T cells are thought to discriminate self from foreign peptides by converting small differences in ligand binding half-life into large changes in cell signaling. Such a kinetic proofreading model has been difficult to test directly, as existing methods of altering ligand binding half-life also change other potentially important biophysical parameters, most notably the mechanical stability of the receptor-ligand interaction. Here we develop an optogenetic approach to specifically tune the binding half-life of a chimeric antigen receptor without changing other binding parameters and provide direct evidence of kinetic proofreading in T cell signaling. This half-life discrimination is executed in the proximal signaling pathway, downstream of ZAP70 recruitment and upstream of diacylglycerol accumulation. Our methods represent a general tool for temporal and spatial control of T cell signaling and extend the reach of optogenetics to probe pathways where the individual molecular kinetics, rather than the ensemble average, gates downstream signaling. The T cell response begins when the T cell antigen receptor (TCR) binds its cognate peptide-major histocompatibility complex (pMHC) on an antigen-presenting cell. Ligand discrimination is essential, as falsely recognizing a self-peptide can lead to autoimmunity, while missing a foreign-peptide allows pathogens to evade detection. Compounding this challenge, self-pMHCs generally outnumber foreign-pMHCs by several orders of magnitude . How theFor many cell surface receptors, occupancy drives the strength of signaling. Lower affinity ligands can signal as well as high affinity ligands when their concentration is increased such that an equal number of receptors are bound . This isA major model of T cell ligand discrimination is kinetic proofreading , which pKinetic proofreading effects need to be strongest when differences in pMHC binding affinities are small, as abundant self-pMHCs will bind more TCRs than scarce foreign-pMHCs. Strong kinetic proofreading ensures that short self-pMHC binding events are unlikely to contribute to downstream signaling. However, recent techniques that measure pMHC binding affinities in more native 2D environments show a larger range of binding affinities than previously measured , suggestTo probe kinetic proofreading in T cells, the field most commonly relies on altered peptide series to change binding half-lives. The limitation of this approach is that when the binding interface is altered, both the bond’s half-life and stability under tension must change together , left. DWe chose the LOVTRAP system for optogenetic control of bind half-life because it is one of the few systems where light stimulates the dissociation of two proteins on the order of seconds , the appLOVTRAP consists of two parts: a blue-light sensitive protein (LOV2) and an engineered binding partner (Zdk). Zdk preferentially binds LOV2 in the dark but quickly unbinds when a photon of blue light excites LOV2, inducing a conformational change. Since photons of blue light stimulate Zdk dissociation, low photon fluxes enforce longer binding half-lives while high photon fluxes enforce shorter binding half-lives. The intrinsic kinetics of Zdk dissociation from the ground and excited states of LOV2 set the upper and lower bounds on the range of achievable half-lives  approach, which has the advantage of being simpler than the TCR, easier to engineer, and medically relevant in its own right . CARs minecessarily more stable under load, and both models predict it would be more stimulatory. Our approach uncouples these parameters by using one ligand-receptor pair to explore a range of half-lives. Blue light, not point mutations, tunes the binding half-life. Because the ligand-receptor pair remains constant in all experiments, so too does the amount of tension they can withstand. Our optogenetic approach directly and specifically tunes ligand binding half-life, allowing us to cleanly measure the degree to which binding half-life influences T cell signaling.A key advantage of this approach is that it allows us to test the kinetic proofreading model directly. For most protein-protein interactions, the binding half-life and the bond’s stability under tension necessarily co-vary with each other, as they are both consequences of the height of the transition state. This makes it hard to distinguish between a kinetic proofreading and mechanosensitive model of T cell signaling. A ligand with a long binding half-life is A point of controversy is whether kinetic proofreading steps occur at the TCR  or furthBy directly controlling ligand binding half-life with light and holding all other binding parameters constant, we show that longer binding lifetimes are a key parameter for potent T cell signaling. Surprisingly, this discrimination occurs in the proximal signaling pathway, downstream of ZAP70 recruitment and upstream of DAG accumulation. This work aids our understanding of how T cell discriminate ligands and expands optogenetics as a tool for controlling the timing of single molecular interactions.We first validated the ability of the LOV2 ligand to photoreversibly bind the Zdk-CAR. Clonal Jurkat cells stably expressing the Zdk-CAR were exposed to SLBs functionalized with purified Alexa-488-labeled LOV2 . BecauseWe next verified that binding of the LOV2 ligand induces cell signaling through the CAR. We measured accumulation of the lipid diacylglycerol (DAG) by quantifying the translocation of the C1-domains from protein kinase C theta from the cytosol to the plasma membrane with Total Internal Reflection Fluorescence (TIRF) microscopy . DAG levWe observed no blue-light-dependent changes in DAG levels when LOV2 was omitted from the bilayer . AdditioNot only the localization, but also the speed of intracellular signaling is consistent with previous measurements. Stimulating T cells with a photoactivatable pMHC shows an ~8 s ‘offset’ time to increased DAG levels. While the slow dark reversion of LOV2 prevents true acute stimulation of our cells, both CAR occupancy and DAG levels rise around 30 s after removing blue light , Video 3These data confirm that the photoreversible binding of LOV2 to the Zdk-CAR leads to photoreversible signaling. Low DAG levels in the presence of blue light was not due to cells detaching from the SLB, as cells were passively adhered with an anti-β2 microglobulin antibody , and Ref3 molecules on the cell surface, which is slightly lower but comparable to the native TCR complex in Jurkats  can have the same receptor occupancy as another cell exposed to a high concentration of LOV2 and a short binding half-life  in the CAR's cytoplasmic domain. In contrast to DAG levels, ZAP70 recruitment showed no detectable evidence of kinetic proofreading . In otheIt is important to recognize how cell adhesion can affect cell signaling, as T cells adhered to a supported lipid bilayer can display ligand-independent signaling . The cloAs the anti-β2m antibody was not necessary, we did not use the it in the ZAP70 experiments. While we did not measure CD45 localization, our results suggest that CD45 exclusion does not contribute to measurable kinetic proofreading at the level of ZAP70 recruitment in our CAR system. However, because CD45 exclusion and T cell signaling are sensitive to the size of the extracellular domains , CD45’s Even though LOV2 was the only means of cell adhesion in the ZAP70 experiments, the cells retained a small RICM footprint under the higher, inhibitory intensities of blue light. The CAR could adhere the cells by many weak interactions with the abundant LOV2 in the low-affinity conformation  or with Our results differ from a study with a DNA-based CAR, which found a delay between ligand binding and ZAP70 recruitment, implicating some kinetic proofreading effects upstream of ZAP70 recruitment. Mechanistically, this delay was related to the time needed for ligand-occupied CARs to form small clusters, the only place where effective ZAP70 recruitment was observed. The putative dimeric state of our CAR  versus tSignificantly, for cells with high CAR occupancy but low ligand binding half-life, we observe ZAP70 recruitment without high DAG levels. These results nicely match the prediction of a two-step ZAP70 activation model. Requiring ZAP70 to bind a phospho-ITAMs and then subsequently be phosphorylated by Lck for full activity allows cells to have significant ZAP70 recruitment without strong downstream signaling . It alsoPrevious work has shown that the recruitment of Lck to the TCR is a key rate-limiting step in discriminating ligand binding lifetimes but left open whether it was mediated through Lck’s phosphorylation of ITAMs or ZAP70 . As we oZAP70 phosphorylation is attractive as a proofreading step because it occurs physically close to the receptor, allowing it to reset quickly upon ligand dissociation. This is a central requirement of kinetic proofreading. Another candidate biochemical step could be the phosphorylation of Y132 on LAT because of its involvement in recruiting PLCγ . Lck wasIn summary, we addressed a fundamental question in immunology: How do T cells discriminate ligands? We overcame previous technical limitations by developing a new optogenetic approach that directly tunes ligand binding half-life while keeping constant all other parameters that could affect CAR signaling. We found direct evidence that ligand binding half-life strongly controls downstream DAG levels but not upstream signaling events, like ZAP70 recruitment.Additionally, our experiments with a CAR revealed a similar degree of kinetic proofreading as complementary experiments that measured calcium release downstream of optogenetic stimulation of the TCR see . As bothTo date, optogenetics has been a powerful approach because it tells us how the timing, amount, and localization of an ensemble of signaling molecules modulate cell signaling . With thStandard molecular biology protocols were used for all cloning. In general, individual DNA segments were amplified by PCR and assembled using the isothermal Gibson assembly method. A plasmid encoding the C1 domains of PKCθ were a kind gift from Mark M Davis . The Zdk6 cells per ml. 293 T cells were grown in DMEM  with 10% fetal bovine serum. All cell lines were grown in humidified incubators at 37°C with 5% CO2.Jurkat cells were grown in RPMI 1640  supplemented with 10% fetal bovine serum  and glutamine . Jurkats were maintained at densities between 0.1 and 1.0 × 106 cells/ml were combined with 0.5 ml each lentiviral supernatant for the Zdk1-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  and washed with IMAC binding buffer until effluent was 60mAu (A280) or less. Protein was eluted with a 10 column volume gradient (IMAC binding buffer to IMAC elution buffer) and collected in 1.5 ml fractions. Visibly yellow fractions contained LOV2 and were pooled and exchanged into IMAC binding buffer using a HiPrep 26/10 desalting column . Purified 6xHis-TEV protease was added 1:10 w/w TEV:LOV2 and incubated overnight covered in tin foil.200 ml of LB Kan (30 ug/ml) was inoculated with E. coli BL21(DE3) transformed with a bicistronic plasmid expressing LOV2 and BirA and grown at 37°C overnight. 