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Genetic variation of staphylococcal LukAB toxin determines receptor tropism

Nature Microbiology volume 6, pages 731–745 (2021)Cite this article

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Abstract

Staphylococcus aureus has evolved into diverse lineages, known as clonal complexes (CCs), which exhibit differences in the coding sequences of core virulence factors. Whether these alterations affect functionality is poorly understood. Here, we studied the highly polymorphic pore-forming toxin LukAB. We discovered that the LukAB toxin variants produced by S. aureus CC30 and CC45 kill human phagocytes regardless of whether CD11b, the previously established LukAB receptor, is present, and instead target the human hydrogen voltage-gated channel 1 (HVCN1). Biochemical studies identified the domain within human HVCN1 that drives LukAB species specificity, enabling the generation of humanized HVCN1 mice with enhanced susceptibility to CC30 LukAB and to bloodstream infection caused by CC30 S. aureus strains. Together, this work advances our understanding of an important S. aureus toxin and underscores the importance of considering genetic variation in characterizing virulence factors and understanding the tug of war between pathogens and the host.

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Fig. 1: CC-specific LukAB variants exhibit different cytotoxic activities towards human leukocytes.
Fig. 2: CC30 and CC45 LukAB kill cells lacking CD11b or CD18.
Fig. 3: Identification of HVCN1 as a cellular target for the CC30 and CC45 LukAB variants.
Fig. 4: CC30 S. aureus kills leukocytes in a LukAB- and HVCN1-dependent manner.
Fig. 5: Human HVCN1 mediates CC30 and CC45 LukAB binding and cytotoxicity.
Fig. 6: LukAB targeting of HVCN1 promotes S. aureus pathogenesis.

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Data availability

The data generated during this study are available upon request with no restrictions. Source data are provided with this paper.

Code availability

Any code generated during this study is available upon request with no restrictions.

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Acknowledgements

We are thankful to members of the Torres and Planet laboratories for insightful discussions and comments on the manuscript, and to H. Behrens, B. Dunn, M. Abrams and R. Plessel for help with reagents, and Z. Chen for help with data analysis. We thank T. Lhakhang (NYU Grossman School of Medicine’s Applied Bioinformatics Laboratories) for help with the bioinformatics analysis of the CRISPR–Cas9 screen, and the NYU Grossman School of Medicine’s Genome Technology Center for help with next-generation sequencing. Last, we acknowledge E. Campeau, P. Kaufman, C. Rice, N. Alto, J. Schoggins, K. Mostov, T. Foster, R. Osicka and F. Zhang for providing reagents. This work was supported in part by the National Institutes of Health–National Institute of Allergy and Infectious Diseases award numbers R01 AI099394, R01 AI105129, R01 AI121244 and contract HHSN272201400019C (to V.J.T.), R01 AI137336 and R01 AI140754 (to B.S. and V.J.T.), T32 AI007180 (to D.B.A.J., E.E.Z. and K.T.), and F32 AI122486 (to D.B.A.J.). S.S.P. was supported by the Jan Vilcek/David Goldfarb Fellowship Endowment Funds and the Bernard B. Levine Program for Postdoctoral Research Fellowships in Immunology. K.M.B. was supported by the Vilcek MSTP Scholars award and T32 GM007308, and C.W.N. was supported by a Gerstner Scholars Fellowship from the Gerstner Family Foundation at the American Museum of Natural History, and a Postdoctoral Research Fellowship from Academia Sinica. P.J.P. and A.M.M. were supported by R01 AI137526. The Genome Technology Center and the flow cytometry technologies are partially supported by the Cancer Center Support Grant P30 CA016087 from the National Institutes of Health–National Cancer Institute at the Laura and Isaac Perlmutter Cancer Center. V.J.T. is a Burroughs Wellcome Fund Investigator in the pathogenesis of infectious diseases.

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Author notes

  1. These authors contributed equally: Sofya S. Perelman, David B. A. James, Kristina M. Boguslawski.

Authors and Affiliations

  1. Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA

    Sofya S. Perelman, David B. A. James, Kristina M. Boguslawski, Juliana K. Ilmain, Erin E. Zwack, Rachel A. Prescott, Adil Mohamed, Kayan Tam, Rita Chan, Miranda B. Pawline, Nikollaq Vozhilla, Meike Dittmann, Damian C. Ekiert, Bo Shopsin & Victor J. Torres

  2. Institute for Comparative Genomics, American Museum of Natural History, New York, NY, USA

    Chase W. Nelson, Apurva Narechania & Paul J. Planet

  3. Biodiversity Research Center, Academia Sinica, Taipei, Taiwan

    Chase W. Nelson

  4. Department of Medicine, Division of Infectious Diseases, New York University Grossman School of Medicine, New York, NY, USA

