Identification and validation of a novel dual small-molecule TLR2/8 antagonist.

Toll-like receptor 2 (TLR2) and TLR8 are involved in the recognition of bacterial and viral components and are linked not only to protective antimicrobial immunity but also to inflammatory diseases. Recently, increasing attention has been paid to the receptor crosstalk between TLR2 and TLR8 to fine-tune innate immune responses. In this study, we report a novel dual TLR2/TLR8 antagonist, compound 24 that was developed by a modeling-guided synthesis approach. The modulator was optimized from the previously reported 1,3-benzothiazole derivative, compound 8. Compound 24 was pharmacologically characterized for the ability to inhibit TLR2- and TLR8-mediated responses in TLR-overexpressing reporter cells and THP-1 macrophages. The modulator showed high efficacy with IC50 values in the low micromolar range for both TLRs, selectivity towards other TLRs and low cytotoxicity. At TLR2, a slight predominance for the TLR2/1 heterodimer was found in reporter cells selectively expressing TLR2/1 or TLR2/6 heterodimers. Concentration ratio analysis in the presence of Pam3CSK4 or Pam2CSK4 indicated non-competitive antagonist behavior at hTLR2. In computational docking studies, a plausible alternative binding mode of compound 24 was predicted for both TLR2 and TLR8. Our results provide evidence that it is feasible to simultaneously and selectively target endosomal- and surface-located TLRs. We identified a small-molecule dual TLR2/8 antagonist that may serve as a valuable pharmacological tool to decipher the role of TLR2/8 co-signaling in inflammation.

The novel small-molecule antagonist MMG-11 preferentially inhibits TLR2/1 signaling.

Toll-like receptor 2 (TLR2) forms heterodimers with either TLR1 or TLR6 to induce protective early inflammatory responses to pathogen- and damage-associated molecular patterns. However, excessive activation is associated with inflammatory and metabolic diseases. Several TLR2 antagonists have been described but pharmacological characterization is still at an early stage. Previously, we identified the potent and selective TLR2 antagonist MMG-11 by computational modelling and experimental validation. Here, we characterized the TLR2 antagonists MMG-11 and CU-CPT22 as well as the TIR-domain binding TLR2 antagonist C29 in TLR-overexpressing promoter cells as well as human and mouse macrophages. In line with our recent studies, MMG-11 abrogated pro-inflammatory cytokine secretion and NF-κB activation induced by different bacterial TLR2 agonists. MMG-11 preferentially inhibited TLR2/1 signaling in promoter cells stably expressing TLR2 heterodimers and mouse macrophages. Furthermore, the TLR2 antagonist blocked ligand-induced interaction of TLR2 with MyD88 and reduced MAP kinase and NF-κB activation. MMG-11 and CU-CPT22 but not C29 displaced Pam3CSK4 in an indirect binding assay confirming the competitive mode of action of MMG-11 and CU-CPT22. Isobologram analysis revealed additive and synergistic effects when the non-competitive antagonist C29 was combined with the competitive antagonist MMG-11 or CU-CPT22, respectively. In conclusion, we provide evidence that MMG-11 acts as a competitive antagonist with a predominance for the TLR2/1 heterodimer in human and mouse cells. Our results also indicate that MMG-11 is a model compound for studying TLR2 signaling.

N-methyl-D-aspartate receptor dysfunction by unmutated human antibodies against the NR1 subunit.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is the most common autoimmune encephalitis related to autoantibody-mediated synaptic dysfunction. Cerebrospinal fluid-derived human monoclonal NR1 autoantibodies showed low numbers of somatic hypermutations or were unmutated. These unexpected germline-configured antibodies showed weaker binding to the NMDAR than matured antibodies from the same patient. In primary hippocampal neurons, germline NR1 autoantibodies strongly and specifically reduced total and synaptic NMDAR currents in a dose- and time-dependent manner. The findings suggest that functional NMDAR antibodies are part of the human naïve B cell repertoire. Given their effects on synaptic function, they might contribute to a broad spectrum of neuropsychiatric symptoms. Ann Neurol 2019;85:771-776.

Acute myeloid leukaemia-derived Langerhans-like cells enhance Th1 polarization upon TLR2 engagement.

