Back

Expanding the toolbox for hypervirulent Klebsiella pneumoniae using the social amoeba Dictyostelium discoideum as a virulence biosensor

Perez, I.; Galvez-Silva, M.; Rojas, D.; Ramos, A.; Chen, Y.; Gan, Y.-H.; Chavez, F. P.; Marcoleta, A. E.

2025-11-15 microbiology
10.1101/2025.11.14.688115 bioRxiv
Show abstract

Hypervirulent Klebsiella pneumoniae (hvKp) represents a critical global threat due to its capacity to cause severe, community-acquired infections in healthy individuals and its convergence with multidrug resistance. However, current methods to evaluate hvKp virulence are limited by their reliance on mammalian models, which are costly, ethically constrained, and unsuitable for high-throughput applications. Here, we introduce a biosensing approach using the social amoeba Dictyostelium discoideum as a living phenotypic sensor for hvKp virulence. Through a suite of quantitative assays integrating predation resistance, social development, and real-time single-cell motility tracking, we show that D. discoideum can discriminate avirulent, virulent, and hypervirulent K. pneumoniae strains. Using the model hvKp strain SGH10 and its capsule-null mutant {Delta}wcaJ, we demonstrate that capsule production is strongly associated with phagocytosis resistance, developmental arrest, and amoebae paralysis, all quantifiable through imaging and motility-based readouts. Multivariate data integration revealed a clear segregation of virulence phenotypes, validating D. discoideum as a sensitive, scalable, and genetically tractable biosensor for functional virulence detection. This work establishes a conceptual and methodological framework for developing cell-based biosensors that report virulence levels through measurable host behavioral responses and outlines a calibration path to vertebrate endpoints. These systems provide a low-cost alternative to mammalian systems, especially for early-stage evaluation, and a potential foundation for automated high-throughput screening and surveillance of virulence in critical priority bacterial pathogens.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.