Many cortical network models use recurrent coupling strong enough to require inhibition for stabilization. Yet it has been experimentally unclear whether inhibition-stabilized network (ISN) models describe cortical function well across areas and states. Here, we test several ISN predictions, including the counterintuitive  suppression of inhibitory firing in response to optogenetic inhibitory stimulation. We find clear evidence for ISN operation in mouse visual, somatosensory, and motor cortex. Simple two-population ISN models describe the data well and let us quantify coupling strength. Although some models predict a non-ISN to ISN transition with increasingly strong sensory stimuli, we find ISN effects without sensory stimulation and even during light anesthesia. Additionally, average paradoxical effects result only with transgenic, not viral, opsin expression in parvalbumin (PV)-positive neurons; theory and expression data show this is consistent with ISN operation. Taken together, these results show strong coupling and inhibition stabilization are common features of the cortex. Extensive recurrent connectivity between nearby neurons is a ubiquitous feature of the cerebral cortex , and theStrong excitatory recurrent coupling, however, can lead to unstable dynamics unless stabilized by inhibition. When recurrent connectivity is weak, excitatory cells can show stable firing rates independent of the activity of inhibitory cells. But in networks with strong recurrent connections, excitatory-to-excitatory (E-E) connections amplify responses so that the excitatory network is unstable if the firing rates of inhibitory neurons are kept fixed. Stable excitatory network operation across a range of firing rates can be restored if inhibitory recurrent connections are sufficiently strong, allowing inhibition to track and balance excitation . Such neWhether cortical networks function in the ISN regime, in which conditions they do so, and which cortical areas may operate as ISNs remain the subject of debate. One key open question has been whether cortical networks function as ISNs only during high levels of network activity , or also operate as ISNs for weak input or even during spontaneous activity states. Merely because a network is stabilized by inhibition during one network state does not imply it is inhibition-stabilized for all network states . For exaOne area, cat primary visual cortex (V1), shows behavior clearly consistent with the ISN regime . This waparadoxical inhibitory suppression — a counterintuitive decrease of inhibitory firing rate as inhibitory cells receive direct input  , severalHere, we examine whether cortical networks operate as ISNs at rest, by combining optogenetic stimulation of inhibitory cells, extracellular recordings in awake animals from several cortical areas, and theoretical analyses. We find clear evidence for ISN operation even without sensory stimulation in multiple mouse cortical areas and both in superficial and deep layers. By providing multiple experimental and theoretical lines of evidence for ISN operation, our results argue against non-ISN explanations .We also address whether stimulating a single genetic subclass of inhibitory neurons in V1, the parvalbumin-positive (PV) cells, can produce paradoxical inhibitory effects. We find differences in paradoxical responses between viral and transgenic expression strategies. These differences are explained by an ISN model where the fraction of stimulated PV cells is varied , and areTogether, these results support the idea that the cortex operates in the ISN regime across a range of areas and brain states.We first describe experiments where all inhibitory neurons were optogenetically stimulated. We expressed an excitatory opsin  in all inhibitory neurons using a transgenic mouse line (VGAT-ChR2). We delivered blue light to the surface of the cortex, while recording activity extracellularly with multi-site silicon recording arrays . Mice weThe majority of recorded units were suppressed by light . When recurrent synapses are substantially suppressed, inhibitory cells expressing ChR2 should increase their firing rate to optogenetic input, while excitatory cells’ firing should not increase. Indeed, by measuring responses to inhibitory stimulation in the presence of E and I blockers, we were able to classify units into two groups . Units i results , examini results , and, fi results . TherefoPharmacologically identified inhibitory and excitatory units showed differences in waveform width and spontaneous firing rate , althougOnce recorded units were classified as excitatory or inhibitory, we could ask whether the same units before adding blockers  showed paradoxical inhibitory effects. Indeed, inhibitory neurons showed strong suppression when stimulated . The meaThese experiments were done without sensory stimulation, as animals viewed an unchanging neutral gray screen. Because visual cortical neurons’ firing rates increase with increasing contrast across a range of overall luminance levels, we expected that we would see little difference between data collected with a neutral gray screen and data collected in the dark. Confirming that expectation, in one experimental session we measured inhibitory responses to stimulation while animals were in the dark , and found paradoxical suppression .In an ISN, paradoxical inhibitory suppression arises because exciting inhibitory cells in turn suppresses excitatory cells, which withdraw excitation from the stimulated inhibitory cells. Then, because more excitatory input from recurrent sources is withdrawn from inhibitory cells than excitatory input from optogenetic stimulation is added, the net steady-state response of inhibitory cells is suppression. For this mechanism to hold, inhibitory cells must transiently increase their firing, even if only a small number of inhibitory spikes are generated, before the paradoxical suppression . TherefoAnother ISN prediction is that inhibitory cells should increase their firing rates at high laser intensities. In a recurrent network operating as an ISN, stimulating inhibitory cells first decreases inhibitory firing rates, but stronger stimulation eventually suppresses excitatory cells until they reach a firing rate where the excitatory network is stable without reactive inhibition. Then, increasing stimulation of inhibitory cells beyond this point produces increases in inhibitory firing rates, as the network moves into a non-ISN regime . Indeed,The link between paradoxical responses and inhibition stabilization was initially derived  assumingWhile our extracellular recording approach has advantages, such as allowing precise measurements of spike rate dynamics, and recording a large number of E and I neurons in the same experiment, one concern might be that misclassification of excitatory waveforms into inhibitory units could mask inhibitory cell responses. However, confirming that our recording procedures accurately measured inhibitory waveforms, (1) paradoxical suppression is maintained when we make our classification thresholds more stringent   inferredtability . The facThe data above show that the superficial layers of mouse V1 are inhibition stabilized. Another open question is how general the ISN regime is across cortical areas. We performed experiments to look for signatures of inhibitory stabilization in two other cortical areas : somatosIn these experiments, we classified neurons as inhibitory based on their activity at high laser intensity. In our V1 experiments, paradoxical effects are clear whether neurons are classified in this way, or with the pharmacological method . This hiIn both the recorded areas , inhibitUp to this point, we have studied paradoxical suppression by stimulating an opsin expressed in all inhibitory cells via the VGAT-ChR2 mouse line. A remaining question is whether stimulation of a subclass of inhibitory neurons also yields paradoxical suppression. Even in an ISN, stimulation of any single subclass of inhibitory cells need not produce paradoxical suppression  neurons, which provide strong inhibitory input to other cells . PV baskWe first used viral methods to express an opsin in PV neurons, injecting a Cre-dependent adeno-associated virus (AAV) encoding an excitatory opsin Chronos; , into a Stimulating the PV cells produced no significant paradoxical effect on average , blue. FOne reason why stimulation with viral expression might produce no paradoxical effect on average is that viral expression could target only a subset of the PV cells . We firWhy, intuitively, can stimulation of a subset of inhibitory cells eliminate paradoxical effects? In networks of strongly-coupled excitatory and inhibitory neurons, increased inhibitory activity produced by stimulation suppresses excitatory activity and results in a withdrawal of recurrent excitation; this, in turn, drives suppression of stimulated cells . When only a fraction of inhibitory cells is stimulated, the withdrawal of excitation coexists with other effects produced by recurrent interactions. In particular, increased activity of stimulated inhibitory cells tends to suppress non-stimulated inhibitory cells and to produce a withdrawal of inhibition to excitatory cells and to stimulated inhibitory cells. This withdrawal of inhibition increases with the fraction of non-stimulated inhibitory cells and, if large enough, can overcome the withdrawal of excitation and prevent paradoxical suppression of stimulated cells.To test these models, and experimentally determine whether differences in number of cells expressing opsin could change the paradoxical effect, we used a transgenic approach to express a different excitatory opsin in PV cells . In these mice, the opsin is expressed in most or all PV cells . Since . Becausep<0.05, χ2 test). The differences in the number of opsin-expressing cells roughly matched our model predictions  of the cortex, recorded within 400 µm of the cortical surface. We focused on the superficial layers because the blue light we used to activate ChR2 and Chronos does not penetrate more than a few hundred microns into the tissue , so the increases in inhibitory response at strong light intensities, when excitatory cells are silent or largely suppressed. But in deep recordings, presumably due to attenuation of stimulation light from scattering and absorption, at our highest stimulation intensities we did not always see clear mean increases in inhibitory firing rates  show paradoxical suppression when all inhibitory cells are stimulated. We used several ISN rate models to fit data from V1, aided by pharmacological manipulations that yield additional informative data about the network. We find that despite having fewer parameters than data degrees of freedom, the ISN model describes the data well. During light anesthesia, mean V1 network firing rates decrease, but we still observed ISN paradoxical suppression. Finally, we explored the effect of stimulating a subclass of inhibitory neurons with strong network effects: the PV neurons. We find that stimulating PV cells also produces paradoxical effects. We compared viral and transgenic expression strategies and found that viral expression did not produce average paradoxical effects. This viral/transgenic difference is consistent with theoretical observations that changing the fraction of stimulated inhibitory cells changes the strength of the paradoxical effect, and is supported by histological data.Our data and theoretical results provide the strongest evidence currently available to rule out