In the retina, synaptic transmission between photoreceptors and downstream ON-bipolar neurons (ON-BCs) is mediated by a GPCR pathway, which plays an essential role in vision. However, the mechanisms that control signal transmission at this synapse and its relevance to behavior remain poorly understood. In this study we used a genetic system to titrate the rate of GPCR signaling in ON-BC dendrites by varying the concentration of key RGS proteins and measuring the impact on transmission of signal between photoreceptors and ON-BC neurons using electroretinography and single cell recordings. We found that sensitivity, onset timing, and the maximal amplitude of light-evoked responses in rod- and cone-driven ON-BCs are determined by different RGS concentrations. We further show that changes in RGS concentration differentially impact visually guided-behavior mediated by rod and cone ON pathways. These findings illustrate that neuronal circuit properties can be modulated by adjusting parameters of GPCR-based neurotransmission at individual synapses.DOI:http://dx.doi.org/10.7554/eLife.06358.001 At the back of the eye, a structure called the retina contains several types of cell that convert light into the electrical signals that the brain interprets to produce vision. Cells called rods and cones detect the light, and then signal to other neurons in the retina that relay this information to the brain. Rods and cones are specialized to respond best to different visual features: cones detect color and can track rapid movement; whereas rods are more sensitive to low light levels and so enable night vision.All rods and cones communicate with particular types of neuron called an ‘ON bipolar cell’: rods send their information to rod-specific ON bipolar cells and cones to cone ON-bipolar cells. To maintain the differences in how visual features are detected, the signals sent by the rod or cone cells need to be tuned separately. Previous studies showed that bipolar cells rely on the action of proteins called RGSs to control how information is passed from rods and cones to ON bipolar cells. However, how the RGS proteins produce their effects is not well understood, and neither is their impact on vision or behavior.Sarria et al. used a genetic approach to create mice that progressively lost RGS proteins from their retina over the course of several weeks. Recording the nerve impulses produced by the bipolar cells as light shone on the retina revealed that RGS depletion affects these neurons in three ways: how sensitive they are to the signals sent by the rod and cone cells, how quickly they respond to a signal, and the size of the electrical response that they produce.Sarria et al. then investigated how these changes affected the behavior of the mice. To test the response of the rod cells, the mice performed tasks in dim light. This revealed that it was only when the sensitivity of the bipolar cells decreased that the mice performed worse. However, in a task involving fast-moving objects that investigated the response of cone cells, only changes to the speed of the response affected vision. Therefore, the RGS protein has different effects on the signals from rod cells and cone cells. These findings will be useful for understanding how different light sensitive cells in the retina communicate their signals to extract important visual features, allowing us to both see well at night and track rapid changes in scenery on a bright sunny day.DOI:http://dx.doi.org/10.7554/eLife.06358.002 Signaling via G protein coupled receptors (GPCR) mediates neurotransmitter actions and shapes many essential neuronal processes including vision, motor control, and cognition . In addiSignaling in GPCR pathways is initiated as the active receptor stimulates GDP/GTP exchange on G protein α subunits, triggering their dissociation from Gβγ subunits. These dissociated G proteins form active species that regulate the activity of second messenger enzymes, and either directly or indirectly modulate the opening of a number of ion channels . It is nInterestingly, the effect of RGS protein loss appears to be cell-type specific. For example, the elimination of the dominant RGS in rod photoreceptors profoundly slows deactivation kinetics while minimally affecting response amplitude or sensitivity , whereaso, which maintains the TRPM1 effector channels closed. The opening of TRPM1 channels and resulting generation of the depolarizing response requires deactivation of Gαo . The high gain and rapid response of visual signals puts exquisite demands on both the timing and sensitivity of signal transmission at this synapse to enable rod reliable vision . The tran of Gαo . Recent n of Gαo . In human of Gαo . Similarn of Gαo , TRPM1 . Evaluation of these mouse models by both electroretinography (ERG) and single cell recordings from rod ON-BCs revealed largely normal responses, that showed only minor delays in the onset time of the ERG b-wave  was introduced in the Rgs11−/− background (cDKO) and the mice were further crossed with a ubiquitous driver line expressing inducible Cre-recombinase  flashes, that activate rods but not cones, elicited a robust b-wave, whose generation requires the activity of rod ON-BCs . These r− strain . Adminis− strain ; Table 2− strain . Accordi− strain . Analysi− strain . Importa− strain , indicatTo determine how the en masse activity of ON-BCs seen in ERGs is set by the responses of single cells, we recorded the light-evoked activity of rod ON-BCs in dark-adapted retinal slices . Whole-cBecause ON-BCs making synapses with cone photoreceptors utilize a similar mGluR6-TRPM1 signaling pathway for generating depolarizing responses, we examined how changes in RGS concentration affected the light-evoked responses in cone ON-BCs , Table 4To assess how changing responses in rod ON-BCs affects dim light vision we developed a behavioral water maze assay. The assay measures the ability of mice to locate an escape platform under varying luminance levels. Under photopic conditions, trained wild-type mice reached the visible platform in ∼10 s . Video rTo validate the test, we evaluated the performance of the constitutive RGS7/11 double knockouts (DKO), which completely lack an ERG b-wave and thus are considered night blind. Under photopic conditions, the escape latency of DKO mice with the visible platform was no different from that of wild-type animals, indicating that they see the platform normally . HoweverRgs7−/− mice did not alter the performance compared to wild-type animals at any light levels . The optomotor response test is based on the ability of mice to respond reflexively to computer-generated rotating sine-wave gratings, which form a virtual cylinder around them . Previou2) OKR performance using DKO mice lacking ON-BC responses and compared them to control Rgs11−/− littermates that show normal ON-BC function. We found that Rgs11−/− and DKO mice had virtually identical contrast sensitivity for slow stimuli (up to 12 deg/s), whereas at higher stimuli speeds (Sp) the DKOs displayed impaired discrimination of low-contrast gratings pattern compared to their Rgs11−/− counterparts  where contrast sensitivity did not start deteriorating until after day 22 following tamoxifen administration, and by day 35 it had reached a sevenfold decline as compared to cDKO:Cre- control mice  detrimentally affects the signal-to-noise ratio of ON-BC responses and increases their variability.Under native conditions, RGS proteins in ON-BCs appear to be present in substantial excess over the minimal amount needed for sustaining wild-type response properties. Rod ON-BCs tolerate an almost 75% reduction in RGS levels before any measurable effects on response properties could be observed. To understand the basis for this overexpression we compared levels of RGS7 and RGS11 to that of the mGluR6 receptors. We found that RGS proteins are present at approximately threefold stoichiometric excess over mGluR6 dimers. Several recent studies suggest that the signal transduction components of the ON-BC are organized in a macromolecular complex . Indeed,These results additionally suggest that G protein deactivation normally is not a rate-limiting step in the cascade of events that lead to generation of a depolarizing response in ON-BCs. Previous studies in rod photoreceptors that utilize similar G protein cascade for light reception demonstrated that G protein deactivation limits the recovery of a photoresponse. In these neurons, increase in the concentration of RGS protein (RGS9) resulted in acceleration of the response termination kinetics . Since aInterestingly, we found that RGS proteins have similar impact on response properties of ON-BCs that form synapses with either rod or cone photoreceptors. Given monosynaptic connectivity between rods and rod ON-BCs, and the progressive night blindness associated with RGS loss, the implications of these changes for rod-mediated behavior are rather unequivocal. However, signaling of cones via parallel ON- and OFF- circuits, together with the heterogeneous nature of cone ON-BCs, complicates the interpretation of the dependence of photopic (cone) vision on cone-to-cone ON-BC signal transmission. The ability to discriminate changes in luminance across a broad range of stimulation frequencies is characteristic feature of photopic vision in many species, including mice . PreviouRgs7 (Rgs7−/−) (Rgs11 (Rgs11−/−) (Rgs7flx/flx) (Rgs11−/−: Rgs7flx/flx), and further with a Cre driver line ubiquitously expressing tamoxifen-inducible Cre-ERT2 recombinase B6.Cg-Tg(CAG-cre/Esr1*)5Amc/J to produce cDKO:Cre+ mice (Rgs11−/−: Rgs7flx/flx: CAG-CreERT2). To induce Cre expression 20 mg/kg tamoxifen , dissolved in corn oil and 10% ethanol, was administered by oral gavage with an 18–24 gauge smooth tip needle. Tamoxifen was administered once daily for 5 consecutive days. All procedures were carried out in accordance with the National Institute of Health guidelines and were granted formal approval by the Institutional Animal Care and Use Committees of the Scripps Research Institute (IACUC protocol number 14-001), Washington University (IACUC protocol number 20140236), and the University of Southern California (IACUC protocol number 10890).Generation of mice with the constitutive deletion in Rgs7−/−) , Rgs11 (gs11−/−) , or bothgs11−/−)  was prevflx/flx) . The resThe generation of sheep anti-TRPM1 is described . Rabbit Escherichia coli and affinity purified on GSTrap HP column . Protein concentration was determined by bicinchoninic acid (BCA) Protein Assay Kit  and adjusted to reflect the protein purity determined by densitometry of Coomassie blue-stained gels.Recombinant His-tagged RGS7 and RGS11 were co-expressed in Sf9 insect cells together with Gβ5, via baculovirus-mediated delivery, then purified by Ni-NTA chromatography as described previously . Glutathg for 15 min at 4°C. Total protein concentration in the supernatant was measured by using BCA Protein Assay Kit . Supernatants were added with SDS sample buffer (pH 6.8) containing 8 M urea and were subjected to 12.5% SDS/PAGE. Protein bands were transferred onto PVDF membranes, subjected to Western blot analysis first with primary antibodies against RGS7, RGS11 or mGluR6 and then with HRP-conjugated secondary antibodies, and detected by using ECL West Pico system . For the quantitative detection of RGS7 we used rabbit anti-RGS7 antibodies . Signals were captured on film and scanned by densitometer, and band intensities were determined by using NIH ImageJ software.Whole retinas were removed from mice and lysed by sonication in ice-cold PBS supplemented with 150 mM NaCl, 1% Triton X-100, and Complete protease inhibitor tablets . Lysates were cleared by centrifugation at 20,800×Dissected eyecups were fixed for 15 min in 4% paraformaldehyde, cryoprotected with 30% sucrose in PBS for 2 hr at room temperature, and embedded in optimal cutting temperature medium. 