The role of deiodinase-mediated TH effects in early vertebrate development is only partially understood. Therefore, we investigated the role of deiodinases during early development of zebrafish until 96 hours post fertilization at the level of the transcriptome (microarray), biochemistry, morphology and physiology using morpholino (MO) knockdown. Knockdown of D1+D2 (D1D2MO) and knockdown of D3 (D3MO) both resulted in transcriptional regulation of energy metabolism and (muscle) development in abdomen and tail, together with reduced growth, impaired swim bladder inflation, reduced protein content and reduced motility. The reduced growth and impaired swim bladder inflation in D1D2MO could be due to lower levels of T3 which is known to drive growth and development. The pronounced upregulation of a large number of transcripts coding for key proteins in ATP-producing pathways in D1D2MO could reflect a compensatory response to a decreased metabolic rate, also typically linked to hypothyroidism. Compared to D1D2MO, the effects were more pronounced or more frequent in D3MO, in which hyperthyroidism is expected. More specifically, increased heart rate, delayed hatching and increased carbohydrate content were observed only in D3MO. An increase of the metabolic rate, a decrease of the metabolic efficiency and a stimulation of gluconeogenesis using amino acids as substrates may have been involved in the observed reduced protein content, growth and motility in D3MO larvae. Furthermore, expression of transcripts involved in purine metabolism coupled to vision was decreased in both knockdown conditions, suggesting that both may impair vision. This study provides new insights, not only into the role of deiodinases, but also into the importance of a correct TH balance during vertebrate embryonic development.Thyroid hormone (TH) balance is essential for vertebrate development. Deiodinase type 1 (D1) and type 2 (D2) increase and deiodinase type 3 (D3) decreases local intracellular levels of T The seqn et al. . All MOsn et al. . This voentation ,11,18. MFor each condition, embryos were reared together in a Petri dish  with a maximum number of 40 embryos. Using a stereomicroscope  and a pair of #55 Dumont forceps , 72 hpf-old zebrafish larvae were manually dissected in ice-cold RNAse-free water to separate the head, abdomen and tail regions. The head region contained the brain, sacculi/otolith and jaw parts, the abdomen region contained the yolk, swim bladder, liver and the entire gastrointestinal tract, and the tail region consisted of the remainder reaching to the tail tip. Four biological replicates of each knockdown condition  and each body region were collected. A total of 20 head, 40 abdomen and 40 tail fragments were pooled per replicate in an RNAse-free Eppendorf tube and immediately snap frozen on dry ice. Samples were kept at -80°C until processing for RNA extraction.Total RNA was extracted from the tissue pools using Qiazol Lysis Reagent  according to the manufacturer’s instructions. RNA quality and quantity were determined using a Nanodrop spectrophotometer ND-1000  while the RNA integrity was verified using the Qiaxcel qiacube . Isolated intact and high quality RNA was further processed for microarray analysis.Total RNA was linearly amplified and labelled using the Low Input Quick Amplification Labelling Kit  according to the manufacturer’s instructions. In brief, 100 ng RNA was reverse transcribed into cDNA using oligo dT primers. Subsequently, cDNA was converted into cRNA and amplified and the resulting cRNA of each sample was labelled once with Cy3-CTP and once with Cy5-CTP. The RNeasy mini spin column kit  was then used to purify the cRNA samples. cRNA yield, quality and dye incorporation efficiency were verified using a Nanodrop spectrophotometer.Danio rerio probes. 825 ng Cy3 and 825 ng Cy5 labelled and purified cRNA were hybridized onto these microarrays for 17 h at 65°C in a rotating (10 rpm) hybridization oven (Agilent Technologies). Four biological replicates of each condition were used and an A-optimal design was used as the hybridization design for each body region separately .Statistical analyses of raw microarray data were performed using the R package Limma  as descr2FC values was calculated. Hierarchical clustering analysis of the samples was performed using MultiExperiment Viewer software 4.8.1  based on Pearson correlation with average linkage with bootstrapping. The biological function of significantly differentially expressed transcripts in D1D2MO and D3MO was analyzed in Blast2GO (www.blast2go.org). The most prominently affected biological processes were identified using pie charts of the blast2GO score which takes into account the number of affected transcripts within each gene ontology (GO) class and the level of specificity of the respective GO class. This score was cut off at 5 to 10% of the total number of annotated transcripts in their respective condition. Subsequently, these GO classes were grouped manually into broader biological categories  to create a general overview of the data  test was performed in three experimental replicates for each condition, resulting in a total sample size of 120 embryos per knockdown condition. The test was performed in 48-well plates with one embryo per well. The first replicate contained 24 embryos per knockdown condition. The second and third replicates each contained 48 embryos per knockdown condition. For the morphological analysis we also included an uninjected control (UC) group and the assessing researcher was blinded with respect to the knockdown condition of the embryos. From 24 hpf up to 96 hpf, lethal and sublethal morphological and physiological endpoints were evaluated every 24 h using a stereomicroscope. The observed endpoints per time point are listed in Based on the transcriptional patterns indicating effects on muscle function and energy metabolism, we performed additional analyses of motility and energy stores. For analysis of motility SCMO, D1D2MO and D3MO embryos were transferred to individual wells of a 48-well plate and were incubated up to 96 hpf. In each plate, embryos from each condition were included and the total sample size for each condition was 24. At 96 hpf, larval movements were recorded and monitored with a Zebrabox  for 75 minutes in a 100% full light period (1160.7 lux), using the video tracking mode with the detection threshold set at 126. The first 30 minutes were used as an acclimation period and were therefore not included in the analysis. The total distance (mm) travelled during the 45 remaining minutes was calculated.For measurement of energy stores, SCMO, D1D2MO and D3MO embryos were raised in Petri dishes and incubated up to 72 hpf. Four biological replicates of each condition, each replicate containing 48 pooled 72 hpf-old whole larvae, were collected. Carbohydrates, lipids and proteins were extracted and measured as described earlier . In brie*Statistical tests were performed using IBM SPSS statistics (version 20) and Graphpad Prism (version 6.0). A p-value below 0.05 was considered significantly different. One-way analysis of variance (ANOVA) with Bonferroni corrected post hoc tests was used to determine significant effects between the MO groups in larval length and heart rate. A Kaplan-Meier survival curve analysis was conducted with log-rank Mantel-Cox test to analyze the hatching results in a time-dependent manner. A chi square test followed by pairwise chi square tests with Bonferroni correction was performed to identify significant differences in frequencies of malformations such as non-inflated swim bladder and pericardial oedema among all conditions. Kruskal-Wallis tests followed by Bonferroni corrected Mann-Whitney U tests were performed to determine significant differences in the travelled distance, the carbohydrate, protein and lipid contents, and the total caloric content. Knockdowns were initially compared to SCMO. If further differences were observed, additional comparisons were made.1056 unique transcripts were differentially expressed in at least one of the studied conditions and/or body regions. Hierarchical clustering analysis across these responding transcripts revealed that the transcriptional response in the head was similar in both deiodinase knockdowns, although the response of D3MO was stronger . Most ofTo functionally characterize the altered mRNA expression signatures in the different body parts of D1D2MO and D3MO in general, the most important GO classes were identified using a score based on both the number of sequences and the specificity of the GO class. In abdomen and tail of both knockdown conditions, the GO classes representing the differentially expressed transcripts were mainly involved in metabolic processes, development and contraction, together with some more general processes such as transport, signal transduction, regulation of transcription and response to stress. Especially in the tail of D1D2MO a lot of transcripts involved in carbohydrate metabolism, including gluconeogenesis/glycolysis, the citric acid cycle, and the electron transport chain, were upregulated, while this was not the case in D3MO. These pathways were also identified in the KEGG pathway analysis and are shown in All endpoints evaluated in the ELS test, are listed in Larval motility at 96 hpf showed that the distance travelled was significantly reduced compared to SCMO in both D1D2MO and D3MO . At 72 hAlthough it is established that THs have a fundamental role in development, many uncertainties remain regarding the role of TH activating and inactivating deiodinases during early vertebrate development. In deiodinase knockdown zebrafish, we investigated both mRNA expression profiles and phenotypic effects. Phenotypic effects were less severe or sometimes absent in D1D2MO compared to D3MO . Based oin situ hybridization experiments have shown localized expression of both D2 and D3 in the retina of developing zebrafish embryos [The GO and KEGG analysis showed that the most prominently affected processes in the head were purine metabolism and vision . In a hu embryos ,31,32. T embryos , this su embryos . For exa embryos  discover embryos . We hypoIn the tail both D1D2MO and D3MO influenced transcriptional expression of transcripts involved mainly in metabolism and development and both conditions were characterized by pronounced upregulation of transcripts . However3 levels. The liver is the central organ responsible for regulating carbohydrate metabolism and so far localized expression of D1 has been shown in liver of 48, 72, 96 and 120 hpf zebrafish embryos [While the transcriptional response related to metabolism in D3MO was mostly limited to protein turnover , the res embryos ,31,37. A3 is known to stimulate gluconeogenesis and cause hyperglycemia [3 stimulates hepatic glucose production and increases GLUT4-mediated transport of glucose into skeletal muscle and adipose cells [In D3MO larvae, the transcriptional response involved in carbohydrate metabolism was limited, but carbohydrate stores were increased. The impact of TH on glucose metabolism has been studied extensively in humans and rats, where Tglycemia –40. Amonse cells –43. Thisse cells , this wase cells . Correspse cells . Among tse cells ,48. In cse cells . Therefose cells  and growse cells . These eTH is also known to stimulate both lipolysis and lipogenesis . Since lhsp90 and unc-45, involved in sarcomere formation during muscle development; mef2ca, a transcription factor regulating muscle gene expression; tropomyosin, myosin heavy polypeptide and myosin regulatory light polypeptide which are sarcomere components and desmin, which connects the sarcomere to the cytoskeleton.TH is known to be involved in muscle development and muscle maturation, which is associated with a switch between myosin isoforms during postnatal development in mammals –54 and