Programmed ‘disarming’ of the neutrophil proteome reduces the magnitude of inflammation

Abstract

The antimicrobial functions of neutrophils are facilitated by a defensive armamentarium of proteins stored in granules, and by the formation of neutrophil extracellular traps (NETs). However, the toxic nature of these structures poses a threat to highly vascularized tissues, such as the lungs. Here, we identified a cell-intrinsic program that modified the neutrophil proteome in the circulation and caused the progressive loss of granule content and reduction of the NET-forming capacity. This program was driven by the receptor CXCR2 and by regulators of circadian cycles. As a consequence, lungs were protected from inflammatory injury at times of day or in mouse mutants in which granule content was low. Changes in the proteome, granule content and NET formation also occurred in human neutrophils, and correlated with the incidence and severity of respiratory distress in pneumonia patients. Our findings unveil a ‘disarming’ strategy of neutrophils that depletes protein stores to reduce the magnitude of inflammation.

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Fig. 1: Diurnal changes in the neutrophil proteome.
Fig. 2: Diurnal loss of NET-forming capacity.
Fig. 3: Degranulation and loss of NET-forming capacity are driven by CXCL2/CXCR2 signaling.
Fig. 4: Diurnal loss of NET formation and pulmonary protection during ALI.
Fig. 5: Diurnal degranulation and pulmonary protection is neutrophil intrinsic.
Fig. 6: Evidence for neutrophil disarming and pulmonary protection in humans.

Data availability

Proteomics data for mouse and human neutrophils are available in the Peptide Atlas with accession number PASS01364. Proteomics data for Bmal1∆N mice at ZT5 and ZT13 and WT vehicle versus AMD3100-treated mice are also available in the Peptide Atlas with the accession number PASS01438. Mouse circadian transcriptomics used for the correlation analysis are available at the Gene Expression Omnibus with accession number GSE102310. Human sequencing data used for the correlation analysis are available at GEO with accession number GSE136632. All other data are available upon request.

Code availability

ImageJ macros for TEM granule quantification and extracellular DNA quantification are available in FigShare (see the relevant section of Methods for specific links). All other code is available upon request.

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Acknowledgements

We thank members of the Comparative Medicine Unit and Advanced Microscopy Unit at CNIC. This study was supported by Intramural grants from the Severo Ochoa program (IGP-SO), a grant from Fundació La Marató de TV3 (120/C/2015-20153032), grant SAF2015-65607-R from Ministerio de Ciencia, Investigacion y Universidades (MCIU) with cofunding from Fondo Europeo de Desarrollo Regional, grant RTI2018-095497-B-I00 from MCIU and HR17_00527 from Fundación La Caixa (to A.H.), and fellowship BES-2013-065550 from MCIU (to J.M.A.), fellowship from La Caixa Foundation (ID 100010434, code LCF/BQ/DR19/11740022, to A.A.-C.) and fellowship Health-PERIS 2016–2020 (to C.C.) Funds were also obtained from Instituto de Salud Carlos III (FIS PI17/01601, to I.L.) and SAF2015-68632-R from MCIU (to M.A.M.); Wellcome Trust Seed Award in Science (206103/Z/17/Z, to D.R.), SFB1123-A1/A10 from Deutsche Forschungsgemeinschaft and ERC-AdG 692511 (to C.W.); SAF2017-84494-C2-R and Programa Red Guipuzcoana de Ciencia, Tecnología e Información 2018-CIEN-000058-01 (to J.R.-C.). Work at CIC biomaGUNE was performed under the Maria de Maeztu Units of Excellence Program from the Spanish State Research Agency (MDM-2017-0720). C.W. is a van de Laar professor of atherosclerosis. The CNIC is supported by the MCIU and the Pro-CNIC Foundation, and is a Severo Ochoa Center of Excellence (MEIC award SEV-2015-0505).

