Regulated thin filaments (RTFs) tightly control striated muscle contraction through calcium binding to troponin, which enables tropomyosin to expose myosin-binding sites on actin. Myosin binding holds tropomyosin in an open position, exposing more myosin-binding sites on actin, leading to cooperative activation. At lower calcium levels, troponin and tropomyosin turn off the thin filament; however, this is antagonised by the high local concentration of myosin, questioning how the thin filament relaxes. To provide molecular details of deactivation, we used single-molecule imaging of green fluorescent protein (GFP)-tagged myosin-S1 (S1-GFP) to follow the activation of RTF tightropes. In sub-maximal activation conditions, RTFs are not fully active, enabling direct observation of deactivation in real time. We observed that myosin binding occurs in a stochastic step-wise fashion; however, an unexpectedly large probability of multiple contemporaneous detachments is observed. This suggests that deactivation of the thin filament is a coordinated active process. Striated muscle contraction is mediated by the interaction of myosin II with actin and is governed by the calcium concentration within the myocyte. In the sarcomere, myosin II is assembled into thick filaments, which cyclically bind to the actin-containing thin filaments in the presence of adenosine triphosphate (ATP) to generate force. However, access to actin is modulated by the control proteins troponin (Tn) and tropomyosin (Tm). Tn is a complex of three proteins that interact with both actin and the 40-nm-long filamentous protein Tm. Calcium binding triggers a conformational change in Tn that permits Tm to slide across actin . This moActivation of the thin filament is hypothesised to be modulated by the accessibility of myosin-binding sites on actin within the context of a three-state mechanism . In the 50), a metastable activation process is observed. Myosin binds to regions of actin for long enough to permit additional myosins to bind in close proximity, without favouring complete propagation of activation as seen at saturating calcium -tagged myosin-S1 (S1-GFP) loading and release from regulated thin filaments (RTFs) offers a direct spatial and temporal view of how activation occurs. In our previous work , we deve50 .https://github.com/Kad-Lab/catastrophic_collapse; swh:1:rev:5137e03d2062938c8d1ff15b7de5a97f5740b05f). Signals were extracted following noise removal by thresholding; therefore, it is possible that some single bound myosins may have been lost. However, Movies of S1-GFP binding to and releasing from thin filament tightropes were converted into kymographs along the contour of actin using ImageJ . The dosKnowing the number of myosins in each active region at every point in time enabled us to calculate transition matrices, which are tables that indicate what happens when the occupancy of an active region changes. To generate a transition matrix, we examined the fate of each active region  by measuring the frequency of transitions from one cluster size to another from one original video frame to the next. From these measurements, we generated a matrix of probabilities . The col–1 ) = 5.6%. As with attachment, this value will scale according to the number of myosins in a cluster; however, any molecule can leave the cluster and, therefore, the probability of detachment becomes the product of a single detachment and the active region size, scaled by a term that takes into account the possible combinations of myosins detaching from a cluster . Using these conditions, we created the values to the left of the central diagonal to complete the predicted table of transition probabilities  was sufficient to detect stepwise detachment based on myosin’s expected attached lifetime (see above), enabling the construction of a predicted transition matrix , consistent with previous studies . The spaIn this study, we have investigated the behaviour of myosin S1 molecules binding metastably to RTFs using a single-molecule imaging approach, offering new insights into how the thin filament relaxes. From our results, it is evident that active regions are turned off in a concerted fashion, a mechanism we term catastrophic collapse mediated by tropomyosin and not requiring the presence of troponin. The role of this ‘catastrophic collapse’ in the process of muscle contraction remains to be determined, although this nuanced understanding of relaxation may help in our understanding of diseases. HCM is characterised by a deficit in relaxation  that is Escherichia coli. Tropomyosin with an N-terminal Ala-Ser modification . This procedure was repeated once more and the pure protein was flash frozen in liquid nitrogen and stored in 20 mM Tris, pH 7, 100 mM KCl, and 5 mM MgCl2 at –80°C.Myosin and actin were prepared from chicken pectoralis following the protocol of g for 30 min at 4°C, the supernatants were combined and subsequently dialysed into 2 M urea, 10 mM imidazole, pH 7, 1 M KCl, and 1 mM dithiothreitol (DTT) for 5 hr at 4°C. To