HIV-1 transmission and viral evolution in the first year of infection were studied in 11 individuals representing four transmitter-recipient pairs and three independent seroconverters. Nine of these individuals were enrolled during acute infection; all were men who have sex with men (MSM) infected with HIV-1 subtype B. A total of 475 nearly full-length HIV-1 genome sequences were generated, representing on average 10 genomes per specimen at 2 to 12 visits over the first year of infection. Single founding variants with nearly homogeneous viral populations were detected in eight of the nine individuals who were enrolled during acute HIV-1 infection. Restriction to a single founder variant was not due to a lack of diversity in the transmitter as homogeneous populations were found in recipients from transmitters with chronic infection. Mutational patterns indicative of rapid viral population growth dominated during the first 5 weeks of infection and included a slight contraction of viral genetic diversity over the first 20 to 40 days. Subsequently, selection dominated, most markedly in env and nef. Mutants were detected in the first week and became consensus as early as day 21 after the onset of symptoms of primary HIV infection. We found multiple indications of cytotoxic T lymphocyte (CTL) escape mutations while reversions appeared limited. Putative escape mutations were often rapidly replaced with mutually exclusive mutations nearby, indicating the existence of a maturational escape process, possibly in adaptation to viral fitness constraints or to immune responses against new variants. We showed that establishment of HIV-1 infection is likely due to a biological mechanism that restricts transmission rather than to early adaptive evolution during acute infection. Furthermore, the diversity of HIV strains coupled with complex and individual-specific patterns of CTL escape did not reveal shared sequence characteristics of acute infection that could be harnessed for vaccine design.
The cloned Porphyromonas gingivalis alkyl hydroperoxide reductase (ahpC) gene complemented an ahpC defect in Escherichia coli. To study the role of ahpC in protecting against oxidative stress in P. gingivalis a 1.8 kb fragment containing the ahpC gene was amplified from the chromosome of P. gingivalis W83. This gene was insertionally inactivated using the ermF-ermAM antibiotic resistance cassette and used to create a ahpC-deficient mutant by allelic exchange. One mutant strain, designated FLL141, demonstrated no change in the growth rate, black pigmentation, beta-hemolysis or level of proteolytic activity compared to the parent strain. Although P. gingivalis FLL141 was more sensitive to hydrogen peroxide than the parent strain, there was no change in its virulence potential in the mouse model compared to the wild-type strain. These findings suggest that the ahpC gene plays a role in peroxide resistance in P. gingivalis but does not contribute significantly to virulence.
A new fuzzy logic model with a genetic algorithm is developed that overcomes some of the inherent uncertainties in the fish stock-recruitment process. This model is applied to stock-recruitment relationships for the Southeast Alaska pink salmon (Oncorhynchus gorbuscha) and the West Coast Vancouver Island Pacific herring (Clupea pallasi) stocks. In both examples, the annual mean sea surface temperature is used as an environmental intervention in the model. The fuzzy logic model provides the functional relationship between the number of fish spawners and the sea surface temperature that is used to reconstruct the historical fish recruitment time series and also to predict the number of fish that will recruit in the future. Globally optimized genetic learning algorithms are used to find the optimal values of the parameters for the fuzzy logic model. The results from this fuzzy logic model are compared with results from both a traditional Ricker stock-recruitment model and a recent artificial neural network model. These comparisons demonstrate the superior capability of the fuzzy logic model for addressing problems of uncertainty and vagueness in both the data and the stock-recruitment relationship. The fuzzy logic model approach is recommended as a useful addition to the analytical tools currently available for fish stock assessment and management.
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