T cells use kinetic proofreading to discriminate antigens by converting small changes in antigen-binding lifetime into large differences in cell activation, but where in the signaling cascade this computation is performed is unknown. Previously, we developed a light-gated immune receptor to probe the role of ligand kinetics in T cell antigen signaling. We found significant kinetic proofreading at the level of the signaling lipid diacylglycerol (DAG) but lacked the ability to determine where the multiple signaling steps required for kinetic discrimination originate in the upstream signaling cascade . Here, we uncover where kinetic proofreading is executed by adapting our optogenetic system for robust activation of early signaling events. We find the strength of kinetic proofreading progressively increases from Zap70 recruitment to LAT clustering to downstream DAG generation. Leveraging the ability of our system to rapidly disengage ligand binding, we also measure slower reset rates for downstream signaling events. These data suggest a distributed kinetic proofreading mechanism, with proofreading steps both at the receptor and at slower resetting downstream signaling complexes that could help balance antigen sensitivity and discrimination. Proper antigen discrimination is a cornerstone of the adaptive immune system. Failure of T cells to activate in response to foreign antigen can enable pathogens to invade the body undetected . ConversOne attractive model for how T cells discriminate antigen is the kinetic proofreading model , where oKinetic proofreading can convert small differences in ligand-binding lifetime into large differences in cellular output. The number of proofreading steps sets the strength of a proofreading system, with stronger proofreading systems requiring a higher number of proofreading steps. Ten steps of kinetic proofreading would roughly amplify a twofold difference in ligand-binding lifetime into a 1000-fold difference in output. Kinetic proofreading allows T cells to ignore numerous shorter-lived self-antigen-binding events, while activating in response to rare longer-lived foreign antigen-binding events .Previously, our group developed a light-gated immune receptor that we used to probe the role of ligand kinetics in T cell antigen signal transduction . We founHere, we improve our assay by incorporating the native integrin ICAM-1 as our light-insensitive mode of adhesion, which improves the robustness of our previous biosensors and enables the use of new biosensors . With inPreviously, we developed a light-gated immune receptor (zdk-CAR) to probe T cell antigen signaling output as a function of both receptor occupancy and average ligand-binding half-life . In the In our previous work, we adhered cells to the SLB using an antibody against their own MHC (Abcam ab21899) , left. H++ concentrations  and a decreased dependence on receptor occupancy (⍴=0.21) compared to Zap70 recruitment . Our proTo assess our model fits, we evaluated the residuals of each model subtracted from their respective dataset. For Zap70 recruitment, our model underestimates the degree of activation at moderate binding half-lives and receptor occupancies, as indicated by the positive region in the center of the heatmap. It is possible that Zap70 recruitment reaches saturation at shorter ligand-binding half-lives than our model predicts . For botTo globally assess how well our fit N values captured the datasets in comparison to other possible N values, we computed the average residual for models fit over a range of constant N values for each dataset . The resIn light of the flat residual landscapes for LAT clustering and DAG generation, we used Akaike information criterion (AIC) to determine if models where LAT and DAG have the same number of upstream proofreading steps  better explained our data than our current model where Zap70, LAT, and DAG all have unique N values . The AICIn our previous work our model fit fewer (N=2.7) steps to DAG generation. We now fit a higher number of steps (N=11.3) to DAG generation. This change could be due to the incorporation of ICAM into our current study, which has been shown to potentiate ligand discrimination . Further–3 . This suggests that our cells are unlikely to be detecting the average ligand-binding half-life of each blue-light condition, but are more likely detecting longer-lived binding events from the underlying distribution of half-lives. Another possibility is that our in vitro washout measurements, which measure average ligand-binding half-lives of soluble ligands diffusing in three dimensions, differ from the half-lives of ligand-receptor interactions between the cell’s plasma membrane and the supported lipid bilayer diffusing in two dimensions .–3  biochemical events that separate short binding events from long binding events. Where these events exist in the antigen signaling cascade is not fully understood.In this study, we improved our optogenetic proofreading assay with ICAM-1 adhesion and measured the dependency of Zap70 recruitment, LAT clustering, and DAG generation on antigen-binding half-life and receptor occupancy. We now find evidence for kinetic proofreading in antigen signal transduction as early as Zap70 recruitment to phosphorylated receptor ITAMs. We measured greater kinetic proofreading at the clustering of the scaffold protein LAT, and the highest degree of kinetic proofreading at the generation of the signaling lipid DAG . We alsoAfter a receptor binds an antigen, many events must occur before Zap70 is recruited to the receptor’s ITAMs. Many strong candidates exist for the mediators of this kinetic processing . Ligand The receptor itself could undergo mechanical conformational changes making it more accessible for phosphorylation . An actiWe measured stronger proofreading downstream of Zap70 recruitment for the clustering of LAT and the generation of DAG. This result implies that additional kinetic proofreading steps exist separate from the receptor. The additional proofreading steps measured at LAT clustering could include the full activation of Zap70 through phosphorylation and the many phosphorylation and binding events on LAT required to initiate clustering. Hyperactive Zap70 mutants biased toward the active conformation increase T cell response to normally weak agonists, suggesting that pre-activation of Zap70 short-circuits one or more proofreading steps .