Furthermore, paired-sample analysis revealed that pretreatment cystatin C levels were reduced significantly in patients who achieved remission after therapy (p = 0.016). High serum cystatin C levels were correlated with age over 60 years (p = 0.049), extra-nodal involvement (p = 0.005) and with high serum lactate dehydrogenase (LDH) (p < 0.013). Elevated serum cystatin C levels were associated with extra-nodal involvement and they were significantly reduced to normal range after the remission. However, Kaplan–Meier curves revealed no survival difference in the pretreatment serum cystatin C levels. Therefore, serum cystatin C may be a novel biomarker that reflects tumor burden in DLBCL but bears no prognostic significance regarding survival.Elevated serum levels of cystatin C are found to be related to poor outcome and metastatic potential of some malignant disorders. To evaluate the clinical prominence of serum cystatin C in diffuse large B-cell lymphoma (DLBCL), blood samples were obtained from 58 patients at the time of diagnosis and paired blood samples were obtained from 22 patients at the time of remission. Also, serum cystatin C level was measured in matched healthy controls. Serum cystatin C levels were significantly more elevated in DLBCL patients than in controls ( The furthermost prevalent subtype of non-Hodgkin lymphoma (NHL) is diffuse large B-cell lymphomas (DLBCL) found in 30%–40% of all cases of lymphoma globally ,2. The cTumor biomarkers are crucial for the screening, diagnosis and follow up of cancer. Definite biomarkers for lymphoma are also relevant to the treatment plan, prognostic mapping and in studying tumorigenesis. Present-day lymphoma clinical biomarkers are non-specific and scarce. Serum lactate dehydrogenase (LDH) is an example of the most commonly used biomarker in the diagnosis of lymphoma and is correlated to prognosis , though Numerous serum biomarkers for lymphomas were proposed for their clinical value such as soluble intercellular adhesion molecule-1 (s-ICAM-1/s-CD54) ; solubleCysteine proteinase inhibitors, cystatins, are involved in mechanisms controlling intracellular and extracellular protein degradation. Under normal physiological conditions, small amounts of catalytically active proteases, released from lysosomes or secreted from infected or dying cells, are effectively blocked by cystatins. Cystatin C, a member of the family II of cystatins, is a non-glycosylated low molecular weight basic protein, as an inhibitor of cysteine proteases, distributed pervasively in almost all extracellular fluids. . A wide-Cystatin C has also been proposed to have a role allied to the modification of the proteolytic system in cancer. In patients with lung and colorectal cancer, elevated serum cystatin C levels are linked to meager outcome of cancer ,19. In pTo further our understanding of the role of cystatin C in DLBCL, we analyzed the level of cystatin C in sera of DLBCL patients and prospectively evaluated its potential influence on disease outcome.We used a prospective, consecutive entry design. Between February 2014 and February 2015, 58 previously untreated patients with DLBCL, in Oncology Center, Mansoura University, Mansoura, Egypt, confirmed by biopsy, participated in this study. Patients with a creatinine level above the upper physiologic limit, patients with autoimmune diseases, asthma, and other malignant diseases have been excluded from the study. Age- and sex-matched healthy blood donors served as controls. The current study was approved by the ethical committee of the Faculty of Pharmacy, Mansoura University, Mansoura, Egypt on February 10, 2014 with code number 2016-72. All procedures implemented in this study concerning human participants were in agreement with the ethical standards of the national and institutional research committee, also in accordance with the 1964 Helsinki declaration and its late amendments.Blood samples were collected with informed consent and institutional ethical committee approval from all patients, at the time of diagnosis, before therapy. In addition, paired serum samples were obtained at the time of disease remission in twenty-two patients just before the fifth cycle of chemotherapy. A complete response (CR) was well-defined as the disappearance of all laboratory, clinical and radiographic evidence of lymphoma. Furthermore, patients who showed response with negligible residual radiographic abnormalities were identified as partial responders (PR).Clinical stages of the patients were categorized according to the Ann Arbor staging system  by means2 on day 1), 19 patients received CHOP without rituximab  [Thirty-six patients received rituximab, cyclophosphamide, Adriamycin, vincristine, and prednisolone (R-CHOP) regimen  .The serum level of cystatin C in lymphoma patients was measured by specific quantitative sandwich enzyme-linked immunosorbent assay (ELISA) using Human Cystatin C Kit ELISA catalog number MBS700210 .Lactate dehydrogenase was measured using ELISA kit catalog number MBS009535 (MyBioSource).t-test and paired match t-test were used instead.Statistical analysis was performed using SPSS statistics software release 22 . Quantitative data were tested for normality using the Kolmogorov–Smirnov test. Abnormally distributed data were given as median (minimum–maximum) while normally distributed data were given as mean ± standard deviation (SD). Non-parametric statistical tests of significance were applied; the Mann–Whitney U test was used to compare two independent groups and the Wilcoxon test was used to compare before/after chemotherapy. While in the case of normal distributed data, the independent Student’s ps below 0.05 were considered significant. Receiver operating characteristic (ROC), carried out using MedCalc statistical software version 12.3 , was used to evaluate the diagnostic and prognostic accuracy of a test. The Youden index was used to find the cut-off point . At this cut- off point, sensitivity and specificity were determined. Correlations were carried out using Pearson correlation (r) and Spearman correlation.Survival analysis was done using the Kaplan–Meier test. Correlations of cystatin C with clinical stages of disease, erythrocyte sedimentation rate (ESR), LDH, and IPI were defined by Spearman rank correlation analysis. All applied statistical tests of significance were two-tailed. In all tests, two-sided p < 0.0001, We examined the circulatory cystatin C levels of DLBCL patients. Median serum cystatin C levels of DLBCL patients at diagnosis were significantly higher than those of healthy controls  .In the 44 patients with DLBCL who achieved response (PR/CR) after chemotherapy treatment, initial levels of cystatin C were significantly lower  compared with the non-responders  was 0.886 a.p = 0.049; Spearman rank correlation coefficients, r = 0.260).Those aged over 60 years showed significantly more elevated levels of cystatin C than the others .Those who had advanced disease with extra-nodal involvement also had higher levels of cystatin C compared to those without extra-nodal disease (r = 0.325 (p < 0.013).Moreover, high cystatin C levels were correlated significantly with high serum LDH level, with a rank correlation coefficient r = 0.056, p = 0.763), or disease stage . Lastly, we found that cystatin C levels could not correlate with IPI score .We did not observe any difference in cystatin C levels among patients according to performance status  and disease-free survival (DFS) of all patients was 91.4% and 87.9%, respectively. Considering the results obtained from factors associated with serum cystatin C levels, we evaluated whether serum cystatin C could be a powerful predictor of disease response. Five patients were excluded from the response analysis, since they died early during the course of disease before starting therapy. Using a cut-off value of 4.2877 for cystatin C, determined by ROC curve analysis b, two sup = 0.612; p = 0.843; Subsequently, we analyzed the survival according to two sub-groups divided according to the prognostic cut-off value of serum cystatin C levels. Only five patients died during the observation period, therefore OS between the two cystatin C subgroups was not statistically significant  [In their study on 52 patients with B-cell lymphoma (41 aggressive lymphomas and 11 indolent lymphomas), Softić et al.  demonstrN = 6) . Along tN = 6) ,20.The optimum cut-off level that yields the finest classification of patients according to response in two main groups was noticed at 3.9829 ng/mL. We found that high levels of cystatin C, above the cut-off value, were correlated negatively with response to therapy. These results recommended that it was valuable to examine whether the negative or poor prognostic impression of cystatin C levels on response was due to its relation to the expression of further poor prognostic markers. In this regard, we compared the levels of cystatin C with established biochemical and clinical parameters.The IPI is the widely accepted prognostic index for patients with aggressive lymphoma . Our resOur study also revealed that cystatin C, at the time of diagnosis, correlates with extra-nodal disease. These results suggest the possibility that there is a specific mechanism in extra-nodal infiltration that drives the increased secretion of cystatin C in body fluids. Moreover, cystatin C is produced by all nucleated body cells, so the correlation of the inhibitor level and extra-nodal disease can be the result of the general response to the increased proteolytic activity. In malignancy, a misbalance among cysteine proteases and their inhibitors, accompanied by the metastatic phenotype of the tumor cell, is believed to promote the invasion of the tumor cell and metastasis . Hence, In our study, there was no significant difference in the serum levels of cystatin C when comparing advanced to non-advanced disease, which was similar to the results in 39 lung cancer patients reported by Ohara et al. . DiffereWe have further demonstrated that there is no correlation between pretreatment cystatin C levels and presence of B symptoms. This is consistent with the results from Softić et al. , who fouMulaomerovi´c et al.  revealedThe fact that cystatin C did not correlate with the IPI raises the possibility that it might independently add to the power of the IPI in predicting survival. Although the correlations with survival are compromised by the short duration of the study, and the low number of events, it should be examined whether combining the IPI and cystatin C makes a model that is more predictive than the IPI alone.In summary, serum cystatin C levels are significantly higher in DLBCL patients than in healthy controls. Moreover, we explored cystatin C levels in DLBCL patients, as a promising biomarker for monitoring response to therapy. On the contrary, it bears no prognostic significance regarding survival.


