Animals can learn to repeat behaviors to earn desired rewards, a process commonly known as reinforcement learning. While previous work has implicated the ascending dopaminergic projections to the basal ganglia in reinforcement learning, little is known about the role of the hippocampus. Here, we report that a specific population of hippocampal neurons and their dopaminergic innervation contribute to operant self-stimulation. These neurons are located in the dentate gyrus, receive dopaminergic projections from the locus coeruleus, and express D1 dopamine receptors. Activation of D1 + dentate neurons is sufficient for self-stimulation: mice will press a lever to earn optogenetic activation of these neurons. A similar effect is also observed with selective activation of the locus coeruleus projections to the dentate gyrus, and blocked by D1 receptor antagonism. Calcium imaging of D1 + dentate neurons revealed significant activity at the time of action selection, but not during passive reward delivery. These results reveal the role of dopaminergic innervation of the dentate gyrus in supporting operant reinforcement. In operant learning, animals modify their action repertoires to earn desired rewards. Previous work on the neural substrates of such learning has focused on the striatum and the midbrain dopaminergic projections that target the striatum . MidbraiThe hippocampus is also a target of dopaminergic projections. Dopamine receptors are expressed in the hippocampus, and in mice D1-class receptor expression is common in the dentate gyrus (DG) region . HoweverIn this study, we examined the contribution of dopaminergic signaling in the DG to operant learning and behavior. We found that mice could learn to perform a new action (pressing a lever) for optogenetic activation of D1 + neurons in the DG. In addition, using both optogenetics and in vivo pharmacological manipulations, we found that activation of LC dopaminergic neurons that project to the DG can also support self-stimulation. This effect depended on the activation of D1-like receptors. Finally, using in vivo calcium imaging in appetitive operant conditioning with food rewards, we found that D1 + DG neurons were more related to the goal-directed actions than simply non-contingent reward presentation.DG or D1::eYFPDG), producing selective expression of the excitatory opsin in D1 + neurons in the dentate gyrus  or a fluorescent control (DIO-eYFP) into D1-Cre mice  mice also showed self-stimulation that is comparable to the stimulation of D1::ChR2DG neurons  into the LC . We foun neurons .The LC-DG projection releases both norepinephrine and dopamine . It is uTo activate DG-projecting LC neurons robustly, we targeted the cell bodies of the DG-LC projections. But the LC has broad projections to many brain areas . To veriBased on our self-stimulation results, we hypothesized that DG D1 + neurons may be preferentially activated during operant conditioning in general, including during actions that result in natural rewards rather than simply optogenetic stimulation of the LC-DG pathway. To test this, we performed in vivo calcium imaging of DG D1 + neurons while performing an operant lever pressing task with a food reward. We implanted a gradient index lens above the DG in D1-Cre mice (n=5) and injected them with a Cre-dependent calcium indicator (AAV9-syn-FLEX-jGCamp7f) .We then recorded calcium transients from DG D1 + neurons during operant lever pressing for food rewards  during lTo determine if the activity of these neurons reflected the spatial locations of the lever pressing or the action of the lever pressing itself, we used a discrete trial design with two levers . On eachIt is difficult to assess the spatial activity of neurons in operant tasks, as animals do not cover the arena equally but instead preferentially occupy specific task-relevant locations . To examTogether our results provide the first evidence that DG D1 + neurons may play a role in operant reinforcement. Using a cell-type-specific approach, we showed that activation of D1 + neurons in the DG is sufficient for self-stimulation . FurtherThese findings build upon previous research that suggests that the LC supplies the primary dopaminergic input to the dorsal hippocampus . While pBoth the hippocampus and the LC are known to be effective sites for intracranial self-stimulation . HoweverSelf-stimulation behavior supported by stimulation of the LC-DG projections or the D1 + DG neurons appears to be different from that supported by stimulation of dopamine neurons in the VTA or SNc. First, the rate of lever pressing is much lower with LC-DG self-stimulation. Yet once established, the lever pressing was surprisingly resistant to extinction, persisting for many days after the termination of stimulation. This is very different from the self-stimulation of VTA or SNc DA pathways and their target regions; such classic self-stimulation behavior is rapidly extinguished in the absence of stimulation . Thus, cOur calcium imaging results showed that D1 + neurons in the DG are preferentially active during goal-directed instrumental actions compared to passive reward delivery. These findings suggest that the DA projections to the hippocampus contribute to the reinforcement of specific instrumental actions. They are broadly in agreement with recent findings on entorhinal grid cells  and hippAll experimental procedures were conducted in accordance with standard ethical guidelines and were approved by the Duke University Institutional Animal Care and Use Committee.Drd1 locus, B6;129-Tg(Drd1-cre)120Mxu/Mmjax, Jackson Labs), and wild type (C57BL/6 J). Optogenetic control of D1-receptor expressing dentate hippocampal neurons was achieved with a double-floxed inverted recombinant AAV5 virus injection to express the excitatory opsin ChR2-eYFP. Viral infection in the dentate of the hippocampus was histologically verified with eYFP imaging colocalized against a D1 receptor antibody and DAPI staining. All mice were aged between 2–12 months old, and housed on a 12:12 light cycle, with tests occurring in the light phase. For calcium imaging experiments, mice were put on food restriction and maintained at 90% of their initial body weights.All behavioral data were collected from D1-cre mice .CAV2-Cre was obtained from Institut de Génétique Moléculaire de Montpellier. rAAV5.EF1α.DIO.hChR2(H134R).eYFP, rAAV5.EF1α.DIO.eYFP, AAV9.hSyn.FLEX.jGCaMP7F, AAV(retro2).hSyn.EF1α.Cre.WPRE was obtained from the Duke University Vector Core. pAAV_hSyn1-SIO-stGtACR2-FusionRed was from Ofer Yizhar Hze/J, Jackson labs). Ai14 reporter mice have a loxP-flanked STOP cassette that is excised in the presence of Cre to promote transcription of a CAG promoter-driven red fluorescent protein variant (tdTomato). 50 nL of either Retro2-Cre 26Sortro2-Cre , or canitro2-Cre  was injetro2-Cre .Three D1-Cre male mice were injected with the AAV9-hSyn-Flex-GCaMP7f in the DG . 3–4 weeks after injections, mice were euthanized with CO2, and their brain was quickly harvested and frozen in OCT for future RNAscope experiments using the Advanced Cell Diagnostics kit and probes (ACD). Brains were sectioned using a cryostat at a thickness of 20 micrometers and directly mounted on superfrost slides; the slides were then stored at –80.On the experiment day, 4% PFA in PBS was chilled at 4 °C in a PFA-safe IHC container. Slides were removed from the –80 and immediately immersed in the pre-chilled PFA for 15 min at 4 °C. After washing the slides with PBS, sections were dehydrated through a 5 min immersion at 50%, 70%, and 100% ethanol. Slides were then air-dried for 5 min at room temperature before incubating them for 30 min with protease IV (ACD #322336), then washed twice in PBS. Sections were then incubated with either the Drd1a probe (ACD #406491), the negative control probe (ACD #320871), or the positive control probe (ACD #320881) for 2 hr at 40 °C. Next, slides were washed twice using the wash buffer (ACD #310091) and then incubated for 30 min with Amp1, 15 min with Amp2, 30 min with Amp3, and finally 15 min with Amp4 Alt B-FL (ACD #320851). After the two washes, sections were then incubated with 5% Neutral Goat Serum (NGS) in 0.2% TBST for 1 hr at RT in the dark, then incubated for 1 hr with a primary antibody against GFP  and for 2 hr with a secondary Alexa-fluorophore (488) conjugated antibodies (Invitrogen). Slides were mounted in Vectashield with DAPI  and a minimum of 5 images per mouse were acquired on an Olympus Fluoview confocal microscopy using a 60 X oil immersion objective.https://imagej.net/Fiji/Download), and GFP + cells were identified and saved as individual ROIs. Using the GFP + cells, a mask was created to identify the presence of puncta  within each ROI using the puncta analyzer plugin. The percentage of GFP + cells having puncta and the average number of puncta per GFP + cell were calculated in all three conditions.  followed by 4% paraformaldehyde (PFA) in order to confirm viral expression as well as optic fiber and GRIN lens placement. To confirm placement, brains were stored in 4% PFA with 30% sucrose for 72 hrs. Tissue was then post-fixed for 24 hr in 30% sucrose before cryostat sectioning (Leica CM1850) at 60 µm. Fiber and lens implantation sites were then verified.TH neurons in the LC  for 1 hr at room temperature. Sections were mounted and immediately coverslipped with Fluoromount G with DAPI medium . The placement was validated using an Axio Imager.V16 upright microscope (Zeiss) and fluorescent images were acquired and stitched using a Z780 inverted microscope (Zeiss).To confirm eYFP expression in LC and DG neurons, sections were rinsed in 0.1 M PBS for 20 min before being placed in a PBS-based blocking solution. The solution contained 5% goat serum and 0.1% Triton X-100 and was allowed to sit at room temperature for 1 hr. Sections were then incubated with a primary antibody  in blocking solution overnight at 4 °C. Sections were then rinsed in PBS for 20 min before being placed in a blocking solution with the secondary antibody used to visualize In order to characterize projections to the dentate gyrus we injected 50 nL of AAV(retro2).hSyn.EF1α.Cre.WPRE into each hemisphere of the DG of Ai14 mice . We then processed the slices and acquired images as described above. We opened the raw images taken from the Axio Imager V16 upright microscope (Zeiss) in Fiji to quantify the number of cells from a single coronal brain slice using eight-bit confocal images. A threshold was set to identify the neuronal cell bodies. The function ‘fill holes’ was then used to remove possible empty spaces within the selected cells. After converting the image to a mask, we ran the ‘Analyze Particle’ plug-in in Fiji to count the cells in each image. Using the Analyze Particle function, the masks taken were then counted to determine the number of co-localizing cells using the ‘Colocalization Threshold’ plug-in in Fiji.