Bayesian statistical analysis of bacterial diversity

Bacteria play an important role in many ecological systems. The molecular characterization of bacteria using either cultivation-dependent or cultivation-independent methods reveals the large scale of bacterial diversity in natural communities, and the vastness of subpopulations within a species or genus. Understanding how bacterial diversity varies across different environments and also within populations should provide insights into many important questions of bacterial evolution and population dynamics. This thesis presents novel statistical methods for analyzing bacterial diversity using widely employed molecular fingerprinting techniques. The first objective of this thesis was to develop Bayesian clustering models to identify bacterial population structures. Bacterial isolates were identified using multilous sequence typing (MLST), and Bayesian clustering models were used to explore the evolutionary relationships among isolates. Our method involves the inference of genetic population structures via an unsupervised clustering framework where the dependence between loci is represented using graphical models. The population dynamics that generate such a population stratification were investigated using a stochastic model, in which homologous recombination between subpopulations can be quantified within a gene flow network. The second part of the thesis focuses on cluster analysis of community compositional data produced by two different cultivation-independent analyses: terminal restriction fragment length polymorphism (T-RFLP) analysis, and fatty acid methyl ester (FAME) analysis. The cluster analysis aims to group bacterial communities that are similar in composition, which is an important step for understanding the overall influences of environmental and ecological perturbations on bacterial diversity. A common feature of T-RFLP and FAME data is zero-inflation, which indicates that the observation of a zero value is much more frequent than would be expected, for example, from a Poisson distribution in the discrete case, or a Gaussian distribution in the continuous case. We provided two strategies for modeling zero-inflation in the clustering framework, which were validated by both synthetic and empirical complex data sets. We show in the thesis that our model that takes into account dependencies between loci in MLST data can produce better clustering results than those methods which assume independent loci. Furthermore, computer algorithms that are efficient in analyzing large scale data were adopted for meeting the increasing computational need. Our method that detects homologous recombination in subpopulations may provide a theoretical criterion for defining bacterial species. The clustering of bacterial community data include T-RFLP and FAME provides an initial effort for discovering the evolutionary dynamics that structure and maintain bacterial diversity in the natural environment.

Phospholipids of lipid-containing bacteriophages and their transbilayer distribution

In this study we used electro-spray ionization mass-spectrometry to determine phospholipid class and molecular species compositions in bacteriophages PM2, PRD1, Bam35 and phi6 as well as their hosts. To obtain compositional data of the individual leaflets, phospholipid transbilayer distribution in the viral membranes was studied. We found that 1) the membranes of all studied bacteriophage are enriched in PG as compared to the host membranes, 2) molecular species compositions in the phage and host membranes are similar, and 3) phospholipids in the viral membranes are distributed asymmetrically with phosphatidylglycerol enriched in the outer leaflet and phosphatidylethanolamine in the inner one (except Bam35). Alternative models for selective incorporation of phospholipids to phages and for the origins of the asymmetric phospholipid transbilayer distribution are discussed. Notably, the present data are also useful when constructing high resolution structural models of bacteriophages, since diffraction methods cannot provide a detailed structure of the membrane due to high motility of the lipids and lack of symmetric organization of membrane proteins.