Mapping the functional versatility and fragility of Ras GTPase signaling circuits through in vitro network reconstitution
- Howard Hughes Medical Institute, University of California, San Francisco, United States
- University of California, San Francisco, United States
Figures
Figure 1
Multiple activities that are frequently perturbed in disease dynamically regulate Ras activity to control the assembly of downstream effectors during signal processing.
(A) Depiction of the proximal architecture of Ras signaling systems. Ras is activated by guanine exchange factors (GEFs) that exchange GDP for GTP and is inactivated by GTPase-activating proteins …
Figure 2 with 1 supplement
A network-level multi-turnover reconstitution of dynamic signal transmission from Ras to downstream effectors.
(A) Bead-based approach used to study how Ras systems assemble effector complexes in response to inputs. By incubating Ni-NTA microspheres that have been loaded with Ras in solutions containing …
Figure 2—figure supplement 1
RasGRF GEF and NF1 GAP dose-dependent effects on effector output behaviors.
(A) Dose-dependent effect of increasing RasGRF GEF concentratiosn on initial rates of the c-Raf RBD effector to Ras-loaded beads. (B) Dose-dependent effect of increasing RasGRF GEF concentrations on …
Figure 3 with 2 supplements
The extent of signal processing distortion by oncogenic alleles of Ras depends on the balance of positive and negative regulatory activities in the network.
(A) Depiction of wild-type (WT) Ras and oncogenic G12V Ras, illustrating the modes by which mutation is thought to impact the network behavior: changing intrinsic hydrolysis rate, blocking …
Figure 3—figure supplement 1
The extent of signal processing distortion by oncogenic G12C and Q61L alleles of Ras depends on the balance of positive and negative regulatory activities in the network.
(A) Depiction of wild-type Ras and oncogenic G12C/Q61L Ras illustrating the modes by which mutation is thought to impact the network behavior: changing in intrinsic hydrolysis rate, blocking …
Figure 3—figure supplement 2
Kinetic modeling and simulations suggest competition and intermediate GTPase states contribute to transient system behavior.
Kintek simulations for a variety of models. Each simulation contains initial conditions of 50 nM effector, 10 nM GDP bound Ras, a GEF activity of ~1 υM, and an 'infinite' supply of nucleotide …
Figure 4
The concentration and identity of each Ras network component can modulate the timing, duration, shape, or amplitude of effector outputs.
(A) Depiction of the experimental setup: a fixed step-input is applied to a panel of Ras signaling systems in which the concentration of a single network component is varied to determine how each …
Figure 5 with 3 supplements
Tuning the levels of GEF, GAP, and GTPase provide access to a rich and diverse space of possible Ras signal processing behaviors.
(A) Depiction of the experimental setup: four different inputs (changes in apparent GEF activity) are applied to a panel of Ras signaling systems sampling four different p120GAP concentrations, and …
Figure 5—figure supplement 1
Normalized (to maximum output) responses of p120GAP/RasGRF/RafRBD/Ras signaling system under a variety of network configurations.
Normalized (to the maximum output value of the response) signaling responses for different network GEF/GAP/Ras density configurations. The RasGRF catalytic domain was used as the activating GEF in …
Figure 5—figure supplement 2
Structure of RasGRF/p120GAP/H-Ras/cRaf response space determined from outputs of 96 system configurations.
Phase diagrams for three different output features – integrated signal, initial rate of response, and overshoot behavior – at three different Ras density levels, constructed by interpolating these …
Figure 5—figure supplement 3
Kinetic modeling and simulations are consistent with experimental observations about how system behavior is influenced by network configuration.
(A) Output of Kintek simulation using a three-state GTPase model with competition between GAP and effectors as described in the main-text 'Materials and methods', in which the Ras density (i.e. …
Figure 6 with 2 supplements
Unique interpretation of Ras•GTP signals by different effectors in multi-effector networks encodes multiple distinct temporal outputs in the system response.
(A) Depiction of the experimental design: a fixed step-input is applied to a particular network configurations in which more than one effector molecule is, resulting in multiple simultaneous system …
Figure 6—figure supplement 1
Additional examples of how the unique interpretation of Ras•GTP signals by different effectors in multi-effector networks encodes multiple distinct temporal outputs in the system response.
