Cell and developmental biology
Drosophila is one of the best models to study autophagy. Rapidly growing larval polyploid tissues like the fat body, the analogue of our liver and white fat, are programmed to undergo autophagy during starvation or metamorphosis, this way providing energy and nutrients to diploid cells that will make the adult fly by the end of the pupal stage (see figure). We study the regulation of autophagy in Drosophila, and how this process is involved in aging, cell growth and cell death, metamorphosis, and stress responses.
Figure 1. Autophagy is induced in response to starvation or during normal development in the liver- and fat tissue-like larval fat body.
Genetics
We have carried out an in vivo loss of function screen, using publicly available transgenic Drosophila lines that allow inducible knockdown of single protein-coding genes in selected cells or tissues. This method allows very sensitive evaluation of autophagy phenotypes, as loss of function cells are surrounded by wild-type cells, and analysis of genes required for early development or for general viability is also possible this way (Figure 2). We take full advantage of the powerful genetics of Drosophila by routinely generating null mutants, transgenic reporters, carrying out genetic rescue experiments, and so on.
Figure 2. Clones of cells expressing an RNAi transgene that silences Atg2 (green) are strongly impaired in starvation-induced autophagy, as detected by Lysotracker Red staining or mCherry-Atg8a.
Biochemistry
We study the post-translational modifications of proteins involved in autophagy, and protein-protein interactions, mainly by co-immunoprecipitation experiments in cultured cells and whole animals (Figure 3). We are also developing tools for proteomics.
Figure 3. The SNARE complex required for the fusion of autophagosomes with late endosomes and lysosomes consists of Syntaxin 17, Usnp (SNAP29) and Vamp7.
Bioinformatics
We analyze the large-scale datasets that we generated from microarray and RNAi experiments by a wide range of bioinformatic methods, and compare these with publicly available data (Figure 4).
