APE-Gen2.0: Expanding Rapid Class I Peptide-Major Histocompatibility Complex Modeling to Post-Translational Modifications and Noncanonical Peptide Geometries.

The recognition of peptides bound to class I major histocompatibility complex (MHC-I) receptors by T-cell receptors (TCRs) is a determinant of triggering the adaptive immune response. While the exact molecular features that drive the TCR recognition are still unknown, studies have suggested that the geometry of the joint peptide-MHC (pMHC) structure plays an important role. As such, there is a definite need for methods and tools that accurately predict the structure of the peptide bound to the MHC-I receptor. In the past few years, many pMHC structural modeling tools have emerged that provide high-quality modeled structures in the general case. However, there are numerous instances of non-canonical cases in the immunopeptidome that the majority of pMHC modeling tools do not attend to, most notably, peptides that exhibit non-standard amino acids and post-translational modifications (PTMs) or peptides that assume non-canonical geometries in the MHC binding cleft. Such chemical and structural properties have been shown to be present in neoantigens; therefore, accurate structural modeling of these instances can be vital for cancer immunotherapy. To this end, we have developed APE-Gen2.0, a tool that improves upon its predecessor and other pMHC modeling tools, both in terms of modeling accuracy and the available modeling range of non-canonical peptide cases. Some of the improvements include (i) the ability to model peptides that have different types of PTMs such as phosphorylation, nitration, and citrullination; (ii) a new and improved anchor identification routine in order to identify and model peptides that exhibit a non-canonical anchor conformation; and (iii) a web server that provides a platform for easy and accessible pMHC modeling. We further show that structures predicted by APE-Gen2.0 can be used to assess the effects that PTMs have in binding affinity in a more accurate manner than just using solely the sequence of the peptide. APE-Gen2.0 is freely available at https://apegen.kavrakilab.org.

Innate extracellular Hsp70 inflammatory properties are mediated by the interaction of Siglec-E and LOX-1 receptors.

Innate immune responses to cell damage-associated molecular patterns induce a controlled degree of inflammation, ideally avoiding the promotion of intense unwanted inflammatory adverse events. When released by damaged cells, Hsp70 can stimulate different responses that range from immune activation to immune suppression. The effects of Hsp70 are mediated through innate receptors expressed primarily by myeloid cells, such as dendritic cells (DCs). The regulatory innate receptors that bind to extracellular mouse Hsp70 (mHsp70) are not fully characterized, and neither are their potential interactions with activating innate receptors. Here, we describe that extracellular mHsp70 interacts with a receptor complex formed by inhibitory Siglec-E and activating LOX-1 on DCs. We also find that this interaction takes place within lipid microdomains, and Siglec-E acts as a negative regulator of LOX-1-mediated innate activation upon mHsp70 or oxidized LDL binding. Thus, HSP70 can both bind to and modulate the interaction of inhibitory and activating innate receptors on the cell surface. These findings add another dimension of regulatory mechanism to how self-molecules contribute to dampening of exacerbated inflammatory responses.

Variation of modulation and expression of biomarkers associated with inflammation in bariatric surgery patients: A systematic review and meta-analysis.