++ mobilization . Consistent with this idea, larger TCR cluster size increases the probability of T cell activation . Lin et off,Work on kinetic proofreading often assumes that the reset rate after ligand binding is uniform across all signaling intermediates koff,. However++ release and ERK phosphorylation upon signal inhibition to be 29 s and 3 min, respectively .Recently, Harris et al. quantified the reset rate of the downstream signaling events Caectively . They shCooperativity between active receptors to complete downstream proofreading steps could explain observations of T cell activation in response to intermediate affinity self- and tumor antigens . IntermeMethods were adapted from our lab’s previous work  with modWe used standard molecular biology protocols for all cloning. In general, we PCR-amplified individual DNA segments and assembled them using the isothermal Gibson assembly method. Mark M Davis  kindly g6 cells per ml. We grew 293T cells in DMEM  with 10% fetal bovine serum. All cell lines grew in humidified incubators at 37°C with 5% CO2.Jurkat cells grew in RPMI 1640  supplemented with 10% fetal bovine serum  and glutamine . Jurkat lines were tested to be negative for mycoplasma. Cell identity (Jurkat E6.1 clone) was validated with STR profiling. We maintained Jurkats at densities between 0.1 and 1.0×106 cells/ml with 0.5 ml lentiviral supernatant for the zdk-CAR and the appropriate reporter construct. Cells recovered overnight in the incubator, 8 ml of media was added the following day and cells were grown to desired density. Cells were labeled with the Halo dye JF549 (We combined 1 ml of wild-type (WT) Jurkat cells at 0.5×10ye JF549  and we sWe produced lentivirus in 293T cells using a second-generation lentiviral system . We tran6 Jurkat cells labeled with the Halo dye JF549 . Using Hamilton syringes , we combined 1 mmoles of lipids in the following molar ratio: 97.5% DOPC, 0.5% PEG-PE, 1% DGS-NTA(Ni), and 1% biotinyl CAP PE . Next, we evaporated the chloroform by slowly rotating the vial at an angle while slowly flowing nitrogen gas . We vacuum desiccated the resulting lipid film for 2 hr to overnight. After desiccation, we rehydrated the lipids in 2 ml of TBS and gently vortexed the vial for 10 min. We transferred the mixture to a cleaned 5 ml round bottom glass tube. We formed small unilamellar vesicles (SUVs) by submerging bottom of the tube in a Branson 1800 ultrasonic cleaner (Branson #M1800) to the level of the lipid mixture in the tube and sonicating for 30–60 min until clear. We added ice periodically to the sonicator bath to maintain a temperature of 0°C. Centrifugation at >21,000 RCF for 30 min at 4C (Eppendorf microcentrifuges 5425R) pelleted large lipid structures. We removed the SUV containing supernatant and used it immediately or stored it in liquid nitrogen until use.2O. Coverslips were washed five times in ddH2O between each bath sonication to remove excess organic solvents. Next, we added 40 ml ddH2O, 10 ml of 30% ammonium  hydroxide, and 10 ml 30% hydrogen peroxide  to the coverslips. We placed the Coplin jar into a 70–80°C water bath and allowed it to react for 10 min after the base solution began to vigorously bubble. We decanted the base solution and washed the coverslips five times in ddH2O. Next, we added 40 ml ddH20, 10 ml of 30% hydrochloric acid , and 10 ml 30% hydrogen peroxide to the coverslips. Again we incubated the reaction in the water bath and allowed it to react for 10 min after the acid solution began to vigorously bubble. We decanted the acid solution and washed the coverslips five times in ddH2O and stored them in ddH2O for up to 1 week.We placed glass coverslips  into a glass Coplin jar  and successively bath sonicated for 10 min each in acetone , isopropyl alcohol , and ddH2O and immediately blown drying it with compressed nitrogen, we firmly attached a six-well Ibidi sticky chamber  to the coverslip. We diluted 30 µl of SUV mixture with 600 µl TBS before adding 100 µl to each well and incubated at room temperature for 25 min. To functionalize a well, we flushed out excess lipids with 500 µl TBS. Next, we added 100 µl of ICAM-1-HIS diluted in TBS-BB to 150 µM to the well and incubated at room temperature for 35 min. After incubation, we washed the well with 500 µl TBS before adding 100 µl Streptavidin  diluted in TBS-BB  to the well and incubated at room temperature for 5 min. After washing again with 500 µl TBS-BB, we added LOV2 diluted in TBS-BB  to the well and incubated in the dark at 37°C for 5 min. We then flushed the well with 500 µl HBSS-BB and incubated with cells previously labeled with the halo dye washed into Imaging media. Cells adhered to the SLB in the dark for 5 min at 37°C before imaging.After removing an RCA cleaned glass coverslip from ddHTBS: 150 mM NaCl, 20 mM Tris Base, pH 7.5TBS-BB: TBS with 2 mg/ml BSA and 0.5 mM βME.2, 2 mM MgCl2, 10 mM glucose, 20 mM HEPES, pH 7.2mHBSS: 150 mM NaCl, 40 mM KCl, 1 mM CaClmHBSS-BB: mHBSS with 2 mg/ml BSA and 0.5 mM βMEImaging media: mHBSS-BB supplemented with 2% fetal bovine serum, 50 µg/ml ascorbic acid and 1:100 dilution of ProLong Live Antifade Reagent . Solution incubated at room temperature for at least 90 min to allow the antifade reagent to reduce oxygen levels.Imaging was performed on an Eclipse Ti inverted microscope (Nikon) with two tiers of dichroic turrets to allow simultaneous fluorescence imaging and optogenetic stimulation. The microscope was also equipped with a motorized laser TIRF illumination unit, ×60 and ×100 Apochromat TIRF 1.49 NA objective (Nikon), an iXon Ultra EMCCD camera (Andor), and a laser launch  equipped with 405, 488, 561, and 640 nm laser lines. For RICM, light from a Xenon arc lamp  source was passed through a 572/35 nm excitation filter  filter and then a 50/50 beam splitter . Microscope and associated hardware was controlled with MicroManager  in combiAfter each day of imaging, we captured TIRF488, TIRF561, and RICM images of slides with concentrated solutions of fluorescein, Rose Bengal, or dPBS, respectively. To flat field correct, we subtracted the camera offset from both the experimental and dye images. By dividing the experimental image by the median dye slide image, we acquired the final flat field corrected image used in analysis .We exposed cells to 5 min blocks of constant blue-light stimulation. Each block consisted of an initial 2 min hold in strong blue light followed by a 3 min stimulation at a fixed intensity of intermediate blue light. To measure the reporter output, we averaged the final four timeframes of each condition. We measured CAR occupancy from a long exposure TIRF488 image taken at the end of the 3 min stimulation, after the last biosensor output measurement made in TIRF561. As fluorescence excitation light from TIRF488 potently stimulates LOV2, the TIRF488 channel could only be imaged once at the very end of a 3 min stimulation. We repeated 5 min blocks over the course of an hour experiment with a variety of blue-light intensities to sample different ligand-binding half-lives.We calculated the average Zdk-binding half-life for each blue-light condition as described previously .We background subtracted and thresholded RICM images at each timeframe to create a mask of cell footprints. The thresholded image was labeled using Python skimage watershed algorithm . A seconTo calculate biosensor output levels at steady state, we averaged the TIRF561 pixel intensity within each labeled cell mask over the last four timeframes  of a 3 min hold in blue light. To account for differences in biosensor expression level, we normalized cells to their average biosensor intensity in the absence of signaling (taken as the average of the last two TIRF561 images of each 2 min reset pulse of intense blue light). We sometimes observed drift in a cell’s basal activity over time. To correct for this drift, we subtracted the mean TIRF561 pixel intensity of each preceding 2 min reset pulse of intense blue light