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Specific cancer-associated mutations in the switch III region of Ras increase tumorigenicity by nanocluster augmentation.

Šolman M, Ligabue A, Blaževitš O, Jaiswal A, Zhou Y, Liang H, Lectez B, Kopra K, Guzmán C, Härmä H, Hancock JF, Aittokallio T, Abankwa D - Elife (2015)

Bottom Line: Here, we show that several cancer-associated mutations in the switch III region moderately increase Ras activity in all isoforms.Nanoclustering dictates downstream effector recruitment, MAPK-activity, and tumorigenic cell proliferation.Our results describe an unprecedented mechanism of signaling protein activation in cancer.

View Article: PubMed Central - PubMed

Affiliation: Turku Centre for Biotechnology, Åbo Akademi University, Turku, Finland.

ABSTRACT
Hotspot mutations of Ras drive cell transformation and tumorigenesis. Less frequent mutations in Ras are poorly characterized for their oncogenic potential. Yet insight into their mechanism of action may point to novel opportunities to target Ras. Here, we show that several cancer-associated mutations in the switch III region moderately increase Ras activity in all isoforms. Mutants are biochemically inconspicuous, while their clustering into nanoscale signaling complexes on the plasma membrane, termed nanocluster, is augmented. Nanoclustering dictates downstream effector recruitment, MAPK-activity, and tumorigenic cell proliferation. Our results describe an unprecedented mechanism of signaling protein activation in cancer.

No MeSH data available.


Related in: MedlinePlus

The computational modeling-derived switch III H-ras mutant exhibits stronger Gal-1 complexation and remains sensitive to GAP-mediated hydrolysis.(A) Schematic representation of the Gal-1-complexation FRET assay in which complexation of mRFP-Gal-1 with mGFP-tagged mutants of Ras was measured in intact BHK cells using FRET (left). Gal-1-complexation FRET analysis of H-rasG12V-D47A,E49A and its parent construct. Numbers in bars give number of analyzed cells from three independent experiments. Error bars represent the standard error of the mean (±SEM). Statistical analysis of differences vs H-rasG12V was performed as described in ‘Materials and methods’ (*p < 0.05) (right). (B) Relative amounts of phosphorylated C-Raf, MEK and ERK in BHK cells transiently expressing vector control, mGFP-H-rasG12V or mGFP-H-rasG12V-D47A,E49A determined by western blotting from three independent repeats. Error bars represent the standard error of the mean (±SEM). Quantification of band intensities and statistical analysis was performed as described in ‘Materials and methods’ (*p < 0.05, **p < 0.01, ***p < 0.001). (C) RBD-pulldown experiment quantification of the active, GTP-bound fraction of H-ras-D47A,E49A in BHK cells. +EGF denotes stimulation with 100 ng/ml EGF. −EGF serum starved cells. +GAP incubation with GAP domain of NF1, to assay for GAP-sensitivity. The graphs represent the averages of active H-ras-D47A,E49A normalized to wt H-ras + EGF-stimulation from three independent experiments. Blue vertical line annotates the activity of wt H-ras when stimulated with EGF. Error bars represent the standard error of the mean (±SEM). Statistical analysis was performed as described in ‘Materials and methods’ (NS, non-significant; **p < 0.01, *p < 0.05).DOI:http://dx.doi.org/10.7554/eLife.08905.006
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fig2s1: The computational modeling-derived switch III H-ras mutant exhibits stronger Gal-1 complexation and remains sensitive to GAP-mediated hydrolysis.(A) Schematic representation of the Gal-1-complexation FRET assay in which complexation of mRFP-Gal-1 with mGFP-tagged mutants of Ras was measured in intact BHK cells using FRET (left). Gal-1-complexation FRET analysis of H-rasG12V-D47A,E49A and its parent construct. Numbers in bars give number of analyzed cells from three independent experiments. Error bars represent the standard error of the mean (±SEM). Statistical analysis of differences vs H-rasG12V was performed as described in ‘Materials and methods’ (*p < 0.05) (right). (B) Relative amounts of phosphorylated C-Raf, MEK and ERK in BHK cells transiently expressing vector control, mGFP-H-rasG12V or mGFP-H-rasG12V-D47A,E49A determined by western blotting from three independent repeats. Error bars represent the standard error of the mean (±SEM). Quantification of band intensities and statistical analysis was performed as described in ‘Materials and methods’ (*p < 0.05, **p < 0.01, ***p < 0.001). (C) RBD-pulldown experiment quantification of the active, GTP-bound fraction of H-ras-D47A,E49A in BHK cells. +EGF denotes stimulation with 100 ng/ml EGF. −EGF serum starved cells. +GAP incubation with GAP domain of NF1, to assay for GAP-sensitivity. The graphs represent the averages of active H-ras-D47A,E49A normalized to wt H-ras + EGF-stimulation from three independent experiments. Blue vertical line annotates the activity of wt H-ras when stimulated with EGF. Error bars represent the standard error of the mean (±SEM). Statistical analysis was performed as described in ‘Materials and methods’ (NS, non-significant; **p < 0.01, *p < 0.05).DOI:http://dx.doi.org/10.7554/eLife.08905.006