12-micrometer frozen sections were obtained and blocked in PT1 (PBS with 0.1% Triton X-100 and 10% donkey serum) for 1 hr, then incubated with primary antibody in PT2 (PBS with 0.1% Triton X-100 and 2% donkey serum) for at least 1 hr. After four washes with PBS with 0.1% Triton, sections were incubated with fluorophore-conjugated secondary antibodies in PT2 for 1 hr. After four washes, sections were mounted in Fluoromount . Images were taken with a Leica SP800 confocal microscope. Quantitative analysis of immunofluorescence from confocal images was performed using Leica software. At each time point  immunohistochemical staining of cDKO:Cre+ was repeated twice and at least two sections obtained from two individual animals were used for analysis and averaging. Sections were double stained for RGS7 and marker protein mGluR6, which co-localizes with RGS7. Positive staining of mGluR6 was used as a reference for the normalization of the recorded fluorescence intensity in the channel containing RGS7 protein. Rod and cone synapses were differentiated by: (i) counter staining with PNA and/or b-arrestin that specifically label cone terminals, (ii) sublamina position in the outer plexiform layer, (iii) specific clustering pattern of synapses at cone terminals. The fluorescence intensity within synaptic puncta was analyzed using line-scan mode and a constant puncta-encircling area, which tightly surrounded the contours of each puncta. Mean intensity (measured in pixels) were averaged across ∼20 individual punctas per imaged section, taking three sections per retina, and two to three retinas used per time point. Imaging parameters were the same for all sections and retinas. Values for groups were normalized using pre-tamoxifen RGS7 values as a 100% percentage of protein remaining. RGS7 protein percentages were plotted vs the tamoxifen time course and fitted nicely with a simple decay function in GraphPad Prism 6.2, 5 min) and prepared for recordings by using dim red light. Mice were anesthetized with an i.p. injection of ketamine and xylazine mixture containing 100 and 10 mg/kg, respectively. Recordings were obtained from the right eye only, and the pupil was dilated with 2.5% phenylephrine hydrochloride , followed by the application of 0.5% methylcellulose. Recordings were performed with a gold loop electrode supplemented with contact lenses to keep the eyes immersed in solution. The reference electrode was a stainless steel needle electrode placed subcutaneously in the neck area. The mouse body temperature was maintained at 37°C by using a heating pad controlled by ATC 1000 temperature controller . ERG signals were sampled at 1 kHz and recorded with 0.3 Hz low-frequency and 300 Hz high-frequency cut-offs.Electroretinograms were recorded by using the UTAS system and a BigShot Ganzfeld . Mice (4–8 wk old) were dark-adapted (≥6 hr) or light adapted (50 cd/m2 or by a Xenon light source for flashes > 2.5 cd·s/m2 (flash duration < 5 ms). ERG responses were elicited by a series of flashes ranging from 2.5 × 10−4 to 2.5 × 101 cd·s/m2 in increments of 10-fold. Ten trials were averaged for responses evoked by flashes up to 2.5 × 10−1 cd·s/m2, and three trials were averaged for responses evoked by 2.5 × 100 cd·s/m2 flashes. Single flash responses were recorded for brighter stimuli. To allow for recovery, interval times between single flashes were as follows: 5 s for 2.5 × 10−4 to 2.5 × 10−1 cd·s/m2 flashes, 30 s for 2.5 × 100 cd·s/m2 flashes, and 180 s for 2.5 × 101 cd·s/m2 flashes.Full field white flashes were produced by a set of LEDs (duration < 5 ms) for flash strengths ≤2.5 cd·s/mERG traces were analyzed using the EM LKC Technologies software, Sigma Plot and Microsoft Excel. The b-wave amplitude was calculated from the bottom of the a-wave response to the peak of the b-wave. To isolate the b-wave peak from sharp varying oscillatory potentials, a local regression smoothing algorithm using a bi-square weighting function was used in SigmaPlot see . Traces 2 for dark-adapted mice; index c). Rmax,r and Rmax,c are maximal response amplitudes, and I0.5,r and I0.5,c are the half-maximal flash intensities. Stimulus responses of retina cells increase in proportion to stimulus strength and then saturate, this is appropriately described by the hyperbolic curves of this function. The maximal amplitudes of rod and cone-driven b-wave stimulus–response curves in The first term of this equation describes rod-mediated responses (r), and the second term accounts primarily for responses that were cone mediated  were made while delivering flashes of light that varied in strength from evoking a just-measurable response to those generating a maximal response. Noise variance was computed from the 0.2 s preceding the flash. Rod ON-BCs were identified in retinal slices based on the distinctive shape of the cell body and its position in the inner nuclear layer jammed up against the outer plexiform layer. The internal solution for whole-cell recordings consisted of (in mM): 125 K-Aspartate, 10 KCl, 10 HEPES, 5 NMG-HEDTA, 0.5 CaCl2, 1 ATP-Mg, 0.2 GTP-Mg; pH was adjusted to 7.2 with NMG-OH. Membrane currents were filtered at 300 Hz by an 8-pole Bessel filter, and digitized at 1 KHz.Recordings of light-evoked currents in rod ON-BCs were performed as described previously . Briefly2 and timed performances from 20 trials  for each mouse at each light-intensity were averaged. Uniform room luminance settings were stably achieved by an engineered adjustable light-source and constantly monitored with a luminance meter LS-100 . To be certain that we were measuring the mice's visual ability only and not memory, the platform was placed pseudo-randomly in the water tank and all external visual cues were eliminated.Mouse visual behavior was assessed using a water maze task with a visible escape platform. The method for assessing visual function in this experiment is principled on a Morris water maze  and prev2 at the mouse eye level), over a range of various Sp, from 3 to 50 deg/s. Fs of stimuli was kept constant at its optimal value of 0.128 cyc/deg for all speed regimes thus providing a range of corresponding temporal frequencies (Ft) from 0.38 to 6.4 Hz, according to following equation: Ft = Sp × Fs  and Sp (0.1 cyc/deg), and a corresponding stimuli speed of 7.5 deg/s. In this mode, the monitor luminance was attenuated to ∼3 × 10−4 cd/m2 by several neutral density film filters that formed a cylinder around the animal. All data were analyzed using independent two-tailed Student t-test, with accepted significance level of p < 0.05.Visual acuity and contrast sensitivity of mice was evaluated from optomotor responses using a two-alternative forced-choice protocol . Briefly Sp × Fs . Photopi review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  Separation of rod and cone components of ERG b-wave signals. 2) Rgs7 changes the kinetics and sensitivity of light-evoked responses in rod ON-BCs”), and the axis label and caption to The text ? For the data in panel C, it would seem much more informative to plot sensitivity (rather than insensitivity), and to determine the level of Rgs7 that halves the sensitivity.3) Plots in Reviewer #2:This paper used a conditional knockout approach to study how altering the concentration of RGS proteins, which regulate G-protein inactivation, influences visual processing. The approach is a powerful one and the paper contains several interesting observations. Several issues, however, make interpretation of the data difficult.Major issues:Comparison of dependence of photopic and scotopic behavioral sensitivity on RGS concentration. The apparent difference in the dependence of photopic and scotopic behavioral sensitivity on RGS concentration is a particularly interesting result of the paper, and it is highlighted as such in the Discussion. Interpretation of this result is difficult, however, since the behavioral task itself differed. How can you rule out that the difference reflects the different task rather than the difference in light level and which retinal circuits are active? Specifically, the concern would be that the sensitivity to changes in the time course of the bipolar responses under photopic conditions reflects the use of a task requiring detection of flickering light inputs, while the task used under scotopic conditions may impose less stringent constraints on temporal sensitivity. This is a critical issue as it undercuts what is probably the most interesting result in the paper. I think it is essential that the same behavioral task be used to evaluate signaling via rod and cone circuits.Time course of changes in behavior and ERG. The lack of sensitivity of the scotopic behavioral measurements on the ERG amplitude , Jeremy Nathans (Reviewing editor) and four reviewers, including the three who reviewed the first version of the manuscript. One of the reviewers, Noga Vardi, has agreed to share her identity.Thank you for sending your work entitled “Sensitivity and kinetics of transmission at the first visual synapse differentially impact visually-guided behavior” for consideration at I am including the four reviews at the end of this letter, as there are many specific comments in them that will not be repeated in the summary here.In general, the reviewers were impressed with the importance and novelty of your work. Overall, the experiments appear to be carefully executed. However, all four reviewers find that the data is difficult to interpret in some places, and that the writing needs to be sharpened. This is a complex body of work and clear writing is critical.eLife is trying to restore some balance to the world of scientific writing. We welcome self-critical comments and we believe that such comments actually enhance the reader's ability to judge the science fairly.We would like to encourage you to resubmit a revised manuscript that addresses the specific issues raised in the reviews below. With respect to the writing, I will offer two examples of scientific writing that I think illustrates the preceding point. They are from two papers on ubiquitin that form the core of the discoveries for which the authors shared the Nobel Prize:1) Proposed role of ATP in protein breakdown: conjugation of protein with multiple chains of the polypeptide of ATP-dependent proteolysis. Hershko A, Ciechanover A, Heller H, Haas AL, Rose IA. Proc Natl Acad Sci U S A. 1980 Apr;77(4):1783-6.In the second paragraph of the Discussion section, after summarizing the evidence that leads the authors to propose the conjugation of APF-1 (=ubiquitin) to proteins as a way of marking them for degradation, the authors write: “Evidence that APF-1-proteins are intermediates in the breakdown of denatured protein as proposed in 2) Activation of the heat-stable polypeptide of the ATP-dependent proteolytic system. Ciechanover A, Heller H, Katz-Etzion R, Hershko A. Proc Natl Acad Sci U S A. 1981 Feb;78(2):761-5.In the last paragraph of the Discussion section, after describing their discovery and characterization of ubiquitin ligase, the authors write: “Evidence suggesting the role of the APF-1-activating enzyme in conjugation and in ATP-dependent protein breakdown is not conclusive at present.”For beautiful examples of clear writing, let me suggest the following:a) Photolyzed rhodopsin catalyzes the exchange of GTP for bound GDP in retinal rod outer segments. Fung B, Stryer L. Proc Natl Acad Sci U S A. 1980 May;77(5):2500-4.b) Flow of information in the light-triggered cyclic nucleotide cascade of vision. Fung BK, Hurley JB, Stryer L. Proc Natl Acad Sci U S A. 1981 Jan;78(1):152-6.Reviewer #1:A general issue of very broad interest in biomedical science is how RGS proteins regulate the kinetics of GPCR signaling. A related specific issue of great interest to neuroscience is whether  the properties of synapses where GPCR signaling is present are tuned by RGS proteins.RGS9 controls the limiting rate of deactivation of the GPCR cascade in rod photoreceptors activated by photon capture  in the responses of ON-bipolar cells.The first issue was addressed quantitatively in the field of phototransduction, where it was shown that the expression level of  capture , Neuron.Rgs7 in Rgs11-/- in adult mice, and reports highly convincing dependence of the kinetics of the in bipolar cells response on the expression level. The quantification of the knockdown, both with Western analysis and with IHC, is impeccable. The electrophysiological data are beautifully presented and convincing in showing that cone- and rod-signaling through ON-bipolars are differentially affected by the knockdowns. Behavioral data show trends consistent with the physiological results, and the general understanding of conditions that favor rod vs. cone signaling. Overall, the paper beautifully characterizes the dependence of the light responses of rod- and cone- ON-bipolars on the expression level of RGS proteins.This investigation elegantly uses an inducible expression system to conditionally knock down the expression levels of 1) In many places paper is not very well written for a general audience, but rather seems addressed to vision experts. Also, in too many places the authors slip into vague broad claims, instead of telling the reader precisely what can be concluded from the results.2) max, rods vs. Rgs7 level, and of time to peak (scotopic b-wave) vs. Rgs7 level.3) Rgs7 expression level declines, while the saturating amplitudes of the individual bipolars is not reduced. Could it be that the loss of Rgs7 and 11 causes the membrane potential of the bipolars to collapse, so that the driving force on the Trpm1 channels declines in vivo (but provided by the experimenter in single-cell recording)?  