Biology Faculty Articles
Document Type
Article
Publication Date
12-14-2022
Publication Title
Science Advances
ISSN
2375-2548
Volume
8
Issue/No.
50
First Page
eadd092
Abstract
Understanding the mechanisms by which populations of bacteria resist antibiotics has implications in evolution, microbial ecology, and public health. The inoculum effect (IE), where antibiotic efficacy declines as the density of a bacterial population increases, has been observed for multiple bacterial species and antibiotics. Several mechanisms to account for IE have been proposed, but most lack experimental evidence or cannot explain IE for multiple antibiotics. We show that growth productivity, the combined effect of growth and metabolism, can account for IE for multiple bactericidal antibiotics and bacterial species. Guided by flux balance analysis and whole-genome modeling, we show that the carbon source supplied in the growth medium determines growth productivity. If growth productivity is sufficiently high, IE is eliminated. Our results may lead to approaches to reduce IE in the clinic, help standardize the analysis of antibiotics, and further our understanding of how bacteria evolve resistance.
Additional Comments
This work was supported by National Institutes of Health award R15AI159902 (R.P.S.) and President’s Faculty and Research Development Grant from Nova Southeastern University no. 334853 (R.P.S.).
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
NSUWorks Citation
Diaz-Tang, Gabriela; Estefania Marin Meneses; Kavish Patel; Sophia Mirkin; Laura Garcia-Dieguez; Camryn Pajon; Ivana Barraza; Vijay Patel; Helana Ghali; Angelica P. Tracey; Christopher Blanar; Allison J. Lopatkin; and Robert P. Smith. 2022. "Growth productivity as a determinant of the inoculum effect for bactericidal antibiotics." Science Advances 8, (50): eadd092. doi:10.1126/sciadv.add0924.
ORCID ID
0000-0002-4900-3099, 0000-0003-2744-7390
DOI
10.1126/sciadv.add0924
Comments
Data and materials availability: All raw data, including raw modeling code, can be found in the Dryad Repository using the following link: https://datadryad.org/stash/share/UwtJUF52RV0KJC_3P5l9qmfX2GDto8rqpm3t_T3CH3o. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.