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LDL delivery of microbial small RNAs drives atherosclerosis through macrophage TLR8

Nature Cell Biology volume 24, pages 1701–1713 (2022)Cite this article

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Abstract

Macrophages present a spectrum of phenotypes that mediate both the pathogenesis and resolution of atherosclerotic lesions. Inflammatory macrophage phenotypes are pro-atherogenic, but the stimulatory factors that promote these phenotypes remain incompletely defined. Here we demonstrate that microbial small RNAs (msRNA) are enriched on low-density lipoprotein (LDL) and drive pro-inflammatory macrophage polarization and cytokine secretion via activation of the RNA sensor toll-like receptor 8 (TLR8). Removal of msRNA cargo during LDL re-constitution yields particles that readily promote sterol loading but fail to stimulate inflammatory activation. Competitive antagonism of TLR8 with non-targeting locked nucleic acids was found to prevent native LDL-induced macrophage polarization in vitro, and re-organize lesion macrophage phenotypes in vivo, as determined by single-cell RNA sequencing. Critically, this was associated with reduced disease burden in distinct mouse models of atherosclerosis. These results identify LDL-msRNA as instigators of atherosclerosis-associated inflammation and support alternative functions of LDL beyond cholesterol transport.

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Fig. 1: nLDL induce inflammatory activation in macrophages.
Fig. 2: nLDL activates NF-κB through TLR8 in macrophages.
Fig. 3: LDL are enriched with msRNA.
Fig. 4: nt-LNAs inhibit TLR8.
Fig. 5: nt-LNAs reduce atherosclerosis in mice.
Fig. 6: nt-LNA treatments re-organize lesion cell types.
Fig. 7: nt-LNA treatments reduce inflammatory macrophages in mouse lesions.

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Code availability

Informatics tools used for sequencing analysis in this manuscript are available for public use via GitHub (https://github.com/shengqh). Additional support is available through the corresponding authors.

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Acknowledgements

The authors thank W. Reichard, M. Kuzmich, S. Landstreet, A. Ifram, L. Sedgeman, C. Wiese and V. Babaev for technical assistance and helpful discussions. We also thank Q. Liu of the Vanderbilt Center for Quantitative Sciences for consultation on single-cell sequencing analysis, and A. Jones of VANTAGE at VUMC for expertise in high-throughput sequencing technologies, the Vanderbilt Flow Cytometry Shared Resource and Translational Pathology Shared Resource. This work is supported by American Heart Association awards 19CDA34660280 (R.M.A.) and 18IPA34180005 (R.M.A.), W.M. Keck Research Foundation Grant (K.C.V., R.M.A., M.F.L. and Q.S.) and National Institutes of Health grants P01HL116263 (M.F.L.) and R01HL128996 (K.C.V.).

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Author notes

  1. Ryan M. Allen

    Present address: Department of Physiology and Cell Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA

  2. Kasey C. Vickers

    Present address: Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Authors and Affiliations

  1. Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Ryan M. Allen, Danielle L. Michell, Ashley B. Cavnar, Wanying Zhu, Neil Makhijani, Danielle M. Contreras, Chase A. Raby, Mark Castleberry, Youmin Zhang, Amanda C. Doran, MacRae F. Linton & Kasey C. Vickers

  2. Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA

    Elizabeth M. Semler & Kasey C. Vickers

  3. Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA

    Carlisle DeJulius & Craig Duvall

  4. Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA

    Marisol Ramirez-Solano, Shilin Zhao & Quanhu Sheng

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Contributions

R.M.A.: conceptualization, methodology, investigation, formal analysis, visualization and writing—original draft. D.L.M.: methodology, investigation, formal analysis and writing—reviewing and editing. A.B.C.: investigation and formal analysis. N.M.: investigation and formal analysis. E.M.S.: formal analysis and investigation. D.M.C.: resources and formal analysis. W.Z.: resources. C. DeJulius: resources. M.C.: resources. Y.Z.: resources. C.A.R.: formal analysis. M.R.-S.: software and visualization. S.Z. software and visualization. C. Duvall: methodology. A.C.D.: methodology and writing—reviewing and editing. Q.S.: methodology, software, visualization and writing—reviewing and editing. M.F.L.: methodology, supervision and writing—reviewing and editing. K.C.V.: conceptualization, methodology, supervision, formal analysis, visualization and writing—reviewing and editing.