100 ml of the overnight culture was diluted into 1 L of TB Kan in a 2.8 L baffled flask and was shaken at 180 rpm at 37°C. At an OD600 of 0.6–0.8, the temperature was reduced to 18°C, and after 30 min media was supplemented with IPTG (250 uM), biotin (50 uM) and flavin mononucleotide . After growing overnight, all future steps were carried out on ice or at 4°C. Cells were pelleted and resuspended in 2 ml IMAC binding buffer for every 1 g wet cell pellet. Cells were homogenized in a dounce homogenizer and one Roche cOmplete Mini, EDTA-free protease inhibitor tablet was added. Phenylmethylsulfonyl fluoride was added  and cells were lysed on an EmulsiFlex-C3 . Lysate was spun in an ultracentrifuge (Beckman Coulter Optima L-90k ultracentrifuge) in a Ti-45 rotor at 40 k rpm for one hour. Using a peristaltic pump, the supernatant was recirculated over a 5 ml HiTrap Chelating column  charged with CoThe digested LOV2 mixture was recirculated over another HiTrap Chelating column with a peristaltic pump. The flow through was exchanged into HBS with a desalting column and concentrated to approximately 4 mg/ml with a Vivaspin protein concentrator . LOV2 was preferentially labeled on the N-terminal KCK tag  by addinDye labeled Zdk1 was purified in an identical manner to LOV2, excepting the following changes.Bacterial growth media was not supplemented with biotin or flavin mononucleotide.The SUMO protease 6xHis-SenP2 is added to the IMAC eluted protein at 1:1000 w/w instead of TEV protease.Protein was labeled with Sulfo-Cyanine3 maleimide  instead of Alexa Fluor 488 C5 Maleimide.At various stages, fractions were collected and analyzed by SDS-PAGE . Final pIMAC binding buffer: 50 mM KH2PO4, 400 mM NaCl, 0.5 mM βME, pH 7.5.IMAC elution buffer: 50 mM KH2PO4, 400 mM NaCl, 500 mM imidazole, 0.5 mM βME, pH 7.5.HBS: 20 mM HEPES, 100 mM KCl, 0.5 mM TCEP, pH 7.5.Nutrient Base: 12 g tryptone, 24 g yeast extract, 4 ml glycerol, 900 ml ddH2O. 10x TB Salts: 170 mM KH2PO4, 720 mM K2HPO4. Autoclave the nutrient base and 10x TB salts separately. Then add 100 ml of 10x TB salts to 900 ml of the nutrient base to make 1L of TB.Terrific Broth (TB) – SUV preparation and glassware cleaning protocols were modified from those previously described . A precl2O. Glassware was covered with a 5% (v/v) Hellmanex III  solution and incubated overnight. The Hellmanex solution was decanted and the glassware was extensively washed with ddH2O to remove all traces of the detergent. Excess water was removed by blow drying with nitrogen gas. Glassware was then dried at 80°C and stored protected from dust. Vial caps were sonicated in ddH2O for 30 min, dried and stored with the glassware.Glass vials  and pasteur pipettes  were added to a 1L beaker, covered with 3M NaOH and bath sonicated (Branson 1800) for 30 min. The NaOH was decanted and glassware was washed five times with ddH2O. Coverslips were washed five times in ddH2O before and after the ddH2O bath sonication to remove excess organic solvents. 3.75 g KOH was dissolved in 45 ml ddH2O and added to the coverslips, followed by 15 ml 30% hydrogen peroxide . The Coplin jar was placed into a 70–80°C water bath and allowed to react for 10 min. The base solution was decanted and coverslips were washed five times in ddH2O. The following were added to the coverslips in order: 38 ml ddH2O, 9.5 ml 37% HCl  and 12.6 ml 30% hydrogen peroxide. Coverslips were incubated for 10 min in the water bath. The acid solution was decanted, coverslips were washed five times in ddH2O and stored in ddH2O for up to one week.Glass coverslips  were added to a glass Coplin jar  and successively bath sonicated for 10 min each in acetone , 190 proof ethanol , and ddHAn RCA cleaned glass coverslip was removed from ddH2O and immediately blown dry with compressed nitrogen. A six-well Ibidi sticky chamber  was firmly pressed on top. Edges were sealed with clear nail polish and let set for five minutes. A 30 ul SUV aliquot was diluted with 800 ul 0.22 um filtered dPBS, added to each well and then incubated at 37°C for one hour. To functionalize a well, excess lipids were flushed out with 500 ul 0.22 um filtered dPBS. Streptavidin  diluted in dPBS-BB  was flushed into the well and incubated at room temperature for 5 min. The well was flushed with 500 ul dPBS-BB. LOV2 diluted in dPBS-BB  was flushed into the well and incubated in the dark at room temperature for 5 min. The well was then flushed with 500 ul dPBS-BB and incubated with cells previously labeled with the halo dye and biotinylated anti-β2 microglobulin antibody  and washed twice into dPBS-BB. Cells adhered to the SLB in the dark for 5 min. The well was flushed with 500 ul of imaging media and transferred to the microscope for imaging.dPBS-BB: dPBS  with 1 mg/ml beta-casein and 0.5 mM βME. Filtered with a 0.22 um filter prior to use.Imaging media: RPMI-1640 without phenol red  supplemented with 1% fetal bovine serum, 1:100 dilution of Glutamax , 10 mM HEPES (pH 7.4), 1 mg/ml β-casein, 0.5 mM beta mercaptoethanol, 50 ug/ml ascorbic acid and 1:100 dilution of ProLong Live Antifade Reagent . Solution was passed through a 0.22 um filter and incubated at room temperature for at least 90 min to allow the antifade reagent to reduce oxygen levels.i 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, a Borealis beam-conditioning unit (Andor Technology), a CSU-W1 Yokogawa spinning disk (Andor Technology), a 60x Apochromat TIRF 1.49 NA objective (Nikon), an iXon Ultra EMCCD camera, an Evolve 512 EMCCD camera (Photometrics) and a laser merge module  equipped with 405-, 440-, 488-, 514-, and 561 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  and flat field correction. After each day of imaging, TIRF488, TIRF561 and RICM images were taken on slides with concentrated solutions of fluorescein, Rose Bengal or dPBS, respectively. Camera offset was determined by taking images with the shutter closed. To flat field correct, the camera offset was subtracted from both the experimental and dye images. The experimental image was then divided by the dye slide image to yield the final flat field corrected image used in analysis.ImageJ  was used for all image manipulation and measurements, while MATLAB was used to analyze the measurements. Prior to analysis, all imaging channels underwent drift correction  of a three-minute hold in blue light. To account for background fluorescence from the DAG reporter in the absence of signaling, cells were treated with saturating amounts of PP2  after every time course. The resulting mean TIRF561 pixel intensity was subtracted from mean TIRF561 pixel intensity in response to blue light see . We someDAG values were either normalized relative to saturation or to treatment with the Src family kinase inhibitor, PP2. See RICM images were thresholded to create a mask of the cell footprint. A second local background mask was made by constructing a thin ribbon along and slightly expanded from the perimeter of the cell mask . Once stWe noticed fluctuations in the 488 nm laser power on the timescale of seconds. To minimize the errors in quantitation from these fluctuations, the 488 nm laser power was tracked by measuring relative changes the integrated whole field TIRF488 fluorescence . On a laImaging of the ZAP70 reporter was done similarly to the DAG reporter, except the anti-β2 microglobulin antibody was omitted. We observed greatly reduced dynamic range of the ZAP70 reporter when imaged with the anti-β2 microglobulin antibody due to high basal recruitment. However, in the absence of the antibody, the cell TIRF footprint was small during the PP2 wash-in experiments (the passive adhesion of the anti-β2 microglobulin antibody helps maintain larger footprints when cells are not stimulated), which led to high variability in the mean pixel intensity of basal ZAP70 recruitment. Therefore, ZAP70 recruitment is reported simply as the mean intensity of pixels within the cell footprint, as determined from the RICM images. The corresponding DAG data sets were acquired and analyzed in the same way as the ZAP70 data to ensure that our DAG kinetic proofreading results were not altered by the absence of the anti-β2 microglobulin antibody.All cells visible within the TIRF field we analyzed, only excluding cells that were dead or dying  or detached from SLB during the acquisition.A biological replicate consisted of four to five time courses see  of stimuobs) was measured by fitting a single exponential decay to the data after the blue LED turned on. All measurements were performed at 37°C. Zdk was modeled as being able to unbind directly from the high-affinity state of LOV2 or quickly unbind from the low-affinity state of LOV2 after a light-dependent conformational change in LOV2.Supported lipid bilayers were functionalized with saturating amounts of LOV2 and imaging media  containing 250 nM purified Zdk-Cy3 was added. To measure Zdk unbinding kinetics, wells were quickly flushed with 200 ul of imaging media without Zdk and the blue LED was immediately turned on. TIRF561 images were acquired every 200 ms. The apparent dissociation rate constant  that occur upstream of a saturable DAG response.Single cell data of CAR occupancies, binding half-lives and DAG levels were fit to a basic model of kinetic proofreading, consisting of i) en route to its active form (Cn). The steps between each of these states are irreversible (i.e. – they consume energy) and only occur while the ligand (L) is bound to the receptor (P). Upon ligand dissociation, the receptor resets to its initial state. Intermediate states are in kinetic competition to either progress to the next state or reset upon ligand dissociation.In kinetic proofreading , the recThe probability of a receptor progressing from one state to the next isConverting from rate constants to half-lives  isR is the total amount of ligand-bound receptors.The amount of receptors productively signaling  were used to calculate the number of CAR and TCR molecules on the cell surface for several cell lines. Cells were first washed in PBS and fixed for 10 min in a 1:1 mixture of PBS:BD Cytofix at room temperature. Cells were washed twice with FACS buffer and stained with the primary antibody  for 30 min at room temperature. Cells were again washed twice with FACS buffer and stained with the secondary antibody . Cells were washed twice with FACS buffer and analyzed by flow cytometry (BD LSR II). An identical staining protocol was used for the Quantum Simply Cellular beads, except they were not fixed. Care was taken to ensure both the primary and secondary antibodies were saturating by using a series of 2-fold serial dilutions of one antibody while the other was held at the highest concentration. No significant decrease of fluorescence was observed for the first 2-fold dilution of either the primary or secondary antibodies, confirming the highest concentration reached saturation. The highest, saturating concentration of OKT3 was 50 ug/ml, OKT8 was 10 ug/ml and the secondary antibody was 20 ug/ml. FACS buffer: dPBS  with 1% (v/v) FBS and 2 mM EDTA.Total surface receptor numbers were calculated per manufacturer’s instructions. In short, log log regression of antibody binding capacity as a function of bead MFI (R2 = 0.9937) was used to convert the MFI of each cell line to total number of bound primary antibodies. Background antibody binding in the no primary control was then subtracted. Because the IgG primary antibodies can bind between one and two surface receptors, a two-fold range is reported.To estimate the absolute molecular density of LOV2 in our bilayer, we used single molecule TIRF microscopy.First, the LOV2 binding capacity of the bilayer at saturation