We examine the implications of a recent report providing evidence that two transducins must bind to the rod phosphodiesterase to elicit significant hydrolytic activity. To predict the rod photoreceptor's electrical response, we use numerical simulation of the two-dimensional diffusional contact of interacting molecules at the surface of the disc membrane, and then we use the simulated PDE activity as the driving function for the downstream reaction cascade. The results account for a number of aspects of rod phototransduction that have previously been puzzling. For example, they explain the existence of a greater initial delay in rods than in cones. Furthermore, our analysis suggests that the ‘continuous’ noise recorded in rods in darkness is likely to arise from spontaneous activation of individual molecules of PDE at a rate of a few tens per second per rod, probably as a consequence of spontaneous activation of transducins at a rate of thousands per second per rod. Hence, the dimeric activation of PDE in rods provides immunity against spontaneous transducin activation, thereby reducing the continuous noise. Our analysis also provides a coherent quantitative explanation of the amplification underlying the single photon response. Overall, numerical analysis of the dimeric activation of PDE places rod phototransduction in a new light. G*, in turn, activates the third protein, the PDE, and it is the stoichiometry of that mechanism that is the focus of this paper. Upon activation, the role of the PDE is to hydrolyse cyclic GMP (cGMP) in the cytoplasm, whereupon the lowered cytoplasmic cGMP concentration triggers the closure of the ion channels, thereby generating the cell's electrical response to light.Three key proteins mediating activation of the light response in rod photoreceptors are located in the disc membranes of the cell's outer segment, and comprise rhodopsin (a G-protein-coupled receptor), transducin (a heterotrimeric G-protein) and the PDE . The fourth key protein, the cyclic nucleotide-gated ion channel (CNGC), is located in the plasma membrane that envelops the outer segment discs. In addition, a number of other proteins mediating recovery of the light response and light adaptation are also located in the disc or plasma membranes. The interaction between the participating proteins occurs primarily via contact resulting from their lateral diffusion in the disc membrane, or at its surface. When rhodopsin is isomerized by the absorption of a photon, it enters an active state (denoted R*) that is able to catalyse the activation of transducin, leading to the formation of Gα and PDE6β with two identical PDE6γ subunits) behaves as a pair of independent catalytic subunits [et al. has provided compelling evidence that the activation of the PDE indeed exhibits pronounced functional asymmetry [Quantitative descriptions of the molecular mechanisms underlying vertebrate phototransduction assume that the rod PDE , and we use this information to provide a new and coherent set of parameters to describe the amplification underlying the single-photon response. Second, we find that a significant delay (of approx. 7 ms) occurs, prior to the ramp-like rise in PDE** concentration, and we show that the existence of this delay resolves an apparent paradox that was recently reported by Rotov 2.2.1.et al. [α and β subunits), nor will we specify whether either of these subunits is preferentially the first to bind a G*. Likewise, we will not specify the exact role of the two γ subunits in the interaction or in activation. These are matters that could be addressed in the subsequent development of the model, when more detailed information is available at a molecular level. In our current version, we simply specify that the singly bound PDE* molecule has minimal activity compared with the doubly bound PDE** molecule, as reported by Qureshi et al. [As proposed by Qureshi et al. ,4, our ma, where the states of the molecule with 0, 1 and 2 transducins bound are indicated by PDE, PDE* and PDE**, respectively. The rightward arrows denote activation steps, and the leftward arrows denote shut-off steps. Furthermore, the possibility of various transition states (such as ‘bound but not yet activated’) is envisaged by the head-to-tail arrows, but again will not be considered here. Contact between PDE and a first G* opens the possibility of their binding, at some rate constant; the diffusion limit to this step occurs when that rate constant approaches infinity. Thereupon (and possibly following some transition state), the molecule is in the PDE* configuration, and for our initial analysis, we assume that this state has no hydrolytic activity. Contact between PDE* and a second G* likewise opens the possibility of their binding, at some rate constant. With two G*s bound (and again following the possibility of some transition state), the PDE** configuration has its full hydrolytic activity, at a level that has been determined in biochemical experiments. Each of the G*-bound states (PDE* and PDE**) decays via hydrolysis of the G*'s terminal phosphate . Here we assume that the decay of PDE** returns it to the PDE* state, and furthermore that the decay of PDE* returns it to the resting PDE state.The postulated interactions of the PDE6 heterotetramer with transducin are illustrated schematically in figure 1b, using the approach described previously [figure 1a. The standard values that we adopted for all the parameters of the reactions at the disc membrane are listed in To estimate the kinetics of formation of PDE* and PDE**, we simulated the lateral diffusion of molecules in the plane of the disc membrane, as sketched schematically in figure 1eviously , with a eviously . For actνRE** is the slope of the linear ramp, and τRE** is the time constant of a first-order delay from the time of photoisomerization.In the Results section, we will show that the simulations predict that a single photoisomerization causes the ensemble mean number of active PDE** molecules to begin rising approximately as a delayed ramp, and we will fit this simulated kinetics with the analytical expression2.2.alkMat coded in Matlab . In addition, and as described below, checks were undertaken using an earlier program Walk2, in some restricted conditions where the two programs could simulate identical scenarios. All code used here is available for download from Dryad [The numerical calculations presented here were performed using a custom program Wom Dryad .x) was 5 nm and the diffusing molecules were represented as ‘single pixels’. For computational speed, we again adopted the ‘shortcut G* production’ method [b). A consequence of this shortcut is that potential local depletion of inactive G-protein is ignored, but this simplification was previously shown to have very little effect for a single R* per disc surface [To estimate the number of PDEs activated during the rod's single-photon response, we simulated the reactions on a disc surface. Lateral diffusion of molecules in the plane of the disc membrane was simulated by the approach described previously , using a’ method , which e surface .−2, giving 141 dimers distributed across the disc surface. The default set of diffusion coefficient and related parameters for a mammalian rod are listed in Contact between the diffusing molecules was defined to occur when they occupied the same grid position (i.e. a 5 nm × 5 nm region). This differs from the previous implementation, where molecules were not permitted to occupy the same grid position and where contact was defined to occur when they were located at adjacent grid positions (at any of the four compass positions); as a result, there will be a slight difference in ‘collision radius’ between the two implementations. The disc surface was simulated as a circular region, 1.5 µm (300 pixels) in diameter. The standard density employed for the PDE holomers was 80 µmνRE = 5 s−1 and Δt = 0.5 µs, the probability of PDE** decay is νRE Δt = 2.5 × 10−6 in each time interval).Two approaches to simulating purely time-dependent reactions  are possible. First, one could take each time increment and determine the probability of reaction (decay) over that time interval, and simulate whether or not the reaction occurred. Alternatively, at the instant of creation of each active molecule, one could simulate its stochastic lifetime. We adopted the latter approach, because the first approach requires more calls to the random number generator and also because it relies on the uniformity of the pseudo-random numbers over a very narrow range , there was no longer any need to continue the simulation of lateral diffusion, because no more G*s could be created and no further ‘contact’ reactions could occur. This ‘exhaustion’ of R* and free G* typically occurred at between 50 and 200 ms of simulation time, and in practice was set by the binding of the last molecule of free G*. Thereafter the remaining reactions comprised only the GTPase-mediated shut-off of PDE** and PDE*, which involved no spatial interactions and could, therefore, be simulated very rapidly. In summary, we simulated lateral diffusion until exhaustion of R* and free G* occurred, and then only the time-dependent reactions until all other molecules of interest (PDE* and PDE**) had decayed.2.2.1.alk2, that had been written in 1996 by Lucian Wischik. That program was coded in C++ and is specific to the Windows operating system. It has the great advantage of executing very rapidly, though in part this is because it uses a ‘quick-and-dirty’ random number generator, whereas Matlab uses the more computationally demanding Mersenne twister algorithm [alk2 has some limitations that prevent its use in our main calculations. For example, it is restricted to a square (rather than circular) region of the disc, but more importantly, it cannot implement multi-stage shut-off of R* of the kind required by multiple phosphorylations and arrestin binding.As a check on our numerical simulation algorithm, we were able to make a comparison with the predictions of a completely independent program, Wlgorithm . On the −1); ‘shortcut’ generation of G*; dimeric PDE; stochastic PDE* and PDE** decay; diffusion-limited reactions; 1000 trials. With the exception of the geometry and the single-stage R* decay, all parameters were set to the default mammalian values listed in alk2 were qualitatively very similar to those for WalkMat, though the trace for the mean PDE** time-course was marginally larger; when the Walk2 PDE** trace was scaled vertically by a factor of 0.86 it was virtually indistinguishable from the WalkMat PDE** trace (data not shown). We think that this difference arose primarily because of the smaller effective collision radius in the WalkMat implementation. Overall, though, the similarity of the responses gives us confidence that the WalkMat program is likely to be generating meaningful predictions.Nevertheless, there were some restricted conditions under which we could compare the predictions of the two programs. We chose the case of a disc surface with: square geometry, of width 1.34 µm (to maintain the area); a single R* positioned randomly at time zero; stochastic R* decay with a single rate constant  acting via the downstream phototransduction cascade. We simulated these downstream reactions using the same equations as presented recently , except . 