The Arctic is warming faster than anywhere else on Earth, prompting glacial melt, permafrost thaw, and sea ice decline. These severe consequences induce feedbacks that contribute to amplified warming, affecting weather and climate globally. Aerosols and clouds play a critical role in regulating radiation reaching the Arctic surface. However, the magnitude of their effects is not adequately quantified, especially in the central Arctic where they impact the energy balance over the sea ice. Specifically, aerosols called ice nucleating particles (INPs) remain understudied yet are necessary for cloud ice production and subsequent changes in cloud lifetime, radiative effects, and precipitation. Here, we report observations of INPs in the central Arctic over a full year, spanning the entire sea ice growth and decline cycle. Further, these observations are size-resolved, affording valuable information on INP sources. Our results reveal a strong seasonality of INPs, with lower concentrations in the winter and spring controlled by transport from lower latitudes, to enhanced concentrations of INPs during the summer melt, likely from marine biological production in local open waters. This comprehensive characterization of INPs will ultimately help inform cloud parameterizations in models of all scales. The Arctic is changing faster than anywhere else on Earth. Interactions between clouds and aerosols play a role in these changes. We report how the quantities and origins of aerosols that affect cloud ice formation change over a full sea ice cycle The largest uncertainties stem from the indirect effects of aerosols on clouds, which are poorly represented in models2. This deficiency is especially true for the central Arctic Ocean, in part, due to a dearth of observations and understanding of clouds and aerosols collocated in space and time3. Evaluating aerosol-cloud interactions in this subregion is critical given the heightened sensitivity of the surface energy budget to cloud phase over sea ice.Aerosols are an important component of the atmosphere through their impacts on climate, yet the magnitude of their effects remains unquantified4. Over the sea ice, they impart seasonally-contrasting radiative impacts, warming the surface for most of the year while cooling it for a short period in summer6. Arctic mixed-phase clouds (AMPCs) composed of liquid and ice are persistent7 and common, occurring 41% of the time on average and up to 70% of the time during the spring and autumn transition seasons8. The ability for models to predict the sea ice seasonal cycle largely depends on accurate depictions of AMPCs because of their strong influence on the surface energy budget, but the magnitude of their impact on downwelling radiation inherently depends on the relative amounts of liquid vs. ice9. However, models struggle to represent seasonal variation in AMPCs without significant biases4. Further, inter-model differences in Arctic cloud occurrence throughout the year are driven by inadequate parameterizations, motivating the need for observations of fundamental microphysical processes10.While the enigmatic behavior of clouds causes an incomplete understanding of their role in the Arctic climate system, previous efforts agree on some general qualities. Arctic clouds are most frequent in autumn and least in winter11. At supercooled liquid temperatures (–38 to 0 °C), the primary pathway for cloud ice formation is immersion freezing by ice nucleating particles (INPs)12, wherein aerosols are first immersed in cloud droplets followed by freezing once optimal conditions are met. The dominant types of Arctic INPs have been shown to comprise of mineral and biological materials, emanating from land  and the ocean . At relatively warm temperatures (above −15 °C)—common among central Arctic clouds throughout the year8—the majority of INPs are typically biological19. Pioneering studies by Bigg et al. during the early 1990s alluded to marine biota as the source of central Arctic INPs21. While previous studies characterizing central Arctic INPs are salient, they are also rare, and typically confined to summer, aside from a single study from March18.Cloud formation requires the presence of aerosols22, specifically enabling a comprehensive investigation of atmospheric, oceanographic, and sea ice observations to characterize the sources of INPs. Our findings portray strong seasonality of INPs, with lower concentrations in the winter and spring controlled by transport from lower latitudes, to enhanced concentrations of INPs during the summer, likely from marine biological production in local open waters.Here, we describe a full annual cycle of size-resolved INPs in the central Arctic. To capture the breadth of INP sources and assess their impacts on Arctic clouds and the surface energy budget, a complete annual cycle of observations is essential. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition offered the opportunity to achieve a holistic characterization of the central Arctic coupled system over the course of a full year (September 2019–October 2020)23 as it drifted from the eastern Arctic Ocean, past the North Pole, and on toward Svalbard, mostly within the central Arctic pack ice  around Polarstern reached a maximum of 6% in early May, while melt pond fraction reached its maximum of 37% in early August. The concentration of sub-ice chlorophyll-a (chl-a)—a proxy for biomass of marine primary producers—was consistent with what has been previously reported for the central Arctic Ocean24, peaking in June–July. The coldest air temperature (down to –42 °C) occurred toward the end of winter, while, following the melt onset in late May, the warmest (up to 3 °C) was observed during the 24-h sunlit summer when air and surface temperature hovered around 0 °C22. The summer experienced relatively calmer wind (4.5 ± 1.8 m s−1 on average) compared to the rest of the year (5.9 ± 2.6 m s−1 on average).As context for our INP observations, MOSAiC measurements presented here were made onboard the research vessel Polarsternce Figs.  and 2. HAirmass trajectory analyses provided a broader perspective for the conditions at Polarstern. Figure 25. The seasonal cycle was shaped by a combination of a relatively consistent presence of cold-temperature INPs (active at <−15 °C) throughout most of the year with a preponderance of warm-temperature INPs (active at ≥−15 °C) during summer. Cold-temperature INPs  peaked during the winter and late summer. The winter peak was likely influenced by dust from continental sources28, as shown by the intermittent transport of continental airmasses from Siberia, Eastern Europe, and Northern Canada . Local wind speed averaged 4.3 m s−1 in June and July, while 3-day airmasses predominantly traveled from over the pack ice (73% of the time) and MIZ (20% of the time) within the typical well-mixed summer boundary layer (below 400 m (ref. 28)) at an average height of 158 m. It is important to note that transit periods when the ship was in open water south of the pack ice did not correspond to enhanced INP concentration, suggesting that the larger area of ice-free open ocean (SIC < 15%) further south was not a significant source of the warm-temperature INPs in the summer. This is supported by the fact that marine INPs from open ocean sea spray are typically low in concentration, unless the waters are subject to shallow shelf regions or collapsing phytoplankton blooms31. Previous work corroborates the fact that the annual variation of sea ice algal presence, derived from chl-a data, is controlled mainly by environmental conditions such as sunlight, and less by latitude32. Additionally, as melt ponds and the MIZ develop, this exposes materials from under-ice and ice-edge algal blooms that manifest during the melt onset and extend into the summer34 and that may become airborne under appropriate wind regimes. As the melt season progresses, these open water sources contain a mixture of ice algal and marine microbial communities that are more densely populated near the surface, while these microorganisms effectively sink in larger areas of open water that has been devoid of ice for longer periods33, and therefore, farther away from potential exchange with the atmosphere. Together, the evidence presented here suggests that: (1) summer INPs were likely from biological productivity in open water sources either locally within the pack ice, from farther north over the pack ice, or from the MIZ; (2) transported airmasses interacted with these surface sources; and (3) winds were strong enough to produce sea spray from localized open water sources within the pack ice36. Future publications will focus on aerosol DNA and fluorescence measurements to corroborate the presence of biological aerosol in the summer.Warm-temperature INPs, which were predominantly proteinaceous  at −15 °C in late August through mid-October and attributed the ice-free ocean as the likely source with smaller contributions from open leads in the pack ice21. Hartmann et al.26 reported summertime concentrations up to 1 × 10−1 l−1 at −15 °C within the MIZ in July and lower concentrations over the pack ice. More recently, Porter et al.44 reported INP concentrations up to 1 l−1 at −15 °C during their Aug/Sep cruise but also observed lower concentrations in the pack ice. In contrast to these three studies, during MOSAiC, INP concentration was highest in the pack ice at 82.1°N in the summer, reaching 2 × 10−2 l−1 at −15 °C. Possible explanations for this contrast could be variability in time of year, airmass transport pathways, ice thickness, local lead and melt pond areal fraction, and primary productivity peak location and date; however, long-term annual studies at multiple locations within the pack ice, MIZ, and ice-free ocean would be required to statistically identify the key limiting factors that control INP concentration. Overall, the observed concentrations are consistent with what has previously been reported in the Arctic but are unique in that: (1) they demonstrate that INPs can be abundant over pack ice with minimal inferred source from the ice-free ocean relative to the pack ice and MIZ regions and (2) they show relationships between INPs and environmental conditions over a full annual cycle.Previous studies report a similar enhancement in summertime Arctic INPs, whereby peak concentration ranged from 2 × 1045. However, this relationship between INP concentration and particle size was not observed during MOSAiC 47, coarse sea spray is present in the autumn prior to freeze up, thus, it is plausible that the low INP concentration observed here (84.8–86.2°N) originated from sea spray, episodically generated by high winds over local leads present in the pack ice  in nature, either existing in that size range  or internally mixed with sea salt50.At the beginning of the study, autumn INPs were coarse ≥3 µm), although total aerosol in this size range was not particularly abundant  and total aerosol, probably due to preferential gravitational settling of larger aerosols during transport over longer distances47 or primary biological organic aerosol52. The highest fraction of INPs to total aerosol in the ~1–3 µm size range was 3–7 in 104 total particles  in winter implies that cold-temperature mineral INPs that are relatively small, like those observed here, could serve an important role in cloud ice formation. However, the fact that the surface is predominantly frozen for most of winter limits