Our data indicated the 8-epitope candidates for helper T-cell and CTL in L1 and L2 sequences. For the L1 and L2 constructs, combination of these peptides in a single universal vaccine could involve all world population by the rate of 95.55% and 96.33%, respectively. In vitro studies showed high expression rates of multiepitope L1 (~57.86%) and L2 (~68.42%) DNA constructs in HEK-293T cells. Moreover, in vivo studies indicated that the combination of L1 and L2 DNA constructs without any adjuvant or delivery system induced effective immune responses, and protected mice against C3 tumor cells (the percentage of tumor-free mice: ~66.67%). Thus, the designed L1 and L2 DNA constructs would represent promising applications for HPV vaccine development.Human papillomavirus (HPV) is the most common sexually transmitted infection in the world and the main cause of cervical cancer. Nowadays, the virus-like particles (VLPs) based on L1 proteins have been considered as the best candidate for vaccine development against HPV infections. Two commercial HPV (Gardasil and Cervarix) are available. These HPV VLP vaccines induce genotype-limited protection. The major impediments such as economic barriers especially gaps in financing obstructed the optimal delivery of vaccines in developing countries. Thus, many efforts are underway to develop the next generation of vaccines against other types of high-risk HPV. In this study, we developed DNA constructs (based on L1 and L2 genes) that were potentially immunogenic and highly conserved among the high-risk HPV types. The framework of analysis include (1) B-cell epitope mapping, (2) T-cell epitope mapping ( More than 150 viral types of HPV have been identified whose classification is based on their association with cervical cancer and precursor lesions. HPV types were classified as high-risk  and low-risk  types consistent with the generation of squamous cell carcinomas in the uterine cervix2. The papillomavirus double-stranded circular DNA genome encodes roughly eight open-reading frames (ORFs), which is responsible for viral replication, maintenance, and cell transformation. These gene products have been classified into six nonstructural regulatory proteins (E1-E6) and two structural capsid proteins (L1 and L2)3. Nowadays, the virus-like particles (VLPs) based on L1 proteins have been considered as the best candidate for vaccine development against HPV infections. Two commercial HPV vaccines are both prophylactic. Gardasil which protects against HPV genotypes 6, 11, 16 and 18, and Cervarix which protects against HPV genotypes 16 and 18; Both of them prevent cervical cancer with almost 100% efficacy4. These HPV VLP vaccines confer protection against limited genotypes5. Although a variable level of cross-protection has also been observed against phylogenetically related HPV genotypes6, major impediments such as economic barriers especially gaps in financing obstructed the optimal delivery of vaccines in developing countries. This might be approached via locally produced generic vaccines. Storage and transportation, the so-called cold chain, is another hindrance, which should be mitigated through lyophilization or protectants, and also it should be noted that many recombinant vaccines rely on multiple immunizations; however, promising results could be obtained with a single dose and certainly 2-dose regimens7.Human papillomavirus (HPV) is the most common sexually transmitted infection in the world and the main cause of cervical cancer. Globally, 4.5% of all cancers worldwide  are attributable to HPV8. Indeed, the current HPV L2 vaccines improved a type-specific protection. Recent studies showed that some regions in the N-terminus of L2 can neutralize antibodies generated during various types of HPV infections9. To overcome the intrinsically low immunogenicity of the recombinant L2 protein, its potency could be increased by various formulations such as the multivalent L2 epitopes (peptide vaccine)12, fusion with L1 and other immunogenic proteins15 and multiepitope DNA-based vaccines17.Recently, a possible approach to broader cross-type protective immunity at a lower cost is to consider L2-based vaccination compared to L1 VLP immunizationin silico analyses of biological queries, mathematics and statistics18. Immunoinformatics tools could help researchers to screen multiple HPV genome and predict high immunogenic epitopes, which provide a T or B cell response against HPV infection21. In this study, the combination of in silico/in vivo approaches was used to evaluate L1 and L2 proteins of high-risk HPV types , and to design a pan genotype L1 and L2 constructs for development of DNA-based vaccines.As a major field of science, bioinformatics has brought together the concepts of To select conserved epitopes between HPV subtypes, L1 and L2 protein sequences were aligned using muscle algorithms. Based on the degree of the conservancy, five regions of L1 proteins  and four regions of L2 proteins  were selected for further immune-bioinformatics analysis such as B- and T-cell epitope prediction. Among them, region 449–473 of HPV-16 L1 protein and region 54–76 of HPV-16 L2 protein had the highest score of conservancy between all high-risk HPV types. In addition, based on sequence variability of conserved regions, the L1 and L2 proteins from two main types of HPV (16 and 18) were selected as a reference for calculation of conservancy by IEDB epitope conservancy analysis tool (Tables L18–22 (EATVYLPPVPVSKVV-type16), L1408–421 (PPPGGTLEDTYRFV-type16) and L1404–417 (NFGVPPPPTTSLVD-type 18) epitopes had the best B cell epitope identification scores. For L2 protein, L222–35 (KQSGTCPPDVVPKV-type18), L2100–113 (PSDPSIVSLVEETS-type16), L294–107 (EPVGPTDPSIVTLI-type18) and L257–70 (GLGIGTGSGTGGRT-type16) epitopes showed the highest epitope identification score between their own protein sequences.B-cell epitopes are recognized by B-cell receptors or antibodies in their native structure. Continuous B-cell epitope prediction is very similar to T-cell epitope prediction, which has mainly been based on the amino acid properties such as hydrophobicity, exposed surface area, charge and secondary structure. At first step, the conserved region sequences were analyzed by BepiPred-2 server to predict potential B-cell epitopes Table . In L1 pSince a linear form of T-cell epitopes are bound to MHCs, the interface between T-cells and ligands can be accurately modeled. In this study, we used three different algorithms  for MHC-I and two algorithms for MHC-II (ANN and quantitative matrix).12–21 (YLPPVPVSKV-type 16 and YLPPPSVARV-type 18), L1460–470 (DQFPLGRKFLL-type 16), L1461–471(DQYPLGRKFLV-type 18), L211–20 (KRASATQLYK-type 16 and KRASVTDLYK-type 18), L2280–291 (DPDFLDIVALHR-type 16) and L2273–284 (DSDFMDIIRLHR-type 18) epitopes had the highest binding affinity among their own protein sequences. In general, the results of three different algorithms confirmed each other. Conservancy and allergenicity analyses were done on the selected epitopes. The sequence of all the epitopes were well conserved among high-risk HPV types and none of them were allergens , L195–111 (TQRLVWACVGVEVGRGQ-type 16 and TQRLVWACAGVEIGRGQ-type 18), L1416–430 (DTYRFVTSQAIACQK-type 16), L1417–431 (DTYRFVQSVAITCQK-type 18), L2100–118 (DPSIVTLIEDSSVVTSGAP-type 16), L2281–297 (PDFLDIVALHRPALTSR-type 16) and L2274–290 (SDFMDIIRLHRPALTSR-type 18) had the highest scores of binding affinity. Also, the sequence of all the epitopes were well conserved among high-risk HPV types and none of them were allergen , L1460–470 (DQFPLGRKFLL-type 16), L1461–471 (DQYPLGRKFLV-type 18), L211–20 (KRASATQLYK-type 16 and KRASVTDLYK-type 18), L2293–303 (DPDFLDIVALHR-type 16) and L2273–284 (DSDFMDIIRLHR-type 18) epitopes had the highest epitope identification scores.The generation of antigenic peptides and their transport across the membrane of the endoplasmic reticulum for assembly with MHC class I molecules are essential steps in antigen presentation to cytotoxic T lymphocytes. Thus, investigating the proteasomal cleavage, Tap transport and affinity prediction of binding is essential in MHC-1 presentation pathway. The NetCTL2.1 server was used to predict TAP transport efficiency and proteasomal cleavage scores Table . Between12–21 (84.71%), L1411–421 (90.87%), L211–20 (73.89%) and L2280–291 (67.72%). For helper T-cell epitopes, the highest population coverage was calculated 86.18% for L18–22, 91.18% for L1327–342, 98.90% for L1416–430, 83.47% for L2100–118 and 97.68% for L2281–297. Overall, these results indicated that high-potential helper T-cell epitopes and CTL epitopes can specifically bind to the prevalent HLA molecules in the target populations where the vaccine will be employed.HLA distribution varies among the diverse geographic regions around the world. Thus, while designing an effective vaccine, population coverage must be taken into consideration to cover the maximum possible populations. In this study, population coverage was estimated separately for each putative epitope in 16 specified geographic regions of the world Tables  and 10. https://www.rcsb.org/). Then, all potential epitopes and MHC PDB files were submitted to the server separately. Top model with the highest cluster density  and interaction similarity score  were selected for each peptide and its MHC  were used to calculate docking scores between MHC allele and peptides. At first, structure data of MHC-I and MHC-II were downloaded from RCSB PDB server (C Tables  and 12. 12–21 (type 16 & 18), L1460–470 (type 16), L1461–471 (type 18), L18–22 (type 16 & 18), L1416–430 (type 16) and L1417–431 (type 18) epitopes were selected. For L2 structure, L211–20 (type 16 & 18), L2280–291 (type 16), L2273–284 (type 18), L2281–297 (type 16), L2274–290 (type 18) and L254–69 (type 16) epitopes were presented  binding affinity between peptide and MHC , 2) epitope identification scores for T- and B-cells, (3) proteasomal cleavage and tap transport scores, (4) conservancy degree between HPV subtypes, (5) population coverage, and (6) scores of peptide-protein docking. Based on L1 and L2 top-ranked epitopes, two different constructs were designed  had a concentration range between 1.5 and 3.5 mg/mL.The designed HPV L1 and L2 genes were correctly cloned in pcDNA3.1 and pEGFP-N1 eukaryotic vectors. The presence of L1 and L2 genes were confirmed by digestion as a clear band of ~765 bp and ~700 bp on agarose gel for L1 and L2, respectively (data not shown). The recombinant endotoxin-free plasmids (In vitro DNA delivery of L1 and L2 into the eukaryotic cell line (HEK-293T) was performed by TurboFect as a transfection reagent. The levels of DNA expression were evaluated using fluorescence microscopy and flow cytometry at 48 h post-transfection. The data indicated that pEGFP-L1 and pEGFP-L2 can effectively penetrate into HEK-293T cells in vitro. The cellular uptake of the L1 and L2 genes into the HEK-293T cells was ~57.86% and ~68.42%, respectively. The delivery of pEGFP-N1 as a positive control was detected in approximately ~92.10% of HEK-293T cells  using anti-GFP antibody. The data indicated the clear bands of ~52, ~50 and ~27 kDa for L1-GFP, L2-GFP and GFP, respectively using DAB substrate . Our data showed progressive tumor growth in control groups on approximately 7–21 days . It was interesting that groups vaccinated with L1 DNA, L2 DNA and L1 + L2 DNA constructs similarly reduced the tumor growth (p > 0.05). As shown in Fig. To evaluate the prophylactic effects of the designed L1 and L2 DNA constructs, tumor growth and survival percentage were assessed in all groups for 60 days after challenging with C3 tumor cells. As shown in Fig. p < 0.05, Fig. p > 0.05, Fig. p > 0.05, Fig. The levels of total immunoglobulin G (IgG), IgG2a and IgG2b in mice immunized with the mixture of L1 + L2 DNA constructs (G3) were significantly higher than other groups . Among all the test groups, the group immunized with the L1 + L2 DNA construct (G3) showed the significant IFN-gamma, IL-5 and IL-10 responses compared to other groups -specific IFN-γ, IL-10 and IL-5 secretions in groups immunized with L1 (G1), L2 (G2) and L1 + L2 (G3) DNA constructs were significantly higher than control groups . The