The volume, extent and age of Arctic sea ice is in decline, yet winter sea ice production appears to have been increasing, despite Arctic warming being most intense during winter. Previous work suggests that further warming will at some point lead to a decline in ice production, however a consistent explanation of both rise and fall is hitherto missing. Here, we investigate these driving factors through a simple linear model for ice production. We focus on the Kara and Laptev seas-sometimes referred to as Arctic “ice factories” for their outsized role in ice production, and train the model on internal variability across the Community Earth System Model’s Large Ensemble (CESM-LE). The linear model is highly skilful at explaining internal variability and can also explain the forced rise-then-fall of ice production, providing insight into the competing drivers of change. We apply our linear model to the same climate variables from observation-based data; the resulting estimate of ice production over recent decades suggests that, just as in CESM-LE, we are currently passing the peak of ice production in the Kara and Laptev seas. Winter sea ice production appears to have been increasing, despite Arctic warming being most intense during winter. Here the authors examine the competing factors controlling sea ice production in the Kara and Laptev seas, and develop a simple model that explains the rise and subsequent fall of ice production under climate change. The retreat of Arctic sea ice is both an expression and driver of Arctic Amplification, which is the phenomenon of intensified climate change in the Arctic region relative to lower latitudes4. Though losses are observed in all seasons, they are more pronounced during late summer than late winter, in terms of both extent5 and thickness6. These trends reveal that increases in summer melting are to some extent compensated by increasing ice production during winter, though not by enough to prevent the continued decline of the annual mean reservoir of sea ice6.Arctic sea ice extent and thickness is in an on-going decline, sustained over at least the length of the satellite record, and directly related to anthropogenic carbon emissions7. Sea-ice growth also plays a vital hydrographic role: brine is rejected from sea ice during freezing, and freshwater is redistributed via sea ice motion between locations of growth and melt10.Ice production is important for a number of reasons. As progressively more ice melts during the summer, winter ice production is critical in restoring the ice pack before the onset of polar day, when the high albedo of sea ice and its snow cover plays a crucial role in the radiative budget of the region. The distribution of sea ice is important during the winter; it limits heat fluxes from the ocean to the atmosphere and plays a complex part in the surface momentum balance11. However, the two phenomena are most likely linked12. As progressively less sea ice survives the summer, the winter ice pack is increasingly thin, promoting both higher winter growth rates13 and increased heat fluxes from ocean to atmosphere14. Heat sequestered in the Arctic Ocean during summer is released into the atmosphere prior to and during winter sea ice growth11. Meanwhile, the sea ice growth rate is tightly coupled to heat fluxes from the ocean to the atmosphere, because it is determined by the energy balance at the lower boundary of the ice. As ice thickens, the conductive heat loss to the atmosphere declines, and growth rate slows in direct proportion. Sea ice thinner than ~0.4 m permits heat fluxes one to two orders of magnitude larger than those through perennial, thicker ice15. Lang et al.14 attribute a rise of ~1 °C in Arctic surface air temperature per decade to declining winter sea ice thickness. In principle, this surface warming may be partially offset by increased long-wave emission16. However, due to the predominantly stable atmospheric stratification, surface-intensified warming is inefficiently transmitted to the top of the atmosphere, and therefore outgoing long-wave emissions only very weakly compensate the warming (the lapse-rate feedback)19.There is an apparent tension in the observation of rising winter sea ice growth in concert with Arctic Amplification, which is intensified during the winter20. There are other effects, however, that also act to promote winter sea ice growth in a warming Arctic. Sea ice that is formed later in the season is more likely to avoid the limiting effects of snow—an effective insulator—on growth rate22. Similarly, snow melt during summer preconditions more rapid growth at the beginning of the freezing season due to the loss of this insulating snow cover23. Additionally, thinner sea ice is weaker and more mobile, leading to an increase in wind-driven sea ice divergence26, which promotes winter growth27. These negative feedbacks provide stability to the Arctic sea ice system, such that rapid or irreversible losses in summer sea ice area are unlikely28.The inverse relationship between sea ice thickness and growth rate underpins a well-known negative feedback on Arctic sea ice loss31, calling into question whether the action of these negative feedbacks may be becoming overwhelmed by warming. Based on simulations with the sea ice model CICE, Stroeve et al.13 suggest that the thermodynamic growth during the winter 2016/2017 was 11–13 cm lower than the 2011–2017 mean, with the overall positive trend in winter ice growth from 1985 ending in 2012. Winter ice growth then weakened until the end of the model run in 2018. Using the same large ensemble of coupled climate model runs we employ here (CESM-LE), Petty et al.32 identify that the positive correlation between temperature and winter growth weakens through the mid-century under the RCP8.5 emissions scenario, eventually becoming a negative relationship—indicative of warming overwhelming the negative feedbacks on sea ice loss. Observational evidence of increasing winter sea ice production, and model-based suggestions of an imminent or recently-begun decline—both associated with anthropogenic climate change—motivate a physical explanation of this rise-then-fall behaviour.In recent warm winters such as 2015/2016 and 2016/2017, growth suppression and even winter melting have been observed33  sea ice in the Arctic basin originating in the Kara and Laptev seas43. High rates of freezing in these shallow seas are also thought to help bolster the cold halocline  of the Arctic Ocean45. Sea ice produced in the Kara and Laptev seas freshens the ocean surface of the Eurasian Basin and northern Barents Sea upon melting during summer, helping to maintain the stratification that limits upwards heat fluxes from Atlantic Waters. The stability of the Arctic halocline has emerged as a key climate change indicator46, in light of episodic collapses of the winter halocline in the Eurasian basin and northern Barents Sea48, which lead to the shoaling of Atlantic Waters and increased heat fluxes to the surface49.The Kara and Laptev shelf seas have been referred to as the “ice factories” of the Arctic Ocean due to their outsized contribution to Arctic sea ice production33 Fig. a. Classieas Fig. b, openinIn this study, we develop a simple linear model for ice production that is informed by the physics of sea ice growth. The simple model can successfully explain both the rise and fall of ice production in the Kara and Laptev seas, as represented in the historical simulations and projections of a state-of-the-art global climate model. Our linear model thus offers insight into the competing processes at play under climate change. We further apply our linear model to observation-based data to estimate historical ice production. This estimate of historical ice production suggests that, as in the global climate model, the Kara and Laptev seas may presently be passing peak ice production.In our analysis of ice production in the Kara and Laptev seas we use 40 ensemble members from CESM-LE. Each ensemble member consists of a 20th Century run (20C) from 1920 to 2005 that employs historical external forcing and a ‘high-emissions’ RCP8.5 run from 2006 to 2080. Ensemble members are initialised in 1920 with small differences in atmospheric properties, which cause the simulations to evolve differently through time. We use this internal variability (the deviations of each ensemble member from the ensemble mean) across all 40 ensemble members to develop our linear model.50. In CESM-LE, the newly formed ice along the Siberian coastline reconnects with the sea ice of the interior Arctic Ocean in an hourglass shape that becomes progressively pinched, and occurs later, through the 21st Century  duration of the freezing season, and the rate of growth where and when it is freezing. Indeed, we can decompose total ice production into these two components Fig. . FirstlyThe decomposition reveals quite different trends. The mean number of freezing area days (or ice factory usage), begins to decline from the 1990s onwards, and then accelerates after 2050 Fig. b. The meR2 = 0.74 during the 20C run, and with R2 = 0.46 during the RCP8.5 run. The mean surface air temperature during Oct-Dec helps to control the heat flux from ocean to atmosphere as a result of the temperature difference across the ocean-atmosphere interface. Including this climate variable as a second regressor improves the fit in the 20C run to R2 = 0.78 and to R2 = 0.61 in the RCP8.5 run.Physically, the ice factory usage during Oct-Apr is controlled principally by the (location-dependent) time taken to cool the upper ocean to the freezing temperature (approximately −1.8 °C). Heat accumulates in the upper ocean during summer due to penetrating solar radiation, and is sequestered within and often below the mixed layer. In the early winter, ice growth is limited by upwards oceanic heat fluxes as mixing removes heat from the (deepening) mixed layer. A simple regression model, using the mean Sep surface ocean temperature as a single explanatory variable, captures the internal variability in the number of freezing area days with 51. The growth of sea ice of thickness, hi through time, t depends on a greater conductive heat loss to the atmosphere through the ice, Fc than heat flux from the ocean at its base, Fw, as shown in Eq. ρi is the density of ice, Li is the latent heat release/uptake on freezing/melting, and fluxes are positive upwards.The growth rate of sea ice on the other hand is controlled—in the absence of incoming shortwave radiation—by the balance of heat fluxes at its baseFc through the ice diminish, bringing the growth rate down towards zero, depending on the size of the oceanic heat flux, Fw. Other factors besides ice thickness also control Fc, including the air temperature and snow cover. Assuming a layer of snow with thickness, hs overlying ice with thickness, hi, with corresponding thermal conductivities λs and λi, respectively, and linear temperature profiles through both layers, the conductive heat flux isk is an effective heat transfer coefficient between the snow/ice surface and the atmosphere, and ΔT = Tw − SAT; in which Tw is the freezing temperature of water and SAT is the surface air temperature. Combining Eqs. As sea ice thickens, the conductive heat fluxes entclass1pt{minimaFw is generally low during the freezing season, of order 1 Wm−2 or less in the basin interiors, but sometimes higher directly over the Atlantic Water boundary current or over rough topography52. Unlike in the Southern Ocean, where Fw is on the order several tens of Wm−2, these low heat fluxes are generally no impediment to the growth of first year ice53.In the Arctic Ocean, the strong surface stratification suppresses upwards heat fluxes from the relatively warm Atlantic Water layer: By revealing that the number of freezing area days and mean winter growth rate exhibit quite different characteristics, the decomposition in Fig. Fw and Fc (Eq. Fw directly from CESM-LE and observations (though the latter are sparse), but we seek to capture Fc in terms of readily observable climate variables, using insight from Eq. As discussed