Biofilms are complex microbial communities that adhere to surfaces and interfaces, exhibiting distinct and emergent characteristics compared to the sessile state of planktonic cells [1]. Biofilms create favorable conditions for colonization on the surface of biomedical devices and inside tissues, show resistance to abiotic and biotic stress factors, and display intense inter-species and intra-species signaling interactions between microorganisms [2]. While most studies focus on single-species models, in real-world scenarios, biofilms are mixed-species communities consisting of different microorganisms such as bacteria, archaea, protists, and fungi. These open systems are open to influx and contamination from the surrounding environment [3].
Mixed-species biofilms might pose significant challenges in healthcare, contributing to difficult-to-treat infections and the emergence of antimicrobial-resistant microorganisms through horizontal gene transfer. However, most studies focus on single-species biofilms, necessitating costly animal models for validation. Thus, there is a knowledge gap regarding the understanding of mixed-species biofilms in open systems. Their composition makes their characterization with current methods (such as sequencing) difficult and expensive. Furthermore, no single technology is available for monitoring mixed-species biofilms in medical and industrial research [4].
The need for the development of quick and low-cost methods for characterizing mixed-species biofilms is crucial for rapid pathogen identification in biofilm infections, water systems, and surface-associated infections to achieve improved health outcomes and reduced maintenance expenses. In this work, we hypothesize that bioelectrochemical methods can be combined with probabilistic machine learning (PML) analysis for rapid characterization of mixed-species biofilms [5]. Specifically, we design and optimize a short-term amperometric method to probe early mixed biofilms in static bioelectrochemical cells.
References
[1] https://doi.org/10.1038/nrmicro.2016.94
[2] https://doi.org/10.1016/j.bjm.2016.10.005
[3] https://doi.org/10.1038/s41467-020-15165-4
