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Identification of structurally diverse menaquinone-binding antibiotics with in vivo activity against multidrug-resistant pathogens

Nature Microbiology volume 7, pages 120–131 (2022)Cite this article

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Abstract

The emergence of multidrug-resistant bacteria poses a threat to global health and necessitates the development of additional in vivo active antibiotics with diverse modes of action. Directly targeting menaquinone (MK), which plays an important role in bacterial electron transport, is an appealing, yet underexplored, mode of action due to a dearth of MK-binding molecules. Here we combine sequence-based metagenomic mining with a motif search of bioinformatically predicted natural product structures to identify six biosynthetic gene clusters that we predicted encode MK-binding antibiotics (MBAs). Their predicted products (MBA1–6) were rapidly accessed using a synthetic bioinformatic natural product approach, which relies on bioinformatic structure prediction followed by chemical synthesis. Among these six structurally diverse MBAs, four make up two new MBA structural families. The most potent member of each new family (MBA3, MBA6) proved effective at treating methicillin-resistant Staphylococcus aureus infection in a murine peritonitis-sepsis model. The only conserved feature present in all MBAs is the sequence ‘GXLXXXW’, which we propose represents a minimum MK-binding motif. Notably, we found that a subset of MBAs were active against Mycobacterium tuberculosis both in vitro and in macrophages. Our findings suggest that naturally occurring MBAs are a structurally diverse and untapped class of mechanistically interesting, in vivo active antibiotics.

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Fig. 1: Identification of BGCs predicted to encode new MBAs.
Fig. 2: Synthesis and antibacterial activity of syn-BNPs based on BGCs predicted to encode MBAs.
Fig. 3: Bactericidal effects and mode-of-action analysis of six new MBAs.
Fig. 4: Anti-M. tuberculosis activity and mode-of-action analysis of new MBAs.
Fig. 5: Six new MBAs grouped by structural family.
Fig. 6: MBA3 and MBA6 are effective against S. aureus infections in mice.

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Data availability

The biosynthetic gene clusters for MBA1–6 have been deposited in GenBank under accession nos. MZ146900 and MZ146905, respectively. All other data are available in the main text or as supplementary information. The following five publicly available resources were used in this study: antiSMASH (https://antismash-db.secondarymetabolites.org/); MIBiG (https://mibig.secondarymetabolites.org/); Norine (https://bioinfo.lifl.fr/norine/); SANDPUMA (https://bitbucket.org/chevrm/sandpuma); and PubChem databases (https://pubchem.ncbi.nlm.nih.gov/). Source data are provided with this paper.

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Acknowledgements

We thank the High Throughput Screening and Resource Center at The Rockefeller University for their assistance with the ITC experiments. We thank J. Rock at The Rockefeller University for M. smegmatis mc2 155. We thank W. Jacobs Jr at Albert Einstein College of Medicine for M. tuberculosis mc2 6206, mc2 7901 and mc2 6206 with the mLux plasmid. We thank V. Fischetti at The Rockefeller University for S. aureus Newman, D712 and O315. We thank Y. Q. Xiong at the David Geffen School of Medicine at University of California Los Angeles for S. aureus A215 and SA684. We thank E. Skaar at Vanderbilt University Medical Center for S. aureus ΔmenA and ΔmenB. We thank J. Peek and J. Burian at The Rockefeller University for careful proofreading. This work was supported by the National Institutes of Health (grant nos. 1U19AI142731 and 5R35GM122559 to S.F.B.).

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Authors and Affiliations

  1. Laboratory of Genetically Encoded Small Molecules, The Rockefeller University, New York, NY, USA

    Lei Li, Bimal Koirala, Yozen Hernandez, Logan W. MacIntyre, Melinda A. Ternei & Sean F. Brady

  2. Department of Medicine, Center for Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Newark, NJ, USA

    Riccardo Russo

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Contributions

L.L. designed the overall experimental plan for the manuscript, performed most of the experiments presented and wrote the manuscript. B.K. performed the peptide synthesis and assisted with the mouse experiments. Y.H. performed the bioinformatics analysis. L.W.M. contributed to the NMR data analysis. M.A.T. performed the MiSeq sequencing. R.R. performed the antibacterial assay against the WT and MDR M. tuberculosis strains. S.F.B. contributed to project management, designed the overall experimental plan for the manuscript and wrote the manuscript. All authors edited the manuscript and approved the final submission.