Mentions: We next explored whether altered nanoclustering also underlies the activity changes that we previously observed for computational modeling-derived switch III mutant H-rasG12V-D47A,E49A (Abankwa et al., 2008b). We compared the nanoclustering of this hyperactive mutant to that of the parent H-rasG12V by statistical analysis of the distribution of immunogold labeled H-ras in electron microscopic images of plasma membrane sheets from BHK cells (Plowman et al., 2005). We observed a significant increase in nanoclustering of this modeling-derived mutant, suggesting that increased signaling originates from augmented nanoclustering (Figure 2A). In order to corroborate these data, we used our recently established Gal-1-dose dependent nanoclustering-response assay (Guzmán et al., 2014b). This assay measures the dependence of H-ras-mutant nanoclustering on different cellular levels of the nanocluster scaffold Gal-1. Due to the tight packing of mGFP- and mCherry-tagged Ras proteins in nanoclusters FRET emerges, thus serving as a read-out for nanoclustering (Figure 2B). As compared to H-rasG12V, nanoclustering-FRET of the modeling-derived switch III mutant H-rasG12V-D47A,E49A was significantly increased at all Gal-1 levels (Figure 2C,D); this was also reflected by the increased complexation of this mutant with Gal-1 in the cells (Figure 2—figure supplement 1A), similar to what we previously observed with a hvr-mutant (Abankwa et al., 2010). Taken together, these data confirmed that nanoclustering is increased in H-rasG12V-D47A,E49A.10.7554/eLife.08905.005Figure 2.Computational modeling-derived switch III mutations D47A,E49A in H-ras increase nanoclustering and RBD-recruitment.


Specific cancer-associated mutations in the switch III region of Ras increase tumorigenicity by nanocluster augmentation.

Šolman M, Ligabue A, Blaževitš O, Jaiswal A, Zhou Y, Liang H, Lectez B, Kopra K, Guzmán C, Härmä H, Hancock JF, Aittokallio T, Abankwa D - Elife (2015)

The computational modeling-derived switch III H-ras mutant exhibits stronger Gal-1 complexation and remains sensitive to GAP-mediated hydrolysis.(A) Schematic representation of the Gal-1-complexation FRET assay in which complexation of mRFP-Gal-1 with mGFP-tagged mutants of Ras was measured in intact BHK cells using FRET (left). Gal-1-complexation FRET analysis of H-rasG12V-D47A,E49A and its parent construct. Numbers in bars give number of analyzed cells from three independent experiments. Error bars represent the standard error of the mean (±SEM). Statistical analysis of differences vs H-rasG12V was performed as described in ‘Materials and methods’ (*p < 0.05) (right). (B) Relative amounts of phosphorylated C-Raf, MEK and ERK in BHK cells transiently expressing vector control, mGFP-H-rasG12V or mGFP-H-rasG12V-D47A,E49A determined by western blotting from three independent repeats. Error bars represent the standard error of the mean (±SEM). Quantification of band intensities and statistical analysis was performed as described in ‘Materials and methods’ (*p < 0.05, **p < 0.01, ***p < 0.001). (C) RBD-pulldown experiment quantification of the active, GTP-bound fraction of H-ras-D47A,E49A in BHK cells. +EGF denotes stimulation with 100 ng/ml EGF. −EGF serum starved cells. +GAP incubation with GAP domain of NF1, to assay for GAP-sensitivity. The graphs represent the averages of active H-ras-D47A,E49A normalized to wt H-ras + EGF-stimulation from three independent experiments. Blue vertical line annotates the activity of wt H-ras when stimulated with EGF. Error bars represent the standard error of the mean (±SEM). Statistical analysis was performed as described in ‘Materials and methods’ (NS, non-significant; **p < 0.01, *p < 0.05).DOI:http://dx.doi.org/10.7554/eLife.08905.006
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Related In: Results  -  Collection

