Statement of the Problem: Nanosilver particles have the potential to serve as a bactericidal agent because of the inherent antimicrobial influences of silver ion. The literature confirmed that specific micro-organisms, especially streptococci, have an important role as an etiological factor for caries.Purpose: The aim of this study was to evaluate the antimicrobial effect of conventional and nanosilver-containing varnishes on oral streptococci.Materials and Method: Pure cultivations of Streptococcus mutans and Streptococcus salivarius were prepared on blood agar media. Thereafter, 0.5 McFarland standard of recently grown bacteria in normal saline was prepared and the bacteria were cultivated monotonously on the culture medium surface by applying a swab. Different concentrations of nanosilver varnishes were prepared in the Mueller- Hinton broth medium in the test tubes and equal amounts of 0.5 McFarland suspension of all the tested bacteria were added separately to all test tubes. A tube without varnish was included as the control sample. The tubes were kept at 37°C for 24 hours, then cultured to determine the numbers of bacteria in each tube by counting colonies. The numbers of bacteria in tubes with varnish were compared to the numbers of bacteria in the tube without varnish. In the instance of observing any reduction in the growth, the minimum inhibitory concentration for growth in the tube with varnish was determined.Results: Nanosilver varnish had an antimicrobial effect on S. mutans and S. salivarius.  S. salivarius was more susceptible than S. mutans to the nanosilver varnish.Conclusion: Based on the results of this study, nanosilver varnishes can be used under amalgam restorations to reduce microbial population and subsequently preventing the recurrent caries. Dental caries is one of the most important dilema in dentistry. Microorganisms, especially Streptococcus, serves as an imperative etiologic factor in dental caries. Extracellular polysaccharides of Streptococcus, in particular Streptococcus mutans, can induce and enhance the dental caries . Nano-siThe antimicrobial effect of nanosilver has been investigated in several studies , 18-20. Only one study has evaluated the antimicrobial  effect of nanosilver on S. mutans and its results showed that nanosilver had a high effect on S.mutans even in low concentrations .To the best of our knowledge, there has been no research about the antimicrobial effect of nanosilver varnish on oral streptococci until the date of commencement of this study.The purpose of the current study was to evaluate the antimicrobial effect of nanosilver varnish on oral streptococci and subsequently, suggesting its application under amalgam restoration to reduce the numbers of bacteria.oC in a refrigerator. It was necessary to provide the required amount of bacterial suspension from a 24-hour culture of frozen bacteria for each test episode. Therefore, immediately after filling each vial with water, 100 microliters of bacterial suspension were transferred to a tube containing 2 milliliters culture medium of streptococcus enrichment broth and kept at 37oC in an oven for 24 hours. Then, in the case of observing any turbidity in each inoculated tube, they were cultured on blood agar and selective streptococcal agar media and kept in an incubator for 24 hours to grow single bacterium colonies. Then to create a bacterial suspension turbidity of 0.5 McFarland;the colonies were removed by sterile phyldo platinum immediately before each test and were added to the tube containing Mueller- Hinton broth medium and stirred properly. All the employed culture media were prepared and kept according to the manufacturer’s  instructions.Streptococcus mutans (PTCC1683) and Streptococcus salivarius (PTCC1448), in a lyophilized state, were purchased from the microbial collection of Iranian Scientific and Industrial Research Center. After adding sterile physiological serum  to each vial of bacteria, they were cultured on blood agar surface in sterile conditions to grow the bacterial colonies. Then a homogenous suspension of bacteria colonies in 3 milliliters sterile vials( containing nutrient broth composition with 15% glycerol) was prepared and kept at -70For preparing the varnishes with different concentrations of nanosilver; specific amounts of nanosilver based on their percentage in the final mixture  were weighed and added to the varnish solution. They were then properly stirred for 3 minutes by adopting a speed mixer machine.The final varnish concentration of the mixture was selected in a way that the inhibitory effect of the varnish itself on bacteria could not be observed.Different concentrations of varnish were provided on Mueller–Hinton broth medium in test tubes and equal amounts of 0.5 McFarland suspension of all the tested bacteria were appended separately to all test tubes. A tube without varnish was considered as the control tube. The tubes were kept at 37◦C for 24 hours. Subsequently, each tube was cultured to define the amount of bacteria. The quantity of bacteria was determined by counting the colonies,. The numbers of bacteria in tubes that contained varnish was compared with the numbers of bacteria in the tube without varnish . In case of observing any reduction in growth, the minimum inhibitory concentration for growth in varnish was verified .Following the inoculation of 8.4×107 S. mutans and 1.2×107 S. salivarius to each tube with different varnish concentrations and after 24 hours of incubation, bacterial growth inhibition was detected in tubes containing varnish.The number of bacteria were increasing in the control tubes without varnish . The minimum concentration of varnish for growth inhibition were determined to be 1/8 and 1/32 for S. mutans and S. salivarius, respectively .The minimum lethal concentration of S. mutans and S. salivarius were recorded as 1/4 and 1/16, respectively.By inoculation of 3.6×106 S. mutans and 9.8×106 S. salivarius to each tube containing different concentrations of nanosilver and after 24 hours of incubation; the numbers of bacteria per millimeter in the 0.5 percent tube were determined to be 3.9×107 and 1.2×104, respectively, whereas in higher concentrations  there were no living bacteria. There were 8×107 and 2.4×108 bacteria, respectively in each millimeter of control tube. This experiment showed that nanosilver concentration of 1 percent or more can kill bacteria. In the 0.5 % nanosilver, S. mutans was still able to grow and after 24 hours, its number had reached up to ten times, while the numbers of S. salivarius were decreased. Therefore, the minimum nanosilver concentration for growth inhibition of S. mutans was more than 0.5 % and less than 1 %.To establish the actual nanosilver concentration to inhibit growth of S. mutans; this experiment was conducted again with 0.5- to- 1% nanosilver for S. mutans. The results showed that in the concentrations  up to 0.7 %  nanosilver, the numbers of bacteria were equal to inoculated bacteria or more, however, they were decreased in concentration of 0.8% and reached to zero in the presence of nanosilver concentration of 1 %.The results of the experiment in determining the effect of nanosilver-containing varnish on oral streptococci are illustrated in For S. salivarius, the varnish containing 0.1 % nanosilver prompted bacterial reduction and varnishes containing higher concentrations showed a significant bacterial reduction. There was no bacterial growth with the 5 % nanosilver. Therefore, S. salivarius in comparison with S. mutans, had a higher susceptibility to antimicrobial and nanosilver varnishes.Considering the vivid importance of oral streptococcus in caries etiology, prevention of the growth of these micro-organisms can be effective in prevention of dental caries.Nanoparticles are defined as particulate dispersions or solid particles with a size of 10-100 nm . These pThe results of this study showed that nanosilver varnish had an antimicrobial effect on S. mutans and S. salivarius. Moreover, the susceptibility of S. salivarius to nanosilver varnish was more than that of S. mutans.25 and antimicrobial products that contained nanoparticles, have been considered as the effective bactericidal materials [In the results of other studies, nanoparticles have demonstrated antibacterial activity aterials .27.The antibacterial and antiviral actions of silver, silver ions, and silver compounds have been reported in literature 26. Microorganisms are unlikely to become resistant against silver since they are susceptible to conventional antibiotics. Therefore, silver ions have been used as an antibacterial component in dental resin composites 14, 28.Results of some studies have demonstrated the bactericidal activity of silver nanoparticles Sondi et al. concluded that silver nanoparticles would aggregate in the membrane of E. Coli, increase the permeability of the membrane and subsequently, damage the bacterial cell. Therefore, nanosilver can be used as a bactericidal material .In another study, Morones et al. found that bactericidal features of nanosilver are dependent on the size of particles. The size of the nanoparticles must be between 1 to 10 nm to be directly exposed to bacteria .According to the study of Lok et al., nanosilver with its own physicochemical characteristics, has similar antimicrobial activities to silver .The conclusions of the aforementioned studies, similar to this study, support the antibacterial effect of nanosilver; however, this study has particularly evaluated the effect of nanosilver on oral streptococci.Dental caries is a worldwide public health problem and S. mutans plays an important role in the etiology of caries. Some studies showed that the antimicrobial properties of silver nanoparticles are proposed  as an effective agent to diminish S. mutans -31.in vitro study [Hernández-Sierra et al. demonstrated nanosilver’s antimicrobial effect against S. mutans in an ro study .In a study by Shvero et al. , the antCavity varnish is normally used to make a barrier against the transmission of stimuli across cements or other restorative materials and to reduce the percolation of oral fluids to underneath dentin . NanosilOnly in one study, Hernandez-Sierra and Ruiz studied the bactericidal and bacteriostatic effects of nanosilver. They compared the antibacterial effects of nanosilver with nanozinc oxide and gold nanoparticles. According to the results of their study, nanosilver in lower concentrations had a higher antibacterial effect on S. mutans compared with nanozinc oxide and gold nanoparticles. In their survey, the effects of nanosilver, nanozinc oxide, and gold nanoparticles on S. mutans were considered and the the high potential of these particles against S. mutans to eliminate the dental caries was concluded .Some of these micro-organisms can produce enough acid to decalcify the dental structure. S. mutans is considered as a main micro-organism in cariogenesis. S. mutans and presumably S. sabrinus and Lactobacillus are human dental pathogens .In the present study, the antibacterial effects of nanosilver-containing varnishes on oral micro organisms, namely S. mutans and S. sanguis, have been investigated. Future studies are suggested to evaluate this effect on other micro-organisms such as S. subrinus and Lactobacillus; which are also important in the occurrence and development of dental caries.It is also suggested that other studies deliberate the micro leakage after implementing nanosilver varnishes.Based on the results of this study, nanosilver varnishes can be used under amalgam restorations to reduce microbial population and consequently; preventing the recurrent dental caries.