Drosophila led to identification of cyclin-dependent kinases (CDKs), Polo-like kinases (PLKs) and Aurora kinases as essential regulators of mitosis. These enzymes have since been found in the majority of eukaryotes and their cell cycle-related functions characterized in great detail. However, genetic studies in another fungal species, Aspergillus nidulans, identified a distinct family of protein kinases, the NEKs, that are also widely conserved and have key roles in the cell cycle, but which remain less well studied. Nevertheless, it is now clear that multiple NEK family members act in networks to regulate specific events of mitosis, including centrosome separation, spindle assembly and cytokinesis. Here, we describe our current understanding of how the NEK kinases contribute to these processes, particularly through targeted phosphorylation of proteins associated with the microtubule cytoskeleton. We also present the latest findings on molecular events that control the activation state of the NEKs and how these are revealing novel modes of enzymatic regulation relevant not only to other kinases but also to pathological mechanisms of disease.Genetic studies in yeast and  Aspergillus nidulans. Two classes of mutant were identified: those that became blocked in mitosis, so-called “bim” mutants, and those that were blocked in interphase and were therefore never in mitosis, so called “nim” mutants  encode a single NEK kinase, while the amoebozoa Dictyostelium that also lacks classical centrioles and cilia has only two. Trypanosoma and Chlamydomonas, with respectively one and two flagella, have ten NEKs and the multiciliated Tetrahymena almost forty. Plasmodium is phylogenetically related to Tetrahymena but expresses only four NEKs. However, three of these are restricted to gametocytes and this is consistent with the male gamete being the only cell with flagella.Strikingly, the number of NEKs encoded within the genome varies widely from organism to organism. Intriguingly, this number correlates with complexity of the molecular structures that contribute to formation of cilia and flagella, which typically involves a basal body or centrosome. This has led to the suggestion that the NEK family evolved in parallel with the ciliary apparatus such that organisms that require a more complex regulation of this structure have expanded the number of NEKs diversifying their functions according to their specific needs . The surprising expansion of the NEK family in Giardia is yet to be explained. The high number of NEKs may be related to the needs of controlling four pairs of different cilia, and their corresponding basal bodies, that are inherited in a specific pattern during cell division. However, this is unlikely to be the sole reason as Giardia NEKs are highly diverse and the majority look by sequence to be enzymatically-inactive pseudokinases. On the other hand, most of these NEKs are expressed and localize in specific manners suggesting that they retain a function. In addition, NEKs appear to be undergoing rapid evolution in these organisms based on observable changes between strains, implying that NEKs may have a role in establishment of strain-specific differences , while C. elegans, with amoeboid sperm and where only sensory neurons are ciliated, has four . Interestingly, planarians have lost centrosomes and only assemble centrioles and cilia in terminally differentiated cells, yet they retain several NEKs, including Nek6/Nek7 and Nek8/Nek9 homologs  and mitotic spindle, NIMA controls chromatin condensation , using a yeast two-hybrid screen to isolate Nek2 binding partners  protein that is almost exclusively composed of predicted coiled-coil motifs. It specifically localizes to the proximal ends of centrioles, where the centrosome linker is assumed to attach. Here, it can bind Cep135, a centriole cartwheel component, with interaction occurring between the C-terminal regions of each protein  associated with interphase centrosomes  catalytic domains  extensions that are N-terminal to the kinase domains. Interestingly, there is now evidence that these N-terminal extensions provide specificity toward substrates and other binding partners in different cellular contexts . However, more targeted studies indicate that the two kinases may be expressed differentially during embryogenesis and in different regions of the adult nervous system , although they do exhibit increased cardiac hypertrophy after transthoracic aorta constriction   and its functions are likely to be crucial during development based on the recent description of a Nek9 mutation in humans  that is associated with a high frequency of abortions  and Asp-Phe-Gly (DFG)]; the Lys-Glu salt bridge within the N-lobe; the regulatory spine of hydrophobic core residues (R-spine); and the activation loop in which the site(s) of activating phosphorylation are located (Bayliss et al., The first structure of a NEK kinase was that of human Nek2, obtained in the presence of the ATP-competitive inhibitor SU11652 (Rellos et al., The catalytic domain of human Nek1 kinase has also been crystallized and the structure determined in apo-form and in the presence of an ATP-competitive inhibitor. The protein used had a T162A mutation, the principal site of activating phosphorylation, and was therefore in an inactive state. Unlike Nek2, the activation loop of inactive Nek1 was fully ordered, albeit in a conformation that is incompatible with substrate binding (Melo-Hanchuk et al., Nek6 and Nek7 activity depends on the phosphorylation of their activation loops at Ser206 and Ser195 respectively (Belham et al., The preference of Nek2 for substrates with a Phe, or another hydrophobic amino acid in the P-3 position, can be rationalized based on crystal structures (Alexander et al., There remain many unanswered questions regarding the biological functions of NEKs, as well as their contributions to disease mechanisms. In terms of centrosome disjunction, we do not fully understand how NEKs regulate the organization and length of the centrosome linker, or its connection to centrioles. Besides C-Nap1 and rootletlin, there are a growing number of proteins that have been proposed to be part of the linker but we know little about their relative importance or regulation. Similarly, while we have made good progress in identifying substrates of Nek2, we have only just uncovered a role for Nek5 in centrosome disjunction. We therefore need to identify both downstream substrates and upstream regulators of Nek5 to understand how it might cooperate with Nek2. In addition, we need to explore how centrosome disjunction is coordinated with other changes in centrosome organization during cell cycle progression, including ciliary resorption and centrosome maturation. It will be equally important to investigate how the centrosome linker is reassembled in late mitosis and how this is coordinated with disengagement of the duplicated centriole pair. In this regard, we hypothesize that there may be competition between C-Nap1 and the centriole duplication factor, SAS-6, for binding to Cep135, and that timely degradation of SAS-6 at the end of mitosis may be necessary for recruitment of C-Nap1 and establishment of a new linker (Strnad et al., Regarding Nek9, Nek6, and Nek7, one of the major challenges will be to understand their relative importance in different cell types and tissues, and whether Nek6 and Nek7 have redundant functions in specific physiological contexts. Indeed, the role of these kinases in normal development needs to be clarified, particularly in light of data showing that mutations in human Nek9 lead to either abortions (Casey et al., in cis regulatory motifs, such as the Nek2 leucine zipper (Croasdale et al., Finally, our understanding of the structural mechanisms within NEK kinase pathways is far from complete. For instance, there is no experimental structural model of a NEK kinase in its active state, principally because protein samples are heterogeneously phosphorylated and unstable when expressed in recombinant form. This issue may be resolved by using genetically encoded phosphorylation to generate purified proteins with phosphorylation on specified serines or threonines. This approach could also be applied to the substrates of NEKs, such as Hsp72, to enable studies on the structural and functional consequences of phosphorylation. To date, structural studies on NEKs have been restricted to individual domains and the complex of Nek7 with a short peptide derived from Nek9, whereas most NEKs are multi-domain proteins. A major aim of future work is to resolve the structures of full-length NEKs that include their AF, RB, and JR contributed to the planning and writing of this review article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Rush desensitization (DS) is a widely used and effective clinical strategy for the rapid inhibition of IgE-mediated anaphylactic responses. However, the cellular targets and underlying mechanisms behind this process remain unclear. Recent studies have implicated mast cells (MCs) as the primary target cells for DS. Here, we developed a murine model of passive anaphylaxis with demonstrated MC involvement and an in vitro assay to evaluate the effect of DS on MCs. In contrast with previous reports, we determined that functional IgE remains on the cell surface of desensitized MCs following DS. Despite notable reductions in MC degranulation following DS, the high-affinity IgE receptor FcεRI was still capable of transducing signals in desensitized MCs. Additionally, we found that displacement of the actin cytoskeleton and its continued association with FcεRI impede the capacity of desensitized MCs to evoke the calcium response that is essential for MC degranulation. Together, these findings suggest that reduced degranulation responses in desensitized MCs arise from aberrant actin remodeling, providing insights that may lead to improvement of DS treatments for anaphylactic responses. Anaphylaxis is a severe form of allergic reaction resulting in systemic symptoms, including respiratory, gastrointestinal, or cutaneous manifestations. This may include hypotension, hypoxia, and poor end-organ perfusion . This alMCs are now widely believed to be the primary target of the DS protocol, as reactivity to MC-dependent skin prick tests is reduced or sometimes disappears after desensitization . These c2) to the second messengers inositol trisphosphate (IP3) and diacylglycerol. Binding of IP3 to receptors on the endoplasmic reticulum results in the emptying of intracellular Ca2+ stores and subsequent store-operated Ca2+ entry (SOCE), which results in the sustained elevation of intracellular Ca2+ required for exocytosis of MC granules  were sensitized with 10 μg anti-TNP (trinitrophenyl) IgE i.p., then challenged i.p. with 500 μg TNP-OVA. Rectal temperatures were measured as an indication of anaphylaxis. MC-deficient Wsh mice were refractory to anaphylaxis, while WT mice showed a drastic drop in temperature 15–30 minutes after challenge  as well the RBL-2H3 cell line . The mod+ cells, which in the peritoneal cavity are