Among abundant reconstructions of Holocene climate in Europe, only a handful has addressed winter conditions, and most of these are restricted in length and/or resolution. Here we present a record of late autumn through early winter air temperature and moisture source changes in East-Central Europe for the Holocene, based on stable isotopic analysis of an ice core recovered from a cave in the Romanian Carpathian Mountains. During the past 10,000 years, reconstructed temperature changes followed insolation, with a minimum in the early Holocene, followed by gradual and continuous increase towards the mid-to-late-Holocene peak , and finally by a decrease after 0.8 kcal BP towards a minimum during the Little Ice Age (AD 1300–1850). Reconstructed early Holocene atmospheric circulation patterns were similar to those characteristics of the negative phase of the North Atlantic Oscillation (NAO), while in the late Holocene they resembled those prevailing in the positive NAO phase. The transition between the two regimes occurred abruptly at around 4.7 kcal BP. Remarkably, the widespread cooling at 8.2 kcal BP is not seen very well as a temperature change, but as a shift in moisture source, suggesting weaker westerlies and increased Mediterranean cyclones penetrating northward at this time. Climate models suggest a poleward shift in the position of mid-latitude storm tracks due to increased warming3. Under this scenario, central and south Eastern Europe are influenced by subtropical high pressure cells, which heighten risk of droughts4 and stronger cyclones5. However, these projections are highly uncertain6 as models have difficulties capturing present-day dynamics of storm tracks. Therefore, model validation against long-term observational data is needed. Over the past decade, important progresses have been made in reconstructing past hydroclimatic variability in ECE8. However, most of these reconstructions are biased towards the warm season; few capture annual climatic information10, and none winter climatic conditions, the latter of which is the period most sensitive to changes in moisture sources and precipitation amounts.Weather and climate patterns in East-Central Europe (ECE) are strongly influenced by the dynamics of the storm tracks carrying moisture from the North Atlantic Ocean and Mediterranean Sea towards the continent. The strength and position of these storm tracks control short and long-term changes of precipitation amount and distribution14C-dated isotopic record from a cave ice deposit in the Apuseni Mountains  of cave ice reflects changes in air temperature during ice formation  offers ECE’s first terrestrial record of changes in the sources of precipitation and associated shifts in the position of mid-latitude storm tracks.Here we present the first continuous record of changes in temperature and precipitation sources from autumn through early winter  in ECE for the past ~10,000 years. It is derived from a precisely 11, defined as the difference of atmospheric pressure between the Icelandic Low and Azores High. The influence of NAO is strong over Europe, especially during the cold season (November through April). When the NAO is in a positive phase , the westerlies carrying moisture are deflected northward, resulting in drier conditions in ECE , the westerlies carry more moisture from the North Atlantic towards South Eastern Europe , on seasonal to millennial time scales. Climate in ECE is strongly seasonal, with hot and dry summers associated with the northward expansion of mid-latitude anticyclonic cells, coupled with cold and wet winters, resulting from complex interplay of southward outbursts of Siberian cold air and northward intrusion of moisture carried by Mediterranean cyclones. The latter originate in the Western and Central Mediterranean, and their path is heavily influenced by the strength of the North Atlantic Oscillation (NAO), one of the principal modes of climate variability in the Northern HemisphereECE Fig. . When thope Fig. .12. The relative contribution of these sources is controlled by the strength of the NAO and East Atlantic-Western Russia (EA-WR) pattern modes of atmospheric variability and the blocking activity of the Siberian High14. δ18O and δ2H values of local precipitation plot along a Local Meteoric Water Line being defined by the equation δ2H = 7.9*δ18O + 8.1415, similar to the Global Meteoric Water Line17. The mean d-excess value is close to 10, although higher (between 12 and 15) in autumn, when moisture from the evaporatively 18O- and 2H-enriched Mediterranean surface waters reaches into ECE18.The mean annual temperature at Scărișoara Ice Cave (SIC) is ~6 °C and precipitation amounts reach 1200 mm/year, with two maxima, one represented by Atlantic-sourced rainfalls in May-June and another of Mediterranean origin in late autumnca. 1–15 cm thick. The cave opens to the surface through two shafts, 47 m and 6 m wide, respectively. The ice block extends from the bottom of the large shaft, continuing under the narrower one. This particular setting allows for direct deposition in the lake on top of the ice block and subsequent incorporation at the base of the newly formed ice layers of surface-derived organic and inorganic matter. Occasional infiltration of seepage water in winter leads to further ice development on top of the already existing one. Spring and summer melting usually removes this ice, so that the ice block mainly consists of ice formed by the freezing of late-autumn and early-winter precipitation. Periods of enhanced melting might have acted in the past, so that the annual layering of the ice is not always preserved20. Observation over the past 70 years have regularly shown that the melt water and the detritus it might contain are flowing out of the surface and over the sides of the ice block, so that the newly formed lake in autumn contains no traces of the previous year’s sediments. This has important implications for the age of the ice, as older organic matter released by the melting of ice is not incorporated in the new ice, and thus the ice and the organic matter it contains are of the same age. Based on observations spanning the past 70 years, the long-term dynamics of the ice block is controlled by the strength of winter cooling, with summer melting playing an secondary role. By analyzing the isotopic composition of precipitation, drip and lake water, and resulting ice, our previous studies21 have shown that the isotopic signature of the ice block reflects precipitation from autumn through early winter and related climatic information.Ice in Scărișoara Cave Fig.  forms asca. 5 kcal BP are likely due to more ice formation in winter (under wetter conditions) and/or less summer ablation (under drier conditions). This hypothesis is supported by proxy  and modeling data22, showing early Holocene warm and dry summers, and wet and somewhat colder late Holocene summers. Summer precipitation amounts were relatively constant (low amounts) between 5 and 9 kcal BP, but increased in two steps at 5 kcal BP and after 2.5 kcal BP. Likewise, higher summer temperatures were documented between 8 and 2.4 kcal BP, followed by a cooling tendency after ca. 2.4 kcal BP10.The ice accumulation rate increased from 0.13 cm/yr prior to 5 kcal BP (thousands of years before AD 1950) to ~0.4 cm/yr between 5 and 2.5-2 kcal BP, and doubled afterwards to ~0.9 cm/yr Fig. . The acc18O record shows an early Holocene minimum (−11‰) between 10.4 and 9.25 kcal BP, followed by a gradual and sustained increase to −8‰ until roughly 5 kcal BP, a relatively stable period until 1.5 kcal BP, and a continuous decrease towards the most recent values (−9.5‰) at AD 1860, before the onset of current warming. Combining the winter temperature with that of the summer22, it appears that the early Holocene had a more continental climate, with a marked contrast between cold winters and warm summers, whereas during the mid-to-late Holocene, the gradual summer cooling and warming winter resulted in diminishing influence of continental-like climate conditions. The past ~1000 years, up to 1860 AD, when the SIC record stops, both summer and winter were cold compared to the preceding millennia. The SIC d-excess record  precipitation sources. We suggest that the cooling in the North Atlantic during the 8.2 ka event led to a weakening of the Icelandic Low and of the westerlies, thus allowing for more Mediterranean-derived precipitation to reach SIC. This source would have been warmer, thus explaining the high d-excess values and reduced cooling, as seen in the SIC record.The stable isotope record from SIC covers almost the entire Holocene, between 10.5 and 0.090 kcal BP Fig. , closelyord Fig.  shows a  BP Fig. , coincid18O values between the early Holocene and ~5 kcal BP, followed by relatively steady conditions until ~0.8 kcal BP and a rapid decrease towards AD 1860 (when the record ends). Second, there is a shift towards higher d-excess values, after ca. 4.7 kcal BP  weather patterns in the wider North Atlantic basin were similar to those occurring during NAO− phases, with dry northwestern and western Europe and wet southeastern Europe  in the early Holocene, with an increasing trend towards 4–5.5 kcal BP30. Strengthening of westerlies and strong zonal flow from 10 to 4.7 kcal BP indicates a shift from NAO− to NAO+ in the North Atlantic, the latter becoming dominant since ~4.7 kcal BP. The SIC δ18O record  suggest a wet early Holocene and rather dry conditions after ca. 4.7 kcal BP31  and more evaporative precipitation source. Further support for this hypothesis comes from the accumulation rate in Scărișoara Ice Cave, which shows an increase at ~5 kcal BP and ~2 kcal BP, coincident with increases in d-excess values in the ice core. In western Romania, Mediterranean cyclones are active mostly in late autumn and early winter33; thus intensification in their activity would explain the increase in d-excess (delivery of more precipitation from the evaporative Mediterranean Sea) and accumulation rate (more precipitation reaching the cave in the ice forming season). The increase in d-excess values that occurred after ca. 4.7 kcal BP, was interrupted at ~4.2 kcal BP by a shift towards lower values that lasted until ~2.5 kcal BP . In the context of the above scenario, the lower  d-excess values during this interval imply a return towards stronger zonal circulation, suggesting negative NAO− like conditions, similar to the conditions documented in SW Greenland32.Two prominent features of the SIC record stand out. First, there is a continuous increase (of about 3.2‰) in δ BP Fig. . The colope Fig. . Additioord Fig.  shows thess Fig.  suggestsess Fig. , and driope Fig.  show conP31 Fig. . Taken tons Fig. , indicatThis is the first study to provide a long term, high resolution record of winter temperature and moisture sources changes in Europe, derived from the stable isotopic composition of underground glaciers.