d4-supplemented larvae showed less compact myotome arrangement and that they showed no free-swimming behaviour. The authors suggested that this was due to the defective swim bladder. (See 4.3 for more detailed discussion of swim bladder inflation)We also observed decreased motility of both knockdowns . Since tIn abdomen, only few transcripts were differentially expressed, probably because of the high remaining tissue diversity in this body part . Most of3 has previously been shown to increase heart rate [TH balance is known to be essential for heart morphology and function, and Tart rate ,59. Furtart rate ,60. We o3 is known to drive growth and development, including the development of the swim bladder. During early development, zebrafish undergo an embryonic-to-larval transition phase marking an important switch from yolk sac- to exogenous feeding larvae around 120 hpf. This transition includes swim bladder inflation, structural and functional maturation of the mouth and gastrointestinal tract, and resorption of the yolk sac [3 supplementation [Tyolk sac . Dong etyolk sac  and Thisyolk sac  showed lyolk sac  showed tyolk sac . Holt etyolk sac  showed tentation .3 supplementation partially mimicked the effects observed in D3MO [3 supplementation. While T3 supplementation was shown to improve swim bladder inflation, promote growth, differentiation and survival in some studies [3 levels but an adverse effect of T3 levels above physiological boundaries.Our observation of similar (and even more prominent) effects on growth, development and swim bladder inflation in D3MO seems counterintuitive because these animals are expected to suffer from hyperthyroidism—confirmed by the previous finding that T in D3MO . Contrad studies –65, it w studies , delay h studies ,66 simil studies . The rea3 levels. Here, we also observed decreased motility, reduced protein content and differential expression of transcripts involved in muscle development. The transcriptional profile in the head was highly similar in both knockdowns and levels of transcripts involved in purine metabolism, phototransduction and eye development were decreased. These findings suggest that a fine-tuned TH balance is essential to coordinate vertebrate eye development and function and that D1D2 and especially D3 deficiency may impair eye development and function.D3MO transiently increased the heart rate and probably resulted in an increased metabolic rate and a less efficient use of energy, contributing to reduced growth. A stimulation of gluconeogenesis, partly by using amino acids as a substrate could have led to the increased carbohydrate content and decreased protein content, also contributing to the reduced growth. Furthermore, transcripts related to muscle development were differentially expressed and motility was reduced. This was possibly also related to impaired swim bladder inflation and delayed hatching. The pronounced upregulation of a large number of transcripts involved in carbohydrate metabolism in D1D2MO could reflect a compensatory response to a decreased metabolic rate due to low TIn conclusion, we have shown that the phenotype following knockdown of the TH activating or inactivating pathway shows several similarities, but the transcriptional patterns combined with the current knowledge of the role of thyroid hormones on vertebrate development indicate that they were caused through distinct molecular mechanisms. Zebrafish express different isoforms of TRs both for TRα (where there are even two genes) and for TRβ. Isoform-specific differences in ligand dependency of TR transactivation activity have been described and combined with tissue-dependent deiodinase expression this may contribute to the large difference in transcriptional expression pattern observed between the two knockdown conditions. Possibly the observed early phenotypic effects are common symptoms, but in a long-term scenario the phenotypes would further diverge due to these underlying differences.S1 TableThe first worksheet shows all GO classes that reached the cutoff value, and shows how they were grouped into biological categories. The pie charts in (XLSX)Click here for additional data file.S2 TableThe first worksheet is a list of all differentially expressed transcripts with logFC and multiple testing corrected p values, and sheets 2–5 specifically show those transcripts shown in Figs (XLSX)Click here for additional data file.


Edge loading can negatively impact the biomechanics and long-term performance of hip replacements. Although edge loading has been widely investigated for hard-on-hard articulations, limited work has been conducted for hard-on-soft combinations. The aim of the present study was to investigate edge loading and its effect on the contact mechanics of a modular metal-on-polyethylene (MoP) total hip replacement (THR). A three-dimensional finite element model was developed based on a modular MoP bearing. Different cup inclination angles and head lateral microseparation were modelled and their effect on the contact mechanics of the modular MoP hip replacement were examined. The results showed that lateral microseparation caused loading of the head on the rim of the cup, which produced substantial increases in the maximum von Mises stress in the polyethylene liner and the maximum contact pressure on both the articulating surface and backside surface of the liner. Plastic deformation of the liner was observed under both standard conditions and microseparation conditions, however, the maximum equivalent plastic strain in the liner under microseparation conditions of 2000 µm was predicted to be approximately six times that under standard conditions. The study has indicated that correct positioning the components to avoid edge loading is likely to be important clinically even for hard-on-soft bearings for THR. Whilst clinical studies have shown encouraging long-term clinical performance, failure of these devices can still occur. Specifically, the clinical complications and unexpected failure of hip prostheses linked to edge loading have been reported recently . This edIn vitro studies have shown that the introduction of microseparation in a hip joint simulator can successfully reproduce clinically relevant wear rates, wear patterns and wear particle distributions for both metal-on-metal (MoM) and ceramic-on-ceramic (CoC) articulations  and 10° laterally were applied through the centre of the femoral head. Four cup inclination angles, varying between 35° and 65° in 10° increments, and 12 lateral microseparation distances of 0 µm, 60 µm, 100 µm, 150 µm, 200 µm, 300 µm, 400 µm, 500 µm, 800 µm, 1000 µm, 1500 µm and 2000 µm were considered in the present study. The definition of the cup inclination angles and head lateral microseparation is shown in 3Edge loading appeared for lower values of microseparation of the head as the cup inclination angles increased , Table 2For all cup inclination angles considered, the maximum von Mises stress of the liner and the maximum contact pressure on the articulating surface and backside surface of the liner increased markedly by 88%–160%, 135%–256%, and 117%–250% respectively when the microseparation distances increased to 2000 µm compared to those under standard conditions . Under s−3 under standard conditions and increased to 34.7×10−3 under 1000 µm microseparation conditions for cup inclination angle of 45° (Plastic deformation in the liner was observed under both standard conditions and microseparation conditions. The maximum equivalent plastic strain in the liner was predicted to be 5.6×10e of 45° .Fig. 6ThFor all cup inclination angles considered, the maximum equivalent plastic strain in the liner increased substantially with increased microseparation distances . The max−3 under standard conditions to 2.7 MPa, 4.19 MPa and 4.13×10−3 under 800 µm microseparation conditions respectively, which then decreased to 0.59 MPa, 1.92 MPa and 0.77×10−3 under 2000 µm microseparation conditions (Under the same microseparation conditions, the model with a load direction of 10° laterally predicted higher maximum von Mises stress, maximum contact pressure and maximum equivalent plastic strain compared to that with a vertical load direction and a load direction of 10° medially . The disnditions .Fig. 8Th4Edge loading as an adverse condition which could cause clinical problems has been widely investigated for hard-on-hard bearing designs . Howeverin vivo and was found to be consistent with previous clinical studies, indicating that cups positioned with high inclination angles were more likely to suffer from edge loading (The FE analysis in the present study showed that with increased cup inclination angles, the microseparation distances required to generate edge loading decreased, suggesting that a steep cup inclination angle could potentially facilitate the occurrence of edge loading when the head lateral microseparation exists. This highlights the increased instability of hip prostheses with steep cups  loading .in vitro study (The contact areas on the articulating surface were located about the superior region of the liner under standard conditions, and were found to centralise at the rim of the liner when the microseparation distances increased to 2000 µm, leading to a stripe contact area at the rim of the liner and elevated stresses in this area. This elevation of stresses would cause severe plastic deformation in the liner. Indeed, the FE predictions from this study showed that plastic deformation of the liner occurs under both standard conditions and microseparation conditions. However, there was a substantial increase in the equivalent plastic strain as the microseparation distance increased, indicating severe plastic deformation of the liner under microseparation conditions, which has also been observed in an ro study . The sevro study , and alsro study .The contact stresses on both the frontside articulating surface and backside surface of the liner were found to increase as the cup inclination angles increased. This was consistent with a previous study . HoweverMicroseparation is believed to generate elevated both localised wear and global wear for hard-on-hard articulations . HoweverThe magnitude and orientation of the contact forces were reported to vary over the gait cycle , which wThere are a number of limitations to this study. First of all, the soft tissues surrounding the hip such as muscles and ligaments may have an important role in the stability of the hip replacement . These wAnother limitation in the present simulation was that the interface between the bone and metal shell was fully bonded to simulate a solid bone-implant osseointegration situation. However, in reality, it is impossible to ensure perfect integration between the bone and the prosthesis with current surgical techniques, especially in the early stage of implantation. In order to assess the effect of such assumption on the predicted results, additional simulations considering a frictional contact interface between the bone and the implant were conducted. The simulation results showed that the contact pressures at the articulating surface and the equivalent plastic strain of the liner decreased by about 13.4% and 17.3% when a friction coefficient of 0.6  was consDespite these limitations listed above, this study did suggest that the head lateral microseparation would cause edge loading and induce a marked increase in the von Mises stresses of the liner and contact stresses on the articulating surfaces, as well as severe plastic deformation of the liner, and that steep cup inclination angles would facilitate edge loading. Therefore, clinically it is important to avoid conditions that may lead to edge loading, which means reducing the levels of rotational and translational mal-positioning of the head and cup.John Fisher is a consultant to DePuy Synthes Joint Reconstruction.