all MCs .Previous studies have suggested that MC desensitization was attributable to the internalization of IgE antibodies during this process. If this were the case, then there should be limited IgE left on these cell surfaces capable of binding sufficient Ag for activation. To test the residual Ag-binding ability of desensitized cells, we collected peritoneal lavages from IgE-sensitized mice that had been desensitized (DS), Ag challenged (Ag), or unchallenged (IgE). Peritoneal cells obtained from the lavage were incubated with Alexa Fluor 647–labeled (A647-labeled) TNP-OVA ex vivo , and the all MCs . As expetivation . Surprisfor FACS . As expeThe residual Ag-binding capacity of desensitized MCs made us speculate that desensitized MCs could perhaps recover the ability to degranulate in response to Ag challenge, much as the effects of DS in clinical settings are temporary, with subjects regaining their reactivity to the allergen after the desensitization process is discontinued . In thisSince residual IgE could be detected on MC surfaces even after desensitization, we sought to identify alternate mechanisms for MC desensitization. First, we investigated whether desensitized MCs still were able to transduce signals, even though degranulation failed to occur in response to additional Ag stimulation, as shown in 2+ mobilization . To demonstrate that MC desensitization was Ag specific, we desensitized MCs with one Ag and examined the degranulation response to heterologous Ag. Groups of MCs were simultaneously sensitized with anti-DNP and anti-OVA IgE antibodies, then desensitized with OVA or DNP-HSA, or left undesensitized . To achiNext, we investigated whether a similar association could be observed between β-actin and desensitized FcεRI following desensitization of MCs with each of the 2 antigens. For this, one of the IgE clones in the sensitization mixture (anti-OVA or anti-DNP IgE) was biotinylated, while the other was left IgE unlabeled. Thus, MCs were sensitized with a mixture of IgE where only IgE of one specificity was biotinylated but not the other. After desensitization or Ag challenge with either Ag (~2 hours of incubation), cell lysates were prepared and immunoprecipitated with streptavidin beads, which only pulled down the biotinylated IgE and associated complexes, leaving the unlabeled IgE in the flow-through. A reciprocal experiment was also performed in which the IgE clone being biotinylated was switched. Unsensitized BMMCs were included in both experiments as negative controls. Western blots of the immunoprecipitates from both sets of experiments revealed β-actin in the pulldown of biotinylated IgE desensitized with its corresponding Ag, but not when heterologous Ag was used for desensitization . Notably2+ flux and to degranulate following exposure to Ag. To this end, we used a cell-permeable peptide construct of the Salmonella effector SptP, SptPC481S-TAT  decrease in core body temperature 30 minutes after challenge with Ag that was unaffected by SptPC481S-TAT treatment  with the plasma membrane Ca2+ channels ORAI1 and TRPC (2+ release–activated channel (CRAC) on the plasma membrane that initiates the influx of extracellular Ca2+. Support for this proposed role of actin rearrangement comes from our observation that upon reversing actin cytoskeletal organization in desensitized MCs with a cell-permeable construct of the Salmonella effector SptP, SptPC481S-TAT, we could restore its Ca2+ mobilization and degranulation activity as well as the anaphylactic phenotype in desensitized mice. This is a highly accelerated recovery of degranulation in desensitized MCs, which without further manipulation took 48 hours to regain responsiveness to Ag challenge  at a density of 0.5 × 106 to 1 × 106 cells/ml with 5 ng/ml recombinant murine IL-3 (R&D Systems) and 5% to 10% stem cell factor-containing supernatant from CHO-KL cells for 4–6 weeks until maturation. CHO-KL cells were a gift from M. Arock . RBL-2H3 cells were purchased from ATCC and maintained in MEM with Earle’s salts, l-glutamine, 1 mM sodium pyruvate, and 15% heat-inactivated FBS. TNP(Anti–phospho-LAT (Y226) , anti-FcεRIγ (06-727), and anti-LAT (06-807) antibodies were from Upstate Technology. Phospho-LYN (Y396) antibody was from Abcam (ab40660). Antibodies against phospho-cofilin (mAb 3313) and total cofilin (mAb 5175), as well as total LYN , were from Cell Signaling Technologies. Antibodies against phospho-ERK (sc-7383) and ERK2 (sc-154) were from Santa Cruz Biotechnology. Anti–β-actin antibody was from Sigma-Aldrich (clone AC-74). BMMCs were obtained from femur bone marrow of 8-week-old C57BL/6 mice and cultured in BMMC medium  unless otherwise specified. For β-hexosaminidase assay, IgE-sensitized BMMCs were seeded at 3 × 105 to 5 × 105 cells per well in V-bottomed 96-well plates on the day of the experiment. RBL-2H3 cells were seeded at 5 × 104 cells per well in MEM with IgE in 96-well flat-bottomed plates overnight. Assays were then performed as previously described  and anti-TNP IgE (BD Biosciences) unless otherwise stated. BMMCs or RBL-2H3 cells were sensitized with 0.25–0.5 μg/ml IgE for 4 hours to overnight and washed 3 times before the start of the experiment. All assays were done in Tyrode’s buffer  for an additional 6 hours . The cells were then washed, permeabilized, blocked with 0.25% saponin in 1% BSA in PBS, and stained for intracellular CCL2 and TNF-α using FITC anti–mouse CCL2 and allophycocyanin anti–mouse TNF-α (eBioscience) before assay via FACS. To assay cytokine secretion, untreated, desensitized, or Ag-challenged BMMCs (5 × 105 BMMCs per sample in triplicate) were incubated in RPMI for 24 hours. Cell culture supernatants were diluted 1:5 and assayed for CCL2 and TNF-α using a kit (eBioscience) according to the manufacturer’s instructions. For cytokine mRNA quantitation, RNA was prepared from 5 × 105 BMMCs per sample in triplicate. RNA was isolated using an RNeasy purification system (Qiagen). cDNA was synthesized using the iScript cDNA synthesis kit (Bio-Rad). SYBR Green and iCycler machine (Bio-Rad) were used for real-time PCR. Primers were obtained from IDT-DNA for β-actin (Actb): 5′-TGAGAGGGAAATCGTGCGTGACAT, 5′-ACCGCTCATTGACGATAGTGATGA; CCL2: 5′-AGCAGGTGTCCCAAAGAAGCTGTA, 5′-AAAGGTGCTGAAGACCTTAGGGCA; Tnfa: 5′-TCTCATGCACCACCATCAAGGACT-3′, 5′-ACCACTCTCCCTTTGCAGAACTCA-3′. All target gene RNA expressions were normalized to Actb expression. Data were expressed as fold over IgE-only controls.In each case, 5 × 106 peritoneal lavage cells were blocked in 1% BSA in PBS before incubation with 100 ng/ml Alexa Fluor 647–labeled TNP-OVA prepared using a protein labeling kit (Molecular Probes) according to the manufacturer’s instructions. After incubation on ice for 1 hour with labeled Ag, cells were washed 4 times in cold PBS before fixation with 4% paraformaldehyde and assay via FACS. For visualization of MC degranulation, 100 μl of peritoneal lavage was cytospun onto charged glass slides at 600 rpm for 5 minutes. Slides were fixed in Carnoy’s fixative  before staining with toluidine blue dye.Six- to eight-week-old female C57BL/6 mice were purchased from The Jackson Laboratory. All experiments were performed in accordance with Duke University’s Animal Care and Use Committee. Mice were sensitized i.p. with 10 μg of anti-TNP IgE (BD Pharmingen) in 100 μl sterile PBS 1–7 days before the experiment. Desensitization doses were delivered via oral gavage in a volume of 100 μl in saline and were spaced 30 minutes apart. Control animals received PBS gavage. For Ag challenge, mice were given 500 μg of TNP-OVA (Protein Sciences) i.p. Core body temperature was monitored with a rectal thermometer before each desensitization dose, and every 15 minutes after challenge with Ag for up to 2 hours. For assay of peritoneal cells via FACS or histology, mice were lavaged with 4 ml cold PBS containing 1 mM EDTA. To assay Ag binding, 3 × 102, 4% polyethylene glycol 8000) before being fixed with 3.7% formaldehyde in CSK buffer, then extracted with 0.5% Triton-X in CSK buffer before staining with antibodies. Coverslips were mounted in ProLong Gold (Life Technologies) to preserve fluorescence before imaging using a Nikon ECLIPSE TE200 laser scanning confocal microscope, using a sequential channel series approach to avoid spectral overlapping. Images were edited using ImageJ software (NIH).RBL-2H3 cells were seeded onto clean glass coverslips in IgE-containing medium. Cells were then stained for cytoskeleton using methods previously described . Briefly7 BMMCs sensitized with a mixture of biotinylated and unbiotinylated IgE per sample were lysed in immunoprecipitation lysis/wash buffer (Pierce) containing protease inhibitors. After clearing of cell debris, 80 μl of streptavidin agarose bead slurry (Thermo Fisher Scientific) was added to 500 μg of total protein and incubated at 4°C for 2 hours. The beads were then washed 5 times with lysis/wash buffer, then boiled with 50 μl 2× SDS-PAGE buffer before electrophoresis. For analysis of phosphotyrosine signaling, 2 × 106 BMMCs per sample were lysed directly in 4× Laemmli sample buffer containing β-mercaptoethanol and immediately boiled for 5 minutes. Samples were loaded on 4%–20% acrylamide gels for SDS-PAGE before transfer and immunoblotting with respective antibodies to phospho-proteins. In some cases, after exposing of blots, membranes were stripped and reprobed for total protein.IgE was biotinylated using a kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. For immunoprecipitation of biotinylated IgE, 102, 1.3 mM CaCl2, 0.5% BSA, pH 7.4) for 15 minutes at 37°C before imaging. Images were acquired every 5 seconds on an Andor XD revolution spinning disk confocal microscope (Olympus), and fluorescence was quantitated using MetaMorph software (Molecular Devices). Data were expressed as fold over baseline fluorescence. Cells that moved out of focus during the experiment or did not respond to a final administration of ionomycin were excluded from analysis.BMMCs in RPMI were loaded with 1 μM Fluo-4 AM (Life Technologies) with 0.02% Pluronic F-127 for 10 minutes at 37°C, washed twice in Tyrode’s buffer, and recovered in Tyrode’s buffer  that has been described (C481S-TAT expressed in BL21-Gold–competent cells (Agilent Technologies) was purified using a cobalt column  and extensively dialyzed in PBS to remove salts before concentrating and quantitation of protein. Serglycin-mCherry–expressing RBL-2H3 cells were created by retroviral transduction. Transduced cells were selected with 500 μg/ml geneticin.We used a His-tagged, phosphatase-inactive, cell-permeable form of SptP  software.All data are expressed as SEM. Significance was calculated by unpaired 2-tailed Student’s All studies were performed with the approval of the Duke University Animal Care and Use Program.AMC and SNA conceived of the study. WXGA and SNA planned most of the experiments, with suggestions by AWB, HWC, and MK. WXGA performed most of the experiments. AMC contributed to experiments. HWC synthesized constructs used in this study. All authors contributed to the writing of the manuscript.