=8, five males, three females, 8–10 weeks old) or D1::eYFP(DG) control groups . Craniotomies were made bilaterally above the hippocampus and AAV5-DIO-ChR2 was microinjected into the dentate gyrus through a pulled glass pipette  using a microinjector . Optic fibers  were then implanted bilaterally above the dentate gyri. For pathway-specific experiments, wild-type mice were used to selectively target LC-Hipp  neurons by bilaterally injecting AAV(retro2).hSyn.EF1α.Cre.WPRE into the dentate gyrus (150 nL each hemisphere) combined with a Cre-dependent ChR2 virus injection into the locus coeruleus  before optic fiber placement . Controls received eYFP injections and fiber implants .Mice were anesthetized with 2.0 to 2.5% isoflurane mixed with 1.0 L/min of oxygen for surgical procedures and placed into a stereotactic frame . Meloxicam (2 mg/kg) and bupivacaine (0.20 mL) were administered prior to the incision. To optogenetically stimulate D1 + neurons in the hippocampus, adult D1-cre mice were randomly assigned to D1::ChR2(DG)  were used in local infusion experiments in the DG. LC and DG surgeries were the same as pathway-specific manipulations, with the addition of cannulas . All optic fibers were secured in place with dental acrylic adhered to skull screws. Mice were group housed and allowed to recover for one week before experimentation.Standard operant boxes  were housed in a light and sound-attenuating cubicles . Each box is equipped with two levers. A Windows XP-based computer system running MED-PC Version IV Research Control & Data Acquisition System software  is used to control the experimental equipment and record the data.For self-stimulation experiments, a single lever was inserted at the start of the session. For each lever press animals received 500 ms of stimulation at 20 Hz . Animals were trained in 30 min sessions. Animals were tested for 32 consecutive days, and received eight sessions of FR1, FR3, and FR5, and then eight extinction sessions. To test for extinction, the lever was inserted but no stimulation was delivered for lever presses.Even low doses of DA antagonists are known to reduce self-stimulation, but low doses of NE antagonists have no effect on self-stimulation behavior . In the For local drug infusions with chronically implanted cannulae, we trained naive animals (n=5) for 8 days on self-stimulation and used the same experimental testing protocol for infusions of SCH23390 (1.8 mM or 3.6 m) or propranolol (10.5 mM 21 mM). The dose is determined based on previous work . The dru=5, five males, 8–10 weeks old). A gradient index (GRIN) lens  was then implanted over the injection site. Viral expression was checked three weeks post-injection, and a base plate was secured to the skull with dental cement. A UCLA miniscope was used to assess in vivo activity of D1 + neurons in the DG in freely moving animals. Images were collected from the miniscope using Bonsai  in four penetrations  of D1-Cre mice . We used two levers in this task. One lever is inserted on a given trial. If pressed the lever would retract, and a food pellet is delivered 1 s later. After a 2 s inter-trial interval, one of the two levers was randomly inserted. Following FR testing, mice received a reward at fixed intervals (20 s), preceded by 1 s of white noise (Non-contingent reward). The animals were not required to press a lever to receive the reward.To assess significant increases in calcium activity, for each neuron we made a peri-event time histogram (PETH) between –3 s and 3 s aligned to relevant events . Event tTo ensure the different compounds were not affecting gross movement we tracked the distance traveled during the vehicle, SCH23390, and propranolol infusions. To do this we used DeepLabCut . For eac These important findings indicate that dopamine signaling, arising from the locus coeruleus, and D1R expressing neurons in the dentate gyrus can support positive reinforcement. This is an exciting finding given the prior dearth of information on the role of dopamine signaling in the dentate gyrus. The evidence to support the claims is compelling. Rigorous optogenetic experiments, site specific pharmacology, tracing, and calcium imaging bring together a compelling argument that dopamine signaling in the dentate can play an important role in positive reinforcement. This manuscript will be of interest to those interested in dopamine, locus coeruleus and/or hippocampal function, learning or motivated behaviors. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Kei Igarashi (Reviewer #2).Thank you for submitting the paper "Elucidating a locus coeruleus-hippocampal dopamine pathway for operant reinforcement" for consideration by Comments to the Authors:eLife.We are sorry to say that, after consultation with the reviewers, we have decided that this work will not be considered further for publication by As you will see below, all the reviewers thought that this work contains significant findings that are of interest to the neuroscience community. In particular, the finding of the reinforcing effect of locus coeruleus (LC) neurons projecting to the dentate gyrus (DG) is potentially of great interest. However, the reviewers found several substantive issues that limit the validity of the conclusions. Specifically, the reviewers are concerned about a lack of experiments to establish the release of dopamine  from LC neurons projecting to DG. Currently, this conclusion was made using intraperitoneal injections of dopamine receptor antagonist. It is important to perform local pharmacological experiments for both norepinephrine and dopamine antagonists within the DG in self-stimulating mice to fully address this. Furthermore, many experiments reported in this paper, including experiments using norepinephrine and dopamine antagonists , are underpowered. The number of animals (samples) needs to be increased throughout this work, in particular, the data supporting the specificity of dopamine as opposed to norepinephrine . Another important concern raised by the reviewers is that it remains unclear whether there is something unique about putative D1R-expressing neurons in the DG for controlling instrumental actions.Overall, the reviewers thought that the above issues are essential. Although these issues could be addressed by additional experiments, it will likely take more than 2-3 months. Because of these reasons, we decided to reject this work, at least in the current form. If the authors can fully address the above issues and each of the reviewers individual concerns, we would be happy to consider a revised manuscript as a new submission.Reviewer #1 (Recommendations for the authors):This manuscript by Petter et al., examines the role of putative D1R expressing neurons in the dentate gyrus in regulating operant behaviors in mice. The authors show that mice will learn to press a lever to receive optogenetic stimulation of D1R-Cre targeted cells compared to eYFP control mice. Next, they show that the DG receives input from the LC as previously described. They then show that mice will self-stimulate for activation of LC neurons that project to the DG, and that optogenetic inhibition of D1-DG neurons reduces the amount of self-stimulation of LC-DG neurons. Finally, the authors use miniscope based calcium imaging to show that DG D1R expressing neurons show changes in their activity timelocked to lever pressing. Overall, the work builds upon previous studies to support a role of LC projections to the DC in instrumental learning and reinforcement. While many of the findings support these conclusions, there are a number of issues with the data and paper in its current form.1) Targeting DG cells in the D1R-Cre mouse. The authors imply that somehow D1R expressing cells in the DG are critical for instrumental behavior. However, it is unclear whether there is robust co-localization with D1R expression and the virally targeted cells. One image is presented to suggest this, but the authors need to quantify the percent of cells that are virally targeted that express D1R vs. those that do not. This has been performed for striatal tissue, but I am not aware of studies that have done this for the DG.2) The amount of nosepoking to receive optical stimulation in all of the self-stimulation experiments seems low, and it is somewhat difficult to really assess this as it is presented as a lever press rate. I would be helpful to present the data in the total number of nosepokes per session for all groups. Even if the level of self-stimulation is low, they do use the appropriate control group (eYFP expressing controls) show the behavior is different between the groups.3) The tracing experiments, although simple and straightforward, suffer from low n's. 2 hemispheres from 2 mice are presented in figure 2 for example. It would be preferable if at least 3-4 biological replicates are included in each dataset. These should also include both male and female mice whenever possible.4) The antagonist experiments presented in Figure 3 are consistent with their hypothesis that dopamine is mediating the self-stimulation effect. However, these drugs are given IP and there is no way to know whether they are actually acting in the DG vs. other areas that may also reduce reinforcement.