Abstract
The clearance of apoptotic cells, termed efferocytosis, is essential for tissue homeostasis and prevention of autoimmunity1. Although past studies have elucidated local molecular signals that regulate homeostatic efferocytosis in a tissue2,3, whether signals arising distally also regulate homeostatic efferocytosis remains elusive. Here, we show that large peritoneal macrophage (LPM) display impairs efferocytosis in broad-spectrum antibiotics (ABX)-treated, vancomycin-treated and germ-free mice in vivo, all of which have a depleted gut microbiota. Mechanistically, the microbiota-derived short-chain fatty acid butyrate directly boosts efferocytosis efficiency and capacity in mouse and human macrophages, and rescues ABX-induced LPM efferocytosis defects in vivo. Bulk messenger RNA sequencing of butyrate-treated macrophages in vitro and single-cell messenger RNA sequencing of LPMs isolated from ABX-treated and butyrate-rescued mice reveals regulation of efferocytosis-supportive transcriptional programmes. Specifically, we find that the efferocytosis receptor T cell immunoglobulin and mucin domain containing 4 (TIM-4, Timd4) is downregulated in LPMs of ABX-treated mice but rescued by oral butyrate. We show that TIM-4 is required for the butyrate-induced enhancement of LPM efferocytosis capacity and that LPM efferocytosis is impaired beyond withdrawal of ABX. ABX-treated mice exhibit significantly worse disease in a mouse model of lupus. Our results demonstrate that homeostatic efferocytosis relies on distal metabolic signals and suggest that defective homeostatic efferocytosis may explain the link between ABX use and inflammatory disease4,5,6,7.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
All data supporting the present study are available within the paper and Supplementary Information. Source data are provided with this paper or can be found in GEO (GSE270512, GSE270514 and GSE270751) and SRA (PRJNA1128534).
References
Sender, R. & Milo, R. The distribution of cellular turnover in the human body. Nat. Med. 27, 45–48 (2021.
Morioka, S., Maueröder, C. & Ravichandran, K. S. Living on the edge: efferocytosis at the interface of homeostasis and pathology. Immunity https://doi.org/10.1016/j.immuni.2019.04.018 (2019).
Doran, A. C., Yurdagul, A. & Tabas, I. Efferocytosis in health and disease. Nat. Rev. Immunol. 20, 254–267 (2020).
Norgaard, M. et al. Use of penicillin and other antibiotics and risk of multiple sclerosis: a population-based case-control study. Am. J. Epidemiol. 174, 945–948 (2011).
Canova, C. et al. Association of maternal education, early infections, and antibiotic use with celiac disease: a population-based birth cohort study in Northeastern Italy. Am. J. Epidemiol. 180, 76–85 (2014).
Sultan, A. A. et al. Antibiotic use and the risk of rheumatoid arthritis: a population-based case-control study. BMC Med. 17, 154 (2019).
Vangoitsenhoven, R. & Cresci, G. A. M. Role of microbiome and antibiotics in autoimmune diseases. Nutr. Clin. Pract. 35, 406–416 (2020).
Zago, G., Saavedra, P. H. V., Keshari, K. R. & Perry, J. S. A. Immunometabolism of tissue-resident macrophages—an appraisal of the current knowledge and cutting-edge methods and technologies. Front. Immunol. 12, 1406 (2021).
Mahajan, A., Herrmann, M. & Muñoz, L. E. Clearance deficiency and cell death pathways: a model for the pathogenesis of SLE. Front. Immunol. https://doi.org/10.3389/fimmu.2016.00035 (2016).
Arandjelovic, S. & Ravichandran, K. S. Phagocytosis of apoptotic cells in homeostasis. Nat. Immunol. https://doi.org/10.1038/ni.3253 (2015).
Trzeciak, A., Wang, Y. T. & Perry, J. S. A. First we eat, then we do everything else: the dynamic metabolic regulation of efferocytosis. Cell Metab. 33, 2126–2141 (2021).
Roberts, A. W. et al. Tissue-resident macrophages are locally programmed for silent clearance of apoptotic cells. Immunity 47, 913–927.e6 (2017).