Author information

J.M.A., A.A.-C, G.C., E.B.-K., E.C. and Y.R.-V. performed experiments. J.M.A., D.R. and A.R.-P. performed bioinformatic analysis. F.O. obtained human samples. C.W., M.A.M., J.R.-C., I.L. and J.V. contributed essential reagents, equipment, expertise and funds. C.C. and A.T. contributed clinical data. J.M.A. and A.H. designed and supervised experiments, and wrote the manuscript, which was edited by all authors.

Correspondence to Andrés Hidalgo.

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Peer review information Ioana Visan was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Validation and analysis of neutrophil proteomics.

a, Experimental strategy for proteomic analysis of day-like (from P- and E-selectin treated mice) and night-like (from AMD3100-treated mice) neutrophils isolated by negative selection (see methods) from blood. b, Intracellular staining of proteins from the proteomics dataset for validation in fresh (blue) and aged (violet) neutrophils obtained as indicated in a. All the proteins analyzed correlated with the proteomics data; n = 3 mice per condition. c, GO terms of the differentially expressed proteins (FDR<0.05, see methods section for 18O proteomics) in the proteomics dataset, showing terms with p < 0.05 (from single samples of 60 million neutrophils pooled from 9 mice (night) and 6 mice (day)). Bubble size represents overlap of query vs. the GO term. d, Scatterplot, correlation coefficient and significance level (pvalue) of the Spearman’s correlation of the direction of change of common proteins and genes from our proteomic analysis of fresh and aged neutrophils (this paper) and circadian RNA-sequencing data previously reported (Adrover et al. 2019, from 3 mice at ZT5 and 3 mice at ZT13), showing poor correlation of RNA and protein content. e, Venn-diagram showing the number of differentially detected proteins (p < 0.05, see methods section for mouse TMT proteomics) between vehicle- and AMD3100-treated mice (at ZT5); n = 3 samples per group. f, Heatmap showing levels of granule proteins in this dataset, Note increased detection of most granule proteins in neutrophils from AMD3100-treated mice. Data in (b) are shown as mean ± SEM. *; p < 0.05; **, p < 0.01; ***, p < 0.001, as determined by unpaired two-tailed t-test analysis.

Extended Data Fig. 2 Degranulation of neutrophils in the circulation and in tissues.

a, Reactome pathway analysis of the proteome of night and day neutrophils (see methods section for 18O proteomics, from single samples of 60 million neutrophils pooled from 9 mice (night) and 6 mice (day)) showing pathways with p-value < 0.05. b, Light-scattering properties (a measure of granularity) of blood neutrophils during a full diurnal cycle, measured as side-scatter in flow cytometry. Data are for WT, CXCR2-, CXCR4- or Bmal1-deficient neutrophils, showing that cell-intrinsic disruption of clock regulators blunts diurnal fluctuation in granularity. Curves are repeated for two cycles (dashed line) to better appreciate the circadian pattern; n = 10 (WT), 3 (CXCR2∆N), 4 (CXCR4∆N) and 4 (Bmal1∆N) mice per time point. c, Kinetics of total (top) or aged (bottom) neutrophils in blood indicating times of release of young or accumulation of aged neutrophils; n = 5 mice (ZT13, ZT17, ZT21, ZT1 and ZT9), n = 4 mice (ZT5). d, Shift of the light cycle alters the pattern of granule content in neutrophils. Left, representative confocal images of sorted neutrophils (MPO, green; DAPI, blue; scale, 5 μm); right, granule content per cell at the indicated times and light regime; n = 3 mice. LD, light-dark cycle; DL, dark-light (inverted) cycle. e, Representative confocal images (scale, 1 μm) and f, quantification of granule content in neutrophils from the blood or tissues of WT mice, showing reduced granule counts in tissues compared with blood; n = 30 (blood, lung and spleen), n = 27 (Liver) cells from 3 mice. Data are shown as mean ± SEM. *; p < 0.05; ***, p < 0.001; n.s., not significant, as determined by one-way ANOVA with Dunnet’s multiple comparison test (d), or using the amplitude vs. zero two-tailed t-test for circadian curves (b).