BACKGROUND: Bariatric surgery is an effective intervention that causes a series of metabolic changes related to inflammatory processes; however, the variation of biomarkers related to these processes is not entirely understood. Our objective was to investigate the variation of modulation and expression of biomarkers associated with inflammation in patients who underwent bariatric surgery. METHODS: We searched the MEDLINE (via PubMed), EMBASE (via Elsevier), Cochrane Central Register of Controlled Trials, Latin American and Caribbean Literature on Health Sciences (via virtual health library), Cumulative Index to Nursing and Allied Health Literature (via EBSCO), Web of Science core collection, and Scopus (via Elsevier) databases, and the gray literature was examined from inception to January 2022. Three pairs of reviewers performed data screening, extraction, and quality assessment independently. Meta-analysis with random effects models was used for general, subgroup, and sensitivity analyses. The I2 statistic was used to assess heterogeneity between studies. RESULTS: In total, 96 articles were included in this systematic review; of these, 87 studies met the criteria for the meta-analysis, involving 3,533 participants. Five biomarkers were included in the meta-analysis (tumor necrosis factor alpha; interleukin 6; leptin; interleukin 1 beta, and lipopolysaccharides). Only leptin showed a significant decrease in the first month after surgery (mean difference -20.71; [95% confidence interval: -28.10 to -13.32, P < .0001; I2 = 66.7%), with moderate heterogeneity. The 12 months after surgery showed a significant decrease in tumor necrosis factor alpha (mean difference -0.89; [95% confidence interval: -1.37 to -0.42], P = .0002; I2 = 94.7%), interleukin 6 (mean difference -1.62; [95% confidence interval: -1.95 to -1.29], P < .0001; I2 = 94.9%), leptin (mean difference -28.63; [95% confidence interval: -34.02 to -23.25], P < .0001; I2 = 92.7%), and interleukin 1 beta (mean difference -2.46; [95% confidence interval: -4.23 to -0.68], P = .006; I2 = 98.3%), all with high heterogeneity. The type of surgery did not show significant differences for the biomarkers at the first month and 12 months, and the results have not changed with high-quality studies. In the 12-month measurement, variations in tumor necrosis factor alpha and leptin were associated with body mass index. CONCLUSION: The findings of this meta-analysis suggest that Roux-en-Y gastric bypass and sleeve gastrectomy bariatric surgeries are associated with a significant reduction in leptin at 1 month after bariatric surgical intervention and tumor necrosis factor alpha, leptin, and interleukin 1 beta after 12 months.

PepSim: T-cell cross-reactivity prediction via comparison of peptide sequence and peptide-HLA structure.

Introduction: Peptide-HLA class I (pHLA) complexes on the surface of tumor cells can be targeted by cytotoxic T-cells to eliminate tumors, and this is one of the bases for T-cell-based immunotherapies. However, there exist cases where therapeutic T-cells directed towards tumor pHLA complexes may also recognize pHLAs from healthy normal cells. The process where the same T-cell clone recognizes more than one pHLA is referred to as T-cell cross-reactivity and this process is driven mainly by features that make pHLAs similar to each other. T-cell cross-reactivity prediction is critical for designing T-cell-based cancer immunotherapies that are both effective and safe. Methods: Here we present PepSim, a novel score to predict T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs. Results and discussion: We show our method can accurately separate cross-reactive from non-crossreactive pHLAs in a diverse set of datasets including cancer, viral, and self-peptides. PepSim can be generalized to work on any dataset of class I peptide-HLAs and is freely available as a web server at pepsim.kavrakilab.org.

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins.

An unprecedented research effort has been undertaken in response to the ongoing COVID-19 pandemic. This has included the determination of hundreds of crystallographic structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had very limited success in producing effective inhibitors against SARS-CoV-2 proteins. A reason might be an often overlooked factor in these computational efforts: receptor flexibility. To address this issue we have implemented a computational tool for ensemble docking with SARS-CoV-2 proteins. We have extracted representative ensembles of protein conformations from the Protein Data Bank and from in silico molecular dynamics simulations. Twelve pre-computed ensembles of SARS-CoV-2 protein conformations have now been made available for ensemble docking via a user-friendly webserver called DINC-COVID (dinc-covid.kavrakilab.org). We have validated DINC-COVID using data on tested inhibitors of two SARS-CoV-2 proteins, obtaining good correlations between docking-derived binding energies and experimentally-determined binding affinities. Some of the best results have been obtained on a dataset of large ligands resolved via room temperature crystallography, and therefore capturing alternative receptor conformations. In addition, we have shown that the ensembles available in DINC-COVID capture different ranges of receptor flexibility, and that this diversity is useful in finding alternative binding modes of ligands. Overall, our work highlights the importance of accounting for receptor flexibility in docking studies, and provides a platform for the identification of new inhibitors against SARS-CoV-2 proteins.

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins.

