Neutrophil ageing is regulated by the microbiome

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Abstract

Blood polymorphonuclear neutrophils provide immune protection against pathogens, but may also promote tissue injury in inflammatory diseases1,2. Although neutrophils are generally considered to be a relatively homogeneous population, evidence for heterogeneity is emerging3,4. Under steady-state conditions, neutrophil heterogeneity may arise from ageing and replenishment by newly released neutrophils from the bone marrow5. Aged neutrophils upregulate CXCR4, a receptor allowing their clearance in the bone marrow6,7, with feedback inhibition of neutrophil production via the IL-17/G-CSF axis8, and rhythmic modulation of the haematopoietic stem-cell niche5. The aged subset also expresses low levels of L-selectin5,9. Previous studies have suggested that in vitro-aged neutrophils exhibit impaired migration and reduced pro-inflammatory properties6,10. Here, using in vivo ageing analyses in mice, we show that neutrophil pro-inflammatory activity correlates positively with their ageing whilst in circulation. Aged neutrophils represent an overly active subset exhibiting enhanced αMβ2 integrin activation and neutrophil extracellular trap formation under inflammatory conditions. Neutrophil ageing is driven by the microbiota via Toll-like receptor and myeloid differentiation factor 88-mediated signalling pathways. Depletion of the microbiota significantly reduces the number of circulating aged neutrophils and dramatically improves the pathogenesis and inflammation-related organ damage in models of sickle-cell disease or endotoxin-induced septic shock. These results identify a role for the microbiota in regulating a disease-promoting neutrophil subset.

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Figure 1: Aged neutrophils represent an overly active subset of neutrophils.
Figure 2: Neutrophil ageing is driven by the microbiota.
Figure 3: Microbiota-driven neutrophil ageing is mediated by neutrophil TLRs and Myd88 signalling.
Figure 4: Microbiota depletion reduces vaso-occlusive events in sickle-cell disease.

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Primary accessions

Gene Expression Omnibus

Data deposits

Microarray data have been deposited in the Gene Expression Omnibus under accession code GSE69886.

Change history

  • 23 September 2015

    A minor change was made to the ‘Antibiotic treatment’ section in the Methods.

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Acknowledgements

We are grateful to C. Prophete and P. Ciero for expert technical assistance. We also thank E. Pamer (Memorial Sloan Kettering Cancer Center) for the gift of Tlr2−/− and Tlr4−/− mice; K. Ireland for assistance with the SCD patient study; Z. Chen for the taxonomic microbiota analysis; R. Ng for assistance with the germ-free mice; O. Uche and G. Wang for assistance in cell sorting; D. Reynolds and W. Tran for the microarray assay; and R. Sellers for histopathological analyses. This work was supported by a predoctoral fellowship from the American Heart Association (15PRE23010014 to D.Z.) and R01 grants from the National Institutes of Health (HL069438, DK056638, HL116340 to P.S.F.). Flow cytometry and cell sorting was supported by a Shared Facilities Award from the New York State Stem Cell Science (NYSTEM) Program.

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Authors

Contributions

D.Z. designed and performed experiments, analysed results and wrote the manuscript; G.C., C.X., Y.K., R.B. and J.J. performed experiments and provided valuable inputs on the manuscript; A.M. provided LysM-cre/Myd88fl/fl and Csf2−/− mice and performed experiments; J.J.F. provided germ-free mice and performed experiments; D.M. provided human samples; C.S. and M.M. discussed data and provided valuable input on the manuscript; P.S.F. designed and supervised the study, discussed data and wrote the manuscript.

Corresponding author

Correspondence to Paul S. Frenette.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Phenotypic and functional characterization of aged neutrophils.

a, Flow cytometry analysis of donor neutrophil ageing after adoptive transfer into recipients. Donor neutrophils gated by CD45.1+ and aged neutrophils gated by CD62LloCXCR4hi. b, Ageing and clearance kinetics of donor neutrophils after adoptive transfer into recipients (n = 3 mice). Left y axis, donor neutrophil number relative to the initial number of neutrophils transferred (black dashed line); right y axis, percentage of the aged subset in donor neutrophils (red line). c, d, MFIM analysis of Mac-1 activation of neutrophils harvested from wild-type or Selp−/− mice, labelled by PKH26 (red) and transferred into wild-type recipients. Scale bar, 10 μm. e, Plasma cytokine levels in wild-type and CD169-DTR mice 5 days after diphtheria toxin treatment (n = 5 mice). f, Percentages of adherent neutrophils that capture more than eight beads in diphtheria-toxin-treated wild-type and CD169-DTR mice (n = 8 mice). g, h, Flow cytometry analysis of surface marker expression (g), cell size (FSC) and granularity (SSC; h; n = 7 mice) on CD62Lhi young and CD62Llo aged neutrophils. i, CXCR4 expression levels on CD62Lhi young and CD62Llo aged neutrophils in wild-type, Selp−/−, and CD169-DTR mice (wild type, n = 13 mice; Selp−/−, n = 4 mice; CD169-DTR, n = 5 mice). Error bars, mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, data representing two or more independent experiments analysed with one-way ANOVA (b) or unpaired Student’s t-test (ei).