In vivo, local and systemic overproduction of SEMA3F reduces tumor growth in NRP2-expressing xenografts. Taken together, SEMA3F regulates mTOR signaling in diverse human cell types, suggesting that it has broad therapeutic implications.Semaphorin 3F (SEMA3F) provides neuronal guidance cues via its ability to bind neuropilin 2 (NRP2) and Plexin A family molecules. Recent studies indicate that SEMA3F has biological effects in other cell types, however its mechanism(s) of function is poorly understood. Here, we analyze SEMA3F-NRP2 signaling responses in human endothelial, T cell and tumor cells using phosphokinase arrays, immunoprecipitation and Western blot analyses. Consistently, SEMA3F inhibits PI-3K and Akt activity, and responses are associated with the disruption of mTOR/rictor assembly and mTOR-dependent activation of the RhoA GTPase. We also find that the expression of vascular endothelial growth factor, as well as mTOR-inducible cellular activation responses and cytoskeleton stability are inhibited by SEMA3F-NRP2 interactions  For instance, cues elicited by netrin-1 are chemoattractive, whereas those dominated by semaphorin 3F (SEMA3F) are chemorepulsive. These processes, known collectively as axon guidance, play an important role in the development of the central nervous system135789Our initial analysis of SEMA3F signaling mechanisms demonstrated that SEMA3F forms a complex with NRP2 and Plexin A1. This complex attracts the ABL2 tyrosine kinase, which activates p190RhoGAP, resulting in the inactivation of RhoA, a small GTPase that converts GTP to GDP, leading to depolymerization of F-actin and the loss of stress fibers with an associated diminished EC and tumor cell migratory responseCaenorhabditis elegans (C. elegans), which is required for morphological changes in its epidermal cells1517182018242556While semaphorins and NRP-elicited responses may regulate multiple intracellular signaling pathways, a common feature is the inhibition of the phosphorylation of the Akt kinase2In this report, we show that SEMA3F interacts with NRP2 and Plexin A1 to reduce PI-3K activity, inhibit the assembly of mTORC2 and reduce downstream Akt signaling. We also show that SEMA3F may elicit anti-tumor and anti-angiogenic effects by inhibiting PI-3K activity and Akt-induced transactivation of VEGF, which is well established to function in tumorigenesis and chronic inflammation. Collectively, our studies define SEMA3F as a novel PI-3K/mTORC2 inhibitor in mammalian cells, indicating that it has broad biological effects and potential as a therapeutic to enhance the resolution of chronic disease.To determine the effect of SEMA3F on intracellular signaling responses, we initially profiled levels of phosphokinases in the NRP2-expressing human glioblastoma cell line U87MG. We found that SEMA3F inhibited the phosphorylation of a number of kinases, notably, Akt (T308 and S473), Erk (T202/Y204 and T185/Y187) and mTOR (S2448) , which wmTORC1 and mTORC2 signaling is critical for cell metabolism30273233343637To further evaluate whether the primary target of SEMA3F is the mTORC2 complex, U87MG cells were transfected with 2DAkt, in which the T308 and S473 sites are mutated to encode a constitutively active form of the kinase36We next wished to determine whether the inhibition of mTORC2 serves as an intermediary response to link SEMA3F activity with cytoskeletal collapse. U87MG cells were treated either with SEMA3F, rapamycin or Torin 1 for 30 minutes and the actin cytoskeleton was visualized using phalloidin staining. As we previously reportedWe next measured RhoA activity2). We found that treatment with DFO induced VEGF promoter activity , which was partially inhibited  by SEMA3F ; tumor size (mm3) was measured at the indicated time points over a period of 3 weeks. We found that tumor growth was essentially absent when SEMA3F-producing cells were implanted vs. parental cells  or a control adenovirus (Ad-Cont). Injection of Ad-3F (1 × 109 pfu) into mice did not result in any toxicity over a 30-day period; mice gained weight and typical behavior was normal. Western blot analysis showed that the administration of Ad-3F resulted in high levels of SEMA3F production in the liver  confirms that SEMA3F is a potent mTOR inhibitor in vivo.Overexpression of SEMA3F in tumor cells, such as lung, brain and breast cancer cells was previously reported to inhibit tumor development in xenograft mouse models5134445in vitro, and it has powerful anti-tumor effects in vivo. We conclude that SEMA3F is a secreted physiological mTOR inhibitor that functions to promote resolution following cellular activation. Our findings suggest that SEMA3F has therapeutic potential, not only in tumor biology, but also for instance to target chronic immune-mediated diseases, allograft rejection and/or angiogenesis related pathology.In conclusion, these findings demonstrate that SEMA3F-NRP2 interactions inhibit intracellular PI-3K activity, mTORC2-dependent signaling, RhoA activity and cytoskeletal stress fiber formation. SEMA3F also inhibits the inducible expression of VEGF at both the transcriptional and protein level The antibodies, rabbit monoclonal anti-phospho-Akt (Thr308) antibody (#2965); mouse monoclonal anti-phospho-Akt (Ser473) antibody (#4051); rabbit polyclonal anti-Akt antibody (#9272); rabbit polyclonal anti-phospho-Erk1/2 (Thr202/Tyr204) antibody (#9101); mouse monoclonal anti-Erk1/2 antibody (#4696); rabbit monoclonal anti-phospho-S6K (Thr389) antibody (#9234); rabbit monoclonal anti-S6K antibody (#2708); rabbit monoclonal anti-phospho-S6 (Ser235/236) antibody (#4856); mouse monoclonal anti-S6 antibody (#2317); rabbit monoclonal anti-phospho-mTOR (Ser2448) antibody (#5536); rabbit polyclonal anti-mTOR antibody (#2972); rabbit polyclonal anti-plexin A1 antibody (#3813); rabbit monoclonal anti-raptor antibody (#2280); rabbit monoclonal anti-RhoA antibody (#2117); rabbit monoclonal anti-PTEN antibody (#9188) were all purchased from Cell Signaling Technology . Goat polyclonal anti-phospho-SGK ; mouse monoclonal anti-NRP2 antibody ; goat polyclonal anti-GIPC antibody  were purchased from Santa Cruz Biotechnology, Inc . Rabbit polyclonal anti-rictor antibody (A300-458A) was purchased from Bethyl Laboratories, Inc , and mouse monoclonal anti-β-actin antibody (AC-15) was from Sigma-Aldrich .The VEGF-A (DVE00) ELISA kit was obtained from R&D Systems . The PI3-Kinase Activity ELISA (K-1000s) was purchased from Echelon Biosciences . The mTOR inhibitors, rapamycin and Torin 1, were purchased from Lc Laboratories  and R&D Systems, respectively. Desferrioxamine (DFO) was purchased from Sigma-Aldrich, and the MEK inhibitor (U0126) was purchased from EMD-Millipore . Wild type (WT) mTOR vector was kindly provided to David M. Briscoe 2 incubator at 37 °C. For all hypoxia experiments, cells were cultured in a hypoxic chamber  in 1% O2 at 37 °C.U87MG and U251 human glioblastoma cells, kidney 293 cells and 293T cells, and Jurkat T lymphocytes were obtained from American Type Culture Collection  and cultured in media containing 10% FBS  and 1% L-glutamine/penicillin G/streptomycin sulfate  as recommended. HUVECs were purchased from Lonza  and cultured in EBM2 medium supplemented with EGM2 SingleQuot. Human melanocytes  were maintained with Medium 254 supplemented with Human Melanocyte Growth Supplement (Life Technologies) in a 5% COA full-length, His-Myc-tagged human SEMA3F construct was transfected into 293T cells using FuGENE HD Transfection Reagent . SEMA3F secreted into culture medium was purified on HiTrap HP Chelating columns The Human Phospho-Kinase Array Kit (ARY003) was obtained from R&D Systems. U87MG cells were treated with SEMA3F which was previously found to induce cytoskeletal collapse and inhibit RhoA activityProteins within each sample were separated by SDS-PAGE and transferred to nitrocellulose membranes. The membranes were blocked with 4% skimmed milk in TBS-T  for 30 minutes, followed by incubation with the primary antibody. After washing with TBS-T, membranes were incubated with the appropriate horseradish peroxidase-conjugated secondary antibody, and immunoreactivity was detected by using ECL detection reagents.Cell lysates were immunoprecipitated using an appropriate antibody at 4 °C overnight. Protein G-Sepharose 4 Fast Flow beads  were added to each sample, followed by mixing for 1 hour at 4 °C. The samples were dissolved in SDS sample buffer and boiled for 5 minutes.Cells were fixed with 4% paraformaldehyde (PFA) followed by permeabilization with 0.2% Triton X-100 in PBS. F-actin and nuclei were stained with Alexa Fluor 488 phalloidin and Hoechst 33342, respectively. Confocal images from 3–5 areas of each culture were reviewed and stress fibers were counted in representative individual cells (~5/experiment) using standard methodology as describedRhoA activity assays were measured by using the RhoA activation assay kit based on rhotekin pull-down, according to the manufacturer’s instructions .Transfection of siRNA (20 nM) was performed with siLentFect Lipid Reagent , according to the manufacturer’s protocol. Control siRNA (Silencer Negative Control #2 siRNA) was purchased from Life Technologies. ON-TARGETplus Human NRP2 siRNA was purchased from Thermo Scientific , Plexin A1 (Hs_PLXNA1_3), rictor (Hs_RICTOR_5), PTEN (Hs_PTEN_6) and GIPC1 (Hs_RGS19IP1_1) siRNA from Qiagen , and Raptor siRNA (sc-44069) from Santa Cruz Biotechnology, Inc. . In general, siRNA transfection was performed for 48 hours prior to assays.U87MG cells were transiently transfected with plasmid constructs  as indicated using Lipofectamine 2000 reagent (Life Technologies) according to the manufacturer’s instructions. After 18 hours, cells were treated as outlined in each experimental design. VEGF promoter activity was analyzed using a Dual-Luciferase Reporter Assay System (Promega), and luciferase activity was normalized by Renilla luciferase as an internal control.10 pfu/ml.Recombinant control (#000047A) and human SEMA3F-His (#129755A) adenovirus were purchased from Applied Biological Materials, Inc. . Each adenovirus was amplified with 293 cells and purified using the Fast Trap Adenovirus Purification and Concentration Kit (EMD-Millipore). The adenovirus titer was determined by Adeno-X™ Rapid Titer Kit . We obtained each adenovirus at titers greater than 1 × 106/injection) were administrated into nude mice  subcutaneously. In one model, tumor size was measured every 3–4 days using a standard calipers. Mice were sacrificed on day 24 and tumors were removed. In a second model, parental U87MG cells were administrated into nude mice subcutaneously. In pilot studies, we injected adenovirus encoding SEMA3F (Ad-3F) or a control adenovirus (Ad-Cont) intravenously via the tail vein 3 days prior to tumor cell injection (1 × 106 U87MG cells/injection); we observed that all tumors in the Ad-3F group failed to grow (data not shown). Thus, we revised our approach, and we delayed administration of Ad-3F until day 2 after the tumor injection, so that tumor growth was initiated prior to peak SEMA3F production in the circulation . Tumor size was measured every other day using a standard calipers, serum samples were collected from the tail vein at day 5 and 8 and mice were sacrificed on day 14 when the tumor, the liver and serum samples were collected. Production of SEMA3F was confirmed by Western blot analysis of liver with anti-His/anti-SEMA3F antibodies and by analysis of serum level of SEMA3F using the human SEMA3F ELISA kit (MBS454602) from MyBioSource . All animal studies were performed with approval in accordance with the guidelines of the Animal Care and Use Committee, Boston Children’s Hospital, Boston, MA.Parental U87MG cells or human SEMA3F stable U87MG clones (1 × 10Paraffin-embedded sections were deparaffinized and activated with proteinase K (36 μg/ml) in 0.2 M Tris buffer (pH7.2) at 37 °C for 30 minutes and processed for immunohistochemical staining. Immunohistochemistry was performed with anti-mouse CD31 antibody , the VectaStain Kit  and the Tyramide Signal Amplification (TSA) Biotin system , according to the manufacturer’s instructions.All assays were independently performed at least three times. The results are represented as mean ± standard deviation (SD). Groups were compared using the Student’s t test and p values < 0.05 were considered statistically significant.How to cite this article: Nakayama, H. et al. Regulation of mTOR Signaling by Semaphorin 3F-Neuropilin 2 Interactions in vitro and in vivo. Sci. Rep.5, 11789; doi: 10.1038/srep11789 (2015).