680 in , and is n [odSim , and the package .2+ concentrations, thereby enabling us to relate steady hydrolytic activity (β) to steady circulating current (J).Using the parameters in the top section of t) waveforms as driving functions for the downstream reactions described above, with the parameters listed in −/− case, we simply held the guanylyl cyclase rate α at the level αDark determined in the dark resting state for WT rods.To predict the electrical responses to single photoisomerizations, we used a large set of simulated PDE**(a) a set of 1000 rectangular events of unit amplitude, each starting at time zero and having a stochastic duration that was exponentially distributed with time constant τ; or (b) a single exponential decay starting from unity at time zero and having the same time constant, τ.To predict the unitary electrical responses elicited by spontaneously created PDE**s, we simulated the individual PDE** events and used these as driving functions for the same downstream reaction equations; this case is much more straightforward to simulate because we do not need to consider lateral diffusional interactions at the disc membrane. The PDE** events were either: (2.4.R(t) is the fractional response, Φ is the flash intensity (in photoisomerizations), A is the amplification constant (in s−2) characterizing the cell, and teff is a short ‘effective delay time’. This description applies only at times sufficiently early that shut-off reactions have not contributed appreciably. Equation (2.2) has been shown to provide a good description of the early rising phase of the electrical response to flashes, in many studies, and for rods from a wide variety of species  can be rewritten asThe standard quantitative model for the response rising phase provides an expression for the amplification constant rameters ,13. Howe2.5.S(f), of the simulated events, using the fast Fourier transform implemented in the ‘fft’ function in Matlab. The one-sided spectrum is defined only for positive frequencies, and so is double the two-sided spectrum. In this case, the variance of the original signal should be equal to the integral of its spectrum over positive frequenciesIn order to predict the power spectrum of the noise elicited by spontaneous occurrences of ‘unitary’ events, we first calculated the one-sided power spectral density, r(t), occurring stochastically in time at a mean rate ν s−1, the zero-frequency asymptote of the one-sided spectral density is predicted to ber(t) = exp(−t/τ), then the predicted spectral density is the well-known Lorentzian functionτ, so that the ensemble mean is unchanged at exp(−t/τ), the resulting power spectral density has exactly the same shape but is simply scaled vertically by a factor of 2, asFor identical events 2.6.t) activity in a single disc surface for multiple isomerizations, in the case of 2, 3, … 10 isomerizations, and also for 20 and 30 isomerizations (though the last two cases were very time-consuming). As described in the Results, we developed an approximate approach for estimating the PDE**(t) time-course for other integer numbers of isomerizations per disc surface, below 30. Then, for each integer k = 0 … 30, we calculated the probability kp that k photoisomerizations would occur in any disc surface, as follows. When the mean number of photoisomerizations per disc surface is φ (and hence the number of photoisomerizations per outer segment is Φ = Nsurfφ), the Poisson distribution gives kp asFor calculating the responses to bright flashes, where multiple disc surfaces receive isomerizations, we used a ‘single compartment’ model of the downstream reactions. Furthermore, we allowed for the fact that individual disc surfaces may receive multiple isomerizations. Thus, we simulated the time-course of PDE**(t) predictions described above, after weighing them according to the proportion of surfaces that would have absorbed that number of isomerizations, and we used the resulting weighted PDE**(t) time-course as the driving function for the downstream reactions.Therefore, to calculate the electrical response, we summed the PDE**(Φ = 25 000 isomerizations per rod (a 0.06% bleach) delivers an average of φ = 17 isomerizations per disc surface. In conjunction with an R* mean lifetime of approximately 70 ms and a shortcut G* activation rate of 1000 s−1, this would create 1200 G*s, representing about 22% of the complement of 5300 transducins per disc surface. As a result, our neglect of transducin depletion should have little effect below 10 000 isomerizations per rod, but at levels of 25 000 isomerizations or more it is likely to lead to overestimation of G* production, and hence overestimation of the time spent in saturation.Our use of the shortcut of a constant mean rate of G* production approach places a restriction on the upper intensity we can investigate, in order to avoid depletion of transducin. For example, a flash of 3.3.1.a shows a time-base to 500 ms, with traces for R* (black), free G* (green), PDE* (blue) and PDE** (red). Note that each trace plots the mean number of molecules remaining present at any instant, rather than the total created, and that for G* we exclude those molecules that are bound to PDE. For the fully activated PDE**, the ensemble mean reaches a peak of 18.0 PDE** at 102 ms after the photoisomerization. At that time, the total number of G*s to have been produced averaged 68, and of those 55 remained active . The production of 68 G*s (on average) corresponds to our simulated activation rate of 1000 G* s−1 times the mean R* lifetime of 68 ms, and is considerably higher than anticipated from biochemical experiments on the incorporation of GTPγS [A small set of sample traces showing the simulated numbers of molecules existing in the different states, in response to a single photoisomerization in each trial, is plotted in Figure 3of GTPγS ; this dib concentrates on the onset phase of the simulated responses, over the first 50 ms; it omits the R* trace, which declined only slightly from its initial value of unity. The earliest rise of the PDE** trace is roughly parabolic, and up until about 35 ms the rising phase is well described by the dashed red curve, which plots the delayed ramp expression given above in equation (2.1), with fitted slope νRE** = 309 PDE** s−1 and first-order delay τRE** = 6.9 ms. Note that our parameter νRE** refers to doubly bound PDE**s rather than to individual PDE subunits.Figure 3et al. [−1, is only 62% of the maximum possible rate, of 500 PDE** s−1 (for a G* activation rate of 1000 G* s−1). We refer to this ratio as the efficacy ηGE** of PDE** activation by G*, defined as ηGE** = 2 νRE**/νRG (where the factor of 2 is required because each PDE** formed has two G*s bound). This coupling efficacy is considerably smaller than unity because a substantial proportion of the total G*s is accounted for by the singly bound form, PDE*. Fourth, the green trace for free G* in figure 3b shows that, on average, only a handful of G* molecules are available to bind at any moment. Hence, although the efficacy of G* in creating PDE** is relatively low, its efficacy in binding to some configuration of the PDE is very high.These results reveal several features of the PDE6 dimeric activation model, in the case of the predicted response of a mammalian rod to a single photoisomerization. First, at early times, prior to any significant shut-off (i.e. before approx. 35 ms), the time-course of PDE** activity rises as a delayed ramp ). Apart from this delay, such a ramp is the standard behaviour predicted by the conventional molecular model of phototransduction that assumes independent PDE catalytic subunits [). Apart et al.  of a ‘pa3.2.a shows a sample of 50 simulated traces for PDE**, and figure 4b shows the corresponding predicted electrical responses for the same set of simulations. At late times in figure 4b, it is possible to discern the electrical response to the last few remaining molecules of PDE**. For the 4000 trials, the ensemble means (blue) and standard deviations (red) are shown for PDE** in d. The ensemble mean single-photon response (SPR) has a peak of 0.0432 (i.e. 4.3% of the dark current). This peak occurs at 125 ms, and the delay of 23 ms from the PDE** peak at 102 ms reflects the filtering properties of the downstream reaction cascade. This filtering results from an integrating stage, with a time constant set by the cGMP turnover time, 1/βDark, due to resting PDE activity, in conjunction with the action of the Ca2+ feedback loop.We next determined the predicted electrical response to a single photoisomerization, by solving the differential equations for the downstream reaction cascade when driven by the simulated kinetics of PDE**. Figure 4c and d provide a representation of the variability of the simulated SPRs, and, as has been reported in experiments on mammalian rod SPRs, the standard deviation  peaks later than does the ensemble mean [area [area was developed [area = 0.671 for PDE** and CVarea = 0.561 for the electrical responses. These CVs are larger than the values of up to 0.35 reported for mammalian rod SPRs [The plots of ensemble standard deviation in figure 4ble mean . In ordean [area . Subsequeveloped , with throd SPRs ,22, and e is quite asymmetric, with a pronounced tail of large peaks (consistent with the occurrence of a few very large traces in figure 4a). For the SPRs, the distribution in figure 4f is more nearly symmetrical. The red curve plots a Gaussian distribution with the measured distribution mean of 0.0453 and with a coefficient of variation for the amplitudes of CVampl = 0.44, which is somewhat higher than typically reported in electrical recordings of mouse rod SPRs.Finally, the bottom row of figure 43.3.νRG = 1000 G* s−1, is higher than the value of 300 G* s−1 that has recently been assumed for mammalian rods [−1 for amphibian rods at room temperature [−1 while holding all other parameters constant. Figure 5a–c presents the results of these simulations, averaged across at least 500 trials in each case. Figure 5a plots the ensemble means of the simulated responses, and it is clear that the activation of PDE** depends strongly on the rate of G* activation. For example, in the third trace (from the bottom), where νRG was reduced to 400 G* s−1 (still higher than the level assumed in recent modelling studies), the mean number of PDE** molecules per trial peaked at only 4.7, roughly a quarter of the peak obtained with our standard rate of 1000 G* s−1. This demonstrates that, in order for the PDE dimeric activation model to have a chance of providing a plausible description of the rod's response, it is necessary that the rate of G* activation be higher than has been assumed in previous modelling of rod phototransduction. Figure 5b plots these same traces on an expanded time-base, and , but itperature –28. In td as for  the dashc, we plot several measures derived from these simulated PDE** responses against the rate of G* activation, and we indicate the standard rate of G* activation with the dotted vertical line. The response amplitudes, measured from figure 5a at a fixed time near the peak, of 100 ms, are plotted as the black symbols, and have been scaled to a maximum of 50 PDE**. The fitted slopes (νRE**) are plotted as the red symbols, scaled to 900 PDE** s−1. The fitted time constants (τRE**) are plotted as the blue symbols, scaled to 25 ms. Finally, the efficacy (ηGE**) of PDE** activation by G* is plotted in green. Clearly, there is a powerful dependence of the amplitude and the fitted slope on the rate of G* activation used in the simulations. And because of the curvature of this relation at low rates of activation, the calculated efficacy of coupling increases as a function of G* activation rate, before approaching a plateau. On the other hand, the fitted time constant declines with increasing G* activation rate. For the two lowest rates of G* activation (100 and 200 G* s−1), the responses were so small that the fitted parameters could not be relied upon; the time constant at the lowest rate was greater than 25 ms and has been omitted from the plot.In figure 53.4.d shows a superimposed plot of ensemble mean responses for six representative membrane densities, of CE = 20, 30, 40, 80, 160 and 250 holomers µm−2; all other parameters (including the rate of G* activation) were held constant at their standard values. The rising phase of each of these traces is plotted in figure 5e, and because of the overlapping nature of the superimposed traces in figure 5d, we have shifted three of them vertically for clarity; the upper group plots the lowest three densities, and the lower group plots the highest three densities. Again, the dashed traces plot the predictions of equation (2.1), and in each case, the fit is very good.We next investigated the effect of altering the membrane density of PDE6 holomers on the simulated activation of PDE**, and found a relatively weak dependence, as shown in the right-hand column of Figure 5f, in a corresponding manner to figure 5c, and again in scaled form. In this case, both the amplitude measured at 100 ms (black) and the fitted rate of rise νRE** (red) decline on either side of a broad peak that occurs at between about CE = 40 and 80 PDE holomers µm−2. The fitted time constant of delay τRE** (blue) decreased with increasing density of PDE. Measurements from these responses are plotted against PDE membrane density in figure 5c of [It is natural to expect the delay time to decrease with increasing PDE density, but the decline in slope and amplitude at higher densities is less intuitively obvious. We interpret those declines to indicate that, at higher densities of PDE, the fractional level of singly bound PDE* (relative to unbound PDE) at any instant is lower than it is at moderate PDE densities, so that each newly created G* has a lower probability of finding a PDE* than it would have at a moderate membrane density of PDE. We suspect that this phenomenon is closely related to the biochemical observation (in fig. 1c of ) that at3.5.