Genome-wide DNA hypermethylation opposes healing in patients with chronic wounds by impairing epithelial-mesenchymal transition

Abstract

An extreme chronic wound tissue microenvironment causes epigenetic gene silencing. An unbiased whole-genome methylome was studied in the wound-edge tissue of patients with chronic wounds. A total of 4,689 differentially methylated regions (DMRs) were identified in chronic wound-edge skin compared with unwounded human skin. Hypermethylation was more frequently observed (3,661 DMRs) in the chronic wound-edge tissue compared with hypomethylation (1,028 DMRs). Twenty-six hypermethylated DMRs were involved in epithelial-mesenchymal transition (EMT). Bisulfite sequencing validated hypermethylation of a predicted specific upstream regulator TP53. RNA-Seq analysis was performed to qualify findings from methylome analysis. Analysis of the downregulated genes identified the TP53 signaling pathway as being significantly silenced. Direct comparison of hypermethylation and downregulated genes identified 4 genes, ADAM17, NOTCH, TWIST1, and SMURF1, that functionally represent the EMT pathway. Single-cell RNA-Seq studies revealed that these effects on gene expression were limited to the keratinocyte cell compartment. Experimental murine studies established that tissue ischemia potently induces wound-edge gene methylation and that 5'-azacytidine, inhibitor of methylation, improved wound closure. To specifically address the significance of TP53 methylation, keratinocyte-specific editing of TP53 methylation at the wound edge was achieved by a tissue nanotransfection-based CRISPR/dCas9 approach. This work identified that reversal of methylation-dependent keratinocyte gene silencing represents a productive therapeutic strategy to improve wound closure.

Keywords: Dermatology; Epigenetics; Molecular biology; Therapeutics.

Figure 1. Increased global DNA methylation is strongly associated with human chronic WE tissue.

(A) Representative IHC analysis of DNMT1 (top), DNMT3A (middle), and DNMT3B (bottom) in paraffin sections from human unwounded (UW) skin and chronic wound-edge (WE) tissue. Bottom panel represent the intensity analysis of the images. (Scale bar: 50 μm; n = 5; *P < 0.05, Student’s t test). (B) Dot blot analysis (left) and its intensity analysis (right) of 5-methylcytosine (5mc) in human chronic WE compared with UW skin (n = 5; *P < 0.05, Student’s t test). (C) Circos plot demonstrating the distribution of significant differentially methylated regions (DMRs) associated with 1 kb upstream and 1 kb downstream of Ref-Seq genes in human chronic WE. Chromosome number marked in the periphery. Red lines and dots represent hypermethylated loci, and green lines and dots represent hypomethylated loci in chronic WE. (D) Hierarchical clustering analysis of 4689 significant DMRs associated with Ref-Seq genes in chronic WE. (n = 3; FDR adjusted P < 0.05; 3661 hypermethylated and 1028 hypomethylated in chronic WE tissue) were obtained. (E) IPA upstream regulator analysis of methylation data identified TP53 to be the most significant hypermethylated upstream regulator in chronic WE. (F) Methylation status of a region of TP53 promoter (–1069 bp to –821 bp) analyzed through bisulfite sequencing (methylated CpG, black; unmethylated CpG, white) (n = 10 clones). (G) Distribution of methylated and unmethylated CpGs in TP53 promoter (human UW skin (top); chronic WE (bottom). (H) qRT-PCR analysis of TP53 expression in human chronic WE and skin. (n = 5, 7; *P < 0.05, Student’s t test). (I) Representative IHC analysis of TP53 in sections from human UW skin and chronic WE and (J) intensity analysis of the images. (Scale bar: 50 μm; n = 6, 7; *P < 0.05, by Student’s t test). Data are presented as the mean ± SEM.

Figure 2. Increase in global DNA methylation represses EMT pathway in chronic WE tissue.