Neochmia phaeton (crimson finch), a sedentary, tropical passerine, for reproductive success and survival over four breeding seasons, and sampled them for commonly used condition indices: mass adjusted for body size, muscle and fat scores, packed cell volume, hemoglobin concentration, total plasma protein, and heterophil to lymphocyte ratio. Our study population is well suited for this research because individuals forage in common areas and do not hold territories such that variation in condition between individuals is not confounded by differences in habitat quality. Furthermore, we controlled for factors that are known to impact condition indices in our study population  such that we assessed individual condition relative to others in the same context. Condition indices that reflect energy reserves predicted both the probability of an individual fledging young and the number of young produced that survived to independence, but only during some years. Those that were relatively heavy for their body size produced about three times more independent young compared to light individuals. That energy reserves are a meaningful predictor of reproductive success in a sedentary passerine supports the idea that energy reserves are at least sometimes predictors of fitness. However, hematological indices failed to predict reproductive success and none of the indices predicted survival. Therefore, some but not all condition indices may be informative, but because we found that most indices did not predict any component of fitness, we question the ubiquitous interpretation of condition indices as surrogates for individual quality and fitness.Body condition may predict individual fitness because those in better condition have more resources to allocate towards improving their fitness. However, the hypothesis that condition indices are meaningful proxies for fitness has been questioned. Here, we ask if intraspecific variation in condition indices predicts annual reproductive success and survival. We monitored a population of  Predicting individual fitness of wild organisms is important because intraspecific variation in fitness has broad implications for evolution and higher ecological processes within populations and communities. Researchers are therefore interested in measures that may be meaningful proxies for fitness and predict individual differences in reproductive success and survival. One such proxy is body condition (hereafter “condition”). Condition is most often defined as the pool of resources that an individual has acquired (and presumably assimilated) that can be allocated toward improving fitness ,2,3, butThere is no direct way to test whether condition predicts fitness because condition is not directly quantifiable. Researchers instead use condition indices as its surrogate. These indices reflect different aspects of an animal’s physiology including resource acquisition and allocation, nutritional status, immunocompetence and stress. We emphasize that condition indices are not the same as condition , but areBecause condition indices measure condition and condition is expected to predict fitness, condition indices are often interpreted as indicators of individual quality and fitness. Indeed, some condition indices have been demonstrated to predict reproductive traits ,7 and super se. Because we have previously determined how condition indices in our study population vary by sex, breeding stage, age, year, and time of day . We therefore performed a principal components analysis using the correlation matrix for all condition indices within the 4-year and 2-year datasets for both the survival and reproductive success analyses. The 4-year datasets include packed cell volume, hemoglobin, scaled mass, muscle score and fat score, and the 2-year datasets additionally include H/L ratio and total plasma protein. We extracted the principal components (PCs) with an eigenvalue > 1 to use as additional condition indices and included them as explanatory variables in our models of survival and reproductive success. We conducted the PCAs in R version 3.0.0 [21].Indicators of individual quality may be less informative singularly than within a multivariate approach [22], then built our models of condition indices upon the structure of these baseline models. To limit the number of models under consideration, we did not include combinations of condition indices. Instead, we included separate models for the principal components (see above) which incorporated information from all indices. We created a model for the additive effect of each condition index (including the PCs), and because condition indices may have non-linear effects on survival and reproductive success, we also considered additional models in which quadratic terms of the condition indices were included. We controlled for covariates known to affect condition indices in our study population [13] by including additional models where these individual covariates were included as additive effects with the condition indices that they impact. In summary, the candidate model sets included six types of models: (1) baseline, (2) baseline + condition, (3) baseline + condition + condition2, (4) baseline + condition + covariate(s), (5) baseline + condition + condition2 + covariate(s), and (6) the set of models that were included in the baseline model selection process  . Because so many individuals have zero annual reproductive success, we asked first (1) do condition indices predict whether or not an individual will fledge young, and then (2) for those that do fledge at least one young, do their condition indices predict the number of young that they fledge? As Neochmia phaeton fledglings have only a 55% survival rate from fledging to independence [17], we also asked (3) whether condition indices of adults that successfully fledge at least one young predict the number of those young produced that survive to independence.Approximately half of breeding pairs in our study area fail to fledge young during a given breeding season with predation being the primary cause of nest failure . We used both recapture and resighting events  within our study area to build the encounter histories. Birds resighted opportunistically outside of the study site were excluded because they were known to have dispersed and were not systematically monitored for survival after dispersal . We only included individuals for whom we had data on condition indices and their breeding stage at the time of sampling , and we tested for apparent monthly survival following the occasion in which the individual was sampled for condition . The datasets and analyses are summarized in Table 1.To test the effects of condition indices on apparent survival (φ) of adult t) p(t)). Where appropriate, we adjusted for the median ĉ value and used QAICc thereafter in evaluating the evidence for our models.We tested for goodness of fit by using the median ĉ test to estimate the variance-inflation factor (ĉ) for the fully time dependent model where both the apparent survival rate (ϕ) and the recapture probability (p) varied with time  in both ϕ and p [t +Year+(t × Year)) and compared alternate versions of p, testing all combinations of t and Year as well as a constant (‘.’) model. Having selected the best structure for p, we then compared alternate versions of ϕ in the same manner as for p, and selected the best structure for ϕ.In the multiple step procedure to determine the most relevant baseline model, we first evaluated the evidence for structural parameters , and, after selecting the best model for p, we repeated the process for ϕ (with p held constant at the best model including nuisance covariates). We excluded some covariates from our 4-year baseline model selection process because we lacked sufficient data to model them: Sex × Age for ϕ and Sex × Stage for p. At each step, if more than one model was competitive (within two delta AICc units of the top model) we selected the most parameterized model to proceed to the next step in an effort to explain the maximum amount of underlying variation. This process allowed us to narrow our candidate model set and to select the best baseline model  for use We evaluated the evidence for our models using an information theoretic approach as previously described and we model-averaged to estimate ϕ and p (95% CI).10.5061/dryad.3n2j5).All data files are available from the Dryad database  = 1.71, P = 0.19); reproductive effort during the current breeding season such as number of nesting attempts , clutches laid , and broods hatched ; reproductive effort and success in the previous breeding season such as number of nesting attempts , clutches laid , broods hatched , young fledged , and young produced that survive to independence ; or survival  = 0.01, P = 0.92).Post-hoc tests comparing individuals in the lower and upper quartiles of PC2 scores indicate that the two groups did not differ in any variables available to us including age  are 7.0 and 22.0 against the baseline model, and 2.2 and 1.7 against the linear model of scaled mass for the 4 and 2-year datasets, respectively. In the 4-year dataset, the third best model (PC2 + PC22) is within two AICc units of the top model and has an evidence ratio of 2.8 against the baseline model, and 2.2 against the linear model of PC2.Scaled mass is the best supported condition index for predicting the number of young produced that survive to independence. The top two models in both the 4 and 2-year datasets are a model with a quadratic term for scaled mass and a model with a linear effect of scaled mass. Combined, these two models have 41% and 70% of the weight in the 4 and 2-year datasets, respectively. The evidence ratios for the top model  from the 4-year dataset, and 0.96 (0.91−0.98) from the 2-year dataset. The model-averaged recapture rate varied monthly from 0.50 (0.32−0.68) to 1 (1−1) and from 0.82 (0.63−0.92) to 1 (1−1) for the 4 and 2-year datasets, respectively. Complete QAICc results are provided in None of the condition indices predict survival as evidenced by high model uncertainty in all analyses with the top models only having 10−16% of the weight . Fat andWe tested the common interpretation of condition indices as proxies for fitness by asking if condition indices predict reproductive success and survival. We found only partial support for this hypothesis because although two condition indices predict annual reproductive success, most condition indices fail to do so and none predict survival. We conclude that condition indices are sometimes, but not always predictors of fitness components, and that a more nuanced interpretation of condition is needed.Although none of the condition indices predict the number of young fledged, less model uncertainty combined with higher evidence ratios suggest that some condition indices predict both the probability of fledging at least one young and the number of young produced that survive to