− prevailed in the course of the early Holocene, followed after ~4.7 kcal BP by a shift towards a predominance of NAO+ type atmospheric circulation, that lasted with interruptions  until present. This shift led to more Mediterranean cyclones penetrating further NE into the European mainland, leading to the differentiation between the Atlantic-dominated European climate in W and NW Europe, and a Mediterranean climate in S, SE, and East-Central Europe.Throughout the Holocene, the subterranean ice block in Scărișoara Ice Cave responded sensitively to changes in both winter temperature and moisture source. During this time period, winter temperature in ECE was mainly controlled by insolation changes. The interplay between insolation variability, SST changes in the North Atlantic, and the influence of the lingering Laurentide Ice Sheet modulated the dynamics of large-scale atmospheric circulation. Conditions mimicking those occurring during NAO34. A total of 56 ice cores, ranging in length between 21 and 63 cm, were recovered and transported in frozen state to Stockholm University, Sweden. In the cold laboratory, the ice core was cut lengthwise, half of it being archived. The remaining half was cleaned by removing the outer layer of ice (0.5–1 cm thick), and subsequently cut in 1–2 cm wide samples that were allowed to melt at room temperature and stored in 20 ml high-density polyethylene flasks before analyses. Organic matter (leaf pieces and pine needles) was handpicked from the ice core for 14C dating.In February 2003, a 22.53 m long core was drilled to the bedrock, using a 10-cm diameter drill, driven by a 220 V engine14C dating to the Poznan Radiocarbon Laboratory (Poland), of which six had not enough carbon for precise age determination. Of the resulting29 determined ages, three were obviously contaminated . The SIC core chronology , we estimated an age of AD 1860 ± 20  for the top of the ice core. The raw 14C data are included in Supplementary Dataset A total of 35 samples were submitted for ogy Fig.  was cons18O and δ2H, respectively  against VSMOW (Vienna Standard Mean Ocean Water), with precision better than ±0.2‰ and ±1‰ for δDataset 1Dataset 2Dataset 3


Preload is applied to screws manually or using a torque wrench in dental implant systems, and the preload applied must be appropriate for the purpose. The aim of this study was to assess screw loosening and bending/torsional moments applied by clinicians of various specialties following application of manual tightening torque to combinations of implants and abutments. Ten-millimeter implants of 3.7 and 4.1 mm diameters and standard or solid abutments were used. Each group contained five implant-abutment combinations. The control and experimental groups comprised 20 and 160 specimens, respectively. Implants in the experimental group were tightened by dentists of different specialties. Torsional and bending moments during tightening were measured using a strain gauge. Control group and implants with preload values close to the ideal preload were subjected to a dynamic loading test at 150 N, 15 Hz, and 85,000 cycles. The implants that deformed in this test were examined using an optical microscope to assess deformities. Manual tightening did not yield the manufacturer-recommended preload values. Dynamic loading testing suggested early screw loosening/fracture in samples with insufficient preload.  Dental implants offer extensive treatment options for patients who are completely or partially edentulous . OsseoinPreload applied by rotational movement of abutment screws is essential for retention in systems in which the connection between the implant and abutment is maintained via a screw. When tightening torque is applied to abutment screws, the screws function as a hard spring. The elastic recovery feature of abutment screws creates a connection force that keeps components together . The preThe preload primarily depends on the applied torque force and secondarily on the material, the design of the screw head and grooves, and the surface roughness between the implant and abutment . The micFor clinical success, screw retention should be stable and constant. For this reason, the magnitude of the preload force is critical. The magnitude of torque applied for the preload depends on the yield strength of the screw and the strength of the bone-implant interface .Use of the optimum tightening torque for implant-abutment complex is vital for clinical success. The risk of loosening is particularly high in abutment screws that are tightened with forces lower than the recommended tightening torque. However, tightening an abutment screw with high forces causes its yield strength to be exceeded and the screw to lose its mechanical characteristics due to plastic deformation .Loosening or fracture of abutment screws is one of the most common mechanical complications . The looScrew loosening can be caused by inadequate tightening torque, excess mechanical loads, inconsistencies in the material and design of the abutment screw, vibration during functional loading, temperature changes in the oral cavity, and misplacement of the implant , 6, 10.Screw loosening can cause implant and screw fracture, unbalanced distribution of occlusal forces, a microgap space between the implant and abutment that can allow bacteria in by causing a micromovement, peri-implant inflammation, and loss of osseointegration due to the microgap , 6, 10.Bickford defined screw loosening as a two-stage process. Initially, external functional forces affect the screw connection and consequently cause the tightening torque to diminish. Vibration and micromovement loosen the screw and decrease the effective preload force. Secondly, the preload force decreases below the critical level, which causes the grooves to rotate and loss of function in the screw joint . After sThe antirotational element and preload of the screw joint are important in preventing screw loosening in implant abutments . After tAlthough some studies have focused on tightening torque, namely, the preload applied to the implant-abutment screw using a ratchet torque wrench, and behaviors during dynamic loading testing, few have evaluated manual application of tightening torque to screws used frequently for implant-supported prostheses. In particular, not many studies have analyzed the bending and torsional moments that occur during manual application of tightening torque by physicians of various specialties and both genders. Therefore, we aimed to analyze the right-left, front-back bending moment and torsional moment during manual application of tightening torque to screws by female and male physicians of different specialties and evaluated the rates of early complications.Implant KA  bone-level implants with platform switching were used, which included Morse conical connections and Oktafiks conical connections that start as the conical connections finish. A total of 90  and 90 (4.1 mm diameter) bone-level implants were used. We also used standard and solid abutments with 2 mm gingival height, which were compatible with the implants.Each specimen consisted of an implant, abutment, abutment screw, manual screwdriver, apparatus on which the implant complex was held using CNC pliers, a strain gauge connected to the strain indicator on which the components were stabilized, and a strain gauge . Twenty The physicians applied torque to the abutment screw using a manual screwdriver before cementation of implant-supported prostheses. The participants were blinded to the implant diameter and abutment type at the time of application of manual tightening torque. The largest torsional moment that occurred during tightening torque application manually or using a ratchet torque wrench was measured. The torque values and bending moments in the right-left or front-back directions during application of tightening torque to the abutment screw were measured using a digital strain gauge indicator  .The experiments were recorded using a digital video camera, and the largest torsional and bending moments were determined. This method reduces the error caused by rapid changes in the moment values.Twenty manually tightened samples from the experimental group that had close to the ideal torque value (5 of each implant diameter and abutment type) and 20 samples from the control group were subjected to dynamic loading testing using a servohydraulic testing machine in accordance with ISO 14801. The parameters 150 N, 15 Hz, and 85,000 cycles, which represent a 1-month mastication cycle and 25°C (room temperature), were used. To imitate the crestal bone loss in endosteal implants, implants attached to plier from 3 mm apically from the implant-abutment connection. A force of 150 N was applied to the test sample, which was placed on a 30° slope at 15 Hz, 2 mm from the abutment center. The tip of the mechanical set-up moved vertically in 5 mm steps until it contacted the inclined surface of the test sample and then slid 2 mm in a lateral direction.Samples that showed no macroscopic deformation after the dynamic fatigue test were checked for deformation and fractures using an optical microscope . The loosening torque value was determined using a torquemeter  .This study did not involve prosthetic restorations due to the difficulty in maintaining standardization and for the elimination of related effective parameters.t-test, and differences according to specialty were subjected to one-way analysis of variance (ANOVA). The reasons for the