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A Gram-negative-selective antibiotic that spares the gut microbiome

Nature volume 630, pages 429–436 (2024)Cite this article

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Abstract

Infections caused by Gram-negative pathogens are increasingly prevalent and are typically treated with broad-spectrum antibiotics, resulting in disruption of the gut microbiome and susceptibility to secondary infections1,2,3. There is a critical need for antibiotics that are selective both for Gram-negative bacteria over Gram-positive bacteria, as well as for pathogenic bacteria over commensal bacteria. Here we report the design and discovery of lolamicin, a Gram-negative-specific antibiotic targeting the lipoprotein transport system. Lolamicin has activity against a panel of more than 130 multidrug-resistant clinical isolates, shows efficacy in multiple mouse models of acute pneumonia and septicaemia infection, and spares the gut microbiome in mice, preventing secondary infection with Clostridioides difficile. The selective killing of pathogenic Gram-negative bacteria by lolamicin is a consequence of low sequence homology for the target in pathogenic bacteria versus commensals; this doubly selective strategy can be a blueprint for the development of other microbiome-sparing antibiotics.

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Fig. 1: Identification of lolamicin.
Fig. 2: Antimicrobial assessment and resistance frequency studies of lolamicin.
Fig. 3: Major binding and transient binding sites of lolamicin in LolCDE.
Fig. 4: In vivo efficacy studies with lolamicin.
Fig. 5: Lolamicin spares the gut microbiome and prevents C. difficile colonization.

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Data availability

All data supporting the findings of this study are available within the paper and the Supplementary Information. Raw sequencing data have been deposited at the NCBI Sequence Read Archive under accession PRJNA1101557. Source data are provided for Figs. 2, 4 and 5, Extended Data Figs. 1–4. Source data are provided with this paper.

Code availability

Source code for data analysis can be found at https://github.com/HPCBio/hergenrother-16S-mouse-2022Sept and https://doi.org/10.5281/zenodo.10980655 (ref. 80).

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Acknowledgements

The authors thank L. Li for LC/MS-MS analysis; L. Dirikolu for the pharmacokinetic data analysis; A. Hernandez, C. Wright, M. Band, E. Hogan and J. Drnevich for PacBio sequencing; and R. Rajabi-Toustani and the Core Facilities at the Carl Woese Institute for Genomic Biology for assistance with confocal imaging. We thank the University of Illinois and the NIH (AI136773 and P41-104601) for funding this work. K.A.M. was a member of the NIH Chemistry–Biology Interface Training Grant (T32-GM136629). R.J.U. is supported by an NIH Ruth Kirschstein Award (F31AI161953) and was a NSF predoctoral fellow. This study used computational resources provided by the Delta Advanced Computing and Data Resource, which is supported by the National Science Foundation (award OAC 2005572) and the State of Illinois. Delta is a joint effort of the University of Illinois Urbana-Champaign and its National Center for Supercomputing Applications. This work used the Anvil system at Purdue University through allocation MCA06N060 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) programme, which is supported by National Science Foundation grants 2138259, 2138286, 2138307, 2137603 and 2138296.

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Authors and Affiliations

  1. Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Kristen A. Muñoz, Rebecca J. Ulrich, Hyang Yeon Lee, Emad Tajkhorshid & Paul J. Hergenrother

  2. Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Kristen A. Muñoz, Rebecca J. Ulrich, Hyang Yeon Lee & Paul J. Hergenrother

  3. Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Archit K. Vasan, Matt Sinclair, Po-Chao Wen & Emad Tajkhorshid

  4. Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Archit K. Vasan, Matt Sinclair, Po-Chao Wen, Emad Tajkhorshid & Paul J. Hergenrother

  5. Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Archit K. Vasan, Po-Chao Wen & Emad Tajkhorshid

  6. High-Performance Computing in Biology, Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Jessica R. Holmes & Christopher J. Fields

  7. Veterinary Diagnostic Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Chien-Che Hung

  8. Department of Veterinary Clinical Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Chien-Che Hung

  9. Department of Pathobiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Gee W. Lau

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  1. Kristen A. Muñoz
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Contributions

K.A.M. and P.J.H. conceived this study. K.A.M. designed and synthesized all novel compounds described and utilized in this Article. K.A.M. and R.J.U. designed and carried out biological experiments. K.A.M. performed aerobic and anaerobic MIC assays, frequency of resistance and time–kill kinetics studies. R.J.U. performed confocal microscopy, co-culture competition experiments, accumulation and frequency of resistance studies. A.K.V., M.S. and P.-C.W. conducted molecular dynamics simulations. J.R.H. and C.J.F. performed bioinformatics and statistical analyses on long read 16S microbiome data. H.Y.L., C.-C.H. and G.W.L. conducted studies with mouse models. K.A.M. and P.J.H. wrote the manuscript with input from R.J.U., A.K.V., M.S., P.-C.W. and E.T. All authors approved the final draft of the manuscript.

Corresponding author

Correspondence to Paul J. Hergenrother.