In adaptive immune responses, T-cell receptor (TCR) signaling impacts multiple cellular processes and results in T-cell differentiation, proliferation, and cytokine production. Although individual protein–protein interactions and phosphorylation events have been studied extensively, we lack a systems-level understanding of how these components cooperate to control signaling dynamics, especially during the crucial first seconds of stimulation. Here, we used quantitative proteomics to characterize reshaping of the T-cell phosphoproteome in response to TCR/CD28 co-stimulation, and found that diverse dynamic patterns emerge within seconds. We detected phosphorylation dynamics as early as 5 s and observed widespread regulation of key TCR signaling proteins by 30 s. Development of a computational model pointed to the presence of novel regulatory mechanisms controlling phosphorylation of sites with central roles in TCR signaling. The model was used to generate predictions suggesting unexpected roles for the phosphatase PTPN6 (SHP-1) and shortcut recruitment of the actin regulator WAS. Predictions were validated experimentally. This integration of proteomics and modeling illustrates a novel, generalizable framework for solidifying quantitative understanding of a signaling network and for elucidating missing links. Protein phosphorylation is a fundamental part of cellular information processing, with a role in controlling numerous physiological functions, including immune defenses A useful reasoning aid is a mechanistic model, meaning a model in which information about molecular interactions is cast in a form that enables simulations consistent with physicochemical principles. Simulation of such a model reveals the logical consequences of the collected knowledge upon which the model is based. Comparisons of model simulations to experimental measurements can drive discovery through generation of hypotheses and identification of knowledge gaps Successful integration of modeling and experimentation depends on both approaches having compatible and relevant levels of resolution. Phosphorylation dynamics can be elucidated using several high-throughput techniques, including reverse-phase protein arrays However, modeling and proteomics have not yet become tightly integrated, in part because of the technical challenges of constructing and parameterizing a model with sufficient detail and scope to be used for analysis of proteomic data. Proteomic measurements give information about phosphorylation levels at specific amino acid residues (sites); thus, a compatible model requires similar site-specific resolution. For this task, traditional modeling approaches  can be difficult or impossible to apply Rule-based modeling has been used to study several immunoreceptor signaling systems We have developed a new approach for characterizing signal initiation using a rule-based model to interpret temporal phosphoproteomic data. We have applied this approach to study initiation of T-cell receptor (TCR) signaling, which is an essential process in the adaptive immune response Our results 1) characterize early TCR signaling with finer time resolution than previous proteomic studies of this system, 2) reveal mechanisms primarily operative on short timescales immediately after stimulation, and 3) demonstrate how mechanistic modeling and high-content measurements can be combined to develop a predictive understanding of cellular information processing.To characterize the first minute of TCR signaling, we performed time-resolved quantitative phosphoproteomic experiments, which allowed direct and accurate measurements of the changes in the levels of phosphorylation at individual tyrosine residues in response to TCR stimulation with anti-CD3, anti-CD28, and secondary antibodies . Three iRegulated changes in phosphorylation (≥2-fold increases or decreases) occurred as early as 5 s after stimulation, with the number of regulated sites increasing to 138 after 60 s of stimulation . Time coIndeed, subsequent enrichment analysis  pY19We simulated the effect of lowered PTPN6 abundance. The model predicted dampening of stimulatory phosphorylation and enhancement of inhibitory phosphorylation, including increased phosphorylation of LCK Y192 , sustainTo test the prediction that PTPN6 positively regulates TCR signaling, we used RNAi to knockdown PTPN6 , bottom.in vitro phosphatase assay in which LCK was immunoprecipitated from PTPN6 KD cells and then incubated with purified recombinant PTPN6. We found that both Y192 and Y505 sites on LCK became dramatically less phosphorylated when incubated with PTPN6 compared to the mock treated sample . Thus, we reasoned that NCK1 may be present at the plasma membrane prior to ZAP70 activation, presumably through binding of its N-terminal SH3 domain to a proline-rich sequence (PRS) in CD3E, which takes place in the absence of TCR phosphorylation A second novel mechanism of TCR signal initiation was suggested by fast phosphorylation of WAS Y291  were rapidly dephosphorylated as the PTPN6 phosphatase was phosphorylated at an activating site. Inclusion of a mechanism causally linking these events allowed our model to reproduce measured time course data and to generate testable predictions. These predictions were validated experimentally, giving credence to the hypothesized link between PTPN6 activation and dephosphorylation of putatively inhibitory pTyr sites. Our model predicted that loss of PTPN6 would result in sustained phosphorylation of these pTyr sites, and reduction of phosphorylation at other, activating sites (including sites in ZAP70 and LAT). These predictions were confirmed through RNAi-mediated knockdown of motheaten mouse, which is deficient in Ptpn6 and suffers from severe autoimmunity PTPN6 expression are distinct from those controlling other negative regulators of TCR signaling The view of PTPN6 as an overall negative regulator of TCR signaling The results presented here suggest that, in Jurkat T cells, PTPN6 (the human ortholog of Ptpn6) has an early positive effect that accelerates signaling, before its negative effects become dominant. The negative effects of PTPN6, such as dephosphorylation of the LCK activation loop in vitro, we have not conclusively determined if PTPN6 directly acts on the sites that are observed to undergo dephosphorylation in our proteomics experiments, or if instead PTPN6 influences some or all of these sites in an indirect manner. Nonetheless, our knockdown results indicate that PTPN6 positively influences specific events in early signaling, and evidence for a more indirect mechanism would not alter this finding. Secondly, the dephosphorylated sites may have roles that are multifaceted, rather than strictly inhibitory. For example, phosphorylation of Y192 in LCK may enhance kinase activity by limiting SH2 association with the C-terminal phosphotyrosine that mediates autoinhibition, which is a regulatory mechanism that may be operative in the case of Src in vivo. Characterization of very early signaling mechanisms in primary T cells poses significant technical challenges and is beyond the intended scope of the present study.Several caveats are worth noting. Firstly, although we have demonstrated that PTPN6 acts directly on LCK Y192 and Y505 LCP2 expression did not attenuate the early WAS phosphorylation, consistent with model predictions and the presence of an alternative pathway. These results indicate that the flow of information through different pathways may shift as signaling progresses. Furthermore, the shortcut pathway may explain how the PRS in CD3E contributes to the ability of the TCR to respond to a range of agonist molecules. The PRS in CD3E and its interaction with NCK1 are known to be more consequential for responses to weak agonists The breadth and fine time resolution of our proteomic data allowed us to determine the order in which events occur. One of the fastest events observed was phosphorylation of the actin regulator WAS, which surprisingly preceded activating phosphorylation of the kinase ZAP70. It has previously been reported that WAS is recruited to the plasma membrane via a pathway involving LAT and LCP2 (SLP-76) Our findings suggest that TCR signaling is initiated by proteins that transition from positive to negative roles. This strategy resembles bang-bang control Physiologically, we speculate that bang-bang control of TCR signal initiation may allow a T cell to launch rapid but controlled responses to infection. T cells scan antigen-presenting cells quickly and have been shown to decide if foreign antigen is present in under 1 min ATCC TIB-152), were grown in RPMI (Invitrogen) supplemented with penicillin/streptomycin , 10% dialyzed fetal calf serum (Invitrogen), and one of three SILAC labels : l-arginine and l-lysine (Arg0/Lys0); l-arginine-13C6-14N4 and l-lysine-2H4 (Arg6/Lys4); or l-arginine-13C6-15N4 and l-lysine-13C6-15N2 (Arg10/Lys8)  and anti-CD28 antibody  (4 µg/ml in RPMI supplemented with 10 mM HEPES). The anti-CD3 and anti-CD28 antibodies were crosslinked by incubation with anti-mouse IgG (Dako) at 4°C overnight. Differentially labeled cells (8 cells per condition). Because of the large number of cells required to obtain sufficient protein for LC-MS/MS analysis, we performed the three replicate temporal phosphoproteomic experiments of Jurkat T cells, clone E6-1 (10/Lys8) . Before ed cells  were stied cells  as descrCells were lysed in 8 M urea with 25 mM Tris, pH 8.0. Lysates from differentially labeled and stimulated cells  and anti-phosphotyrosine antibody . Briefly, peptides were dissolved in 2 ml of IAP buffer per experiment , refrigerated, centrifuged to remove undissolved peptides in the pellet, and immunoprecipitated with 80–100 µl of the anti-phosphotyrosine antibody resin for 3–4 h at 4°C. Beads were washed three times with the IAP buffer and two times with a salt solution (50 mM NaCl), and phosphopeptides were eluted using 0.15% TFA solution. Three sequential elutions were performed; each time, the volume of the 0.15% TFA solution used was equal to that of the bead volume. Eluted phosphopeptides were further purified by using TiOm/z from 350 to 1,600) were subjected to MS/MS fragmentation using the CID activation technique with the following settings: R = 60,000 (MS resolution), a normalized collision energy of 35%, and an isolation window of 2.0 Th. In MS/MS acquisition, we used q = 0.25 (collision endothermicity) and an activation time of 30 ms. Slightly different settings were used for the third biological replicate: the range of m/z was 300–2000 and multistage activation (MSA) was used. For all MS runs, ions selected for fragmentation were dynamically excluded for 45 s and lock mass ions were used for internal mass calibration Peptides were analyzed using LC-MS/MS as previously described http://www.matrixscience.com) to search the generated peak-lists files (*.msm) against the IPI database (version 3.69) Raw data files from three biological replicates were processed using MaxQuant (version 1.0.13.13) as described earlier c-means algorithm as implemented in GProX with default parameters p-value less than 0.05 after correction for multiple testing using the Benjamini and Hochberg algorithm were regarded as significant. For identification of enriched pathways, UniProt accession keys were uploaded to DAVID For bioinformatics analysis, the peptide abundance ratios obtained from the three biological replicates were averaged. For cases where a pTyr site was detected in multiple peptides, we considered the least modified peptide when evaluating pTyr site dynamics. Clustering of pTyr sites showing ≥2-fold dynamics at a minimum of one time-point was performed using the fuzzy PTPN6 and LCP2 knockdowns were modeled by setting the copy numbers of PTPN6 and LCP2 to zero. The model visualization in Fig. S4 in A chemical-kinetics model for TCR signaling in a single cell was formulated using a rule-based approach, which enabled concise representation of biomolecular interactions and efficient simulation of multi-site phosphorylation A lentiviral vector pSicoR (Addgene plasmid 11579) PTPN6 and LCP2 transcripts were generated using available Web resources (http://www.dharmacon.com) according to published recommendations for siRNA/shRNA design An EGFP cassette in the vector pSicoR was exchanged with a puromycin resistance gene cassette, resulting in a modified pSicoR-puro vector allowing puromycin resistance-based selection of shRNA expressing cells. RNAi sequences potentially targeting the 