the potential local sources of INPs. Additionally, the highly stratified boundary layer, characterized by high variance in sub-cloud equivalent potential temperature, limits the vertical movement of INPs. Given that there are seasonal episodic transport pathways in the lower troposphere from lower latitudes in winter55, it is most plausible that winter INPs affecting clouds have remote sources. The INPs measured near the surface are likely transported aloft and transported downward by mixing and/or precipitation and sublimation.The coldest and highest clouds were present in winter, as observed previously56) offers the potential for more interaction between clouds and the near-surface environment where coarse, biological or organic sea spray INPs active at cold temperatures16 could be produced from more localized leads, melt ponds, or the MIZ. Indeed, high ice fractions in low-level clouds are observed at these times of year. Previous work has indicated that cloud formation in the spring is promoted by strong evaporation from newly-opened water, increasing cloud presence and downward longwave radiative flux, contributing to even more sea ice melt57.Spring and autumn clouds were still relatively cold and somewhat elevated above the surface, but enhanced below-cloud mixing , including onboard the German research icebreaker Polarstern and from Met City, which was an installation on the ice in the CO between 300–600 m from Polarstern22. GPS devices operated on Polarstern and at Met City measured latitude and longitude at hourly and 1-min resolution, respectively. Arctic Mapping Tools67 for MATLAB were used for plotting the maps for the positioning data .The MOSAiC international drift expedition took place in the central Arctic pack ice, leaving Tromsø, Norway on 20 September 2019 and returning to Bremerhaven, Germany on 12 October 2020. The expedition was broken down into legs, some of which required transit of Polarstern back to land for exchange of personnel, fuel, and provisions (transits are noted in figures in the main text). Dates for legs 1, 2, 3, 4, and 5 are 20 Sep–13 Dec 2019, 13 Dec 2019–24 Feb 2020, 24 Feb–04 Jun 2020, 04 Jun–12 Aug 2020, and 12 Aug–12 Oct 2020, respectively68 from the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) Program, which was positioned on the bow of Polarstern, ~18 m above the sea ice surface69. Air temperature was also measured at 2 m above the sea ice using a Vaisala PTU307 sensor package situated on a 10-m meteorological tower at Met City70 (not shown but used for as a cross check for the temperature measured on Polarstern). Surface skin temperature was measured at a 1-min resolution using an Apogee SI-4H1 infrared thermometer mounted at 2 m on the Met City tower and pointed down at the surface. Winds were measured at a 1-min resolution using a sonic anemometer mounted at a nominal height of 10-m on the Met City tower22. For the May–June period when Met City was not installed, these measurements were obtained from an identical sonic anemometer mounted at a nominal height of 3 m on an Atmospheric Surface Flux Station that was left in place of the Met City installation71. U (eastward) and V (northward) wind vectors were calculated to obtain daily averages using Eqs. 72. Equivalent potential temperature (θe) was calculated following Eqs. (22) and (43) in ref. 73 for the radiosonde profiles. From that, daily mean vertical θe variance between the launch platform and the daily mean cloud base height as retrieved from ceilometer (see details below) were employed as a proxy for sub-cloud mixing. Likewise, daily mean temperature at the estimated cloud base was extracted from the soundings.Radiosondes measuring profiles of pressure, temperature, humidity, and winds were launched every 6 h from the helicopter deck on the stern of Polarstern up to ~20 km76. The first or lowest cloud base height (out of three possible for multilayer cloud scenarios) was used as that is the most relevant for interactions with aerosols detected near the surface. Cloud fraction over time was calculated using the first cloud base height data from the ceilometer. Each 15-s interval was classified as 0 (no cloud base detected) or 1 (first cloud base detected) and monthly mean cloud fractions were calculated from there. Cloud ice water path (IWP) at a 1-min resolution was retrieved via a multi-sensor cloud property retrieval method77. The fractional occurrence of cloud ice was calculated based on when IWP > 0 for data below 3 km, then taking the total minutes flagged for ice divided by the total minutes where clouds were detected below 3 km per day.Cloud fraction and base height were estimated using 15-s data from a ceilometer  operated on the upper deck of Polarstern as a part of the second ARM Mobile Facility (AMF2)https://github.com/rich-iannone/SplitR)79. Reanalysis data from the National Centers for Environmental Prediction were used as the meteorological fields in HYSPLIT simulations . Trajectories were initiated from 30 m AMSL and from the latitude and longitude of Polarstern at 00:00 daily. The initiation height of 12 m above the AOS inlet was used to estimate larger-scale airmass movements outside of the possible wind shear interference from Polarstern’s decks. An initiation height of 100 m AMSL was also used to demonstrate that possible disturbances from the underlying surface did not change the main conclusions and to validate the results from the trajectories ending at 30 m. Trajectories were calculated 3 days back in time to account for average atmospheric aerosol residence times for supermicron aerosol 53, as these aerosol sizes were the most proficient INPs observed during MOSAiC. Additionally, 5- and 7-day trajectories were calculated to account for possible longer residence times of smaller particles. Recent work utilized 7-day trajectories for coarse mode aerosol source apportionment in Svalbard46. It is important to note that the determination of the length of back trajectories is a compromise between typical aerosol lifetimes and the uncertainty in calculation that increases with time. Additionally, particle sphericity impacts the rate of gravitational settling80 but neither particle sphericity or gravitational settling are accounted for here, as the relevant measurements to determine particle sphericity were not made and HYSPLIT is a passive air parcel model. Figure Airmass backward trajectories were calculated using HYSPLIT, the HYbrid Single Particle Lagrangian Integrated Trajectory model with the SplitR package for RStudio  were obtained from the National Snow and Ice Data Center81 and were derived from the Multi-sensor Analyzed Sea Ice Extent Advanced Microwave Scanning Radiometer 2 MASIE-AMSR2 (MASAM2) daily 4-km SIC product that is a blend of two other daily sea ice data products: ice coverage from the product at a 4-km grid cell size and ice concentration from the AMSR2 at a 10-km grid cell size82. MASAM2 was used to meet a need for greater accuracy and higher resolution in ice concentration fields. Daily SIC averaged within a 1-degree grid box around Polarstern and within a 0.1-degree grid box around airmass backward trajectory endpoints were computed and shown in Figs. Daily SIC data  thermal infrared satellite imagery85. Most samples were analyzed at the Alfred Wegener Institute (AWI) after the campaign had ended. During leg 3, one subsample per sampling event was measured on board within 3 days after sampling, while a second subsample was analyzed at AWI (5–9 months after sampling). No systematic difference between the replicates could be detected, indicating no significant degradation of chl-a took place during storage and transport.Samples for the determination of surface chlorophyll (chl-a) were taken from 11-m water depth via Polarstern’s underway system. Depending on chl-a levels, 2–4 l of seawater were filtered onto glass microfiber filters  in duplicates or triplicates and frozen at −80 °C until further analyses. Samples were extracted in 90% acetone over night at 4 °C and subsequently analyzed on a fluorometer , including an acidification step (1 M HCl) to determine phaeopigments following ref. 87. Submicron aerosol size distributions from 60 nm to 1 µm were measured at a 10-s resolution using an ultra-high sensitivity aerosol spectrometer  also located in the AOS89. The UHSAS measurements were converted to aerodynamic diameter (Da), assuming optical diameter is equivalent to the true diameter (Dtrue), using the following equation and density of 1.5 g cm−3 based on estimates from previous studies based in pristine and polluted locations91:94. The whole air inlet of the Swiss container is built after the Global Atmosphere Watch recommendations95. The AOS inlet contained a back-pressure purge system that was applied to minimize sampling of ship emissions, such that during obvious periods of ship emission influences, the purge system would cause the inlet to stop sampling while all instruments remained operational96. However, not all periods were detected and purged. Thus, developing and applying a pollution mask to omit aerosol data affected by local contamination  was essential. The basic principle of the pollution detection algorithm is based on the time derivative of the particle number concentration. Local pollution was characterized by strong fluctuations (and thus high time derivatives) in particle number concentration. If this time derivative exceeds a certain threshold, the data were flagged as polluted. The basic principle of this method was developed and used for the 2018 Microbiology-Ocean-Cloud-Coupling in the High Arctic campaign on the Swedish ice breaker Oden97.Aerosol number concentrations from 10 nm to 10 µm at a 1-s resolution were measured using a condensation particle counter  located in the AOS99 through the AOS inlet, in parallel with the CPC and UHSAS. The DRUM collected daily integrated samples at 29–34 l min−1 (average flow rate of 31.6 ± 1.6 l min−1) from 0.15 to >12 µm in diameter  on sterilized perfluoroalkoxy substrate strips coated with petrolatum—a material containing very few artifacts that interfere with INP analysis. It is important to note that the AOS inlet has a high transmission efficiency for particles from 10 nm to 4 µm but has large uncertainties in transmission efficiency above 4 µm due to low ambient aerosol signal in that size range, thus it is possible the DRUM samples produced lower INP concentration estimates in that size range as well92. Sample start times ranged from 10:25–15:15 UTC (average time of 14:30 UTC) and collected continuously for 24 h. Aerosols were “smeared” onto the sample strips as the discs in each stage rotated slowly over time (5 mm of aerosol loading) followed by quick 2 mm rotation, affording a sample blank in between each daily sample. Weekly flow and pressure checks were conducted throughout the year to ensure obstructions were not present in the orifices. Samples were preserved and shipped frozen (≤–20 °C) until analysis at CSU.Size-resolved aerosols were collected from 23 October 2019 to 1 October 2020 using the Colorado State University (CSU) 4-stage Davis Rotating-drum Unit for Monitoring cascading impactor 100. For preparation, aerosol samples were added to 2 ml of 0.1-μm filtered deionized water and mixed for at least 20 min at 200–500 rpm in a laminar flow clean hood to resuspend particles into a suspension. After preparation, 100 × 2.5-µl aliquots were created on a 3-inch diameter copper