The Gr64 cluster of gustatory receptors promotes survival and proteostasis of epithelial cells in Drosophila.

Gustatory Receptor 64 (Gr64) genes are a cluster of 6 neuronally expressed receptors involved in sweet taste sensation in Drosophila melanogaster. Gr64s modulate calcium signalling and excitatory responses to several different sugars. Here, we discover an unexpected nonneuronal function of Gr64 receptors and show that they promote proteostasis in epithelial cells affected by proteotoxic stress. Using heterozygous mutations in ribosome proteins (Rp), which have recently been shown to induce proteotoxic stress and protein aggregates in cells, we show that Rp/+ cells in Drosophila imaginal discs up-regulate expression of the entire Gr64 cluster and depend on these receptors for survival. We further show that loss of Gr64 in Rp/+ cells exacerbates stress pathway activation and proteotoxic stress by negatively affecting autophagy and proteasome function. This work identifies a noncanonical role in proteostasis maintenance for a family of gustatory receptors known for their function in neuronal sensation.

The PECAn image and statistical analysis pipeline identifies Minute cell competition genes and features.

Investigating organ biology often requires methodologies to induce genetically distinct clones within a living tissue. However, the 3D nature of clones makes sample image analysis challenging and slow, limiting the amount of information that can be extracted manually. Here we develop PECAn, a pipeline for image processing and statistical data analysis of complex multi-genotype 3D images. PECAn includes data handling, machine-learning-enabled segmentation, multivariant statistical analysis, and graph generation. This enables researchers to perform rigorous analyses rapidly and at scale, without requiring programming skills. We demonstrate the power of this pipeline by applying it to the study of Minute cell competition. We find an unappreciated sexual dimorphism in Minute cell growth in competing wing discs and identify, by statistical regression analysis, tissue parameters that model and correlate with competitive death. Furthermore, using PECAn, we identify several genes with a role in cell competition by conducting an RNAi-based screen.