results found by ANOVA were investigated using the Bonferroni test. The analyses were performed using a 95% confidence interval and significance level of 0.05. The relationship between the initial tightening torque values and the final state of the implant-abutment screw complex was evaluated by calculating Spearman's Rho correlation coefficient and by performing ANOVA.Differences in bending and torsional moments according to gender were evaluated by The right-left bending of the implant-abutment complex during application of manual torque is referred to as the Ch1 bending moment. The front-back bending is known as the Ch2 bending moment, and the torque force applied is termed the Ch3 torsional moment.The Ch3 torsional moment values in the control group were those recommended by the manufacturer. Therefore, the Ch3 torsional moment values of the control group were included in the statistical evaluation for comparison.The mean Ch1, Ch2, and Ch3 moments according to specialty are shown in p = 0.002) and the Ch3 torsional moment (p < 0.001), but not the Ch1 bending moment (p = 0.067), were statistically significant (p = 0.035), and oral and maxillofacial surgeons and dentists showed statistically significant difference in Ch2 values (p = 0.025). The control group exhibited a significant difference in Ch3 values compared to subjects of all specialties (p < 0.001)  .Males applied higher Ch1 and Ch2 bending moments on the abutment screws than females  . Howeverp = 0.021), but not Ch2 bending moment (p = 0.137)   . There wfemales) .n = 20), 9 (45%) had screw fractures and 8 (40%) had an opening on the pressured side. Furthermore, 3 (15%) showed only loosening, not deformation. The mean initial tightening torque values of the group with fractures, an opening on the pressured side, and loosening were 13.79, 12.85, and 16.34 Ncm, respectively.Of the experimental group samples subjected to dynamic fatigue testing  .In the group that experienced opening due to pressure, the mean loosening torque increased by 15% compared to the initial tightening torque value. In contrast, the mean loosening torque decreased by 34% in the group with only loosening .The mean loosening torque value of the control group with 3.7 mm diameter implants and standard abutments decreased by 29%, compared to 34% in the group with 3.7 mm diameter implants and solid abutments.The mean loosening torque value of the control group samples that were exposed to dynamic fatigue decreased 22% in the group with 4.1 mm diameter implants and standard abutment and 26% in the group with 4.1 mm diameter implants and solid abutment.The dynamic fatigue test results for the control groups are shown in the right column of Although the preload force used on abutments of various types, diameters, and connection types using a ratchet wrench and the mechanical resistance of the implant-abutment complex have been reported, few studies have focused on manual tightening of abutment screws, which is generally preferred by dentists –15.Application of the optimum torsional moment to the implant-abutment complex is critical for long-term successful prosthetic implant restoration. The implant-abutment connection loosens over time, resulting in microgaps, bacterial colonization, and peri-implantitis. Over time, microgaps progress to macrogaps. In this situation, the surface connection between the implant and abutment is lost, leading to abnormally directed forces on the screw. These phenomena cause complications such as inflammation/infection of the soft tissues and fracture of the screw. To prevent this, it is crucial to apply the optimum torsional force to the implant-abutment connection, ideally using a torque-calibrated ratchet wrench , 6, 10. We evaluated the preload  on abutment screws depending on specialty and gender, as this can affect implant dental practice. We also assessed the condition of the implants after manual application of a preload force under dynamic loading conditions.A 30° loading angle, which is close to the tubercle slope of posterior teeth, is also recommended in the ISO 14801 protocol, which was developed for evaluating the mechanical resistance of dental implant materials. In this protocol, the experimental conditions were defined to mimic oral-cavity conditions under dynamic loading conditions. In studies by Steinebrunner et al. (2005), Balfour and O'Brien (1995), and Steinebrunner et al. (2008), a 30° loading angle was used in dynamic loading tests –18. TherIt is specified in ISO 14801 that, during dynamic load testing, the 3 mm crestal part of the implant within the material should be excluded to imitate the worst clinical condition and possible crestal bone loss. In the studies by Khraisat et al. (2004), Balfour and O'Brien 1995), and Tsuge and Hagiwara (2009), 3 mm crestal portions of implants were stabilized outside the complex to simulate possible crestal bone loss , and Tsu, 19. The6 cycles have been reported not to experience complications; however, abutment screws tightened to 20 Ncm experienced complications at 357.162 cycles [6 cycles are recommended at 2 Hz, while lower speeds and 5 × 106 cycles are advised for velocities of 2–15 Hz.Abutment screws tightened to 30 Ncm for up to 5 × 102 cycles . These p2 cycles , 22. The5 cycles [6 cycles [We used 150 N loading in the dynamic loading test based on the work of Shin et al., who performed a dynamic fatigue test at 10–150 N, 10 Hz, and 106 cycles .The Ch1 bending moment, that is, the right-left inclination of the implant-abutment screw during manual application of tightening torque, applied by prosthodontists was greater than that applied by dentists. Moreover, males applied a greater right-left bending moment than females. Similarly, the Ch2 bending moment, that is, the front-back inclination of the screw, differed between the oral and maxillofacial surgeons and dentists. No study of this phenomenon has to our knowledge been published. We believe that a possible reason for this difference is the application of greater torsional moment by prosthodontists and oral and maxillofacial surgeons to the abutment screw compared with dentists. The reason may be the high probability of the physician sliding towards the lateral forces while applying more torsional moment to the screw.The difference in bending moments during manual tightening of implant screws would probably not affect an osseointegrated implant. However, in patients with loose trabeculation, these lateral forces may affect the primer stability of the implant during surgery.p = 0.001). This was probably because prosthodontists are more experienced in implant superstructures and possibly also abutments and abutment screw systems compared with those of other specialties. Similarly, the fact that nonspecialist dentists showed the lowest values was due to their lack of knowledge and experience with clinical osseointegration and implant-supported prosthetic components. The male participants applied significantly greater tightening torque than the female participants.In a study by Goheen et al. of 5 oral and maxillofacial surgeons and 11 prosthodontists, the physicians were asked to apply 10, 20, and 32 Ncm torque forces to implants manufactured by Brånemark  using manual wrenches of the same brand. Oral and maxillofacial surgeons applied 23–48% lower torque values with target torque values of 10 and 20 Ncm, respectively. The prosthodontists showed a margin of error of 15% with these target torque values. Therefore, oral and maxillofacial surgeons apply less manual force to abutment screws. Goheen et al. emphasized the importance of standardizing torque applied to abutment screws using a torque device to prevent screw loosening . In our Gross et al. reported habitual tightening torque values of 7–14.6 Ncm ; the mean maximum tightening torque values were 9.4–19.9 Ncm . Therefore, manual application of tightening yielded torque values lower than that recommended by the manufacturer, suggesting that mechanical torque devices are required . The manp = 0.01). Therefore, professional experience influenced torque values. However, 90.8% of the participants applied <15 Ncm to the healing abutments, and 8% applied 15–20 Ncm [p = 0.001), probably because the prosthodontist group had more experience in dental implant prostheses than the dentists. This agrees with the report by Bousquet et al. describing that professional experience influences tightening torque values.In a study by Bousquet et al., 30 lecturers at universities that provided dentistry education  and 24 students in their final year of undergraduate education  manually applied the highest tightening torque possible to implant healing abutments in the right and left first molar tooth area and lower left first incisor tooth area. <10, 10–15, 15–20, and 20–25 Ncm tightening torque groups were formed. There were no significant differences between the areas or between the female and male physicians. However, the lecturers applied a significantly greater tightening torque than the students , at the recommended torque value (20 Ncm), and 20% greater than the recommended torque value 24 Ncm) to abutments of implants of diameters 3.3, 3.75, and 5 mm and performed dynamic loading testing. The three torque values differed significantly among the implant diameter groups; however, abutment screw and implant fractures occurred in the 3.3 mm diameter implants. The number of complications due to mechanical fatigue under load is higher in implants of lesser diameter [ Ncm to a6 cycles. The low tightening torque values negatively affected the screw joints, and the dynamic loading led to loss of preload force [In a study by Xia et al., 30 screws of implant-abutment combinations were tightened to 24, 30 (recommended), and 36 Ncm. Dynamic fatigue testing was performed at 30–300 N, 15 Hz, and 5 × 10The preload force values manually applied to dental implant-abutment screws did not reach those recommended by the manufacturer. This negatively influenced the continuity of the screw connection against dynamic loads, and so manual application of preload force is not sufficient for clinical success; therefore, the calibrated ratchet torque wrench provided by the manufacturer should be used.