Morioka, S. et al. Efferocytosis induces a novel SLC program to promote glucose uptake and lactate release. Nature https://doi.org/10.1038/s41586-018-0735-5 (2018).
Tirelle, P. et al. Comparison of different modes of antibiotic delivery on gut microbiota depletion efficiency and body composition in mouse. BMC Microbiol. 20, 340 (2020).
Okabe, Y. & Medzhitov, R. Tissue-specific signals control reversible program of localization and functional polarization of macrophages. Cell 157, 832–844 (2014).
Khosravi, A. et al. Gut microbiota promote hematopoiesis to control bacterial infection. Cell Host Microbe 15, 374–381 (2014).
Churchill, G. A., Gatti, D. M., Munger, S. C. & Svenson, K. L. The diversity outbred mouse population. Mamm. Genome. 23, 713 (2012).
Oliveira Corrêa, R. et al. Regulation of immune cell function by short-chain fatty acids. Clin. Transl. Immunol. 5, e73 (2016).
Erny, D. et al. Microbiota-derived acetate enables the metabolic fitness of the brain innate immune system during health and disease. Cell Metab. 33, 2260–2276.e7 (2021).
Raymond, M. H. et al. Live cell tracking of macrophage efferocytosis during Drosophila embryo development in vivo. Science 375, 1182–1187 (2022).
Davidson, A. J. & Wood, W. Macrophages use distinct actin regulators to switch engulfment strategies and ensure phagocytic plasticity in vivo. Cell Rep. 31, 107692 (2020).
Schulthess, J. et al. The short chain fatty acid butyrate imprints an antimicrobial program in macrophages. Immunity https://doi.org/10.1016/j.immuni.2018.12.018 (2019).
Elliott, M. R. & Ravichandran, K. S. The dynamics of apoptotic cell clearance. Dev. Cell https://doi.org/10.1016/j.devcel.2016.06.029 (2016).
Heng, T. S. P. et al. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008).
Lantz, C., Radmanesh, B., Liu, E., Thorp, E. B. & Lin, J. Single-cell RNA sequencing uncovers heterogenous transcriptional signatures in macrophages during efferocytosis. Sci. Rep. 10, 14333 (2020).
Wu, C., MacLeod, I. & Su, A. I. BioGPS and MyGene.info: organizing online, gene-centric information. Nucleic Acids Res. 41, D561–D565 (2013).
Kalaaji, M. et al. Glomerular apoptotic nucleosomes are central target structures for nephritogenic antibodies in human SLE nephritis. Kidney Int. 71, 664–672 (2007).
Kemppainen, K. M. et al. Association between early-life antibiotic use and the risk of islet or celiac disease autoimmunity. JAMA Pediatrics 171, 1217–1225 (2017).
Nicolás-Ávila, J. A. et al. A network of macrophages supports mitochondrial homeostasis in the heart. Cell 183, 94–109.e23 (2020).
Wright, J. R. Clearance and recycling of pulmonary surfactant. Am. J. Physiol. Lung Cell Mol. Physiol. https://doi.org/10.1152/ajplung.1990.259.2.L1 (1990).
DI Tommaso, P. et al. Nextflow enables reproducible computational workflows. Nat. Biotechnol. 35, 316–319 (2017).
Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 276–278 (2020).
Straub, D. et al. Interpretations of environmental microbial community studies are biased by the selected 16S rRNA (gene) amplicon sequencing pipeline. Front. Microbiol. 11, 550420 (2020).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Yousif, A. et al. NASQAR: a web-based platform for high-throughput sequencing data analysis and visualization. BMC Bioinf. 21, 267 (2020).
Aguilar, S. V. et al. ImmGen at 15. Nat. Immunol. 21, 700–703 (2020).
Perry, J. S. A. et al. Interpreting an apoptotic corpse as anti-inflammatory involves a chloride sensing pathway. Nat. Cell Biol. https://doi.org/10.1038/s41556-019-0431-1 (2019).