+.5) For the self-stimulation of LC neurons experiment  it is unclear why a non-specific viral strategy was employed. The authors could have easily targeted TH expressing neurons that project from the LC to the DG instead of only targeting neurons based on their projection target. At the very least, the authors should perform a careful quantification of the percentage of the LC cells that are targeted using the retroAAV that are TH6) The presentation of the miniscope data could use some improvement. For example, how many cells for each animal were recorded? How many trials were analyzed per session? All of the image data looks processed, and it would be nice to see some raw data presented alongside the processed data. I was also struck by how short the recording sessions were (10 minutes). Why did the authors record for such a short period of time?Reviewer #2 (Recommendations for the authors):In this manuscript, Petter and colleagues identify the neuromodulatory pathway from the locus coeruleus to the dentate gyrus of the hippocampus. The manuscript has a novelty on focusing in this previously undescribed circuit. However, I have several major concerns that some of the claims are not justified by the results.1. I have a concern on the propranolol experiment . Although the authors state that they did not see significant effect with propranolol, there is a trend of decreases (as mentioned by the author). The sample number here looks like n=5, which would not be achieving a sufficient power. Thus, it may be the case that this circuit functions using both dopamine AND norepinephrine.Reviewer #3 (Recommendations for the authors):2. I have a difficulty in interpreting LC-stimulation + DG inhibition experiment , as this can be interpreted with many possibilities. Rather, LC axon inhibition would achieve simple conclusion about the involvement of this circuit.In this study, the authors show that stimulation of Drd1-expressing dentate gyrus (DG) neurons in the mammalian brain promotes operant reinforcement, thereby expanding the role of the hippocampus traditionally studied with respect to episodic/spatial memory to instrumental learning. This is a very interesting finding, supported by a substantial array of behavioural observations. Yet, I do have three sets of comments/questions regarding the selectivity of the main observations, and thus the strength of the related conclusions.First, the authors conclude from their experiments that D1-expressing DG cells constitute a specific population of hippocampal neurons that supports operant reinforcement learning. But how cell-type selective is this behavioural contribution? Notably, can Drd1-non-expressing DG neurons also be involved in this behavioural effect? That is, can self-stimulation be achieved with other DG neuron types? Interestingly, many retro-Cre labelled LC neurons that project to DG are not expressing tyrosine hydroxylase (see Table 1). Likewise, is this behavioural effect selective to LC-to-DG inputs? That is, can this effect be obtained with any input targeting Drd1-expressing DG neurons ?Second, while the data supporting the direct contribution of the LC-to-DG pathway in operant reinforcement is convincing (irrespective of whether or not such a contribution is selective), the dopaminergic identity of this pathway remains elusive. Throughout the manuscript the authors keep making this sort of strong statement: e.g., "surprisingly, these neurons receive dopaminergic projections from the locus coeruleus …" . As far as I understand the results, neither the release of dopamine from, nor the dopaminergic identity of, the LC inputs to DG are demonstrated. This claim is central to the work but seems to rely on two indirect observations. First, DG-targeting LC neurons are immuno-positive for tyrosine hydroxylase. Indeed, because this marks catecholamine neurons and is thus expressed by both dopamine and norepinephrine neurons. Second, systemic injections of the β-adrenoceptor antagonist propranolol does not prevent self-stimulation while the D1-antagonist SCH 23390 does. But the direct blockade of the LC-DG pathway is not established in these pharmacological experiments. Should other pathways be recruited in this behaviour , then the current interpretation of this pharmacological blockade would be misleading.Finally, hippocampal activity is strongly coupled to animal's speed (and thus the corresponding network states that report active exploration versus immobility). The observation that D1+ DG neurons are modulated by lever pressing during operant conditioning is interesting. The inclusion of the passive reward delivery is a good control. But speed should be formally controlled for . This can be done in at least two ways: by using speed-matched time windows across the two reward delivery conditions , and by reporting the measure of DG activity (Δ F/F) as a function of speed at the active reward delivery.eLife. Your revised article has been evaluated by Kate Wassum (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Elucidating a locus coeruleus-hippocampal dopamine pathway for operant reinforcement" for further consideration by The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:As you can see, we very much appreciated your thorough response to the prior review. The manuscript is much improved. However, there are some remaining concerns that need to be addressed. These are summarized for your convenience below but are described in more detail in the reviewers' comments. In your revision please provide a point x point response to each reviewer's comment.– There are some concerns, especially regarding Figure 3, whether drug effects on behavior are specific to reinforcement and not confounded by general effects on motor behavior. We feel this could be ideally addressed experimentally, though there may be other avenues to alleviate this concern.– There are concerns that the conclusions that the reinforcement effects are mediated by dopamine and not norepinephrine hinge on the strong conclusions made about null results from experiments that could be underpowered  and also have high variability. As important conclusions rely on these findings, we think this concern should be addressed with additional experiments to increase power.– Reviewer 3 makes a number of additional important points that we all agree should be addressed.Reviewer #1 (Recommendations for the authors):Most of my previous concerns are now resolved.Reviewer #2 (Recommendations for the authors):All of my previous concerns have been addressed.Reviewer #3 (Recommendations for the authors):1. There are concerns with some of the reinforcement data that clouds interpretation. The biggest concern I have is in Figure 3 where it is not possible to tell whether responding during the drug treatment challenges is truly being reinforced by LC-DG stimulation in these tests. These tests were run after 8d of extinction of a previous operant response, and there is no difference in responding between EXT day 8 and test day 1. In fact, they respond less during the testing. In figure 4, basal response rates are very low at the vehicle (10 responses/30min). Again, these rates are lower than extinction levels previously established. Are the authors sure that the drug effects on behavior are reinforcement-specific and not just general effects on motor behavior?2. The only evidence that supports their claim that this is mediated by dopamine and not norepinephrine is the microinjection study, which is quite underpowered and also involves the question of reinforcement (above). This hinges on the strong conclusions made about null results from an underpowered set of experiments. For the authors are trying to rule out the contributions of NE, which is hard to do given n=5 where the mean effect size is a reduction of ~5-6 responses vs. ~2-3 responses on a task with fairly high variance to begin with. In particular, the 21nM propranolol group may be lower than the vehicle if the outlier with the increased responding is removed. This is important as the conclusion of the manuscript is that an LC-DG circuit mediates reinforcement via a dopamine input+?3. It is not clear why in Figure 3 they didn't use a TH-specific viral strategy here. Since the manipulation in not cell-type specific, they really should have quantified these neurons. How many of the ChR2+ neurons are TH+ population of LC neurons projecting to the DG – this is potentially a very small group of cells and the authors have not linked these responses to this specific input.4. The imaging studies were conducted on D1+ neurons, but not demonstrably related to LC inputs. With such small cell numbers activated here, and such a small TH5. The discussion overstates a large number of the conclusions.a. The authors point out in the text that it is interesting that this behavior doesn't really extinguish… yet this observation is left entirely unexplored. Perhaps, this circuitry is important for the acquisition of [reward-related] memories… but perhaps it is not critical to the maintenance of conditioned behavior. Some inhibition (or DA depletion) studies may give some insight into this possibilityb. Authors write: "These findings suggest that the LC supplies the primary dopaminergic input to the dorsal hippocampus (1), especially to a population of D1+ neurons in the DG (2), and that this dopaminergic pathway plays a critical role in operant reinforcement (3)."i. It is not clearly demonstrated here that the LC provides dopaminergic input to the DG; however, it has been shown more clearly in previous studies. This language could be softened to suggest that they have data to support previous work.ii. There is no direct evidence that LC projections modulate the D1+ population, so this may need to be softened as well.iii. There is no evidence that this pathway is necessary, or even involved in natural reinforcement. This would require some circuit inhibition studies or pharmacological manipulations during sucrose reinforcement. It's also not clear what the authors believe the function of D1+ DG neurons to be.c. Authors note "These findings suggest that the hippocampus contributes to the reinforcement of specific instrumental actions.". While this is possible, there is no clear evidence for this function – only evidence the calcium events were organized around lever presses. What about lever presses prior to learning? What about lever presses after extinction?6. Is figure 1C showing a response isolated from an FR5 (as stated in the legend)? Were there no other responses within the 15-second timeframe of each response? How were these signals isolated and how was it ensured that the same data traces weren't being averaged several times but slightly offset from each other because of the temporal relationship between responses?7. The response and the reinforcer delivery are only separated by 1 second… so it is challenging to distinguish