was calculated. Limiting amounts of LOV2-Alexa488 were diluted into LOV2-Cy3 and added to bilayers already functionalized with saturating amounts of streptavidin until single molecules of LOV2-Alex488 were resolvable. The number of LOV2-Alexa488 molecules were counted by hand and the total LOV2 molecules on the bilayer were calculated knowing the dilution factor, original concentrations of LOV2-Alexa488 and LOV2-Cy3 and their labeling efficiencies .2), DF is the LOV2-Alexa488 dilution factor, [LOV2Cy3] and [LOV2Alexa488] are the concentrations of the undiluted protein stocks and fAlexa488 is the fraction of the LOV2-Alexa488 protein stock labeled with Alexa488. Saturation at these LOV2 concentrations was verified by diluting the LOV2 solution 2-fold and measuring no observable decrease in the LOV2-Cy3 intensity.Where ρ is the molecular density on the bilayer  with a fluorophore and the Zdk1 domain  is expressed as a first gen CAR. The system is cycled between the dark/dim (binding on) or light (binding off) states with the dark/dim periods leading to increased cell spreading. Even in the light state a contact area is maintained, probably by the low affinity interaction of the off Lov2 with Zdk1. As a readout the authors mostly focus on DAG sensor readout as the ZAP-70 readout for CAR itself seems to be recruited in simple proportion to engagement (even in light/off state?). The DAG reporter signal is proportional to kinetics rather that occupation. Suggesting that the step between the ITAM mediated recruitment of ZAP-70 and the LAT dependent recruitment of PLCγ (based on literature) can be involved in kinetic proofreading with a threshold around 10 seconds.In this system the Lov2 domain binds Zdk1 with a low nM Kd in the dark and a low µM Kd in with intense blue light. Intense blue light will induce rapid conversion to low affinity form and dimmer blue light will induce slower conversion (even when Zdk1 is bound) and some degree of dark recovery of those molecules equilibrate to a concentration of active Lov2 that binds Zdk1 and dissociates with a kAs a technical paper that develops a tool, this is an interesting paper and could be published in the Tools and Resources section. The paper is more relevant to synthetic biology and design of CAR based therapies, but limited relevance to how a TCR works or self/non-self discrimination. We do not think that the paper resolves the latter question, which the authors set out to address. A number of technical points about the method need to be addressed. Furthermore, somewhat disturbingly, the vast literature in the field is cited in a selective manner. Below, we provide guidance on both these important issues.Essential revisions:1) The Abstract and Introduction should be reshaped to reflect more of the background of CARs and T cell ligand discrimination and what a simplified signaling model with light based kinetic control could teach us.2) Many aspects of the system are poorly described- at this point in the technologies they are working with they should provide:a) Absolute quantification (molecules/cell) of the CAR on the Jurkat cells. This could be done by flow cytometry with 488 or 640 labelled Luv2 (dark) and MESF beads from Bangs labs.b) The density of the Lov2 domain in the SLB in molecules/µm2 across the range that they are using. This could be done by line scanning FCS (dilute fluorescent version with non-fluorescent version if density is too high.) See PMID: 19254560.c) The estimate of occupancy will be inaccurate if they don't include a control for exclusion of non-binding Luv2 mutant or similar sized protein in the bilayer with a difference fluorophore. See: PMID: 17911103.3) It would be useful to see at least diffraction limited higher magnification images of the different signals to establish the correlation between IRM contact, CAR occupancy, the ZAP-70 and C2 domain sensor. It the CAR forming microclusters or is forming larger domains as it appears in the zoomed out views provided.4) A number of relevant papers need to be cited that are pertinent, and the present results related to these studies. A small sampling of the missing literature are:a) The work from the Davis lab from several years ago on using caged pMHCs that were sensitive to light, which allowed measurements that described the speed at which signals propagate downstream.b) The role of rebinding that allows ligands with short half-lives to signal, described by work from eight years ago by Van der Merwe and co-workers (Immunity) and Huseby and co-workers.c) The role of co-receptors, amount of active Lck and diffusion in kinetic proof reading described by Stepanek et al., (2014).d) You also ignore the data the Germain lab, and others, have presented showing that the same ab TCR signals differently in DP thymocytes vs. mature cells , meaning that intracellular events can control signal propagation separately from occupancy, half-life or mechanosensing.5) The model for proofreading is a bit strange. Many people have made models that are far more realistic and incorporate feedback, including several papers from the Germain lab , Chakraborty and Palmer, Chakraborty and Weiss, and the more theoretical efforts by Murugan, Huse, and Leibler. These papers should at least be cited and briefly discussed.6) The estimate of 3 proofreading steps is undoubtedly wrong. The various steps do not have the same time scales and there are feedback loops. These effects make the model's fit to obtain n very difficult to interpret. This part needs to be deleted.7) The comments about ZAP70 not being subject to kinetic proof reading in the Introduction need to be made with great care. Two points to note in this regard: a] There are data suggesting that partial phosphorylation of ITAMs is at least correlated if not connected to antagonism. b] in vivo, ZAP that is bound to TCR is not phosphorylated. Also note that in vivo the T cells are constantly stimulated by self ligands .eLife. Your revised article has been favorably evaluated by Arup Chakraborty (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Light-based tuning of ligand half-life supports kinetic proofreading model of T cell signaling" for further consideration at The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:The reviewing editor has asked that you address one point prior to a final decision. Please describe the steps in optimisation of the adhesion system and the specific requirement for the system chosen. Also, the implications of this adhesion system for kinetic proofreading – aka kinetic segregation model – should be discussed. For example, could the lack of kinetic proofreading in TCR proximal signaling be due to the presence of the adhesion system, which takes care of CD45 exclusion and makes signaling a matter of occupancy? If the light dependent kinetics were in control of CD45 segregation, would this step show a sensitivity to kinetics?Recent relevant papers include:Chang.et al., 2016.Cai.et al., 2018.The editor is not asking for additional experiments, just a transparent discussion of the results of the adhesion system, why anti-β2m, and if this is actually required for the signaling by the light inactivated CAR.Reviewer #1:The study continues to struggle with proving "kinetic proofreading" (KP) due to the issue that the light dependent acceleration of dissociation of the LOV2 domain from Zdk1 doesn't impact the dark reversion rate (on rate) so the affinity is decreased by blue light. This is a similar limitation faced by people working with mutation-based systems for TCR or CARs. The mass action equation makes it very hard to change the half-life while maintaining a constant affinity and occupation. They partly compensate for this by doing the experiments at different densities of the LOV2 domain to have matched receptor occupancy at all light intensities. These densities are now specified, but there is a surprise there. This is partly successful as they do see a better correlation of DAG to kinetics rather than occupation, but it’s expected that this system fires fully at low occupancy, so this lack of linear relationship to occupation is expected. Thus, the proof of KP is not advanced that much in practice, but the potential is there. Additional innovations seem necessary to fully exploit it.Essential Revisions:The authors have added a synthetic adhesion system, which was only mentioned in the methods and some legends. They use a biotinylated mAb to β2m, which should pull the cells very close to the substrate, perhaps less than 10 nm. I assume this will exclude CD45 at least partially if not quite robustly (the authors should check this) so all they need to do to trigger the CAR is pull them into these close contacts. This changes everything as they are not making the LOV2-Zdk1 interaction achieve KP through "kinetic-segregation". This is thought by many to be a critical process that needs to be mediated by the ITAM bearing receptor to initiate signalling in many contexts. So, this adhesion system and its implications need to be described in the experimental design and schematics in figures as its likely critical to interpretation. Can the system work at all without this synthetic adhesion system? This may explain why they see no kinetic proofreading at the level of ZAP-70- as its recruitment is taken care of by the adhesion system and then bringing the CAR into the close contacts.The other surprise is that the binding of the LOV2 domain displays significant positive cooperativity in binding to the SLB, suggesting that it oligomerises. How does this impact the measurements and interpretation?eLife. Your revised article and response to our request has been reviewed by a Reviewing Editor and Arup Chakraborty as the Senior Editor.Thank you for submitting your article "Light-based tuning of ligand half-life supports kinetic proofreading model of T cell signaling" for consideration by Unfortunately, your response has not addressed the only issue that needs to be resolved. When the question about adhesion first came up, we asked about low affinity interaction in the off state that might support residual adhesion. You noted that this was not a relevant question because the adhesion system was added to make the system independent of the CAR interactions with the ligand to keep the cells in place over many cycles. Now you say the adhesion system is not needed and in fact you did not use it for the studies looking at ZAP-70's role in kinetic proofreading. Given this, unfortunately, we need to ask again if this indicates that there is a low affinity interaction in the 100% "off" state that could mediate some residual adhesion, but without signaling. If the cells remain attached in the presence of the most intense blue light that would completely turn off the interaction, then it seems there would be support for the presence of such an interaction. Can you acknowledge this, or do you have another explanation for the system working with or without the parallel adhesion system? Summary:off defined by the light intensity. The Lov2 domain is attached to supported lipid bilayer (SLB) with a fluorophore and the Zdk1 domain  is expressed as a first gen CAR. The system is cycled between the dark/dim (binding on) or light (binding off) states with the dark/dim periods leading to increased cell spreading. Even in the light state a contact area is maintained, probably by the low affinity interaction of the off Lov2 with