above, variability in the number of freezing area days during Oct-Apr is dominantly captured by variation in the Sep sea surface temperature. The mean growth rate, meanwhile, must be controlled by the variables affecting the heat fluxes d Fc Eq. . We can λs ≈ 0.1λi), even a thin layer of snow may significantly affect the conductive heat flux51. Equation T lessens—the conductive heat flux decreases in linear relation, as does the growth rate  as an initial condition for growth. The minimum sea ice area  determines the area over which hi = 0 at the start of the season: the Sep open water area. In addition, open water is created near-continually throughout the freezing season by divergence. Under tension, ice cracks in a brittle manner rather than thinning; the open water area is equal to the area diverged.The sea ice thickness tion Eq.  due to ihi = 0 in the diverged area—we can expect the effects of convergence and divergence on growth rate to be asymmetric. We must therefore separately consider not only net divergence over the region, but also divergence that is balanced by convergence elsewhere within the region: we term this the “compensated” divergence.Divergence of the Arctic ice pack is spatially and temporally complex, and to understand its effect on thermodynamic growth we must also consider the role of convergence, which thickens ice through ridging and rafting, and may partially balance divergence that occurs in a given area. Because the growth rate of ice is an inverse function of its thickness Eq. , and becT and to the area that has been opened. In our linear model, we neglect the timing of divergence and simply use the total area diverged during the winter season in both cases.We thus classify three settings in which open water is made available for ice growth: the Sep open water, created by summer melting and ice advection; the open water area due to net divergence; and the open water area due to compensated divergence. The sea ice growth in open water areas will be linearly related to ΔT/hs, as per Eq. hs is the mean winter snow depth wherever ice is present. Gathering together these terms, our linear model for total winter ice production, including regression coefficients, βn is then:We can include the effect of snow as ΔASep is the open water area available in September, Anet is the net area diverged integrated over the winter season, Acomp is the compensated area diverged integrated over the winter season, SSTSep is the Sep sea surface temperature (the top 10 m ocean temperature), and ΔT is the difference between the freezing temperature of seawater and the surface air temperature. We neglect the ocean-to-ice heat flux term, Fw, simply because when it is included as an additional term in this multiple linear regression, the resulting regression coefficient is statistically insignificant : Fw has no significant impact on ice production in the Kara-Laptev region in CESM-LE.Where βn, using the departures of regressors and regressands in each ensemble member from ensemble-mean values, i.e. the internal variability, during the 20C run, RCP8.5 run and the full timeseries , Sep open water × ΔT (β2), and inverse snow depth × ΔT (β1). This reinforces the important role of variability in wind-driven sea ice divergence in explaining year-to-year changes in ice production in the Kara-Laptev region36.Three regressors are dominant in their contribution to the internal variability: net divergence × ΔHaving seen that our linear model can explain a large fraction of the internal variability in winter sea ice production across all ensemble members of CESM-LE, we now ask whether it can explain the forced changes in sea ice production associated with climate change, and, in doing so, help us to understand the process at play. The linear model reconstruction of these forced changes is determined by the trends in the ensemble-mean values of the climate variables used Fig.  and the We construct timeseries of ice production using the regression coefficients calculated from internal variability in (a) 20C data only, (b) RCP8.5 data only and (c) the full timeseries Fig. a. All thT) and another climate variable. The trends of the underlying timeseries , resulting in a negative trend in the ice production attributable to the Sep open water setting.The greatest contribution to increasing ice production up to 2020 comes from the 20 Figs. b, this t20 Figs. b, this nβ1ΔT/hs term are dominated by warming SAT, leading to a decline in ice production from this term in spite of the thinning snow cover.Decreasing snow thickness on sea ice also provides an important contribution to rising ice production between approximately 1990 and 2010 Fig. b. While TAnet regressor has the largest standardised coefficient  in the region are generally low in CESM-LE: the ensemble-mean values are c. 0.5 W/m2 during 20C, rising throughout RCP8.5 to nearly 2 W/m2 by 2080. This, however, does not appear to noticeably impede ice growth in CESM-LE: Fw is statistically insignificant when included as an additional regressor in the linear model for ice production.The ity Fig. e. There We next estimate changes in ice production in the Kara-Laptev region over recent decades Fig.  by apply30 trigger relatively low ice production. The 10-year running mean  between the Sep SIA and SAT timeseries , and could be included as an additional regressor. Sea-ice thicknesses at the start of winter  can additionally be included as an additional regressor (as ΔT/hi). We did not include this term because observational estimates of sea ice thickness are much more time-limited than the other variables we use, precluding a multi-decadal observation-based estimate of ice production. Further, its inclusion only marginally increased the skill of the linear model. However in other regions this term may be more important. Application of this technique to other regions and models will help to build physical understanding of the controls on sea ice production, and motivate understanding of the impact of future changes in ice production.Our methodology could be applied to other regions in the polar oceans and other climate models. While much of the process-based understanding we have developed for the Kara-Laptev region is transferable to other areas, there are important differences in the mean sea ice state and oceanic setting that affect the balance of arge Eq. . On the There is observational evidence for an Arctic-wide increase in ice production. Here, however, we focus on one region which shows this rise-then-fall behaviour in CESM-LE. Note, however, that other climate models may not necessarily show a rise-then-fall in ice production in this same region. We adopt a regional perspective because the growth conditions of sea ice across the Arctic are highly regionally variable, and focus on one region with fairly consistent characteristics: the Kara and Laptev seas. Moreover, these shelf seas have special status due to their outsized contribution to Arctic sea ice production.65, precluding the regional investigation we undertake here, though not undermining the Arctic-wide evidence for increasing sea ice production described in the Introduction.Analysis of ice production from satellite data is possible with a combination of sea ice thickness, concentration and velocity data. However, we do not perform it here for two main reasons: a) the satellite record of sea ice thickness is very short, precluding the development of robust statistical relationships; b) there is a large amount of uncertainty in sea ice thickness measurements where ice is thin66. A large ensemble such as CESM-LE permits investigation of forced responses to climate change in the context of internal climate variability66. We require an ensemble approach in our methodology in order to develop a linear model based on internal variability that we then use to reconstruct forced changes. CESM-LE is based on the fully coupled model CESM1.1, and comprises the Community Atmosphere Model, version 5 (CAM5); the Parallel Ocean Programme, version 2 (POP2); the Community Land Model, version 4; and the Los Alamos Sea-Ice Model CICE, version 4 as its sea ice component. The version of CICE used features improvements to shortwave radiation interactions, including the effects of melt ponds and aerosol deposition on ice67. The spatial resolution of the CESM1.1 ocean and sea ice models is nominally 1° × 1° longitude by latitude, while the atmospheric model is 0.9° × 1.25°.To analyse the forced response of sea ice production in the Kara and Laptev seas to climate change, we use data from all 40 ensemble members of the Community Earth System Model Large Ensemble (CESM-LE)68 that we refer to as the 20C run; secondly, from 2006 to 2100, a high-emissions RCP8.5 run69, leading to over 4 ∘C warming by 2100. The ensemble spread is entirely generated by simulated internal climate variability originating from very small, random differences in the initial air temperature fields. Here we rely on monthly data between 1920–2080, sufficient time to assess major forced changes in ice production in the Kara and Laptev seas.All CESM-LE ensemble members are forced by the same external forcing data, separated into two runs. Firstly, a run from 1920 to 2005 forced by historical external forcing data71, the Intergovernmental Panel on Climate Change (IPCC) consider it a scenario of low likelihood in their sixth assessment report (AR6), due to recent developments in the energy sector72; in the context of currently pledged climate and energy policies more plausible scenarios project between 2 °C and 3 °C by 210073. Despite this, the RCP8.5 emissions scenario retains great value for fundamental science: it provides a large warming signal, which is useful for interpreting climate dynamics and relationships in the climate system in the presence of noise.Although recent emissions have tracked the RCP8.5 emissions scenario75. While CESM1.1 and CESM-LE have their own biases, they have been used in a broad swathe of Arctic climate studies and generally show a serviceable correspondence to (sparse) observations83. Arctic sea ice thickness in CESM-LE broadly corresponds in spatial mean pattern and trend during 1980–2015 with PIOMAS84. On an Arctic-wide basis it exhibits thicker  sea ice than PIOMAS, though the differences versus PIOMAS are smaller and vary either side of zero spatially in the Kara-Laptev region85. Although the satellite record of sea ice thickness is short, CESM-LE compares well with estimates of inter-seasonal thickness changes and interannual variability32. Declining Arctic sea ice concentration and extent in CESM-LE has been more comprehensively compared to observations, which fit within its ensemble spread and compare well across all seasons81. The changing open water season also shows correspondence with satellite observations, including in the Laptev Sea81.Coupled climate models of the CMIP5 and CMIP6 generations exhibit substantial biases in aspects of their representations of the Arctic Ocean, particularly in the temperature of the Atlantic Water layer, and the salinity of the halocline and surface fresh layer32. In contrast with Petty et al. we analyse freshwater exchanges with the ocean, rather than changes in ice thickness, to explicitly isolate thermodynamic changes. Sea ice production is calculated as the sum of only the ocean-to-ice part of thermodynamic ice-ocean freshwater fluxes (it does not include melting).To capture winter thermodynamic sea ice production, we use monthly data from October to April, inclusive, as per Petty et al.Our calculation of freezing area days using CESM-LE data is an approximation, dependent on the spatial discretisation of the ocean and sea ice components , temporal averaging (monthly) and number of ensemble members used (40). The mean winter growth rate in all such freezing grid cells is given by the total winter ice production divided by the number of freezing area days.Climate variables are either explicitly taken in September (SST and open water area), or calculated as a mean  or sum (divergence) throughout the winter period of October to April. All variables