Lighting up gene sets in specific regions of bacterial biofilms

Biofilms are microbial communities that are attached to surfaces and embedded in a matrix of exopolysaccharides (EPS), protecting them from external stresses such as antibiotics. To fight and eliminate cheaters and competitors, some bacteria are equipped with the type-VI-secretion system (T6SS), a molecular nano-weapon for injecting lethal toxins into other microorganisms. Pseudomonas aeruginosa, a multi-resistant organism (number 3 in the WHO list) produces three distinct types of EPS, 3 distinct T6SSs, and more than a dozen of T6SS toxins during biofilm growth, known to be controlled by a complex regulatory network of two component systems, c-di-GMP signaling pathways and non-coding small RNAs. However, the stimuli triggering the various regulatory cascades and the spatio-temporal distribution of various cells/clusters that express different subset of these genes in a growing biofilm are poorly understood.

In this project, the student will (1) visualize the expression of EPS and T6SS genes using reporter fluorescent constructs in cells growing in biofilms/macrcolonies in order to produce spatio-temporal expression maps, (2) monitor mixed bacterial communities comprising different lineages of P. aeruginosa strains with various set of T6SS genes, or mixed population of T6SS active P. aeruginosa and T6SS inactive E. coli (the various lineages will be tagged with different fluorophores and imaged using confocal microscopy), and (3) integrate the experimental data to build an integrative mathematical model of the ongoing warfare in biofilms populations as well as profiling EPS and T6SS-producing cells in distinct sub-populations.

Some of the short-term goals include engineering of different P. aeruginosa strains lacking T6SS and fluorescently labelling them. This would enable experimental setup that allows for monitoring mixed bacterial population interactions and lineages distribution. Initial aim is to analyse how differences in interactions between populations change the overall architecture of a bacterial distribution in mixed population. This will be supplemented by use of agent-based modelling of equivalent interactions using an existing simulation platform - Simbiotics. The further plan is to explore these interactions at single cell level by imaging and analysing bacterial biofilms, this being a part of a proposed collaboration with a group from MPI-Marburg