The x-axis now reads “Principal Component 1”; y-axis, “Principal Component 2”; and panel label, “Principal component analysis.” This article has been corrected.1In the Original Investigation titled “Development and Validation of an Esophageal Squamous Cell Carcinoma Detection Model by Large-Scale MicroRNA Profiling,”


The KINARM robot produces a granular dataset of participant performance metrics associated with proprioceptive, motor, visuospatial, and executive function. This comprehensive battery includes several behavioral tasks that each generate 9 to 20 metrics of performance. Therefore, the entire battery of tasks generates well over 100 metrics per participant, which can make clinical interpretation challenging. Therefore, we sought to reduce these multivariate data by applying principal component analysis (PCA) to increase interpretability while minimizing information loss.Ns = 101–208) were assessed using 6 behavioral tasks and automated software generated 9 to 20 metrics related to the spatial and temporal aspects of subject performance. Data from these metrics were converted to Z-scores prior to PCA. The number of components was determined from scree plots and parallel analysis, with interpretability considered as a qualitative criterion. Rotation type  was decided on a per task basis.Healthy right-hand dominant participants were assessed using a bilateral KINARM end-point robot. Subjects , while still accounting for a large amount of variance (range 70–82%). The number of KINARM parameters reduced to 3 components for 5 out of 6 tasks and to 5 components for the sixth task. Many components were comprised of KINARM parameters with high loadings and only some cross loadings were observed, which demonstrates a strong separation of components.Complex participant data produced by the KINARM robot can be reduced into a small number of interpretable components by using PCA. Future applications of PCA may offer potential insight into specific patterns of sensorimotor impairment among patient populations.The online version of this article (10.1186/s12984-018-0416-5) contains supplementary material, which is available to authorized users. Robotic technology, such as KINARM , provides objective metrics using the participants’ upper limbs to assess proprioception and sensorimotor function, as well as executive function. In stroke survivors, this technology has identified subtle neurocognitive deficits not apparent on routine clinical testing , and varPrincipal component analysis (PCA) is a data reduction technique used to identify linear combinations of measured variables that account for the most overall variance in responses –8. The fThe primary objective of this study was to reduce the dimensionality of healthy participant data produced by the KINARM End-Point robot by using PCA. This analysis should increase overall interpretability by reducing redundant KINARM parameters into behaviorally meaningful components, which has the potential to demonstrate the clinical utility of PCA by capturing a minimal number of performance measures that could assist with the characterization of deficits among various patient populations.Healthy participants were community-based and were randomly recruited via advertisements on lab and departmental websites, as well as in local classifieds (online and print). Trained research staff screened each adult participant (> 17 years old) to ensure that task instructions could be easily understood, no prior neurological deficits were reported, and that subjects had no prior medical conditions that could affect upper limb mobility . These identified components were then analyzed for interpretability, which was defined as parameters having substantial loadings on each component and groupings of performance parameters having behaviorally meaningful conclusions . To further characterize participant performance, components and their respective parameters that loaded highly were used to broadly generate intuitive labels for each component per task rather than successively numbering components.PCA analysis was conducted in R version 3.3.2  using thn = 11) completed all 6 KINARM tasks. For more detailed demographics per task, see Table The number of participants across each task varied Ns = 101–208). Participant level of education consistently ranged from high school education to postdoctoral researcher. The percent of participants that were male ranged from 40 to 46%, and the mean age of subjects (range: 41–46 years) was fairly consistent across all tasks. Only a small subset of participants  by grouping related parameters into components. APM, BonB, VGR, RVGR, and OH reduced from 9 to 14 performance metrics to 3 components, which accounted for 70–79% of variance. OHA was reduced from 20 performance items to 5 components, while still accounting for 82% of the variance . See Table position accuracy , 2) movement variability , and 3) contraction/expansion .Scree plot and parallel analysis yielded three models  that warranted further examination. For the 2- and 3-component solutions, the use of an orthogonal rotation was justified, as the inter-component correlations ranged from |-0.18| to |0.16|. Both the 1- and 2-component solutions included parameters that did not load onto any of the components  or had sizeable cross loadings. Although several parameters had substantial loadings (≥|0.40|) on at least one component in the 3-component solution, the 3-component solution was the most interpretable , 2) bar angle  and 3) hand-speed maxima which is related to movement smoothness . The contribution of speed and success to task performance suggests that speed is highly influential for high success rates.Three models  were further examined based on the findings of the scree plot and parallel analysis. For the 2- and 3- component solutions, we used an oblique rotation because inter-component correlations ranged from |0.15| to |0.46|. Most parameters had substantial loadings (≥|0.40|) on at least one component in the 3-component solution. Bar length variability and hand path bias did not load substantially onto any of the 3 components. However, the 3-component solution was the most interpretable , 2) total movement , and 3) posture and reaction time .Scree plot and parallel analysis yielded three models  for examination. For the 2- and 3-component solutions, we used an orthogonal rotation (varimax) as inter-component correlations ranged from |0.06| to |0.16|. Both 1- and 2-component solutions included a number of parameters that did not load onto any of the components (loadings <|0.40|) or had sizeable cross loadings, but all parameters had substantial loadings (≥|0.40|) on at least 1 component in the 3-component solution. The 3-component solution also was the most interpretable , 2) total movement , and 3) reaction time . Initial movement and smoothness accounted for more variance in this task than it did in VGR. Therefore, initial movement is imperative for success in RVGR.Scree plot and parallel analysis yielded three models  for further examination. For the 2- and 3-component solutions, we used an orthogonal rotation, as the inter-component correlations ranged from |-0.09| to |0.13|. Both the 1- and 2-component solutions included items that did not load onto any of the components , but all items had substantial loadings (≥ |0.40|) on at least one component in the 3-component solution. The 3-component solution also was the most interpretable , 2) speed and area , and 3) accuracy . Hits with left and right hands cross loaded onto both accuracy and hand bias.According to the scree plot and parallel analysis, three models  warranted further examination. For the 2- and 3-component solutions, we used an orthogonal rotation, as the inter-component correlations ranged from |0.03| to |0.24|. Both the 1- and 2-component solutions included items that did not load onto any of the components (loadings <|0.40|) or had sizeable cross loadings, but all items had substantial loadings (≥|0.40|) on at least one component in the 3-component solution. The 3-component solution also was the most interpretable , 2) accuracy , 3) hand bias , 4) speed and area , and 5) miss bias . Three of these components  are shared with OH, whereas miss bias and distractor avoidance are components unique to OHA.According to the scree plot and parallel analysis, five models  warranted further examination. For the 2-, 3-, 4-, and 5-component solutions, we kept the oblique rotation because the inter-component correlations ranged from |-0.12| to |0.30|. The 1-, 2-, 3-, and 4-component solutions included items that did not load onto any of the components  or had substantial cross loadings, but all items had substantial loadings (> |0.40|) on at least one component in the 5-component solution, which was also the most interpretable . Furthermore, KINARM parameters were separated reproducibly across multiple tasks. For example, OHA was divided into 5 components: impulsivity, accuracy, hand bias, speed and area, and miss bias. Three of these components  are comparable to the PCA results observed from the OH task, thereby providing evidence that these tasks assess similar underlying behaviors. However, despite assessing similar underlying behaviors, the variance explained by each component differs. For example, speed and area accounts for more variability than accuracy, whereas in OHA accuracy accounts for more variability than speed and area. The addition of two unique components  during the OHA may quantitatively represent the increase in cognitive load during this task. These two components that are absent from OH, and which account for 28% of overall variability, likely reflect the different impacts of impulsive movements in OH and OHA. In OH, quick and effectively impulsive movements are rewarded; quick movements with little inhibition will result in many targets being hit. In contrast, in OHA there is a penalty for impulsive movements that are not processed thoroughly prior to execution . Furthermore, behaviorally during the OH task, the subject can hit a total of 300 targets during the trial. In contrast, there are only 200 targets during OHA, plus 100 distractors. Therefore, hand bias and speed and area account for less variability in OHA, as now the subjects must inhibit their automatic responses , which also results in an increased need for improved accuracy. This contrast in cognitive load between tasks may anecdotally account for the difference in variance explained by the components mentioned above across OH and OHA. This result of similar inter-task grouping of parameters across related KINARM tasks was also observed during VGR and RVGR. Taken together, our results demonstrate the potential utility of PCA when applied to granular datasets, as well as the increased interpretability of KINARM parameters when categorized into behaviorally meaningful components that characterize performance.We initially used PCA to reduce our multivariate dataset, and we then investigated if this statistical technique identified interpretable and classifiable components of participant performance. Within a task, components explained intuitive performance metrics. For example, in APM (a task that assesses limb proprioception), the 3 components identified were distinct measures of function , which demonstrates that these tasks assess similar underlying behaviours and further supports the validity of using this statistical technique on KINARM performance metrics. Our current analysis has substantially reduced these granular datasets into biologically plausible, interpretable, and behaviorally meaningful components.Furthermore, parameter associations, positive or negative, strong or weak, within their respective components were logically related to participant performance. For example, the PCA of the BonB task identified the component position accuracy, movement variability, and contraction/expansion), which is fairly consistent with previous research that described these 3 variables as observed patterns of impairment after stroke [position accuracy and movement variability components, with an inverse sign from negative to positive respectively. Shift indicates a systematic bias to move either left/right or front/back in the workspace. Therefore, in the position accuracy component, shift X (left/right) indicates, with a negative association, that shift X biases to the left as absolute error in the X and XY plane increases. In contrast, component 2, movement variability, indicates that as X shifts in the positive direction , overall variability increases.PCA indicated that some of the KINARM parameters cross-loaded with multiple components. For example, the APM task identified 3 components . Unexpectedly, the parameter total hits, which should be intuitively related to hits with left and right, did not cross-load onto the accuracy and hand bias components. However, this result demonstrates that total hits is highly related to accuracy, which is separable from hand bias. These components are not highly interrelated, as indicated by the use of orthogonal rotation, and measure separable aspects of participant performance. Therefore, hand bias measures the bias towards the participants limb preference, and use in space, which is separable from overall accuracy and the total targets being hit .Furthermore, certain parameters did not intuitively cross-load onto multiple components. For example, the OH task identified 3 components , and thus these parameters were excluded from this analysis. We conducted PCA only on the End-Point robot data, and therefore, we will need to conduct