A nutrient bottleneck controls antibiotic efficacy in structured bacterial populations

The included code includes MATLAB function lines.m and script runsim_codeocean.m for simulations reported in the manuscript “A nutrient bottleneck controls antibiotic efficacy in structured bacterial populations” by Anna M. Hancock, Arabella S. Dill-Macky, Jenna A. Moore, Catherine Day, Mohamed S. Donia, and Sujit S. Datta, submitted for consideration as an article in Nature Communications. Antibiotic resistance is a growing global health threat. Although antibiotic activity is well studied at the single cell level, many infections are caused by spatially structured multicellular populations where consumption of scarce nutrients establishes strong spatial variations in their abundance. These nutrient variations have long been hypothesized to help bacterial populations tolerate antibiotics, since single-cell studies link antibiotic tolerance to metabolic activity, and thus, local nutrient availability. Here, we test this hypothesis by visualizing cell death in structured \textit{Escherichia coli} populations exposed to nutrient (glucose) and antibiotic (fosfomycin). We find that nutrient availability acts as a bottleneck to antibiotic killing, causing death to propagate through the population as a traveling front. By integrating our measurements with biophysical theory and simulations (sample code included here), we establish quantitative principles that explain how collective nutrient consumption can limit the progression of this ``death front,'' protecting a population from a nominally deadly antibiotic dose. While increasing nutrient supply can overcome this bottleneck, in some cases, excess nutrient unexpectedly \emph{promotes} the regrowth of resistant cells. Altogether, this work provides a key step toward predicting and controlling antibiotic treatment of spatially structured bacterial populations, yielding biophysical insights into collective behavior and guiding strategies for effective antibiotic stewardship. The parameters in runsim_codeocean.m, the code run in this demo, recreate data reported in Fig. 3E-G. By changing the input parameters within the script, this same code, which calls the function lines.m to evaluate the model reported in the manuscript, can generate simulation results reported elsewhere in the manuscript. All code was demo’ed on MATLAB 2024a, which can be installed following directions found here: https://www.mathworks.com/help/install/index.html Please contact Anna Hancock (annahancock@princeton.edu) with questions.