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fig2s1: The computational modeling-derived switch III H-ras mutant exhibits stronger Gal-1 complexation and remains sensitive to GAP-mediated hydrolysis.(A) Schematic representation of the Gal-1-complexation FRET assay in which complexation of mRFP-Gal-1 with mGFP-tagged mutants of Ras was measured in intact BHK cells using FRET (left). Gal-1-complexation FRET analysis of H-rasG12V-D47A,E49A and its parent construct. Numbers in bars give number of analyzed cells from three independent experiments. Error bars represent the standard error of the mean (±SEM). Statistical analysis of differences vs H-rasG12V was performed as described in ‘Materials and methods’ (*p < 0.05) (right). (B) Relative amounts of phosphorylated C-Raf, MEK and ERK in BHK cells transiently expressing vector control, mGFP-H-rasG12V or mGFP-H-rasG12V-D47A,E49A determined by western blotting from three independent repeats. Error bars represent the standard error of the mean (±SEM). Quantification of band intensities and statistical analysis was performed as described in ‘Materials and methods’ (*p < 0.05, **p < 0.01, ***p < 0.001). (C) RBD-pulldown experiment quantification of the active, GTP-bound fraction of H-ras-D47A,E49A in BHK cells. +EGF denotes stimulation with 100 ng/ml EGF. −EGF serum starved cells. +GAP incubation with GAP domain of NF1, to assay for GAP-sensitivity. The graphs represent the averages of active H-ras-D47A,E49A normalized to wt H-ras + EGF-stimulation from three independent experiments. Blue vertical line annotates the activity of wt H-ras when stimulated with EGF. Error bars represent the standard error of the mean (±SEM). Statistical analysis was performed as described in ‘Materials and methods’ (NS, non-significant; **p < 0.01, *p < 0.05).DOI:http://dx.doi.org/10.7554/eLife.08905.006
Mentions: We next explored whether altered nanoclustering also underlies the activity changes that we previously observed for computational modeling-derived switch III mutant H-rasG12V-D47A,E49A (Abankwa et al., 2008b). We compared the nanoclustering of this hyperactive mutant to that of the parent H-rasG12V by statistical analysis of the distribution of immunogold labeled H-ras in electron microscopic images of plasma membrane sheets from BHK cells (Plowman et al., 2005). We observed a significant increase in nanoclustering of this modeling-derived mutant, suggesting that increased signaling originates from augmented nanoclustering (Figure 2A). In order to corroborate these data, we used our recently established Gal-1-dose dependent nanoclustering-response assay (Guzmán et al., 2014b). This assay measures the dependence of H-ras-mutant nanoclustering on different cellular levels of the nanocluster scaffold Gal-1. Due to the tight packing of mGFP- and mCherry-tagged Ras proteins in nanoclusters FRET emerges, thus serving as a read-out for nanoclustering (Figure 2B). As compared to H-rasG12V, nanoclustering-FRET of the modeling-derived switch III mutant H-rasG12V-D47A,E49A was significantly increased at all Gal-1 levels (Figure 2C,D); this was also reflected by the increased complexation of this mutant with Gal-1 in the cells (Figure 2—figure supplement 1A), similar to what we previously observed with a hvr-mutant (Abankwa et al., 2010). Taken together, these data confirmed that nanoclustering is increased in H-rasG12V-D47A,E49A.10.7554/eLife.08905.005Figure 2.Computational modeling-derived switch III mutations D47A,E49A in H-ras increase nanoclustering and RBD-recruitment.

Bottom Line: Here, we show that several cancer-associated mutations in the switch III region moderately increase Ras activity in all isoforms.Nanoclustering dictates downstream effector recruitment, MAPK-activity, and tumorigenic cell proliferation.Our results describe an unprecedented mechanism of signaling protein activation in cancer.

View Article: PubMed Central - PubMed

Affiliation: Turku Centre for Biotechnology, Åbo Akademi University, Turku, Finland.

ABSTRACT
Hotspot mutations of Ras drive cell transformation and tumorigenesis. Less frequent mutations in Ras are poorly characterized for their oncogenic potential. Yet insight into their mechanism of action may point to novel opportunities to target Ras. Here, we show that several cancer-associated mutations in the switch III region moderately increase Ras activity in all isoforms. Mutants are biochemically inconspicuous, while their clustering into nanoscale signaling complexes on the plasma membrane, termed nanocluster, is augmented. Nanoclustering dictates downstream effector recruitment, MAPK-activity, and tumorigenic cell proliferation. Our results describe an unprecedented mechanism of signaling protein activation in cancer.

No MeSH data available.


Related in: MedlinePlus