this method. ^