The authors need to comment on this apparent discrepancy. I think this is a clue to normal RGS function in the cells (see point #7).4) Rgs7 and 11 in ON-bipolars in regulating vision, but they are not really explained in a manner that a general reader could understand. There from the physiological data, it seems that there are two general effect of the cKO: (1) the loss of ON-bipolar signaling amplitude (as manifest in the b-waves); (2) the slowing of ON-bipolar response kinetics. The authors should attempt to explain to the reader how the cellular losses explain the specific behavioral deficits. To me, most of the defects (except for that in panel F) seem modest, and thus the behavioral data mainly show that normal ON-bipolar signaling is needed for completely normal visual function. However, the results also show that the loss of these signals does not lead to blindness .5) Rgs7 and 11 in cone ON-bipolars apparently results in a more severe phenotype.6) The paper seems to reach for the stars and miss the moon at times. For example, to me the conclusion of the Abstract—“we demonstrated that key parameters of synaptic transmission: sensitivity, kinetics, and maximal response amplitude have unique ranges of dependence on RGS concentration. We further show that these parameters differentially impact visually guided- behavior mediated by rod and cone ON pathways. These findings illustrate that neuronal circuit properties can account for varying needs by adjusting parameters of synaptic transmission at individually-defined synapses”—is amazingly obtuse. The phrase “a key parameter of synaptic transmission” isn't conventional or even clear. The parameters in question are values of extracted from ERGs or electrical responses of ON-bipolar cells: presumably the actual synaptic signal transmission kinetics—the decrease in glutamate release and consequent mGluR6 and Go de-activation—are not changed. The “unique ranges of dependence” on RGS concentration is never shown (see comment #3 above about how it might be introduced.) The phrase “these parameters differentially impact…” is gobbledy gook. Rather, the loss of 7) The paper needs a schematic  that would illustrate how the loss of RGS proteins produces the main ON-bipolar response defects. Even though this would necessarily remain speculative, the general reader needs to be given some understanding of how RGS depletion less to loss of ON-bipolar response amplitude and slowing of the responses. The authors don't ever return to the issue of how their results might impact the understanding of the post-synaptic responses of other neurons known to express RGS proteins.8) The Introduction could be shortened by at least 30-50% without loss of clarity. The bottom line should be: “… and thus we did the following experiments.”Reviewer #2:This “new” submission is essentially a resubmission of the previous manuscript, and I'm afraid I have found it very difficult to evaluate. In some ways the authors have addressed previous criticisms, but on examining the paper more deeply many of the flaws raised previously still seem to be present. For example, it is still problematic to compare the ERG recordings  with either the behavioral experiments (done at a range of room luminances) or with the OKR measurements . The new data in The authors have clearly done a lot of interesting experiments and they have some very valuable data. But what I can't really see is that as a paper it all hangs together in a sufficiently sound and coherent way. As a bottom line, the authors have not actually convinced me of the interpretation in their Abstract that “We demonstrate that key parameters of synaptic transmission… have unique ranges of dependence on RGS concentration”. Nor have they convinced me “that these parameters differentially impact visually guided-behavior mediated by rod and cone ON pathways”.Reviewer #3:The paper by Sarria et al. is a nice comprehensive attempt to understand how the quantity of RGS proteins affects parameters of the light response in ON bipolar cells and further influence different behavioral tasks. An emphasis is placed on comparing scotpic to photopic vision. My previous concern about the photopic ERG has been addressed; it seems that the concern about the behavioral assays  has also been addressed.Rgs11:Rgs7. The authors used whole retinas for their quantitative Western blottings; however, mGluR6 and Rgs11 are present only in ON bipolar cells while Rgs7 is present also in the IPL. I saw no attempt to correct for that. If Rgs7 is distributed equally in OPL and IPL , then the amount of Rgs7 will be reduced to about 10 fM and Rgs7+Rgs11 will equal mGluR6 amount. This quantification is important for understanding the composition of the mGluR6-related macromolecule and the mechanism for the light response. As analyzed here, it appears that RGSs are at excess, but if the IPL is factored in, then each mGluR6 molecule may have only one RGS molecule to modify its cascade. In fact, if the mGluR6 receptor functions as a dimer, then each receptor will have two RGS molecules, a more realistic outcome. This concern however, does not affect the conclusions of the paper regarding the difference between rods and cones (it may affect the Discussion).My major concern is the quantification of mGluR6:Another concern is the quantification of immunostaining in rod bipolar vs ON cone bipolar cells. The authors do not mention how they determined the puncta identity. A common way is to counter-stain with PNA, but this is not demonstrated or mentioned in Methods. I therefore have to conclude that the authors simply used the pattern for this distinction. While the pattern gives some indication about the identity of the puncta, it is far from being adequate for quantification.max and I0.5 than on time to peak. The authors should add a graph showing this parameter vs. days after tamoxifen (as they did for scotopic conditions). In particular, we learn later that in photopic conditions, the behavior was mostly affected by amplitude, so it will be nice to see these graphs next to each other.Reviewer #4:This paper uses ON bipolar signaling pathways in the retina to correlate the decrease in RGS expression with synaptic transmission and behavior. This is potentially a very powerful approach, and many of the results in the paper are clear and compelling. However, several substantial issues keep the paper from realizing its potential.Comparison of scotopic and photopic behavior:One of the central conclusions of the paper is that scotopic and photopic behavior have different sensitivities to properties of bipolar signaling . This is based on Morphology:A key point in interpreting the results is whether the synapses are altered morphologically or only functionally. The data on this point is not convincing. First, there is little quantification. Second, the resolution of the images provided is not sufficient to evaluate the possibility that synaptic components are rearranged. Third, some key procedures – such as how components at rod and cone synapses were isolated – are not described (or at least I did not find them easily).Cone bipolar recordings:Recordings from cone bipolar cells would help interpret the photopic behavioral results and substantially enhance the paper.Writing:The writing in many places is sufficiently confusing so as to alter or obscure the conclusions of a sentence. The entire paper needs to be gone through carefully with this issue in mind. [Editors’ note: the author responses to the first round of peer review follow.]eLife, and while the paper was formally rejected at the time we were encouraged to resubmit it as new version provided that we address points that required additional work. We are happy to report that we performed extensive additional studies which we hope should clear the concerns raised by the Reviewers. From the summary of discussion among the reviewers there appears to be two major concerns about our study: 1) separation of rod vs cone components measured by ERG (points A and B) and 2) different behavioral paradigms used to evaluate scotopic (rod-dominated) and photopic (cone-dominated) vision (points C and D).The earlier version of this work has been evaluated by To address the first concern, we performed additional set of experiments where we measured ERG responses on a rod-suppressing light background across all time points following tamoxifen administration. This intensity of this background was very similar to what was used for the behavioral study to attain proper correspondence of the results. We further ensured that truly scotopic light intensities, which do not activate cones, were used for the analysis of rod pathway by ERG. We added this new information, and re-analyzed all the data. In sum, we are convinced that rod and cone driven ERG responses are cleanly separated.To address the second concern, we established another behavioral paradigm where we evaluated temporal contrast sensitivity under scotopic conditions using optomotor test. Thus, we are now using the same behavioral test to evaluate vision under both scotopic and photopic conditions. Importantly, the results of the optomotor experiments closely matched the results observed in the water maze task, lending further credence to the interpretation that tuning of rod vs cone ON-BC pathways in respect to their reliance on RGS concentration ranges differentially contributes to behavior. Below are our responses to specific reviewers’ comments.Reviewer #1:1) Separation of rod and cone components of ERG b-wave signals.purports to plot the response-intensity dependence of the rod-driven component of the ERG b-wave, under control conditions and after tamoxifen application. However, it is crucial to demonstrate that the amplitudes plotted here are genuinely rod-driven, and unfortunately the raw data are not illustrated. What needs to be shown are sets of actual b-wave responses, across the full range of flash intensities, together with the extraction of the rod-driven and cone-driven components; these responses are not available, and so it is not possible to evaluate the measurements. Likewise, the fits of(in the subsection headed “Electroretinography”) to the measured amplitudes are not illustrated; these fits need to be illustrated for two reasons. First, it seems unusual that an exponent of unity (rather than a power law) is being applied. But secondly, the magnitude of the cone-driven component (listed in) is so large  that the extraction of the rod-driven component appears susceptible to the precise fitting procedures; however, the significance of this potential problem cannot be evaluated with seeing the results.We now provide the raw plot of full range of b-wave amplitudes elicited across all light intensities (scotopic to photopic) before extraction of rod vs cone components . We plot2): Sensitivity (6B), and half-saturation normalized response (6C). In, the measurements of sensitivity appear to have been taken from, where only the responsive cells have been included in the averages. The authors need to deal with the