Corresponding authors

Correspondence to Ryan M. Allen or Kasey C. Vickers.

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Competing interests

M.F.L. has received research funding from Amgen, Regeneron, Ionis, Merck, REGENXBIO, Sanofi and Novartis, and has served as a consultant for Esperion, Alexion Pharmaceuticals and REGENXBIO. All other authors have no competing interests.

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Extended data

Extended Data Fig. 1 nLDL induces inflammatory activation of macrophages.

(a) mRNA expression determined by qPCR of THP-1 macrophages treated with indicated doses of nLDL (matched to Fig. 1f) for 24 h (n = 3 biological replicates). (b) Quantification of immunoblots presented in Fig. 1f (n = 3 biological replicates). (c) Secreted cytokines in the media of THP-1 macrophages stimulated with 0.5 mg/ml nLDL for 3 h, 6 h, 24 h relative to cells receiving no treatment for 24 h (Ctr) (matched to Fig. 1e; n = 3 biological replicates) (d) Primary mouse bone-marrow derived macrophages (BMDM) differentiated with GM-CSF were treated with nLDL (0.5 mg/ml) for 24 h and assayed for mRNA expression by qPCR (n = 3 biological replicates), (e) cytokine secretion by ELISA (n = 3 biological replicates), and (f) protein expression in cell lysates by immunoblot (n = 2 biological replicates). Data are mean ± SEM. (a-c) One-way ANOVA and (d) Two-way ANOVA with Benjamini, Krieger and Yekutieli FDR (Q = 0.05), *q < 0.05, **q < 0.01, ***q < 0.001, ****q < 0.0001. e, Student’s t-test (unpaired, two-sided), **p < 0.01. Numerical source data, statistics, exact p values and q values are provided.

Source data

Extended Data Fig. 2 nLDL and macrophage TLR responses quality control.

(a) Total protein (top) and neutral lipid (bottom) of ox LDL, bovine serum albumin (BSA), and 10 human nLDL of independent donors resolved by agarose gel electrophoresis. Image represents two independent experiments. (b) TBARS assay of nLDL samples, or matched LDL samples treated with copper sulfate as indicated (limit of detection = 0.625 μM) (n = 10 independent preparations). (c) Quantification of total protein and lipids of DGUC-VLDL, -LDL, -HDL, or BSA following fractionation with 2x-Superose-6 columns. Lipoprotein data are matched to a single donor representative of >10 independent experiments. (d) mRNA expression of primary human macrophages (CD14 + ; GM-CSF/IFNγ) pre-treated with C29 (200 μM) for 30 min, then stimulated with PAM3CSK4 (2 ng/mL) for 4 h (n = 3 biological replicates(BR)). (e) Normalized NF-κB-driven luciferase activity of HEK293T cells over expressing an empty vector, hTLR7 or hTLR8 following treatments with vehicle (Ctr, n = 4 BR), nLDL (0.5 mg/ml, n = 4 BR), ssRNA40 (TLR8 ligand; 2 μg/mL, n = 4 BR), R848 (TLR7 ligand; 10 μM, n = 3 BR), or CL075 (TLR8 ligand; 2.5 μg/mL, n = 3 BR). (f-h) mRNA expression of THP-1 macrophages electroporated with siRNA against TLR7,TLR8, or no siRNA (Control, n = 4 BR) and then treated with (f) nLDL (0.5 mg/mL, n = 6 BR), (g) R848 (10 μM, n = 6 BR) or (h) ssRNA40 (0.5 μg/mL, n = 6 BR). (i) Relative NF-κB-driven luciferase activity of HEK293T cells over expressing an empty vector (n = 7–8), mTLR7 (n = 7–8) or mTLR8 (n = 6-8) treated with mock transfection, R848 (10 μM), ssRNA40 (2 μg/mL), or nLDL (0.5 mg/ml) for 24 h. (j) mRNA expression in wild-type (WT) and Tlr7-/- BMDMs following treatment with 0.5 mg/ml nLDL, or 1 μg/mL ssRNA40 for 24 h (n = 3 BR). (k) Relative NF-κB-driven luciferase activity of HEK293T cells over expressing human or mTLR8 pre-treated with CU-CPT9a and exposed to nLDL for 24 h (n = 4 BR). Data are mean ± SEM. (d) Two-way ANOVA; Sidak’s multiple comparisons test, ***p < 0.001, ****p < 0.0001. (e, i-k) Two-way ANOVA; Benjamini, Krieger and Yekutieli FDR (Q = 0.05), *q < 0.05, **q < 0.01, ***q < 0.001). (f-h) One-way ANOVA; Dunnett’s multiple comparisons test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Numerical source data, statistics, exact p values and q values are provided.

