Optimal dose of lactoferrin reduces the resilience of in vitro Staphylococcus aureus colonies

Abstract

The rise in antibiotic resistance has stimulated research into adjuvants that can improve the efficacy of broad-spectrum antibiotics. Lactoferrin is a candidate adjuvant; it is a multifunctional iron-binding protein with antimicrobial properties. It is known to show dose-dependent antimicrobial activity against Staphylococcus aureus through iron sequestration and repression of β-lactamase expression. However, S. aureus can extract iron from lactoferrin through siderophores for their growth, which confounds the resolution of lactoferrin's method of action. We measured the minimum inhibitory concentration (MIC) for a range of lactoferrin/ β-lactam antibiotic dose combinations and observed that at low doses (< 0.39 μM), lactoferrin contributes to increased S. aureus growth, but at higher doses (> 6.25 μM), iron-depleted native lactoferrin reduced bacterial growth and reduced the MIC of the β-lactam-antibiotic cefazolin. This differential behaviour points to a bacterial population response to the lactoferrin/ β-lactam dose combination. Here, with the aid of a mathematical model, we show that lactoferrin stratifies the bacterial population, and the resulting population heterogeneity is at the basis of the dose dependent response seen. Further, lactoferrin disables a sub-population from β-lactam-induced production of β-lactamase, which when sufficiently large reduces the population's ability to recover after being treated by an antibiotic. Our analysis shows that an optimal dose of lactoferrin acts as a suitable adjuvant to eliminate S. aureus colonies using β-lactams, but sub-inhibitory doses of lactoferrin reduces the efficacy of β-lactams.

Fig 1. Kinetic model of lactoferrin/ β-lactam interaction with bacterial population that produces β-lactamase when treated with an antibiotic.

Fig 2. Kinetic model prediction vs experimental data.

The kinetic ode model was parameterised to fit the experimental data, the model results (solid lines) are plotted against the experimental data (points). One model ([Fe³⁺]) parameter was a function of the lactoferrin Fe³⁺ saturation level, all others remained the same.

Fig 3. Absorbance and bioluminescence estimates of S. aureus 12–18 hours post treatment by Cefazolin+lactoferrin adjuvant.

Markers correspond to concentrations of Cefazolin (eight for absorbance and four for bioluminescence). Each point on the graph represents a biological replicate of three estimates for that lactoferrin concentration. Bioluminescence qualitatively replicates the optical density-based estimate of the population for all the four combined treatments. The data highlights the nonlinear dependence of bacterial growth on lactoferrin adjuvance and its iron-saturation. a, d) Native lactoferrin b, e) Mixed lactoferrin c, f) Saturated lactoferrin.

Fig 4. Model predicted population recovery profiles as a function of lactoferrin dose for 1.0 μg/ml Cefazolin.

(a) Log density of bacteria, the dots show the times at which the population reaches 50% of the carrying capacity. (b) Log densities of each subpopulation N (susceptible), N_Lf (LF bound susceptible), P (Persister), P_Lf (LF bound Persister).

Fig 5. Resilience and differential growth rates of the bacterial population as a function of native lactoferrin/ β-lactam concentrations.

a) Average resilience data from a stochastic (N = 10000) simulation for 8 different doses of Cefazolin, 12 different doses of native lactoferrin, and with different initial susceptible and persister cell fractions are plotted. The variability in the initial population leads to non-unitary value for no treatment condition. b) Difference in maximum growth rates between susceptible (fast growing) and persister (slow growing) cells as function of native lactoferrin/ β-lactam concentrations. Values are the average difference between the maximum growth rates for these populations from a stochastic (N = 10000) simulation with different initial susceptible and persister cell fractions.