Activation of EGF-R and PDGF-R triggers autophosphorylation and the recruitment of Eps15 and Hrs. These two endosomal proteins are important for specific receptor sorting. Hrs is recruiting ubiquitinated receptors to early endosomes to further facilitate degradation through the ESCRT complex. Upon receptor activation Hrs becomes phosphorylated and is relocated to the cytosol, important for receptor degradation. In this work we have studied the endosomal binding dynamics of Eps15 and Hrs upon EGF-R and PDGF-R stimulation. By analysing the fluorescence intensity on single endosomes after ligand stimulation we measured a time-specific decrease in the endosomal fluorescence level of Eps15-GFP and Hrs-YFP. Through FRAP experiments we could further register a specific change in the endosomal-membrane to cytosol binding properties of Eps15-GFP and Hrs-YFP. This specific change in membrane fractions proved to be a redistribution of the immobile fraction, which was not shown for the phosphorylation deficient mutants. We here describe a mechanism that can explain the previously observed relocation of Hrs from the endosomes to cytosol after EGF stimulation and show that Eps15 follows a similar mechanism. Moreover, this specific redistribution of the endosomal protein binding dynamics proved to be of major importance for receptor degradation. Binding of ligand is the activation signal for all the RTKs, which triggers trans-autophosphorylation of the receptor. This step is crucial for RTK dependent activation and recruitment of a variety of signalling proteins. Binding of ligand leads to ubiquitination of the receptor and recruitment of Hepatocyte growth factor-regulated tyrosine kinase substrate (Hrs) and Epidermal growth factor receptor pathway substrate 15 (Eps15). This process targets RTKs to the lumen of multivesicular bodies (MVBs) for lysosomal degradation3. Sorting of membrane receptors into MVBs is orchestrated by the sequential recruitment of members of the endosomal-sorting complex required for transport (ESCRT complex) (for review see3).Receptor tyrosine kinases (RTK) play an important role in the control of fundamental cellular processes, including the cell cycle, cell migration, cell metabolism and survival, cell proliferation and differentiation4. The N-terminal Eps15 homology (EH) domains bind NPF motifs on a variety of other endocytic adaptor proteins. The central coiled-coil domain mediates Eps15 oligomerization and binding to other proteins including Hrs. The DPF domain of Eps15 binds to adaptor protein-2 (AP-2) and is important in the formation of clathrin-coated vesicles (CCV)5. In the C-terminus of Eps15 the two ubiquitin interacting motifs (UIM domains) are located. Eps15 has been reported to bind directly to ubiquitinated EGF-R through these UIM domains6. Activation of EGF-R triggers both monoubiquitination and phosphorylation of Eps158.Epidermal growth factor receptor substrate 15 (Eps15) is an adaptor protein important for endocytosis11. Hrs recognizes ubiquitinated receptors through the ubiquitin interacting motif (UIM), and together with signal-transduction adaptor-molecule (STAM) it acts as part of the sorting machinery for degradation via the ESCRT machinery13. Upon EGF-R activation, Hrs is tyrosine phosphorylated and monoubiquitinated15.Hrs is a 115-kDa multidomain coat protein that binds to the endosomal membrane either through the FYVE-  or the coiled-coiled domain20.In this study we have described a downstream effect of EGF-R ligand binding on the phosphorylation and membrane binding kinetics of Hrs and Eps15. To facilitate the analysis of the membrane binding kinetics on single endosomes we enlarged the endosomal size by transfecting the cells with the major histocompatibility complex class-II associated invariant chain (Ii) under the control of an inducible metallothionein promotor. Expression of Ii in model cell lines has been found to increase the early endosomal fusion rate and resulting in an enlargement of the endosomes22. We can additionally show that the binding of Eps15 is regulated by phosphorylation in a similar manner. Our work provides an important mechanistic link between the receptor-induced phosphorylation of Hrs and Eps15 and their redistribution to cytosol. We furthermore show that this specific change in the endosomal binding kinetics is of major importance for the RTK degradation.Eps15 and Hrs cycle between a membrane bound and a cytosolic state, and in this study we could measure that their binding properties change upon EGF and PDGF stimulation. We show that the membrane-to-cytosol cycling of both Eps15 and Hrs is dependent on their state of phosphorylation. For the first time we can document how RTK induced phosphorylation of Hrs and Eps15 regulates their endosomal binding kinetics. Receptor induced phosphorylation of Hrs and Eps15 stimulate a particular change in the equilibrium between the immobile and the mobile fractions. This redistribution changes the amounts of Hrs and Eps15 in the cytosol and can be accounted for by the release of Hrs or Eps15 from the immobile fraction. These results confirm and provide an explanation to previously published biochemical assays, which show a redistribution of the membrane bound fraction of Hrs to the cytosolic upon phosphorylation23 was subcloned into the pMep4 vector (Invitrogen). The pMep4 vector contains a metallothionein promoter and expression is induced by addition of 5 μM cadmium chloride (CdCl2) to the culture medium24. The fusion constructs; pEGFP-C2-Eps15, pEYFP-C1-Hrs and pEGFP-CtEEA1  has all been described earlier27.cDNA encoding Ii-wtGGATCCATGGCTAATCGAGGAGCAAC-3′ (the BamHI site is in bold) and 5′-AGAGACTCGAGTTAGTTACTACAACACTG-3′ (XhoI site is in bold); for mCherry, 5′-AGAGAGGTACC ATGGTGAGCAAGGGCGAGGAG-3′ (KpnI site) and 5′-AGAGAGGATCCCTTGTACAGCTCGTCCATGCC-3′ (BamHI site). mCherry was ligated to the N-terminus of Rab5 by the BamHI site and subsequently subcloned into the pcDNA3 vector  at the KpnI and XhoI sites. All the restriction enzymes used in this cloning experiment is from New England Biolabs .Rab5–mCherry was made by amplifying canine Rab5 and mCherry separately by PCR using the following primers: for Rab5, 5′AGAGATo mutate tyrosine 850 to phenylalanine in pEGFP-C2-Eps15 a QuickChange Site-Directed Mutagenesis Kit  was used according to the protocol. Microsynth  supplied the primers and XL10-Gold Ultracompetent cells  were used in the transformation of the plasmid. pEGFP-C2-Eps15 was sequenced at GATC  and the mutation was confirmed. The same protocol as described above was used to mutate tyrosine 329 and 332 to phenylalanine in pEYFP-C1-Hrs.2 incubator with 37 °C. Cells stable transfected with Ii-pMep4  were grown in medium containing 0.15 mg/mL Hygromycin B . M1 cells stably expressing Hrs-YFP/Eps15-GFP and Ii-pMep4 were grown in complete medium containing 0.2 mg/mL G418 bisulphate  and 0.15 mg/mL Hygromycin B . All the FRAP experiments were done on cells stably expressing Hrs-YFP or Eps15-GFP. Lipofectamine 2000 reagent  was used according to the supplied protocol in all transfections when preparing the stable cell lines.Human fibroblast (M1) cells were grown in complete medium: DMEM  supplemented with 10% FCS , 2mM L-glutamine, 25U/ml penicillin and 25 μg/mL streptomycin  in a 5% COIn the preparations of stabile cell lines, M1 cells were transfected with 1 μg DNA and 1.5 μl Lipofectamine 2000  ON, in 500 μl OptiMEM and 1.5 ml DMEM medium. Stable clones with expression of the respective constructs were tested to prove EGFR degradation. Clones with inhibited EGFR degradation were discarded.28). Anti-Eps15 and anti-GFP were obtained from Abcam . Anti-Phosphotyrosine clone 4G10 was from Millipore  and anti-EGFP was from Fitzgerald . All the secondary antibodies were purchased from GE Healthcare Life Science . Both human rec EGF and human rec PDGF BB were obtained from Bachem . The EGF-Alexa-647 used in this study was from Life Technologies .Anti-Hrs was a gift  or 60 ng/ml of human rec PDGF BB  for 4, 8, 20 or 40 minutes at 37 °C. Lysis buffer  was supplemented with phosphatase inhibitor cocktail II  and protease arrest . Immunoprecipitation (IP) was performed according to protocol with Dynabeads Protein G . All the biochemical blots presented in this paper were repeated at least three times.Cells were grown on chambered cover glass  and for all the live cell experiments DMEM medium without phenol red and sodium bicarbonate  were used. Prior to the FRAP experiments the cells were starved for four hours in serum-free