2 research outputs found
Xrp1 and Irbp18 trigger a feed-forward loop of proteotoxic stress to induce the loser status
Cell competition induces the elimination of less-fit “loser” cells by fitter “winner” cells. In Drosophila, cells heterozygous mutant in ribosome genes, Rp/+, known as Minutes, are outcompeted by wild-type cells. Rp/+ cells display proteotoxic stress and the oxidative stress response, which drive the loser status. Minute cell competition also requires the transcription factors Irbp18 and Xrp1, but how these contribute to the loser status is partially understood. Here we provide evidence that initial proteotoxic stress in RpS3/+ cells is Xrp1-independent. However, Xrp1 is sufficient to induce proteotoxic stress in otherwise wild-type cells and is necessary for the high levels of proteotoxic stress found in RpS3/+ cells. Surprisingly, Xrp1 is also induced downstream of proteotoxic stress, and is required for the competitive elimination of cells suffering from proteotoxic stress or overexpressing Nrf2. Our data suggests that a feed-forward loop between Xrp1, proteotoxic stress, and Nrf2 drives Minute cells to become losers
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The PECAn image and statistical analysis pipeline identifies Minute cell competition genes and features.
Acknowledgements: We wish to thank Rafael Carazo-Salas and his laboratory for their guidance in designing, building, and validating the image analysis pipeline. We also thank Stephen Cross and Anatole Chessel for their help in evaluating the software and Daniel Lawson and Susan Connolly for assisting us in implementing, interpreting, and understanding statistical packages in R. Thanks to Cristina Villa del Campo and Miguel Torres for sharing sample images of a postnatal mouse heart. Finally, we thank the Wolfson Bioimaging Facility for access to microscopes. This work was supported by a Cancer Research UK Programme Foundation Award to E.P. (Grant C38607/A26831) and Wellcome Trust Senior Research Fellowships to E.P. (205010/Z/, 16/Z and 224675/Z/21/Z). J.L. is supported by an EMBO Postdoctoral Fellowship (EMBO ALTF 947-2021).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