5′-T(N19)TTCAAGAGA(rN19)TTTTTTC-3′ and5′-TCGAGAAAAAA(N19)TCTCTTGAA(rN19)A-3′5′-GAGCATGACACAACCGAAT-3′ for PTPN6 and 5′-GGACCAGACAGAAGAGAGA-3′ for LCP2. Verified DNA constructs were used to produce lentiviral particles as described earlier where N19 is the sense strand of the target sequence and rN19 is the antisense strand. The oligonucleotides were annealed as described earlier PTPN6 (PTPN6 KD) or LCP2 (LCP2 KD) were stimulated for either 0, 30, and 60 s or 0, 10, and 60 s using our qPACE setup (http://imagej.nih.gov/ij/docs/guide/user-guide.pdf). Chemiluminescence was measured, and we considered different exposure times to ensure that images were analyzed well before saturation. Values for bands corresponding to site-specific antibody staining were normalized using values for corresponding total protein loading controls. The following antibodies were used for western blots: Phospho-Lck (Tyr 505), Phospho-Zap-70 (Tyr 493), Phospho-LAT (Tyr 191), Zap-70, WASP, NCK1 ; LAT ; Phospho-WASp (Tyr 290) (Sigma-Aldrich); Phospho-Lck (Tyr 192) (Abcam); and Shp-1, Shp-2, and Slp-76 mouse mAb (BD Biosciences). Secondary anti-mouse and anti-rabbit antibodies were obtained from GE Healthcare, UK.Equal amounts of normal Jurkat T cells (WT) and Jurkat T cells with stable knockdown of CE setup . HarvestPTPN6 were starved for 16 hours and harvested by centrifugation. The cells were lysed using ice-cold lysis buffer . The cell lysate was centrifuged; a supernatant was supplemented with the SDS up to 0.5% and incubated for 30 minutes on ice. The cell lysate was diluted with the lysis buffer up to 0.1% SDS. Mouse anti-LCK antibody (6 µg) bound to Protein G beads was used for immunoprecipitation of LCK for 5 hours at 4°C. The beads were washed three times with the lysis buffer and five times with ice-cold in vitro assay buffer . Washed beads were divided into two equal parts with 50 µl in vitro assay buffer. The first sample was supplemented with 1 µg of an active human recombinant protein PTPN6  and second sample was vehicle treated. Both samples were incubated at 37°C for 30 minutes with gentle shaking. Thereafter, the samples were mixed with 6× Laemmli buffers and resolved on Novex 4–12% Bis-Tris gradient gels (Invitrogen) using MOPS running buffer followed by protein transfer to nitrocellulose membrane, blocking with 5% BSA and incubation with primary and HRP-conjugated secondary antibodies.Jurkat T cells with stable knockdown of For immunoprecipitation, an anti-LCK antibody  was used. For immunoblotting, antibodies specific for phosphorylated Y192 in LCK (Abcam) and LCK  were used.The Supporting Information consists of 17 items: Tables S1 and S2 and Files A and B in File S1This Zip file combines Tables S1 and S2 (Excel spreadsheets) and Files A and B (plain-text files).Table S1 in File S1. Proteomic data. This Excel spreadsheet provides a listing time courses and residue numbers of phosphopeptides detected in each of three LC-MS/MS experiments. Table S2 in File S1. Parameter estimates. This Excel spreadsheet provides a listing parameter estimates used in the model for TCR signaling. File A in File S1. Executable model specification. This plain-text file provides an executable model specification, which can be processed by BioNetGen. The filename extension should be changed to “.bngl” for processing by BioNetGen. File B in File S1. Simulation protocol. This plain-text file provides a definition of a simulation protocol, which can be processed by NFsim. The filename extension should be changed to “.rnf” for processing by NFsim.(ZIP)Click here for additional data file.File S2This PDF file combines Figures S1–S9 and Tables S3–S5.Figure S1 in File S2. Overview of methodology and summary of main results. (A) An integrated experimental and model-based approach was used to characterize initial phosphorylation events in TCR signaling, generate non-trivial predictions, and test these predictions. A model based solely on previously elucidated mechanisms of TCR signaling did not reproduce the phosphorylation dynamics observed for the following five sites: LCK Y192, DOK1 Y449, DOK2 Y299, PAG1 Y417, and WAS Y291. Incorporation of novel mechanisms enabled the dynamics of these sites to be reproduced and led to generation of predictions that were tested experimentally. (B) Proposed roles of PTPN6 in early and late signaling. In early signaling (bold lines), PTPN6 plays a positive role by dephosphorylating negative regulatory sites, including LCK Y192, PAG1 Y163, DOK1 Y449, and DOK2 Y299. Later in signaling (thin lines), the negative regulatory capabilities of PTPN6 become dominant. (C) Proposed dual pathways for activation of WAS. In early signaling (bold lines), WAS is recruited to the plasma membrane through interaction with NCK1/2 in association with CD3E. As signaling progresses over time, the longer pathway for WAS recruitment (thin lines), which is dependent on LCP2, becomes dominant. (D) A PTPN6-mediated positive feedback loop in which PTPN6 dephosphorylates LCK Y192, thereby enhancing the ability of the LCK SH2 domain to interact with pTyr sites. LCK activates PTPN6 through phosphorylation and direct interaction. (E) In a second PTPN6-mediated positive feedback loop, LCK phosphorylates and activates PTPN6. PTPN6 dephosphorylates PAG1, which reduces the ability of PAG1 to co-localize LCK and CSK, which reduces phosphorylation of LCK at its inhibitory C-terminal tyrosine and relieves autoinhibition. Figure S2 in File S2. Enrichment analysis. For proteins containing regulated pTyr sites, we tested for enrichment of associated GO terms compared to proteins containing detected but unregulated pTyr sites . (A) Cluster-specific enrichment analysis based on “biological process” terms. (B) Cluster-specific enrichment analysis based on “molecular function” terms. (C) Cluster-specific enrichment analysis based on “cellular compartment” terms. (D) We also tested for enrichment of associated Pfam domain names. In A through D, color is used to indicate the negative logarithm (base 10) of the z-transformed p-value associated with each term. The lighest shade of green corresponds to the highest level of enrichment. Black corresponds to no enrichment. (E) Information about detected pTyr sites was uploaded to the DAVID resource and processed using default parameters to identify pathways enriched for regulated pTyr sites. The y-axis reports the negative logarithm (base 10) of the p-value for each of the indicated pathways. Pathway enrichment scores are reported on the y-axis. The most enriched pathway is the “T cell receptor signaling pathway.” Figure S3 in File S2. Phylogenetic relationships of protein kinases with detected pTyr sites. The tree shown was built based on kinase domain sequences. Protein kinases that contain regulated pTyr sites are indicated with red lettering; these kinases are also represented in Figure S4 in File S2. Visualization of model. Proteins, domains, and linear motifs are represented as boxes, which are nested to indicate structural relationships. Lines that begin and end with an arrowhead represent direct binding interactions. Arrowheads point to functional components that mediate protein-protein interactions. Lines that originate at a box representing an enzyme (a kinase or phosphatase) and end with an open circle, or open circle overlayed with a diagonal bar, indicate enzyme-substrate relationships. An open circle denotes phosphorylation and an open circle overlaid with a diagonal bar denotes dephosphorylation. Flags  represent sites of post-translational modification. All of these sites are pTyr sites. Compartmental locations of proteins are indicated by boxed labels near the lower left corners of protein boxes. The following symbols are used to denote locations: E, extracellular; M, membrane anchored; and C, cytosol. Locations that can be inferred are not indicated. Protein boxes are organized in layers, which are indicated by shading. Stimulating antibodies are represented in the top layer, TCR/CD3 and CD28 are represented in the next layer, their direct interaction partners are indicated in the next layer, and so on. Elements of this map directly related to elements of the underlying rule-based model that it visualizes. A rule-based model is composed of molecule type definitions and rules, as well as specifications of rate laws, parameters, and initial conditions. Molecule type definitions of the model are illustrated here by protein boxes. Rules are illustrated by arrows. Each arrow corresponds to a single rule or a set of related rules. Numbers next to arrows reference rules presented in File A in Figure S5 in File S2. Principal component analysis of time-course data used to guide model specification and estimate model parameter values. Experimental time courses for the 16 pTyr sites included in the model were analyzed by principal component analysis and found to separate into three classes, which are distinguished by different background colors and labeled 1–3. Time courses in Class 1 correspond to pTyr sites that were observed to undergo increases in phosphorylation; according to the model, these increases occur through mechanisms that do not require prior ITAM phosphorylation. An example of a pTyr site in Class 1 is CD3G Y171, which in the model can be phosphorylated by LCK bound to CD28 through a constitutive interaction that does not require ITAM phosphorylation. Time courses in Class 2 correspond to pTyr sites that were also observed to undergo increases in phosphorylation; however, according to the model, these increases in phosphorylation occur through mechanisms that require ITAM phosphorylation. For example, ZAP70 must be recruited to a phosphorylated ITAM before it can be phosphorylated by LCK at Y493. Time courses in Class 3 correspond to pTyr sites were observed to undergo decreases in phosphorylation. Figure S6 in File S2. Experimental and simulated time courses. (A through P) Phosphorylation dynamics of the 16 pTyr sites used to guide model construction and estimate model parameters are plotted. Points represent the average of measurements from three independent phosphoproteomic experiments, with error bars representing standard deviations. Simulation results are plotted as solid lines. Experimental measurements and simulation results are normalized to baseline and log2 transformed. (Q and R) Measured phosphorylation dynamics of pTyr sites in PAG1, additional to the site shown in Panel m. The dynamics of these sites are similar to the dynamics of PAG1 pY163; these sites are not explicitly considered in the model. Note that the results presented here were presented earlier in Figure S7 in File S2. PTPN11 levels in normal cells and in cells depleted of PTPN6. Immunoblots of PTPN11 (SHP-2) in normal cells (WT) and in cells depleted of PTPN6 (SHP-1 KD). Blots are representative of the results from multiple (at least two) experiments. Figure S8 in File S2. In vitro phosphatase activity of PTPN6. Immunoprecipitated LCK was treated or untreated with recombinant PTPN6 and immunoblotted using phospho-tyrosine specific antibodies as indicated. LCK specific antibodies were used to show equal amounts of immunoprecipitated LCK. Blots are representative of the results from multiple (at least two) experiments. Figure S9 in File S2. Disengagement of the shortcut pathway and engagement of the longer LCP2-dependent pathway to WAS activation. (A) Predicted association of NCK1/2 with CD3E at 0 and60 s of stimulation. The y-axis indicates the number of NCK molecules associated with TCR/CD3 complexes per cell. (B)Predicted association of NCK1/2 with phosphorylated LCP2 at 0 and 60 s of stimulation. The y-axis indicates the number of NCK molecules associated with LCP2 per cell. (C) Immunoblot of LCP2 phosphorylation at Y145 in normal (WT) and LCP2 KD cells stimulated for the indicated times. (D) Immunoblot of LCP2 phosphorylation at Y113 in normal (WT) and LCP2 KD cells stimulated for the indicated times. Blots are representative of the results from multiple (at least two) experiments. Table S3 in File S2. Proteins and pTyr sites included in the model for TCR signaling. Table S4 in File S2. Summary of earlier phosphoproteomic studies of TCR signaling. Table S5 in File S2. Comparison of selected models for immunoreceptor signaling in which phosphosite dynamics were considered.(PDF)Click here for additional data file.File S3This PDF file provides the Supplementary Text.Supplementary Text S1 in File S3. This PDF file provides extensive annotation of the model.(PDF)Click here for additional data file.


