Predicted coreceptor usage at end-stage HIV disease in tissues derived from subjects on antiretroviral therapy with an undetectable plasma viral load.

HIV cure research is increasingly focused on anatomical tissues as sites for residual HIV replication during combined antiretroviral therapy (cART). Tissue-based HIV could contribute to low-level immune activation and viral rebound over the course of infection and could also influence the development of diseases, such as atherosclerosis, neurological disorders and cancers. cART-treated subjects have a decreased and irregular presence of HIV among tissues, which has resulted in a paucity of actual evidence concerning how or if HIV persists, replicates and evolves in various anatomical sites during therapy. In this study, we pooled 1806 HIV envelope V3 loop sequences from twenty-six tissue types (seventy-one total tissues) of six pre-cART subjects, four subjects with an unknown cART history who died with profound AIDS, and five subjects who died while on cART with an undetectable plasma viral load. A computational approach was used to assess sequences for their ability to utilize specific cellular coreceptors (R5, R5 and X4, or X4). We found that autopsied tissues obtained from virally suppressed cART+ subjects harbored both integrated and expressed viruses with similar coreceptor usage profiles to subjects with no or ineffective cART therapy (i.e., significant plasma viral load at death). The study suggests that tissue microenvironments provide a sanctuary for the continued evolution of HIV despite cART.

Special section on machine intelligence approaches to systems biology.

The three papers in this special section focus on machine intelligence approaches to systems biology.

Multiple-vector self-adaptation in evolutionary algorithms.

Self-adaptation is a common method for learning online control parameters in an evolutionary algorithm. In one common implementation, each individual in the population is represented as a pair of vectors (x, sigma), where x is the candidate solution to an optimization problem scored in terms of f(x), and sigma is the so-called strategy parameter vector that influences how offspring will be created from the individual. Experimental evidence suggests that the elements of sigma can sometimes become too small to explore the given response surface adequately. The evolutionary search then stagnates, until the elements of sigma grow sufficiently large as a result of random variation. A potential solution to this deficiency associates multiple strategy parameter vectors with a single individual. A single strategy vector is active at any time and dictates how offspring will be generated. Experiments are conducted on four 10-dimensional benchmark functions where the number of strategy parameter vectors is varied over 1, 2, 3, 4, 5, 10, and 20. The results indicate advantages for using multiple strategy parameter vectors. Furthermore, the relationship between the mean best result after a fixed number of generations and the number of strategy parameter vectors can be determined reliably in each case.

Temperature gradient chamber for relative growth rate analysis of yeast.

Relative growth is often used as a phenotypic measure to distinguish mutant and wild-type yeast or bacterial strains. Differential growth as a function of temperature is a convenient and accurate means of analyzing differences between strains. Slight differences in the genotypes of two strains frequently result in differential growth of the two strains as a function of temperature. We have developed a chamber for the simultaneous growth of multiple strains in microtiter plates along a temperature gradient. Image analysis was used to determine colony area and number at various times as a function of temperature. This chamber reduces the time required and increases the accuracy in measuring growth as a function of temperature. This occurs by allowing relative growth to be measured along a temperature gradient where all other conditions are constant. Two strains of yeast (Saccharomyces cerevisiae) with a known difference in temperature dependence of growth were used to demonstrate the performance of this chamber.

Expression of Tetrahymena histone H4 in yeast.

Histone H4 is one of the most conserved proteins known. The very low rate of nonsynonymous substitution in H4 suggests that it fulfills an essential function in virtually all eukaryotes. While the majority of histone H4 sequences differ only slightly from the general consensus H4 sequence, yeast and Tetrahymena sequences diverge substantially from both the consensus and from each other. This study demonstrates that despite this divergence, when Saccharomyces cerevisiae cells are forced to use the Tetrahymena thermophila histone H4 protein, they are viable although they have a reduced growth rate, are temperature-sensitive relative to wild-type, have a lengthened G2 phase, and show a dramatic repression of mating. An amino acid replacement at position 33 in the protein improves the growth rate of these cells growing at temperatures above 28 degrees C. This replacement changes a proline to a serine and is a further divergence from both the Tetrahymena thermophila and Saccharomyces cerevisiae histone H4 sequences. Thus, the replacement and expression of a non wild-type histone H4 in yeast offers measurable effects on cell growth, identifying amino acids required for optimal yeast functioning.

On the instability of evolutionary stable strategies.

Evolutionary stable strategies (ESSs) are often used to explain the behaviors of individuals and species. The analysis of ESSs determines which, if any, combinations of behaviors cannot be invaded by alternative strategies. However, two of the assumptions required to generate ESSs, an infinite population and payoffs described only on the average, are not particularly realistic in natural situations. Previous experiments have indicated that under more natural conditions of finite populations and stochastic payoffs, populations may evolve in trajectories that are unrelated to an ESS, even in very simple evolutionary games. Those earlier simulations are extended here under a variety of conditions. The results suggest that ESSs may not provide a good explanation of a finite population's behavior even when the conditions correspond closely with the infinite population model. The implications of these results are discussed briefly in light of previous literature claiming that ESSs generated suitable explanations of real-world data.