Zdk1. As a readout the authors mostly focus on DAG sensor readout as the ZAP-70 readout for CAR itself seems to be recruited in simple proportion to engagement (even in light/off state?). The DAG reporter signal is proportional to kinetics rather that occupation. Suggesting that the step between the ITAM mediated recruitment of ZAP-70 and the LAT dependent recruitment of PLCγ (based on literature) can be involved in kinetic proofreading with a threshold around 10 seconds.In this system the Lov2 domain binds Zdk1 with a low nM Kd in the dark and a low µM Kd in with intense blue light. Intense blue light will induce rapid conversion to low affinity form and dimmer blue light will induce slower conversion (even when Zdk1 is bound) and some degree of dark recovery of those molecules equilibrate to a concentration of active Lov2 that binds Zdk1 and dissociates with a kAs a technical paper that develops a tool, this is an interesting paper and could be published in the Tools and Resources section. The paper is more relevant to synthetic biology and design of CAR based therapies, but limited relevance to how a TCR works or self/non-self discrimination. We do not think that the paper resolves the latter question, which the authors set out to address. A number of technical points about the method need to be addressed. Furthermore, somewhat disturbingly, the vast literature in the field is cited in a selective manner. Below, we provide guidance on both these important issues.We would like to thank you and the other reviewers for providing thoughtful, constructive feedback. The experiments and literature references you shared helped to make our paper stronger and clearer. We have outlined the most significant changes to the paper below and provided a detailed point-by-point response separately.1) The reviewers wanted us to focus more explicitly on our use of a CAR, both in the setup and interpretation of our results. During revision, we made modifications throughout the manuscript to emphasize our use of a CAR more directly. We included comparisons to historic insights from chimeric antigen receptors and the increased medical relevance of understanding their signaling mechanisms. In interpreting our results, we explicitly delineate what conclusions apply to CAR signaling and which ones could be more general features of T cell signaling.3 molecules/cell) and LOV2 densities (between 40-1200 molecules/um2) showed that our system performs in similar molecular ranges as previously published CAR-based approaches. High magnification images showed that both of our reporters localized as expected: ZAP70 co-localized with areas of CAR occupancy while DAG was found roughly diffusely throughout the cell footprint. To address concerns about inaccurate receptor occupancies as a result of unbound LOV2 molecules being excluded from the cell footprint, we reanalyzed our data under a worst-case scenario: that all unbound LOV2 molecules are excluded from the footprint. Under these conditions, we find our value of n increases from 2.7 to 7.0, indicating that our reported results are a conservative estimate.2) Experimentally, the reviewers wanted a more detailed description of the performance of our new system, especially in terms of absolute numbers of molecules. Happily, we were able to address all of their points. Absolute quantification of the levels of cell-surface CARs (62-238 x103) The reviewers wanted more comparisons between our approach and results to other papers in the field. Towards this end, they suggested several excellent papers to include. We have incorporated these and other references in our revised submission. These helped to strengthen our paper and give the reader a broader lay of the field by enabling more points of comparison in both the setup of our approach and discussion of the results. We appreciate that they took the time share specific, constructive feedback, which allowed us to easily understand and directly address their concerns.Essential revisions:1) The Abstract and Introduction should be reshaped to reflect more of the background of CARs and T cell ligand discrimination and what a simplified signaling model with light based kinetic control could teach us.In motivating our experimental approach, we have more heavily emphasized the advantages of a synthetic, CAR-based input. The simple, modular design of CARs facilitates easier engineering with our optogenetic approach and allows us to bypass many of the mechanisms the endogenous TCR might use to discriminate ligands. The fact that we observe kinetic proofreading with the more minimal CAR places constraints on the possible receptor mechanisms that contribute to sensing the duration of ligand binding. By directly controlling ligand binding half-life with a simple receptor, paired with live cell readout at different levels of the pathway, we are able to better isolate and study the extent to which the proximal signaling pathway aids in the kinetic discrimination of ligands.2) Many aspects of the system are poorly described- at this point in the technologies they are working with they should provide:We agree that a more thorough quantitative evaluation of our system would be helpful for readers to interpret our results and would also aid in replication of our work by others. We have addressed all of the experimental concerns below.a) Absolute quantification (molecules/cell) of the CAR on the Jurkat cells. This could be done by flow cytometry with 488 or 640 labelled Luv2 (dark) and MESF beads from Bangs labs.3 CAR molecules on the surface of our cells lines, compared to 273-547 x103 TCR molecules on wt Jurkats. The quantification and controls are detailed in Figure 1—figure supplement 4.We used beads with known antibody binding capacity (Bang’s lab Simply Cellular beads) to measure the absolute number of CARs on each cell line. We also measured TCR levels on wt Jurkats for comparison. We find that there are between 62-238 x10b) The density of the Lov2 domain in the SLB in molecules/µm2 across the range that they are using. This could be done by line scanning FCS (dilute fluorescent version with non-fluorescent version if density is too high.) See PMID: 19254560.In lieu of a line scanning FCS, we used single molecule TIRF microscopy to measure the absolute density of LOV2 on the bilayer. Fluorescent LOV2 was diluted into non-fluorescent LOV2 and added to the bilayer at saturating concentrations. By counting the single molecules, we calculated the total LOV2 binding capacity of the bilayer at saturation. We then measured what fraction of saturation our experimental conditions achieved and used this value to calculate the absolute concentration of LOV2 on the bilayers in our experiments.2. This is in a similar range to what others have reported with a CAR on planar lipid bilayers .We find that over the range of LOV2 concentrations we used, there are between 40 and 1200 molecules/umc) The estimate of occupancy will be inaccurate if they don't include a control for exclusion of non-binding Luv2 mutant or similar sized protein in the bilayer with a difference fluorophore. See: PMID: 17911103.n of 2.7 to 7.0. Thus, we believe we report a conservative estimate of the degree of proofreading.It is true that exclusion of non-bound LOV2 from the synapse could affect our receptor occupancy measurements. To assess how this might affect our results, we recalculated our results under a worst-case scenario: all unbound LOV2 molecules are excluded from synapse. We found that this only increased the magnitude of observed kinetic proofreading from a value of 3) It would be useful to see at least diffraction limited higher magnification images of the different signals to established the correlation between IRM contact, CAR occupancy, the ZAP-70 and C2 domain sensor. It the CAR forming microclusters or is forming larger domains as it appears in the zoomed out views provided.We have included higher magnification images of CAR occupancy, reporter localization and IRM contact for both the ZAP70 and DAG biosensors . ZAP70 shows a clear co-localization with sites of receptor occupancy, as expected. DAG has a diffuse localization throughout the synapse. Both the sites of receptor occupancy and reporter activity are contained within the IRM footprint. The CAR does appear to form larger domains when bound to LOV2. However, we cannot rule out that single CAR molecules are also occupied and signal, as background free LOV2 prevents single molecule imaging.4) A number of relevant papers need to be cited that are pertinent, and the present results related to these studies. A small sampling of the missing literature are:Overall, these papers are excellent and will help us more fully discuss the context of our work. Below, we briefly describe how we discussed each of the suggested works and provide their location in the revised text.a) The work from the Davis lab from several years ago on using caged pMHCs that were sensitive to light, which allowed measurements that described the speed at which signals propagate downstream.The speed of onset to DAG production in our system is comparable to the ~8 second “offset time” described in this work. While we both use optical methods of stimulation, we cannot directly measure such an “offset time” because blue-light acutely terminates signaling in our system. Once blue light is removed, LOV2 goes through a slow reversion to an active state with a half-life of ~30 seconds. That DAG levels reach their maximum on this timescale suggests that once a CAR is bound, DAG can be produced within seconds. .b) The role of rebinding that allows ligands with short half-lives to signal, described by work from eight years ago by Van der Merwe and co-workers (Immunity) and Huseby and co-workers.This work is important because it shows that sufficiently fast on-rates can effectively occupy receptors longer than their binding half-life. Not only does this highlight the importance that other binding kinetics can play in ligand discrimination, it also allows us to point out that the 4-7 second transition between stimulatory and non-stimulatory half-lives we observe may in fact be an underestimate. The immobilization time the CAR experiences may be longer if LOV2 rebinding is sufficiently fast. .c) The role of co-receptors, amount of active Lck and diffusion in kinetic proof reading described by Stepanek et al., (2014).This work made a great discussion point to contrast our results with, given that we don’t observe kinetic proofreading effects at the level of ZAP70 recruitment. We used it to remind readers that our system does not engage co-receptors and so may be blind to additional kinetic proofreading steps in the endogenous TCR. (Discussion section).d) You also ignore the data the Germain lab, and others, have presented showing that the same ab TCR signals differently in DP thymocytes vs. mature cells , meaning that intracellular events can control signal propagation separately from occupancy, half-life or mechanosensing.Since our system specifically manipulates ligand binding half-life, we tended to focus our attention on how binding half-life affects T cell activation. This work reminded us to emphasize that the T cell response is not entirely dictated by a ligand’s properties, but also is sensitive to the internal state of the T cell. (Subsection “Quantifying the degree of kinetic proofreading”).5) The model for proofreading is a bit