PCA on healthy participant data generated using the KINARM Exoskeleton. It is unclear how antigravity support provided by the Exoskeleton will affect participant performance and the generated components. Therefore, PCA of Exoskeleton data will be imperative to future investigations to implement PCA across KINARM platforms. Also, the current analysis was conducted in healthy participants, and future PCA will need to examine participant performance using a clinical sample, such as stroke, to validate these components and characterize performance in clinical populations to further demonstrate the utility of our current findings. Furthermore, the current analysis does not address individual or group differences in our healthy participant sample, which may complicate future comparisons between healthy subjects and clinical populations. However, using PCA across 6 KINARM tasks, which assesses a broad range of upper limb sensorimotor function, has led to a substantial reduction in the dimensionality of our data, and produced interpretable components of performance.Therefore, PCA of KINARM data has the potential to become a valuable clinical tool. Applying PCA and identifying main sources of variability in a clinical examination can help make data from research tools, such as the KINARM, more concise and easily interpretable. Furthermore, identifying sources of strong variability may improve the detection of fluctuations in performance, which could increase the clinical relevance of robotic assessment as an evaluation tool. Therefore, analyzing data produced by a clinical population offers the potential to increase the clinical utility of the KINARM robot by maximizing interpretability of participant performance, while also minimizing information loss, to increase the characterization of performance among various populations. It is not clear if patient performance will result in similar components of performance patterns as healthy participants. Therefore, future applications of this analysis may offer potential insight into specific patterns of sensorimotor impairment among patient populations.Using PCA, granular KINARM performance data can be substantially reduced to a small number of interpretable components with minimal information loss and still accounting for a large amount of variance. To validate our current findings and to further characterize sensorimotor function and impairment, the components derived from our sample of healthy participants will serve as normative data for future comparisons to patient populations.Additional file 1:Volunteer screening checklist. This form was administered to participants by trained researchers to screen for previous neurological and musculoskeletal complications. (PDF 53 kb)Additional file 2:KINARM Standard Tests Parameter Tables. These tables provide detailed descriptions of each parameter per KINARM task. These tables were extracted from Dexterit-E 3.6 User Guide with permission from BKIN Technologies Ltd. (PDF 145 kb)Additional file 3:Correlation matrices of KINARM parameters across all tasks. (DOCX 30 kb)


The TRPC1, TRPC4, and TRPC5 proteins form homotetrameric or heterotetrameric, calcium‐permeable cation channels that are involved in various disease states. Recent research has yielded specific and potent xanthine‐based TRPC1/4/5 inhibitors. Here, we investigated the possibility of xanthine‐based activators of these channels.2+ ([Ca2+]i) and by patch‐clamp electrophysiology. Selectivity of AM237 was tested against TRPC3, TRPC6, TRPV4, or TRPM2 channels.An analogue of the TRPC1/4/5 inhibitor Pico145, AM237, was synthesized and its activity was investigated using HEK cells overexpressing TRPC4, TRPC5, TRPC4–C1, TRPC5–C1, TRPC1:C4 or TRPC1:C5 channels, and in A498 cells expressing native TRPC1:C4 channels. TRPC1/4/5 channel activities were assayed by measuring intracellular concentration of Ca50 15–20 nM in [Ca2+]i assay) and potentiated their activation by sphingosine‐1‐phosphate but suppressed activation evoked by (−)‐englerin A (EA). In patch‐clamp studies, AM237 activated TRPC5:C5 channels, with greater effect at positive voltages, but with lower efficacy than EA. Pico145 competitively inhibited AM237‐induced TRPC5:C5 activation. AM237 did not activate TRPC4:C4, TRPC4–C1, TRPC5–C1, TRPC1:C5, and TRPC1:C4 channels, or native TRPC1:C4 channels in A498 cells, but potently inhibited EA‐dependent activation of these channels with IC50 values ranging from 0.9 to 7 nM. AM237 (300 nM) did not activate or inhibit TRPC3, TRPC6, TRPV4, or TRPM2 channels.AM237 potently activated TRPC5:C5 channels (ECThis study suggests the possibility for selective activation of TRPC5 channels by xanthine derivatives and supports the general principle that xanthine‐based compounds can activate, potentiate, or inhibit these channels depending on subunit composition. Compounds were added from a compound plate after recording for 60 s.Intracellular concentration of Ca2.4d‐lysine‐coated 96‐well plates, at 25,000 cells per well, and TRPC5 protein expression was induced with 1 μg·ml−1 tetracycline. Cells were cultured for 24 hr in DMEM‐F12 GlutaMAX  containing 10% FBS, with the addition of blasticidin (10 μg·ml−1) and zeocin (400 μg·ml−1). For wells containing methanol‐treated positive control cells, medium was removed and 70% methanol/distilled water (100 μl) was added and cells returned to the incubator for 30 min. After incubation, the solution in each well was replaced with recording buffer . The LIVE/DEAD®  cell viability kit was used to stain the cells. Calcein AM (5 μl) and ethidium homodimer‐1 (20 μl) were added to SBS with 0.01% SBS (10 ml). Compound and vehicle dilutions were carried out directly in LIVE/DEAD® solution and 100 μl of each was added to the recording buffer in each corresponding well. Cells were incubated for 30 min and immediately imaged. Imaging and analysis was carried out on the IncuCyte® ZOOM .Cell viability was assayed using the LIVE/DEAD® cell viability kit . HEK T‐REx expressing tetracycline‐inducible hTRPC5 were plated onto black, clear bottom poly‐2.5−1 tetracycline 24 hr before experimentation. Experiments were carried out at room temperature. Extracellular solution consisted of SBS. Patch‐clamp recordings were performed in whole‐cell mode , 5‐mM ATP , and 0.1‐mM NaGTP, titrated to pH 7.2 with CsOH. All solutions were filtered using a 0.2 μm filter . Comparisons of peak current sizes mediated by EA‐activated TRPC1/4/5 channels in whole cells and excised outside‐out patches are provided in For electrophysiology experiments, cells were plated at a low density of 20–30% onto round or square glass coverslips (13 mm diameter or 5 × 5 mm), and TRPC expression in HEK T‐REx was induced with 1 μg·mlell mode  and in e2.6British Journal of Pharmacology on experimental design and analysis in pharmacology  for multiple groups (post hoc test was only applied when ANOVA gave P < .05). Data were analysed using GraphPad Prism. Representative data are presented as raw data. Where required for the construction of concentration–response curves and statistical analysis, data were normalized to the appropriate control responses as detailed in the figures. Unless indicated otherwise, data were obtained from 5 independent experiments. Each independent experiment consisted of six technical replicates , unless stated otherwise. No blinding or randomization was undertaken. Compound additions in calcium recording experiments were performed using automated liquid handling by the FlexStation according to predefined plate layouts and appropriate controls were included on every plate. Concentration–response curves were fitted in GraphPad Prism as using a four parameter curve fit, using raw data for EC50 calculations and normalized responses for IC50 calculations. The amplitudes of Ca2+ responses for different channels were measured at time points indicated in the corresponding figure legends.The data and statistical analysis comply with the recommendations of the 2.71H NMR, 13C NMR, 19F NMR, and HPLC analysis  were made up in DMSO and stored at −80°C. Sphingosine‐1‐phosphate  was dissolved in MeOH and stored at −80°C. http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=4357 was obtained from Sigma‐Aldrich .AM237 was synthesized by modification of published procedures  recordings. AM237 activated TRPC5 currents in excised outside‐out membrane patches of HEK T‐REx cells expressing TRPC5  significantly increased the activation of TRPC5:C5 channels by S1P , no activation or inhibition was seen in cells expressing TRPC3, TRPC6, TRPV4, or TRPM2 channels  or Pico145 (0.03–30 nM) for 30 min before addition of 100‐nM AM237 and i vs. rapid current recordings).The finding that Pico145 is a competitive antagonist of AM237 at TRPC5:C5 channels , TRPC1:C5, and TRPC4:C5 channels, but not TRPC4:C4 or TRPC1:C4 channels  [Ca2+]i measurements from a single 96‐well plate (N = 6) showing that 0.3‐300 nM AM237 has no effect on [Ca2+]i in TRPC5‐SYFP2 expressing (Tet+) HEK T‐REx cells in the absence of extracellular calcium. Both recording buffer and compounds were made up in Ca2+‐free SBS, where CaCl2 was replaced by 0.4 mM EGTA.Figure S6. AM237‐mediated increase of [Ca2+]i is dependent on the expression of TRPC5. A) [Ca2+]i measurements from a single 96‐well plate (N = 6) showing that 0.3–300 nM AM237 has no effect on [Ca2+]i in wild‐type HEK 293 cells. B) Example whole‐cell patch clamp data from a WT HEK 293 cell showing current sampled at ‐100 mV and +100 mV during ramp changes in voltage. AM237 and EA had no significant effect on currents recorded. C) Representative currentvoltage relationship (I‐Vs) from experiments of the type illustrated in (B). D) Mean response ± SEM (n = 5/6 independent recordings) as illustrated in (B) for +100 mV and ‐100 mV.Figure S7. AM237‐mediated increase of i measurements from a single 96‐well plate (N = 6) showing an increase in [Ca2+]i in response to 0.3‐300 nM AM237 in hTRPC5‐SYFP2 expressing (Tet+) HEK T‐REx cells. B) Representative [Ca2+]i measurements from a single 96‐well plate (N = 6) showing an increase in [Ca2+]i upon co‐application of 10 μM S1P and 0.3‐300 nM AM237 in hTRPC5‐SYFP2 expressing (Tet+) HEK T‐REx cells. C) Concentration‐response data for experiments in (F) and (G), showing mean responses ± SEM (n/N = 5/30). Responses were calculated at 250‐295 s compared to [Ca2+]i at baseline (0‐55 s).Figure S9. AM237 potentiates the S1P response of TRPC5:C5 channels. A) Representative [Ca2+]i measurements from single 96‐well plates (N = 6) showing an increase in [Ca2+]i in response to 100 nM EA, and lack of activation with 3‐300 nM AM237 in (Tet+) HEK T‐REx cells expressing hTRPC5‐C1 (A), hTRPC4‐SYFP2 (C) and hTRPC4‐C1 (E). B, D, F) Mean ± SEM for experiments in (A), (C), and (E) respectively. Responses were calculated at 250‐295 s compared to basal [Ca2+]i 0‐55 s (n/N = 3/18) G) Representative [Ca2+]i measurements from a single 96‐well plate (N = 6) showing an increase in [Ca2+]i in response to 100 nM EA, and lack of activation with 3‐300 nM AM237 in A498 cells. H) Mean ± SEM for experiments in (G). Responses were calculated at 250‐295 s compared to basal [Ca2+]i 0‐55 s (n/N = 3/18)Figure S10. AM237 does not activate TRPC5:C1, TRPC4 and TRPC4:C1 channels A, C, E). Representative [Ca2+]i measurements from single 96‐well plates (N = 6) showing no effect of AM237 on activation of hTRPC3 (A) or hTRPC6 (C) by 100 μM OAG in WT HEK 293 cells transiently transfected with hTRPC3 (A) or hTRPC6 (C). The baselines before OAG application indicate that AM237 does not activate TRPC3 (A) or TRPC6 (C). Cells not expressing TRPC3 (A) or TRPC6 (C) do not respond to 100 μM OAG. B,D) Mean responses ± SEM (n/N = 3/18) for experiments shown in , for hTRPC3 (B) and hTRPC6 (D). Responses were calculated at 75‐ 95 s (TRPC3) and 80‐100 s (TRPC6) compared to basal [Ca2+]i (0‐55 s). E) Representative [Ca2+]i measurements from a single 96‐well plate (N = 6) showing no effect of AM237 on activation of TRPV4 by 5 μM 4α‐PDD in CHO cells stably expressing hTRPV4. The baselines before 4α‐PDD application indicate that AM237 does not activate TRPV4. F) Mean responses ± SEM (n/N = 3/18) for experiments shown in (E). Responses were calculated at 250‐295 s compared to basal [Ca2+]i (0‐55 s). G) Representative [Ca2+]i measurements from a single 96‐well plate (N = 6) showing no effect of AM237 on activation of TRPM2 by 1 mM H2O2 in (Tet+) HEK T‐REx cells expressing TRPM2. The baselines before H2O2 application indicate that AM237 does not activate TRPM2. Cells not expressing TRPM2 do not respond to H2O2. H) Mean responses ± SEM (n/N = 3/18) for experiments shown in (F). Responses were calculated at 550‐595 s compared to basal [Ca2+]i (0‐55 s).Figure S11. AM237 has no effect on TRPC3, TRPC6, TRPV4 or TRPM2 channels. A, C) Representative [CaFigure S12. Differences in current amplitudes of TRPC5:C5, TRPC5:C1, TRPC4:C4 and TRPC4:C1 channels recorded by whole‐cell patch and outside‐out patch clamp electrophysiology. A) Mean ± SEM peak currents evoked by 100 nM EA in HEK 293 cells expressing either TRPC5, TPRC1 and TRPC5, TRPC4, or TRPC1 and TRPC4, measured by whole‐cell patch clamp electrophysiology, with current sampled at ‐80 mV (white bars) and +80 mV (black bars) (n = 4‐12). B) Mean ± SEM peak currents evoked by 100 nM EA in HEK 293 cells expressing either TRPC5, TPRC1 and TRPC5, TRPC4, or TRPC1 and TRPC4, measured in excised outside‐out membrane patches, with current sampled at ‐80 mV (white bars) and +80 mV (black bars) (n = 4‐8). Note the difference in scale of the y‐axes between (A) and (B).Click here for additional data file.