Acknowledgements
We thank members of the Perry laboratory and coauthors for edits and discussions related to this paper, and the Microbiome Core Laboratory of Weill Cornell Medicine for 16S sequencing. This work was supported by grants to J.S.A.P. from the NIH (grant nos. NCI 5R00CA237728; NIGMS 1DP2GM146337), to S.Z.J. from the NIH (grant nos. NIAID R01AI148416; NIGMS RM1GM139738), BWF-Path Award and the Hirschl Weill-Caulier Award, a Wellcome Trust Career Development Award to C.J.A. (grant no. 225923/Z/22/Z), a Medical Research Council (MRC) Clinician Scientist award (grant no. MR/X019314/1) and an MRC Programme grant (no. MR/W019264/1) to C.D.L., and a MSKCC Cancer Center Support grant no. P30CA008748. Figures 1a,c,g,h, 2a, 3a,b,g and 4d were created using the commercial version of BioRender.
Author information
Authors and Affiliations
Contributions
P.H.V.S. and J.S.A.P. conceived and directed the study, with P.H.V.S. performing most experiments. A.J.T., A.L., A.J.O., Z-L.L., Z.W., W.S.R., G.Z. and J.E.R-P. assisted with some experiments. A.W.D. and S.Z.J. performed ChIP–seq experiments. M.R.M.v.d.B., C.D.L., C.J.A. and A.Y.R. provided essential insights and reagents. P.H.V.S. and J.S.A.P. wrote the paper with input from all authors.
Corresponding authors
Ethics declarations
Competing interests
J.S.A.P. is a cofounder of Atish Technologies. A.Y.R. is a Scientific Advisory Board member and has equity in Sonoma Biotherapeutics, Santa Ana Bio, RAPT Therapeutics and Vedanta Biosciences. He is a Scientific Executive Board member of Amgen and BioInvent and is a co-inventor or has intellectual property (IP) licensed to Takeda that is unrelated to the content of the present study. M.R.M.v.d.B. has received research support and stock options from Seres Therapeutics, and stock options from Notch Therapeutics and Pluto Therapeutics; he has received royalties from Wolters Kluwer; he has consulted, received honorarium from or participated in advisory boards for Seres Therapeutics, Vor Biopharma, Rheos Medicines, Frazier Healthcare Partners, Nektar Therapeutics, Notch Therapeutics, Ceramedix, Lygenesis, Pluto Therapeutics, GlaksoSmithKline, Da Volterra, Thymofox, Garuda, Novartis (Spouse), Synthekine (Spouse), Beigene (Spouse) and Kite (Spouse); he has IP Licensing with Seres Therapeutics and Juno Therapeutics; and he holds a fiduciary role on the Foundation Board of DKMS (a nonprofit organization). Memorial Sloan Kettering has institutional financial interests relative to Seres Therapeutics. The other authors declare no competing interests.
Peer review
Peer review information
Nature Metabolism thanks Zaida Ramirez-Ortiz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Yanina-Yasmin Pesch, in collaboration with the Nature Metabolism team.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 The intestinal microbiome supports peripheral efferocytosis.
(a) Representative flow cytometry gating to identify large peritoneal macrophages (LPMs; CD11b + F4/80+ cells), the primary tissue-resident macrophage and phagocyte in the peritoneum. (b) Representative flow cytometry plots of LPMs from peritoneal lavage of mice following antibiotics treatment for indicated days (Day 0 = blue, Day 3 = light green, Day 7 = dark green). (c) Percentage of LPMs isolated from peritoneal lavage in (a) (D0 n = 5, D3 = 6, D7 n = 6). (d) Percentage of LPMs from SPF (n = 6) (blue) and GF (n = 6) (purple) mice. (e) In vitro efferocytosis analysis of primary macrophages from SPF (n = 2) or GF (n = 2) mice. (f) Representative flow cytometry plots of PI and AnnexinV staining of thymocytes 6 h after dexamethasone injection. (g) Percentage of LPMs from C57BL6/J (n = 8) (blue) and J:DO (n = 9) (brown) mice. All bar graphs represent means + s.e.m. Statistics were performed by one-way ANOVA in c and d, two-tailed t-test in e and f. *p < .05; ****p < .0001. ns - not significant.
Extended Data Fig. 2 Butyrate boosts efferocytosis via induction of efferocytotic transcriptional programs.