strange. Many people have made models that are far more realistic and incorporate feedback, including several papers from the Germain lab , Chakraborty and Palmer, Chakraborty and Weiss, and the more theoretical efforts by Murugan, Huse, and Leibler. These papers should at least be cited and briefly discussed.We decided to use a simple model of kinetic proofreading to reduce the number of free parameters and provide a conservative estimate of the degree of proofreading. However, as noted, the trade-off is that the model does not have direct analogy to the numerous biochemical steps that others have shown to be important in ligand discrimination. Thus, our model differs from the data in some important ways, most notably that our data shows cells are less sensitive to receptor occupancy than the model predicts. We now explicitly discuss these limitations of our model and how it could be improved by incorporating aspects of previously published models. (Subsection “Quantifying the degree of kinetic proofreading”) and elsewhere throughout the paper.6) The estimate of 3 proofreading steps is undoubtedly wrong. The various steps do not have the same time scales and there are feedback loops. These effects make the model's fit to obtain n very difficult to interpret. This part needs to be deleted.We agree that 3 proofreading steps is likely an oversimplification and may be confusing to some readers. The corresponding section has been removed.7) The comments about ZAP70 not being subject to kinetic proof reading in the Introduction need to be made with great care. Two points to note in this regard: a] There are data suggesting that partial phosphorylation of ITAMs is at least correlated if not connected to antagonism. b] in vivo, ZAP that is bound to TCR is not phosphorylated. Also note that in vivo the T cells are constantly stimulated by self ligands .We think that the reviewer is raising the important point that equivalent amounts of ZAP70 recruitment do not necessarily mean equivalent amounts of receptor phosphorylation or ZAP70 activation. We are more careful in the current manuscript to restrict our claims to our direct experimental measurements—kinetic proofreading is not observed at the level of ZAP70 recruitment but is observed at the level of DAG accumulation.The reviewing editor has asked that you address one point prior to a final decision. Please describe the steps in optimisation of the adhesion system and the specific requirement for the system chosen. Also, the implications of this adhesion system for kinetic proofreading – aka kinetic segregation model – should be discussed. For example, could the lack of kinetic proofreading in TCR proximal signaling be due to the presence of the adhesion system, which takes care of CD45 exclusion and makes signaling a matter of occupancy? If the light dependent kinetics were in control of CD45 segregation, would this step show a sensitivity to kinetics?Recent relevant papers include:Chang et al., 2016.Cai et al., 2018.The editor is not asking for additional experiments, just a transparent discussion of the results of the adhesion system, why anti-β2m, and if this is actually required for the signaling by the light inactivated CAR.Reviewer #1:The study continues to struggle with proving "kinetic proofreading" (KP) due to the issue that the light dependent acceleration of dissociation of the LOV2 domain from Zdk1 doesn't impact the dark reversion rate (on rate) so the affinity is decreased by blue light. This is a similar limitation faced by people working with mutation-based systems for TCR or CARs. The mass action equation makes it very hard to change the half-life while maintaining a constant affinity and occupation. They partly compensate for this by doing the experiments at different densities of the LOV2 domain to have matched receptor occupancy at all light intensities. These densities are now specified, but there is a surprise there. This is partly successful as they do see a better correlation of DAG to kinetics rather than occupation, but it’s expected that this system fires fully at low occupancy, so this lack of linear relationship to occupation is expected. Thus, the proof of KP is not advanced that much in practice, but the potential is there. Additional innovations seem necessary to fully exploit it.Essential Revisions:The authors have added a synthetic adhesion system, which was only mentioned in the methods and some legends. They use a biotinylated mAb to β2m, which should pull the cells very close to the substrate, perhaps less than 10 nm. I assume this will exclude CD45 at least partially if not quite robustly (the authors should check this) so all they need to do to trigger the CAR is pull them into these close contacts. This changes everything as they are not making the LOV2-Zdk1 interaction achieve KP through "kinetic-segregation". This is thought by many to be a critical process that needs to be mediated by the ITAM bearing receptor to initiate signalling in many contexts. So, this adhesion system and its implications need to be described in the experimental design and schematics in figures as its likely critical to interpretation. Can the system work at all without this synthetic adhesion system? This may explain why they see no kinetic proofreading at the level of ZAP-70- as its recruitment is taken care of by the adhesion system and then bringing the CAR into the close contacts.The other surprise is that the binding of the LOV2 domain displays significant positive cooperativity in binding to the SLB, suggesting that it oligomerises. How does this impact the measurements and interpretation?n in our model) at the level of DAG. Thus, the passive adhesion system was not used for the ZAP70 data and could not impact those results. However, we point out that the kinetic segregation of CD45 may impact kinetic proofreading differently in the native TCR-pMHC context, where the size of the extodomains may differ from our CAR-LOV2 pair. Revisions to reflect these changes in the Discussion section. Additionally, we added two more references of recent papers that highlight kinetic proofreading outside of the TCR as examples of areas where our approach may have future utility.We initially included the passive adhesion system to ensure that a decrease in the TIRF signal could not be due to the cells detaching from the bilayer. Ultimately, this system is dispensable, as experiments included in the supplement  without the adhesion system show an equivalent degree of kinetic proofreading  were not needed to maximally suppress cell signaling, cells were never exposed to saturating amounts of blue light, meaning a small fraction of LOV2 likely still existed in the high-affinity conformation. This small population of LOV2 may also account for the residual adhesion we observed. Both of these possibilities are now discussed to provide a more complete technical description of the new system.


Previous studies provide conflicting evidence on whether metformin is protective against cancer. When studying time-varying exposure to metformin, covariates such as body mass index (BMI) and glycated haemoglobin (HbA1c) may act as both confounders and causal pathway variables, and so cannot be handled adequately by standard regression methods. Marginal structural models (MSMs) with inverse probability of treatment weights (IPTW) can correctly adjust for such confounders. Using this approach, the main objective of this study was to estimate the effect of metformin on cancer risk compared with risk in patients with T2DM taking no medication.Patients with incident type 2 diabetes (T2DM) were identified in the Clinical Practice Research Datalink (CPRD), a database of electronic health records derived from primary care in the UK. Patients entered the study at diabetes diagnosis or the first point after this when they had valid HbA1c and BMI measurements, and follow-up was split into 1-month intervals. Logistic regression was used to calculate IPTW; then the effect of metformin on all cancers (including and excluding non-melanoma skin cancer) and breast, prostate, lung, colorectal and pancreatic cancers was estimated in the weighted population.A total of 55 629 T2DM patients were alive and cancer-free at their study entry; 2530 people had incident cancer during a median follow-up time of 2.9 years [interquartile range (IQR) 1.3–5.4 years]. Using the MSM approach, the hazard ratio (HR) for all cancers, comparing treatment with metformin with no glucose-lowering treatment, was 1.02 (0.88–1.18). Results were robust to a range of sensitivity analyses and remained consistent when estimating the treatment effect by length of exposure. We also found no evidence of a protective effect of metformin on individual cancer outcomes.We find no evidence that metformin has a causal association with cancer risk. Evidence for a protective effect of metformin on cancer risk remains under debate.Existing studies of metformin and cancer may not appropriately deal with time-dependent confounders affected by previous treatment.Inverse probability weighting of marginal structural models can deal with such confounders.Using this approach in a cohort of 55 000 patients with newly diagnosed diabetes from the Clinical Practice Research Datalink [median follow-up 2.9 years (IQR 1.3–5.4 years)] produced no evidence of an association between metformin use and cancer incidence. ,,,Metformin is the preferred first-line treatment for type 2 diabetes (T2DM) in general practices in the UK.The highest-quality existing observational studies have compared new users of metformin with new users of sulphonylureas, ignoring subsequent changes in treatment (intention to treat approach),,,One possible randomized controlled trial (RCT) designed to examine the causal association between metformin use and cancer incidence might randomize patients with newly diagnosed type 2 diabetes to receive either metformin monotherapy or placebo, with all participants additionally advised to follow a standard diet and exercise regimen, and long follow-up to detect cancer outcomes. Provided there were no/minimal protocol deviations, notably that patients stayed on their allocated treatment through follow-up, such a trial would reliably estimate the effect of metformin on cancer risk. In reality, although practical constraints preclude such a trial, we may be able to recreate a similar comparison in routinely collected primary care records by comparing initiators and non-initiators of metformin through time. In the absence of randomization, there is likely to be time-dependent confounding by factors associated with both treatment initiation and cancer risk. In the presence of time-dependent confounders that are themselves likely to be affected by previous treatment, such as body mass index (BMI) and glycated haemoglobin (HbA1c),To date, no studies have used MSMs with IPTW to compare cancer risk between new users of metformin, and patients with a diabetes diagnosis who are yet to initiate any treatment (no medication). Nor have they investigated the potential for time-dependent confounders when modelling time-varying treatment in the context of metformin and cancer. The main objective of this study was to estimate the causal effect of metformin monotherapy vs no medication on risk of cancer in patients with newly diagnosed T2DM, using MSMs with IPTW to appropriately deal with time-dependent confounding. We further