Extended Data Figure 2 Antibiotic treatment efficiently depletes and alters the composition of the microbiota.

a, Copy numbers of 16S ribosomal DNA in feces from control and antibiotics (ABX)-treated mice (n = 5 mice). b, Principal component analysis of the microbiome composition in control and ABX-treated mice (n = 5 mice). c, d, Percentage of each bacteria genus in total microbiome (n = 5 mice). Error bars, mean ± s.e.m. *P < 0.05, ***P < 0.001, data representing two or more independent experiments analysed with unpaired Student’s t-test (a, d) or permutational multivariate ANOVA (b).

Extended Data Figure 3 Microbiota-derived molecules regulate neutrophil homeostasis and ageing.

a, Numbers of circulating leukocyte subsets in control and antibiotics (ABX)-treated mice (n = 9 mice). b, Bone marrow cellularity and numbers of leukocyte subsets in the bone marrow of control and ABX-treated mice (n = 14 mice). c, Numbers of bone marrow haematopoietic stem and progenitor cells in control and ABX-treated mice (n = 9 mice). d, Spleen cellularity and numbers of leukocyte subsets in the spleen of control and ABX-treated mice (n = 7 mice). e, Flow cytometry analysis of neutrophil–LPS interactions in blood, bone marrow (BM) and spleen 1 h after LPS–FITC gavage (Ctrl, n = 4 mice; LPS–FITC, n = 5 mice). Histogram showing fluorescence intensity on neutrophils gated by Gr-1hi CD115lo SSAhi. f, Numbers of circulating aged neutrophils in control, ABX-treated, and ABX-treated mice fed with peptidoglycan (PGN) or mTriDAP (left, n = 11 (Ctrl), 9 (ABX), 9 (ABX+PGN) mice; right, n = 10 (Ctrl), 10 (ABX), 5 (ABX+mTriDAP) mice). Error bars, mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, data representing two or more independent experiments analysed with unpaired Student’s t-test.

Extended Data Figure 4 Neutrophil homeostasis is altered in germ-free mice.

a, Total white blood cell (WBC) counts and numbers of leukocyte subsets in blood of specific-pathogen-free (SPF) and germ-free (GF) mice (n = 5 mice). b, Total bone marrow (BM) cellularity and numbers of leukocyte subsets in the bone marrow of SPF and germ-free mice (SPF, n = 5 mice; germ-free, n = 4 mice). c, Total spleen cellularity and numbers of leukocyte subsets in the spleen of SPF and germ-free mice (SPF, n = 5 mice; germ-free, n = 4 mice). d, Copy numbers of 16S ribosomal DNA in feces from SPF mice, germ-free mice, germ-free mice reconstituted by fecal transplantation (GF-FT), and antibiotic-treated germ-free mice (GF-ABX; n = 5, 5, 5 and 4 mice, respectively). e, Numbers of total circulating neutrophils in SPF, germ-free, GF-FT, and GF-ABX mice (n = 5, 5, 5 and 3 mice, respectively). Error bars, mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, data representing two or more independent experiments analysed with unpaired Student’s t-tests.

Extended Data Figure 5 Microbiota-driven neutrophil ageing is independent of clearance mechanisms, and mediated by TLRs and Myd88 signalling.

a, Adhesion molecule expression on endothelial cells (ECs) in control and antibiotics (ABX)-treated mice (n = 4 mice). MFI, mean fluorescence intensity. b, Numbers of spleen and liver macrophages in control and ABX-treated mice (left, n = 7 mice; right, n = 4 mice). c, d, Numbers of bone marrow (BM) macrophages (c; n = 19, 19, 10, 10 mice, left to right) and circulating aged neutrophils (d; n = 12, 11, 10, 9 mice, left to right) in diphtheria toxin (DT)-treated control, ABX-treated mice, CD169-DTR, and ABX-treated CD169-DTR mice. e, Flow cytometry analysis of aged neutrophils in wild-type and LysM-cre/Myd88fl/fl mice (n = 12, 10 mice, respectively). f, Percentages of aged neutrophils in wild-type, Tlr4−/− and Tlr2−/− mice (n = 10, 10, 12 mice, respectively). g, Flow cytometry analysis of aged neutrophils in wild-type and Tnf−/− or Csf2−/− mice. h, Percentages of wild-type and LysM-cre/Myd88fl/fl or Tlr4−/− or Tlr2−/− neutrophils in total leukocyte population in chimaeric mice (n = 5 mice). i, Percentages of wild-type and LysM-cre/Myd88fl/fl or Tlr4−/− or Tlr2−/− neutrophils that capture more than eight beads in chimaeric mice (n = 5 mice). Error bars, mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, data representing two or more independent experiments analysed with unpaired Student’s t-test (af) or paired Student’s t-test (h, i).