In this study, distributions of volatile organic compounds (VOCs) and the roles on O3 pollution have been investigated in a typical industrial city of Baoji in Northwestern China by means of monitoring of their concentrations and other trace gases. The air samples have been collected at three sites according to urban function area. Concentration of VOCs in Weibin site, which near to industrial zone, was higher than most of other cities in China, and the ambient VOCs were dominated by aromatics and alkenes. The temporal variations of VOCs and O3 coincided with the surface wind, implying that the formation of O3 was impacted by both exports of plumes upwind and local photochemical reactions. Result of source apportionment indicated that industrial emission, vehicular exhaust, and solvent evaporation were three major pollution origins. Alkenes and aromatics contributed to the largest fractions of photochemical reactivity, suggesting the strong influences from industrial and traffic sectors. The study presents the characteristic VOCs and other factors in the contribution of O3 formation in China.Level of surface ozone (O Surface or tropospheric O3 originated mainly from regular transportation from stratosphere2, direct emissions in source-dominated regions3, and photochemical reactions between volatile organic compounds (VOCs) and nitrogen oxides (NOx)5. In between, photochemistry plays an important role on both sources and sinks of O3, subjected to the atmospheric NOx levels6. Xue et al.3 reported that the in-situ photochemical production is the main source for the surface O3 in megacities in China based on calculations with observation-based model. In urban site, the mixing ratio of NOx is often high (up to 70 ppbv) due to large quantities of emissions from heavy industries and vehicular engines. The O3 budget is thus sensitive to photochemical reactivity of VOCs and NOx9. Under short term regulatory measures, reduction of anthropogenic VOCs emission is efficient in shrinking of O3 peak. However, the approaches only lead the reaction mechanism between VOCs-NOx-O3 transferred from NOx-dependent to VOCs-dependent while VOCs becomes a limiting factor, in the case of no decline of NOx emission. Instead, to further reduce surface O3 level, regulations on both VOCs, biogenic VOCs (BVOCs) and NOx are needed in a long term run9.Ozone , chemical mass balance (CMB), and UNMIX23. In Northern China , the atmospheric VOCs were mainly influenced by combustions of coal, gasoline, petrochemicals, and compressed natural gas (CNG), while solvent use and biogenic sources also had minor contributions26. However, solvent use was the major source contributor reported in Pearl River Delta (PRD) region in China, where influenced by the emissions from a large number of factories 28.Understanding of the sources of VOCs is a base for O3 are the causes for severe air pollutions, which pose high health risk as well31. In the recent years, the surface O3 level has been continuously increasing in most Chinese cities32. In northern region, the concentration in rural site had an increasing rate of 1.13 ± 0.01 ppbv year−1 from 2003 to 201532, comparing with 0.87 ppbv year−1 from 2001 to 2007 at a background station in Hong Kong, where is located in Southern China33. And in the urban site, it was found that surface O3 increased as rate of 1.1 ± 0.5 ppbv year−1 in the city of Beijing between 2001–200634, while in the city of Hong Kong, surface O3 increase as a rate 0.5–0.8 from mid 1960s to 201035.Fine particle and ground-level O36. The consumption of coal from industry sector is about ten millions tons annually . And the emissions plumes from both industry and traffic emissions can highly impact the air quality and elevate the surface O3 pollution to downwind regions39. In summer, the mixing ratios of surface O3 in Baoji increased with ambient temperature and radiation. Its hour-average exceeded the China National Standard of 160 μg m−3 in a frequency of 9 to 14 days per month from May to August in 2016 (https://www.aqistudy.cn/). Ozone is the dominant factor in air pollution (https://www.aqistudy.cn/). Either speciation or evaluation of VOCs was sparsely conducted in Northwestern China40, and no any data were even reported in Baoji. A one-week simultaneous observation was used to evaluate the levels and compositions of both VOCs and O3 in this industrial city in summer, 2016. This study aims to investigate the impacts of VOCs emissions on the pollution in this typical industrial city. The Photochemical Assessment Monitoring Stations (PAMS) organized by United States Environmental Protection Agency (U.S.EPA) had defined 57 critical ozone precursors (VOCsPAMS)26. A total number of 57 VOCs that contributed mostly on the O3 formation in atmosphere were quantified41. Temporal variation and potential pollution sources were identified. The correlations among the levels of VOCsPAMS and NOx and meteorological factors contributed in the O3 formation at different locations were discussed.Baoji is an industrial city in the western edge of Guanzhong basin, and was in Shaanxi province in Northwestern China. The main industries include power plants, coal chemical industry, metal smelting, and coke productionsPAMS quantified at the three sites  were 48.03 ± 18.15, 17.00 ± 11.36, 17.27 ± 10.18 ppbv measured in the Weibin, Chencang and Miaogou sites, respectively. Comparing to the ambient levels in the Chinese megacities, our VOCsPAMS in Weibin site were higher than those in Beijing in summer of 2005 (36.4 ± 12.1 ppbv), but close to Southern regions such as Guangzhou (40.58 ± 0.89 ppbv) and Hong Kong (45.83 ppbv) where influenced by industrial and traffic emissions in major23.Table PAMS descended notably in the order of Weibin > Chencang > Miaogou. The composition of VOCsPAMS significantly varied among the sites as well. The highest levels of aromatics and alkenes were observed in the Weibin site, with the mixing ratios of 16.6 ± 8.0 and 7.7 ± 4.5 ppbv, respectively. The high abundances can be ascribed to the existence of fossil fuel combustion sources installed in the industries nearby the site42. Particularly, aromatics had a molar contribution of 51.0% of TVOCsPAMS in the Weibin site  and less factories nearby. At the Miaogou site, the mixing ratios of most VOCsPAMS were the lowest among the three sites, with exceptions of toluene, styrene, undecane, isoprene, and p-diethylbenzene. Toluene and styrene are universal components in solvents used in manufactures43, while C9-C12 alkanes and isoprene are tracers for diesel exhaust and biogenic sources, respectively18. This VOCsPAMS distribution indicates that the air quality at Miaogou site was impacted by a mix of natural and anthropogenic pollution sources. Scattered small-scale paint factories presented around the site in the on-site survey, and the plants in the forests can contribute greatly on the biogenic emissions43.In general, the abundances of most VOCsPAMS levels at the Weibin site is illustrated in Fig. PAMS in Weibin site varied with surface wind directions. Higher mixing ratios of VOCsPAMS were shown on the sampling days of June 16, June 17 and June 19, when the dominant wind was easterly , with slopes of 0.51, 0.37, and 0.49 for the Weibin site, Chencang and Miaogou, respectively 10, demonstrating the inalienable input from vehicular emission in Baoji. The regressions of aromatics and long-chain alkanes  varied significantly among the sampling sites, appointing to a wide variety of source contributions  with other aromatics  and n-decane (R2 = 0.65) but fairly to poorly with undecane and dodecane . Reversibly, good correlations (R2 > 0.65) between benzene and the long-chain alkanes (except n-decane with an R2 = 0.22) were found. Benzene was also correlated poorly with ethylbenzene, xylenes, and styrene (R2 = 0.24–0.29) , ascribed to the strong benzene emissions from the local factories and transportation from the industrial zone with the easterly wind. In contrast to the Weibin site, the mixing ratios of toluene, styrene and dodecane were unexpectedly high in the Miaogou site. This also indicates that the solvent involved manufacture processes in the scattered paint and printing factories had a large contribution to the emissions of toluene and other aromatics43.Correlation analysis between individual VOCsely Fig. . The ratons Fig. . In the 9) Table . Benzenem-/p-isomers) and ethylbenzene is often used to access the air mass aging, considering that the differences in degradation rates , in which xylenes have higher KOH of 1.36–2.30 × 10–11 in comparison of lower KOH of 7. 0 × 10−12 for ethylbenzene49. In this study, the ratios of xylenes to ethylbenzene were both high at the Weibin site and Chencang sites . It is reasonable that relatively more aged air mass in Miaogou region due to its geographical position48, even though its average VOCsPAMS levels were close to that at Chencang site.The molar ratio of xylenes  close to the theoretical Q value] with reasonable explanation. The results are illustrated in Fig. 10. Factor 2 is filled with propane, n-butane, n-pentane, iso-pentane and toluene. Most of these VOCPAMS were relevant to the fuel evaporation, in particular of gasoline and CNG. Factor 3 is characterized by n-decane, undecane, xylenes, ethylbenzene, 1,2,3-trimethylbenzene, 1,2,4-trimethylbenzene, p-diethylbenzene, 3-methylpentane, ethylene, 1-pentene. The composite was corresponding to and dominated by the diesel-fuel combustion10. However, it must be noted that 3-methylpentane, a common marker for gasoline exhaust, was unexpectedly high in this factor. Even though the major contribution of Factor 3 could be characterized by the diesel combustion, the gasoline emission might still contribute a few. Factor 4 is consistent with a typical gasoline exhaust profile. Factor 5 is singly filled with high abundance of iso-butane but no representative source could be identified, thus is marked as others. Factor 6 is dominated by those VOCsPAMS from solvent evaporation 43. Isoprene is the major component in vegetation emission, even it is always detected in the vehicle exhaust10. Considering that high contribution of vegetation, isoprene is identified as a biogenic source marker. Factor 7 can be identified as biogenic source since isoprene had the highest contribution10.Source apportionment was conducted with PMF receptor model. The mixing ratios and uncertainties for the VOCsPAMS in Baoji , industrial discharges (20.1%), and solvent usage (19.7%) are the top three major pollution sources contributed to ambient VOCs4.5%, indPAMS, the contributions of the potential sources on each sample were obtained, and their strengths were compared by taking the mean values at the different sites  in the Weibin site. In the Chencang, diesel exhaust was the most important source, contributing to 59.4% of the TVOCsPAMS. Solvent-related emission contributed 36.7% of TVOCsPAMS in the Miaogou due to trace commercial painting activities. Covering with a high density of forest area, the mixing ratio of isoprene at the Miaogou was high. Therefore, a stronger strength of biogenic emission of 13.3% was found, in comparison with only 5.1 and 7.1% at the Weibin site and Chencang, respectively.With normalized to the mass of TVOCses Table . The loaPAMS was studied to investigate their potential oxidation ability with OH•, which can consequently lead the formation of surface O37. The sum of OH• loss rate (LOH) was calculated on basis of reaction rate constant between an individual VOCsPAMS and OH• and its mixing ratio. Table OH accounted at the three sites. As much higher TVOCsPAMS mixing ratios at the Weibin site, higher LOH (27.20 ± 12.55 s−1) was obtained in comparison of those at other two sampling sites. The LOH in Weibin site was mainly dominated by the groups of alkenes and aromatics due to the strong influences of industrial and traffic emissions. In addition to their high chemical reactivity, the two pollution sources are thus considered to play key roles on hydroxyl radical-driven oxidations in Weibin site Baoji. The air quality at the Chencang was less impacted by the direct industrial activities, and the LOH was thus less than half of that in the Weibin site. The group of alkanes, which has a lower chemical reactivity, was the major contributor (35.6%) to LOH. At the remote site, even though the mixing ratios of most VOCsPAMS were relatively low, the high abundance of extremely-reactive isoprene dominated 32.5% to LOH, resulting of an equivalent level of oxidation potential as that accounted in the Chencang.The chemical reactivity of VOCsOH, while the impacts from anthropogenic sources shrined as a