Anne-Claude Gingras (Lunenfeld-Tanenbaum Research Institute, University of Toronto), Claire Martin (Lunenfeld-Tanenbaum Research Institute), Geoffrey Hesketh (Lunenfeld-Tanenbaum Research Institute), Rasha Al Mismar (Lunenfeld-Tanenbaum Research Institute, Lunenfeld-Tanenbaum Research Institute, University of Toronto), James Knight (Lunenfeld-Tanenbaum Research Institute), Christopher Go (Lunenfeld-Tanenbaum Research Institute, University of Toronto), Ugo Dionne (Lunenfeld-Tanenbaum Research Institute)
Presenting Author Lunenfeld-Tanenbaum Research Institute, University of Toronto
Understanding dynamic subcellular organization in living cells is key to unravelling mechanisms of intracellular signaling that goes awry in disease. Using proximity-dependent biotinylation (BioID), we first established a reference map for the human cell at steady state (Go et al., Nature, 2021; humancellmap.org) that serves to identify protein baits that can report on the recruitment of proteins to selected organelles or subcellular structures. These BioID “sensors” can then be applied to look at dynamic changes in signaling. For example, exploiting late endosome/lysosome BioID sensors (VAMP7 and VAMP8), we recently revealed new intricacies in the regulation of amino acid sensing pathways (Hesketh et al., Science, 2020). We also coupled BioID sensor profiling to CRISPR-mediated ablation of pathway components to investigate the consequences on the environment detected by the sensor. Using these approaches, we recently used fast-acting biotinylation enzymes, including miniTurbo, to provide a space and time-resolved analysis of EGFR pathway activation, revealing previously uncharacterized associations. This presentation will revisit key principles of dynamic organelle mapping in living cells, and the utilization of coincidence detection for dynamic proximal interactomes.
Support or Funding Information
This work was also supported by a Canadian Institutes of Health Research Foundation Grant (CIHR FDN 143301 to ACG), a Canadian Cancer Society Research Institute Innovation to Impact grant (to ACG) and a Terry Fox Research Institute team grant (to ACG). Proteomics work was performed at the Network Biology Collaborative Centre at the Lunenfeld-Tanenbaum Research Institute, a facility supported by Canada Foundation for Innovation funding, by the Government of Ontario and by Genome Canada and Ontario Genomics (OGI-139). ACG is the Canada Research Chair (Tier 1) in Functional Proteomics. CM is supported by the KRESCENT Kidney Foundation of Canada and a CIHR Postdoctoral Fellowship. GH was supported by a CIHR Postdoctoral Fellowship and a Basic Research Fellowship, Parkinson Society Canada’s National Research Program. RAM is supported by an Ontario Student Opportunity Trust Fund award. CG was supported by a CIHR doctoral award.
lt;pgt;This work was also supported by a Canadian Institutes of Health Research Foundation Grant (CIHR FDN 143301 to ACG), a Canadian Cancer Society Research Institute Innovation to Impact grant (to ACG) and a Terry Fox Research Institute team grant (to ACG). Proteomics work was performed at the Network Biology Collaborative Centre at the Lunenfeld-Tanenbaum Research Institute, a facility supported by Canada Foundation for Innovation funding, by the Government of Ontario and by Genome Canada and Ontario Genomics (OGI-139). amp;nbsp;ACG is the Canada Research Chair (Tier 1) in Functional Proteomics. CM is supported by the KRESCENT Kidney Foundation of Canada and aamp;nbsp;CIHR Postdoctoral Fellowship. GH was supported by a CIHR Postdoctoral Fellowship and a Basic Research Fellowship, Parkinson Society Canadaamp;rsquo;s National Research Program. RAM is supported by anamp;nbsp;Ontario Student Opportunity Trust Fund award. CG was supported by a CIHR doctoral award.lt;/pgt;
This figure shows a schematic for the optimization of the duration of labeling with miniTurbo for signaling baits. The right panel displays selected high confidence proximal interactors for EGFR-miniTurbo (unstimulated) after labeling for different times. See legend inset for details.; Designing a proximity-dependent biotinylation sensor experiment. Quantitative proteomics approaches are used to observe the change in recruitment of proteins in the vicinity of the sensor across conditions. Conditions can include cell stimulation, pharmacological inhibition, CRISPR-mediated ablation of pathway components, etc.