level of Granzyme B in group receiving L1 DNA construct was similar to that in group receiving L2 DNA construct (p > 0.05).The secretion of Granzyme B in all test groups was significantly higher than the control groups  to provide the protection against high-risk HPV types30. Panahi and colleagues used a two-step method (consist of molecular docking and sequence-based approach) to determine immunogenic epitopes for induction of immune system against the oncoproteins of HPV 16, 18, 31 and 45 types31. In 2016, Wang and coworkers suggested the regions 51–58, 87–97, 214–220, 290–296, 335–341, 351–366, 408–418, 430–442 and 475–496 as putative B-cell epitopes for HPV16 L1 protein32. Sabah et al. used in silico immunoinformatics tools and reported a conserved 9 mer epitope (ESTVHEIEL) among all HPV58 types33. Bristo et al. designed MHC-I/MHC-II hybrid ras oncopeptide that could elicit T-cell reponse in an animal model34.While around 13 high-risk HPVs were recognized, current vaccines just protect humans from few types. An important limitation of the current vaccines is their narrow coverage. The accessibility of fully sequenced proteome from high-risk HPV strains provides a prospect for + and CD8+), (3) allergenicity assessment, (4) tap transport and proteasomal cleavage, (5) population coverage, (6) global and template-based docking and (7) data collection, analysis, and design of the L1 and L2 DNA constructs. For experimental analysis, the final L1 or L2 DNA constructs were cloned into mammalian expression vector with green fluorescent tag (pEGFP vector) and their expression was evaluated in the eukaryotic cells using flow cytometry, fluorescent microscopy and western blotting. Moreover, the L1/L2-specific antibody and T-cell immune responses induced by L1 and L2 DNA constructs were assessed in mouse tumor model.In this research, we designed a framework for the comprehensive analysis of L1 and L2 conserved regions of high-risk HPV types containing both MHC-I and MHC-II epitopes. The framework begins with conservancy analysis of all 13 high-risk HPV strains following with (1) B-cell epitope mapping, (2) T-cell epitope mapping . In the case of T-cell epitope prediction, in silico analysis has been significantly improved, thus, the results are more reliable. In this study, for MHC-I epitopes, L112–21 (YLPPVPVSKV-type16 and YLPPPSVARV-type18), L1460–470 (DQFPLGRKFLL-type16), L1461–471 (DQYPLGRKFLV-type18), L211–20 (KRASATQLYK-type16 and KRASVTDLYK-type18), L2280–291 (DPDFLDIVALHR-type16) and L2273–284 (DSDFMDIIRLHR-type18) epitopes had the highest binding affinity scores. In addition, above-mentioned epitopes had the highest T-cell epitope prediction scores which were obtained from proteasomal cleavage and tap transport analysis. High degree of conservancy was observed between subtypes for these epitopes (Table 460–470 (DQFPLGRKFLL-type16) and L2280–291 (DPDFLDIVALHR-type18).At first, L1 and L2 sequences obtained from high-risk HPV types were aligned using MUSCLE algorithms. Conservancy analysis showed that five regions of HPV16,18 L1 protein  and four regions of HPV16,18 L2 protein  were more conserved among other subtypes and could be analyzed as an immunoinformatics input. In B-cell epitope prediction, L1460–470 sequences were identical with HPV 16, 31, 33, 35, 39, 52, 58, 59, 68 types and had high similarity rate with HPV 51, 56, 18 and 45. In addition, L2280–291 sequences had higher degree of conservancy with HPV 16, 31, 33, 35, 51, 52, 58, and 56. For MHC-II prediction, L18–22 (EATVYLPPVPVSKVV-type16), L1416–430 (DTYRFVTSQAIACQK-type16), L1417–431 (DTYRFVQSVAITCQK-type18), L2281–297 (PDFLDIVALHRPALTSR-type16), L2274–290 (SDFMDIIRLHRPALTSR-type18) and L254–69 (FFGGLGIGTGSGTGGR-type16) epitopes had the highest binding affinity scores. Among them, L254–69 had the greatest degree of conservancy . One of the remarkable points is that L18–22 and L257–70 epitopes are the same , but in MHC-II was 115.4–136. After the completion of the analysis and according to all of the above-mentioned parameters, two separate constructs were designed. In addition, accumulative population coverage of helper T-cell and CTL epitopes for the designed constructs were estimated. For the L1 and L2 constructs, the combination of 8 epitope candidates for helper T-cell and CTL in a single universal vaccine could involve all world population by the rate of 95.55% and 96.33%, respectively 21 have been reported as potentially immunogenic epitopes. The ability of in vitro expression of the designed L1 and L2 DNA constructs was determined in HEK-293T cells using flow cytometry and western blot analysis. The transfection efficiency of the L1 and L2 DNA constructs was ~57.86% and ~68.42%, respectively indicating their high potency for delivery into the eukaryotic cells. As known, the use of a polytope DNA vaccine containing multiple T-cell and B-cell epitopes is an attractive strategy for developing a therapeutic and prophylactic vaccine against HPV infections. After in vitro assay, immunological experiments were performed in mice to determine the efficiency of the designed L1 and L2 DNA constructs without the use of adjuvant or delivery system for vaccine development. Similarly, some studies used the pcDNA vector harboring the gene of interest for immunization without any adjuvant39. Our data indicated that the groups immunized with L1, L2 and L1 + L2 DNA constructs increased antibody and T-cell responses as compared to control groups. Furthermore, the (L1 + L2)-specific immunity in mice receiving the mixture of L1 + L2 DNA constructs (G3) resulted in higher secretion of total IgG, IgG2a, IgG2b, IFN-γ, IL-5 and IL-10 cytokines as well as Granzyme B than other groups. The higher levels of IgG2a and IgG2b as well as IFN-gamma (as a Th1 cytokine) in this group drive T-cell responses toward Th1-type immunity. The studies showed that immunoglobulin G1 (IgG1) is related to a Th2-type response, while a Th1 response is associated with the induction of IgG2a and IgG2b in mice40. Regarding to our observations in protective studies, this regimen (L1 + L2 DNA construct: G3) could confer further protection against C3 tumor-challenged mice  depending on stimulation of CD4+ T cell-dominated Th1 responses as well as Granzyme B secretion (indicating CTL activity) as compared to the L1 or L2 DNA constructs, alone . These data showed high potency of the combined L1 + L2 DNA constructs versus each DNA construct alone as a prophylactic HPV vaccine. Taken together, immunoinformatics approaches have been emerged as a critical field for accelerating immunological researches. Yet, the immunoinformatics techniques applied to T-cells have more advancement than those dealing with B-cells30. Moreover, recently, due to the limited options for choosing an adjuvant in clinical trials, bioinformatics analyses have been developed to predict the best adjuvant. In this way, in silico studies help researchers saving time and resources, and also can guide the experimental work with higher probabilities of finding the desired solutions and with fewer trial and error repeats of assays. The accessibility of HPV genomic sequences and functional characterization of the genes involved in the virulence has significantly improved our understanding of the molecular foundation for the pathogenesis of HPV and offered a wealth of data that can be used to design new plans for vaccine design. Nowadays, powerful immune system simulators have been developed using bioinformatics tools which predict artificial immunity provided by the vaccine. These approaches could predict the best adjuvant for using in human vaccine studies. There is a multi-scale computational infrastructure approach which can stimulate the dynamics of the immune response induced by several vaccination formulations and predict optimal combination in terms of adjuvant type, dosage and timing. NetLogo is an agent-based modeling of the immune system running different simulations with different parameter settings. It also can interact with different modeling strategies including the investigation of pathogen growth, life cycle modeling environment for simulation complex phenomena43. Therefore, using these methods can increase efficiency and reduce costs in vaccine studies. In this study, for the first time, comprehensively integrated methods (using sequence-based tools in combination with flexible peptide-protein docking) were used to design highly immunogenic and protective vaccine candidates which were able to boost both humoral and cellular immune responses against all high-risk HPV types. In addition, in vivo analysis demonstrated high potency of the designed L1 and L2 constructs as combined in DNA-based vaccines without the use of adjuvant or delivery system. However, we will improve the efficiency of these DNA-based vaccines using a delivery system and also will compare their efficacy with the designed peptide-based vaccines along with adjuvants in near Future.L1https://pave.niaid.nih.gov/) and used as input for future bioinformatics analysis.The reference L1 and L2 protein sequences of 13 high-risk HPV strains  were extracted from PaVE database (http://tools.immuneepitope.org/tools/conservancy/) were used.To determine conserved epitopes between different subtypes, L1 and L2 sequence datasets were first aligned using SnapGene software 4.2.2 . After protein alignments analysis using muscle algorithms, the conserved epitopes of each protein were selected for immune-bioinformatics analysis such as B- and T-cell epitope prediction. Also, to calculate the degree of variability and conservancy of each epitope, IEDB epitope conservancy tools  was employed. In this study, epitope threshold value was set as 0.5 41.A successful vaccine must elicit a strong T-cell and B-cell immune response, but above all, provide protection against the disease being targeted. Therefore, it is essential to show that constructed immunogens are able to induce protective cellular and humoral immunity. Since the antibodies are induced against linear B-cell epitopes, it would be very difficult to synthesize long peptides with the native protein conformation resembling for the induction of protective antibodies. However, optimal peptide-based vaccines should be presented in a desired secondary structure of peptides in order to induce a specific humoral responseMHC-I epitope prediction: The initial step on applying bioinformatics to vaccine researches is to assess potentially immunoprotective epitopes. T-cell epitopes presented by MHC molecules are typically in a linear form containing 12 to 20 amino acids. This fact facilitates accurate modeling for the interaction of ligands and T-cells44. Thus, the most selective step in the presentation of antigenic peptide to T-cell receptor (TCR) is the binding of the MHC molecule45. In this study, we tried to use three different algorithms including Artificial Neural Networks (NetMHCpan 4.0 server43 (http://www.cbs.dtu.dk/services/NetMHCpan/), Quantitative matrix (Propred I43 (http://crdd.osdd.net/raghava/propred1/) and Published motifs (syfpeithi server46 (http://www.syfpeithi.de) to predict high-potential T-cell epitopes. For NetMHCpan, percentile rank was set at 0.5% for strong binders and 2% for weak binders and for Propred I threshold was set at 4%.MHC-II epitope prediction: For MHC class II, NetMHCIIpan 3.2 server47 (http://www.cbs.dtu.dk/services/NetMHCIIpan/) and ProPred48 (http://crdd.osdd.net/raghava/propred/) were employed to predict potential interaction of helper T-cell epitope peptides and MHC-II. In this case, the threshold for strong and weak binders was set at 2% and 10%, respectively.http://www.cbs.dtu.dk/services/NetCTL/) to access the prediction of antigen processing through the MHC class I antigen presentation pathway. In this method, parameters of weight on the C-terminal cleavage, Tap transport efficiency, and epitope identification were set to default 49.Investigating the Tap transport and proteasomal cleavage as well as affinity prediction of binding is essential in MHC-I presentation pathway. In this study, we used NetCTL 1.2 server combined with Tap transport/proteasomal cleavage tools (50 (/population/iedb_input). In this study, individual epitope and its