Mitochondria are the major sub-cellular organelle of ATP production. With a negative inner membrane potential, mitochondria take up Ca2+ from their surroundings, a process called mitochondrial Ca2+ uptake. Under physiological conditions, Ca2+ uptake into mitochondria promotes ATP production. Excessive uptake causes mitochondrial Ca2+ overload, which activates downstream adverse responses leading to cell dysfunction. Moreover, mitochondrial Ca2+ uptake could shape spatio-temporal patterns of intracellular Ca2+ signaling. Malfunction of mitochondrial Ca2+ uptake is implicated in muscle degeneration. Unlike non-excitable cells, mitochondria in muscle cells experience dramatic changes of intracellular Ca2+ levels. Besides the sudden elevation of Ca2+ level induced by action potentials, Ca2+ transients in muscle cells can be as short as a few milliseconds during a single twitch or as long as minutes during tetanic contraction, which raises the question whether mitochondrial Ca2+ uptake is fast and big enough to shape intracellular Ca2+ signaling during excitation-contraction coupling and creates technical challenges for quantification of the dynamic changes of Ca2+ inside mitochondria. This review focuses on characterization of mitochondrial Ca2+ uptake in skeletal muscle and its role in muscle physiology and diseases.Muscle uses Ca While ifunction , recipro2+ and ATP. Thus, muscle physiology largely depends on two intracellular organelles: the sarcoplasmic reticulum (SR) for Ca2+ storage and release  of mitochondria in cardiac cells  ester of rhod-2 (Rhod-2-AM) preferentially targets mitochondria . Rhod-2mulation . The shomulation . The unentensity . Rhod-2 mulation . Howevere fibers . In this2+ uptake in skeletal muscle remains to be challenging. GFP and other functionally similar fluorescent proteins have modernized the research in cell biology , Rudolf et al. demonstrated that a single twitch could cause measurable dynamic changes in mitochondrial Ca2+ levels in live skeletal muscle fibers. However, they also noted some limitations of 2mtYC2 for mitochondrial Ca2+ measurement in muscle cells, for instance, YC2 had a small dynamic range with an increase of the emission ratio <26% in the cytosol and <14% in mitochondria during muscle contraction  cause malignant hyperthermia (MH) and central core disease (CCD). The MH and CCD mutations lead to altered Ca2+ release from the SR. By overexpressing the MH and CCD RyR1 mutant proteins in HEK-293 cells, Brini et al. reported a correlation between the level of cytosolic Ca2+ transient and the amount of mitochondrial Ca2+ uptake, demonstrating that the MH mutation with enhanced cytosolic Ca2+ transients simultaneously leads to enhanced mitochondrial Ca2+ uptake  mouse model (G93A) with transgenic overexpression of the human ALS-associated SOD1G93A mutant  expressed in ALS muscle fibers, these authors recorded the dynamic change of mitochondrial free Ca2+ levels during voltage-induced SR Ca2+ release and detected a reduced Ca2+ uptake by mitochondria in the fiber segment with depolarized mitochondria, which mirrored the elevated Ca2+ transients in the cytosol in the same region , the molecular identity of the putative MCU had remained mysterious for decades . The MCU−/− mice survived well with a smaller body size, but showed impaired skeletal muscle performance along with absence of mitochondrial Ca2+ uptake in isolated skeletal muscle mitochondria, indicating that mitochondrial Ca2+ uptake plays an important role in skeletal muscle development and performance (2+ uptake in protecting denervation-induced skeletal muscle atrophy was provided by Mammucari et al. and Chemello et al., in which, the authors have shown that virus-mediated overexpression or silencing of MCU had significant impact on skeletal muscle atrophy through regulation expression of genes involved in hypertrophic pathways in skeletal muscle (2+ channel, other auxiliary subunits participate forming the mitochondrial Ca2+ uniportor complex (2+ uniporter complex in skeletal muscle still has more to be determined.Although, it was well known that mitochondria from all cell types were able to take up Ca decades . It was  decades . This neformance . Recentll muscle . Althoug complex . The ide complex  in patie complex  indicate2+ uptake is a double-edged sword for muscle function. While the Ca2+ influx into mitochondria is required for promoting ATP synthesis, excessive Ca2+ accumulation in mitochondria initiates a series of molecular malfunctions leading to mitochondrial damage and cell death. Under diseased conditions, such as muscular dystrophy, gene-mutation related myopathies and aging, enhanced SR Ca2+ release activity overloads mitochondria with Ca2+, leading to mitochondrial dysfunction and muscle cell degeneration. In those cases, mitochondrial damage seems to be a consequence of extensive elevation of cytosolic Ca2+ levels. In ALS G93A skeletal muscle, the mitochondrial membrane potential is depolarized, which leads to a reduced Ca2+ buffering capacity of mitochondria. This reduced mitochondrial Ca2+ uptake further overloads those polarized mitochondria with Ca2+ and causes further mitochondrial damage in the same cell. In this case, the compromised mitochondrial Ca2+ uptake is a leading cause of the disrupted intracellular Ca2+ signaling that initiates muscle cell degeneration. In summary, any dysregulation in the amount and kinetics of mitochondrial Ca2+ uptake will cause mitochondrial dysfunction and abnormal intracellular Ca2+ signaling that leads to muscle cell degeneration. It is predicted that identification of molecular basis associated with mitochondrial Ca2+ uptake will further advance the understanding of the role of mitochondrial Ca2+ uptake in skeletal muscle health and diseases.Mitochondrial Ca


Our objective was to quantify the metabolic energy associated with the generation of Ca2+ APs in the dendrites. We used morphology-based computational models to simulate the dendritic Ca2+ spikes in layer 5 pyramidal neurons. We calculated the energy cost by converting Ca2+ influx into the number of ATP required to restore and maintain the homeostasis of intracellular Ca2+ concentrations. We quantified the effects of synaptic inputs, dendritic voltage, back-propagating Na+ spikes, and Ca2+ inactivation on Ca2+ spike cost. We showed that much more ATP molecules were required for reversing Ca2+ influx in the dendrites than for Na+ ion pumping in the soma during a Ca2+ AP. Increasing synaptic input increased the rate of dendritic depolarization and underlying Ca2+ influx, resulting in higher ATP consumption. Depolarizing dendritic voltage resulted in the inactivation of Ca2+ channels and reduced the ATP cost, while dendritic hyperpolarization increased the spike cost by de-inactivating Ca2+ channels. A back-propagating Na+ AP initiated in the soma increased Ca2+ spike cost in the apical dendrite when it coincided with a synaptic input within a time window of several milliseconds. Increasing Ca2+ inactivation rate reduced Ca2+ spike cost, while slowing Ca2+ inactivation increased the spike cost. The results revealed that the energy demand of a Ca2+ AP was dynamically dependent on the state of dendritic activity. These findings were important for predicting the energy budget for signaling in pyramidal cells, interpreting functional imaging data, and designing energy-efficient neuromorphic devices.Pyramidal neurons consume most signaling-related energy to generate action potentials (APs) and perform synaptic integration. Dendritic Ca The brain has powerful capacity of information processing, which makes a substantial contribution to the body’s energy consumption. The limited availability of energetic resources constrains the processing power, size, and architecture of the brain , which d2+ channels, a Ca2+ AP is generated in the apical dendrites (2+ AP produces long (up to 50 ms) plateau-type depolarization in the distal dendrites, which spreads to the soma/axon to activate voltage-dependent Na+ channels, resulting in high-frequency bursting of Na+ APs  firing  firing . As an aocessing .