from the mean value of the proceeding 3 min stimulation of blue light. Thus, the reported biosensor output value is the fold-change from the cell’s average basal activity minus the fold-change from resent basal activity.Prior to model fitting, the biosensor output values of a biological replicate dataset (consisting of multiple wells acquired on the same day) were normalized by the 90th percentile output value in the dataset to properly normalize the data to the model, and to allow comparison between biosensors of variable dynamic ranges.We fit each biological replicate dataset to the simple model of kinetic proofreading described previously . Each daAssuming errors are identically and normally distributed, AIC was calculated as 2K+n*ln(RSS/n) for several nested models where various conditions of equality were set on the N parameters in the equilibrium model across the data. K is the total number of parameters used to describe the outcomes, n is the total number of measurements across the system, and RSS is the sum of squared residual from the fit. The model with the lowest AIC score  was chosen as the best model .We restricted our analysis to cells that were present for the full-time course. Cells that detached partway through the time course or arrived after the experiment began were excluded. Each replicate began with a no-light  condition to identify non-responding cells. Cells that did not exhibit at least a 10% increase in reporter output above their basal output were excluded from analysis.Kinetic proofreading datasets  – A biolSignaling reset datasets  – A biolOptoCAR expressing Jurkats were activated for 5 min on a LOV2-AF647 and ICAM-1 functionalized bilayer. At T=0, we illuminated the cell with intense blue light to unbind receptor-bound LOV2 ligands while imaging LOV2 and either Zap70 or LAT biosensors every 3 s. We manually segmented cells in ImageJ before tracking clusters of LOV2 and Zap70/LAT biosensors using the TrackMate plugin . We sele The authors have used an ontogenetic approach to advance understanding of the kinetic proofreading steps down-street of the antigen receptors. The authors have provided evidence for longer lifetimes of down-stream signaling events in relation to physiological receptor-ligand binding kinetics. This accounts for the ability of immune cells to respond across a large range of agonist pMHC while retaining the ability to prevent responses to very short lived interactions associated with self ligands. The results advance our knowledge of chimeric antigen receptor signaling and provides an approach to identify optimal receptor binding kinetics to couple to down-stream signaling machinery. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Aleksandra Walczak as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Progressive enhancement of kinetic proofreading in T cell antigen discrimination from receptor activation to DAG generation" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.There is a consensus that your work is an advance on your prior study looking at ontogenetic control of T cell activation. The addition of the adhesion system enables a better analysis of how the kinetics of the light driven receptor-ligand interactions impact receptor proximal and distal signals. The work reinforces that early events fit within a framework of a kinetic proofreading (KP) model. Reviewers are concerned that later events are too slow in relation to receptor-ligand binding to support a kinetic KP process, unless the proofreading process incorporated down stream transcriptional and epigenetic steps in the model where early signals influenced by the receptor-ligand binding kinetic through KP controlled slower immune synapse levels on and off rates that then feed forward on the slower downstream events. This might be an interesting thing to discuss, but seems beyond the scope of the current modelling related to receptor-ligand binding processes and KP. The most obvious revision path would be to moderate claims as suggested by reviewers or to propose a set of plausible alternative models to KP as to how the later steps relate to the receptor-ligand binding kinetics and determine if KP is really the best model or if other models fit equally well or better at later stages.1) An estimate of the goodness-of-fit is important to provide some confidence that the model used to fit the experimental data is sound. Alternative models might be fit just as well and might provide very different conclusions otherwise; the data fits to KP 'by default'. It would also be helpful to be more explicit in the methods about how this fitting was done. Could the authors model the data assuming a series of reversible reactions (ie not KP), with the rate constant for each being sufficiently low to still fit the delay observed between receptor engagement and downstream output. How would this compare to the KP model?2) In the paragraph beginning with line 144: It should be noted that the kinetic proofreading model that the authors use is very simplified. The scaling with the power of steps, n, is only correct under certain approximations . While the authors' note one of these approximations later (line 155), this is not the only one, and so mentioning the approximate nature of the analyses earlier on would be good.3) As Figure 3 shows, there is quite a bit of cell-to-cell variability. Can this variability data be quantified to provide insights into this "noise", which is important in determining the response of the population of cells?4) The authors may be interested in a paper by R. Ganti et al., , which studies related issues using computational modeling, information theory, and experiments. In that paper, the consequences of "distributed" kinetic proofreading, a model for the reset steps along the pathway, and experiments considering some spatial aspects of kinetic proofreading are considered.5) I think it would be appropriate to cite the original kinetic proofreading papers by Hopfield and Ninio.Reviewer #1 (Recommendations for the authors):1) In the paragraph beginning with line 144: It should be noted that the kinetic proofreading model that the authors use is very simplified. The scaling with the power of steps, n, is only correct under certain approximations . While the authors' note one of these approximations later (line 155), this is not the only one, and so mentioning the approximate nature of the analyses earlier on would be good.2) As Figure 3 shows, there is quite a bit of cell-to-cell variability. Can this variability data be quantified to provide insights into this "noise", which is important in determining the response of the population of cells?3) The authors may be interested in a paper by R. Ganti et al., , which studies related issues using computational modeling, information theory, and experiments. In that paper, the consequences of "distributed" kinetic proofreading, a model for the reset steps along the pathway, and experiments considering some spatial aspects of kinetic proofreading are considered.4) I think it would be appropriate to cite the original kinetic proofreading papers by Hopfield and Ninio.Reviewer #2 (Recommendations for the authors):An