binding to HLA alleles were analyzed, and different geographic areas were also selected.Since the response to T-cell epitopes is restricted by MHCs, the selection of epitopes with multiple HLA-binding increases population coverage in defined geographical regions where the peptide-based vaccine might be employed. The coverage rate of population for each epitope was computationally validated using the IEDB population coverage tooletc.51. The food and agriculture organization (FAO) and world health organization (WHO) protocol includes three terms to evaluate the allergenicity of proteins which are defined as following: the term sensitivity refers to correctly predicted allergens (%), whereas specificity refers to correctly predicted non-allergens (%), and also accuracy refers to the proportion of correctly predicted proteins19. The allergenicity of the epitopes was analyzed by the PA3P (http://lpa.saogabriel.unipampa.edu.br:8080/pa3p/pa3p/pa3p.jsp) using Allergen online (8aa and 80 wordmatch) and AFDS-motif algorithms based on amino acid composition. The specificity of these methods is 95.43% (8aa), 92.88% (80aa) and 88.1% (ADFS)52. To assess cross-reactivity between peptide and human proteome, top-ranked epitope were analyzed by peptide matching program (https://research.bioinformatics.udel.edu/peptidematch/index.jsp)53.Since proteins are very important in inducing allergenic reactions, the prediction of potential allergenicity is an important item in the safety assessment especially in the field of genetically modified foods, therapeutics, bio-pharmaceuticals 54. With the rapid development of peptide therapeutics in rational drug design, the use of new techniques such as protein-peptide docking is inevitable. In this study, two different algorithms  were performed by GalexyPepDock server55  and CABS Dock server56 (http://biocomp.chem.uw.edu.pl/CABSdock). To estimate the formation of MHC-peptide complex, the GalaxyPepDock server effectively models the structural 3D peptide-protein complexes from input peptide and protein sequences using the structure database and energy-based optimization (Template-based Docking). CABS-Dock server performs Global docking procedure which at first explicit fully flexible docking simulation and then clustering-based scoring. Receptor flexibility was limited by default to small backbone fluctuation but could be increased to include selected receptor fragments57. This study presented an example of MHC-peptide docking performed by each individual epitope and available PDB file  tools58, VaxiJen59 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) and Protein-Sol (https://protein-sol.manchester.ac.uk/) server60.Based on L1 and L2 top-ranked epitopes, two different constructs were designed. The physicochemical properties of top-ranked epitopes such as solubility, molecular weight, estimated half-time, instability index and antigenicity were determined by ProtParam . All the recombinant vectors were transformed into Escherichia coli (E. coli) DH5α strain. After extraction of plasmids from single colonies using Mini-Kit (Qiagen), the presence of inserted L1 and L2 fragments was confirmed by digestion with restriction enzymes and sequencing. For in vivo immunological assessment, the pUC57-L1 and pUC57-L2 vectors were digested by BamHI/HindIII and the L1 and L2 genes were subcloned into BamHI/HindIII sites of pcDNA3.1 (-) vector containing cytomegalovirus early promoter and enhancer sequence, individually . Indeed, we used the pcDNA vector harboring CpG motif for in vivo studies. As a final point, the recombinant DNA vectors harboring L1 and L2 genes were purified by an endotoxin-free plasmid Extra EF kit . The concentration and purity of the recombinant L1 and L2 DNA constructs were determined by NanoDrop spectrophotometry61.After bioinformatics analysis, the selected peptides were assembled in two separated constructs Fig. . The pUC2 atmosphere. After some passages, the cells were seeded in a 12-well plate. The optimal cell confluency for effective transfection was considered 70–80%. For the generation of TurboFect-plasmid DNA complex, 10 μl of TurboFect (Thermo Scientific) and 2 μg of each plasmid  were mixed and incubated for 15 min at room temperature. Then, the complex was added to each well in serum-free media. In addition, the non-transfected HEK-293T cells were used as negative control. After six hours, the media was replaced with the completed RPMI medium. Finally, the cells were harvested, washed and resuspended in PBS buffer, to analyze the expression of L1 and L2 DNA constructs using flow cytometry, fluorescent microscopy and western blotting at 48 hr after transfection61.Human embryonic kidney cells (HEK-293T) were cultured in RPMI supplemented with 10% fetal bovine serum (FBS) at 37 °C and 5% CO61.HEK-293T cells were scraped from their plates and washed with PBS1X. After washing steps, the cells were lysed in whole-cell lysis buffer . The extracted protein samples  were separated by SDS-PAGE in 12.5% (w/v) polyacrylamide gel and transferred to nitrocellulose membrane (Millipore). The membrane was equilibrated with TBST  solution containing 2.5% BSA  overnight. The anti-GFP polyclonal antibody  was used to recognize the expressed proteins under standard procedures. The immunoreactive protein bands were visualized by detection of peroxidase activity using a substrate named as 3, 3′-diaminobenzidine i.e., secretion of antibody, cytokine and Granzyme B), two peptide constructs  with 50 µg of each plasmid DNA (pcDNA-L1 or pcDNA-L2: G1 or G2) or their combination (pcDNA-L1+ pcDNA-L2: G3) at the right footpad as shown in Table 62. Moreover, all of the animal experimental procedures were approved by Animal Care and Use Committee of Pasteur Institute of Iran and carried out according to the Animal Experimentation Regulations of Pasteur Institute of Iran  for scientific purposes (code: 976).Five groups of six female C57BL/6 mice  were immunized on days 0, 14, and 28 , two weeks after the last injection. The C3 tumor cells contain whole HPV16 genome, and the presence of L1 and L2 genes was confirmed in the previous studies63. Tumor growth and the percentage of tumor-free mice were monitored twice a week by palpation for 60 days post-challenge. At each time, tumor volume was calculated by this formula: V = (a2b)/2 62.For 64.Two weeks after the last injection, serum samples were collected from each group. The levels of goat anti-mouse immunoglobulin G1 (IgG1), IgG2a, IgG2b and total IgG antibodies  secreted from B-cells were measured in the pooled sera of each group by indirect ELISA. The coated antigens were the mixture of L1 and L2 synthetic peptides (5 μg/mL). Moreover, mice sera were diluted 1:100 in 1% BSA/PBS-Tween6 cells/ml) were cultured in 48-well plates for 72 h in the presence of 5 μg/mL of L1 + L2 peptides, RPMI 5% as negative control and 5 μg/mL of concanavalin A (ConA) as positive control in complete RPMI culture medium. The supernatants were harvested to assess the secretion of IFN-γ, IL-5 and IL-10 from T-cells using the sandwich-based ELISA method (R&D Systems) according to the manufacturer’s instructions. All data were represented as mean ± SD for each sample65.Three mice from each group were sacrificed and the spleens were removed. The red blood cell-depleted pooled splenocytes (2 × 104 cells/well) incubated with the mixture of L1 and L2 peptides (~30 μg/mL) for 24 h. Then, the prepared splenocytes  were counted and added to the target cells at E: T ratio of 100: 1 in complete RPMI culture medium for 6 h incubation. Finally, the supernatants were harvested to measure the concentration of GrB by ELISA (eBioscience kit) according to the manufacturer’s instruction64.To measure Granzyme B (GrB) by ELISA, the P815 target cells (T) were seeded into 96-well plates  test. The value of p < 0.05 was considered statistically significant.Statistical analyses were performed by Prism 7.0  to determine the differences between the control and test groups using one-way ANOVA and student’s


Clinicopathological and molecular features of responders to nivolumab for advanced gastric cancer (AGC) are not well understood.Patients (pts) with AGC who were treated with nivolumab after two or more chemotherapy regimens in a single institution from September 2017 to May 2018 were enrolled in this study. PD-L1 expression in tumor cells (TC) and mismatch repair (MMR) were analyzed by immunohistochemistry. Epstein-Barr virus (EBV) was detected by in situ hybridization. Cancer genome alterations were evaluated by a next-generation sequencing-based panel. High tumor mutation burden (TMB) was defined as more than 10 mutations/megabase.PIK3CA mutation in those with PIK3CA wild-type. ORR was 31% in pts with at least one of the following factors; MMR-D, high TMB, EBV+ and PD-L1+ in TC vs. 0% in those without these factors. Progression-free survival was significantly longer in pts with PS 0 than in those with PS 1 or 2, MMR-D than in those with MMR-P, and PD-L1+ in TC than in those with PD-L1- in TC.A total of 80 pts were analyzed in this study. Tumor response was evaluated in 72 pts with measurable lesions and 14 pts (19%) had an objective response. Overall response rate (ORR) was significantly higher in pts with ECOGPS 0 in those with PS 1 or 2, MMR-deficient (MMR-D) in those with MMR-proficient (MMR-P), PD-L1+ in TC in those with PD-L1- in TC and Some features were associated with favorable response to nivolumab for AGC. Combining these features might be useful to predict efficacy.The online version of this article (10.1186/s40425-019-0514-3) contains supplementary material, which is available to authorized users. Recently, blockade of immune checkpoint molecules with monoclonal antibodies has emerged as a promising strategy in several malignancies –6. ProgrA phase III ATTRACTION-2 trial of nivolumab, a fully human IgG4 monoclonal antibody (mAb) against PD-1, for patients (pts) with advanced gastric cancer (AGC) after two or more previous line chemotherapies showed a survival benefit, leading to the approval of nivolumab for AGC in Japan . ExploraPembrolizumab, another PD-1 mAb, also demonstrated encouraging anti-tumor activity with acceptable safety for PD-L1 positive AGC in phase II and III trials , 10, wheIn order to establish the better selection of pts who may derive greater benefit from PD-1 blockade, we investigated clinicopathological and molecular features of responders to nivolumab for AGC.A prospective study was performed to evaluate the efficacy of nivolumab in pts with AGC from September 2017 to May 2018 at the National Cancer Center Hospital East. The eligibility criteria were the presence of histologically proven adenocarcinoma; Eastern Cooperative Oncology Group performance status (ECOG PS) of 0–2; adequate bone marrow, hepatic, and renal function; history of previous treatment with two or more regimens and at least one treatment with nivolumab. All patients provided written, informed consent prior to participating in this observational study. The study protocol was approved by the Institutional Review Board at the National Cancer Center.Molecular characteristics, such as status of human epidermal growth factor receptor 2 (HER2), PD-L1, MMR, and EBV, and genomic alterations, were analyzed with formalin-fixed paraffin-embedded tissue specimens from archival tissue samples if available. Immunohistochemistry (IHC) using a monoclonal anti-HER2 antibody  and fluorescence in situ hybridization (FISH) using the PathVysion HER-2 probe kit  were performed to assess HER2 status, and HER2 positive was defined as IHC 3 + or IHC 2+ and FISH positive. PD-L1 IHC was performed using an anti-PD-L1 rabbit monoclonal antibody , and PD-L1 positive in tumor cells (TC) or immune cells (IC) was defined as the presence of ≥1% of TC or IC with membrane staining. CPS, which was the number of PD-L1 staining cells  divided by the total number of viable TC multiplied by 100, was also assessed. MMR status was assessed by IHC using monoclonal antibodies for anti-mutL homolog 1 , anti-mutS homolog 2 , anti-postmeiotic segregation increased 2 , and anti-mutS homolog 6  , and tumors lacking either MLH1, MSH2, PMS2, or MSH6 expression were considered MMR-deficient (MMR-D), whereas tumors that maintained expression of MLH1, MSH2, PMS2, and MSH6 were considered MMR proficient (MMR-P). Chromogenic in situ hybridization for EBV-encoded RNA (EBER) using fluorescein-labeled oligonucleotide probes  was performed to assess EBV status . All theWe assessed ORR, disease control rate (DCR), and progression-free survival (PFS). Tumor response was assessed in pts with measurable lesions according to the guidelines of the Response Evaluation Criteria in Solid Tumors version 1.1. ORR was defined as the proportion of pts with the best overall response of complete response (CR) or partial response (PR). DCR was defined as the proportion of pts with the best overall response of CR, PR, or stable disease (SD). Responder was defined as pts who achieved CR or PR, while non-responder was defined as those who showed SD or progression disease (PD). PFS was defined from the date of initiation of nivolumab to the date of disease progression or death from any cause.p < 0.05 was considered to indicate statistical significance.Statistical comparisons of ORR according to baseline characteristics was performed using Chi-square test or Fisher’s exact test. PFS was estimated by the Kaplan–Meier method and compared according to baseline characteristics using Cox proportional hazards models and presented as hazard ratios (HRs) with 95% confidence intervals (CIs). Multivariate analysis for PFS was conducted using clinical factors which were associated with significant impact on PFS as well as potential predictive biomarkers reported in previous studies. Statistical analyses were performed using SPSS® Statistics software V21 . All tests were two-sided; A total of 80 pts were analyzed in this study. Baseline patient characteristics were shown in Additional file Of 80 pts with AGC, tumor response was evaluated in 72 pts with measurable lesions. Best responses were CR, PR, SD, and PD in 0 (0%), 14 (19%), 20 (28%), and 38 (53%), respectively, resulting in ORR of 19% and DCR of 47%.p < 0.01) (Table p = 0.05), although the differences were not statistically significant.ORR was significantly higher in pts with PS of 0 than in those with PS of 1 or 2 , PD-L1+ in TC than in those with PD-L1- in TC , and PIK3CA mutation in those with PIK3CA wild-type   Table . There w%, 3 TablPIK3CA mutations     and 0.4 , respectively  months with median follow-up period of 3.8 months   of pembrolizumab was reported in 6 pts with EBV+ AGC [ORR was significantly higher in AGC pts with d tumors . It is abstrates . MoreoveEBV+ pts . Meanwhisubgroup . Among 4EBV+ AGC , which wEBV+ AGC . Our perEBV+ AGC , thus boHigh TMB was not associated with response to nivolumab in our study, though it was notable that 4 of 8 responder pts with MMR-P had high TMB. It has been reported that the estimated TMB based on panel sequencing showed relatively high discordance compared with TMB calculated from whole exome sequencing in tumors with relatively low number of mutations , which mInterestingly, ORR was 31% in pts with at least one of the following factors; MMR-D, high-TMB, EBV+, and PD-L1+ in TC vs. 0% in those without these factors, suggesting that pre-screening of these biomarkers might be useful to predict clinical benefit of anti-PD-1/PD-L1 blockade in AGC.It is important to note the limitations of the present study. First, this was a single-institution study with a limited sample size. Second, we did not analyze PD-L1 expression, MMR, EBV status, and cancer genome alterations in all the pts enrolled in this study, which warrants further evaluations in a larger cohort.In conclusion, we identified some clinicopathological and molecular characteristics associated with responders to nivolumab for pts with AGC. Combining these features might be useful for the better selection of pts who may derive greater benefit from PD-1 blockade. However, further investigations in larger cohorts are needed to confirm precise biomarkers of PD-1/PD-L1 blockade for AGC.Additional file 1:Table S1. Gene list of the Oncomine™ Comprehensive Assay version 3. (DOCX 15 kb)Additional file 2:Table S2. Patient characteristics. (DOCX 17 kb)Additional file 3:Figure S1. Response to nivolumab by tumor mutation burden. (DOCX 22 kb)Additional file 4:Table S3. Clinicopathological features of responders to nivolumab in patients with MMR-P. (DOCX 16 kb)Additional file 5:Table S4. Molecular features of responders to nivolumab in patients with MMR-P. (DOCX 16 kb)Additional file 6:Table S5. Characteristics of patients with MMR-D and EBV positive gastric cancer. (DOCX 18 kb)Additional file 7:Table S6. Subgroup analysis of progression-free survival. (DOCX 16 kb)


We define 18 turn types compared to the 8 classical turn types in common use. We propose a new 2-letter nomenclature for all 18 beta-turn types using Ramachandran region names for the two central residues . We identify 11 new types of beta turn, 5 of which are sub-types of classical beta-turn types. Up-to-date statistics, probability densities of conformations, and sequence profiles of beta turns in loops were collected and analyzed. A library of turn types, BetaTurnLib18, and cross-platform software, BetaTurnTool18, which identifies turns in an input protein structure, are freely available and redistributable from dunbrack.fccc.edu/betaturn and github.com/sh-maxim/BetaTurn18. Given the ubiquitous nature of beta turns, this comprehensive study updates understanding of beta turns and should also provide useful tools for protein structure determination, refinement, and prediction programs.Protein loops connect regular secondary structures and contain 4-residue beta turns which represent 63% of the residues in loops. The commonly used classification of beta turns  was developed in the 1970s and 1980s from analysis of a small number of proteins of average resolution, and represents only two thirds of beta turns observed in proteins (with a generic class Type IV representing the rest). We present a new clustering of beta-turn conformations from a set of 13,030 turns from 1074 ultra-high resolution protein structures (≤1.2 Å). Our clustering is derived from applying the DBSCAN and  Folded proteins consist of elements of regular secondary structure, such as alpha helices and beta sheets connected by irregular structures called loops. Loops have a varying length and typically contain U-shaped beta turns which abruptly change the direction of the chain. Beta turns are formed by four sequential amino acid residues and adopt specific conformations which have been classified into eight types since the 1970s. Based on a larger set of very detailed protein structures and thorough statistical data analysis, the previous set of beta-turn types was revised to include 7 existing turn types, 5 subtypes of the existing turns, and 6 new types. Their properties and amino-acid sequence propensities are analyzed. We propose a self-explanatory turn nomenclature, based on the conformations of residues 2 and 3 of the beta turn, that is much easier to remember than the old nomenclature. We developed a library of 18 turn types and software to assign them to an input protein structure. The software and new turn types should advance fundamental understanding of protein structure as well as benefit applications for protein structure prediction, determination, and refinement. Ordered protein structures consist of elements of regular secondary structure, such as alpha helices and beta-sheet strands, and irregular elements of structure referred to as loops or coil regions. Loops comprise half of residues in protein structures. Due to the globular nature of folded proteins, the direction of the peptide chain often has to change radically within a few short residues within loops. These changes in direction are often accomplished by turns, which can occur multiple times in loop regions. Turns consist of segments between 2 and 6 amino acids , and are identified by the distance between the Cα atoms of the first and last residues and sometimes by the existence of specific hydrogen bonds within the segment –6. TheseFour-residue beta turns are by far the most common turn type , constitnd and 3rd residues around  and  respectively. Type VI turns contained a cis proline at the 3rd residue. Type VII turns had ψ2 of 180° or φ3 of 180°. The turns not fitting the established definitions, because two or more of the dihedrals of residues 2 and 3 were not within 40° of the defined values, were placed in a miscellaneous category, type IV by Lewis et al.In 1973, Lewis et al. observed that the backbone 1–4 hydrogen bond is not present in a large number of peptide fragments that might still be considered turns . Instead2,ψ2 = -60°,-30° and φ3,ψ3 = -90°,0° for residue 3) because they occupy similar regions of the Ramachandran map at positions 2 and 3, and many type III turns that could be identified at that time were part of 310 helices. The similarity of 310 helices of length 3 to two consecutive Type I turns has been analyzed by Pal et al.  and φ3,ψ3 = . Instead, a small number of points near these values occur in the Other group, but they are too spread out in dihedral angle space and not numerous enough to produce a cluster. By contrast, we do find two types within a cis peptide bond and a β-like conformation at residue 3. One of these is close to the classical VIb turn; the other is new.Our clustering procedure results in a set of 18 turn types , 3 and 4nd and 3rd residues of each beta-turn type  and a set of intricate prime symbols and alphanumeric sub-indices , we propose a new nomenclature using the Latin alphabet with type names assigned according to the one-letter region codes of Ramachandran map for the 2urn type . The figOur scheme is based on a detailed analysis of the distribution of residues from very high-resolution structures by Hollingsworth and Karplus . Most ofOther group. As expected, glycine and proline play important roles in many turn types. Proline is required for the two cis2 turn types, cisDA and cisDP, and the three cis3 turn types, BcisP, PcisP, and PcisD. It is also important in position 4 of the AZ turn where it is responsible for the zeta conformation at position 3, which occurs for residues immediately before proline % confidence intervals respectively. A border-case flag (“+”) is returned when the integer confidence level is 6 or below, and a “.” (dot) is returned when it is 7 or higher. In border cases we recommend considering both turn types for modeling. Our tool accounts for chain breaks in coordinates when it prints a single-character alignment of the complete amino acid sequence, its DSSP secondary structure, turn types, integer confidence levels, and border-case flags. Downloads and complete documentation of our tool or library are available at From a set of 13,030 beta turns identified in 1,074 proteins of resolution 1.2 Å or better, we have identified 18 beta-turn types, compared to the 8 beta-turn types in common use . Our nomenclature is based on positions in the Ramachandran map of residues 2 and 3. Six of the turn types are essentially new: dD, dN, Dd, pG, cisDA, and cisDP. Among them there are two new types of turns with left-handed residue 2 and right-handed residue 3 (“dD” and “dN”), and the new complementary turn type with right-handed residue 2 and left-handed residue 3 (“Dd”). There is a new turn type “pG” and two new turn types with a cis peptide bond at position 2: “cisDA” and “cisDP.”2. Turns of Type VIa2 with a cis peptide bond at residue 3, φ2, ψ2 =  and φ3, ψ3 = , were too spread out and contained fewer than 25 points in RefinedSet (0.2%), too few to form a cluster; they were left in the Other group.Four of the existing turn types remain essentially unchanged but have been renamed: Type I is now “AD” and its complement Type I’ is now “ad”; Type II’ is now “pD”; and Type VIa1 with a cis peptide bond at residue 3 is now “PcisD”. Eight turn types are subtypes of the classical turn types: Type VIII turns have been replaced with 4 subtypes  with distinct peaks in the density in the Ramachandran map of residue 3 and distinct residue preferences at one or more of the 4 turn residue positions. Type II turns now consist of two subtypes, “Pd” and “Pa”, defined by the presence or absence of Gly