Sub-inhibitory antibiotic treatment selects for enhanced metabolic efficiency

Author:

Aduru Sai Varun1ORCID,Szenkiel Karolina2,Rahman Anika2,Ahmad Mehrose2,Fabozzi Maya2,Smith Robert P.3ORCID,Lopatkin Allison J.12456ORCID

Affiliation:

1. Department of Chemical Engineering, University of Rochester, Rochester, New York, USA

2. Department of Biology, Barnard College, New York, New York, USA

3. Cell Therapy Institute, Kiran Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA

4. Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, New York, USA

5. Data Science Institute, Columbia University, New York, New York, USA

6. Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester, New York, USA

Abstract

ABSTRACT Bacterial growth and metabolic rates are often closely related. However, under antibiotic selection, a paradox in this relationship arises: antibiotic efficacy decreases when bacteria are metabolically dormant, yet antibiotics select for resistant cells that grow fastest during treatment. That is, antibiotic selection counterintuitively favors bacteria with fast growth but slow metabolism. Despite this apparent contradiction, antibiotic resistant cells have historically been characterized primarily in the context of growth, whereas the extent of analogous changes in metabolism is comparatively unknown. Here, we observed that previously evolved antibiotic-resistant strains exhibited a unique relationship between growth and metabolism whereby nutrient utilization became more efficient, regardless of the growth rate. To better understand this unexpected phenomenon, we used a simplified model to simulate bacterial populations adapting to sub-inhibitory antibiotic selection through successive bottlenecking events. Simulations predicted that sub-inhibitory bactericidal antibiotic concentrations could select for enhanced metabolic efficiency, defined based on nutrient utilization: drug-adapted cells are able to achieve the same biomass while utilizing less substrate, even in the absence of treatment. Moreover, simulations predicted that restoring metabolic efficiency would re-sensitize resistant bacteria exhibiting metabolic-dependent resistance; we confirmed this result using adaptive laboratory evolutions of Escherichia coli under carbenicillin treatment. Overall, these results indicate that metabolic efficiency is under direct selective pressure during antibiotic treatment and that differences in evolutionary context may determine both the efficacy of different antibiotics and corresponding re-sensitization approaches. IMPORTANCE The sustained emergence of antibiotic-resistant pathogens combined with the stalled drug discovery pipelines highlights the critical need to better understand the underlying evolution mechanisms of antibiotic resistance. To this end, bacterial growth and metabolic rates are often closely related, and resistant cells have historically been characterized exclusively in the context of growth. However, under antibiotic selection, antibiotics counterintuitively favor cells with fast growth, and slow metabolism. Through an integrated approach of mathematical modeling and experiments, this study thereby addresses the significant knowledge gap of whether antibiotic selection drives changes in metabolism that complement, and/or act independently, of antibiotic resistance phenotypes.

Funder

HHS | NIH | National Institute of General Medical Sciences

HHS | NIH | National Institute of Allergy and Infectious Diseases

Publisher

American Society for Microbiology

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