MOTIVATION: Recent efforts to computationally identify inhibitors for SARS-CoV-2 proteins have largely ignored the issue of receptor flexibility. We have implemented a computational tool for ensemble docking with the SARS-CoV-2 proteins, including the main protease (Mpro), papain-like protease (PLpro) and RNA-dependent RNA polymerase (RdRp). RESULTS: Ensembles of other SARS-CoV-2 proteins are being prepared and made available through a user-friendly docking interface. Plausible binding modes between conformations of a selected ensemble and an uploaded ligand are generated by DINC, our parallelized meta-docking tool. Binding modes are scored with three scoring functions, and account for the flexibility of both the ligand and receptor. Additional details on our methods are provided in the supplementary material. AVAILABILITY: dinc-covid.kavrakilab.org. SUPPLEMENTARY INFORMATION: Details on methods for ensemble generation and docking are provided as supplementary data online. CONTACT: geancarlo.zanatta@ufc.br , kavraki@rice.edu.

Structural Modeling and Molecular Dynamics of the Immune Checkpoint Molecule HLA-G.

HLA-G is considered to be an immune checkpoint molecule, a function that is closely linked to the structure and dynamics of the different HLA-G isoforms. Unfortunately, little is known about the structure and dynamics of these isoforms. For instance, there are only seven crystal structures of HLA-G molecules, being all related to a single isoform, and in some cases lacking important residues associated to the interaction with leukocyte receptors. In addition, they lack information on the dynamics of both membrane-bound HLA-G forms, and soluble forms. We took advantage of in silico strategies to disclose the dynamic behavior of selected HLA-G forms, including the membrane-bound HLA-G1 molecule, soluble HLA-G1 dimer, and HLA-G5 isoform. Both the membrane-bound HLA-G1 molecule and the soluble HLA-G1 dimer were quite stable. Residues involved in the interaction with ILT2 and ILT4 receptors (α3 domain) were very close to the lipid bilayer in the complete HLA-G1 molecule, which might limit accessibility. On the other hand, these residues can be completely exposed in the soluble HLA-G1 dimer, due to the free rotation of the disulfide bridge (Cys42/Cys42). In fact, we speculate that this free rotation of each protomer (i.e., the chains composing the dimer) could enable alternative binding modes for ILT2/ILT4 receptors, which in turn could be associated with greater affinity of the soluble HLA-G1 dimer. Structural analysis of the HLA-G5 isoform demonstrated higher stability for the complex containing the peptide and coupled ß2-microglobulin, while structures lacking such domains were significantly unstable. This study reports for the first time structural conformations for the HLA-G5 isoform and the dynamic behavior of HLA-G1 molecules under simulated biological conditions. All modeled structures were made available through GitHub (https://github.com/KavrakiLab/), enabling their use as templates for modeling other alleles and isoforms, as well as for other computational analyses to investigate key molecular interactions.

Host expression system modulates recombinant Hsp70 activity through post-translational modifications.

The use of model organisms for recombinant protein production results in the addition of model-specific post-translational modifications (PTMs) that can affect the structure, charge, and function of the protein. The 70-kDa heat shock proteins (Hsp70) were originally described as intracellular chaperones, with ATPase and foldase activity. More recently, new extracellular activities of Hsp70 proteins (e.g. as immunomodulators) have been identified. While some studies indicate an inflammatory potential for extracellular Hsp70 proteins, others suggest an immunosuppressive activity. We hypothesized that the production of recombinant Hsp70 in different expression systems would result in the addition of different PTMs, perhaps explaining at least some of these opposing immunological outcomes. We produced and purified Mycobacterium tuberculosis DnaK from two different systems, Escherichia coli and Pichia pastoris, and analyzed by mass spectrometry the protein preparations, investigating the impact of PTMs in an in silico and in vitro perspective. The comparisons of DnaK structures in silico highlighted that electrostatic and topographical differences exist that are dependent upon the expression system. Production of DnaK in the eukaryotic system dramatically affected its ATPase activity, and significantly altered its ability to downregulate MHC II and CD86 expression on murine dendritic cells (DCs). Phosphatase treatment of DnaK indicated that some of these differences related specifically to phosphorylation. Altogether, our data indicate that PTMs are an important characteristic of the expression system, with differences that impact interactions of Hsps with their ligands and subsequent functional activities.

