Building multicellular microbial consortia that communicate with each other and perform programmed functionalities is the next milestone for synthetic biology. Achieving cell-cell communication within these communities requires programming of the transduction of an extracellular signal into a customized intracellular response. G-protein-coupled receptors (GPCRs) are attractive candidates for engineering signal transduction as they can sense extracellular events with high sensitivity and specificity and transduce them into complex intracellular programs. We recently developed a scalable cell-cell communication language based on fungal mating GPCRs and their secreted peptide ligands. This language allows the assembly of engineered yeast strains into multicellular communication networks and allows them to be made interdependent by peptide signaling. In peptide signaling, one cell secretes a peptide that supports the growth of another cell at nanomolar concentrations, a scalable approach for engineering interdependence. Here we address the challenge of correlating the doubling time of Saccharomyces cerevisiae cells with an increasing external peptide concentration by linking GPCR activation to the expression of an essential gene. The required fine-tuning of downstream signaling is achieved via the transcriptional titration of a set of orthogonal GPCR-activated transcription factors, a series of corresponding promoters with different output dynamics, and the use of chemically recoded peptide ligands with varying activation potentials. As such, our work establishes three control points that allow the tuning of the basal and maximal activation of the GPCR response, fold change activation, and response sensitivity. The presented results enable the implementation of peptide-dependent and peptide-tunable growth but could also facilitate the design and calibration of more complex GPCR-controlled synthetic functionality in the future.
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Evidence ID | Analyze ID | Gene/Complex | Systematic Name/Complex Accession | Qualifier | Gene Ontology Term ID | Gene Ontology Term | Aspect | Annotation Extension | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Phenotype | Experiment Type | Experiment Type Category | Mutant Information | Strain Background | Chemical | Details | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Disease Ontology Term | Disease Ontology Term ID | Qualifier | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Regulator | Regulator Systematic Name | Target | Target Systematic Name | Direction | Regulation of | Happens During | Regulator Type | Direction | Regulation Of | Happens During | Method | Evidence | Strain Background | Reference |
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Site | Modification | Modifier | Source | Reference |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Allele | Assay | Annotation | Action | Phenotype | SGA score | P-value | Source | Reference | Note |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Assay | Annotation | Action | Modification | Source | Reference | Note |
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Complement ID | Locus ID | Gene | Species | Gene ID | Strain background | Direction | Details | Source | Reference |
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Evidence ID | Analyze ID | Dataset | Description | Keywords | Number of Conditions | Reference |
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Evidence ID | Analyze ID | File | Description |
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