Biomedical subjects
Publications and source records attributed to Matthew J Brauer.
We demonstrate a new method, microarray-assisted bulk segregant analysis, for mapping traits in yeast by genotyping pooled segregants. We apply a probabilistic model to the progeny of a single cross and as little as two microarray hybridizations to reliably map an auxotrophic marker, a Mendelian trait, and a major-effect quantitative trait locus.
We studied the physiological response to glucose limitation in batch and steady-state (chemostat) cultures of Saccharomyces cerevisiae by following global patterns of gene expression. Glucose-limited batch cultures of yeast go through two sequential exponential growth phases, beginning with a largely fermentative phase, followed by an essentially completely aerobic use of residual glucose and evolved ethanol. Judging from the patterns of gene expression, the state of the cells growing at steady state in glucose-limited chemostats corresponds most closely with the state of cells in batch cultures just before they undergo this "diauxic shift." Essentially the same pattern was found between chemostats having a fivefold difference in steady-state growth rate (the lower rate approximating that of the second phase respiratory growth rate in batch cultures). Although in both cases the cells in the chemostat consumed most of the glucose, in neither case did they seem to be metabolizing it primarily through respiration. Although there was some indication of a modest oxidative stress response, the chemostat cultures did not exhibit the massive environmental stress response associated with starvation that also is observed, at least in part, during the diauxic shift in batch cultures. We conclude that despite the theoretical possibility of a switch to fully aerobic metabolism of glucose in the chemostat under conditions of glucose scarcity, homeostatic mechanisms are able to carry out metabolic adjustment as if fermentation of the glucose is the preferred option until the glucose is entirely depleted. These results suggest that some aspect of actual starvation, possibly a component of the stress response, may be required for triggering the metabolic remodeling associated with the diauxic shift.
We studied the physiological response to limitation by diverse nutrients in batch and steady-state (chemostat) cultures of S. cerevisiae. We found that the global pattern of transcription in steady-state cultures in limiting phosphate or sulfate is essentially identical to that of batch cultures growing in the same medium just before the limiting nutrient is completely exhausted. The massive stress response and complete arrest of the cell cycle that occurs when nutrients are fully exhausted in batch cultures is not observed in the chemostat, indicating that the cells in the chemostat are "poor, not starving." Similar comparisons using leucine or uracil auxotrophs limited on leucine or uracil again showed patterns of gene expression in steady-state closely resembling those of corresponding batch cultures just before they exhaust the nutrient. Although there is also a strong stress response in the auxotrophic batch cultures, cell cycle arrest, if it occurs at all, is much less uniform. Many of the differences among the patterns of gene expression between the four nutrient limitations are interpretable in light of known involvement of the genes in stress responses or in the regulation or execution of particular metabolic pathways appropriate to the limiting nutrient. We conclude that cells adjust their growth rate to nutrient availability and maintain homeostasis in the same way in batch and steady state conditions; cells in steady-state cultures are in a physiological condition normally encountered in batch cultures.
We investigated the usefulness of a parallel genetic algorithm for phylogenetic inference under the maximum-likelihood (ML) optimality criterion. Parallelization was accomplished by assigning each "individual" in the genetic algorithm "population" to a separate processor so that the number of processors used was equal to the size of the evolving population (plus one additional processor for the control of operations). The genetic algorithm incorporated branch-length and topological mutation, recombination, selection on the ML score, and (in some cases) migration and recombination among subpopulations. We tested this parallel genetic algorithm with large (228 taxa) data sets of both empirically observed DNA sequence data (for angiosperms) as well as simulated DNA sequence data. For both observed and simulated data, search-time improvement was nearly linear with respect to the number of processors, so the parallelization strategy appears to be highly effective at improving computation time for large phylogenetic problems using the genetic algorithm. We also explored various ways of optimizing and tuning the parameters of the genetic algorithm. Under the conditions of our analyses, we did not find the best-known solution using the genetic algorithm approach before terminating each run. We discuss some possible limitations of the current implementation of this genetic algorithm as well as of avenues for its future improvement.