+ influx into ATP values, the AP energy cost in the soma/axon was determined in different cell types. It is shown that reversing the Na+ influx producing APs makes a significant contribution to the overall usage of signaling-related energy in the brain  pyramidal cells.The electrical signaling within neurons is sustained and propagated via ionic currents through their membranes. The different ion concentrations on the inside and the outside of a cell create electrochemical gradients. They are the major driving forces of neural activity, which store the potential energy and create a cellular battery for each ion . The genhe brain . The AP he brain , Na+ chativation , membrantivation , dendrittivation , body tetivation , and stitivation  all cont2+ APs. We used computational models of L5 pyramidal cells to simulate a Ca2+ spike initiated in the apical dendrites. Our results revealed that the ATP cost of a Ca2+ AP was dependent on the state of dendritic activity, which was determined by the synaptic inputs, membrane voltage, bAP, and Ca2+ inactivation. These results suggested that the pyramidal neurons dynamically adjust their energy demand according to dendritic responses.The objective of this study was to estimate the energy consumption associated with the dendritic Cahttps://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=83344. As shown in 2+ APs in the apical dendrites of neocortical pyramidal neurons. We implemented the computational model in NEURON simulation environment , high-voltage activated Ca2+ (ICaH), low-threshold T-type Ca2+ (ICaT), fast K+ (IKV), A-type K+ (IKA), slow non-inactivating K+ (IKm), Ca2+-dependent K+ (IKCa), and passive leakage (IL) currents. The channel densities in the active dendrite were as follows (in pS/cm2): 2+ channel densities were different, which were INa, ICaH, IKV, IKA, IKm, IKCa, and IL  = et/τ1−(1−et/τ2−), which was a validated waveform commonly applied to activate Ca2+ APs in experiments (1 = 4 ms and τ2 = 0.8 ms (f(t) was adjusted so that the peak of Isyn corresponded to the value referred to as stimulus amplitude.All the synaptic inputs were injected in the dendrites after an initial period of 1000 ms. Unless otherwise stated, the site of distal recording/injection was at the main bifurcation of the apical dendrites  was computed by:Here x was the forward rate and βx was the backward rate. The steady-state value was x∞ = αx/(αx + βx), and the time constant was τx = 1/(αx + βx). For current ICaT, they were:where αICaH, the rate functions were given by:For current 2+ currents was not uniform within a dendrite during Ca2+ spikes. The NEURON divided each dendrite into several discrete segments with different membrane areas, and the membrane currents and potentials were distributed uniformly within each segment , and their rate functions were:Here q was replaced by:Note that the steady-state value for inactivation gating variable INa at the center of each somatic segment, and calculated the Na+ influx per membrane area by integrating the Na+ current curve during 100 ms after the onset of Isyn. This integral interval was identical to the calculation of Ca2+ influx. Since the Na+/K+-ATPase hydrolyzed one ATP for every three Na+ ions exported , which was Ith = 1.47 nA. We increased Isyn from a subthreshold value (< Ith) to suprathreshold (> Ith), and recorded the evoked transmembrane voltage and transmembrane currents in the dendrites. We computed the ATP cost on Ca2+ ion pumping in the active dendrite at each Isyn, and the surface area of this single dendrite was 635.9640 μm2.We first quantified the ATP cost of a synaptically evoked Ca2+ ions consumed much more ATP during a Ca2+ spike than that in the absence of Ca2+ spike  was about fourfold larger than the active dendrite, but more ATP molecules were required for Ca2+ extrusion in this dendrite than for Na+ ion pumping in the soma  resulted in the inactivation of Ca2+ channels and reduced channel availability. Then, the ICa underlying each dendritic spike was reduced as Vinit increased  de-inactivated Ca2+ channels and increased channel availability. This effectively increased the Ca2+ current underlying a Ca2+ spike and extended the interval of its plateau level  increased the rate of dendritic depolarization and extended the width of a Ca2+ spike  reduced the rate of dendritic depolarization , indicated by a red triangle in 2+ spike was Ith = 1.27 nA. At Isyn < Ith, the dendrites generated passive responses and no Ca2+ spike was activated . Thus, more ATP molecules were required for reversing Ca2+ influx in the whole dendrites than for Na+ ion pumping in the soma (Isyn < 1.4Ith, the Ca2+ spike only activates one Na+ spike in the soma (Isyn ≥ 1.4Ith, two Na+ spikes were generated during a Ca2+ spike. Thus, the ATP cost in the soma at Isyn = 1.4Ith was about twofold more than that at Isyn = 1.3Ith. This indicated that the generation of a Ca2+ AP made a significant contribution to the signaling-related energy in the pyramidal models, which was consistent with above simulations.We also compared the ATP consumption on reversing Cathe soma . Howeverthe soma . At Isynthe soma . At Isyn2+ spike initiated in the apical dendrites. The energy cost was estimated with the following approaches. We integrated the inward Ca2+ currents to give the total Ca2+ influx, and then calculated the number of ATP molecules that were hydrolyzed to restore the homeostasis of intracellular Ca2+ concentrations, operating with a ratio of one Ca2+ ion per ATP. These calculations allowed us to develop a qualitative understanding of the effects of synaptic inputs, dendritic voltage, bAP, and Ca2+ inactivation on the energy consumption associated with a Ca2+ spike.We used biologically realistic models of L5 pyramidal neurons to investigate the energy cost of a Ca2+ spike is a typical suprathreshold response in the apical dendrites. Our simulations suggested that the Ca2+ spikes consumed substantial ATP molecules in the dendrites, which makes a significant contribution to the overall usage of signaling-related energy in pyramidal neurons. These data were consistent with earlier predictions of energy budgets in the cerebral cortex (Energy budgets for neural computation indicated that the synaptic transmission mediated by pre- and postsynaptic mechanisms consumed about 55% of the total ATP used in neocortex cells . In partl cortex .2+ AP propagates distal signals to the soma/axon, which provides a cellular mechanism for coupling inputs arriving at different cortical layers (2+ ion pumping during a Ca2+ spike. This high ATP demand may be related to the dendritic function of integrating signals.The single dendritic branch is a fundamental functional unit of signaling in the nervous system . In L5 pl layers . Our sim+ AP were dependent on the membrane potential. Our simulations showed that the dendritic voltage determined the inactivation and availability of voltage-gated Ca2+ channels. Thus, the ATP cost of a Ca2+ spike in L5 pyramidal model neurons also depended on dendritic voltage, which was consistent with the findings of 2+ spike cost in neocortical neurons makes it complicated to estimate the signaling-related energy with experimental approaches. In this sense, the computational modeling provides a powerful tool to understand the energy budget for signaling in a specific neuron.Experimental recordings  made in + spike in the soma/axon reduced the threshold of a Ca2+ spike and activated BAC firing in the apical dendrites when it coincided with synaptic inputs within a time window of several milliseconds (2+ spike, which depended on the timing of synaptic inputs relative to the onset of bAP. Our results also indicated that the effects of a bAP on Ca2+ spike cost were due to its interactions with dendritic depolarization, which altered the availability of dendritic Ca2+ channels. Further, 2+ influx during a BAC firing may be for coupling cortical signals despite the additional metabolic cost.iseconds . We exte+ inactivation governed the metabolic energy of a Na+ AP in the soma/axon (+ inactivation increased Na+ entry efficiency and limited the ATP cost of Na+ spikes. On the contrary, slowing Na+ inactivation rate reduced the efficiency of Na+ influx, increasing spike cost. Our simulations were consistent with these previous studies, and we extended these prior observations to show that slowing Ca2+ inactivation increased the ATP cost of a dendritic Ca2+ spike, whereas accelerating Ca2+ inactivation reduced the Ca2+ spike cost. These data suggested that the inactivation kinetics of ion channels played considerable roles in the energy consumption of neuronal computation.Earlier studies predicted that Naoma/axon . Fast Na2+ APs in pyramidal cells. Our simulations predicted that the ATP cost of a Ca2+ spike depended on the state of dendritic activity. Synaptic inputs, membrane voltage, bAP, and Ca2+ inactivation all contributed to determining the energy consumption of a dendritic Ca2+ spike. The results suggested that the pyramidal neurons were able to dynamically adjust their energy demand based on dendritic responses, which should be considered when understanding the dendritic processing in the pyramidal cells, the energy budget for signaling in the cerebral cortex, as well as the metabolism-dependent functional brain imaging signals. Based on computational modeling, we provided a qualitative interpretation of how subcellular processes and membrane biophysics were organized effectively and efficiently to perform neural computation. The predictions were important for inspiring engineers to design energy-efficient neuromorphic devices.To the best of our knowledge, this computational study was the first investigation of the metabolic energy associated with dendritic CaAll datasets generated for this study are included in the article/supplementary material.GY and JW conceived and designed the work, and wrote the manuscript. GY and YF performed the simulations. GY, YF, and JW analyzed and interpreted the data.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.