long distance far from the two sites. At a higher mixing ratio of TVOCsPAMS , propene and 1-butene were the two most important contributors to LOH, representing that the industrial and traffic emissions not only can elevate the mixing ratios of VOCsPAMS and amounts of OH· oxidation, and also lead further surface O3 formation.Considering that alkenes play important roles on the chemical reactivity, the contributions of the most significant individuals at different concentration levels were displaced in Fig. 3 at the three sites were depicted in Fig. 3 concentrations exceeded the national standard in China of 160 μg m−3 in the five sampling dates. The diurnal patterns of surface O3 levels were similar at the Weibin site and Miaogou sites, showing maxima between noon and early evening  and Miaogou (R2 = 0.61) during daytime, when associated with higher light intensity and radiations. Besides, water vapor could scavenge O3 and its precursors in atmosphere by wet deposition52. As expected, the surface O3 levels was found to be negatively correlated with RHs at Weibin site (R2 = 0.46) and Miaogou (R2 = 0.51) sites.The correlations between surface O3 levels were also coincided with the surface zonal winds within our observation period , while the second peak could be resulted by the transportation of the industrial plume with high VOCs emission from the upwind locations.The Oiod Fig. 3, 53. Insly Fig. . More obOH was used to assess the VOCs activities, ozone formation potential (OFP) could be more direct method to measure the contributions of VOCsPAMS in the O3 formation54. Ozone formation potential (OFP) were calculated based on the average mixing ratios and the maximum incremental reactivity coefficients (MIR) of VOCsPAMS quantified in the present study54:i, represents the mixing ratio (ppbv) for species i. The MIRs were obtained from Carter54. The estimated OFP for the top 20 contributors were given in Table PAMS to overall OFP varied from the sites, that might be driven by their degrees of degradation and dilution in transportation processes48. In the Weibin site, alkenes and aromatics were dominated and totally contributed >90% of the overall OFP loading. The marker species of propene and m-diethylbenzene were the two most important VOCsPAMS which contributed to more than half of overall OFPs, indicating that vehicle and industrial emissions greatly affected the surface O3 formation. At the Miaogou site, isoprene and solvent-characterized VOCs such as toluene, styrene, xylenes, and long-chain alkanes contributed most of OFPs loading. In another aspect, air mass aging can also contribute the variation of OFPs28. Even though the overall estimated OFPs was low at the Miaogou site, the actual O3 levels were not the lowest as expected  Weibin, (ii) Chencang and (iii) Miaogou, respectively, in this study Figure . The Wei52. Two sorbent tube samples were thus collected in each time interval. The sampling inlet was set-up at 1.5 m above the ground level. Prior to the sampling, all sorbent tubes were cleaned in a thermal conditioner  at 330◦C for 20 min before use. All pre-conditioned and sampled tubes were sealed with Difflok caps  and stored in desiccators at 0◦C for a maximum of two weeks. The desiccators were filled with silica gel and activated carbon to avoid passive absorption of any water vapor and VOCs, respectively. The pump was calibrated with a mass flow calibrator  before each sampling event. A Teflon filter assembly  and a home-made ozone scrubber, manufactured by a 1 m long and ¼” o.d. saturated potassium iodide (KI) coated copper tube, were installed in the air upstream to remove any influences from particulate matter (PM) and O3, respectively. The O3 removal efficiency was >99% at a concentration level of 100 ppbv for 60 min in laboratory test. One field blank was collected on each sampling day. The sorbent samples were properly transported to the laboratory for chemical analysis.Samplings were conducted at 08:00–09:00, 15:00–16:00, 16:00–17:00 and 21:00–22:00, respectively, from June 15 to June 21, 2016. The VOCs in the air was drawn into a ¼” o.d. stainless steel multi-bed adsorbent tube filled with Tenax-TA, Carbograph I TD and Carboxen 1003  using a low-flow module pump  at a flow rate of 50 mL min−1 for 60 min . Insignificant breakthrough (<5%) was observed either in field or laboratory demonstration under this sampling flow and volume2/NOx was measured with a chemiluminescence detector , and surface O3 was monitored with an ultra-violet (UV) photometric O3 analyzer . The time resolution for these instruments was 5 min and the minimum detection limit (MDL) for both NO2/NOx and O3 were 0.5 ppbv. The meteorological data  were recorded with an multi-parameter automatic weather stations .Real-time concentrations of the trace gases were monitored continuously during the sampling event. NO55. A tube was connected into the TD unit at room temperature (~25◦C) and purged with ultra-high purity (UHP) helium (He) gas at a flow rate of 40 mL min−1 for 60 s to eliminate air and oxygen intrusion. For the primary desorption stage, the analytes were desorbed at 330◦C for 5 min and refocused onto a cryogenic-trap  to capture high volatility target compounds at −15 °C. For the secondary desorption stage, the trap was dry-purged for 6 s and rapidly heated from −15 °C to 320◦C and maintained for 5 min. The analytes were passed via a heated transfer line at 160 °C, and re-refocused onto a cold GC capillary column head  at −45 °C with an aid of liquid nitrogen (N2) in GC oven. Once the second desorption is completed, the oven temperature program started at an initial temperature of −45 °C for 4 min, ramped to 230 °C at a rate of 6 °C min−1, and maintained at 230 °C for 5 min. The constant flow rate of He carrier gas was 1.0 mL min−1 throughout the GC analysis. The MSD was operated in selective ion monitoring (SIM) mode at 230 °C and 70 eV for electron ionization. Identification was achieved by comparing the mass spectra and retention times of the chromatographic peaks with those authentic standards. Certified PAMS standard mixtures (Restek Corporation) were used in calibrations. A multi-point calibration curve was established to quantify each of the target compounds with linearity >0.999. The minimum detection limits (MDLs) were in the range of 0.003–0.808 ppbv with a sampling volume of 3 L. The measurement precision for the analysis of eight replicates of standard samples at 2 ppbv were ≤5%.A total of 72 valid sorbent tube samples were collected. They were all analyzed using a thermal desorption (TD) unit  coupled with a gas chromatograph/mass spectrometric detector  within one week. The chemical analysis procedure can be found in our previous workxij is the concentration of the jth congener in the ith sample of the original data sets; gik is the contribution of the kth factor to the ith sample; fkj is the fraction of the kth factor arising from congener j; eij is the residual between the measured Xij and the predicted Xij using p principal components. μij is the uncertainty of the jth congener in the ith sample of the original data sets containing m congener and n samples. Q is the weighted sum of squares of differences between the PMF output and the original data sets. One of the objectives of PMF analysis is to minimize the Q value.A standard receptor model of PMF (Version 3.0) recommended by U.S.EPA was used for source apportionment in this study. In principle, the PMF model is based on the following equations:Supporting Information


NDVI) across the Svalbard Archipelago to describe local/sub-regional and large-scale components of sea ice/terrestrial productivity coupling. Whereas the local/sub-regional component is attributed to sea breeze (cold air advection from ice-covered ocean onto adjacent land during the growing season), the large-scale component might reflect co-variability of sea ice and tundra productivity due to a common forcing, such as large-scale atmospheric circulation . Our study clarifies the range of mechanisms in sea ice/terrestrial productivity coupling, allowing the generation of testable hypotheses about its past, present, and future dynamics across the Arctic.The rapid decline in Arctic sea ice poses urgent questions concerning its ecological effects, such as on tundra terrestrial productivity. However, reported sea ice/terrestrial productivity linkages have seldom been constrained, and the mechanism governing them remains elusive, with a diversity of spatial scales and metrics proposed, at times in contradiction to each other. In this study, we use spatially explicit remotely sensed sea ice concentration and high-resolution terrestrial productivity estimates (Normalised Difference Vegetation Index,  However, reported correlations between sea ice dynamics and Arctic terrestrial primary productivity are far from coherent and robust over the Arctic – even in places with recent large sea ice reductions8, while the mechanism that drives this coupling remains elusive. Without exception, the Arctic Amplification (hereafter AA9) has been implicitly or explicitly inferred to drive the terrestrial productivity/sea ice connection. AA consists of a suite of processes operating at northern high latitudes at different temporal and spatial scales9 resulting in high rates of temperature variability and strong climate change trends in the Arctic. These have been calculated to be ~1.6–2 times the global average in recent decades using observational climate data10, and 3–4 times over longer term – millennia to millions of years – using proxy data11.The steep decline in Arctic sea ice extent, concentration, and volume observed in recent decades has been linked to changes in tundra productivityAA12, and are thought to mostly operate in autumn and winter, when the heat stored during the warm sunlit season by the upper ocean mixed layer is released into the atmosphere. Summer is the season with the weakest AA, potentially affected by snow albedo early in the season9. Processes other than sea ice and snow albedo feedbacks play important roles in the AA, especially those related with temperature feedbacks in the vertical structure of the warming (lapse-rate feedback) and the relationship between radiative forcing, temperature, and longwave emission (Planck’s feedback)13, among others9. Thus, the effects of an AA-enhanced warmer Arctic on terrestrial ecosystem functioning cannot be linked to sea ice decline without previously defining and scaling candidate mechanisms able to effectively couple sea ice and such ecological processes. In the case of tundra productivity, and given its widely reported strong relationship with growing season temperatures16, we will focus on processes responsible for sea ice-related summer AA.Positive feedbacks linked with the high albedo and thermal insulation properties of ice and snow constitute a large component of the et al.10 described large-scale (hemispheric) and local/sub-regional spatial components in AA. The former is seen as the amplification of zonally averaged surface air temperature variations from tropical through high-latitude regions, whereas local/sub-regional AA was related to ice-albedo feedbacks in maritime and coastal areas in autumn when the ice cover is seasonally at a minimum10. A further local/sub-regional manifestation of AA was described by Haugen and Brown17, termed sea breeze and consisting of cold air advection in summer from the partially to fully ice-covered ocean onto the adjacent land. Both local/sub-regional processes can influence temperatures several hundreds of km inland, depending on topography17. By definition, large-scale AA has a loose spatial resolution and its sea ice component is difficult to quantify without climate modelling13 or the study of many climatological variables18, especially given the similar recent trends between pan-Arctic sea ice and Arctic temperatures: temperature-sensitive terrestrial ecological processes might thus show high correlations with hemispheric/regional sea ice extent even when this is distant. Local/sub-regional AA reflects much more spatially and temporally constrained phenomena: (1) if present, sea ice cools air above it and enhances cold air advection onto adjacent, warmer landmasses (summer), and/or 2) open ocean water releases heat that warms coastal areas (autumn). Local/sub-regional AA can effectively decouple surface air temperature from synoptic-scale circulation. The dimension of the coupling mechanism between sea ice and tundra productivity will leave a signature in the spatial patterns of co-variability between both variables and allows the formulation of hypotheses:AA, spatially-constrained