non-ISN interpretations. Three prominent features of our data supporting a strongly-coupled recurrent origin for the results are: (1) that inhibitory response dynamics match ISN predictions , (2) thaThese observations allow us to rule out several potential alternatives to the ISN explanation for the paradoxical effects we observed. One potential alternative might be a model with multiple inhibitory populations and weak recurrent coupling, perhaps driven to fire spontaneously by strong external input such as thalamic or corticocortical input. In principle, paradoxical inhibitory responses could be produced in such a non-ISN model via feedforward, inhibitory-onto-inhibitory, interactions. Specifically, in this alternative, stimulation of one inhibitory subclass (e.g. PV cells) would inhibit a second set of inhibitory cells whose firing rate would decrease . However, in a feedforward inhibitory model, there would be little reason to predict inhibitory neurons to change from decreasing to increasing rate as stimulation intensity increases. ISN models, on the other hand, are predicted to transition into a non-ISN state for strong inhibitory stimulation where inhibitory cells begin to increase their firing, as our data show e.g. .The large fraction of inhibitory cells that we found to be suppressed  at low stimulation intensity  further Other non-ISN scenarios would also rely on feedforward inhibitory effects made unlikely by our data. For example, alternative models could have feedforward inhibition arising from rare, powerful inhibitory cells, perhaps so rare that we could not record them, or input from deeper-layer inhibitory cells. These feedforward inhibition scenarios are also argued against by the observations outlined above . And our recordings across depth showed that a majority of inhibitory cells’ firing rates were paradoxically suppressed by stimulation, independent of the depth of recording, making it unlikely that our effects were produced by feedforward inhibition arising from a deeper cortical layer. The ISN model remains a consistent explanation of all the data we observed.In the last decade, multiple experiments have performed optogenetic stimulation of cortical interneurons, but these studies have yielded mixed results on inhibition stabilization. In layer 2/3 of mouse V1, optogenetic excitation of PV cells with viral expression was shown to generate non-paradoxical modulation of inhibition . In mousOur results, combining theoretical analyses with a range of experimental conditions, suggest some explanations why paradoxical inhibitory suppression has not been previously widely reported. First, it appears a significant fraction of inhibitory cells must be stimulated to produce large paradoxical changes in firing rate . StimulaWhile paradoxical effects in a single inhibitory subclass do not imply the overall network is inhibition stabilized , PV neurSeveral influential models of cortical function include inhibition-stabilized parameter regimes, and our data provide new constraints on such models. The ‘balanced network’ model  predictsk models  used fink models , which ak models .The SSN also makes a specific prediction about the ISN regime that our data constrains. The SSN predicts that cortical networks are inhibition-stabilized for strong inputs, but not an ISN for sufficiently weak inputs. Thus, the SSN predicts a transition between two operating regimes as network activity increases. Finding this transition point is important for understanding computation, as the two regimes show different  modes of input summation. Prior ISN studies  have lefTogether, the results reported here suggest that inhibition stabilization is a ubiquitous property of cortical networks. This data is consistent with a network that is in the strong coupling regime, but is not extremely strongly coupled as some balanced network models would predict . It is tSlc32a1 locus), three PV-Cre , and two PV-Cre;ReaChR (Gt(ROSA)26Sor locus) mice were used .All procedures were conducted in accordance with the guidelines of the National Institutes of Health. Seven VGAT-ChR2 (2). Using aseptic technique, a headpost was affixed using Metabond (Parkell), and a 3 or 5 mm diameter craniotomy made.Mice were implanted with a titanium headpost and a transparent window  in the left cerebral hemisphere. The windows provided access to the primary visual (V1) and somatosensory cortex, or motor cortices, for imaging and silicon neural probe recordings with optogenetics. Mice were given dexamethasone , 2 hr before surgery. Animals were anesthetized during surgery with isoflurane . We targeted a multisite silicon probe electrode : poly(styrenesulfonate)], For recording experiments, we first affixed a 3D printed ring to the cranial window to retain fluid. With the ring in place, we removed the cranial window and flushed the craniotomy site with sterile normal saline to remove debris. Kwik-Sil silicone adhesive (World Precision Instruments) was used to seal the craniotomy between recording days. Using a stereomicroscope on an articulating arm we positioned the end of an optical fiber  fitted with a light-tight coupler to an optical cannula  over our target cortical area, with a slight 10–30° angle from vertical to provide space for the electrodes. We used fiber coupled LED light sources  to deliver illumination with peak at 470 nm or 625 nm to the brain. We calibrated total intensity at the entrance of the cannula using a power meter and photodiode . The cannula distance from the dura was adjusted to provide a light spot with a full width at half maximum intensity of 0.8–1.2 mm ; thus, our reported spot irradiance in mW/mmresponse . To avoiite data . Populatite data  were calTo classify cells using pharmacological weakening of cortical synapses, we divided the experiment into three phases, each 30–45 min in duration: first no blockers, then excitatory blockers, then excitatory plus inhibitory blockers. For each section, we delivered the same optogenetic stimulation protocol (above). To apply the pharmacological agents, we made a hole near the recording electrodes in the agarose on top of the brain, removed the normal saline covering the agarose and hole, and replaced it with a solution containing the agent in saline. After 15 min, the initial application of blocking solution was removed via aspiration and refreshed. We waited a total of 20 min for the pharmacological agent(s) to take effect before recording. Excitatory synaptic blockers  affected AMPA, kainate, and NMDA synapses. Inhibitory blockers (1 mM bicuculline) affected GABA-A synapses. Source: Sigma-Aldrich .To determine the location and depth of recording electrodes, we coated the shanks with 1,1’-Dioctadecyl-3,3,3’,3’-tetramethylindocarbocyanine perchlorate (DiI) , prior to insertion into the brain. We dipped the electrode tips 5 times into the DiI solution and allowed the coating to dry for 30 s between immersions. Fluorescent electrode tracks were then visualized in coronal sections of fixed brain tissues. Mice were anesthetized with isoflurane and injected intraperitoneally with pentobarbital sodium (150 mg/kg). They were then perfused transcardially with cold (4°C) PBS followed by cold 4% paraformaldehyde. Brains were extracted and fixed in 4% paraformaldehyde for 6–12 hr and then cryoprotected in a 30% (w/v) sucrose solution in PBS until they sank. Brains were sectioned at 50 µm on a freezing microtome (Leica), mounted on glass slides, and coverslipped with mounting media containing DAPI . Slides were imaged using an Olympus slide-scanner .To quantify opsin expression differences between viral  and transgenic (PV-Cre::ReaChR-mCitrine) animals, we used a confocal microscope (Zeiss LSM780) to image fluorescent neurons in L2/3 using 50 µm thick coronal brain tissue sections. . For ea2) to put the animal into a lightly anesthetized but awake state during recordings. At the lower 0.25% isoflurane concentration, recordings in V1 displayed no synchronized oscillatory Up/Down state activities and the animals eyes were open throughout. The anesthesia experiments were divided into four sections: awake animal, anesthesia with no blockers, anesthesia with excitatory blockers, anesthesia with inhibitory blockers. Anesthesia was delivered for 20 min before starting recording.To test the effects of lowering overall activity on the stability of the cortical network we fitted animals with an isoflurane inhalation mask system  and provided anesthesia . Single units were identified as waveform clusters that showed clear and stable separation from noise and other clusters, unimodal width distributions, and inter-spike interval histograms consistent with cortical neuron absolute and relative refractory periods. Multiunits were clusters that were distinct from noise but did not meet one or more of those criteria, and thus these multiunits likely group together a small number of single neurons. Signal-to-noise ratio (SNR)  of singlTo quantitatively analyze the network response, we used a two population rate model , in whicain text  have beesity see . We alsorameters .In the experiments, recordings of the response are done in three separate phases: (1) A ’normal’ phase in which recurrent interactions are intact; (2) A phase with E synaptic blockers; and (3) A phase with both E and I synaptic blockers. The addition of blockers in the model is described with two parameters, Combining To compare the model response with the data, we find the equilibrium solution of Note that this number of parameters is smaller than the number of parameters needed for piecewise-linear fits of the data (average firing rates vs light intensity for both E and I neurons). Such piecewise-linear fits of the data need 15 parameters: 3 phases times five parameters per phase . Thus, the model does not have enough parameters to fit successfully the data, unless the model structure accurately describes the cortical responses. Indeed, The fixed point solutions of For every value of the laser intensity, the excitatory and inhibitory nullclines are defined as the functions Because of these invariances, using data from a single experimental phase, only ratios between parameters can be inferred. This is no longer true when the three phases are considered; in this case all the 11 relevant model parameters can be found.Given the dataset of excitatory and inhibitory responses, the best set of parameters describing the data is found via global optimization as follows:Select random initial parameters in the interval .curve_fit’, variables are constrained to be positive and, for Find the optimal set of parameters through a least-squares optimization , data from all recording sessions from VGAT-ChR2 animals in V1  were pooled together for fitting. For the second and third phases , in order to describe the variability from day to day of the efficacy of the blockers, we use a different set of These parameters are used in 4 random resamplings (random with replacement) are shown in We use a bootstrap approach, combined with the global optimization described above, to estimate the precision with which model parameters can be inferred from the data. The optimal (minimum-error) estimates obtained from 10To analyze the stability of the system, we compute the eigenvalues of the dynamical matrix defined by shown in  from theshown in .To understand how the network response to PV stimulations depends on its structure, here we analyze a three-population model  with oneThe model predictions in Using the rectified linear transfer function of In the large  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This study thoroughly addresses a long-standing question in cortical neurophysiology, namely the extent to which cortex resides in an inhibition stabilised regime. Using careful experiments and mathematical modelling the authors reveal several hallmarks of this regime in multiple cortical areas, including so-called paradoxical inhibition of inhibitory interneuron firing rate, in both awake and anaesthetised states. Together, these results provide further compelling evidence of the role of inhibition in stabilising neural activity.