PPT-A mRNA expression and the substance P release. The up-regulation of both the PPT-A mRNA expression and the substance P release by either a GSK-3β inhibitor TWS119 (10 μM) or a Wnt signaling agonist Wnt-3a (100 ng/ml) were significantly abolished by an inhibitor of cyclooxygenase-2 . Collectively, these data suggest that nociceptive input-activated β-catenin signaling plays an important role in regulating the biosynthesis and release of substance P, which may contribute to the inflammation responses related to chronic pain.To examine regulatory effects of β-catenin on the biosynthesis and release of substance P, a rat chronic constriction injury (CCI) model and a rat dorsal root ganglion (DRG) cell culture model were used in the present study. The CCI treatment significantly induced the overall expression of β-catenin (158 ± 6% of sham) in the ipsilateral L5 DRGs in comparison with the sham group (109 ± 4% of sham). The CCI-induced aberrant expression of β-catenin was significantly attenuated by oral administration of diclofenac . Importantly, aberrant nuclear accumulation of β-catenin in cultured DRG cells resulted in up-regulation of the  The nociceptive stimuli-evoked release of substance P from cultured DRG cells could be significantly attenuated by the inhibition of COX-2 [2 release and thermal hyperalgesia can also be reversed by spinal COX-2 inhibition [PPT-A gene could reduce the stimulus-induced surface insertion of delta-opioid receptors and abolish delta-opioid receptor-mediated spinal analgesia and morphine tolerance [Substance P, encoded by the of COX-2 –3. Intrahibition ,5. Moreoolerance . Hence, olerance .2, a cyclooxygenase (COX) product contributing to inflammatory pain, has been reported to induce the accumulation of nuclear β-catenin through inactivating glycogen synthase kinase 3β (GSK-3β) [As a key mediator in the Wnt signaling pathway, β-catenin regulates multiple cellular functions, including proliferation, differentiation, inflammation and oncogenesis –10. Rece(GSK-3β) ,15. BaseThe above-mentioned observations strongly support that the β-catenin signaling might serve as an important regulator in the process of chronic pain. To uncover the novel molecular mechanisms underlying chronic pain, we investigated the effect of β-catenin nuclear accumulation on the biosynthesis and release of substance P in the DRG cells of chronic constriction injury (CCI) rats. A further examination of their relationship was demonstrated in cultured DRG cells by up- or down-regulating the Wnt/β-catenin signaling by various activators  or inhibitors , respectively.The care and use of animals and experimental protocols for this study were performed according to the Guide for Animal Experimentation, South-Central University for Nationalities and the Committee of Research Facilities for Laboratory Animal Sciences, South-Central University for Nationalities, China. The protocols were approved by the Committee on the Ethics of Animal Experiments of the South-Central University for Nationalities, China (Permit Number: 2011-SCUEC-AEC-001). All surgery was performed under trichloroacetaldehyde monohydrate  anesthesia, and the Wistar rats for DRG neurons were sacrificed by decapitation after being anesthetized by CO2. All efforts were made to minimize suffering.The chronic constriction injury of the sciatic nerve had been performed in adult male Wistar rats as in our previous study . To inveImmunofluorescence analysis of β-catenin localization was performed with anti-β-catenin polyclonal antibody  on 4-μm paraffin-embedded L5 DRG sections and paraformaldehyde-fixed cultured DRG cell sections . Then, t2 for 5 days before the initiation of experiments [2. Thereafter, the cells pretreated with various drugs were continuously stimulated with TWS119  or Wnt-3a (R&D Systems) for 1, 6, 24 or 48 h in DMEM at 37°C. The DRG cells treated by peptidase inhibitors alone were used as a control. The substance P content in the cultured rat DRG cells was measured using a highly sensitive radioimmunoassay as previously described [The isolated DRG cells from adult Wistar rats (6–9 weeks of age) were cultured at 37°C in a water-saturated atmosphere with 5% COescribed .GAPDH was used as an internal standard. The following primer pairs were used: β-catenin, 5’-tggacaatggctactcaagctgac-3’ and 5’-gggatgagcagcgtcaaactgcgt-3’; COX-2, 5’-tgtatgctaccatctggcttcgg-3’ and 5’-gtttggaacagtcgctcgtcatc-3’; PPT-A, 5’-ggtgccaacgatgatct-3’ and 5’-gcatcccgtttgcccatt-3’; GAPDH, 5’-tgtgtccgtcgtggatctga-3’ and 5’-ttgctgttgaagtcgcaggag-3’.Total RNA harvested from the isolated L5 DRGs of rats treated with or without CCI of the sciatic nerve and the cultured DRG cells treated with or without TWS119 or Wnt-3a according to the manufacturer’s instructions  and an acid guanidinium thiocyanate-phenol-chloroform extraction method  was sepaThe cell membranous, cytoplasmic and nuclear proteins were extracted from cultured DRG cells using a Nucl-Cyto-Mem Preparation Kit, according to the manufacturer’s instructions . The total proteins of the isolated L5 DRGs were extracted according to the manufacturer’s instructions . The protein concentrations of each fraction were determined using a Lowry protein assay . Then eqP value less than 0.05 was considered statistically significant.Results are shown as the means ± SEM. The statistical analyses were performed by one- or two-way ANOVA as indicated in the text using Instat software . A th day after the operation. The total level of β-catenin expression  in contralateral L5 DRGs of CCI group was also higher than that in contralateral L5 DRGs of sham . Moreover, the CCI-induced total expression of β-catenin in ipsilateral L5 DRGs was significantly inhibited by diclofenac  in ipsilateral L5 DRGs  and whole cell localization (indicated as M+C+N) of β-catenin in the same specimens of DRGs described above were calculated according to a randomly selected field in each image . The nucth day after the operation  on the 28peration . Consistperation . All togTo clarify the relationship between β-catenin and COX-2, we further mimicked the Wnt/β-catenin signaling in cultured DRG cells with either TWS119 (a distinct GSK-3β inhibitor) or Wnt-3a , in combination with or without NS-398 (a selective inhibitor of COX-2) or Dkk1  ,19. As sβ-catenin  and COX-2  in cultured DRG cells in a Dkk1-sensitive manner  in a time-dependent manner within 48 h  and TWS119 (180 ± 14% of the control) were significantly attenuated by pre-treatment with either Dkk1 (113 ± 9% of the control) or NS-398  modulates the biosynthesis and release of substance P release from DRG cells through an induction of COX-2.2 has been reported to induce the accumulation of nuclear β-catenin in human colon cancer cells [COX-2 mRNA expression in a dose- and time-dependent manner [As an inducible enzyme pivotal in the inflammatory response, COX-2 converts arachidonic acid to the prostaglandins required for the biosynthesis and release of substance P during the inflammatory process ,22,23. Mer cells . Purifiet manner ,25. In at manner , HMGA1 wt manner  and panct manner ,29, suggt manner . Compareβ-catenin mRNA expression and/or its protein translocation induced by the CCI operation or Wnt/β-catenin signaling activators  As shown in in vivo and in vitro experiments and the above-mentioned observations reported previously, we herein considered that the possible molecular mechanisms of chronic pain by which the β-catenin signaling was required for evoking the biosynthesis and release of substance P (PPT-A genes to increase the substance P synthesis [Based on our present results from stance P . Insteadynthesis . The relynthesis  and enhaynthesis . Just thThe current studies demonstrated that a novel crosstalk between β-catenin and COX-2 plays an important role in regulating the biosynthesis and release of substance P, which may contribute to the inflammation responses related to chronic pain.


Humans can select actions by learning, planning, or retrieving motor memories. Reinforcement Learning (RL) associates these processes with three major classes of strategies for action selection: exploratory RL learns state-action values by exploration, model-based RL uses internal models to simulate future states reached by hypothetical actions, and motor-memory RL selects past successful state-action mapping. In order to investigate the neural substrates that implement these strategies, we conducted a functional magnetic resonance imaging (fMRI) experiment while humans performed a sequential action selection task under conditions that promoted the use of a specific RL strategy. The ventromedial prefrontal cortex and ventral striatum increased activity in the exploratory condition; the dorsolateral prefrontal cortex, dorsomedial striatum, and lateral cerebellum in the model-based condition; and the supplementary motor area, putamen, and anterior cerebellum in the motor-memory condition. These findings suggest that a distinct prefrontal-basal ganglia and cerebellar network implements the model-based RL action selection strategy. Using exploration and reward feedback, humans and other animals have a remarkable capacity to learn new motor behaviors without explicit teaching2The theory of reinforcement learning (RL)Computational models of RL and fMRI studies with humans have explored the neural substrates of model-based and motor-memory strategies, given their strong resemblance to classical, psychological, dichotomous behavior control employing deliberative and automatic processes, respectively. Activity in the dorsolateral prefrontal cortex (DLPFC) has been associated with the use of model-based strategies when an internal model of environmental dynamics is available and can be used for forward planning and prediction of an action’s future outcomes78910451214161718121221121423Our working hypothesis is that humans rely on distinct action selection strategies for learning, depending on their level of experience with a task: (1) in the early stage of learning with no prior knowledge of state transition or reward setting, the exploratory strategy is used; (2) as learning continues and an internal model of action results is developed, the model-based strategy is used to expedite learning; (3) in the late stage, after many successful experiences, a motor-memory strategy is used for robust performance with minimal computational load. In order to test this hypothesis and to identify brain mechanisms supporting different action selection strategies, we designed a task paradigm, the “grid-sailing taskThe grid-sailing task requires a subject to move a cursor from its start position to a goal position in a 5 × 5 grid by performing a sequence of key presses with the right index, middle, and ring fingers . DiffereWe asked subjects to perform each task condition either with or without a delay after a KM and SG positions were presented. We expected that subjects would use this pre-start delay period for mental simulation of imaginary actions if an internal model of the KM rule was available. We examined behavioral measures to test our hypotheses on differential use of action selection strategies and analyzed fMRI signals especially during the pre-start delay period, to identify neural networks associated with behavior suggestive of the use of model-based action planning.Subjects (n = 18) performed the “grid-sailing task”In the training session on day 1, subjects trained with two of the KMs paired with two SGs each . The KM-During the test session on day 2 , subject = 186.54, p < 0.0001), with a significantly (p < 0.001) lower overall reward score in Condition 1  than in Condition 2  and the largest reward score in Condition 3   = 2.74, p = 0.0649, indicating that the effect of task condition, trial block, and start time was comparable among groups.One important prediction based on our hypothesis is that the use of a distinct action selection strategy in each task condition would be associated with a distinct behavioral performance. The lack of prior experience and of an internal model due to the introduction of a new KM in Condition 1 would be associated with slow learning and a lower reward score relative to performance in Condition 2, where the presence of an internal model of the KMs would lead to fast learning and higher reward score, especially in trials preceded by a delay when the internal model of a KM could be used for forward planning. In Condition 3 we expected a stable, fast, accurate performance for the pre-learned action sequences. In support of this prediction, we found a significant effect of task condition (F.33) See  and S5. To further investigate the effect of the availability of an internal model of KMs on behavior performance, we estimated subjects’ overall trial-by-trial probability of reaching the goal  and the probability of reaching the goal by performing the shortest action sequence . Analysis of the goal reach probability found that subjects could reach a learning criterion of 80% success at trial 4 in delayed-start trials in Condition 2  . We alsoIn order to further quantify performance differences among the task conditions, we classified trials into three trial types based on reward score subjects received at the end of each trial: error trials , suboptimal trials , and optimal trials . We expected to find a higher rate of error trials in Condition 1 relative to Condition 2 due to the lack of experience with the new KM. Conversely, we expected higher rate of optimal trials in Condition 2 given that the internal model of KMs could be useful for successful action planning. As for Condition 3, we expected to find the lowest rate of error trials and the highest rate of optimal trials, given the well-learned action sequences. These predictions were supported by the analysis, which revealed a significant effect of task condition (p < 0.0001), with a higher percentage of optimal trials in Condition 2 than in Condition 1 (p < 0.006), especially in delayed start trials  as a measure of exploratory behavior. MAD was estimated separately for reward score, the number of steps to reach the goal, and execution time, and entered into a group analysis with a three-way ANOVA . This analysis revealed higher performance variability in reward score, number of steps, and action sequence execution time in Condition 1, than in Condition 2 (p < 0.001) or 3 (p < 0.001). These results suggest that the high error rate and variable performance in Condition 1 may be explained by active action exploration and learning of the new KM.These results show that subject performance in terms of reward score, overall goal reach and optimal goal reach were improved quite significantly in Condition 2, specifically with a delayed start, a finding that replicates our previous workOur main purpose was to identify the neural network that accomplishes mental simulation of sequential actions using internal models. Due to the beneficial effect of the pre-start delay in accelerating learning in Condition 2 when subjects could use the internal model of the KMs to plan sequential actions, we analyzed how the BOLD fMRI signal in the delay period was modulated by different task conditions. Using Condition 3 as the control task for Condition 1 (Condition 1 > Condition 3) and for Condition 2 (Condition 2 > Condition 3), the analysis revealed that both Condition 1  and CondIn order to investigate whether distinct neural networks were associated with the distinct performance profiles found in the behavior analysis, we conducted a direct contrast of the delay period signal between Conditions 1 and 2 . Cortical activation in Condition 1 was found in the ventromedial orbital frontal cortex (VMOFC), the somatosensory cortex, the inferior parietal lobule, and primary visual cortex .We also investigated neural mechanisms associated with well-learned action sequences in Condition 3. When contrasting the activity of Condition 3 versus the activities in Condition 1, Condition 2 or both, no significant activation was found . However, when analyzing the positive activation (contrast image) of Condition 3 we found significant activity in the dorsal premotor cortex, the supplementary motor area (SMA), and the superior parietal cortex  in the identified prefrontal and striatal clusters . Signal intensity in the VMOFC and the anterior-ventromedial striatum was highest in Condition 1  or even when using a more lenient statistical threshold of p < 0.001, uncorrected. However, using Condition 3 as the control, significant activation was found in Condition 1  the brain arbitrates between decision strategies. Switching from deliberative to automatic control may also depend on prefrontal-subcortical systems5556Eighteen healthy, right hand-dominant subjects , non-musicians, with normal vision, or vision corrected to normal (by contact lenses), participated in this study. All subjects were screened for history of psychiatric, neurological problems, or drug use at the time of the experiment. Subjects gave written informed consent and the experiment was approved by the ethics and safety committee of the Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan, and they meet the Declaration of Helsinki requirements. Methods were carried out in accordance with approved guidelines. A fixed payment was made to subjects for their participation in this study. Due to technical fMRI pre-processing problems, we report the imaging results of only sixteen subjects.In the training session, performed outside the fMRI scanner, subjects sat comfortably at a table approximately 40–50 cm from the computer monitor. The task was run on a regular desktop computer and keyboard. Subjects used the index, middle, and ring fingers of the right hand to press the ‘v’, ‘b’, and ‘n’ keyboard buttons, respectively. Stimulus generation and behavioral data recording were done by custom-made programs written in MATLAB (version 7.0), using Psychophysics Toolbox functions (PTB-3). The test session was performed inside the fMRI scanner and the task was displayed through an imaging projector on a mirror inside the scanner mounted on top of the head coil. An fMRI-compatible response optic button box was used, obeying the same finger-key association used in the training session.et al., 2010). Subjects were given verbal instructions about task rules before training began, and received brief verbal feedback during the first training trials to ensure that they fully understood how to execute the task. The task started with an inter-trial interval of 3~5 s in which the computer displayed a 5 × 5 grid (10 cm × 10 cm) with a red fixation cross (FC) on top of the center square matching the monitor center position  and was assigned to one of three different key-mappings (KM), which were associated with a three-move direction rule . Five diSubjects performed one training session and one test session on consecutive days, with one night of sleep between sessions. Training and test sessions were further divided into three and two trial blocks, respectively, with trial blocks separated by 5-min rest intervals. Subjects were randomly assigned to one of three groups and remained in that group during the two experimental sessions. During training, only two KMs and four SG positions were employed by each group . Each ofDuring a test session, subjects performed the task inside the fMRI scanner, under three task conditions : ConditiSubjects performed a short version  of the training task right before the start of fMRI scanning in order to ensure that they still remembered the task procedures, and more importantly, the action sequences learned for each KM-SG set on the previous day. At the end of this short pre-fMRI practice, subjects received explicit instruction regarding introduction of different task conditions that they had to perform inside the fMRI scanner.We analyzed the following behavioral performance variables: a) reward score – number of points earned in a trial; b) over-step – number of excessive keystrokes executed to reach the target goal; c) reaction time (RT) – time elapsed from go signal to onset of the first keystroke; d) execution time (ET) – time from onset of the first keystroke to the execution of the last. These behavioral measures were separately subjected to a series of n-way ANOVA with Group (3), Task Condition , Trial Block , and Start Time Condition (immediate and delay) as factors. The statistical analysis also included multiple comparison procedures with Scheffe’s post-hoc test to seek for significant differences between pairs of levels.Scanning took place at the ATR Brain Activity Imaging Center, Kyoto, Japan. Detailed anatomical data were collected using a multiplanar, rapidly acquired gradient echo (MP-RAGE) sequence. T2*-weighted echo planar images (EPIs) with blood oxygen level-dependent (BOLD) contrast were acquired on a Siemens Tesla 3 Magnet Trio MRI scanner . Thirty coronal oblique slices (3 × 3 × 5 mm) were acquired parallel to the plane containing the anterior and posterior commissures (AC-PC plane) after prescribing slice position based on automatic measurements of rotation, translation, and tilt of the structural images relative to an average image. The fMRI session was split into two runs, each lasting approximately 22 min, with the number of acquired imaging volumes dependent upon subject performance.Statistical parametric mapping with SPM8 software  was used to preprocess all fMRI data, which included correction for slice time acquisition, spatial realignment of all volumes to the first image, spatial normalization using regressor parameters estimated by the unified segmentation process with voxel size resampled to 3 × 3 × 3 mm, and smoothed using a Gaussian kernel with an isotropic full width at half maximum of 8 mm. High-pass temporal filtering with a cutoff of 128 s was applied to remove low-frequency drifts in signal, and global changes were removed by proportional scaling.http://rfxplot.sourceforge.net/) and neuro-anatomically defined mask images generated using the WFU Pickatlas toolbox for the caudate and putamen, and the Anatomy Toolbox for the cerebellum. The Multicolor toolbox was used to plot voxels extracted from the basal ganglia and the cerebellum.Statistical analysis was conducted using a general linear model and a set of boxcar functions. Subject-specific design matrices were created, including two-trial events (Response Period and Reward delivery) of immediate-start condition trials and three events  of the delayed-start condition trials modulated separately by subject performance variables  for each of the three task conditions. A nuisance partition containing head motion regression parameters estimated in the realignment procedure was also entered in the design matrix. A series of contrast images were generated for each subject using a subtraction approach and were subsequently taken to a second-level for a random effects group analysis using one-sample t-tests. We were primarily interested in the specific effect of task condition on the delay-related period activity. Region of interest analysis (ROI) was carried out using the rfxplot toolbox .