plate and covered to prevent contamination. The plate was cooled at ~1–10 °C min−1 from room temperature until all drops froze on the plate or until the cold plate limit of ~ –29 °C. Drop freezing was detected and recorded through monitoring software to provide the freezing temperature and cooling rate for each drop. Each sample was tested three times .The CSU cold plate is an established method used to measure immersion mode INPs101. However, this recent work analyzed deposition-mode INPs, which likely do not overlap with the properties of immersion-mode INPs, as other previous studies have demonstrated how pollution aerosol, in general, is not a sufficient source of immersion INPs106.For MOSAiC, a subset of the 1280 DRUM samples was processed and analyzed using the cold plate. Approximately every third day’s sample set was chosen—a “sample set” corresponds to all four samples from each of the size ranges, per 24-h period, equating to analysis of 388 samples. However, this time resolution varied as samples that were not under the AOS inlet purge were selected. The purging reduced possible contamination from Polarstern, as recent work has shown that ship pollution potentially introduces INPs at low temperatures 107. The filter units were open-faced, secured outside to the AOS railing and shielded from precipitation and blowing snow. Vacuum line tubing connected the filter unit to the flow meter followed by vacuum pump, both of which were housed inside the AOS container. Average flow rate was 21.9 l min−1. Samples were started at 14:15 UTC on average and collected continuously for 72 h. Following collection, filters were removed from the single-use units, placed in sterile Petri dishes, and preserved and shipped frozen (≤ −20 °C) until analysis at CSU.Total aerosol samples were collected on top of the AOS container near the inlet from 23 October 2019 to 1 October 2020 using single-use Nalgene™ Sterile Analytical Filter Units that were prepared by replacing their cellulose nitrate filters with 0.2-μm polycarbonate filters backed with 10-μm polycarbonate filters (each are 47 mm diameter Whatman® Nuclepore™ Track-Etched Membranes), both pre-cleaned at CSU113, a technique which is comparable to the cold plate method114. Filters were placed in sterile centrifuge tubes, 8 ml of 0.1 µm-filtered deionized water added, and particles re-suspended by tumbling end-over-end on a rotator for 20 min. From these suspensions, 32 50-μl aliquots were distributed into sterile, 96-well PCR trays in a laminar flow cabinet. Serial dilutions were applied to cover INP concentrations over the full temperature range. Plates were then placed into the blocks of the IS, the device covered with a plexiglass window, and the headspace purged with filtered N2. The device was cooled at 0.33 °C min−1 until ~ –29 °C using a recirculating low temperature bath, and the freezing of wells recorded every 0.5 °C via a CCD camera system. Thermal treatments and peroxide digestions provide valuable insights into INP composition. Heat treatments were performed on approximately one third of the IS filter samples by heating 2 ml of suspension to 95 °C for 20 min to denature heat-labile INPs, such as proteins115. Hydrogen peroxide (H2O2) digestions were performed on a further 2 ml of suspension to remove all bio-organic material. This procedure, and the neutralization of remnant H2O2 to prevent freezing point depression112. The samples were then re-analyzed to assess the reduction caused by the decomposition of heat-labile and all organic INPs, respectively.Samples were processed using the CSU Ice Spectrometer (IS)116:f is the proportion of droplets frozen, Vdrop is the volume of each drop, Vsuspension is the volume of the suspension, and Vair is the volume of air per sample. See Supplementary Fig. 117. Treatment results for select temperatures throughout the MOSAiC expedition are shown in Supplementary Fig. 119.For both techniques, the fraction of drops frozen at each temperature interval and the known total volume of air sampled were used to calculate INP concentration with a universally-used equationSupplementary InformationPeer Review File


Unlike other TMEM16s exhibiting a membrane-exposed hydrophilic groove that serves as a translocation pathway for lipids, the experimentally determined structures of TMEM16F shows the groove in a closed conformation even under conditions of maximal scramblase activity. It is currently unknown if/how TMEM16F groove can open for lipid scrambling. Here we describe the analysis of ~400 µs all-atom molecular dynamics (MD) simulations of the TMEM16F revealing an allosteric mechanism leading to an open-groove, lipid scrambling competent state of the protein. The groove opens into a continuous hydrophilic conduit that is highly similar in structure to that seen in other activated scramblases. The allosteric pathway connects this opening to an observed destabilization of the Ca2+ ion bound at the distal site near the dimer interface, to the dynamics of specific protein regions that produces the open-groove state to scramble phospholipids.TMEM16F is a Ca Molecular dynamics simulations reveal the allosteric mechanism leading to an open, lipid scrambling competent state of a mammalian TMEM16F. The scramblase members of the family can also function as non-selective ion channels. They regulate the exposure of phosphatidylserine (PS) lipids on the cell surface and play essential roles in fundamental physiological processes, from blood coagulation, bone formation, and cell fusion to membrane repair and immune response8. Dysfunction of TMEM16 PLS proteins is associated with genetically inherited disorders of muscle10, bone13, blood15, and brain19, and mutations in TMEM16 PLS have been associated with various disease conditions24. Specifically, mutations in human TMEM16F (hTMEM16F) PLS are responsible for Scott syndrome25, a bleeding disorder caused by impairment of Ca2+-dependent externalization of PS lipids in activated platelets.The TMEM16F belongs to the TMEM16 family of integral membrane proteins. The human genome encodes for 9 TMEM16 protein homologs that have been classified as Ca33, but for mammalian TMEM16 PLS such structure-based functional insights are only now starting to emerge35. These insights have led to seemingly contradictory inferences, and it is still unclear to what extent the molecular mechanisms underlying activity or regulation of TMEM16 PLS are similar between mammalian and fungal homologs, or even among different mammalian ones. Thus, the X-ray and cryo-electronmicroscopy (cryo-EM) structures of Ca2+-bound fungal nhTMEM16 and afTMEM16 proteins36 have revealed a common homo-dimeric fold, in which the ten transmembrane helices (TMs) of each protomer generate a hydrophilic groove facing the membrane on the side opposite to the dimer interface ; a “membrane-exposed” open state  side to restrict the passage of lipid headgroups due to a constriction formed by the polar interaction network between TMs 3, 4, and 6); and a “lipid-conductive” state . Overall, these insights support the ‘credit card’ model mechanism for lipid scrambling39 by the TMEM16 PLS, whereby lipids traverse the bilayer by populating the hydrophilic groove pathway with their headgroups while keeping their hydrophobic tails perpendicular to the groove axis, in the bilayer environment.Much of what is known about structure/function relationships in TMEM16 PLS is inferred from breakthrough studies on fungal nhTMEM16 and afTMEM16 homologs35 have provided further support for this mechanism, as the Ca2+-bound groove in hTMEM16K was found to assume conformations wide enough to constitute a lipid translocation pathway. Indeed, MD simulations suggested that such an open-groove state in hTMEM16K is necessary for scramblase activity35. However, structural investigations of mouse TMEM16F (mTMEM16F)34 showed in striking contrast to the fungal TMEM16 PLS and the hTMEM16K, the groove in mTMEM16F remains closed even under conditions of maximal activation  lipids). This led to the proposal that mTMEM16F PLS may function according to a different paradigm40, where lipid permeation occurs outside a closed scrambling pathway.More recent structure/function studies on mammalian hTMEM16K PLS2+ ion bound at a “distal Ca2+ binding site” in the dimer interface region. The ion is stabilized at this site by the sidechains of two negatively charged residues—E395 on TM2, and D859 on TM10—as well as by two backbone carbonyls from residues S854 and I857 in TM10. While the analogous Ca2+ ion binding site exists in the hTMEM16K structures as well, the unique structural feature of mTMEM16F is that the intracellular (IC) end of TM2 coordinates the third Ca2+ ion while the EC end of the same helix participates in the interactions with TM4 to provide stability to the closed groove structure. These characteristics present an intriguing possibility that the distal Ca2+ ion site in mTMEM16F may be allosterically coupled to the EC side of the groove. Indeed, Ca2+ binding to the analogous site in the TMEM16A CaCC site, has been shown to facilitate TMEM16A channel opening41, suggesting the existence of an allosteric connection between the distal Ca2+ ion site and the groove region.Comparison of existing structural data for the different TMEM16 PLS identifies two intriguing structural characteristics of mTMEM16F that together distinguish it from the fungal and hTMEM16K PLS see Fig. : (1)-the2+ ion in the distal binding site may allosterically affect conformational dynamics of the groove region in mTMEM16F and lead to its opening for lipid scrambling. To test this hypothesis we carried out massive, ~400 µs-long, all-atom ensemble molecular dynamics (MD) simulations of mTMEM16F. The analysis of the MD data revealed a gradual opening of the groove region and its transformation into a continuous lipid conduit. This conformational transition severs the interactions along the interfaces between TM4 and TM6, and between TM4 and TM2. Importantly, the analysis of MD trajectory data with Markov State Model (MSM) and transition path theory (TPT) approaches revealed that the unraveling of the TM4–TM2 interface was correlated in time with the destabilization of the Ca2+ ion in the distal binding site. To discern the mode of allosteric coupling between the ion binding site and the groove region we used N-body Information Theory (NbIT) analysis42 and quantified it with the thermodynamic coupling function (TCF) approach44. These revealed the allosteric path of communication between the two regions and showed that the mTMEM16F groove conformation that permits lipid scrambling is indeed energetically stabilized by the allosteric communication. These results provide specific mechanistic details in a structural context for the allosteric mechanism leading to an open, lipid conductive state of the groove in mTMEM16F.We, therefore, reasoned that dynamics of the Ca2+ ion in its binding site at the dimer interface , but also in the apparent stabilization of the closed groove structure of mTMEM16F by interactions with TM4  a widened TM2–TM4 interface; and (3) a widened TM4–TM6 interface. These frames were continued, in independent triplicates, in Stage 2. Following the same analysis protocol and criteria we selected 8 frames from the Stage 2 set for Stage 3 simulations, which also included a conformation from the highest population region -conformational changes at the TM4–TM6 and TM4–TM2 interfaces of the groove, and (ii)-changes in the coordination of the Caite Fig. . EvaluatIn the 2D tICA space spanned by the first two vectors, the opening of the TM4–TM6 interface is represented by microstates along the first eigenvector, tIC1 Fig.  and S6. 