−/− case, we simply held the guanylyl cyclase activity fixed at the dark resting level obtained for WT rods.We next investigated the consequences that dimeric activation of PDE has for the magnitude and properties of the spontaneous ‘continuous’ component electrical noise in rod photoreceptors. To do this, we calculated the electrical response elicited by the spontaneous activation of an individual PDE** molecule, and we calculated the noise spectrum predicted for such events occurring stochastically in time (see §2.5). For comparison with experimental recordings reported in the literature, where the ‘continuous’ noise is much greater in the rods of GCAPs knockout mice than in the rods of WT mice, we calculated the responses and power spectra for both genotypes. For the GCAPskE** (taken to be 5 s−1). We shall refer to this spontaneous occurrence as a ‘unitary PDE** event’; its ensemble mean ) will be an exponential decay from unity, û(t) = exp(−kE**t). We therefore solved the downstream reactions for two scenarios of driving functions: (a) a large set of rectangular PDE** events with stochastic lifetimes that were exponentially distributed with a time constant 1/kE**; and (b) a single event in the form of an exponential decay with the same time constant. The ensemble mean of the electrical responses for (a) should be identical to the single electrical response for (b), provided that the downstream reactions behave linearly for such small perturbations. But, interestingly, for events of these types occurring randomly in time, the power spectral density is predicted to be scaled vertically by a factor of 2 in the former case, in comparison with the case for random occurrences of identical exponential events. This result was derived in 1976 by Prof. Peter Whittle of the Statistical Laboratory, University of Cambridge; see §2.5.Spontaneous activation of PDE** would be expected to result from the binding of a spontaneously activated G* to a molecule of singly bound PDE*. The resulting PDE** would be expected to remain present until inactivated stochastically as a result of GTPase activity occurring with a rate constant a, as the black trace for WT, and as the red trace for GCAPs−/−. The two unitary responses rise without delay, along a common initial time-course. The WT trace reaches a peak amplitude of 2.7 × 10−3 (i.e. 0.27% of the dark current) at 77 ms; this peak is 1/16 that of the mean single-photon electrical response (0.0432) in figure 4d, broadly as expected given that a mean of 18 PDE**s are present at the peak of the mean simulated WT single-photon response (figure 4c). The GCAPs−/− trace reaches a much larger peak amplitude of 6.0 × 10−3 at 229 ms. As a check, we calculated the response of the downstream cascade when the driving function was the ensemble mean unitary PDE** event, û(t) = exp(−kE**t), and for both genotypes the response was almost identical to the illustrated traces (not shown), consistent with the idea that, for stimuli as small as a single PDE**, the downstream reactions operate effectively as a linear filter. Thus, for both genotypes, one can think of the traces in figure 6a as representing the convolution of an exponential decay (having a 200 ms time constant) with the small-signal transfer function of the downstream reactions for the genotype; this generates a peak at 77 ms for WT, and at 229 ms for GCAPs−/−.We solved the differential equations for the downstream reaction cascade in response to a set of 1000 simulated unitary PDE** events having unit amplitude and exponentially distributed lifetimes, and the predicted mean unitary PDE** electrical response is plotted (as a fraction of the dark current) in figure 6b,c, we have plotted as the solid traces the power spectral density averaged from the individual downstream responses to the 1000 stochastic unitary PDE** events, for GCAPs−/− and WT genotypes, respectively. For these solid traces, the vertical scaling corresponds to a stochastic event rate of ν = 1 s−1 (see §2.5). As a check, we also calculated the power spectral density for the mean unitary electrical response (for each genotype), and confirmed that upon vertical scaling by a factor of 2 this was closely similar to the illustrated spectrum (not shown). In these log–log coordinates, the power spectral density at high frequencies declines with a slope of −4 for both genotypes, as expected with two ‘integrating’ stages, representing the mean PDE** lifetime, 1/kE**, and the cGMP turnover time, 1/βDark, in the downstream cascade.In figure 6et al. [−/− mice in their fig. 4b. Although they did not separate the ‘continuous’ component of noise from the effects of spontaneous photon-like events, inspection of their fig. 3 suggests that their recordings were dominated by the continuous component. Because all of our analysis is in terms of normalized response (response divided by circulating dark current), we normalized their power scale by dividing by the square of the rod dark current . The resulting values from fig. 4b of [figure 6b,c. The dotted traces near these open symbols plot the corresponding solid traces scaled vertically by assumed rates of stochastic PDE** events of 40 s−1  and 11 s−1 . In addition, in figure 6c, we have plotted as the ‘+’ symbols the measured spectra for WT monkey rods taken from fig. 14b of Baylor et al. [−1.To compare our predicted spectra with spectra reported in the literature for the continuous noise recorded from mammalian rods, we began with recordings from GCAPs knockout mice, where the noise is substantially larger. Burns et al.  have preg. 4b of , for mear et al.  and norm−/−, 40 s−1; mouse WT, 11 s−1; monkey WT, 50 s−1. Overall, we think that these fits are as good as can be expected, given the difficulties that potentially result from very slow drift (causing problems at the lowest frequencies) and the occasional occurrence of spontaneous photon-like events. The required vertical scaling for the three sets of data from the literature suggests that stochastic activation of the PDE** occurs at a rate of the order of a few tens per second, and we shall address the interpretation of this estimate in the Discussion.Thus, the rate of stochastic PDE** events used for the three dotted traces were: mouse GCAPs3.6.R) and flash intensity (Φ) saturates according to an exponential function, R/Rmax = 1 – exp(−kΦ) [Up to this point, we have considered only the very smallest responses . As the flash intensity delivered to a rod increases from the lowest levels, the rod's response initially scales in direct proportion to flash intensity, with negligible change in time-course; this is termed the ‘linear range’ and typically applies for responses up to about 20% of maximal. At higher intensities, the peak initially moves slightly earlier, in a manifestation of light adaptation. When the response amplitude is measured at a fixed time, before this peak, the relationship between response amplitude (exp(−kΦ) . Saturata plots the simulated PDE**(t) activity for multiple photoisomerizations delivered to a single disc surface at time zero; the solid traces are ensemble mean responses for simulations in which at least 1000 photoisomerizations were delivered . The smallest red trace is for a single isomerization, and has been taken from figure 3a, with 4000 trials; the black traces are for two to nine isomerizations, and the remaining three red traces are for 10, 20 and 30 isomerizations. Each trial began with the specified integer number of photoisomerizations, distributed at random locations across the disc surface. One interesting aspect of the traces in figure 7a is a small degree of super-linearity for flashes delivering a few isomerizations per surface. For example, for two isomerizations the response peaks at 39.5 PDE** (28.0%), in comparison with 18.0 PDE** (12.8%) for a single isomerization. We chose not to run simulations for numbers of isomerizations other than those shown by the solid traces, because the computation time became long with large numbers of isomerizations, as a longer time was required for all the transducin molecules to be inactivated. Instead, we predicted the response waveforms for other intensities as described below.Figure 7t) response approaches its maximal level (representing the binding of two G*s to every PDE holomer) within about 100 ms of stimulus delivery. For intensities above this level, with the PDE fully bound, the peak level of free G* increases dramatically (not shown); for example, for 20 isomerizations per surface, the peak level of free transducin was 984 G* at 143 ms. Furthermore, when the PDE is fully bound in this way, our model assumes that the decline in G*(t) level results solely through decay from the PDE** state, which occurs at a rate NE,surfkE**, where NE,surf is the number of PDE holomers in the disc surface and kE** is the rate constant of PDE** inactivation. Denoting the effective lifetime of R* as TR*, we see that each additional isomerization will produce νRGTR* additional molecules of activated transducin, G*. As a result, at intensities that cause all PDEs in the disc surface to be doubly bound, each additional isomerization is predicted to elicit a rightward shift of the response falling phase of νRGTR*/. Substituting νRG = 1000 G* s−1, TR* = 68 ms (as measured for our R* shut-off scheme), NE,surf = 141 holomers per surface and kE** = 5 s−1, we predict a rightward shift in the recovery phase of 96.4 ms for each additional photoisomerization per disc surface. For the simulations using 10, 20 and 30 isomerizations per surface, the mean rightward shift per extra photoisomerization was measured to be 94.1 ms. To estimate the expected responses for intermediate numbers of isomerizations (11–19 and 21–29), we interpolated between the simulations for 10 and 20 isomerizations, and between those for 20 and 30 isomerizations, by shifting and weighting appropriately. Those estimated responses are plotted as the blue traces in figure 7a, and will be used in the next section.With at least eight photoisomerizations per disc surface, the mean PDE**, which is produced by about 2700 PDE** in the outer segment. Given that the PDE content of the outer segment is 2 × 105 holomers, suppression of 90% of the circulating current would be achieved when the average level of PDE** in the outer segment is only approximately 1.3% of the total. On average, the fractional level in each disc surface will be the same (a mean of 1.8 PDE** per surface), but the actual number of PDE**s per disc surface will be Poisson distributed with this mean, and will therefore fluctuate widely.To put the predictions of figure 73.7.From the results in the preceding section, we can calculate the predicted responses to bright flashes. To do this, we first need to know the number of disc surfaces per outer segment, which we calculate as around 1500 for a mammalian rod . Because this number is quite large, it turns out to be sufficient to use the single-photon simulation results for flash intensities up to several hundred photoisomerizations per rod. For example, a just-saturating flash of 300 photoisomerizations per rod would deliver a mean of 0.2 photoisomerizations per disc surface. As a result, at that intensity only a very small proportion (less than 2%) of disc surfaces would receive multiple photoisomerizations, so that the response can be determined accurately from knowledge of the PDE** activity elicited by a single photoisomerization.a, to determine the total PDE**(t) time-course throughout the outer segment, which we then use as the driving function for the downstream reaction cascade.However, at higher intensities, we need a different approach. As set out in §2.6, we use the Poisson probability distribution, specifying the proportion of disc surfaces experiencing