Phosphosite mapping indicated that multisite serine/threonine autophosphorylation probably occurs within the activation loop of the kinase. Phosphoablative mutations differentially impair kinase activity in vitro and receptor function in planta.Phytosulfokine is perceived by a leucine-rich repeat receptor-like kinase with auto- and  Arabidopsis thaliana plants identified Ser696 and Ser698 in the JM (juxtamembrane) region and probably Ser886 and/or Ser893 in the AL (activation loop) as in planta phosphorylation sites. In vitro-expressed kinase was autophosphorylated at Ser717 in the JM, and at Ser733, Thr752, Ser783, Ser864, Ser911, Ser958 and Thr998 in the kinase domain. The LC–ESI–MS/MS spectra provided support that up to three sites  in the AL were likely to be phosphorylated in vitro. These sites are evolutionarily highly conserved in PSK receptors, indicative of a conserved function. Site-directed mutagenesis of the four conserved residues in the activation segment, Thr890, Ser893, Thr894 and Thr899, differentially altered kinase activity in vitro and growth-promoting activity in planta. The T899A and the quadruple-mutated TSTT-A (T890A/S893A/T894A/T899A) mutants were both kinase-inactive, but PSKR1(T899A) retained growth-promoting activity. The T890A and S893A/T894A substitutions diminished kinase activity and growth promotion. We hypothesize that phosphorylation within the AL activates kinase activity and receptor function in a gradual and distinctive manner that may be a means to modulate the PSK response.PSK (phytosulfokine) is a plant peptide hormone perceived by a leucine-rich repeat receptor kinase. Phosphosite mapping of epitope-tagged PSKR1 (phytosulfokine receptor 1) from  Proteins were separated by SDS/PAGE (15% gel), stained with Coomassie Blue, dried and exposed to an X-ray film. The protein bands were excised, mixed with Ultima Gold (PerkinElmer) and radioactivity was determined by liquid-scintillation counting in a Tri-Carb 2910 TR instrument (PerkinElmer). The background signal of the gel was subtracted.35S:PSKR1-GFP and wild-type seedlings. The plant material was ground in liquid nitrogen and the tissue was extracted in 1 ml of extraction buffer per g of plant material. The extraction buffer contained 50 mM Tris/HCl (pH 7.5), 150 mM NaCl and 1% (v/v) Igepal CA 630 (Sigma–Aldrich). After incubation of protein samples on ice for 1 h, suspensions were centrifuged at 300 g for 3 min at 4°C. The protein concentration was determined with Roti®-Quant (Carl Roth). Then 10 ml (1 mg/ml) of total plant protein in extraction buffer was incubated with 100 μl of 50% (v/v) Protein A–Sepharose CL-4B beads  in 50 mM Tris/HCl (pH 7.5) and 150 mM NaCl. The samples were centrifuged at 300 g for 3 min at 4°C and supernatants were incubated with anti-GFP antibody (Life Technologies) as indicated for 1 h at 4°C with gentle mixing of samples followed by the addition of 200 μl of fresh 50% (v/v) Protein A–Sepharose CL-4B beads and overnight incubation. The Protein A–Sepharose CL-4B beads were pelleted at 300 g for 3 min and washed four times with 1 ml of washing buffer containing 50 mM Tris/HCl (pH 7.5) and 150 mM NaCl. The protein was separated on a denaturing gel, immunoblotted on to a PVDF membrane, detected with anti-GFP antibody linked to horseradish peroxidase (Life Technologies) and visualized using the ECL detection system .GFP-tagged full-length PSKR1 protein was extracted from 14-day-old To generate peptides for LC–ESI–MS/MS analysis, protein bands were excised from the SDS gels, washed with 500 μl of water for 15 min, and destained twice in 800 μl of 30% (v/v) acetonitrile for 30 min. Supernatants were discarded and gel slices were re-equilibrated with 500 μl of incubation buffer  for 10 min. After removal of the supernatants, the gel slices were dried by vacuum evaporation using a SpeedVac (Eppendorf) for 30 min at room temperature. For reduction of disulfide bonds, 116 μl of reducing solution (10 mM DTT) was added to the gel pieces and the samples were incubated at 56°C for 30 min, followed by cooling at 4°C for 10 min and a washing step with 150 μl of incubation buffer for 10 min. Alkylation of cysteine thiols was performed in the dark for 20 min by addition of 100 μl of 40 mM iodacetamide followed by quenching with 75 μl of reducing solution for 10 min and an additional washing step with 150 μl of incubation buffer for 15 min. To remove reducing and alkylation contamination, the samples were washed with 150 μl of incubation buffer for 5 min and 150 μl of pure acetonitrile was added to initiate gel shrinking. The samples were shaken for 15 min before the supernatant was removed, and dried using a SpeedVac for 30 min at room temperature before digestion.in vitro sample, all four enzymes were used in a multi-protease approach as described previously  and with 150 μl of pure acetonitrile for 15 min. The three extracts were combined, evaporated to dryness using a SpeedVac at room temperature, made up to 10 μl with loading buffer A [3% (v/v) acetonitrile and 0.1% TFA] and stored at −20°C before LC–ESI–MS/MS analysis.The protein was digested with chymotrypsin, elastase, GluC and trypsin. Enzyme/protein ratios of 1:10 were used, except for trypsin which was used at 1:20. For the eviously . For thein vitro sample were enriched using TiO2 beads  similar to the method described previously  for 15 min with shaking. The supernatant was discarded followed by peptide dilution in 50 μl of loading buffer. Diluted peptides were transferred and incubated with the TiO2 beads for 15 min while shaking. Beads were pelleted and the supernatant (NB fraction) was transferred to a new tube. Then, 50 μl of washing buffer I  was added and samples were shaken for 15 min. Beads were pelleted followed by a second washing step with washing buffer II . Combined supernatants of both washing steps were transferred to a new tube (W fraction). Before eluting the bound phosphopeptides, the beads were evaporated to dryness using a SpeedVac at room temperature for 30 min. Elution was performed twice using 50 μl and 100 μl of elution solution (1% NH4OH in H2O). Supernatants from both elution steps were combined (P fraction) and acidified with 5 μl of pure TFA and dried using a SpeedVac. Before MS analysis, each sample  was diluted in 16.5 μl of eluent A (0.05% formic acid in water).Phosphopeptides from the eviously . First, Protein digests were separated and analysed by RP (reverse-phase) LC using an UltiMate 3000 nano-HPLC system (Dionex) coupled online to an LTQ Orbitrap Velos equipped with ETD support (Thermo Fisher). Solvents used for LC were: solvent A (0.05% formic acid in water) and solvent B [80% (v/v) acetonitrile and 0.04% formic acid in water]. For sample loading, a solution of 3% (v/v) acetonitrile and 0.1% TFA was used. The flow rates were 30 μl/min for the loading and 30 nl/min for the micro-pump. Isocratic elution (5% B) was used for the first 4 min, followed by an increase to 10% B within 1 min. Then a linear gradient to 50% B within 80 min was used. Column washing was performed by a linear increase of B to 95% within 10 min and held for 5 min. Re-equilibration was set to 10 min at 5% B.m/z 3; remaining settings were default). To increase the number of acquired peptide ions, dynamic exclusion was activated . For LC–ESI–MS/MS analysis, 14 μl of the in vivo sample, 10 μl of the phosphopeptide fraction and 5 μl of the non-phosphopeptide fraction (in vitro sample) were injected.A dual top five CID (collision-induced dissociation)–HCD  method was written manually using XCalibur 2.1. Here, a MS full scan was acquired at a resolution of 60000 with activated pre-scan. For fragmentation, the five most intense precursor ions were selected for fragmentation by CID (default settings) and analysis in the ion trap. Afterwards, the same five most intense precursor ions were selected for fragmentation by HCD  from Uniprot. A PD workflow consisting of the following nodes was created; if not specified otherwise, the default settings were used: (0) Spectrum Files; (1) Spectrum Selector; (2) MS2 - Spectrum Processor, (3) Scan Event Filter [splitting