STEM fields. Despite these clear and measurable benefits, faculty generally do not receive mentorship training, and feedback mechanisms and assessment to improve mentoring in academia are limited. Ineffective mentoring can negatively impact students, faculty, departments, and institutions via decreased productivity, increased stress, and the loss of valuable research products and talented personnel. Thus, there are clear incentives to invest in and implement formal training to improve mentorship in STEM fields. Here, we outline the unique challenges of mentoring in academia and present results from a survey of STEM scientists that support both the need and desire for more formal mentorship training. Using survey results and the primary literature, we identify common behaviors of effective mentors and outline a set of mentorship best practices. We argue that these best practices, as well as the key qualities of flexibility, communication, and trust, are skills that can be taught to prospective and current faculty. We present a model and resources for mentorship training based on our research, which we successfully implemented at the University of Colorado, Boulder, with graduate students and postdocs. We conclude that such training is an important and cost‐effective step toward improving mentorship in STEM fields.Effective mentoring is a key component of academic and career success that contributes to overall measures of productivity. Mentoring relationships also play an important role in mental health and in recruiting and retaining students from groups underrepresented in  All of the incomplete responses were cases where people began filling out demographic questions, which included a “choose not to respond” option, and then stopped taking the survey. We chose to retain only those respondents employed or enrolled in the United States, as this was the largest sample size and American graduate training methods differ from other models Kehm, . With thThe survey responses reveal several interesting themes. First, academics recognize the negative impact of breakdowns in the mentoring relationship: 70% of self‐described mentees reported that a breakdown had affected their mental health, and 70% said it affected their research productivity. Over half, 58%, of self‐described mentors also said a breakdown had affected their research productivity. Breakdowns in mentoring relationships are also not rare events, with 39% of all respondents experiencing poor mentoring “frequently.” However, 70% of self‐described mentors that took our survey felt that they “rarely” mentor poorly. Thus, among our survey respondents, nearly everyone appeared to recognize the consequences of ineffective mentorship, but mentors perhaps did not recognize their own role in a mentoring breakdown. Although this survey is not exhaustive, this discrepancy may reflect a general lack of communication between mentors and mentees about the state of the mentoring relationship.Second, many academics do not receive formal mentorship training: 69% of respondents received no formal mentorship training, even among those that spent at least 1 hr/week on mentoring‐related activities. Of those that did receive training, 74% reported receiving only “a little” training. In short responses, that small amount of training consisted mainly of short workshops, seminars, or online trainings. Personal experience as a mentee (54%) and “learning by doing” (57%), rather than formal training, were also chosen as “extremely important” in development of a mentoring style. Furthermore, even though mentorship was selected as the most important factor in graduate school retention and completion rates (68.9% of respondents chose mentoring as “extremely important”), mentoring graduate students was not selected as an important factor influencing faculty hiring and tenure decisions .Finally, based on short responses, many respondents think mentors need more training and expressed the desire for more training themselves. A word‐frequency analysis of important characteristics of mentors and mentees revealed broad agreement among respondents: Patience, honesty, listening, and communication were considered top qualities for both mentors and mentees Figure . Trainin4Our survey data clearly indicate that while scientists think mentorship is important and want more formalized mentorship training, there is not currently a broadly implemented framework for mentorship training in STEM fields. The first step in implementing such training is to define effective mentorship. Our survey indicates that people use words like “patience,” “honesty,” “communication,” “empathy,” and “listening” as characteristics of good mentors. Here, we take these impressions further by examining the primary literature to develop a definition of effective mentorship appropriate to the academic setting.Defining effective mentorship is challenging in part because of the many different roles that the primary mentor  must assume for undergraduates, graduate students, and postdocs  Many graduate students and postdocs already mentor undergraduates and other less experienced researchers; (b) this training will prepare graduate students and postdocs who intend to become faculty before, rather than after, they take on critical mentoring responsibilities; (c) graduate students and postdocs may have more time to devote to mentorship training than new professors, who are faced with a barrage of new tasks; and (d) mentorship training will benefit graduate students and postdocs who do not intend to become academic faculty, as nearly any career requires skills in leadership, communication, and conflict management . In contrast to academia, companies in the private sector have long recognized that good management practices increase employee productivity and retention and have consequently made management training a priority  Step II personality test Furnham,  and the 5.3Your graduate student wants to delay their candidacy exam because they don't feel ready, but you want them to have it this semester and you think that they will do fine. What do you say?You set a deadline to review a paper that your graduate student is writing. You did not hear anything from your student for a week prior, and now the deadline has passed. What do you do?You feel like something is wrong in the personal life of your grad student (they have seemed tired and stressed lately and slow on email). What do you do?Many of the activities in the mentoring seminar focused on building practical mentoring skills. Participants worked on applying methods from outside STEM to academic mentoring scenarios. Often, this took the form of case studies: Participants took turns playing the role of a mentor in pairs or small groups and responded to hypothetical, challenging mentoring scenarios. For example:Additional examples are included in the Supporting Information 5.4Throughout the seminar, we discussed professional behavior in the context of mentoring relationships, as well as ethical issues that may arise in academic mentoring. These issues are very important in academia, where mentoring relationships are typically long term and interactions often take place outside the traditional workplace. The close nature of these relationships can often blur professional and personal boundaries  may be attractive to only a subset of people. Instead, mentorship performance and student success should be a critical component of hiring, tenure review and promotion decisions and should inform decisions about which faculty have the opportunity to take on new students.There are important challenges to consider when evaluating mentorship. Effective mentoring can be difficult to measure as it involves both metrics of graduate student success , and less tangible metrics such as student mental health, support, and satisfaction. Additionally, evidence from research on teaching evaluations suggests that students can be biased when evaluating certain groups, such as women, and thus evaluations based on student opinion alone must be used with caution Boring, . FinallyDespite these challenges, we feel that evaluating mentorship and providing guidance for improvement, as well as a structure for accountability, are important steps forward in changing the culture of how mentorship is valued in STEM fields. Evaluations will need to be carefully designed and executed to address potential biases. Similar concerns surrounding teaching evaluations have led to the development of new metrics and evaluation techniques that better measure student success and are less prone to bias (Golding & Adam, 6Effective mentoring has demonstrable impacts on productivity, creativity, inclusion, equity, and mental health, and thus, effective mentoring should be a central goal of STEM faculty members, departments, and institutions. There is no doubt that most scientists want to be effective mentors, yet they receive little to no training and often lack essential skills for accomplishing this goal. Despite these challenges, we believe that as a community we should make mentorship a priority, and, in doing so, make substantive improvements by providing scientists with the skills they need to be effective mentors. Our proposed training model is an effective and affordable step in the right direction. It seeks to improve mentorship by providing formal training to graduate students and postdocs, who are the future leaders and mentors of STEM fields. This training draws on strategies and best practices from the corporate world and from empirical academic research. We have successfully implemented this model at CU Boulder, where it is now expanding to other STEM departments. We encourage other institutions to establish and support similar courses.All authors participated in the first iteration of the mentoring seminar and contributed to writing and editing the formal document and supplement. AKH and ESCS developed and led the original mentoring seminar and completed a majority of the writing. AKH reviewed a majority of the academic mentoring literature and compiled the majority of the All questions, results, and raw data from the mentoring survey are presented in the  Click here for additional data file.


However, little is known about the mechanistic details of the Ca2+ ion impact and the Ca2+ binding capabilities of PAF outside the fungal cell, which might be the reason for the activity loss. Using nuclear magnetic resonance (NMR), isothermal titration calorimetry and molecular dynamics (MD) simulations we demonstrated that PAF weakly, but specifically binds Ca2+ ions. MD simulations of PAF predicted one major Ca2+ binding site at the C-terminus involving Asp53 and Asp55, while Asp19 was considered as putative Ca2+ binding site. The exchange of Asp19 to serine had little impact on the Ca2+ binding, however caused the loss of antifungal activity, as was shown in our recent study. Now we replaced the C-terminal aspartates and expressed the serine variant PAFD53S/D55S. The specific Ca2+ binding affinity of PAFD53S/D55S decreased significantly if compared to PAF, whereas the antifungal activity was retained. To understand more details of Ca2+ interactions, we investigated the NMR and MD structure/dynamics of the free and Ca2+-bound PAF and PAFD53S/D55S. Though we found some differences between these protein variants and the Ca2+ complexes, these effects cannot explain the observed Ca2+ influence. In conclusion, PAF binds Ca2+ ions selectively at the C-terminus; however, this Ca2+ binding does not seem to play a direct role in the previously documented modulation of the antifungal activity of PAF.Calcium ions (Ca Penicillium chrysogenum. Since PAF shows no toxic effect on mammalian cells it represents a promising bio-moleculec concentration that is triggered by an influx of extracellular Ca2+ ions. In consequence, the PAF treated fungal cells are disturbed in polar growth and cell proliferation, and show apoptotic markers[2+]c homeostasis is a common mechanism that is also responsible for the growth inhibitory activity of other antifungal proteins, e.g. the antifungal protein AFPNN5353 of Aspergillus giganteusc and counteracting cell death[2+ ions on the antifungal action of PAF outside the fungal cell is still unclear.Importantly, the