Modulation of Th1/Th2 immune responses by killed Propionibacterium acnes and its soluble polysaccharide fraction in a type I hypersensitivity murine model: induction of different activation status of antigen-presenting cells.
Carla Cristina Squaiella-BaptistãoDaniela TeixeiraJuliana Sekeres MussalemMayari Eika IshimuraIeda Maria Longo-MaugériPublished in: Journal of immunology research (2015)
Propionibacterium acnes (P. acnes) is a gram-positive anaerobic bacillus present in normal human skin microbiota, which exerts important immunomodulatory effects, when used as heat- or phenol-killed suspensions. We previously demonstrated that heat-killed P. acnes or its soluble polysaccharide (PS), extracted from the bacterium cell wall, suppressed or potentiated the Th2 response to ovalbumin (OVA) in an immediate hypersensitivity model, depending on the treatment protocol. Herein, we investigated the mechanisms responsible for these effects, using the same model and focusing on the activation status of antigen-presenting cells (APCs). We verified that higher numbers of APCs expressing costimulatory molecules and higher expression levels of these molecules are probably related to potentiation of the Th2 response to OVA induced by P. acnes or PS, while higher expression of toll-like receptors (TLRs) seems to be related to Th2 suppression. In vitro cytokines production in cocultures of dendritic cells and T lymphocytes indicated that P. acnes and PS seem to perform their effects by acting directly on APCs. Our data suggest that P. acnes and PS directly act on APCs, modulating the expression of costimulatory molecules and TLRs, and these differently activated APCs drive distinct T helper patterns to OVA in our model.
Keyphrases
- dendritic cells
- poor prognosis
- immune response
- induced apoptosis
- cell wall
- cell cycle arrest
- randomized controlled trial
- regulatory t cells
- signaling pathway
- case report
- oxidative stress
- endoplasmic reticulum stress
- electronic health record
- heavy metals
- cell death
- risk assessment
- big data
- deep learning
- combination therapy
- data analysis
- high resolution