aimed to evaluate the impact of adjusting for time-dependent confounders by comparing MSMs with standard methods.This study is based in part on data from the Clinical Practice Research Datalink obtained under licence from the UK Medicines and Healthcare products Regulatory Agency. The data are provided by patients and collected by the NHS as part of their care and support. The interpretation and conclusions contained in this study are those of the author/s alone. The study was approved by the Independent Scientific Advisory Committee . The approved protocol was made available to the journal and reviewers during peer review. Generic ethical approval for observational research using the Clinical Practice Research Datalink (CPRD) with approval from ISAC has been granted by a Health Research Authority (HRA) Research Ethics Committee . In addition, the study was approved by the London School of Hygiene and Tropical Medicine ethics committee .https://www.cprd.com], using an algorithm developed previouslyIJE online). The algorithm required a diagnosis code for T2DM alongside either a diabetes-care related code or a prescription for an oral glucose-lowering medication [identified using British National Formulary (BNF) codesPatients with incident T2DM were identified from the CPRD [IJE online).Patients with previous cancer, aged <30 or >90 at diagnosis, or with missing smoking/alcohol information at diabetes diagnosis, were excluded. A minority of patients lacked a valid BMI or HbA1c (measured within the previous 3 months) at diagnosis; for these individuals, study entry (baseline) was delayed until the point in follow-up when complete data were available.Follow-up ended at the earliest of the following: death, leaving the practice, first cancer record, initiation of any glucose-lowering medication other than metformin, or the last data collection date from the practice (31 July 2014 at the latest).The exposure of interest was metformin monotherapy, and the comparator group was patients with T2DM not taking any pharmacological therapy (hereafter referred to as ‘no medication’ controls). The date of metformin initiation was defined as the date of the first prescription record for metformin in CPRD (BNF code 6.1.2.2.2). The exposure status of individuals starting in the no medication group, who initiated metformin during follow-up, was time-updated in the month of the first metformin prescription. Patients were assumed to stay on metformin after their first prescription, since cessation of metformin without introduction of another glucose-lowering medication would be unusual and contrary to national guidance.Cancer outcomes were identified using Read codes recorded in the patient’s CPRD record as described previously.,IJE online. Briefly, each patient’s data were expanded into monthly intervals, and pooled logistic regression models were fitted to estimate stabilized IPTW.,IJE online; and 2) ; HbA1c ; and indicator variables for: use of statins in the year preceding baseline; use of non-steroidal anti-inflammatory drugs (NSAIDs) in the year preceding baseline; use of anti-hypertensives in the year preceding baseline; history of cardiovascular disease (CVD); and history of chronic kidney disease (CKD). Time-varying covariates included: HbA1c in the previous month; BMI in the previous month; and indicator variables for: use of statins in the previous year, use of NSAIDs in the previous year, use of anti-HTs (anti-hypertensives) in the previous year; history of CVD; and history of CKD. Last observation carried forward (LOCF) was used to impute time-varying covariates going forward from study entry, if not measured in a particular interval. All baseline risk factors and time since study entry were entered into the model for the numerator of the weight.,Inverse probability of treatment weighting (IPTW) of marginal structural models (MSMs) has been described elsewhere.The effect of metformin use on risk of cancer was estimated using pooled logistic regression models with time since study entry included in the model (as a cubic spline with the same knot points as the weighting model) to approximate a Cox proportional hazards model allowing for time-varying weights.Four outcome models were fitted to evaluate the effect of metformin on cancer risk. The first three models were unweighted models with varying levels of confounder adjustment, namely: model 1—minimal adjustment ; model 2—full baseline adjustment ; model 3—adjustment for all baseline and time-dependent covariates.The fourth model (the MSM) was a weighted model using the joint treatment and censoring weights. All baseline covariates included in the model for the numerator of the weights were included as covariates in the MSM. Both weighting and outcome models were repeated using restricted cubic splines for age, HbA1c and BMI, to investigate the impact of possible model mis-specification from inappropriate covariate form. All analysis was performed in Stata v.14.IJE online.The three main sensitivity analyses were as follows. First, we varied how far forward the cancer diagnosis dates were brought (0 and 12 months), to assess the impact of different latency periods. Second, the interval length used was changed from 1 to 3 months. Third, we explored the effect of fitting treatment models separately by calendar period. More detail regarding the methods for these sensitivity analyses, and details of further (secondary) sensitivity analyses, are given in A total of 98 080 patients with incident T2DM, aged 30–90 and with no history of any cancer at the time of diabetes diagnosis, were identified; 55 629 patients were eligible to enter the study . The maiIJE online.During follow-up 2530 cancers were observed, with crude event rates for no medication and for metformin of 11.4 per 10 000 person-years and 9.4 per 10 000 person-years. respectively. There were 266 prostate, 241 breast, 185 lung, 226 colorectal and 50 pancreatic cancers observed during follow-up. The full breakdown of incident cancer types as defined by ICD-10 codes is given in IJE online. Following truncation, the mean of the weights was 1.00 (SD 1.09), with 1st and 99th percentiles of 0.1 and 6.48, respectively (IJE online).The individuals who were more likely to initiate metformin had higher HbA1c, higher BMI, were of younger age and had a later calendar year of diagnosis. Model outputs for the estimation of both treatment and censoring weights are presented in IJE online).When stratifying by cumulative exposure to metformin, the MSM gave results consistent with no effect of metformin on risk of all cancers combined for all time periods, though precision of the estimates reduced as the length of exposure increased, due to loss of power . The unwIJE online). Outcome models using cubic spline parameterizations of continuous covariates gave similar results (IJE online).None of the sensitivity analyses produced meaningfully different results to those observed in the primary analysis ,It is also possible that in combining all cancers, any potential time-dependent confounding was masked because the confounding acts in opposite directions for different cancers. In particular, the association between BMI and risk of cancer has been shown to differ by cancer type.,An important limitation of this analysis is that the average follow-up time of patients in this study was relatively short. With a median time of 2.9 years, we acknowledge that there may have been insufficient follow-up in enough patients to detect any causal effect of metformin on cancer. The decision to censor at initiation of any other therapy in our analyses contributed to this reduced follow-up time, but with use of IPCW to adjust for informative censoring, this approach was deemed the most appropriate way to remove issues of treatment switching. It should also be noted that previous studies of metformin and cancer with contrasting results also had average follow-up times that are broadly comparable to our study.,Previous studies have found that cancer diagnoses taken from CPRD primary care data have good concordance with external sources, and have a low false-positive rate.In our analysis, only a single prescription was required to be considered exposed to metformin, and it was assumed that the patient remained exposed until there was evidence of a change in medication; however, this approach would not take into account non-adherence to prescribed medication or cessation of all antidiabetic therapy. It should also be acknowledged that patients apparently off treatment may have been receiving medication from specialist diabetes clinics or other sources. However, since diabetes is predominantly managed in primary care,Even after weight stabilization, there were extremely large weights for some individuals, usually driven by the characteristic of having a high HbA1c but not initiating metformin. Truncation of the stabilized weights was therefore necessary, which may have resulted in re-introducing time-dependent confounding. However, we only truncated the top 0.6% of the weights, meaning that under the assumption that the weighting model was correctly specified, the amount of confounding re-introduced is likely to be small.We found that many patients had missing HbA1c or BMI at the time of diabetes diagnosis. Use of multiple imputation was not considered since there is limited research on the use of multiple imputation with MSMs, and it is likely impractical to combine these methods in a large dataset, due to computational intensity. To increase numbers, patients entered the study at the first point (at or after diabetes diagnosis) at which they had data on all covariates, as long as this was before any treatment was initiated. Using this approach instead of a complete case analysis increased the sample size by about 20 000 patients, and since 75% of these patients entered the study within 6 months of their diagnosis date, it was considered that this would not cause serious bias. However, this approach resulted in the exclusion of a large number of individuals who initiated treatment before they obtained measurements for HbA1c and BMI. This could induce selection bias if the reason for not having measurements was related to cancer risk, though there is no clear reason why this would be true.We believe this to be the first published study to assess cancer risk associated with metformin use vs no medication while appropriately adjusting for time-dependent confounders affected by previous treatment. We found no evidence that metformin has a protective effect on cancer risk in patients with type 2 diabetes—a result consistent with some existing studies using new-user active comparator designs and an intention to treat approach. Although we acknowledge loss of precision, our results had consistent estimates close to the null when looking by length of exposure. As such, these results add weight to the view that the large protective effects previously observed were not causal.This study was funded by a Medical Research Council doctoral studentship award to R.F. K.B. holds a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (107731/Z/15/Z). R.M. is supported by a Wellcome Postdoctoral Fellowship from the Wellcome Trust (201375/Z/16/Z). D.F. and R.K. are supported by core support to the Medical Research Council .Conflict of interest: The authors declare no conflicts of interest.Supplementary DataClick here for additional data file.