into CID-IT (CID in an ion trap) or HCD data]; (4) Mascot or SEQUEST HT (see below); (5) phosphoRS 3.0; (6) Percolator; (8) Scan Event Filter; (11) Event Detector and (12) Precursor Ions Area Detector. In total, four (independent) database searches were performed using CID-IT or HCD MS/MS data and Mascot and SEQUEST HT respectively. The resulting four .msf files were opened together and filtered for high confidence with a validation threshold of 0.01 based on the q-value. The combination of four independent searches was required as phosphoRS 3.0 +m/z values. Using these data, the start and end position of the peptide within the protein of interest was extracted. Furthermore, it was determined  whether this peptide was identified by Mascot or SEQUEST with CID and HCD respectively. Additionally, the number of hits for each of the four combinations was counted. The average peptide [M+H]+ mass, its average retention time and precursor mass (obtained from the Precursor Ions Area Detector) were calculated.For data evaluation, GAMBAS (version 3) scripts were written in house. These scripts were used to group peptide identification features of the phosphopeptides identified. The .txt files of the PSMs were loaded and phosphorylated peptide identifications were grouped together on the basis of their sequence, retention time difference (<20 min) and +: 1043.4–3667.7) with different physicochemical properties . This enabled us to localize the phosphorylation site with high confidence. Overall, using this multi-protease strategy, a sequence coverage of 99% was achieved for the His6–PSKR1-KD protein.Site mapping was significantly facilitated by using the four proteases and both the phosphoRS score  and the 717 in the JM domain, Ser733, Thr752, Ser783, Ser864, Ser911, Ser958 and Thr998  in the AL of PSKR1 spanning residues 886–894 is likely to be phosphorylated (890 represented the most likely phosphorylation site on the basis of the automated phosphorylation site mapping approaches (phosphoRS score and database search engines). In addition, peptides covering this potential phosphorylation site show a high diversity of sequence length (between 8 and 33 amino acids) and starting positions (883–886). This resulted in 29 unique peptide sequences encompassing different modification forms of a peptide with the same sequence that, as a group, can be used for phosphorylation site localization. Together with these singly phosphorylated peptide species, a second, doubly phosphorylated, form was also detected, probably a combination of Thr890 and Ser893 or Thr890 and Thr894. However, owing to the close proximity of Ser893 and Thr894, an unambiguous localization of the second site was not possible. At present, we cannot exclude that both forms exist in parallel.On the basis of the dataset and on the number of PSM and phosphoRS scores, eight phosphorylation sites were identified: Serd Thr998 . Moreoveorylated . Here, TE. coli kinases we expressed and affinity-purified kinase-inactive His6–PSKR1(K762E)-KD protein  was obtained. Similar values were calculated for the PSM [89.7% for His6–PSKR1-KD and 10.3% for His6–PSKR1(K762E)-KD]. To evaluate the extent of phosphorylation between His6–PSKR1-KD and His6–PSKR1(K762E)-KD, the same approach as described before was performed. Comparing the peptide sequences, the percentage distribution [His6–PSKR1-KD/(His6–PSKR1-KD+His6–PSKR1(K762E)-KD] was 94% and 90% for the label-free and SPC  approach respectively (Supplementary Table S2), indicating that the extent of phosphorylation in His6–PSKR1(K762E)-KD was much lower. Nonetheless, the kinase-inactive His6–PSKR1(K762E)-KD protein is a suitable substrate for an as yet unknown bacterial kinase. Probably, phosphorylation takes place during ectopic expression of the mutant protein in E. coli. It is unlikely that the phosphorylation event originates from any kinase activity of the mutant His6–PSKR1(K762E)-KD protein, since a replacement of the invariant lysine residue in subdomain II by glutamate results in a loss of kinase-mediated phosphotransfer ATP. As controls, the wild-type His6–PSKR1-KD and the His6–PSKR1(K762E)-KD variant were analysed [890 was a target of trans-phosphorylation by E. coli, it cannot be ruled out that it is also a target of autophosphorylation. On the basis of this reasoning, we included analysis of this residue. The T890A mutation resulted in overall reduced kinase activity compared with the wild-type, whereas the T890D mutant displayed wild-type activity -1,2,3,4; 35S:PSKR1(T899A)-1,2,3,4,5, 35S:PSKR1(S893A/T894A)-1,2,3,4,5, 35S:PSKR1(TSTT-A)-1,2,3 and 35S:PSKR1(T890D)-1,2,3,4,5 by RT–PCR . In the null background line pskr1-3 pskr2-1 (denoted r1r2), no PSKR1 mRNA was detected, indicating that growth promotion was driven exclusively by the respective receptor variant.The high conservation of potential phosphosites in the AL of PSKR orthologues and the differential effect of individual phosphosites on PSKR1 kinase activity prompted us to analyse the impact of AL phosphorylation and of the adjacent Thrr1r2. Root lengths of 5-day-old wild-type, r1r2, r1r2 35S:PSKR1-GFP-1, r1r2 35S:PSKR1(T890A), r1r2 35S:PSKR1(T890D), r1r2 35S:PSKR1(T899A), r1r2 35S:PSKR1(S893A/T894A) and r1r2 35S:PSKR1(TSTT-A) seedlings as well as their responsiveness to exogenously supplied PSK were measured -1,2,3,4 seedlings were still responsive to PSK, indicating that activation of the receptor by ligand binding is not fully dependent on Thr890. Replacing Thr890 by a phosphomimic aspartate residue resulted in wild-type root lengths in five independent lines (r1r2 35S:PSKR1(T890D) lines (4 and 5) had an overexpression phenotype [in vitro kinase activity (893/Thr894 abolished responsiveness to PSK in four of the five PSKR1(S893A/T894A) lines and diminished it considerably in the fifth line.The phosphoablative mutations in the AL and of the adjacent Thrs mutant B and 5A.nt lines B. At leaactivity B and 5A.activity A. Unlikein vitro kinase activity and in planta receptor function was unveiled for the PSKR1(T899A) mutant. Even though this residue was not identified as being phosphorylated, it is a conserved residue in all PSKRs, and the BRI1 AL residue corresponding to PSKR1 Thr899 is a likely phosphosite in planta [899 in PSKR1 abolished the kinase activities of both receptors. Whereas the T899A mutation in PSKR1-KD similarly abolished kinase activity in vitro (in planta (in planta may depend on other factors. It is conceivable that the PSKR1(T899A) receptor is complemented by heterodimerization with another kinase in planta that aids in signal transduction as demonstrated recently for BAK1 [An intriguing difference between n planta . Mutatioin vitro B and 2C,n planta A. This sfor BAK1 . The PSK899 to alanine not only abolished kinase activity, but also, upon in planta expression, resulted in roots that were as short as or shorter than the roots of r1r2 seedlings, indicating that this receptor was inactive in planta  and of r1r2 35S:PSKR1(S893A/T894A) plants as well as the flower stalks of 6-week-old plants were intermediate between r1r2 and wild-type plants (r1r2 35S:PSKR1(T899A) plants had wild-type size as was observed for seedling roots (r1r2 35S:PSKR1(TSTT-A)-1,2,3 plants were no bigger than those of r1r2. Hence the concerted phosphoablative mutations in the activation segment of PSKR1 resulted in the inability of the PSKR1(TSTT-A) receptor to rescue the short-root and small-shoot phenotype of receptor-null plants.Analysis of soil-grown plants revealed that the phosphoablative mutations had a similar effect on plant shoot growth as it had on seedling root growth and hence confirmed the differential effect of specific phosphosites on receptor activity . The rose plants A and 6B.ng roots A and 6A.In summary, our results showed that PSKR1 belongs to the RD-type kinases that require phosphorylation within the AL for their full activation. Phosphoablative mutations of one or two AL phosphosites partially reduced kinase activity and receptor function, but did not abolish it. We hypothesize that differential phosphorylation of AL residues of PSKR1 can modulate receptor activity.


