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. 2023 Apr 12;13(1):5941.
doi: 10.1038/s41598-023-32452-4.

5-methyladenosine regulators play a crucial role in development of chronic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis

Yiyi Zhou  1 , Zhenli Hu  1 , Qinying Sun  1 , Yuchao Dong  2
Affiliations

5-methyladenosine regulators play a crucial role in development of chronic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis

Yiyi Zhou et al. Sci Rep. .

Abstract

5-methyladenosine (m5C) modification regulates gene expression and biological functions in oncologic areas. However, the effect of m5C modification in chronic hypersensitivity pneumonitis (CHP) and idiopathic pulmonary fibrosis (IPF) remains unknown. Expression data for 12 significant m5C regulators were obtained from the interstitial lung disease dataset. Five candidate m5C regulators, namely tet methylcytosine dioxygenase 2, NOP2/Sun RNA methyltransferase 5, Y-box binding protein 1, tRNA aspartic acid methyltransferase 1, and NOP2/Sun RNA methyltransferase 3 were screened using random forest and nomogram models to predict risks of pulmonary fibrosis. Next, we applied the consensus clustering method to stratify the samples with different m5C patterns into two groups (cluster A and B). Finally, we calculated immune cell infiltration scores via single-sample gene set enrichment analysis, then compared immune cell infiltration, related functions as well as the expression of programmed cell death 1 (PD-1, PDCD1) and programmed death protein ligand-1 (PD-L1, CD274) between the two clusters. Principal component analysis of m5C-related scores across the 288 samples revealed that cluster A had higher immune-related expression than B. Notably, T helper cell (Th) 2 type cytokines and Th1 signatures were more abundant in clusters A and B, respectively. Our results suggest that m5C is associated with and plays a crucial role in development of pulmonary fibrosis. These m5C patterns could be potential biomarkers for identification of CHP and IPF, and guide future development of immunotherapy or other new drugs strategies for pulmonary fibrosis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flowchart of this study.
Figure 2
Figure 2
Landscape of the m5C regulators in CHP and IPF. (A) The boxplot of 14 m5C regulators expression in tests (CHP and IPF) and controls. (B) Expression of 12 significantly differentially expressed m5C regulators in tests and controls. *p < 0.05, **p < 0.01, and ***p < 0.001. (C) Chromosomal positions of those m5C regulators.
Figure 3
Figure 3
Correlation between m5C erasers and writers. Eraser gene: TET2. Writer genes: DNMT1, DNMT3A, DNMT3B, NOP2, NSUN2, NSUN3, NSUN4, NSUN5, NSUN6, NSUN7, TRDMT1. The threshold value: |R|> 0.4 and P < 0.001. R, correlation coefficient.
Figure 4
Figure 4
The RF and nomogram model construction. (A) The red line represented the error levels of treat groups, the green line represented control groups and the black line represented overall samples. (B) The importance of the m5C regulators were calculated based on the RF model. (C) The construction of nomogram model and gene score was used to predict prevalence. (D) The accuracy of nomogram model was assessed by calibration curve. (E) The decision curve might be benefit to the disease. (F) Clinical impact curve was applied for assessing clinical impact of the model.
Figure 5
Figure 5
Consensus clustering of m5C regulators. (A) Consensus matrices of the 12 m5C regulators (k = 2–5). (B) Expression of 12 significant m5C regulators in the heatmap. (C) Expression of 12 significant m5C regulators in the boxplot. (D) PCA showed the striking difference in modification cluster A (the blue pattern) and cluster B (the red pattern). *p < 0.05, **p < 0.01, and ***p < 0.001.
Figure 6
Figure 6
Single sample gene set enrichment analysis of immune cells infiltration. (A) The relationship between immune cells infiltration with two m5C patterns. (B) The heatmap of the 12 significant m5C regulators and infiltrating immune cells. (C) Immune cell infiltration between high NSUN4 expression pattern and low NSUN4 expression pattern. *p < 0.05, **p < 0.01, and ***p < 0.001.
Figure 7
Figure 7
Single sample gene set enrichment analysis of the two m5Cclusters. (A) The boxplot of immune function in the two m5C patterns. *p < 0.05, **p < 0.01, and ***p < 0.001. (B) The heatmap of immune function in the two m5C patterns. Other characteristics including estimate score, immune score, and stromal score. Differential estimate score (C), immune score (D), and stromal score (E) between m5Ccluster A and B.
Figure 8
Figure 8
Analyses of immune checkpoints, small molecule drug therapy, GO, as well as KEGG Pathway in two m5Cclusters. (A) The boxplot of immune checkpoints in the two m5C patterns. *p < 0.05, **p < 0.01, and ***p < 0.001. (B) The expression of PDCD1 (PD-1) in the two m5C patterns. (C) The expression of CD274 (PD-L1) in the two m5C patterns. Two-dimensional molecular structure (D) and three-dimensional molecular structure (E) of ruxolitinib. The GO (F) and KEGG (G) enrichment analysis for the m5C-related differentially expressed genes (DEGs).
Figure 9
Figure 9
Consensus clustering of the 3346 m5C-related DEGs. (A) Consensus matrices of the DGEs (k = 2–5). (B) Expression of the DEGs in gene cluster A and cluster B. (C) Expression of the 12 m5C regulators in gene cluster A and cluster B. (D) Principal component analysis for the expression profiles of gene subtypes, also showing a remarkable difference between different modification patterns. *p < 0.05, **p < 0.01, and ***p < 0.001.
Figure 10
Figure 10
Analyses of immune cells infiltration and immune checkpoints in two m5Cclusters. (A) The relationship between immune cells infiltration with two m5C patterns. (B) The boxplot of immune checkpoints in the two m5C patterns. *p < 0.05, **p < 0.01, and ***p < 0.001.
Figure 11
Figure 11
Single sample gene set enrichment analysis of the two gene clusters. (A) The boxplot of immune function in the two gene patterns. *p < 0.05, **p < 0.01, and ***p < 0.001. (B) The heatmap of immune function in the two gene patterns. Other characteristics including estimate score, immune score, and stromal score. Differential estimate score (C), immune score (D), and stromal score (E) between m5Ccluster A and B.
Figure 12
Figure 12
Comparison of m5C patterns and gene patterns. (A) Sankey diagram of the relationship between two m5C patterns, two gene patterns, and m5C scores. Differences in m5C score based on PCA algorithm between the two m5C patterns (B) or the two gene patterns (C). Differential expression levels of IL-4, IL-5, IL-10, IL-13, TSLP and MUC5B between m5C cluster A and cluster B (D) or gene cluster A and cluster B (E). *p < 0.05, **p < 0.01, and ***p < 0.001.
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