(A) Depiction of the experimental design: a fixed step-input is applied to a particular network configurations in which more than one effector molecule is, resulting in multiple simultaneous system …
Figure 6—figure supplement 2
Kinetic modeling and simulations show that competition between effectors allows multiple temporal responses to be encoded in the system output.
(A) Output of Kintek simulation using a three-state GTPase model with competition between GAP and effectors as described in the main-text 'Materials and methods', in which two effectors (one c-Raf …
Figure 7 with 1 supplement
Introducing recruitment-based positive feedback into the Ras signaling network alters output dynamics and amplifies weak signals in high-GAP contexts.
(A) Illustration of Ras system that now includes recruitment-based positive feedback and the synthetic GEF (RasGRF-RBD) that was used to implement the feedback. (B) Experimentally determined …
Figure 7—figure supplement 1
Normalized (to maximum output) responses of p120GAP/ RasGRF-RBD feedback /RafRBD/Ras signaling system under a variety of network configurations.
Normalized (to the maximum output value of the response) signaling responses for different network GEF/GAP/Ras density configurations. The recruitment-based positive feedback GEF RasGRF-RBD was used …
Figure 8 with 1 supplement
Introducing allosteric-based positive feedback into the Ras signaling network reduces transient overshoot and smooths the OUTPUT dynamics.
(A) Illustration of Ras system that now includes allosteric-based positive feedback and the naturally occurring GEF (SOScat) that was used to implement the feedback. (B) Experimentally determined …
Figure 8—figure supplement 1
Normalized (to maximum output) responses of p120GAP/ SOScat feedback /RafRBD/Ras signaling system under a variety of network configurations.
Normalized (to the maximum output value of the response) signaling responses for different network GEF/GAP/Ras density configurations. The allosteric-based positive feedback GEF SOScat was used as …
Figure 9
One system, many behaviors: versatility and fragility in the space of Ras GTPase signal processing behaviors.
(A) Illustration of direct and indirect diversity that exists in Ras network configurations. In the direct case, the distribution of p120GAP, H-Ras, and Raf gene expression levels across a variety …
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Figure 9—source data 1
Relative gene expression level data from a variety of human tissue and cell types that was used to produce the plot in Figure 8A.
This table contains the relative expression-level data that was used to prepare the plot in Figure 9A. These data were obtained from Genevestigator as outlined in the main-text 'Materials and methods'.
- https://doi.org/10.7554/eLife.12435.024
Videos
Video 1
Response of wild type and G12V Ras systems in GAP-free network context.
The effector output (red) from a representative bead loaded with wild-type Ras (blue) or G12V Ras (green) is shown. 2 μM RasGRF was provided as an activating input. Time-steps are separated by 15 …
Video 2
Response of wild type and G12V Ras systems in high-GAP network context.
The effector output (red) from a representative bead loaded with wild-type Ras (blue) or G12V Ras (green) is shown. 2 μM RasGRF was provided as an activating input and the system contained 1 μM …
Tables
Table 1
List of plasmids used this study. A description of each construct used in this study, the bacterial antibiotic resistance associated with that plasmid, and a pSC reference index to facilitate any …
| Description | Bacteria Marker | ||||
|---|---|---|---|---|---|
| pSC | 353 | pMal-H.s.SOS1cat-StrepII | amp | ||
| pSC | 354 | pMal-H.s.p120GAP(RASA)-StrepII | amp | ||
| pSC | 369 | pMalStrep-RasGRF(MusGRF1cat ) | amp | ||
| pSC | 427 | pSNAP-Mal-cRaf-RBD-StrepII | amp | ||
| pSC | 451 | pSNAP_Mal_H-Ras_2xHis(6xHis-linker-10xHis) | amp | ||
| pSC | 465 | pMalStrep-RasGRF-30xGAGS-RBD | amp | ||
| pSC | 485 | pMalStrep-NF1 Ras GAP | amp | ||
| pSC | 486 | pSNAP-Mal-H-rasG12v-2xHis | amp | ||
| pSC | 488 | pSNAP-Mal-RafRBD(N64A)-StrepII | amp | ||
| pSC | 490 | pSNAP-Mal-H-RasG12C-2xHis | amp | ||
| pSC | 492 | pSNAP-Mal-H-RasQ61L-2xHis | amp | ||
| pSC | 501 | pSNAP-Mal-ARafRBD-StrepII | amp | ||
| pSC | 502 | pSNAP-Mal-BRafRBD-StrepII | amp | ||
