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Distinct transcription factor networks control neutrophil-driven inflammation

Nature Immunology volume 22, pages 1093–1106 (2021)Cite this article

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Abstract

Neutrophils display distinct gene expression patters depending on their developmental stage, activation state and tissue microenvironment. To determine the transcription factor networks that shape these responses in a mouse model, we integrated transcriptional and chromatin analyses of neutrophils during acute inflammation. We showed active chromatin remodeling at two transition stages: bone marrow–to-blood and blood-to-tissue. Analysis of differentially accessible regions revealed distinct sets of putative transcription factors associated with control of neutrophil inflammatory responses. Using ex vivo and in vivo approaches, we confirmed that RUNX1 and KLF6 modulate neutrophil maturation, whereas RELB, IRF5 and JUNB drive neutrophil effector responses and RFX2 and RELB promote survival. Interfering with neutrophil activation by targeting one of these factors, JUNB, reduced pathological inflammation in a mouse model of myocardial infarction. Therefore, our study represents a blueprint for transcriptional control of neutrophil responses in acute inflammation and opens possibilities for stage-specific therapeutic modulation of neutrophil function in disease.

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Fig. 1: Neutrophil transcriptional rewiring en route to the site of inflammation is parsimonious.
Fig. 2: Neutrophils dynamically alter their chromatin landscape en route to the site of inflammation.
Fig. 3: RUNX1 and KLF6 deficiency impairs the maturation of neutrophils.
Fig. 4: Runx1−/− and Klf6−/− neutrophils are deficient in migration and recruitment to inflamed tissue.
Fig. 5: RELB, IRF5 and JUNB control the immune responses of neutrophils.
Fig. 6: RELB, IRF5 and JUNB deficiency impairs neutrophil inflammatory responses.
Fig. 7: Neutrophil-specific knockout of JunB limits pathological destruction of tissue during neutrophil-driven inflammation.

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Data availability

The RNA-seq (Fig. 1) and ATAC-seq (Fig. 2) data that used neutrophils from mice subjected to air pouch and zymosan stimulation and the RNA-seq data using the HoxB8 neutrophils (Figs. 5 and 7 and Extended Data Figs. 7–9) are available at the Gene Expression Omnibus (GEO) under accession no. GSE161765. The previously published RNA-seq data with accession nos. GSE147910 and GSE109125 were retrieved from the GEO. To align the neutrophil subpopulations from the bone marrow, blood, air pouch membrane and exudate in the current study (Extended Data Fig. 1i), we also utilized published scRNA-seq data (accession no. GSE137540). Source data are provided with this paper.

Code availability

All code used in current study has been stored at https://github.com/Tariq-K?tab=repositories.

Change history

  • 30 July 2021

    In this article the editor in the ‘Peer review information' was incorrectly given as L. A. Dempsey but should have been Zoltan Fehervari. The original article has been corrected.

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Acknowledgements

We are grateful to Jonathan Webber (Kennedy Institute of Rheumatology (KIR), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS)) for providing the cell sorting service, T. Nicol and M. Crabtree (Nuffield Department of Medicine) for help with the Seahorse assay the and Genomic core facility (Wellcome Centre for Human Genetics) for sample sequencing and technical support. We thank F. Powrie for scientific discussions and E. Thompson and D. Berthold (KIR, NDORMS) for helpful suggestions on the manuscript and assistance with figures. This work was supported by the Oxford-Celgene fellowship (T.E.K.); the Chinese Science Council (Z.A.); Wellcome Trust Investigator Award no. 209422/Z/17/Z to I.A.U., E.v.G. and H.E.; Novo Nordisk Foundation Tripartite Immunometabolism Consortium (no. NNF15CC0018486 to S.M. and L.W.); and the Erasmus Foundation (N.W. and V.v.W.).