at residue 3 respectively. Type VIb turns have been split into two: BcisP  and PcisP with a change in φOther. We conclude that the 18 types cover 98% of turn conformations and that we have not overlooked any major turn conformations. We have also taken a census of beta turns in loops in our data set and determined that there is one turn per 4.8 residues, regardless of loop length. Many turns overlap other turns, such that 63% of residues in loops are in beta turns.Our decisions in clustering were based on two criteria: keeping the total number of turn types at a minimal practical level for existing and future applications; and justifying new cluster formation either by a significant distance from other formed clusters or uniqueness of a four-residue turn profile. Thus, we abandoned formation of a new cluster if its amino acid turn profile was not unique enough or its conformation spread could be justified by continuous, low-energy torsion angle variation. After running DBSCAN and subdividing some clusters, 2.4% of the turns were left as BetaTurnLib18 of the new 18 turn types. For each type, we list proposed and existing names, percentage, and geometrical conformation expressed with torsion angles of a real dataset sample. For each cluster, two verified sample conformations with reliable electron density are given: closest to a mode and closest to a medoid. This library may be used in third-party software for modeling and prediction of beta turns. In addition, we prepared an easy-to-use Python tool, BetaTurnTool18  that reads a PDB-formatted or mmCIF-formatted coordinate file of a protein structure as input. The tool assigns secondary structure with included redistributable DSSP program, uploads our turn library, and finally locates turns and identifies their types. The output includes the first and second most probable types with assigned confidence levels for each detected beta turn.We provide a tab-delimited library file, Other” turn type IV. We have found that some of these turn types represent more than one mode in the density and should be subdivided into further turn types. One of the more interesting results of the new clustering are the turn types that represent combinations of right-handed helical and left-handed helical conformations of residues 2 and 3. We find both combinations: left-right (types dN and dD) and right-left (Dd). These are not the same as the type V and V’ turns defined by Lewis et al.  and  restricted to well-known regions of the Ramachandran map for each cluster. This was possible for all the clusters returned by DBSCAN with parameters eps = 14° and minPts = 25, with the exception of Type I and Type VIII turns. These turns could not readily be separated by DBSCAN since the density for residue 3 is more or less continuous from the alpha to the beta regions of the Ramachandran map. Kernel density estimates of this cluster showed 5 peaks in the density. Three other clusters also showed more than one peak in the density of residue 3. The k-medoids algorithm was applied sequentially to these four clusters in order to find clusters associated with each peak in the  density.Clustering of 2-φ2-ψ2-ω3-φ3-ψ3-ω4 dihedral space. The medoid and mode serve as representative conformations for each turn type. We scanned a distance matrix of each cluster to find a sample with the most number of neighbors within the smallest radius covering at least 20% of cluster members and at least 20 data points. We approximate a mode with such a turn sample closest to it. The medoid conformation is more sensitive for turn type detection while the mode conformation is more useful in modeling of an unknown protein structure.For each of resulting 18 clusters, we identified and reported both medoid and mode of a cluster density in the seven-dimensional ωWe calculated 1D and 2D kernel density estimates (KDE) for a single circular variable (a single torsion angle) and two circular variables (two torsion angles) respectively with a von Mises kernel which is suitable for circular statistics. For example, for the Ramachandran map distribution, we use a two-dimensional kernel density estimate that we developed for the backbone-dependent rotamer library :ρ^We utilized a 10-fold maximum-likelihood cross-validation to establish the concentration parameter κ for each kernel density estimate.i-1 to i+4 residues. We manually selected turns that satisfy these criteria.For 36 medoid / mode representatives, we report beta turns satisfying these conditions: 1) close to medoid / mode by our distance metric, 2) atomic EDIA backbone and side-chain atom electron density better than the median electron density in each cluster, 3) passing a visual inspection of protein geometry and 2Fo-Fc and Fo-Fc electron density maps for any inconsistencies in the placement of backbone and side-chain atoms of the www.ebi.ac.uk and ran EDIA software to analyze agreement between turn conformations and their electron density [Our Python tool for beta-turn type assignment depends on widespread python2 language, numpy  and biop density .S1 Supporting information(PDF)Click here for additional data file.S2 Supporting information(PDF)Click here for additional data file.S1 Text(TXT)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S1 Data set(TXT)Click here for additional data file.


Research in the past few decades generally supported a nonselective view of bilingual lexical access, where a bilingual’s two languages are both active during monolingual processing. However, recent work by Costa et al. (2017) brought this into question by reinterpreting evidence for nonselectivity in a selective manner. We manipulated the factor of first language (L1) attrition in an event-related potential (ERP) experiment to disentangle Costa and colleagues’ selective processing proposal versus the traditional nonselective processing view of bilingual lexical access. Spanish–English bilinguals demonstrated an N400 effect of L1 attrition during implicit L1 processing in a second language (L2) semantic judgment task, indicating the contribution of variable L1 lexical access during L2 processing. These results are incompatible with Costa and colleagues’ selective model, adding to the literature supporting a nonselective view of bilingual lexical access. Determining the exact nature of bilingual lexical access remains an important question given the ramifications for its use in bilingualism research. For instance, nonselectivity was utilized as a mechanism for both cognitive and linguistic differences between bilinguals and monolinguals [For decades, researchers investigated the nature of bilingual lexical access during single-language processing: are bilinguals capable of processing only the target language or are both languages activated? The former view, known as language-selective access, proposes that bilinguals activate lexical units in the target language without non-target language translation activation. With language nonselective access, on the other hand, a bilingual’s two languages are activated in parallel even when processing a single language . Bilinguals, therefore, appear to not only be sensitive to the lexical properties of non-target language words but are also unable to restrict processing to a target language alone. While the previously mentioned bilingual experiments show results consistent with the language-selective view, much of the work on bilingual lexical access suggests evidence of language nonselectivity where both target and non-target languages are activated in parallel ,9,10. A The most convincing evidence for language nonselectivity involves paradigms that do not explicitly display both target and non-target languages. In such cases, bilinguals are exposed to one language only, and any effects of the non-target language reliably show influences of parallel activation despite the monolingual setting. One way to manipulate implicit non-target language effects is using variable cross-language orthographic neighbors, words in the non-target language that differ from the target word by one letter. In lexical decision tasks, bilinguals are slower to respond to target words with higher cross-language neighbors and faster to respond to targets with higher within-language neighbors . Such efhuo (“train” translates to huo che and “ham” translates to huo tui). If bilingual lexical access is nonselective in nature, this implicit manipulation of the L1/non-target language (Chinese) should affect L2 processing. ERP results in the Chinese–English bilinguals indicated such an effect; the target word of the L1 translation related pairs elicited attenuated N400s (priming effect) compared to other unrelated L2 word pairs without related L1 translations. Critically, this difference was shown in monolingual Chinese and not monolingual English participants, suggesting that (1) the N400 priming effect can be attributable to lexical repetition priming, and (2) L1 translations are activated even when bilinguals are engaged in L2 only. Of particular importance to the present paper is Thierry and Wu’s experiment of implicit non-target language effects in Chinese–English bilinguals . In theiAlthough Thierry and Wu’s results were interpreted as the parallel activation of a bilingual’s two languages, a recent proposal aims to provide an alternative explanation . Costa ahuo tui in L1). Upon learning the English word “train” for the first time through translation association, activation would spread to its L1 translation huo che. Through the same mechanism of spreading activation, huo che would subsequently activate huo tui (an L1 orthographic neighbor), which would then activate its L2 translation “ham” , bilinguals can selectively activate L2 lexical items during L2 processing without activation of their corresponding L1 representations . AlthougIf L1 and L2 are in fact separated and independent after learning, then bilinguals who are fluent in L2 should no longer experience any L1 influence during L2 processing regardless of their L1 proficiency. Differences in L1 lexical access could, thus, be used to evaluate the two competing explanations for unrelated L2 priming . An attractive method of manipulating L1 lexical access is through testing bilinguals with variable levels of L1 attrition. L1 attrition refers to the internal restructuring of the L1 lexicon that could lead to L1 vocabulary loss and lexical activation reduction, occurring when bilinguals shift from L1 to L2 use in daily life . This laStudies of L1 attrition effects on bilingual L1 access struggled to define a clear-cut and quantifiable measure of L1 attrition due to the multitude of factors than can lead to diminished L1 language proficiency . Of thesResearch indicates drastic L1 degradation—to the extent of undetectable behavioral and neural measures of L1 processing —if attriL1 use in daily life also contributes to L1 attrition ,29. Becahuo tui and huo che association) alters L2 lexical organization  during learning and not after, allowing bilinguals to selectively access their L2 upon reaching high L2 proficiency. If the model is correct, then L1 attrition should not affect L2 processing in proficient bilinguals, since the lexical influences from L1 disappear after L2 vocabulary learning is completed. Specifically, highly proficient L2 bilinguals should have similar L2 lexical organization established during learning, after which the L1 has little input on L2 organization due to selective L2 processing. Therefore, the model anticipates no differences in L2 processing between bilingual L1 attriters and those with proficient L1, so long as their L2 proficiency level and learning experiences are comparable. Altogether, research suggests that L1 attrition leads to diminished L1 lexical access. In Costa et al.’s proposal , L1 lexi(1)Variable levels of L1 attrition in L2 fluent bilinguals will test the validity of Costa et al.’s model : if L1/L(2)Any seen effects of L1 attrition on L2 processing would provide support for bilingual lexical nonselectivity; if L1 lexical access varies as a function of L1 attrition and such variation influences L2 language task performance, then the L1 can be assumed to be activated during L2 processing.The goals of the present study are