Western Diet Triggers NLRP3-Dependent Innate Immune Reprogramming

Abstract

Long-term epigenetic reprogramming of innate immune cells in response to microbes, also termed "trained immunity," causes prolonged altered cellular functionality to protect from secondary infections. Here, we investigated whether sterile triggers of inflammation induce trained immunity and thereby influence innate immune responses. Western diet (WD) feeding of Ldlr^-/- mice induced systemic inflammation, which was undetectable in serum soon after mice were shifted back to a chow diet (CD). In contrast, myeloid cell responses toward innate stimuli remained broadly augmented. WD-induced transcriptomic and epigenomic reprogramming of myeloid progenitor cells led to increased proliferation and enhanced innate immune responses. Quantitative trait locus (QTL) analysis in human monocytes trained with oxidized low-density lipoprotein (oxLDL) and stimulated with lipopolysaccharide (LPS) suggested inflammasome-mediated trained immunity. Consistently, Nlrp3^-/-/Ldlr^-/- mice lacked WD-induced systemic inflammation, myeloid progenitor proliferation, and reprogramming. Hence, NLRP3 mediates trained immunity following WD and could thereby mediate the potentially deleterious effects of trained immunity in inflammatory diseases.

Keywords: ASC; NLRP3 inflammasome; Western diet feeding; atherosclerosis; epigenetic reprogramming; granulocyte macrophage progenitors; innate immune memory; non-communicable diseases; sterile inflammation; trained immunity.

Figure 1. WD Feeding Induces Systemic Inflammation and Functional Reprogramming

(A) Schematic of dietary interventions. Female Ldlr^−/− mice were fed either CD, WD for 4 weeks, or WD for 4 weeks followed by CD for 4 weeks (WD > CD). (B) Systemic serum cholesterol in response to dietary intervention in Ldlr^−/− mice. (C) Heatmap representing normalized serum cytokine levels from mice fed as indicated. (D and E) Bone marrow cells (D) or splenic CD11b⁺ monocytes (E), isolated from Ldlr^−/− mice following dietary intervention treated ex vivo with vehicle or different TLR stimuli for 6 hr. Log 2 transformed data represented as spider plots for the following stimulations: Pam3Csk4, LPS, R848, and CpG. For (B) n = 6–10 animals; for (C) n = 3–5 animals per group; ± SEM, p < 0.05 versus CD (B and C); versus un-stimulated cells (D and E). Experiments were performed twice independently and data are representative of a single experiment. See also Figure S1 and Table S1.

Figure 2. WD Induces Hematopoiesis and Transcriptional Reprogramming of GMPs

(A) Total counts of the indicated blood cell populations in CD- or WD-fed (4 weeks) female Ldlr^−/− mice. (B and C) Relative numbers (%) (B) and activation status (C) of circulating myeloid subsets isolated from female Ldlr^−/− mice. (D) Percentage of hematopoietic precursor cells as indicated in female Ldlr^−/− mice fed either CD or WD (4 weeks). (E) PCA of RNA-seq data of GMPs isolated from CD- or WD-fed mice. PCA is based on variable genes (non-adjusted [adj.] p value < 0.05, n = 4,672). (F) Gene and sample wise hierarchical clustering based on the 1,000 genes with the highest variance within the dataset of GMPs purified from WD- or CD-fed mice. Gene expression values are Z score standardized. (G) MA-plot showing DE genes in GMPs of WD- or CD-fed mice. DE genes (|FC| > 1.5, non-adj. p value < 0.05) are colored in red (upregulated in WD) and blue (downregulated in WD) and notable genes are highlighted. (H) Trajectory analysis of single-cell RNA-seq data (GEO: GSE70235 and GEO: GSE70240) with computational clustering (top) representing the expression of Csf1R or S100A8, overlaid onto the developmental trajectory to identify monocytic or granulocytic lineage determination. Cells of the monocytic (turquoise) and granulocytic (dark green) branches were used to determine signature genes to test for lineage potential in (I) and (J). (I) Expression differences of monocytic (turquoise) and granulocytic (dark green) signature genes in GMPs from mice fed as indicated. (J) Enrichment of monocytic and granulocytic signatures in GMPs isolated from CD- and WD-fed mice and computationally inferred by linear support vector regression analysis. Enrichments are significant (non-adj. p value < 0.001). (K) GO term enrichment network analysis of differentially expressed genes from GMPs isolated from WD- and CD-fed mice. Each dot represents a significantly enriched GO term and connections indicate shared genes between GO terms. Significance (false discovery rate [FDR]) is indicated by color (lower FDR: more intense color) and size (lower FDR: bigger nodes/thicker borders) of nodes (upregulated genes) or borders (downregulated genes); ± SEM, p < 0.05 versus CD; for (A)–(D) n = 3–5 animals per group. Experiments were performed twice independently and data are representative of a single experiment. See also Figure S2.

