Fig 1: Agreement with in vitro and in vivo estimates of methylation-dependence.(A–B) Comparison of motif enrichment for mSTARR-seq-identified MD enhancers (x-axis) versus affinity for methylated DNA in SELEX experiments (y-axis: higher values represent preferential binding to methylated DNA). Results are plotted for each TF tested in both mSTARR-seq and in Yin et al. (2017) (A) TFs enriched for MD enhancers that were more active when unmethylated. (B) TFs enriched for MD enhancers that were more active when methylated. Each TF is colored by whether its binding motif was significantly enriched in the given MD enhancer set in mSTARR-seq. (C) CDF of the effect of CpG methylation levels on gene expression levels (rho) for 32,843 gene pairs measured in 1202 human monocyte samples (Reynolds et al., 2014). The mean correlation between CpG methylation levels and the expression of the closest gene is near zero. Inset shows the same distribution plotted as a histogram. (D) Enrichment (Log2 odds ratio from a Fisher’s exact test) of CpG sites with significant DNA methylation-gene expression correlations (FDR < 10%) in MD enhancers relative to non-MD enhancers. Bars show (i) enrichment versus all CpG sites measured in both mSTARR-seq and the monocyte dataset; (ii) enrichment in the subset of CpG sites with negative DNA methylation-gene expression correlations (of any magnitude); and (iii) enrichment in the subset of CpG sites with negative DNA methylation-gene expression correlations located in gene promoters. Error bars represent 95% confidence intervals. Evidence that major environmental perturbations to gene expression (IFNA challenge) leads to correlated changes in DNA methylation is shown in Figure 5—figure supplement 1.
Fig 2: Activation of JAK-STAT pathway in CLAD basal cells mediates overexpression of MHC-I.(A) Schematic showing JAK-STAT activation leads to nuclear localization of STAT1 where it binds to IFN-sensitive response element (ISRE) leading to upregulation of IFN stimulating genes (ISGs), including MHC-I–expressing genes (B2M, HLA-A, HLA-B, HLA-C). (B) Immunofluorescence shows increased nuclear localization for STAT1 (red) in CLAD compared with control basal cells (KRT5+, green) (n = 6 CLAD cases and 6 controls). DAPI, blue. Scale bar: 10 μm. (C) Quantification of nuclear localization of STAT1 in CLAD compared with control basal cells. (D) Immunoblotting for phospho-STAT1, STAT1, B2M, and GAPD in primary basal cells isolated from donor controls shows activation of JAK1-STAT1 signaling and upregulation of MHC-I following stimulation with IFNA1 (100 ng/μL). This response is partially rescued with cotreatment with the JAK1 inhibitor, brepocitinib (0.5 μM). (E and F) Quantification showing a significant decrease in IFN-mediated activation of JAK-STAT pathway in cells treated with a JAK1 inhibitor (n = 3 biological replicates). Statistical analysis for immunofluorescence experiments was performed using the unpaired parametric t test, while analysis for Western blots was performed using the 1-way ANOVA (P < 0.05) with post hoc Tukey test. Data are shown as mean ± SEM. *P < 0.05, ***P < 0.001.
Fig 3: Transcriptional targets of IFN response, including MHC-I expression, are upregulated in CLAD basal cells compared with donor controls.(A) Volcano plot of gene expression changes in CLAD (n = 4) compared with control (n = 3) basal cells showing upregulation of MHC-I–expressing genes. (B) Hallmark pathway analysis of 855 preranked genes (Supplemental Data File 2) showing pathways significantly different (Padj < 0.05) in CLAD compared with control basal cells. (C) GSEA plot showing enrichment of IFN-α and IFN-γ response genes in CLAD basal cells. (D) Violin plots demonstrating upregulation of IFN response genes in CLAD compared with control basal cells. (E) Immunofluorescence staining of control (top) and CLAD (bottom) airways showing increased expression of MHC-I (red), as measured by antibody staining of B2M, in KRT5+ basal cells (green) from CLAD airway tissue compared with control. Yellow in merged panel shows colocalization of MHC-I expression present in CLAD but not donor controls. Scale bar: 30 μm. (F) Quantification of MHC-I expression in basal cells in every airway across 3 CLAD and 3 control samples. (G) Immunoblotting showing increased protein expression of B2M in epithelial-enriched protein lysates from CLAD compared with control airway tissue. Differential gene expression (A–D) was performed using the Wilcoxon rank sum test with Bonferroni correction to generate adjusted P values. Statistical analysis for protein quantification (F) was performed using the unpaired parametric t test. Data are shown as mean ± SEM. **P < 0.01.
Fig 4: CD169+ clas_mono expansion is related to high levels of interferons in GO(A) Network visualization of upregulated TFs and their target genes. The internal nodes annotate TFs; the circular edge denotes the downstream target upregulated DEGs of these TFs; bar plots showing the pathway enrichment of these DEGs. The node sizes are positively correlated to the number of associated DEGs.(B) Representative histograms and quantitative analysis of phosphorylated STAT1 levels (normalized MFI values) in CD169+ clas_mono from the GO and GD groups (n = 6 per group). The data are summarized as the mean ± SD. Statistical analysis was performed using independent-sample t test. ∗∗p < 0.01.(C) Monocytes were stimulated with IFN-γ, IFN-α and IFN-β for 6 h, and relative mRNA expression (CD169 genes) was measured using qRT-PCR (n = 6). The data are summarized as the mean ± SD. Statistical analysis was performed using one-way ANOVA. ns, not significant; ∗p < 0.05; ∗∗∗p < 0.001.(D) Representative histograms and quantitative of the mass cytometry analysis results showing the proportions of CD169+ clas_mono after 24 h of IFN-γ, IFN-α and IFN-β stimulation (n = 6). Statistical analysis was performed using one-way ANOVA. All data with error bars are presented as mean ± SD. ns, not significant; ∗∗∗p < 0.001.
Fig 5: Single-cell transcriptomics of PBMCs identifying CD169+ classical monocytes associated with GO progression(A) Workflow chart of the overall experimental design.(B) Left, UMAP plot showing the distribution of different cell types in the peripheral blood from 5 GO patients and 4 GD patients. Right, UMAP plots showing expression of representative marker genes for the major cell types in PBMCs.(C) Heatmap showing the distribution of upregulation DEGs in each cell type. Each column represents one cell type, and each row represents upregulated genes (LogFC >0.5, adjusted p value <0.05).(D) Combined volcano plot showing up- and downregulated genes across the most effectively changed cell types in GOs (an adjusted p value <0.01 is indicated in red; an adjusted p value ≥0.01 is indicated in black; genes within IFN pathways and inflammation are labeled). Bar plot depicting enriched pathways of the significantly upregulated genes in combined volcano plot.(E) UMAP visualization of monocytes from the transcriptomic atlas of PBMCs, colored by the identified monocyte subtypes. Boxplots comparing the differences in cell proportions between GOs (orange) and GDs (green) in the selected monocytes. The data are summarized as the mean ± SD. Statistical analysis was performed using Wilcoxon test. ns, not significant; ∗p < 0.05.(F) Mass cytometry plot (Top) and bar plot (Bottom) showing the proportions of CD169+ classical monocytes in peripheral monocytes from GOs (n = 22) and GDs (n = 20). The data are summarized as the mean ± SD. Statistical analysis was performed using independent-sample t test. ∗∗p < 0.01.
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