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by John Huguenard as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Hillel Adesnik (Reviewer #3).Thank you for submitting your article "Inhibition stabilization is a widespread property of cortical networks" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.This work address whether excitatory cortical activity is stabilised by rapid inhibitory feedback across cortical areas. One important prediction of such a model is so-called paradoxical suppression of inhibitory neurons in response to inhibitory stimulation. This prediction is validated experimentally and analysed with mathematical modelling.eLife caters for, the authors should make more effort to explain the importance and relevant functional consequences of the ISN regime.The reviewers broadly agree that the study is thorough, well executed and well presented. Several issues were raised, including the need to show/quantify more raw data and population data. There are also some potential experimental confounds, explained in detail below, that should be addressed either by appropriately adjusting conclusions or if possible, by reanalysis or modelling. Finally, for the wide readership Reviewer #1:This is a well written paper without any major deficiencies. The authors address the question whether the activity of excitatory neurons is stabilised by rapid inhibitory feedback, and how common this feature is across cortical areas. The hypothesis that such inhibition-stabilised networks (ISN) would display the so-called 'paradoxical suppression' is intuitive and nicely backed up by a Wilson-Cowan model. The data does indeed show the paradoxical suppression, supporting the ISN hypothesis nicely.The authors also address another possible explanation of the paradoxical suppression, namely the suppression of inhibition-to-inhibition coupling, and explain why this is less convincing, again supported by data. They also go into some depth on the strength of the coupling, which seems to be moderate, rather than very strong.It would help the wider readership if the authors could go into more depth on why this question is important. It seems self-evident that cortical networks are stabilised by inhibition: few other mechanisms are rapid enough and moderate loss of inhibition generically leads to seizures.For example, what are the computational benefits of an ISN? This is only briefly touched upon in the end of the Discussion section at the moment, and could be elaborated upon in the Introduction more.Reviewer #2:The paper of Sanzeni et al., demonstrates that the firing rate of inhibitory cells expressing ChR2 is suppressed at low light intensity but as intensity increases firing rate return and then becomes even higher. This is a paradoxical behavior since we expect that firing rate will increase monotonically with light intensity. The results are in a good agreement with predictions made by inhibition-stabilized network (ISN) models. Such behavior was found in superficial and deep layers of different cortical areas of awake mice and also in anesthetized mice.The methods of the experiments are straightforward and were conducted with high standards. The presentations of the results are clear and nice. For example, the classification of cells into inhibitory and excitatory cells based on different methods is convincing. The virus vs. transgenic experiments are also clear and important. The comparisons of the effect of light before and after addition of synaptic blockers are important and help in clarification of the experiments. Finally, the demonstration of the effect in deep layers and under anesthesia are crucial. Thus, the experimental work, regardless the model, is interesting and important.However, this study is not novel enough as similar conclusions were shown in previous studies and in particular in the 2018 study of Moore et al., from Wehr lab which presented a paradoxical increase of PV firing rate when these cells were optogenetically suppressed (the authors cite this study). Since Moore's paper includes a substantial modeling of ISN network, I am not sure if this under review study introduces novel concepts (the major difference is that in this study inhibitory cells were activated and in the 2018 paper the cells were inactivated).As an experimentalist I have some concerns related to some experimental aspects. Unlike the previous studies, no direct measurements of excitatory and inhibitory currents were made in this study and thus my enthusiasm was reduced. Yet, the study can benefit from pharmacology, which can be better trusted compared to optogenetics.Essential revisions:1) Unlike the light-inactivation of inhibitory cells using Arch, illumination of the cortex of VGAT-ChR2 mice can directly cause synaptic release from the terminals, regardless the firing of the cells. I mention this issue although I don't think that it is fully understood. In other words, it is unclear if the firing of the cells directly reports the effect of light in the network (which could be better assessed using patch recordings). Ideally this could be addressed by limiting the expression of ChR2 to cell bodies. Since none of my proposed experiments are realistic for a revision, I propose to discuss this issue in detail or even try to model it.2) Pharmacology: the effect of GABAr blockers should be tested before blocking excitatory transmission. Adding such blockers at low concentration to partially reduce the effect of inhibition perhaps will result with a similar (but opposite) effect to the optogenetic and perfusion of 3 or 4 concentrations in an increasing manner may work.3) It is not clear if and where the population distribution of the paradoxical reduction in the firing rate of inhibitory cells is presented in the paper. It is possible that firing rate of excitatory cells is suppressed due a small fraction of inhibitory cells for which firing rate increases monotonically with light intensity.4) As far as I understand the model is not a conductance based model. I think that providing predictions for currents can greatly help in illustrating the mechanisms (simply feed the firing rates of the cells into a single cell model).5) The roles of short-term synaptic plasticity were not discussed or checked both in the model and in the experiments. Both depression and facilitation can contribute. Different levels of depression/facilitation of inhibitory synapses due to their activation may contribute to the effect. Without modeling them it is hard to predict their contributions.6) Rather than changing light intensity the authors should consider increasing the surface area that is illuminated. I think that the model will predict that this will result with similar effect to that for increasing light intensity. I believe that this approach is better.Reviewer #3:eLife a suitable journal.Sanzeni et al., present a compelling new data set supporting the notion that neocortical areas operate in the inhibition stabilized regime in which recurrent inhibition is needed to balance recurrent excitation to stabilize the network. This had been theoretically proposed more than 20 years ago by Tsodyks and colleagues, and experimental data to support it was originally put forward by Ferster, Miller and colleagues in 2009 using whole cell recording in anesthetized cats. Subsequently, additional whole cell recordings in awake mice in A1 and V1 supported this model. What has been lacking was a direct test of a core prediction of the ISN model: that direct excitation of inhibitory neurons (I cells) would paradoxically lead to a net suppression of I cells at the steady state (followed by a transient increase in I cell activity). It should be clear this result was indirectly shown by measuring synaptic inhibition in the Ferster and Miller paper. The current study elegantly and rigorously used direct optogenetic stimulation of I cells in transgenic mice to show that 'paradoxical' prediction is borne out in mouse V1. The authors then go on to repeat this core result in S1 and M1 and in deeper cortical layers of these areas. Overall, I find the study well performed and the arguments sound. I previously already subscribed to this view based on the existing data, but the data in this study really is the nail in the coffin. For this reason, this study is timely and warrants publication, with I do have some questions for the authors to address . While I agree with almost all of the authors conclusions, I would like them to address a few points in which I will act as a devil's advocate. I'd like them to conceptually, experimentally, or theoretically rule out alternative explanations for the data, however less likely they might seem than the ISN model.1) Could it be that a very small subset of PV+ interneurons are extremely photo-excitable  and that these cells are in fact responsible for the suppression of most other I cells? These cells might be so rare (inhibitory 'hub' cells) that they would be very hard to catch with extracellular recording . Perhaps they have a slightly delayed response to optogenetic stimulation which explains the transient increase in I cell activity prior to its suppression?2) Could a pure feed-forward model explain their results coupled with the possibility that L4 interneurons could be more photo-excitable than L2/3 interneurons, despite being deeper in the brain? In this scenario, the 'paradoxical' I cell suppression in L2/3 could be entirely explained by loss of feedforward excitation from L4. It seems like in the data set some I cells didn't show paradoxical effects (and most of the authors data comes from above L4), so maybe this is the case?3) In any case, the authors should show much more raw data (rather than just average data) in the main figures. I agree it's the average that matters to test the ISN model, but it would be helpful to the reader to see the distribution across units for the various effects: transient activation, shape of 'paradoxical response' etc. Some of this is in the supplement, but not much. This work address whether excitatory cortical activity is stabilised by rapid inhibitory feedback across cortical areas. One important prediction of such a model is so-called paradoxical suppression of inhibitory neurons in response to inhibitory stimulation. This prediction is validated experimentally and analysed with mathematical modelling.The reviewers broadly agree that the study is thorough, well executed and well presented. Several issues were raised, including the need to show/quantify more raw data and population data. There are also some potential experimental confounds, explained in detail below, that should be addressed either by appropriately adjusting conclusions or if possible, by reanalysis or modelling. Finally, for the wide readership eLife caters for, the authors should make more effort to explain the importance and relevant functional consequences of the ISN regime.Reviewer #1:This is a well written paper without any major deficiencies. The authors address the question whether the activity of excitatory neurons is stabilised by rapid inhibitory feedback, and how common this feature is across cortical areas. The hypothesis that such inhibition-stabilised networks (ISN) would display the so-called 'paradoxical suppression' is intuitive and nicely backed up by a Wilson-Cowan model. The data does indeed show the paradoxical suppression, supporting the ISN hypothesis nicely.Thank you for the positive comments.The authors also address another possible explanation of the paradoxical suppression, namely the suppression of inhibition-to-inhibition coupling, and explain why this is less convincing, again supported by data. They also go into some depth on the strength of the coupling, which seems to be moderate, rather than very strong.Yes, our results support moderate, not very strong, recurrent connectivity. We have revised the Discussion section to highlight this point .It would help the wider readership if the authors could go into more depth on why this question is important. It seems self-evident that cortical networks are stabilised by inhibition: few other mechanisms are rapid enough and moderate loss of inhibition generically leads to seizures.We agree with the reviewer that the presence of epileptic seizures upon blockade of inhibition seems at first sight to imply that cortex is inhibition-stabilized. However, this does not rule out that cortex is inhibition-stabilized in some states, but not others – in particular, it could be inhibition-stabilized in the presence of sensory inputs, but not during spontaneous activity. In fact, to the best of our knowledge, while there was already strong evidence for inhibition stabilization during sensory processing, the question of whether cortex is inhibition stabilized at baseline remained an open question until our study. We now make this point more clearly in the Introduction.For example, what are the computational benefits of an ISN? This is only briefly touched upon in the end of the Discussion section at the moment, and could be elaborated upon in the Introduction more.We have added to the first paragraph of the Introduction to discuss the computational benefits of ISNs, including now citing work on ‘balanced amplification’ , and work on how strongly coupled recurrent networks can generate complex spatiotemporal patterns of activity .Thank you for these suggestions; we think the manuscript has been greatly improved.Reviewer #2:The paper of Sanzeni et al., demonstrates that the firing rate of inhibitory cells expressing ChR2 is suppressed at low light intensity but as intensity increases firing rate return and then becomes even higher. This is a paradoxical behavior since we expect that firing rate will increase monotonically with light intensity. The results are in a good agreement with predictions made by inhibition-stabilized network (ISN) models. Such behavior was found in superficial and deep layers of different cortical areas of awake mice and also in anesthetized mice.The methods of the experiments are straightforward and were conducted with high standards. The presentations of the results are clear and nice. For example, the classification of cells into inhibitory and excitatory cells based on different methods is convincing. The virus vs. transgenic experiments are also clear and important. The comparisons of the effect of light before and after addition of synaptic blockers are important and help in clarification of the experiments. Finally, the demonstration of the effect in deep layers and under anesthesia are crucial. Thus, the experimental work, regardless the model, is interesting and important.Thank you for the positive comments and thorough review.However, this study is not novel enough as similar conclusions were shown in previous studies and in particular in the 2018 study of Moore et al., from Wehr lab which presented a paradoxical increase of PV firing rate when these cells were optogenetically suppressed (the authors cite this study). Since Moore's paper includes a substantial modeling of ISN network, I am not sure if this under review study introduces novel concepts (the major difference is that in this study inhibitory cells were activated and in the 2018 paper the cells were inactivated).While it is true that ISN effects have been seen before , there has been an open question of whether cortical networks always operate as ISNs. Theoretical work has shown that networks can transition out of an ISN regime as network activity goes down, and theoreticians have actively speculated that as sensory stimuli grow in intensity, even cat V1 may transition from a non-ISN to an ISN state . Further, even the question of whether the ISN regime generalizes outside cat V1 was questioned . In fact, a recent paper from David Hansel and Nuo Li’s group , argues that PV cell stimulation as performed by Kato et al., etc, does not imply ISN operation.Our present work shows clear evidence for the ISN state, relying on experimental stimulation of all classes of inhibitory neurons together.The Moore et al. work is interesting, as they find paradoxical changes in firing rates in mouse A1, but that work neither proves nor disproves whether mouse auditory L2/3 is inhibition stabilized. Suppressing only L2/3 PV neurons, they find Arch-expressing cells decrease their firing rates (as expected for Arch-induced suppression), and Arch-negative cells increase their firing rate (as one might expect from disinhibition due to Arch suppression of other I cells). These observations do not provide evidence for an ISN interpretation, because, subject to some assumptions, in an ISN the cells stimulated by the opsin will produce paradoxical effects. In fact, the authors argue for a non-recurrent explanation. They find that a feedforward disinhibitory circuit , where L4 inhibitory cells disinhibit L2/3 inhibitory cells, explains their data. This feedforward disinhibition can be implemented in weakly-coupled networks, and does not require the network to be stabilized by inhibition.There are several differences between the Moore paper and ours. First, they used viral expression of opsin, which, as we show, can change how paradoxical effects manifest . Second, we showed, using synaptic blockers, that cells which are stimulated by the laser respond paradoxically, and we provide several lines of evidence  for the ISN mechanism, and against this effect being produced by disinhibitory circuits. Our result, showing strong recurrent coupling, combined with the Moore result, suggests that it will be interesting for future work to examine both across and within brain areas whether these two mechanisms might be used for different types of computation.We have added text to better explain the above issues. In the Introduction we have revised the discussion of the non-ISN to ISN transition topic, and in both the Introduction and Discussion section we have revised for clarity the discussion of the Moore result.As an experimentalist I have some concerns related to some experimental aspects. Unlike the previous studies, no direct measurements of excitatory and inhibitory currents were made in this study and thus my enthusiasm was reduced. Yet, the study can benefit from pharmacology, which can be better trusted compared to optogenetics.Yes. We would add that the pharmacology work made possible the modeling  results, which would have been unobtainable without the extra pharmacological measurements.Essential revisions:1) Unlike the light-inactivation of inhibitory cells using Arch, illumination of the cortex of VGAT-ChR2 mice can directly cause synaptic release from the terminals, regardless the firing of the cells. I mention this issue although I don't think that it is fully understood. In other words, it is unclear if the firing of the cells directly reports the effect of light in the network (which could be better assessed using patch recordings). Ideally this could be addressed by limiting the expression of ChR2 to cell bodies. Since none of my proposed experiments are realistic for a revision, I propose to discuss this issue in details or even try to model it.Thanks for raising this point. For certain potential effects, such as those that depend on stimulating only the cells that are being measured, optogenetically-induced synaptic release from terminals  might well change the interpretation of experiments. However, for the network effects produced in an ISN, the paradoxical effect is generated by I cells affecting the population of E cells, which then withdraw input from the population of I cells. Since ISN phenomena are population effects , whether target neurons are affected by direct synaptic release or by eliciting spikes at inhibitory neurons’ somata should not change the conclusions. Specifically, for both induced spiking and induced synaptic release, if the network is an ISN we expect firing rates to decrease with I stimulation , as we found. Also, our pharmacology data supports the idea that many cells were induced to fire directly by stimulation. When we added E and I blockers, we still found that many cells fired in response to optogenetic stimulation. We have added to the Discussion section to discuss this.2) Pharmacology: the effect of GABAr blockers should be tested before blocking excitatory transmission. Adding such blockers at low concentration to partially reduce the effect of inhibition perhaps will result with a similar (but opposite) effect to the optogenetic and perfusion of 3 or 4 concentrations in an increasing manner may work.Yes, we also tried to verify the ISN prediction in this way. Adding GABA blockers in an ISN could in principle lead to a paradoxical effect of inhibitory rates increasing as excitatory rates increase. To look for this, we did indeed do pilot experiments in which we added GABA blockers first. While this did raise spontaneous excitatory firing rates, it was difficult to find a useful concentration that did not lead to epileptiform activity, so we settled on the sequence used in Figure 4. Due to this difficulty in precise control, we were not able to determine whether changes in inhibitory firing rates via pharmacological blockade also led to paradoxical effects. What made such measurements possible with optogenetics was the ability to more precisely control stimulation intensity and stimulation timing.3) It is not clear if and where the population distribution of the paradoxical reduction in the firing rate of inhibitory cells is presented in the paper. It is possible that firing rate of excitatory cells is suppressed due a small fraction of inhibitory cells for which firing rate increases monotonically with light intensity.We have added population data to the paper. We have plotted responses for every V1 inhibitory cell in the supplement . We had previously shown the distribution across all cells of the paradoxical effect , plotted as raw and normalized responses, and we have chosen to retain these. We have also added a set of raw example cells to Figure 2 (new panel I).Also, the reviewer mentions the possibility that rare, non-paradoxical inhibitory cells could suppress all other I cells. Our data make this possibility unlikely. First, the observed dynamics  is inconsistent with this scenario. Second, we observed that suppressed inhibitory cells respond in a non-paradoxical way for large laser intensities, which is also inconsistent with the rare-I-cell scenario. If a small number of I cells control the effect, I cells should show similar responses at low and high stimulation intensity , not for I cells to transition from suppression to excitation in the way we observed.  