The expression of the tumor suppressor Merlin is compromised in nervous system malignancies due to genomic aberrations. We demonstrated for the first time, that in breast cancer, Merlin protein expression is lost due to proteasome-mediated elimination. Immunohistochemical analysis of tumor tissues from patients with metastatic breast cancer revealed characteristically reduced Merlin expression. Importantly, we identified a functional role for Merlin in impeding breast tumor xenograft growth and reducing invasive characteristics. We sought to determine a possible mechanism by which Merlin accomplishes this reduction in malignant activity. We observed that breast and pancreatic cancer cells with loss of Merlin show an aberrant increase in the activity of β-catenin concomitant with nuclear localization of β-catenin. We discovered that Merlin physically interacts with β-catenin, alters the sub-cellular localization of β-catenin, and significantly reduces the protein levels of β-catenin by targeting it for degradation through the upregulation of Axin1. Consequently, restoration of Merlin inhibited β-catenin-mediated transcriptional activity in breast and pancreatic cancer cells. We also present evidence that loss of Merlin sensitizes tumor cells to inhibition by compounds that target β-catenin-mediated activity. Thus, this study provides compelling evidence that Merlin reduces the malignant activity of pancreatic and breast cancer, in part by suppressing the Wnt/β-catenin pathway. Given the potent role of Wnt/β-catenin signaling in breast and pancreatic cancer and the flurry of activity to test β-catenin inhibitors in the clinic, our findings are opportune and provide evidence for Merlin in restraining aberrant activation of Wnt/β-catenin signaling. NF2 gene [NF2 gene or mutations within the gene manifests frequently as neurofibromatosis 2, schwannomas, meningiomas, and ependymomas. Mutations of the NF2 gene are also found in mesotheliomas [Merlin, also known as schwannomin or neurofibromin 2, was first discovered in 1993 as the protein encoded by the heliomas -6, prostheliomas , colorecheliomas , melanomNF2 gene in breast cancers [Three independent studies failed to identify mutations of the  cancers -12. Conc cancers . We iden cancers -16. Merl cancers , 17. Mer cancers . We have cancers .Detachment of cells involves profound rearrangements of structural molecules characterized by disruption of the cadherin-catenin complex with the cytoskeleton. β-catenin is an integral member of adherens junctions that regulates the cellular dynamics of cell attachment or detachment . β-catenIn this study we present novel, exciting observations that Merlin binds β-catenin and restricts its nuclear entry. By mediating increased cellular levels of Axin1, Merlin also facilitates proteasomal degradation of β-catenin, thereby reducing the overall pool of β-catenin. Loss of Merlin enables nuclear translocation of β-catenin with a concomitant increase in β-catenin-mediated transcriptional activity in breast and pancreatic carcinoma cells. Importantly, we show that loss of Merlin sensitizes tumor cells to inhibition by compounds that inhibit β-catenin-mediated activity. Thus, our study provides compelling evidence for Merlin in reducing the malignant activity of pancreatic and breast cancer, in part by suppressing the Wnt/β-catenin pathway.Given the important role of Merlin in contact inhibition and its role in stabilizing adherens junctions in Schwann cells and mouse embryo fibroblasts (MEFs) , 31, we in vitro and in vivo, results in enhanced non-membranous β-catenin [We tested the effect of Merlin on β-catenin-mediated transcriptional activity using TOPFlash activity as a read-out. Abrogating Merlin from MCF10AT and MCF7 breast cancer cells caused a significant (p < 0.05) upregulation of TOPFlash activity indicating activation of β-catenin–mediated transcription Figure . Interes-catenin , 33. Als-catenin , 35. In The osteopontin (OPN) oncoprotein has been reported as one of the transcriptional targets of β-catenin in rodent mammary, bladder, and kidney carcinoma cells , 36. MCFOur evidence suggests that loss of Merlin causes re-localization of β-catenin and decreases its transcriptional activity. The increased time for visualizing β-catenin in cells restored for Merlin suggested probable changes in cellular β-catenin levels. Therefore we evaluated the levels of total β-catenin from the perspective of Merlin. The total cellular level of β-catenin is decreased in SUM159 cells restored for Merlin expression, simultaneous with a decrease in the transcriptional targets, cyclin D1 and c-Myc Figure . There wSince we observed that Merlin decreases β-catenin protein levels, we aimed to determine the mechanism regulating this decrease. In the absence of active Wnt signaling, β-catenin is phosphorylated by casein kinase Iα (CKIα) at Serine 45 -39 and bWe previously determined that Merlin inhibits tumor growth and invasive characteristics in breast cancer cells . To asseGiven our findings that cells compromised for Merlin expression show robust β-catenin-mediated signaling, we tested the sensitivity of Merlin-silenced MCF10AT breast cancer cells to two inhibitors of Wnt/β-catenin signaling. We tested iCRT-14, an inhibitor of β-catenin-responsive transcription and JW55, an inhibitor of the PARP domain of tankyrase 1 and tankyrase 2 (TNKS1/2), regulators of the β-catenin destruction complex. By virtue of inhibiting TNKS1/2 poly(ADP-ribosyl)ation activity, JW55 stabilizes AXIN2, leading to increased degradation of β-catenin. Our findings revealed that cells silenced for Merlin and having greater activity of β-catenin were sensitive to iCRT-14 and JW55 Wnt/β-catenin signaling inhibitors Figure . As suchin vivo CRISPR-Cas9 screen in mice revealed a set of genes regulating lung metastasis. In particular, the Nf2 gene locus was found to be significantly enriched in late primary tumor and lung metastases suggesting that Merlin likely plays an important role in promoting metastasis as well as the growth of the primary tumor [Functionally, Merlin is best characterized for its role in contact inhibition of growth. Merlin is directly involved in cytoskeletal organization via its ability to bind actin and tubulin and inhibits the actin nucleation promotion factor N-WASP, thereby regulating actin polymerization . Merlin ry tumor .bonafide targets c-Myc, cyclin D1, and OPN. In support of this observation, MCF7 cells silenced for Merlin registered a robust increase in the level of β-catenin associated with the TCF4 transcription factor   and β-caSUM159 and SUM149 cells were grown in DMEM/F-12 supplemented with heat-inactivated FBS , insulin, and hydrocortisone. MCF7 cells were cultured in DMEM/F-12 supplemented with FBS and insulin. MCF10AT cells were cultured as described previously . Stable 2 incubator at 37°C.Stable Merlin-expressing transfectants of BxPC-3 cells were generated by transfecting pcDNA3-Merlin or an empty-vector (pcDNA3) as previously described  and wereCells were plated at a density of 5,000 cells per well in 96 well tissue culture plates and treated with Wnt/β-catenin inhibitors iCRT-14  or JW55 (Tocris Bioscience) or vehicle control (DMSO). Cell viability was determined after 72 hours using the PMS/MTS reagent . Absorbance was read at 490 nm.M50 TOPFlash luciferase construct was generously gifted by Dr. Randall Moon . pACT-Merlin was generated using the following primers: Forward primer 5′(GCACTAGTCTAGAGCCGGGGCCATCGCTTC)3′ and reverse primer 5′(CGTCGGGGTACCGAGCTCTTCAAAGAAGGCCA)3′. pcDNA3.1-Merlin was used as a starting template . pBIND-β-catenin was generated using the forward primer: 5′(GCACTAGTCTAGAGCTACTCAAGCTGATTTGATG)3′ and reverse primer 5′(CGTCGGGGTACCCAGGTCAGTATCAAACCAGG)3′. pcDNA3.1-β-catenin was used as a template. pcDNA3.1-β-catenin and the S37A degradation-resistant plasmids were generated in the lab.Cells were co-transfected with 200 ng/well each of β-galactosidase and TOPFlash vector and the effector plasmids using Lipofectamine 2000 (Invitrogen) as per the manufacturer's instructions. Each group was assayed in triplicate with luciferase readings being normalized to β-galactosidase. Data are expressed as relative luciferase activity, where control is 100%. For the TOPFlash assay with degradation-resistant β-catenin, the same basic procedure was followed as described above. Cells were co-transfected with 200 ng/well empty pcDNA3.1 vector or pcDNA3.1 S37A β-catenin as the effector and 100 ng/well TOPFlash construct. After 36 hours, luciferase activity was measured using the Luciferase Assay System (Promega) and normalized to total protein concentration.pACT-Merlin (50ng/well), pBIND-β-catenin (50ng/well), and pGL4.31 (100ng/well) were co-transfected in SUM159 cells using Lipofectamine™ 2000 (Invitrogen) as per manufacturer's instructions. Total protein was harvested and luciferase activity was measured.OPN promoter (200ng)  was co-tSUM159 cells transfected with Merlin were lysed and 1 mg of lysate was immunoprecipitated with anti-Merlin or anti-β-catenin as a reverse co-IP using the following protocol: Primary antibody was added to the lysate at a 1:50 dilution and samples were incubated at 4°C for 2 hours with gentle rocking. Protein A/G agarose beads  were added and the samples were returned to 4°C for rocking overnight. Samples were centrifuged and pellet was washed with ice-cold PBS. Pellet was re-suspended in sample buffer, vortexed and centrifuged at maximum speed for 30 seconds at room temperature. Samples were resolved on a 10% SDS PAGE gel. The immunoprecipitates were assessed by immunoblotting. The interaction between TCF4 and β-catenin in MCF7 cells was assessed by immunoprecipitating the lysate with with anti-TCF4  followed by immunoblotting for β-catenin.SUM159 and MCF7 transfectants were fixed using 4% paraformaldehyde. Following two washes with ice-cold PBS, cells were permeabilized in PBS containing 0.1% Triton X-100 (PBST) and incubated with 1% BSA in PBST to block non-specific binding of the antibodies, followed by incubation with anti-Merlin  and anti-β-catenin  antibodies. Following three washes with PBS, cells were incubated with the mixture of two secondary antibodies each at 1:100 dilution  in 1% BSA in the dark. Cells were washed three times in PBS and mounted in DAPI . Cells were visualized using a Nikon Eclipse TE2000-U microscope and photographed at 60X magnification using the F3.8 setting of a Nikon CoolPixId700D camera.Immunoblotting was performed with the following antibodies from Cell Signaling Technologies : anti-β-catenin, anti-pS552-β-catenin, anti-pS675-β-catenin, anti-pS33/S37/T41-β-catenin, anti-Axin1, anti-TCF4, and anti-GAPDH. Anti-Merlin antibodies were purchased from Santa Cruz Biotechnology . Anti-c-Myc antibody was purchased from Clontech. Anti-active β-catenin antibody was purchased from Millipore. Anti-rabbit or anti-mouse HRP conjugated secondary antibody was used for detection and blots were developed with SuperSignal substrate  and exposed using a Syngene G:Box Imager.MCF10AT cells were grown to ~70 % confluence; cells were lysed in NP-40 lysis buffer and fractionated using a Superose 6 HR 10/30 size exclusion column  on a BIO-RAD BioLogic DuoFlow coupled with a BioLogic Fraction Collector. The column was run using PBS at a flow rate of 0.25 ml/min. Fractions (1ml) were collected and subjected to immunoblotting. Fractions 14, 15, & 16 were pooled and immunoprecipitated with anti-Merlin or anti-β-catenin using the protocol outlined in co-immunoprecipitation section. The immunoprecipitates were evaluated by immunoblotting.Following the manufacturer's instructions for the CHIP-IT™ Express Enzymatic Kit , SUM159 cells were cultured to 70-80% confluence and the cells were fixed  for 10 minutes at room temperature. Cells were scraped and transferred to an ice-cold dounce homogenizer and homogenized on ice 20 times to release the nuclei and centrifuged at 5,000 rpm for 10 minutes at 4°C. The nuclear pellet was re-suspended in the kit provided Digestion Buffer and the DNA was sheared enzymatically following the manufacturer's directions and immunoprecipitated for β-catenin. GAPDH primers were provided in the kit (Active Motif). The immunoprecipitate was subjected to PCR using primers specific to JS and non-specific primers. The non-specific primers  are as follows: Forward (5′ GCAACTTTATAATCTGTGTGC 3′); Reverse (5′ GGTAGGGGGAAATATGTCTC 3′). The JS primers  are as follows: Forward (5′ TGTCACTAGTGCCATTTGTCT 3′); Reverse (5′ CTCTTGCCTGTATGATTGTACT 3′). PCR products were visualized on a UVP  transilluminator under ultraviolet light.Following DNA isolation in the final step of the ChIP, quantitative PCR was done to quantify the extent of specific pull-down. The negative Human Control Primer Set supplied in the ChIP kit was used as endorse control and the fold change of expression was calculated using the Isotype Antibody Control as the reference set.Invasion assay was accomplished using 8μM polyethylene terpthalate filters , as described earlier . Cells wStatistical differences between groups were assessed using the student's t-test or ANOVA, using GraphPad Prism 5 software. Statistical significance was determined if the analysis reached 95% confidence. The precise p-values are listed in the corresponding Figure Legends.