supplementation of the fungal growth medium with CaClell death, 9. The 2+ in the mode of action of PAF. In particular, we addressed the hypothesis that this process involves a direct interaction of Ca2+ with this antifungal protein. Molecular dynamics (MD) simulations indicated that the preferred Ca2+ binding site is formed by the negatively charged C-terminal carboxylate group, together with the carboxylate groups of the C-terminally located Asp53 and Asp55. We examined the roles of these critical amino acid (aa) residues in Ca2+ binding affinity, 3D solution structure and the antifungal toxicity by comparing the recombinant expressed PAFD53S/D55S, in which the Asp residues were substituted for serines (Ser), with PAF. We also analysed the recently described single mutant PAFD19S, in which the less probable Ca2+ binding residue Asp19 was substituted for a Ser[2+-bound PAF (PAF-Ca2+).In this study, we combined a molecular biology approach with structural analyses and functional tests to understand the role of extracellular Cafor a Ser. FinallyP. chrysogenum shaking cultures were grown as described before[P. chrysogenum minimal medium (PcMM) was inoculated with 2x108 conidia and grown for 72 h at 25 °C under continuous shaking. Isotopic 15N-labeling of proteins for nuclear magnetic resonance (NMR) analysis was performed by replacing the nitrogen source with 0.3% Na15NO3 (Euriso-Top)[N. crassa was used as a PAF-sensitive model organism and cultivated in 5-fold diluted Vogel’s medium[Fungal strains used in this study are listed in Table A in ed before. In brieriso-Top). For antD19S were generated as described previously[paf gene was applied to prepare the expression plasmid pSK275pafD53S/D55S for PAFD53S/D55S production. The primers used are listed in Table B in paf nucleotide sequence by Sanger sequencing (Eurofins/MWG Operon), the plasmid pSK275pafD53S/D55S was transformed into a P. chrysogenum Δpaf strain[D53S/D55S in liquid culture and the verification of the correct aa exchanges by electro spray ionization mass spectrometry (ESI-MS) at the Protein MicroAnalysis Facility  were performed as described[Recombinant PAF and PAFreviously. Site-diaf strain as previaf strain. The expdescribed, 14.3N. crassa conidia were incubated with 2-fold increasing concentrations of PAF and PAFD53S/D55S (0–128 μM) in liquid medium in a total volume of 200 μL per well. CaCl2 was added to the growth medium in a concentration range of 0–5 mM. Caspofungin (Merck) was applied at concentrations of 0.55 mM. Samples were prepared in triplicates. The fungal growth was monitored microscopically and by measuring the optical density (OD620nm) after 24–48 h of incubation at 25 °C with a FLUOstar Omega spectrophotometer (BMG Labtech). The growth rates of the treated samples were compared with untreated controls. All experiments were repeated at least twice and statistical calculations were done with Microsoft Excel.Fungal growth inhibition assays were carried out in 96-well plates  as described, 9. BrieD53S/D55S. The PAF structure was derived from the RCSB database[D53S/D55S was generated in silico by removing the Oδ atoms of Asp side chains and renaming the Cγ atoms. The protein models were solvated in octahedral boxes in such a way that the closest distance between the box and the protein was 15 Å and then it was filled up with explicit TIP3P water molecules. The systems were neutralized and the sodium-chloride concentrations were set up to a total 40 mmol/dm3. After a short energy minimization, a two-step equilibration was carried out. In the first stage the minimized systems were heated up to 298 K under NVT conditions that lasted 50 ps and followed by a 500 ps long stage under isothermal-isobaric conditions, the protein heavy atoms were kept restrained under the whole equilibration protocol by a force constant of 1000 kJ/mol-1nm-2. After the equilibration procedure 2 μs long NPT MD simulations were performed using periodic boundary conditions with the aid of virtual sites that allowed the 4 fs step size for integrating the Newtonian laws of motions and all bonds were constrained with the LINCS algorithm. A cutoff of 10.0 Å was used for the Lennard-Jones interactions and short-range electrostatic interactions and the force-switch was applied to smoothly switch the forces between 7.0 Å and 10.0 Å. The long-range electrostatics interactions were calculated by Particel Mesh Ewald summation.In order to reveal the dynamical impact of the D53S/D55S aa exchanges, MD simulations were performed both on PAF and on the computationally mutated protein PAF database  instrument was used. Thirty-eight microliters of 10 mM CaCl2 were titrated in 2 μL steps (90 s delays between the injections) into the measuring cell containing 200 μL of 100 μM protein solution. Proteins and Ca2+ were dissolved in the same buffer  in each experiment. All ITC experiments were performed at 25 °C. To determine the heat of dilution, a blind experiment was also set up, where Ca2+ was injected to the buffer in the same experimental setup. Furthermore, the control experiments were also run with the same parameters using Na+, K+ and Mg2+ ions, respectively, as ligands. The obtained isotherms were analysed and figures were prepared with Origin 7.0 software .To investigate the Ca3PO4, 40 mM NaCl, 0.04% NaN3, pH = 6.0). In case of Ca2+ saturation experiments 10 mM MOPS buffer  was used instead of phosphate buffer to avoid complex formation. NMR experiments were performed at 298 K using NEO or AVANCE-II type spectrometers (Bruker) at 700 or 500 MHz proton frequency. 1H chemical shifts were referenced to DSS  and indirect chemical shift referencing was used for 15N and 13C nuclei calculated from the gyromagnetic ratios. Spectra were processed with TopSpin 3.1 (Bruker) and analysed with CCPNmr Analysis 2.1[1H–1H NOESY (130 ms), 3D 15N-HSQC-TOCSY (60 ms mixing time) and 3D 15N-HSQC-NOESY (130 ms) spectra were used. The completeness of 1H- and 15N-assignment of PAF-Ca2+ was acceptable . However, most unassigned 1H atoms were HD or HE hydrogens of lysine (Lys) side chains where high number of NOEs is not expected. For the structure calculation NOE cross peaks were collected from 2D 1H-1H NOESY and 15N-edited 3D HSQC-NOESY spectra. No further structure refinement was used for PAF-Ca2+; the structure is based on NOE distance restraints. Disulphide pattern was assumed to be identical with that of the wild-type PAF[In NMR experiments 1.6 mM protein concentration was applied in phosphate buffer  in order to detect 15N-1H chemical shift changes in response to the increasing Ca2+ concentration of the solution. To avoid dramatic increase of the sample volume and loss of sensitivity, small quantities of high-salt concentration stock solutions were used for the titration (Tables C and D in 15N (70.966 MHz) relaxation data of 0.45 mM/L 15N-labelled PAF and of the same solution with 20 equivalent CaCl2 for Ca2+ bound state  were determined with the standard pulse programs and the Dynamics Center 2.4.3 fitting routine of the manufacturer. Waiting times between scans of 3s  and 7s (NOE) were allowed. The M2 model of the Lipari-Szabó method[2 order parameters.For NMR titrations, a series of bó method, 32 was 2+ binding of PAF, considering the site-specific mutations at putative binding sites Asp19, Asp53 and Asp55. Since the latter must be the main binding site, the PAF variant PAFD53S/D55S was produced with the P. chrysogenum based expression system as described recently[-1 were reached. The purity and the identity of PAFD53S/D55S were verified by ESI-MS. One peak representing the average molecular mass of 6.187 kDa was detected. The calculated theoretical mass (web.expasy.org/protparam) of the reduced form of PAFD53S/D55S was 6.194 kDa. This suggested that three intra-molecular disulphide bonds were formed in this PAF variant that showed no further post-translational modifications, except for the correct processing of the N-terminus  averaged for Ca2+ sensitive residues of PAF was 463 ± 24 M-1 for 15N and 490 ± 70 M-1 for 1H resonances  occurred at cysteine (Cys) 54, in the middle of the predicted and preferred C-terminal binding site. ITC measurements with PAF and Ca2+ resulted in similar binding constants as the NMR analysis (Ka = 400 ± 9 M-1) .To address the hypothesis that PAF binds Casonances . Further2+ binding to PAF is weak, but specific, and we confirmed unambiguously that Asp53 and Asp55 together with the C-terminal carboxylate group form the preferred Ca2+ binding site in PAF.All these findings demonstrate that CaD53S/D55S with that of PAF on the test organism N. crassa. We determined the minimal effective concentration (MEC) that inhibited the growth of N. crassa germlings by 90%. The MEC for PAFD53S/D55S and PAF was 0.12 μM and 0.06 μM, respectively  was supplemented with increasing concentrations of CaCl2 (D53S/D55S and PAF effectively inhibited the growth of N. crassa at a concentration of 0.06 μM. Supplementation of the test medium with CaCl2 (0–5 mM) ameliorated the fungal growth in a concentrations dependent way. Five mM CaCl2 restored fungal growth to 111 ± 9.6% and 85 ± 2.8% in the presence of PAFD53S/D55S and PAF, respectively. The sensitivity for Ca2+ supplementation affecting antifungal efficacy was even higher for the previously described PAFD19S[2 concentration as low as 0.3 mM, restored fungal growth to 50%[2 concentration and reached only 12% in PAFD53S/D55S treated and 6% in PAF treated samples.As demonstrated earlier, the antifungal activity of PAF and PAFntrations, 9, 33. of CaCl2 . The pro2 on the antifungal activity of caspofungin against N. crassa . In the 1H-15N HSQC spectra of PAF-Ca2+ all amide NH-s and Asp side chain NH2 resonances were well dispersed indicating the folded state of PAF-Ca2+. With 15N-labelled PAF the average number of NOE distance restraints in the complex was 14.7 per residues, which is slightly lower than for the 15N-/13C-labelled PAF structure[2+ is less well defined, especially at the N-terminus and in some loops regions. However, the quality of the Ca2+-bound structure was sufficient for comparison with the Ca2+-free PAF . This conformational change increased the negative charge of the protein surface at the C-terminus, which supported the theoretical calculations indicating Asp53 and Asp55 to be the key residues for Ca2+ binding in this region . The 15N-1H chemical shift dispersion pointed towards a similarly folded structure as that of PAF. This was further supported by the well comparable 13C-chemical shifts from natural abundance 13C-HSQC spectra, which indicated similar backbone conformation  in the absence of Ca2+ ions. (2) In the second set the same simulation systems were built up in the presence of one Ca2+ ion which means approximately 7 mM (corresponding to the whole volume of the simulation box). (3) To study the protein dynamics and the effect of high Ca2+ concentration, in the third set ten Ca2+ ions were placed into the simulation box, which fit to a super-saturated simulation cell and an approx. 70 mM calcium ion concentration. (4) The last three runs were controls to prove that the MD simulations are able to clearly distinguish the selective ion binding ability of proteins. In these simulations, we placed one Mg2+ ion, then one Mg2+ and one Ca2+ and finally two Ca2+ ions into the simulation box.Four sets of simulations were run to study the CaD53S/D55S was only 0.46 Å. The RMSD revealed no significant discrepancies, nevertheless after the first 1.25 μs of the simulation, the total radius of gyration suggested a somewhat more compact structure in the case of PAFD53S/D55S, as can be seen in panel B and C of D53S/D55S became more rigid compared to those in PAF. The reason for these observations is related to the change of salt bridge patterns on the protein surface  Panel A in 1H-15N chemical shifts upon Ca2+ titration were observed near to the Ca2+ binding site  timescales  but in the case of PAF the radius of gyration is significantly decreased (from ~1.11 nm to ~1.09 nm), confirming again that the Ca2+ binding results a less flexible, compact structure.(2) The diminished Cate group . As oppo of Ca2+ . Althoug2+ rich media, several simulations were conducted under similar condition, which means approx. 