Moreover, the transition  happens at the point at which E cells become suppressed, as predicted by ISN models. We have added a paragraph to make these points in the Discussion section.4) As far as I understand the model is not a conductance based model. I think that providing predictions for currents can greatly help in illustrating the mechanisms (simply feed the firing rates of the cells into a single cell model).We have added text to the Discussion section to say that ISN models make similar predictions for paradoxical effects in recorded intracellular currents and in the firing rates we recorded, and to connect this work to the past intracellular work by Kato et al., Moore et al., etc.5) The roles of short-term synaptic plasticity were not discussed or checked both in the model and in the experiments. Both depression and facilitation can contribute. Different levels of depression/facilitation of inhibitory synapses due to their activation may contribute to the effect. Without modeling them it is hard to predict their contributions.This is a very good question. To our knowledge, the way short-term plasticity impacts the relationship between paradoxical suppression and ISN operation has not been studied (surprisingly). Therefore, we explored this analytically in detail. We have added an appendix (Appendix A) to the manuscript with a figure. Our results show that when a paradoxical inhibitory response is observed in a network with synaptic plasticity, that implies the excitatory network is unstable and thus that the network is an ISN. We now explain these results in the main text .6) Rather than changing light intensity the authors should consider increasing the surface area that is illuminated. I think that the model will predict that this will result with similar effect to that for increasing light intensity. I believe that this approach is better.Illumination area and stimulation strength are partly related and partly orthogonal. Stimulating small numbers of inhibitory cells  is similar to stimulating a small fraction of inhibitory cells in a network. Therefore, for small illumination areas, paradoxical effects and ISN operation are no longer related , Sadeh et al., (2017) characterized in a model the impact of changing illumination area, and confirmed that for small areas (100 µm with their parameters), the network no longer shows paradoxical effects. In a sense, then, increasing area does produce changes in inhibitory response sign, as changing laser power does, though the mechanisms are not the same: increasing laser power shifts the network to a non-ISN state while decreasing illumination area leaves the network in the ISN state but hides the paradoxical effect. Thanks for this comment; to clarify this for readers we have added text to the Discussion section to explain this effect of area, citing Sadeh et al., there.Reviewer #3:Sanzeni et al., present a compelling new data set supporting the notion that neocortical areas operate in the inhibition stabilized regime in which recurrent inhibition is needed to balance recurrent excitation to stabilize the network. This had been theoretically proposed more than 20 years ago by Tsodyks and colleagues, and experimental data to support it was originally put forward by Ferster, Miller and colleagues in 2009 using whole cell recording in anesthetized cats. Subsequently, additional whole cell recordings in awake mice in A1 and V1 supported this model. What has been lacking was a direct test of a core prediction of the ISN model: that direct excitation of inhibitory neurons (I cells) would paradoxically lead to a net suppression of I cells at the steady state (followed by a transient increase in I cell activity). It should be clear this result was indirectly shown by measuring synaptic inhibition in the Ferster and Miller paper. The current study elegantly and rigorously used direct optogenetic stimulation of I cells in transgenic mice to show that 'paradoxical' prediction is borne out in mouse V1. The authors then go on to repeat this core result in S1 and M1 and in deeper cortical layers of these areas. Overall, I find the study well performed and the arguments sound. I previously already subscribed to this view based on the existing data, but the data in this study really is the nail in the coffin. For this reason, this study is timely and warrants publication, with eLife a suitable journal.Thank you for the positive comments about the experiments and the arguments in the paper.On existing data, we agree that support for the ISN regime in cat V1, with sensory stimulation, was demonstrated by Ozeki et al. However, our work goes beyond this and other existing results in several ways. Our most important new finding that goes beyond Ferster and Miller is new evidence for ISN operation at baseline (at rest) – without the additional input that comes with sensory stimulation.Theoretical work on the stabilized supralinear network , shows that networks can be in the non-ISN state at baseline, and then with sensory drive, transition into the ISN state. You showed relevant experimental data in your 2017 Neuron paper, giving evidence for increasing inhibition stabilization (via increasing I/E ratio) as sensory stimulation strengthens. But before our work, an open question had been at what level of activity the ISN transition point occurred – whether it was above or below the level of spontaneous activity, and therefore whether weak inputs were processed by a network operating in the ISN regime or not. Ferster and Miller noted this as well : “Our data [with visual stimulation] do not address whether the network operates as an ISN at rest.”Our data does show the network operates as an ISN at rest. And our experiments can also give insight into how far spontaneous activity is from the transition. We found that the transition out of the ISN state is substantially below the level of spontaneous activity We have significantly revised the Introduction to better explain the above topic, including adding more discussion of the non-ISN to ISN transition predicted by Miller and colleagues (Introduction). We appreciate the comments and the opportunity to improve our discussion of these issues.I do have some questions for the authors to address . While I agree with almost all of the authors conclusions, I would like them to address a few points in which I will act as a devil's advocate. I'd like them to conceptually, experimentally, or theoretically rule out alternative explanations for the data, however less likely they might seem than the ISN model.1) Could it be that a very small subset of PV+ interneurons are extremely photo-excitable  and that these cells are in fact responsible for the suppression of most other I cells? These cells might be so rare (inhibitory 'hub' cells) that they would be very hard to catch with extracellular recording . Perhaps they have a slightly delayed response to optogenetic stimulation which explains the transient increase in I cell activity prior to its suppression?Thanks for bringing up these possible alternative explanations.Our data argues against the possibility of rare, non-paradoxical PV+ interneurons that increase their rate to suppress all other inhibitory cells. We observed that inhibitory cells respond in a non-paradoxical way for large laser intensities, which is inconsistent with the rare-I-cell scenario. If a small number of I cells control the effect, each I cell would be predicted to be either suppressed or excited by the laser, not to transition in the way we observed.While this firing rate transition (predicted by the ISN model) is the most clear evidence, several other observations also argue against rare inhibitory cells providing feedforward inhibition. First, we found that almost all I cells show paradoxical effects in transgenic animals; e.g. see initial response slopes in Figure 2JK and new Figure 2—figure supplement 7.) Second, the observed dynamics, with initial increase in firing rate followed by suppression in the same inhibitory cells , places tight constraints on such potential rare I cells. As you mention, for stimulated inhibitory cells to show short-latency excitation followed by longer latency feedforward inhibition would presumably require most inhibitory cells to respond quickly to stimulation, but for the rare inhibitory cells to be, oddly, both very photoexcitable but also slow to respond.  Moreover, the transition  happens at the point at which E cells become suppressed, as predicted by ISN models. While it might be possible to achieve this alignment by careful choice of network parameters, it seems unlikely, especially in light of the fact that our ISN model describes these transition points and simultaneously describes the pharmacology data well.We have added to the Discussion to make these points.2) Could a pure feed-forward model explain their results coupled with the possibility that L4 interneurons could be more photo-excitable than L2/3 interneurons, despite being deeper in the brain? In this scenario, the 'paradoxical' I cell suppression in L2/3 could be entirely explained by loss of feedforward excitation from L4. It seems like in the data set some I cells didn't show paradoxical effects (and most of the authors data comes from above L4), so maybe this is the case?This scenario, which was examined by Moore et al., (2018), is also unlikely in our case, for similar reasons as above including our observation that I cells transition from negative firing rate change  to positive change  with increasing stimulation. If the main stimulation effect was to excite L4 inhibitory cells, which suppressed excitatory cells, producing loss in feedforward excitation in layer 2/3, we would not expect a slope transition (as in prev. point above) or an unchanged slope in pharmacology, as we observed. Also, in this scenario, our observed response dynamics are hard to explain. In addition, we did not see evidence in depth recordings for increasing excitability in L4 , and only a small fraction of I cells show non-paradoxical effects with the transgenic approach .In addition to revisions indicated above for your prior comments, we have revised the Introduction on Moore et al.,’s data.3) In any case, the authors should show much more raw data (rather than just average data) in the main figures. I agree it's the average that matters to test the ISN model, but it would be helpful to the reader to see the distribution across units for the various effects: transient activation, shape of 'paradoxical response' etc. Some of this is in the supplement, but not much.We have added population data to the paper. We had shown the distribution across units for the transient response in the supplement, and we have now moved into the main figures  a scatterplot showing the transient size vs. the paradoxical suppression effect size. We have also added a set of raw example cells to Figure 2 (new panel I), and have plotted responses for every V1 inhibitory cell in the supplement . We had previously shown the distribution across all cells of the paradoxical effect , plotted as raw and normalized responses, and we have chosen to retain these. However, these were clearly not immediately interpretable, and we think combining these with the single cell examples, the transient distributions, and the new supplementary figure improves the presentation.