are computed within the study region shown in Fig. The variables that contribute to the regressors are as follows. (1) Winter mean snow depth on sea ice, averaged over all cells with more than <15% sea ice. (2) Sep sea ice concentration, converted to sea ice area using grid cell areas. (3) Sea-ice area diverged throughout the winter, (a) summed over all grid cells where ice is present (net divergence), (b) summed over cells in which cells are divergent only (positive divergence). (4) Winter mean surface air temperatures over the whole oceanic region. (5) Sep ocean temperatures averaged across the whole oceanic region in the top (10 m thick) grid cell.We investigate two types of sea ice divergence. Firstly, the net divergence, which is area-integrated divergence, including convergence. We are also interested in divergence within the region that is compensated by convergence somewhere else in the region; this divergence can open or expand leads but still not lead to net divergence. To capture this compensated divergence, we record the “positive divergence”, which is counted only over cells where the divergence is positive. The compensated divergence is then found as compensated div = positive div − net div. There is therefore no double counting between net and compensated divergence. The two are therefore only weakly correlated—preferable for the purposes of building a linear model. The divergence measures are converted to areas diverged before inclusion in the linear model for ice production.T by taking the difference between the freezing temperature of seawater, −1.8 °C, and the surface air temperature. Thus, the colder the surface air temperature, the larger and more positive ΔT. Sep open water area is derived by taking the difference between the total oceanic area of the region and the Sep sea ice area.Surface air temperature is converted to ΔData are plotted according to the second year in each winter, e.g. winter 2015/2016 is labelled 2016. The variables extracted from the observation-derived estimates are computed in a consistent manner to their equivalents from CESM-LE.T/hs (2) ΔTASep (3) ΔTAnet (4) ΔTAcomp (5) SSTSep. In each case these are deviations from the ensemble mean for each year, such that the linear model is centred around zero. Through multiple linear regression, we derive the associated regression coefficients, βn that minimise the least squares difference of the linear model to the original ice production values. In Eq. e indicates that the variable is taken from all ensemble members, and for all years under consideration.We derive the regression coefficients for the linear model by using the internal variability in all 40 ensemble members of CESM-LE. The regressand is total winter ice production in the Kara and Laptev seas, and the regressors are: (1) ΔThe standardised values of the coefficients are shown in Fig. While we can find the root mean square error associated with the linear model, we view a more useful estimate of uncertainty as that derived from the spread of estimates when limiting the dataset to single ensemble members. This connects the methodology to the constraints of a single climate realisation, which is all we may observe, and all we have from many climate models. To do this, we subtract the ensemble mean from each model realisation for each climate variable . We then compute the regression coefficients as before, but with a dataset that is 1/40th of the size of the whole ensemble estimates. All 40 resulting versions of each regression coefficient are displayed as grey dots in Fig. β coefficients are derived from internal variability across the whole ensemble as per Eq. m denotes that the variable is from the ensemble mean. Superscript m recon indicates that the ice production timeseries is a reconstruction of the ensemble mean.To reconstruct the ensemble-mean ice production in CESM-LE, we use regression coefficients derived from internal variability across the whole ensemble, but use climate variables from the ensemble mean. This is illustrated in the equation below: As our linear model is trained on internal variability, which numerically is comprised of deviations either side of zero because we subtract the ensemble mean, we add a constant to compare the reconstruction with the original ice production data. We choose the constant such that the mean of the reconstruction and the mean of the original ice production timeseries are the same.We process the climate variables from observation-based sources for application with the linear model in the same manner as climate variables from CESM-LE.86, gridded at 0.25 ∘ × 0.25 ∘ resolution. ERA5 is the fifth generation of atmospheric reanalyses from the European Centre for Medium-Range Weather Forecasts (ECMWF). We use the terms surface air temperature and 2 m temperature interchangeably here. Surface air temperatures from ERA5 generally perform well in the Arctic relative to in situ observations, however consistently show warm biases of order several degrees over sea ice in winter months87. Our analysis of these data spans the winters 1979/1980 to 2019/2020.We use monthly-mean 2 m temperatures from the ERA5 global reanalysis88. Polar Pathfinder data are masked during processing to only provide vectors where ice concentration is >15%. We analyse data from Polar Pathfinder encompassing the winters 1979/1980 to 2019/2020.To plot mean sea ice velocities and calculate sea ice divergence, we make use of the National Snow and Ice Data Centre (NSIDC) Polar Pathfinder v4.1 product, gridded at 25 × 25 km resolution and in weekly means89, and compare well with other estimates91. We use data spanning the winters 1978/1979 to 2018/2019.To document changes in sea ice area, we use sea ice concentration data from passive microwave satellite retrievals, prepared by the NASA Goddard Space Flight Centre (NASA GSFC). We use the ‘merged’ data, which combine data derived from the Bootstrap and Nasa Team algorithms92 and the Polar Pathfinder ice motion vectors described above. The snow depth data are observations-based in the sense they are produced by observationally-informed atmospheric reanalysis and ice motion products. However we note that the snow depth data themselves should not be treated as observations. SnowModel-LG output compares well to observational data sets in spatial and seasonal variability of snow depth and density93. The data span winters 1981/1982 to 2018/2019. Consistent with our snow depth acquisitions from CESM-LE, we present snow thickness on sea ice where present.For snow depth, we use the output of a Lagrangian snow evolution model, SnowModel-LG, which provides daily data on a 25 km × 25 km grid. SnowModel-LG is forced by the MERRA-2 atmospheric reanalysis94. The monthly data are provided on a 1° × 1° grid. We use data spanning the winters 1961/1962 to 2019/2020. The SST data are taken from the Met Office Marine Data Bank (MDB), as well as data from the Comprehensive Ocean-Atmosphere Data Set (COADS) (now ICOADS) where there were no MDB data. The sea ice data are taken from a variety of sources including passive microwave retrievals and digitised sea ice charts. Additionally, we use monthly-mean SST data at a 1° × 1° resolution from the National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature v2 (NOAA OISSTv2) dataset95. We use NOAA OISSTv2 data from the winters 1981/1982 to 2019/2020.For sea surface temperatures, we use the Met Office Hadley Centre’s sea ice and sea surface temperature (SST) data set, HadISSTo estimate), we utilise our linear model and the observation-based climate variables (superscript o), as perTo estimate historical ice production (superscript In order to just have one central estimate and one set of uncertainty windows, we pool all 40 estimates of the regression coefficients across the 20C, RCP8.5 and ‘full’ runs, and evaluate the linear model with each, yielding 120 estimates. We take the mean as the central estimate, and display the one standard deviation envelope associated with each term and the total ice production estimate (Fig.


Background and Objectives: Navigated pedicle screw placement is becoming increasingly popular, as it has been shown to reduce the rate of screw misplacement. We present our intraoperative workflow and initial experience in terms of safety, efficiency, and clinical feasibility with a novel system for a 3D C-arm cone beam computed-tomography-based navigation of thoracolumbar pedicle screws. Materials and Methods: The first 20 consecutive cases of C-arm cone beam computed-tomography-based percutaneous pedicle screw placement using a novel navigation system were included in this study. Procedural data including screw placement time and patient radiation dose were prospectively collected. Final pedicle screw accuracy was assessed using the Gertzbein–Robbins grading system. Results: In total, 156 screws were placed. The screw accuracy was 94.9%. All the pedicle breaches occurred on the lateral pedicle wall, and none caused clinical complications. On average, a time of 2:42 min was required to place a screw. The mean intraoperative patient radiation exposure was 7.46 mSv. Conclusions: In summary, the investigated combination of C-arm CBCT-based navigation proved to be easy to implement and highly reliable. It facilitates the accurate and efficient percutaneous placement of pedicle screws in the thoracolumbar spine. The careful use of intraoperative imaging maintains the intraoperative radiation exposure to the patient at a moderate level. For numerous indications, dorsal stabilization with the placement of pedicle screws is a common treatment option ,2,3,4,5.Different combinations of 3D-imaging devices and navigation systems for spinal navigation are available. Accordingly, numerous studies have been performed to investigate the image quality and radiation dose of the imaging devices as well as the accuracy and usability of navigation systems ,17,18,19Since June 2021, a novel device enabling 3D C-arm CBCT-based navigation for the placement of thoracolumbar pedicle screws has been implemented. However, to date, no reports on its clinical use are available in the literature.The aim of this study is to present our workflow for navigated minimally invasive pedicle screw placement, to report our initial experience in terms of clinical feasibility and efficiency, and to evaluate screw accuracy, patient radiation exposure, and the time needed for screw placement in the first clinical cases performed with the system.The study was reviewed and approved by the ethics committee of the Medical Association Rhineland-Palatine, Germany (application number 2021-16061). All included patients gave verbal and written consent. All the procedures were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.The first 20 consecutive cases of percutaneous pedicle screw placement were performed by a single experienced spine surgeon (S.Y.V.) after implementation of the system in our institution. All patients had preoperative imaging with conventional anteroposterior and lateral X-rays as well as computed tomography (CT).Screw placement was performed using the navigation software of the Pulse platform . This device is composed of a monitor cart with two vertically stacked touch-sensitive flat screens and a mobile camera unit with two infrared cameras. Navigation was based on datasets acquired with a newest-generation mobile 3D C-arm CBCT . To allow for referencing, two arrays were mounted on both sides of the flat panel detector of the C-arm.All the patients were placed on a radiolucent carbon-fiber surgical table in prone position. The orientation of the patient depended on the planned spinal levels for the instrumentation to avoid the interference of the C-arm with the table column and to account for the different curvature of the spine at upper thoracic and lumbar levels . In eithThe correct spinal levels were identified using low-dose fluoroscopy before the preparation and draping of the patient. After a small midline incision and soft-tissue preparation, the patient reference array was mounted to a spinous process or the posterior iliac