Expansive pedicle screws significantly improve fixation strength in osteoporotic spines. However, the previous literature does not adequately address the effects of the number of lengthwise slits and the extent of screw expansion on the strength of the bone/screw interface when expansive screws are used with or without cement augmentation. Herein, four designs for expansive pedicle screws with different numbers of lengthwise slits and different screw expansion levels were evaluated. Synthetic bones simulating severe osteoporosis were used to provide a comparative platform for each screw design. The prepared specimens were then tested for axial pullout failure. Regardless of screw design, screws with cement augmentation demonstrated significantly higher pullout strength than pedicle screws without cement augmentation (p < 0.001). For screws without cement augmentation, solid screws exhibited the lowest pullout strength compared to the four expansive groups (p < 0.01). No significant differences in pullout strength were observed between the expansive screws with different designs (p > 0.05). Taken together, our results show that pedicle screws combined with cement augmentation may greatly increase screw fixation regardless of screws with or without expansion. An increase in both the number of slits and the extent of screw expansion had little impact on the screw-anchoring strength. Cement augmentation is the most influential factor for improving screw pullout strength. Transpedicular screws have been widely used to achieve stabilization of the posterior spine for treatment of a variety of spinal disorders –5. HowevNumerous reports have demonstrated that several factors, such as bone mineral density, screw design, cement augmentation, and surgical technique, are related to screw pullout resistance –18. AmonWhen PMMA is applied to spinal vertebrae with osteoporosis, solid screws with retrograde cement pre-filling are generally used to achieve robust screw fixation in osteoporotic bone. In such augmentation techniques, PMMA is squeezed directly into the prepared pilot hole in the vertebral body prior to screw insertion. The solid pedicle screw is then inserted into the cement to enhance screw-anchoring strength . AnotherAlthough extensive studies have demonstrated improvement in pullout strength using expansive screws, reports that address the influences of design parameters such as the degree of screw expansion and the number of lengthwise wings on the axial pullout strength of the expansive screws, with or without cement augmentation, are lacking. In this study, we address three features that might alter the pullout strength of expansive screws: 1) the effect of expansive range, 2) the effect of the number of the lengthwise wings and 3) the effect of insertion techniques with or without cement augmentation.3. These test blocks provided a homogeneous and consistent material similar to human cancellous bone with extreme osteoporosis [Synthetic composite bone  was chosen as the test object to represent human vertebrae with severe osteoporosity, which minimized experimental variation caused by the variability of bone properties and morphometry. The synthetic bone was constructed from open-cell rigid polyurethane foam with a density of 0.09 g/cmoporosis . The tesFive screw designs were employed in the present study: solid screws (control) and four types of expansive screws. All of the screws had the same outer diameter of 6 mm, a length of 40 mm from hub to tip, a thread pitch of 2 mm and a thread depth of 0.8 mm. The lengthwise slit length from the screw tip was 27 mm for all of the screws. All of the expansive screws had an internal hole 3 mm from the screw head, which was connected to a smaller hole (1.5 mm in diameter) that extended to the screw tip . Four tyThe allocation of specimens to experimental groups is presented in Group 1: Solid screws, cementless.Group 2: Expansive screws, 4-slit with 16 mm EEL, cementless.Group 3: Expansive screws, 4-slit with 22 mm EEL, cementless.Group 4: Expansive screws, 6-slit with 16 mm EEL, cementless.Group 5: Expansive screws, 6-slit with 22 mm EEL, cementless.Group 6: Solid screws, cemented.Group 7: Expansive screws, 4-slit with 16 mm EEL, cemented.Group 8: Expansive screws, 4-slit with 22 mm EEL, cemented.Group 9: Expansive screws, 6-slit with 16 mm EEL, cemented.Group 10: Expansive screws, 6-slit with 22 mm EEL, cemented.Prior to screw insertion, a pilot hole was drilled into the test block using a 3-mm drill bit, and a pedicle screw was then inserted into the test block through the prepared pilot hole. The insertion rate for all of the screws was 3 rev/min , and a cScrew pullout tests were conducted according to the ASTM F543-07 testing standards . Each prAll of the measurements were collected in six trials and are expressed as the mean ± standard deviation (SD). To evaluate the effects of screw design  and different modes of screw implantation (cemented compared to cementless) on the stability of spinal fixation, the magnitudes of the ultimate pullout force were statistically compared. ANOVA test with post-hoc analyses were performed to evaluate difference among groups. Differences were considered significant at p < 0.05.The average diameters at the screw tip after screw expansion for 4-slit screws with 16- and 22-mm effective expansion lengths (EELs) were 10.61 ± 0.54 mm and 13.02 ± 0.46 mm (p < 0.001), respectively. For 6-slit screws with 16- and 22-mm EELs, the average diameters were 11.07 ± 0.67 mm and 13.24 ± 0.28 mm (p < 0.001), respectively. Expansive screws with 16- and 22-mm EELs after expansion are shown in et al. [Pedicle screw fixation remains a challenge for patients with severely osteoporotic spines. The screw-anchoring strength is greatly compromised in poor-quality bone, which can result in subsequent screw loosening or fixation failure. Numerous reports have demonstrated that expansive pedicle screws can significantly enhance the initial fixation strength compared to conventional screws ,31,35. Oet al. , who repAlthough previous literature has demonstrated that expansive screws effectively enhance screw-fixation strength, our results demonstrate that expansive screws combined with cement augmentation may further increase screw-fixation strength compared to an expandable screw alone. Increasing both the number of slits and the extent of screw expansion had little impact on screw-anchoring strength. For expansive screws, we did not observe any noticeable difference in pullout strength among the various screw designs  for screws using the same augmentation technique (cemented or cementless). However, regardless of the screw design, pullout strength in the cemented groups was significantly greater than in the cementless groups . This stObservations of the failed specimens after the pullout tests indicated that cement infiltration into the open cell of the test block led to the formation of a cement/bone composite structure . All of 3 [3 and were chosen to model human osteoporotic vertebrae. Although the mechanical tests did not measure the pullout strength from actual cadaveric vertebrae, they measured the screw/cement/bone interfacial strength, which allowed examination of the relative differences between different screw designs. Unlike a more clinically relevant loading combining pullout, transeverse, moment loading performed by Choma et al, who investigated bioactive cements augmenting pedicle screw resistance to loosening using multicomponent loading [In this study, synthetic osteoporotic bones (test blocks) were used as a substitute for human osteoporotic vertebrae. Because variations in the apparent density, trabeculae orientation and mechanical properties of cancellous bone within and among specimens are large, numerous tests are required to isolate the effects of screw design when cancellous bone is used. The use of synthetic cancellous bone simplified the experimental setup, thus limiting experimental error. Cancellous bone is generally reported to have a density in the range of 0.09 to 1.25 g/cm3 . The tes loading , this st loading ,30,31, a loading ,32, and  loading ,13,26. T3 was used to simulate a cadaveric spinal bone with extreme osteoporosis. Bone density might significantly influence differences in the screw/bone interfacial strength among various screw designs. Further investigation into the influence of bone density on pullout strength should be conducted in animal models. Fourth, the measurement of pullout strength at the screw/bone interface did not take into account the cortical shell of spinal vertebrae, which might have an impact on the interfacial bonding strength. However, we believe that the results regarding pullout strength provide a preliminary platform for the comparison of the postoperative stability of pedicle screws with various slit designs in spinal fusion surgery. Fifth, the volume of injected cement tested was constant (3 ml). The amount of injected cement might be an important factor determining screw holding power, and the effects of the amount of injected cement on the bone/screw interfacial strength should be studied in the future. Finally, only static loading (pullout tests on the synthetic bone) was used; other types of physiological loading were not considered. In real-life situations, the screw/bone interface is subjected to dynamic multidirectional loading. Although our loading mode did not necessarily represent actual physiological loading conditions, all of the specimens were prepared and tested in a uniform and reproducible manner, and we believe that this study provides information that could be useful for orthopedic surgeons that perform spinal fusion surgery. Further investigation into the effects of other loading methods, such as dynamic fatigue testing, might be necessary in the future.Our study has some limitations. First, the pedicle screws were manufactured in-house using only one type of metal, diameter, thread and outer geometry . The possible effects of variations in the above-mentioned properties were not considered. Second, a synthetic composite bone was used as a substitute for human bone. Although synthetic bone provides a platform for the comparison of screw fixation stability, there must be some differences between the mechanical characteristics of the synthetic bone and those of actual bone; therefore, extrapolation of our results to clinical applications should be performed with caution. Third, only open-cell rigid polyurethane foam with a density of 0.09 g/cmWe conclude that pedicle screws combined with cement augmentation may greatly increase screw fixation regardless of screws with or without expansion. An increase in both the number of slits and the extent of screw expansion had little impact on the screw-anchoring strength. Cement augmentation is the most influential factor for improving screw pullout strength for expansive pedicle screws.


We used a spatial noise stimulus for mapping interactions between MTCs belonging to different glomeruli in the dorsal bulb. Analyzing firing responses of MTCs to the stimulus, we did not find robust lateral inhibitory effects that were spatially specific. However, analysis of sub-threshold changes in the membrane potential revealed evidence for inhibitory interactions between MTCs that belong to different glomerular units. These lateral inhibitory effects were short-lived and spatially specific. MTC response maps showed hyperpolarizing effects radially extending over more than five glomerular diameters. The inhibitory maps exhibited non-symmetrical yet distance-dependent characteristics.Mitral and tufted cells (MTCs) of the mammalian olfactory bulb are connected via dendrodendritic synapses with inhibitory interneurons in the external plexiform layer. The range, spatial layout, and temporal properties of inhibitory interactions between MTCs mediated by inhibitory interneurons remain unclear. Therefore, we tested for inhibitory interactions using an optogenetic approach. We optically stimulated MTCs expressing channelrhodopsin-2 in transgenic mice, while recording from individual MTCs in juxtacellular or whole-cell configuration  Neurons in sensory systems are often spatially arranged such that cells responding to similar stimuli cluster in functional or even morphologically defined modules. In the mammalian olfactory bulb (OB) this modular organization is governed by the convergence of axons from olfactory sensory neurons (OSNs) expressing the same receptor type onto the same functional unit, the glomeruli . Principin vivo  in studies .in vivo. Mapping these interactions with high temporal precision is crucial because changes of odor representations occur on the timescale of milliseconds . This confirmed that a neuronal element was causing the resistance increase and that the neuron could be driven by optical stimulation with a short latency. For whole-cell recordings we used the same pipettes and recording system as for the juxtacellular configuration. To achieve the whole-cell patch clamp configuration we followed the standard protocol described by K-gluconate 135, HEPES 10, phosphocreatine-Na 10, KCl 4, MgATP 4, GTP 0.3 and biocytin 0–2%, pH 7.2. The junction potential was not subtracted. Data were acquired using an Axoclamp 900A amplifier (Molecular Devices), low pass filtered at 10 kHz and recorded simultaneously on