3.1 Rhodopsin-effector coupling
3.1.1 Fewer activated effector molecules
First of all, I wanted to modify the model to arrive at a lower number of activated PDE molecules. My plan was to retune the parameters of the model which had been determined by parameter adaptation. In the next step, I also wanted to incorporate the new insight that the double-activated ∗PDE∗ carries most of the activity.
In the interest of keeping the model as close to experimental results as possible, my modifications to the model should obey two criteria. First, they should leave the output of the model as close to the original model as possible, in different light regimes (dim and bright flash responses, responses to prolonged stimuli and back-ground plus flashes). Second, they should leave all parameters untouched that are sourced from experimental results. Therefore, I wanted to modify only parameters that had previously been manually tuned.
The parameters of interest are those governing the activation of the G-protein by rhodopsin and the activation of the PDE by the G-protein. In the Invergo 2014 model, all parameters describing the interaction of the G-protein and PDE were known from experimental results. However, there are a few parameter governing the activation of the G-protein by rhodopsin that had been determined by param-eter tuning. These paramparam-eters are listed in table 3.1. The underlying reaction network is illustrated in figure 3.1 with all the reaction rate constants.
Please note that the reaction rate constant kG1,n for n = 0 to 6 phosphorylation states is calculated from the reaction rate constant kG1,0 as follows:
kG1,n =kG1,0·e−ωG·n, (3.3) with ωG = 0.6 from (Gibson et al., 2000). Thus, when we change kG1,0, all other kG1,n are changed as well.
Table 3.1: A list of all manually tuned parameters in the activation of the G-protein by rhodopsin.
Parameter Significance Value
kG1,0 Rate constant of binding of R0 and Gt 1·10−3/s kG2 Rate constant of the dissociation of R0 ·Gt (without activation) 2200/s kG3 Rate constant of the dissociation of GDP from R0·Gt 8500/s kG4,GDP Rate constant of the association of GDP to R0·Gt 400/s kG5,GTP Rate constant of the association of GTP to R0·Gt 3500/s kG6 Rate constant of the dissociation of R0 ·GGTP 8500/s
Rn·Gt Rn·G Rn·GGTP GGTP
GDP GTP
Rn
Rn
Gt
kG1n
kG2
kG3 kG4GDP
kG5GTP kG6
Figure 3.1: Reaction network illustrating the activation of the G-protein by rhodopsin and the reaction rate constants.
I used the parameter tuning interface of the IQM toolbox IQMparamestGUI, and investigated the influence of changing these parameters. The interface for manual tuning is shown in figure 3.2. In the interface, one can choose between different models to compare to experimental data. The experimental data I used for com-parison was simply a simulation of the Invergo 2014 model, since this was the benchmark that I did not want to deviate from. Parameters can be changed using the sliders at the bottom of the window. The result from the simulation with the new parameters is shown as a solid line, compared with the experimental data as a dotted line.
I initially selected a dim flash response for comparison and was looking for param-eter changes that would decrease the overall amount of activated G-protein, and
thus PDE, without changing the kinetics of the response in term of the change in circulating current, the photocurrent ∆J, which is the output variable of the model.
I found that inkG3: this parameter is the rate constant of the dissociation of GDP from R0 · Gt and thus a crucial step in the phototransduction cascade. When decreasing kG3, the amount of activated G-protein and PDE (and thus also ∆J) was decreased, but the kinetics were unchanged. When scaling the responses of the modified and old model to the same amplitude, they overlapped. The same held for different types of light stimuli.
Figure 3.2: TheIQMparamestGUImanual tuning interface. After loading a project, different models and experimental data can be selected for comparison. Parameters can be changed by moving the sliders in the lower part of the interface. After the parameters have been changed, a new simulation of the model is performed and plotted as a line along with the experimental data as a dotted line. In this case, a dim flash response is compared after the parameter kG3 has been changed.
When scalingkG3 from its original value of 8500/s down to 250/s, the peak effector during the single photon response was reduced to 13 instead of 110, as can be seen in figure 3.3 on the left: the effector resulting from a deterministic simulation of the single photon response of the Invergo 2014 (black line) and the new, modified model (red dashed line) are compared. The effector was still calculated according to the old definition here, where the total number of activated subunits are counted
regardless of whether the PDE is single- or double-activated. The new number of activated PDE subunits is now in agreement with (Yue et al.,2019).
Next, I had to make sure that enough cGMP gets hydrolyzed by this smaller amount of activated PDE, to arrive at a comparable light response in term of ∆J.
For this, I increased the catalytic activity of the effector. This is the parameter βsub in the model. Increasing it from its previous value of 2.183·10−3/s to 0.019/s resulted in the normal decrease in cGMP due to the hydrolytic activity of the PDE and thus in the previous amplitude of ∆J. This did not change the kinetics of the response.
In figure 3.3 on the right, the ∆J resulting from deterministic simulations of the single photon response of the Invergo 2014 model (black solid line) and the new modified model (red dashed line) are compared. While the effector (left in the figure) is now at a much smaller level, the overall light response in terms of ∆J is hardly altered.