(A) Circos plot demonstrating the distribution of significant differentially expressed genes (DEGs) obtained through RNA-Seq analysis in chronic WE. Chromosome number marked in the periphery. Red lines represent upregulated genes and green lines represent downregulated genes in chronic WE. (B) Hierarchical clustering analysis of significantly different genes in chronic WE. (C) Bar plot representing integration of hypermethylated genes (by MethylCap-Seq analysis) with downregulated genes (by RNA-Seq analysis) was performed to look for the common canonical pathways using the comparison analysis tool of IPA. The dot over each bar represents the –log(P value) of each pathway. (D) Representative IHC analysis of ADAM17, (E) TWIST1, (F) NOTCH1, and (G) SMURF1 in paraffin sections of human UW skin and chronic WE. Right panels represent the intensity analysis of the images. (Scale bar: 100 μm; *P < 0.05, Student’s t test, n = 4–11). (H) H&E staining and (I) representative IHC analyses for E-cadherin–ZEB1 colocalization, (J) E-cadherin–slug colocalization, (K) E-cadherin–vimentin colocalization, and (L) E-cadherin–N-cadherin colocalization in ischemic and nonischemic murine bipedicle wounds at different time points after wounding. Right panels represent the Pearson colocalization coefficient calculation at days 1, 3, and 7 after wounding. (Scale bar: 50 μm; n = 6, *P < 0.05). Data represented as mean ± SEM.

Figure 3. Single-cell RNA-Seq analysis identifies 2 epithelial clusters in human unwounded skin, one of which, high in metabolic genes, is diminished in chronic WE tissue.

(A) tSNE (t-distributed stochastic neighbor embedding) plots showing single-cell transcriptomes of 25,561 cells from UW skin (obtained from 4 individuals) (left) and 25,168 cells from chronic WE (right) (obtained from 3 individuals) analyzed using the 10x Genomics platform. Unsupervised clustering revealed cellular heterogeneity with 11 distinct clusters of cells identified and color-coded. Each cell is represented as a dot. (B) tSNE clustering of the epithelial cells showing 2 identified keratinocytes, Kera1 (cluster 5) and Kera2 (cluster 6), in human UW skin. (C) Violin plots showing the expression level of the top 3 upregulated transcription regulators and (D) top 3 upregulated enzymes in the Kera2 cluster of human UW skin compared with the Kera1 cluster. (*adjusted P < 0.00001, Wilcoxon rank-sum test). (E) Heatmap showing the relative expression of genes involved in cellular metabolism in the 2 keratinocyte clusters (Kera1 and Kera2). (F) Heatmap showing the relative expression of genes involved in glycolysis in the 2 keratinocyte clusters (Kera1 and Kera2). (G) Spatial transcriptomics identified distinct localization of Kera1 (marked by high KRT14 and KRT1 expression) and Kera2 (marked by high KRT19 and KRT7 expression) in human UW skin through spatial feature plots. Scale bar for expression levels: KRT14 (scale: 0–3), KRT1 (scale: 0–2), KRT19 (scale: 0–1.5), and KRT7 (scale: 0–1.6). H&E staining of human skin section (left) was processed for Visium spatial gene expression analysis for classifying tissue based on mRNA levels. Further characterization of the Kera2 cluster is illustrated in Supplemental Figure 4A.

Figure 4. KRT14⁺ Kera1 cluster expresses low transcripts of identified EMT-related genes.

(A) Violin plot showing expression of KRT14 in the identified 11 clusters obtained through scRNA-Seq analyses. (B) tSNE plot with KRT14⁺ Kera1 (cluster 5) color-coded purple (left) and expression of KRT14 in the Kera1 cluster (right). (C) Expression level of TP53 in Kera1 derived from human UW skin (left) and chronic WE (right). (P < 0.0001; χ² test). (D) Expression levels of ADAM17, NOTCH1, SMURF1,and TWIST1 in human UW skin and chronic WE. Scale bar for expression levels: scale 1–3. (P < 0.05, except for TWIST1; χ² test). (E) Network of Reactome pathways enrichment of the downregulated pathways in Kera1 epithelial cells of chronic WE (CW) compared with human UW skin (adjusted P < 0.001 and at least 8% of the pathway genes found in the DEG). NGF, nerve growth factor; NMD, nonsense-mediated decay; EJC, exon junction complex; SRP, signal recognition particle; SLIT, slit guidance ligand 1; ROBO, roundabout.

Figure 5. TP53 methylation hinders human keratinocyte migration.