(a) Mature macrophages were conditioned with butyrate (1 mM) for 3d prior to efferocytosis assay. Conditioned macrophages were subsequently incubated with apoptotic mouse thymocytes co-labeled with CypHer5E and CTY at a 1:10 ratio for 1 h. Shown is summary data from flow cytometry analysis of efferocytosis efficiency (left, CypHer5E+ macrophages) and capacity (right, CTY MFI in macrophages). Data are from three independent experiments. (b) Representative flow cytometry plots of vehicle and butyrate (1 mM)-conditioned primary macrophages for the indicated time points from experiments shown in Fig. 2d. In some conditions, primary macrophages were conditioned for 3d followed by withdraw of butyrate for the indicated time point prior to efferocytosis assay. (c) Representative transluminescence microscopy image of vehicle and butyrate-treated primary macrophages from experiments performed in Fig. 2e. Scale bar is 50 µm. (d) Flow cytometry analysis and quantification of in vitro latex bead (n = 3) and fungal (zymosan particles) (n = 3) phagocytosis in primary macrophages conditioned with vehicle or butyrate (1 mM) for 3d. Data are from three independent experiments. (e) Efferocytosis-associated programs identified in butyrate-treated primary macrophages from experiments performed as in Fig. 2d. Data corresponds to analyses presented in Fig. 2i. (f) Putative efferocytosis-supporting programs identified in butyrate-treated primary macrophages from experiments performed as in Fig. 2d. Data corresponds to analyses presented in Fig. 2j. All bar graphs represent means + s.e.m. Statistics were performed by two-tailed t-test in a and d. *p < .05; ***p < .001. ns - not significant.
Extended Data Fig. 3 Exogenous butyrate rescues antibiotic-induced defects in peripheral efferocytosis.
(a) Single cell RNA sequencing analysis of peritoneal cells from control mice as performed in Fig. 3c. (left) Shown is the dimension reduction analysis via Uniform Manifold Approximation and Projection (UMAP) of control peritoneal cells. (right) Functional analysis of differentially expressed genes extrapolated from the three main LPM clusters (C1-C3). The circle size was determined by applying a scaling factor to the number of genes involved in the program. The upper bound features 120-150 differentially expressed genes, which is scaled to 0.35” whereas the lower bound features 0-19 differentially expressed genes, which is scaled to 0.16”. The color intensity was determined by applying a scaling factor to the log2 p value. The upper bound (100% intensity) features log2 p values greater than 100 whereas the lower bound (0% intensity) features log2 p values less than 15. (b) Experiments performed similar to those in Fig. 3a. (left) Shown is a summary plot of TIM4 expression in large peritoneal macrophages (LPMs), expressed as geometric mean fluorescence intensity (MFI). Control (n = 4), Abx (n = 4) and Abx + But (n = 4). (right) XY plot showing the linear correlation between TIM4 expression and efferocytosis capacity. Data are from 4 to 5 biological replicates. See also Fig. 3d. All bar graphs represent means + s.e.m. Statistics were performed by one-way ANOVE in b. *p < .05. ns - not significant.
Extended Data Fig. 5 Exogenous butyrate rescues antibiotic-induced defects in peripheral efferocytosis.
Similar to Extended Data Fig. 4, except showing overlays of key cell type identifiers for immune cell subtypes in scRNA-seq data from Fig. 3c. Itgam and Fcer1g are found in large peritoneal macrophages (LPMs), Clec4b1 and Ltb4r1 are found in small peritoneal macrophages (SPMs), Cd79b and Ms4a1 are found in B1 B cells, and Trbc2 and Cd3d are found in T cells. See also Supplementary Tables 2–8.
Extended Data Fig. 6 Butyrate boosts efferocytosis via histone acetylation, not cognate GPCR signaling.
