Candidiasis is often observed in immunocompromised patients and is the 4th most common cause of bloodstream infections. However, antifungals are limited, so novel antifungal agents are urgently needed. Antimicrobial peptides (AMPs) are considered as potential alternatives of conventional antibiotics. In the present study, antimicrobial peptide protonectin was chemically synthesized and its antifungal activity and mode of action were studied. Our results showed that protonectin has potent antifungal activity and fungicidal activity against the tested fungi cells. Its action mode involved the disruption of the membrane integrity and the inducing of the production of cellular ROS. Furthermore, protonectin could inhibit the formation of biofilm and kill the adherent fungi cells. In conclusion, with the increase of fungal infection, protonectin may offer a new strategy and be considered as a potential therapeutic agent against fungal disease.

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During early embryonic development, a network of regulatory interactions among genes dynamically determines a pattern of differentiated tissues. We show that important timing information associated with the interactions can be faithfully represented in autonomous Boolean models in which binary variables representing expression levels are updated in continuous time, and that such models can provide a direct insight into features that are difficult to extract from ordinary differential equation (ODE) models. As an application, we model the experimentally well-studied network controlling fly body segmentation. The Boolean model successfully generates the patterns formed in normal and genetically perturbed fly embryos, permits the derivation of constraints on the time delay parameters, clarifies the logic associated with different ODE parameter sets and provides a platform for studying connectivity and robustness in parameter space. By elucidating the role of regulatory time delays in pattern formation, the results suggest new types of experimental measurements in early embryonic development.

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A common approach to the modeling of gene regulatory networks is to represent activating or repressing interactions using ordinary differential equations for target gene concentrations that include Hill function dependences on regulator gene concentrations. An alternative formulation represents the same interactions using Boolean logic with time delays associated with each network link. We consider the attractors that emerge from the two types of models in the case of a simple but nontrivial network: a figure-8 network with one positive and one negative feedback loop. We show that the different modeling approaches give rise to the same qualitative set of attractors with the exception of a possible fixed point in the ordinary differential equation model in which concentrations sit at intermediate values. The properties of the attractors are most easily understood from the Boolean perspective, suggesting that time-delay Boolean modeling is a useful tool for understanding the logic of regulatory networks.