medium. The cells were stimulated with 100 ng/ml human rec EGF  or 60 ng/ml human rec PDGF BB  to stimulate the specific RTKs.29. GFP and YFP were imaged with the multilane Argon laser  and mRFP/mCherry with the 559 nm laser. GFP and YFP were bleached with the 405 nm laser, whereas the 559 nm laser was used to bleach mCherry. The endosomes were bleached for 800 milliseconds with maximum laser power.Live imaging experiments were performed on an inverted Olympus iX81 FluoView 1000 confocal microscope , equipped with a PlanApo 60x/1.10 oil objective. This microscope has a 4 channel PMT detector unit and a dual SIM scanner specifically designed for high speed FRAP analysisAll photomontages presented were made in Adobe Photoshop CS4 and Adobe InDesign CS4.http://rsbweb.nih.gov/ij/). For the analysis of single images and the analysis of Eps15-GFP and Hrs-mRFP colocalization the JACoP plugin (http://rsbweb.nih.gov/ij/plugins/track/jacop.html) was used to calculate the “overlap coefficient”. Analysis of EGF-Alexa-647 colocalization with Eps15-GFP or Hrs-mRFP through time was an object based analysis and calculating the Pearsons´s coefficient was performed in GraphPad Prism 4 (http://www.graphpad.com/scientific-software/prism/).The colocalization analysis was performed with ImageJ  or 60 ng/ml human rec PDGF BB . The fluorescence endosomal intensity and the total cellular intensity were measured with the Series Analysis profile in the FV10-ASW4.2 software from Olympus . To correct for the general bleaching the total cellular fluorescence intensity was subtracted from the measured endosomal fluorescence intensity. The endosomal fluorescence intensity at time point zero was set to 1 and calculated for each time point for each individual endosome was normalized according to this value.30 and fitted by nonlinear regression to a function that assumes a single diffusion coefficient31;Obtained data from FRAP was normalized and corrected for bleachingF(0), F(∞) and T1/2 were calculated using GraphPad Prism 6 (http://www.graphpad.com/scientific-software/prism/) and the immobile fractions (IF) were calculated as described in Lippincott-Schwartz et al.32.The values for The fraction of molecules that is slowly exchanged between bleached and non-bleached regions is called the immobile fraction (IF). The mobile fraction, defines the molecules freely exchanged between the bleached and non-bleached region presented by: MF = (1-IF). If the two fractions at the fluorescent endosomal membrane proteins are altered after RTK activation we need independent measurements monitoring the total endosomal fluorescence intensity, tEFI. The tEFI for each endosome is normalized to 1 before stimulation. The corresponding total immobile fraction (tIF) and total mobile fractions (tMF) may then be calculated as followed:In the experiments in this study the tEFI values and the immobile fraction (IF) and mobile fraction (MF) represents the average of at least 10 endosomes for each time point.http://www.imsupport.com/).Cells stably expressing Hrs-YFP, HrsY329/334F-YFP, Eps15-GFP or Eps15Y850F-GFP were incubated with or without EGF (60 ng/ml)/PDGF (60 ng/ml) and cyclohexamid (10 ug/ml) for 0 h, 2 h or 4 h at 37 °C. The receptor degradation assays for each of these four cell lines was repeated 3 times and the quantification was performed with ImageQuant  signalling, and is phosphorylated in response to activation of the receptorF-R Fig. .26). After EGF stimulation the endosomal fluorescence intensity of these proteins remained constant . We could measure a significant decrease of the immobile fraction for Eps15-GFP and Hrs-YFP and also a reduction in the T1/2  experiments we calculated the endosomal T1/2 Fig. . We coulWe further tested whether PDGF-R activation could induce a similar change in the cytosol to endosome binding kinetics for Eps15-GFP and Hrs-YFP. Reminiscent of EGF-R activation, a transient change in the binding kinetics of Eps15-GFP and Hrs-YFP four and eight minutes after PDGF stimulation was measured Fig. .1/2 recovery remained constant after EGF stimulation  with measured immobile and mobile fractions (Fig. 22 and we could further show similar rearrangement for Eps15.We have above described an essential EGF induced change in the Tons Fig.  (see matons Fig. . Similarons Fig. . This chTogether these changes in endosomal fluorescence intensity and membrane to cytosol cycling indicate an altered mechanism in binding dynamics of Eps15-GFP and Hrs-YFP. This time dependent change in membrane fraction proved to be a redistribution of the immobile fraction.39. We wanted to further, biochemically analyse whether there could be an association between the change in binding kinetics and the phosphorylation of the Eps15-GFP and Hrs-YFP. When probing with an antibody against phosphorylated tyrosine residues, we could measure an increased level of phosphorylation for Eps15-GFP after four minutes and for Hrs-YFP after eight minutes, subsequently of EGF stimulation (Fig. It has previously been described that endogenous Eps15 and Hrs become phosphorylated upon EGF-R activationion Fig. . The incion Fig. . This su15.The preceding experiments indicated a phosphorylation–mediated mechanism regulating the binding kinetics of Eps15-GFP and Hrs-YFP. To test whether phosphorylation is a prerequisite for the change in binding kinetics we point-mutated the known phosphorylation site Y850 in Eps15-GFP and in Hrs-YFP, the two adjacent phosphorylation sites Y329 and Y334M1 cells stably transfected with Ii were co-transfected with either Eps15Y850F-GFP or HrsY329/334F-YFP. Ii was induced overnight, resulting in characteristic enlarged endosomes and fluorescently labelled EGF was added to the cells. Concomitant with cells expressing the wt fusion constructs Fig. , indicat1/2 recovery in EGF nor PDGF stimulated cells expressing the mutant fusion proteins (Fig. Similar measurements as we did for the wt Fig. , were doins Fig. .Figure 6Our previous analysis showed us that Eps15-GFP and Hrs-YFP became phosphorylated upon EGF-R activation Fig. . SimilarThis indicated that the EGF-R cannot regulate the endosomal binding dynamic of tyrosine mutated Eps15-GFP or Hrs-YFP and that phosphorylation of Eps15-GFP and Hrs-YFP is crucial for regulating their endosomal binding kinetics.Impaired endosomal progression will inhibit receptor degradation. When analysing the degradation of the EGF-R and PDGF-R, in our stably transfected cells, we observed a major degradation after two and four hours Fig. . However41. Protein modifications such as phosphorylation or ubiquitinylation have the potential to modify this complex process. By stimulating the RTKs, EGF-R and PDGF-R, we have been able to study their downstream effects on endosomal membrane binding kinetics of Eps15-GFP and Hrs-YFP.Specific regulation of the RTKs is mediated through signalling and endocytic trafficking42. This is a well-established method where Ii expression enlarges the size of the endosomes without interfering with the important Rab5 to Rab7 transition43. Importantly, previous studies could show that the Ii induced enlargement did not change the membrane binding dynamics of the endosomal tethering-molecule EEA137. We could furthermore confirm a similar EGF-R induced phosphorylation pattern for Eps15 and Hrs in M1 cells expressing Ii as in untransfected cells44.To better study single endosomal vesicles, we enlarged their size by expressing the Ii in the human cell line M133.In M1 cells transfected with Eps15-GFP and Hrs-mRFP we observed that the two proteins are present on the same Ii enlarged endosomes prior to ligand stimulation Fig. . When stEGF-Alexa-647 was transported from the PM to the endosomes positive for Eps15-GFP/Hrs-mRFP in just over two minutes Fig. . When EG1/2 for the recovery and the immobile fraction after ligand stimulation (Fig. The binding kinetics of Eps15-GFP and Hrs-YFP measured by FRAP show significant variations both in the Tion Fig. . Interes37. Given this, it is interesting to observe that for Eps15 and Hrs the intensity drop after EGF activation can be accounted for entirely by a reduced immobile fraction (Fig. From our experiments, we could measure a differential EGF-R regulation of the tMF and the tIF of both Eps15-GFP and Hrs-YFP on the endosomes. Activated EGF-R could free more of the immobile fraction of Eps15-GFP after 4 minutes compared to the immobile fraction of Hrs-YFP after 8 minutes. We have in an earlier study proposed that the immobile fraction of the tethering molecule EEA1 bound to the endosomes is