issue of the unresponsive cells, because response amplitude and sensitivity appear to be correlated. Thus, the omission of sensitivity measurements from the unresponsive (and presumably very insensitive) cells would seem to bias the plotted measurements.We looked into the issue of unresponsive cells in more detail. After analyzing the data more carefully it became apparent that the fraction of unresponsive cells across the time-course of tamoxifen administration did not substantially change and stayed in the 31% to 46% range.We thus retracted our earlier claim regarding correlation between tamoxifen administration and increase in the number of unresponsive cells. The appearance of these cells is likely related to stochastic hyperactivation of Cre-recombinase and resultant complete loss of RGS proteins that renders cells completely unresponsive. Given that no parameters are extracted from these cells and that their fraction remains relatively constant throughout the experiment, we think that they are not significantly biasing our sensitivity measurements. Consistent with this, we found almost perfect correspondence in sensitivity measured by both patch clamp and ERG. We clarified these points in the text.The text (in the subsection headed “Progressive loss of Rgs7 changes the kinetics and sensitivity of light-evoked responses in rod ON-BCs”), and the axis label and caption to, give very confusing information about what is plotted. The caption and axis label refer to “Half-saturation  response” as a percentage of maximal. But my understanding of a half-saturating response is that it is half-maximal (i.e. 50%), so I cannot understand what is being plotted. The text refers to “sensitivity” but I cannot see any measure of sensitivity plotted. There seems to be something important here that the authors are not explaining to the reader.We corrected 3) Plots in. In each of panels A, C and D in, the authors show the extrapolated behavior at very low Rgs7 concentrations asymptoting to a level similar to that obtained at around 20% concentration. In the absence of measurements at lower concentrations, this appears somewhat implausible. For example, in panel C, why would the half-saturation intensity suddenly stop increasing as the Rgs7 concentration dropped below 20%? And hence, how reliable is this asymptotic level (which is used for curve fitting)? For the data in panel C, it would seem much more informative to plot sensitivity (rather than insensitivity), and to determine the level of Rgs7 that halves the sensitivity.2=0.0991. Hence, we believe that our estimates of RGS concentration required to change response parameters and behavior are accurate. We further compared all measured parameters according to level of Rgs7 that halves the response as the reviewer suggested and present this data in panels E of We have changed data presentation in these figures now , which nReviewer #2:Comparison of dependence of photopic and scotopic behavioral sensitivity on RGS concentration. The apparent difference in the dependence of photopic and scotopic behavioral sensitivity on RGS concentration is a particularly interesting result of the paper, and it is highlighted as such in the Discussion. Interpretation of this result is difficult, however, since the behavioral task itself differed. How can you rule out that the difference reflects the different task rather than the difference in light level and which retinal circuits are active? Specifically, the concern would be that the sensitivity to changes in the time course of the bipolar responses under photopic conditions reflects the use of a task requiring detection of flickering light inputs, while the task used under scotopic conditions may impose less stringent constraints on temporal sensitivity. This is a critical issue as it undercuts what is probably the most interesting result in the paper. I think it is essential that the same behavioral task be used to evaluate signaling via rod and cone circuits.We completely agree with the reviewer’s comment. To deal with this issue we evaluated mouse vision using the scotopic contrast sensitivity test. This is exactly the same paradigm that we used to evaluate photopic vision, therefore we think that the data are now directly comparable. Strikingly, we still detect a very large difference in behavioral sensitivity of mice to the RGS loss under photopic vs. scotopic conditions. Importantly, the results obtained in the scotopic contrast sensitivity task also agree very well with measurements in object recognition task in the water maze. Together, these data further boost confidence in the conclusions from this work.Time course of changes in behavior and ERG. The lack of sensitivity of the scotopic behavioral measurements on the ERG amplitude  is somewhat unexpected. Why would this be the case? Sensitivity is a somewhat artificial measurement from a biological perspective because of the normalization, so it is particular unexpected that behavior tracks it rather than response amplitude. Could initially both signal and noise decline with declining RGS concentration such that behavioral threshold remains fixed? If this is the case, it should be apparent from noise recorded in the rod bipolar cells. An expanded discussion of this issue is needed.We think that the reviewer’s intuition here is correct. The traditional definition of sensitivity needs to consider the ability to discriminate light-driven signals from the noise, or a signal-to-noise ratio. While most of the emphasis of the manuscript is on evaluating changes in the light induced changes in signal, we had not examined how elimination of RGS proteins affects noise. In the revised manuscript we separately evaluated changes in noise and this information can now be used to approximate changes in signal-to -noise ratio. While the signal per photon absorbed changes in magnitude , we detected no change in rod ON-BC noise . This inThe paper could be written much more clearly. There are numerous sentences that I struggled to interpret. A few examples are:Abstract: “We demonstrate that key parameters of…”;Introduction: “Combined with observed modulation…”;Subsection “Progressive loss of Rgs7 changes the kinetics and sensitivity of light-evoked responses in rod ON-BCs”: “we first examined retinal responses…”;Subsection “Saturation of RGS concentration in ON-BC ensures reproducibility of signal transfer”: “Notably, a substantial…”.There are other examples, and generally the paper needs a careful editing job. There are also some very long paragraphs that could get broken up to help a reader, for example the first paragraph of Results.We have edited the manuscript, hopefully improving its readability.Other issues:The Abstract does not give a good idea of the results in the paper. For example, it does not state that the key manipulation in the paper is to alter the concentration of RGS proteins.We modified the Abstract and it now contains reference to RGS proteins.Introduction: Sampath and Rieke is probably not the best reference for the role of Go in On-BC response recovery.Sampath and Rieke were the first to show directly the specific role of G protein deactivation in limiting the onset of ON-BC responses. This reference is cited in the context of Go deactivation. We have additionally included reference to Dhingra and Vardi  who idenResults, first paragraph, and: the similarity of the synaptic connectivity shown inis important as the authors point out. But nothing is done to quantify such similarity. A similar issue arises in the discussion ofWe are unsure about the type of the quantification that might be required here. We see perfect apposition of pre-synaptic (e.g. CtBP2) and post-synaptic (e.g. mGluR6) markers with no difference between genotypes. In the event this alignment is disrupted  the synaptic morphology is usually drastically affected and/or either pre- or post-synaptic markers do not reveal staining in the synaptic layer. We have previously examined the morphology of photoreceptor to ON-BC synapses  PNAS in The evidence for 1:1:1 stoichiometry  is not particularly strong and not uniquely supported by the data. That part of the text needs to be revised to more accurately reflect the actual data.We revised the manuscript and are now providing exact values for RGS and mGluR6 content measured in our experiments. We would like to note that these experiments were carefully designed and included purified recombinant protein standards spiked into respective mouse knockout retinas. We conducted three independent experiments and generally believe that the data are reliable. We also modified the text to provide more accurate approximation of RGS:mGluR6 stoichiometry.Heterogeneity of rod bipolar responses: The ERG data and plots based on it suggest a smooth change in response properties as RGS concentration declines. But the single cell recordings seem to provide a much different picture, with large variability among cells. It would help to document that variability in more detail and explain to a reader why the heterogeneity is not a concern in the comparison of the ERG and behavioral data. Why do rod bipolar responses change in shape so much? It is not immediately obvious why the dependence of the slope of the rising phase of the response on flash strength should change.By its nature, ERG recordings reflect average behavior of thousands of cells. It is common to see some variability in a single cell electrophysiological recordings directly from individual bipolar cells. One of the major conclusions of the paper is that in ON-BC RGS are present in substantial excess and this in part ensures reproducibility of their responses and high sensitivity. Thus, it is quite expected that manipulation with concentration beyond the point where it starts affecting the response properties, produces greater variability. This situation is further compounded by the variability associated with the activation of Cre-recombinase which also varies on cell-by-cell basis. Nevertheless, despite the higher than normal variability we were able to perform quantitative analysis of changes in response sensitivity measured in the single cell patch clamp experiments with a reasonable precision . ImportaChange in the slope of the light response onset upon RGS loss is expected from the deceleration of the G protein inactivation and resultant slowing of the TRPM1 channel opening and documented previously  PNAS. AgIn the subsection headed “Kinetics and sensitivity of rod ON-BC responses and behavioral performance correlate with different RGS levels”: the error bars for the dependence of response properties on RGS concentration seem very small given that they should incorporate error in both determination of RGS concentration and in the response parameter of interest.In the revised figures  we have Inclusion of recordings from ON cone bipolar cells would enhance the paper. It is not clear why these are not part of the paper at present.Cone ON-BCs are rare when sampling randomly from a slice for patch recordings. Although we agree that having single cell data would have been ideal, obtaining a sufficient data set for the analysis of tamoxifen inducible changes in this rare neuronal population was not feasible in the set of experiments presented. However, we do evaluate the average cone-driven responses of cone ON-BC by ERGs and compare their properties to rod-driven ON-BC responses, with little difference in respect to how parameters of interest in these two neuronal populations respond to change in RGS concentration.Reviewer #3:The paper is well written, but the Abstract is not sufficiently informative.We have revised the Abstract to make it more informative.