estimate of the goodness-of-fit is important to provide some confidence that the model used to fit the experimental data is sound. Alternative models might be fit just as well and might provide very different conclusions otherwise; the data fits to KP 'by default'. It would also be helpful to be more explicit in the methods about how this fitting was done. Could the authors model the data assuming a series of reversible reactions (ie not KP), with the rate constant for each being sufficiently low to still fit the delay observed between receptor engagement and downstream output. How would this compare to the KP model? Reviewer #1 (Recommendations for the authors):1) In the paragraph beginning with line 144: It should be noted that the kinetic proofreading model that the authors use is very simplified. The scaling with the power of steps, n, is only correct under certain approximations . While the authors' note one of these approximations later (line 155), this is not the only one, and so mentioning the approximate nature of the analyses earlier on would be good.We apologize for not clearly explaining our model’s limitations in the manuscript. We have incorporated the following paragraph on page 10 line 206 to better communicate the features of our simple kinetic proofreading model:“Our model represents the expected output of a kinetic proofreading signaling network at steady-state. It assumes a network of sequential and non-reversible biochemical events where the rate of progression to the next step is much slower than the rate of reset upon ligand unbinding. We cannot determine the exact number of proofreading steps in the network, as the output of many weak proofreading steps could behave the same as the output of a few strong proofreading steps. However, if we assume equivalent strength for all proofreading steps, we can estimate a lower limit of the number of steps. Our model captures the expected output of a saturable signaling step downstream of such a proofreading system. When we apply our model to a measured signaling event in our system, we assume that event is downstream of the proofreading cascade and ask how many upstream steps of equivalent proofreading best explain its output.”2) As Figure 3 shows, there is quite a bit of cell-to-cell variability. Can this variability data be quantified to provide insights into this "noise", which is important in determining the response of the population of cells?Thank you for highlighting this feature of our dataset. We have included the following paragraph on page 7 line 153 discussing the sources of the cell-to-cell variability and how we leverage it to better explore the occupancy axis of our model.“The lentiviral vectors we used to express our zdk-CAR and live-cell biosensors result in cell-to-cell variability in the expression level of each construct, even after sorting for cells with CAR expression levels similar to native TCR expression in T cells . To account for variability in biosensor expression, we normalize each cell to its baseline biosensor intensity measured in the absence of ligand engagement. The cell-to-cell variability in zdk-CAR expression has the benefit of expanding the receptor occupancies that we sample, as cells expressing more zdk-CAR receptors bind more LOV2 under all half-life conditions . If all cells expressed identical amounts of receptor, it would be laborious to generate and analyze the large number of independent CAR expressing clones needed to sample a wide range of receptor occupancies. Importantly, the variability in receptor expression is not unique to our CAR system; native T cells also vary in TCR expression level and can respond to antigen even at very low levels of TCR expression . Kinetic proofreading could enable cells to tolerate variability in receptor expression level, as it results in cells that are increasingly sensitive to ligand binding half-life instead of receptor occupancy.”3) The authors may be interested in a paper by R. Ganti et al., , which studies related issues using computational modeling, information theory, and experiments. In that paper, the consequences of "distributed" kinetic proofreading, a model for the reset steps along the pathway, and experiments considering some spatial aspects of kinetic proofreading are considered.Thank you very much for calling this paper to our attention. In light of this suggestion, we have implemented the steady-state model from Ganti et al., to explore the plausibility of the number of proofreading steps our model fits to our datasets. From this work we have uploaded a new supplementary figure  with the figure caption on page 52. We discuss our exploration the model from Ganti beginning on page 14 line 314.-3 . We chose the error rate of 10-3, as it is an order of magnitude less than the theorized 10-4 upper limit error rate of the native TCR . At moderate half-life ratios, an 11-step network can discriminate cognate peptides present in small concentrations .“To assess the plausibility of a larger number of proofreading steps, we implemented the steady state kinetic proofreading model from Ganti et al., . The model estimates the minimum number of proofreading steps required to discriminate between cognate-ligands and self-ligands with different binding half-lives present at a given concentration ratios at a given Hopfield error-rate . First, we evaluated what combinations of ligand half-lives and concentration ratios an 11-step kinetic proofreading network could discriminate at an error rate less than 10In our optogenetic system, the ratio of the average ligand binding half-life between the longest suppressive half-life and the shortest fully activated half-life is about 2. However, an 11-step network is insufficient to discriminate between ligands with a half-life ratio of 2, even at the high ligand ratio of 1 . This suggests our cells are unlikely to be detecting the average ligand binding half-life of each blue-light condition, but are more likely detecting longer-lived binding events from the underlying distribution of half-lives. Another possibility is that our in vitro washout measurements, which measure average ligand binding half-lives of soluble ligands diffusing in three dimensions, differ from the half-lives of ligand-receptor interactions between the cell’s plasma membrane and the supported lipid bilayer diffusing in two dimensions .