the regulatory fraction of the binding kineticsion Fig. . Specifi15. When mutating the main phosphorylation sites in Eps15-GFP and Hrs-YFP, the EGF stimulation did not induce any change in the endosomal membrane to cytosol binding of the mutated proteins (Fig. The Eps15 and Hrs regulation is also time dependent as discussed above and occurred 4 and 8 minutes after addition of ligand to the cells similar to the earlier studies of sequential phosphorylationins Fig. .21.However, both mutants inhibited degradation of the EGF-R and PDGF-R Fig. . This is3. Our data support previous work showing that non-phosphorylated Hrs blocks the entry into the ESCRT mediated EGF-R degradation pathway21. Previous data published have shown that the majority of phosphorylated Hrs is present in the cytosol and that phosphorylation occurs on the endosome22. Our data shows that Eps15 and Hrs are present on the endosomal membranes and in cytosol before ligand activation. Receptor stimulation induces endosomal sequential phosphorylation of Eps15 and Hrs on the endosomes as an incentive for degradation. One may speculate if the sequential phosphorylation constitutes a regulatory factor for receptor degradation, however, this needs further analysis to confirm. Endosomal phosphorylation of Eps15 and Hrs induce a shift in the membrane bound fractions of the respective proteins. FRAP analysis shows us that there is a significant redistribution of the mobile fraction, presumably to the cytosol (Figs 21. Immobile fraction relocation of Eps15 and Hrs proved to be functionally significant to ensure receptor degradation.We have here shown that RTK induced phosphorylation of Eps15 and Hrs reduced endosomal binding of these molecules and the phoshorylation deficient Eps15 and Hrs inhibit EGF-R and PDGF-R degradation. The ESCRT complex mediates degradation by controlling the entry into the MVBssol Figs  and 4a,bWe have here shown that RTK activation induces a sequential phosphorylation of Eps15 and Hrs. This time dependent phosphorylation activates the translocation of Hrs and Eps15 from endosomes to cytosol. This particular translocation is dominant and specifically regulated by a redistribution of the immobile membrane bound fractions of Eps15 and Hrs. We have here identified an endosomal RTK ligand-induced mechanism that describes a new link to better understand the regulatory path of ligand activated receptors.Supplementary Movie 1Supplementary Movie legends and supplementary table 1 and 2


Phosphorylation of the regulatory light chain (RLC) of myosin modulates cellular functions such as muscle contraction, mitosis, and cytokinesis. Phosphorylation defects are implicated in a number of diseases. Here we focus on striated muscle where changes in RLC phosphorylation relate to diseases such as hypertrophic cardiomyopathy and muscular dystrophy, or age-related changes. RLC phosphorylation in smooth muscle and non-muscle cells are covered briefly where relevant. There is much scientific interest in controlling the phosphorylation levels of RLC in vivo and in vitro in order to understand its physiological function in striated muscles. A summary of available and emerging in vivo and in vitro methods is presented. The physiological role of RLC phosphorylation and novel pathways are discussed to highlight the differences between muscle types and to gain insights into disease processes. RLC phosphorylation acts as a modulator and not as a switch in striated muscle  is a member of the EF-hand superfamily . The N-terminal domain is similar in structure to calmodulin. RLC is non-covalently bound to the C-terminus of myosin S1 subfragment  and type II (NCBI Accession # P02608), and one slow isoform , where fast and slow describe the physiological behavior of different muscle groups. These three isoforms are encoded by different genes in the human chromosome 16, but with a high degree of sequence homology Szczesna .In human heart, there are two different RLC isoforms expressed from separate genes, namely the atrial isoform (MYL2a) (NCBI Accession # M94547) encoded from a gene in chromosome 7 and the ventricular isoform (MYL2v) (NCBI Accession # P10916) encoded from a gene in chromosome 12, with the ventricular ones expressed in both ventricular myocardium and slow twitch skeletal muscle (MYL2f)   residues, Ser14 and Ser15 Fig. , by a skIn striated cardiac muscle from mouse or rat, RLC exists in three states: unphosphorylated, singly phosphorylated at serine 14 or 15, and doubly phosphorylated at serine 14 and 15. These sites are mostly phosphorylated by a cardiac-specific MLCK (cMLCK), and partly by zipper-interacting kinases (ZIP Kinase). While mouse and rat possesses phosphorylatable sites at both Ser14 and Ser15, human cardiac RLC (cRLC) has only one single phosphorylatable site at Ser15 Fig.  Lyon et. Interes2+-phos-tag gel electrophoresis technology for detecting phosphorylated variants of proteins, Aguilar et al. showed three differentially charged species of RLC in cultured uterine smooth muscle myocyte: unphosphorylated, singly and doubly phosphorylated  phosphorylates Ser1, Ser2, and Thr9 of smRLC, which reverses the increase in ATPase activity associated with smMLCK phosphorylation at Ser19  in the presence of calcium and calmodulin  of muscle  mouse, the extensor digitorum longus (EDL) muscle exhibits no post-tetanic force potentiation after potentiating stimuli compared to that in wild-type control mice  mutations known in human cardiac RLC are found in the region of the Ser15 phosphorylation site, affect RLC phosphorylation and have a physiological effect  and troponin I (TnI), are also affected by in vivo mutations of cRLC phosphorylation sites  biopsy samples from patients either with LV hypertrophy and preserved systolic function or with LV dilation and reduced ejection fraction  by crossing cMLCK/neo+ to each other in order to assess myosin RLC phosphorylation and cardiac performance in vivo , smMLCK expression is tissue-specifically abolished  or glutamic acid (E)], which carries a negative charge that mimics the charged state of a phosphorylated residue. Based on the known cRLC crystal structure and on molecular dynamics (MD) simulations, the in vivo phosphorylatable site of Ser15 is proximal to the D166V  mutation site of cRLC known to cause familial hypertrophic cardiomyopathy (FHC). Muthu et al. created a S15D (phosphorylated Ser15) pseudophosphorylated mutation site in the cRLC protein carrying the D166V mutation  activity , which induces cardiac hypertrophy in wild-type mice and is associated with an increase in MYL2v phosphorylation in mice that lack cMLCK, by activating the ZIP Kinase  plays an important role in regulating vascular tone as well as in maintaining systemic blood pressure. Volatile anesthetics may inhibit AngII-induced vascular contraction via inhibiting RLC phosphorylation  and Michaelis constant (Km)  whereas smMLCK proceeds by an ordered sequential mechanism in which Mg-ATP binds first, followed by the RLC substrate  is expressed in most tissue including smooth muscle, skeletal muscle, and cardiac muscle. smMLCK catalyzes CaDrosophila  and incubated with 0.1–0.5 µM RLC for 0.5–2 h at 25–37 °C or at 4 °C overnight. This method yields almost 100 % cRLC phosphorylation, i.e., the cRLC species are phosphorylated at Ser15 position with almost 100 % efficiency  dissolved in buffer . ATPγS was used because thiophosphorylated proteins are relatively resistant to dephosphorylation by contaminating or endogenous protein phosphatases in the muscle tissue samples  are also used to reduce RLC phosphorylation in vitro  and protein kinase C (PKC). Rho-kinase is also involved in RLC phosphorylation in striated muscle. For example, adult rat ventricular myocytes were treated with the Rho-kinase inhibitor Y-27632 at a concentration of 10 µM so that it does not phosphorylate the myosin-binding phosphatase targeting subunit (MYPT), the catalytic subunit of protein phosphatase 1 (PP1)  and P34 kinase also affect RLC phosphorylation, but most reports refer to smooth muscle and non-muscle cells  have several disadvantages, especially when targeting a specific kinase to modulate RLC or other sarcomeric protein phosphorylation. Since the ATP-binding sites of kinases are highly conserved, it is difficult to target kinases specifically using pharmacological inhibitors. On the other hand, genetic approaches to deactivate kinases are very specific but take time to be effective and levels of inactivation are difficult to control. Small molecule pharmacological inhibitors