Figure 3. WD Primes for Inflammatory Responses to LPS

(A) Schematic representation of the WD and LPS manipulations. Female Ldlr^−/− mice were fed CD or WD for 4 weeks. 6 hr prior to sacrifice mice were intravenously challenged by LPS or PBS. (B) Serum cytokine levels in LPS-treated CD- or WD-fed mice were normalized to levels in PBS control groups and represented as fold-change. (C and D) Activation status of circulating (C) and splenic (D) myeloid subsets from female Ldlr^−/− mice treated as indicated. (E) Pearson correlation analysis of top 1,000 DE genes of transcriptomes from GMPs isolated from mice treated as indicated. (F) Volcano plot indicating transcriptomic changes between CD/LPS and WD/LPS. Significantly upregulated (FC > 1.5, FDR-adj. p value < 0.05, red) and downregulated (FC < −1.5, FDR-adj. p value < 0.05, blue) genes are shown and the most significantly regulated genes are highlighted. (G) Number of significantly upregulated (red) and downregulated (gray) GMP genes within immune system associated GO terms in WD/LPS compared to CD-/ LPS-treated mice. n = 3–8 animals per group in (B); n = 3 animals in (C) and (D); ±SEM, p < 0.05 versus CD (B)–(D). Experiments were performed twice independently and data are representative of a single experiment. See also Figure S3.

Figure 4. WD Induces Long-Lasting Reprogramming in GMPs

(A) Schematic of diet and LPS manipulations. (B) PCA of genes with highest variance (non-adj. p value < 0.05, n = 4672). (C) Pearson correlation analysis of top 1,000 DE genes of transcriptomes from GMPs isolated from mice treated as indicated. (D–I) Co-expression network analysis (genes = 4,360, correlation >0.85) based on highly correlated genes among the 11,306 expressed genes. To allow identification of specific gene signatures, Z score transformed average gene expression (D–F) or fold changes in gene expression (G–I) of respective conditions were overlaid onto the co-expression network. See also Figure S4.

Figure 5. WD Induces Epigenetic Reprogramming of GMPs

(A) FC-FC plot comparing the LPS response of GMPs with amplitude of difference in LPS responses visualized as color code (red, upregulated genes; blue, downregulated genes). (B) Total counts of the indicated blood cell populations in female mice fed as shown. (C and D) Activation status of circulating (C) and splenic (D) myeloid subsets from mice treated as indicated. (E) Volcano plot displaying open chromatin loci as determined by ATAC-seq in GMPs isolated from mice treated as indicated. Average signal is represented as log2 fold change. Significantly up- (non-adj. p value < 0.05, log2FC > 1) and downregulated (non-adj. p value < 0.05, log2FC < −1) peaks are shown. (F) Hierarchical clustering, standardized, and visualized as a heatmap show the significant differentially accessible genomic loci (p value < 0.05) in GMPs isolated from mice treated as indicated. (G) Coverage of ATAC-seq signal for TET2 (top) and TLR4 loci (bottom). n = 3–5 for groups in (B)–(D); ±SEM, p < 0.05 versus CD. Experiments were performed twice independently and data are representative of a single experiment. See also Figure S4.

Figure 6. PYCARD and IL-1RAP SNPs Influence oxLDL-Induced Training Effects

(A) Schematic overview of the in vitro FTI-QTL protocol. (B–D) Manhattan plots representing reference single nucleotide polymorphisms (rs) in the PYCARD gene locus (B) or in the IL1RAP gene locus (D), (C) TNF production capacity in different genotyping groups from (B). (E) TNF and IL-6 production capacity between different genotyping groups from (D). (F) Monocyte oxLDL training was performed in presence or absence of recombinant IL-1ra followed by LPS stimulation. Shift in cytokine levels represented as fold-change (oxLDL/^+/− IL-1ra). (G–I) Systemic serum cholesterol levels (G), cytokines (H), and acute phase response (I) in Ldlr^−/− mice treated as indicated. n = 3–4 mice per group (G–I); means ± SEM, p < 0.05 versus non-IL-1ra treatment (RPMI only) (F); versus CD and PBS treatment (G–I).

Figure 7. NLRP3-Dependent Myeloid Progenitor Priming

(A) Total counts of circulating cell subsets from female Ldlr^−/− and Nlrp3^−/−/Ldlr^−/− mice treated as indicated. (B and C) Activation status (B) and proliferative capacity (C) of GMPs isolated from Ldlr^−/− or Nlrp3^−/−/Ldlr^−/− mice fed as indicated. (D) Atherosclerotic plaque lesion size in WD-fed (8 weeks) female Ldlr^−/− or Nlrp3^−/−/Ldlr^−/− mice. (E) Serum cytokine response 6 hr post LPS injection in female Ldlr^−/− and Nlrp3^−/−/Ldlr^−/− mice fed as indicated. (F) Co-expression network analysis of transcriptional changes induced by WD in female Ldlr^−/− and Nlrp3^−/−/Ldlr^−/− mice. Fold changes are overlaid onto co-expression networks. (G) Rank plot visualization of fold changes of genes represented in (F). n = 2–5 for groups in (A)–(C) and (E) (experiments were performed twice independently, and data are representative of a single experiment), n = 5–9 animals in (D); ±SEM, p < 0.05 versus CD (A–C and E) versus Ldlr^−/− (D).