Extended Data Fig. 3 CXCR2-deficient neutrophils are responsive to activating stimuli.

a, Representative confocal images (scale, 2 μm) and b, quantification of granule content (top) and MPO intensity (bottom) in CXCR2-deficient neutrophils upon LPS or PMA stimulation. Granule loss indicates that CXCR2-deficient neutrophils are responsive to inflammatory stimuli; n = 15 cells per group. Data are shown as mean ± SEM. **; p < 0.01; ***, p < 0.001, as determined.

Extended Data Fig. 4 Regulation of circadian patterns by Bmal1.

a, Representative confocal images (left) and quantification of granule content (right) in Bmal1-deficient neutrophils at ZT13 (night) and ZT5 (day). n = 30–31 cells from 3 mice; scale 2 μm. b, Ex vivo NET formation by Bmal1-deficient neutrophils at ZT5 and ZT13. Note that Bmal1-deficient neutrophils fail to display circadian oscillations in both granule and NET formation. n = 3 mice per time point. c, Experimental design of circadian proteomic analysis of Bmal1-deficient neutrophils. d, Granule proteins (left) and NET-associated proteins (right) in the circadian Bmal1∆N neutrophil proteome (n = 3 mice at ZT5 and n = 2 at ZT13). Black dots show all granule or NET-associated proteins, respectively, none of which reached significance in differential expression between night and day (FDR < 0.05, see methods section for TMT proteomics of mouse neutrophils). e, Heatmap of granule proteins in the circadian proteome of wild-type (same as in Fig. 1) and Bmal1∆N neutrophils. Note that the diurnal pattern is lost in Bmal1-deficient neutrophils. Data in (a-b) are shown as mean ± SEM; n.s., not significant, as determined by unpaired two-tailed t-test.

Extended Data Fig. 5 Normal circadian oscillations in Balb/c mice.

a, Total (left) and CD62LO aged (right) neutrophil counts in the blood of Balb/c mice; n = 4–5 mice per time. b, Circadian oscillations in CD62L and CXCR2 expression in neutrophils from Balb/c mice, measured as median fluorescence intensity (MFI) by flow cytometry n = 5 mice (ZT13, ZT17, ZT21, ZT1 and ZT9), n = 4 mice (ZT5). c, Side scatter values plotted together with surface levels of CD62L in neutrophils, showing similar fluctuation patterns as reported for C57BL/6 neutrophils; n = 5 mice (ZT13, ZT17, ZT21, ZT1 and ZT9), n = 4 mice (ZT5). All curves are repeated for two cycles (dashed line) to better appreciate the circadian pattern. Data are shown as mean ± SEM. P values were determined by the amplitude vs. zero two-tailed t-test.

Extended Data Fig. 6 Neutrophils and platelets in the lung microvasculature during ALI.

Quantification of neutrophil a, and platelet b, numbers per field of view over time in wild-type mice subject to ALI at night (ZT13, blue line) or during daytime (ZT5, red line), in the intravital imaging experiments shown in Fig. 4c. Insets show area under the curve (AUC) values; n = 15 fields from 4 mice in each group. c, Neutrophil numbers in the lungs of naïve, LPS-only and wild-type mice in which ALI was induced at ZT5 (n = 8 mice) or ZT13 (n = 5 mice), or at ZT5 in the presence of Cl-amidine (n = 5 mice), as determined by flow cytometry. Neutrophils d, and platelets e, numbers in mutant mice (Bmal1∆N purple line; CXCR4∆N blue line) from the intravital imaging experiments shown in Fig. 5d; n = 15 fields from 4 mice in each genotype. Insets show area under the curve (AUC) values. f, Neutrophil numbers in the lungs of LPS-only control mice (n = 5) or during ALI in Bmal1ΔN (n = 4 mice) or Cre- control (n = 4 mice); and CXCR4ΔN (n = 5 mice) or Cre- control mice (n = 4 mice), as determined by flow cytometry. g, Interactions between platelets and the uropod (U) or leading edge (LE) of adherent neutrophils, in the inflamed cremasteric microvessels of wild-type (n = 45 from 3 mice), Bmal1ΔN (n = 28 from 3 mice) and CXCR4ΔN mice (n = 31 from 3 mice); scale, 5 μm. Data are shown as mean ± SEM. **; p < 0.01; ***, p < 0.001; n.s., not significant, as determined by unpaired two-tailed t-test analysis (a-d) or one-way ANOVA with Dunnet’s multiple comparison test (e-g). In the insets in a-d, individual data points are not shown as this graph uses a mean ± SEM value for the area under the curve calculated from the data shown in the respective panels.