sea ice adjacent to land will be highly related with on-land terrestrial productivity;in the local/sub-regional AA, distant and less spatially-constrained sea ice will be related to tundra terrestrial productivity.in the large-scale/hemispheric Bekryaev, sea breeze mechanism proposed by Haugen and Brown17), we show the large-scale component to be mostly related with large-scale/hemispheric atmospheric patterns  affecting both sea ice and temperatures during the growing season, and causing their co-variability. Svalbard encapsulates much of Arctic’s range of environmental conditions in a relatively small area: whereas the West Spitsbergen Current delivers warm North Atlantic surface water to its western half (hereafter W-Sb), which is often free of ice, the East Spitsbergen Current delivers cold, Arctic surface water to the eastern parts of the archipelago20 (hereafter E-Sb), which are colder and covered by sea ice for much of the year, well into the growing season. Moreover, Svalbard’s rugged topography favours large environmental gradients over short distances, maintaining the contrasting east-west environmental divide driven by ocean surface currents.Here we show evidence for strong large and local/sub-regional scale co-variability between sea ice concentration and tundra terrestrial productivity in the Svalbard Archipelago, demonstrating and scaling the ways in which sea ice might affect terrestrial primary productivity in the Arctic. While we attribute the local/sub-regional component of this coupling to cold air advection , with a diversity of Dryas tundra vegetation on ridges and hillslopes (>650 km2), and arctic meadows on south-facing slopes (~150 km2). Vegetation in E-Sb is concentrated in Barentsøya and Edgeøya, where glaciers cover ~44% of the area: here, vegetation types range from dense Dryas vegetation to well-developed mires and bryophyte-dominated vegetation along the eastern shores, to polar desert in the eastern lowlands21.Glaciers cover >60% of the total area in the Svalbard archipelago, whereas much of the non-glaciated land is dominated by open, non- or sparsely vegetated landscapesNDVI at 0.5 km pixel resolution – widely used as an indicator of terrestrial primary productivity in the Arctic25, covering the whole archipelago and adjacent ocean waters for the period 2000–2014, and focused on the growing season . Singular Value Decomposition (SVD)28 was applied to both sets of data (fields). SVD identifies pairs of coupled spatial patterns and their temporal variation – modes – explaining the maximum mean-squared covariance between two spatially separate fields (in this case sea ice and NDVI). SVD analyses were performed for the whole growing season and for three separate periods – defined as Early Growing Season – EGS, Peak Growing Season/July – JL, and Late Growing Season – LGS, and for W-Sb and E-Sb (see Methods).We used spatially explicit, 8-day averaged, remotely sensed data on sea ice concentration (resampled to 2.3 km pixel resolution) and vs. terrestrial productivity coupling emerged from the SVD analysis. In all instances, the 1st mode in the SVD was found to be significant and to explain >60% of the covariance (ranging from 62.63% to 89.38%), with the percentage of the explained total variance in the two fields ranging from 17.86% to 31.27%  in Svalbard was characterised by a sharp difference between its west and east halves  shifted to an arrangement resembling that of W-Sb, with strong paired coupling between NDVI in vegetated land masses and sea ice concentration anomalies in distant northern ocean areas, coinciding with the retreat of sea ice from the region in late summer . Moreover, the spatial arrangement of NAO vs. sea ice concentration mimicked that of the heterogeneous correlations between sea ice and NDVI , the spatial arrangement of NAO & AO vs. sea ice concentration did not agree with that of the heterogeneous correlations between sea ice and NDVI . NDVI vs. NAO showed field significance for W-Sb June through August, as did NDVI vs. AO in June and July in W-Sb and in July only in E-Sb  related with the DVI Fig. . WhereasDVI Fig. . MoreoveNDVI vs. NAO patterns of spatio-temporal co-variability are in agreement with the generalised positive correlation between monthly NAO and surface air temperatures from the same period 2000–2014 obtained from the NCEP-NCAR Reanalysis project29 , except for the northernmost Reinsdyrflya peninsula. Sea ice concentration in areas closer to these inner fjords was not found to affect tundra productivity, perhaps unsurprisingly given the fact that during the growing season there is very little sea ice in the central and south ocean regions of W-Sb. The relationships between sea ice and NAO, NAO and meridional wind, NAO and NDVI, and NAO and surface air temperatures are strong in W-Sb during the period analysed – and mostly in JL. NAO has long been known to play a role in the North Atlantic storm track and thus in the advection of warmer water and air masses into the Barents Sea through the West Spitsbergen Current, affecting the position of the sea ice edge in the region31. The flow of warm water and air advection associated with NAO can thus affect both sea ice and tundra productivity without them needing to be coupled mechanistically. Hence, the existence of a correspondence between sea ice and NDVI anomalies in this region (and in E-Sb during the LGS) may be due to the influence of a lurking variable or ultimate process driving both. Finally, the relationships of NAO and AO vs. sea ice and NDVI in E-Sb for JL showed different spatial patterns to those in W-Sb: the lack of correspondence between the NAO/AO correlation fields and the SVD paired patterns of co-variability suggests that the role of these teleconnection modes is not linked with the local/sub-regional NDVI/sea ice coupling captured by the SVD in E-Sb. The fact that only NDVI – and not sea ice concentration – showed field significance with AO and NAO  in E-Sb further suggests that this mechanism might not be led by sea ice  affects both sea ice concentration and NDVI, a possible influence of sea ice on the regional temperatures cannot be completely ruled out. The coupling between sea ice dynamics and the climate system is not unidirectional and changes in sea ice might feedback to the climate system over a wide range of spatial and temporal scales, affecting not only the Arctic but the Northern Hemisphere mid-latitudes through their influence on storm tracks, the jet stream, and planetary waves and their associated energy propagation33, as well as the NAO34. Such evidence – stilly bound by large uncertainties33 – further complicates the interpretation, understanding, and quantification of the large-scale linkages between sea ice and tundra productivity, which require more than correlational studies between sea ice and tundra productivity estimates, involving the employment of regional climate and coupled sea ice/climate models, in which sea ice forcing on climate is quantified in the same manner as in the [CO2]atmosphere attribution exercises36.The local/sub-regional vs. tundra productivity. Local/sub-regional summer AA – sea breeze – agrees with the findings by Dutrieux, et al.7 in a set of transects in the Siberian Arctic, who found a decreasing strength in the sea ice concentration vs. summer warmth index (SWI) and time-integrated NDVI (TI-NDVI) correlations with distance to the coast . The same mechanism can be applied to Macias-Fauria, et al.8 in Yamal and Nenets Autonomous Okrugs, who found sea ice in the Barents and Kara Seas to be only correlated with NDVI in May and argued that during the growing season sea ice might be too distant from the tundra to affect productivity dynamics. Hence, the presented local/sub-regional sea breeze is a strong candidate to operate over the whole Arctic and its spatial signature may be found elsewhere provided that sea ice is close to vegetated land during the growing season.The framework presented herein can characterise candidate mechanisms driving the patterns reported elsewhere on sea ice 4 are less well constrained and potentially more complex: in these cases, large-scale mechanisms might be at play, which would require further analyses prior to a robust sea ice attribution on tundra dynamics3. The identified NAO (and to a lesser degree AO) large-scale linkage in the case of the Svalbard Archipelago might not be operating in the same way elsewhere in the Arctic. Further, studies reporting large-scale sea ice/tundra productivity relationships are often based on summary statistics (e.g. TI-NDVI) whose signal is likely compounded as it incorporates and averages several processes and events over the growing season. Kerby and Post5 reported strong relationships between winter and June pan-Arctic sea ice extent and the phenology of several tundra species in Kangerlussuaq (west Greenland). The poor explanatory power of local sea ice and summer temperatures in this study suggests a minor role played by local/sub-regional AA. In this case, the already challenging task of quantifying the influence of large-scale sea ice variability on climate is compounded by the unknown suite of climatic conditions that might be controlling plant phenology and which pan-Arctic sea ice extent might integrate5. Relationships between plant productivity and sea ice outside of the growing season6 represent a further nuance: Hollesen, et al.6 – working in Disko Island, <300 km north of Kangerlussuaq – proposed a mechanism through which local winter sea ice conditions might be coupled with local winter temperatures, which are related with increased radial growth of Betula nana through earlier soil warming. In this case, the nature of the coupling might be due to (1) local coupling or (2) collinearity between winter air temperatures and local sea ice extent driven by a third process (e.g. ocean circulation). Although we focused on temperature as it is the main variable controlling terrestrial productivity in the Arctic and in Svalbard in particular16, other climate variables might be important locally or regionally, such as precipitation15: in this cases, the effects of sea ice on Arctic precipitation38 will likely explain a significant amount of tundra productivity variability. Finally, sea ice/tundra productivity coupling might also incorporate long-term, low-frequency processes that are out of the scope of this 15-year study.Reported coarser-scale/pan-Arctic relationships39 implies sea ice retreat from tundra-covered land masses earlier in the year, increasing the tundra areas that might become decoupled from the local/sub-regional AA and (1) increasingly behave like W-Sb or (2) stop showing any local/sub-regional relationship with sea ice: E-Sb-like areas might have recently transitioned or will soon transition into W-Sb-like ones. Although the dynamics of sea breeze without sea ice may include further feedback mechanisms3, our results strongly suggest that once sea ice is absent from the ocean adjacent to tundra regions, sea breeze does not significantly affect tundra productivity . In these cases, predictions and observations are still in a black-box stage and are thus extremely sensitive to training data.Knowledge of the mechanism allows for the generation of predictions and hypotheses: year-round current and projected future sea ice declinel AA and  increasisea breeze17. The large-scale paired pattern of co-variability is clearly identified and dominates regions where sea ice is distant from vegetated land masses. In this case, we advise against attributing tundra dynamics to sea ice based solely on correlation between both fields: in our study area in Svalbard, we present evidence that supports large-scale atmospheric and sea surface dynamics (captured by the NAO index) affecting both sea ice and growing season temperatures and thus causing high agreement between both fields without the need of a mechanistic link. Attribution studies and/or the detailed analyses of many more climatological variables would be needed in this case to quantify the relative effect of sea ice on tundra productivity.In this study, we showed that distinctly paired sea ice/tundra productivity spatial signatures result depending on the scale and characteristics of the dominant coupling mechanism. We clearly identified local/sub-regional and large-scale modes of variability in the Svalbard Archipelago which are geographically robust and which can be explained by the spatial arrangement of sea ice in the ocean areas surrounding tundra-covered land masses. Presence of abundant sea ice near the coast during the growing season favours local control of tundra