(a) Analysis of Ffar2 (protein name: GPR43), Ffar3 (protein name: GPR41), and Hcar2 (protein name: GPR109a) normalized gene expression in bulk RNA-seq of isolated large peritoneal macrophages (LPMs; CD11b + F4/80 + ) from male, C57BL/6 mice (n = 4). (b) Schematic illustrating experiments performed to interrogate the mechanism of butyrate action, testing specifically if butyrate acts via cognate GPCR or inhibition of HDAC3. (c) Efferocytosis efficiency (left) and capacity (right) by macrophages from wildtype (WT; white), Ffar2-/- (green) and Hcar2-/- (pink) mice and conditioned with vehicle or butyrate (1 mM) for 3d. Data are from three independent experiments. (d) Efferocytosis efficiency (left) and capacity (right) by macrophages conditioned with vehicle, butyrate (1 mM), or niacin (500 µM) for 3d prior to efferocytosis assay. Data are from three independent experiments. (e) Percentage of LPMs from Ffar2-/- (n = 3) and Hcar2-/- (n = 5) mice. (f) Efferocytosis efficiency by LPMs from Ffar2-/- (n = 5) and Hcar2-/- (n = 3) mice. Data are from two independent experiments. (g,h) Macrophages were conditioned with vehicle or butyrate (1 mM) for 3d, then analyzed for indicated histone modifications. Shown are summary (g) and representative (h) plots of the geometric mean fluorescence intensity (MFI) observed in butyrate-treated macrophages relative to untreated macrophages. All samples were normalized to total H3. Data are from three independent experiments. (i, j) Principal component analysis (PCA) of H3K27ac from butyrate (orange) or vehicle control (green) treated macrophages (i) and a representative CUT&RUN track of Rac1, with black arrows denoting the presence or absence of H3K27ac binding regions in the putative promoters of denoted genes. Data are from three independent experiments. See Supplementary Table 9 for list of significantly differentially bound genes. All bar graphs represent means + s.e.m. Statistics were performed by two-tailed t-test in a, e, f, and g, two-way ANOVA in c, one-way ANOVA in d. ***p < .001; ****p < .0001. ns - not significant. Schematics were created with BioRender.com.
Extended Data Fig. 7 Treatment with antibiotics induces prolonged peripheral efferocytosis defects.
(a) Schematic of experimental design (top). Mice were treated with antibiotics (ABX) in drinking water for 10d followed by withdrawal of antibiotics. On indicated days post-antibiotics withdrawal, mice were injected intraperitoneally (i.p.) with CypHer5E-labeled apoptotic cells. After 1 h, peritoneal CD11b + F4/80+ macrophages were analyzed for efferocytosis. (bottom) Shown are representative images of ceca. Data are representative of day 1 (control n = 6, ABX n = 6), day 7 (control n = 6, ABX = 6), day 14 (control n = 4, ABX = 6), and day 21 (control = 4, ABX = 5) post-antibiotics withdrawal, across three independent experiments. (b, c) 16 S sequencing analysis of mouse stool from experiments performed in Fig. 5a. Shown is the principal component analysis of beta diversity (b) and the number of observed unique bacterial OTUs (n = 3) (c). See also Fig. 5b for the Faith’s phylogenetic (alpha) diversity and Fig. 5c for the relative frequency of key bacterial orders. Data are from three biological replicates. All bar graphs represent means + s.e.m. Statistics were performed by one-way ANOVA in c. ****p < .0001. Schematics were created with BioRender.com.
Supplementary information
Supplementary Table 1
Bulk RNA-seq analyses.
Supplementary Table 2
scRNA-seq: cluster marks.
Supplementary Table 3
scRNA-seq: LPM cell IDs.
Supplementary Table 4
scRNA-seq: LPM gene counts.
Supplementary Table 5
scRNA-seq: control-Gene Ontology (GO) function analysis.
Supplementary Table 6
scRNA-seq: ABX-Gene Ontology (GO) function analysis.
Supplementary Table 7
scRNA-seq: ABX + But-Gene Ontology (GO) function analysis.
Supplementary Table 8
scRNA-seq: functional scaling.
Supplementary Table 9
H3K27ac CHIP–seq analyses.
Supplementary Table 10
P values.
Source data
Source Data for Figs. 1–4 and Extended Data Figs. 1–3, 6 and 7.
Statistical source data.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Saavedra, P.H.V., Trzeciak, A.J., Lipshutz, A. et al. Broad-spectrum antibiotics disrupt homeostatic efferocytosis. Nat Metab 6, 1682–1694 (2024). https://doi.org/10.1038/s42255-024-01107-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s42255-024-01107-7