Author information

Author notes

  1. These authors contributed equally: Tariq E. Khoyratty, Zhichao Ai.

Authors and Affiliations

  1. Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK

    Tariq E. Khoyratty, Zhichao Ai, Hayley L. Eames, Sara Mathie, Lihui Wang, Ashleigh Hemmings, Nicola Willemsen, Valentin von Werz, Erinke van Grinsven & Irina A. Udalova

  2. Area of Cell & Developmental Biology, Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain

    Ivan Ballesteros, Sandra Martín-Salamanca & Andres Hidalgo

  3. Institute of Cardiovascular Physiology and Pathophysiology, Biomedical Center and Walter Brendel Center of Experimental Medicine, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany

    Annette Zehrer & Barbara Walzog

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Contributions

I.A.U. conceptualized the study. I.A.U., S.M., H.E., T.E.K. and Z.A. devised the methodology. T.E.K., Z.A., S.M., H.E., I.B., S.M-S., L.W., N.W., V.v.W. and A. Hemmings carried out the experiments. T.E.K., Z.A., I.B., E.v.G. and I.A.U. analyzed and/or interpreted the experimental data. T.E.K. and Z.A. carried out the computational genomic analysis. A.Z. and B.W. provided the materials. I.A.U., Z.A., A. Hidalgo, T.E.K., E.v.G. and H.E. wrote, reviewed and edited the manuscript.

Corresponding author

Correspondence to Irina A. Udalova.

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The authors declare no competing interests.

Additional information

Peer review information Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Z. Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Administration of zymosan into the air pouch cavity induces neutrophil transcriptional remodeling.

a, Representative H&E staining showing the formation of the granulation tissue (membrane) and infiltration of leukocytes. Representative (left) and zoomed-in (right) images of air pouch cavity from mice subjected to air pouch and zymosan challenge are shown with indicated scale bars. b, Representative image of immunofluorescence imaging on the pouch cavity of Ly6gcre-tdTomato x Cx3cr1GFP illustrates typical neutrophil infiltration. Neutrophils and macrophages appear reddish and green, respectively, and cell nuclei stained with DAPI appear blue. a, b) Representative images from four mice are shown. c, Total number of neutrophils per milliliter of air-pouch exudate (AP) post challenge with zymosan. d, Total number of neutrophils per milligram of air-pouch membrane (MEM) post challenge with zymosan. e, Expression of pro-IL1β, as measured by flow cytometry, in neutrophils from indicated tissues recovered from mice subjected to the air pouch model and zymosan challenge. c, d, e) Results are the means and SD of 15 mice. Significant differences are denoted as: *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001; (DM one-way ANOVA with Dunnett’s multiple comparisons test). f, Gating strategy of neutrophil sorting from Ly6gcre-tdTomato x Cx3cr1GFP mice for subsequent RNA sequencing analysis and ATAC sequencing analysis. g, Gating strategy used to quantify neutrophils under different maturation stages. h, Percentage distribution of neutrophil subsets in BM and blood between steady condition and zymosan-induced inflammation. Data are shown as means and SD from three naïve mice and four mice with zymosan challenge. Significant differences are denoted as: **P < 0.01, ****P < 0.0001 (RM two-way ANOVA with Šidák’s multiple comparisons test). i, Correlation between indicated neutrophil samples with scRNA-seq-defined neutrophil populations reported by Xie et al.11. The mean fraction of indicated scRNA-seq defined clusters (G0-G5) in each group of neutrophil samples. G0-G4: BM neutrophils (G0-CMP, G1-GMP, G2-pre-neutrophils; G3-immature neutrophils; G4-mature neutrophils). G5a, G5b, G5c: peripheral mature neutrophils.

Source data

Extended Data Fig. 2 Analysis of DEG expression reveals gene set enriched pathways.

a, Global changes in gene expression between the bone marrow and air pouch (padj < 0.05, fold change > 1.5). b, Top gene ontology terms associated with the bone marrow and air pouch identified by gene set enrichment analysis (GSEA) padj < 0.01. c, Gene set enrichment analysis (GSEA) of genes differentially expressed between the bone marrow and air pouch. Line plot shows the distribution of genes ranked by their normalised enrichment score (ES). Heatmaps show variance stabilised counts of the leading-edge genes most associated with each tissue.

