Abstract
The permeability barrier of Gram-negative cell envelopes is the major obstacle in the discovery and development of new antibiotics. In Gram-negative bacteria, these difficulties are exacerbated by the synergistic interaction between two biochemically distinct phenomena, the low permeability of the outer membrane (OM) and active multidrug efflux. In this study, we used Pseudomonas aeruginosa and Escherichia coli strains with controllable permeability barriers, achieved through hyperporination of the OMs and varied efflux capacities, to evaluate the contributions of each of the barriers to protection from antibacterials. We analyzed antibacterial activities of β-lactams and fluoroquinolones, antibiotics that are optimized for targets in the periplasm and the cytoplasm, respectively, and performed a machine learning-based analysis to identify physicochemical descriptors that best classify their relative potencies. Our results show that the molecular properties selected by active efflux and the OM barriers are different for the two species. Antibiotic activity in P. aeruginosa was better classified by electrostatic and surface area properties, whereas topology, physical properties, and atom or bond counts best capture the behavior in E. coli. In several cases, descriptor values that correspond to active antibiotics also correspond to significant barrier effects, highlighting the synergy between the two barriers where optimizing for one barrier promotes strengthening of the other barrier. Thus, both barriers should be considered when optimizing antibiotics for favorable OM permeability, efflux evasion, or both.
Original language | English |
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Pages (from-to) | 1223-1234 |
Number of pages | 12 |
Journal | ACS Infectious Diseases |
Volume | 4 |
Issue number | 8 |
DOIs | |
State | Published - Aug 10 2018 |
Funding
This study was sponsored by the Department of Defense Defense Threat Reduction Agency (HDTRA1-14-1-0019) and by NIH/NIAID Grant No. RO1AI132836. This study was sponsored by the Department of Defense, Defense Threat Reduction Agency (HDTRA1-14-1-0019) and by NIH/NIAID Grant No. RO1AI132836. The content of the information does not necessarily reflect the position or the policy of the federal government, and no official endorsement should be inferred. S.J.C. was supported by NIH/NIGMS-IMSD Grant No. R25GM086761 and a National Science Foundation Graduate Research Fellowship under Grant No.
Funders | Funder number |
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Department of Defense Defense Threat Reduction Agency | |
NIH/NIAID | |
NIH/NIGMS-IMSD | |
National Science Foundation | |
National Institutes of Health | |
U.S. Department of Defense | |
National Institute of General Medical Sciences | R25GM086761 |
National Institute of Allergy and Infectious Diseases | RO1AI132836 |
Defense Threat Reduction Agency | HDTRA1-14-1-0019 |
Keywords
- Gram-negative bacteria
- antibiotic permeation
- machine learning
- multidrug efflux
- outer membrane
- physicochemical properties