Mutational signatures are specific patterns of somatic mutations introduced into the genome by oncogenic processes. Several mutational signatures have been identified and quantified from multiple cancer studies, and some of them have been linked to known oncogenic processes. Identification of the processes contributing to mutations observed in a sample is potentially informative to understand the cancer etiology.We present here SigsPack, a Bioconductor package to estimate a sample’s exposure to mutational processes described by a set of mutational signatures. The package also provides functions to estimate stability of these exposures, using bootstrapping. The performance of exposure and exposure stability estimations have been validated using synthetic and real data. Finally, the package provides tools to normalize the mutation frequencies with respect to the tri-nucleotide contents of the regions probed in the experiment. The importance of this effect is illustrated in an example.SigsPack provides a complete set of tools for individual sample exposure estimation, and for mutation catalogue & mutational signatures normalization.The online version of this article (10.1186/s12859-019-3043-7) contains supplementary material, which is available to authorized users. Throughout their lives, cells are exposed to many different influences that can compromise the integrity of their DNA by introducing changes to the genome . These sThe catalogue of somatic mutations in cancer, short COSMIC , hosts vcontext of the mutation. Mathematically, the mutational profile derived from sample data , can be expressed as vector m of K=96 somatic mutation frequencies that have been observed in the cancer sample. Mutational signatures are described by a matrix P which elements Pkn reflect the frequency with which the mutational process corresponding to the nth signature causes the kth mutational feature [Different frameworks have been proposed to mathematically decipher whether provided reference signatures are present in sequencing data from a single patient and how much they each contributed to its mutational load , 12. Fol feature . As the Pe is the product between the mutational profiles matrix P and the exposure vector e, when individual exposures en are restricted to non-negative values. This formalism assumes that different processes have additive contributions to the mutational load.The reconstructed catalogue We describe SigsPack, a Bioconductor package to estimate exposures to processes described by a known mutational signature matrix, for example from COSMIC. SigsPack also provides estimates of exposure stability, using bootstrapping. Its performance is benchmarked against synthetic & real data, using multiple tumor samples collected from the same patient. The effect of mutation context frequency is discussed, as well as the stability of individual COSMIC signatures and the loss of accuracy suffered by small mutational catalogues.We provide R package SigsPack for easy computation of exposures from mutational catalogues. The package provides several features, allowing to read the primary mutation data, normalize the mutational catalogues if necessary & compute the exposures with their bootstrapped variation estimates.Extract a sample’s mutational catalogue from a file in VCF formatThe function vcf2mut_cat allows to extract a mutational catalogue from a vcf file in a format so that can be used with the package (and most other packages from this field)Signature exposure estimation (or ’signature fitting’)The signature exposure is calculated using quadratic programming, in the same way as . This caBootstrapping & variability estimationbootstrapping estimates). The operation is achieved by creating multiple catalogues, each obtained by re-sampling the original catalogue with replacement. The number of re-sampled catalogues is under user control, and by default is set to 1000. That value has been used throughout the validation runs shown here.Following , SigsPacThe basic functions requried to compute exposure estimates are listed below. The COSMIC signatures have been included in the package  only), and are used by default. However, it is possible for the user to import her own signature matrix, or use a sub-set of COSMIC signatures, instead of the whole matrix. Extracting trinucleotide context frequencies from genomes or exomesget_context_freq computes the trinucleotide distribution of exomes and/or genomes, which is needed to normalize the data or signatures.Normalizationnormalize can be called to normalize the data to fit the signatures or vice versa. This requires the user to provide the trinucleotide frequencies of the data’s reference genome or exome (SigsPack’s function get_context_freq can extract these frequencies from an exome bed file or a BSgenome entity). The same has to be provided for the signatures in case the user chooses to provide their own signatures.SigsPack provides several functions which allow the user to put any mutational catalogue on a scale compatible for their choice of signature matrix. These functions can also be used to perform the inverse operation, i.e. to re-scale one or more signatures to match the frequencies on which the data have been collected. Normalization is required to correct differences in tri-nucleotide context frequencies of the catalogues and the signature matrix, typically from exome and whole genome respectively. PlotsGiven a mutational catalogue, the function summarize_exposures bootstraps it and provides a table and a plot illustrating the results of the signature estimation for this sample and the bootstrapped re-samples. The plot shows the distribution of estimated signature exposure for all the re-samples, highlighting the one of the original mutational catalogue, thus providing insights on the reliability of the estimates.Data simulatorThe create_mut_catalogues function allows to create mutational catalogues with exposure to specified signatures by sampling mutations from a distribution of those signatures’ weighted profiles. The signatures can either be known consensus signatures from COSMIC (whose signature profiles are included in the package for convenience) or signatures provided by the user. These can be specified on any kind of features so that the application is not limited to the 96 mutational contexts but can also be used, for example, to simulate profiles with strand bias.The package also provides convenience functions to visualise the results, and to generate synthetic data that can be used to analyse signatures stability. create_mut_catalogues. Samples in set1 are drawn from a distribution formed with equal contributions of signatures 7, 13, 21, 24 and 28 (so with each a weight of 0.2), likewise, the mutational catalogues of set2 have been samples from a distribution of signatures 3, 5, 8, 16 and 25 each having a weight of 0.2. The number of mutations in the catalogues were set to 1000, except when assessing the catalogue’s size effect on reconstructed exposures accuracy , and 5 relatively similar signatures , and generated mutational catalogues from each of them. Figure set1 are very small  and 5 (patient 99). In two out of three cases, the exposure estimation is higher than 0 (0.2% & 6%) for one sample, while the range is limited to 0 for another sample. In the third case, (signature 27 in patient 80), the exposure is estimated to be 2.49% in sample 2, while the bootstrapping range computed from sample 3 is between 0 and 2.43%. We conclude from these examples that in most cases, the boostrapping variability estimates can provide a proxy for the exposures’ variability due to clonal heterogeneity of mutational catalogues.Across all patients, the contribution from unstable signatures vary considerably from one sample to the next, and there is more than 10% difference between estimates from the 4 samples for signatures 5, 9 & 16 for patient 69, and for signatures 3 & 16 for patient 99 . As for synthetic data, bootstrapping provides large variation estimate for those signatures: for signatures 5 & 16, the extreme values reached by bootstrapping differ by more than 30% for 10 out of the 13 samples. These difference appear to be compensating: across the 13 samples, the correlation between signatures 3 & 16 exposures is −0.75, the most negative correlation between all signature pairs. This observation supports the hypothesis that the unstable mutations can be explained almost equally well by their contributions. The complete set of exposures is found in Additional file P value 4.7·10−4). This simple example shows that taking sequence composition into consideration by normalisation of mutational catalogues is required to ensure accurate exposure values.COSMIC signatures have been normalized to the trinucleotide frequencies of the human reference genome version GRCh37 . The regTo gain understanding on possible causes of the unstability of estimation of some signatures, we have created 1000 mutational catalogues of 1000 events each, drawn with probability proportional to the underlying tri-nucleotide frequencies in the human genome. Exposures were computed from these mutational catalogues, and the sum of estimated exposures over the 1000 samples is shown in Fig. Using both synthetic data & multiple samples from the same donors, we have shown that stability of exposure estimates can be accurately represented by bootstrapping mutational catalogues. It should be noted that reliable exposure estimation can only be achieved for a relatively large mutational catalogue. When the number of available mutations falls below a few hundreds, the number of observations in each of the 96 mutation classes is not sufficient to ensure a stable exposure reconstruction. SigsPack will warn the user when the mutational catalogue size is below 125.Even for larger mutational catalogues, some exposures displayed considerable variability upon re-sampling of the mutational catalogue. Inspection of relationships between signatures suggest that similarity between signatures, and the fact that some signatures can be approximated by others, may cause unwanted sensitivity to mutational catalogue details for some signatures’ exposures.To overcome the problem of unstable signatures, it may be possible to follow for example Letouzé and co-workers  and use We have shown that accurate exposures estimation requires matching tri-nucleotide frequencies between regions on which mutational catalogues and signature matrix have been collected. When using the COSMIC signature matrix provided with SigsPack, exome data must be put on “genomic scale” prior to exposures estimation. Choice of reference tri-nucleotide frequencies and normalization should be carefully selected when creating new mutational signature matrix from a large cohort. Even though SigsPack doesn’t provide algorithms to generate such matrix, it allows for easy normalization of each of its component. We suggest that scaling observations to the whole genome’s tri-nucleotide frequencies should be encouraged, as it renders the results independent of the experimental particulars.The analysis above has been carried out on the full set of COSMIC version 2 signatures. However, as the recently released version 3 contains more signatures, the signatures will remain non-orthogonal, and there might be cluster of signatures very similar to each other. These features could presumably lead to the same stability problems for exposure estimation as with COSMIC version 2 signatures.Many computational methods aimed at mutational signatures discovery already exist . The SigProject name SigsPackProject home page https://github.com/bihealth/SigsPackOperating system(s) any OS running R [unning R  & Bioconunning R Programming language ROther requirements BioconductorLicense GPL-3Any restriction to use by non-academics NoneAdditional file 1Derivation of the tri-nucelotide frequency effect on exposures. The effect of tri-nucleotide frequencies on the exposure reconstruction is detailed. (PDF 95 kb)Additional file 2set1 , and set2 . A perfect reconstruction would have original exposure values very close to 0.2 for these 5 signatures, and 0 for all the others. The mutational catalogues was resampled 1000 times, and for each signature, the minimum, first quartile, median, third quartile and maximum values of exposures computed from the re-sampled data are shown. (TSV 5 kb)Exposure estimation for synthetic data example. Exposure estimation from synthetic data. 1000 mutations have been generated at random, using frequencies from Additional file 3Mutational catalogues of the 13 samples used to estimate exposure stability in presence of biological noise. There are 4 samples for patient 69, 5 for patient 80 and 4 for patient 99. All catalogues have been collected using the Agilent SureSelect XT Human All Exon V4 exome enrichment kit. (TSV 3 kb)Additional file 4Exposure estimation for 13 samples from 3 colon cancer patients. Exposure estimation for 13 samples from 3 colon cancer patients . The mutational catalogues was resampled 1000 times, and for each signature, the minimum, first quartile, median, third quartile and maximum values of exposures computed from the re-sampled data are shown. (TSV 34 kb)Additional file 5Tri-nucleotide frequencies in the human genome & exome kit. Counts of tri-nucleotides in the human genome GRCh37 (Genome) and in the regions covered by the Agilent SureSelect XT Human All Exon V4 kit (Exome). The counts aggregate the tri-nucleotide sequence shown in the Cotext column and its reverse complement. (TSV 1 kb)Additional file 6Tri-nucleotide frequency effect in 13 samples from 3 colon cancer patients. Exposure estimation for 13 samples from 3 colon cancer patients , after normalization of the mutational catalogue to the genome tri-nucleotides frequencies (Genome) and after normalization of the mutation signatures to the regions enriched by the Agilent SureSelect XT Human All Exon V4 kit (Exome). (TSV 14 kb)Additional file 7Plots of exposure estimation for 13 samples from 3 colon cancer patients. Plots of exposures presented in Additional file