36 , the Ca2+ ion is gradually destabilized as indicated by the loss of coordination with the backbone moieties of S854 and I857 . This is then followed by the transitions to the states in which the TM4–TM6 interface begins to gradually open while the distal Ca2+ binding site remains destabilized and TM4–TM2 interface—partially open . Lastly, the system transitions to the ensemble of states in which both TM4–TM6 and TM4–TM2 interfaces of the groove are fully open and the distal Ca2+ binding site is destabilized  to provide a quantitative description of how particular states, and transitions between them, are favored or opposed by allosteric coupling between reaction coordinates . As we had demonstrated that TCFs could be constructed in the context of MSMs, using the first tIC eigenvectors as CVs and the microstate free energies inferred from the MSM43, we used TCF here to quantify the allosteric coupling between the distal Ca2+ binding site and the groove opening.As our results show that the opening of the groove in mTMEM16F is preceded by the restructuring of the distal CaA calculated at each  grid point of the space (see “Methods”)). According to Eq.  of the tICA space with ∆∆A < 0 is stabilized by the thermodynamic coupling between tIC1 and tIC2 vectors, while the states with ∆∆A > 0 are destabilized by such coupling43. The TCF map on Fig. a and b). The allosterically stabilized states include: (1) the states with fully open TM4–TM2 and TM4–TM6 interfaces , and the other corresponds to the conformations with the TM4–TM6 interface in the beginning stages of destabilization, but closed TM4–TM2 interface and intact distal Ca2+ binding site (top left region marked by red rectangle).Figure g to Eq. , the stasee Fig. ; and (2)2+ ion in the distal site, the only conformations stabilized by the allosteric coupling between tIC1 and tIC2 are those with fully open groove ; and (ii) the fully open TM4–TM6 interface can be allosterically stabilized under two conditions—the fully open TM4–TM2 interface and the reorganized distal Ca2+ binding site, or closed TM4–TM2 interface and intact distal Ca2+ binding site. Indeed, allosteric coupling between tIC1 and tIC2 makes it unfavorable for the fully open TM4–TM6 interface to exist under conditions of destabilized Ca2+ ion if the TM4–TM2 interface is only partially open.The two main mechanistic inferences from these results are: (i) under the conditions of destabilized Ca2+ binding site and the TM4–TM2 and TM4–TM6 interfaces, we used the N-body Information Theory (NbIT) analysis formalism to calculate the coordination information (CI) between various sites (see “Methods”). The S1 transmitter site combined the residues coordinating the distal Ca2+ ion, while receiver sites in the S2–S10 sites defined in “Methods” for the TM4–TM2  involved in the pathway of allosteric coupling between the distal CaCI values between the S1 site and the sites along TM4–TM6 interface (S6–S10) are given in Fig. CI values between the S1 site and the sites along TM4–TM2 interface (S2–S5) are shown in Fig. CI values in microstate 8 along the groove opening pathway. This microstate includes the ensemble of structures in which the TM4–TM6 interface is already open, the distal Ca2+ ion is fully destabilized, but the TM4–TM2 interface is still not fully open . As shown in Fig. MCI values calculated for the microstate 8 trajectory are high not only in the vicinity of the transmitter and receiver sites but also at other structural loci, most notably in helices TM1, TM2, TM6, and TM7, suggesting that the fluctuations throughout the protein are strongly coupled during the transition towards the groove opening. In contrast, the MCI values calculated from the trajectory of microstate 4 (in which the CI shows the Ca2+ binding site and the TM4–TM6 region to be weakly coupled to each other) are markedly lower . The opening of the TM4–TM2 interface appears to facilitate this last step of the scrambling process. Indeed, in a different trajectory, in which the TM4–TM6 interface fully opened but TM4–TM2 remained closed, a lipid traveling from the IC to EC side performed only a partial flip. This is shown in Fig. As expected from the dynamic properties of the system described above, the initial stages of the IC to EC scrambling process Fig.  are assoIn the three trajectories in which lipid scrambling in the EC to IC direction was observed Fig. , the opedZ plots in Fig. dZ plot in Fig. In two of the three trajectories Fig. , these eTogether, these results provide detailed mechanistic descriptions under the simulation conditions, of the key steps in the scrambling process of lipids through the open groove conformation of mTMEM16F that forms spontaneously through an allosteric process.2+ ion in the distal site has a mechanistic role in mTMEM16F groove opening when the primary Ca2+ site is occupied, we investigated whether the groove can fully open when the distal Ca2+ site has lost its bound ion. To this end, we performed MD simulations of the mTMEM16F lacking the Ca2+ ions in the distal sites. The starting points for these simulations were two separate frames from the original simulation set , our findings represented in Figs. 2+ ion may facilitate the transition to the open groove conformation.Since these results suggest that the groove opening mechanism is functional when the distal binding site is empty, we asked if an mTMEM16F system with a destabilized Caace Fig.  to repre45, those studies employed structural models built by homology to the known open and closed groove structures of the fungal nhTMEM16 PLS. In contrast, we report here in atomistic detail the dynamic changes that lead from the closed groove structure of mTMEM16F to a scrambling-competent open groove state of this system in MD trajectories totaling ~400 µs, which allow us to define and quantify the allosteric mechanisms and kinetic model of the process. Thus, we show that the groove opening in mTMEM16F is mechanistically enabled by the allosteric coupling between the two structural motifs that are uniquely present in mTMEM16F but have not been observed together thus far in any available experimentally determined structures of other TMEM16 PLS. One of these motifs is the distal Ca2+ ion binding site at the IC end of the dimer interface. The coordination shell of the Ca2+ ion at this location includes two acidic residues of which one, E395, resides on TM2. The second structural element involves the extended helix-loop-helix motif which connects the same TM2 helix to TM1 at its other, EC, side. This linker region in mTMEM16F is much longer than the analogous loop region in fungal TMEM16 PLS or in hTMEM16K and reaches over the groove area to contact the EC end of TM4 . It is clear, however, that the same type of allosteric communication is unlikely in the fungal TMEM16 PLS, which lack a distal Ca- channel member of the family have shown that Ca2+ ion binding at an analogous distal site in this protein was able to facilitate allosterically the channel opening process41, we note that the Ca2+ ion coordination shell in TMEM16A consists of three anionic residues in contrast to the coordination shell in mTMEM16F which contains only two anionic sidechains . The additional negative charge may entail a different mode of interaction with the protein and Ca2+ stabilization. It will be of interest to compare the allosteric communication between the distal Ca2+ binding site and the groove region to the findings presented here.Because recent studies of the TMEM16A Cl2 lipids in TMEM16F scramblase activity34, and a regulation of scrambling mechanisms of TMEM16 PLS by their lipid environment has been demonstrated40. The possibility that lipids with different tail lengths and saturation may affect the scrambling by an open-groove state of TMEM16F remains open, as is that of an alternative mechanism of lipid scrambling that envisions lipid translocation with a closed groove34. The open groove scrambling mechanism uncovered in our work may complement the proposed closed groove scrambling mechanism. An interplay between these mechanisms in the context of different lipid environments remains a tantalizing possibility yet to be explored.Thus far, our studies of TMEM16F functional dynamics have not addressed the role of specific lipids, although earlier work had suggested involvement of PIP6) in which each monomer includes two Ca2+ ions bound in the primary binding site, and a third Ca2+ ion bound in the distal site. The missing segments from the structure  were modeled using Rosetta’s AbinitioRelax application46. For the 876–911 segment, 1000 structures were generated and clustered. The most populated cluster (with clustering threshold of 4 Å) consisted of 315 structures, in all of which the 891–904 fragment was alpha helical. Based on the positioning of the analogous helical fragment in the structure of fungal homolog nhTMEM16 (PDBID 6QM547), we positioned the 891–904 segment of mTMEM16F to interact with the N-terminal part of the opposite monomer 47 and hTMEM16K(5OC9)35 as structural templates. In the completed structure, the first 20 N-terminal 20 were excluded due to their high flexibility, so that the final model contained residues 21–911  and POPG ) lipids, the same lipid composition as used in our previous simulations of nhTMEM16 PLS29. The protein to lipid ratio was 1:1050. After adding a solvation box containing 150 mM K+Cl− the total system contained ~ 561,219 atoms.Using the CHARMM-GUI web interface51 using a standard set of equilibration scripts provided by CHARMM-GUI. After this initial equilibration, the velocities of all the atoms were randomly regenerated and the system was subjected to an extensive 3-stage adaptive ensemble MD simulation protocol . Monte Carlo barostat and Langevin thermostat were used to maintain constant pressure and temperature, respectively. Additional parameters for these runs included: “friction” set to 1.0/picosecond, “EwaldErrorTolerance” 0.0005, “rigidwater” True, and “ConstraintTolerance” 0.000001. For the van der Waals interactions we applied a cutoff distance of 12 Å, switching the potential from 10 Å.The ensemble MD simulations were carried out with OpenMM 7.453, as well as the recently revised CHARMM36 force-field for ions which includes non-bonded fix (NBFIX) parameters54.For all simulations, we used the latest CHARMM36m force-field for proteins and lipids55 as previously described57. Briefly, in the tICA approach the MD simulation trajectories are used to construct two covariance matrices: a time-lagged covariance matrix (TLCM) CTL(τ) = <X(t)XT(t + τ)>, and the usual covariance matrix C = <X(t)XT(t)>, where X(t) is the data vector at time t, τ is the lag-time of the TLCM, and the symbol <…> denotes the time average. To identify the slowest reaction coordinates of the system, the following generalized eigenvalue problem is solved: CTLV = CVΛ, where Λ and V are the eigenvalue and eigenvector matrices, respectively. The eigenvectors corresponding to the largest eigenvalues define the slowest reaction coordinates. These reaction coordinates depend on the choice of data vector X, i.e., the choice of collective variables (CV). Here, to define the tICA space, we considered CVs that quantify conformational dynamics