Respiratory syncytial virus reduces STAT3 phosphorylation in human memory CD8 T cells stimulated with IL-21.

Respiratory syncytial virus (RSV) is a common cause of childhood lower respiratory tract infections. The recent failure of a vaccine candidate based on recombinant F protein underlines the urgent need to better understand the protective human memory immune response against RSV. Signal transducer and activator of transcription 3 (STAT3) protein is a transcription factor that promotes the maturation of the memory CD8 T cell response in cooperation with IL-10 and IL-21. However, the role of STAT3 in the memory CD8 T cell response during RSV infection remains to be elucidated. We found that in infants with bronchiolitis infected with RSV, the expression of STAT3 detected in nasal washes is reduced when compared to that in infants infected by other viruses. In vitro, RSV impairs STAT3 phosphorylation induced by IL-21 in purified human memory CD8 T cells. In addition, RSV decreases granzyme B production by memory CD8 T cells, reducing its cytotoxic activity against RSV-infected epithelial pulmonary cell lines. Together, these data indicate that RSV modulates the IL-21/STAT3 pathway in human memory CD8 T cells, and this could be a mechanism to be further explored to improve the memory response against the infection.

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes.

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

Interpreting T-Cell Cross-reactivity through Structure: Implications for TCR-Based Cancer Immunotherapy.

Immunotherapy has become one of the most promising avenues for cancer treatment, making use of the patient's own immune system to eliminate cancer cells. Clinical trials with T-cell-based immunotherapies have shown dramatic tumor regressions, being effective in multiple cancer types and for many different patients. Unfortunately, this progress was tempered by reports of serious (even fatal) side effects. Such therapies rely on the use of cytotoxic T-cell lymphocytes, an essential part of the adaptive immune system. Cytotoxic T-cells are regularly involved in surveillance and are capable of both eliminating diseased cells and generating protective immunological memory. The specificity of a given T-cell is determined through the structural interaction between the T-cell receptor (TCR) and a peptide-loaded major histocompatibility complex (MHC); i.e., an intracellular peptide-ligand displayed at the cell surface by an MHC molecule. However, a given TCR can recognize different peptide-MHC (pMHC) complexes, which can sometimes trigger an unwanted response that is referred to as T-cell cross-reactivity. This has become a major safety issue in TCR-based immunotherapies, following reports of melanoma-specific T-cells causing cytotoxic damage to healthy tissues (e.g., heart and nervous system). T-cell cross-reactivity has been extensively studied in the context of viral immunology and tissue transplantation. Growing evidence suggests that it is largely driven by structural similarities of seemingly unrelated pMHC complexes. Here, we review recent reports about the existence of pMHC "hot-spots" for cross-reactivity and propose the existence of a TCR interaction profile (i.e., a refinement of a more general TCR footprint in which some amino acid residues are more important than others in triggering T-cell cross-reactivity). We also make use of available structural data and pMHC models to interpret previously reported cross-reactivity patterns among virus-derived peptides. Our study provides further evidence that structural analyses of pMHC complexes can be used to assess the intrinsic likelihood of cross-reactivity among peptide-targets. Furthermore, we hypothesize that some apparent inconsistencies in reported cross-reactivities, such as a preferential directionality, might also be driven by particular structural features of the targeted pMHC complex. Finally, we explain why TCR-based immunotherapy provides a special context in which meaningful T-cell cross-reactivity predictions can be made.

GRPR antagonist protects from drug-induced liver injury by impairing neutrophil chemotaxis and motility.

