Mapping dynamic protein interactions in MAP kinase signaling using live-cell fluorescence fluctuation spectroscopy and imaging

Slaughter, Brian D.; Schwartz, Janet; Li, Rong

doi:10.1073/pnas.0710336105

Cited by 128 publications

(136 citation statements)

References 50 publications

(82 reference statements)

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“…Each protein icon represents 200 molecules/cell. differences are a lower Ste5 abundance compared with an earlier whole-genome immunoblotting study (9), a higher Fus3 abundance compared with two studies that relied on fluorescence measurements (11,12), and a higher value for Ste11 compared with all three studies. In the case of Fus3, we suspect that the underestimation of Fus3 abundance might be caused by omission of correction for fluorophore maturation and Fus3 degradation rate.…”

Section: Resultsmentioning

confidence: 53%

See 1 more Smart Citation

Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range

Thomson

Benjamin

Bush

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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Although the proteins comprising many signaling systems are known, less is known about their numbers per cell. Existing measurements often vary by more than 10-fold. Here, we devised improved quantification methods to measure protein abundances in the Saccharomyces cerevisiae pheromone response pathway, an archetypical signaling system. These methods limited variation between independent measurements of protein abundance to a factor of two. We used these measurements together with quantitative models to identify and investigate behaviors of the pheromone response system sensitive to precise abundances. The difference between the maximum and basal signaling output (dynamic range) of the pheromone response MAPK cascade was strongly sensitive to the abundance of Ste5, the MAPK scaffold protein, and absolute system output depended on the amount of Fus3, the MAPK. Additional analysis and experiment suggest that scaffold abundance sets a tradeoff between maximum system output and system dynamic range, a prediction supported by recent experiments.quantitative immunoblotting | single-cell fluorescence quantification | genetic algorithmic model optimization T he Saccharomyces cerevisiae pheromone response system has been useful for understanding eukaryotic cell signaling (1-3). In haploid cells, the pheromone response system detects mating pheromone in the extracellular environment secreted by yeast of the opposite mating type. The system triggers a number of responses, including induction of gene expression, arrest in cell cycle progression, changes in morphology, and eventually, mating. The molecules and interactions by which the system operates are relatively well-understood (Fig. 1).Changes in the number of molecules per cell (hereafter called abundances) of signaling proteins can alter quantitative signaling behaviors. For example, in the pheromone response system, overexpression of Fus3 increases pheromone-induced sensitivity to cell cycle arrest (4), and overexpression of Gpa1 decreases pheromone-induced transcription (5). Similarly, changes in the abundances of MAPK cascade scaffold proteins (Ste5 in the yeast pheromone response system) ( Fig. 1) can alter system behavior. In models, when scaffold is limiting, increasing scaffold abundance first increases system output and then, eventually sequesters the associated protein kinases onto separate scaffolds, diminishing the number of fully assembled complexes and system output (6). This peak and decline behavior has been experimentally shown for Ste5 in this yeast system (7) and the Kinase Suppressor of Ras (KSR) scaffold protein in Ras signaling in Xenopus oocytes (8).Previous investigators reported focused and genome-wide inventories of pheromone system protein abundances (9-13). These measurements differed by up to 12-fold (Fig. 2) (14, 15). We thought some discrepancies might be because of differences and systematic biases in quantification methods (Discussion). We developed improved measurement methods and used these methods to quantify system proteins. We used...

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Section: Resultsmentioning

confidence: 53%

“…Previous investigators reported focused and genome-wide inventories of pheromone system protein abundances (9)(10)(11)(12)(13). These measurements differed by up to 12-fold ( Fig.…”

mentioning

confidence: 95%

Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range

Thomson

Benjamin

Bush

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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“…In this study, we investigated the absolute concentration, dynamics, and complex formation of the 26S proteasome in living yeast cells by fluorescence correlation spectroscopy (FCS) [27][28][29][30] , a method for quantitative live-cell imaging. Unexpectedly, we found that the 26S proteasome is a highly mobile complex, and that almost all proteasome subunits are stably incorporated into 26S proteasomes in both the cytosol and nucleus.…”