of the TM4–TM6 and TM4–TM2 interfaces of the mTMEM16F groove, as well as the coordination of the Ca2+ ion bound at the third binding site, referred throughout simply as the third Ca2+ ion ; (2) the Cα–Cα distance between the residue pairs 497–360, and 499–363 (TM4–TM2 interface); (3) any minimum distance between the third Ca2+ ion and residues E395, S854, I857, and D859.To facilitate analysis of conformational dynamics in the simulations, we performed dimensionality reduction using tICA 59. Briefly, the 2D tICA space was discretized into 50 microstates using automated clustering k-means algorithm, and a transition probability matrix (TPM) was built58. To ensure Markovian behavior, multiple TPMs were constructed for different time intervals between transitions (MSM lag times), and the relaxation timescales of the system were calculated as τ′ is the lag-time used for building the TPM, λi denotes the ith eigenvalue of the TPM, and τi represents relaxation timescale  corresponding to the ith relaxation mode of the system. The Markovian property of the TPM was established by verifying the independence of τi from τ′. This analysis, presented in Fig. 60. As shown in Fig. τ′ = 160 ns lag-time, the model predictions match well with the observed dynamics. Further, we compared the top MSM relaxation modes for lag times of 160, 320, and 480 ns. As illustrated in Fig. τ′ = 160 ns is a valid lag-time for the MSM analysis.To perform MSM analysis, we used Python 2.7.14 scripts included in MSMBuilder software57. Briefly, using a Robust Perron Cluster Analysis (PCCA+) algorithm61, the 50 microstates within the tICA space were clustered into 16 macrostates based on their kinetic similarity. Using the Dijkstra graph theory algorithm62 implemented in the MSMbuilder software, a flux matrix63 was then constructed for macrostates, and the most probable pathways were identified as those with the highest flux between the starting macrostate and a final microstate.In order to identify the most probable pathways, we applied TPT analysis as previously described44. Briefly, given two CVs, A, is a function of specific values  of those CVs:The TCF analysis was carried out as previously describedkB is the Boltzmann constant, T is the temperature, and p, p(x), and p(y) are, respectively, the observed joint and marginal probability distributions of molecular conformations. Here, 43:In the above, m was given a weight:p(m) represents the equilibrium weight of the microstate taken from the MSM, and Nm is the number of configurations associated with this microstate. The 2D space of the first two tIC vectors was then divided into 100 equally spaced bins in each direction, and the joint probability density corresponding to a bin centered at  was estimated over the configuration probability density within that bin43:fp(x) and p(y) were calculated by summing over the corresponding dimensions.Each configuration in microstate 43.Estimation of confidence levels for each bin was performed using the bootstrapping procedure described previously2+ binding site and the mTMEM16F groove region we have applied N-body Information Theory (NbIT) analysis42 to the trajectories of metastable MSM microstates which represent local energy minimum conformations on the tICA space (see “Results”). The NbIT method relies on the Information Theory representation of configurational entropy H calculated from the covariance matrix (C) of all atomic positions (X) in the protein:To quantify the allosteric coupling between the distal CaTC), defined as the total amount of information that is shared among N atoms in a set, was then quantified as:Xi-s represent components of 3N-dimensional vector corresponding to x, y, z atomistic coordinates of the atoms in the set, and H is the joint entropy of the set.Total correlation  which quantifies the amount of information shared by a set of variables of arbitrary size that is also shared with another variable:Using Xm.In the above, CI, we calculated the mutual coordination information (MCI), defined as the amount of coordination information that is shared between two residues and the same set:From 2+ binding site  was defined as the transmitter.Here we have applied NbIT to all the non-hydrogen atoms of the mTMEM16F protein. The distal Careceiver regions42 of the protein involved in allosteric coupling with the distal Ca2+ binding site during the groove opening process is a two-stage procedure. First, a set of NbIT sites was created, which included all the residue pairs along the TM2–TM4 and TM4–TM6 interfaces that form stable interactions prior to the groove opening. An interaction was considered stable if its occupancy was >75%; the cut-off for the minimum distance between the two sidechains was set to 3.5 Å for polar interactions, and to 6 Å for nonpolar ones. For this preliminary extended set, we evaluated the Coordination Information (CI) shared between each of these sites and the transmitter Ca2+ binding site, for all the microstates visited by the trajectories capturing the scrambling process. Since the highest values of CI were observed for microstate 8 (see “Results”), we focused on the CI values obtained for this microstate for further analysis of the most prominent receivers of the allosteric signal from the Ca2+ binding site. Specifically, the CI data obtained for microstate 8 was clustered with a Fisher-Jenks algorithm (https://github.com/mthh/jenkspy) into three groups of sites with various levels of allosteric coordination of the Ca2+ binding sites: low (CI < 24%), average (24% ≤ CI ≤ 50%), and high (CI > 50%). Residue pairs from each of the two interfaces that exhibited high levels of CI were then selected as the most prominent receiver sites in these regions. These sites were the following: S2 site (W359/Y502); S3 site (Q506/Q351); S4 site (Q351/Y502); S5 site (Y502/Q506); S6 site (M522/W619); S7 site (F518/K616); S8 site (S514/ Q608); S9 site (S514/F518/M522); S10 site (Q608/K616/W619). The allosteric channels connecting the distal Ca2+ binding site with the TM4–TM2 and TM4–TM6 interfaces were then identified by evaluating the MCI42 between the S1 site and the combined sites S2+S3+S4+S5, and S6+S7+S8.The selection of the The MSM analysis was performed on 544 replicates generated by the adaptive ensemble MD simulation protocol, as described above.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary DataSupplementary Movie 1Supplementary Movie 2Reporting Summary


To adapt to changing hemodynamic demands, regulatory mechanisms modulate actin-myosin-kinetics by calcium-dependent and -independent mechanisms. We investigate the posttranslational modification of human essential myosin light chain (ELC) and identify NIMA-related kinase 9 (NEK9) to interact with ELC. NEK9 is highly expressed in the heart and the interaction with ELC is calcium-dependent. Silencing of NEK9 results in blunting of calcium-dependent ELC-phosphorylation. CRISPR/Cas9-mediated disruption of NEK9 leads to cardiomyopathy in zebrafish. Binding to ELC is mediated via the protein kinase domain of NEK9. A causal relationship between NEK9 activity and ELC-phosphorylation is demonstrated by genetic sensitizing in-vivo. Finally, we observe significantly upregulated ELC-phosphorylation in dilated cardiomyopathy patients and provide a unique map of human ELC-phosphorylation-sites. In summary, NEK9-mediated ELC-phosphorylation is a calcium-dependent regulatory system mediating cardiac contraction and inotropy. Phosphorylation of the essential myosin light chain (ELC) influence actin-myosin crossbridge cycling in the heart. Here, the authors show upregulated ELC-phosphorylation in systolic heart failure and identify NIMArelated kinase 9 to bind to ELC mediating its calcium-dependent phosphorylation. Myosin light chains (MLCs) are integral parts of the sarcomere located at the myosin motor complex2. The MLC protein family consists of two classes of EF-hand calcium (Ca2+) -binding proteins, the regulatory cardiac myosin light chain (RLC/cMLC2) and the essential cardiac myosin light chain (ELC/cMLC1). Each MLC exhibits two cardiac isoforms in humans: RLC is encoded by myl7  and myl2 (ventricular form) and ELC is encoded by myl4  and myl3 (ventricular form)3. Mutations in genes encoding MLCs account for up to 5% of inherited cardiomyopathy cases4, but the mechanisms which render a given genotype in a phenotype are largely unknown.Cardiomyopathies are frequent genetic disorders involving more than 40 different disease genes. In the last years, much attention was paid to understand the mechanisms involved in sarcomeric dysfunction and first compounds have proven clinically effective in modulating myosin kinetics6. The MLC binding region stabilizes the neck region and contributes to the conversion of chemical energy into cardiac contraction. Furthermore, the N-terminal ELC protein can interact with actin10, which positively affects force generation in patients with congenital heart disease expressing pathological levels of atrial ELC11.Both MLC proteins, ELC and RLC, bind to the neck region of the myosin heavy chain (MHC), being directly involved in the cross-bridge cycle. The myosin neck region acts as a lever arm, transducing force generated by the catalytic motor domain2+ -dependent RLC-phosphorylation by the myosin light chain kinase (MLCK)13 is pivotal for the initiation of contraction in smooth muscle cells15. Cardiac MLCK function is stimulated by Ca2+ and calmodulin16. Consequently, RLC-phosphorylation potentiates force and speed of cardiac contraction by higher myosin-actin binding affinity18. Noticeable, relatively small alterations of cross-bridge formation can significantly affect cardiac function. Therefore, it is crucial to precisely decipher sarcomeric regulation to ultimately translate those findings into therapeutic approaches. Although highly conserved ELC-phosphorylation sites have been annotated20, only recently we were able to demonstrate the functional relevance of ELC-phosphorylation in the context of cardiac contractility. Lack of ELC or the ELC-phosphorylation site at the carboxy-terminus leads to severely impaired cardiac contractility in zebrafish22. Subsequent studies underlined the relevance of ELC-phosphorylation during physiological and pathological cardiac stress. In detail, the level of ELC-phosphorylation significantly increased during forced swimming of adult zebrafish, which is accompanied by increased sliding velocity of actin filaments on immobilized myosin proteins in-vitro23. Taken together, the ELC-phosphorylation was shown to be responsible not only for maintenance of cardiac force generation, but also for the modulation of sarcomere function during stress23.An important role is attributed to posttranslational phosphorylation of MLC proteins. For example, Ca2+-dependent mechanism and influences cardiac function in-vivo. ELC-phosphorylation is detected in the human heart and upregulated in systolic heart failure. The ELC-phosphorylation status in patients inversely correlates with the left ventricular ejection fraction. Understanding the regulation of ELC-phosphorylation in human myocardium will help to develop targeted pharmaceutical interventions.Here, we aim to identify potential ELC kinases and analyze ELC-phosphorylation sites in the human heart. NIMA-related kinase 9 (NEK9) interacts with ELC and is highly expressed in the heart. NEK9-mediated ELC-phosphorylation is a CaTo identify ELC interacting proteins, we covalently