Mammalian αE-catenin is allosterically regulated: the monomer binds the β-catenin·cadherin complex, whereas the homodimer does not bind β-catenin but interacts with F-actin. As part of the cadherin·catenin complex, αE-catenin requires force to bind F-actin strongly. It is not known whether these properties are conserved across the mammalian α-catenin family. Here we show that αT (testes)-catenin, a protein unique to amniotes that is expressed predominantly in the heart, is a constitutive actin-binding α-catenin. We demonstrate that αT-catenin is primarily a monomer in solution and that αT-catenin monomer binds F-actin in cosedimentation assays as strongly as αE-catenin homodimer. The β-catenin·αT-catenin heterocomplex also binds F-actin with high affinity unlike the β-catenin·αE-catenin complex, indicating that αT-catenin can directly link the cadherin·catenin complex to the actin cytoskeleton. Finally, we show that a mutation in αT-catenin linked to arrhythmogenic right ventricular cardiomyopathy, V94D, promotes homodimerization, blocks β-catenin binding, and in cardiomyocytes disrupts localization at cell-cell contacts. Together, our data demonstrate that αT-catenin is a constitutively active actin-binding protein that can physically couple the cadherin·catenin complex to F-actin in the absence of tension. We speculate that these properties are optimized to meet the demands of cardiomyocyte adhesion. The adherens junction (AJ)1–9–In mammals, αE -catenin is allosterically regulated: the monomer binds the β-catenin·cadherin complex, whereas the homodimer does not bind β-catenin but interacts with F-actin , 10. β-C14–α-Catenin functions in adhesion and mechanical signaling must be integrated in all tissues. In cardiomyocytes, the AJ functions with the desmosome to physically link opposing cells in a specialized adhesive structure called the intercalated disc (ICD) . Contrac21–Here we show that αT-catenin is a constitutive actin-binding α-catenin that can directly couple the AJ to the actin cytoskeleton. Our data also reveal that the V94D mutation linked to ARVC alters αT-catenin dimerization potential to disrupt β-catenin binding and cellular localization. We postulate that αT-catenin protein conformation and ligand binding properties are tuned to meet the specific demands of cardiomyocyte adhesion.A, N1 and N2), binds β-catenin, and mediates homodimerization  and binds vinculin in response to mechanical force . We compared the amino acid sequence of Mus musculus αT-catenin with M. musculus αE-catenin and M. musculus αN-catenin. αT-Catenin is 58% identical and 77% similar to αE-catenin; likewise, it is 59% identical and 77% similar to αN-catenin . αE-Catenin and αN-catenin are 81% identical and 91% similar, making αT-catenin the most divergent of the mammalian family. We then analyzed sequence homology across domains between αT-catenin and αE-catenin . The region with the lowest degree of homology is N2 , whereas the region with the highest degree of homology is M2 .Structural studies of αE-catenin have revealed that the protein is a series of helical bundles , 27, 28.Fig. 1force 14–, 29, 30.M. musculus αT-catenin and M. musculus αE-catenin from Escherichia coli and used limited trypsin proteolysis to examine domain organization. As shown previously (D). Tryptic digestion of αT-catenin revealed three stable fragments at 30, 25, and 18 kDa (D). N-terminal sequencing revealed that the 30-kDa fragment started at aa 379 and contained bundles M2 and M3 (D). The entire M2-M3 region forms a protease-resistant fragment in mouse αE-catenin (D)   and fishE. coli was first purified by Mono Q ion exchange chromatography (A). Two peaks were routinely observed during elution off a Mono Q column , and SDS-PAGE analysis of peak fractions revealed they both contained full-length αT-catenin . A similar ion exchange chromatography profile is observed with M. musculus αE-catenin (data not shown), and the two peaks correspond to the monomer (peak 1) and homodimer (peak 2) species. Both αT-catenin peak fractions were subsequently purified over a Superdex 200 (S200) size exclusion chromatography (SEC) column. The Mono Q peak 1 fraction eluted in a single, discrete peak , consistent with it being a single, likely monomeric, species. The S200 elution profile of Mono Q peak 2 was similar to peak 1, although a second, small peak was sometimes observed where a dimer species would be expected to elute .We assessed the oligomerization state of αT-catenin by chromatography. Recombinant αT-catenin protein prepared from tography A. Two pem) of αT-catenin with αE-catenin monomer and homodimer by analytical SEC. At all concentrations tested (25–50 μm), αT-catenin eluted in a single peak after both αE-catenin homodimer and monomer, suggesting that αT-catenin is a monomer (C). We then used SEC and sucrose density gradient centrifugation to determine the molecular mass of αT-catenin, αE-catenin monomer, and αE-catenin homodimer (C) were compared with known standard proteins to calculate the Stokes radius (D). The calculated Stokes radius of αE-catenin homodimer was similar to past observations .We then compared the primary S200 peak . The Svedberg coefficients were determined to be 7.0S for αE-catenin homodimer . The molecular mass of αT-catenin was calculated to be 109 kDa, similar to that of αE-catenin monomer (106 kDa). Finally, αT-catenin migrated as a single band by native PAGE, faster than either αE-catenin monomer or dimer, consistent with the SEC analysis (H). We conclude that αT-catenin is primarily a monomer in solution.We then used sucrose density gradient centrifugation to determine the sedimentation coefficients of αT-catenin, αE-catenin monomer, and αE-catenin homodimer. Proteins were separated on 5–20% sucrose gradients, and the fraction peak was determined and compared with a standard curve to calculate the sedimentation coefficient , E and Fculation ), 5.2S fculation G. The moanalysis H. We conA) and the minor peak in the peak 2 SEC elution  suggest that αT-catenin might exist as a homodimer. However, if the Mono Q peak 2 elution represented a homodimer species of αT-catenin, then the majority of these dimers dissociated during SEC . We were never able to purify a sufficient quantity of the potential dimer species for analysis by SEC or native-PAGE. Also, attempts to promote dimerization by incubation of the monomer at physiological (37 °C) temperatures caused the protein to aggregate and fall out of solution. Although we were unable to analyze the dimerization kinetics of wild-type (WT) αT-catenin, our analysis of the V94D mutant revealed that αT-catenin, similar to αE-catenin and αN-catenin, has dimerization potential (described below). Nonetheless, we took advantage of the lack of a stable dimer in solution to study the behavior of αT-catenin monomer binding to F-actin.Dimerization kinetics differ significantly between mouse αE-catenin and αN-catenin at physiological temperatures . αE-CateFig. 2m F-actin, the samples were centrifuged, and the resulting pellets were analyzed. αT-Catenin cosedimented with F-actin above background (A), and the bound protein was quantified and plotted over free protein to calculate the affinity of the interaction (B). Bovine serum albumin (BSA) and αE-catenin were run as negative and positive controls, respectively . Plotted data were fit to a hyperbolic function . αT-Catenin bound to F-actin with a dK of 0.4 ± 0.2 μm, similar to αE-catenin dimer . In contrast, robust bundling of 2 μm F-actin was observed with 4 μm αE-catenin homodimer (C and 5D). The weak bundling observed with αT-catenin could result from either the dimer species being stabilized on the actin filament or activation of a cryptic dimerization domain as observed in the vinculin tail , we were able to calculate the affinity of the complex for F-actin. The β-catenin·αT-catenin complex bound to F-actin with a dK of 1.1 ± 0.2 μm . Although β-catenin lowers the affinity of αT-catenin for F-actin slightly, the interaction strength is considerably stronger than that of the Danio rerio β-catenin·αE-catenin complex (>10 μm) and similar to the strength of αE-catenin homodimer association with F-actin . We then ran the V94D peak over an S200 SEC column where it eluted as a single peak before WT αT-catenin and similar to the possible homodimer peak . We then compared the concentrated V94D protein  with WT αT-catenin by analytical SEC. The V94D mutant eluted as a single species before WT αT-catenin with a larger Stokes radius . The V94D mutant also displayed a higher sedimentation coefficient than WT αT-catenin . The Stokes radius and sedimentation coefficient produced a molecular mass of 183 kDa , roughly double that of WT αT-catenin. We conclude that the V94D mutation creates a stable αT-catenin homodimer.Two mutations in αT-catenin have been linked to ARVC: replacement of a valine for an aspartic acid at aa 94 (V94D) in the N1 domain and deletion of a leucine at aa 765 (L765del) in the ABD . Yeast tFig. 4E–G). Similar to the full-length construct, the V94D mutation altered the elution and sedimentation profiles of the ΔABD construct (E and F). The calculated molecular mass of αT-catenin V94D ΔABD was 146 kDa compared with 90 kDa for αT-catenin ΔABD, consistent with it forming a homodimer.Because full-length αT-catenin V94D is difficult to purify, we deleted the ABD (aa 660–895) in both WT and V94D αT-catenin to improve protein yield. We analyzed the SEC and sedimentation properties of the ΔABD constructs , E–G. Sionstruct , E and FH). In the presence of BS3, however, V94D migrated as a 150-kDa protein at all concentrations tested, indicating a cross-linked dimer. Incubation with BS3 did not affect αT-catenin ΔABD migration at low concentrations, although at higher concentrations (2 and 4 μm), a 150-kDa species was detected. We speculate that this could reflect a transient homodimer species. We conclude that the V94D mutation promotes dimerization of αT-catenin.We analyzed the oligomeric state of the αT-catenin ΔABD proteins by cross-linking. Increasing concentrations of αT-catenin ΔABD and αT-catenin V94D ΔABD were incubated with or without the cross-linker bis(sulfosuccinimidyl)suberate (BS3), and the resulting products were analyzed by SDS-PAGE. As expected, αT-catenin ΔABD and αT-catenin V94D ΔABD ran as 75-kDa proteins in the absence of cross-linker H. In theI). However, the β-catenin/homodimerization domain  was protected relative to WT , consistent with this domain being stabilized in the homodimer state.We used limited proteolysis to determine whether the V94D mutation affected domain organization. Like WT αT-catenin, three fragments were resistant to trypsin cleavage in V94D I. HoweveJ). Thus, the V94D mutation creates an obligate αT-catenin homodimer that cannot bind β-catenin.We then questioned whether the V94D homodimer could interact with β-catenin. We mixed increasing concentrations of WT or V94D αT-catenin with GST-tagged β-catenin, pulled down the β-catenin, and assessed binding. Wild-type αT-catenin bound GST-β-catenin at stoichiometric levels; however, little to no V94D bound J. Thus, A) with an affinity similar to that of WT αT-catenin , suggesting that homodimerization does not increase the affinity of αT-catenin for F-actin.Dimerization promotes αE-catenin binding to F-actin , 10. We Fig. 5m F-actin with 4 μm αT-catenin V94D relative to 4 μm WT αT-catenin (C and D). Although increased, the level of bundling was still less than that observed with 4 μm αE-catenin homodimer (C and 5D). We conclude that the V94D mutation promotes αT-catenin-mediated F-actin bundling.We then tested whether the V94D homodimer could bundle F-actin. We consistently observed increased bundling of 2 μ-catenin , C and Domodimer C and 5D.A, C, and zoom in E). In contrast, V94D was largely peripheral to cell-cell contacts  and localized to actin fibers . This was confirmed by directly measuring colocalization between N-cadherin and EGFP-αT-catenin or EGFP-αT-catenin V94D signals at AJ clusters in transfected cells using Pearson's r (F). This analysis revealed a significant reduction in colocalization between N-cadherin and EGFP-αT-catenin V94D at AJs (F). Thus, the V94D mutation disrupts αT-catenin subcellular localization in cardiomyocytes.