[Editors' note: the author responses to the re-review follow.]Reviewer #1:1) In many places paper is not very well written for a general audience, but rather seems addressed to vision experts. Also, in too many places the authors slip into vague broad claims, instead of telling the reader precisely what can be concluded from the results.We have edited the paper substantially to increase its readability for a general audience. This includes elaborating and clarifying how we reach conclusions and substantiate claims.2)presents a nice set of control data, but it's very tedious and wasteful of the reader's time, and should be relegated to the supplement with one or two brief sentences in the main text. The figure presentation should start with the current, which presents the knockdown strategy and highly convincing evidence for its success at the molecular and cellular level.eLife policies state that every piece of supplemental material should be related to a particular main figure. In the case of our eLife had this data in the supplement but we were asked to make it a main figure. The figure represents an important pretext to the current work, the fact that knocking down RGS expression to 25% of normal has little effect on the light response properties.We understand the reviewer’s point and agree that moving current 3)is very powerful. To help the reader two more panels should be added: (1) a plot of Rmax, rods vs. Rgs7 level, and of time to peak (scotopic b-wave) vs. Rgs7 level.These analyses are provided in 4). It's very puzzling that the saturating ERG b-wave amplitude is systematically reduced as Rgs7 expression level declines, while the saturating amplitudes of the individual bipolars is not reduced. Could it be that the loss of Rgs7 and 11 causes the membrane potential of the bipolars to collapse, so that the driving force on the Trpm1 channels declines in vivo (but provided by the experimenter in single-cell recording)?  The authors need to comment on this apparent discrepancy. I think this is a clue to normal RGS function in the cells (see point #7).We found that response amplitudes of rod ON-BC are reduced when measured by either ERG or single cells recordings with very good correspondence between these very different methods. The Y scale in 5). The behavioral data provide support for the role of Rgs7 and 11 in ON-bipolars in regulating vision, but they are not really explained in a manner that a general reader could understand. There from the physiological data, it seems that there are two general effect of the cKO: (1) the loss of ON-bipolar signaling amplitude (as manifest in the b-waves); (2) the slowing of ON-bipolar response kinetics. The authors should attempt to explain to the reader how the cellular losses explain the specific behavioral deficits. To me, most of the defects (except for that in panel F) seem modest, and thus the behavioral data mainly show that normal ON-bipolar signaling is needed for completely normal visual function. However, the results also show that the loss of these signals does not lead to blindness .We revised the manuscript to provide a more detailed explanation of the behavioral data and their interpretation. The behavioral deficits that we measure for the rod pathway is straightforward: complete loss of scotopic vision. The phenotype is not modest by any means and relationship between the lack of b-wave and night blindness is well established in humans and animal models. “How the cellular losses explain the specific behavioral deficits” is the main investigative line of the paper and we present our thinking about this point in the Discussion, which we have carefully revised and edited for clarity.6) The paper seems to reach for the stars and miss the moon at times. For example, to me the conclusion of the Abstract—“we demonstrated that key parameters of synaptic transmission: sensitivity, kinetics, and maximal response amplitude have unique ranges of dependence on RGS concentration. We further show that these parameters differentially impact visually guided- behavior mediated by rod and cone ON pathways. These findings illustrate that neuronal circuit properties can account for varying needs by adjusting parameters of synaptic transmission at individually-defined synapses.”—is amazingly obtuse. The phrase “a key parameter of synaptic transmission” isn't conventional or even clear. The parameters in question are values of extracted from ERGs or electrical responses of ON-bipolar cells: presumably the actual synaptic signal transmission kinetics—the decrease in glutamate release and consequent mGluR6 and Go de-activation—are not changed. The “unique ranges of dependence” on RGS concentration is never shown (see comment #3 above about how it might be introduced.) The phrase “these parameters differentially impact...” is gobbledy gook. Rather, the loss of Rgs7 and 11 in cone ON-bipolars apparently results in a more severe phenotype.We have done our best to revise the manuscript and improve clarity. Specifically, we rephrased the statements mentioned by the reviewer hopefully clarifying the points that we were trying to make. The data establishing the dependence of ERG parameters on RGS concentration are presented in 7) The paper needs a schematic  that would illustrate how the loss of RGS proteins produces the main ON-bipolar response defects. Even though this would necessarily remain speculative, the general reader needs to be given some understanding of how RGS depletion less to loss of ON-bipolar response amplitude and slowing of the responses. The authors don't ever return to the issue of how their results might impact the understanding of the post-synaptic responses of other neurons known to express RGS proteins.We have added a schematic to provide our interpretation of this process .8) The Introduction could be shortened by at least 30-50% without loss of clarity. The bottom line should be: “… and thus we did the following experiments.”We revised the manuscript and shortened the Introduction section where we could. However, we felt that dramatic reduction in length might not do the service to a general reader, as it would not allow us to introduce broadly relevant problems at hand.Reviewer #2:For example, it is still problematic to compare the ERG recordings  with either the behavioral experiments (done at a range of room luminances) or with the OKR measurements .All experiments were performed under 2 conditions: scotopic to measure rod pathway and photopic to assess cone pathway function.2 in ERG while applying 5 to 1000 cd*s/m2 flashes with 10 to 260 cd*s/m2 half-saturating light intensities used to derive kinetic parameters and 70 cd/m2 in photopic OKR making the amount of light delivered to the retinas under different paradigms comparable and representative of the truly photopic conditions.For the photopic conditions, we used a rod-suppressing background of 50 cd/m-4 to 2.5x10-1 to cd*s/m2 and equivalent flash intensities were used for the single cell recordings. Half-saturating light intensities under different tamoxifen treatment time points ranged between 7x10-4 and 6x10-2 cd*s/m2 and were used for the analysis of kinetic parameters (time to peak). For the behavior, we used 3x10-4 (OKR) and 5x10-3 (water maze) cd/m2, which are significantly above the visual threshold. The reviewer is correct in pointing out the caveats of comparing electrical responses to behavior under scotopic conditions, mainly because behavioral assessment requires steady light illumination whereas electrophysiological recordings use flashes on a dark background. However, we matched the conditions, to the extent possible, and used alternative behavioral paradigms to evaluate scotopic vision. The light intensities used for the electrophysiological characterization and behavioral evaluation were in the same range corresponding to pure scotopic conditions. Using a mouse model of abrogated rod function (Gnat1-KO) we have previously determined that under those conditions cone-driven responses are negligible and do not impact OKR behavior . In our analysis, we have used the smoothing approach for normalizing the data. However, we do show raw data before filtering. In the revised manuscript we are providing a more detailed description of this approach and show the results on the manipulation in Also, it had previously escaped my notice that, while the authors make a big point of the slowing of the ERG b-wave time course in, they do not comment on the corresponding results for single-cell recordings; inspection ofsuggests that the phenomenon may be very different in the rod ON-BCs themselves.In fact, we do see similar slowing in the onset kinetics of the currents recorded from the rod ON-BC by single cell as we do by ERG. Referencing trace peaks in Reviewer #3:My major concern is the quantification of mGluR6:Rgs11:Rgs7. The authors used whole retinas for their quantitative Western blottings; however, mGluR6 and Rgs11 are present only in ON bipolar cells while Rgs7 is present also in the IPL. I saw no attempt to correct for that. If Rgs7 is distributed equally in OPL and IPL , then the amount of Rgs7 will be reduced to about 10 fM and Rgs7+Rgs11 will equal mGluR6 amount. This quantification is important for understanding the composition of the mGluR6-related macromolecule and the mechanism for the light response. As analyzed here, it appears that RGSs are at excess, but if the IPL is factored in, then each mGluR6 molecule may have only one RGS molecule to modify its cascade. In fact, if the mGluR6 receptor functions as a dimer, then each receptor will have two RGS molecules, a more realistic outcome. This concern however, does not affect the conclusions of the paper regarding the difference between rods and cones (it may affect the Discussion).Rgs7 in the retina is specifically present in appreciable quantities only in the OPL. It is true that immunostaining picks up the signal in the IPL, however we found that signal to be completely non-specific as it was also present in DKO retinas lacking Rgs7. We now present this data in Rgs7 by western blotting as well. Therefore, we think that our estimate of absolute Rgs7 content in the retinas is accurate and reflect primarily the amount present in the ON-BC synapses.Our investigations indicate that We thank the reviewer for reminding us that mGluR6 exists as a constitutive dimer and functions as a single unit. This consideration, indeed calls for an upward adjustment of our RGS:mGluR6 ratio from ∼ 2:1 to ∼ 3:1. We discuss this calculation and its implications in the revised version of the manuscript.Another concern is the quantification of immunostaining in rod bipolar vs ON cone bipolar cells. The authors do not mention how they determined the puncta identity. A common way is to counter-stain with PNA, but this is not demonstrated or mentioned in Methods. I therefore have to conclude that the authors simply used the pattern for this distinction. While the pattern gives some indication about the identity of the puncta, it is far from being adequate for quantification.We identify cone synapses by counter-staining with b-arrestin which specifically labels cone terminals. In addition, as reviewer mentioned cone synapses are located in a distinct sublamina of OPL and can be easily identified by a characteristic clustering pattern. We use both hallmarks to distinguish between rod and cone synapses. We clarified this point in the revised manuscript and provided a supplementary figure  to illustrate how we distinguish cone synapses.. It seems to me that the effect of reduced RGS is much greater on Rmaxand I0.5 than on time to peak. The authors should add a graph showing this parameter vs. days after tamoxifen (as they did for scotopic conditions). In particular, we learn later that in photopic conditions, the behavior was mostly affected by amplitude, so it will be nice to see these graphs next to each other.max, I0.5 and time-to-peak depend on RGS concentration (days after tamoxifen) side by side.We provide the analysis requested by the reviewer in Reviewer #4:Comparison of scotopic and photopic behavior:One of the central conclusions of the paper is that scotopic and photopic behavior have different sensitivities to properties of bipolar signaling . This is based on, which nicely plot physiological signaling properties and behavior against RGS expression levels. These plots show that scotopic behavior correlates with bipolar sensitivity, while photopic behavior correlates with bipolar kinetics and amplitude. My concern about this analysis is that the behavioral tasks differ. Scotopic behavior was measured using the OKR at speeds corresponding to peak sensitivity. Photopic behavior was measured at higher speeds, since only at those speeds did ON bipolar signaling appear to influence the OKR under photopic conditions. To be convincing, the authors need to show that the particular bipolar feature that behavior correlates with does not depend on task parameters.Rgs7/11 DKO mice did not exhibit any deficits in their photopic OKR behavior at lower stimuli speeds .Rgs7 and Rgs11 even early in development does not affect synaptic morphology and the ability of photoreceptors to make synaptic contacts with ON-BC  required nearly 1.5 years to collect, and this is for one time point). However, we do evaluate the average cone-driven responses of cone ON-BC by ERGs and compare their properties to rod-driven ON-BC responses, with little difference in respect to how parameters of interest in these two neuronal populations respond to change in RGS concentration.Writing:The writing in many places is sufficiently confusing so as to alter or obscure the conclusions of a sentence. The entire paper needs to be gone through carefully with this issue in mind.We appreciate the reviewer’s attention to writing issues. We revised the manuscript editing it for grammar and usage, hopefully making the result more readable.