whether there is a calcium response to reward (+1s) or not since fluorescence is already elevated at the time of reward consumption.8. Similarly, the lever protracted 2sec after reward delivery – are the animals still consuming the sucrose pellet at this time?9. Ultimately, it's unclear how they were able to categorize the neurons given the condensed task parameters and high variability exact spike time for the neurons shown in Figure 7B. [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]As you will see below, all the reviewers thought that this work contains significant findings that are of interest to the neuroscience community. In particular, the finding of the reinforcing effect of locus coeruleus (LC) neurons projecting to the dentate gyrus (DG) is potentially of great interest. However, the reviewers found several substantive issues that limit the validity of the conclusions. Specifically, the reviewers are concerned about a lack of experiments to establish the release of dopamine  from LC neurons projecting to DG. Currently, this conclusion was made using intraperitoneal injections of dopamine receptor antagonist. It is important to perform local pharmacological experiments for both norepinephrine and dopamine antagonists within the DG in self-stimulating mice to fully address this.We have performed local pharmacological experiments using norepinephrine and dopamine antagonists within the DG . The results support our original conclusions.Furthermore, many experiments reported in this paper, including experiments using norepinephrine and dopamine antagonists , are underpowered. The number of animals (samples) needs to be increased throughout this work, in particular, the data supporting the specificity of dopamine as opposed to norepinephrine .We have also increased the number of animals for pharmacological experiments as well as tracing experiments.Another important concern raised by the reviewers is that it remains unclear whether there is something unique about putative D1R-expressing neurons in the DG for controlling instrumental actions.We have included discussion of this issue (see discussion) – “Future work will have to address whether this self-stimulation effect requires D1+ neurons or if it is a property of hippocampal neurons in general.”Reviewer #1 (Recommendations for the authors):This manuscript by Petter et al., examines the role of putative D1R expressing neurons in the dentate gyrus in regulating operant behaviors in mice. The authors show that mice will learn to press a lever to receive optogenetic stimulation of D1R-Cre targeted cells compared to eYFP control mice. Next, they show that the DG receives input from the LC as previously described. They then show that mice will self-stimulate for activation of LC neurons that project to the DG, and that optogenetic inhibition of D1-DG neurons reduces the amount of self-stimulation of LC-DG neurons. Finally, the authors use miniscope based calcium imaging to show that DG D1R expressing neurons show changes in their activity timelocked to lever pressing. Overall, the work builds upon previous studies to support a role of LC projections to the DC in instrumental learning and reinforcement. While many of the findings support these conclusions, there are a number of issues with the data and paper in its current form.1) Targeting DG cells in the D1R-Cre mouse. The authors imply that somehow D1R expressing cells in the DG are critical for instrumental behavior. However, it is unclear whether there is robust co-localization with D1R expression and the virally targeted cells. One image is presented to suggest this, but the authors need to quantify the percent of cells that are virally targeted that express D1R vs. those that do not. This has been performed for striatal tissue, but I am not aware of studies that have done this for the DG.We used RNAscope to quantify the number of virally target cells that colocalized with D1R expression. We found robust colocalization .2) The amount of nosepoking to receive optical stimulation in all of the self-stimulation experiments seems low, and it is somewhat difficult to really assess this as it is presented as a lever press rate. I would be helpful to present the data in the total number of nosepokes per session for all groups. Even if the level of self-stimulation is low, they do use the appropriate control group (eYFP expressing controls) show the behavior is different between the groups.Lever pressing was used. We now include total number of presses for all data.3) The tracing experiments, although simple and straightforward, suffer from low n's. 2 hemispheres from 2 mice are presented in figure 2 for example. It would be preferable if at least 3-4 biological replicates are included in each dataset. These should also include both male and female mice whenever possible.We added 2 mice with retro-Cre injected into the DG of Ai-14 mice (n = 4).4) The antagonist experiments presented in Figure 3 are consistent with their hypothesis that dopamine is mediating the self-stimulation effect. However, these drugs are given IP and there is no way to know whether they are actually acting in the DG vs. other areas that may also reduce reinforcement.We have performed the same experiments using local infusion of these drugs . Similar results were found.+.5) For the self-stimulation of LC neurons experiment  it is unclear why a non-specific viral strategy was employed. The authors could have easily targeted TH expressing neurons that project from the LC to the DG instead of only targeting neurons based on their projection target. At the very least, the authors should perform a careful quantification of the percentage of the LC cells that are targeted using the retroAAV that are TH+  and found that ~40% of the retro-cre labeled cells are also TH+.We added quantification of the percentage of LC cells that are TH6) The presentation of the miniscope data could use some improvement. For example, how many cells for each animal were recorded? How many trials were analyzed per session? All of the image data looks processed, and it would be nice to see some raw data presented alongside the processed data. I was also struck by how short the recording sessions were (10 minutes). Why did the authors record for such a short period of time?We updated the Figure to reflect how many neurons were recorded per animal and included a table (Table 2) to show the number of neurons recorded from each animal for each task. We added more imaging data from other tasks that show the number of presses  per session for each animal . Sessions were short because we wanted to avoid photobleaching.Reviewer #2 (Recommendations for the authors):In this manuscript, Petter and colleagues identify the neuromodulatory pathway from the locus coeruleus to the dentate gyrus of the hippocampus. The manuscript has a novelty on focusing in this previously undescribed circuit. However, I have several major concerns that some of the claims are not justified by the results.1. I have a concern on the propranolol experiment . Although the authors state that they did not see significant effect with propranolol, there is a trend of decreases (as mentioned by the author). The sample number here looks like n=5, which would not be achieving a sufficient power. Thus, it may be the case that this circuit functions using both dopamine AND norepinephrine.We added animals to bring the sample number to N=8 for the systemic injections . We also performed local infusions with both NE and DA antagonists (cannulae in DG) to further confirm our effects.2. I have a difficulty in interpreting LC-stimulation + DG inhibition experiment , as this can be interpreted with many possibilities. Rather, LC axon inhibition would achieve simple conclusion about the involvement of this circuit.We removed this data, as it could be difficult to interpret.Reviewer #3 (Recommendations for the authors):In this study, the authors show that stimulation of Drd1-expressing dentate gyrus (DG) neurons in the mammalian brain promotes operant reinforcement, thereby expanding the role of the hippocampus traditionally studied with respect to episodic/spatial memory to instrumental learning. This is a very interesting finding, supported by a substantial array of behavioural observations. Yet, I do have three sets of comments/questions regarding the selectivity of the main observations, and thus the strength of the related conclusions.First, the authors conclude from their experiments that D1-expressing DG cells constitute a specific population of hippocampal neurons that supports operant reinforcement learning. But how cell-type selective is this behavioural contribution? Notably, can Drd1-non-expressing DG neurons also be involved in this behavioural effect? That is, can self-stimulation be achieved with other DG neuron types? Interestingly, many retro-Cre labelled LC neurons that project to DG are not expressing tyrosine hydroxylase (see Table 1). Likewise, is this behavioural effect selective to LC-to-DG inputs? That is, can this effect be obtained with any input targeting Drd1-expressing DG neurons ?We focused on the role of D1+ neurons and the DA projections from LC. We did not examine the role of other cell types in the DG. Future work will have to investigate how specific the effects are to D1 neurons. We found ~41% of the retrogradely labeled neurons colocalized with TH.Second, while the data supporting the direct contribution of the LC-to-DG pathway in operant reinforcement is convincing (irrespective of whether or not such a contribution is selective), the dopaminergic identity of this pathway remains elusive. Throughout the manuscript the authors keep making this sort of strong statement: e.g., "surprisingly, these neurons receive dopaminergic projections from the locus coeruleus …" . As far as I understand the results, neither the release of dopamine from, nor the dopaminergic identity of, the LC inputs to DG are demonstrated. This claim is central to the work but seems to rely on two indirect observations. First, DG-targeting LC neurons are immuno-positive for tyrosine hydroxylase. Indeed, because this marks catecholamine neurons and is thus expressed by both dopamine and norepinephrine neurons. Second, systemic injections of the β-adrenoceptor antagonist propranolol does not prevent self-stimulation while