-3 . To discriminate between ligands with a half-life ratio of two, at least 14 steps are needed when the ligands are at equal concentrations, and more than 25 steps are needed if cognate-ligands are 1 per 1000 self-ligands. The required number of proofreading steps decreases rapidly as the half-life ratio increases, reaching a minimum of 8-steps needed for a concentration ratio of 1/1000 and a half-life ratio of 10, which is more in line with physiological half-life ratios between agonist and non-agonist peptides .To better explore the kinetic proofreading model space, we generated heatmaps reporting the required number of steps to discriminate combinations of ligand and half-life ratios at an error rate of 10After comparing our results with the Ganti model, this analysis suggest that our number of fit proofreading steps may be somewhat inflated as a function of our use the average ligand binding half-lives of three dimensional washout experiments in place of the two dimensional single molecule information T cells use to make activation decisions. However, the higher fit N values are more consistent with the required number of steps to discriminate ligands under more physiological conditions than our previous measurements of ~3 steps, which would not be expected to discriminate ligands with half-life ratio of 10 even at a ligand ratio of 1 .”4) I think it would be appropriate to cite the original kinetic proofreading papers by Hopfield and Ninio.We apologize for omitting these key works. Citation of these papers existed in earlier versions, but we inadvertently deleted that section without bringing the references back elsewhere. This has been now corrected (page 2 line 43).Reviewer #2 (Recommendations for the authors):An estimate of the goodness-of-fit is important to provide some confidence that the model used to fit the experimental data is sound. Alternative models might be fit just as well and might provide very different conclusions otherwise; the data fits to KP 'by default'.It would also be helpful to be more explicit in the methods about how this fitting was done.We have expanded our methods section on model fitting on page 32 line 719 as reproduced below. We also moved this section to succeed relevant sections describing the data acquisition and processing methods for our occupancy and biosensor output measurements used before fitting the data.“We fit each biological replicate dataset to the simple model of kinetic proofreading described previously  . Each datapoint consists of an average ligand-binding half-life, a CAR occupancy measurement, and a biosensor output measurement (see above). With those three measured values, we fit the remaining three parameters of our model n, K, and β  using the Levenberg–Marquardt least-squares algorithm as implemented by the curve_fit function from the python SciPy library .”Could the authors model the data assuming a series of reversible reactions (ie not KP), with the rate constant for each being sufficiently low to still fit the delay observed between receptor engagement and downstream output. How would this compare to the KP model?Importantly, our analysis is for the effects of different half-lives for signal outputs at stead-state and does not rely on the analysis of delays between steps. A series of reversible reactions would not be able to capture the dependance of signaling output we measured on ligand-binding half-life at steady state. Such a system’s output at steady state would depend on the steady-state receptor occupancy, with the reaction rate constants influencing how long it took the system to reach steady state, regardless of the number of reversible steps. We apologize for not emphasizing that our measurements are at steady state as opposed being based on temporal delays between steps following ligand binding. We thank the reviewers for suggesting that we compare with other models, as discussing the differences between kinetic proofreading systems and linear systems of reversible reactions has made our manuscript stronger. This discussion has been included on page 8 line 179, and reproduced below.“To overcome this technical limitation of our system, we chose instead to measure the steady-state output of the antigen signaling cascade achieved several minutes after ligand binding. Kinetic proofreading systems behave differently than non-proofreading systems at steady-state. A non-proofreading system’s steady-state output is set by the number of ligand-bound receptors and not the binding half-lives of those ligands . In contrast, a kinetic proofreading system can produce different steady-state outputs in response to ligands of different binding half-lives, even when ligand densities are adjusted to achieve equivalent occupancy  . Signaling events take varying amounts of time to occur after ligand binding . However, the temporal delays between steps are on the order of tens of seconds. By imaging the cells after minutes of constant exposure to a set ligand binding half-life, we measure the steady state output achieved at a signaling event in the cascade on a longer timescale than these delays .


During the COVID-19 pandemic, video consultations became a common method of delivering care in general practice. To date, research has mostly studied acute or subacute care, thereby leaving a knowledge gap regarding the potential of using video consultations to manage chronic diseases.This study aimed to examine general practitioners’ technology acceptance of video consultations for the purpose of managing type 2 diabetes in general practice.A web-based survey based on the technology acceptance model measuring 4 dimensions—perceived usefulness, perceived ease of use, attitude, and behavioral intention to use—was sent to all general practices (N=1678) in Denmark to elicit user perspectives. The data were analyzed using structural equation modeling.P<.001). Perceived ease of use had a positive influence on perceived usefulness and attitude. Attitude was positively influenced by perceived usefulness. Attitude had a positive influence on behavioral intention to use, although perceived usefulness did not. Goodness-of-fit indices showed acceptable fits for the structural equation modeling estimation.The survey sample comprised 425 general practitioners who were representative of the population. Structural equation modeling showed that 4 of the 5 hypotheses in the final research model were statistically significant (Perceived usefulness was the primary driver of general practitioners’ positive attitude toward video consultations for type 2 diabetes care. The study suggests that to improve attitude and technology use, decision-makers should focus on improving usefulness, that is, how it can improve treatment and make it more effective and easier. Technological change and the use of new technologies in health care are driven by objectives to increase access to health care, reduce care costs, coordinate health care, and facilitate chronic disease prevention and management . The COVThe recent uptake of video consultations in general practice is intriguing as the use of new health care technology and its implementation typically takes years ,13. ThisHowever, research has not systematically elicited general practitioners’ attitudes toward video consultations or their perceptions of the ease of use or usefulness in general practice. This research gap is unfortunate as it is well established in IT literature that attitude and perception influence physicians’ use of other types of health care technology such as electronic patient records or telemedicine -35. The Using the insight that chronic disease prevention and management are key drivers of technological change, this paper studies the potential of using video consultations in general practice to manage type 2 diabetes for 3 reasons. First, type 2 diabetes is a chronic disease for which video consultation appears promising in general practice -45. SecoThe aim of this paper is to use TAM to study general