2026/02/06 by Giulia Bottacin, Benjamin Raach, Leonard Fröhlich +10 · 1 voice
Biochemistry, Genetics and Molecular Biology · Social Sciences · #Bacterial biofilms and quorum sensing #Evolutionary Game Theory and Cooperation #Gene Regulatory Network Analysis
paper · doi:10.64898/2026.02.04.703747
Abstract Many microbial communities form multispecies biofilms where cells interact through diffusible molecules. In these biofilms, multiple interactions, often with opposing effects, occur simultaneously, yet we lack quantitative frameworks to predict how they combine to shape community functions. Here, we hypothesized that complex spatial patterns can emerge when opposing interactions have distinct spatial ranges. To test this, we studied how two Pseudomonas aeruginosa exoproducts, HQNO and rhamnolipids, jointly modulate Staphylococcus aureus antibiotic tolerance by respectively increasing and decreasing it. Using microfluidics-based imaging, we quantified spatial-tolerance patterns at single-cell resolution and found that tolerance indeed shows a complex spatial pattern: S. aureus cells survived treatment only at intermediate distances from P. aeruginosa , while cells closer or farther away did not. Combining experiments and modelling, we showed that this remarkable pattern emerges because rhamnolipids have a stronger but short-ranged effect, while HQNO has a weaker but longer-ranged effect. We found that spatial arrangement affects overall tolerance by shifting the balance between the two opposing interactions. Finally, using bioprinting, we confirmed that HQNO and rhamnolipids modulate tolerance in highly mixed biofilms. In more segregated biofilms, spatial arrangement still strongly modulated tolerance, but independently of these compounds, suggesting additional interactions. Together, our results show that spatial-tolerance patterns emerge from the combined effect of opposing range-dependent interactions and cannot be predicted from either alone. By predicting how opposing interactions jointly determine community properties, our framework provides a foundation for understanding and ultimately engineering microbiome functions.