the D1-antagonist SCH 23390 does. But the direct blockade of the LC-DG pathway is not established in these pharmacological experiments. Should other pathways be recruited in this behaviour , then the current interpretation of this pharmacological blockade would be misleading.As mentioned above, we also included experiments using local infusion of antagonists to address this problem.Finally, hippocampal activity is strongly coupled to animal's speed (and thus the corresponding network states that report active exploration versus immobility). The observation that D1+ DG neurons are modulated by lever pressing during operant conditioning is interesting. The inclusion of the passive reward delivery is a good control. But speed should be formally controlled for . This can be done in at least two ways: by using speed-matched time windows across the two reward delivery conditions , and by reporting the measure of DG activity (Δ F/F) as a function of speed at the active reward delivery.In order to address these concerns, we report the measure of DG activity (df/f) as a function of speed during the reward delivery period. Specifically, we used a 4 second window (+/- 2 seconds) centered on reward delivery. For each trial and neuron pair, we take the average df/f in this 4 second window and plot it as a function of speed. In order to compare neurons on the same scale df/f was normalized by the maximum df/f for each neuron.2=0.0591, p value: 0.2924) or the delay task . The FR5 task shows a weak negative relationship between speed and df/f The data is plotted for all three tasks in [Editors’ note: what follows is the authors’ response to the second round of review.]The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:As you can see, we very much appreciated your thorough response to the prior review. The manuscript is much improved. However, there are some remaining concerns that need to be addressed. These are summarized for your convenience below but are described in more detail in the reviewers' comments. In your revision please provide a point x point response to each reviewer's comment.– There are some concerns, especially regarding Figure 3, whether drug effects on behavior are specific to reinforcement and not confounded by general effects on motor behavior. We feel this could be ideally addressed experimentally, though there may be other avenues to alleviate this concern.We analyzed total distance traveled for each drug condition and did not find effects on motor behavior .– There are concerns that the conclusions that the reinforcement effects are mediated by dopamine and not norepinephrine hinge on the strong conclusions made about null results from experiments that could be underpowered  and also have high variability. As important conclusions rely on these findings, we think this concern should be addressed with additional experiments to increase power.We have performed additional experiments to increase N.Reviewer #3 (Recommendations for the authors):1. There are concerns with some of the reinforcement data that clouds interpretation. The biggest concern I have is in Figure 3 where it is not possible to tell whether responding during the drug treatment challenges is truly being reinforced by LC-DG stimulation in these tests. These tests were run after 8d of extinction of a previous operant response, and there is no difference in responding between EXT day 8 and test day 1. In fact, they respond less during the testing. In figure 4, basal response rates are very low at the vehicle (10 responses/30min). Again, these rates are lower than extinction levels previously established. Are the authors sure that the drug effects on behavior are reinforcement-specific and not just general effects on motor behavior?We believe that the drug effects on behavior are reinforcement specific. The animals used for LC-hippocampal pathway stimulation with intrahippocampal cannula were all naïve to start the experiments. These animals showed an increase in responding compared to controls  and significant decreases in lever pressing in response to SCH23390 but not propranolol . Further, we analyzed the movement of these animals using DeepLabCut and found that the head movement of these animals was unchanged by the drug infusions . The change in lever pressing but not gross movement suggests these are likely reinforcement specific.2. The only evidence that supports their claim that this is mediated by dopamine and not norepinephrine is the microinjection study, which is quite underpowered and also involves the question of reinforcement (above). This hinges on the strong conclusions made about null results from an underpowered set of experiments. For the authors are trying to rule out the contributions of NE, which is hard to do given n=5 where the mean effect size is a reduction of ~5-6 responses vs. ~2-3 responses on a task with fairly high variance to begin with. In particular, the 21nM propranolol group may be lower than the vehicle if the outlier with the increased responding is removed. This is important as the conclusion of the manuscript is that an LC-DG circuit mediates reinforcement via a dopamine inputWe performed additional experiments and increased the N in each group to 8.+?3. It is not clear why in Figure 3 they didn't use a TH-specific viral strategy here. Since the manipulation in not cell-type specific, they really should have quantified these neurons. How many of the ChR2+ neurons are TH+ in figure 2H.We did quantify the number of ChR2+ neurons that are TH+ population of LC neurons projecting to the DG – this is potentially a very small group of cells and the authors have not linked these responses to this specific input.4. The imaging studies were conducted on D1+ neurons, but not demonstrably related to LC inputs. With such small cell numbers activated here, and such a small TH+ LC projections cannot be determined, as doing pathway specific stimulation while imaging is not technically feasible. In the paper we did not make any specific claims about the relationship between TH+ LC projections and neurons we imaged. We do know from previous work that TH+ axons strongly innervate the hippocampus . While we have not linked the responses of the cells directly to LC activity, we find it probably that the LC modulates these cells based on the changes in behavior with D1-DG activation of LC-DG activation.The imaging experiments merely attempted to show the properties of D1+ neurons in the DG during operant behavior. Whether these neurons receive TH5. The discussion overstates a large number of the conclusions.a. The authors point out in the text that it is interesting that this behavior doesn't really extinguish… yet this observation is left entirely unexplored. Perhaps, this circuitry is important for the acquisition of [reward-related] memories… but perhaps it is not critical to the maintenance of conditioned behavior. Some inhibition (or DA depletion) studies may give some insight into this possibilityTesting reward-related memories is beyond the scope of these studies. We agree it would be interesting for future studies to inhibit D1-DG neurons during acquisition and extinction of conditioned behavior.b. Authors write: "These findings suggest that the LC supplies the primary dopaminergic input to the dorsal hippocampus (1), especially to a population of D1+ neurons in the DG (2), and that this dopaminergic pathway plays a critical role in operant reinforcement (3)."i. It is not clearly demonstrated here that the LC provides dopaminergic input to the DG; however, it has been shown more clearly in previous studies. This language could be softened to suggest that they have data to support previous work.We softened the language to reflect that this claim about DA inputs to the DG is shown by previous work, rather than our findings.ii. There is no direct evidence that LC projections modulate the D1+ population, so this may need to be softened as well.We have revised our discussion to point out the lack of direct evidence.iii. There is no evidence that this pathway is necessary, or even involved in natural reinforcement. This would require some circuit inhibition studies or pharmacological manipulations during sucrose reinforcement. It's also not clear what the authors believe the function of D1+ DG neurons to be.We have revised our discussion to point out the lack of direct evidence, but our calcium imaging data shows systematic changes in press-related activity during training.c. Authors note "These findings suggest that the hippocampus contributes to the reinforcement of specific instrumental actions.". While this is possible, there is no clear evidence for this function – only evidence the calcium events were organized around lever presses. What about lever presses prior to learning? What about lever presses after extinction?An additional piece of evidence supporting the role of these hippocampal neurons in operant conditioning is that there are more neurons responsive to lever pressing compared to noncontingent reward delivery. There are no lever presses prior to learning in such a task, since lever pressing is learned.6. Is figure 1C showing a response isolated from an FR5 (as stated in the legend)? Were there no other responses within the 15-second timeframe of each response? How were these signals isolated and how was it ensured that the same data traces weren't being averaged several times but slightly offset from each other because of the temporal relationship between responses?In figure 5C there was a typo and the example neuron was actually taken from an FR1 session.We have updated the legend to reflect this. There are no other responses within the time frame.7. The response and the reinforcer delivery are only separated by 1 second… so it is challenging to distinguish whether there is a calcium response to reward (+1s) or not since fluorescence is already elevated at the time of reward consumption.This is partially addressed by the non-contingent reward task, where there is no operant conditioning component.8. Similarly, the lever protracted 2sec after reward delivery – are the animals still consuming the sucrose pellet at this time?It is possible that the animals are still consuming the sucrose pellet at this time. If they are still consuming the pellet they can wait to respond on the lever. The lever does not retract after a fixed amount of time, and therefore the animal can press it whenever it is ready.9. Ultimately, it's unclear how they were able to categorize the neurons given the condensed task parameters and high variability exact spike time for the neurons shown in Figure 7B.We used a non-contingent reward task so we could examine how many cells were responsive when no operant behavior was required for a reward. In this task we found significantly fewer modulated neurons compared to a task where lever pressing was required.