coupled custom-specific ELC antibodies to magnetic beads for immunoprecipitation (IP). Identification of precipitated proteins was performed by liquid chromatography followed by mass spectrometry (LC-MS) analysis Fig. . In tota25, CamK2G expression levels are highest in brain and skeletal muscle tissue -influx is mandatory for cardiac muscle contraction, we analyzed whether increasing Ca2+ concentrations influence the kinase-target interaction. Figure 2+-sensitive, potentially enabling increased interaction of ELC and its kinase during physical exercise or under pathological conditions that go along with high intracellular Ca2+-load.To further evaluate the mode of interaction of ELC with the identified NEK9 kinase candidate, plasmid overexpression of human myc-tagged ELC protein was performed in human cells followed by myc-specific IP and kinase specific IB. Importantly, ELC is not expressed in HEK293 cells Fig.  under ba2+ -influx  of NEK9    and a predicted additionally phosphorylated form (+1P: 19.0 ± 6.5%). In response to ionophore stimulation, phosphorylated ELC (+1P: 33.3 ± 5.9%) was significantly increased and a second phosphorylated ELC form (+2P: 8.0 ± 2.6%) became detectable Fig. . The Ca2.1) Fig. . In addi01) Fig. . Ca2+ stext Fig. , hintingnek9 in zebrafish hearts can be shown by fluorescence antisense in-situ hybridization   transient knockdown by antisense oligonucleotides, (2) CRISPR/Cas9-mediated knockout and 3) genetic sensitizing in an ELC phospho-deficient transgenic zebrafish line. To establish these approaches several prerequisites were considered. First, NEK9 shows high cross-species conservation as shown by immunoblots of heart protein Fig.  and prot genetic  transien01) Fig. . The spenek9 locus by using a heteroduplex assay  and the 500 bp deletion results in loss of functional NEK9 protein by a premature stop codon (nek9+/500del) . As shown in Fig. nek978del/+ embryos showed impaired cardiac function, around 68% of heterozygous nek9+/500del embryos developed heart failure and 100% of analyzed compound heterozygous embryos (nek978del/500del) presented with the cardiac phenotype at 72 hpf  and Mendelian ratios were not achieved   in-vitro in combination with zebrafish ELC. NEK9500del was not expressed in-vitro, pointing towards non-sense mediated RNA decay or instable partial protein. In contrast, truncated NEK978del, which misses a part of the protein kinase domain del) repeat profile of NEK9.Next, we generated transgenic zebrafish using the CRISPR/Cas9 system targeting the NEK9 locus Fig. . F0 embrhpf Fig. . Importaek9 Fig. . The heaek9 Fig. . To charlazy susan (lazm647)23. Laz mutants harbor a mutation in the cmlc1 gene leading to a truncated, but stably expressed ELC protein with loss of carboxy-terminal phosphorylation. Complete loss of the phosphorylation sites in homozygous embryos (lazm647/m647) is associated with impaired cardiac contractility at rest (lazm647/m647 phenotype), while heterozygous fish (lazm647/+) are not affected. First, a nek9 gene dose titration system was established by using wild-type embryos    siblings of the same clutches   Fig. . As such7%) Fig. . Next, c3%) Fig. . In deta01) Fig. . Sensitiyos Fig. . Thus, sIn conclusion, these experiments underline the role of NEK9 for cardiac development and maintenance of contractility by interacting with ELC via its protein kinase domain.30 and T64, T127, as well as T129, were already described in-vitro by Arrell et al.19 and Cadete et al.20. In contrast to RLC, the ELC-phosphorylation map shows very dense modification events   Fig.  and by p24) Fig.  of the s24) Fig. , it coul11, analysis of a DCM cohort by mRNA-seq did not show significant isoform expression changes of ELC  and systolic dysfunction undergo adaptive changes in the expression and post-translational modification of various sarcomeric proteins. While in congenital heart disease isoform shifting occurs from ventricular (vELC) to atrial myosin light chain (aELC) expressionELC Fig. . Hence, n = 11, HTX: n = 6, healthy controls: n = 3). The patients suffering from DCM had a severely reduced LV ejection fraction (LVEF) compared to controls  (Supplementary Table R = −0.6193 and P = 0.0036) . In a validation cohort of 4 DCM patients and 2 healthy control samples we performed LC-MS and analyzed the detected phospho peptides semiquantitativly  biopsies of 11 DCM patients and 9 control samples (DCM: 36) Fig.  and the vly Fig. . We coulvly Fig. . Visualivly Fig. . Not all2+) -influx, myosin isoform expression, and phosphorylation of sarcomere proteins. Although ELC is shown to be thio-phosphorylated by MLCK in-vitro33, the kinases and phosphatases responsible for regulating ELC-phosphorylation under physiological conditions and in-vivo remained largely elusive. Here, we revealed that NIMA-related kinase 9 (NEK9), which is highly expressed in cardiac tissue, interacts with ELC in a Ca2+-dependent manner and regulates its phosphorylation in the heart. Loss of NEK9 is associated with heart failure and causally linked to ELC and cardiac contractility regulation.Finetuning of sarcomere function depends on many factors, including calcium 38, which could be recapitulated in zebrafish. In-silico kinase-specific phosphorylation prediction for ELC ranked NIMA-related kinases as the most likely candidates39.NEK9 belongs to the NIMA (‘never in mitosis gene A’) serine/threonine kinase protein family and is associated with cell cycle control during mitosis2+ -concentrations, which is the main trigger of cardiac contraction coupling and which is different from smooth muscle cell contraction. Since NEK9 does not seem to have EF-hand motifs, Ca2+ might rather improve the binding likelihood of NEK9 and ELC as shown for other protein-protein interactions, potentially also including additional mediators41. We postulate increased ELC-phosphorylation with increased interaction of ELC with NEK9, which is blunted after loss of NEK9 in Ca2+-saturated cells. By characterization of the interaction regions of NEK9 with ELC, we could identify the protein kinase domain as relevant part of NEK9 in two species, human and zebrafish. Other interactors are likely, e.g. by the NEK9 RCC domain, which is known to interact with Ran GTPase regulating mitotic progression42.Ionophore stimulation of human cells signified that the interaction of NEK9 and ELC can be mediated by increased intracellular Ca23. KD of the identified ELC kinase NEK9 results in a significant decrease of ELC-phosphorylation at baseline and also blunting of Ca2+ mediated phosphorylation increase.Our in-vitro data corroborate in-vivo studies in transgenic zebrafish lacking the carboxy-terminal ELC-phosphorylation site, where homozygous founders die during development and adult heterozygous fish exhibit progressive deterioration of cardiac performance in response to physical stress43. The absence of NEK6 promotes the progression of cardiac hypertrophy by activation of the Akt signaling pathway. In addition, elevated NEK6 protein levels are detected after transaortic constriction in murine heart tissue and in end-stage patients suffering from dilated cardiomyopathy43. Here, we observed that the transient or genetic loss of nek9 leads to heart failure and severely impaired cardiac contractility in zebrafish embryos. Transgenic nek9 zebrafish lines lacking the protein kinase domain develop the same heart failure phenotype indicating an essential role of NEK9 for cardiac function. In addition to the induction of heart failure, the mutant nek9 alleles lead to early embryonic degradation. This is in contrast to transgenic zebrafish lines harboring mutations in cardiac restricted genes, like lazy susan21, flatline44, or bungee45, which compensate a cardiac dysfunction up to day 5 post fertilization by oxygen diffusion via the surrounding medium. The lethal nature of the nek9 mutation might be explained by the expression of NEK9 in non-cardiac tissues  show lower lethality and could be characterized at 72 hpf. Heterozygous embryos (nek978del/+ and nek9+/500del) can be occasionally raised to adulthood if heart failure is not severe and are available for line propagation.To the best of our knowledge, NEK9 was previously not described in cardiac pathophysiology. However, NIMA-related kinase 6, another member of the NIMA kinase family, was analyzed in a mice model of cardiac hypertrophyues Fig.  and a br(lazy susan) is associated with severely impaired cardiac contractility of both chambers. While the ELC mutation is lethal in homozygous fish embryos, heterozygous mutant embryos do not show obvious spontaneous phenotype and have a normal life span21. Using this model in a heterozygous state for genetic-sensitizing, hypomorphic nek9 leads to the phenocopy of the homozygous loss of C-terminal ELC-phosphorylation, underpinning functional coupling of NEK9 kinase and ELC.In the present study, we focused on the identification of NEK9 regulating ELC-phosphorylation in the heart, since ELC is a key player of force transmission in cardiomyocytes. The relevance of ELC-phosphorylation for cardiac contractility was previously shown in zebrafish hearts in-vivo. Complete loss of a C-terminal ELC-phosphorylation site in a stable mutant zebrafish line 46, it was important to combine the assays with dephosphorylation protocols that have been meticulously validated in this study. It is not possible to verify which of the two mainly detected 2D-IB spots are originating from these residues47, since they might not be phosphorylated in the same ELC molecule at the same time or with differing abundance. Hence, phospho-specific antibodies for the four mainly differing serine/threonine phosphorylation-sites  need to be generated for future studies.ELC-phosphorylation has sparsely been reported previously. This might be due to the fact that many other protein species are accumulated in the region of ELC on 2D electrophoresis. We have overcome this issue by coupling LC-MS and specific immunoblotting assays. We could identify as much as nine active phosphorylation sites of ELC, from which we could validate five in additional peptide databases. Since phosphorylation events in bottom-up MS-experiments are close to modifications such as sulfonation 50. In addition, previous studies on RLC suggest decreased RLC-phosphorylation level in ventricles of end-stage heart failure patients53 and mouse models for cardiac dysfunction55. Here, we demonstrate the presence of phosphorylation of ELC in human hearts and observed significantly upregulated ELC-phosphorylation in LV tissue of DCM patients, showing an inverse correlation with ventricular ejection fraction. This might indicate a diverging role of RLC and ELC during the pathogenesis of heart failure, or it could be stage dependent, since we included living DCM patients in earlier disease stages .The functional relevance of RLC-phosphorylation is demonstrated by the expression of pseudo-phosphorylated RLC protein leading to reconstitution of inotropy in rodent models of heart failure2+ -load. Hence, ELC-phosphorylation may be up-regulated to salvage the circulatory supply/demand mismatch. Mechanistically, ELC-phosphorylation appears to result in enhanced actin-myosin cross-bridge formation and increased force development23 with RLC- and ELC-phosphorylation being increased after physical stress in the zebrafish heart23. In heart failure, intracellular Ca2+ -levels are increased, which might stimulate the ELC kinase NEK9 as shown in this