Source data

Extended Data Fig. 3 The small RNA on LDL is predominantly exogenous and removed by LDL re-constitution.

a) Normalized abundance of taxa identified upon alignment of LDL-sRNA to non-host tRNA database (tRNA-db). RPM, reads per million total reads. b) Normalized abundance of bacterial phyla (human microbiome database) contributing sRNA to LDL. c) Normalized abundance of fungal sRNA and representative genomes present on LDL. d) Normalized abundance of algal and protist sRNA and representative genomes present on LDL. RPM, reads per million total reads. Matched nLDL and rLDL samples were fractionated by size-exclusion chromatography (SEC) using two superose-6 columns in tandem and assessed for e) phospholipid and protein content by colorimetric kit (representative data of three independent experiments), f) fluorescence (TopFluor Cholesteryl ester), and g) APOB protein by immunoblot (representative image of three independent experiments). h) Relative expression of exogenous sRNA in matched rLDL and nLDL of a single preparation relative to buffer controls. i) Oil-Red-O staining and fluorescence microscopy (TopFluor Cholesterol ester) (representative images of three biological replicates). Scale bar = 200 μm. Numerical source data, statistics, exact p values and q values are provided.

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Extended Data Fig. 4 Microbial small RNA on lipoproteins is not depleted in germ-free mice.

a) Plasma from two cohorts of adult mice - specific pathogen free (SPF; n = 6 mice total) and facility-matched germ-free (GF; n = 17 mice total) fed a chow diet were harvested at the National Gnotobiotic Rodent Resource Center (NGRRC; North Carolina, USA) for lipoprotein sRNA-seq b) Plasma was fractionated by size-exclusion chromatography (SEC) and cholesterol-rich fractions corresponding with HDL were selected for sRNA-seq. c) Relative percentage of reads aligned to host and non-host databases, as well as reads too short for analysis or reads that failed to align to either database (unmapped). d) Percentage of sRNA reads aligned to host miRNA, host tRNA and host rRNA transcripts. e) Percentage of reads aligned to the non-host rRNA database and tRNA database. f) Percentage of reads aligned to genomes of fungi and algae. g) Percentage of reads aligned to bacterial genomes associated with a human microbiome (HMB) database. h) Reads per million total reads (RPM) mapped to indicated bacterial phyla within the HMB database. i) Percentage of reads aligned to bacterial genomes within an environmental bacteria (ENV) database. j) Differential abundance (log2) of bacterial sRNA (dots represent individual genomes of the HMB and ENV databases) between GF and SPF mice categorized by phyla. Gray bar represents a 1.5 fold change. Data are mean ± SEM. (d-g, i) Statistical differences between GF and SPF were assessed by Mann-Whitney U-test, but no evaluations were statistically significant. j) Differences in abundance of individual genomes within each database were assessed between groups by the Wald Test, but applying False Discovery Rate correction (α = 0.05) resulted in no differentially abundant genomes. Numerical source data, statistics, exact p values and q values are provided.