European windstorms cause socioeconomic losses due to wind damage. Projections of future losses from such storms are subject to uncertainties from the frequency and tracks of the storms, their intensities and definitions thereof, and socio-economic scenarios. We use two storm severity indices applied to objectively identified extratropical cyclone footprints from a multi-model ensemble of state-of-the-art climate models under different future socio-economic scenarios. Here we show storm frequency increases across northern and central Europe, where the meteorological storm severity index more than doubles. The population-weighted storm severity index more than triples, due to projected population increases. Adapting to the increasing wind speeds using future damage thresholds, the population weighted storm severity index increases are only partially offset, despite a reduction in the meteorological storm severity through adaptation. Through following lower emissions scenarios, the future increase in risk is reduced, with the population-weighted storm severity index increase more than halved. Storm severity indices of European winter storms in climate models show future increased storm losses in northwestern Europe, caused by changes in the location and intensity of storms, and increasing population. The extreme wind speeds associated with particularly intense cyclones (‘windstorms’) can cause significant and widespread damage to buildings and utilities infrastructure2, and have large impacts on aviation, shipping, forestry and agricultural activities3. Examples of severe windstorms include storms Lothar and Martin from December 1999, which resulted in losses of 8bn and 3.3bn USD, respectively. Storm Kyril from January 2007 also caused 6.7bn USD insured losses4, and had total economic losses much greater than this.Across the European continent, extratropical cyclones dominate the prevailing weather conditions and are one of the most significant natural drivers of insured losses, with individual storms having the potential to cause more than €6 billion in losses3, with the frequency of storms peaking in the early 1990s, a declining trend followed this until 2014, associated with changes in the North Atlantic Oscillation5. These varying trends are also associated with uncertain projections of future storm tracks, with this being especially notable across the North Atlantic and for windstorms across western Europe7. Some consistent signals are a decrease over the south of Europe, including the Mediterranean, and an increase over the British Isles and parts of Northwestern Europe9. How the winds associated with the storms will change can be quantified in a number of different ways10. In the Northern Hemisphere winter, the wind intensity of extreme storms is expected to increase in the future11 despite decreases in overall storm numbers. Other studies show a decrease in the cyclones associated with strong winds across the NH but with increases over NW Europe8, indicating that these results often come with strong regional variations12. Winds associated with individual cyclones could increase in strength14, and mesoscale wind features (up to 50 km), such as sting jets, could increase in intensity15. These mesoscale changes seem to be related to the robust projections of increased precipitation within European extratropical cyclones18, and the associated increased latent heating11.There is considerable inter-annual variability of winter storms over western Europe21, consistent with storm track changes. A synthesis of previous work suggests that a future 2.5 °C increase in temperature could lead to a 23% increase in European windstorm losses22. Future losses from windstorms over Europe will depend on several factors that can be related to the IPCC framework of risk being a function of hazard, exposure, and vulnerability. The hazard here depends on the frequency of the windstorms and the intensity of the winds within the storms, the exposure depends on the track or location of the windstorms, and the population of the region  while the vulnerability depends on the degree of adaptation to extreme wind speeds in the present and future (amongst other factors not considered here).Previous examinations of potential future storm losses indicate an increase over the northwest of Europe, although with varying magnitudes23 to assess the future changes of the five factors that determine the losses associated with winter  European windstorms between the recent past (1980–2010) and the end of the current century (2070–2100)24. Previous studies considering losses may have used only single models21, consider only daily maximum wind speeds26, or use previous generations of models with resolutions considerably lower than offered by CMIP6 models19, meaning we can provide a better estimate of uncertainty and robustness, using models that can better represent extratropical cyclone wind features27. We consider two different Shared Socio-economic Pathways (SSPs) forming part of the IPCC’s Sixth Assessment Cycle24: the first envisages policy efforts to control greenhouse gas emissions that follow current trends (SSP2-4.5)28 and has an end of 21st-century global mean warming of 2.6 °C, while the other envisages very high emissions driven by increased exploitation of fossil fuel resources (SSP5-8.5) with an end of 21st-century warming of over 5 °C relative to pre-industrial29.In this study, we used eight state-of-the-art general circulation models (GCMs) participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6)30; the meteorological SSI (METSSI), which only depends on the storm winds; and the sociological SSI (SOCSSI), which includes population data and correlates strongly and significantly with actual storm losses . We, therefore, consider an idealised adaptation scenario by using varying wind/damage thresholds for the historical and future climates, finding that this can substantially impact future SOCSSI changes. We also account for potential changes in sociological factors, such as population demographics, which will interact with changes in cyclone severity to affect actual wind risk from social and economic perspectives34.To quantify storm impacts, we use two versions of a storm severity index (SSI)ses Fig. . Other m37. The robust nature of the changes is despite the CMIP6 models strongly overestimating the baseline cyclone frequency in south-eastern Europe relative to ERA5 reanalysis  and SSP5-8.5 (–6%) Fig.  relativesis Fig. , associa30, or 9 m/s if the 98th percentile is below that threshold39. Extreme wind speeds over Europe are most commonly associated with extratropical cyclones and the fronts that come with them41, which can be seen by the similar pattern of high 98th percentile winds , any future decreases in these extremes as shown in Fig. Having shown that parts of Europe will experience increases in the intensity of the extreme winds, we can consider two different sub-scenarios to investigate future storm severity and risk. One represents an ‘adaptation’ sub-scenario, where the local 98th percentile value used to calculate the storm severity indices (see Methods) is extracted from the considered socio-economic scenario; and a ‘no-adaptation’ scenario, where the storm severity threshold is calculated based only on the historical baseline distribution1. First, we consider the METSSI, which is a measure of the severity of the storms in a meteorological sense only. While the CMIP6 model average METSSI is biased relative to the reanalysis , equivalent to the annual exceedance probabilitysis Fig. , the modsis Fig. . For theead Fig. . Model vope Fig.  and one Much of the increase in METSSI over Europe corresponds to locations where increases in the 98th percentile winds Fig. , and inc30, by scaling the METSSI by the historical and future population estimates  from the historical value over NW Europe for the SSP5-8.5. This increase is associated with the robust location shift of the highest storm frequencies, rather than changes in the within-storm wind speeds. There is, however, considerable variability between models in their storm track and METSSI response, demonstrating the benefit of the ensemble used in this study. The multi-model changes are consistent with the findings of the latest IPCC report46 and from studies assessing wind risk in CMIP6 models43. However, our results find markedly higher potential increases in risk, with a potential tripling of windstorm impacts across NW Europe. There are several potential reasons for this difference; internal variability; higher resolution of current global climate models; differences in climate scenarios; or the higher climate sensitivity of CMIP6 models47. Regarding climate sensitivity, the models we have used here consist of a broad range, with a mean that is lower than the CMIP6 all-model mean47. All studies agree on an increase in windstorm risk as we move through the 21st century; however, further investigation is required to determine the magnitude of this change.The increased windstorm risk for NW Europe is consistent with studies using previous generation climate models42 and differing vulnerabilities on a national and regional level. Policy decisions regarding adaptation are also made more complex by the uncertainties in the METSSI projections with different models43. We consider adaptation in the perfect sense, and therefore our two loss estimates provide an upper and lower bound for future windstorm impacts. Despite this, our results provide a clear benefit indicating where impacts are likely to change through the 21st century, with results acting as a baseline for further investigation by loss modellers and future high-resolution studies.The future projections of the population have been used to estimate the impact of population change on the projected