twofold: hongo and honda, or unrelated—e.g., luna and sol) [huevo in L1) prime “bone” (hueso in L1) in these Spanish–English bilinguals and, if so, does this priming effect differ as a function of L1 attrition? To address these goals, we revisited Thierry and Wu’s 2007 study by recording behavioral and ERP measures of L2 priming in Spanish–English bilinguals with varying levels of L1 attrition. The bilinguals had comparable levels of highly proficient/fluent L2 (English), but critically differed in their levels of L1 (Spanish) attrition (the factors used to derive the L1 attrition measure are detailed in and sol) . The criPredictions were made for the N400 (ERP component) priming effect based on whether bilingual lexical access is selective as per Costa et al.’s model  or nonse1. Costa et al.’s language-selective access view : If Costa and colleagues’ model is correct, then the priming seen between two lexically and semantically unrelated L2 words with related L1 translations are attributable to L2 lexical organization  and not due to L1 lexical activation. In these highly proficient L2 bilinguals, no effect of L1 attrition on the L2 N400 priming effect should be seen, since L1 to L2 connections are severed after L2 acquisition.2. Language nonselective access view ,12,13,14L1 attrition should influence L2 processing as both languages are activated in parallel even during single-language processing. Specifically, the higher a bilingual’s level of L1 attrition is, the less L1 lexical access they have. Bilinguals with high L1 attrition should experience less L1 lexical activation during L2 processing than those with low L1 attrition, reflected in smaller N400 priming effects for the critical condition L2 word pairs  as L1 attrition levels increase. Twenty-five Spanish–English bilinguals  recruited from San Diego State University participated in the study. All participants were native Spanish (L1) speakers and high-proficiency/fluent bilinguals of English (L2). Every bilingual learned English after Spanish and, therefore, utilized translation association L2 learning . All but two of the participants studied Spanish in a classroom setting, with approximately half taking classes in the United States of America (USA) and the other half studying in a Spanish-speaking country . All participants used Spanish and English daily in speaking, writing, and reading. Their percentage of daily use of Spanish and English varied, with L1 averaging 45.6% (SD = 23.9%) and L2 averaging 65.8% (25.2%); note that these percentages did not add up to 100% due to the heavily bilingual environment of the study (a predominantly L2 environment close to the border of an L1-speaking country), where participants are frequently exposed to both their L1 and L2 in daily life. Participants were right-handed as per the Edinburgh Handedness Inventory  with nor(1)Bilingual language questionnaire—participants self-reported L1 and L2 history and use. Questions were categorized into four subsections to account for factors contributing to L1 attrition: L1 proficiency, L1 language use, L2 age of acquisition/length of residency, and L1/L2 language dominance.(2)Shipley vocabulary test—participants completed a multiple-choice test of the best synonym for a low-frequency English word as a measure of L2 proficiency. The test contained 40 English words with four possible choices, from which participants selected the best fit meaning of the target word  .(3)Multilingual naming test (MINT)—participants completed a Spanish and English adaption of the Boston naming test  as a meaParticipants completed the following battery of linguistic measures as an assessment of L1/L2 proficiency, history, and use: (1)zorro and “skunk”/zorrillo);Semantically related/L1 translation lexically related sed and “hunger”/hambre);Semantically related/L1 translation lexically unrelated hueso and “egg”/huevo);Semantically unrelated/L1 translation lexically related nube and “floor”/piso).Semantically unrelated/L1 translation lexically unrelated  of 3–11-letter English (L2) nouns with 3–10-letter non-cognate Spanish (L1) translations organized in a 2 × 2 design. Half of the pairs were lexically  related in their Spanish translation and the remaining half were not. Each was further halved as semantically related or semantically unrelated. The final 200 pairs consisted of four total conditions crossing semantic relatedness and L1 translation lexical relatedness , with 50Twelve additional pairs (three in each condition) were made for the practice session prior to the actual task. n = 3) determined the categorization of semantic relatedness for the English word pairs (only pairs eliciting unanimous “yes”/“no” responses were considered).The 400 English words used for the 200 word pairs were selected and translated from a corpus of over 1000 Spanish nouns derived from Mexican government-mandated Spanish textbooks (grades 4–6) . TextbooThe prime and target for each word pair were matched for their English length, Spanish translation length, grade level, frequency, English orthographic and phonological neighborhood , and conIn order to verify the ability of the participants to correctly translate the L2 word pairs into their respective L1 translations , participants completed a translation task of all seen stimuli after completion of the ERP task. This task showed all 400 English words from the task in randomized order, and participants were asked to translate each word into Spanish. All participants completed the L1 and L2 proficiency measures (described in Eligibility was determined based on (1) ERP study requirements , (2) bilingual sample requirements (native Spanish speakers who learned English after Spanish with high proficiency/fluency in English), and (3) score cutoffs for the Shipley vocabulary test and MINT to ensure high L2 proficiency and variable L1 proficiency (L1 attrition). The Shipley vocabulary test cutoff for adequate L2 proficiency was a score of 21 out of 40, derived from previous research on English monolinguals, English-dominant bilinguals, and balanced  bilinguals ,42,43. T(1)L1 proficiency—calculated from Spanish MINT accuracy and self-ratings of L1 reading, L1 speaking, and L1 comprehension. Higher ratings/accuracy correspond to a lower L1 attrition score.(2)L1 language use—calculated from time spent in an L1 environment (percentage of life spent in an L1-speaking country) and percentage of daily L1 use. Longer L1 environment time spent and higher percentage of daily L1 use correspond to a lower L1 attrition score.(3)L2 AoA and environment—calculated from L2 age of acquisition , time spent in an L2 environment (the higher the length of residency (LOR) is, the higher the L1 attrition score is), and percentage of daily L2 use .(4)Language dominance—calculated by self-rating of language dominance on the bilingual language questionnaire and the difference between the English and Spanish MINT accuracies, with greater English (L2) dominance corresponding to a higher L1 attrition.Each participant obtained an L1 attrition score based on results from the screening questionnaire and language tasks. The scores were derived from four categories of factors contributing to L1 attrition ,30,31,32Each category accounted for 25 points  to provide a quantifiable L1 attrition score that weighted each factor evenly. The individual L1 attrition scores allowed for correlation analyses of participant results. Additionally, the scores were used to divide participants into low versus high attrition groups for behavioral and ERP group analyses. ERPs were measured while participants completed a semantic judgment task for each sequentially presented L2 word pair . Participants pressed the left or right shoulder gamepad button if the target word was related to the previous word and the other button if not . After each decision, the next word pair was shown. Note that the L1 translation lexical relatedness manipulation was implicit and, therefore, participants never saw any Spanish stimuli during the ERP experiment. The 200 total word pairs were shown in two blocks of 100 in pseudorandomized order (no more than three pairs of the same condition in a row), with each block containing 25 pairs in each condition. A practice block of 12 word pairs (three in each condition) was presented prior to the experiment in order for participants to familiarize themselves with the task and button presses. For the purposes of ERP recording, participants were instructed to remain still and minimize blinking when each word appeared on the screen. All words were presented in white Arial font in the center of a black background on a stimulus monitor approximately 140 cm in front of the participant. Each trial (word pair) see  initiateERPs were recorded from a 29-channel electrode cap (electro-cap) while participants sat in a chair in a light and sound attenuated room. Electrode positioning on the cap was based on the 10–20 electrode positioning system . AdditioApproximately 1–2 weeks after the ERP session, participants came back for a behavioral follow-up translation task. Using Excel, they viewed each of the 400 English nouns previously seen in the ERP task and typed the Spanish translation for these words. The instructions explicitly stated that these words were all nouns and that participants should type the first Spanish translation that came to mind for each word. Translations were corrected against the original Spanish stimuli used to create the 200 English word pairs. Only correctly translated prime–target pairs were used for subsequent analyses to maintain validity in the translation lexical relatedness condition  into its designated translation(s), the prime and target would no longer be lexically related in their L1 translation, and we should not expect to see any priming effects attributable to that).ERPs time-locked to the onset of each target word were averaged together based on stimuli condition with a −100 ms to 0 ms baseline and a low-pass filter (15 Hz). No trials with ocular artefacts (blinks or saccades) were included in the average . The N400 component mean amplitude was calculated from averaging the EEG from 300–500 ms, a time range that corresponds with the negative deflection in the brainwave reflective of this component . AnalyseIn addition, the N400 effect for the critical comparison  was calculated by subtracting the average voltage between 300 and 500 ms for targets whose translations were lexically related from those whose translations were not lexically related in the semantic unrelated conditions see . The resBehavioral statistics on accuracy and reaction time (RT) of the task were conducted between the independent variable of L1 attrition score and the four conditions. We also assessed L1 attrition against two additional dependent variables: the semantic priming effect due to the nature of the experiment (semantic judgment) and, importantly, the L1 translation lexical priming effect. The latter effect was captured in Thierry and Wu (2007) and is thought to reflect nonselective bilingual lexical access. The semantic priming effect was constructed by subtracting the average RT and accuracy for the semantically related conditions from that of the semantically unrelated conditions. The L1 translation lexical priming effect for semantically unrelated pairs . t-tests.Analyses included pairwise correlations  and mixed ANOVAs. ANOVAs utilized a between-subject factor of attrition (low versus high) and within-subject factor of semantic relatedness (related versus unrelated) and L1 translation lexical relatedness (related versus unrelated). Additionally, a two-way repeated measures ANOVA  was utilized to look at the overall behavioral responses elicited by the four conditions. All significant interactions were further analyzed using RT data were trimmed to reduce effects of unrelated external influences  using a combination of two methods: an absolute cutoff and relative cutoff per participant . An uppen = 12; high L1 attrition n = 8) allowed us to capture the greatest effects of L1 attrition on L2 processing. Unpaired t-tests demonstrated that, while L1 attrition and factors contributing to the L1 attrition score  were significantly different (t(18) < 