independence (in at least some years).Neochmia phaeton fail to fledge any young in a given breeding season [The strongest support for a relationship between a condition index and fitness is that the probability of an adult successfully fledging young varies by PC2. Because nearly half of all breeding g season , the proOur finding that the average value of PC2 is least adaptive and that the extremes are most optimal was unexpected and the reason for this pattern is not immediately obvious. We attempted to elucidate this pattern by using post-hoc tests to compare individuals in the lower and upper quartiles of PC2, but we found no differences between the groups. This leaves unexplained the pattern that those with low energy reserves and oxygen-carrying capacity are equally as successful at fledging young as those with high energy reserves and oxygen-carrying capacity.Moderate support from evidence ratios and model weights suggest that individuals that were heavier for their body size produced more independent young than those with average or below average mass for their body size. Some caveats to this conclusion are that (a) considerable model uncertainty exists suggesting that other models have some  support, (b) evidence ratios for the effect of scaled mass are moderate but not strong, (c) the pattern is only evident in some, but not all years, and (d) data limitations caused wide margins of error in our model-averaged predictions see  and shouThat an individual may increase their annual reproductive success three-fold by optimizing their mass is striking. This pattern suggests that those individuals able to maintain energy reserves are most likely to be able to carry reproduction through to completion. Thus, although individuals with low energy reserves  have the same probability of fledging at least one young as do those with high energy reserves, they are less likely to have their young survive to independence, indicating that this is a less effective strategy for maximizing fitness than that represented by high PC2 scores.Chen caerulescens  and Somn eider, ,29). Hown eider, ). Here wn eider,  and our Neochmia phaeton. Among Branta bernicla (Brent geese), unfavorable environmental conditions limited the positive effect of scaled mass on reproductive success [However, additional energy reserves do not always improve reproductive success. Although scaled mass predicted reproductive success in three out of four years in our study, it was uninformative in 2006 − 2007 . This br success  and a si success . Thus, w success , and the success  suggestiNeochmia phaeton. Others have also found that hematological condition indices do not predict fecundity parameters such as the probability of double-brooding [Unlike the traditional condition indices that reflect energy reserves, hematological indices did not predict reproductive success among brooding , number brooding , and laybrooding . Howeverbrooding , glycosybrooding , and meabrooding . TherefoNeochmia phaeton, as evidenced by high model uncertainty and competitive null models. Others have found that condition indices do predict survival [Aphelocoma coerulescens through a disease epidemic [Parus major when they were food limited but not otherwise, and only among subordinate but not dominant classes [Condition indices have no relationship to the survival of survival  or that epidemic  but not  classes . Also, s classes ,42, and Enhydra lutris (sea otter) while fat reserves did not, and the natural history and physiology of Enhydra lutris may explain this difference: fat is quickly utilized due to the otters’ high metabolic rate, and as diving foragers, it is oxygen-carrying capacity that determines their ability to acquire food [Neochmia phaeton, for which the costs and benefits of fat reserves and their associated trade-offs are poorly understood. Predicting which condition indices are most relevant may be easiest for species that have extreme physiological demands.One reason that condition indices failed to predict survival in our study may be that the indices we measured are not relevant to the survival of our study species. For example, hemoglobin concentration predicted the survival of ire food . Therefoire food . It is nire food ,10,39,43Neochmia phaeton because they were sampled at the wrong time of year. To predict survival condition indices likely need to be sampled prior to, or during, the challenge that contributes most to mortality. We do not know the primary sources of mortality for adults in our study population. However, Neochmia phaeton occupies a seasonal, tropical environment with distinct dry and wet seasons, and we suspect that the most food-limiting time is the end of the dry season when grass seeds are most depleted [Neochmia phaeton are less food-stressed, condition indices may have predicted survival (but perhaps not subsequent reproductive success). We encourage researchers to consider the relevance of both the condition indices and the timing of sampling in employing these indices as predictors of fitness.Alternatively, condition indices in our study may have failed to indicate survival of depleted . If we hNeochmia phaeton, most of the indices that we measured fail to do so, and none predict survival. These results and other literature indicate that condition indices are only sometimes potentially meaningful proxies for fitness. We therefore question the ubiquitous interpretation of condition indices as proxies for fitness. How then, should condition indices be interpreted? First, as exemplified by our result relating PC2 to reproductive success, indices may be more informative when integrated via a multivariate approach than when assessed individually. Second, where we found that condition indices predicted a component of fitness, its quadratic effect was always better supported than a linear effect of that index. We therefore echo the suggestion of others that more is not necessarily better and that the relationship between a condition index and fitness is not necessarily linear .The wide discrepancy in whether condition indices predict fitness, and if so, which indices in particular are meaningful, suggests that not all indices are relevant to all species or contexts ,45. ReseThe interpretation of condition indices as proxies for individual quality and fitness must be refined. We previously found that condition indices are only repeatable within individuals across short, but not long time periods  suggestiS1 Tablet = time dependence, PCV = packed cell volume, Hb = hemoglobin concentration, SMI = scaled mass index, Muscle = muscle score, Fat = fat score, HL = heterophil to lymphocyte ratio, TPP = total plasma protein, Time = time of day, and PCs = principal components. See Included are all of the models from the candidate model set where ϕ = apparent monthly adult survival, ρ = recapture probability, (DOCX)Click here for additional data file.S2 TableWe asked three questions of each dataset for a total of six analyses. Included are all of the models from the candidate model set. PCV = packed cell volume, Hb = hemoglobin concentration, SMI = scaled mass index, Muscle = muscle score, Fat = fat score, HL = heterophil to lymphocyte ratio, TPP = total plasma protein, Time = time of day, and PCs = principal components. See (DOCX)Click here for additional data file.S3 TableWe asked three questions of each dataset for a total of six analyses. Included are all of the models from the candidate model set. PCV = packed cell volume, Hb = hemoglobin concentration, SMI = scaled mass index, Muscle = muscle score, Fat = fat score, HL = heterophil to lymphocyte ratio, TPP = total plasma protein, Time = time of day, and PCs = principal components. See (DOCX)Click here for additional data file.


The majority of IKK complex functions are attributed to NF-κB activation; however, there is increasing evidence for NF-κB pathway-independent signalling. Here we combine quantitative mass spectrometry with random forest bioinformatics to dissect the TNF-α-IKKβ-induced phosphoproteome in MCF-7 breast cancer cells. In total, we identify over 20,000 phosphorylation sites, of which ∼1% are regulated up on TNF-α stimulation. We identify various potential novel IKKβ substrates including kinases and regulators of cellular trafficking. Moreover, we show that one of the candidates, AEG-1/MTDH/LYRIC, is directly phosphorylated by IKKβ on serine 298. We provide evidence that IKKβ-mediated AEG-1 phosphorylation is essential for IκBα degradation as well as NF-κB-dependent gene expression and cell proliferation, which correlate with cancer patient survival  Inflammatory cytokines such as TNF-α influence inflammation, apoptosis and tumour development through regulation of the kinase IKKβ. Krishnan and Nolte et al. apply quantitative proteomics to identify potential IKKβ targets, and reveal phosphorylation of AEG-1 by IKKβ as a mechanism controlling NF-κB signalling. NF-κB activation-mediated gene transcription is critical for physiological and pathological processes including stress responses, apoptosis and tumour developmentThe I-kappa-B kinase (IKK) complex is a crucial activator of NF-κBIKKβ is also capable of phosphorylating Hamartin TSC1), a known repressor of the small GTPase Rheb within mammalian target of rapamycin (mTOR) signalling, a knownAlthough considerable progress has been made in understanding NF-κB pathway-dependent and -independent functions of IKKβ, the recent evidence for new IKKβ targets suggest that many of its functions still remain to be discovered.To search for new potential substrates of the IKK complex, we study IKKβ-mediated phosphorylation in stable isotope labelling by amino acids in cell culture (SILAC)-labelled breast carcinoma cells. Here we test the effect of the TNF-α stimulation and IKKβ inhibition on the phosphoproteome. High-resolution mass spectrometry and comprehensive bioinformatics enable us to identify a plethora of new proteins being potential novel targets of IKKβ. We show that one of these candidates—Astrocyte-Elevated Gene-1 (AEG-1), also known as Metadherin (MTDH) or lysine-rich CEACAM1 co-isolated (LYRIC) is a direct substrate of IKKβ. On TNF-α activation, IKKβ phosphorylates particular serine residue of AEG-1, which is a crucial step required for downstream cellular effects of TNF-α.We used the breast tumour cell line MCF-7 ref. .2) beads. All samples were analysed using nanoLC-MS/MS on the QExactive mass spectrometer and raw spectra were processed using MaxQuant (We mixed the related cell populations (Exp. 1–Exp. 4) and the extracted proteins were in solution digested with trypsin. Next, the peptides were separated using strong cation exchange chromatography (SCX) and the phosphopeptides were enriched with titaniumdioxide  protein with a 22-fold upregulation on TNF-α stimulation, whereas treatment of SC-514 showed a significant downregulation (53%). Similarly, one of the known substrates of IKKβ, serine 312 of