In a laminitic horse, the maximal loading of the toe region occurs during the breakover phase. To date, no kinetic data demonstrates the effect of supportive orthopaedic therapy in horses with laminitis on breakover phase. Thus, the purpose of this study was to examine the effect of heel elevation on the breakover phase. Eight horses with acute laminitis treated medically as well as with application of a hoof cast with heel wedge (HCHW) were included in this study. Immediately following cessation of clinical signs of acute laminitis, two measurements using the Hoof™ System were taken: the first with HCHW and the second immediately following removal of the HCHW, i.e. in barefoot condition (BFC). The hoof print was divided into three regions: toe, middle hoof, and heel. Kinetic parameters included vertical force (VF), stance duration, contact area (CA) for all hoof regions during stance phase, duration of breakover, VF in the toe region at onset of breakover and location of centre of force.The VF and CA were higher in the heel region  and decreased significantly after removal of the HCHW . The breakover phase in horses with HCHW lasted 2% of stance phase and was significantly shorter than that in BFC, which lasted 6% of stance phase. The VF at onset of breakover for the toe region in horses with HCHW was significantly lower than that in BFC. The centre of the force was located at the heel region in all horses with the HCHW, and at the middle the hoof region in BFC.Heel elevation in horses with laminitis as examined on a concrete surface significantly shortens breakover phase and decreases the vertical force in the toe region during breakover. HCHW provides adequate support to the palmar hoof structures by increasing the contact area in the heel region and incorporating the palmar part of frog and sole into weight bearing, thus decreasing the stress on the lamellae. Hoof cast with heel elevation could be a beneficial orthopaedic supportive therapy for horses suffering from acute laminitis. Acute laminitis is defined as the onset of clinical signs including bounding digital pulse, increased heat at the coronary band in combination with a positive response to the hoof tester over the toe. Moreover, affected horses display a stilted gait and are reluctant to move , 2. The History and clinical as well as radiographic findings are listed in Tables There were no significant differences in any of the variables between the left and right forelimbs (t-test for dependent samples). Therefore, the measured variables for both limbs were pooled by using arithmetic mean over both sides.p = 0.002)  lower than those in BFC  Fig. . For theFC Table .Fig. 1Tp = 0.002, p = 0.001, respectively)  Table .p = 0.001 and p = 0.001 respectively) after removal of the HCHW . The breakover phase in horses with HCHW (tHO 98 ± 1.2% of stance phase) lasted 2% of the stance phase and was significantly shorter than those in BFC (tHO 94 ± 1.3%), which lasted 6% of stance phase  with laminitis following application of the HCHW and in the BFC is shown in Fig. To the best of our knowledge, the study presented here is the first kinetic study to examine the effect of heel elevation on breakover phase in horses with laminitis.Our results demonstrate that the breakover phase in laminitic horses with HCHW was significantly shorter than those in BFC. Onset and duration of breakover phase are highly dependent on hoof angle and toe length . BreakovA recently presented kinetic study demonstrated that in laminitic horses the VF in the heel region was significantly higher as compared to toe and middle hoof regions, whereas the VF in the toe region was highest in sound horses . TherefoOur results showed that the VF in the heel region of laminitic horses with an HCHW was higher than that observed in the BFC. This finding is in agreement with that of a study demonstrating, that the load on the palmar half of the hoof constitutes 81% of the total load after heel elevation in standing horses suffering from acute laminitis . FurtherThe proposed theory behind the use of the HCHW is to shift loading forces to the undamaged heel and frog regions to provide relief to the toe region . HCHW shFurthermore, the HCHW is an inexpensive, quick, and easy method to elevate the heel. The plaster bandage can be adapted to any hoof shape and moulded into the frog sulci, increasing the weight-bearing surface area. This distributes the load over a larger area without the need for impression material, which is required in other methods such as wooden shoes and the modified ultimate cuff . HoweverIt has been demonstrated that digital hypothermia would ameliorate experimentally induced laminitis due to the hypometabolic and anti-inflammatory effects , althougThis study had some limitations. Ideally, four measurements should have been taken: the first upon admission in the BFC during the acute phase, the second immediately after the application of the hoof cast, and the third and fourth after the acute signs had subsided with and immediately after removal the hoof cast. However, measurement prior to the application of the hoof cast would have taken place during a more acute phase, and would therefore, have been during the more painful and unstable phase of laminitis, as both application and calibration of the measuring system take about 15–20 min. Moreover, horses are required to stand during these procedures on one forelimb with the contralateral limb held off the ground for approximately 1–2 min. Thus, there was a potential risk of further damage to the lamellae, which could have a negative effect on the healing process. Therefore, we only took two measurements, as opposed to four. Other limitations include the small sample size and the variation in breeds included in the study. Laminitis is not a very common disease in horses, so we were forced to include horses from different breeds and sizes. Furthermore, there was not a normal control group in the study; however, the forelimbs from each horse were subjected to two measurements and served as its own control.The current study demonstrated that heel elevation in horses with laminitis as examined on a concrete significantly shortens breakover phase and decreases the vertical force in the toe region during breakover. HCHW provides adequate support to the palmar hoof structures by increasing the contact area in the heel region and incorporating the palmar part of frog, sole and into weight bearing, thus decreasing the stress on the lamellae. Hoof cast with heel elevation could be a beneficial orthopaedic supportive therapy for horses suffering from acute laminitis.Horses with clinical signs of acute laminitis that were admitted to the Clinic for Horses of the Justus-Liebig-University in Gießen, Germany were included in the study prospectively within a period of 1 year. Inclusion criteria were horses suffering from acute laminitis  on both forelimbs with rotation of the distal phalanx. Horses with distal displacement of the distal phalanx, horses affected on only one forelimb or on hindlimbs, as well as those with dorsal capsular rotation were excluded from the study.Diagnosis of acute laminitis was based on the results of clinical and lameness examination  , as wellThereafter horses received medical therapy consisting of meloxicam  acepromazine  and dalteparin-sodium  and were stalled in a box with a soft bedding (peat). One horse with equine pituitary pars intermedia disease received pergolide mesylate . Moreover, all horses received soaked hay 1 kg/100 kg/day. After resolution of the acute signs of laminitis, all medications were discontinued, however the dose of meloxicam was reduced gradually over 2 days and then completely discontinued. The acute episode of laminitis was considered to be overcome when the horses with HCHW were sound at a walk without medication while walking on hard as well as on soft surfaces. Thereafter, the horses were equipped with the Hoof™ System on both forelimbs and two measurements were performed as described in a previous study [2 and %) was defined as the surface area of all loaded sensing elements. Stance phase duration (sec) and the breakover phase, which starts at the time of heel-off (tHO) (% of stance phase), which is the time at which the heel is lifted off the ground, and ends at time of toe-off (end of stance phase), were also recorded. Furthermore, vertical force (Newton) at time of heel-off (VF-tHO) (only for the toe region) was also calculated. Moreover, the location of centre of force was recorded.To evaluate the kinetic data of the pressure measurement system, the mean of 5 valid strides was calculated and averaged into one pressure image using the FastSCAN Mobile Research software program . Each individual hoof print was divided into three equal regions by two lines: the toe, middle hoof, and heel . P-values < 0.05 were considered as statistically significant.For each variable, the data were analysed using two-way repeated-measures analysis of variance (ANOVAs). Side (left and right forelimb) and hoof region were considered as fixed effects. The measured HCHW values were compared to the barefoot values. The difference between the two measurements  for each hoof region was computed and analysed. Afterward, pairwise comparisons were computed for each of the hoof regions using dependent samples Additional file 1.


Furthermore, the combination of BMS-754807 and U0126 efficiently decreased the cell viability and increased cleaved caspase 3 and apoptosis in vitro and in vivo. Our data suggest that the treatment of colon tumor cells with IGF-1R inhibitors stimulates p70S6K1 activity via MEK1/2 to promote survival, providing a new strategy for colorectal cancer therapeutics.The insulin-like growth factor 1 receptor (IGF-1R) governs several signaling pathways for cell proliferation, survival, and anti-apoptosis. Thus, targeting IGF-1R appears as a reasonable rationale for tumor treatment. However, clinical studies showed that inhibition of IGF-1R has very limited efficacy due to the development of resistance to IGF-1R blockade in tumor cells. Here, we discovered that prolonged treatment of colon cancer cells with IGF-1R inhibitors (BMS-754807 and GSK1838705A) stimulates p70 KDa ribosomal protein S6 kinase 1 (p70S6K1) activation, a well-known kinase signaling for cell survival. We also found that p70S6K1 activation by IGF-1R inhibition is independent of K-Ras and PIK3CA mutations that frequently occur in colon cancer. Besides the increased p70S6K1 phosphorylation, the phosphorylation of mitogen-activated protein kinase kinase 1 and 2 (MEK1/2) was elevated in the cells treated with BMS-754807. Interestingly, the increases in MEK1/2 and p70S6K1 phosphorylation were also observed when cells were subjected to the treatment of AKT inhibitor or genetic knockdown of AKT2 but not AKT1, suggesting that AKT2 inhibition stimulates MEK1/2 phosphorylation to activate p70S6K1. Conversely, inhibition of MEK1/2 by MEK1/2 inhibitor (U0126) or knockdown of MEK1 and MEK2 by corresponding  Elevation of IGF-1R expression or activity is frequently found in many types of cancer, which