| 1. |
- Garcia, Ignacio, et al.
(författare)
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Kel1 is a phosphorylation-regulated noise suppressor of the pheromone signaling pathway
- 2021
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Ingår i: Cell Reports. - : Elsevier. - 2211-1247. ; 37:13
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Tidskriftsartikel (refereegranskat)abstract
- Mechanisms have evolved that allow cells to detect signals and generate an appropriate response. The accuracy of these responses relies on the ability of cells to discriminate between signal and noise. How cells filter noise in signaling pathways is not well understood. Here, we analyze noise suppression in the yeast pheromone signaling pathway and show that the poorly characterized protein Kel1 serves as a major noise suppressor and prevents cell death. At the molecular level, Kel1 prevents spontaneous activation of the pheromone response by inhibiting membrane recruitment of Ste5 and Far1. Only a hypophosphorylated form of Kel1 suppresses signaling, reduces noise, and prevents pheromone-associated cell death, and our data indicate that the MAPK Fus3 contributes to Kel1 phosphorylation. Taken together, Kel1 serves as a phosphoregulated suppressor of the pheromone pathway to reduce noise, inhibit spontaneous activation of the pathway, regulate mating efficiency, and prevent pheromone-associated cell death.
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| 2. |
- Zackrisson, Martin, et al.
(författare)
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Scan-o-matic: High-Resolution Microbial Phenomics at a Massive Scale
- 2016
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Ingår i: G3: Genes, Genomes, Genetics. - : Oxford University Press (OUP). - 2160-1836. ; 6:9, s. 3003-3014
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Tidskriftsartikel (refereegranskat)abstract
- The capacity to map traits over large cohorts of individuals—phenomics—lags far behind the explosive development in genomics. For microbes, the estimation of growth is the key phenotype because of its link to fitness. We introduce an automated microbial phenomics framework that delivers accurate, precise, and highly resolved growth phenotypes at an unprecedented scale. Advancements were achieved through the introduction of transmissive scanning hardware and software technology, frequent acquisition of exact colony population size measurements, extraction of population growth rates from growth curves, and removal of spatial bias by reference-surface normalization. Our prototype arrangement automatically records and analyzes close to 100,000 growth curves in parallel. We demonstrate the power of the approach by extending and nuancing the known salt-defense biology in baker’s yeast. The introduced framework represents a major advance in microbial phenomics by providing high-quality data for extensive cohorts of individuals and generating well-populated and standardized phenomics databases
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