two analog digital recording systems. Sweep based data were acquired at a rate of 20 kHz using a NI PCI-6251 board  driven by custom written software in the Matlab programming environment. A continuous data stream was simultaneously recorded using an Axon Digidata 1440 board and the PClamp10 software  for the majority of experiments.For juxtacellular recordings of MTCs we used low-resistance borosilicate patch pipettes  pulled on a DMZ puller . Pipettes were filled with standard artificial cerebral spinal fluid. We searched MTCs by 2 μm stepwise advancement of the electrode in voltage clamp mode, while monitoring the electrode resistance. Upon an increase of the electrode resistance the exposed OB surface was flashed for 20 ms .The dorsal OB was visualized using a Movable Objective Microscope , a Zeiss water immersion lens  and an AxioCam camera . Care was taken not to expose the OB to strong light. Similar to studies performed in vitro , in braiin vitro , and in .The entire surface of the DMD was tiled with hexagons with a size of 64 μm (diameter of inner circle) unless noted otherwise. Only spots that were projected onto the exposed OB surface were included in the analysis of the whole-cell data. Binary white noise movies were generated such that each spot was active for 10% of the stimulation time. We allowed the number of active spots to vary between frames. The dense stimulus movies were played in 1 min epochs at a rate of 50 Hz. Movie epochs were spaced by 1 min pauses except for three experiments in juxtacellular configuration where pauses ranged from 25 to 40 s and seven experiments in juxtacellular configuration without baseline recorded  were centered on the stimulation hotspot. Intensity of the inner disk was 6 mW/mm2. Intensity of the surrounding ring varied from 0 to 0.5 mW/mm2. Stimulation was triggered on the respiration cycle and lasted 100 ms.In four whole-cell experiments we generated a neuron specific stimulation pattern: a disk (50 μm radius) and a ring , a 16x water immersion objective  and a MaiTai DeepSee laser . Images (512 × 512 pixels) were acquired at 5 μm steps in the z-direction.Data from extracellular recordings were separated oﬄine into a LFP band and an AP band using a 200 Hz non phase-shifting digital filter . APs were detected and sorted oﬄine using either a threshold and template based algorithm or the OSort software . Only daIn order to determine the effect of the stimulation on AP rate and the membrane potential with millisecond temporal resolution we calculated the first order forward correlation. To this end, we generated a peristimulus time histogram (PSTH) for each spot triggering on the time points when the respective spot was flashed. For the whole-cell recordings we calculated the average membrane potential triggered on all flashes of each spot after removing the APs (see above). To subtract the non-specific general modulation of the AP frequency or the membrane potential, we generated a PSTH or averaged membrane potential triggered on all frame transitions. This average trace was subtracted from the PSTHs or the membrane potential averages.We present data from 45 MTCs. 31 MTCs were recorded in juxtacellular configuration, 14 MTCs in the whole-cell patch clamp configuration. Unless otherwise noted we report the median with interquartile range. For non-parametric statistical testing we used the Wilcoxon rank sum test. For testing the uniform distribution of circular data we used the Rayleigh Test .As a measure of hotspot tuning width we estimated the full width at half height (FWHH). We fitted a Gaussian function to the AP rate changes at different distances (d) from the hotspot .To test if the hotspot centers corresponded to the glomerulus outlines we determined the centers of mass for all glomeruli identified using 2-photon laser scanning microscopy (see above). We also calculated the center of mass of the hotspot including the six neighboring hexagons to the spot with the strongest stimulation efficacy. Within the area of outlined glomeruli we calculated the distance of each camera pixel to the closest glomerulus center. For statistical testing we randomly drew 100,000 times one pixel from each of the three experiments with reconstructed glomeruli and averaged their distance values. Finally we compared the average distance of the three hotspot centers to the next glomerulus center  with the distribution of the averaged random distance values .Figure 5C) for each MTC we averaged the values of equidistant spots (100 μm bins) with respect to the hotspot. Profiles for the time points (4 ms bins) at which excitatory and inhibitory components were largest across all experiments were normalized to the values of maximal excitation and inhibition, respectively. Finally the median of all 45 normalized profiles was calculated. Confidence intervals (±SEM) of this median profile were determined by bootstrapping 100 times. Control profiles were generated using the 500 shuﬄed data sets.To quantify the spatial extent of AP rate change  separately for each time bin (1 or 4 ms), depending on the experiment. In the analysis of the spatial distribution of excitatory and inhibitory spots  we took into account the different amplitudes and signal-to-noise ratios of depolarizing and hyperpolarizing membrane potential changes. For this, we combined two significance criteria: (1) Spots had to pass the threshold calculated using shuﬄed control data, and (2) Spots had to pass a threshold calculated as percentage of the maximal depolarization or the maximal hyperpolarization.In order to calculate significance thresholds for forward correlation results and to control for analysis-induced effects we shuﬄed the stimulation time points of individual spots randomly and repeated the analysis above 500 times. Since the analysis was performed for 522 spots in parallel, this provided a distribution of random values with 261,000 entries. To account for the varying AP probability and the varying distribution of membrane potential values within each 20 ms stimulation interval we calculated the 10Figure 8C) for the detection of inhibitory spots were calculated for MTCs recorded intracellularly. We plotted the frequency of inhibitory spots during the time window of inhibition  versus the frequency of inhibitory spots during the base line period  while varying the detection threshold for inhibitory spots, in terms of significance level.Receiver operating characteristics  was calculated as the average difference between traces with surround stimulation and traces with center stimulation only in a time window specific for each MTC. This time window started with the end of the stimulus. The duration of the time window was 2x the time to half maximal inhibition, thalf . Confidence intervals (mean ± SEM and mean slopes ± SEM) were calculated by bootstrapping 100 times across the 50 repetitions of the same stimulus.In the experiments with center and surround stimulation, the amount of lateral inhibition . The blue light patterns consisted of hexagons with an inner circle diameter of 64 μm. Every single hexagon, from now also termed “spot,” had an on-probability of 10%. During mapping we recorded from individual MTCs in juxtacellular  or whole-cell patch clamp configuration. The optical stimulation increased the average AP rate of MTCs from 0.0 Hz at baseline  to 8.7 Hz during stimulation periods .Mitral and tufted cells typically receive sensory input from a single glomerulus (parent glomerulus). In the transgenic mouse line used in this study, MTCs express ChR2 from the Thy1 promoter and can be stimulated by illumination of the dorsal OB surface . First, Figure 2A) we obtained maps with clear hotspots  in 45 out of 59 neurons . Only data of these 45 neurons  were further analyzed. With increasing distance from the hotspot, the stimulation efficacy dropped sharply with a median FWHH of 105 μm . Hotspots were generally shifted relative to the electrode tip ; this shift corresponds to the tilt of MTC apical dendrites  and the hotspot centers were significantly closer to the nearest glomerulus center than expected by chance .Calculating the stimulation efficacy for the individual hexagons . Accordingly, during stimulation periods the LFP power spectra of single recordings contained sharp peaks at stimulation frequency . The LFP-power at 50 Hz increased from 5.3∗10-5 mV2/Hz  before stimulation to 6.0∗10-4 mV2/Hz  during stimulation. Averaging the LFP responses and calculating PSTHs from the AP responses to all stimulation frames (with a length of 20 ms each) confirmed the stimulus-locked temporal modulation of neuronal activity . The observed oscillatory AP rate modulation was significant in 29 out of 45 MTCs . Presumably, the rhythmicity of the optical dense noise stimulus (activating many MTCs simultaneously) and synaptic interactions within the OB circuitry generated the oscillatory activity and locked the LFP and AP activity tightly to the frame transition frequency. Changing the stimulation frequency around 50 Hz also changed the frequency of the LFP (data not shown). In the following analysis of MTC responses, we aimed to reveal effects of stimulating single spots, not the global effects of the whole stimulus pattern. Therefore, we subtracted the average modulation of the AP rate, i.e., the average response to all stimulus frames, from the AP rate changes elicited by flashing single spots .The optical stimulation with frame transitions at 50 Hz evoked prominent oscillations of the local field potential  resulted in an increase in AP rate of of 21.7 Hz . As described above, this increase in firing was restricted to the hotspots. Stimulation of remote spots did not lead to an increase in AP rate . Following the end of the hotspot stimulation, the AP rate decreased and dropped below the average AP rate . In 32 of 45 cells the AP rate significantly decreased below baseline about 8 ms after the end of hotspot stimulation . Cells that did not show this decrease in AP rate generally had lower firing rates than other MTCs. As a measure of the amount of post-stimulus AP rate reduction, we calculated the mean AP rate change in a window of 8–100 ms after stimulus end . The post-stimulus changes significantly correlated with the overall firing activity of the cells during stimulation . These findings are well in line with the description of recurrent inhibition and inhibition due to sister MTCs belonging to the same glomerulus . Increasing the color-contrast in the ‘movie’ , the delayed self-inhibition described above became more obvious. However, we did not detect significant (α = 10-5) inhibitory spots in the surround. The distance distribution of excitatory and inhibitory spots detected in the maps from all recordings did not differ . Likewise, 80% of significant inhibitory spots were within 200 μm from the hotspot. From this spatial restriction of excitatory and inhibitory spots we argued initially that both the increase in AP rate of a given MTC and the following delayed self-inhibition were a result of stimulating MTCs belonging to the same parent glomerulus.We asked if stimulation of MTCs belonging to neighboring or more distant glomeruli also affected the firing rate of a given MTC. To analyze the effects resulting from stimulation of remote spots we calculated PSTHs in 4 ms time bins for every single spot on the exposed OB surface. Again, the average AP rate modulation was subtracted. To visualize the contribution of all the spots to firing rate changes, we displayed these PSTHs jointly as a movie using false colors  and across all recordings . We calculated the median AP rate change at a given time point across all spots, across all the cells recorded : consistent with the previous analysis, the median AP rate change increased shortly after stimulation onset  and decreased below baseline within 10 ms after the end of stimulation (‘late’). To better examine the distance and time dependence of the inhibitory effects, we plotted the color coded AP rate change as a function of distance from the hotspot , and time relative to the stimulus onset . This visualization  allowed the detection of two inhibitory components: (1) Delayed self-inhibition following stimulation at the hotspot, and (2) a reduction of the AP rate by stimulation even at distances more than 500 μm from the hotspot (5–12 ms after the stimulus end).To rule out the possibility that we did not detect subtle inhibitory effects that might be caused by stimulation of the surrounding area, we analyzed the time course of overall excitatory and inhibitory components in the responses pooling data across equidistant spots (n = 45) at the time point of maximal inhibition : for every spot, the data in this single time bin were normalized with respect to the minimal value in this time bin. We then averaged equidistant spots relative to the hotspot (100 μm bins), which gave us a normalized spatial profile of the inhibitory effects with the hotspot in its center . The observed AP rate reduction was significant up to 550 μm from the hotspot . Excitatory effects, determined at the time point of maximal excitation (‘early’), also extended several μm away from the hotspots. However, the normalized excitatory effects were only significant up to a distance of 250 μm from the hotspot  despite the generally bigger excitatory amplitude. These excitatory