IRS1, was found to be upregulated fivefold on TNF-α stimulation and downregulated after treatment with SC-514. We also saw a downregulation by 80% of the IKKβ autophosphorylation site serine 698 on SC-514 treatment. Of note, we observed a clear activation of wild-type IKKβ on TNF-α stimulation, whereas the IKKβ kinase dead mutant did not show any activation in response to TNF-α. In a parallel experiment, SILAC-labelled MCF-7 cell lysates, used for mass spectrometry analysis, were subjected to immunoblotting and we observed a moderate degradation of IκBα in control MCF-7 cells, suggesting a basal activity of the IKK complex. We were able to see an increased degradation of inhibitor of the nuclear factor-κB (IκBα) on TNF-α stimulation; however, this effect was abolished on SC-514 treatment or on the overexpression of the kinase dead IKKβ (K44M), thus proving the basal functionality of the experimental conditions . In thisin vivo regulatory mechanism. Furthermore, we detected a IKKβ-dependent phosphorylation of autophagy-related protein 2 homologue B (ATG2B) on serine 497 and synaptotagmin-like protein 2 (SYTL2) on serine 649. ATG2B is involved in the regulation of the autophagosome formation and lipid droplet morphology, whereas SYTL2 acts as a RAB27A effector protein and plays a role in cytotoxic granule exocytosis in lymphocytes13To gain a more systematic view of the potential target proteins in TNF-α-dependent IKKβ activity, we used a random forest-based approach to identify substrates of IKKβ under physiological conditions , 4 and 59Another candidate that we identified with a high IKKβ score  is prote151617151620212324AEG-1 shares no homology to the identified domains; however, its sequence includes several potential nuclear localization signals . Serine in vitro kinase assay using P32-ATP. We observed that AEG-1 but not its serine 298 mutant is directly phosphorylated by IKKβ , oesophageal squamous cell carcinoma, and breast and prostate cancers172728293031Next, we analysed the role of serine 298 of AEG-1 in the activation of NF-κB. It has been proposed that AEG-1 mediates IκBα degradationIκBα becomes phosphorylated and ubiquitinated on its way to degradation. To learn more about the role of AEG-1 phosphorylation in IκBα degradation, we decided to analyse post-translational modifications of IκBα in cells transfected with either wild-type AEG-1 or its S298A mutant  and founFinally, we attempted to analyse the protein–protein interactions involved in AEG-1-mediated degradation of IκBα. We employed HEK293 cells and tested the possibility that AEG-1 forms complexes with both IKKβ and IκBα. In agreement with our previous data from MCF-7 cells , we coulAEG-1 was shown to directly interact and translocate to the nucleus with the p65 subunit of the p50/p65 complex of NF-κBAEG-1 mediates interaction between p65, CBP and the other basal transcriptional components, facilitating NF-κB-driven gene transcriptionNext, we performed an analysis of the impact of IKKβ-mediated AEG-1 phosphorylation on other known biological functions. AEG-1 has been associated with the Wnt/β-catenin pathway in several cancer conditions. In HCC cells AEG-1 activates Erk1/2, leading to an upregulation of LEF1 expression resulting in the activation of WntWe also further investigated the effects of serine 298 on cell proliferation. First, we studied the cell cycle distribution in stable 4T1 cells using fluorescence-activated cell sorting (FACS)TNF-α is known to enhance cell migration and metastasis of various cancer types through different mechanisms, including the NF-κB-dependent induction of the chemokine receptor CXCR4, upregulation of LOX-1 or activation of matrix metaloproteinases37in vivo and that S298 phosphorylation levels can provide additional information on patient prognosis.AEG-1 expression levels were correlated with patient prognosis and metastases in various studies on ovarian cancer3940Taken together, our data imply that on activation of the IKK complex by TNF-α, IKKβ, phosphorylates serine 298 of the protein AEG-1. This step is crucial for IκBα degradation, the interaction of AEG-1 with p65, the subsequent activation of gene expression downstream of NF-κB and, ultimately, cell proliferation .One of the major functions of the TNF-α pathway is the regulation of gene expression through IκBα-kinase-complex (IKK complex)-mediated degradation of IκBα and the subsequent activation of the transcription factor NF-κBAt the same time, however, we discovered a variety of novel phosphopeptides, regulated by IKKβ. For example, we observed that IKKβ might be able to phosphorylate the mitogen-activated protein kinase kinase kinase 14 . NIK is believed to be an exclusive target for IKKα Similar to serine 320 of RIPK1, phosphorylation of ATG2B on serine 497 has already been reportedTwo other IKKβ-dependent phosphorylation sites we identified, seine 457 of TOM1L2 and serine 649 of SYTL2, are completely novel. Interestingly, both, SYTL2 and TOM1L2, are involved in various forms of cellular trafficking. SYTL2 is an effector of the small GTPase Rab27 and was found to participate in the cytotoxic granule secretion and regulation of localization of proteins to the apical cell surface48Another novel IKKβ substrate we identified is the protein AEG-1/MTDH/LYRIC, previously shown to localize to different cellular compartments, including the cell membrane22222250in vitro. Together with the proteomics data showing that inhibition of IKKβ completely abolishes TNF-α-induced phosphorylation of AEG-1, these findings establish AEG-1 as a novel, direct target of IKKβ.Serine 298 of AEG-1 that we identified in this study represents a novel TNF-α-IKKβ-dependent regulatory site of AEG-1. On the basis of our analysis and on the basis of the fact that the sequence surrounding serine 298 matches the degenerated motif for IKKβ substrates, we hypothesized that AEG-1 may be directly phosphorylated by IKKβ. Indeed, we not only observed that AEG-1 interacts with IKKβ, but also found that IKKβ phosphorylates AEG-1 et al.AEG-1 has been reported to serve as a positive regulator of the TNF-α-dependent NF-κB pathwayTNF-α was proposed to mediate the cell motility of various cell types. There are, however, various and sometimes conflicting mechanisms proposed to explain these processes54TNF-α is also known to induce apoptosis of tumour cells; however, this process seems to be NF-κB-independentin vivo.AEG-1 overexpression was correlated with bad prognosis in various types of cancer. Our data suggest that the specific phosphorylation of AEG-1 can be an additional factor regulating its activity, possibly independently of the expression levels. To this end, we correlated AEG-1 phosphorylation levels with patient survival and observed that high levels of phosphorylated AEG-1 negatively correlated with patient survival, implicating a role of IKKβ-mediated phosphorylation in tumour development. We performed our analysis in samples with S298-phosphorylated tumours. We normalized the amount of phosphorylated AEG-1 to its individual total protein expression. Therefore, our data suggest that high phosphorylation levels of AEG-1 may be correlated with negative patient survival independently of gene overexpression. Taken together, our data demonstrate the importance of IKKβ-mediated phosphorylation of AEG-1 In summary, a direct combination of the SILAC-based quantitative phosphoproteomics with the random forest computational prediction allowed us to identify novel IKKβ substrates in breast cancer cells. On the basis of this approach, we demonstrate that a transmembrane protein AEG-1 is a novel substrate of IKKβ. IKKβ phosphorylates AEG-1 on serine 298, a step required for TNF-α-induced IκBα degradation and subsequent activation of NF-κB-dependent cellular processes.−1 insulinlight, Lys 4 and Arg 6 for middle and Lys 8 and Arg 10 for heavy condition. Lysine and arginine were added to a concentration of 0.028 and 0.0735, mg ml−1, respectively, in all SILAC-labelling conditionsL-Glutamine−1). Stable cells were transfected with control pcDNA3.1-FLAG vector or the vector carrying wild-type/S298A AEG-1. The stable cells were selected using 400 μg ml−1 (MCF-7) or 250 μg ml−1 (4T1) G418 sulphate.MCF-7 cells were purchased from the German Collection of Microorganisms and Cell Cultures (Deutsche Sammlung von Mikroorganismen und Zellkulturen-DSMZ) and were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS), 1 mM sodium pyruvate, 1% nonessential amino acids and 10 ng ml−1. Labelled amino acids for SILAC were purchased from Silantes . G418 sulphate (PAA) was used at 400 μg ml−1 for MCF-7 and 250 μg ml−1 for 4T1 cells.The following antibodies were purchased from commercial sources: mouse monoclonal anti-AEG-1/MTDH clone 2F11C3 , rabbit monoclonal anti-IκBα, anti-Erk1/2-p44/42 MAPK, anti-P-Erk1/2-p44/42 MAPK, anti-p38 MAPK, anti-P-p38 MAPK and anti-Histone 3 , mouse monoclonal anti-α-Tubulin, anti-FLAG-horseradish peroxidase (HRP), anti-HA (haemagglutinin)-HRP, anti-γ-Tubulin , anti-FLAG-fluorescein isothiocyanate (FITC) and anti-FLAG affinity gel (Sigma-Aldrich), anti-IκBα (C-15), rabbit polyclonal anti-IKKα/β  and anti-Ki-67 . SC-514 and MG132 were purchased from Calbiochem and used at the final concentration of 25 or 20 μM, respectively. TNF-α (PeproTech) was used at 10 ng mlHuman AEG-1 shRNAs  and mouse AEG-1 shRNAs  were purchased from QIAGEN. Reverse transcriptase–PCR (RT–PCR) primers for mouse FOS (5′-gggaggaccttacctgttcg-3′ and 5′-aggccagatgtggatgctt-3′); human FOS (5′-tagcaaaacgcatggagtgt-3′ and 5′-gcctggctcaacatgctact-3′) and human IL-8 (5′-agacagcagagcacacaagc-3′ and 5′-aggaaggctgccaagagag-3′) were synthesized by SIGMA.The pCMV6-Entry plasmid carrying MTDH/AEG-1 wild-type cDNA was purchased from Origene. Wild-type AEG-1 and the mutant AEG-1 (S298A) were generated using PCR and cloned into pcDNA3.1-FLAG. Clone identity was confirmed by sequencing. HA-tagged, wild-type and kinase dead (K44M) clones of IKKβ were a kind gift from D. Brandt ; HA-tagged ubiquitin was a generous gift from Ivan Dikic −1) for 30 or 10 min, respectively, whereas transfected cells remained nonstimulated or were stimulated with TNF-α for 10 min. Cells were directly lysed in 4% SDS/0.1 M Tris HCL (pH=8.5), followed by sonication and boiling at 70 °C for 10 min. Lysates were clarified by centrifugation  and protein concentration was determined using DC assay (Bio-Rad). Approximately 3 mg protein of each labelling condition  were pooled and reduced using 100 mM dithiothreitol (Sigma-Aldrich) for 10 min at 56 °C and then subjected to the FASP digestion technique2PO4 in 30% acetonitrile (ACN) (pH=2.65) and B 7 mM KH2PO4, 350 mM KCl in 30% ACN (pH=2.65). All fractions were pooled conducting absorbance at 280 nm to a total of 8–10 fractions, concentrated and adjusted to binding conditions for Titan sphere (TiO2) bead-based extraction of phosphorylated peptides . Fractions were incubated twice with 2.5 mg of TiO2 beads and