Extended Data Fig. 3 Discrete changes occur in neutrophil chromatin landscape en route to the site of inflammation.

a, Venn diagrams showing the overlap of differentially accessible peaks (padj < 0.05, fold change > 1.5) between the indicated transitions. b, Heat maps of normalised read counts (FPKM) over 2.5 k.b regions centred on ATAC-seq peaks from each tissue. Only differentially accessible peaks between the blood and bone marrow, and between blood and membrane are shown (padj < 0.05, fold change > 1.5). c, Hierarchical clustering of all differentially accessible peaks (LRT test padj < 0.01) based on Manhattan distances using the Ward method. Data are presented as a heatmap normalised to the minimum and maximum of each row. d, Gene ontology analysis for differentially accessible peaks between the blood and bone marrow, and between blood and membrane (padj < 0.05, fold change > 1.5). The top 10 most significant results are shown and the number of differentially accessible peaks within each GO term are annotated.

Extended Data Fig. 4 Expression of predicted transcription factor family members in neutrophils.

a, Expression of identified transcription factor (TF) family members across immune cell populations (ImmGen), subpopulation level counts are averaged. Highlighted TFs have high levels of neutrophil expression. b, Expression of predicted transcription factor (TF) family members across neutrophil sub-populations (ImmGen). Highlighted TFs have high levels of neutrophil expression and/or neutrophil specific expression compared with other family members. c, Expression of predicted transcription factors in each of G0 to G5 clusters, mapped to progressively maturing neutrophils in scRNA-Seq analysis11, before and after intraperitoneal E. coli challenge, coloured by the average expression of each gene in each cluster scaled across all clusters. d, Western blot analysis of JUNB activation (P-JUNB) and levels of expression (JUNB) over the time course of HoxB8 neutrophil differentiation/maturation (D0 to D5) and under zymosan stimulation, and representative blot from three independent experiments is shown.

Source data

Extended Data Fig. 5 Validation and maturation of TF knockout neutrophils.

a, Targeted TF knockout of HoxB8 neutrophils. Left: Schematic of in vitro generation, CRISPR-Cas9 lentivirus transduction and subsequent differentiation into neutrophils in the presence of G-CSF. Right: Immunoblots for validating TF knockout from HoxB8 neutrophils, and representative blots from three independent experiments are shown. b, Representative flow cytometry plots(top) of WT or CEBPβ−/−, Klf6−/−, Runx1−/−, Rfx2−/−, RelB−/−, Irf5−/− or JunB−/− HoxB8 neutrophils co-labelled with Ly6C, Ly6G and CD101. Quantification of flow cytometry data as percentages of preneutrophils Ly6G-CXCR4+, immature Ly6G+CD101- and mature Ly6G+CD101+ neutrophils (bottom). Data are shown as means and SD and are representative of three independent experiments. Significant differences compared with the WT control group are denoted as: ****P < 0.0001 (two-way ANOVA with Dunnett’s multiple comparisons test). c, Myeloperoxidase (MPO) expression in HoxB8 neutrophils. A representative Western blot probed with antibodies specific for MPO and b–Actin is shown. MPO expression is normalized against b-Actin amount in the lysates (and expressed as arbitrary unit of MPO). d, Mitochondrial transmembrane potential of WT and indicated KO HoxB8 neutrophils measured by flow cytometry using TMRM. e, Early apoptosis rates of HoxB8 neutrophils differentiated for five days as assessed by the percentage of cells positive for the Annexin V staining and negative for the live/dead staining. Data are shown as means and SD from three independent experiments, each with duplicate. (c, d, e) Data are shown as means and SD and are representative of three independent experiments. Significant differences compared with WT neutrophils are denoted as: **P < 0.01, ***P < 0.001, ****P < 0.0001; (Oridinary one-way ANOVA with Dunnett’s multiple comparisons test).