allow the proteins to refold, the urea was removed by a second dialysis into 10 mM imidazole, pH 7, 0.75 M KCl, and 1 mM DTT overnight at 4°C before a final dialysis into 10 mM imidazole, pH 7, 0.5 M KCl, and 1 mM DTT for 5 hr at 4°C. The samples were then centrifuged at 17,000×g for 10 min at 4°C and the protein in the supernatant precipitated by adding 30% ammonium sulphate ((NH4)2SO4) and gently stirring at 4°C for 1 hr. After centrifugation at 8000×g for 30 min at 4°C, the sample was precipitated on ice for 30 min by bringing the (NH4)2SO4 concentration to 50%. The solution was centrifuged at 17,000×g for 10 min at 4°C, and the pellet was resuspended in 10 mM imidazole, pH 7, 200 mM NaCl, 100 μM CaCl2, and 1 mM DTT before an overnight dialysis in the same buffer to remove the (NH4)2SO4. After dialysis, the sample was spin concentrated in a 10 kDa molecular weight cutoff (MWCO) column (Amicon Ultra) at 2360×g for 45 min and further purified using a Sephacryl S-300 column (Cytiva). Finally, the protein sample was stored in the same buffer, with the addition of 3% sucrose, at –80°C. Reconstituted RTFs, that is, actin fully decorated with Tm and Tn, were obtained through an overnight incubation at 4°C in reconstitution buffer as described previously  column. The purified construct was then exchanged for myosin S1 RLC (1:3 S1:RLC-GFP-6xHis ratio) in 50 mM potassium phosphate buffer, pH 7, 600 mM KCl, 10 mM EDTA, 2.5 mM ethylene glycol tetraacetic acid (EGTA), and 2 mM ATP for 1 hr at 30°C. The reaction was stopped with 15 mM MgCl2 and the construct further purified using a Sephacryl S-200 and a HisTrap HP (Cytiva). Following purification, the ratio of the absorbance at 280 nm and 488 nm was used to ensure that the material was 100% exchanged. Purified proteins were then stored in 50% glycerol and 3% sucrose at –20°C.S1-GFP was prepared as described by 2, 1 mM EGTA, and 10 mM DTT. The calcium and S1 concentrations used are given in the figure legends; when multiple concentrations of S1 were used, these data were pooled. We have shown previously that cluster size is affected by [S1]; however, we observed no correlation in collapse probability with [S1]. To reduce background noise from surface-adhered S1-GFPs, the surface of the flow-cell was photobleached for 1 min at 20 mW prior to recording data. Movies were recorded at 3.3 frames/s for 2 min, exciting the GFP with 5 mW laser power, resulting in fluorescent molecules where the S1-GFP co-localises with an RTF  coated in poly-L-lysine adhered to coverslip of a microfluidic flow cell , as detah an RTF . To furtATP binding to an attached S1-GFP leads to its detachment. The probability of detachment can therefore be expressed as a function of ATP association:th power of where where th power of the attachment probability, Likewise, the probability of a myosin joining an already active region can be calculated using the Considering the nature of our assay, As a secondary means to confirm the existence of collapse events, and to ensure that no bias exists in the analysis, we applied a statistical approach to analyse the original rolling-ball-subtracted kymographs. This approach used a Gaussian mixtures model via a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, based on a custom-written software in R that utilizes the package miscF  and codaAs a secondary measure to quantify the binding and release of S1-GFP within active regions, we applied a Bayesian statistics approach via RJMCMC. The first step in our analysis was to identify and assign a peak intensity for each point in time for every active region in a kymograph. This was achieved using a custom MATLAB-based fitting routine that uses a Gaussian fit centred on the active region to provide a peak intensity . The next step was to convert this intensity into the number of S1-GFP molecules bound. To do this, for one kymograph, we generated four independent Markov chains using the RJMCMC algorithm, and from this, we calculated the average mean pixel intensity values associated with that number of binders. This value depends on the maximum number of fluorescence states (components) used in the RJMCMC algorithm. Since the fluorescence intensity is linear with the number of binders, this was used as a prior constraint for the RJMCMC algorithm.The average of four simulated RJMCMC chains with two different numbers of components is shown in From the values in We present a full Bayesian analysis of finite mixtures of univariate normal distributions with an unknown number of clusters. The size of the clusters refers to the number of bound S1-GFP molecules within that active region. Each observation in the dataset is assumed to have emerged from one of the K clusters. Thus, the purpose of the mixtures analysis is to infer the number (K) of components, the component parameters, and the proportion of each cluster.RJMCMC methods are adopted to determine the number of clusters in an active region. In this manuscript, the RJMCMC algorithm is employed similarly to α = 2; g = 0.2; and h = 10/R2, where R is the range of the observations.The hierarchical model employed in this paper has been proved to be weakly informative by The results from the RJMCMC analysis qualitatively agree with those reported using the Trackmate method see , althoug In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:Your work highlights the complexity of myosin binding and release from thin filaments. The mechanism of concerted release is both important from a molecular point of view, as it illustrates how important it is to consider cooperative effects both in terms of binding and unbinding to actin filaments, and physiologically for all its implications in further understanding muscle contraction. We hope that you will be able to provide in the future a quantitative molecular model accounting for all the subtleties of this mechanism.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Anna Akhmanova as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Josh Baker (Reviewer #3).Thank you for submitting your article "Single molecule imaging reveals the concerted release of myosin from regulated thin filaments" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1. The observation that clusters form with tropomyosin but without troponin is curious and questions about the function of troponin and calcium in this system. Does tropomyosin alone inhibit actin-myosin binding? If not, what is the mechanism by which clusters form with only tropomyosin? That is, if tropomyosin alone does not inhibit actin-myosin binding, then how can myosin heads cooperatively activate specific regions?The authors should provide a kinetic scheme for actin myosin detachment both with and without tropomyosin. This would allow the reader and possibly the authors to better explore what is going on. Specifically, the results seem to imply a detachment rate that depends on the number of bound myosin heads in a cluster. The observation that detachment of the last one or two heads is relatively slow suggests that there is an N-dependent relationship. Is it possible to develop an equation for that N-dependent rate? This would provide significant insights into the mechanism for cooperative detachment. Could such model also explain why the data show only myosin head detachment and not rebinding during the detachment of clusters?2. The Methods section would benefit from more experimental details. Could the authors specify precisely buffer/protein concentrations in each experiments? For example, it is not clear in Figure 3A whether data at 5 and 10 nM S1-GFP were pulled together or used in different experimental conditions. Is there an influence of myosin-S1 concentration for cluster formation and detachment? What is the rate of photobleaching in these experiments and is it comparable to the rate (not estimated but should be) of detachment?It is also difficult to assess the precision of the measurements from the few images provided in this work. It would be useful if the authors could detail how the data are processed from typical unmodified images . It would also require examples of how quantitative information is extracted, show individual measurements and not only straight lines as in Figure 2b. For example, the authors should indicate how transient binding of one molecule is distinguished from typical background. The authors should also clarify whether they included every events in their analysis or excluded some weak signals. Such analysis should also include a notion of error, i.e. the degree of confidence when detecting x GFPs in this system, and how such precision impacts the precision of the final transition probabilities.3. The choice of using GFP-tagged proteins in this system is surprising because these fluorophores are not the brightest for single molecule imaging. Moreover, GFP maturation is slow, which results usually in an important fraction of the proteins being non-fluorescent. Could the authors evaluate by spectrophotometry the fraction of fluorescent vs. non-fluorescent S1-GFP molecules, and tell us how non-fluorescent GFP-S1 contribute to the "real-size" of clusters. How does this change probability values measured in Figures 3 and 4?Reviewer #1:4. All reviewers were curious to know whether release of myosin would be altered if the mechanical properties of the thin filaments were modulated. Such experiments are probably too complicated to perform for this specific paper, but could the authors discuss this point and provide estimates of filament tension in these experiments?In this paper, the authors use single molecule imaging to reveal how clusters of myosin S1 unbind from reconstituted thin filaments. One of the difficulty of this work was to find experimental conditions where cluster formation is only transient. Data quantification include transition matrices, showing how clusters change in size at these experimental time intervals. These results are compared to theoretical predictions, where the probability of detachment of each molecule is calculated from the parameters that govern