0.001) between the low and high L1 attrition groups, potential confound variables such as sex and age (t(18) = 0.484) were not. Each participant received a composite L1 attrition score based on the factors listed in r = −0.68, p < 0.001). As participants’ L1 attrition level increased, the worse their L2 to L1 translation accuracy became. This was corroborated by the significant translation accuracy difference between low (78.5%) and high (68.4%) L1 attrition groups, t(18) = 3.54, p = 0.002.Participants completed the semantic judgment task with an average accuracy of 85.7%, SD = 3.5%, with correct trials taking an average of 1397.5 ms (SD = 247 ms) for RT. Average accuracy on the post-ERP translation task was 73.9% (SD = 7.4%) with a significant correlation between L1 attrition score and accuracy . Similarly, a 2 × 2 mixed design ANOVA with within-subject factors of semantic relatedness  and L1 translation lexical relatedness  and the between-subject factor of L1 attrition group (low versus high) did not show effects of L1 attrition group on either RT or accuracy metrics. The following behavioral results reveal the effects seen in the overall group of Spanish–English bilinguals.F = 13.31, p = 0.001. This effect was modulated by L1 translation lexical relatedness  = 9.07, p = 0.006); participants were faster at correctly judging semantically related targets as being related if their translations were lexically related than unrelated, and slower at correctly judging semantically unrelated targets as being unrelated if their translations were lexically related than unrelated (see t(24) = 2.41, p = 0.02.Reaction time analysis revealed a main effect of semantic relatedness; participants responded to semantically related targets (mean = 1349.8 ms) faster than to semantically unrelated targets (mean = 1420.6 ms), ated see . This inF = 8.39, p = 0.008) and translation lexical relatedness  = 4.298, p = 0.05) (see t-test for the “no” (the two words are unrelated) and “yes” (the two words are related) responses, indicating a significant “no” response bias, t(24) = 3.17, p < 0.001. The translation lexical relatedness main effect revealed that participants were more accurate on the semantic judgment task when L1 translations were unrelated (86.8%) than related (84.6%). There was no significant interaction between the semantic relatedness and L1 translation lexical relatedness factors.Accuracy results showed the main effects of both semantic relatedness (.05) see . ParticiF = 43.96, p < 0.001; semantically unrelated targets elicited larger-amplitude N400s than semantically related targets. This is shown in Participants displayed a strong N400 semantic priming effect, F = 21.59, p < 0.001, and high-attrition bilinguals, F = 9.47, p = 0.0179, demonstrated the N400 semantic priming effect, with no group by priming effect interaction  or attrition group comparisons.The main effect of interest was the whether L1 translation lexical relatedness influenced N400 processing of semantically unrelated L2 word pairs. To explore this effect, ERPs to semantically unrelated L2 targets were compared based on whether their L1 translations were lexically related or not. Across all participants, there were no significant differences between the N400 amplitudes of these two conditions, although semantically unrelated targets with lexically unrelated translations elicited slightly smaller N400s than semantically unrelated targets with lexically related translations (opposite direction priming). This can be seen in F = 0.4, p = 0.538), but was significant in the high L1 attrition group, F = 11.37, p = 0.012. The opposing-direction priming effects across the two groups contributed to the overall lack of main effect in the entire bilingual sample. Despite this lack of main effect in the overall group, we were interested in seeing whether the low versus high L1 attrition groups showed differing priming effects of L1 translation lexical relatedness. In fact, the group comparisons indicated opposite priming directions; low L1 attrition bilinguals showed a priming effect in the typical direction  see , but higF = 61.78, p < 0.001), as shown in the voltage map  = 0.414, p = 0.04) at electrode Pz  prior to L1 attrition. A composite L1 attrition score, derived from factors shown to contribute to language attrition, correlated significantly with participants’ L2–L1 translation ability as seen in the post-ERP translation task; low L1 attrition bilinguals outperformed high L1 attrition bilinguals on English-to-Spanish translation, indicating that our calculated L1 attrition score could be reliably used as a measure of L1 lexical access. The behavioral results did not reveal significant effects of L1 attrition for either individual correlation or by group. However, significant interactions between low and high L1 attrition groups from the ERP data suggest contributions of L1 attrition on L2 lexical access that align with the nonselective bilingual lexical access view. honda and “mushroom”/hongo) than unrelated (“cloud”/nube and “floor”/piso). Supporters of the bilingual nonselective lexical access view would argue that this reflects the activation of the non-target language during target language processing [The Spanish–English bilinguals completed the English semantic judgment task with high accuracy (85.7%), indicating a proficient level of L2 required for the study. L1 translation lexical relatedness affected RT and accuracy results when the L2 prime–target pairs were semantically unrelated (“sling” and “mushroom” or “cloud” and “floor”); participants performed worse (higher RT and lower accuracy) for these trials when the L1 translations were lexically related . Bilinguals selectively activate their L2 during the task, but have a harder time correctly rejecting certain semantically and lexically unrelated L2 pairs because of their learned associations (and not because of non-target language activation). However, since L1 attrition level did not significantly affect the behavioral results, Costa et al.’s selective model  can alsoBrain waves collected during the semantic judgment task revealed an N400 semantic priming effect in all bilinguals. Targets that were semantically related to their primes . The N400 semantic priming effect is an established ERP effect that reflects the ease of processing a word’s meaning when the previously seen word shares semantic overlap . CombineWe did not find an N400 effect of L1 translation lexical priming in the overall grand mean. However, this was explained by the opposite priming effect directions in the low versus high L1 attrition groups. For semantically unrelated target conditions, low L1 attrition bilinguals elicited a standard N400 translation lexical priming effect; targets whose L1 translations were lexically related with those of the prime elicited smaller N400 amplitudes than those whose translations were lexically unrelated . The revThe significant interaction between low and high L1 attrition groups and their L1 translation lexical priming N400 effect, in conjunction with the significant positive correlation between individual L1 attrition scores and the effect size, provides evidence of L1’s influence on L2 processing. Greater L1 lexical access, attributable to lower L1 attrition levels, facilitates processing of L2 words if their L1 translations are lexically related to that of a previously seen word. This is in spite of the lack of overt relatedness between the prime and target. As the amount of L1 attrition increases, L1 translation lexical relatedness appears to inhibit target processing, resulting in larger N400s to translation related L2 words and reversal of a typical lexical priming effect. Highly L1 attrited bilinguals elicited larger N400s to L2 target words whose L1 translations were lexically related to that of the primes. This may reflect the more effortful processing of L1 lexical items due to diminished access to the L1 lexicon as a result of L1 attrition, as smaller N400 amplitudes reflect ease of lexicosemantic processing . HoweverAs considered within the framework of inhibitory models of bilingual processing, related lexical candidates  compete for activation during reading . Such coAlthough low L1 attrition bilinguals displayed a L1 translation lexical priming N400 effect in the standard direction, the effect was not significant on its own and, therefore, did not replicate the results of Thierry and Wu’s study . A few mTo the first point, Chinese and English differ in orthography and phonology  to a greater extent than Spanish and English . Logographic languages elicit different ERPs  from alphabetic languages ,55 and iAnother consideration is the nature of L1 translation lexical priming between our experiment and that of Thierry and Wu . Differehuo shared between both “ham” (huo tui) and “train” (huo che) has a meaning on its own: fire. When the Chinese–English bilinguals activated huo che to the target “train” after activation of huo tui to the prime “ham”, processing was facilitated not only by lexical repetition, but by meaning repetition as well. This additive semantic priming would further attenuate the N400, leading to a stronger priming effect [The final difference is the presence of semantic priming in Thierry and Wu’s L1 translations that was absent in our L1 translations. The Chinese characters used in the previous study not only shared complete form and phonological overlap, but semantic overlap as well. For example, the Chinese character g effect . As previously mentioned, our attenuated L1 translation lexical priming N400 effect relative to that of Thierry and Wu’s study could be due to the script similarity between our bilinguals’ L1 and L2. Spanish and English, by virtue of sharing the same script, compete for lexical activation during word processing. Such competition was seen in the increased reaction time to name L2 interlingual homographs  compared to L2 control words . Shared To better dissociate between L1 and L2 and reduce the between-language competition that may be attributed to reduced priming effects, we can turn to studies of bimodal bilinguals who use spoken/written and signed languages. Spoken and signed languages utilize separate perceptual systems during language comprehension , which results in diminished between-language competition relative to that between two spoken languages . AmericaThe main goal of the present study was to evaluate the validity of a selective model of bilingual lexical processing  against However, the ERP results do in fact show such an influence; when performing in an exclusively L2 language task, low L1 attrited bilinguals significantly differed from high L1 attrited bilinguals in their L1 translation lexical priming N400 effect. This effect size was significantly correlated with L1 attrition scores, with lower L1 attrition facilitating L2 target processing. This finding is compatible with a nonselective view of bilingual lexical access, where the nontarget language impacts target language processing. Differential L1 lexical access due to differential L1 attrition levels led to markedly different neural signatures of L2 processing. Our results, therefore, support Thierry and Wu’s original conclusion over Costa et al.’s reinterpretation. The present study tested for behavioral and ERP effects of L1 attrition on L2 processing to investigate the nature of bilingual lexical access. Manipulation of L1 attrition in an L2 semantic judgment task with implicit L1 translation relatedness allowed for the disentangling of two opposing views: Costa et al.’s model of selective L2 processing  and the Additionally, while the behavioral results did not show L1 attrition effects, the ERP data did. This study, therefore, adds another line of support for the use of more sensitive cognitive neuroscience measures in linguistic research, which can uncover attributes of cognitive processing not revealed by behavior . Influen