Source data

Extended Data Fig. 6 RUNX1 deficiency impairs neutrophil maturation.

a, Irf5 deficiency does not affect neutrophil maturation. Left: representative flow cytometry of neutrophil subsets (pre-neutrophils, immature and mature neutrophils) from WT and Irf5−/− mice. b, JunB deficiency does not affect neutrophil maturation. Left: representative flow cytometry of neutrophil subsets (pre-neutrophils, immature and mature neutrophils) from JunBfl/fl and S100a8cre/crexJunBfl/fl mice. Right: Statistical analysis of percentages of indicated neutrophil subsets. Data are shown as means and SD derived from three mice from each group. Statistical comparison was made by ordinary one-way ANOVA with Dunnett’s multiple comparisons test: ns, no significant difference. c, Absolute quantification of neutrophil subsets (pre-neutrophils, immature and mature neutrophils) in the bone marrow (left) and the blood (right) from Runx1fl/fl and Lyz2Cre/cre xRunx1fl/fl mice. d, Morphology assessment of CD11b+Ly6G+ neutrophils sorted from Runx1fl/fl and Lyz2Cre/crexRunx1fl/fl mice. e, Morphology assessment of CD11b + Ly6G+ neutrophils sorted from JunBfl/fl and S100a8cre/crexJunBfl/fl mice. (d, e) Results are expressed as percentages of segmented, banded neutrophils and metamyelocyte out of at least 200 cells counted from different fields and independent replicates. Scale bars represent 10 µm. a, c, d, e) Data are shown as means and SD from three mice from each group within one experiment. Significant differences compared between two individual groups are denoted as: *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001 (RM two-way ANOVA with Šidák’s multiple comparisons test). f, Differential expression of Cebpe in CD11b+Ly6G+ neutrophils sorted from Runx1fl/fl and Lyz2Cre/crexRunx1fl/fl mice, measured by flow cytometry. Gating strategy (top) of neutrophils for identifying CD11b+Ly6G+ neutrophils. Fluorescence histogram (middle) and geometric mean (bottom) of CEBPE expression in CD11b+Ly6G+ neutrophils. Data are shown as means and SD derived from three mice from each group. Significant differences compared with Runx1fl/fl and Lyz2Cer/crexRunx1fl/fl mice are denoted as: **P < 0.01 (unpaired student t test).

Source data

Extended Data Fig. 7 RUNX1 and KLF6 in transcriptional control of neutrophil migration.

a, Hierarchical clustering of all differentially expressed genes (LRT test padj < 0.01, |log2FC|>1), based on Manhattan distances using the Ward method. Data are presented as heatmap normalised to the minimum and maximum of each row. b, Gene ontology (GO) analysis, showing the top 10 enriched GO categories for each cluster from Extended Data Fig. 7a. c, Leukocyte-migration-related gene expression in WT, Klf6−/−, or Runx1−/− HoxB8 neutrophils. (b, c) Significant differences compared with Runx1fl/fl and Lyz2Cer/crexRunx1fl/fl mice are denoted as: **P < 0.01, ****P < 0.0001 (RM two-way ANOVA with Šidák’s multiple comparisons test). d, Early apoptosis rates of HoxB8 neutrophils recovered from blood, air pouch membrane and exudate from mice subjected to into air pouch model of acute inflammation and zymosan stimulation, assessed by the percentage of cells positive for the Annexin V staining and negative for the live/dead staining. Data are shown as means and SD from four mice from each group. e, Percentage distribution of neutrophil subsets (pre-neutrophils, immature and mature neutrophils) in BM and blood under zymosan-induced inflammation. Data are shown as means and SD from six mice from each group.