practitioners’ technology acceptance of video consultations to manage type 2 diabetes in general practice. The hypotheses were that higher levels of attitude, PU, and PEOU positively affect general practitioners’ BI to use video consultations to manage type 2 diabetes. Bringing to bear TAM on video consultations in general practice allows exploring the potential of using the technology for a type of chronic care where health care systems need to find new ways of increasing health care access and cutting care costs.The research model  builds oPEOU influences BI to use indirectly through both attitude and PU. A high PEOU represents the belief that using the technology will require little to no effort . PU concHypothesis 1: PEOU has a positive impact on the PU of video consultations for type 2 diabetes care.Hypothesis 2: PEOU has a positive impact on attitudes toward video consultations for type 2 diabetes care.Hypothesis 3: PU has a positive impact on attitude toward video consultations for type 2 diabetes care.The BI to use represents an individual’s intention to use a new technology . BI to uHypothesis 4: PU has a positive impact on the BI to use video consultations for type 2 diabetes care.Hypothesis 5: Attitude toward video consultations for type 2 diabetes care has a positive impact on the BI to use the technology.Data were collected through a cross-sectional web-based survey distributed to all general practitioners in Denmark (n=3326). The Danish health care system is mostly tax financed, and citizens can receive care from general practice free of per service charge. Danish general practitioners are self-employed but work on contracts for the public funder. Most general practitioners work in partnership practices, and their income is generated as a combination of fee for service and capitation . The incdo not know . Demographic measures (12 items) such as age and sex were collected to analyze the representativeness of the study sample in comparison with the total population of general practitioners. Before distribution and to test face validity, the survey was evaluated and revised according to inputs from 5 general practitioners working in each of the 5 Danish Regions.The main measures (13 items) central to our hypotheses originated from TAM  and healPerceived usefulness (PU)improve my treatmentPU1: can effectivePU2: can make my treatment more easierPU3: can make my treatment Perceived ease of use learning to use was (would be) easyPEOU1: (would be) easy to get software to do what I needPEOU2: masterPEOU3: (would be) easy to usePEOU4: (would be) easy to Attitude (ATT)good ideaATT1: using is a unpleasantATT2: using is beneficialATT3: using is Behavioral intention (BI)often as possibleBI1: intend to use as do not intend to useBI2: even when possible, use to the extent possibleBI3: would The survey was administered using SurveyXact (Rambøll Management) . To idenThe survey was distributed to general practices as an electronic letter on January 7, 2021, via the Danish public electronic mailbox system (e-Boks Business) using publicly available data from MedCom. The letter contained information about the study and a survey link. Participants were informed about data protection measures, anonymity of participation, and the option to be paid—DKK 276.72 (US $44) based on a General Practitioners'’ Organization tariff—for the 20 minutes it maximally takes to complete the survey. The letter was addressed to the clinic, and all trained general practitioners were encouraged to participate. Unfortunately, it was not possible to contact each general practitioner directly as this information was not publicly available. The survey link was open and only available in a letter to ensure anonymity and availability for all general practitioners in a clinic. Data entry for payments was conducted in a separate survey to preserve anonymity. Two reminders were sent on January 21, 2021, and February 2, 2021. The data collection ended on February 7, 2021.The Committee of Multipractice Studies in General Practice  evaluated the study and recommended that general practitioners participate in the survey. This study was reported to the Danish Data Protection Agency ..”The Research Ethics Committees for Central Denmark Region (1-10-72-181-20) concluded that the study could be conducted without approval from the committee as “According to the Consolidation Act on Research Ethics Review of Health Research Projects, Consolidation Act number 1083 of 15 September 2017, section 14(2) notification of questionnaire surveys or medical database research projects to the research ethics committee system is only required if the project involves human biological materialP<.05 was set as the threshold for statistical significance.Data were analyzed using Stata  . To compWe report the unstandardized and standardized path coefficients from structured equation modeling. The unstandardized path coefficients reflect the expected (linear) change in the dependent variable with each unit change in the independent variable, given the other variables in the model. The standardized path coefficients express relationships in the same unit; that is, SDs. The interpretation is that when an independent variable  changes by 1 SD, then the dependent variable  changes by an SD as well. By placing all coefficients in the same unit, the SDs for different variables measured in different metrics become interpretationally equivalent.A total of 457 general practitioners answered the survey, from which 32 (7%) incomplete responses were excluded, resulting in 425 (93%) respondents. The sample represented 12.78% (425/3326) of all Danish general practitioners. The sample represented 18.82% (315/1674) of Danish general practices. Compared with the population of general practitioners, Pearson chi-square tests showed that the individual characteristics of the study sample  were representative of the population not participating . The samThe internal consistency of the items that comprise the 4 dimensions in TAM had Cronbach α >.8 . Cronbacχ21=90.9, P<.001; PEOU 43.3, χ21=694.6, P<.001; attitude 8.22, χ21=0.3, P=.57; BI 17.9, χ21=26.0, P<.001). As nonnormality invalidates the assumption for the maximum likelihood method of structural equation modeling estimation, we used Satorra-Bentler adjustments to relax the assumption of normality. The measures in TAM were also assessed for convergent validity and discriminant validity  with moderate kurtosis (ranging from 2.134 to 3.841). Normality was further evaluated using the Mardia multivariate kurtosis test, in which all dimensions failed except attitude, thereby indicating nonnormally distributed measures . PEOU4 was also an outlier in terms of missing data, with 12.7% (53/425) of missing responses, leading to the suspicion that the data were not missing at random. We excluded PEOU4 from the analysis and ran a new confirmatory factor analysis, which had factor loadings ranging from 0.77 to 0.92, thereby confirming that the latent variables of TAM were explained by the observed variables. Goodness-of-fit indices confirmed that the confirmatory factor analysis was a good fit for the data  ) . The finWe used structural