different numbers of photoisomerizations, in conjunction with the individual the traces in figure 7b shows the calculated responses to a series of flashes at intensities that increase by 0.3 log10 units between traces (i.e. approximately doubling), from Φ = 1 to Φ = 16 000 photoisomerizations per rod. We repeated these calculations for many more flash intensities than are illustrated in figure 7b, and for each intensity, Φ, we measured the saturation time, Tsat, defined as the time at which the falling phase of R(t) crosses the 90% level, corresponding to recovery of 10% of the dark current. We then plotted these values against flash intensity as the red curve in figure 7c, which gives Tsat as a continuous function of Φ. The dotted red line has a slope corresponding to 215 ms, and provides a good description of our simulated results up to about Φ = 3000 isomerizations per rod; for reasons that are not immediately obvious, the slope of this line is marginally higher than the time constant of PDE** shut-off, 1/kE** = 200 ms, that we used in the simulations.Figure 7c. For intensities up to approximately 2000 isomerizations the red curve for our model provides a good description of their experiment, with each doubling of intensity eliciting a constant rightward shift (corresponding to the lower intensities in figure 7b). However, at higher intensities there is a significant discrepancy. Whereas the experimental measurements indicate a second time constant (dotted blue line) of 650 ms, our model in its present form predicts a steeper rise. As we shall return to in the Discussion, this defect in our model stems from the assumption (that we made for simplicity in the case of a single R* per disc surface) that shut-off of the free G* can be ignored.To compare this prediction of our model with experimental measurements, we have plotted the values presented by Burns & Pugh   behaves as a pair of independent catalytic subunits. However, in the light of earlier reports [In the past, it has often been assumed that the rod's hetero-tetrameric PDE6 reports ,3 that t reports , we felt4.1.νRE** ≈ 300 s−1, and that at the peak of the single-photon response there are approximately 18 PDE**s simultaneously active. In order to account for the experimentally observed amplitude of the single-photon response in mammalian rods, of approximately 4–5% of the dark current, we found it appropriate to set the PDE hydrolytic activity in the downstream reactions to be βE** = 0.025 s−1, giving the amplitude of the ensemble mean of the simulated SPRs as 4.3% in figure 4d.Our model and simulations suggest that, during the rising phase of the rod's single-photon response, PDE**s are activated at a rate of βE** can be predicted from physical and biochemical parameters of the rod outer segment, using equation (2.4). That equation combines three parameters  that can each be estimated from experimental measurements in the literature, though, it has to be said, with a fair bit of leeway in each estimate. For example, if we adopt values of kcat/Km = 6 × 108 s−1 M−1, Vcyto = 0.02 pL and BcG = 2, then substitution into equation (2.4) predicts βE** = 0.025 s−1, exactly as we used in the simulations. We do not consider any of the three underlying parameters to have been determined accurately for a mammalian rod in vivo, and we are simply showing that plausible values predict an appropriate magnitude for βE**. Thus, we conclude that an entirely reasonable set of physical and biochemical parameter values enables the downstream cascade of reactions to convert the simulated PDE** traces into electrical responses with properties closely emulating the experimentally measured single-photon responses in mammalian rods.From the analysis of the phototransduction cascade , the magA of phototransduction is given by equation (2.3) asncG = 3. Accordingly, our mean value of νRE** ≈ 310 PDE** s−1, for simulations with the standard set of parameters, predicts an amplification constant of A ≈ 23 s−2 for mouse rods, consistent with the upper end of estimates in the photoreceptor electrophysiology literature (§2.3). Therefore, we conclude that the PDE dimeric activation model, in conjunction with our standard set of parameters for lateral diffusion in the disc membrane and our suggested set of other physical and biochemical parameters, is able to account accurately for the amplification and rising phase kinetics of mammalian rod phototransduction.Viewed from a slightly different perspective, the amplification constant 4.2.teff, representing the cumulative effect of multiple short delays expected to arise from several steps in the cascade, as well as any filtering delay in the recording system [et al. [et al. [In the ‘delayed Gaussian’ description of the rising phase of the rod's flash response given in equation 2.1), there is an in-built ‘effective delay time’, , there i [et al.  examined [et al. , which u [et al.  analysedc,f show that the magnitude of the time constant declines with increasing transducin activation rate and also with increasing density of PDE holomers. For our standard set of parameters, of νRG = 1000 G* s−1 and CE ≈ 80 PDE holomers µm−2, the fitted time constant was τRE** ≈ 7 ms. If the PDE density were a little higher, at 125 PDE holomers µm−2, then the delay would drop to approximately 5 ms. The magnitude of this time constant will also depend on the assumed values of the lateral diffusion coefficients (primarily those of transducin and the PDE), and although we are not aware of any direct measurements of those parameters, the values that we have adopted are consistent with the literature .Furthermore, we do not think there is a problem, as suggested in , regardi4.3.νRG = 1000 G* s−1, whereas several modelling studies in the literature have proposed approximately 300 G* s−1 at mammalian body temperature [Of the parameter values that we have adopted, perhaps the one that is most likely to raise eyebrows is the rate of G* activation by a single R*, for which we have taken perature ,23,24. Wet al. [et al. [−1 at room temperature, and with bovine rods, Kahlert & Hofmann [−1 at 20°C. More recently, using purified bovine rod disc membranes reconstituted with purified transducin at different concentrations, Heck & Hofmann [−1 at 34°C; adjustment to physiological conditions  converted this to νRG ≈ 1000 G* s−1.First, it conforms with measurements made from rods using light-scattering approaches –28. Withet al.  and Bruc [et al.  estimate Hofmann  obtainedCPDE = 80 µm−2; the lateral diffusion coefficient of transducin, DG = 2.2 µm2 s−1; and the mean lifetime for an R*, of approximately 70 ms. In order to obtain more than a handful of PDE** molecules at the peak of the response, our simulations required νRG ≥ 500 G* s−1 (c), and our default value of νRG = 1000 G* s−1 generated 18 PDE** at the peak.Second, a value of this order of magnitude is needed to account for the amplitude of the rod single-photon response in our model of dimeric activation of PDE, when used in conjunction with values for the other parameters that are reasonably well-grounded in the literature. The most important of these other parameters are: the membrane density of PDE holomers, 0 G* s−1 c, and ouνRG < 500 G* s−1 would be inadequate. Our fourth line of evidence comes from considering the intensity at which the rod's bright flash response transitions from a first to a second dominant time constant. This will be presented in §4.9.A third rationale comes from considering the variability of the single-photon responses. If only a few PDE** molecules were produced at the peak of the SPR, then the fluctuations in amplitude (and area) would be predicted to be very large, and so once again a rate of in vivo? Another issue that deserves investigation is whether the catalytic activity of R* remains unchanged, from the early times (tens of millisecond) that are relevant to the electrical response and to the light-scattering measurements, out until the very late times (tens of seconds) at which biochemical measurements are made. In our recent analysis of the shut-off of R* activity during the single-photon response [−/− mice. If a large drop in R* catalytic activity does occur for some reason, then this would have major ramifications for any measurements made at late times.Clearly, there is a marked difference between the high rates of transducin activation, estimated in light-scattering experiments and required in our description, and the much lower rates estimated in biochemical experiments measuring nucleotide incorporation (e.g. ). For a response , we foun4.4.f, the rate of PDE** activation has a fairly flat-topped peak for PDE membrane densities in the vicinity of 40–80 holomers µm−2. On the other hand, the initial delay is substantial at low densities, and declines with increasing holomer density. We would predict, therefore, that experiments with altered levels of PDE expression would reveal relatively subtle effects, with minor changes in amplitude and kinetics of the SPR; we are not aware of any experiments in the literature that test this prediction.According to our simulated measures in figure 52). On the basis that a density of 80 holomers µm2 is sufficient to account for the onset phase of the response, and the expectation that increasing the PDE density would increase the noise resulting from spontaneous activation, we chose to standardize on the value reported by Pentia et al. [Values in the literature for the PDE holomer density, relative to rhodopsin, in bovine retina range from as high as 1 : 65  down to a et al. . Very sia et al.  and 1 : a et al. .4.5.area = 0.671 and CVampl = 0.545, while for the electrical response the corresponding values were CVarea = 0.561 and CVampl = 0.414.For the parameters that we adopted as standard, we determined the variability of the response to a single photon as the coefficient of variation, CV, both for the number of PDE** molecules active and also for the electrical response, and in both cases for the amplitude and also the area  of the response. For PDE**, the coefficients of variation for the SPR were CVarea is typically found to be no greater than 0.35. However, we suggest that underestimation of the experimental values could have arisen from the process of selection of singleton responses from the full set of responses comprising failures, singletons and responses to multiple hits. With the use of amplitude criteria to select singletons, we think it inevitable that the smallest singletons may get overlooked (as they are assumed to be failures) and that the largest singletons may likewise get overlooked (as they are assumed to be responses to multiple hits). Omission of the extrema will bias the sample and lead to underestimation of the true variance.These values are larger than reported in SPR experiments on mammalian rods, where CVWith this factor in mind, we regard the correspondence between simulation and experiment as adequate for preliminary modelling in which we have made no attempt to optimize parameters. We envisage that by adjusting various parameters in the model it would possible to achieve closer agreement with SPR experiments.4.6.