αT-Catenin localizes to the adherens junction at the ICD in cardiomyocytes . To deterson's r F. This aD at AJs F. Thus, in vitro results show that, in solution, αT-catenin binds F-actin as a monomer and in complex with β-catenin, properties that separate it from mammalian αE-catenin. αT-Catenin monomer binds F-actin with a slightly higher affinity than αE-catenin homodimer (0.4 versus 1.0 μm) (m). We conclude that αT-catenin binding to F-actin, unlike mammalian αE-catenin, is not allosterically regulated. This would permit αT-catenin to directly couple the cadherin·catenin complex to the actin cytoskeleton in the absence of tension, although mechanical force could strengthen the αT-catenin-actin interface.Our  1.0 μm) . AlthougM. musculus αE-catenin and αN-catenin homodimerize in solution, although the kinetics of dimerization differ significantly between the two mammalian α-catenins (versus 25 μm). However, the kinetics of dissociation differ markedly: αN-catenin equilibrates quickly, whereas a kinetic block limits αE-catenin dissociation  precluded a detailed analysis of dimerization kinetics. Nonetheless, our results lead us to postulate that αT-catenin has dimerization potential and that the homodimer species, similar to αN-catenin, dissociates quickly (i.e. no kinetic block).Both catenins . At physociation . The αE-in vivo are unclear. The V94D mutation, which drives αT-catenin into the dimer state in vitro, shifted localization from cell-cell contacts and promoted recruitment to F-actin bundles when expressed in cardiomyocytes. Actin filament cross-linking is essential for cardiomyocyte cytoskeletal organization and function. The barbed ends of actin filaments from adjoining sarcomeres interdigitate at the Z-disc where they are cross-linked primarily by α-actinin to form a structural lattice  and plakoglobin,Our limited proteolysis experiments revealed that both the β-catenin/homodimerization domain and middle domain were more protease-sensitive in αT-catenin than in αE-catenin. Notably, the N2 bundle within the β-catenin/homodimerization domain of αT-catenin is the region with the least conservation compared with αE-catenin. αT-Catenin binds β-catenin D and plaThe core M region (M1–M3) of αE-catenin is required for its function as a mechanosensor in which tension alters α-catenin conformation to promote ligand binding , 29, 43.Increased flexibility between the M2 and M3 domains could also promote ligand binding within this region. Notably, αT-catenin, but not αE-catenin, was shown to bind plakophilin-2, a desmosomal protein that links to intermediate filaments, and the binding interface was mapped to M3 . αT-Catein vitro studies showed that αT-catenin could directly couple the actin cytoskeleton to cadherin·catenin in the absence of tension. We speculate that this property of αT-catenin might permit the cadherin·catenin complex to maintain a static linkage to the actomyosin network over a range of forces such as those produced by repeated cycles of contraction and relaxation in cardiomyocytes. Our biochemical analyses also suggest that αT-catenin dimerization properties and M region stability differ from those in αE-catenin. How these differences impact in vivo function is unclear, but we speculate that they could impact molecular interactions and tension sensing. In the mammalian heart, αT-catenin may have evolved to complement αE-catenin functions in adhesion and signaling.α-Catenin functions in adhesion and mechanical signaling must be integrated in all tissues. Contractile forces place physical demands on heart junctional complexes: not only must they withstand repeated cycles of force but tension-sensing proteins within these complexes must be tuned to regulate signaling and maintain homeostasis. Our M. musculus αT-catenin was cloned into pGEX-TEV  and then incubated with tobacco etch virus protease overnight at 4 °C to cleave protein from the GST tag. All proteins were purified by Mono Q anion exchange chromatography followed by S200 gel filtration chromatography in 20 mm Tris, pH 8.0, 150 mm NaCl, 10% glycerol, and 1 mm DTT. Eluted protein was concentrated to 20–50 μm working concentrations using a Millipore column concentrator, flash frozen in liquid nitrogen, and stored at −80 °C.GST-tagged αT-catenin, αE-catenin, and β-catenin were expressed in BL21(DE3) escribed , 36. GSTm Tris, pH 8.0, 150 mm NaCl, and 1 mm DTT. Protein was injected at 25–30 μm.Analytical SEC was performed at 4 °C on a Superdex 200 column in 20 mm Tris, pH 6.8, 150 mm NaCl, 300 mm sucrose, 100 mm DTT, and 0.02% bromphenol blue) and loaded onto a 5% native gel . Gels were run at 80 V for 5 h at 4 °C, stained with Coomassie Blue, and imaged on a LI-COR Biosciences scanner.FPLC-purified αE-catenin and αT-catenin were diluted in cold native gel sample buffer (20 mm αT-catenin was incubated at room temperature in 0.05 mg/ml sequencing grade trypsin (Roche Applied Science) in 20 mm Tris, pH 8.0, 150 mm NaCl, and 1 mm DTT. Reactions were stopped with 2× Laemmli buffer at the indicated times, and samples were analyzed by SDS-PAGE. For N-terminal sequencing, digested peptides were blotted onto PVDF membrane; stained with 0.1% Coomassie Blue R-250, 40% methanol, and 1% acetic acid; destained; and dried. Individual bands were excised and sequenced by Edman degradation (Iowa State University Protein Facility).12 μSR) was determined by analytical size exclusion chromatography using a Superdex 200 column equilibrated with 20 mm Tris, pH 8.0, 150 mm NaCl, and 1 mm DTT. Standard proteins were bovine carbonic anhydrase (SR = 2.4 nm), bovine serum albumin (SR = 3.5 nm), yeast alcohol dehydrogenase (SR = 4.6 nm), sweet potato β-amylase (SR = 5.4 nm), horse spleen apoferritin (SR = 6.7 nm), and bovine thyroglobulin (SR = 8.5 nm). The partition coefficient, Kav, was calculated for all standards and α-catenin proteins used in this study. The Stokes radius was calculated from a standard curve of (−logKav)1/2Sversus R.The Stokes radius  for 4 h at 4 °C. All α-catenin proteins were loaded at concentrations ≥20 μm. After centrifugation, 200-μl fractions were collected and analyzed by SDS-PAGE. Gels were imaged on a LI-COR Biosciences scanner, and the percentage of protein in each fraction was measured in ImageJ. Plotted data were fit to a Gaussian curve to determine the peak fraction in Prism software. Standard proteins were bovine carbonic anhydrase (2.8S), bovine serum albumin (4.3S), yeast alcohol dehydrogenase (7.4S), sweet potato β-amylase (8.9S), and horse spleen apoferritin (16.6S). The sedimentation coefficient of α-catenin proteins was determined from a standard curve of sedimentation coefficient (S) versus fraction.Gradients of sucrose were made by layering sucrose dissolved in 20 mescribed . Each laThe molecular mass of α-catenin proteins used in this study was calculated from the measured Stokes radius and sedimentation coefficient as described , 45.m HEPES, pH 7.5, 100 mm KCl, 2 mm MgCl2, 0.5 mm ATP, and 1 mm EGTA) for 1 h at room temperature to polymerize filaments. Gel-filtered αT-catenin or αT-catenin·β-catenin heterocomplex was diluted to the indicated concentrations in 1× reaction buffer  with and without 2 μm F-actin and incubated for 30 min at room temperature. Samples were centrifuged at 50,000 rpm  for 20 min at 4 °C in an S100-AT3 rotor. Pellets were resuspended in Laemmli sample buffer, separated by SDS-PAGE, and stained with Coomassie Blue. Gels were imaged on a LI-COR Biosciences scanner and measured and quantified in ImageJ. To determine the amount of bound protein, background sedimentation (no F-actin pellet) was first subtracted from cosedimentation (F-actin pellet). Bound protein across samples was then normalized to the F-actin pellet. The amount of bound protein was calculated from a standard curve created from the starting material. All binding data were processed with Prism software.Chicken muscle G-actin  was incubated in 1× actin polymerization buffer  in 20 mm HEPES, pH 7.4, 150 mm NaCl, and 1 mm DTT for 30 min at room temperature, separated by SDS-PAGE, stained with Coomassie dye, and imaged on a LI-COR Biosciences scanner.Purified αT-catenin ΔABD and αT-catenin V94D ΔABD were incubated with or without 1 mm Tris, pH 8, 150 mm NaCl, and 5 mm DTT. Samples were incubated with gentle mixing for >2 h at 4 °C and then washed five times in PBS + 0.05% Tween 20 and 5 mm DTT before elution in Laemmli sample buffer. Samples were separated by SDS-PAGE, stained with Coomassie dye, and imaged on a LI-COR Biosciences scanner.Increasing amounts of αT-catenin or αT-catenin V94D (1–15 μg) were added to 15 μg of GST-β-catenin bound to glutathione-agarose in 20 mm Ara-C, and 1 μm isoproterenol. Transfections were performed 24 h postplating using Lipofectamine 2000 (Life Technologies).All animal work was approved by the University of Pittsburgh Division of Laboratory Animal Resources. Cardiomyocytes were isolated from mouse neonates (P1–P3) as described . Cardiomm 1,4-piperazinediethanesulfonic acid, pH 7.0, 25 mm HEPES, pH 7.0, 10 mm EGTA, pH 8.0, 2 mm MgCl2, and 0.12 m sucrose), washed with PBS, blocked for 1 h at room temperature in PBS + 10% BSA, washed three times in PBS, incubated with primary in PBS + 1% BSA for 1 h at room temperature, washed three times in PBS, incubated with secondary in PBS + 1% BSA for 1 h at room temperature, washed three times in PBS, and mounted in Fluoromount G (Electron Microscopy Sciences). F-actin was stained using Alexa Fluor-phalloidin (Invitrogen) and antibodies against αE-catenin (Enzo Life Sciences) or N-cadherin (Invitrogen). Cells were imaged on a Nikon Eclipse Ti inverted microscope outfitted with a Prairie swept field confocal scanner, Agilent monolithic laser launch, and Andor iXon3 camera using NIS-Elements imaging software. Maximum projections of 4-μm image stacks were created for image analysis and presentation. For Pearson's r calculations, signal colocalization was measured between user-defined N-cadherin-positive AJ clusters and EGFP signals using ImageJ. Colocalization data were plotted and analyzed with Prism software.Cells were fixed in 4% paraformaldehyde in PHEM buffer (60 mA. V. K. conceived and coordinated the study and designed experiments. E. D. W., I. W. D., C. D. M., J. A. H., D. B. S., and A. V. K. performed research. E. D. W., C. D. M., J. A. H., and A. V. K. analyzed data. A. V. K. wrote the paper.