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confidence: 99%

Quantitative live-cell imaging reveals spatio-temporal dynamics and cytoplasmic assembly of the 26S proteasome

et al. 2014

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The 26S proteasome is a 2.5-MDa multisubunit protease complex that degrades polyubiquitylated proteins. Although its functions and structure have been extensively characterized, little is known about its dynamics in living cells. Here, we investigate the absolute concentration, spatio-temporal dynamics and complex formation of the proteasome in living cells using fluorescence correlation spectroscopy. We find that the 26S proteasome complex is highly mobile, and that almost all proteasome subunits throughout the cell are stably incorporated into 26S proteasomes. The interaction between 19S and 20S particles is stable even in an importin-a mutant, suggesting that the 26S proteasome is assembled in the cytoplasm. Furthermore, a genetically stabilized 26S proteasome mutant is able to enter the nucleus. These results suggest that the 26S proteasome completes its assembly process in the cytoplasm and translocates into the nucleus through the nuclear pore complex as a holoenzyme.

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“…Their measurements lead them to conclude that there is no change in the cytoplasmic complex formation upon α-factor treatment for any of the complexes investigated. In contrast to this, Slaughter et al 73 performing similar measurements found a regulated interaction for Fus3p and Ste7p upon α-factor stimulation. They could not detect an interaction between the two proteins in cycling cells but measured the formation of a complex upon α-factor stimulation.…”

Section: Fluorescent-based Quantitative Measurements Of Signal Transdmentioning

confidence: 81%

“…Two groups investigated complex formation in the mating pathway by FCS 62,73 . Maeder et al studied the pair-wise interactions between Ste11p, Ste7p, Ste5p and Fus3p in the cytoplasm of α-factor treated and untreated cells.…”

Section: Fluorescence Correlation Spectroscopymentioning

confidence: 99%

Fluorescent-Based Quantitative Measurements of Signal Transduction in Single Cells

Pelet¹,

Peter²

2011

Design and Analysis of Biomolecular Circuits

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Budding yeast (Saccharomyces cerevisiae) has been widely used as a model system to study fundamental biological processes. Genetic and biochemical approaches have allowed in the last decades to uncover the key components involved in many signaling pathways. Generally, most techniques measure the average behavior of a population of cells, and thus missed important cell-to-cell variations. With the recent progress with fluorescent proteins, new avenues have been opened to quantitatively study the dynamics of signaling in single living cells. In this chapter, we describe several techniques based on fluorescence measurements to quantify the activation of biological pathways. Flow cytometry allows for rapid quantification of the total fluorescence of a large number of single cells. In contrast, microscopy offers a lower throughput but allows to follow with a high temporal resolution the localization of proteins at sub-cellular resolution. Finally, advanced functional imaging techniques such as FRET and FCS offer the possibility to directly visualize the formation of protein complexes or to quantify the activity of proteins in vivo. Together these techniques present powerful new approaches to study cellular signaling and will greatly increase our understanding of the regulation of signaling networks in budding yeast and beyond. FLUORESCENT-BASED QUANTITATIVE MEASUREMENTS OF SIGNAL TRANSDUCTION IN SINGLE CELLS Serge Pelet and Matthias Peter Institute of Biochemistry, Department of Biology, ETH Zürich Abstract:Budding yeast (Saccharomyces cerevisiae) has been widely used as a model system to study fundamental biological processes. Genetic and biochemical approaches have allowed in the last decades to uncover the key components involved in many signaling pathways. Generally, most techniques measure the average behavior of a population of cells, and thus missed important cell-tocell variations. With the recent progress with fluorescent proteins, new avenues have been opened to quantitatively study the dynamics of signaling in single living cells. In this chapter, we describe several techniques based on fluorescence measurements to quantify the activation of biological pathways. Flow cytometry allows for rapid quantification of the total fluorescence of a large number of single cells. In contrast, microscopy offers a lower throughput but allows to follow with a high temporal resolution the localization of proteins at sub-cellular resolution. Finally, advanced functional imaging techniques such as FRET and FCS offer the possibility to directly visualize the formation of protein complexes or to quantify the activity of proteins in vivo.Together these techniques present powerful new approaches to study cellular signaling and will greatly increase our understanding of the regulation of signaling networks in budding yeast and beyond.

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Mapping dynamic protein interactions in MAP kinase signaling using live-cell fluorescence fluctuation spectroscopy and imaging

Cited by 128 publications

References 50 publications

Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range

Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range

Quantitative live-cell imaging reveals spatio-temporal dynamics and cytoplasmic assembly of the 26S proteasome

Fluorescent-Based Quantitative Measurements of Signal Transduction in Single Cells

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