equation modeling to analyze our hypotheses and the final research model. The goodness-of-fit indices model showed an acceptable fit .P<.005), except for the path from PU to BI to use (P=.84). PEOU had a positive influence on PU  and attitude . PU had a positive influence on attitude . The influence of attitude and PU on BI to use was also positive ; however, the latter was statistically insignificant. The calculated R2 values . Similarly, earlier research in general practice found that this relationship appeared to be highly important ,54. The Third, our analysis confirmed the expectation that general practitioners’ PU of video consultations would be positively influenced by their ratings of PEOU . This mirrors findings from studies of other types of health care technology -52. The Fourth, the BI to use video consultations to provide type 2 diabetes was positively influenced by the attitude toward the technology . This particular relationship has also been found in other studies in the domain of primary health care ,60,61. ABy studying chronic care in our context—type 2 diabetes—our research findings contribute to an emerging literature on video consultations in general practice that has hitherto mostly studied acute or subacute or out-of-hours care ,25,31. AThe potential of using video consultations in general practice to deliver chronic disease management is promising ,10,11 anRelatedly, our findings provide suggestions for mitigating change hesitance, which remains a barrier to implementing video consultations in routine practice . As reseTwo modifications were made to the original TAM, underlining the final research model. First, an item (attitude item 2) was removed as it decreased the Cronbach α of the attitude dimension. Another item (PEOU4) was dropped because of the low factor loading from the confirmatory factor analysis. To assess the extent to which removing these items changed the findings, a structural equation modeling estimation, including these items, was performed, which showed path coefficients very similar to our final model, thereby supporting the accuracy of the final structural equation model. Second, structural equation modeling estimations were not performed with all respondents as those skipping questions were omitted. Running a structural equation modeling estimation that included respondents with missing answers resulted in similar path coefficients but had poorer goodness of fit. The final research model met the recommended indices of the goodness of fit but failed the chi-square test. Failing the chi-square test is a known issue with structural equation modeling, which, similar to our study, has a high number of respondents and survey answers that are not normally distributed . The issWith the widespread research validation of TAM in combination with acceptable goodness-of-fit indices, the final research model is considered valid. However, as this study surveyed general practitioners from a tax-financed health care system, the findings may be most generalizable to countries with similar health care systems such as the English National Health System. Some authors also raise the concern that the original TAM and later extensions lack precision in health care because of their inability to consider the influence of external variables and barriers to technology acceptance  such as The relatively low response rate of 12.8% of all 3326 Danish general practitioners increased the risk of selection bias. Nevertheless, it improved confidence in the findings that the individual characteristics of the sample of general practitioners were comparable with the population, and the share of respondents in the sample who used video consultations was similar to that of other sources . This fiThe study design was cross-sectional and, thus, only capable of capturing the views of general practitioners at the time of data collection. Although the cross-sectional design is standard in most studies on TAM ,38, longThis study explored the potential of using video consultations to provide type 2 diabetes care in general practice by eliciting the technology acceptance of a representative survey sample of Danish general practitioners. On the basis of TAM, our study suggests 2 main drivers: PU positively affects attitude toward using video consultations for diabetes care, and attitude positively affects the BI to use the technology. For policy makers interested in scaling up general practitioners’ use of video consultations to provide diabetes care, our findings indicate that they should emphasize how the technology can improve treatment and make it more effective and easier. To this end, policy makers may need to explore what these aspects of usefulness mean to general practitioners working in different organizational contexts.


CDK4/6 inhibitors have become the mainstay of treatment for patients with advanced hormone receptor positive and Human Epidermal Receptor -2 [ HER-2 ] negative breast cancer. Three CDK 4/6 inhibitor drugs are currently approved and available, including Palbociclib, Ribociclib and Abemaciclib. All three of these drugs have similar mechanism of action and other pharmacokinetic and pharmaco-dynamic properties and hold equivalent positions in cancer care guidelines. Surprisingly, however, in the adjuvant setting of early breast cancer, two trials of palbociclib have failed to show any benefit while abemaciclib has shown some early benefits in disease-free survival and has received approval for its use in adjuvant setting. In this article, we explore several reasons for this discrepancy in the results of CDK4/6 inhibitors in the adjuvant setting. We also question if we should already adopt adjuvant abemaciclib in our clinical practice given the uncertainty in data. Cyclin–dependent kinase (CDK) 4/6 inhibitors in combination with endocrine therapy (ET) have become the mainstay of treatment for advanced hormone receptor (HR) positive breast cancer over the last few years. Palbociclib was the first CDK 4/6 inhibitor to receive the FDA approval as accelerated approval in 2015 and full approval in 2017 for HR positive and human epidermal receptor-2 (HER-2) negative metastatic breast cancer. Since then, two more CDK 4/6 inhibitors, ribociclib and abemaciclib have received full approval for the same indication. Although ribociclib is the only agent to show an overall survival (OS) benefit in the first line so far, there have been some concerns about lack of standard post-protocol treatment in the trial .However, interestingly, the results of the three CDK 4/6 inhibitors have differed when tested in the adjuvant setting. Thus, it is important to ask if these differences seen in the adjuvant setting are a play of statistics and trial design or if there actually is a meaningful difference among the CDK 4/6 inhibitors in the adjuvant setting.In the PENELOPE-B trial, 1250 patients with early HR positive breast cancer at high risk of relapse who had no pathological complete response after neo-adjuvant chemotherapy were randomly assigned to one year of palbociclib vs placebo . High-riIn the PALLAS trial involving 5685 patients, patients with stage II or III HR positive breast cancer