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

Investigating organ biology requires sophisticated methodologies to induce genetically distinct clones within a tissue. Microscopic analysis of such samples produces information-rich 3D images. However, the 3D nature and spatial anisotropy of clones makes sample analysis challenging and slow and limits the amount of information that can be extracted manually. Here we have developed a pipeline for image processing and statistical data analysis which automatically extracts sophisticated parameters from complex multi-genotype 3D images. The pipeline includes data handling, machine-learning-enabled segmentation, multivariant statistical analysis, and graph generation. This enables researchers to run rigorous analyses on images and videos at scale and in a fraction of the time, 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 competition and identify, by statistical regression analysis, tissue parameters that model and predict competitive death.

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PECAn, a pipeline for image processing and statistical analysis of complex mosaic 3D tissues

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PECAn, a pipeline for image processing and statistical analysis of complex mosaic 3D tissues