is associated with tumor cell proliferation, survival, anti-apoptosis, and drug resistance.2 For example, IGF-1R-mediated activation of AKT, in turn, phosphorylates and inhibits pro-apoptotic proteins such as Bad and caspase 9 to promote survival.4 In colorectal cancer, IGF-1R expression levels are commonly elevated in cancerous tissues relative to adjacent normal tissues.5 Most importantly, the elevation of IGF-1R expression is associated with a worse prognosis in colorectal cancer patients.6 Since the IGF-1R signaling pathway is critical for the development, maintenance, and survival of cancers, many small molecule compounds and antibodies targeting IGF-1R have been developed. Unfortunately, these anti-IGF-1R agents have very limited efficacy in clinical trials,9 suggesting the existence of a mechanism to antagonize the IGF-1R inhibition in cancer cells.The insulin-like growth factor 1 receptor (IGF-1R) belongs to the tyrosine kinase receptor family. By binding with IGF-1, IGF-2, and insulin, IGF-1R activates downstream pathways including phosphoinositide 3 kinase (PI3K)/AKT and Ras/extracellular signal-regulated protein kinase (ERK) pathways,10 The induction of p70S6K1 activation in colon cancer cells is associated with resistance of IGF-1R inhibition since knockdown of p70S6K1 or inhibition of p70S6K1 activity enhances the cell sensitivity to IGF-1R inhibition. p70S6K1 is a serine/threonine kinase, which is the key downstream point of the AKT-mTOR pathway.11 p70S6K1 activation has been shown to boost cell survival.11 The well-known mechanism for p70S6K1 to promote survival is that p70S6K1 phosphorylates mouse double minute 2 (MDM2) leading to polyubiquitination and degradation of p53, and thus represses p53-dependent apoptosis.12 Interestingly, p70S6K1 is one of the downstream targets of AKT. Inhibition of AKT activation by IGF-1R inhibitor, OSI-906, is supposed to suppress p70S6K1 activation. However, colon tumor cells treated with OSI-906 suppresses AKT activation but induces p70S6K1 phosphorylation.10 This finding raises the question of whether the activation of p70S6K1 by OSI-906 is through IGF-1R inhibition or it is a non-specific effect of OSI-906. If it is through IGF-1R inhibition, what is the molecular mechanism to induce p70S6K1 activation?Previously, we found that prolonged treatment of colon cancer cells with an IGF-1R inhibitor, OSI-906, induces p70S6K1 activation.10 Colon tumor cells with high Pdcd4 expression is sensitive to OSI-906 inhibition, while Pdcd4 knockdown gains the resistance to OSI-906 inhibition.10 Conversely, colon cells with low Pdcd4 level are resistant to OSI-906 inhibition and this resistance is relieved by Pdce4 overexpression.10 Pdcd4 has been demonstrated to inhibit tumor promotion and progression,13 whose expression is often downregulated in colorectal cancer.15 Pdcd4 physically interacts with eukaryotic initiation factor 4A (eIF4A) and attenuates protein translation.16 eIF4A is an ATP-dependent RNA helicase belonging to DEAD-box RNA helicase protein family.17 eIF4A mutants that are defective in RNA helicase activity inhibits translation of mRNAs with stable secondary structures more efficiently than less-structured mRNAs,18 indicating that eIF4A’s helicase activity is required for translation of structured mRNAs. Thus, inhibiting eIF4A activity by Pdcd4 is expected to suppress protein translation, especially the translation of structured mRNAs. This notion was recently confirmed by identification of the Pdcd4 target, stress-activated-protein kinase interacting protein 1 (Sin1), whose translation is inhibited by Pdcd4 through suppressing eIF4A activity.19We also discovered that the level of the tumor suppressor, Programmed cell death 4 (Pdcd4), correlates with the chemosensitivity to OSI-906 in colon tumor cells.21 We found that colon tumor cells treated with an IGF-1R inhibitor, BMS-754807 or GSK1838705A, induced phosphorylation of p70S6K1 as OSI-906 does. We also found that Pdcd4 inhibited the induction of p70S6K1 phosphorylation by IGF-1R inhibitor was through suppression of p70S6K1 translation. In addition, BMS-754807 treatment induced phosphorylation of mitogen-activated protein kinase kinase 1 and 2 (MEK1/2), which contributed to BMS-754807-induced p70S6K1 activation for survival in colon cancer cells. These findings provide new mechanistic insights into combating the resistance of IGF-1R inhibition.In this study, we intend to resolve the mechanism of induction of p70S6K activation by IGF-1R inhibition using a different IGF-1R inhibitor, BMS-754807. BMS-754807 is a potent IGF-1R inhibitor, which has been reported to have a limited off-target effect since the transcriptional profile is similar between wild-type MEF cells treated with BMS-754807 and IGF-1R null MEF cells.10 To test whether the induction of p70S6K1 phosphorylation is due to inhibition of IGF-1R signaling pathway not non-specific outcome of OSI-906, the HCT116 cells were treated with other IGF-1R inhibitors, BMS-754807 and GSK1838705A, for 0–72 h. Since IGF-1R inhibition results in suppression of downstream AKT activation, the IGF-1R inhibitors (BMS-754807 and GSK1838705A) efficiently inhibit AKT phosphorylation, indicating that the drugs function properly. Phosphorylation of p70S6K1 was significantly induced at 48 and 72 h in cells treated with BMS-754807 or GSK1838705A ,24 we treated SW480 ,24 LoVo ,24 and RKO 24 cells with BMS-754807 (240 nM) for 0–72 h. The p70S6K1 phosphorylation was induced in all cells treated with BMS-754807 for 48–72 h  and LacZ shRNA transfected control (HT29-L) cells with BMS-754807 for 0–72 h. As expected, Pdcd4 knockdown cells (HT29-P) dramatically induced p70S6K1 phosphorylation when cells were treated with BMS-754807 for 24–72 h . This discrepancy may be due to the different mechanisms employed by different inhibitors to inhibit MEK1/2 activity, which requires further investigations.To test whether MEK inhibition can increase AKT phosphorylation to activate AKT, we knocked down MEK1 and MEK2 expressions with lls Fig. , b. To vlls Fig. , d. Howells Fig. , d. Thes126 Fig.  but not Our results suggested that activation of MEK1/2 is a key event for the induction of p70S6K1 phosphorylation in resistance to IGF-1R inhibition. We, next, examined whether inhibition of MEK1/2 leads to suppression of IGF-1R inhibition-induced p70S6K1 activation and promotes cell death. We treated HCT116 cells with vehicle (DMSO), BMS-754807 (240 nM), U0126 (5 μM), and BMS-754807 (240 nM)+U0126 (5 μM) for 3 days. As shown in Fig. To confirm the results from cultured cells, we tested the efficacy of the combination of BMS-754807 and U0126 on suppressing tumor growth in HCT116 cells derived tumor xenograft. The average tumor volume in mice treated with BMS-754807 + U0126 increased about twofold during the 12-day treatment while the tumor volume increased about sevenfold in the vehicle-treated mice Fig. . The aveIn this study, we found that extensive treatment of IGF-1R inhibitor induced phosphorylation of p70S6K1 in colon tumor cells Fig.  and 3. I28 For example, p70S6K1 phosphorylates MDM2 at Ser166 resulting in polyubiquitination and degradation of p53 and thereby inhibits p53-dependent apoptosis.12 Inhibition of p70S6K1 results in the dephosphorylation of the pro-apoptotic protein, Bad, leading to mitochondria-dependent apoptosis.29 The finding that prolonged treatment with IGF-1R inhibitor induced p70S6K1 phosphorylation suggests the importance of p70S6K1 phosphorylation in cell survival when IGF-1R is inhibited , which allows the translation initiation complex moving from 5′ to 3′ direction to locate at the AUG codon.17 Thus, inhibition of eIF4A helicase activity results in suppression of translation, especially the translation of mRNAs with stable secondary structures at 5′UTR. Pdcd4 was initially demonstrated to preferentially inhibit translation of the luciferase mRNA possessing an artificial stem-loop structure at 5′UTR with the free energy of −44.8 kcal/mol.31 This concept was recently confirmed by the identification of Sin1 as a Pdcd4 target, whose mRNA forms a secondary structure at 5′UTR with the free energy of −145 kcal/mol.19 In addition to inhibiting eIF4A activity, Pdcd4 may repress protein translation through binding with RNA by preventing the formation of the translation initiation complex.32 Since the partial 5′UTR of p70S6K1 mRNA forms a secondary structure with the free energy of −82.30 kcal/mol, it is most likely that Pdcd4 inhibits the p70S6K1 translation via repressing eIF4A activity. Yet, the mechanism needs to be further investigated. Interestingly, it has been reported that Pdcd4 is phosphorylated at Ser67 by p70S6K and followed by proteasomal degradation.33 Here, the result of Pdcd4 regulating p70S6K1 translation indicates a feedback regulation between Pdcd4 and p70S6K1.The result that Pdcd4 knockdown increased the distribution of ion Fig. . By bind1 Previous studies have shown the crosstalk between Ras/ERK and PI3K/AKT pathway and thus inhibition of one pathway may induce activation of the other one.34 For example, in response to various stimulations, AKT negatively regulates ERK activation through inhibition of c-Raf phosphorylation.36 Conversely, inhibition of AKT activity by MN-2206 upregulates EKR phosphorylation in multiple myeloma cells.37 In agreement with this scenario, we observed that inhibition of AKT by BMS-754807 or MN-2206 notably increased the phosphorylation of MEK1/2 , ERK does not likely play a role in AKT activation. A recent study by Procaccia et al. suggested that AKT and MEK may directly bind each other.40 It is necessary to further investigate whether AKT and MEK bind each other to mutually suppress the counterpart.Our data showed that inhibition of MEK1/2 by a pharmacological inhibitor U0126) abolished the IGF-1R inhibition-induced p70S6K1 phosphorylation  and MEK inhibitor (U0126) significantly suppressed cell viability and colony formation in cultured cells . HT29, HCT116, and RKO cells were grown in McCoy’s medium (Invitrogen). LoVo and SW480 cells were cultured in RPMI-1640 medium (Hyclone). HT29-shLacZ (HT29-L) and HT29-shPdcd4 (HT29-P) cells were previously generated.41 Briefly, 5 × 103 cells/well were seeded onto a 96-well plate 1 day before drug treatment. The sodium 3′--bis (4-methoxy-6-nitro) benzene sulfonic acid hydrate (XTT) labeling mixture was added to the cell culture for 30 min and subsequently, the absorbance at 490 nm was measured using the iMark microplate reader (Bio-Rad Laboratories). For clonogenic assays, 1000 cells were seeded on a six-well plate and subsequently treated with drugs for 7 days. The number of colonies was determined after staining with 1% (w/v) crystal violet.The TACS XTT cell proliferation assay kit (Trevigen) was used for cell proliferation assays as described previously.The Annexin V–FITC Apoptosis Kit (Biolegend) was used for apoptosis assays according to the manufacturer’s protocol. The percentage of apoptotic cells was measured by a FacsCalibur cell analyzer (BD Biosciences).Cells were lysed in lysis buffer  on ice for 1 h. Twenty to forty microgram of protein was