losses. Despite decreases in the projected meteorological intensity of windstorms in the future with risk adaptation, increases in population by the end of the century lead to larger risk potential through an increase in socio-economic exposure. This is a feature that is robust across all of the models examined. Many of the local increases in SOCSSI concentrated across North Western Europe can be offset somewhat by adapting to increases in the extreme wind speed climatology. For the SSP5-8.5 scenario, the increase in SOCSSI is reduced by 19%  by including adaptation to the increased extreme windspeeds (compared increase of 74.1% with no adaptation and an increase of 40.5% with adaptation). The increased risk over Northwestern Europe in the SSP5-8.5 scenario, even when including adaptation, is more than double the historical value, indicating the adaptation is useful, but not sufficient, at reducing the future storm risk. Even if the population does not grow, adaptation to the future wind speed climatology would be necessary to avoid an increase in storm losses (i.e. the METSSI). For the more moderate climate change scenario, almost all of the population- and wind-driven increases in the impacts are mitigated by adapting to the higher wind speeds (34.1% increase with no adaptation and 7.6% increase with adaptation). So mitigating the greenhouse gas emissions, or following lower emissions scenarios, would be another way to limit the future increases in storm loss and would require less adaptation. Our approach to losses and adaptation in this study is an idealised approach and does not account for added complexities in land use change48, it is not clear whether this will impact the future of these losses. There are large uncertainties in the projections of storm clustering49. Despite this and other uncertainties associated with future changes in the storm tracks and storm intensities51, we are able to give robust projections of changes to the windstorm risk. This does, of course, depend strongly on the population changes that we will see in the coming years. Previous studies have shown that individual models can show non-linear future changes in the storm tracks52 with different warming scenarios, and projections from different models depend on a lot of different processes. By using a multi-model ensemble, we show a robust increase in losses in the north of Europe in SSP5-8.5. This highlights that, as well as adapting to future wind speeds, efforts must be in place to follow a lower emissions trajectory to reduce future risk. We have shown that to understand the future changing risk associated with European windstorms, there is a need to go beyond physical hazard modelling to consider risk and adaptation from a socio-economic perspective.While the serial clustering of extratropical cyclones is an important contribution to the seasonal losses from European windstorms53. Six-hourly data from 1980–2010 are used to identify the extratropical cyclones. The fields used are 850-hPa zonal and meridional winds and 10-m winds. Model data come from 8 models taking part in the latest Coupled Model Intercomparison Project , ERA5All analyses were conducted on a core rectilinear grid defined by longitudinal and latitudinal domains of 23.5°W–31°E and 35°N–71°N respectively, and repeated for three regional sub-domains. The sub-domains are defined as NW Europe , NE Europe , and S Europe . Prior to any analysis taking place, all data were first re-gridded to a 1.875° × 1.875° grid, which is equivalent to the model with the coarsest resolution. To maintain an exclusive focus on statistics over inhabited land, grid points are masked prior to analysis if their re-gridded human population value is less than one person per square km.55 applied to 850-hPa vorticity. The specific values of all parameters employed within the algorithm, and the methodological details, are identical to those outlined in refs. 38. The 850-hPa relative vorticity is smoothed to T42 resolution to focus on the synoptic scales before the identification and tracking are applied. From the resulting track lists, only those of a sufficient lifetime (>48 h) and displacement from the point of cyclogenesis (>1000 km), and with at least one track point within the ‘All Europe’ rectilinear domain are retained for analysis.Storm tracks are identified using a Lagrangian objective cyclone tracking algorithm based on the method of Hodges4. The maximum of these values within the 24-h period at each location is then used. This area around the cyclone is chosen as it is the area within which the strongest windspeeds tend to occur56, while the temporal range maximises the proportion of cyclone-attributable wind speeds that are accounted for. This step is used to produce a ‘footprint’ map of maximum cyclonic wind speed values recorded across the domain  as those used for the storm data. The population base year 1 and uses the grid point 98th percentile of the 6-hourly wind speeds as the threshold above which damage occurs. Prior to calculating storm severity indices, we adjusted all local 98th percentile wind speed values below a threshold of 9 m/s up to the value of this threshold, consistent with the methodology used in ref. 58. This step ensures that cyclonic wind speeds below the selected threshold do not contribute to the loss metrics. For the SSI in future climatic scenarios, the case of adaptation is considered when the scenario 98th percentile (2070–2099) is greater than the historical value. In this case, the 98th percentile is adjusted upwards to the future scenario value. In cases when the 98th percentile is lower in the future scenario, the historical value is retained as it is assumed that buildings/exposure do not de-adapt. The METSSI is calculated at each grid box as follows:i and j are the spatial co-ordinates of the grid point of interest, vmax is the maximum wind speed value associated with the cyclone, v98 is the adjusted 98th percentile value of the winter wind speed distribution, and N is the number of grid points within the domain of interest30.The SSI is applied as in ref. 59.−2 and 102, respectively, to ensure both metrics are of the same order of magnitude.Since the losses associated with windstorms will be strongly related to the exposure of people and buildings, the METSSI can be scaled by the population estimate at each grid boxSupplementary Information


To determine whether genetic risk single nucleotide polymorphisms (SNPs) for age-related macular degeneration (AMD) influence short-term response to intravitreal ranibizumab treatment.Forty-four treatment-naive AMD patients were included in a prospective observational study. They underwent three monthly injections of intravitreal ranibizumab for neovascular AMD. After an initial clinical examination (baseline measurement), a follow-up visit was performed to determine treatment response one month after the third injection . Patients were evaluated based on ophthalmoscopy, fluorescein angiography, optical coherence tomography (OCT), and OCT angiography. Peripheral venous blood was collected for DNA analysis at baseline visit. Patients were genotyped for single-nucleotide polymorphisms within AMD-relevant genes and classified on good or poor responders based on visual acuity, central retinal thickness, intraretinal fluid, and subretinal fluid.CYP2J2), rs11200638 (HTRA1), rs405509 (APOE), rs9513070 (FLT1), and rs8135665 (SLC16A8) predisposed patients to a good response, whereas rs3093077 (CRP) was associated with a poor response. FTL1, SLC16A8, and APOE were the SNPs that showed significance (P < 0.05) but did not pass Bonferroni correction.One hundred ten AMD-associated SNPs have been analyzed. Six were found to be relevant when associated to ranibizumab treatment response. The genetic variants rs890293 (CRP, SCL16A8, or CYP2J2 to treatment response to ranibizumab therapy. On the other hand, HTRA1, FLT1, and APOE are linked to a good ranibizumab response. These SNPs may be good candidates for short-term treatment response biomarkers in AMD patients. However, further studies will be necessary to confirm our findings.This is the first study that links novel polymorphisms in genes such as  Patients were recruited and evaluated by ophthalmologists from University Clinical Hospital of Santiago de Compostela, Galicia, Spain, from May 2019 to August 2021. After an initial clinical examination (baseline measurement), patients underwent a loading phase that consisted of three-monthly injections with ranibizumab , and a follow-up visit was performed to determine treatment response one month after the third injection . Patients were evaluated based on ophthalmoscopy, fluorescein angiography, optical coherence tomography (OCT)  and OCT angiography . This instrument has an A-scan rate of 100,000 scans per second, using a light source with a wavelength of 1 µm for a deeper penetration into tissue. All patients in this study were imaged using the 3 × 3 mm, 6 × 6 mm, and 9 mm scan patterns centered on the fovea. OCT angiography images were processed, and measures were analyzed using IMAGEnet 6.The study and the data collection strictly adhered to the declaration of Helsinki principles. Ethical approval was given by the local Institutional Review Board and the autonomous region of Galicia (2017/614).Patients had to meet all the inclusion criteria, no exclusion criteria and had to give their consent for the collection and storage of blood samples. Inclusion criteria were defined as neovascularization secondary to AMD, age over 55 years, and visual acuity (VA) greater than 25 Early Treatment Diabetic Retinopathy Study (ETDRS) letters at baseline. Exclusion criteria included any retinal disease other than nAMD. In addition, patients who had previously undergone any ophthalmologic surgery other than cataract surgery or had signs of intraocular inflammation were also excluded. None of the subjects had received previous anti-VEGF treatment for nAMD.Treatment response analysis to ranibizumab therapy was performed four months after the initiation of ranibizumab treatment. The classification of this response is based on Amoaku et al.g for 15 minutes at room temperature. After the leukocytes were separated from the other components of blood, they were washed twice with saline solution, and they were resuspended in 500 µL of saline