inactivate kinases rapidly and in a dose-dependent manner. Moreover, interpretation of results using genetic or pharmacological techniques is complicated by functional redundancy of kinases and the overlapping substrates of multiple kinases. To overcome this, Kevan Shokat and his colleagues  as substrate. Using SDS-PAGE and autoradiography, RLC was found to be the direct substrate of ZIP kinase in situ. Thus, this approach manipulates RLC phosphorylation in different tissue systems by selectively inhibiting kinases.This chemical genetic approach has been exploited to identify RLC as a direct substrate for phosphorylation by ZIP kinase to regulate smooth muscle contraction  pathway  gel analysis is most common to detect phosphovariants of proteins according to their isoelectric point by isoelectric focusing and subsequently by molecular weight via SDS-PAGE (O’Farrell DE gel an®) selectively stain phosphoproteins in acrylamide gels, without the need for blotting or phosphoprotein-specific antibodies and Western-blot analysis  as it can be conjugated with protease digestion and tandem mass spectrometry   and TnI in cardiac muscle . There are several molecular players that control this dynamic process. Along with regulation via MLCK activity (reviewed above), MLCP also keeps the activity balance  was identified in smooth muscle  studies may be used to observe the dynamics of cross-bridge interactions in different phosphorylation states of RLC. These studies may focus on the structure of the cross-bridges, but also in the inter-filament spacing and myofibrillar organization. Pseudophosphorylation may provide stable systems to aid structural studies and molecular dynamic simulations. Results will assist the elaboration of models of contraction in which the contractile performance and efficiency of muscle is related to disease states encountered in hypertrophic or dilated cardiomyopathies.RLC phosphorylation regulates muscle contraction and other cellular functions, with important consequences for muscle behavior in health and disease. RLC phosphorylation plays a part in muscle disorders and may be a future therapeutic target. This review summarizes the structure and function of RLC and gives an overview of the methods available for manipulating and testing the effect of RLC phosphorylation in muscle tissue, focusing on striated muscle. We also compare the advantages and disadvantages of in vivo and in vitro methods and suggest their potential applications.


Simultaneous inference of both excitatory and inhibitory SCs sheds light on the neural circuits underlying the neural activity and advances our understanding of neural information processing. Conventional Bayesian methods can infer excitatory and inhibitory SCs based on a single trial of observed membrane potential. However, if multiple recorded trials are available, this typically leads to suboptimal estimation because they neglect common statistics (of synaptic inputs (SIs)) across trials. Here, we establish a new expectation maximization (EM) algorithm that improves such single-trial Bayesian methods by exploiting multiple recorded trials to extract common SI statistics across the trials. In this paper, the proposed EM algorithm is embedded in parallel Kalman filters or particle filters for multiple recorded trials to integrate their outputs to iteratively update the common SI statistics. These statistics are then used to infer the excitatory and inhibitory SCs of individual trials. We demonstrate the superior performance of multiple-trial Kalman filtering (MtKF) and particle filtering (MtPF) relative to that of the corresponding single-trial methods. While relative estimation error of excitatory and inhibitory SCs is known to depend on the level of current injection into a cell, our numerical simulations using MtKF show that both excitatory and inhibitory SCs are reliably inferred using an optimal level of current injection. Finally, we validate the robustness and applicability of our technique through simulation studies, and we apply MtKF to  Inferring the excitatory and inhibitory synaptic conductances (SCs) from single trials of membrane-potential recordings, and their underlying trial-to-trial variability, is crucial for understanding various functional aspects of neuronal sensory response, such as the neuron's receptive fields , also estimates excitatory and inhibitory SCs from single trials of the sub-threshold MP recorded in response to repeated sensory stimulation. However, unlike previous algorithms, our proposed recursive EM algorithm uses all repeatedly recorded trials of the MP to better estimate the common time-varying statistics of the excitatory and inhibitory SIs. It does this without requiring any parametric or non-parametric smoothing step that could blur the fine temporal features of the conductances. Our recursive algorithm consists of two steps. Firstly, the Kalman filtering (KF) algorithm estimates excitatory and inhibitory SCs from each membrane-potential trace, assuming certain statistics underlying the excitatory and inhibitory SCs. Secondly, these statistics are updated based on all estimated values of excitatory and inhibitory SCs from different trials.Our simulations demonstrate the accuracy and robustness of our proposed MtKF approach compared to the single-trial KF (StKF) . All details about the recordings, pre-processing of data, and animal preparations have been reported previously  is white Gaussian noise of variance NE(t), and NI(t) are the instantaneous excitatory and inhibitory SIs to the neuron at time step t, respectively, and dt is the time bin, which may differ from the voltage recording sampling. Equation (1) is the first-order (Euler method) approximation of the underlying continuous-time differential equation. Note that the time index t takes integer values between 0 and T, where T × dt is the entire physical recording time. We assume that these time steps are equidistant. Similar to Kobayashi et al. .Here, i et al. , Paninski et al. , and Lani et al. , the revgE and gI for each trial during sensory stimulation, based on all repeated trials. Here, we assume that non-negative SIs are generated by a truncated Gaussian distribution. The probability distribution functions of the excitatory and inhibitory SIs are given by:Our objective in this study is to develop a new algorithm, MtKF, to estimate the time course of excitatory and inhibitory SCs E (t) and μI (t) are the mean of the excitatory and inhibitory SIs, respectively, at time t, and ΓE (t) and ΓI (t) are the time-varying variances of these SIs at time t.where μNE and NI gives gE and gI directly according to Equation (1). It has been shown previously , μI(t), ΓE (t), and ΓI (t) in Equation (2)] from all recorded traces of the MP. Similar to the recursive framework in Lankarany et al.  = {xi(0), xi(1), …, xi(t)} to represent the time trace of variable x =  corresponding to the ith recorded trial from time 0 to t. Figure We present the MtKF algorithm for identifying the excitatory and inhibitory SCs of a single neuron expressed by Equation 1) from noisy MPs recorded during multiple stimulation trials. In the following sections, we use the notation  from noii is defined asThe dynamical system underlying the generation of the MP in trial xi(t) and yi(t) indicate the state vector (including the true MPs and SCs) and the observation (a noisy measurement of MP), respectively, of the ith trial at time t. Functions F and H are the transition and observation functions, respectively, and v(t) and εi (t) are the system noise (comprising the common inputs) and the observation noise, respectively. An explicit form of these equations is introduced in the next section. While we assume above that the system is observed at each time step, it is easy to consider a case, in which observations are less frequent. In Figure v and ε, e.g., the mean and variances. Our objective is to estimate the dynamics of each trial (xi(t) i = {1, …, L}, where L is the number of trials) as well as to estimate the statistical parameters of the common synaptic input v(t) and observation noise εi(t). The MtKF algorithm begins with an arbitrary initiation followed by forward and backward Kalman filtering. These filtering steps are necessary to identify the hidden dynamics xi(t) for each trial given the noisy observation. Once this step is accomplished and the statistics (such as mean and variance) of the dynamics are calculated, the parameters of the common (shared) stochastic sources are inferred by using an EM