Extended Data Figure 6 Microbiota depletion inhibits NET formation.

a, Flow cytometry analysis of aged neutrophils in isotype and anti-P/E-selectin antibody-treated mice (n = 6, 5 mice, respectively). b, ROS production of neutrophils from isotype and anti-P/E-selectin antibody-treated mice, as analysed by flow cytometry using dihydrorhodamine 123 (DHR-123; Isotype, n = 10; Abs (P/E), n = 11 mice). Grey lines, background fluorescence of neutrophils from both groups without LPS stimulation. ns, not significant. c, LPS-induced NET formation of neutrophils from control and antibiotics (ABX)-treated mice, as analysed by immunofluorescence staining of DNA (sytox orange), neutrophil elastase (NE) and citrullinated histone 3 (CitH3). Inset, isotype control. Scale bars, 10 μm. d, Quantification of NET formation of neutrophils from isotype and anti-P/E-selectin antibody-treated mice, or from control and ABX-treated mice (left, n = 4 (Isotype), 5 (Abs) mice; right, n = 4 mice). Error bars, mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, data representing two or more independent experiments analysed with unpaired Student’s t-test.

Extended Data Figure 7 Microbiota depletion benefits endotoxin-induced septic shock.

a, Representative images and quantification of in vivo NET formation in liver vasculature of control and antibiotics (ABX)-treated mice challenged with 30 mg kg −1 LPS (n = 3, 4 mice, respectively). Scale bar, 10 μm. b, Quantification of NET biomarkers, plasma nucleosome and DNA, in septic control and ABX-treated animals (n = 4 mice). c, d, Representative images showing CitH3+ neutrophil aggregates (c) and fibrin deposition associated with neutrophil aggregates (d) in septic liver of control and ABX-treated mice. Arrows, diffusive CitH3 and neutrophil elastase (NE) proteins. Insets, isotype controls. Scale bars, 10 μm. e, f, Numbers of CitH3+ neutrophils and neutrophil aggregates (e; left: n = 4 mice; right: n = 40 vessels from 4 mice) and quantification of fibrin deposition (f; n = 4 (Ctrl), 3 (ABX) mice) in septic liver of control and ABX-treated mice. g, Survival time of control, ABX-treated mice, and ABX-treated mice infused with 2 × 106 aged or young neutrophils in septic shock induced by 30 mg kg −1 LPS (n = 16, 10, 13, 6 mice, respectively). Error bars, mean ± s.e.m. *P < 0.05, **P < 0.01, data representing two or more independent experiments analysed with unpaired Student’s t-test (a, e (left), f), Mann–Whitney U-test (b, e (right)) or log-rank test (g).

Extended Data Figure 8 Microbiota depletion affects disease progression in sickle-cell disease.

a, Numbers of circulating leukocyte subsets in hemizygous control (SA), control SCD (SS Ctrl) and antibiotics-treated SCD (SS ABX) mice (SA: n = 8 mice; SS Ctrl: n = 9 mice; SS ABX: n = 9 mice). b, Haemodynamic parameters of mice analysed for neutrophil adhesion and integrin activation. c, Percentages of adherent neutrophils that capture more than eight beads in SA, SS Ctrl and SS ABX mice (n = 4, 3, 3 mice, respectively). d, Correlation between the survival times of SS control and SS ABX mice in acute vaso-occlusive crisis and their spleen weights. R2 = 0.45. e, Scoring of liver damage, liver fibrosis, inflammation and necrosis in SS control and SS ABX mice (n = 8, 9 mice, respectively). f, Flow cytometry analysis of aged neutrophils in healthy controls, SCD patients (SS), and SCD patients on penicillin V prophylaxis (SS-PV). g, Demographics of human subjects analysed for aged neutrophil numbers. ACS, acute chest syndrome; VOC, vaso-occlusive crisis. h, Aged neutrophil numbers in SCD patients grouped by age, gender, hydroxyurea (HU) and penicillin V (Pen V) treatment (Ctrl, n = 9 subjects; SS, n = 23 subjects; SS-PV, n = 11 subjects). Error bars, mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, data representing two or more independent experiments analysed with unpaired Student’s t-test (a, c, h) or Mann–Whitney U-test (e).

Extended Data Table 1 Pathways selected for the analysis of neutrophil functions
Extended Data Table 2 Gene set enrichment analysis of selected pathways in aged and activated neutrophils

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Zhang, D., Chen, G., Manwani, D. et al. Neutrophil ageing is regulated by the microbiome. Nature 525, 528–532 (2015). https://doi.org/10.1038/nature15367

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