effects might be explained by subtle stimulation of MTC apical and lateral dendrites that lie close to the OB surface . However, the inhibitory effects were also significant at larger distances, over 250 μm away from the hotspot. The further spread of inhibitory compared to excitatory effects suggested that we were not simply observing a self-inhibitory effect stemming from the preceding stimulation of the recorded MTC.We tested for significance of this AP rate reduction in the surrounding area by pooling data from all experiments  glomerular units. However, by analyzing AP responses, we could not resolve potential inhibitory effects that were elicited by the stimulation of single spots in the surrounding area of a given MTC. Thus for the analysis of specific interactions between MTCs a more sensitive recording technique or substantially longer recordings are required.Figure 6) and analyzed membrane potential changes resulting from optical stimulation. Recorded membrane potential traces were analyzed after removing APs. The optical stimulus resulted in a median membrane potential depolarization of 5.9 mV . As observed for LFP and AP rate, a 50 Hz oscillation was also present in the subthreshold membrane potential modulation . This oscillation was absent or much weaker during periods without stimulation .In order to measure inhibitory input on MTCs more directly, we performed whole-cell current-clamp recordings in 14 MTCs (n = 14 MTCs). Figure 7A shows, for a representative recording, the superimposition of the AP hotspot with the membrane potential hotspot. Shortly after the end of the stimulus, the membrane potential at the hotspot returned to pre-stimulus values . In comparison to the hotspot, the specific depolarization was very weak at a nearby spot . However, by flashing this neighboring spot the membrane potential was significantly hyperpolarized . It is noteworthy that stimulating at this location did not elicit a depolarization of the membrane potential preceding the hyperpolarization. This finding suggests a distinct form of hyperpolarization which is not mediated by recurrent or delayed self-inhibition. These first observations indicated that whole-cell recordings allowed not only the detection of excitatory but also of inhibitory inputs, and that these inhibitory effects could be attributed to the stimulation of single spots around the parent glomerulus of a given MTC.During the first 10–30 ms after the stimulus start, the stimulus spot that evoked most APs (the hotspot) also induced the strongest depolarization on top of the membrane potential oscillation  and displayed the values for all spots jointly as a movie in false colors . We observed a significant depolarization of the membrane potential directly at and close to the hotspot. The weak, however, obvious excitatory components around the hotspot is in accordance with the observation of subtle MTC activation by stimulation of the close surrounding area , which might be explained by stimulation of MTC dendrites. After a short depolarization, numerous spots that hyperpolarized the membrane potential emerged up to 500 μm away from the hotspot. This hyperpolarization was very short, i.e., occurring in a time window of <20 ms. Comparing the spatial distribution of significant membrane potential fluctuations from all recordings, we found a clear separation of excitatory and inhibitory spots: 90% of excitatory spots were closer than 130 μm  to the hotspot. Spots that significantly hyperpolarized the membrane potential were distributed within 500 μm from the stimulation hotspot .We examined the spatial distribution of excitatory and inhibitory sub-threshold inputs to individual MTCs in more detail. For each stimulated spot, we calculated the resulting average membrane potential in 4 ms time bins. From this, we again subtracted the average membrane potential modulation . In 4 ms time bins, we calculated the number of significant inhibitory spots before the stimulation of the respective spot  and between 0 and 40 ms after stimulation onset (true positive rate), varying the significance criterion. The resulting values were plotted against each other : in 5 out of 14 MTCs the ROC curves separated well from the line of identity, indicating detection of specific hyperpolarizing spots at the end or shortly after the stimulation.Although, all MTCs recorded in whole-cell configuration had excitatory hotspots, we did not detect inhibitory spots in the surrounding area of all neurons. To identify these MTCs we calculated ROC curves for all MTCs . From the ROC classification, we were able to separate the two groups of MTCs with and without surround inhibition (yellow and black traces respectively). For the MTCs with surround inhibition, the membrane potential revealed the excitatory phase followed by a predominantly inhibitory phase. From these time courses we determined the time bin of maximal excitatory effects , and the time bins of maximal inhibitory effects . The membrane potential maps at the late time points are presented for all MTCs with surround inhibition (yellow) and for five representative MTCs without surround inhibition . The maps of MTCs with surround inhibition showed spatially diverse inhibitory receptive fields, whereas only excitatory components were present in the remaining MTCs. To evaluate the distance dependence of the effects with respect to the hotspot, we generated spatial profiles of the different maps at early and late time points . The spatial profiles were nearly identical for all MTCs during the early period of excitation . They differed, however, between the two groups during the phase of maximal inhibition . In all five MTCs with surround inhibition, the average inhibition was strongest within 200–500 μm from the hotspot .To visualize the time course of the overall excitatory and inhibitory components around stimulation, we calculated the median membrane potential change in every time bin across all spots of a given recording . Our data support the notion that the strength of specific inhibitory interactions between MTCs is distance dependent and can extend up to 500 μm.Taken together, the maps that revealed inhibitory effects in the surrounding area were highly heterogeneous between different MTCs. However, with increasing distance from the glomerular unit we recorded from, the hyperpolarization induced by the optical stimulation dropped . To this end we color-coded the average membrane potential change at different distances from the hotspot  and plotted it at different time points relative to the stimulus start . The profiles show depolarizing effects around the hotspots during the stimulus, delayed and long lasting hyperpolarizing effects at the hotspots, as well as phasic hyperpolarizing effects in the surrounding area shortly after the stimulus: the most negative values at the hotspot occurred 60–200 ms after stimulus onset. On the contrary, hyperpolarization corresponding to surround-stimulation occurred mainly within a very short period of 15–40 ms after stimulus onset.The data presented so far revealed complex spatial and temporal features of inhibitory effects on MTCs. For the five MTCs with surround inhibition, we therefore analyzed the spatial profiles evolving over time  in combination with moderate stimulation of the surrounding area . Illuminating only the hotspot caused the MTCs to depolarize and fire . This was followed by hyperpolarization after the end of the stimulus. Increasing the stimulation intensity in the surrounding area, the post-stimulus hyperpolarization became stronger . Notably, the number of evoked APs between the different conditions was stable: increasing the surround intensity hardly changed the overall AP rate of the recorded MTCs . Thus the increase in inhibition could not be explained by recurrent inhibition that would get stronger with more APs elicited in the MTC itself.To confirm the timing differences, we applied a simpler stimulus in four MTCs. We flashed the hotspot of a given MTC for 100 ms at full intensity . The resulting traces had a faster time course  than the long lasting hyperpolarization after stimulation of the hotspot alone. In all four cells the slow hyperpolarization after exclusive hotspot stimulation never peaked earlier than 130 ms after stimulus end. Thus with a temporally and spatially much simpler stimulus than the white noise, we could confirm the finding that the hyperpolarization arising from surround stimulation is phasic and acts on a faster time scale than the slow hyperpolarization observed after hotspot stimulation.To isolate the time course of hyperpolarization induced by surround stimulation, we subtracted the membrane potential time courses of exclusive hotspot stimulation from the data with maximal surround stimulation of 0.5 mW/mmIn this study we examined the functional connectivity between MTCs in the mouse dorsal OB by using an optogenetic approach combined with electrophysiological recordings, in juxtacellular or whole-cell patch clamp configuration. We drove MTCs using light spots the size of single glomeruli, i.e., the functional unit of OB. In this setting, we investigated the effect of stimulating MTCs at varying distances to the MTC we recorded from. In Thy1-ChR2-YFP mice the majority of MTCs express channelrhodopsin2 (ChR2) while no other neurons in the OB express the light-sensitive ion channel . Using tThe optical stimulus we applied revealed distance-dependent lateral inhibition at distances even beyond 500 μm (corresponding to approximately five glomerular diameters) from the parent glomeruli of the MTCs we recorded from. The functional maps that describe the spatial features of lateral inhibition were heterogeneous: they did not exhibit symmetry around the parent glomerulus of a given MTC, but still they contained distance-dependent characteristics. These findings functionally confirm anatomical results from a study of Our results are significant in the context of previous approaches studying inhibitory interactions using odor stimuli . These sin vivo, In the other approach to estimate functional connectivity In this study, we used artificial stimuli . Therefore, the layout of MTC activation in our paradigm may differ from the activation based on naturally occurring odorant stimuli. It has been shown, that most of odorants in naturally occurring concentrations activate approximately 10–30% of all glomeruli on the dorsal OB . Based oFurthermore, experiments in this study were performed on animals anesthetized with Urethane. Several studies have shown that spontaneous MTC activity as well as odorant-evoked MTC activity differs between awake and anesthetized states . Diverse2) evoked a γ-oscillation and illumination with dense noise stimuli  induced a pronounced oscillation locked to the frame transition rate within the gamma band. The MTC/GC network has a tendency to oscillate in vivo . Thus, the dissimilar spatial profiles of excitation and inhibition we observed point toward a different mechanism. (2) Feedback from activated cortical neurons could drive GCs at their somata and indirectly result in MTC inhibition. However, data from another study  Direct light-induced depolarization of MTC lateral dendrites could result in recurrent inhibition via inhibitory interneurons. However, this mechanism is unlikely since lateral dendrites were illuminated to a lesser extent than the glomerular tufts due to their deeper location. In addition, AP propagation in the lateral dendrite of a neighboring MTC is more efficient than pure electrotonic conduction of inhibitory post-synaptic potentials in the recorded MTC. IPSPs elicited far away from the soma of a recorded MTC  would experience a significant amplitude drop during their propagation to the soma. Thus, it is more likely that we detected IPSPs that were elicited close to the soma by APs of neighboring MTCs that traveled in the lateral dendrites of these neighboring MTCs. Also, if one assumes that the observed surround inhibition is a result of precedent activation of lateral dendrites at these locations, then excitatory and inhibitory components in the maps would cover the same areas (compare to er study  show thaer study , this laAn important additional finding of our study is that we did not identify lateral inhibitory influence on all MTCs: a large proportion of recorded cells did not show inhibitory receptive fields at all. This could be explained by different integration of MTCs in the inhibitory network of the EPL . The funonly distance dependent nor only sparse and specific. There are studies reporting no chemotopy on a fine scale in the OB (Our results suggest that inhibitory interactions between MTCs are neither n the OB ; howevern the OB  and thatn the OB . In addi2 field of view, and substantially facilitate the computation of higher order correlations and allow the examination of activity-dependent changes of connectivity (Neuronal architecture favoring distance-dependent inhibition does not prove that this organization is used for physiological function . An impoectivity . CompariHS and AL designed the study. MV and HS built the optical stimulation setup including the stimulation and data acquisition software. AL performed the experiments. AL and HS analyzed the data. AL, AD, and HS wrote the manuscript.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.