Extended Data Fig. 7 Vascular leakiness and types of NETs during ALI.

Vascular leakiness in a, wild-type, b, Bmal1∆N, and c, CXCR4∆N mice after induction of ALI (LPS + antibody) or in control mice treated with LPS only. WT and Bmal1∆N mice displayed increased leakiness only in lungs upon ALI induction, while CXCR4∆N mice were protected; n = 3 (LPS only) and 5 (ALI) mice per genotype; d, Time-lapse images showing examples of flowing and adherent NETs (asterisks) as observed by intravital imaging of the lung microvasculature during ALI, representative of n = 3 independent experiments. See also Supplementary Movie 5. e, Relative frequency of NET types in WT, Bmal1∆N and CXCR4∆N mice during ALI, n = 15 fields from 3 mice (WT), 10 fields from 3 mice (Bmal1∆N) and 15 fields from 3 mice (CXCR4∆N). Data are shown as mean ± SEM. *; p < 0.05; ***, p < 0.001; n.s., not significant, as determined by two-way ANOVA (a-c; unless otherwise specified, comparisons did not reach significance; and e).

Extended Data Fig. 8 Loss of circadian patterns in Bmal1∆N and CXCR4∆N mice.

a, Survival of wild-type, Bmal1∆N and CXCR4∆N mice subjected to ALI at night (ZT13, solid line) or daytime (ZT5, dashed line); n = 16 mice (ZT5) and 18 mice (ZT13) for wild-type, n = 10 mice per time point for Bmal1∆N; n = 12 mice (ZT5) and 14 mice (ZT13) for CXCR4∆N. b, Representative confocal images (top) and quantification of granule content (bottom) in CXCR4-deficient neutrophils at ZT13 and ZT5. Note the loss of diurnal fluctuations compared with wild-type mice (see Fig. 1e); n = 30 cells (from 3 mice) per time point; scale, 2 μm. c, Ex vivo NET formation after PMA stimulation by CXCR4∆N neutrophils analyzed at ZT13 (n = 3 mice) and ZT5 (n = 3 mice). Note the loss of diurnal changes in NET-formation compared with wild-type cells (see Fig. 2b); d, Neutrophil counts in blood at ZT5 and ZT13 in wild-type (n = 5 mice at ZT5 and 4 mice at ZT13) and Bmal1∆N mice (n = 4 mice per time point). Data are shown as mean ± SEM. *, p < 0.05; **, p < 0.01; n.s., not significant, as determined by two-sided log rank (Mantel-Cox) test (a) or unpaired two-tailed t-test (b-d).

Extended Data Fig. 9 Analysis of the human neutrophil proteome.

a, Experimental design. Blood from 10 healthy volunteers was extracted at 8 am, 2 pm and 7 pm. Neutrophils were purified for proteomic analysis, granule quantification and NET-formation assays. b, GO terms of the differentially expressed proteins between 8am and 2 pm in human neutrophils. c, Correlation analysis (Spearman) of the direction of change of common proteins and genes from paired human proteomic (n = 5 per time) and RNA sequencing (n = 3 per time) analysis, showing poor correlation of RNA and protein content. d, Volcano plot of the human neutrophil proteome highlighting proteins found in NETs. Red dots and labels show proteins that are significantly different among samples (p < 0.05), and black dots show all other NET proteins, and dots show the whole proteome dataset.