solution. Afterward, the leukocytes samples were stored at −80°C until the DNA isolation.Peripheral venous blood was collected in two EDTA tubes (4 mL) at the baseline visit. The separation of leukocytes in blood was done within 24 hours. Five milliliters of blood were spun in a centrifuge at 2280DNA was isolated following the manufacturer's instructions with the Wizard Genomic DNA Purification Kit . DNA concentration and purity were measured with Qubit 2.0  and NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific). For the panel design, genotyping was performed using a custom made OpenArray technology using the QuantStudio 12K Flex System (Thermo Fisher Scientific), which is a real-time PCR system that uses Taqman technology. The results were analyzed using two software, the QuantStudio 12K Flex Real-Time PCR Software and Taqman Genotyper Software, which allows a cluster analysis by biomarker.A selection of DNA biomarkers has been carried out through the development of a bibliographic search based on scientific evidence so far on articles related to AMD and pharmacogenetics. Fifty-seven articles between the years 2004 to 2021 have been reviewed.Pharmacogenetic data were analyzed with PASW Statistics 20  and R for Windows. Differences between good responder versus poor responder groups were explored using statistical tests; nonparametric Mann-Whitney test in the case of quantitative variables, and chi-square test or Fisher's exact test in the case of categorical variables. A logistic regression analysis was performed to detect the genetic association for each SNP and response to ranibizumab treatment using codominant, dominant, recessive, and log-additive genetic models, with sex used as covariate. Correction (for multiple tests) was performed via the Bonferroni method.P = 0.014).This was an observational study where 44 naïve patients have been included. The demographic and response parameters of the study population are summarized in Regarding the analysis of the response at the end of the loading phase, 52.3% of our series presented a complete response to treatment (gain ≥5 ETDRS letters and disappearance of fluid), 43.2% presented a partial response (gain <5 ETDRS letters or persistence of any type of fluid), and 4.5% did not present any type of response. Nonresponders and poor responders were pooled in a single group for analysis purposes.The median visual acuity was 60 ETDRS letters  and the median CRT was 298 µ  at the initial visit. In the OCT parameters: 61.7% of the eyes had IRF at diagnosis, 75% SRF. None of the patients presented areas of geographic atrophy as defined by the Classification of Atrophy Meetings at baseline.At the first visit after completing the loading phase, the median VA was 66 ETDRS letters  and the median CRT was 214 µm . We found IRF in just 18.1 % of the cases, and SRF in 20.5%. No intraretinal hemorrhages were found in any of our patients. Regarding the improvement of VA, an overall improvement of 7 ± 13 letters has been quantified after the loading phase , 2.P = 0.715), but we found significant differences in VA between groups after the loading phase (P = 0.029). These differences were not found between responders and poor responders in the baseline CRT, nor after treatment.Good responders did not differ in VA from poor responders at baseline (P < 0.0001) in both responders  and poor responders . VA surged over time in both groups, but it was found to have statistical significance only in the responders’ group (P < 0.0001).Median CRT decreased significantly over time  gene, rs11200638 of HTRA1 (human high temperature requirement serine protease A1) and rs8135665 of SLC16A8 (solute carrier family 16 member 8) gene. On the other hand, risk alleles of rs3093077 of C-reactive protein (CRP) and rs9513070 of FTL1 suggested worse response. However, these SNPs did not reach significance after the Bonferroni correction.Six SNPs were found to be relevant in their association to treatment response with ranibizumab. The SNPs that had better response to ranibizumab treatment were rs890293 of SLC16A8) was associated with a risk of better response to treatment with ranibizumab after the loading dose in a dominant model. This SNP not only preserved significance, but it also increased once the model was adjusted for sex.The allele T  of rs8135665 (CYP2J2) were found, and heterozygotes only appeared in the group of non-responders, so the C allele may be beneficial when patients were treated with ranibizumab. The allele G of rs405509 (APOE) was associated with improved ranibizumab treatment outcomes after the loading dose in a dominant model when the model was adjusted for sex. In the case of rs9513070 of FLT1 gene, the G allele is the risk allele and tends to be frequent in patients with a substandard response to treatment with ranibizumab therapy in a recessive model.No homozygotes for the rare allele (A) of rs890293 . It should be noted that the count was excessively low in the response group of this SNP.The SNP that appeared to have worse response was rs3093077 of HTRA1, had the worst response to treatment because it appeared more frequently in the group of nonresponders , but it also did not signify any association once the analysis was corrected for sex (P = 0.1318).The frequent allele (G) of rs11200638; The main purpose of pharmacogenetics is the possibility of stratifying patients based on their individual genetic profile and to provide them with the most appropriate therapeutic decision to avoid lack of efficacy and adverse effects of drugs. Therefore early identification of nonresponders or poor responders is essential for the implementation of personalized therapies in the hope of maximizing visual outcomes and minimizing the number of treatments and associated risks.We have analyzed biomarkers related both to response and to the development of macular neovascularization. Although the former is more scarce than the latter in the literature, both provide new insights of treatment response.SLC16A8 gene encodes a monocarboxylate transporter protein (MCT3) that is specifically expressed by the RPE and is involved in the transport of pyruvate, with lactate and ketonesSLC16A8 gene knock-out mice have changes in visual function,The SLC16A8 has been linked to susceptibility to AMD,,,CYP2J2 belongs to the family of cytochromes that are common in the heart and vascular endothelium.CYP2J2 in nAMD. It has been observed that products from CYP2J2 metabolism, induced angiogenesis in vivo and ex vivo in mice, and inhibition of CYP2J2 may suppress pathological vessel growth in choroidal neovascularization.CYP2J2 gene in a Lithuanian population. The CYP2J2 TT genotype was statistically less frequent in patients >65 years with AMD than in controls, suggesting that polymorphism rs890293 TT could be a protective genotype.CYP2J2 and response to ranibizumab treatment.In our case, we found no homozygotes for the rare allele (A) and heterozygotes were only found in the group of non-responders, so the C allele could be beneficial when patients are treated with ranibizumab. However, it is true that the frequency of the TT genotype is very low according to the scientific literature and it is similar to that obtained in our population (4.7%). This rarity has also been observed in other pathologies such as Alzheimer's diseaseFLT1 is a gene which encodes a member of the vascular endothelial growth factor receptor (VEGFR) family. This protein binds to VEGFR-A, VEGFR-B, and placental growth factor and plays an important role in angiogenesis.,,The APOE polymorphic protein acts by revamping the homeostasis of cholesterol and other lipids. It is the major apolipoprotein of the central nervous system, and it has been shown to be a component of drusen, and can accumulate in the cytoplasm of RPE cells.CRP have been shown to determine serum levels of this protein, particularly in response to inflammatory stimuli.CRP gene have been studied in relation to treatment response but related to photodynamic therapy (PDT).Genetic variants of HTRA1 gene encodes a heat shock serine protease that regulates TGF-β.HTRA1 gene is one of the few genes that have been linked to a poor response to anti-VEGF treatment and is included in PharmGKB database,,VEGF, CFH or ARMS as detailed in PharmGKB probably because of the small sample size that was recruited for this study.Finally, the ,,,,The primary limitation of this study is undoubtedly the sample size. However, this limitation originates from the strict inclusion and exclusion criteria, along with the use of a sole center for recruitment. Likewise, this work only shows the early response associated to loading phase. Short-term response was chosen because previous studies revealed that the optimal point to measure morphological and functional response is following the loading phase where maximum response is seen.,Overall, we have only analyzed the response to treatment of nAMD with ranibizumab as anti-VEGF agent as our goal was to be able to identify the patients who could benefit the most from this treatment. This grants an opportunity to further expand this area of research as other approved agents for nAMD also exert their effect in the VEGF pathway, such as brolucizumab, aflibercept or faricimab. Surveying the genetic profiles associated to response to varying agents could offer the opportunity to customize the treatment of patients. On the other hand, due to the multifactorial pathophysiology of the disease, the treatment response may be further be explored with studies that bridge the gap between genetic markers and clinical imaging biomarkersSCL16A8 or CYP2J2 that are associated with better outcome to treatment response. This is the first study that links CRP genotype, which previously was only studied in PDT, to anti-VEGF therapy obtaining a good response in homozygotes. FTL1 confirms prior published data regarding positive response, on the other hand, in this study HTRA1 risk allele was associated with a good ranibizumab response. Based on our results, SCL16A8, CYP2J2, FLT1,APOE, and CRP may be good candidates for treatment response biomarkers in AMD patients. However, further studies will be necessary to confirm our findings.Our study provides further insight into the pharmacogenetics of the clinical response of nAMD to ranibizumab therapy, by identifying novel polymorphism in genes such as