70 mM Ca2+ above the existing 40 mM NaCl. We could find differences between the used general dynamical descriptors of PAF and PAFD53S/D55S  the unusually high ion strength can induce temporary Ca2+ binding sites  Due to the fact that some NMR measurements were performed in Ca53S/D55S  and the 2+ binding site but does not interact with other cations such as K+, Na+ or Mg2+. To verify that our MD investigations are able to correctly predict Ca2+ (and other cation) binding properties of PAF, three additional simulations were performed. In the first simulation cell one Mg2+ ion was placed randomly and retained its position, one Ca2+ was added, and finally two Ca2+ ions were considered in the simulation box. The general analysis of these simulations can be found in Figures Q and R in 2+ accessed the negative side-chains only temporarily as well as in the Ca2+-Mg2+ containing box, Ca2+ can be found in the C-terminal binding pocket  We could show that PAF has a weak Ca2+ to PAF might be the reason for the loss of its antifungal activity. Our investigations shed new light on the Ca2+ binding ability outside the fungal cell and its impact on the structure-function relation of the antifungal protein PAF, which may support our understanding of the mode of action of other PAF- related antifungal proteins from filamentous fungi.In this study, we investigated the hypothesis that binding of extracellular Ca2+ at its C-terminus (Asp53 and Asp55). The binding is rather weak (Ka ~ 460/M), which may be explained by the absence of a well-formed binding pocket in PAF. No interactions between PAF and other cations  could be detected. This confirms selective Ca2+ binding of PAF, though we wondered why this interaction is weak. Therefore, the Asp mutants PAFD53S/D55S and the previously described PAFD19S[2+ ions showed significantly reduced binding to PAFD53S/D55S. Since the secondary Ca2+ binding site at Asp19 is less probable, its mutation in PAFD19S has little impact on the overall Ca2+ binding affinity. Characteristic 13C NMR chemical shifts suggested that the exchange of two negatively charged Asp at the binding site in PAFD53S/D55S did not affect appreciably the overall solution structure with respect to PAF. The same structural stability is true for PAFD19S as discussed earlier[2+ complex structure we observed small differences in the big loop 3, whereas the second β-pleated sheet has become slightly shorter and less regular compared to PAF. Still, the global fold and the 3D structure is almost the same both in the apo and holo forms, i.e. Ca2+ binding to PAF (PDB ID: 2NBF for bound state) does not influence its essential structure. The MD of PAF and PAFD53S/D55S were extensively studied by all-atom MD simulations, including the Ca2+ binding features. We could prove that PAFD53S/D55S cannot bind Ca2+ ions; a consequence of missing Asp53 and Asp55 side chains, that causes the change of long-range electrostatic surface, as the only significant difference we found between PAF and PAFD53S/D55S.We proved by NMR spectroscopy and MD simulations that PAF primarily binds Cad PAFD19S were card earlier. In the 2+ were in acceptable agreement (RMSD = 2.19 Å). MD simulations gave new insights into the dynamics of disulphide bonds and salt bridges in PAF and PAFD53S/D55S in accordance with NMR chemical shift movements upon Ca2+ titration of PAF. The overall PAF dynamics represented by the average 15NH S2 order parameters did change upon Ca2+ binding neither by experimental (0.83 ± 0.05 vs. 0.83 ± 0.07) nor theoretical (0.84 ± 0.06 vs. 0.83 ± 0.07) results. Locally however, the experimental S2 order parameter differences (obtained from 15N NMR relaxation)[Experimental (2NBF) and calculated , 32 dispD53S/D55S tested on N. crassa is very similar to that of PAF . The antifungal activity of the recently studied protein variant PAFD19S was highly sensitive[2+ ions, while PAFD53S/D55S exhibited a similar Ca2+ ion dependent antifungal activity as PAF. Hence, similar Ca2+ -dependent activities may be explained by the structural and dynamical similarities of PAF and PAFD53S/D55S. In summary, we conclude that the Ca2+ sensitive mode of the PAF action is independent of its direct Ca2+ binding capability, corroborating the earlier hypothesis that extracellular Ca2+ ions regulate the fungicidal mode of action of PAF indirectly in a more complex way, e.g. by influencing the expression and/or activity of PAF-specific interaction molecules/receptors or Ca2+ transporters in fungal cells[It also turned out that the antifungal activity of PAFsensitive to Ca2+ gal cells, 9.S1 FileSupplementary Figures and Tables: Table A. Fungal strains used in this study. Table B. Oligonucleotides used in this study. Mutation primers are in bold; mismatches for aa exchange are underlined. Table C. Setup of NMR titration of native PAF with CaCl2. Table D. 15N Chemical shift changes (ppm) of the Ca2+-sensitive residues of PAF in the function of CaCl2concentration. Table E. Cαand Cβchemical shifts (ppm) of PAF and PAFD53S/D55S. Missing resonances are due to low intensity NH and NH correlated peaks which is a consequence of H/D exchange of these peaks at pH = 6.0. Table F. Ion binding probabilities of PAF and PAFD53S/D55Sproteins. Calculations of ion binding probabilities were based on the number of frames where the ion was closer to the corresponding carboxylate carbon than 0.4 nm, divided by the total number of frames multiplied by 100. Only those values are shown which differed from 0.00%. Figure A. Molecular mass determination of PAFD53S/D55Sby ESI-MS. (A) Overview on the average isotopic pattern spanning 4–8 kDa identifies the correct mass of PAFD53S/D55S (6.187 kDa). (B) Detailed view (6.1–6.6 kDa) reveals that additional peaks can be attributed to chemical adducts. MS results for PAF and PAFD19S were published previously [KFigure B. evalue of Ca2+binding of PAF calculated for individual aa residues. Ca2+ binding constants as determined from the fit of 15N chemical shifts upon Ca2+ titration yielded similar results, independent of the sequential distance from the effective binding site. Figure C. Ca2+-sensitive amide proton chemical shift changes of PAF (MOPS buffer) upon CaCl2titration. Experimental points were fitted as a function of CaCl2 concentration according to the one site binding model. Equation 3 was used from reference [1H chemical shift changes by using an in-house written MATLAB code. The average equilibrium constant Ka = 490 ± 70 M-1 is in agreement with 15N and ITC results. Figure D. ITC isotherms for CaCl2-titrations. (A) PAF (B) MOPS buffer (negative control) (C) PAFD19S(D) PAFD53S/D55S. Figure E. Antifungal effect of Caspofungin (1.25 μg/mL) on Neurospora crassa is not affected by increasing concentrations of extracellular CaCl2 (0–10 mM). Figure F. Top: Cartoon model of Ca2+-bound PAF structure  and structure of Ca2+-free PAF . Beta-strands are shown as flat arrows, and disulphide-bridges are represented in yellow. Bottom: Surface representation of Ca2+-bound PAF (left) and Ca2+-free PAF (right) coloured according to electrostatic potential calculated in vacuum . A negatively charged patch can be seen at the C-terminus of the protein. Figure G. Cαchemical shifts of PAF, PAFD19Sand PAFD53S/D55S. Figure H. Cβchemical shifts of PAF, PAFD19Sand PAFD53S/D55S. Figure I. Comparison of the dynamics of disulphide dihedrals and salt bridge distances in the absence of Ca2+ion. (A) Changes of the Cβ-Sγ-Sγ-Cβ torsion angle (native and mutant respectively). (B) The C-terminal related salt bridge distances are shown and correspond to the distances between the Lys Nζ—Asp Cδ, Lys Nζ—Ser Oγ, and Lys Nζ—C-terminal carboxylate carbon atoms. Figure J. Electrostatic potential surfaces of PAF and PAFD53S/D55Smutant proteins. (A) In the case of PAF, the electrostatic surface map clearly shows a negative surface potential in the vicinity of Asp53 and Asp55, which explains the Ca2+ binding ability of the native protein. (B) In contrast to PAF, the PAFD53S/D55S shows significantly positive surface at the same position . Figure K. Secondary structures of cluster representatives of PAF  and differences in the generalized correlations and in the chemical shifts. (A) Graphical representations of secondary structure elements (cartoon) of most populated cluster representatives of PAF in the absence of Ca2+ ions (left), and in the presence of ten Ca2+ ions (right), residues with notable chemical shift change were depicted with purple . In panel B above, absolute difference between generalized covariance matrices, on the edges the individual secondary structure elements were depicted . The absolute difference of matrices was calculated by: |CijPAF − CijPAF-10Ca2+|. (B) Chemical shift differences upon titration experiments, the background color represents the SSEs of the saturated PAF. SFigure L. Theoretical and experimental 2order parameters. (A) Comparison of NMR experimental (pink) and MD theoretical (cyan) S2 order parameters of free PAF. Missing point is Pro29 due to the lack of an NH group. The overall trends of theoretical and experimental S2 are similar, and differences might be attributed to the different time scopes (μs vs. ns) of the methods and the experimental uncertainties. (B) NMR experimental differences between Ca2+ loaded and free PAF. Error bars (yellow) are shown on the top of the differences (blue). Positive bars mean rigidification upon binding, while negative differences mean enhanced dynamics. Figure M. Comparison of the dynamics of disulphide bonds and salt bridge distances in the presence of one Ca2+ion. (A) Changes of the Cβ-Sγ-Sγ-Cβ torsion angle (native and mutant respectively). (B) The C-terminal related salt bridge distances are shown and correspond to the distances between the Lys Nζ—Asp Cδ, Lys Nζ—Ser Oγ, and Lys Nζ—C-terminal carboxylate carbon atoms. Figure N. Minimum distances between Ca2+ions and negatively charged side-chains. Those residues were considered where the Ca2+ binding was clearly observable. In the left panel PAF is depicted, in the right panel the PAFD53S/D55S mutant. These simultaneous, artificial binding sites can be induced by the high ionic strength but clearly demonstrate that the most effective binding can occur at the C-terminus of PAF. Figure O. Comparison of the dynamics of disulphide bonds and salt bridge distances in the presence of ten Ca2+ions. (A) Changes of the Cβ-Sγ-Sγ-Cβ torsion angle (native and mutant respectively). (B) The C-terminal related salt bridge distances are shown and correspond to the distances between the Lys Nζ—Asp Cδ, Lys Nζ—Ser Oγ, and Lys Nζ—C-terminal carboxylate carbon atoms. Figure P. Salt bridge distances in PAF and PAFD53S/D55S. The Ca2+ concentration induced changes are shown in (A) for PAF, in (B) for PAFD53S/D55S, depicted and based on the distance between the Lys Nζ—Asp Cδ, Lys Nζ—Ser Oγ, and Lys Nζ—C-terminal carboxylate carbon atoms. Figure Q. Salt bridge distances in control simulations. The Ca2+ and Mg2+ concentration induced changes in the salt bridge connections are shown for PAF (A) one Mg2+, (B) one Mg2+ and one Ca2+, (C) two Ca2+, based on the distances between the Lys Nζ—Asp Cδ, Lys Nζ—Ser Oγ, and Lys Nζ—C- terminal carboxylate carbon atoms. Figure R. General dynamical properties of control simulations. (A) and (B) The MD simulations are summarized and based on calculations to all heavy atoms, in (C) the C-terminal related salt-bridge distances are collected and correspond to the distances between Lys Nζ—Asp Cδ or C-terminal carboxylate carbon atoms. Figure S. Minimum distances of Ca2+and Mg2+ions in control simulations. (A) The applied Mg2+ concentration does not result in any significant binding. (B) and (C) In the case of one or two Ca2+, the C-terminal binding site was filled in the second half of the simulations and in the latter case the second Ca2+ ion bound weakly to the Asp19 in the last ~50 ns. The corresponding values fit to the distances between the ion and the Asp Cδ or Ser Oγ or C-terminal carboxylate carbon atoms. Figure T. Comparison of the experimental  and theoretical  structures of Ca2+loaded PAF. The RMSD between the representative of the largest cluster and the first frame of 2NBF is 2.19 Å.eference  for fitt(DOCX)Click here for additional data file.S2 File(PDF)Click here for additional data file.