A key challenge in analyzing high throughput Single Nucleotide Polymorphism (SNP) arrays is the accurate inference of genotypes for SNPs with low minor allele frequencies. A number of calling algorithms have been developed to infer genotypes for common SNPs, but they are limited in their performance in calling rare SNPs. The existing algorithms can be broadly classified into three categories, including: population-based methods, SNP-based methods, and a hybrid of the two approaches. Despite the relatively better performance of the hybrid approach, it is still challenging to analyze rare SNPs.3-S, for rare SNP calling. This new approach can improve genotyping accuracy through clearly defining the boundaries of genotype clusters from samples with known genotypes, and enlarge the call rate by combining the simulated data based on the inferred genotype clusters information with the study population.We propose to utilize information from samples with known genotypes to develop a two stage genotyping procedure, namely M3-S outperforms existing methods in calling rare SNPs.Applications to real data demonstrates that this new approach MThe online version of this article (doi:10.1186/s12859-015-0824-5) contains supplementary material, which is available to authorized users. Genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants contributing to hundreds of human diseases in the past decade , 2. TheiA and B. The goal of genotype calling is to infer the genotype  carried by an individual across the SNPs in the genome. All the genotype calling algorithms share the common feature of first defining genotype clusters and then assigning individuals to these clusters. They differ in how the clusters are defined and how the samples are allocated to these clusters. One class of genotyping algorithms is population-based where all individuals are genotyped SNP-by-SNP. The genotype clusters are defined through the joint analysis of all samples at a given SNP separately. Although commonly used, its performance highly depends on the size of the study population. It may not perform well for SNPs with low minor allele frequencies (MAF) where there may be very few individuals for certain clusters. For example, GenCall [In this article, we focus on the analysis of Illumina arrays, which use two color single base extension (SBE) biochemistry technology  to infer GenCall , a repre GenCall . In cont GenCall . The goo3 that combines the population-based strategy with the SNP-based approach to improve calling accuracy for rare SNPs [3 borrows genotype cluster information from reference SNPs to improve the calling performance for rare SNPs. It also improves upon GenoSNP by utilizing the population-based calling scheme. However, the effectiveness of M3 depends on the quality of the reference SNP. If the reference SNP behaves very different from the target rare SNP, the inferred genotype will likely be incorrect. In this article, we consider using additional information to further improve the quality of the reference SNP. In particular, we consider the use of samples with known genotypes, e.g. HapMap samples, that are often included for quality control purposes. Because the HapMap samples have been extensively studied and their true genotypes can be considered known with almost certainty, the genotype calling results from these samples can provide a good metric for the performance of calling algorithms. Hence, we incorporate known genotype information from these quality control samples into the reference SNP selection procedure under the general framework for M3, and this new method is named M3-S where S denotes samples with known genotypes for calling. More specifically, M3-S utilizes known genotype information to construct three genotype clusters in the first stage, and the entire samples together with simulated data based on the inferred cluster information are then genotyped in the second stage.To address the limitation of these two approaches, we developed Mare SNPs . Compare3 to the samples with known genotypes. It can directly define boundaries of genotype clusters before genotyping other study subjects. In addition, our proposed method is computationally efficient and applicable to the large-scale intensity data , and then explain how this new method helps calling for rare SNPs. Finally we compare the performance of our method with existing methods to demonstrate its better performance.The rest of the paper is organized as follows. We first describe the two stages of our new method  denote the measured intensity values of the sth SNP for the ith individual where  with S being the total number of SNPs and n being the total number of subjects. For every SNP, we use ahs= to denote the raw intensity values of the hth subject with known genotype for the sth SNP, where h = 1, …, na and na is the total number of samples with known genotypes. In the study that motivated our work, HapMap samples were included and we obtained their “true” genotypes from the International HapMap Project [na is less than n, and each element in M = {1,…,na} can be matched to one unique element in a set N = {1,…,n}. To distinguish SNPs with study population and SNPs only containing individuals with known genotypes, the latter is named as featured SNPs. We propose the following five-step procedure to estimate the parameters of the genotype clusters.la samples, and the other is the testing set. Generally, the training samples are used to infer the parameters of genotype clusters, and the testing individuals are used to evaluate the performance of the method. In this study, we define different allocation ratios between the training samples and testing samples (ratio 1:1 or 2:1) to evaluate the impact of the allocation ratio on genotyping. For the data set that motivated this research, there are 141 samples with known genotypes. Under the allocation ratio 1:1, we consider 71 training samples, i.e. la=71, and 70 testing samples. For the allocation ratio 2:1, 94 subjects are assigned to the training group, i.e. la=94, and 47 individuals are in the testing set.(1) Step I: randomly assign samples with known genotypes into two groups. One will be called the training set with 1 collects those featured SNPs having three distinct genotypes, whereas G2 collects those featured SNPs with fewer than three distinct genotypes.(2) Step II: evaluate the quality of each featured SNP via analyzing the training samples. When the training samples are randomly selected, the number of distinct genotypes and the sample size of each genotype can be inferred from known genotypes, then all the featured SNPs with training subjects  Step III: select reference featured SNPs for target featured SNP in sth featured SNP is the target featured SNP, the two dimensional raw intensity vector bhs [sth featured SNP and all Ref featured SNPs are classified into three genotype clusters in terms of this univariate variable.(4) Step IV: calculate the Mahalanobis distance between the target featured SNP with training samples and each Ref featured SNP containing the same training samples. Here, the Mahalanobis distance measures the similarity between the target featured SNP and each Ref featured SNP, where the optimal Ref featured SNP is selected based on minimizing this distance. To simplify the calculation, when the ble bhs , and theds) is obtained by the following equation,SR is the set of Ref featured SNPs selected for the sth featured SNP; bhs and bhr are the projected intensities of the hth training sample for the sth featured SNP and the rth Ref featured SNP separately; and sh is the standard deviation of bhs and bhr. So, the best Ref featured SNP selected through ds may be most informative for the clustering of the sth featured SNP.The minimum Mahalanobis distance (sth featured SNP. If sth featured SNP and the best Ref featured SNP (such as: the rth Ref featured SNP). In the second case, the training samples in the sth featured SNP are not adequate to construct the three genotype clusters, and an appropriate Ref featured SNP could help the sth featured SNP estimate three cluster information. A new combined matrix  Step V: estimate parameters of three genotype clusters from the training individuals. When lask and lark denote the number of training samples at the kth cluster for the sth featured SNP and rth Ref featured SNP, respectively; lask+lark) x 1 column vector with all elements equal to 1; lask x 1 column vector with all elements equal to 1; and kth genotype group for the sth featured SNP.where μask is an 1 × 2 vector measuring the average intensity of training samples in the kth cluster for the sth featured SNP; Σask is a 2 × 2 covariance matrix of the kth cluster at the sth featured SNP.Note that In summary, the first stage focuses on selecting reference featured SNPs to better estimate the parameters of the three genotype clusters.In the first stage, samples with known genotypes are first analyzed separately to infer three genotype clusters for each SNP denoted by AA, AB, and BB. We use B to denote the less common allele, and all possible genotypes are denoted as: AA, AB, or BB. Let  Project , 4. From(1) Step I: simulate intensity data according to the inferred parameters of the three genotype clusters from the training samples with known genotypes.m additional individuals at each SNP from the above Gaussian mixture distribution. Parameters μask and Σask  could adequately provide the center and variability of the three genotype clusters for each SNP, and each cluster contains equal number of simulated subjects  Step II: genotype calling using both observed and simulated data.xis=, and the pair of simulated raw intensities are yjs=, then the combined raw intensity values at the sth SNP ts, where the number of components is fixed at three. Besides, we introduce a null component for those individuals whose genotypes are difficult to be assigned to one of the three clusters. In principle, this model assigns the wth pair of the combined raw intensities tws to one component with probability πsk where k measures three components corresponding to the three genotypes. The latent genotype class is denoted by the indicator variable zws generated from a multinomial distribution where zws takes the value of 1, 2, or 3. Then the three-component GMM can be expressed as:The pair of original raw intensities are mponents , to ts,n∗ is the total number of individuals collected at the sth SNP and simulated data where n∗=n+m, and S is the total number of SNPs. The normal density Φ has mean μsk and variance-covariance matrix Σsk in the kth cluster for the sth augmented SNP data; all pairs of raw intensity at the sth augmented data are measured by ts=  where πs= , μs=, and Σs=.where u+1)th iteration of the indicator variable zws=k  is inferred byThrough solving the score equation, the maximum likelihood estimates (MLEs) of the above parameters can be easily estimated . The (u+μsk and variance-covariance matrix Σsk areThe relevant iterative estimates for the mean qs), for the sth augmented data are calculated to quantify the quality of SNP calling [Two values, Posterior Rate , GenoSNP (a SNP-based approach), and M3 (a hybrid of the previous two approaches). In this data set, 38 HapMap samples were measured multiple times, using a total of 141 arrays. We focused on SNPs from chromosome 22 with a total of 15,020 SNPs. The performance of each genotyping method was evaluated by the comparison results between SNP calls inferred by each calling method and those from the International HapMap Project for these HapMap samples [M3 and M3-S. The effects of different thresholds on genotyping are summarized in Additional file We analyzed Illumina Omni 1M array data collected from 3258 samples to compare the performances of MM3-S (99.38 %) has the best call accuracy and high call rate, followed by M3, GenoSNP and GenCall. We can also see that M3 gives the highest call rate (99.77 %), followed by M3-S, GenoSNP, and GenCall.Because there were 141 HapMap subjects, we used their “true” genotypes to evaluate the accuracy of different calling algorithms. We varied the numbers of training and testing samples  to explore their impacts on genotyping. The effectiveness of two allocation designs is summarized in Table 3-S to see the impact of simulated data on the genotyping, especially for rare variants. 