crest. For highest accuracy, it is recommended that a spinous process is chosen inside, or at least close to, the surgical field. Mounting on a fractured/instable spinous process should be avoided. The design and the comparatively small size of the array allowed for sufficient mobility of the instruments, even with the array mounted inside the region of interest .After applying a sterile transparent cover to the C-arm, a registration scan was performed. Ventilation should be paused during scan acquisition to avoid motion artifacts and enhance both accuracy and image quality. While in most cases, the image quality in the low dose mode (200 images in 30 s) of the C-arm is highly sufficient for screw placement, to reduce artefacts, we recommend a referencing scan with 400 images for better image quality in obese patients or in patients with spinal implants.After acquisition of the 3D scan, image data were automatically transferred to the navigation device via the NaviLink interface of the C-arm. After selection of the spine levels for instrumentation, screw placement was performed: First, navigation accuracy was verified with the navigated pointer on different anatomical landmarks. If the accuracy was adequate, the navigated drill guide was used to find the appropriate screw trajectory and make a small incision in the skin. Then, subcutaneous tissue and the fascia is performed. Of course, the technique presented here can also be used in open-approach surgery. To avoid soft-tissue traction on the instruments or the screw under insertion and the resulting effect on accuracy, blunt preparation of the trajectory down to the lamina vertebrae is recommended. Subsequently, the navigated drill guide was used to find the adequate trajectory with the help of the simultaneous visualization of the trajectory and the corresponding screw on the screen. Screw diameter and length can easily be adjusted on the touchscreen by OR personnel. After selecting the right screw size and diameter, electric drilling was performed with a 3.5 mm drill. The trajectory can be saved by clicking the corresponding field on the touchscreen, we recommend saving the trajectory during drilling. Using the drill guide, the drilling depth can be flexibly adjusted. From our experience, we recommend 30 mm for thoracolumbar screw placement, which is sufficient for drilling through the pedicle and beyond the posterior wall into the spongious bone of the vertebral body. The drill was removed with the drill guide held in place, and a flexible guide wire was inserted into the pedicle. Finally, the drill guide was removed entirely.While some surgeons prefer to place every screw immediately after drilling, we recommend the drilling and placement of guide wires for all planned screws to avoid the mechanical manipulation of the spine, which may occur during screw insertion and might affect the navigation accuracy of the subsequent screws. Between the placement of different guide wires, accuracy should be checked using the navigated pointer.On surgeon discretion, standard AP and lateral fluoroscopic views are performed to check guide-wire position. This allows potential misplacement to be detected before the screw is inserted, which is significantly thicker in diameter, and thus potentially more destructive. If guide-wire position cannot be evaluated or seems inadequate in standard fluoroscopic views, a control scan may be performed, followed by a correction of guide-wire position, if necessary.The guide wire also facilitates locating the exact entry point for the cannulated screw , which is then inserted according to the drilled trajectory continuously visualized in axial and sagittal planes on the screen . After sPatient data including diagnosis, spinal levels to be instrumented, as well as the number of segments instrumented, and screws placed.Operating time ;Navigation time ;Screw placement time (SPT) for every single screw .Procedural times during the procedures:Intraoperative patient radiation exposure as dose area product (DAP) and fluoroscopy time (FT) as documented in the imaging system dose report; for calculation of the effective dose (ED) in mSv, ED/DAP conversion factors of 0.29 , 0.26 (BMI 25–30) and 0.23 (BMI > 30) were used .Intraoperative revision of guide wires or screws.Intraoperative complications or technical problems.Postoperative complications Pedicle screw accuracy was assessed in postoperative CT by an independent investigator with extensive experience according to Gertzbein–Robbins grading system with grades A (no pedicle breach) and B (pedicle breach < 2 mm) considered clinically acceptable and Grades C (pedicle breach < 4 mm), D (pedicle breach < 6 mm), and E (pedicle breach ≥ 6 mm) deemed unacceptable because of potential neurovascular injury .The following parameters were prospectively collected:p < 0.05.Statistical analysis was performed using Prism 8 . Kolmogorov–Smirnov test was used to check for normal distribution of data. Descriptive statistics are shown as means and 95% confidence interval (95% CI) for continuous variables. Mann–Whitney U test was used to analyze the central tendencies of the differences. The significance level was set at Overall, 156 screws were placed in 20 patients during 20 procedures.Eight patients were female, and twelve patients were male. The mean age at the time of surgery was 68.6 years (95% CI 63.4–73.4 years), and mean BMI was 28.2 (95% CI 25.3–31.1). Eight procedures were performed at the thoracic level, four on the thoracolumbar junction, seven on the lumbar spine, and one on the lumbosacral junction. Fourteen procedures were performed for vertebra fractures and six for spondylodiscitis. The general patient data and procedure characteristics are displayed in Overall, the mean operating time from the first incision to suture was 92.3 min (95% CI 82.6–102.0 min). For navigation alone, 35.7 min (95% CI 28.6–42.9 min) was required. The time needed to place every screw was also recorded. On average, a time of 2:42 min was required to perform wire and screw placement in a pedicle (95% CI 2:31–2:57 min).The intraoperative dose area product, fluoroscopy time and calculated ED are shown in p = 0.001).In addition to the registration scan, additional intraoperative 3D imaging was performed in 12 of 20 patients. Wire or screw position was checked in one and nine cases, respectively, while in two cases, a 3D scan was performed both after wire and screw placement (Case 4 and Case 10). As expected, DAP was significantly higher in cases with a control scan compared with cases without additional intraoperative 3D imaging . No intra- or postoperative complications or technical malfunctions were reported. Revision surgery was not performed in any cases.The final screw accuracy was 94.9% with 96 screws graded as A, 52 graded as B, 7 graded as C, and 1 graded as D using Gertzbein–Robbins grading system . All eigThe purpose of this study was to present our intraoperative workflow for percutaneous pedicle screw placement using a novel navigation system and to report our initial experience concerning its feasibility and reliability, as well as the results of the first performed clinical cases. To the best of our knowledge, there are currently no other studies on the in-patient use and evaluation of this system.The first 20 patients in which minimally invasive screw placement was performed were included in the study after implementation of the system. Due to our institution’s focus on trauma cases, most procedures were performed on patients with vertebral fractures. Since both the performing surgeon and the OR staff already had experience with navigation in general, particularly the navigation of pedicle screws in the cervical spine, the presented workflow could be established within a very short time. The efficiency of the workflow is confirmed by an average screw placement time of 2:42 min, which is a competitive time compared with other studies ,23,24.There is extensive evidence in the literature that navigated procedures achieve a higher accuracy of screw placement in the pedicle compared to freehand and fluoroscopically assisted procedures ,13,14,15Although no comparison was made with conventional screw placement in this study, the average effective dose to the patient of 7.46 mSv for the included procedures appears to be reasonable compared with data in the literature ,32.In their experimental study, Foster et al. investigated the effective dose for different imaging devices including the C-arm used in the present study. In a human specimen with pedicle screws inserted into the lumbar spine, they reported an effective dose of 1.9 mSv, 4.0 mSv, and 4.6 mSv for a single 3D scan with low-, medium-, and high-dose protocols, respectively, using similar ED/DAP conversion factors .Keil et al. were able to show in their study that the C-arm CBCT used in this study provides sufficient image quality to evaluate the relevant structures with regard to pedicle screw placement . In the In two cases, the revision of an inserted screw was necessary following intraoperative 3D imaging. An evaluation by an independent investigator resulted in a final screw accuracy of 94.9%. In 8 of 156 screws, a potentially relevant perforation of the pedicle was present, which occurred on the lateral side in all cases. In seven out of eight cases, these screws showed an in–out–in configuration and/or were larger in diameter than the pedicle itself. Pedicle screws placed in an in–out–in configuration have been described to be biomechanically superior compared to a fully intrapedicular position . In addiOur results are in accordance with other studies investigating the accuracy of navigated pedicle screw placement. In the study by Tkatschenko mentioned above, an accuracy of 95.8% was reached with percutaneous C-arm-based navigation, which also used the Gertzbein–Robbins classification with a cut-off value of 2 mm . In theiIn a recently published experimental study, the combination of a C-arm CBCT and navigation system reached an accuracy rate of 97.5%, which was non-inferior to iCT navigation and superior to O-arm CBCT-based navigation . WhetherAs with all navigated procedures, potential pitfalls must be considered. The presumed constant visualization of anatomy and implants can provide a deceptive sense of security. Therefore, regular verification using anatomical landmarks must be performed to detect potential navigation inaccuracies ,40.Compared with ‘mobile’ iCT, which has been frequently investigated in the literature, the C-arm CBCT can be more easily relocated . HoweverOverall, this study has several limitations:First, we present an observational case series with a limited number of cases. Nevertheless, this study is the first description of the clinical application of this system under investigation. The aim of this study was not to test the accuracy on a large number of screws, but to describe the feasibility and reliability, as well as a highly efficient intraoperative workflow from which other users may benefit.Second, because the first clinical cases were included, we did not consider a potential learning curve with the new system; however, this would be more likely in an inexperienced surgeon .Because regular navigated pedicle screw placement was not performed in the thoracolumbar spine prior to the implementation of the system, it was not possible to create a control group based on conventional procedures performed by the same surgeon.Further studies should aim to investigate relevant parameters—such as accuracy, radiation exposure, and time required—using larger patient populations, comparing these parameters with fluoroscopically assisted pedicle screw placement and using other combinations of imaging devices and navigation systems.In summary, the investigated combination of C-arm CBCT-based navigation proved to be easy to implement and highly reliable. This facilitates the accurate and efficient percutaneous placement of pedicle screws in the thoracolumbar spine. The careful use of intraoperative imaging maintains the intraoperative radiation exposure for the patient at a moderate level.


