Project description:BackgroundSex in higher diploids carries a two-fold cost of males that should reduce its fitness relative to cloning, and result in its extinction. Instead, sex is widespread and clonal species face early obsolescence. One possible reason is that sex is an adaptation that allows organisms to respond more effectively to endless changes in their environment. The purpose of this study was to model mutation and selection in a diploid organism in an evolving environment and ascertain their support for sex.ResultsWe used a computational approach to model finite populations where a haploid environment subjects a diploid host to endlessly evolving change. Evolution in both populations is primarily through adoption of novel advantageous mutations within a large allele space. Sex outcompetes cloning by two complementary mechanisms. First, sexual diploids adopt advantageous homozygous mutations more rapidly than clonal ones under conditions of lag load (the gap between the actual adaptation of the diploid population and its theoretical optimum). This rate advantage can offset the higher fecundity of cloning. Second, a relative advantage to sex emerges where populations are significantly polymorphic, because clonal polymorphism runs the risk of clonal interference caused by selection on numerous lines of similar adaptation. This interference extends allele lifetime and reduces the rate of adaptation. Sex abolishes the interference, making selection faster and elevating population fitness. Differences in adaptation between sexual and clonal populations increase markedly with the number of loci under selection, the rate of mutation in the host, and a rapidly evolving environment. Clonal interference in these circumstances leads to conditions where the greater fecundity of clones is unable to offset their poor adaptation. Sexual and clonal populations then either co-exist, or sex emerges as the more stable evolutionary strategy.ConclusionsSex can out-compete clones in a rapidly evolving environment, such as that characterized by pathogens, where clonal interference reduces the adaptation of clonal populations and clones adopt advantageous mutations more slowly. Since all organisms carry parasitic loads, the model is of potentially general applicability.
Project description:Transcription profiling of aerial parts of Arabidopsis wild type and arr10 arr12 double mutant seedlings treated with the cytokinin trans-zeatin
Project description:Transcription profiling of nodose or dorsal root ganglion visceral sensory neurons from mice infected with N. brasiliensis and/or subjected to environmental stress
Project description:We investigated the ability of HDAC inhibitors (HDACi) to target CML stem cells. Treatment with HDACi combined with IM effectively induced apoptosis in quiescent CML progenitors resistant to elimination by IM alone, and eliminated CML stem cells capable of engrafting immunodeficient mice. In vivo administration of HDACi with IM markedly diminished LSC in a transgenic mouse model of CML. The interaction of IM and HDACi inhibited genes regulating hematopoietic stem cell maintenance and survival. HDACi treatment represents a novel and effective strategy to target LSC in CML patients receiving tyrosine kinase inhibitors. CML CD34+CD38- cells were selected using flow cytometry sorting and treated with IM, LBH and the combination of IM and LBH or cultured without exposure to drugs (controls) for 24 hours (n=3 each). Total RNA from 5000 cells was extracted using the RNeasy kit (Qiagen), amplified and labeled using GeneChip Two-Cycle Target Labeling and Control Reagents (Affymetrix, Santa Clara, CA). 15 µg cRNA from each sample was hybridized to Affymetrix GeneChip Human Genome U133 Plus 2.0 Arrays. Microarray data analyses were performed using R (version 2.9) with genomic analysis packages from Bioconductor (version 2.4). Expression data were normalized using the robust multiarray average (RMA) algorithm, with background adjustment, quantile normalization and median polish summarization. Probesets with low expression levels or low variability across samples were filtered. For genes with multiple probesets, the gene level expression was set to be the median of the probesets. Linear regression was used to model the gene expression with the consideration of 2x2 factorial design and matched samples. Differentially expressed genes were identified by calculating empirical Bayes moderated t-statistic, and p-values were adjusted by FDR using the “LIMMA” package. Gene Set Enrichment Analyses (GSEA) was performed using GSEA software version 2.04 [http://www.broadinstitute.org/gsea/] to detect enrichment of predetermined gene sets using t-scores and gene sets in C2 (curated gene sets) category from the Molecular Signature Database (MsigDB). Gene sets representing common functional categories were categorized and grouped. We also analyzed enrichment of gene sets with common transcription factor binding sites (586 sets) from MsigDB.