flow-throughs were incubated three times with 5 mg of TiO2 beads (SLSC Science). Beads were washed several– times with decreasing content of TFA (6–3%) and loaded on C8 material-containing tips. Peptides were eluted with 40% ammonia/acetonitrile (pH=11.6), concentrated in a speed vac at room temperature to almost complete dryness and diluted in acidified (0.1% formic acid or 0.5% acetic acid) H2O before mass spectrometry. All experiments were at least performed in duplicates.MCF-7 cells were transfected with wild-type IKKβ or its kinase-inactive mutant (K44M) after at least six passages of SILAC labelling. After 48 h of transfection, cells were starved overnight and nontransfected cells were stimulated with SC-514 (25 μM) or TNF-α  column with 1.7 μm (3.0 μm) C18 beads (Dr Maisch GmbH) using a binary buffer system consisting of solution A: 0.1% formic acid (0.5% acetic acid) and B: 80% acetonitrile, 0.1% formic acid . Linear gradients from 7–38% B in 150–240 min were applied with a following increase to 80% B within 5 min and a re-equilibration to 5% B.QExactive settings: MS spectra were acquired using 1E6 as an AGC target, a maximal injection time of 20 ms and a 70,000 resolution at 200 mz−1. A Top10 method was applied for subsequent acquisition of higher-energy collisional dissociation (HCD) fragmentation MS/MS spectra of the top 10 most intense peaks. Resolution for MS/MS spectra was set to 35,000 at 200 mz−1, AGC target to 5E5, max injection time to 120 ms and the isolation window to 1.3 Th.Velos settings: the resolution for MS spectra was set to 30,000 at 400 mz−1 after accumulation of 1E6 ions (AGC target) within a maximal injection time of 60 ms. Top 15 method was applied for MSA MS/MS spectra at a resolution of 7,500 at 200 mz−1. AGC target and maximal injection time were set to 1E4 and 30 ms, respectively. CID fragmentation was carried out with multistage activation and normalized collision energy was set to 35.More than 120 raw files from all four experimental set-ups in at least duplicates were processed using MaxQuant (1.4.1.2)n=1,500), downsampling, random forest tuning (receiver operating characteristic (ROC)-based, tune length=5, parameter tuned: mtry—no. of features used at each split) and prediction as well as evaluation and comparison with other machine-learning algorithms66To perform Random forest analysis to predict novel IKKβ substrates, we first manually filtered known TNF-α/IKKβ-dependent phosphorylation sites. In total, we found 11 IKKβ and 25 TNF-α targets, which represent the positive data sets. Conversely, the negative data set was created by resampling of the remaining phosphorylation sites, which were quantified in all experiments >10,000; . The cla−1 of each leupeptin, aprotinin and pepstatin, 1 mM 4-(2-aminoethyl)-benzosulfonylfluoridhydrochloride and 1 mM Na3VO4). Protein extracts were purified with centrifugation and were incubated with the antibody for the respective protein in presence of Protein A/G PLUS Agarose (Santa Cruz Biotechnology). Anti-FLAG affinity gel was used to immunoprecipitate FLAG-tagged MTDH. Precipitated proteins were then centrifuged and washed several times using ice-cold RIPA buffer. Finally, antibody–antigen complexes were eluted by boiling in Laemmli buffer and samples were subjected to western blotting. To determine the phosphorylation of Erk1/2 MAPK, p38 MAPK or IκBα degradation, MCF-7 cells were transfected with wild-type or mutated (K44M) IKKβ. Forty-eight hours after transfection, cells were starved for 12 h and incubated with SC-514 (25 μM) and/or TNF-α (10 ng ml−1). Cells were lysed and lysates were subjected to western blot analysis as described above. To determine AEG-1 phosphorylation of Erk1/2 and IκBα degradation, MCF-7 cells were transfected with siRNA targeting AEG-1 or HEK293 cells transfected with wild-type/S298A AEG-1. After 48 h, cells were starved, stimulated with control or TNF-α (10 ng ml−1) for 10 min and lysed. For subcellular localization, proteins were fractionated using the Subcellular Protein Fractionation Kit for Cultured Cells (Thermo). Cell lysates, immunoprecipitates and fractions were separated using SDS–PAGE and transferred to a nitrocellulose membrane. Proteins were detected using their respective antibodies and visualized using enhanced chemiluminescence system . Full-size western blot images are presented in For protein immunoprecipitation, MCF-7 or HEK293 cells were lysed in ice-cold radioimmunoprecipitation (RIPA) buffer . Purified recombinant IKKβ (ProQinase GmbH) was incubated with immunoprecipitate in the presence of 2 × kinase buffer, 10 μM cold ATP , 5 μCi γ32-ATP  for 30 min at 30 °C. The reaction was stopped by adding 4 × Laemmli buffer and was subjected to boiling for 5 min. The samples were separated using SDS–PAGE gel and the gel was dried and exposed to an X-ray film .To visualize protein AEG-1 phosphorylation −1) for 10 min and fixed in 4% paraformaldehyde (PFA) for 15 min at room temperature. Fixed cells were permeabilized by 0.5% triton and immunostained using anti-AEG-1 or anti-FLAG-FITC for endogenous AEG-1 or overexpressed AEG-1, respectively. Anti-IκBα was used for counterstaining. Nuclei were stained with Hoechst 33342 (Invitrogen) and imaged under epifluorescent microscope.For immunostaining, MCF-7 cells were either transfected with siRNA targeting AEG-1 or FLAG-tagged wild-type/S298A AEG-1 cDNA and were incubated for 48 h. After serum starvation overnight, cells were stimulated with control buffer or TNF-α (10 ng ml−1) for 12 h. Cells were then trypsinized and the cell pellet was fixed in 4% PFA, permeabilized by 0.5% triton and stained using anti-FLAG-FITC. Cells were counterstained with anti-Ki-67. Ki-67-positive cells were quantified in the sorted FLAG-positive stained cells in BD FACS Canto II. For cell cycle analysis, MCF-7 and 4T1 cells stably expressing scrambled shRNA or shRNA against AEG-1 together with FLAG-tagged wild-type or S298A AEG-1 were serum-starved, treated with control or TNF-α (10 ng ml−1) and trypsinized. Cells were then pelleted and the pellets were fixed in 70% ethanol. Cells were stained using 7-aminoactinomycin D (7-AAD) and cell cycle phases were analysed in BD FACS Canto II.For proliferation assay using flow cytometry (FACS), serum-starved FLAG-tagged wild-type/S298A AEG-1 cDNA-transfected HEK293 cells were treated with TNF-α (10 ng ml−1) or left untreated for 12 h. For ChIP experiments, ∼2–5 × 107 cells were crosslinked with 1% formaldehyde for 10 min at room temperature. The reaction was then quenched using 0.125 M glycine for 5 min at room temperature and washed with ice-cold PBS. Cells were lysed using cell lysis buffer  and nuclei were enriched for sonication. The nuclei were then sheared by sonication to an average chromatin fragment size of 200–400 bp using Biopruptor (Diagenode). Chromatin was precleared and incubated with anti-FLAG affinity gel overnight at 4 °C, and immune complexes were precipitated by protein A beads (Diagenode), which was pre-equilibrated with sonicated salmon sperm DNA and BSA. Immunoprecipitated material was then washed extensively, and the crosslinks were reversed using elution buffer  overnight at 65 °C (ref. Ct method with results presented as the mean plus or minus s.e.m.MCF-7 cells were transiently transfected with wild-type or S298A AEG-1 cDNA and incubated for 48 h. Cells were then starved and stimulated with TNF-α (10 ng ml °C ref. . ChIP DN4)/4T1 (104) cells stably expressing wild-type or S298A AEG-1 on the background of stable AEG-1 knockdown were starved and seeded into either 96-well-migration upper chambers or 24-well matrigel chamber (Corning). Control buffer or TNF-α (10 ng ml−1) were added to the lower chamber of the respective wells. The cells were then allowed to migrate/invade for 24 h and the migrated/invaded cells at the lower surface of the filter were fixed in methanol, stained using toluidine blue and counted.MCF-7 (3 × 10Cell proliferation was measured using the xCELLigence system (Roche), according to the manufacturer's instructions. Briefly, MCF-7 cells were transfected with wild-type or S298A protein AEG-1 cDNA. After 48 h of transfection, cells were trypsinized, seeded on E-Plate (ACEA Biosciences Inc.) and cell impedance measurement was carried out for 48 h.Colony formation assay was adapted from ref. t-test. To identify significantly regulated phosphorylation sites, the FDR was controlled (multiple testing correction) using a permutation-based algorithm, adapted from ref. P<0.05.Comparison of two groups was carried out by a two-sided https://cptac-data-portal.georgetown.edu/cptac/ Data set S020). To check for the impact of AEG-1 phosphorylation, we utilized the iTRAQ-based phosphoproteome and proteome report files, analysed by the CDAP pipeline. First, we filtered for patients with AEG-1 S298 phosphorylation. Next, we extracted AEG-1 protein and phosphoprotein (only those containing phosphorylated serine 298) expression values for the filtered patients and mapped the expression values on the clinical data provided by CPTAC. Phosphoprotein expression was normalized to protein expression. We used both ends of the AEG-1 phosphorylation expression distribution (top 20 versus lowest 20) for a survival analysis based on the clinical data . To access statistical significance, we performed a log-rank test. Curves were plotted using GraphPad Prism v 5.04.Proteomics and phosphoproteomics data from a clinical studyJ.M.S., M.K., R.K.K. and H.N designed experiments and analysed results. H.N. and R.K.K. performed peptide enrichment. H.N. performed mass spectrometry-based measurement of phosphopeptides as well as machine-learning for computational prediction. R.K.K. performed molecular cloning and mutagenesis of AEG-1, together with biochemical and biological analyses. H.K. performed FACS analysis, sorting and quantified gene expression using real-time PCR and K.S. performed ChIP assay. M.L. analysed patients' survival. T.S. performed migration and invasion assays. J.M.S., H.N., M.K. and R.K.K. wrote the manuscript.Accession codes. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD001873.How to cite this article: Krishnan, R. K. et al. Quantitative analysis of the TNF-α-induced phosphoproteome reveals AEG-1/MTDH/LYRIC as an IKKβ substrate. Nat. Commun. 6:6658 doi: 10.1038/ncomms7658 (2015).Supplementary Figures 1-11Identified phosphosites in the complete dataset (Experiment 1-4). Sites used for training the classifiers are indicated. Description of each column is inside the file.Identified protein groups by acquired MS raw data and processing by MaxQuant. Description of each column is inside the file.Phosphorylation sites that were quantified with 13 SILAC ratios in the 4 experiments including the training data set. The classifiers were used to determine a scores for IKKβ and TNF-a dependent sites. This table is related to Fig. 1e and Supplementary Fig. 3. Description of each column is inside the file.