Vaccination remains the leading control method against infectious bronchitis (IB) in poultry despite the frequently observed IB outbreaks in vaccinated flocks. Here, two vaccination regimes were evaluated against challenge with the Massachusetts (Mass) infectious bronchitis virus (IBV) strain that was linked to egg production defects in Western Canada. One vaccination strategy included live attenuated IB vaccines only, and the other used both inactivated and live attenuated IB vaccines. The two immunization programs involved priming with a monovalent live attenuated IB vaccine (Mass serotype) at day-old, followed by intervals of bivalent live attenuated IB vaccines containing the Mass and Connecticut (Conn) serotypes given to the pullets at 2-, 5-, 9-, and 14-week-old. Inactivated IB vaccine (Mass serotype) was administrated to only one group of the vaccinated birds at 14-week-old. At the peak of lay, the hens were challenged with the Mass IBV isolate (15AB-01) via the oculo-nasal route. The efficacy of the vaccines was assessed following the challenge by observing clinical signs, egg production, egg quality parameters, seroconversion, and systemic T-cell subsets (CD4+ and CD8+ cells). Moreover, the viral genome loads in the oropharyngeal (OP) and cloacal (CL) swabs were quantified at predetermined time points. At 14 days post-infection (dpi), all the hens were euthanized, and different tissues were collected for genome load quantification and histopathological examination. Post-challenge, both vaccination regimes showed protection against clinical signs and exhibited significantly higher albumen parameters, higher anti-IBV serum antibodies, and significantly lower levels of IBV genome loads in OP swabs (at 3 and 7 dpi) and trachea and cecal tonsils compared to the mock-vaccinated challenged group. However, only the birds that received live attenuated plus inactivated IB vaccines had significantly lower IBV genome loads in CL swabs at 7 dpi, as well as decreased histopathological lesion scores and IBV genome loads in magnum compared to the mock-vaccinated challenged group, suggesting a slightly better performance for using live attenuated and inactivated IB vaccines in combination. Overall, the present findings show no significant difference in protection between the two vaccination regimes against the Mass IBV challenge in laying hens. Infectious bronchitis (IB) is an economically devasting disease in chickens, and its etiological agent is infectious bronchitis virus (IBV) . InitialIBV is one of the most widespread agents around the world, so it can spread rapidly through chicken flocks, particularly in non-vaccinated birds, which shed the virus in their excretions for long periods . TherefoAlthough many experimental IBV vaccination/challenge trials have been performed to evaluate the protective efficacy in young chicks, few of these experiments have been conducted in laying birds . The maiIn young chicks, two antigenically different live-attenuated IBV vaccines have been used successfully in a vaccination program to combat diseases associated with recent IBV variants ,30. As aIBV serotypes are distributed differently across different regions in Canada. The majority of current IBV outbreaks in Eastern Canada are caused by the Delmarva (DMV/1639) and 4/91 serotypes ,34. MeanAll the experimental procedures in this study were ethically approved by the University of Calgary’s Health Sciences Animal Care Committee (HSACC) and the Veterinary Sciences Animal Care Committee (VSACC) with the protocol number AC19-0113.n = 50) were reared in the University of Calgary’s Veterinary Science Research Station (VSRS). The birds were housed on deep litter with strict biosecurity measures, and they received water and feed ad libitum for the duration of the experiment. During the laying period, a light schedule of sixteen hours of light:eight hours of darkness was maintained.A total of one hundred and twenty 1-day-old specific-pathogen-free (SPF) chicks (White Leghorn) were purchased from the Canadian Food Inspection Agency (CFIA) . The day-old SPF chicks were sexed based on a molecular assay described previously . The SPF50) of the virus was determined from 9-day-old SPF ECE following Reed and Muench’s method [8 EID50/mL. Two commercial live attenuated IB vaccines were employed; one was composed of the Mass serotype  and the other contained the Mass and Conn serotypes . Each vial of the live vaccine was reconstituted in 30 mL of sterile phosphate-buffered saline  to be administrated via eye drop according to the manufacturer’s instructions (30 µL per bird). The commercial inactivated vaccine used for this trial was BRON-NEWCAVAC® SE, which contained the Mass serotype of IBV , and it was given intramuscularly (I/M) based on the manufacturer’s instructions (0.5 mL per bird).In this study, we challenged the birds with a Mass IBV isolate designated as 15AB-01 in its fourth serial passage through SPF embryonated chicken eggs (ECE). The Mass IBV isolate was isolated from tissue samples from a Western Canadian layer flock with a history of SES . The 50%s method ; the virn = 36) and mock-vaccinated (n = 14) groups in two separate negative pressure rooms. At 14-week-old, the vaccinated group was randomly and evenly subdivided into two groups (L+LK and L+L), and each group was kept in a separate room. In terms of immunization, all birds in the vaccinated groups were primed with a Mass serotype live attenuated vaccine via eye drop at 1-day-old and then boosted with live attenuated vaccine containing the Mass and Conn serotypes at 2-, 5-, and 9-week-old. At 14-week-old, the L+LK group was vaccinated with a combination of live attenuated IB vaccine containing the Mass and Conn serotypes and a Mass serotype inactivated vaccine, while the L+L group was only vaccinated with live attenuated IB vaccine. Simultaneously, the mock-vaccinated (MV) group was administrated a placebo (PBS) via eye drop. At 26-week-old (peak of lay), the MV, L+LK, and L+L groups were further subdivided into MV/MI and MV/I; L+LK/MI and L+LK/I; and L+L/MI and L+L/I denoting mock-infected and infected groups, respectively. The hens in the L+LK/I (n = 10), L+L/I (n = 10), and MV/I (n = 9) groups were infected with a total volume of 150 µL containing 1 × 106 EID50 Mass IBV isolate (15AB-01) per hen through the oculo-nasal route. Meanwhile, the birds in the MV/MI (n = 5), L+LK/MI (n = 8), and L+L/MI (n = 8) group were mock-infected oculo-nasally with 150 µL of PBS.The design is presented in g for 10 min (min) at 4 °C, and the serum was separated, aliquoted, and kept at −20 °C. At 14 dpi, all hens were euthanized via over-inhalation of isoflurane anesthesia followed by cervical dislocation, and then a post-mortem examination was performed to observe any gross lesions. Tissue specimens from the trachea, lung, kidney, cecal tonsil (CT), ovary, and different parts of the oviduct  were collected and preserved in RNA Save®  for IBV genome load quantification. Other parts from the ovary and different regions of oviduct  were fixed in 10% neutral buffered formalin  for histopathological examination.After infection, all birds were observed for clinical signs, egg production, and egg quality for 14 days post-infection (dpi). Oropharyngeal (OP) and cloacal (CL) swabs were also collected at 3, 7, and 14 dpi to detect viral shedding. Then, the swabs were preserved in PBS supplemented with 2% fetal calf serum and 2% penicillin and streptomycin , aliquoted, and stored at −80 °C until processing. At 5 dpi, 2 mL of blood was collected from the wing vein of all hens and kept in tubes containing anticoagulant to isolate the peripheral blood mononuclear cells (PBMCs). At 3 weeks post last vaccination, as well as at 10 and 14 dpi, 1 mL of blood was collected in plain tubes from all birds to monitor serum anti-IBV antibodies. Following 1 h at room temperature, the blood samples were centrifuged at 2000× Following the IBV challenge, the clinical signs in all groups were observed twice a day, and they were scaled according to their severity into four scores (from 0 to 3) using a previously described method . BrieflyThe collected eggs were analyzed daily up to 2 weeks post-infection (p.i.). All eggs were examined for external egg quality parameters such as egg length (L) and egg width (W), which were measured using a digital caliper with a minimum value 0.01 mm. Based on these measurements, the egg shape index (SI) was calculated following a formula previously described . For intA commercial enzyme-linked immunosorbent assay (ELISA) kit  was used to quantify anti-IBV antibodies in serum samples. The procedure was performed and the data were analyzed following the manufacturer’s instructions. The positive IBV samples had titers > 396 (cut-off).6 cells per well in 96-well U-bottom microtiter plates . Then, the cells were washed in a buffer composed of 1% bovine serum albumin (BSA)  in PBS. The plate was centrifuged at 233× g for 5 min at 4 °C, and the supernatant was discarded. The fragment crystallizable (Fc) region of the antibody was quenched via the resuspension of cells in 0.2% chicken serum diluted in 1% BSA for 15 min at 4 °C. After spinning the cells as mentioned above, the cell pellets were stained with a suspension of two monoclonal antibodies: mouse anti-chicken CD8-APC  and mouse anti-chicken CD4-PE  for 20 min at 4 °C in the dark. The controls were their relevant isotypes (APC and PE labelled), and the unstained control was 1% BSA. Following two washings in 1% BSA, 1% paraformaldehyde solution  was used to fix the stained PBMCs. Finally, the samples were submitted to the University of Calgary’s Flow Cytometry Core Facility  for analysis. The data were generated using BD FACSDivaTM 6.1.3 software .The PBMCs were isolated from the collected blood at 5 dpi using a previously de-scribed procedure . The PBM® reagent  according to the manufacturer’s instructions. In order to measure the concentration of extracted RNA, a Nanodrop 1000 spectrophotometer  was used. A total of 1000 ng and 2000 ng of the obtained RNA were used to synthesize the cDNA following the manufacturer’s guidelines using random primers  for the swabs and tissues, respectively. The quantification of the viral RNA was performed via a SYBR green real time quantitative polymerase chain reaction (RT-qPCR) assay using IBV nucleocapsid (N) gene-specific primers as previously described [7 to 103) of N-gene plasmid prepared in-house to quantify the IBV genome copies [7 to 103 copies of the standard plasmids. A plot of threshold cycle (Ct) values against standard curve data was made to determine the limit of detection of this RT-qPCR assay. The detection limit of RT-qPCR refers to the lowest concentration of viral particles that can be detected.The viral ribonucleic acid (RNA) was extracted from different tissues and swabs using Trizolescribed . A stande copies . The limThe formalin-fixed tissue specimens from the ovary and different parts of oviduct  were submitted to the Diagnostic Services Unit (DSU) at the University of Calgary, Faculty of Veterinary Medicine, for further processing and to obtain hematoxylin and eosin (H & E)-stained sections. We examined the slides under a light microscope  and found