Analysis of Heterokaryon Formation in Fusarium oxysporum f. sp. cubense.

Fusarium oxysporum forma specialis cubense is a soilborne phytopathogen that infects banana. The true evolutionary identity of this so called species, Fusarium oxysporum, is still unknown. Many techniques have been applied in order to gain insight for the observed genetic diversity of this species. The current classification system is based on vegetative compatibility groups (VCG's). Vegetative compatibility is a self non-self recognition system in which only those belonging to a VCG can form stable heterokaryons, cells containing two distinct nuclei. Heterokaryons in turn, are formed from hypha! anastomosis, the fusion of two hyphae. Furthermore, subsequent to heterokaryon formation potential mechanisms exist which may generate genetic variability. One is through viral transfer upon hyphal anastomosis. The other mechanism is a form of mitotic recombination referred to as the parasexual cycle. Very little research has been performed to directly obser.ve the cellular events; hypha! anastomosis, heterokaryon formation, and the parasexual cycle in Fusarium oxysporum f. sp. cubense. The purpose of this research was to design and use methods which would allow for the detection of hypha! anastomosis and heterokaryon formation, as well as any characteristics surrounding this event, within and between VCG's in Foe. First, some general growth properties were recorded: the number of nuclei per hypha, the size ofthe hyphal tip cell, the size of the cell adjacent to the hypha! tip (pre-tip) cell, and the number of cells to the first branch point. Second, four methods were designed in order to assay hyphal anastomosis and heterokaryon formation: 1) pairings on membrane: phase or brightfield microscopy, 2) pairings on membrane: fluorescence microscopy, 3) spore crosses: fluorescence microscopy, and 4) double picks in fractionated MMA. All of these methods were promtsmg.

Phylogenetic analysis of within -host serially-sampled viral data

The primary goal of this dissertation is the study of patterns of viral evolution inferred from serially-sampled sequence data, i.e., sequence data obtained from strains isolated at consecutive time points from a single patient or host. RNA viral populations have an extremely high genetic variability, largely due to their astronomical population sizes within host systems, high replication rate, and short generation time. It is this aspect of their evolution that demands special attention and a different approach when studying the evolutionary relationships of serially-sampled sequence data. New methods that analyze serially-sampled data were developed shortly after a groundbreaking HIV-1 study of several patients from which viruses were isolated at recurring intervals over a period of 10 or more years. These methods assume a tree-like evolutionary model, while many RNA viruses have the capacity to exchange genetic material with one another using a process called recombination. ^ A genealogy involving recombination is best described by a network structure. A more general approach was implemented in a new computational tool, Sliding MinPD, one that is mindful of the sampling times of the input sequences and that reconstructs the viral evolutionary relationships in the form of a network structure with implicit representations of recombination events. The underlying network organization reveals unique patterns of viral evolution and could help explain the emergence of disease-associated mutants and drug-resistant strains, with implications for patient prognosis and treatment strategies. In order to comprehensively test the developed methods and to carry out comparison studies with other methods, synthetic data sets are critical. Therefore, appropriate sequence generators were also developed to simulate the evolution of serially-sampled recombinant viruses, new and more through evaluation criteria for recombination detection methods were established, and three major comparison studies were performed. The newly developed tools were also applied to "real" HIV-1 sequence data and it was shown that the results represented within an evolutionary network structure can be interpreted in biologically meaningful ways. ^