600, 1500, or 3000 simulated samples are roughly 1, 1/2 or 1/5 times the original study population. In brief, the performance of our proposed calling algorithm based on three different simulation designs is evaluated through the comparison between the genotypes of testing HapMap samples inferred from M3-S and the genotypes of these subjects inferred from the HapMap project. Table 3-S to infer genotypes. Because GenoSNP, M3 and M3-S have been developed to focus on calling less common SNPs, Table 3-S has the best call accuracy (99.22 %~ 99.28 %) and high call rate (99.59 %~ 99.81%) for rare SNPs with MAF < 0.1, 0.05, and 0.01, followed by M3, GenoSNP and GenCall.We simulated different sizes of samples  in the second stage of M3-S) depends on the accurate estimations of the three genotype clusters from the subjects with known genotypes in the first stage, and adequately simulated subjects from the Gaussian mixture distribution in the second stage. For parameter estimations of the three genotype clusters, the reference SNP selection method [The successful application of this proposed calling procedure   to infer genotypes at rare SNPs, but M3-S uses one SNP’s individuals plus 3000 simulated subjects (3258+3000) to infer genotypes at these rare SNPs. The change in sample size of two methods results in the difference of call rate between these two approaches. Moreover, Additional file 3-S, GenCall and GenoSNP.Here, we extend our analysis to the entire study population, not just testing HapMap samples. For all study subjects, the call rate and the concordance rate of the four calling algorithms are compared, where the call rate is the ratio of genotypes that can be inferred from each calling method to those that need to be genotyped, and the concordance rate is the genotype agreement between two algorithms. The overall comparison results are summarized in Table %) Table . Besides3 and M3-S fall in between in terms of the number of SNPs not meeting HWE. Specifically, we find that M3-S may make more SNPs fail the HWE test when this approach enlarges the number of samples for simulated data. It seems that improving call accuracy for rare SNPs is in conflict with guaranteeing the quality of SNPs via HWE test. People have to select the appropriate samples size for simulated data to balance the above two criteria.Hardy-Weinberg Equilibrium (HWE) test is an important criterion to examine genotyping quality as failing HWE test may indicate calling errors. We performed HWE tests for the four population groups: Hispanic African-American, non-Hispanic African-American, Hispanic European-American, and non-Hispanic European-American, separately. Table 3-S was motivated from a data set with HapMap samples genotyped as part of the study, but many studies do not have these valuable samples collected. In this case, we may use pairs of raw intensity of HapMap subjects provided by the International HapMap Project [M Project , but raw Project –21, we a3-S applies the reference SNP selection step to samples with known genotypes for improving the missing rate and call accuracy for rare SNPs, especially for SNPs with very low MAF. The successful application of M3-S is to select the appropriate Ref featured SNP from the whole genome. In practice, it is not practical to search the Ref featured SNPs from the entire genome due to plenty of tedious calculation involved, then the instrumental featured SNPs near the target featured SNP are picked out. The assumption about identical probe response of all SNPs allows each SNP to borrow information from other good quality SNPs. When some probes break this assumption, searching for the most optimal Ref featured SNP is still an open question. Besides, the reference SNP selection is based on maximizing the mathematical similarity between the target featured SNP and the Ref featured SNP. Because the probe intensity is highly correlated with 50 base probe sequence, incorporating the probe sequence information in the reference SNP selection procedure may greatly improve the quality of SNP calls.M3-S is based on the collection of samples with known genotypes. However, some SNP-array data may not contain individuals with known genotypes, which results in the restricted use in M3-S. Fortunately, M3-S is a supplement to our previous method M3 [3 method if their SNP array data do not contain any HapMap samples. If the data have samples with known genotypes, they could apply M3-S. Scientists could try these methods according to their requirements. Recently, a large amount of rare variants are widely captured in many SNP array data, some new powerful calling algorithms have been proposed for accurately calling rare SNPs, such as: optiCall [The prerequisite for running Method M3 . We strooptiCall  and zCaloptiCall . To bett3 to combine the benefits of both population-based and SNP-based strategies. Although M3 outperformed other methods, it is not able to use samples with known genotype information when they are available in a study, often as quality control samples. In this study, we propose M3-S to take advantage of genotype cluster information from the samples with known genotypes to further improve M3 on genotyping at rare SNPs. M3-S is a two-stage procedure where the reference SNP selection method is applied to samples with known genotypes at rare SNPs to estimate the parameters of the three genotype clusters, followed by simulating additional samples from the Gaussian mixture distribution, and fitting GMM to the augmented data for genotype calling. The superiority of this method rests on two aspects. First, samples with known genotypes help define the boundaries of three genotype clusters before finally genotyping. Second, adding simulated data in the original population greatly enlarges the sample size while genotyping. These two aspects help us improve genotyping technique for rare SNPs.Most genotyping approaches for microarray data are population-based methods, e.g. GenCall with GenTrain, that infer genotype clusters from a large number of samples to achieve a certain call accuracy. Although it may work well for common SNPs, it may perform poorly for rare SNPs where genotype clusters cannot be reliably established unless a very large number of samples are available. A SNP-based method, GenoSNP, was designed to address this problem, but many more SNPs inferred from GenoSNP tend to fail the HWE test which indicates the violation of the strong underlying assumption for GenoSNP to succeed. Recently, we proposed M


The aim of the study was to estimate risk factors for hospitalization due to sepsis and to determine whether these risk factors vary by age and gender.We performed a population-based case-control study of all adult patients admitted to a medical ED from September 2010 to August 2011. Controls were sampled within the hospital catchment-area. All potential cases were manually validated using a structured protocol. Vital signs and laboratory values measured at arrival were registered to define systemic inflammatory response syndrome and organ dysfunction. Multivariable logistic regression was used to elucidate which predefined risk factors were associated with an increased or decreased risk hospitalization due to sepsis.A total of 1713 patients were admitted with sepsis of any severity. The median age was 72 years (interquartile range: 57–81 years) and 793 (46.3%) were male. 621 (36.3%) patients were admitted with sepsis, 1071 (62.5%) with severe sepsis and 21 (1.2%) with septic shock. Episodes with sepsis of any severity were associated with older age (85+ years adjusted OR 6.02 [95%CI: 5.09–7.12]), immunosuppression (4.41 [3.83–5.09]), alcoholism-related conditions (2.90 [2.41–3.50]), and certain comorbidities: psychotic disorder (1.90 [1.58–2.27]), neurological (1.98 [1.73–2.26]), respiratory (3.58 [3.16–4.06]), cardiovascular (1.62 [1.41–1.85]), diabetes (1.82 [1.57–2.12]), cancer (1.44 [1.22–1.68]), gastrointestinal (1.71 [1.44–2.05]) and renal (1.46 [1.13–1.89]). The strength of the observed associations for comorbid factors was strongest among younger individuals.Hospitalization due to sepsis of any severity was associated with several independent risk factors, including age and comorbid factors. Severe sepsis is a life-threatening condition defined by a systemic inflammatory response (SIRS) caused by infection, hypoperfusion and organ dysfunction. The synWe conducted a population-based case control study of all patients hospitalized to a medical ED with sepsis from 1 September 2010–31 August 2011. We used admissions to a large primary hospital with tertiary functions to identify cases and national health registries to identify population-based controls. As virtually all medical care in Denmark is furnished by the national health authorities, these data resources allow true population-based studies covering all inhabitants in the selected region of Denmark.In compliance with Danish law, the study was notified to and approved by the Danish Data Protection Agency (J No 2008-58-0035), and the access to patient clinical records was approved by the Danish National Board of Health (J No 3-3013-35). No further ethical approval, or consent from participants, is needed for register-based studies in Denmark. Data were anonymised and de-identified prior data analysis.The study was conducted as a population-based case-control study where both cases and controls were sampled from Odense University Hospitals’ catchment-area. Cases, defined as patients hospitalized with sepsis of any severity, were compared to controls, to elucidate which factors were associated with being a case, i.e. were associated with an increased or decreased risk of hospitalization due to sepsis.Odense University Hospital is a tertiary academic hospital with all specialities represented. Although the hospital provides high level care for persons living in the region of South Denmark (pop: 1.2 million) it also serves as the primary care hospital for all residents in the hospital catchment-area of 235,000 adults.The medical ED served as a medical admission unit for the following medical specialities: general internal medicine, infectious diseases; gastroenterology; geriatric medicine; rheumatology; endocrinology and respiratory medicine. The medical ED received all acutely hospitalized medical patients from either primary care or the open general ED. The open general ED serves as a level-1 trauma centre with mixed medical-surgical patients.Baseline characteristics of the case material are described in detail elsewhere. BrieflyTime of arrival to the hospital, clinical information, and vital signs at the time of admission were electronically extracted from the patient’s records and validated by trained data abstractors with the aim to identify cases.In order to identify patients with community-acquired infection, we manually reviewed all admissions to the medical ED or directly to the medical ICU