Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment.

With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.

Chemorepellent-Loaded Nanocarriers Promote Localized Interference of Escherichia coli Transport to Inhibit Biofilm Formation.

To mitigate antimicrobial resistance, we developed polymeric nanocarrier delivery of the chemorepellent signaling agent, nickel, to interfere with Escherichia coli transport to a surface, an incipient biofilm formation stage. The dynamics of nickel nanocarrier (Ni NC) chemorepellent release and induced chemorepellent response required to effectively modulate bacterial transport for biofilm prevention were characterized in this work. Ni NCs were fabricated with the established Flash NanoPrecipitation method. NC size was characterized with dynamic light scattering. Measured with a zincon monosodium salt colorimetric assay, NC nickel release was pH-dependent, with 62.5% of total encapsulated nickel released at pH 7 within 0-15 min, competitive with rapid E. coli transport to the surface. Confocal laser scanning microscopy of E. coli (GFP-expressing) biofilm growth dynamics on fluorescently labeled Ni NC coated glass coupled with a theoretical dynamical criterion probed the biofilm prevention outcomes of NC design. The Ni NC coating significantly reduced E. coli attachment compared to a soluble nickel coating and reduced E. coli biomass area by 61% compared to uncoated glass. A chemical-in-plug assay revealed Ni NCs induced a chemorepellent response in E. coli. A characteristic E. coli chemorepellent response was observed away from the Ni NC coated glass over 10 µm length scales effective to prevent incipient biofilm surface attachment. The dynamical criterion provided semiquantitative analysis of NC mechanisms to control biofilm and informed optimal chemorepellent release profiles to improve NC biofilm inhibition. This work is fundamental for dynamical informed design of biofilm-inhibiting chemorepellent-loaded NCs promising to mitigate the development of resistance and interfere with the transport of specific pathogens.