−/− genotypes. In panels b,c, we plotted the power spectral density predicted for the random activation of PDE** molecules, each having a stochastic lifetime determined by the GTPase reaction, and with the vertical scaling corresponding to a rate of one such spontaneous event per second per rod. In both genotypes, the fall-off at high frequencies has a slope corresponding to two filtering stages. The predicted zero-frequency asymptote and half-power frequency are 1.4 × 10−6 Hz−1 and 0.9 Hz for WT, and 5.2 × 10−5 Hz−1 and 0.45 Hz for GCAPs−/−.In order to test whether the stochastic occurrence of PDE** events could underlie the continuous noise recorded from mammalian rods, we subjected simulated stochastic PDE** events to the downstream cascade, and our results are shown in b of Burns et al. [b of Baylor et al. [−1 per rod for GCAPs−/− mouse (b), and to 11 s−1 per rod and 50 s−1 per rod for WT mouse and monkey rods, respectively (c). A shortcoming here is that the experimental recordings did not separate the continuous noise from the noise component arising from spontaneous photon-like events, but we think that this had relatively little impact because the photon-like event rate is very low in mammalian rods; for the future, it would valuable to make dedicated recordings isolating the continuous component of noise in mammalian rods. We think it possible that the WT mouse measurements may have underestimated the noise, because the noise in darkness was only marginally larger than that in saturating light, so that there may have been issues with the subtraction to obtain the biological noise. On balance, we will adopt 40 PDE** events s−1 per rod as a representative event rate for both genotypes.Then we examined the spectral density measurements of dark noise from two studies in the literature  of spontaneous transducin activation events required to account for the observed rate of PDE** events, if we know the fraction, NE*/NE, of PDE molecules that are singly bound in the resting state. Thus, on the assumption  that the fate of each G* depends simply on whether it contacts a singly bound PDE* or an unbound PDE, we can writeA potential source of random activation of PDE**s would be the binding of spontaneously activated transducins (G*) to singly bound PDE* molecules (but see next section). For such a molecular mechanism, we can estimate the required rate (kE* is the rate constant of PDE* shut-off by GTPase activity. Combining equations (4.1) and (4.2), we obtainIf we further assume that, in the dark resting state, the singly bound PDE*s had arisen solely as a result of spontaneously activated G* binding to unbound PDE, then the resting number of singly bound PDE*s is the product of their rate of formation and their mean lifetimeNE = 2 × 105 PDE holomers per rod, kE* = 2.5 s−1 and νE** = 40 s−1 per rod, we obtain the required rate of spontaneous transducin activation as νG* = 4500 s−1 per rod. The ratio of estimated rates of spontaneous activation for transducin versus PDE** is therefore approximately 100-fold, which indicates that the amplitude of the electrical response elicited on average by a single transducin would be around 1% of that elicited by a single PDE**.When we substitute −1 per rod represents an upper limit, because it is conceivable (a) that there might be other contributions to the resting PDE* level and/or (b) that there might be a source of spontaneous PDE** activation other than via G*. An example of the latter mechanism would be the spontaneous flickering release of inhibition by the γ-subunits; however, we would anticipate that such events would be quite fast, so that the noise they elicit would extend to frequencies higher than is observed in the spectrum of the rod's dark noise.The above estimate of 4500 G* s−1 over the entire outer segment, is not particularly high, and represents only 3 G* s−1 per disc surface. As the outer segment contains around 8 × 106 transducins (at a density of 3000 µm−2), it corresponds to a G* activation rate of about 5 × 10−4 s−1 per molecule. For comparison, the rate of spontaneous release of GDP by bovine transducin has been measured in biochemical experiments as 1–2 × 10−4 s−1 at 37°C [−1 obtained for WT mouse rods to be correct, then our estimate for the G* activation rate would have halved, and would have been very close to the biochemical estimate.Even this upper limit, of 4500 G* s at 37°C , so that−1 by the mean PDE** lifetime of 1/kE** = 0.2 s, then we obtain an estimate for the mean number of PDE**s present in the outer segment under resting conditions, as c, the SPR (at its peak) should generate a random number of PDE**s with a mean of 18 and an s.d. of approximately 10. As a result, for SPRs occurring at random times, it seems likely that the majority would be resolvable above the noise. Clearly, this is a simplistic calculation, and the detectability of the SPR could be analysed more rigorously in terms of the electrical response, once the spontaneous rate of unitary PDE** events has been established more accurately.If we multiply the rate of PDE** activation of 40 s4.7.For toad rods, it has been reported that the continuous noise in darkness arises from spontaneous fluctuations in PDE activity . In an eet al. [Our analysis was undertaken in light of the preliminary report of Qureshi et al.  and as aet al. , and it et al. [kon and koff) for the binding of G* to PDE and to PDE*; in particular, they reduced the value of kon for the second reaction, in light of their measurement of a higher dissociation constant in that case. Instead, we chose not to implement a finite value of kon for two reasons. First, we feel that specifying such rates on the basis of equilibrium measurements introduces uncertainty. Second, in preliminary trials, where we did simulate a lowered kon for binding to form PDE**, we found that the main effect appeared to be an increase in the delay prior to the ramp-like rise of PDE** (data not shown), and that this increase was beyond what is expected from experimental recordings. The effect deserves investigation in future work, once kinetic measurements of kon and koff are available, but it is beyond the scope of the present paper.One difference in the numerical simulations is that we have used a simpler  description of lateral diffusion and intermolecular contacts than the more comprehensive approach adopted by Qureshi et al. , and as 4.8.βE** = 0.025 s−1, we obtain a hydrolytic rate of βspont ≈ 0.2 s−1, far smaller than the resting dark rate of hydrolysis, of βDark = 4 s−1. As a result, we can conclude that βDark is not set by stochastic activation of the PDE to its fully activated PDE** state.From the mean number of PDE**s active in the dark resting state, in conjunction with the hydrolytic activity per PDE**, we can readily estimate the mean dark level of cGMP hydrolysis generated by the stochastic activation mechanism. By setting βDark results from the residual activity of either the unbound PDE holomers or the singly bound PDE* molecules, for example, by flickering relief of inhibition by the γ-subunit(s). Up until now, we have ignored any hydrolytic activity of the singly-bound PDE*, and we regard this simplification as entirely justifiable in calculating the kinetics of activation, because Qureshi et al. [βDark. If PDE* were the primary source of the resting hydrolytic rate, then its residual activity would need to be βDark/NE* ≈ 4 s−1/1800 = 0.0022 s−1. This would be approximately 9% of the hydrolytic rate of the fully active PDE** (βE** = 0.025 s−1), a level considerably higher than the reported upper limit of 2.5% [βDark/NE ≈ 4 s−1/2 × 105 = 2 × 10−5 s−1, or around 1/1000 that of the fully activated PDE**. We have no basis for rejecting residual activity of 0.1%, and so it is plausible that βDark represents the very low residual activity of the unbound PDE holomers, or a combination of residual activity of both PDE and PDE*.The two most likely alternatives are that i et al.  reported4.9.Φ and the time Tsat that the response to that flash remains in saturation . For intensities up to Φ ≈ 3000 photoisomerizations per rod, our model predicted exactly the relationship reported in the literature [figure 7c. We interpret this discrepancy to indicate a fault in our assumption that activated transducin (G*) can only decay while it is bound to PDE, because it is this assumption that leads to the linear dependence of Tsat on Φ at very high intensities. We made that assumption as a simplification to avoid introducing an additional parameter, and the simplification has a negligible effect when there is only a single R* per disc surface, because the level of free G* is normally quite small. However, at higher intensities that saturate the binding of G* to PDEs, the level of free G* rises, and hence the lifetime of free G* needs to be taken into account.Using our model, we predicted the relationship expected between flash intensity terature ,40,41, win vivo, on the basis of experiments incorporating RGS9-2 into rods [γ, the rate of GTP hydrolysis by activated transducin is around 1 s−1 [−1 [The second, longer ‘dominant time constant’ (of 600–800 ms) observed in electrophysiological measurements on mouse rods has been proposed to reflect the time constant of such decay of free G* nto rods . We concnd 1 s−1  (in cont s−1 [−1 ). Accord−1 per R*. The transition intensity, Φtrans, for the change from smaller to larger dominant time constant should correspond to the creation of just enough G*s to bind both sites on the PDE holomers. Hence, the rate of G* activation required to account for the transition intensity should satisfyΦtrans has been measured as approximately 4000 R* per rod [TR* ≈ 0.07 s, and the rod contains NE = 2 × 105 PDE holomers. Consequently, the required rate of G* activation is calculated as νG* ≈ 1400 G* s−1 per R*, even higher than the value we have adopted.Importantly, this interpretation provides the fourth line of support (see §4.3) for our use of a G* activation rate of at least 1000 G* s per rod , the R* 4.10.α’ subunits (encoded by PDE6C) and is presumably a symmetrical molecule. It is therefore plausible to think that activation of the cone PDE by transducin may not display functional asymmetry, and that the two catalytic subunits may instead operate independently of each other, as has in the past been envisaged to be the case for the rod PDE. However, as we do not know the precise molecular mechanisms involved in activation, this suggestion for cones remains speculative. Nevertheless, if it were the case, then cones could be viewed as having avoided the combination of advantages and disadvantages that are afforded to rods by dimeric activation.Our analysis so far has been directed solely towards rods, but we can suggest some consequences for cones, based on the fact that the cone PDE6 is composed of a pair of identical c). In theory, this factor could scale the amplification constant of cones relative to rods by up to 1/0.6, thus giving an increase of greater than 50%.Such a scenario could well explain at least two known differences between cones and rods, and it might predict other differences as well. First, it would explain the shorter delay in the rising phase of the response that has recently been characterized . Second,figure 54.11.If our interpretations in this study are broadly correct, then they have multiple implications for the reassessment of previous analysis and modelling of vertebrate phototransduction, and they also enable us to make suggestions for future work on refining and extending the concepts.−1 [γS binding, and that the activation rate is the same in disrupted rod outer segments as it is in vivo. If, however, the rate of transducin activation at late times in disrupted outer segments differs from that at early times in vivo, then the biochemical measurements could potentially be very misleading.The first matter to consider for reassessment is the report in the literature that the rate of transducin activation in amphibian rods at room temperature is as low as 120–150 G* set al. [Likewise, we suggest that all previous modelling of vertebrate phototransduction based on this value  should be reassessed in light of the critical question of whether the parameters of the model were well constrained. As discussed recently by Gross et al. , ‘Ill-co−1, but it is possible that the true value in vivo might be even higher. If so, this would increase the mean number of PDE**s produced during the response to a single photon, and thereby lower the variability (i.e. lower the CVarea). At the other extreme of the intensity range, for the predicted responses to very bright flashes, we anticipate that by incorporating inactivation of free G* it may well be possible to account quantitatively for the second, longer ‘dominant time constant’. In addition, as set out in §4.9, we think it likely that a higher rate of G* activation may make it easier to account for the relatively low transition intensity for the change from the first to the second dominant time constant.For the future, we intend to investigate whether it is possible to extend the predictions of the dimeric PDE activation model to account more comprehensively for experimental measurements in the literature. For this present analysis, we concentrated on tests with an assumed rate of G* activation per R* of 1000 G* sOverall we conclude that, by breaking free of the unsubstantiated assumption that rod PDE subunits activate independently, it has been possible to obtain a more realistic model of phototransduction in rods, and that further refinement will be possible in the future.