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Extended Data Fig. 8 RELB, IRF5, and JUNB deficiency impair neutrophil inflammatory responses.

a, Hierarchical clustering of all DEGs (padj < 0.05, |log2FC|>1). Data are presented as heatmap normalized to the minimum and maximum of each row. b, Gene ontology (GO) analysis showing the log2 odds ratio of genes regulated by specific TF knockout with the indicated GO annotation. c, Representative immunofluorescence images of NETosis from WT, RelB−/− and JunB−/− HoxB8 neutrophils stimulated with 5 μM ionomycin and PMA overnight, stained for DNA (blue), MPO (green) and citrullinated histone3 (red). Representative images from three independent experiments are shown. Scale bar indicates 10 µm. d, Phagocytosis-related gene expression in in Cebpβ−/−, RelB−/−, Irf5−/− or JunB−/− HoxB neutrophils. e, ROS-biosynthetic-process-related gene expression in WT, Cebpβ−/−, RelB−/−, Irf5−/− or JunB−/− HoxB8 neutrophils.

Extended Data Fig. 9 RELB, IRF5, and JUNB affect neutrophil inflammatory gene expression and production.

a, Cytokine-production-related gene expression in WT, Cebpβ−/−, RelB−/−, Irf5−/− or JunB−/− HoxB8 neutrophils. b, Chemokine-production-related gene expression in Cebpβ−/−, RelB−/−, Irf5−/− or JunB−/− HoxB8 neutrophils. c, Il1α, Il6 and Cxcl2 mRNA induction in HoxB8 neutrophils stimulated with zymosan (50ug/ml) for 0, 1, 2 hours. Gene expression was measured by qPCR. Data are shown as means and SD from three independent experiments. Significant differences compared between KO and WT neutrophils are denoted as: *P < 0.05, **P < 0.01, ****P < 0.0001, and ****P < 0.0001; (DM one-way ANOVA with Dunnett’s multiple comparisons test). d, Cytokines and chemokines secreted from WT HoxB8 neutrophils challenged with Zymosan for 2 h, measured by the proteome array shown in Fig. 6b. e, Densitometric quantification of IL1β secretion from HoxB8 neutrophils stimulated with stimulated with zymosan (50ug/ml) for 2 hours, measured with 30 minutes exposure time. Data are shown as mean and SD of three biologically independent samples. Significant differences compared between KO and WT neutrophils are denoted as: *P < 0.05, **P < 0.01; (Ordinary one-way ANOVA with Dunnett’s multiple comparisons test).

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Extended Data Fig. 10 Assessment of chimerism in MBMC and proposed TF blueprint.

a, Representative flow cytometry (left) and the chimerism ratio (right) of neutrophils in the bone marrow, blood, and heart in mice subjected to permanent myocardial infarction 6 weeks after bone marrow transplant. Data are shown as means and SD derived from three mice from each group within one experiment. Statistical comparison was made by paired student-t test. ns, no significant difference. b, Model of transcriptional regulation of neutrophils during inflammation. In the process of differentiation in bone marrow, lineage-determining transcriptional factors, including RUNX1, KLF6, CEBPE, and PU.1, are highly expressed and ensure gene expression programmes that promote proper neutrophil maturation. During the mobilization from the bone marrow into the blood, RFX2, RELB, IRF5 and JUNB become upregulated and transcriptionally accessible to support neutrophil cell survival and establish their effector function repertoire, whereas RUNX1 and KLF6 expression are silenced. Upon inflammation, circulating neutrophils migrate into the inflammatory sites, where they are exposed to inflammation-derived signals and become activated. Neutrophil activation leads to the activation of TFs, including RELB, IRF5 and JUNB, and subsequent TF binding to already accessible binding sites, thereby resulting in diverse TFs genomic occupancy and distinct transcriptional outputs.

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Khoyratty, T.E., Ai, Z., Ballesteros, I. et al. Distinct transcription factor networks control neutrophil-driven inflammation. Nat Immunol 22, 1093–1106 (2021). https://doi.org/10.1038/s41590-021-00968-4

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