presumptive IBV-induced lesions , which wThe Pearson’s chi-squared test was performed to compare the percentage of weekly egg production performance between the groups. Whereas, the egg quality parameters (SI and HU), clinical signs scores, serum anti-IBV antibodies at post-challenge, and IBV genome loads in OP and CL swabs were analyzed at different time points using the mixed-effects model. In order to identify the group differences, Tukey’s multiple comparisons test was employed. The differences among the groups in other parameters  were identified using the Kruskal–Wallis test followed by Dunn’s multiple comparison test. The normality was assessed by D’Agostino-Pearson normality test before the analysis of data. All statistical tests and graphs were conducted and created, respectively, in GraphPad Prism 9.2.0 Software .p < 0.05), 5 dpi (p < 0.01), and 6 dpi (p < 0.05), thus indicating a significant protection against the clinical signs induced by Mass IBV challenge among these groups. Primary respiratory distress signs were observed , as well as non-specific signs including depression and ruffled feathers were detected.Prior to the necropsy examination at 14 dpi, all hens were scored for respiratory and non-specific signs. Overall, the birds in the MV/I group exhibited clinical signs starting at 3 dpi and subsiding at 10 dpi . On the p > 0.05) among all the treatment groups (data not shown). Regarding external egg quality, the SI exhibited no significant differences between all groups over two weeks post-infection  than those of the mock-infected groups at the first and second weeks following IBV infection  than those of the MV/I group at the 1st week of IBV infection; however, the L+LK/I group showed significantly lower (p < 0.01) HU compared to the L+LK/MI group at the same time point. At the second week of IBV infection, the HU in the L+LK/I and L+L/I groups were significantly higher (p < 0.0001) than the MV/I group. Over the two weeks p.i., 2 eggs with watery albumen were detected in the MV/I group at 6 and 8 dpi, whereas in the L+L/I group, only one egg with watery albumen was detected at 12 dpi.Over the first- and second-week post-challenge, egg production was not significantly different  and post-challenge (10 and 14 dpi) are shown in p < 0.0001 and p < 0.01, respectively) at the pre-challenge period.Statistically, both vaccinated groups (L+LK and L+L) had higher serum anti-IBV titers than the mock-vaccinated group (MV) (p < 0.0001), the L+LK/MI group (p < 0.05), and the L+L/MI group (p < 0.0001) at 10 and 14 dpi. Likewise, the anti-IBV antibody concentrations in the L+L/I group were significantly higher (p < 0.001) than in the MV/MI group and the L+L/MI group at 10 and 14 dpi. Compared to the L+LK/I and L+L/I groups at 10 and 14 dpi, the serum anti-IBV antibody titers in the MV/I group were significantly lower . The anti-IBV antibody titers were not significantly different (p ˃ 0.05) between the L+LK/I and L+L/I groups throughout the study. There were no statistically significant differences (p ˃ 0.05) between the mock-infected groups; however, the anti-IBV titers in the L+LK/MI group were significantly higher (p < 0.05) than in the MV/MI group at 14 dpi.In terms of antibody titers post-challenge, the titers in the L+LK/I group were significantly higher than in the MV/MI group (p > 0.05). Similarly, the percentages of CD8+ T cells showed no statistical difference between the groups at 5 dpi (p > 0.05).The gating strategy used to identify the T cell subsets (CD4+ and CD8+) among the other cell populations in PBMCs is shown in p < 0.0001) than those observed in the mock-infected groups at 3 and 7 dpi. The MV/I also had a significantly higher IBV genome load than that quantified in the L+LK/I group at 3 and 7 dpi (p < 0.0001), and in the L+L/I group at 3 and 7 dpi . At 3 dpi, the IBV genome loads in the L+L/I and L+LK/I groups were significantly higher when compared to the mock-infected groups (p < 0.01) (pairwise comparison not shown). The IBV genome loads in the L+LK/I and L+L/I groups showed no significant difference (p > 0.05) throughout the observation period. At 14 dpi, there was no significant difference (p > 0.05) between all treated groups. In terms of the CL swabs, the IBV genome loads in the MV/I group were significantly higher (p < 0.05) than those observed in the mock-infected groups and the L+LK/I group at 7 dpi. There were no significant differences (p > 0.05) detected between all groups at 3 and 14 dpi.The IBV genome loads in OP and CL swabs are shown in p < 0.0001), the L+LK/I group (p < 0.0001), and the L+L/I group (p < 0.05). In the case of CT, the MV/I group had significantly higher IBV genome loads when compared to the mock-infected groups (p < 0. 001), the L+LK/I group (p < 0.05), and the L+L/I group (p < 0.05). In the magnum, the IBV genome loads in the MV/I group were significantly higher (p < 0.05) than those of the other groups except the L+L/I group. The IBV loads in other tissues such as lung, kidney, ovary, isthmus, and uterus did not exhibit significant differences (p > 0.05) between the groups. Furthermore, there were no statistically significant differences in IBV genome loads between the L+LK/I and L+L/I groups in any of the tested tissues (p > 0.05).The IBV genome loads quantified by RT-qPCR in tissues are shown in The mock-infected groups demonstrated no obvious histopathological lesions in the ovary and different parts of the oviduct during the observation period a,e,i,m. In the MV/I group, the ovary showed focal sloughing of the ovarian epithelium, and the cortical stroma was infiltrated with either multi-focal heterophils or a few mononuclear cells. Furthermore, there were some aggregates of macrophages laden with hemosiderin and congested blood vessels in the cortical stroma b. In theIn the L+L/I, the ovary was characterized by sloughed epithelium, and the interstitial connective tissue of cortical stroma was filled with multiple areas of heterophilic cell infiltrations c, and thp < 0.01). In the case of magnum, the MV/I group showed significantly higher lesion scores compared to those of the L+LK/I group (p < 0.001) and the mock-infected groups (p < 0.0001). However, the lesion scores were not significantly different between groups in the uterus (p > 0.05). Although the lesion scores varied between the L+L/I and L+LK/I groups in all tissues, no significant differences could be detected (p > 0.05).The histopathological lesion scores are shown in It was shown that the Mass IBV isolate (15AB-01) could experimentally reproduce egg production and egg quality deteriorations in SPF layer chickens in 2018, and this was in accordance with the field observations caused by the same isolate among Western Canada layer flocks in 2010 . TherefoOur results were three-fold. First, compared to the MV/I group, both vaccination strategies conferred a significant protection against clinical signs post-challenge, and they showed reduced viral shedding in OP and CL swabs. In addition, they revealed significantly lower genome loads in the trachea and CT. Furthermore, the vaccinated/challenged groups had higher serum anti-IBV antibody titers at 10 and 14 dpi and higher HU at the first and second week following IBV infection when compared to the MV/I group. Second, only the group that received live attenuated and inactivated IB vaccines exhibited a significant decrease in histopathological lesion scores and IBV genome loads in the magnum, as well as lower IBV genome loads in CL swabs at 7dpi when compared to those observed in the MV/I group. Third, there was no significant difference observed between the L+LK/I group and the L+L/I group on the basis of clinical signs, egg production and quality parameters, seroconversion, and genome load quantification following IBV challenge.Previous studies have shown that using live attenuated IB vaccines combined with inactivated IB vaccines given just before the point of lay provide greater protection than the use of live vaccines alone ,28,46. MTypes of IBV vaccine strains approved for use may vary between countries. The epidemiological knowledge of the regionally or locally predominant strains should guide this process. In Canada, the licensed live attenuated IB vaccines contain either Mass-type IBV alone  or a combination of Mass and Conn strains of IBV . While the inactivated vaccine involves either Mass-type IBV alone or a combination of Mass- and Ark-types of IBV. This enabled us to employ two homologous regimes containing Mass-type IBV against the relevant field IBV (Mass-type) that is commonly circulating among poultry flocks in the Eastern part of Canada . PreviouIn this study, the Mass IBV isolate (15AB-01) did not induce significantly lower egg production among challenged groups. This was not consistent with the previous experimental study that was conducted on the same IBV isolate . SimilarThere is controversy surrounding the precise role of antibodies in controlling IBV infections. A number of studies have shown that humoral immunity against IBV plays a key role in minimizing the clinical signs, histopathological lesions in some organs, and viral elimination ,52. AddiThe cell-mediated immune response represents the other arm of adaptive immune protection against IBV challenge, and its development is associated with reduced clinical severity, viral clearance, and rapid recovery from the disease ,55. CD8+Our results showed that the Mass IBV isolate induced microscopic lesions in the reproductive tissues, which included epithelial and ciliary losses, inflammatory cell infiltrations, dilated glands, and some vascular changes such as congestion and edema. Most of the observed histopathological findings were noticeable in other studies using different IBV strains ,61,62. TAlthough the current study adds to the existing knowledge about the immunopathogenesis of the Mass IBV (15AB-01) infection in vaccinated and non-vaccinated layers, it has some limitations. One of these limitations is the lack of analysis of antigen specific T cell responses in PBMCs or tissues such as the reproductive tract. However, it can be argued that our results showed the presence of IBV-presumed microscopic lesions in the functional ovary and oviduct of challenged birds, so we speculated that some of the recruited T cells from circulation toward the site of injury may have been antigen-specific, as previously described . TherefoIn summary, the present study provides insights into the efficacy of two different vaccination strategies against the Mass IBV strain affecting Western Canada’s layer flocks. The current findings demonstrate that the two vaccination strategies conferred protection against Mass IBV despite the minor advantages of using the combination of live attenuated and inactivated IB vaccines that are licensed in Canada. Additionally, these results indicate that the vaccine strain could induce better protective efficacy against the challenge with homologous IBV strains. Further work is required to identify the development of reproductive tract lesions until recovery. This could be achieved by conducting a trial with a long experimental period. Furthermore, these vaccination regimes should be validated in the commercial layers.