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Competing interests

The University of Illinois has filed patents on compounds described in this Article on which K.A.M. and P.J.H. are named inventors.

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Nature thanks Gerard Wright and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Previously identified LolCDE inhibitors.

(a) Antibiotic assessment of compounds 1 and 2 against a panel of gram-negative pathogens performed as part of this study. MICs were performed in Mueller Hinton broth per CLSI guidelines and are reported in µg/mL. All experiments were performed in biological triplicate. (b) eNTRy rule parameters (Rotatable bonds, Globularity, Functional Group) of compounds 1 and 2 calculated using eNTRyway33,81. Accumulation determined via previously reported accumulation assay33 and reported in nmol per 1012 colony-forming units (CFUs). Line indicates average accumulation of low accumulating control antibiotics novobiocin, fusidic acid, erythromycin, and rifampicin. Data shown represents the average of three independent experiments with standard deviation of the mean.

Source data

Extended Data Fig. 2 Growth competition of lolamicin resistant mutants generated in E. coli BW25113 as compared to wild-type (WT) E. coli BW25113.

Fitness of E. coli isolates that harbor the mutations in lolC (a) or lolE (b) conferring resistance to lolamicin at concentrations 32-fold above the MIC (64 µg/mL) were evaluated for growth in culture relative to the parental strain. Bacteria were cultured in cation-adjusted Mueller Hinton broth at a starting density of 5 × 103 CFUs/mL and grown for 48 h at 37 °C. At time = 0 hr, 24 hr, and 48 hr, cultures were serially diluted and plated on LB agar and LB agar containing 8 µg/mL lolamicin to quantify number of wild-type and lolamicin resistant mutants. Each competition between mutant and the parental strain was assessed in biological triplicate. Measurements were compared using a two-sample Welch’s t-test (one-tailed test, assuming unequal variance). NS, not significant (P ≥ 0.05); * (P < 0.05); ** (P ≤ 0.01). LolC-E195K t = 24 hr (P = 0.01), t = 48 hr (P = 0.03); LolC-E255D t = 24 hr (P = 0.87), t = 48 hr (P = 0.05); LolC-Q258P t = 24 hr (P = 0.20), t = 48 hr (P = 0.35); LolC-M262I t = 24 hr (P = 0.44), t = 48 hr (P = 0.18); LolC-N265k t = 24 hr (P = 0.79), t = 48 hr (P = 0.55); LolE-D264N t = 24 hr (P = 0.57), t = 48 hr (P = 0.24); LolE-L199P t = 24 hr (P = 0.22), t = 48 hr (P = 0.02); LolE-I206N t = 24 hr (P = 0.16), t = 48 hr (P = 0.42); LolE-F367S t = 24 hr (P = 0.14), t = 48 hr (P = 0.04). ǂLolE F367S displayed morphological changes and smaller colonies compared to wild-type E. coli BW25113.

Source data

Extended Data Fig. 3 Time-kill kinetics of lolamicin against gram-negative pathogens.

(a) The effect of lolamicin and ciprofloxacin on E. coli BW25113 growth. (b) The effect of lolamicin, tetracycline, and ciprofloxacin on K. pneumoniae ATCC 27736 growth. (c) The effect of lolamicin and ciprofloxacin on E. cloacae ATCC 29893 growth. All experiments were performed in biological triplicate and are represented as mean ± s. e. m.

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Extended Data Fig. 4 Lolamicin induces cell swelling in wild-type E. coli and K. pneumoniae but not in lolamicin-resistant mutants.

Confocal microscopy of (a) E. coli BW25113; lolamicin-resistant mutants, (b) BW25113 LolC-N265K, (c) BW25113 LolE-D264N; (d) K. pneumoniae ATCC 27736; and lolamicin-resistant mutants, (e) LolC-Q258L and (f) LolE-V59L. Scale bar is 10 µm. Antibiotics were tested at the following concentrations (3X MIC or just below the solubility limit for lolamicin in resistant mutants): E. coli—DMSO 2%; lolamicin 8 µg/mL for E. coli BW25113, 64 µg/mL for resistant strains; globomycin 24 µg/mL; mecillinam 0.4 µg/mL; aztreonam 0.1 µg/mL. K. pneumoniae—DMSO 2%; lolamicin 3 µg/mL for K. pneumoniae ATCC 27736 or 64 µg/mL for resistant strains; globomycin 64 µg/mL; mecillinam 3 µg/mL; aztreonam 1.5 µg/mL. Cell size (n = 25) in E. coli (g) and K. pneumoniae (h) and resistant mutants was quantified. Length and width were measured in ImageJ and cell area calculated using the area formula for an ellipse (A = π*ab where a = ½ length and b = ½ width). Measurements were compared using two-sample Welch’s t-test (one-tailed test, assuming unequal variance). NS, not significant (P > 0.05); *** (P < 0.0005). E. coli BW25113: lolamicin (P = 3.44 × 10−18), globomycin (P = 1.00 × 10−15); E. coli BW25113 LolC-N265K: lolamicin (P = 0.28), globomycin (P = 4.16 × 10−10); E. coli BW25113 LolE-D264N: lolamicin (P = 0.09), globomycin (P = 4.44 × 10−12). K. pneumoniae ATCC 27736: lolamicin (P = 3.59 × 10−17), globomycin (P = 3.22 × 10−16); K. pneumoniae ATCC 27736 LolC-Q258L: lolamicin (P = 0.22), globomycin (P = 1.26 × 10−8); K. pneumoniae ATCC 27736 LolE-V59L: lolamicin (P = 0.24), globomycin (P = 2.59 × 10−11).

