505C Poster - 06. Regulation of gene expression
Saturday April 09, 1:30 PM - 3:30 PM

Authors:
Yisi Louise Lu; Hiroshi Nakato; Michael O'Connor

Affiliation: University of Minnesota, Minneapolis, MN

Keywords:
n. networks; n. networks

Drosophila is an ideal model system for the study of TGF-β signaling networks and their roles in organism development and homeostasis. In Drosophila, there are three TGF-β/Activin-like ligands, Myoglianin (Myo), activin-β (Actβ), and Dawdle (Daw). These factors have been implicated in regulation of different physiological activities in a tissue specific manner. For example, loss of actβ results in small muscles with altered carbohydrate metabolism and changes in NMJ electrophysiology. Loss of daw results in major metabolic disfunction including altered carbohydrate metabolism and a disruption in the TCA cycle, while loss of myo leads to smaller brain and imaginal disc sizes.
Although loss of each Activin ligand gives rise to a unique phenotype, all three ligands signal through dSmad2, a common intracellular transcriptional transducer. Our goal is to elucidate the mechanisms by which the three Activin-like ligands produce specific responses and phenotypic outcomes in different tissues. We hypothesize that tissue-specific responses result from several attributes of the signaling network. First, there are cross regulatory interactions between the different ligands. Second, each ligand preferentially signals through one of three isoforms of the Type I receptor encoded by the Babo locus. Third, each tissue expresses its own complement of Babo isoforms. Finally, the transcriptional transducer dSmad2 associates with different tissue-specific cofactors to stimulate an appropriate downstream response for that tissue. The lab’s long-term goal is to elucidate how these three ligands coordinate to produce the appropriate balance of signals to maintain viability and physiological homeostasis.
We have begun to investigate the cross-regulation relationships among the three Activin ligands by qPCR studies as well as by employing transgenic GFP reporter fly lines. Our preliminary data suggested Daw is negatively auto-regulated by dSmad2 while Myo may be positively autoregulated and that these ligands likely cross-regulate each other’s expression in a tissue-specific manner. We have also begun efforts to identify direct downstream targets of Activin signaling through transcriptomic and ChIP-seq analyses. Preliminary analysis of this data will be presented. These studies will provide a deeper understanding of the Activin signaling network and the regulatory interactions that it provides in controlling important physiological activities such as proliferation and energy utilization in several key tissues including muscle, fat and brain.