within the study period (N = 8358). We used a structured protocol, based on The National Healthcare Safety Network criteria in combiIn order to exclude possible hospital-acquired infections we excluded cases with a prior hospitalization up to 7 days before the current admission. Patients transferred from other hospitals, patients residing outside the hospitals catchment-area at the time of admission and patients who were unidentified throughout the entire course of admission were excluded as well.As controls, we sampled all adults (≥15 years) with residence in the hospital catchment-area  in the study period. Each subject was assigned a random index date during the study period, and if the subject was a resident that day, had not been hospitalized up to 7 days before the index date, and had not previously been included as case, then the subject was included as control.We examined the following potential risk factors for hospitalization with sepsis: age; gender; a history of alcoholism-related conditions; immunosuppression and chronic medical conditions including a history of psychotic disorder as described in the following.By using the unique Danish personal identification number assigned to all Danish residents, we werePatients were categorized as having sepsis, severe sepsis or septic shock at arrival to the hospital according to the ACCP/SCCM published criteria. Severe Comorbidity is reported based on discharge diagnoses the last 10 years up to 7 days prior to the current admission or index date for cases and controls. To avoid the possibility of a strong correlation in the covariates cancer and immunosuppression, we defined cancer in the “cancer comorbidity” category as a stable state (from 10–1 years prior the index date), whereas active cancer (new cancer diagnosis from 365–7 days prior the index date) was included in the immunosuppression covariate. We defined alcohol-induced liver disease (ICD-10: K70*) as alcoholism-related conditions and excluded these ICD-10 codes from the “Gastrointestinal” comorbidity definition. We defined a history of psychotic disorder by one or more redeemed prescriptions of antipsychotic medication (ATC: N05A*) from 2007 and up to the current admission for cases, or index date for controls .Age was categorized in three categories . We tested if age could be included as a continuous variable in the model using fractional polynomials, but age violated the assumption of linearity. Also, dIn the main analysis, the outcome of interest was sepsis of any severity and exposure of interest was the predefined potential risk factors. For all risk factors, three analyses were performed: 1) A crude, univariate analysis looking at the single risk factor, 2) A multivariate analysis for the single risk factor adjusted for gender and age, 3) A multivariable analysis adjusted for all of the remaining potential risk factors (fully adjusted model).In order to identify potential effect measure modification we repeated the analyses stratified by gender and age.Similarly, to assess whether the effect of the single risk factors varied when examining patients with sepsis or severe sepsis, we repeated the fully adjusted model in these two groups separately.Data were presented as medians with interquartile range (IQR) or proportions with 95% confidence intervals (95%CI) calculated under the assumption of a binomial distribution. Missing data was treated as within normal range if data were continuous and defined as within the reference category if data were categorized.All calculations were performed using Stata Release 13.0 .A total of 8358 admissions to the medical ED or directly to the ICU within the study period, 3605 admissions (43.1%) where the patient had signs of an infection and 2490 (29.8%) met the criteria for sepsis of any severity. In total we excluded 730 due to: re-admissions with sepsis (N = 377), living outside catchment-area (N = 209) and discharged <7 days prior inclusion (N = 144). We ended up with 1713 patients with a first-time admission with sepsis of any severity within the study period.The median age of the included patients was 72 years (IQR: 57–81 years) and 793 (46.3%) were male. 621 (36.3%) presented with sepsis, 1071 (62.5%) presented with severe sepsis and 21 (1.2%) presented with septic shock .All of the potential risk factors were associated with a significantly increased risk of hospitalization with sepsis of any severity in the univariate risk factor estimates . In the Differences in the strength of association between patients hospitalized with sepsis and severe sepsis include age and psychotic disorder, where patients hospitalized with severe sepsis had a higher odds of being older and with a history of psychotic disorder. The lower 95% confidence intervals of the adjusted OR for cancer and renal comorbidity, for patients with sepsis were just below 1  compared to severe sepsis . See We found no significant difference in risk factors when stratifying by gender, except the distribution of age in the age categories, where the risk of hospitalization of older males was significantly higher compared to females in the same age category (85+ years) .Females in the young age category were more likely to be hospitalized with sepsis of any severity compared to males in the same age category, whereas males were more likely to hospitalized in the 85+ age category. The association between immunosuppression as well as the sum of comorbidities with hospitalization with sepsis of any severity were less pronounced with increased age .In this large population-based cohort study we found several independent risk factors associated to hospitalization at a medical ED with community-acquired sepsis of any severity. We found a large difference in the contribution of the individual risk factors between young and old patients.We found that the risk factor most strongly associated to hospitalization with sepsis of any severity was old age. Increasing age has previously been associated with hospitalization with severe sepsis, both in studies focusing on age stratified incidence rates,26–29 asThe comorbid condition exhibiting the strongest association with an incident hospitalization with sepsis of any severity was chronic respiratory disease with an adjusted OR 3.56. This is in accordance with a study by Wang et al (OR 1.5),31.To account for potential clinically meaningful predefined effect modifiers, we stratified by age and gender and applied to a multivariate model. The association of the different risk factors was different when comparing the young and old patients. Like a previous study we found that female gender was a risk factor in the youngest age categories but was protective among the oldest. This coThe strength of immunosuppression and most of the evaluated comorbidities as risk factors for hospitalization with sepsis of any severity showed a distinct variation by age with the highest OR in youngest age category and lowest among the oldest. This could either be due to survivor bias , where patients who had lived to old age have lived with their comorbid condition for so long, that the effect as a risk factor for admission with sepsis decreases. Another possible explanation is, that age is such a strong risk factor for hospitalization with sepsis, that other risk factors are less important.An interesting finding was, that the risk factors’ strength of association were in large parts similar among patients hospitalized due to sepsis and severe sepsis. Only old age, and psychotic disorder were stronger risk factors of hospitalization due to severe sepsis compared to sepsis. This similarity between the severity of sepsis could be due to the structure of the Danish healthcare system in regard to acute care. All patients included in the study were seen by either an ED physician or a general practitioner, who decided that the patient was in need of an admission to the medical ED. If we included all contacts from the open general ED (including those who were sent home without being admitted), perhaps this would have lead to more differentiated risk factors strength of association in the different severity groups, but this is beyond the scope of the study.Information regarding the risk factors and mortality is important for several reasons. It is important to identify the characteristics of individuals at increased risk of developing a given illness, when devising disease risk stratification or prevention strategies. Public health benefits have, over the last century, resulted from evidence-based risk stratification for conditions like cardiovascular disease, and it is a hope that it is possible for sepsis as well,34.For the physician working at an ED, knowledge of risk factors could potentially aid in earlier identification of patients with sepsis compared to patients hospitalized to the medical ED due to conditions other than sepsis, but this would require another selection of controls than we chose, as it was not the aim of the study.Other risk factors have previously been evaluated, including marital status, where hospitalization with sepsis are more common among single, widowed, and legally separated individuals. FurtherDue to the uniformly organized Danish public healthcare system we could identify all patients included in the study in the different population-based registers for a full medical record. We used manual chart review with a structured protocol to collect data regarding the presence of infections at the time of admission, and based our study on symptoms and clinical findings at the time of arrival to the hospital.The current work was a single-centre study from a medical ED at a university hospital, which potentially could affect the generalizability of the study. However, the hospital is the only hospital in the area and is a primary hospital for all residents in the hospital catchment-area, in parallel to highly specialized tertiary functions. Therefore we do not expect that residents of the catchment-area to be admitted to other hospitals.The study was conducted at a medical ED describing acutely hospitalized medical patients. As such, the risk factors’ strength of association might be different among patients hospitalized to an abdominal surgery department, or patients in active chemotherapy. This could also lead to an underreporting of the number of patients hospitalized due to sepsis, and underestimate the risk factors strength of association among the chronic medical conditions associated with admission to departments not included in the current study.Subsequent validation of the cohort identified by the review, yielded a kappa value of 0.67. This shows, that even when a well-defined classification of infection is applied, it is difficult to obtain a high inter-rater agreement.Another limitation is that hospitalisation is a complex outcome with multiple components. There is the emergence of the medical condition itself, sepsis, but also extraneous factors such as the patient’s decision to seek medical attention, the patient’s follow-though in acquiring it and the physician’s decision to hospitalise. Depending on the circumstances, some of the extraneous factors may play an important role in the outcome and thus make the interpretation of our results challenging.The seven day exclusion period is shorter compared to other definitions of hospital acquired infections. However, we did not find any significant differences in the risk factors strength of association when using a 30 day exclusion period (data not shown).There is always the possibility of residual confounding, which could lead to different estimates when interpreting the independent risk factors in the multivariate models. Potential confounders could include socioeconomic status, and atrial fibrillation,36.We found that age, comorbidity including a history of psychotic disorder, immunosuppression and a history of alcoholism-related conditions served as independent risk factors for hospitalization with sepsis of any severity. We found a large difference in the risk factors’ strength of association in the different age categories, where comorbid conditions displayed stronger associations among young patients.S1 Appendix(DOCX)Click here for additional data file.