Quantifying Variation in Bacterial Reproductive Fitness: a High-Throughput Method

Area of Science:

• Microbiology and Infectious Diseases
• Bacterial Pathogenesis and Evolution
• Computational Biology and Bioinformatics

Background:

• Assessing bacterial reproductive fitness is crucial for understanding antimicrobial resistance (AMR) evolution and persistence.
• Existing methods for analyzing bacterial fitness are often costly and not high-throughput, limiting broad applicability.
• There is a need for accessible, cost-effective tools to quantify bacterial fitness, especially for AMR surveillance.

Purpose of the Study:

• To develop and validate a low-cost, high-throughput method for analyzing bacterial growth and reproductive fitness on agar plates.
• To evaluate changes in bacterial fitness, particularly in strains with and without antimicrobial resistance.
• To establish a link between bacterial fitness and potential clinical outcomes in severe infections.

Main Methods:

• Bacterial quantitative fitness analysis (BaQFA) involves arraying bacterial cultures on agar, sequential time-lapse photography, and image analysis.
• Open-source software is used to derive normalized image intensity (NI) values, which correlate with bacterial counts (CFU/ml).
• A Gompertz growth model is fitted to NI values to calculate fitness parameters and relative competitive fitness (RCF).

Main Results:

• BaQFA accurately constructed bacterial growth curves and fitted Gompertz models, with NI values strongly associating with CFU/ml counts (P < 0.001).
• Significant fitness differences were detected between bacterial strains, including methicillin-resistant *Staphylococcus aureus* (RCF, 1.58; P < 0.001) and vancomycin-resistant *Enterococcus faecium* (RCF, 1.59; P < 0.001).
• The method demonstrated the higher competitive fitness of resistant strains compared to their susceptible counterparts.

Conclusions:

• BaQFA is a robust, high-throughput, and cost-effective method for quantifying bacterial reproductive fitness and detecting fitness differences.
• This method can provide valuable phenotypic data for antimicrobial resistance analysis and aid in predicting epidemiological persistence.
• BaQFA has the potential to inform patient management and risk stratification in severe bacterial infections, particularly in resource-limited settings.