On the relationship between Pathogenic Potential and Infective Inoculum

Area of Science:

• Microbiology
• Pathogen-host interactions
• Mathematical modeling in biology

Background:

• Pathogenic Potential (PP) quantifies a microbe's disease-causing ability based on inoculum, disease signs, mortality, and host survival.
• Understanding the relationship between inoculum size and disease outcome is crucial for assessing microbial virulence.

Purpose of the Study:

• To investigate the relationship between pathogenic potential (PP) and infective inoculum (I) in pathogenic fungi using Galleria mellonella as a model host.
• To introduce a new parameter, the pathogenicity constant (kPath), for quantitative comparison of microbial pathogenicity.
• To analyze existing literature data to further understand the variability in PP-inoculum relationships across different microbial species.

Main Methods:

• Infection experiments using two pathogenic fungi in Galleria mellonella, with mortality as the outcome measure.
• Mathematical analysis of the relationship between log(infective inoculum) and the fraction of animals with signs over median survival time (Fs/T).
• Extrapolation of data to determine the lowest pathogenic inoculum (LPI) and calculation of the pathogenicity constant (kPath).

Main Results:

• A negative exponential relationship was observed between PP and I for C. neoformans.
• A linear relationship was found between log(I) and Fs/T, with the slope (kPath) varying between fungal species.
• Microbes were categorized into two groups based on their PP vs. I and Fs/T vs. I relationships: linear (bacteria) and exponential (fungi/eukaryotes).

Conclusions:

• The pathogenicity constant (kPath) provides a novel metric for comparing the relative pathogenicity of different microbial species.
• Distinct mathematical relationships between inoculum and disease progression suggest fundamental differences in host-microbe interactions between bacteria and fungi.
• The study highlights the utility of mathematical modeling in understanding and quantifying microbial pathogenicity.