separated onto 4–12% SurePAGE gel (Genscript) and transferred to nitrocellulose membrane. After incubation with primary antibodies followed by horseradish peroxidase-linked secondary antibody, the target protein was visualized by Immobilon Western Chemiluminescent HRP substrates (Millipore). The antibodies against phospho-AKT(Ser473) (4060), AKT(pan) (4691), phospho-p70S6K(Thr389) (9205), p70S6K (2708), phospho-ERK1/2(Thr202/Tyr204) (4370), ERK1/2 (4695), phospho-S6 ribosomal protein (Ser235/236) (4858), S6 ribosomal protein (2217), phospho-MDM2(Ser166) (3521), phospho-MEK1/2(Ser217/221) (9154), MEK1/2 (8727), MEK1 (12671), and cleaved caspase 3 (9664) were purchased from Cell Signaling (1:1000 or 1:2000 dilution). The MEK2 (67410–1-Ig) antibody was purchased from Proteintech . The GAPDH (sc-47724) antibody was purchased from Santa Cruz Biotechnology (1:5000 dilution).19 Briefly, cycloheximide (10 μg/ml) was added into the culture medium and incubated for 10 min before cells were harvested. The cells were lysed in lysis buffer  for 30 min on ice. After centrifugation, the protein concentration of the supernatant was determined. An equal amount of protein in the supernatant was layered onto 10–45% (w/w) sucrose gradient and centrifuged at 36000 r.p.m. for 2 h to separated free RNAs and ribosome-bound RNAs. Fractions were collected using Piston Gradient Fractionator (Biocomp) and Econo UV monitor (Bio-Rad). Data were acquired by Gradient Profiler software (Biocomp).Sucrose gradient fractionation was carried out as described previously.p70S6K1 or p70S6K2 mRNA levels were calculated by the 2−ΔΔCt method.42 The average Ct values of three replicates in each fraction for p70S6K1 or p70S6K2 mRNA was normalized to the average Ct value of internal control GAPDH mRNA using the formula ΔCt = Ct(p70S6K1)-Ct(GAPDH). The relative level of p70S6K1 or p70S6K2 mRNA was determined by comparison of p70S6K1 or p70S6K2 mRNA in each polysomal fraction to that in the corresponding free RNAs fraction, respectively, using the formula 2−[ΔCt−ΔCt(free RNAs fraction)].Total RNA was extracted from each sucrose gradient fraction using Trizol reagent (Invitrogen). The first strand of cDNA was synthesized from each fraction using the Superscript First-Strand Synthesis System (ThermoFisher). The p70S6K1, p70S6K2, and GAPDH mRNA levels were quantified by qPCR using primers purchased from Qiagen. The relative 5) were transfected with 110 pmol of akt1, akt2, mek1, or mek2 DsiRNA (Integrated DNA Technologies) using INTERFERin in vitro siRNA transfection reagent (Polyplus) as instructed by the manufacturer’s protocol. After 48 h, cells were harvested for proliferation and Western blot analyses.Cells  and i.p. injected with (i) vehicle [10% (v/v) DMSO in corn oil); (ii) BMS-754807 (25 mg/kg); (iii) U0126 (15 mg/kg); and (iv) BMS-754807 (25 mg/kg) + U0126 (15 mg/kg) every day for 12 days. All mice under these treatments showed no sign of toxicity, i.e., bodyweight loss more than 15%, diarrhea, or decreased food intake.Male and female athymic nude mice (CrTac:NCr-Foxn1nu) were purchased from Taconic at age of 4- to 5-week. The mice were housed under pathogen-free conditions with commercial diet, water ad libitum, and 12 h light/12 h dark cycle. The experimental protocol was approved by IACUC of the University of Kentucky based on the NIH guidelines. Two million HCT116 cells were injected s.c. into the flank of each mouse and the tumor size was monitored and determined as previously described.t-test for quantification of the band intensity of western blots, the two-sample t-test for comparing the means of two groups, and the one-way ANOVA with Dunnett’s multiple comparison for comparing means among multiple groups. Data are shown as the mean ± standard deviation (SD). The difference is considered statistically significant at the P < 0.05 level.Statistical analyses were performed using the one-sample Supplemental Materials


Insertion of sacro-iliac (SI) screws for stabilization of the posterior pelvic ring without intraoperative navigation or three-dimensional imaging can be challenging. The aim of this study was to develop a simple method to visualize the ideal SI screw corridor, on lateral two-dimensional images, corresponding to the lateral fluoroscopic view, used intraoperatively while screw insertion, to prevent neurovascular injury. We used multiplanar reconstructions of pre- and postoperative computed tomography scans (CT) to determine the position of the SI corridor. Then, we processed the dataset into a lateral two-dimensional slice fusion image (SFI) matching head and tip of the screw. Comparison of the preoperative SFI planning and the screw position in the postoperative SFI showed reproducible results. In conclusion, the slice fusion method is a simple technique for translation of three-dimensional planned SI screw positioning into a two-dimensional strict lateral fluoroscopic-like view. Fractures of the pelvic ring are severe injuries, which occur with an incidence between 0.3 to 8.0% of all fractures . TherebyThe aim of the present investigation is to develop a simple method to visualize the perfect SI screw corridor on a preoperative CT scan, and transfer screw reference points of an ideal SI screw to lateral two-dimensional images, corresponding to the lateral fluoroscopic view used intraoperatively while screw insertion.CT scans of patients treated with percutaneous screw fixation (PSF) of the posterior pelvic ring between 2018 and 2020 were analyzed. Inclusion criteria were PSF for a traumatic instability of the posterior pelvic ring, a complete set of images (pre- and postoperative CT scans), and age over 18 years. Exclusion criteria were missing data  or an imperfect SI screw position in the postoperative CT images.Retrospectively, we processed the SFI based on the preoperative CT with an axial slice thickness of 1 mm in a bone kernel reconstruction. The DICOM  datasets were further processed with the freeware software package OsiriX MD . After determination of the ideal screw position and identification of ideal entry point and ideal screw tip on SFI, we correlated the SFI based on the preoperative scan to the SFI based on the postoperative scan to verify transferability.Each CT scan was reformatted along the axis of the sacrum to have an exact reproducible, symmetric orientation using the multi-planar reconstruction function (MPR) of Osirix MD. Then, the perfect osseous corridor for a safe SI screw placement from the lateral ileum, through the ala sacralis into the body of the first and/or second sacral vertebrae was identified in axial and inlet/outlet orientated angulations .Additionally, the actual screw position of the postoperative CT scans was determined in the axial, parasagittal, and coronal view. In the preoperative as well as in the postoperative CT slices, the entry point of the SI corridor and the end point of the corridor were marked by the setting of ROI (region of interest) points.After identification and marking of the SI corridor entry and end points, an image overlay of the CT slices was performed to visualize the marked ROI in a strictly lateral two-dimensional projection of the dorsal pelvis. First, exact lateral reconstructions of the pelvis were made and then a fusion of the relevant layers was performed. For this purpose, the thick slab function of OsiriX MD was applied in the multi-planar (MPR) reconstruction tool .The resulting pre- and postoperative two-dimensional lateral projections of the SI screw corridor were compared with the intraoperative lateral fluoroscopic images.In order to visualize the position of the SI screw corridor on the strictly lateral view on the processed image, the S1 and/or S2 vertebral body was divided into three columns: an anterior, a central, and a posterior column. Those columns were then further divided into cranial, intermedium, and caudal segments. This resulted in a total of nine separate segments of the vertebrae body and enabled an accurate assessment of the entry-, endpoint, and direction of the SI screw corridor . Using tIn total, 30 SI corridors and placed SI screws were analyzed in this radiological investigation.The identification of the perfect safe osseous corridor was reproducible in each preoperative CT scan . The fusComparing the preoperative predicted entry-, endpoint, and direction of the perfect osseous corridor to the actual postoperative position of every investigated SI screw resulted in almost identic SI screw positions. The nine-segment division of the vertebral bodies showed matching of the pre- and postoperative entry- and endpoints and made the direction of the screws comprehensible.Transferring these findings to the available intraoperative fluoroscopy images showed that the entry- and endpoint of the SI screw can be traced during surgery. These results apply to straight osseous corridors , as wellThe main finding of this study is, that the ideal position of SI screws in a safe osseous corridor can be planned preoperatively on CT scans and the entry-, endpoint, and direction of the SI screws can be transferred into a two-dimensional lateral, fluoroscopic-like image. This technique may be helpful for surgeons to re-identify the planned screw corridor intraoperatively.Percutaneous SI screws insertion is a successful and minimal invasive technique for fixation of fractures of the posterior pelvic ring ,24,25. PThe results of this study show that accurate planning of SI screw placement in preoperative CT scans, in combination with the slice fusion of sagittal CT images can provide a two-dimensional visualization of screw reference points (head and tip of the screw) in a lateral view, which may be reproducible intraoperatively by two-dimensional fluoroscopy. This technique might be helpful for the surgeon to identify the ideal position of an ideal SI screw, by using the fluoroscopy only in a strict lateral position. This could lead to a reduction of radiation exposure and might also reduce the operation time, as the surgeon does not need to change the position of the c-arm into inlet/outlet views several times to verify the direction of the k-wire. The technique also combines the advantages of three-dimensional planning on preoperative CT scans, with the widespread available and simple to use intraoperative fluoroscopy and a potential accuracy of intraoperative CT scans or 3D navigation.This study has some limitations. First, the accuracy of the described technique is very sensitive to exact positioning of the c-arm. An exact, true- lateral view of the sacrum is crucial. This leads to a high subjective dependence of the performing surgeon.Second, the evaluation was performed retrospectively on pre-, intra-, and postoperative CT scans and x-rays. Further clinical studies are needed to test this novel method with regard to intraoperative practicability and accuracy.In conclusion, the slice fusion method is a simple technique for translation of three-dimensional planned SI screw positioning into a two-dimensional strict lateral view and might help intraoperatively to place SI screws using a lateral fluoroscopic image.