Drug-induced liver injury (DILI) is a major cause of acute liver failure (ALF), where hepatocyte necrotic products trigger liver inflammation, release of CXC chemokine receptor 2 (CXCR2) ligands (IL-8) and other neutrophil chemotactic molecules. Liver infiltration by neutrophils is a major cause of the life-threatening tissue damage that ensues. A GRPR (gastrin-releasing peptide receptor) antagonist impairs IL-8-induced neutrophil chemotaxis in vitro. We investigated its potential to reduce acetaminophen-induced ALF, neutrophil migration, and mechanisms underlying this phenomenon. We found that acetaminophen-overdosed mice treated with GRPR antagonist had reduced DILI and neutrophil infiltration in the liver. Intravital imaging and cell tracking analysis revealed reduced neutrophil mobility within the liver. Surprisingly, GRPR antagonist inhibited CXCL2-induced migration in vivo, decreasing neutrophil activation through CD11b and CD62L modulation. Additionally, this compound decreased CXCL8-driven neutrophil chemotaxis in vitro independently of CXCR2 internalization, induced activation of MAPKs (p38 and ERK1/2) and downregulation of neutrophil adhesion molecules CD11b and CD66b. In silico analysis revealed direct binding of GRPR antagonist and CXCL8 to the same binding spot in CXCR2. These findings indicate a new potential use for GRPR antagonist for treatment of DILI through a mechanism involving adhesion molecule modulation and possible direct binding to CXCR2.

Improved structural method for T-cell cross-reactivity prediction.

Cytotoxic T-lymphocytes (CTLs) are the key players of adaptive cellular immunity, being able to identify and eliminate infected cells through the interaction with peptide-loaded major histocompatibility complexes class I (pMHC-I). Despite the high specificity of this interaction, a given lymphocyte is actually able to recognize more than just one pMHC-I complex, a phenomenon referred as cross-reactivity. In the present work we describe the use of pMHC-I structural features as input for multivariate statistical methods, to perform standardized structure-based predictions of cross-reactivity among viral epitopes. Our improved approach was able to successfully identify cross-reactive targets among 28 naturally occurring hepatitis C virus (HCV) variants and among eight epitopes from the four dengue virus serotypes. In both cases, our results were supported by multiscale bootstrap resampling and by data from previously published in vitro experiments. The combined use of data from charges and accessible surface area (ASA) of selected residues over the pMHC-I surface provided a powerful way of assessing the structural features involved in triggering cross-reactive responses. Moreover, the use of an R package (pvclust) for assessing the uncertainty in the hierarchical cluster analysis provided a statistical support for the interpretation of results. Taken together, these methods can be applied to vaccine design, both for the selection of candidates capable of inducing immunity against different targets, or to identify epitopes that could trigger undesired immunological responses.

Lessons from molecular modeling human α-L-iduronidase.

Human α-L-iduronidase (IDUA) is a member of glycoside hydrolase family and is involved in the catabolism of glycosaminoglycans (GAGs), heparan sulfate (HS) and dermatan sulfate (DS). Mutations in this enzyme are responsible for mucopolysaccharidosis I (MPS I), an inherited lysosomal storage disorder. Despite great interest in determining and studying this enzyme structure, the lack of a high identity to templates and other technical issues have challenged both bioinformaticians and crystallographers, until the recent publication of an IDUA crystal structure (PDB: 4JXP). In the present work, four alternative IDUA models, generated and evaluated prior to crystallographic determination, were compared to the 4JXP structure. A combined analysis using several viability assessment tools and molecular dynamics simulations highlights the strengths and limitations of different comparative modeling protocols, all of which are based on the same low identity template (only 22%). Incorrect alignment between the target and template was confirmed to be a major bottleneck in homology modeling, regardless of the modeling software used. Moreover, secondary structure analysis during a 50ns simulation seems to be useful for indicating alignment errors and structural instabilities. The best model was achieved through the combined use of Phyre 2 and Modeller, suggesting the use of this protocol for the modeling of other proteins that still lack high identity templates.

New insights into the in silico prediction of HIV protease resistance to nelfinavir.

The Human Immunodeficiency Virus type 1 protease enzyme (HIV-1 PR) is one of the most important targets of antiretroviral therapy used in the treatment of AIDS patients. The success of protease-inhibitors (PIs), however, is often limited by the emergence of protease mutations that can confer resistance to a specific drug, or even to multiple PIs. In the present study, we used bioinformatics tools to evaluate the impact of the unusual mutations D30V and V32E over the dynamics of the PR-Nelfinavir complex, considering that codons involved in these mutations were previously related to major drug resistance to Nelfinavir. Both studied mutations presented structural features that indicate resistance to Nelfinavir, each one with a different impact over the interaction with the drug. The D30V mutation triggered a subtle change in the PR structure, which was also observed for the well-known Nelfinavir resistance mutation D30N, while the V32E exchange presented a much more dramatic impact over the PR flap dynamics. Moreover, our in silico approach was also able to describe different binding modes of the drug when bound to different proteases, identifying specific features of HIV-1 subtype B and subtype C proteases.