Canonical WNT/β-catenin signaling is a central pathway in embryonic development, but it is also connected to a number of cancers and developmental disorders. Here we apply a combined in-vitro and in-silico approach to investigate the spatio-temporal regulation of WNT/β-catenin signaling during the early neural differentiation process of human neural progenitors cells (hNPCs), which form a new prospect for replacement therapies in the context of neurodegenerative diseases. Experimental measurements indicate a second signal mechanism, in addition to canonical WNT signaling, being involved in the regulation of nuclear β-catenin levels during the cell fate commitment phase of neural differentiation. We find that the biphasic activation of β-catenin signaling observed experimentally can only be explained through a model that combines Reactive Oxygen Species (ROS) and raft dependent WNT/β-catenin signaling. Accordingly after initiation of differentiation endogenous ROS activates DVL in a redox-dependent manner leading to a transient activation of down-stream β-catenin signaling, followed by continuous auto/paracrine WNT signaling, which crucially depends on lipid rafts. Our simulation studies further illustrate the elaborate spatio-temporal regulation of DVL, which, depending on its concentration and localization, may either act as direct inducer of the transient ROS/β-catenin signal or as amplifier during continuous auto-/parcrine WNT/β-catenin signaling. In addition we provide the first stochastic computational model of WNT/β-catenin signaling that combines membrane-related and intracellular processes, including lipid rafts/receptor dynamics as well as WNT- and ROS-dependent β-catenin activation. The model’s predictive ability is demonstrated under a wide range of varying conditions for in-vitro and in-silico reference data sets. Our in-silico approach is realized in a multi-level rule-based language, that facilitates the extension and modification of the model. Thus, our results provide both new insights and means to further our understanding of canonical WNT/β-catenin signaling and the role of ROS as intracellular signaling mediator. β-catenin signaling in the process of cell fate commitment in hNPCs, which has been reported to play a crucial role for the differentiation process of hNPCs. Based on a combined in-vitro and in-silico approach we demonstrate an elaborate interplay between endogenous ROS and lipid raft dependent WNT/beta-catenin signaling controlling the nuclear beta-catenin levels throughout the initial phase of neural differentiation. The stochastic multi-level computational model we derive from our experimental measurements adds to the family of existing WNT models, addressing major biochemical and spatial aspects of WNT/beta-catenin signaling that have not been considered in existing models so far. Cross validation studies manifest its predictive capability for other cells and cell lines rendering the model a suitable basis for further studies also in the context of embryonic development, developmental disorders and cancers.Human neural progenitor cells offer the promising perspective of using in-vitro grown neural cell populations for replacement therapies in the context of neurodegenerative diseases, such as Parkinson’s or Huntington’s disease. However, to control hNPC differentiation within the scope of stem cell engineering, a thorough understanding of cell fate determination and its endogenous regulation is required. Here we investigate the spatio-temporal regulation of WNT/ In the nucleus β-catenin associates with the Lef/Tcf transcription factors triggering a pathway-specific gene response relevant for the regulation of various physiological and developmental processes, including neuronal differentiation ), whereas only ∼ 25% LRP6 molecules are raft-associated (LR[LRP6]), which corresponds to experimentally derived values in [Representative simulation trajectory demonstrating the separation of membrane bound CK1alues in .(TIF)Click here for additional data file.S1 Tableβ-catenin concentration).PRCC values for input parameters significantly correlated with model outcome (nuclear (PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 TextSource file for WNT/beta-catenin model implemented in ML-Rules.(PDF)Click here for additional data file.S4 TextSource file for combined ROS/WNT/beta-catenin model implemented in ML-Rules.(PDF)Click here for additional data file.S1 Datasheet(CSV)Click here for additional data file.S2 Datasheet(CSV)Click here for additional data file.S3 Datasheet(CSV)Click here for additional data file.S4 Datasheet(CSV)Click here for additional data file.


Methyldopa, an antihypertensive, is a very close analogue of DOPA. Drug interaction accompanied by degradation in a banana juice mixture was reported for DOPA. However, the effect of banana on methyldopa has not been reported. Therefore, we have investigated the impact of banana juice on methyldopa. The drug and supernatant of banana pulp were mixed, and the mixture was observed for changes in color, drug concentration, and ultraviolet-visible absorption spectra at 30 °C. The originally clear and colorless mixture started to acquire a yellow coloration after about 30 seconds after the mixing. The color tone increasingly deepened, then blistered solid particles that do not dissolve were observed after 3 hours. Concentration of methyldopa in the mixture decreased by 60% after 5 min, to 0.5% after 30 min of the mixing. From these findings, it was suggested that the drastic alterations were caused by banana polyphenol oxidase that plays a role in the biosynthesis of melanin pigment from levodopa in banana pulp. Because the degradation of methyldopa occurs extremely fast, it was suggested concomitant use of this anti-hypertensive and banana juice consumption should be avoided in clinical practice. Methyldopa (MD) is an antihypertensive that controls the sympathetic nervous system via a central action . This meAlterations in the coloring of banana supernatant containing MD (1 mM) over 3 h are shown in Alterations in the absorption spectrum of banana supernatant with MD at 30 °C during 60 min are shown in Alterations in concentration of MD in banana supernatant solution (1 mM) at 30 °C over a time span of 60 min are shown in Methyldopa was obtained from LKT Laboratories, Inc. . Filipino banana was purchased from a local market in Japan. LC-MS grade acetonitrile was used . All other chemicals were of reagent grade (Wako).Banana pulp was minced and homogenized in distilled water at 50% or 10% (w/v) concentrations. The homogenate was centrifuged at 15,000 g for 10 min at 4 °C. Each supernatant was stored at −40 °C until use. Enzymatically inactivated supernatant from banana pulp was also produced by autoclave treatment (121 °C for 20 min) of the fresh banana juice supernatant.At 30 °C, alteration of ultraviolet-visible absorption spectrum of 1 mM MD dissolved in 10% banana supernatant was measured by U-3210 Spectrophotometer . The absorbance of the mixture was scanned between 300 nm and 800 nm at 1, 5, 10, 15, 30, and 60 min after addition of MD into the supernatant. + ion at m/z 212.1 for MD was monitored for positive ions; the interface voltage was 4.5 kV, and the detector voltage was 1.5 kV. The heat block and CDL temperatures were 200 and 250 °C, respectively. Nitrogen was used as the nebulization gas at flow rates of 1.5 L/min. A mobile phase consisting of 3% acetonitrile in 1 mM ammonium formate was pumped through the column at a flow rate of 0.2 mL/min. Calibration curves (2 μM to 1 mM of MD) were constructed using regression analysis.MD (1 mM) was incubated in 50% supernatant of banana pulp at 30 °C. After 5, 10, 20, 30, 45, and 60 min of incubation, 100 times the volume of ice-cold acetonitrile was added to the reaction mixture. The sample was mixed vigorously for 20 s and centrifuged at 16,000 g for 5 min at 4 °C; then the supernatant (5 μL) was injected into LC/ESI/MS. ESI mass spectra were obtained using Shimadzu LCMS-2010EV LCMS system with an ESI probe  equipped with a Capcell Pak SCX UG80 column . The flow rate was set at 0.2 mL/min. The [M+H]in vivo experiments or clinical settings. MD should not be administered concomitantly with banana pulp to facilitate administration of the drug. It was demonstrated that MD is degraded in the supernatant of banana pulp following mixing. The degradation reaction was accompanied by alterations of the coloring. Observation of the changes in the absorption spectrum revealed that the reaction produced multiple products with different absorbance wavelengths. These observations suggest that the degradation reaction of MD is caused by polyphenol oxidase, which is involved in melanin biosynthesis in plants, in banana as well as DOPA degradation. This drug-incompatibility might become a significant drug-food interaction because the reaction is rapidly progressive. As in the case of DOPA, MD might also have reduced bioavailability when administered concomitantly with banana juice . The eff


Conversely, knockdown of δ-catenin in LECs impaired lymphangiogenesis in vitro and in vivo. Biochemical analysis shows that δ-catenin regulates activation of Rho family small GTPases, key mediators in cell motility. δ-catenin activates Rac1 and Cdc42 but inhibits RhoA in LECs. Notably, blocking of Rac1 activation impaired δ-catenin mediated lymphangiogenesis in a Matrigel assay. Consistently, loss of δ-catenin in mice inhibited the growth of tumor metastases. Taken together, these findings identify a new function of δ-catenin in lymphangiogenesis and tumor growth/metastasis, likely through modulation of small Rho GTPase activation. Targeting δ-catenin may offer a new way to control tumor metastasis.δ-catenin, an adherens junctions protein, is not only involved in early development, cell-cell adhesion and cell motility in neuronal cells, but it also plays an important role in vascular endothelial cell motility and pathological angiogenesis. In this study, we report a new function of δ-catenin in lymphangiogenesis. Consistent with expression of δ-catenin in vascular endothelial cells, we detected expression of the gene in lymphatic endothelial cells (LECs). Ectopic expression of δ-catenin in LECs increased cell motility and lymphatic vascular network formation  Lymphangiogenesis is the process that forms lymphatic vessels, during which lymphatic endothelial cells proliferate, migrate and assemble into lymphatic vascular networks. Lymphangiogenesis plays important roles in tissue homeostasis, metabolism and immunity. Lymphatic vessel formation also contributes to pathological conditions such as tumor invasion to lymph nodes and metastasis . The rolδ-catenin is a member of the catenin family belonging to the p120 catenin (p120ctn) subfamily . Geneticin vitro and in vivo, likely through regulating small RhoGTPase activities. Consistently, inactivation of δ-catenin resulted in a significant reduction of lymphangiogenesis and growth of tumor metastases in mouse models. Thus, this study expands out knowledge and identifies a new mediator in δ-catenin in lymphangiogenesis in tumor progression.To further explore the function of δ-catenin in adult vasculature, we investigated the role of this protein in lymphangiogenesis. We detected expression of δ-catenin in lymphatic endothelial cells. δ-catenin regulates lymphangiogenesis 2 asphyxiation.The mice were maintained in pathogen-free facilities at Vanderbilt University and the National Cancer Institute . The study was approved by the Vanderbilt University Animal Care and Use Committee and the NCI Animal Care and Use Committee, and in accordance with the ARRIVE guidelines. δ-catenin null mice on a C57/Bl6 background were used in our previous study [Human lung lymphatic endothelial cells (HMVEC-LLy) were purchased from Lonza , and cultured according to manufacture’s protocol. All the experiments were performed on cells between 3–7 passages. Lewis lung adenocarcinoma cells (3LL) were maintained in DMEM supplemented with 10% serum.Eight weeks old of wild type and δ-catenin null mice were sacrificed. Single cell suspension was made from lungs as described , followeTotal RNA was isolated by RNeasy Mini kit . iScriptcDNA Synthesis kit (Bio-Rad) was used to make cDNA, followed by RT-PCR using specific primer