The molecular regulation of Hox gene RNA processing during Drosophila embryonic development

The Hox genes encode a family of developmental regulators that are essential for the normal patterning of the animal body axis. Their correct expression is controlled by a number of mechanisms including RNA processing, a molecular system that allows the formation of alternative mRNAs from a single gene. Previous work in the Alonso Lab has demonstrated that RNA processing of the Drosophila Hox gene Ultrabithorax (Ubx) by means of alternative splicing and alternative polyadenylation plays an important role during Drosophila development, but the mechanisms underlying these regulatory processes are not well understood. In this project we found that the Drosophila neural RNA binding protein ELAV has an important role in the control of both Ubx alternative splicing and polyadenylation during embryonic development. Furthermore, by conducting a series of in vitro experiments we demonstrate that ELAV is able to interact with two specific RNA elements located within Ubx intronic sequences and mutation of such elements abolishes the interaction. We also establish that embryos carrying a loss of function mutation in the elav gene produce lower levels of Ubx mRNA and protein suggesting a role of ELAV on Hox gene expression. Finally we investigated the roles that other Drosophila factors including RNA binding proteins, chromatin regulators and splicing regulatory proteins may have on Ubx RNA processing and found several potential regulators. All in all our work contributes to the understanding of the molecular basis of Hox RNA processing control during Drosophila development

Tunable phenotypic variability through an autoregulatory alternative sigma factor circuit.

Genetically identical individuals in bacterial populations can display significant phenotypic variability. This variability can be functional, for example by allowing a fraction of stress prepared cells to survive an otherwise lethal stress. The optimal fraction of stress prepared cells depends on environmental conditions. However, how bacterial populations modulate their level of phenotypic variability remains unclear. Here we show that the alternative sigma factor σV circuit in Bacillus subtilis generates functional phenotypic variability that can be tuned by stress level, environmental history and genetic perturbations. Using single-cell time-lapse microscopy and microfluidics, we find the fraction of cells that immediately activate σV under lysozyme stress depends on stress level and on a transcriptional memory of previous stress. Iteration between model and experiment reveals that this tunability can be explained by the autoregulatory feedback structure of the sigV operon. As predicted by the model, genetic perturbations to the operon also modulate the response variability. The conserved sigma-anti-sigma autoregulation motif is thus a simple mechanism for bacterial populations to modulate their heterogeneity based on their environment

Escherichia coli can survive stress by noisy growth modulation.

Gene expression can be noisy, as can the growth of single cells. Such cell-to-cell variation has been implicated in survival strategies for bacterial populations. However, it remains unclear how single cells couple gene expression with growth to implement these strategies. Here, we show how noisy expression of a key stress-response regulator, RpoS, allows E. coli to modulate its growth dynamics to survive future adverse environments. We reveal a dynamic positive feedback loop between RpoS and growth rate that produces multi-generation RpoS pulses. We do so experimentally using single-cell, time-lapse microscopy and microfluidics and theoretically with a stochastic model. Next, we demonstrate that E. coli prepares for sudden stress by entering prolonged periods of slow growth mediated by RpoS. This dynamic phenotype is captured by the RpoS-growth feedback model. Our synthesis of noisy gene expression, growth, and survival paves the way for further exploration of functional phenotypic variability