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Extended Data Fig. 5 Locked nucleic acid (LNA) bases mediate antagonism of single-stranded RNA ligands of TLR8.

a) HEK293T cells over-expressing human TLR8, UNC93B1 and CD14 were pre-treated with vehicle (DOTAP) or corresponding DNA/LNA oligonucleotides (2.5 μg/mL) for 30 min and then stimulated with TLR8 nucleoside analogue agonist CL075 or TLR8 ORN agonists ssRNA40 or ORN06 (2 μg/mL) for 24 h (n = 5 biological replicates). Two-way ANOVA with Dunnett’s multiple comparison test (statistical significance relative to untreated within each group; **p < 0.0001). b) THP-1 macrophages were pre-treated + /- nt-LNA (1 µg/mL) for 45 min and treated with LPS (500 ng/mL), Poly I:C (1 µg/mL), CL075 (2.5 μg/ml), or ssRNA40 at 1 µg/mL (1:1, nt-LNA:ssRNA40) or 0.2 µg/ml (5:1) for 24 h (n = 3 biological replicates). Relative mRNA expression of IL1B, IL6 and TNF were then assessed by qPCR. For each treatment, the relative fold change of each treatment in the presence of nt-LNA was expressed as a percentage of the relative fold change of each treatment without nt-LNA pre-treatment (% inhibition). Two-way ANOVA, Benjamini, Krieger and Yekutieli FDR (Q = 0.05), **q < 0.01. c-d) Primary human CD14 + PBMC differentiated with GM-CSF and IFNγ were pre-treated with 2.5 μg/mL nt-LNA or vehicle (DOTAP) for 30 minutes and then stimulated with or without ssRNA40 (0.5 μg/mL) for 24 h. c) mRNA expression was quantified by qPCR (n = 4 biological replicates) d) Cytokine (IL-6) secretion was quantified by ELISA (n = 4 biological replicates). One-way ANOVA with Dunnett’s multiple comparison test. **p < 0.01. ***p < 0.001, ****p < 0.0001 (e) mRNA expression (n = 4 biological replicates), (f) cytokine secretion (n = 3 biological replicates) and (g) immunoblotting (representative image of three independent experiments) of BMDMs following up to 24 h treatment with IFNγ (100 U/mL) +/- 0.5 mg/ml nLDL in the presence or absence of 2.5 μg/mL nt-LNA. Two-way ANOVA, Benjamini, Krieger and Yekutieli FDR (Q = 0.05), *q < 0.05, **q < 0.01. Data are mean ± SEM. Numerical source data, statistics, exact p values and q values are provided.

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Extended Data Fig. 6 nt-LNA treatment reduces atherosclerosis without altering lipid or lipoprotein metabolism in Apoe-/- mice.

a) Female and male Apoe-/- mice fed a western diet were administered saline (Ctr; n = 4 mice per sex), nt-LNA-A (20 mg/kg; n = 4 mice per sex) or nt-LNA-B (20 mg/kg; n = 4 mice per sex) by intraperitoneal injection once weekly for four weeks. Treatments for each were randomized between cohabitating animals separated by sex. At sacrifice, the aortic sinus was serially sectioned and stained with Oil-Red O to identify atherosclerotic lesions. Scale bar = 500 μm. b) Quantification of lesion area in serial sections and c) sex-normalized, relative lesion area under the curve (n = 8 mice per treatment). d) Plasma of female Apoe-/- mice treated for 4 weeks with saline (Ctr; n = 10) or nt-LNA (n = 10) were fractionated by size-exclusion chromatography and assessed for total cholesterol (TC) e) Plasma protein levels were assessed by immunoblot of individual cages receiving either Saline/Ctr (n = 5 mice) or nt-LNA (n = 5 mice) treatments (representative images of two independent assessments). f) Quantification of independent immunoblots by densitometry (n = 10 mice per treatment) normalized to C3. g) Lesion area (Oil-red O) of matched sections of the aortic root following treatment with saline (Ctr) or nt-LNA for 4 weeks (n = 10 mice per treatment). h) Hepatic mRNA expression determined by qPCR (n = 10 mice per treatment). Data are mean ± SEM. (c) One-way ANOVA, Sidak’s multiple comparison test, *p < 0.05. **p < 0.01. (f-h) Two-way ANOVA with Benjamini, Krieger and Yekutieli FDR (Q = 0.05), *q < 0.05, **q < 0.01,***q < 0.001. Numerical source data, statistics, exact p values and q values are provided.