    Miranda B. Pawline & Bo Shopsin

  5. Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Ahmed M. Moustafa & Paul J. Planet

  6. Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA

    Sang Y. Kim & Sergei B. Koralov

  7. Office of Collaborative Sciences, NYU Grossman School of Medicine, New York, NY, USA

    Sang Y. Kim

  8. Skirball Institute of Biomolecular Medicine and Department of Cell Biology, New York University Grossman School of Medicine, New York, NY, USA

    Damian C. Ekiert & Gira Bhabha

  9. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Paul J. Planet

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  1. Sofya S. Perelman
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Contributions

S.S.P., D.B.A.J., K.M.B. and V.J.T. designed the study. S.S.P., D.B.A.J., K.M.B. and J.K.I. performed experiments. C.W.N., A.N., A.M.M. and P.J.P. performed the bioinformatics analyses, K.T. generated the monoclonal antibody. E.E.Z. performed isolation and sorting of the primary human leukocytes, and R.C. purified recombinant toxins. B.S. provided bacterial strains and insight into the clinical relevance of CC30 strains. S.S.P., K.M.B., S.Y.K. and S.B.K. generated the hHVCN1 mice. N.V. and M.B.P. bred the mice. R.A.P., A.M. and M.D. provided guidance and assisted with microscopy and image analyses. D.C.E. and G.B. provided expertise with purification of recombinant HCVN1–Strep as well as with Octet measurements. S.S.P. and V.J.T. wrote the manuscript, and all authors commented on the manuscript.

Corresponding author

Correspondence to Victor J. Torres.

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Competing interests

V.J.T. is an inventor on patents and patent applications filed by New York University, which are currently under commercial license to Janssen Biotech Inc. (patent nos. US10669329; US10202440; US10087243; MY-175062-A; BR112013032774-0; 60 2014 068 192.1; 40000719B; 1190640B9; 10-2050267; 2012273125; 2012273123; 10,781,246; 10,301,378; 9,783,597; 9,657,103; 9,644,023; 9,481,723; 9,480,726; 9,091,689; 8,846,609; 6758363; 6452765; 6,170,913; 6,093,760; 3441474; 3403669; 3011012; 2,720,754; 2720714; 2,635,462; 2,613,135; 2,609,650; 730359; 710,439; 619,942; 619,938; 357,938; 343,589; 340,446; and 229922). Janssen Biotech Inc. provides research funding and other payments associated with the licensing agreement. All other authors declare no conflicts of interest.

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Extended data

Extended Data Fig. 1 Nonsynonymous (dN) and synonymous divergence (dS) values and ratios for lukA and lukB between clades.

a-b, Mean between-clade dN and dS values are shown below the diagonal, after applying a Jukes-Cantor correction for multiple substitutions at the same sites. Error bars show the standard error of the mean (SEM), estimated using 10,000 bootstrap replicates (codon unit). Corresponding mean between-clade dN/dS ratios are shown above the diagonal, with significance evaluated using a Z-test of the null hypothesis that dN = dS (10,000 bootstrap replicates, codon unit). All P-values are <1.82×10-6 (see corresponding Figure Source Data) such that asterisks *** indicate a significance level of 0.0001 after Bonferroni correction. Mean between-clade estimates were derived by comparing each member of one clade to each member of a second clade for all pairs of clades. For lukA, the numbers of taxa (n) and independent aligned codon positions (L) for each clade are n = 49 and L = 1013 (clade 1/5/8); 6 and 1013 (30/45); 2 and 1008 (10/395); 8 and 1012 (398); 3 and 860 (S. schweitzeri, labeled as S. sch.); and 6 and 1010 (S. argenteus, labeled as S. arg.). For lukB, the numbers of taxa (n) and independent aligned codon positions (L) for each clade are n = 51 and L = 1004 (clade 1/5/8); 8 and 1004 (30/45); 3 and 1004 (10/395); 3 and 1004 (398); 5 and 1004 (S. schweitzeri); and 6 and 966 (S. argenteus). The number of aligned codon positions (L) are given after eliminating positions with ≤2 defined (non-gap, non-ambiguous) codons in either clade, separately for each between-clade comparison.

Source data

Extended Data Fig. 2 Comparison between amino acid sequences of selected mature LukA and LukB.

a,c, Phylogenetic tree based on amino acid sequences of mature LukA (a) and LukB (c) produced by S. aureus belonging to the indicated clonal complexes (CCs). The branch length in the tree is proportional to the number of amino acid substitutions per 100 residues. b,d, Percent identity and divergence of mature LukA (b) and LukB (d) proteins produced by S. aureus belonging to the indicated CCs.