(A) Immunofluorescence analysis of 5-methylcytosine (5mc) in keratinocytes (HaCaT cells) exposed to vehicle control or S-adenosylmethionine (SAM) (80 μM, 48 h) and (B) their intensity analysis. DAPI intensity was used for normalization. (Scale bar: 20 μm; n = 6–8; *P < 0.001, Student’s t test). (C) Quantitation and (D) representative images of scratch-wound migration assay of keratinocytes. Scratch wound migration was performed after 48 hours of pretreatment with vehicle control or SAM (80 μM) and followed for 44 hours after wounding. (Scale bar: 200 μm; n = 6; *P < 0.05, Student’s t test). (E) Schematic diagram showing DNA methylation strategy of TP53 promoter mediated by dCas9-DNMT3A. Transfection efficiency of GFP-labeled plasmids shown in Supplemental Figure 9K through flow cytometry. (F) Left: schematic diagram of TP53 promoter (–1069 bp to –821 bp) analyzed through bisulfite genomic sequencing of DNA. Diagrammatic representation of the promoter methylation status shown (methylated CpG, black; unmethylated CpG, white). Right: Venn diagram showing the distribution of methylated and unmethylated TP53 promoter in HaCaT cells transfected with control (dCas-9-DNMT3A-ANV) (top) and in dCas-9-DNMT3A (bottom) in presence of TP53-guide RNAs. (G) Western blot analysis showing the expression of TP53, (H) activated NOTCH1, and (I) ADAM17 in HaCaT cells transfected with dCas-9-DNMT3A-EGFP or control (dCas-9-DNMT3A-EGFP-ANV) in presence of TP53-guide RNAs. Data expressed as fold change; β-actin used as loading control. (n = 6, 7; *P < 0.05, Student’s t test). Data represented as mean ± SEM. (J) Representative images and (K) quantitation of scratch-wound migration assay of HaCaT keratinocytes. Scratch-wound assay was performed after 48 hours of transfection with dCas-9-DNMT3A or control (dCas-9-DNMT3A-ANV) in presence of TP53-guide RNAs and followed for 44 hours after wounding. (n = 6; *P < 0.05, Student’s t test). Data represented as mean ± SEM.

Figure 6. Correction of P53 hypermethylation improves ischemic wound closure.

(A) Topical delivery of 5-azacytidine to murine ischemic bipedicle wounds. (B) Representative IHC and intensity analysis (right panel) of 5-methylcytosine in ischemic wounds treated with either vehicle control or 5-azacytidine. (Scale bar: 50 μm; n = 5; *P < 0.05, Student’s t test). (C) Wound closure determined by digital planimetry (top). Data presented as percentage of wound area (bottom). n = 7, 8, *P < 0.05 (Student’s t test). Data represented as mean ± SEM. (D) Vector components used for targeted demethylation of P53 promoter in keratinocytes using CRISPR/dCas9 approach. Keratinocyte targeting was achieved by KRT14 promoter–driven guide RNAs. (E) The mouse P53 promoter locus used for demethylation events. The locations of the targets (1–3) for sgRNAs are indicated by red pointers. (F) Topical delivery of TET1 CD and targeted sgRNAs to the ischemic wound employing tissue nanotransfection (TNT2.0) technology. (G) Schematic diagram of the TNT process. (H) Western blot analysis showing the expression of P53 in bipedicle ischemic wounds of mice nanotransfected with TET1CD and peptide repeat in presence or absence of KRT14 promoter–driven P53 gRNA targets. Data expressed as fold-change using β-actin as loading control. (n = 6; *P < 0.05, Student’s t test). (I) Demethylation activity was measured by bisulfite sequencing of murine P53 promoter region (mm10_chr11:69,578,954-69,579,215). (J) Wound closure was monitored at different days after wounding in bipedicle ischemic wounds of mice subjected to TNT by digital planimetry (left). Data presented as percentage of wound area (right). n = 8, *P < 0.05 (Student’s t test). (K) Representative IHC analysis of P53 in ischemic wounds subjected to TNT. (L) Intensity analysis of the images. (Scale bar: 100 μm; n = 6; *P < 0.05, Student’s t test). nairing, hair-removal technique using a depilatory agent (Nair, Church and Dwight).