study in-vitro. This discovery could be important for targeting the sarcomere as a modulator of inotropy, a treatment strategy with high clinical potential56.We hypothesize that the increase in ELC-phosphorylation serves as an adaptive mechanism in DCM, potentially due to increased ß-adrenergic stress and/or increased Ca2+-dependent manner. By demonstrating elevated ELC-phosphorylation levels in DCM patients, important insights could be provided, combining posttranslational ELC modification and human heart disease. Enhancing ELC-phosphorylation might be a mechanism that positively affects cardiac output and hence might be a potential pharmacological target.In summary, we identified NEK9 as a bona fide ELC kinase, regulating ELC posttranslational phosphorylation in a Ca57. Biopsies were rinsed with NaCl (0.9%) and immediately transferred and stored in liquid nitrogen until protein isolation. Besides the 6 HTX controls, 3 healthy control samples were included and have been used according to the protected health information (45 C.F.R. 164.514 e2) (bioserve) as well as the BCI informed consent F-641-5 (biochain).The characterization of samples and patient data within a central biobank approach has been approved by the ethics committee, medical faculty of Heidelberg (S390-2011). The participants have given written informed consent. A prerequisite for enrollment was leftover myocardial tissue from the routine diagnostic workflow. Altogether, remaining tissue biomaterial of 11 DCM patients was analyzed. Further inclusion criteria for DCM patients were defined as left ventricular ejection fraction (LVEF) of lower than 45% and availability of physical activity NYHA classification of the patients as well as N-terminal prohormone of brain natriuretic peptide (NT-proBNP) levels and high sensitivity measurement of troponin T (hs-TnT) levels. For controls 6 biopsies of patients after successful heart transplantation (HTX), who underwent routinely pathological check, were used. Their LVEF was defined as at least 55% or higher with good physical activity (NYHA I) and hs-TnT lower than 51 ng/L. Biopsies were obtained from the apical part of the left ventricular wall from patients undergoing cardiac catheterization using a standardized protocol3, 1 NF, 0.2 PMSF, protease inhibitor  and phosphatase inhibitor . All steps were performed at 4 °C to avoid native dephosphorylation of the protein extract. The concentration of the supernatant was measured using Bradford Assay . 45 μg protein of each sample was precipitated for 45 min at 4 °C with one volume 10% TCA. Pellets were washed twice with ice-cold acetone and stored in acetone at −80 °C until use.For protein extraction human biopsies were homogenized by peqlab mill (program: 2 × 5000 rpm 20 s with 10 s pause) in lysis buffer (in mM): 50 Tris-HCl, pH 7.5, 120 NaCl, 5 EDTA, 0.1% NP-40, 1 DTT, 1 NaVO2, 1% NP-40, 1 DTT, protease inhibitor . One half of each sample was treated with phosphatase inhibitor . The other half was incubated with calf intestine phosphatase  for 1 h at 37 °C. 45 μg protein of each sample was precipitated for 45 min at 4 °C with one volume 10% TCA. Pellets were washed twice with ice-cold acetone and stored in acetone at −80 °C until use.For in-vitro dephosphorylation assays, protein was isolated in EDTA-free lysis buffer without addition of phosphatase inhibitor (in mM): 50 Tris-HCl, pH 8, 100 NaCl, 10 MgClTM WesternCTM Blotting Standards, Biorad #1610385). 2D separated gels were either used for protein transfer to PVDF membranes and immunostaining or high-sensitive silver staining followed by cutting of protein-spots and mass spectrometry analysis.Dried protein pellets were dissolved in 2D sample buffer (in mM): 7000 urea, 2000 thiourea, 4% Chaps, 1% TritonX-100, 32.4 DTT, carrier ampholytes (Bio-Lyte® 3/10 Ampholyte Biorad #163-2094); at 37 °C. The PROTEAN® IEF System from Biorad and 17 cm IPG strips pH 4-7 (IPG ReadyStrip™ Biorad #163-2008) were used for isoelectric focusing (IEF). The strips were allowed to rehydrate overnight and focused as described . After IEF each strip was equilibrated in DTT and IAA equilibration buffer (in mM): 50 Tris pH 8.8, 6000 urea, 33% glycerol, 4% SDS, EB1: 130 DTT, EB2: 162 IAA; for 20 min. Half of each strip  was opposed to the second dimension gel  and run for 1 h at 7 mA per gel and 3.5 h at 15 mA per gel in Tris-Glycine buffer. A molecular weight marker  was moved into a small piece of filter paper and placed in front of the IPG strip before the gel run. For some validation experiments an additional marker was placed at the end of the strip . Indicated spots were cut out and de-stained according to manufacturer’s protocol.Staining of proteins in polyacrylamide gels was performed by the 2D polyacrylamide gels were stained for 1 h with Pro-Q® Diamond Phosphoprotein Stain . Fixation, washing, and destaining were performed according to the manufacturer’s guidelines. For total protein stain gels were stained overnight with SYPRO® Ruby .All primary antibodies used in this study are: rabbit polyclonal antibody against human ELC , custom-made rabbit polyclonal antibody against piscine ELC , mouse polyclonal antibody raised against the full-length protein of human CamK2G , mouse monoclonal antibody against human NEK9 , mouse monoclonal antibody against actin , mouse monoclonal antibody against beta-actin , mouse monoclonal antibody against beta-actin , mouse monoclonal antibody against flag-tag , rabbit polyclonal antibody against beta-tubulin , rabbit polyclonal antibody against troponin I  and rabbit polyclonal antibody against phospho-troponin I (cardiac) (Ser23/24) . HRP-coupled secondary antibodies  and HRP-conjugated monoclonal antibody against myc-tag  were used. Antibody dilutions are provided within the Source Data file.TM M-270 Epoxy  was used. Custom specific ELC antibodies (Eurogentec) against zebrafish ELC were covalently coupled by the Dynabeads Antibody Coupling Kit . All steps were carried out according to the manufacturer’s instructions. Precipitated proteins were separated by polyacrylamide gels and stained with custom-made rabbit polyclonal antibody against piscine ELC  for validation of successful co-immunoprecipitation. One representative experiment was sent for mass spectrometry analysis. To do so, the separated proteins were stained with Quick Coomassie® Stain  according to the manufacturer’s instructions. For every sample three gel pieces were cut out  and liquid chromatography followed by mass spectrometry (LC-MS) analysis was performed. After peptide identification Scaffold  was used to visualize MS/MS based peptide and protein identifications. The identified exclusive peptide counts were summarized for each protein. Peptide identifications were accepted if they could be established at greater than 95.0% probability by the Peptide Prophet algorithm58. Protein identifications were accepted if they could be established at greater than 95% probability and contained at least 2 identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm59. Specific interaction partners were identified by calculating the foldchange of unique peptide counts enriched by ELC compared to isotype control (IgG Ctrl). The identified proteins were categorized manually: a, sarcomeric proteins; b, enzymes; c, cytoskeleton elements; d, kinases and phosphatases; e, others; f, uncharacterized. Uncharacterized proteins refer to predicted but uncharacterized proteins in the data base. Finally, the proteins were ranked by descending foldchange expecting high abundant proteins to be candidates for further validation.For ELC-specific co-immunoprecipitation the Dynabeads Co-immunoprecipitation Kit  in combination with Dynabeads58. In brief, after reduction with dithiothreitol and alkylation with iodoacetamide trypsin digestion was done overnight at 37 °C. The reaction was quenched by addition of 20 µL of 0.1% trifluoroacetic acid  and the supernatant was dried in a vacuum concentrator before LC-MS analysis. Nanoflow LC-MS2 analysis was performed with an Ultimate 3000 liquid chromatography system coupled to an QExactive HF mass spectrometer . Dried samples were dissolved in 0.1% TFA and loaded on a C18 Acclaim PepMap100 trap-column (Thermo Fisher Scientific) with a flow rate of 30 µL/min 0.1% TFA. Peptides were eluted and separated on an C18 Acclaim PepMap RSLC analytical column  with a flow rate of 300 nL/min in a 60 min gradient of 3% buffer A (0.1% formic acid) to 40% buffer B (0.1% formic acid/acetonitrile). The mass spectrometer was operated in data-dependent acquisition mode, automatically switching between MS and MS2. Collision induced dissociation MS2 spectra were generated for up to 20 precursors with normalized collision energy of 29%. Dynamic exclusion was set to 10 s. Raw LC-MS files were processed using Proteome Discoverer 2.3 with SEQUEST HT: ISE  (Thermo Scientific) for peptide identification and quantification. MS2 spectra were searched with the SEQUEST software (Thermo Scientific) against the Uniprot human proteome database (UP000005640_9606) and the contaminants database 59 with the following parameters: Carbamidomethylation of cysteine residues as fixed modification and Acetyl (Protein N-term), Oxidation (M), deamidation (NQ) and phosphorylation  as variable modifications, trypsin/P as the proteolytic enzyme with up to 2 missed cleavages. Target FDR for the total number of identified peptide spectra matched to a protein (PSM) was set to 0.01 and results were filtered for high confident peptides. Peptide identifications were accepted if they could be established at greater than 95.0% probability by the Peptide Prophet algorithm60. Protein identifications were accepted if they could be established at greater than 95% probability and contained at least 2 identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm61. Peptides of the same sequence with different modifications were counted as different peptides. The assignment of all fragment spectra of peptides labeled as phosphorylated by database search were manually verified. No additional algorithm for posttranslational modification localization had to be used. Due to the high degree of neutral loss , phosphorylation at tyrosine was excluded.Separated proteins were processed as described by Fecher-Trost et al. with minor modificationsVentricle , atrium , skeletal muscle (R1234171-50), stomach (R1234248-50), brain (R1234035-50), lung (R1234152-50), liver (R1234149-50) and kidney (R1234142-50) were purchased at Amsbio LLC, UK, quantified with the Denovix DS-11 spectrophotometer and 1000 ng were transcribed into cDNA.nek9-F: 5′ GTGGTCACAGTGGAGAAGGA 3′, nek9-R: 5′ GCTGTCGATAGGAGGCTTTG 3′, camk2g-F: 5′ GTGAAGAAAACCTCCACGCA 3′, camk2g-R: 5′ ATCCGAGCCTCACGTTCTAG 3′, rplp0-F: 5′ TCGACAATGGCAGCATCTAC 3′, rplp0-R: 5′ ATCCGTCTCCACAGACAAGG 3′. PCR amplificates were separated by agarose gel electrophoresis.Reverse Transcription was performed by MuLV Reverse Transcriptase  in combination with: 25 mM MgCl2 solution and 10x PCR Buffer II , dNTP Mix , Random Hexamer Primer  as well as RNase Inhibitor . For quantitative real time-PCR SYBR® Green PCR Master Mix  in combination with the ViiA 7 Real-Time PCR System (Applied Biosystems) was used according to the manufacturer’s instructions. The primer sequences against human genes were as follows: 2.HEK 293A  cells were cultured in DMEM  supplemented with 10% FCS , 1% Penicillin-Streptomycin  and 1% glutamine  at 37 °C, 5% CO38 or zebrafish cDNA of the different transgenic mutant nek9 lines using primers flanking the coding sequence. PCR reaction was performed using KOD Hot Start DNA Polymerase . Cloning of the PCR amplicons was obtained into the mammalian expression vector pcDNA3.1TM (+)  with N-terminal myc for human ELC . Cells were stimulated or harvested 48 h post transfection.ELC and NEK9 were amplified from human left ventricular cDNAMerck, # 1086. Clo6 cells were seeded in 150 mm dishes in DMEM media without antibiotics and cultured overnight. After 24 h cells were transfected using lipofectamine . The siRNA against human NEK9; sihNEK9: 5′ AAUUGAUCCAAAUGGUUCA[dT][dT] 3′ and were used. The final amount of siRNA was 180 pmol.For siRNA mediated knockdown in HEK293 cells, 2 × 102  were diluted in prewarmed HBSS . Cells were washed once with prewarmed DPBS  and treated with stimulation solution for 10 min at 37 °C, 5% CO2. Unstimulated controls were treated only with high CaCl2 concentrations. Cells were harvested and proteins were isolated. For 2D gel electrophoresis 70 µg total protein was directly precipitated by TCA to avoid native dephosphorylation.Ionophore stimulation of HEK293 cells was performed to increase intracellular calcium concentration. Ionomycin calcium salt from Streptomyces conglobates  was solved in 50% ethanol and stored in aliquots at −20 °C. Ionophore  and CaCl62 HEK293 cells were lysed in lysis buffer (in mM): 10 Tris-HCl, pH 7.5, 150 NaCl, 0.5 EDTA, 0.5% NP-40, 1 PMSF, protease inhibitor  and phosphatase inhibitor . 