KChIPs constitute a group of specific auxiliary β-subunits for Kv4 channels, the molecular substrate of transient potassium currents in both neuronal and non-neuronal tissues. Moreover, KChIPs can interact with presenilins to control ER calcium signaling and apoptosis, and with DNA to control gene transcription. Ca2+ binding via their EF-hands, with the consequence of conformational changes, is well documented for KChIPs. Moreover, the Ca2+ dependence of the presenilin/KChIP complex may be related to Alzheimer’s disease and the Ca2+ dependence of the DNA/KChIP complex to pain sensing. However, only in few cases could the Ca2+ binding to KChIPs be directly linked to the control of excitability in nerve and muscle cells known to express Kv4/KChIP channel complexes. This review summarizes current knowledge about the Ca2+ binding properties of KChIPs and the Ca2+ dependencies of macromolecular complexes containing KChIPs, including those with presenilins, DNA and especially Kv4 channels. The respective physiological or pathophysiolgical roles of Ca2+ binding to KChIPs are discussed.Kv channel-interacting proteins (KChIPs) belong to the neuronal calcium sensor (NCS) family of Ca Shal gene of Drosophila melanogaster .–33.2+ an2+ withdrawal from KChIP1 .–51.2+-deansients . Taken t disease . Physiol2+-induced Ca2+ release, by interacting with presenilins [2+ transients, associated with a reduced sarcomeric shortening. These effects were not due to compromized sarcoplasmic reticulum (SR) Ca2+ loading, and correlated with a dispersion of PS1 from SR membranes to the cytoplasm [2+ transients caused by KChIP2 knock-down [2+ binding properties, but also the different cell systems used in these studies may be responsible for the seemingly contradictory findings. Certainly, more research is needed to elucidate the KChIP/presenilin-mediated control of Ca2+-induced Ca2+ release.More recent studies in cardiomyocytes suggest that, similar to KChIP3 , KChIP2 may also influence ryanodine receptor-mediated Casenilins . It was ytoplasm . Surprisock-down  resembleock-down . Structu2+-dependent transcriptional repressor, then dubbed “downstream regulatory element antagonist modulator” (DREAM) [2+ is low KChIP3 (DREAM) was shown to specifically bind to the DRE site of the human prodynorphin gene, thereby preventing its transcription. An increase in Ca2+ leads to conformational changes and a dissociation of KChIP3 (DREAM) from the DRE site in an EF-hand-dependent manner, which allows transcription of the prodynorphin gene [2+ levels, to modulate pain perception via κ-opioid receptors [+/Ca2+ exchanger (NCX) 3 expression in cerebellar neurons is under the control of the transcriptional repressor KChIP3 (DREAM), which may occupy a doublet of DRE sites in the NCX3 gene when Ca2+ is low [2+ is elevated, which has been interpreted as a negative feed-back control loop of Ca2+ homeostasis [2+ signaling is out of control [2+- and Ca2+/calmodulin-dependent protein kinase (CaMK) II-dependent manner. In the nucleus KChIP3 (DREAM) represses the transcription of the CACNA1c gene, which codes for the L-type Ca2+ channel α-subunit Cav1.2 [2+ on the transcription repressor function of KChIP3 (DREAM), but in the end also serves Ca2+ homeostasis. For KChIP2, despite its prominent expression in cardiomyocytes, a gene transcription control mechanism similar to the ones involving KChIP3 (DREAM) could not be shown [Ca via a direct interaction with the Cav1.2 protein independently of protein expression or trafficking (see section “Acute modulation of the Kv4/KChIP channel complex by cytoplasmic Ca2+”) [2+ and EF-hand dependence of the KChIP2-mediated control of miR-34b/c expression remains to be studied mechanistically in more detail.There are numerous examples showing that KChIPs are involved in the control of gene expression in both excitable and non-excitable cells. Especially KChIP3, but also KChIP1, KChIP2 and KChIP4, may fulfill the function of a transcriptional repressor, either directly, by binding to downstream regulatory element (DRE) sites of genes ,55–61, o (DREAM) . When Cahin gene . Thus, ieceptors ,41,65. I control . Intrigut Cav1.2 . Apparenbe shown . Rather,c Ca2+”) ,69. Notac Ca2+”) . These r2+ binding state of the KChIP β-subunit. This aspect also includes the intriguing possibility of a Ca2+-dependent dynamic Kv4:KChIP stoichiometry. Also, trafficking of Kv4/KChIP complexes to the plasma membrane or its internalization may depend on Ca2+ binding to the KChIP β-subunit. All of these putative mechanisms are expected to modify the molecular composition and/or the number of Kv4 channels in the plasma membrane. Alternatively, or in addition to that, membrane-bound Kv4/KChIP complexes may be preserved when Ca2+ binds to the KChIP β-subunit, however, conformational changes of the KChIPs may be directly transmitted to the channel in order to modulate its gating  domain contribute to KChIP binding [2+ dependencies of KChIP binding to these two sites differ. In support of this notion, it was recently found that, with strong cytoplasmic Ca2+ buffering by BAPTA-AM starting immediately after cell transfection, KChIP2b coexpression caused an acceleration of Kv4.3 recovery kinetics, but not the otherwise typical slowing of the initial phase of macroscopic current decay [2+ markedly accelerated Kv4.3 recovery kinetics but did not considerably slow macroscopic current decay [2+ is bound to KChIP2 or not. Thus, in the absence of Ca2+ KChIP2 binding to the Kv4.3 N-terminus may not be strong enough to suppress N-type inactivation [2+ binding in Kv4/KChIP complex formation was also supported by the finding that binding of wild-type KChIP2c to a 90 amino acid NTF of Kv4.2 is enhanced, whereas the binding of KChIP2cΔEF mutants to that fragment, although possible, is not enhanced by Ca2+ [KChIPs are specific β-subunits of Kv4 potassium channels . the mols gating . As descxpressed . These rormation . Likewisormation , and theependent . An and oworkers  found thmay vary . However binding ,74, and  binding ,75. It int decay . Accordint decay . These dnt decay . Notablynt decay . It is ptivation , while Ktivation ,78. Such by Ca2+ ,37.2+-sensitive KChIP binding to the Kv4 N-terminus (see above) would also confer Ca2+ sensitivity to channel trafficking. Hasdemir and coworkers [2+ dependence, in more detail: They found that Kv4.2 expressed alone is not retained in the ER but can reach the Golgi compartment, and that coexpression of KChIP1 allows further trafficking to the plasma membrane. The Ca2+ binding-deficient mutant KChIP1ΔEF2,3,4 was not only unable to promote membrane trafficking but even prevented Kv4.2 from reaching the Golgi compartment. Apparently, Kv4.2 interacts with KChIP1 (wild-type or mutant) somewhere along the ER-to-Golgi transport pathway in a Ca2+-independent manner, but Ca2+ binding to KChIP1 is required for further trafficking to the plasma membrane. The Ca2+-dependent step of Kv4.2/KChIP1 trafficking has not been identified yet, but Ca2+-dependent targeting of the channels to the right transport vesicles seems unlikely since KChIP1ΔEF2,3,4 and wild-type KChIP1 showed the same subcellular localization [2+/myristoyl switch, so the myristoyl moiety is available for membrane association already at resting Ca2+ concentrations [It has been recognized early on that, with the exception of the KChIP4a splice variant, complex formation with KChIPs promotes the trafficking of Kv4 channels to the plasma membrane and increases their stability ,70,79. Ioworkers  studied lization . In KChIlization , but in trations .2+-dependent function of recoverin (2+ binding site of KChIP4a (2+ (10 μM) increases the Kv4.3 NTF (amino acid residues 6 – 145) binding affinity of KChIP4a by an order of magnitude [2+ binding.The KChIP4a splice variant is special in various respects: Coexpression of KChIP4a does not lead to increased Kv4 channel surface expression  and leavecoverin  Like in  KChIP4a , and Ca2agnitude . However2+ with modified gating. Chen and coworkers [2+ concentration on voltage-dependent potassium currents. These authors managed to exchange the patch-pipette solution, in order to vary the cytoplasmic Ca2+ concentration, while performing whole-cell recordings from individual cultured guinea pig hippocampal neurons. They observed an increase in ISA amplitude during initial cell perfusion with low Ca2+ and a suppression of ISA during subsequent cell perfusion with high Ca2+. The cytoplasmic Ca2+ increase caused opposing and overlapping effects on ISA and the delayed-recifier potassium current (ID), but ISA suppression (> 50%) persisted if the authors applied Cs+ or tetraethylammonium to block ID [2+ concentrations in separate recordings from cultured rat cerebellar granule cells to study possible differences in ISA properties. Unlike Chen et al., these authors observed almost 100% larger ISA amplitudes when recording with the high Ca2+ patch solution, which did match their simultaneously observed slower current decay kinetics but not the more negative voltage dependence of steady-state inactivation, and no Ca2+ dependence at all was seen for the kinetics of recovery from inactivation [2+ concentration in the patch-pipette solutiuon , which complicates proper judgement of the described effects. Nevertheless, Wang et al. reported that Co2+ was not capable of reproducing their Ca2+ effects, supporting the notion that these required a Ca2+-binding protein and were not caused just by increasing divalents on the internal side of the membrane [2+ effects on ISA the authors concluded that these were somehow mediated by KChIPs. Thus, the study by Wang and coworkers [2+-dependent modulation of ISA-related Kv4/KChIP channels. More recently, Groen and Bähring [2+-dependent gating modulation of Kv4.3/KChIP2 channel complexes expressed in human embryonic kidney (HEK) 293 cells. These authors accounted for the actual free Ca2+ concentration in the patch-pipette, and their results suggested that the kinetics of recovery from inactivation were affected by varying cytoplasmic Ca2+ between nominal Ca2+-free (BAPTA or BAPTA-AM) and 50 μM Ca2+. In that study Kv4.3 was not only coexpressed with wild-type but also with specifically EF-hand-mutated KChIP2, and the observed Ca2+ dependences were further examined in the presence of the CaMKII inhibitor KN-93. The results were explained by a mechanism in which direct Ca2+ binding to KChIP2 EF3 and EF4 may speed up the recovery of Kv4.3/KChIP2 channels from inactivation. However, CaMKII action may apparently override this effect, causing a net Ca2+-mediated slowing of Kv4.3/KChIP2 recovery kinetics with KChIP2 EF2 and EF3 being critically involved. In view of this putative mechanism the findings suggest a shift from the functional importance of both high affinity Ca2+ sites (EF3 and EF4) to a combined functional importance of a Ca2+ and a Mg2+ site  [2+ entry  or Ca2+ release . In their seminal studies, Anderson and coworkers [2+ entry via Cav3 channels, known to mediate a subthreshold-activating transient (T-type) Ca2+ current, as a possible source of Ca2+ for the modification of A-type channel gating. What these authors initially observed, was a negative shift of the voltage dependence of ISA steady-state inactivation when they applied the T-type Ca2+ channel blocker mibefradil, but not when they applied the high voltage-activated (HVA) Ca2+ channel blocker SNX-483, to stellate cells in rat cerebellar slices [2+ for an acute gating modulation of membrane-bound Kv4.2/KChIP3 channels. In fact, Heath and coworkers [SA availability, and blocking Ca2+ currents abolished regional differences in ISA density. The novel form of subcellular Ca2+ signaling occurs within a protein complex, and the radius of action actually represents a Ca2+ nanodomain (distance < 50 nm) [2+ channel blocker (negative shift of the inactivation curve) it can be inferred that Ca2+ entry and Ca2+ binding to KChIP3 causes a positive shift of the inactivation curve, which represents a strengthening of the actual KChIP effect on Kv4 channel gating  receptors, or ER Ca2+ channels like the ryanodine and the IP3 receptor, are able to acutely modulate the gating of Kv4/KChIP channel complexes.The above-described experimental approaches have all imposed changes in Caistance) ,90 of Caoworkers ,21 identr slices . Their cr slices ,21. Thusoworkers  found ou< 50 nm) ,90. Fromting see . Thus, Cte cells . When ex neurons . It will neurons , or in c neurons ,69, maybchannels , may parchannels –96. Fina2+ control both Kv4/KChIP/DPP channel function and the transcription of the prodynorphin gene in nociceptive neurons, or can Ca2+ control both Kv4/KChIP/DPP channel function and NCX3 expression in cerebellar neurons? Alternatively, KChIPs may fulfill one function under physiological and a different one under pathophysiological conditions. It is also intriguing that KChIPs themselves may be able to influence relevant sources of Ca2+, by controlling calcium channel gene transcription or ER Ca2+ release via presenilin. We need to know whether and how KChIPs are involved in different Ca2+ signaling mechanisms, and whether different mechanisms converge to fulfill the same physiologic function, maybe to fine-tune Kv4 channel gating. On the other hand, the role of KChIPs as neuronal and non-neuronal calcium sensors under conditions with disturbed Ca2+ signaling, like apoptosis or cardiac stress, has to be defined more clearly in the future.A more far-reaching question to be answered is whether both Kv4/KChIP/Cav nanodomain signaling and KChIP functions related to presenilins and/or to DNA may synergistically coexist in the same cell. One may ask, for instance: Can Ca