Recognition of Cell Wall Mannosylated Components as a Conserved Feature for Fungal Entrance, Adaptation and Survival Within Trophozoites of Acanthamoeba castellanii and Murine Macrophages.

ABSTRACT

Acanthamoeba castellanii (Ac) is a species of free-living amoebae (FLAs) that has been widely applied as a model for the study of host-parasite interactions and characterization of environmental symbionts. The sharing of niches between Ac and potential pathogens, such as fungi, favors associations between these organisms. Through predatory behavior, Ac enhances fungal survival, dissemination, and virulence in their intracellular milieu, training these pathogens and granting subsequent success in events of infections to more evolved hosts. In recent studies, our group characterized the amoeboid mannose binding proteins (MBPs) as one of the main fungal recognition pathways. Similarly, mannose-binding lectins play a key role in activating antifungal responses by immune cells. Even in the face of similarities, the distinct impacts and degrees of affinity of fungal recognition for mannose receptors in amoeboid and animal hosts are poorly understood. In this work, we have identified high-affinity ligands for mannosylated fungal cell wall residues expressed on the surface of amoebas and macrophages and determined the relative importance of these pathways in the antifungal responses comparing both phagocytic models. Mannose-purified surface proteins (MPPs) from both phagocytes showed binding to isolated mannose/mannans and mannosylated fungal cell wall targets. Although macrophage MPPs had more intense binding when compared to the amoeba receptors, the inhibition of this pathway affects fungal internalization and survival in both phagocytes. Mass spectrometry identified several MPPs in both models, and in silico alignment showed highly conserved regions between spotted amoeboid receptors (MBP and MBP1) and immune receptors (Mrc1 and Mrc2) and potential molecular mimicry, pointing to a possible convergent evolution of pathogen recognition mechanisms.