productivity by sea ice, very likely through Our study (1) provides a framework to investigate the potential ways in which the sea ice/tundra productivity coupling takes place through remote sensing; (2) improves the understanding of these mechanisms, refining their spatial dimension; and (3) generates testable hypotheses for sea ice/tundra productivity dynamics in the Arctic, both at present and in past and future scenarios, which might involve spatiotemporal shifts between local/sub-regional and large-scale controls.http://wms.met.no/icechart/). The dataset has close to daily time resolution and is based on the best possible satellite data available as found from visual inspection. In our study area, up to 100 m resolution Synthetic Aperture Radar – SAR – data, and sometimes passive microwave data at ~25 km resolution were used. If cloud free, NOAA-AVHRR data was employed, and in only very few cases  Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was included. The dataset was converted to raster and consists of mean 8-day sea ice concentration values for the period 2000–2014. Sea ice concentration was categorised into six discrete classes, as recommended by the Norwegian Sea Ice Service: 5% – Open Water (0/10); 25% – Very Open Drift Ice (1/10 to 4/10), 55% – Open Drift Ice (4/10 to 7/10), 80% – Close Drift Ice (7/10–9/10), 90% – Very Close Drift Ice (9/10–10/10), 100% – Fast Ice (10/10). The spatial resolution of the sea ice data was 2,320 m * 2,320 m.Sea ice concentration data for the ocean areas surrounding Svalbard were obtained from the Norwegian Sea Ice Service (NDVI) data was obtained from MODIS40. To process a clear-sky NDVI dataset, we used the MODIS Terra data surface reflectance products MOD09Q1 and MOD09A1, both with 8-day temporal composites. Due to the short and intense period of plant growth in the High Arctic and the occurrence of frequent cloud cover, all possible cloud free data is needed, which requires reliable cloud masks to be computed. A combination of three cloud-removing methods was used40. The quality assessment (QA) flags in the MOD09A1 product give information about different types of atmospheric noise. For each 8-day period, we made a visual quality control in ArcGIS of which of the different State QA values detected the cloud cover and cloud shadows. In most cases (>60%), one of the State QA values or a combination of the State QA values could detect the clouds in the data. This approach was not sufficient in the remaining <40% of the cases, depending on the cloud type, time of the season, and type of land cover, and especially in the detection of cloud shadows and clouds over sparsely vegetated areas. Therefore, we developed our own cloud detection algorithms based on the 7 bands in the MOD09A1 products40. In the few cases where both algorithms failed, we manually masked out the noisy parts by drawing polygons around them. Altogether, 15 different combinations of State QA values/own algorithms/manual masking were employed in the cloud detection and removal procedures. For each of these combinations, a python script to remove the clouds was developed. The cloud mask was then applied to the NDVI values calculated form the MOD09Q1 product. Savitzky-Golay spatial interpolation was applied to pixels and periods with no available data. The spatial resolution of the NDVI data was resampled to 464 m * 464 m.Eight-day mean clear-sky Normalised Difference Vegetation Index (GS) was defined as the period encompassing Day-of-Year (DOY) 161 (June 10) to 249 (September 6); that is, eleven 8-day periods, forming a time series of 165 observations over the 15-year period. To study the potential change of the paired spatial patterns as sea ice recedes during summer, GS was divided into 3 sub-periods: Early Growing Season (EGS), DOY 161 (June 10) to 185 (July 4), consisting of three 8-day periods forming a time series of 45 observations over the 15-year period; Peak Growing Season, spanning mostly the month of July (JL), DOY 177 (June 26) to 217 (August 5), consisting of five 8-day periods forming a time series of 75 observations over the 15-year period; and Late Growing Season (LGS), DOY 217 (August 5) to 249 (September 6), consisting of four 8-day periods forming a time series of 60 observations over the 15-year period.The growing season  and in one with overall high sea ice concentration (E-Sb) during the growing season  by decomposing the covariance matrix of the two fields into singular values and two sets of paired orthogonal vectors, one for each field. SVD maximises the covariance between the expansion coefficients of the leading pattern in each field27, identifying pairs of spatial patterns that explain as much as possible of the mean-squared temporal covariance between them. Iwasaka and Wallace27 explain in detail the SVD and the methodology described in here is largely based on their work.The SVD was computed over (1) the whole study region and for the GS, (2) for W-Sb and E-Sb separately, and (3) for EGS, JL, and LGS for the whole region, W-Sb, and E-Sb. We first constructed anomaly datasets from the raw sea ice and NDVI data by subtracting from the 8-day values their climatological mean, and normalised them by dividing this value by the standard deviation of the 8-day period. This resulted in a Sea Ice Anomaly field (S) and an NDVI anomaly field (P). For the GS, S consisted of a 165 * 80,555 matrix , whereas P was a 165 * 12,774 matrix. S and P had 45 rows for EGS, 75 rows for JL, and 60 rows for LGS. Matrices for W-Sb and E-Sb consisted of the same number of rows (i.e. they were computed over the same period) but fewer pixels each .C = S′ * P/N, where N = length of the period. C was then used to compute the SVD. SVD decomposes C in two matrices of singular vectors: U – for sea ice, and V – for NDVI). Each singular vector is normalised and non-dimensional, and each pair of singular vectors is a mode of co-variability between the fields S & P . From the singular vectors and the fields, the expansion coefficients can be computed , which are time series describing how each mode of variability changes with time, and have been dimensioned to the original data  for each mode – ρk – indicates the similarity between the time variations of the patterns of the two fields. These statistics are shown in Supplementary Material To investigate the strength of the relationship found between two fields, the entclass1pt{minimaSVD modes was computed by estimating confidence intervals on SCF and NCF via a Monte-Carlo approach based on 100 runs of the SVD for each pair of fields , but with one of them being randomly shuffled in the time sequence27.Statistical significance of the SVD mode (SVDk), two expansion coefficient vectors exist (Ak & Bk), one for each field (sea ice and NDVI). Homogeneous correlation maps are computed by correlating the expansion vector for a given mode with the time series corresponding to the same field variable at each grid point . Heterogeneous correlation maps are computed correlating the expansion vector of one field for a given mode with the time series corresponding to the other field at each grid point . The latter show how the two fields are related to one another and how much of the amplitude of their variation is explained by the SVD27: although they are expected to be lower than the homogeneous correlation maps, they deliver very important information that hints at the nature of the coupling between the two fields. Hence, heterogeneous correlation maps are shown in Fig. The computation of homogeneous and heterogeneous correlation maps provides important information on the spatial structure of the covariance between the two fields. For each NAO – and Arctic Oscillation – AO – indices where obtained from the Climate Prediction Center of the National Oceanic & Atmospheric Administration . Both modes of large-scale atmospheric circulation have been linked to sea ice dynamics in the Arctic43. Monthly NAO indices are based on Rotated Principal Component Analysis (rPCA) to monthly mean standardized 500-mb height anomalies using the procedure described by Barnston and Livezey19. Monthly AO are defined as the leading mode of Empirical Orthogonal Function (EOF) analysis of monthly mean 1000 mb height poleward of 20°N. Monthly NAO and AO time series for June, July, and August (period 2000–2014) were correlated with the time series for each pixel corresponding to (1) sea ice concentration, (2) NDVI, (3) the expansion coefficients of sea ice (A), and (4) the expansion coefficients of NDVI (B) for EGS, JL, and LGS, respectively. Field significance, accounting for the effects of multiplicity in spatially autocorrelated fields, was addressed following the Monte-Carlo-based approach  described in Livezey and Chen44. Field significance addresses the probability that a given spatial pattern of strong correlations between a given time series and time series in a highly spatially autocorrelated field might be a chance occurrence.Monthly North Atlantic Oscillation – 29, which provides assimilated data with an horizontal resolution of 2.5° by 2.5° degree, and correlated with monthly indices of the NAO and AO  for the period 2012–2014 for which daily data was available in all stations. Daily values were averaged into 8-day periods to match the temporal resolution and format of the study (Supplementary Material Daily meteorological station surface air temperature data for a set of 12 stations in the Svalbard Archipelago (6 in Supplementary Information


Protein kinase C (PKC) has been shown to activate the mammalian target of rapamycin complex 1 (mTORC1) signaling pathway, a central hub in the regulation of cell metabolism, growth and proliferation. However, the mechanisms by which PKCs activate mTORC1 are still ambiguous. Our previous study revealed that activation of classical PKCs (cPKC) results in the perinuclear accumulation of cPKC and phospholipase D2 (PLD2) in recycling endosomes in a PLD2-dependent manner. Here, we report that mTORC1 activation by phorbol 12,13-myristate acetate (PMA) requires both classic, cPKC, and novel PKC (nPKC) isoforms, specifically PKCη, acting through distinct pathways. The translocation of mTOR to perinuclear lysosomes was detected after treatment of PKC activators, which was not colocalized with PKCα- or RAB11-positive endosomes and was not inhibited by PLD inhibitors. We found that PKCη inhibition by siRNA or bisindolylmaleimide I effectively decreased mTOR accumulation in lysosomes and its activity. Also, we identified that PKCη plays a role upstream of the v-ATPase/Ragulator/Rag pathway in response to PMA. These data provides a spatial aspect to the regulation of mTORC1 by sustained activation of PKC, requiring co-ordinated activation of two distinct elements, the perinuclear accumulation of cPKC- and PLD-containing endosomes and the nPKC-dependent translation of of mTOR in the perinuclear lysosomes. The close proximity of these two distinct compartments shown in this study suggests the possibility that transcompartment signaling may be a factor in the regulation of mTORC1 activity and also underscores the importance of PKCη as a potential therapeutic target of mTORC-related disorders. The mammalian target of rapamycin (mTOR) is a crucial signaling hub in eukaryotes, functioning to sense and integrate environmental changes such as alterations in nutrients, growth factors, energy stress and oxygen levels into cellular responses . Current+-ATPase (v-ATPase) [Given this central position, the regulation of mTORC1 is understandably complex and involves a variety of factors depending on the stimulus. Thus, amino acids regulate mTORC1 through the RAG GTPases  that rec-ATPase) , follicu-ATPase) , 6–8. Mo-ATPase) . By cont-ATPase) . For exa-ATPase) , 12. In -ATPase) . More re-ATPase) , thus deProtein kinase C (PKC) is a family of 10 isoforms grouped into 3 subfamilies  based on their structures and activators . ClassicIn contrast to this well-established paradigm, we have previously reported that sustained activation of cPKCs by PMA or GPCRs results in internalization and translocation of cPKCs to a perinuclear subset of RAB11-positive recycling endosomes (which we termed the ‘pericentrion’) –22. NotaConsiderable evidence has implicated the mTOR pathway as a downstream effector of PKC signaling in various cell lines. For example, in adult cardiac muscle cells, PKCε and PKCδ are required for ET-1 induced mTORC1 activation, and PKCδ is involved in insulin-stimulated activation of mTORC1 . In glioIn this study, we have investigated the mechanisms by which sustained PMA treatment regulates the activation of mTORC1. We find that PMA activation