Structural in silico analysis of cross-genotype-reactivity among naturally occurring HCV NS3-1073-variants in the context of HLA-A*02:01 allele.

Cellular immune response plays a central role in outcome of Hepatitis C Virus (HCV) infection. While specific T-cell responses are related to viral clearance, impaired responses can lead to chronic infection, turning HCV variability into a major obstacle for vaccine development. In a recent work, Fytili et al. (2008) studied the cross reactive potential of HCV specific CD8+ T-cells and observed a large variation in immunogenicity among 28 naturally occurring NS3(1073) variants. In this work, we intend to evaluate this immunogenic variation at molecular level, through bioinformatics approaches. The D1-EM-D2 strategy was used to build in silico MHC:peptide complexes (pMHC) of these HCV-derived peptides in the context of HLA-A*02:01 allele. The TCR-interacting surface of these complexes were evaluated using the GRASP2 program. Structural analysis indicated a sharing of topological and electrostatic features among complexes that induced strong response in vitro. Besides, complexes that induced low response presented an important positively charged spot in the center of TCR-interacting area. This spot was seen even in complexes with conservative amino acid changes and is consistent with the impairment of recognition by wild-type-specific T-cells, observed in vitro. Furthermore, the most remarkable difference in electrostatic potential was seen precisely in the only complex unable to induce in vitro stimulation. All these observations were confirmed by Principal Component Analysis (PCA) and this approach was also applied to a set of 45 non-related immunogenic viral epitopes, indicating possible new targets for cross-reactivity studies. Our results suggest structural in silico analysis of pMHC complexes as a reliable tool for vaccine development, affording to predict the impact of viral escape mutations and selection of epitopes with potential to induce cross-reactive immune responses.

Structural allele-specific patterns adopted by epitopes in the MHC-I cleft and reconstruction of MHC:peptide complexes to cross-reactivity assessment.

The immune system is engaged in a constant antigenic surveillance through the Major Histocompatibility Complex (MHC) class I antigen presentation pathway. This is an efficient mechanism for detection of intracellular infections, especially viral ones. In this work we describe conformational patterns shared by epitopes presented by a given MHC allele and use these features to develop a docking approach that simulates the peptide loading into the MHC cleft. Our strategy, to construct in silico MHC:peptide complexes, was successfully tested by reproducing four different crystal structures of MHC-I molecules available at the Protein Data Bank (PDB). An in silico study of cross-reactivity potential was also performed between the wild-type complex HLA-A2-NS31073 and nine MHC:peptide complexes presenting alanine exchange peptides. This indicates that structural similarities among the complexes can give us important clues about cross reactivity. The approach used in this work allows the selection of epitopes with potential to induce cross-reactive immune responses, providing useful tools for studies in autoimmunity and to the development of more comprehensive vaccines.

S100B secretion is stimulated by IL-1beta in glial cultures and hippocampal slices of rats: Likely involvement of MAPK pathway.

S100B is an astrocyte-derived cytokine implicated in the IL-1beta-triggered cytokine cycle in Alzheimer's disease. However, the secretion of S100B following stimulation by IL-1beta has not been directly demonstrated. We investigated S100B secretion in cortical primary astrocyte cultures, C6 glioma cells and acute hippocampal slices exposed to IL-1beta. S100B secretion was induced by IL-1beta in all preparations, involving MAPK pathway and, apparently, NF-small ka, CyrillicB signaling. Astrocytes and C6 cells exhibited different sensitivities to IL-1beta. These results suggest that IL-1beta-induced S100B secretion is a component of the neuroinflammatory response, which would support the involvement of S100B in the genesis of neurodegenerative diseases.