Figure 1.
Modeling of the in vitro growth in A. fumigatus.
In the figure is depicted an example of model fit to a single isolate. The growth is characterized by five phases: the lag phase, where no OD-growth changes were observed; the 1st transition period, where the OD change rate increased; the log phase, where the OD change rate was maximal; and the 2nd transition period, where the OD change rate decreased to reach a stationary phase. The output of the mathematical model developed herein shows that two phases are critical to measure fungal growth in vitro: a) the lag phase and b) the linear OD decaying growth phase, which includes the 1st and 2nd transition periods and the log phase. The proposed model (Equation 1) for simulating the growth of A. fumigatus in vitro (blue line) fitted well to the observed growth curves (red line).
Table 1.
Sample correlation coefficients between the growth parameters (τ,ν,λ).
Table 4.
The F-test statistics of nested CSI models compared to the original CSI model.
Table 2.
Differential survival outcome reflects strain-dependent virulence distinction.
Figure 2.
Clustering analysis of strains based on the fungal growth.
The growth-curve parameter (τ,ν,λ) estimates of 30 strains revealed interstrain variability in growth within the WT groups and the non-wild-type isolates. The subscripts indicate the ID numbers of each A. fumigatus strain used for the current study.
Figure 3.
Relationship between the virulence markers MST, and SUR % and CSI and the function g(τ,ν,λ).
The logistic function was used to fit three different virulence markers: the median survival time (MST, panel A), the survival percentage (SUR %, panel B) and the composite survival index (CSI, panel C). No in vitro-in vivo correlation was found between MST and g(τ,ν,λ), whereas a strong correlation was found for the other two virulence markers. The symbols correspond to the observed CSI values, while the solid line is the outcome of the prediction model.
Table 3.
Descriptive statistics of fitting the g(τ,ν,λ) function against the composite survival index (CSI), the percentage of survival (SUR%) and the median survival time (MST) for the inoculum 107 CFU.
Figure 4.
Leave one out-cross validation analysis.
The CSI model describes the relationship between fungal growth and virulence better than the SUR% model because 12 of the 15 isolates had less standardized residuals for CSI than for SUR% (markers below the diagonal) (p<0.02). The shapes and colors of the symbols used for the observed data represent the same isolates as those defined in Figure 3.
Figure 5.
Relationship between CSI and g(τ,ν,λ) by systemic infection of mice with four different inocula.
No clear sigmoidal relationship was found between CSI and g(τ,ν,λ) for the groups infected with 1×106, 5×106, and 5×107 CFU per mouse. Changes in the virulence of each single strain due to different inocula indicate that CSI was also dependent on the degree of infection by the inoculums.
Table 5.
Rank of the fifteen clinical A. fumigatus isolates used for the evaluation of virulence in a murine model of disseminated aspergillosis based on the observed CSIs.