of mTORC1 involves dual pathways and mechanisms. The first pathway requires the formation of the cPKC-containing endosomes through activation of PLD as we have described previously. The second, previously unknown pathway requires translocation of mTORC1 to the lysosomal surface through activation of the novel PKCη. These data identified a hitherto unknown regulator of mTORC1. Given the tendency of this pathway to be hyperactivated in many cancers, the possibility of targeting PKCη as a means to disrupt this activity warrants further investigation.All cell lines were purchased from American Tissue Culture Collection . Lipofectamine RNAiMAX were from Life Technologies . X-treme GENE 9 were from Roche . PLD2 inhibitor, VU 0364739 was a kind gift from Dr. Alex Brown . PMA, Gö 6976, Bisindolylmaleimide I and U0126 were from EMD Millipore . Anti-phospho-p70S6K (Thr389), S6K, mTOR, phospho-ERK1/2 (Thr202/Tyr204), ERK1/2, EGFR, HA antibodies were from Cell Signaling Technology  and anti-Actin antibody was purchased from Sigma Aldrich . Anti-LAMP1, LAMP2, PKCδ and PKCε antibodies were from Santa Cruz Biotechnology . Anti-EEA1 antibody was from BD Biosciences . siRNA for PKCη, PKCδ, PKCε, RAGB, LAMTOR 1, and LAMTOR 3 were from Qiagen .HEK293 cells were maintained in MEM supplemented with 10% (V/V) fetal bovine serum. HeLa cells were grown in DMEM supplemented with 10% (V/V) fetal bovine serum. All cells were grown at 37°C in a humidified atmosphere with 5% CO2.The wild type pBK-CMV-GFP-PKCα was described previously  and was Protein samples were collected in LDS sample buffer (Invitrogen) and boiled for 10 min. Proteins were separated on 4–20% polyacrylamide gels (BioRad), transferred to nitrocellulose membranes (BioRad), which were blocked in PBS-T with 5% non-fat dried milk for 1 hour at room temperature. After three washes with PBS-T, membranes incubated with primary antibody overnight at 4°C. Primary antibody solutions were prepared according to manufacturer’s recommendations. On the following day, blots were washed three times (PBS-T) and incubated with HRP-conjugated secondary antibodies in blocking solution for 1 hour. After three washes, proteins were detected with enhanced chemiluminescence reagent (Pierce).5 cells/dish. After 24 hours, cells were transfected as described above. After transfection, cells were grown in for 24 to 48 hours before serum starvation for 5 hours prior to treatment. Cells transfected with green fluorescence protein (GFP) were fixed with 3.7% paraformaldehyde and nuclei were stained with DAPI. Indirect immunofluorescence was performed as described before [Cells were plated on 35-mm confocal dishes (MatTek) at a density 3 to 5 ×10d before . All ima5 cells/dish. After 24 to 48 hours, cells were starved for 5 hours followed by treatment with vehicle or PMA as shown. Cells were then fixed and permeabilized as described before. After incubation with primary antibodies (LAMP1 and mTOR), the proximity ligation assay (PLA) was performed as described previously [HeLa cells were plated on 35-mm confocal dishes at a density 3–5 ×10eviously . The sigStatistical significance was calculated with student’s t-test or by two-way ANOVA with Bonferroni Post test where appropriate. A p-level below 0.05 was considered to be statistically significant.Fig 1A). As can be seen, the phosphorylation of S6K started 15 min and achieved a maximum at 60 min after PMA treatment. To confirm that the observed increase in S6K phosphorylation was mediated by mTORC1, cells were pre-treated with rapamycin prior to PMA stimulation . As can be seen, there was complete inhibition of PMA-induced S6K phosphorylation by rapamycin confirming this phosphorylation was mediated by mTORC1.Considerable research has implicated mTORC1 as a downstream effector of PKC signaling –29. PrevFig 1B). Similarly, 10µM BI-D1870 decreased PMA-induced GSK3β phosphorylation by more than 75% with no effect on S6K phosphorylation . Taken together, these results confirmed that MEK/ERK and RSK are largely dispensable for activation of mTORC1 induced by PMA in our system.While early research implicated the MEK/ERK pathway in PMA activation of mTORC1 through activation of RSK , a more Fig 1C). Moreover, the degree of colocalization of mTOR and LAMP1 increased with longer PMA treatment . To consolidate these results in a more quantifiable manner, PLA were performed to evaluate the interaction of mTOR with LAMP1. PMA significantly increased the proximity of mTOR and Lamp1 compared to the vehicle treatment  suggesting that PMA increased localization of mTORC1 to the lysosomal surface.Accumulating evidence has suggested that the lysosomal localization of mTORC1 is essential for its full activation by bringing mTORC1 in close proximity to its upstream activator Rheb . DespiteFig 1E and 1F). Taken together, these results suggested that the sustained activation of PKCs induces mTORC1 translocation to lysosomes.In addition, we confirmed these results using another PKC activator, 10-methyl-aplog-1  to proveFig 2A and 2A’). Strikingly, upon PMA treatment, both PKCα-GFP and mTOR localized to the perinuclear region  with some overlap between the two proteins indicated . Similar overlap was observed with PKCα-GFP and LAMP1 upon PMA treatment . This raised the possiblity that, while the mTOR-containing lysosomes and cPKC-containing endosomes are both in the perinuclear region, they are distinct compartments. To confirm this, we examined the localization of mTOR and EEA1 at earlier time points as we had previously observed the trafficking of PKCα-GFP through the early endosomes en route to the perinuclear region [Fig 2D and 2D’), further supporting the hypothesis that mTOR and PKCα are present in distinct compartments.We have reported that sustained stimulation of PKC with PMA leads to sequestration of recycling endosomes and cPKCs into a RAB11-positive juxtanuclear compartment . We specr region , 35. As Fig 3A–3C and 3A’–3C’), despite preventing perinuclear translocation of PKCα-GFP . Importantly, we have previously shown that these inhibitors blocked PMA-induced S6K phosphorylation [To consolidate these data, we took advantage of our previous observations that the formation of the perinuclear cPKC-containing endosomes requires endocytosis, as well as PKC and PLD activity. Accordingly, cells were pre-treated with a cPKC inhibitor (Gö 6976), PLD inhibitor (1-butanol), PLD2 inhibitor (VU 0364739), or endocytosis inhibitor (hypertonic sucrose) prior to stimulation with PMA. Notably, none of these inhibitors affected PMA-induced perinuclear translocation of mTOR and its colocalization with lysosome markers  on PMA-induced accumulation of mTOR in the perinuclear lysosomes raised the possibility that other members of the PKC family are regulators of mTOR translocation. As only the subtypes of classical and novel PKC are PMA sensitive, we examined the effect of bisindolylmaleimide I (Bis), an inhibitor of both cPKC and nPKC, on the mTOR localization. Bis strongly inhibited the perinuclear accumulation and colocalization of mTOR in the lysosomes and S6K phosphorylation induced by PMA .The lack of effect of the cPKC inhibitor Gö 6976 . Indeed, PMA could not induce the lysosomal accumulation of mTOR in the cells transfected with PKCη siRNA . Taken together, these data suggest that PKCη plays a major role in the accumulation of mTOR induced by PMA.To define the specific nPKC isoform involved in mTORC1 translocation, an siRNA approach was employed. Transfection of siRNAs for PKCη, PKCδ, PKCε and PKC-theta significantly reduced the levels of their respective isoforms. Strikingly, while knockdown of PKCδ, PKCε, and PKC-theta had little or no effect, PKCη siRNA significantly inhibited PMA-induced S6K phosphorylation suggesting this may be the relevant isoform required for lysosomal translocation of mTOR  and the effects on S6K phosphorylation and mTOR translocation were determined. Consistent with the essential role for RAGs in localizing mTORC1 to the lysosome, the treatment of RAGB siRNA inhibited both PMA-induced S6K phosphorylation  and mTOR translocation to the lysosome  while having no effect on PMA-induced translocation of PKCα to the perinuclear region . Moving further upstream, two components of the Ragulator complex–LAMTOR1 and LAMTOR 3—were knocked down by siRNAs. As can be seen, each siRNA reduced the levels of their respective targets. Moreover, as with RAGB knockdown, effects of PMA on S6K phosphorylation were inhibited in the absence of LAMTOR1 or LAMTOR3 . Finally, concanamycin A–an inhibitor of the v-ATPase–was utilized and, again, PMA-induced S6K phosphorylation were inhibited . Collectively, these results place PKCs upstream of the v-ATPase/Ragulator/RAGB in regulating mTORC1 translocation to the lysosome.Research into the mechanisms underlying mTORC1 localization to the lysosomal surface has identified a number of regulatory mechanisms. While the RAG GTPases function as key proteins that recruit mTORC1 to the lysosome through interacting with RAPTOR, a number of upstream regulators of RAGs have been reported including the Ragulator complex, v-ATPase, GATOR2 complex and Sestrins (as positive regulators) with Folliculin and GATOR1 as negative regulators , 6–9. Hain vitro and in vivo yet the mechanisms by which PKCs activate mTORC1 remain unclear. In our previous study, we found that sustained activation of PKC with PMA treatment activated mTORC1 in a temporally delayed manner. Here, we have further explored the mechanisms underlying this regulation. We found that PMA-induced activation of mTORC1 requires two distinct signaling pathways through the concurrent activation of two specific classes of PKC as summarized schematically . Importantly, these results reveal a hitherto unknown role for PKCη in regulating lysosomal translocation and activity of mTORC1. Moreover, it is further confirmed that PMA activation of mTORC1 is largely independent of ERK and RSK. Collectively, these findings define novel mechanisms by which PKCs regulate mTORC1 activity and show, for the first time, that PKCs are involved in the spatial regulation of mTORC1.Research to date has established the mTOR pathway as a downstream effector of PKC signaling both The mTOR pathway is a central integrator of environmental cues and is highly regulated by multiple pathways and inputs. In recent years, the spatial regulation of mTORC1 has become appreciated beginning with the discovery that mTORC1 can cycle on and off the late endosome/lysosome surface through interactions with the RAG proteins . SubsequConsiderable research has defined multiple upstream pathways that converge on the Rag proteins to regulate lysosomal mTOR localization including the Ragulator complex, v-ATPase, folliculin, and the GATOR complexes amongst others , 38, 39.In addition to the finding of the unappreciated effect of PMA on localization of mTORC1, we further observed that lysosomal translocation of mTORC1 alone is insufficient for full mTORC1 activity. Instead, PMA activation of mTORC1 required the co-ordinated activation of two separate pathways–the nPKC-dependent arm that induced lysosomal mTOR translocation and perinuclear accumulation of the lysosomes discussed above and a cPKC-dependent arm that results in accumulation of PKC and PLD2 in a perinuclear subset of recycling endosomes. Notably, this agrees with a number of reports suggesting that glucose, amino acids and growth factors all influence mTORC1 activity through multiple pathways , 43. ImpA number of previous studies have defined PLD and its downstream product, phosphatidic acid as important players in the regulation of mTORC1 activity in response to serum, growth factors and amino acids. Together with our earlier study, we now extend these findings to implicate PLD activity in PMA activation of mTORC1 which strengthens the idea that PLD is a central hub of the non-lysosomal translocation arm of mTORC1 regulation. Interestingly, studies on the role of PLD in mTORC1 activation have primarily implicated the PLD1 isoform , 44. In In conclusion, this study has explored the mechanisms underlying PMA activation of mTORC1. We show for the first time that there is a spatial aspect to the regulation of mTORC1 by PMA, requiring concurrent activation of cPKCs and nPKCs that serves to bring endosomal PLD2 and lysosomal mTOR in close proximity to elicit full activation. We also identify a novel upstream regulator of mTOR translocation to the lysosomes. Given that the mTORC1 pathway is often hyperactivated in many cancers and other pathologies, this opens up the targeting of PKCs, particularly the novel PKCη, as a potential avenue for therapeutic intervention.