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Correspondence to Sean F. Brady.

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The authors declare no competing interests.

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Peer review information Nature Microbiology thanks Michael R. Barbachyn, Stephan A. Sieber and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Phylogenetic analysis of eSNaPD hits from six conserved A-domains found in the BGCs of the three known MBAs.

Phylogenetic analysis of eSNaPD hits (a) and predicted peptide sequences of recovered clones (b). All the hits at an e-value ≤10–45 from A-domain analysis of l-Leu-6 encoded new MBAs and formed a separate, well-defined clade with A-domains of three known MBAs, which suggested that l-Leu-6 in the proposed minimal MK-binding motif were encoded by the most highly conserved A domain in MBA-family peptides.

Extended Data Fig. 2 Three potential MBA BGCs from eSNaPD-guided soil metagenomic mining.

Comparison of NRPS gene organization (a) as well as amino acid substrates (b) between the three known MBA BGCs and the three potential MBA BGCs we cloned from soil metagenomes. The blue residues represent building blocks that are conserved across all MBAs. The green circles represent residues that are shared between known and potential MBAs.

Extended Data Fig. 3 Predicted MBA peptide sequences identified in a motif search of the p-NRP database (a) and the BGCs associated with these predicted peptides (b).

The blue residues represent building blocks that are conserved across all MBAs.

Extended Data Fig. 4 The structures (a) and anti-bacterial activities (b) of the N-acylated peptides associated with known MBAs cyclized in two different ways.

The (R)-3-hydroxy-octanoic acid analogs of lysocin E, WBP-29479A1 and the deoxy version of WAP-8294A1 shown here were synthesized in this study. B. subtilis 168 1A1, S. aureus USA300, S. epidemidis RP62A and M. tuberculosis H37Rv were used as tested strains. The blue residues represent building blocks that are conserved across all MBAs.

Extended Data Fig. 5 Membrane depolarization activity and resistance frequency of MBAs 1 through 6.

a, The effect of each MBA on S. aureus membrane potential was measured using 3,3′-Dipropylthiadicarbocyanine iodide [DiSC3(5)]. Vancomycin (Van) and lysocin were used as the negative and positive controls, respectively. b, Resistance frequency of MBAs 1 through 6 against S. aureus USA300 in the presence of 4x the MIC of each antibiotic.

Source data

Extended Data Fig. 6

Isothermal titration of 1:1 (mol/mol) DOPC:DOPG vesicles containing MK into each MBA.

Source data

Extended Data Fig. 7 Correlation between antibiotic activity and MK binding affinity for active or inactive syn-BNP MBAs.

a, Isothermal titration of 1:1 (mol/mol) DOPC:DOPG vesicles containing MK into the four additional syn-BNPs we generated in Fig. 2. b, Comparison of Kd values and MICs against S. aureus USA300 for all syn-BNP MBAs in Fig. 2.

Source data

Extended Data Fig. 8 Antibiotic activity and MK binding of MBA3 with single point mutations in the proposed minimal MK-binding motif.

MIC in μg/mL, highest concentration tested was 64 μg/mL.

Source data

Supplementary information

Supplementary Information

Supplementary Discussion, Tables 1–5 and Figs. 1–19.

Reporting Summary

Supplementary Data 1

Numerical data for the graphs in Supplementary Fig. 3.

Supplementary Data 2

Numerical data for the graphs in Supplementary Fig. 4.

Source data

Source Data Fig. 3

Numerical data for the graphs in Fig. 3a–c.

Source Data Fig. 4

Numerical data for the graphs in Fig. 4b,c.

Source Data Fig. 6

Numerical data for the graphs in Fig. 6.

Source Data Extended Data Fig. 5

Numerical data for the graph in Extended Data Fig.5a.

Source Data Extended Data Fig. 6

Numerical data for the graphs in Extended Data Fig.6.

Source Data Extended Data Fig. 7

Numerical data for the graphs in Extended Data Fig.7a.

Source Data Extended Data Fig. 8

Numerical data for the graphs in Extended Data Fig.8b.

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Li, L., Koirala, B., Hernandez, Y. et al. Identification of structurally diverse menaquinone-binding antibiotics with in vivo activity against multidrug-resistant pathogens. Nat Microbiol 7, 120–131 (2022). https://doi.org/10.1038/s41564-021-01013-8

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