Mesangial cell (MC) proliferation is one of the important pathological features of obesity-associated nephropathy with unknown etiology. Excessive MC proliferation can cause glomerulosclerosis and renal function loss. Thus, targeting MC proliferation may be a potential strategy for the treatment of obesity-associated kidney disease. The present study was undertaken to investigate the role of celastrol in MC proliferation induced by ox-LDL, as well as the potential mechanisms. Following ox-LDL treatment, MC proliferation was induced and the NLRP3 inflammasome was activated, as evidenced by increased NLRP3 levels, caspase 1 activity, and IL-18 and IL-1β release. Significantly, NLRP3 siRNAs inhibited MC proliferation and delayed cell cycle progression, as indicated by the cell cycle assay and the expression of cyclin A2 and cyclin D1. Given the anti-inflammatory effect of celastrol, we pretreated MCs with celastrol before ox-LDL treatment. As expected, celastrol pretreatment strikingly inhibited NLRP3 inflammasome activation and MC proliferation triggered by ox-LDL. In summary, celastrol potently blocked ox-LDL-induced MC proliferation, possibly by inhibiting NLRP3 inflammasome activation. These findings also suggest that celastrol may be a potential drug for treating proliferative glomerular diseases related to obesity and lipid disorders. Lipid metabolism disorders are commonly associated with hyperlipidemia and the glomerular accumulation of atherogenic lipoproteins, of which the oxidatively modified low-density lipoprotein (ox-LDL) is a compelling contributor to glomerular mesangial proliferation, inflammation and extracellular matrix (ECM) expansion, which ultimately leads to glomerulosclerosis and nephron loss12. Thus, targeting mesangial cell (MC) proliferation in obesity and MetS is of importance for treating or preventing proliferative glomerulopathy.Considerable evidence has shown that obesity-related metabolic syndrome (MetS) is a major risk factor for glomerulopathy15. In kidney MCs, recent research has demonstrated that activation of the NLRP3 inflammasome contributes to high-glucose-induced rat mesangial cell inflammation and immunoglomerulonephritis17. However, the role of the NLRP3 inflammasome in lipid-related mesangial cell proliferation remains unclear.The nucleotide binding and oligomerization domain-like receptor family pyrin domain containing three (NLRP3) inflammasome was recently reported to be involved in various diseases, and the activation of the NLRP3 inflammasome results in the cleavage of pro-IL-1β and pro-IL-18 into the pro-inflammatory cytokines IL-1β and IL-1820. Accumulating evidence indicates that celastrol exhibits potent anti-inflammation, anti-immunity, and anti-cancer effects23. Previous studies have found that celastrol can inhibit the activation of the NLRP3 inflammasome in the innate immune defense25. Additionally, celastrol-albumin nanoparticles are effective in treating mesangioproliferative glomerulonephritis in rats stimulated by anti-Thy1.126. However, the mechanism underlying the effect of celastrol against mesangial cell overproliferation in such models is not understood. Moreover, whether celastrol has a similar effect against lipid-induced MC proliferation still needs to be studied. Considering the contribution of the NLRP3 inflammasome to MC pathology and the effect of celastrol on NLRP3 inflammasome activation, we hypothesize that celastrol may be beneficial against lipid disorder-associated MC proliferation and NLRP3 inflammasome activation.Celastrol is a triterpene extracted from the traditional Chinese medicinal plant Tripterygium wilfordii Hook F and is listed by the journal Cell as one of the five traditional medicinal compounds most likely to be developed as a modern drugTo confirm that ox-LDL treatment can induce MC proliferation, in the present study, we treated MCs with various concentrations of ox-LDL  for 24 or 48 h. As expected, ox-LDL-induced MC proliferation in a dose- and time-dependent manner Fig. . We alsoWe investigated whether celastrol is effective in preventing ox-LDL-induced MC proliferation. First, we tested a series of concentrations of celastrol to determine its influence on MC viability. Celastrol did not have an obvious cytotoxic effect on MCs, even at 100 mM Fig. . Then, 5Owing to the known role of cell cycle progression in cellular proliferation and the above evidence showing the effect of celastrol on the regulation of cyclin A2 and cyclin D1, we further explored the action of celastrol on cell cycle progression. The number of cells in each cell cycle phase was analyzed by DNA propidium staining. As shown in Fig. A previous study suggested the contribution of the NLRP3 inflammasome to high-glucose-induced MC proliferation. Therefore, we detected the effect of ox-LDL on the activation of the NLRP3 inflammasome. As expected, ox-LDL upregulated the protein expression of NLRP3 and pro-caspase 1 Fig. , enhanceTo evaluate the role of celastrol on NLRP3 inflammasome activation, MCs were treated with celastrol before ox-LDL stimulation. As shown in Fig. To further explore the role of the NLRP3 inflammasome in MC proliferation induced by ox-LDL, NLRP3 small-interfering RNA (siRNA) was used in MCs. As shown by the data, two NLRP3 siRNA candidates markedly blocked NLRP3 protein expression Fig. . Then, wNext, we examined the effect of NLRP3 inflammasome in activation on MC proliferation induced by ox-LDL. As expected, inactivating the NLRP3 inflammasome by silencing NLRP3 significantly blocked the upregulation of cyclin A2 and cyclin D1 protein expression caused by ox-LDL Fig. . By the Finally, we determined the effect of NLRP3 inflammasome inactivation on cell cycle progression induced by ox-LDL in MCs. As shown in Fig. With the elevated prevalence of obesity worldwide, the occurrence of obesity-related glomerulopathy has increased. Mesangial cell proliferation is an important pathological feature of obesity-related glomerulopathy, which may contribute to glomerular hypertrophy and subsequent glomerulosclerosis and renal function decline. Therefore, finding a drug to control MC proliferation associated with obesity and hyperlipidemia is important. Here we found that ox-LDL treatment promotes MC proliferation, which is effectively attenuated by celastrol. In addition, we found that NLRP3 inflammasome activation may be involved in ox-LDL-induced MC proliferation, and the antagonizing effect of celastrol on MC proliferation may occur through the inhibition of this inflammasome.30. Interestingly, a study showed that celastrol-albumin nanoparticles are effective in treating anti-Thy1.1-induced mesangioproliferative glomerulonephritis in rats. The study focused on determining the optimal size of celastrol-albumin nano particles to obtain more accumulation in the glomeruli, as well as their role in glomerular injury, but the detailed molecular mechanism of celastrol against mesangial cell proliferation was not defined. Additionally, whether celastrol can affect ox-LDL-induced MC proliferation has not been reported. In this study, we found that celastrol attenuates ox-LDL-induced MC proliferation and cell cycle progression, suggesting a role for celastrol in antagonizing lipid disorder-related glomerular disease.Celastrol is a natural product with potential pharmacological activities, including anti-cancer, anti-inflammation, and anti-immunity activities. In recent years, research on celastrol has shown its effect against obesity and metabolic diseases9. Additionally, previous studies have suggested that the NLRP3 inflammasome is involved in high-glucose-induced MC proliferation. However, the role of the NLRP3 inflammasome in ox-LDL-related MC proliferation is still unknown. Our data showed a notably upregulated expression of NLRP3 inflammasome components in line with increased caspase 1 activity and IL-18 and IL-1β release in MC culture medium, which confirmed that the NLRP3 inflammasome was activated in MCs stimulated with ox-LDL. NLRP3 knockdown by siRNA not only inhibited the activation of the NLRP3 inflammasome but also blocked MC proliferation induced by ox-LDL. The above results demonstrate that the NLRP3 inflammasome is an important contributor to MC proliferation induced by ox-LDL. Our data also show that celastrol inhibits NLRP3 inflammasome activation induced by ox-LDL, which suggests that celastrol inhibits ox-LDL-induced MC proliferation and cell cycle progression by blocking NLRP3 inflammasome activation.Hyperlipidemia and the accumulation of atherogenic lipoproteins are commonly accompanied by inflammation, which is positively related to the development of glomerular mesangial proliferative diseasesIn summary, in the present study, we report that celastrol effectively attenuates MC proliferation induced by ox-LDL, possibly through inhibiting NLRP3 inflammasome activation. The above findings strongly suggest that the clinical application of celastrol may play multiple beneficial roles in obesity and obesity-associated nephropathy.Ox-LDL was purchased from Yiyuan Biotech . Celastrol was purchased from Sigma . Dulbecco’s modified Eagle’s medium (DMEM), fetal bovine serum (FBS), and 0.25% trypsin-EDTA were purchased from Gibco . A cyclin A2 rabbit polyclonal antibody and a cyclin D1 mouse monoclonal antibody were purchased from Proteintech (USA). Anti-NLRP3 and anti-β-actin were provided by Abcam . Anti-mouse caspase 1 (p20) was purchased from Adipogen .2. The cells were subcultured at 80–90% confluence using trypsin-EDTA (0.25%).Mouse mesangial cells (MCs) were obtained from the China Center for Type Culture Collection  and were cultured in DMEM supplemented with 10% FBS at 37 °C with 5% COMCs were plated in 96-well plates and treated with different doses  of ox-LDL for 24 h. Then, the cells were incubated with 10 μl of CCK-8 solution for 2 h. The optical density at 450 nm was determined.g for 15 min at 4 °C. The protein concentration was determined with a BCA protein assay kit . The proteins were separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred onto polyvinylidene difluoride (PVDF) membranes (Bio-Rad). After blocking in defatted milk, the membranes were incubated with primary antibodies against cyclin A2, cyclin D1, NLRP3, caspase 1 (p20), and β-actin, followed by incubation with secondary antibodies. The immunoreactive bands were detected with ECL reagents using a gel imager (Bio-Rad). The signals obtained from western blotting were quantified with ImageJ software. The internal control β-actin was used to normalize loading variations.After treatment, cell lysates were collected using RIPA buffer supplemented with protease and phosphatase inhibitors. Whole protein supernatant was obtained by centrifuging at 12,000 × MCs were plated in 24-well plates. The cells were pretreated with celastrol (50 μM) or transfected with siNLRP3/NC, followed by treatment with ox-LDL for 24 h. At each time point, the cells were incubated with EDU solution, fixed with paraformaldehyde, and stained with Apollo 488, followed by nucleus staining using Hoechst 33342. Finally, the cells were imaged using a fluorescence microscope .MCs were harvested using 0.25% trypsin, followed by 70% ethanol fixation, RNase A treatment and propidium iodide staining according to the protocol of the cell cycle detection kit . The number of cells in each cell cycle phase  was determined by flow cytometry using a BD FACS Calibur flow cytometer , and data analysis was performed with ModiFIT 3.0 software.The cell culture medium was collected after treatment and centrifuged at 2000 rpm for 10 min. The supernatant was collected for cytokine analysis. The concentrations of the cytokines IL-18 and IL-1β were determined according to the manufacturer’s instructions using ELISA kits .Caspase 1 activity was determined using a caspase 1/ICE colorimetric assay kit . Once the treatment was terminated, the cell lysates were collected using chilled cell lysis buffer, and the supernatant was collected after centrifugation. The protein concentration was determined with a BCA protein assay kit (Beyotime), and 100–200 μg of protein from each sample was incubated with reaction buffer and YVAD-ρNA substrate at 37 °C for 2 h. The samples were read at 405 nm using the Multiskan FC (Thermo Scientific).P < 0.05 was considered statistically significant.Statistical analysis was conducted using SPSS version 21.0 . Each experiment was repeated at least three times. The data are presented as the mean ± SD and were analyzed by ANOVA.