Source data

Extended Data Fig. 5 Conformational landscape of LolCDE.

(a) High-frequency residue/lolamicin contact probability. (b) Probability density of lolamicin’s center of mass locations projected onto two reaction coordinates: distances to BS1 and BS2 (blue). Overlaid color traces show compound 3 unbinding in five simulation replicates, highlighting consistent and immediate unbinding from BS1. (c) Reaction coordinates (RCs) used to project free energy landscape of transporter. Two orientation-based RCs, opening of the TMD periplasmic region (α) and opening of the TMD intracellular region (β), and two distance-based RCs, distance between the nucleotide binding domains (dNBD), and distance between periplasmic domains (dPD), were used for conformation projections. (d) Free energy landscape projected onto RCs. Red dots indicate position of starting Cryo-EM structure (7MDX). (e) Lolamicin-resistant mutations overlap with binding pocket for lipoprotein substrates of LolCDE. Predicted luminal tunnel displayed as gray surface. (f) Molecular rendering of LolC K195-LolE D264 salt bridge interaction. Initial (transparent purple) and final (opaque purple) conformations of LolC loop demonstrate change in binding pocket shape upon mutation. Donor-acceptor heavy atom distance of 2.69 Å is highlighted for final conformation. Density plot of salt-bridge distance between LolC E/K195-LolE D264 from WT and mutant simulations highlights salt bridge formation with E195K peak density. (g) Lolamicin bound in BS1. Conformational sampling of lolamicin rendered as a density with a single conformer (stick). Mutation sensitive LolE residues D264 and I268 (density and stick), and distant Q198 (stick) shown. (h) LolE-N265K mutation relative to lipid bilayer. Also highlighted is LolC-G357 for which the N/K265-door bar distance was measured. Wider distribution for mutant state demonstrates instability of nearby binding pocket. (i) Root-mean squared fluctuation (RMSF) values from replica simulations of LolE residues 357 to 377 calculated from multiple simulation replicas for WT and F367S mutants. F/S367 is marked with a dashed line. Increased RMSF for mutant is apparent.

Extended Data Fig. 6 Bacterial composition of mouse fecal microbiota obtained by full-length 16 s rRNA sequencing at the Class level.

Taxonomic analysis showing bacterial population shifts over a 31-day period before (Day 0) and after (Day 7, Day 10, and Day 31) administration of antibiotic. CD-1 mice were treated with vehicle (20% DMSO, 30% water, 50% PEG400, n = 6 biologically independent animals) or compound (clindamycin, 100 mg/kg; amoxicillin, 100 mg/kg; or lolamicin, 200 mg/kg; n = 6 biologically independent animals for each compound) twice a day for three days via oral gavage.

Extended Data Fig. 7 Bacterial composition of mouse fecal microbiota obtained by full-length 16 s rRNA sequencing at the Order level.

Taxonomic analysis showing bacterial population shifts over a 31-day period before (Day 0) and after (Day 7, Day 10, and Day 31) administration of antibiotic. CD-1 mice were treated with vehicle (20% DMSO, 30% water, 50% PEG400, n = 6 biologically independent animals) or compound (clindamycin, 100 mg/kg; amoxicillin, 100 mg/kg; or lolamicin, 200 mg/kg; n = 6 biologically independent animals for each compound) twice a day for three days via oral gavage.

Extended Data Fig. 8 PCoA ordination of Bray-Curtis dissimilarity values for samples before (day 0) and after (day 7, day 10) lolamicin, amoxicillin, and clindamycin administration.

Vehicle-treated samples are included as negative controls. Samples are grouped by day and color coded as shown in the key. Points represent individual samples for each grouping. CD-1 mice were treated with vehicle (20% DMSO, 30% water, 50% PEG400, n = 6 biologically independent animals) or compound (clindamycin, 100 mg/kg; amoxicillin, 100 mg/kg; or lolamicin, 200 mg/kg; n = 6 biologically independent animals for each compound) twice a day for three days via oral gavage.

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This file contains synthetic methods and NMR spectra for reported compounds and Supplementary Tables 1–12.

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Muñoz, K.A., Ulrich, R.J., Vasan, A.K. et al. A Gram-negative-selective antibiotic that spares the gut microbiome. Nature 630, 429–436 (2024). https://doi.org/10.1038/s41586-024-07502-0

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