As the number of older adults increases, increased prevalence of cognitive and sensory impairments pose growing public health challenges. Research on the relationship between hearing impairment and cognition, however, is minimal and has yielded mixed results, with some studies finding that hearing impairment is associated with cognitive decline, and others reporting that the association is weak or non-existent. Most of this research has been conducted outside of the U.S., and the few U.S.-based longitudinal studies have relied mostly on small, non-representative samples involving short follow-up periods. Further, despite known gendered patterns in cognitive and hearing impairments, no studies to date have examined whether the relationship between the two varies by gender. Our study addresses these weaknesses in the literature by utilizing nine waves of the Health and Retirement Study , a large, nationally representative, longitudinal study that facilitates examination of long-term interrelationships between hearing and cognitive impairments. In this study, we use autoregressive latent trajectory (ALT) methods to model: 1) the relationship between hearing impairment and cognitive decline, and 2) sex differences in the relationship. ALT models enable us to determine whether hearing impairment and cognitive impairment are associated, net of their common tendency simply to co-trend with age. Results indicate that hearing and cognitive impairments are strongly interrelated processes that trend together over time. Moreover, hearing impairment has an increasing impact on cognitive impairment across age while the effect of cognitive impairment on hearing impairment levels out over time. Sex differences in these patterns are discussed.


ILDR1 gene is a high profile candidate, as it contains our top SNP, is a known hearing loss gene, has been linked to age-related hearing impairment before, and in addition is preferentially expressed within hair cells of the inner ear. By verifying all previously published SNPs, we can present a paper that combines all new and existing findings to date, giving a complete overview of the genetic architecture of age-related hearing impairment. This is of importance as age-related hearing impairment is highly prevalent in our ageing society and represents a large socio-economic burden.Previous research has shown that genes play a substantial role in determining a person’s susceptibility to age-related hearing impairment. The existing studies on this subject have different results, which may be caused by difficulties in determining the phenotype or the limited number of participants involved. Here, we have gathered the largest sample to date , and examined phenotypes that represented low/mid and high frequency hearing loss on the pure tone audiogram. We identified 7 loci that were either replicated and/or validated, of which 5 loci are novel in hearing. Especially the  Presbycusis or age-related hearing impairment (ARHI) may lead to loss of productivity at work3, social withdrawal4 and depression5. ARHI is associated with cognitive decline6 and dementia although the precise relationship between the two is debated7. ARHI typically affects hearing thresholds bilaterally which is most pronounced in the higher frequency range8, while the age of onset and rate of progression are variable. The cochlea plays a vital role in its pathophysiology and signs of cochlear degeneration are present in cases with ARHI10.Hearing loss with age is highly prevalent and accounts for a large socio-economic burden11. Heritability estimates of ARHI vary depending on the precise phenotype studied but are substantial, ranging from 36–70%15. However, the genetic architecture of ARHI remains unclear and to date few genetic variants have been convincingly identified in humans. There are many known genetic variants associated with hearing loss of different types16, but few have been identified as underlying ARHI, which include TJP217, MYO618, and WFS119.The etiology of ARHI is multifactorial and includes genetic factors, environmental factors, and their interaction20. Several loci – named Ahl - have been associated with ARHI, resulting in the identification of one causative gene: cadherin 2321. Mutations in the human homologue of Ahl are implicated in Usher syndrome 1D and nonsyndromic autosomal recessive deafness DFNB1222. Evidence of their contribution to ARHI in humans is still lacking.Owing to the difficulty of obtaining human cochlea tissue, much research into ARHI has been performed in mice where genetic manipulation and biochemical studies are relatively easy to perform23, pure tone averages23, principal components26, and Z-scores27. Health record ICD-9 diagnoses28 have also been used in absence of audiometry. This has led to associations reported between ARHI and variants in the following genes: GRM727, IQGAP224, SIK326, ISG20, TRIOBP, EYA4 and ILDR128. There is a lack of consistency between these studies though, which is probably explained by relatively modest sample sizes, given the likely small effect sizes of the variants. However, suboptimal definition of the phenotype, large genetic heterogeneity and the study of isolated populations may also play a role in the failure to date to replicate many of the reported findings.ARHI is not easily quantifiable. The gold standard measure of hearing, pure tone audiometry, provides an audiogram with hearings thresholds at multiple frequencies. Normal values for thresholds are dependent on age and sex and show a skewed distribution. In previous genome-wide association studies (GWAS) investigators have used a variety of traits, including different ways of deriving information from the pure tone audiogram: absolute thresholdsHere we present a large, 1000Gv3 imputed GWAS meta-analysis of ARHI using pure tone audiometry from multiple cohorts in Northern Europe and the USA, providing a total discovery cohort of 9,675 individuals. We were interested in exploring correlates of high and low/mid frequency hearing loss across samples of different ethnic backgrounds. Because previous work suggested an influence of ethnicity on ARHI, we performed the analyses both stratified by ancestry and in all ancestries combined.−10) was located on 3q13.33 in the intron region within the ILDR1 gene. For LOW/MID, the two genome-wide significant SNPs identified were rs6740893 (P = 3.22*10−08), located on 2p16.2 in the intron region within the SPTBN1 gene, and rs9298078 (P = 3.36*10−08), located on 8q12.3 in an intronic non-coding RNA region. Interestingly, the HIGH and LOW/MID phenotypes did not show any overlap in associated signals, with all suggestive and significant loci being different between the two . Of note, the second and third strongest trends from the LD Hub analyses were also related to hearing loss (‘Hearing aid user’ and ‘Hearing difficulty/problems with background noise’).The use of UK Biobank questionnaire data (as well as to identify other phenotypes linked to ARHI), was validated by exploring the phenotype genetic correlations using LDHub  achieved significance after Bonferroni correction , ILDR1 (hair cells), TRIL (supporting cells), SCUBE2 , DOCK9 , ISG20 , SHC2 (hair cells) and ODF3L2 . The greatest differential expression was noted for ILDR1, which is 7.65 times more expressed in hair cells than in supporting cells (FDR = 1.33*10−4).The following genes showed signs of differential expression in the inner ear: All 10 genetic variants identified as genome-wide suggestive or significant were either on different chromosomes or were far apart from each other (>10 Mb) and can therefore be considered independent loci. Only a very minor attenuation of signal was observed in the joint analysis (Supplementary Table −5) with SNP-based heritability (h2) more or less equal for the two: HIGH = 0.15 (P = 0.002); LOW/MID = 0.11 (P = 0.032). This implies that between 0.11–0.15 of the phenotypic variance can be explained by common SNPs with minor allele frequency > 0.05.A significant genetic correlation was identified between HIGH and LOW/MID phenotypes  or the IQGAP2 gene (rs457717) was found. We were unable to replicate the SIK3 SNP (rs681524), which had been identified in the G-EAR consortium and TwinsUK. However, positive support was established for ILDR1 (rs2877561 was highly significant in HIGH and LOW/MID), ISG20  and TRIOBP  (Table EYA4 (rs9493627) just failed to reach significance (P = 0.006 in LOW/MID).Results of the 7 candidate gene variants previously linked to ARHI are given in Table FXYD5, IPP, SPIRE2, SPTBN1 and TRIL) and 2 have been previously related to hearing loss . Suggestive and significant associations showed no overlap between the low/mid and high frequency hearing loss phenotypes, indicating different pathophysiological mechanisms. In addition, we have confirmed some of the SNPs previously reported as related to ARHI , while others were not replicated . Our study has again shown that ARHI is highly polygenic as many genes - each with small effect - contribute to the estimated heritability of 36–70%15. In this study we identify a SNP-based heritability of 11–15%. This is in line with the observation that twin and family studies produce higher heritability estimates of common complex traits because GWAS estimates only the contribution made by common variants while other heritable variation (such as indels and CNVs) are largely ignored29.This study is the largest GWAS meta-analysis of ARHI to date and identified 7 associated loci, of which 5 are novel . Despite different approaches to phenotyping, the UK Biobank questionnaire responses are useful in that they indicate the genetic variants responsible for pure tone audiometry changes are relevant to individuals’ clinical symptoms, something that is increasingly important in publically funded research.The UK Biobank was used to validate our results based on responses to questions regarding hearing loss. Previous work on this dataset had examined the speech-in-noise phenotype and found it unreliable. A significant correlation was found between our HIGH phenotype and self-reported hearing loss in the UK Biobank, indicating that these responses may be a useful alternative to pure tone audiometry, in line with literature35.Data on gene expression in the auditory system can provide supportive evidence, although such data should be interpreted with caution. The effects of aging may be manifest through other mechanisms, for example via the circulation, and may have an important influence on hearing function. Almost all associated genes identified showed signs of expression within the human or mouse cochlea, spiral ganglion cells or both. Of specific interest is the differential expression because most genes involved in hearing loss to date have been shown to be overexpressed within cochlear hair cellsSLC15A2 and ILDR1. The SLC15A2 gene was initially considered a candidate gene for the nonsyndromic hearing loss locus, and was designated DFNB4236. However, sequencing of the SLC15A2 gene yielded no causal variants. Six years later, the Ig-like domain containing receptor 1 (ILDR1) gene was identified as the causal gene37. Several mutations within the ILDR1 gene have been associated with autosomal recessive nonsyndromic hearing loss to variable extent41. In addition, this gene has been linked previously to ARHI through a candidate gene approach28. The ILDR1 protein mediates the recruitment of tricellulin to tight