attachment time, ADP release and ATP binding. Discrepancies between experimental and theoretical values reveal a mechanism of cooperative disassembly, i.e. where myosin S1 molecules detach by groups and not progressively as expected. However, this theoretical calculation does not explain the mechanism of these "catastrophic" detachments, and only possible explanations are provided in the discussion.Reviewer #2:Overview: The article authored by Smith et al., describes the importance of understanding how myosin binds and is released from thin filaments with implications in further understanding muscle contraction. To determine the binding interactions of myosin with thin filaments, the authors incorporate single molecule fluorescence imaging in addition to using silica beads to produce attachment sites for the thin filaments. The beads and thin filament complex generates thin filament tightropes which act as key locations for the binding of myosin as they are passed through the flow chamber. From the data, the authors were able to determine the differences between myosin binding and the differing mechanisms of cluster catastrophic collapse and stochastic detachment of myosin. Interestingly, from the imaging data they can show the probabilities for the final cluster sizes dependent on their experimental results and compare to their own predictive model. Overall, this article explores how determining the attachment and detachment of myosin's to thin filaments adds an interesting way to visualize and quantify the active interactions of myosin binding with thin filaments.The conclusions of this work are mostly supported by the data presented, however, the discussion could be further extended.Reviewer #3:The goal of this paper is to better understand the molecular mechanisms underlying relaxation of striated muscle. On the surface, this seems like a relatively simple process; however, the results of this study suggest that it is more interesting and significant than this reviewer originally thought. To determine the mechanism of relaxation, the authors use a remarkable technique that allows for the direct observation of myosin binding to actin (the mechanism of contraction) and myosin heads detaching from actin (the mechanism of relaxation). Unlike in most kinetic assay that average binding kinetics both spatially and temporally, this assay reveals binding events at specific locations along an actin filament and detachment events from specific clusters of myosin heads. Thus, the authors were able to observe clusters of myosin heads that bind to actin at specific regions and detachment of these clusters at a rate that exceeds that of a single myosin head, implying cooperative mechanisms. The authors were not able to account for the cooperative detachment mechanism, but for this reviewer that is more intriguing than it is disappointing.The authors successfully achieved their goal and discussed the significant implications of their results for muscle relaxation in normal and disease states. Essential revisions:1. The observation that clusters form with tropomyosin but without troponin is curious and questions about the function of troponin and calcium in this system. Does tropomyosin alone inhibit actin-myosin binding? If not, what is the mechanism by which clusters form with only tropomyosin? That is, if tropomyosin alone does not inhibit actin-myosin binding, then how can myosin heads cooperatively activate specific regions?The authors should provide a kinetic scheme for actin myosin detachment both with and without tropomyosin. This would allow the reader and possibly the authors to better explore what is going on. Specifically, the results seem to imply a detachment rate that depends on the number of bound myosin heads in a cluster. The observation that detachment of the last one or two heads is relatively slow suggests that there is an N-dependent relationship. Is it possible to develop an equation for that N-dependent rate? This would provide significant insights into the mechanism for cooperative detachment. Could such model also explain why the data show only myosin head detachment and not rebinding during the detachment of clusters?Tropomyosin alone does inhibit myosin’s interaction with actin. This is evidenced from the literature where it has been shown that tropomyosin impedes the motion of actin filaments in the in vitro motility assay (1). In our assay this is also evident from the reduced interactions of myosin with the tightropes in the presence of Tm only, relative to naked actin (2). We have made a statement in the revised version clarifying this point (see end of Results section). Therefore, given that tropomyosin does inhibit interaction with actin then this provides the mechanism for cooperative activation. This has also been shown for the effects of Tm alone on myosin’s ATPase and binding, which we mention both in the Results section and in the discussion.We thank the reviewers for suggesting we create a model, as a result we have now included a sub-panel in