Plant immunity is crucial to plant health but comes at an expense. For optimal plant growth, tight immune regulation is required to prevent unnecessary rechannelling of valuable resources. Pattern- and effector-triggered immunity (PTI/ETI) represent the two tiers of immunity initiated after sensing microbial patterns at the cell surface or pathogen effectors secreted into plant cells, respectively. Recent evidence of PTI-ETI cross-potentiation suggests a close interplay of signalling pathways and defense responses downstream of perception that is still poorly understood. This review will focus on controls on plant immunity through phosphorylation, a universal and key cellular regulatory mechanism. Rather than a complete overview, we highlight “what’s new in protein kinase/phosphatase signalling” in the immunity field. In addition to phosphoregulation of components in the pattern recognition receptor (PRR) complex, we will cover the actions of the major immunity-relevant intracellular protein kinases/phosphatases in the ‘signal relay’, namely calcium-regulated kinases , mitogen-activated protein kinases (MAPKs), and various protein phosphatases. We discuss how these factors define a phosphocode that generates cellular decision-making ‘logic gates’, which contribute to signalling fidelity, amplitude, and duration. To underscore the importance of phosphorylation, we summarize strategies employed by pathogens to subvert plant immune phosphopathways. In view of recent game-changing discoveries of ETI-derived resistosomes organizing into calcium-permeable pores, we speculate on a possible calcium-regulated phosphocode as the mechanistic control of the PTI-ETI continuum. PTI is mediated by cell surface pattern recognition receptors (PRRs) after sensing of conserved microbial molecules , e.g. EF-Tu, flagellin, peptidoglycans), or via the detection of endogenous plant molecules released from damaged tissues (damage-associated molecular patterns (DAMPs), e.g. AtPep1) [defense continuum of increasing amplitude rather than separate entities [surface- or intracellular-immunity have emerged to designate the site of immune activation. In this spatially defined context, the plant immune system continuum can be extended beyond subcellular localization to local/systemic immunity at the plant level or even at the community level through interplant signalling mechanisms. Using this spatial definition, we summarize recent phosphorylation-related studies from the surface PRR complexes to the regulation of intracellular (de)phosphorylation and manipulation of these phosphopathways by pathogen effectors  amplification loop may delineate signalling generated by the recently discovered calcium-permeable pores formed by ETI resistosome complexes in host membranes [In the ‘zig-zag’ model of plant immunity , the ter AtPep1) . Binding AtPep1) ,4. In re AtPep1) ,6. ETI ientities , but theentities ,9 would embranes ,6.PRR recognition of immunogenic microbial patterns. Individual PRRs possess distinct ectodomains that bind ligands and convert this into intracellular signalling processes through phosphorylation events. To achieve this, RLPs, which lack a cytoplasmic kinase domain, would require protein kinase-containing partners for transmembrane phosphosignalling, while RLKs may directly employ their intracellular kinase domains. Yet, even RLKs show ligand-induced receptor/coreceptor oligomerization and recruitment/release of additional kinases (e.g. receptor-like cytoplasmic kinase (RLCK) family members) [1 (BIK1) [in vitro kinase activities compared with their RD counterparts [complex transphosphorylation described above is consistent with the notion of the RD-kinase, BAK1, activating the non-RD kinases, FLS2 or EFR. Surprisingly, the catalytic activity of EFR cytoplasmic kinase domain was recently found to be dispensable for initiating antibacterial immunity although transphosphorylation of the EFR activation loop was essential for downstream signalling [conformational changes within EFR that initiate downstream signalling, for instance, by enhancing the BAK1 coreceptor activity. This challenges the above-mentioned concept of signal activation through activity enhancement of non-RD-type RLKs. It additionally questions the role of the kinase domain within the PRRs for the signal relay: can the substrates downstream of the PRR complexes be redundantly phosphorylated by either the receptor or coreceptor, or is this exception restricted to EFR?PTI signalling begins at the cell surface upon members) . Some ofmembers) . On the 1 (BIK1) , which i1 (BIK1) . Recipro1 (BIK1) ,15. Mostterparts . Thus, tgnalling . The aut (GPI)-anchored protein (LLG)) heterotypic PRR complexes to bind endogenous rapid alkalinization factor (RALF) peptides [scaffold to facilitate ligand-induced FLS2-BAK1 or EFR-BAK1 complex formation. Studies of single FLS2-GFP particle trajectories and their diffusion coefficients show that FER contributes to membrane nanoscale spatial partitioning and assembly of other PM PRR complexes [Together with the above-mentioned EFR data, other recent studies are revealing that the regulation of kinase activities within PRR complexes is more complicated than previously thought. Feronia (FER), an RLK with malectin-like ectodomains, was found to regulate EFR-BAK1 or FLS2-BAK1 complexes independent of its intracellular kinase activity . FER funpeptides . For immomplexes . Severalomplexes ), so thamitogen-activated protein kinases (MAPKs) [calcium-regulated kinases [calcium-dependent protein kinases (CDPKs) [To transduce the danger signal from the surface PRRs to intracellular responses, two major cellular phosphorylation pathways are activated downstream of the PRR complexes, namely  (MAPKs)  and calc kinases ,24. In r (CDPKs) . Large g (CDPKs) ,25, and  (CDPKs) . While a (CDPKs) , it has BIK1 and PBL1 [PTI-relevant RLCKs include the family VII (46 members) and family XII (12 members) [missing link between RLCKs and MKKK activation. (II) Subsets of RLCK members are differentially recruited to distinct PRR complexes. EFR, FLS2, and BAK1 mainly engage BIK1 and PBL1 (clade 8) [signal specificity by connecting the PRR complex to activation of distinct MAPK cascade components, thus tailoring the activation of different stages of the signalling cascade. Additionally, as described in the next section, RLCKs also trigger the release of additional signalling molecules such as calcium and ROS.RLCKs, as central players in receptor complexes linking PRRs to downstream signalling, are obvious candidates to target MKKKs , but anaand PBL1 , althougmembers) . Recentlmembers)  and clarclade 8) ,29, wherclade 8) ,31,32 anclade 8) , while dclade 8) ,35 occurclade 8) . (III) Tclade 8) . Altogetcalcium and ROS production closely linked to phosphorylation [phosphoregulation of the plasma-membrane-localized calcium channels. Several cyclic nucleotide-gated channels (CNGCs) have been proposed to be the putative MAMP-responsive calcium channels; and indeed, CNGC2/4 were found to be direct substrates of BIK1 [Downstream of PRR activation, elevation of cytosolic calcium levels and production of ROS are hallmarks of PTI/ETI signalling, with rylation ,38. A gerylation ,39, thus of BIK1 , while C of BIK1 . Similar of BIK1 . Further of BIK1 . However6, CIPK6, translate calcium sensing into phosphorylation and play a role in plant immunity [calcium-phosphorylation-ROS amplification loop [Various calcium sensors read and decode the PTI/ETI-induced calcium signature and directly or indirectly translate it into changes in phosphorylation capacity . While oimmunity , we focuimmunity . In Arabion loop . Multiplion loop , thus ation loop . Hence, ion loop , CPK28 pion loop  or DAMP ion loop . The resion loop .phosphocodes for PTI, ETI, and possibly even SAR.RBOHD-derived ROS induces secondary calcium fluxes and contributes to the MAMP-induced calcium signature . Altogetgraded and tuneable response that is dependent on the level of the input protein kinase and its opposing phosphatase  phosphatases and much less is known about protein Tyr phosphatases [kapp mutants show correspondingly increased resistance against Botrytis cinerea [Y428 dephosphorylation by CERK1-interacting protein phosphatase 1 (CIPP1) [S77 (feedback) phosphorylation by its own substrate, BIK1, is needed for BIK1-PP2C38 dissociation from the PRR complex. Recent data also pinpoint similar mechanism for additional PP2Cs, namely poltergeist-like 4 and 5 (PLL4 & 5) where PLL4 dissociation is preceded by phosphorylation through BIK1 [While protein kinases write the phosphorylation marks, counteracting phosphatases serve as phatases . Startin cinerea . Similar cinerea  or CERK1 (CIPP1) . The pho (CIPP1) . Interesugh BIK1 .1 (MKP1) is promoted by MPK6-mediated phosphorylation, thus promoting MPK6 dephosphorylation and shutting down of defense responses [Plectosphaerella cucumerina fungal resistance [Similar feedback control is also observed for the protein phosphatases that inactivate the intracellular protein kinases such as MAPKs. In a feed-forward loop, stability of the MAPK phosphatase esponses . While tesponses  or varioesponses , althougsistance  or the asistance .Botrytis cinerea [1). Immunity through stomatal closure may thus be regulated by the RBOHF-mediated ROS generation [1-aminocyclopropane-1-carboxylate synthases, ACS6 [+channel (GORK) that is involved in jasmonic acid (JA)-induced stomatal closure [Currently, there are fewer examples of protein phosphatases that target the key immunity-related calcium-regulated protein kinases. Although not previously associated with immunity, CPK1 activity was dampened after dephosphorylation by the protein phosphatase, PP2A-B’γ, thereby affecting resistance to  cinerea . Comparaneration . Here, ies, ACS6  and ACS7es, ACS6 . Similar closure . Hence, Pseudomonas syringae strain DC3000 (Pto), at least 5  dire direPseu  block d  and HopBF1 (Pto) were recently identified as representatives of two new clades of ‘atypical’ protein kinases. The XopC2 family harbors additional α-helix subdomains making the kinase domain atypically long , on the other hand, inhibits ETI through the phosphorylation and resultant inactivation of the chaperone protein Hsp90. The authors suggest that Hsp90 phosphorylation ultimately prevents proper folding of client proteins such as NLRs and/or kinases important for signalling [Besides interfering with phosphorylation,  265 aa; ), while  265 aa; ). In an  265 aa; . HopBF1 gnalling .mimicking substrates of conserved protein kinases to facilitate their own phosphorylation/activation. For example, in susceptible plant species, the phosphorylation of Pto effectors, AvrPto (S149) and HopQ1 (S51), via unidentified plant kinases, and subsequent recruitment of the plant phosphoform-recognizing 14-3-3 proteins to HopQ1, contribute significantly to their virulence functions [In addition to mimicking and inhibiting protein kinase activity to suppress immunity, some effectors hijack plant phosphopathways by unctions . Recentlunctions .Pto effector AvrPtoB [Xanthomonas campestris pv. campestris effectors, XopK, XopAC, and XopJ [target elements of calcium signalling deserve more attention in the future.The evolution of multiple effectors, employing a variety of strategies, to converge on PRRs, MAPK elements and other signalling nodes only serves to emphasize their importance. However, given that calcium influx and decoding of this signal by CDPKs are equally crucial to both PTI and ETI ,107, it  AvrPtoB  or CPK6 and XopJ ), these and XopJ ,6, we prIn this review, we highlighted recent advances in our molecular understanding of phosphorylation-dependent immune regulation. We also highlight the need to further explore interplay/cross-talk between complex phosphocoding (e.g. between phosphosites of multiphosphorylated proteins) and additional PTMs - either through plant machineries or pathogen effectors. A current proteomics bottleneck for this endeavor is the analytic limitation to obtain full coverage of all PTM events in a given protein at a specific time, especially for low-abundance proteins. Besides technical advances, a holistic approach combining structural biology , machine-learning-based phosphosite prediction tools and structural modelling (e.g. AlphaFold) will be beneficial to decipher the PTI/ETI phosphocode. For instance, how does ETI trigger a stronger and more sustained calcium influx and phosphosignalling (e.g. MAPK activation profile) compared with PTI? The demonstration of shared signalling components and interdependence between PTI and ETI ,9,109 fuThree recent breakthrough discoveries in the understanding of ETI components will likely pave the way for future understanding of how calcium fluxes and perception shape immunity. First is the previously mentioned resistosome, which is made up of coiled-coil-type NLR pentamers that form calcium-permeable pores in host membranes ,6. SeconIntracellular protein kinases/phosphatases control immunity in the PTI-ETI-SAR continuum.Multisite phosphorylation  defines the phosphocode for downstream mechanistic interpretation of immune signalling.Phosphocodes enable tuneable ‘graded response’ or more complex ‘logic gating’ to control response outcomesIn light of recently discovered calcium influx generated by resistosome pores or calcium-mobilizing molecules, understanding the phosphocode edited through calcium-regulated enzymes will be central in future studies to dissect immune signalling.Effectors manipulate multiple points of immune phosphosignalling but those targeting calcium-sensing processes deserve greater attention.