Langerhans cells (LCs) represent a highly specialized subset of epidermal dendritic cells (DCs), yet not fully understood in their function of balancing skin immunity. Here, we investigated in vitro generated Langerhans-like cells obtained from the human acute myeloid leukaemia cell line MUTZ-3 (MUTZ-LCs) to study TLR- and cytokine-dependent activation of epidermal DCs. MUTZ-LCs revealed high TLR2 expression and responded robustly to TLR2 engagement, confirmed by increased CD83, CD86, PD-L1 and IDO expression, upregulated IL-6, IL-12p40 and IL-23p19 mRNA levels IL-8 release. TLR2 activation reduced CCR6 and elevated CCR7 mRNA expression and induced migration of MUTZ-LCs towards CCL21. Similar results were obtained by stimulation with pro-inflammatory cytokines TNF-α and IL-1ß whereas ligands of TLR3 and TLR4 failed to induce a fully mature phenotype. Despite limited cytokine gene expression and production for TLR2-activated MUTZ-LCs, co-culture with naive CD4(+) T cells led to significantly increased IFN-γ and IL-22 levels indicating Th1 differentiation independent of IL-12. TLR2-mediated effects were blocked by the putative TLR2/1 antagonist CU-CPT22, however, no selectivity for either TLR2/1 or TLR2/6 was observed. Computer-aided docking studies confirmed non-selective binding of the TLR2 antagonist. Taken together, our results indicate a critical role for TLR2 signalling in MUTZ-LCs considering the leukemic origin of the generated Langerhans-like cells.

Development of immune-specific interaction potentials and their application in the multi-agent-system VaccImm.

Peptide vaccination in cancer therapy is a promising alternative to conventional methods. However, the parameters for this personalized treatment are difficult to access experimentally. In this respect, in silico models can help to narrow down the parameter space or to explain certain phenomena at a systems level. Herein, we develop two empirical interaction potentials specific to B-cell and T-cell receptor complexes and validate their applicability in comparison to a more general potential. The interaction potentials are applied to the model VaccImm which simulates the immune response against solid tumors under peptide vaccination therapy. This multi-agent system is derived from another immune system simulator (C-ImmSim) and now includes a module that enables the amino acid sequence of immune receptors and their ligands to be taken into account. The multi-agent approach is combined with approved methods for prediction of major histocompatibility complex (MHC)-binding peptides and the newly developed interaction potentials. In the analysis, we critically assess the impact of the different modules on the simulation with VaccImm and how they influence each other. In addition, we explore the reasons for failures in inducing an immune response by examining the activation states of the immune cell populations in detail.In summary, the present work introduces immune-specific interaction potentials and their application to the agent-based model VaccImm which simulates peptide vaccination in cancer therapy.

CancerResource: a comprehensive database of cancer-relevant proteins and compound interactions supported by experimental knowledge.

During the development of methods for cancer diagnosis and treatment, a vast amount of information is generated. Novel cancer target proteins have been identified and many compounds that activate or inhibit cancer-relevant target genes have been developed. This knowledge is based on an immense number of experimentally validated compound-target interactions in the literature, and excerpts from literature text mining are spread over numerous data sources. Our own analysis shows that the overlap between important existing repositories such as Comparative Toxicogenomics Database (CTD), Therapeutic Target Database (TTD), Pharmacogenomics Knowledge Base (PharmGKB) and DrugBank as well as between our own literature mining for cancer-annotated entries is surprisingly small. In order to provide an easy overview of interaction data, it is essential to integrate this information into a single, comprehensive data repository. Here, we present CancerResource, a database that integrates cancer-relevant relationships of compounds and targets from (i) our own literature mining and (ii) external resources complemented with (iii) essential experimental and supporting information on genes and cellular effects. In order to facilitate an overview of existing and supporting information, a series of novel information connections have been established. CancerResource addresses the spectrum of research on compound-target interactions in natural sciences as well as in individualized medicine; CancerResource is available at: http://bioinformatics.charite.de/cancerresource/.

Theoretical modeling techniques and their impact on tumor immunology.

Currently, cancer is one of the leading causes of death in industrial nations. While conventional cancer treatment usually results in the patient suffering from severe side effects, immunotherapy is a promising alternative. Nevertheless, some questions remain unanswered with regard to using immunotherapy to treat cancer hindering it from being widely established. To help rectify this deficit in knowledge, experimental data, accumulated from a huge number of different studies, can be integrated into theoretical models of the tumor-immune system interaction. Many complex mechanisms in immunology and oncology cannot be measured in experiments, but can be analyzed by mathematical simulations. Using theoretical modeling techniques, general principles of tumor-immune system interactions can be explored and clinical treatment schedules optimized to lower both tumor burden and side effects. In this paper, we aim to explain the main mathematical and computational modeling techniques used in tumor immunology to experimental researchers and clinicians. In addition, we review relevant published work and provide an overview of its impact to the field.