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Extended Data Fig. 7 nt-LNA treatment promotes atherosclerotic regression in Ldlr-/- mice.

(a) Schematic for regression study design. Male(M) and female(F) Ldlr-/- mice were fed a chow diet (n = 6 mice) or an atherogenic diet (n = 50 mice) for 14 weeks. After 14 weeks, chow-fed mice and a subset of mice from the atherogenic diet group (baseline; n = 7 M/8 F mice). Remaining diet-fed mice were then switched to a chow diet to allow lesion regression (Reg.) and were injected once weekly with saline control (Reg. Ctr; n = 9 M/9 F mice) or Reg. nt-LNA (30 mg/kg; n = 9 M/8 F mice). b) Plasma total cholesterol (TC) or c) triglycerides (TG) following fractionation by SEC; chow (n = 6; 3 M/3 F), baseline (n = 8; 4 M/4 F), Reg. Ctr; (n = 10; 5 M/5 F) and Reg. nt-LNA (n = 10; 5 M/5 F) d) Immunoblots of plasma proteins in Reg. Ctr (n = 10) or Reg. nt-LNA (n = 9) groups. Representative images of two independent experiments are shown. e) Quantification of immunoblots by densitometry. f-g) Lesion area of serial sections of the aortic root in baseline (n = 15 mice; 7 M/8 F), Reg. Ctr(n = 18 mice; 9 M/9 F) or nt-LNA (n = 17; 9 M/8 F) groups. h) Lesion area under the curve (AUC) for both sexes of mice as determined by Oil Red O staining in the aortic root (Baseline: n = 15; Reg. Ctr: n = 18; Reg. nt-LNA; n = 17). One-way ANOVA; Dunnett’s multiple comparison test, **p < 0.01, ***p < 0.001. i) Lesion AUC for mice of each group separated by sex. Two-way ANOVA; Dunnett’s multiple comparison test, **p < 0.01. j-k) Masson’s Trichrome staining and quantification of fibrosis in aortic roots of baseline (n = 7 mice; 3 M/4 F) Reg. Ctr (n = 9 mice; 4 M/5 F) or Reg. nt-LNA (n = 10 mice; 5 M/5 F) groups. l-m) MAC2 (green) immunofluorescence and quantification within aortic roots obtained of at baseline (n = 7 mice; 3 M/4 F), Reg. Ctr (n = 10 mice; 5 M/5 F), or Reg. nt-LNA (n = 10 mice; 5 M/5 F) groups. Two-way ANOVA; Tukey’s multiple comparison test, **p < 0.01, ***p < 0.001. Data are mean ± SEM. Scale bar = 500 μm. Numerical source data, statistics, exact p values and q values are provided.

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Extended Data Fig. 8 Gating strategy of leukocytes from mouse aortas for single-cell RNA sequencing.

Sequential gating fluorescent activated cell sorting for single and live cells, followed by non-red blood cells. Cells were then sorted that were CD45 + but CD3−.

Extended Data Fig. 9 Single-cell RNA sequencing of the atherosclerotic lesion to identify anti-atherosclerotic mechanisms of nt-LNA treatment.

a) Apoe-/- mice fed an atherogenic diet for 4 weeks were injected once weekly with saline control (Ctr; n = 8) or nt-LNA (30 mg/kg; n = 8). b) UMAP projection of unbiased clusters obtained from atherosclerotic lesions. c) Relative contribution of cells from saline (Ctr) and nt-LNA treated mice to each cluster of (b). d) Relative expression (color) and % of cells reaching threshold of detection (size) of transcripts pertaining to T cell and NK cell phenotypes (top) or B-cell phenotypes (bottom) in atherosclerosis for each cluster. Numerical source data, statistics, exact p values and q values are provided.

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Allen, R.M., Michell, D.L., Cavnar, A.B. et al. LDL delivery of microbial small RNAs drives atherosclerosis through macrophage TLR8. Nat Cell Biol 24, 1701–1713 (2022). https://doi.org/10.1038/s41556-022-01030-7

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