sets. Murine δ-catenin, 5’-GCCCAGTTTGAGAAGCTG 3’ and 5’-TCCTCGGTGTAGGTTTCC-3’; Βeta-actin 5’- GACAACGGCTCCGGCATGTGC -3’ and 5- TGGCTGGGGTGTTGAAGGTC- 3’.Cell lysates were analyzed by Western blot and incubated with a specific antibody against δ-catenin (Santa Cruz).HMVEC-LLy cells were transiently transfected with either a δ-catenin expression pCMV vector or empty vector using a Nucleofector device (Lonza). Knockdown of δ-catenin was performed by using a mixture of shRNA (Sigma-Aldrich) transfected into cells by a Nucleofector device (Lonza).In vitro lymphatic angiogenic assays and Rac1, Cdc42, and RhoA activation assay.LEC migration, lymphatic vascular network formation and Rho GTPase activity measurement were done according to a published protocol except using VEGF-C . The exp5) in 100μl PBS were injected into the tail vein of wild-type littermates and δ-catenin null mice. At 14 days after inoculation of the tumor cells, the lungs were excised, and the number of pulmonary tumor colonies was counted and lungs were weighted.For lymphangiogenesis, 600 μl of growth factor reduced Matrigel (BD Bioscience) mixed with VEGF-C at 100 ng was injected subcutaneously into the dorsal region of sex and age matched mice for about 2 weeks. Matrigel plugs were harvested, sectioned and analyzed by Lyve-1 immunofluorescent staining. The number of Lyve-1 positive vascular structures was counted from 10 randomly selected fields under microscopy. For metastasis, 3LL cells  pooled from multiple donors. Semi quantitative RT-PCR analysis clearly demonstrated expression of δ-catenin in human lymphatic endothelial cells . To furtLike vascular endothelial cells, lymphatic endothelial cells have the tendency to form vascular networks in 3-D culture. Consistent with the increase in cell motility, ectopic expression of δ-catenin significantly increased lymphatic vascular network formation  and convSmall Rho family of GTPase regulates cell motility. Previously, it was reported that δ-catenin regulates neuronal cell motility through Rho GTPase . We showTo validate if the RhoGTPase is responsible for δ-catenin mediated lymphangiogenesis, we co transfected HMVEC-LLy cells with vectors for δ-catenin and dominant-negative Rac1 (Rac1N17). As expected that blocking Rac1 activation significantly inhibited δ-catenin-mediated lymphangiogenesis in a Matrigel tubule formation assay . These din vivo, we used δ-catenin null mice. Besides deficiency in memory and synaptic plasticity, the null mice are healthy, fertile, and they grow normally [To further explore the function of δ-catenin in lymphangiogenesis normally . We estain vitro and in vivo findings clearly established an important role of δ-catenin in lymphangiogenesis. Loss of δ-catenin in mice impairs lymphangiogenesis and consequently contributes to reduced growth of tumor metastases.As lymphatic vessels contribute to tumor metastasis, we next examined the function of δ-catenin in tumor growth using an experimental vascular metastasis model. We injected 3LL Lewis lung carcinoma cells into the tail vein of δ-catenin null and wild type littermate control mice for two weeks, followed by evaluation of lung metastasis. Gross examination of lungs showed a reduction of tumor metastases in the null mice compared to wild type controls . Quantitin vitro and lymphangiogenesis in vivo, likely through regulating Rho GTPase activity. Consequently, deletion of δ-catenin in mice impaired growth of tumor metastases in a murine vascular metastasis model. This finding plus our previous data of δ-catenin in pathological angiogenesis [The spread of cancer cells, either by local invasion or distant metastasis, is a hallmark of malignancy. The majority of cancer-associated death is due to metastasis. The lymphatic system is important for the metastatic spread of cancer. In this study, we identified a new mediator in δ-catenin in lymphangiogenesis and growth of tumor metastases. δ-catenin is expressed in lymphatic endothelial cells. It regulates cell motility, lymphatic vascular network formation ogenesis  suggest δ-catenin was originally identified as a neuronal catenin . It is lδ-catenin belongs to the p-120ctn sub family. It is well known that p120ctn regulates actin cytoskeleton organization and cell motility through Rho family GTPase . SimilarIn conclusion, this study identifies a new function of δ-catenin in lymphangiogenesis. δ-catenin is expressed in lymphatic endothelial cells. It regulates cell motility and vascular network formation likely through regulation of Rho GTPase signaling. Consistently, inactivation of δ-catenin in mouse models inhibits lymphangiogenesis and growth of tumor metastases. The study expands our understanding of δ-catenin in vascular biology. Considering the critical function of δ-catenin in pathological angiogenesis  and lympThe experiments comply with the current laws of USA in which they were performed.


D, or the cell proliferation rate, λ. Estimating D and λ is important for investigating the efficacy of a potential treatment and provides insight into the mechanism through which the potential treatment acts. While a few methods for estimating D and λ have been proposed, these previous methods lead to point estimates of D and λ, and provide no insight into the uncertainty in these estimates. Here, we compare various types of information that can be extracted from images of a scratch assay, and quantify D and λ using discrete computational simulations and approximate Bayesian computation. We show that it is possible to robustly recover estimates of D and λ from synthetic data, as well as a new set of experimental data. For the first time, our approach also provides a method to estimate the uncertainty in our estimates of D and λ. We anticipate that our approach can be generalized to deal with more realistic experimental scenarios in which we are interested in estimating D and λ, as well as additional relevant parameters such as the strength of cell-to-cell adhesion or the strength of cell-to-substrate adhesion.Quantifying the impact of biochemical compounds on collective cell spreading is an essential element of drug design, with various applications including developing treatments for chronic wounds and cancer. Scratch assays are a technically simple and inexpensive method used to study collective cell spreading; however, most previous interpretations of scratch assays are qualitative and do not provide estimates of the cell diffusivity,  Invoking the standard assumption that Pm and Pp are constant, the parameters in the computational simulation are related to D and λ by , Pp ∈ , to generate parameter values and corresponding simulations. Given that the time scale of cell proliferation is typically much larger than the time scale of cell motility, ABC is a useful method for computing posterior distributions of unknown model parameters in situations where the likelihood function is intractable . ABC algtility,  , we antitility,  ,36, an A4.4.1.Pm, Pp) = , which with Δ = 25 μm and τ = 1/24 h corresponds to  = . We perform a single realization of the simulation with these parameters and calculate the relevant summary statistics at time t = 4, 8, 12 h. The average distance between the summary statistic for the synthetic data and the simulation prediction at t = 4, 8, 12 h is calculated and compared to either accept or reject potential parameter values to estimate the posterior distribution . The evolution of the computational simulation is presented in figure 2a–d. We apply the ABC-MCMC algorithm  and present the resulting posterior distribution for the pair correlation function q(i), in figure 2e. If the pair correlation function were a close-to-sufficient summary statistic, we would observe a well-defined posterior distribution centred at  = , with a narrow spread about the mean in the distributions of Pm and Pp. Instead, we observe that the posterior distribution is centred at  ≈ , with significant spread. These observations suggest that additional information ought to be incorporated into our ABC algorithm. We note that identically prepared simulations using the same values of Δ and τ, but different values of Pm and Pp, can occasionally produce similar or indistinguishable summary statistics. This is due to the fact that our random walk computer simulations are stochastic. For this reason, we feel it is more appropriate to interpret our experimental results using a probabilistic ABC approach, leading to a distribution of D and λ, rather than using a more traditional approach and arriving at point estimates of D and λ.To examine the robustness of our method and the validity of using the pair correlation function as a summary statistic, we first attempt to recover parameter values from data generated synthetically. We choose a biologically relevant parameter set , as summary statistics in figure 2f and g, respectively. Intuitively, we expect that N may provide some information about Pp but less information about Pm. Indeed, the posterior distribution in figure 2f suggests that all values in the range Pm ∈  are potentially acceptable and there is little correlation between Pm and Pp. The counts of pair distances correspond to the pre-normalized pair correlation functions. Since information regarding Pm and Pp may be lost in the normalization that converts c(i) into q(i), we anticipate that c(i) could be a more relevant summary statistic than q(i). We observe in figure 2g that c(i) is indeed an excellent summary statistic as the calculated posterior distribution is centred on  ≈ .We now attempt to refine our estimates of q(i) and N, K = {d[q(i)] + d[N]/2}, where d[L] is a measure of the difference between two datasets using an arbitrary summary statistic L . Therefore, the summary statistic K includes information about the number of cells or agents lost due to the formulation of the pair correlation function, q(i). We present the posterior distribution calculated using K in figure 2h and we find that the distribution is similarly centred on  ≈ . Owing to the explicit inclusion of N in the summary statistic, we observe a reduction in the spread of the posterior distribution in the Pp direction, compared to figure 2g, while maintaining a similar spread in the Pm direction. While in theory we could continue to incorporate additional information in our summary statistic to obtain a further refined posterior distribution, there is an important computational trade-off between the more complicated summary statistic and the improvement in the posterior distribution  and 104 equally spaced values of Pp in the interval Pp ∈ , and count the number of successful observations for each parameter combination, and use this information to calculate DKL(f|π). We find that DKL(f|π) is higher for the posterior distribution calculated using K rather than c(i): DKL(f|π) = 7.98 and DKL(f|π) = 7.61, respectively. For perspective, the DKL(f|π) values for q(i) and N were 6.32 and 6.93, respectively. Therefore, a difference in DKL(f|π) of approximately 0.3 is relevant. Guided by this information, we will interpret our experimental data using K as the summary statistic.As both the vergence , which iribution . We disc4.2.q(i) and c(i), and counted N, at t = 4, 8, 12 h. The position of the cells in the experiments was mapped to a square lattice with Δ = 25 μm . Figure 4a–i illustrates the process of mapping the cell positions to the lattice. We applied the ABC-MCMC algorithm  to the experimental data using K as a summary statistic and the average distance between the summary statistic for the experimental data and the simulation prediction at t = 4, 8, 12 h to either reject or accept potential parameter values in the estimation of the posterior distribution, given in figure 4j. We observe that the resulting bivariate posterior distribution is well defined and contains a relatively narrow spread in both the D and λ directions. To provide further insight, we estimate the corresponding univariate distributions of D and λ, presented in figure 4k–l, by averaging the posterior distribution in each of the λ and D directions, respectively. Since the univariate posterior distributions do not appear to be significantly skewed, we choose to report the mean of the univariate posterior distributions as our estimate of D and λ. To provide quantitative insight