figure 4 containing a cartoon of our interpretation of the results. The principle of catastrophic detachment leads to the release of all heads in a single step. However, without having a clear measure of the detachment rates we feel it is too speculative to suggest an N-head dependent model for detachment. This is a really important measurement that we aim to discover in future studies. Therefore, we have made reference to the importance of discovering if the rate of detachment depends on the number of attached heads, and added a prediction for those values (see discussion).The last point refers to the attachment and detachment of heads. Our data does reveal both, as seen in the transition tables where association lies to the right of the diagonal. These tables show what happens in a transition, in the case of multiple events we are unable to resolve these due to the time resolution of our measurements, however the probability of these events occurring forms the basis of our interpretation throughout the manuscript.2. The Methods section would benefit from more experimental details. Could the authors specify precisely buffer/protein concentrations in each experiment? For example, it is not clear in Figure 3A whether data at 5 and 10 nM S1-GFP were pulled together or used in different experimental conditions. Is there an influence of myosin-S1 concentration for cluster formation and detachment? What is the rate of photobleaching in these experiments and is it comparable to the rate (not estimated but should be) of detachment?It is also difficult to assess the precision of the measurements from the few images provided in this work. It would be useful if the authors could detail how the data are processed from typical unmodified images . It would also require examples of how quantitative information is extracted, show individual measurements and not only straight lines as in Figure 2b. For example, the authors should indicate how transient binding of one molecule is distinguished from typical background. The authors should also clarify whether they included every events in their analysis or excluded some weak signals. Such analysis should also include a notion of error, i.e. the degree of confidence when detecting x GFPs in this system, and how such precision impacts the precision of the final transition probabilities.and pooled for analysis” into the legend to make our approach clearer. No behavioral correlation for the collapses was found with [S1-GFP], as expected for a process that is not concentration dependent. However, cluster size is affected by the concentration of myosin, leading to larger clusters with higher [S1-GFP]. We published a comprehensive analysis of this behavior previously (2), and we have amended the Methods section to make this clear.We apologize for not organizing the details of the method better. The data in Figure 3 was obtained at different S1-GFP concentrations as mentioned in the methods and figure legend, however we were not clear that these data were pooled. This was necessary because the concentration of S1-GFP was tuned for every experiment until we saw metastability. We have now added “With regards photobleaching, we did not carry out a measurement of photobleaching because this is an unreliable measurement to make on tightropes, since their height above the surface is not controlled. We could calculate the photobleaching rate on the surface but the laser power density is stronger. Therefore, we altered the laser power and found S1-GFP lifetimes were unchanged, and we also characterized the second order ATP binding rate constant as previously  and found it agrees with solution studies. Moreover, the effects of photobleaching cannot explain the collapse that we see here, because photobleaching is also stochastic. We have mentioned these aspects of photobleaching in the manuscript (lines 135-137 and 270).In response to the request for more analytical details we have created a new figure detailing the steps in the analysis using a second dataset. We highlight the inclusion criteria and indicate estimates on the errors in determining the number of bound myosins in the figure legend. The impact of our confidence in assigning the number of myosins is also discussed (see Results section starting at line 134-159). Details of this analysis have been moved out of the methods and into the results to fit the paper narrative more clearly. Most of this analysis, including the assignments on numbers of myosins has been previously published (2), and the programs used to analyze the data have all been made available on GitHub, linked in the manuscript.3. The choice of using GFP-tagged proteins in this system is surprising because these fluorophores are not the brightest for single molecule imaging. Moreover, GFP maturation is slow, which results usually in an important fraction of the proteins being non-fluorescent. Could the authors evaluate by spectrophotometry the fraction of fluorescent vs. non-fluorescent S1-GFP molecules, and tell us how non-fluorescent