tricellular junctions, which plays a crucial role in the epithelial barrier function. ILDR1 knock-out mice initially show normal development of inner and outer hair cells and the organ of Corti42. At 2 weeks of age however, outer hair cells begin to degenerate at the basal turn of the cochlea, corresponding to the higher frequencies. Later, this progresses to outer hair cells of the lower frequencies as well, and hearing function is severely diminished at 3 weeks of age. This process describes an accelerated version of the biology of ARHI in humans.The most highly associated SNP in our meta-analysis lies on chromosome 3 in close proximity to two genes: SPTBN1, encoding the spectrin beta, non-erythrocytic 1 protein, also known as βII spectrin, contains the top SNP of one of the low/mid frequency hearing loss loci. Spectrins are a major component of the cell membrane cytoskeleton and are located in hair and supporting cells of the cochlea43. As the cytoskeleton has a close relationship with outer hair cell electromotility, namely a shortening and elongation of the cell in response to sound stimuli, this may provide a mechanism through which SPTBN1 contributes to hearing loss.CTH) is involved in the formation of hydrogen sulfide and this gas has been demonstrated to regulate cochlear blood flow and has the ability to protect against noise-induced hearing loss44. Odf3l2 has been listed as a candidate gene involved in mild hearing loss in a large-scale screen in mice45 and is differentially expressed in hair cells35. To our knowledge, no relationship has been established yet between hearing loss and any of the other annotated genes . The precise mechanism through which they act on hearing function is yet unknown. Pathway analysis performed on our significantly associated variants was unrewarding.Cystathionine-γ-lyase  is another gene that was replicated in our discovery cohort and is a well-established hearing loss gene, labelled as DFNB2849. The TRIOBP protein is localized in the rootlets of hair cell stereocilia and provides stability and rigidity. In knock-out mice, stereocilia develop normally but are easier to deflect and damage50. Initially thought to be responsible for profound, early onset hearing loss, mutations in TRIOBP have been recently shown to be also linked to late onset28 and more moderate hearing loss51, fitting the description of ARHI well.To conclude, this study provides a large step forward in unravelling the genetic architecture of ARHI. Future studies have to confirm the associated loci and elucidate the pathophysiological pathways in which they may lead to hearing loss.52 that have collected pure tone audiometry were included. Participants from Age, Gene/Environment Susceptibility Study , Cardiovascular Health Study , Framingham Heart Study , Health ABC , and the Rotterdam study  were combined to provide a discovery sample of n = 9,675. The replication samples had also obtained pure tone audiometry and included the Antwerp study , the G-EAR consortium , the Jackson Heart Study , Hispanic Community Health Study/Study of Latinos  and TwinsUK (n = 819), leading up to a total available sample of n = 10,963. Findings were further validated in the UK Biobank sample (https://www.ukbiobank.ac.uk/) using responses to questions on self-reported hearing loss because the speech-in-noise measures were found to be unreliable. In every analysis, only males and females aged 45 years or older were included. All participants provided written informed consent. Ethics approval was obtained locally at each study site: the Icelandic National Bioethics Committee (AGES); the institutional review boards at the University of Pittsburgh, the Johns Hopkins School of Public Health, Wake Forest University Health Sciences and the University of California Davis (CHS); the Boston Medical Center Institutional Review Board (FHS); the institutional review boards at the University of Pittsburgh and the University of Tennessee, Memphis (HABC); the Medical Ethics Committee of the Erasmus Medical Center (RS); the Committee for Medical Ethics UZA-UAntwerp; the Institutional Review Board of IRCCS Burlo Garofolo (G-EAR); the institutional review boards at the San Diego State University, the University of Miami, the University of North Carolina, the University of Illinois and the Albert Einstein College of Medicine (HCHS/SOL); the Institutional Review Board of the University of Mississippi Medical Center (JHS); and the National Research Ethics service London-Westminster (TwinsUK). The Declaration of Helsinki was adhered to. A more detailed description of the discovery and replication cohorts is available in the supplementary information.Six population-based studies from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortiumPure tone audiometry  was collected on participants in all studies. We elected to examine hearing as a quantitative trait, rather than arbitrarily assign participants to case or control status. For each participant, the results of the better ear were used, defined as having the lowest threshold averaged over 0.5, 1, 2 and 4 kHz; when these were equal across ears we took the ear with the lowest threshold averaged over 4 and 8 kHz; if these were similar, the ear with the lowest threshold averaged over 0.5, 1 and 2 kHz was chosen; and if these too were similar, the results of the left ear were selected.53 did not summarise our data well so decided that a standardised approach would allow each cohort to use its own data to generate means and standard deviations. Thus, each cohort provided its own reference panel and the effect of variations between audiometry centers was hereby minimised. Definition and calculation of the phenotypes was established before commencing the genetic analysis and not altered post hoc. Age-related hearing impairment mainly affects thresholds of the higher frequencies  or suggestively associated (P < 1*10−6). Comparison of findings between the phenotypes was considered to be informative of the genetic relationship between specific characteristics of the audiogram (low/mid versus high frequency hearing loss).Inverse variance-based meta-analysis of summary statistics was performed in METALThe same phenotype definition was used in five independent studies in which we attempted to replicate the most highly associated SNPs: the Antwerp study, the G-EAR consortium, JHS, HCHS/SOL and TwinsUK (details on genotyping are listed in Supplementary Table −6) were considered for replication. Inverse variance-based meta-analysis of summary statistics was performed in METAL.All suggestive and significant loci from the discovery meta-analysis . Participants were invited to local examination centers and underwent a battery of clinical tests and completed online questionnaires including questions pertaining to hearing ability. We elected at the outset to use responses to hearing ability questions to see if we could validate the genetic findings using a subjective measure that reflects clinical hearing disability. Participants had been asked the question ‘Do you have any difficulty with your hearing?’ Cases were defined as responding ‘Yes’ or ‘I am completely deaf’ while control status was assigned to those answering ‘No’. Participants with missing data and those aged below 45 years at the time of participation were excluded from the present study. GWAS was performed using Northern Europeans alone as defined by the response to the ethnicity question and principal component analysis.We sought to validate our findings further using UK Biobank. This national bioresource in the UK comprises ~500,000 participants aged 40–69 years registered with a general practitioner, and has been extensively described elsewhererd degree relatives in the kinship table), putative sex chromosome aneuploidy, heterozygosity and missing rates57. The final sample, selecting Northern Europeans as determined genetically, comprised n = 98,816 cases and n = 257,325 controls. Since 10 independent SNPs were investigated results were considered significant if P < 0.005, with Bonferroni correction.PLINK2 logistic regression models were used to test for association adjusting for age, genetic sex (inferred from genotype data), UK Biobank PCs 1–10 and genotyping platform. Data were pre-processed by UK Biobank before release, and further QC was performed based on their recommended filters for excess relatedness . Interpretation was performed in a qualitative manner, assigning results to one of four categories: not available ; absent (expression < 1 FPKM); marginal (expression > 1 FPKM in only one sample); or present (expression > 1 FPKM in more than one sample).The cochlea is a distinct, highly differentiated organ with its own specific gene expression pattern. Human cochlea material is difficult to obtain so expression data are relatively lacking. We assessed a database, the only existing one as far as we are aware, derived from three human cochlear specimens61. We looked at whether the candidate genes were expressed in adult cochlear inner and outer hair cells, at P25–P3062, and spiral ganglion cells of the cochlear nerve, at P1547. Expression was designated “present” when, respectively, levels exceeded 10.9 or >=50% of detection calls were positive. In addition, differential expression in the inner ear, defined as >2-fold change, was noted, looking at: cell type (hair cells vs supporting cells); tissue source (cochlea vs utricle); and developmental stage 35. False discovery rate was used to assess statistical significance.The Shared Inner Ear Laboratory Database (SHIELD) was also consulted, as it contains a wide variety of information on gene expression in the mouse auditory system that is unavailable in the human variant63. A combined dataset of the Rotterdam II and III cohorts was used as genetic background.Independence of all suggestively and significantly associated variants was examined. Genome-wide Complex Trait Analysis (GCTA) was used to identify secondary signals employing the cojo-slct function64. Only traits investigated in European ancestry samples were considered. If a trait was included multiple times, the largest dataset was considered. Traits for which no estimates could be obtained were excluded. This approach resulted in 518 traits being investigated, including 339 unpublished traits from the UK Biobank, of which three were hearing-related phenotypes. Significance was set at Bonferroni adjusted P < 9.65*10−5.LD score regression was employed to investigate the genetic correlation between the phenotypes, as well as to examine the genetic correlation between our phenotypes and publically available data on LD HubGRM727; IQGAP224; SIK326; EYA4, ILDR1, ISG20, and TRIOBP28. Significance level was Bonferroni adjusted for 9 SNPs and set at P = 0.0056.We were interested to determine the strength of evidence in this dataset supporting SNPs previously identified as associated with ARHI. We used the discovery cohort to examine 9 genetic variants in 7 genes that have been previously associated with ARHI using agnostic study methods and replicated, or identified through GWAS meta-analysis: Supplementary informationSupplementary Table S2Supplementary Table S5