into the uncertainty in our estimates, we calculate the 90% credible interval by finding the interval, symmetric about the mode, containing approximately 90% of the total area under the univariate distribution. The mean and 90% credible intervals are D ≈ 1350(675–1800) μm2 h−1 and λ ≈ 2.5 × 10−2(1.7 × 10−2–3.1 × 10−2) h–1. We note that our estimates of D and λ are consistent (within a factor of two) with previously reported point estimates [D and λ, our approach provides a well-defined quantitative estimate of the uncertainty present in the parameter recovery. Furthermore, our approach does not require overly complicated and time-consuming experimental procedures such as tracking individual cells [We obtained experimental data from a scratch assay, calculated stimates . Howeveral cells ,16, consal cells  or perfoal cells .Figure D ≈ 1350 μm2 h−1 and λ ≈ 2.5 × 10−2 h−1) with Δ = 25 μm and τ = 1/24 h, equation (3.1) gives Pm = 0.36 and Pp = 0.00104. Therefore, the average time between motility events for an isolated agent τ/Pm is approximately 7 min, whereas the average time between proliferation events for an isolated agent τ/Pp is approximately 40 h. These quantities are biologically realistic since the time scale of cell motility is much shorter than the time scale of cell proliferation, and these quantities are consistent with previous estimates of the time scale of cell motility [For our parameter estimates  and the number of cells or agents present N provided a simple yet insightful summary statistic. After confirming the validity of our approach using synthetic data, we applied the same approach to a new experimental dataset. Our posterior distribution of D and λ allowed us to estimate  ≈ , which was consistent with previously reported estimates [D = (675−1800) μm2 h−1 and λ = (1.7 × 10−2−3.1 × 10−2) h–1 very simply using a single experimental dataset.To investigate the validity of applying ABC to spatio-temporal experiments such as scratch assays, we initially attempted to recover estimates of stimates . Howeveret al. [D, λ and D and λ in the lattice-based and lattice-free frameworks. While this comparison is, in principle, possible to carry out, we note that ABC techniques rely on repeated simulations of the random walk, and that lattice-free models of collective cell behaviour with crowding effects are significantly more computationally demanding than lattice-based models. Therefore, we leave the extension of applying ABC techniques to a lattice-free model for future analysis.Our approach of interpreting scratch assays using ABC together with a combination of the pair correlation function and the number of cells present in the experimental images can be extended in several ways. For example, in this work we have only considered experimental data where cell-to-cell adhesion is negligible . Howeveret al. , could bferation , other mferation ,42 or diferation . Howeverferation –45. Alteferation ,47, and


Computational observations indicated that Ex-527 may also target SIRT6 active site residues under high salt concentration, thus, validating in vitro findings. Based on these evidences, a novel regulatory step by SIRT6, modifying AR expression under hyperosmotic stress was presented and its possible interactions with intracellular machinery was discussed.SIRT6 is a protein deacetylase, involved in various intracellular processes including suppression of glycolysis and DNA repair. Aldose Reductase (AR), first enzyme of polyol pathway, was proposed to be indirectly associated to these SIRT6 linked processes. Despite these associations, presence of SIRT6 based regulation of AR still remains ambiguous. Thus, regulation of AR expression by SIRT6 was investigated under hyperosmotic stress. A unique model of osmotic stress in U937 cells was used to demonstrate the presence of a potential link between SIRT6 and AR expression. By overexpressing SIRT6 in HeLa cells under hyperosmotic stress, its role on upregulation of AR was revealed. In parallel, increased SIRT6 activity was shown to upregulate AR in U937 cells under hyperosmotic milieu by using pharmacological modulators. Since these modulators also target SIRT1, binding of the inhibitor, Ex-527, specifically to SIRT6 was analyzed  A well-established target of the transcription factor NFAT5 (nuclear factor of activated T-cells 5), aldose reductase (AR) is responsible from reduction of various substrates including glucose, as well as, atherogenesis related aldehydes –3. Despi+) dependent protein deacetylase, was suggested to regulate wide range of intracellular processes including metabolism and genomic maintenance [SIRT6, a nicotinamide adenine dinucleotide  was added to all controls. HeLa cells were cultured in DMEM supplemented with 5 mM glucose, 10%FBS and 100 IU/ml penicillin/streptomycin, maintained similar to U937 cells [Cell culture and their treatments were performed as previously described with minor modifications . U937, h at 37°C . Cells w37 cells . In all RPMI-1640, DMEM, FBS and antibiotics were purchased from Pan Biotech GmbH . MTT Cell Proliferation Kit I, X-tremeGENE 9 DNA transfection reagent, protease and phosphatase inhibitor cocktails were purchased from Roche . Primary NFAT5  and AR  antibodies were purchased from Santa Cruz Biotechnology Inc.  and used with 1:200 dilution in immunoblotting. SIRT6 antibody  was purchased from Sigma  and used with 1:1000 dilution in immunoblotting. Primary SIRT1 , PARP1 , myc , Lamin A/C  and Beta-Actin  antibodies were purchased from Cell Signaling Technology Inc.  and used with 1:1000 dilution in immunoblotting. Secondary HRP-conjugated anti-rabbit (#7074) and anti-mouse (#7076) antibodies were also purchased from Cell Signaling Technology Inc.  and used with 1:5000 dilution in immunoblotting. NaCl, glucose, tris, glycine, and tween-20 were purchased from Molekula Ltd. . All other chemicals were obtained from Sigma , unless otherwise stated.Protein extraction and immunoblotting protocols were applied as previously described, with minor modifications . For totTransfections were performed as previously described, with minor modifications . Flag ta+ [The crystal structures of SIRT6 (PDB entry: 3K35 Chain A) and of SIRT1 (PDB entry: 4I5I Chain A) were used as the receptor for docking of the ligand, Ex-527. , 22. Thi+ . Structu+ . Ex-527 + . Final dProtein design tool FoldX (version 4) was utilized to assess the effect of hydrophobic residue mutations on the stability of SIRT1 and SIRT6 –28. By c3, respectively. Then the systems were neutralized with NaCl at 10 mM or 100 mM of final concentrations. Simulations completed in 10 mM were referred as low salt (LS) while simulations in 100 mM NaCl, were referred as high salt (HS). Simulations under LS were utilized to control for the outcomes of the simulations done under HS environment. The resulting systems were used in MD simulations using the NAMD program [+ (version 1.4) tool was used to evaluate the interactions under high salt condition at the 20th ns of MD simulations. [+ selected Ex-527 atoms to the Ligplot+ selected atoms of molecules interacting with Ex-527 at 20th ns of MD simulations, were measured throughout the simulations completed under high salt environment, to present the variation of Ex-527 dynamics.Molecular dynamics (MD) simulations were performed, as previously described, using the structures obtained from docking step . As the  program  with the program , 31 whic program , 33. Wat program . An NpT  program  was usedlations. . Distancin vitro results were representative of at least three independent experiments, while in silico results were representative of two. All numerical data were expressed as mean ± SEM. Statistical significance was determined using student’s t-test. p value of < 0.05 was accepted as statistically significant.All A unique model of hyperosmotic stress, recently characterized by our laboratory for its non-canonical osmotic stress response, was used to construct the main frame of this study . This moIn order to decipher the variation in the expression of SIRT6 when NFAT5 dependent AR expression was upregulated in this model, pretreatment of TSA, an agent that has been shown to induce binding of NFAT5 to its target promoter sites and to upregulate its target genes, was utilized , 38. RemWith the purpose of testing this hypothesis, wildtype flag tagged SIRT6 was overexpressed in HeLa cells, followed by similar hyperosmotic stress treatment, and AR expression was explored Figs  and S3B.+, was used as activator [+ prior to hyperosmotic stress demonstrated that more SIRT6 accumulated to nucleus and total AR expression was upregulated, compared with hyperosmotic stress only control (p < 0.05)  . Accordi+ pretreatment experiments, a well-established SIRT1 inhibitor which was also recently identified as SIRT6 inhibitor, Ex-527, was employed [+ pretreatment under hyperosmotic stress.To confirm the outcome obtained from NADemployed . Pretreaemployed . Thus, dMoreover, increased SIRT6 expression in osmotic stress only group compared with normosmotic control in in silico studies were conducted to demonstrate specific targeting of SIRT6 by the inhibitor, Ex-527, under hyperosmotic milieu.Together, it was evident that enzymatic activity of SIRT6 positively regulated its own nuclear accumulation in parallel to upregulation of AR expression. Nevertheless, the pharmacological modulators utilized in these studies did not only target SIRT6 but also targeted SIRT1 . Thus, iin vitro model with lysine 56 of histone H3 as the substrate, it was questioned if Ex-527 directly targeted SIRT6 in silico under hyperosmotic environment [Since targeting of Ex-527 on SIRT6 was suggested once through an ironment . In ordeironment . Since Eth ns (p <0.05) (th ns of the simulations (th ns (p < 0.05) (th ns (th ns (p < 0.05)   comparedp <0.05)   . Instead)  . Hence,  < 0.05) , gray.th ns were utilized and selected distances from the interaction schemes were investigated throughout the simulations. Parallel to the findings in th ns of MD simulations  indicates U937 cells treated with 5 mM glucose containing SFM for 16 hrs and HeLa cells treated with serum containing medium with 5 mM glucose for 16 hrs. 100 mM N indicates U937 and HeLa cells further treated with 100 mM NaCl (N) for 16 hrs. The reduction in cell viability (~13% for U937 and ~15% for HeLa) was not statistically significant in both cell lines (p > 0.05). Metabolic activity status was analyzed by MTT based colorimetric assay. Average absorbance values of the controls were set to 100%.(TIF)Click here for additional data file.S2 FigNuclear NFAT5, SIRT6, PARP1 and cytoplasmic SIRT1 expressions were analyzed using densitometry for (TIF)Click here for additional data file.S3 FigTotal AR expressions were analyzed using densitometry for (TIF)Click here for additional data file.S4 FigNuclear SIRT6 and total AR expressions were analyzed using densitometry for (TIF)Click here for additional data file.S5 FigAll simulations showed RMSD values converged to a plateau value, indicating suitable simulations for further analysis. Å: Angstrom.(TIF)Click here for additional data file.S6 Figth ns, the distance between center of mass of Ex-527 and center of mass of hydrophobic pocket residues was significantly lower in simulation of SIRT6-Ex-527 complex under high salt, compared with simulations of SIRT1-Ex-527 under high salt and low salt. Moreover, this distance was significantly lower in simulation of SIRT6-Ex-527 complex under low salt, compared with the simulation of SIRT6-Ex-527 complex under high salt, at 20th ns. (C) Based on RMSF values of Cl atom of Ex-527 molecule, Ex-527 atom of SIRT1 under low salt displayed similar fluctuation compared with the Ex-527 of SIRT6 under high salt. * indicates statistically significant difference vs simulation of SIRT6-Ex-527 under high salt (p < 0.05). # indicates statistically significant difference vs simulation of SIRT6-Ex-527 under low salt (p < 0.05). HS: High salt, LS: Low salt.(A) Based on RMSF values C atom of Ex-527 molecule, Ex-527 of SIRT6 simulated under high salt condition has significantly reduced fluctuations, thus flexibility, compared with the Ex-5257 molecules of the other simulations. (B) At 20(TIF)Click here for additional data file.S7 Fig(A) Legend for (TIF)Click here for additional data file.