10 µg of total protein extract was used as input on SDS-Page. 20 µL Myc-Trap magnetic beads  were equilibrated in washing buffer (in mM): 10 Tris-HCl, pH 7.5, 150 NaCl, 0.5 EDTA, 0.1% NP-40. 1 mg of protein lysate per sample was incubated with beads over night at 4 °C. Beads were washed and eluted in 30 µL elution buffer (in mM): 200 glycine, pH 2.5. Before SDS-Page followed by immunoblot analysis eluates were neutralized (in mM): 1000 Tris-HCl, pH 8.For myc tag specific co-immunoprecipitation38 and integrated in pcDNA3-APEX2-NES . HEK293 cells were seeded in 150 mm dishes in DMEM media without antibiotics and cultured overnight. For overexpression cells were transfected with lipofectamine . Empty pcDNA3-APEX2-NES serves as negative control. 24 h after transfection, all samples were treated with tyramide  for 30 min. Half of the samples were additionally stimulated with 30% H2O2 . Streptavidin pull-down was performed using MyOne C1 beads  following manufacture’s instruction. In brief, 1 mg of isolated proteins was used for precipitation. Eluates were washed with 6 M Urea before analysis on SDS-PAGE. To check for assay efficiency gels were blotted and stained with streptavidin-horseradish peroxidase . LC-MS analysis of one representative experiment was performed as described after trypsin digestion. One LC-MS run was performed for each sample . After peptide identification Scaffold  was used to visualize MS/MS based peptide and protein identifications. The identified exclusive peptide counts were summarized for each protein. The inclusion criteria were as follows: peptide thresholds: 90% minimum, 2 peptides minimum, peptide FDR: 1.7%; protein FDR: 0%. Specific interaction partners were identified by calculating the foldchange of unique peptide counts of biotin-labeled proteins in the proximity of ELC  and unique peptide counts of the same protein in the APEX-Ctrl sample.ELC was amplified from human left ventricular cDNAProtein samples were mixed with Laemmli sample buffer  and denatured for 5 min at 95 °C. Protein samples were run on hand-cast polyacrylamide gels  in 1× running buffer (in mM): 25 Tris, 192 glycine, 0.1% SDS. Gel run was performed for approximately 1.5 h at 20 mA per gel.Proteins separated by 2D gel electrophoresis or SDS-Page were transferred to a PVDF membrane (Mini Trans-Blot® Electrophoretic Transfer System) for 2 h at 400 mA in 1× blotting buffer (in mM): 19 Tris, 144 glycine, pH 8.3, 15% methanol. 5% milk powder in TBST buffer was used to block unspecific binding. After staining with primary antibody and secondary HRP-antibody, chemiluminescent substrate (ECL Western Blotting Substrate Pierce™ #32106) was used to detect HRP activity from antibodies. Densitometric analysis was performed with ImageJ version 1.51.PVDF membranes were incubated for 24 min at 50 °C in a harsh stripping buffer: 62 mM Tris-HCl pH 6.8, 2% SDS, 0.8% beta-mercaptoethanol. Membranes were washed intensively with tap water and incubated twice with TBST buffer for 5 min. After additional blocking with 5% milk powder in TBST buffer for at least 1 h, membranes were stained with primary antibodies.https://zfin.org/ZDB-GENO-960809-7) and the transgenic zebrafish line lazy susan (lazm647)21  were used. For breeding of zebrafish, piscine eggs were collected, pooled, rinsed, and placed in an incubator at 28 °C until 7 days post fertilization (dpf) upon which they were transferred into the fish room system. Maintenance took place at constant temperature of water and the surrounding at 28 °C and a defined light/dark cycle of 14/10 h. The aquarium system was manufactured by Schwarz  and enabled constant circulation of water for oxygen supply. Mature fish were fed twice a day with a combination of freshwater aquarium flake food  and live artemia shrimps .Care and breeding of zebrafish (Danio rerio) experiments were performed under institutional approval of the Regierungspräsidium Karlsruhe (T52/17), which conform to the Guide for the Care and Use of Laboratory Animals published by The US National Institute of Health . The AB zebrafish line . The primer sequences were as follows: nek9-F: 5′ ATGTCTCTGGAGGAGTACG 3′, nek9-R: 5′ CACACTGCTGATGTTGGTC 3′, camk2g1-F: 5′ GACATCGTCGCCAGAGAGTA 3′, camk2g1-R: 5′ GCTCTGCGGTGATTCTCTTG 3′. PCR amplicons were cloned into the pCR® II-TOPO vector  with dual promoter sites according to the manufacturer’s instructions. Plasmid sequences were checked by Sanger sequencing before use. After linearization of the plasmids, digoxigenin-(DIG) labeled RNA probes and antisense control probes were synthesized by using the DIG-RNA Labeling Kit . Fluorescence in-situ hybridization was performed as described by Brend et Holley63 with minor modifications. In brief, zebrafish embryos were fixed with 4% paraformaldehyde  at 72 hpf and dehydrated with 100% methanol. Permeabilization of embryos was performed with proteinase K digestion  for 35 min at room temperature. To block unspecific binding prehybridization is performed with heparin and 5 µg/mL torula (yeast) RNA. Hybridization of DIG-labeled probes was carried out at 65 °C overnight. After washing embryos were incubated with anti-Digoxigenin-POD antibody  overnight at 4 °C. For fluorescence labeling and signal amplification the Tyramide Signal Amplification (TSA) plus Fluorescein System  was used. Imaging was obtained in 3% methylcellulose using a Leica MZ FLIII confocal microscope.In-situ probes against zebrafish Morpholinos-modified antisense oligonucleotide were obtained from Gene Tools  and injected into one cell stage zebrafish embryos. Sequences are as follows:nek9: 5′ CTCCTAGTCGTCCAGACACATCATC 3′;MO-5bp-mm-control-nek9: 5′ TCGAGTCTCCACACTGAACTACACA 3′;MO-5’UTR-nek9: 5′ CTCGTACTCCTCCAGAGACATGATC 3′;MO-ilk26: 5′ GGCACTGAGTGAAGATGTCATCCAT 3′;MO-control: 5′ CCTCTTACCTCAGTTACAATTTATA 3′.MO-An injection ramp made of 3% agarose was produced with an injection mold TU-1 , which contained asymmetric gullies (5 cm length × 3 mm width). The injection needles were made of 1 mm glass capillaries  with a Narishige PC-10 device  with two heating steps of 72 and 67 °C. The embryos were placed into the asymmetric gullies and were injected with the injection device Femtojet®  at a capillary pressure of 15 hPa and an injection duration of 0.1 s. The injection pressure was adjusted to the injection needle. After microinjection, the embryos were incubated at 28 °C for six hours. Living embryos were transferred into fresh petri dishes. At 20 to 22 h post fertilization (hpf) 0.2 mM 1-phenyl-2-thiourea (PTU) was added to suppress pigmentogenesis.nek9 and camk2g1 was generated by using the mMESSAGE mMACHINE™ SP6 Transcription Kit . RNA quality and concentration were determined by Fragment Analyzer (Agilent). For rescue experiments the mRNA concentration was titrated in regard to toxicity. Injection needles were calibrated to 1 nL for each injection by using an optical micrometer. Successful injection of mRNA into one-cell stage embryos was followed by Morpholino (MO) injection. Embryos were incubated at 28 °C and analyzed at 72 hpf. Functional analyses of each injected zebrafish clutch were performed blinded to the MO and mRNA injected.Sense-capped RNA of zebrafish www.benegfx.de).Cardiac function in zebrafish embryos was assessed at 24 or 72 hpf. The embryos were anesthetized with 0.02% tricaine  and mounted in 2.5% methylcellulose. Video documentation was performed by recording 10 sec video frames using the LEICA DM IRB microscope with a magnification of 1.6×, a LEICA 506059 objective and a LEICA DFC360 FX camera . Images were processed with VirtualDub Version 1.7. Fractional shortening of heart chambers was calculated with help of the zebraFS software  targeting nek9 were designed. Sequences are as follows:CRISPR/Cas9 mediated knockout of zebrafish nek9 gRNA1: 5′ AACTGTTCACCACGATACT 3′; nek9 gRNA2: 5′ GGCCGGTCTGCAGCGTCCGG 3′; nek9 gRNA3: 5′ GGGCAAGGGCGCGTTCGGAG 3′; tyr gRNA: 5′ GGACTGGAGGACTTCTGGGG 3′. Full length scaffold gRNAs were synthesized by using the in-vitro transcription kit (New England BioLabs #E3322). Cas9 (PNA Bio #CP01) and gRNAs were combined in a ratio of 2:1 to a final concentration of 400 pg/nL Cas9 protein and 200 pg/nL gRNA. Injection was performed in zebrafish one-cell stage. Additional injection of gRNA against tyrosinase generates mosaic pigmentation phenotypes65. For the analysis of efficiency and indel formation the heteroduplex motility assay was used. Two flanking primer pairs were designed: HMA-nek9-F1: 5′ GTTCAGTGTGGAGACTCGAGA 3′, HMA-nek9-R1: 5′ CCGTCAGGTAAACAGTGCTG 3′, HMA-nek9-F2: 5′ ATGTCTCTGGAGGAGTACGAGC 3′, HMA-nek9-R2: 5′ AATGTAACGATGTGCAGTGGTC 3′. After PCR amplification samples were denatured for 3 min and cooled down to room temperature for about 1 h. Heteroduplex products were separated by polyacrylamide gel electrophoresis.DNA was isolated from fin biopsy of 3-month-old adult zebrafish or from whole embryo at 24 hpf or 72 hpf. PCR amplifications with specific primer pairs flanking the gRNA binding site were performed. F1: 5′ ATGTCTCTGGAGGAGTACGAGC 3′; R1: 5′ ATCTCCTTCCACACCACCAG 3′; F2: 5′ CACTACGCCTCCCTGAACTC 3′; R2: 5′ GCTGATGTTGGTCTGGTTCC 3′.P-values < 0.05 were considered statistically significant. *P < 0.05; **P < 0.01; ***P < 0.001 and ****P < 0.0001 are shown in figures. The exact p-values are provided in the Source Data file. All statistical analysis was performed using GraphPad Prism Version 5.0 for Windows.If not stated otherwise, the results are expressed as mean ± standard deviation (SD). The sample size is listed as n. For zebrafish experiments the total number of analyzed individuals  and the number of independent experiments  is stated. Information about statistical analysis is provided in the figure legends. Further information on research design is available in the Supplementary information fileReporting SummarySupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Description of Additional Supplementary FilesPeer Review File