GFP-S1 contribute to the "real-size" of clusters. How does this change probability values measured in Figures 3 and 4?We agree with the reviewer that GFP is not the brightest fluorophore and we have begun incorporating a different fluorescent protein onto the RLC, however GFP is very good at ensuring enough expressed protein remains in solution. RLC itself forms inclusion bodies, therefore our expression approach biases the RLC-GFP into solution, as much as possible. The process of expressing with another fluorescent protein involves re-optimizing the best procedure for protein production.90 times for the GFP we use here are ~90min (3) and maturation yields have been assessed to approach 100% (4). We routinely measure the OD280/OD488 ratio for our proteins following exchange, which also involves two purification steps to separate from unlabeled protein. Our data show 100% fluorophore labelling. Based on this the cluster sizes we determine here are accurate measures. We thank the reviewers for raising this point and have now included a statement in the Methods explaining that the labelling is 100%.With regards the maturation time, the RLC-GFP is purified from bacteria before exchange with the endogenous RLC from myosin S1. Going from expression to use in the assay minimally takes days to complete, and therefore the GFP has ample time to mature. The published maturation τ4. All reviewers were curious to know whether release of myosin would be altered if the mechanical properties of the thin filaments were modulated. Such experiments are probably too complicated to perform for this specific paper, but could the authors discuss this point and provide estimates of filament tension in these experiments?This is a wonderful experiment, and one that we wish to pursue in the near future. It is, of course, potentially very relevant to stretch-activated muscle systems, including cardiac. Could tension lead to changes in the thin filament and recruit myosins? Since tropomyosin is a single molecule spanning the whole of the actin filament, it is reasonable to guess that pulling on the ends would change the energy of the whole protein and could possibly change the occupancy of the thin filament. The tension of the thin filament in these experiments is very difficult to determine, we previously determined the tension on a single DNA molecule tightrope as 2.2 pN. This relatively low force is unlikely to affect acto-myosin interactions, and has been used for decades for single molecule myosin measurements (5). That being said, the tension on thin filaments is likely smaller because of the much greater persistence length of actin, which would reduce the end-to-end tension experienced by the molecule. Arguments based on this have been added to the Discussion section.ReferencesProc Natl Acad Sci U S A 96(22):12488-12493.1. VanBuren P, Palmiter KA, and Warshaw DM (1999) Tropomyosin directly modulates actomyosin mechanical performance at the level of a single actin filament. J Biol Chem 290(4):1915-1925.2. Desai R, Geeves MA, and Kad NM (2015) Using fluorescent myosin to directly visualize cooperative activation of thin filaments. Nat Methods 15(1):47-51.3. Balleza E, Kim JM, and Cluzel P (2018) Systematic characterization of maturation time of fluorescent proteins in living cells. Anal Biochem 421(1):291-298.4. Macdonald PJ, Chen Y, and Mueller JD (2012) Chromophore maturation and fluorescence fluctuation spectroscopy of fluorescent proteins in a cell-free expression system. J.Muscle Res.Cell Motil. 18(1):17-30.5. Dupuis DE, Guilford WH, Wu J, and Warshaw DM (1997) Actin filament mechanics in the laser trap.


To investigate the impact of COVID-19 in Sweden on rates of knee and hip surgeries.We used healthcare data for the population of the southernmost region in Sweden (1.4 million inhabitants). We did an interrupted time-series analysis to estimate changes in rates and trends of joint replacements (JR), arthroscopies, and fracture surgeries for knee or hip in April–December 2020 compared to pre-COVID-19 levels adjusting for seasonal variations.We found a drop of 54%  and 42% , respectively, in the rate of JRs and arthroscopies in April 2020 when compared to the counterfactual scenario. This was followed by an increase that brought the rates of JRs and arthroscopies back to their predicted levels also during the beginning of the second wave (November–December 2020). Acute fracture surgeries were largely unaffected, i.e. did not show any decrease as observed for the other surgeries.In southern Sweden, we observed a marked decrease in elective knee and hip surgeries following the first wave of Covid-19. The rates remained close to normal during the beginning of the second wave suggesting that important elective surgeries for patients with end-stage osteoarthritis can still be offered despite an ongoing pandemic provided adequate routines and hospital resources.The online version contains supplementary material available at 10.1186/s40634-021-00382-7. The coronavirus disease 2019 (COVID-19) emerged in late 2019 in China causing over a million deaths by the end of the Summer of 2020, and nearly a million more deaths by the end of 2020 when the “second wave” was spreading through the world .In March 2020, many countries imposed lockdowns, in anticipation of the COVID-19 “first wave” and the massive healthcare resources required to meet medical needs, with many hospitals severely curtailing the performance of elective surgeries , 16. SweSurgeries to treat or alleviate symptoms in RMDs include both elective (e.g. joint replacements (JRs)) as well as emergency surgical procedures, typically for acute fractures. Surgical treatments are often the last resort to alleviate chronic pain, improve function and (or) prevent serious complications , 10, 11.Thus, our aim was to investigate the impact of COVID-19 and the Swedish government’s response on the rate of JR, arthroscopies, and surgeries due to fractures in the knee or the hip during the first and second wave of COVID-19.We used register data from Skåne healthcare register for the entire population of Skåne, the southernmost region in Sweden with about 1.4 million inhabitants . We included all residents aged ≥18 years anytime during the years 2015 to 2020. Among those, we identified the following surgical procedures of the knee or hip: JR (JR due to fracture excluded), arthroscopy, and surgery due to fracture (including JR) using a combination of the Swedish version of NOMESCO Classification of Surgical Procedures and ICD10  codes  elective primary JRs which includes all the JR performed every month which were not due to fractures, (ii) emergency knee and hip surgeries due to fractures including JR and other surgical procedures to reduce or fixate the fracture, (iii) elective arthroscopic surgeries such as meniscectomy, synovectomy, exploratory arthroscopies. If more than one surgery (e.g. meniscectomy and ligament reconstruction) was performed at the same time, the procedures were counted separately.We did an interrupted time-series (ITSA) analysis using segmented ordinary least-squares regression to estimate changes in the levels and trends of surgical procedures compared to pre-COVID-19 levels, adjusting for seasonal variations  arthroscopies per 10,000 adults in April, which corresponds to a 43% relative decrease  followed by a positive trend signifying a monthly increase of 0.13  arthroscopies per 10,000 adults which brought the rate of arthroscopic surgery performed to the rate predicted by the counterfactual scenario by August 2020. The rate of fractures was largely unaffected, with a small increase of 0.28  in April 2020. The reason for surgeries (i.e. main diagnostic code) had a similar distribution in the years pre-COVID and in 2020  during the first and second wave reported similar results showing a rapid decrease in the rate of surgeries in March–April 2020 followed by a recovery that brought the level of surgeries to the expected levels by the fall of 2020 . The autA similar drop in the rate of elective surgeries for the first wave was recently reported in Norway where an official lockdown was enforced early in the pandemic . Thus, iOur results indicate no relevant change in the rate of emergency surgeries due to fracture. Despite this result may be expected due to the impossibility to postpone emergency surgeries, numerous reports from countries that implemented strict lockdowns suggest a reduction in the number of emergency surgeries due to fractures –14. PossSome limitations need to be acknowledged. Due to the limited data available through the register, we were not able to determine if the surgical procedure were postponed by the health care providers or were cancelled by the patient. In addition, we were not able to verify the reason for the fractures which limits our ability to identify a causal factor behind the observed pattern.In southern Sweden, the first wave of the COVID-19 pandemic was associated with a marked and rapid drop in the rate of elective knee and hip surgeries which was more evident among older adults. However, the rates returned to normal over few months and stayed that way during the beginning of the second wave, suggesting that adequate preparations had already been made to meet the expected increase of hospital and intensive care admissions. This observation suggests that adequate preparations for a pandemic have the potential to minimise disruption to orthopaedic elective surgeries, often important to RMD patients, can still be carried out.Additional file 1: Supplementary file 1A. Swedish version of NOMESCO Classification of Surgical Procedures and ICD10  codes used to classify hip surgeries. Supplementary file 1B. Swedish version of NOMESCO Classification of Surgical Procedures and ICD10  codes used to classify knee surgeries. Supplementary file 2. STATA code for main analysis. Supplementary file 3. monthly rate of surgery per 10,000 Skåne inhabitants aged > 18. Supplementary file 4. Frequency of diagnosis by type of surgery before and during the COVID-19 pandemic.