The Wnt system is highly complex and is comprised of canonical and non-canonical pathways leading to the activation of gene expression. Our aim was to examine changes in the expression of Wnt ligands and regulators during hepatic stellate cell (HSC) transdifferentiation and assess the relative contributions of the canonical and non-canonical Wnt pathways in fibrogenic activated HSC. The expression profile of Wnt ligands and regulators in HSC was not supportive for a major role for β-catenin-dependent canonical Wnt signalling, this verified by inability to induce Topflash reporter activity in HSC even when expressing a constitutive active β-catenin. We detected expression of Wnt5a in activated HSC which can signal via non-canonical mechanisms and showed evidence for non-canonical signalling in these cells involving phosphorylation of Dvl2 and pJNK. Stimulation of HSC or Kupffer cells with Wnt5a regulated HSC apoptosis and expression of TGF-β1 and MCP1 respectively. We were unable to confirm a role for β-catenin-dependent canonical Wnt in HSC and instead propose autocrine and paracrine functions for Wnts expressed by activated HSC via non-canonical pathways. The data warrant detailed investigation of Wnt5a in liver fibrosis. Hepatic stellate cells (HSC) are widely recognised as the major cellular origin of activated pro-fibrogenic myofibroblasts in chronic liver disease, irrespective of disease aetiology. In response to liver damage HSC undergo an epigenetically-regulated transdifferentiation to adopt a myofibroblast-like phenotype characterised by proliferation, contractile ability and the secretion of vast amounts of fibril-forming extracellular matrix (ECM) proteins. The perGrowing evidence suggests that tissue injury is accompanied by the reactivation of embryonic signalling pathways such as those controlled by the morphogen families Hedgehog (Hh), Notch and Wnt. During 2+) pathway. The relative contributions of these distinct intracellular Wnt signalling pathways towards the regulation of myofibroblast fate and function, as well as to the control of fibrogenesis per se, remains poorly defined.The Wnt systems is comprised of signalling proteins that are highly evolutionary conserved secreted glycoproteins with a critical role in developmental regulation. In the Recent work from a number of laboratories has suggested that Wnt signalling is activated in HSCs upon liver injury and that the expression of many components of the different Wnt pathways are modulated during HSC transdifferentiation including Wnt ligands, Frizzled receptors, the LRP5 co-receptor and the expression of nuclear β-catenin–13. More4- or BDL-injured livers  receptors or a variety of non-canonical receptors including ROR1, ROR2, Ryk and Ptk7 . TranscrThe interaction of Wnt ligands with their cell surface receptors is under tight extracellular regulation by soluble proteins that compete for their binding; these include members of the secreted frizzled-related proteins (sFRP1-5) and the dickkopf (Dkk1-4) family. HSC activation was accompanied by induction of sFRP1, sFRP4 and Dkk3 mRNAs which was detected by both semi-quantitative  and quanTo determine the ability of aHSC to respond to canonical Wnt signalling, human LX-2 were co-transfected with the canonical Wnt luciferase reporter Topflash and expression vectors for Wnt3a and Wnt10b. To control these assays similar transfections were carried out in HEK293 cells which are known to support β-catenin-dependent Wnt signalling. As antiHaving failed to find evidence for β-catenin-dependent canonical Wnt activity in HSC we turned our attention to non-canonical Wnt signalling which is mediated via JNK- and CamKii/NFAT-dependent pathways,24. ExpoOur observation that HSC secrete and respond to Wnt5a by activating Dvl2 and downstream non-canonical Wnt signalling encouraged us to further examine the functional contribution of this Wnt ligand to fibrogenesis. As before, primary rat aHSC were exposed to conditioned media from cultures of Wnt5a over-expressing LX-2 cells prior to determining effects on morphology , prolifeThe Wnt signalling system has been the subject of considerable attention in HSCs and liver fibrosis with recent reports suggesting functions in HSC transdifferentiation, proliferation, apoptosis, control of fibrogenic gene expression and regulatory interactions of aHSC with other liver cell types and physiological responses such as hepatocellular regeneration,13,14. Hin vitro there is little concrete evidence in favour of a role for canonical β-catenin-dependent Wnt signalling in aHSC. Rather on the contrary, the aHSC is lacking a number of factors for this pathway to be active . Studies in lung fibroblasts have implicated a role for Wnt5a as a regulator of proliferation and apoptosis, hence our finding for an anti-apoptotic role for Wnt5a in aHSC may be indicative of this being a core function for Wnt5a in tissue fibrosis[Our second conclusion was that aHSC abundantly express and secrete Wnt5a, which may through non-canonical signalling pathways and via both autocrine and paracrine mechanisms promote fibrogenesis. In previous work Wnt5a was reported as an induced transcript with culture-activation of rat HSC,12 and b fibrosis. The mec fibrosis,36. Our  fibrosis. Further fibrosis. The rol fibrosis indicatiIn conclusion, the Wnt signalling system is a functional feature of HSC-derived myofibroblasts and has the potential to influence fibrogenesis via autocrine and paracrine routes. However, from our data it seems that the degree to which aHSC utilise the canonical Wnt pathway is minimal and as such it will be important in future work to interrogate the nature and function of non-canonical Wnt signalling.2 in air at 37°C, cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 16% foetal bovine serum (FBS), 100 U/mL penicillin, 100 g/mL streptomycin and 2mM L-glutamine. The LX-2 cell line, a spontaneously immortalized human hepatic stellate cell, was kindly provided by Dr. Scott L. Friedman . Treatment reagents used were recombinant sFRP1 and sFRP5 (5nM) (R&D systems), CT99021 (Axon MedChem), IWP2 (20μM) (Sigma), Dvl-PDZ Domain inhibitior  (5μM), ICG001 (5μM)(Tocris). Amicon Ultra-0.5 mL centrifugal filters (Millipore) were used to concentrate cell culture medium for western blotting.All experiments were approved by Newcastle University’s Animal Welfare and Ethical Review Board. Animals were bred in the Newcastle University animal unit, maintained as specific pathogen free according to the FELASA Guidelines. Work was carried out under project and personal licences approved by the UK Home Office. HSCs were isolated from normal livers of male Sprague-Dawley rats (following Ketamine/Xylazine anaesthesia and sacrifice by exsanguination) by collagenase/pronase perfusion by density centrifugation in Optiprep (Invitrogen). Isolated cells were pre-plated on plastic flasks for 20 mins to allow attachment and separation of Kupffer cells. Cells harvested directly after isolation (Day 0 of culture) were considered quiescent. Cells harvested after 7 days of culture were considered activated. Dermal fibroblasts were obtained from freshly harvested rat skin by collagenase A digestion. Primary cells were maintained in humidified atmosphere of 5% CO4 injured mouse liver was donated by H. Tsukamoto, as used in Mann et al [Total RNA was purified using the RNeasy kit (Qiagen) and cDNA templates prepared using a random hexamer primer and Moloney murine leukemia virus reverse transcriptase (MMLV-RT) (Promega). RNA from HSCs isolated from BDL or CClnn et al . RNA fronn et al . PCR priSodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) and immunoblotting were done as previously described. Primary Antibodies used were CaMKII (pan) (#3362S), Dvl2 (#3216), LEF1(C12A5)(#2230), PJNK (Thr183/Tyr185) (#9251), TCF1 (C63D9) (#2203), TCF3 (D15G11) (#2883), TCF4 (C48H11) (#2569), TGFβ (56E4) (#3709), TJNK (#9252), Wnt5a (C27E8) (#2530) and αTubulin (#2144) (1 in 1000) , Wnt5a (#55184-1-AP), Wnt3a (#21414-1-AP), Fzd2 (#24272-1-AP) (1 in 1000), sFRP4 (#15328-1-AP) (1 in 500) (Proteinech). GAPDH (ab9485), β-Actin(AC-15) (ab6276), β-Catenin  (ab6302), Fzd8 (ab75235) (1 in 2000) (Abcam), p84 (5E10) (GT70220) 1 in 1000, (Genetex), β-Catenin(Active) (8E7) (#05–665) 1 in 500 (Millipore).Cells in 6 well plates were pre-treated with Mitomycin-C (Sigma) for 30 minutes and single scratch made in each well. Wound was measured over a 24 hour period. Results presented are averages of ten measurements per field. Experiments were performed in triplicate.Cells were seeded in 12-well plates at 20–30% confluency. Apoptotic cells were detected by Acridine Orange staining (1μg/ml) (Sigma) and visualisation under a FITC filter. Five random fields per well were counted at x20 magnification in duplicate wells.Cells seeded in 96 well plates were treated with MTT  for 4 hours. Medium was then aspirated and cells incubated with DMSO (Sigma) for 30 minutes. Plate was read using plate reader at 570nm absorbance.Cells were transfected with 1μg of plasmid DNA using Effectene transfection reagent (Qiagen) for 48 hours before harvest. For TOPFLASH reporter assay, 0.5μg of TOPFLASH plasmid (Millipore) was contransfected with 0.05μg of renilla luciferase plasmid serving as a transfection control. Luminescence was measured using the Dual Luciferase Reporter Kit (Promega) and data presented as average ratios of firefly to renilla luciferase activity. Conditioned medium was collected from transiently transfected LX-2 cells over expressing Wnt5a, Wnt10b or an empty control construct.et al[Active β-catenin construct (S37A) was provided by D.L.Johnson, as used in Orford et al. Wnt3a, Data are expressed as mean ± standard error of the mean (SEM). P values were calculated using a two-tailed paired or unpaired Student t test. For figures with statistically significant data *, **, and *** indicate P values of <0.05, <0.01 and <0.001, respectively.S1 Fig(A) Western Blot analysis confirms protein expression of Fzd2 and FZd8 in qHSC and aHSC. Fzd overexpessing LX2 cells serve as a positive control. (B) qRT-PCR analysis of sFRPs and Dkk 3 in rat qHSC and aHSC. Results expressed as fold change normalised to control ± SEM (n = 4).(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigNuclear βcatenin is increased upon overexpression of S37 mutant. αTubulin and p84 serve as controls of cytoplasmic and nuclear purity respectively.(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 FigComplete media (16% FBS containing DMEM) serves as a negative control.(TIF)Click here for additional data file.S6 Fig(A) Western Blot demonstrating increased expression of Dvl2 in day 7 rat HSCs upon Wnt5a or Wnt10b conditioned medium treatement (B) qRT-PCR for profibrotic markers in rat HSCs treated with Wnt5a conditioned medium at Day3 or Day 7 of in vitro culture. Results expressed as fold change normalised to control ± SEM (n = 3).(TIF)Click here for additional data file.S7 Fig(A) mRNA levels of profibrotic markers were analysed by qPCR. Results expressed as fold change normalised to control ± SEM; n = 3 (B) Bright field microscopy images (C) Apoptotic response analysed by Acridine Orange staining. Data presented as number of apoptotic cells as percentage of total cell number (n = 3).(TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.S9 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.