Extended Data Fig. 10 Graphical abstract.

Neutrophils are released from the bone marrow into the bloodstream enriched in granule-held antimicrobial, cytotoxic and NET-forming proteins. As they spend time in the circulation, they undergo a homeostatic process of proteome ‘disarming’ that is regulated by the clock gene Bmal1 and signaling through CXCR2. This process causes a reduction in granule content and their ability to form NETs, ultimately reducing their toxicity towards host tissues. During acute lung injury, the presence of granule-poor neutrophils at specific times of day or in CXCR4 mutants protects the lungs and increases survival. Disabling homeostatic degranulation in Bmal1 mutants, in contrast, increases organ damage and death at all times of day.

Supplementary information

Reporting Summary

Supplementary Video 1

Whole-mount immunostaining of clarified lungs showing almost complete absence of NETs in naïve lungs before ALI induction. Lungs were stained for MPO (neutrophils), DNA, citrunillated-histone 3 and CD31. NETs were defined as triple-positive MPO-DNA-citH3 events. Representative of three independently cleared lungs.

Supplementary Video 2

Whole-mount immunostaining of clarified lungs during ALI, showing abundant NETs scattered throughout the lung. Lungs were stained for MPO (neutrophils), DNA, citrunillated-histone 3 and CD31. NETs were defined as triple-positive MPO-DNA-citH3 events. Representative of three independently cleared lungs.

Supplementary Video 3

Intravital imaging of lungs during ALI, showing the formation of NET-like events in the pulmonary microvasculature. When ALI is induced at night (ZT13), more NET-like events (arrows) are produced compared with induction at noon (ZT5). Mice were treated intravenously with fluorescent anti-Ly6G to label neutrophils, and Sytox-green for extracellular DNA. NET-like events are defined as those in which DNA is extruded from a neutrophil. Representative of four independent mice per time.

Supplementary Video 4

Three-dimensional reconstruction of CT scans of lungs from WT, Bmal1∆N and CXCR4∆N mice before (t = 0) and after acute lung injury induction (t = 21). The appearance of edema is shown by fluid accumulation (red) in the lungs, and bones are shown for reference. Representative of seven independent mice per genotype.

Supplementary Video 5

Intravital microscopy of the lung microcirculation showing the two identified types of NETs produced by neutrophils during ALI: flowing NETs in which DNA is extruded out of the neutrophil body and is washed away by the blood flow; and adherent NETs, in which DNA is slowly extruded as the neutrophil crawls on the vessel, and adheres to the endothelial surface. Examples of both are shown. Representative of nine independent experiments.

Supplementary Video 6

Video abstract. Neutrophils are released from the bone marrow into the bloodstream enriched in granule-held antimicrobial, cytotoxic and NET-forming proteins. As they spend time in the circulation, they undergo a homeostatic process of proteome ‘disarming’ that is regulated by the clock gene Bmal1 and signaling through CXCR2. This process causes a reduction in granule content and their ability to form NETs, ultimately reducing their toxicity towards host tissues. During acute lung injury, the presence of granule-poor neutrophils at specific times of day or in CXCR4 mutants protects the lungs and increases survival. Disabling homeostatic degranulation in Bmal1 mutants, in contrast, increases organ damage and death at all times of day.

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Adrover, J.M., Aroca-Crevillén, A., Crainiciuc, G. et al. Programmed ‘disarming’ of the neutrophil proteome reduces the magnitude of inflammation. Nat Immunol (2020). https://doi.org/10.1038/s41590-019-0571-2

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