Figure 3.3: Comparison of the single photon response in the Invergo 2014 model (black solid lines) and the new deterministic model (red dashed lines). On the left, the effector is shown, and on the right, the photocurrent ∆J is compared.
The effector is calculated according to the old definition as the total number of activated subunits of the PDE.
To work with this new deterministic model, I obviously had to check that it also works well in other light regimes. To make sure of that, I compared the light responses of the Invergo 2014 model (black lines) to those of the new modified model (red lines) in figures 3.4 and 3.5. The figures shows the light responses to different stimulus paradigms: flashes of different brightness and combinations of flashes and prolonged background stimuli, for a wild type (WT) model and a simulated knockout of the GCAPs. This tests the new model’s ability to produce
varied features of the light responses. The different paradigms follow (Invergo et al., 2014).
Figure 3.4: Responses of a dark adapted rod to flashes of increasing brightness compared between the Invergo 2014 model (black) and the new deterministic model (red). The flashes lasted for 0.02 s and the intensities were 1.7, 4.8, 15.2, 39.4, 125, 444, 1406 and 4630 photons/µm2.
In figure 3.4, responses in terms of photocurrent ∆J to brief flashes with increas-ing brightness are shown, without any background stimulus. The flashes lasted for 0.02 s and the intensities were 1.7, 4.8, 15.2, 39.4, 125, 444, 1406 and 4630 photons/µm2, respectively. The new model reproduces the kinetics of the light response in this stimulus paradigm well, as the curves almost overlap.
In the three brightest flashes, we can see the phenomenon ofsaturation: the flashes are so bright that the circulating current is maximally suppressed. The higher the intensity of the flash, the longer the rod stays in this state of saturation. This is accurately reproduced in the new model.
Figure 3.5 contains the light responses in terms of ∆J for three different stimu-lus paradigms, combined in one figure. The first stimustimu-lus consisted of a constant dim background stimulus (dot-dashed line). The second stimulus combined the constant background with a bright flash at t = 100 s (dashed line). Finally, the third stimulus just consisted of a bright flash at t = 100 s (solid line). The in-tensity of the background was 81 photons/µm2s and the intensity of the flash was 1590 photons/µm2.
In the left figure, we can see that the background leads to a higher ∆J in the wild
type. The response to the flash is saturating, so its amplitude is not influenced by the presence of the background. The shut-off of the response to the flash is impacted, though. We can see that the modified model reproduces these effects well, despite a very slightly increased response to the background and a slightly slower recovery.
Figure 3.5: Responses to different light stimuli in a wild type model and a GCAPs knockout model, compared between the Invergo 2014 model (black) and the new deterministic model (red). In the left and right plot, the responses to three different combinations of stimuli are combined in one plot, respectively. These were: 1. a constant dim background stimulus (dot-dashed line), 2. a constant dim background stimulus and a saturating flash at t= 100 s (dashed line), 3. no background and a saturating flash att= 100 s (solid line). In the left plot, these stimuli were applied to the wild type (WT) model, while they were applied to a GCAPs knockout model on the right.
The knockout of the GCAPs for the right plot of figure3.5 was implemented using an experiment and modifying the synthesis rate of cGMP, which normally reads:
vf = αmax 1 +Ca2+
free
KC1
m1 + αmax 1 +Ca2+
free
KC2
m2, (3.4)
with the following parameters: the maximal activity of the GC αmax = 60µM/s from (Koch and Stryer, 1988), the EC50 values KC1 = 171 nM and KC2 = 59 nM, which specify the calcium concentration at which the GCAPs activate the GC to half-maximum, and the Hill coefficients m1 = 3 and m2 = 1.5.
When the GCAPs are knocked out, there is no calcium-dependent regulation of the GC anymore. However, this does not mean that the GC does not produce
any cGMP whatsoever - it is still producing cGMP at its basal rate. This can be determined by inserting the dark calcium concentration Ca2+dark = 0.25µM into equation (3.4), which results in a dark activity of αdark = 20.72µM/s.
In the simulation, we changed the following parameters using the IQMexperiment syntax: set αmax to αdark = 20.72µM/s, and set m1 = m2 = 0 to prevent any change of the activity with changing free calcium concentration.
Going back to figure 3.5, we can see that the knockout of the GCAPs has a sig-nificant impact on the different light responses. The saturating response still has the same amplitude of ∆J, but the response to the background is a much larger change in ∆J. Furthermore, the shut-off of the bright flash response is slower both with and without the background. These changes are due to the fact that the synthesis of cGMP to return to the dark state is not regulated in a calcium-dependent manner anymore, since the GCAPs are missing. Thus, it takes a longer time to recover from a bright flash, which has reduced the cGMP concentration.
Furthermore, the adaptation to a background stimulus, which reduces the ∆J in a prolonged stimulus, is not working anymore due to the missing calcium feedback.
These effects triggered by a knockout of the GCAPs are also well reproduced in the new model.
These results demonstrate that the modified model combines the new result of a smaller number of activated effector molecules while still producing reliable and robust results in a number of different light stimulus conditions and even a knock-out of the GCAPs. We can thus use it for simulations of different light conditions and different genetic modifications, as long as the specific dimeric nature of the effector does not have to be taken into account.