algorithm. Since these parameters determine the initial values of the next iteration, the algorithm can be stopped with an appropriate criterion, i.e., no significant change in likelihood function in two consecutive iterations.where x(t) = H denote the vector of neuron dynamics corresponding to ith recorded voltage at time t, where [·]H represents the matrix transpose operation. We can represent the neuronal model (Equation 1) in the dynamical-system form of Equation (3), where the observation function for each trial is given by H[xi(t)] = Cxi(t), with a vector C = , meaning that only the MP is observed. Similarly, the transition function F is given byWe derive the MtKF algorithm in this section. Let v (t) = H is given byThe distribution of the system noise  NE and NI describe excitatory and inhibitory SIs, respectively. Since we apply the EKF for each recorded MP |yi(0:t)) and p(xi(t)|yi(0:T)) in the forward and backward KF, respectively |yi(0:t)} and state correlation matrix E{xi(t)xi(t)H|yi(0:t)} in the forward filtering step and E{xi(t)|yi(0:T)} and E{xi(t)xi(t)H|yi(0:T)} in the backward filtering (smoothing) step using the KF approach for each observed recorded MP. Here, E{·} stands for the expected value and xi is the state vector of the ith trial  and NI(t) in this paper. We derive the EM algorithm to use all recorded traces of MP, and infer the statistics underlying common SIs. The EM algorithm infers the statistical parameters of Equations (3–5), i.e., the time-varying mean (μv (t)) and the variance of the states (v(t)), and the variance of the observation noise  as follows:The EM algorithm is a robust optimization technique for inferring the parameters of models involving unobserved data  as well as the variance of the observation noise. As a result, we can update the statistical parameters of the excitatory and inhibitory SIs as well as the variance of the observation noise in the M-step   and the variance (Γ) of the excitatory and inhibitory SIs corresponding to each single trial .Here, μi indicates the ith trial) and the corresponding SIs The proposed multiple–trial framework Figure  is extenNE(t) and μNI(t) are the trial means of the excitatory and inhibitory SIs, respectively. Similar to Equation (6), we wish to maximize the logarithm of the joint probability of the states and observation as follows:Here, μThen, given It is clear from Equation (11) that the means of the SIs have the same update rule (only for mean SIs) as those presented in Equations (7) and (8). These means are then used for the next iteration of MtPF. Comparing the EM derivation of MtPF with that in Paninski et al. , we see 4). As a result, these traces  exhibit adaptation . We clarify that inferences by MtKF and MtPF are, respectively, based on Equations (2) and (9) in which the distributions of SIs are truncated Gaussian and exponential. In our simulations, we have generated SIs from Poisson distributions in order to evaluate the performances of those algorithms in non-ideal scenarios.The time-varying mean of the Poisson distribution for excitatory and inhibitory inputs is either structured or non-structured. A non-structured mean is modeled as an OU process (the absolute white noise (non-negative) is filtered based on excitatory and inhibitory time constants). To build a structured mean that mimics the post-synaptic potential (PSP) evoked by whisker stimulation (in our simulations), we used excitatory and inhibitory SCs of real neurons. Simulation results (those belonging to structured inputs) are shown for neuron #3 calculated using the least squares (LS) method  and non-structured mean SI . Given the common mean, the SIs in each trial  are randomly generated by a Poisson distribution . Synaptic conductances and the resulting MP trace are then generated according to Equation (1) and are continued for 2 s for each trial. A white Gaussian observation noise of standard deviation (std) 1 mV is added to the MP at each time step. The StKF and MtKF algorithms are then applied to infer the excitatory and inhibitory SCs from these membrane-potential traces. In order to quantify the trial-to-trial performances of each algorithm, we calculate the root-mean-square error (RMSE) between the true and estimated SCs for each single trial. The error bars are plotted in Figure non-structured and structured SIs. Multiple-trial Kalman filtering outperforms StKF in estimating both excitatory and inhibitory SCs. The trial-to-trial variabilities of excitatory and inhibitory SCs are shown in Figure non-structured input) are plotted in Figure Iinj = 0; we suggest a better current level later).Examples are shown in Figure We note that StKF and MtKF are equivalent if only one trial is applied. The main advantage of our proposed MtKF algorithm is that it uses all recorded trials to estimate the statistics of the common SI and therefore is better at inferring the SCs in each single trial. In order to highlight this advantage of MtKF and to demonstrate that our algorithm is not just a simple averaging of StKF, the normalized error of the SCs is plotted against the number of trials (for zero injected current) in Figure E/I describes the estimated SC. This normalized error weights the error of excitatory and inhibitory SCs with the same contribution. As we can see from Figure where Consistent with Figure L = 10 trials) for three different injected currents, Iinj =  pA. Each current level is simulated by adding a constant current to the right-hand side of the MP dynamics of Equation (1). As can be observed from Figure inj = 50 pA .Inference of SC is less accurate if the MP is close to its reversal potential because the synaptic driving force is small there. For example, this typically happens for the inhibitory conductance when no current is injected to a recorded neuron but a Bayesian approach is applied nonetheless to simultaneously estimate excitatory and inhibitory SCs  and presented as an error box. Figure inj = 50 pA) for both SNRs. Therefore, we suggest applying the MtKF algorithm with this best level of the injected current as a new technique. This way, excitatory and inhibitory SCs can be accurately estimated simultaneously using a single level of current injection in the current-clamp recordings.Furthermore, we quantify the performance of the proposed MtKF algorithm at estimating excitatory and inhibitory SCs at the above holding currents. The RMSE between true and estimated conductances is calculated for each trial (n = 5) responding to whisker stimulation in the rat barrel cortex. As mentioned before, all details about the recording can be found in Heiss et al. . We note that although four different holding potentials were available in Heiss et al. . The estimation results of MtKF that used a single level of current injection matched well with those of the LS technique that required multiple levels of current injection.We also applied the MtKF algorithm to An alternative approach can be used to extract specific joint statistics between excitatory and inhibitory SCs. For example, Tan et al.  proposedIn this study, to improve the estimation performance of MtKF, we utilized multiple recording trials with the same stimulation protocol. However, application of the stimulus is not a strict requirement for MtKF to benefit from multiple recording trials. Improvement should be observed as long as excitatory or inhibitory SI is correlated across trials. In this sense, multiple recording trials without any stimulation should also benefit the estimation because some statistics about SI are expected to be common across trials. Obviously, additional information that can better align SIs across different trials would allow more efficient extraction of common underlying features about SIs. This could be, for example, motor actions, onsets of up/down state transitions , the method would not work so well. One approach to resolve this issue is to align trials precisely so as to minimize the variability of trials. As described above, if the distribution of SI is bimodal because of up and down states (Katz et al., in vivo data suggest that this framework is capable of extracting the intricate interplay between excitatory and inhibitory SCs underlying an animal's neuronal activity.In summary, the general applicability of the multiple-trial EM framework and the promising results of MtKF and MtPF obtained from both the synthetic and in-vivo data. ML, JH, IL, and TT wrote the paper.ML, TT developed algorithm. JH, IL recorded and provided The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.