The Mo‐dependent formate dehydrogenase (FDH) from Rhodobacter capsulatus catalyzes reversible formate oxidation to CO2 at a bis‐molybdopterin guanine dinucleotide (bis‐MGD) cofactor. To elucidate potential substrate binding sites relevant for the mechanism, we studied herein the interaction with the inhibitory molecules azide and cyanate, which are isoelectronic to CO2 and charged as formate. We employed infrared (IR) spectroscopy in combination with density functional theory (DFT) and inhibition kinetics. One distinct inhibitory molecule was found to bind to either a non‐competitive or a competitive binding site in the secondary coordination sphere of the active site. Site‐directed mutagenesis of key amino acid residues in the vicinity of the bis‐MGD cofactor revealed changes in both non‐competitive and competitive binding, whereby the inhibitor is in case of the latter interaction presumably bound between the cofactor and the adjacent Arg587.Biological carbon dioxide  are capable of oxidizing formate to CO2 reversibly. Here, we compared IR spectroscopic data with DFT calculations and inhibition kinetics to study the putative binding mode of CO2/formate at the bis‐MGD cofactor with the help of isoelectronic and charged product analogues. Experimental and theoretical data exclude a direct binding of the inhibitor molecules to the bis‐MGD. Both have been proposed to ensure optimal substrate binding to and proton transfer away from the active site Figure .6a, 6a, 72 analogues with RcFDH. The negative charge and hence the large transition dipole moment of the inhibitory anions causes high extinction coefficients of the antisymmetric stretching modes, which are exceptionally well‐suited for IR measurements as they are located in a spectral region free of any protein or water absorptions.RcFDH and associated variants, potential interaction sites at the bis‐MGD and in its secondary coordination sphere could be identified. In addition, we have investigated the azide inhibition of the inactive oxo‐ligated bis‐MGD that was produced in absence of its maturation precursor, RcFDHΔFdsC.In the present study, infrared (IR) spectroscopy was employed in combination with density functional theory (DFT) and inhibition kinetic studies to elucidate the specific interaction of such inhibitory CORcFDH proteins incubated with 10‐fold excess inhibitor in which contributions of free inhibitors in solution were subtracted. The spectra of the as isolated protein sample treated with azide and cyanate   and the detected band shape suggest a conformation of the azide, which is more similar to that of free azide in water . The second distinct band, observed at 2031 cm−1 for azide, and 2153 cm−1 for cyanate treatment, could only be detected in the bis‐MGD‐containing enzyme .The small frequency shift (ΔAint) within a semi‐quantitative evaluation . They were found to be in a similar order of magnitude as the protein concentration, yielding in sum values about 0.5 anions per RcFDH molecule. As this observation was consistent for all azide‐incubated samples, it rather points to a modified extinction coefficient due to the interaction with the protein environmentTo elucidate the specific role of the different binding sites, the concentrations of both types of bound azide molecules were estimated from their band intensities Aint withi−1, forms the binding site indicated by the band at 2031 cm−1. This finding is supported by the fact that the overall integrated absorbance Aint of both bands (Aint(2031+2040 cm−1)) in the RcFDHWT sample is equivalent to the total integral Aint of the band at 2040 cm−1 in the apoenzyme, RcFDHΔbis−MGD. The calculated difference spectrum, in which spectral contribution of the apoenzyme with respect to the Mo loading are subtracted, shows exclusively the remaining bis‐MGD dependent band centered at 2031 cm−1 . This observation suggests that the two observed bands represent two populations of potential binding sites within the sample (apo‐ and holoenzyme), to which in total only one inhibitory molecule can bind.Moreover, this result indicates that in presence of the bis‐MGD cofactor, the azide molecule from the interaction site, related to the absorption at 2040 cmVI), the ligated Cys386, and an additionally incorporated inhibitor anion were conducted . The corresponding Mo coordination geometry was derived from the oxidized structure of the related bis‐MGD of the nitrate‐inducible formate dehydrogenase (FDH−N) from E. coli.VI oxidation state and the respective inhibitor was kept in an energetically favored position near the MoVI ion but excluding a covalent Mo−N bond . DFT calculations were also performed for the corresponding interactions with isotopically labeled azide (15N=N=N−) and cyanate (O=13C=15N−) for a proper comparison with the experimental spectra , whereas the lower frequency band appears to refer to a specific interaction site in the RcFDHWT (holoenzyme). Notably, the observed binding‐site‐specific bands remain independent from the cofactor's oxidation state, indicating that none of the inhibitors coordinate directly the Mo ion. This has been also recently proposed within a combined quantum mechanical and molecular mechanic calculations.Enzyme reduction is illustrated in the corresponding UV‐visible absorption spectra by the respective intensity loss of bands characteristic for Fe−S clusters and FMN . In addition, previous XAS and EPR spectroscopic studies have proven that such treatments change the oxidation state of the Mo ion to MoRcFDHH387M and RcFDHR587K  as an electron acceptor at the bis‐MGD‐site, kcat values of about 1800 min‐1 were obtained for RcFDHWT, while lowered kcat values were observed for the RcFDHH387M and RcFDHR587K variants.KM (formate) value was obtained, displaying a significantly lower binding affinity. While RcFDHWT and RcFDHH387M revealed a mixed‐type inhibition for azide and cyanate, the amino acid exchange in the RcFDHR587K variant resulted in a strictly competitive inhibition, which underlines the importance of Arg587 for both substrate and competitive inhibitor binding.In catalytic assays for formate oxidation, using NADKi (azide) of RcFDHWT, the Ki (azide) of the active site variants RcFDHH387M and RcFDHR587K increased by a factor of ∼10 and 120, respectively (Table Ki (cyanate) of the RcFDHWT has increased by a factor of ∼15 relative to Ki (azide). This suggests a stronger influence of these amino acids on the binding of azide compared to cyanate. Furthermore, azide is also a more efficient inhibitor as reflected by the lowered Ki and αKi values.Notably, significant differences were observed for the inhibition constants of azide and cyanate. In comparison to Ki values was observed in experiments using DCPIP  as electron acceptor at the bis‐MGD, confirming that both forms of inhibition rather occur near the active site instead of being related to an interruption of the intramolecular electron transfer.Additionally, no influence on the RcFDHH387M variant for each of the two inhibitors, albeit slightly shifted compared to the WT protein, only the higher frequency component, as found for the RcFDH▵bis−MGD, was detected in the spectrum of the RcFDHR587K variant at 2042 and 2157 cm−1 for azide and cyanate, respectively. These data suggest that the binding site of the inhibitor, giving rise to the lower frequency IR bands at 2031 and 2153 cm−1 for azide and cyanate, respectively, is localized between the bis‐MGD cofactor, presumably the sulfido ligand, and the Arg587 in a more defined manner. This binding site would correspond to the competitive inhibition mode component as derived from the kinetic experiments of RcFDHWT and RcFDHH387M.Complementary to the biochemical assays, IR spectra of the azide‐ and cyanate‐treated variants were recorded Figure . While tRcFDHΔFdsC with the active RcFDHWT enzyme  . Presumably, it is bound to the protein in a manner, which provides a higher degree of translational freedom. This assumption would explain the small spectral shifts in the inhibitor‐treated RcFDHWT, RcFDHR587K and RcFDHH387M holoenzyme variants  should be addressed to the authors.Supporting InformationClick here for additional data file.