journal article·2010-08Open access

The role of multiple toll-like receptor signalling cascades on interactions between biomedical polymers and dendritic cells

Behnaz Shokouhi, Cevayir Coban, Vasif Hasirci, Erkin Aydin, Anandhan Dhanasingh, Nian Shi, Shohei Koyama, Shizuo Akira, Martin Zenke, Antonio S. Sechi

Biomaterials· Volume 31· Issue 22

Abstract

Biomaterials are used in several health-related applications ranging from tissue regeneration to antigen-delivery systems. Yet, biomaterials often cause inflammatory reactions suggesting that they profoundly alter the homeostasis of host immune cells such as dendritic cells (DCs). Thus, there is a major need to understand how biomaterials affect the function of these cells. In this study, we have analysed the influence of chemically and physically diverse biomaterials on DCs using several murine knockouts. DCs can sense biomedical polymers through a mechanism, which involves multiple TLR/MyD88-dependent signalling pathways, in particular TLR2, TLR4 and TLR6. TLR-biomaterial interactions induce the expression of activation markers and pro-inflammatory cytokines and are sufficient to confer on DCs the ability to activate antigen-specific T cells. This happens through a direct biomaterial-DC interaction although, for degradable biomaterials, soluble polymer molecules can also alter DC function. Finally, the engagement of TLRs by biomaterials profoundly alters DC adhesive properties. Our findings could be useful for designing structure-function studies aimed at developing more bioinert materials. Moreover, they could also be exploited to generate biomaterials for studying the molecular mechanisms of TLR signalling and DC activation aiming at fine-tuning desired and pre-determined immune responses.
Source: europepmc · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Immunotherapy and Immune Responses · Cell Adhesion Molecules Research · Monoclonal and Polyclonal Antibodies Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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