There are four current hypotheses in relation to the benefit of vitamin C dependence in the context of adaptation and reproduction. Here we advance and test a new ‘electron transfer hypothesis’, which focusses on the role of the expression of glucose transporter 1 (Glut-1) in red blood cells (RBCs) in recycling vitamin C, thereby increasing the efficiency of micronutrient uptake.During evolution, some species including humans, monkeys and fruit bats lost the ability for ascorbic acid (AA) biosynthesis due to inactivation of the enzyme l-Gulono-lactone-oxidase (GLO-loss) and pigs with functional GLO.To evaluate the benefit of Glut-1 expression, we determined vitamin C uptake into RBCs and potential release from two different species, humans with The oxidized form of vitamin C  was transported into human RBCs via Glut-1. There was no transport of either the reduced (AA) or the oxidized vitamin in pig erythrocytes.We propose that the transport of vitamin C increases an intracellular electron pool, which transfers electrons from intracellular ascorbate to extracellular substances like ascorbyl free radical or DHA, resulting in 100-fold smaller daily requirement of this essential redox sensitive micronutrient. This would be an advantage during seasonal changes of the availability from food and may be the key for the survival of individuals without vitamin C biosynthesis.40 million years ago some individuals lost the ability to synthesize vitamin C. Why did they survive such as humans until now? Individuals with a specific glucose transporter Glut-1 on their erythrocytes which transports vitamin C need less and are protected from scarcity due to seasons and food competitors. From then on, these species, including some primates, guinea pigs and Indian fruit bats, have been dependent on dietary, daily intake of AA.Most animals are able to synthesize ascorbic acid (AA) from glucose in either the kidney or the liver . About 6l-gulono-lactone through the presence of adequate vitamin C within the diet. In the habitat of our ancestors, abundant fruits containing ascorbate were available ), tris (2-carboxyethyl)phosphine (TCEP) was added to each sample, so that the complete vitamin C amount could be determined. The mobile phase was a 3.7 mM sodium dihydrogen phosphate buffer which was adjusted to pH 3.0 with meta-phosphoric acid. For sample separation, a reversed phase C18-aq column  with diameter of 5 µm was used. Detection occurred via electrochemical detection with a coulometric cell . Concentration of the samples was interpolated via standards with LabSolutions software .Samples were all stabilized 1:1 with 5% PCA and stored at −80°C until analysis. RBCs were lysed in ultrapure water 1:5 (v:v) before stabilization with PCA. Samples were thawed on ice and centrifuged . To all datasets, a mixed model was adapted including qualitative and quantitative parameters. Individuals were taken as random effects. Degrees of freedom were adjusted by Kenward and Roger . For allTo verify that DHA transport into RBCs is significant and specific for species without vitamin C synthesis, uptake kinetics of AA and DHA into RBCs from pigs and humans were made. The results show that pig RBCs transport neither AA nor DHA . The meaIf RBCs may act as a kind of vitamin C storage, the micronutrient should accumulate during the lifetime of a cell. To verify the ‘storage hypothesis’ described above, RBCs from six healthy individuals were separated by their cell age. Three different density layers were used to isolate the 10% youngest and 10% oldest cells from all others (middle-aged cells). The intracellular ascorbate content was analysed by HPLC and was corrected to the haemoglobin absorbance (546 nm) and, therefore, the cell number. The results are shown in There is a significant disposal of vitamin C from RBCs. Loaded cells release significantly more vitamin C than unloaded cells, but the absolute amount (1 and 0.5 µM) is negligible compared with intracellular concentration 89.8 and 36.5 µM, respectively. If there is an active transport of AA out of RBCs, the amount must be the same by loaded and unloaded cells. If the disposal of the antioxidant occurs by diffusion driven by a gradient, different released amounts were expected but on the whole, we would have expected a faster and, therefore, bigger amount that should have been found in the extracellular PBS. The results look more like a random, passive diffusion than directed transport. So the recycling hypothesis could not be confirmed by our findings.Our experimental results in relation to the uptake of DHA via the Glut-1 receptor, which only occurs in species that lost vitamin C synthesis, suggests a new hypothesis as to why the loss of vitamin C synthesis may represent an evolutionary advantage. We considered two other hypotheses. The electron transfer hypothesis shows that extracellular ascorbyl radicals (AfR), as produced during the redox defence reaction with other radicals, can be reduced again by electron transfer derived from DHA through the RBC membrane. As a consequence, AA is available again as redox partner in blood and interstitial fluid to inactivate free radicals. This is indeed a benefit because even at low levels of dietary vitamin C supply, this electron transfer helps to reduce the burden of free radicals. In contrast, the species not lacking GLO need far more vitamin C to ensure an equivalent redox defence reaction in the blood. We propose that this hypothesis offers a more complete explanation for the loss of vitamin C synthesis in primates and in some mammals.In the following sections, we summarize up our problems with these four theories:The theory of ‘ubiquitous availability’ of ascorbate-rich food explains only why species without vitamin C biosynthesis may have survived in a vitamin C-rich habitat. It is frequently argued that all species which have lost the endogenous vitamin C synthesis have a vitamin C-rich diet. However, if a vitamin C-rich diet is a selective pressure for gene silencing, there should be far more species with an inactive GLO. Most species living in the environment of the last 100 million years experienced a diet rich in vitamin C, which is not only present in fruits, but in green leaves, nuts, beans, roots, seeds (e.g. seed coat baobab), insects and all kinds of small and large animals which consume a vitamin C containing diet. Consequently, it is not an advantage of frugivores and thus not a selective pressure.The hypothesis of ascorbate as ‘fertility factor’ argues, that older creatures require more vitamin C and if they not get it will die and younger species with higher fertility will survive . PrimateThe ‘electron ratio’ hypothesis discusses the advantage of oral vitamin C uptake versus biosynthesis regarding the net electron supply. The dietary intake of ascorbate has a positive electron ratio, whereas the synthesis is electron neutral. Further, the dietary intake of the micronutrient allows the organism to down regulate the synthesis and to use these glucose molecules for energy metabolism (glycolysis or glycogen synthesis). In an energetic view, this theory is plausible, but it does not explain why ‘dietary vitamin C dependent’ species are preferred to ‘independent’ species.The most widely accepted hypothesis involves ‘free radicals’. Due to the loss of the ascorbate biosynthesis, more radical species accumulated in plasma and cells. Driven by more frequent DNA attacks, adaptation to environmental changes should be accelerated due to more frequent mutations. According to the proponents of this hypothesis, more frequent mutations should produce a greater diversity within one species and at least more or less successfully adapted to the changing environment. The most important dietary radical scavengers  are primarily present in plant-derived food. Based on this hypothesis, frugivores should have less mutations and consequently less phenotypic variability than carnivores with a poorer antioxidant supply.All these hypotheses do not describe the real advantage of the loss of GLO and they fail to address the extraordinary aspect of Glut-1 expression in RBCs of the non-synthesizing species.Glut-1 expression in RBCs—the common characteristic of all species without vitamin C synthesis—enables the energy independent and controlled transport of DHA into RBCs. The interaction of Glut-1 with stomatin results in a fast and selective transport of this molecule. We assume that this selective transport of vitamin C into RBCs is the reason for the selective pressure.To explain the advantage of the loss of vitamin C synthesis in connection with the occurrence of RBC-Glut-1, we have proposed three hypotheses.First: RBC may act as a storage site for AA. Indeed, the uptake of DHA and subsequent conversion to AA prevents the irreversible breakdown and consequently the loss of this important vitamin. Our data however, do not justify this assumption because we do not find a significant accumulation of AA during the RBCs lifespan, which could increase blood ascorbate after erythrophagocytosis. Nevertheless, the uptake of vitamin C into RBC can contribute to a lower need for this vitamin through diet.et al. [et al. [in vitro measurements, but also the reducing capacity which was observed by Mendiratta et al. and this supports our ‘electron transfer hypothesis’  like diabetes mellitus (DM). Based on preliminary data (unpublished), we found differences of intracellular vitamin C concentrations in RBCs. Further the distribution of Glut-1 and stomatin into DRM in patients with DM type 1 and their ratio show differences to controls despite equivalent plasma concentrations of both groups. There is an ongoing discussion on the real cause for secondary diseases in DM. Often lowered vitamin C plasma levels or increased OS is mentioned, but there is no evidence for one or both of these hypotheses due to contradictory results. If the expression of Glut-1 and its interaction with stomatin in RBCs was the key event for the compensation of the lacking vitamin C biosynthesis, a missing or disturbed protein interaction could also be the reason for the lowered VC plasma level and increased OS as described frequently in healthy and diseased individuals critically ill [All these facts support our third hypothesis, the electron transfer hypothesis. Van Dujin  showed ially ill  and metaally ill . No or pally ill .Sufficient uptake of DHA and conversion to AA into RBC might also contribute to a protection of RBC against OS. Incubation of RBC with vitamin C alone or in combination with N-acetylcysteine prevents the damage caused by OS in stored transfusion blood, such as increased osmotic fragility or membrane damage , 38. MorBut still one question remains: Was induced GLO inactivation the selective pressure, leading to survival of RBCs Glut-1 positive individuals or was RBCs Glut-1 expression the benefit and the GLO-loss was just a consequence due to better energy balance?Newborns of all species express in their first weeks of life Glut-1 in RBCs , 39. In In a population there are different phenotypes, that is, there might have been individuals with Glut-1 or Glut-3 or one or more of the other 12 Glut transporters in their RBCs. If the loss of GLO was caused by a retrovirus , this shRousettus leschenaultii, Hipposideros armiger) and others with expression of inactive GLO  [It is argued  that theequinum) . Howeverequinum) , 10. NotThis erythrocyte recycling capacity and AfR reduction should be further elucidated in diseases that are linked to increased radical production, like diabetes. The low levels of vitamin C in blood and RBC observed in diabetics may be the result of competition between high blood glucose levels and DHA intake via Glut-1 . At the More fragile and stiffer RBC are described in diabetics  and are There are few methodological limitations: The separation of RBCs regarding their lifetime occurred by a density gradient at room temperature. Vitamin C content is influenced by light and temperature. Further, free haemoglobin possibly diminished the AA concentrations due to his oxidizing character. The absolute determined amount of vitamin C might have been decreased during this process. Out HPLC-method based on the indirect measurement of DHA and AfR, that is, only AA (the reduced form) is detected and DHA is calculated by the difference of whole AA (complete reduced samples with TCEP) to the stabilized sample without any reducing agents.de novo synthesis of the micronutrient. RBCs Glut-1 expression and resulting vitamin C recycling decreased the required daily amount by up to 100-fold and led to the evolutionary selection of this phenotype which is better adapted to a changing and unsecure supply of this important micronutrient.Transport and accumulation of vitamin C into RBCs increases intra-RBCs electron pool and cross membrane electron transfer. This results in efficient extracellular recycling of vitamin C from AfR, produced during the redox reaction of vitamin C with free radicals. This recycling is energetically more economic compared with the