were randomized to adjuvant ET + 2 years of palbociclib versus placebo [Abemaciclib was tested in the monarchE Trial that included 5637 patients with high-risk disease and comprised of two cohorts; cohort 1 enrolled patients with ≥4 positive axillary lymph nodes (ALNs), or 1-3 positive ALNs and either grade 3 disease or tumor ≥5 cm, and cohort 2 enrolled patients with 1-3 positive ALNs and high Ki-67 index (≥20%) [These results led to U.S. FDA approval of 2 years of adjuvant abemaciclib in patients with early HR positive breast cancer at a high risk for relapse, but only when Ki-67 ≥20%. The FDA limited its indication to Ki-67 ≥ 20% because in this sub-population of monarchE trial an iDFS benefit was seen without a potential detriment in overall survival (HR for OS was 0.767 ), however the HR for OS in the ITT population was 1.091, indicating potentially detrimental effect on OS among patients with low Ki-67. However, the European Medicines Agency (EMA) approved this indication without any Ki-67 based restrictions .The results of the trial testing three years of adjuvant ribociclib in early breast cancer has not yet been reported.The positive results of abemaciclib compared to the negative results of palbociclib in early breast cancer raises several questions. Why would two CDK 4/6 inhibitors with similar efficacy in metastatic setting have differential outcomes in adjuvant setting?One proposed explanation is the selection of high-risk patients with positive lymph nodes in monarchE unlike PALLAS that included all stage II and III patients . The perCan drug exposure or duration of treatment explain these differences? Even though the rate of discontinuation due to drug related adverse events was similar (21.1% in PALLAS vs 17.2 % in monarchE), PALLAS had more drug stoppage intervals, due to neutropenia, as per trial protocol. In a post hoc analysis, no significant relationship was seen between longer palbociclib duration or exposure intensity and improved iDFS .Finally, with the OS results of PALOMA-2 trial presented at ASCO 2022 Annual Meeting, it is also worthwhile asking if palbociclib truly is different among the CDK 4/6 inhibitors . In PALOAlthough most debates start with the premise of why palbociclib results were negative, it is equally important to think if it is the abemaciclib results that should be questioned. Could it be that PALLAS and PENELOPE are true negative but monarchE was a false positive? We are not suggesting that that is the case, but that it is also an equally plausible alternative. However, the question that matters the most in clinical practice is whether adjuvant ademaciclib should be the standard of care.First, iDFS is not a valid surrogate for OS in early breast cancer, except in HER2 positive subgroup . As of nFurthermore, we need to ensure that patients in the control arm of monarchE have received CDK 4/6 inhibitors at the time of relapse. If they have received a CDK 4/6 inhibitor at relapse and achieve similar OS, that means irrespective of iDFS results, patients could very well reserve the drug to be used at the time of relapse. If patients in the control arm haven’t received CDK4/6 inhibitors at relapse, even OS gains do not prove early receipt of CDK 4/6 inhibitors is better than receiving them at the time of relapse. The possibility of a worse OS also exists, if early CDK 4/6 exposure leads to the development of an aggressive, treatment resistant clone. Given the HR for OS favoring the control arm in the ITT population, the FDA restricted the use of abemaciclib, based on an exploratory analysis, to Ki-67 high patients.The FDA approval of abemaciclib based upon Ki-67 more than 20% happens to be first of its kind. Ki-67 is not a genomic marker or a target of abemaciclib. Ki-67 is a marker of cellular proliferation. It is a prognostic marker for early-stage breast cancer and not a predictive marker for treatment efficacy . There aSecond, the follow-up time is too short for an adjuvant breast cancer trial with assessment of the primary outcome at only 19 months of median follow up, with only 25% of patients having completed their 2-year CDK 4/6 inhibitor treatment. An additional follow-up analysis, conducted at regulatory request performed at 27 months (with 90% of patients having either completed or prematurely discontinued their the 2-year treatment) confirmed the iDFS benefit but with no OS gains. This is still a short follow-up. In PenelopeB, iDFS benefit was seen at 2-3 years, similar to monarchE, only for the iDFS curves to converge at the final analysis at 42.8 months. We do not understand the haste in approving an agent in adjuvant setting and including it in guidelines based on premature data and dubious companion diagnostic when other agents of the same class have produced negative results.Third, the protocol of monarchE did not specify the imaging modality to be used for staging of visceral disease. Some patients might thus have had subclinical metastatic disease, where we already know CDK 4-6 inhibitors are effective.Fourth, the addition of abemaciclib to ET results in substantial side effects. With abemaciclib, patients experienced a higher incidence of Grade≥3 AEs (49.7% vs 16.3%). In adjuvant setting, compared with placebo, one can only produce detrimental, or at best stable, effects on QoL while on treatment. The frequency of PRO assessments in monarchE was insufficient to capture patient-reported side effects and QoL within the first 3 months.Finally, the cost of 2 years of abemaciclib is estimated at >300,000 USD. That’s a substantial financial toxicity on health care system for a drug with uncertain benefit but known toxicities.Even if abemaciclib is declared a winner in early breast cancer receiving the nod from the FDA and EMA based on very early positive iDFS results, we urge caution before accepting this as a routine standard of care by the guidelines and the breast cancer community. Considering the added therapeutic burden for 2 years, the jeopardizing of an important treatment option at relapse, the adverse effects, the financial toxicity to the patients and healthcare system, and negative results from other two trials of a similar class of drug, we ask the breast cancer community if it’s really worth rushing to prescribe this drug in adjuvant setting without waiting for mature survival results?When the results from two trials are conflicting, one should question not only if the negative trial is a false negative, but also whether the positive trial is a false positive. In the case of adjuvant trials of CDK 4/6 inhibitors in early breast cancer, even the “positive trial” of abemaciclib is marginally positive for an unvalidated surrogate endpoint at a very early look of the data. Given the potential for overtreating several patients for a dubious or even non-existent benefit in a small percentage of patients, we believe it is premature to recommend adjuvant abemaciclib to patients with early breast cancer without waiting for mature data.Dr Van Wambeke has received speaker fees from Janssen and IPSEN. Dr Gyawali reports receiving consulting fees from Vivio Health, outside of the submitted work. No other conflicts of interest to disclose.