Extended Data Fig. 3 Biolayer interferometry binding curves of CC30 and CC8 LukAB variants binding to CD11b I-domain.

a,b, Binding curves of CC30 (a) and CC8 (b) LukAB toxins to CD11b I-domain. The association and dissociation kinetics of LukAB with the I-domain coated sensor are represented in blue. Toxin concentrations are 400 nM, 200 nM, and 100 nM for CC30 LukAB, or 125 nM, 62.5 nM, and 31.3 nM for CC8 LukAB. Red curves show the best global fit using a 1:1 binding model.

Source data

Extended Data Fig. 4 HVCN1 expression in human white blood cells.

Consensus human blood cell type expression of HVCN1 derived from RNA-seq data from internally generated Human Protein Atlas (HPA) data1. Transcript expression values are presented as Normalized eXpression (NX), resulting from the internal normalization pipeline for 18 blood cell types and total peripheral blood mononuclear cells (PBMC). Data is available at v20.proteinatlas.org/ENSG00000122986-HVCN1/blood, Human Protein Atlas available from www.proteinatlas.org34.

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Extended Data Fig. 5 Generation and evaluation of the HVCN1 humanized mouse model.

a, Schematic representation of murine Hvcn1 locus and DNA template used to humanize exon 4. b, Genotyping strategy using genomic DNA isolated from wild type (WT), heterozygous (het), and homozygous (homo) hHVCN1 mice using primers VJT2065 and VJT2069. Images are representative of multiple independent experiments as routinely performed for hHVCN1 mouse genotyping. c-g, CFUs in the kidneys (c), livers (d), hearts (e), spleens (f), and lungs (g) collected from WT and hHVCN1 mice infected intravenously with 1 × 107 CFU of lukAB-deficient USA300 strain LAC. Data from 11 WT and 10 hHVCN1 mice are represented as mean values ±SEM. Statistical significance was determined by t-test (two-tailed), numbers above bars indicate P values. H-K, CFUs in the livers (h), hearts (i), spleens (j), and lungs (k) collected from WT and hHVCN1 mice infected intravenously with 5-10x107 CFU CC30 S. aureus MUZ211 (CFU obtained from 11 WT and 24 hHVCN1 mice) and 62300D1 (CFU obtained from 11 WT and 10 hHVCN1 mice). Data for each isolate are from mice infected over three independent experiments and is represented as mean values ±SEM. Statistical significance was determined by t-test (two-tailed), numbers above bars indicate P values.

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Extended Data Fig. 6 Flow cytometry gating (part 1).

a, Flow cytometry gating scheme utilized to measure surface CD11b levels in scramble shRNA (top) and ITGAM shRNA (bottom) expressing THP1 cell (Fig. 2a) using APC-conjugated anti-CD11b antibody. b, Flow cytometry gating scheme utilized to measure binding of biotinylated LukAB (CC30 LukAB is shown as an example) to CHO cells expressing Fluc (top) or HVCN1 (bottom) using PerCP/Cy5.5-conjugated streptavidin staining (Fig. 5b,c). c, Flow cytometry gating scheme utilized to measure membrane damage in B cells following treatment with PBS control (top) and LukAB (CC30 LukAB is shown as an example, bottom) using Fixable Viability Dye eFluor™ 450 (Fig. 5e). d-e, Flow cytometry gating scheme utilized to measure membrane damage in CD4-positive (d) and CD8-positive (e) T cells following treatment with PBS control (top) and LukAB (CC30 LukAB is shown as an example, bottom) using Fixable Viability Dye eFluor™ 450 (Fig. 5e).

Extended Data Fig. 7 Flow cytometry gating (part 2).

a, Flow cytometry gating scheme utilized to measure membrane damage in PECs after treatment with PBS control (top) and leukocidins (LukED is shown as an example, bottom) using Fixable Viability Dye eFluor™ 450 (Fig. 6a). b, Flow cytometry gating scheme utilized to measure membrane damage in Lenti-X 293 T cells expressing C-terminal GFP-tagged wildtype HVCN1 and chimeric proteins (human HVCN1 is shown as an example) following treatment with PBS control (top) and CC30 LukAB (bottom) using Fixable Viability Dye eFluor™ 450 (Fig. 6d).

Supplementary information

Supplementary Information

Supplementary Tables 4 and 5 and supplementary references.

Reporting Summary

Supplementary Tables

Table 1: Related to Fig. 1. S. aureus sequences used in the phylogeny analyses. Table 2: Related to Fig. 1. Amino acid sequences of selected mature LukA and LukB produced by S. aureus belonging to the selected CCs. Table 3: Related to Fig. 3. sgRNAs enriched following CC30 LukAB selection.

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Perelman, S.S., James, D.B.A., Boguslawski, K.M. et al. Genetic variation of staphylococcal LukAB toxin determines receptor tropism. Nat Microbiol 6, 731–745 (2021). https://doi.org/10.1038/s41564-021-00890-3

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