The severity of age‐related hearing and vision impairments increases with age. Such sensory impairments are risk factors for functional decline, reduced social participation, withdrawal, depression and accidents, and hence they make people vulnerable and adversely affect their quality of life.The aim of this study was to explore how the oldest old recipients of home care experience sensory impairments in daily life.An inductive, descriptive research design was used.Ten recipients of home care with a mean age of 89 years were interviewed in their homes. The study was implemented in accordance with the suggestions from Elo and Kyngäs for inductive content analysis.The main theme concerned acceptance and adjustment in daily life. Subcategories concerning the category of reduced hearing were identified as “acceptable though annoying” and “hesitant about using hearing aids.” Subcategories concerning the category of reduced vision were “reading is increasingly challenging” and “living with vision diseases.” The third category of feeling weak was elucidated in the subcategories “troublesome bodily changes” and “strenuous days with limited energy.”It is imperative to recognise that the oldest old are in a distinct phase of the lifespan. Despite this population being aware of their hearing and vision impairments, they do not always have the strength to alter the situation. Instead they accept it; they often struggle with more serious health challenges. Therefore, they are not prioritising using their limited energy reserves to try to improve or optimise their hearing and vision impairments themselves.The oldest old with sensory impairments cannot be expected to perform all the necessary activities of daily living or address their functional sensory impairments. Close monitoring and assistance need to be applied to the oldest old. The oldest old typically face several serious health challenges that limit their capacity to prioritise hearing and vision impairments.The oldest old are characterised by feeling weak and having few energy reserves.Troublesome bodily changes together with sensory impairments make the oldest old vulnerable and at risk of being socially isolated.Many of the oldest old have limited possibilities due to their poor health condition to get out of their homes in order to receive specialised treatment for their hearing and vision impairments.We suggest engaging the oldest old in energy conservation techniques and addressing their sensory impairments through education of the benefits of intervention to preserve as much hearing and vision as possible using compensatory techniques.To prevent feeling of social isolation, it is important to offer the oldest old assistance for improving their hearing and vision functions.We suggest that home care nurses and other community healthcare providers increase their attention towards informing the oldest old clients about age‐related hearing and vision impairments and possible interventions to improve these functions.We suggest home care nurses and other community healthcare providers to increase their awareness towards possible consequences of sensory impairments in everyday matters for the oldest old; such as telephone calls, changing light bulbs and transportation.The oldest old and the providers of home care should be offered knowledge in how to optimise the indoor lighting conditions and its meaning for vision and hearing.The oldest old need help to improve their hearing and vision function in order to increase the probability of them maintaining social contact with others.We suggest establishing interdisciplinary teams involving nurses, audiologists, low‐vision occupational therapists and optometrists that can visit older people in their homes to provide advice on how to alleviate changes in hearing and vision function.Attention is needed on the importance of the senses to maintaining social contact and activity among the oldest old.Preventive teams in municipalities or home care services should start monitoring hearing and vision from an earlier age.More research is needed to establish individualised interventions to help the oldest old to preserve and improve their hearing and vision.1The increase in life expectancy is resulting in more people experiencing hearing and vision impairments. Even the fact that there is close relationship between ageing and sensory impairment, people affected would not always understand this  mean altered or impaired vision. The pupil becomes smaller, which reduces the amount of light entering the eye, the lens thickens, gets stiffer, and is less able to accommodate, and the ciliary muscle weakens Wyller, . In addiSeveral studies have documented that hearing and vision impairments  are common among people older than 80 years  in the southern Norway. Age and different diseases meant that all participants received public home care ranging from once a week to several times a day Table . Seven l4.2One in‐depth interview was conducted with each participant in their homes by the first author. The interviews focused on experiences with hearing and vision impairments in daily life. To encourage the participants to freely express themselves, the interviewer adopted an open discussion approach  and by the Norwegian Social Science Data Service (reference number 28025).4.4The interviews were transcribed verbatim into a 134‐page document. Following the suggestions for inductive content analysis , and they all received public home care at least weekly. This study was designed to explore how the oldest old manage their hearing and vision impairments when living at home and receiving home care. The interviews revealed that hearing and vision impairments formed an integral part of their living experience. They considered hearing and vision impairments to belong to the image of being very old; it was something they accepted having to live with even though it was sometimes quite annoying.Reduced hearing was particularly troublesome when they were with many other people at social gatherings; it had the effect of impairing their social life. Reduced vision was annoying when it made reading the newspaper difficult or when they had to ask for help with changing light bulbs or other simple practical everyday tasks. Such findings concur with previous reports on how hearing and vision impairments affect everyday competencies , their ability to socialise with others was limited by both bodily and sensory challenges. Their physical decline resulted in them spending most of the time indoors. They were tired, had few energy reserves and their hearing and vision impairments were barriers in communicating with others as well as being informed about current events. Altogether, our understanding was that they seemed to consider that nothing could be done about their impairments. The oldest old in our study demonstrated a wish to take care of themselves as much as possible, not wanting to bother others with daily trifles. Although adjustment was not always easy, they expressed feelings of gratitude for living a long life and for feeling safe and still being able to manage in everyday life. However, they constantly had to struggle with health issues and weakness from being old; their strength and capability to perform even small tasks was decreased, which meant that their sensory impairments were not prioritised. These findings concur with the model of Rowe and Fried  of how rGood lighting is important for both hearing‐impaired and vision‐impaired people, so that they can see and read the face and mouth of the person they are talking to. Since advancing age results in less light entering into the eye, it makes one more dependent on good lighting conditions to see well. Eilertsen, Horgen, Kvikstad, and Falkenberg  found th. Small things really matter. We would argue that providers of care in the home should be more proactive about solving even the tiniest problem, since the oldest old, as part of their self‐care, need help in setting goals during their often‐strenuous daily lives. At the same time, it is challenging for these care providers to understand the unique situation of the care receiver. This study has revealed that the oldest old who need home care because of age‐related declines and diseases have reduced energy reserves. Thus, they need to carefully consider how to spend their limited energy. The oldest old used their strength in accepting and adjusting to the most urgent issues in their daily lives and therefore decided that their hearing and vision were acceptable. They had to take this approach, and so it might not be realistic to expect them to use their limited energy reserves to try to improve or optimise their hearing and vision themselves. Accordingly, we suggest establishing interdisciplinary teams that include nurses, audiologists, optometrists, low‐vision occupational therapists and other professionals to visit older people in their homes and advise them on how to alleviate their hearing and vision impairments. Cacchione (Høy, Wagner, and Hall  describeacchione  emphasis6.1In this interview study, we explored experiences of the oldest old and generated knowledge about how they feel forced to prioritise their resources to manage more serious health challenges than their sensory impairments. Nevertheless, we were not sufficiently aware of how weak the participants were when planning for the interviews and subsequently found that most of the participants were easily tired and could not manage long conversations. Therefore, transferring the present findings to other settings or groups of oldest old should be done with caution. Two spouses were present in two of the interviews, which according to Norlyk, Haahr, and Hall  might ha7We suggest engaging the oldest old in energy conservation techniques and adressing their sensory impairments through education of the benefits of intervention to preserve as much hearing and vision as possible using compensatory techniques.To prevent feeling of social isolation, it is important to offer assistance for improving their hearing and vision functions. Home care nurses and other health care providers should increase their attention towards informing the oldest olds about age‐related hearing and vision impairments and possible interventions to improve these functions.Home care nurses and other health care providers should increase their awareness towards possible consequences of sensory impairments in everyday matters for the oldest old, such as transportation, telephone calls and changing light bulbs.Oldest old and providers of home care should be offered knowledge in how to optimise the indoor lighting conditions and its meaning for hearing and vision.8The oldest old are in a distinct phase of the lifespan, and home care providers can offer support and care in a variety of meaningful ways. Even when the oldest old are aware of their hearing and vision impairments, they do not always have the strength to improve the situation. Instead, they accept it, often struggling with more serious health challenges, unable to prioritise their limited energy for hearing and vision impairments. We encourage policymakers to facilitate hearing and vision monitoring at an early age and to obtain the information necessary to detect age‐related changes in functioning of the senses. When people know about normal age development of their senses early in the lifespan, they can start the self‐monitoring process that helps improve their hearing and vision function in old age.No conflict of interest. All authors have confirmed agreement with the final statement of the study.Study design: GGH, GE; Data collection: GGH; Data analysis: GGH, EH, GE; Manuscript preparation: GGH, EH, GE.