Transcriptomic Insights into Different Stimulation Intensity of Electroacupuncture in Treating COPD in Rat Models

Abstract

Background: Electroacupuncture (EA), with varying stimulation intensities, has demonstrated therapeutic potentials in both animal and clinical studies for the treatment of chronic obstructive pulmonary disease (COPD). However, a comprehensive investigation of the intensity-related effects, particularly 1mA and 3mA of EA, and the underlying mechanisms remains lacking.

Methods: A COPD rat model was established by prolonged exposure to cigarette smoke and intermittent intratracheal instillation of lipopolysaccharide. EA treatment was administered at acupoints BL13 (Feishu) and ST36 (Zusanli), 20 minutes daily for 2 weeks, with intensities of 1mA and 3mA. EA effectiveness was evaluated by pulmonary function, histopathological change, serum level of inflammatory cytokines, and level of oxidative stress markers in serum and lung tissues. Transcriptome profiling and weighted gene co-expression network analysis (WGCNA) were performed to reveal gene expression patterns and identify hub genes. Real-time quantitative PCR (RT-qPCR) and Western blot (WB) were performed to detect the mRNA and protein expression levels, respectively.

Results: EA at both 1mA and 3mA exerted differing therapeutic effects by improving lung function and reducing inflammation and oxidative stress in COPD rats. Transcriptome analysis revealed distinct expression patterns between the two groups, functionally corresponding to shared and intensity-specific (1mA and 3mA) enriched pathways. Eight candidate genes were identified, including Aqp9, Trem1, Mrc1, and Gpnmb that were downregulated by EA and upregulated in COPD. Notably, Msr1 and Slc26a4 exclusively downregulated in EA-1mA, while Pde3a and Bmp6 upregulated solely in EA-3mA. WGCNA constructed 5 key modules and elucidated the module-trait relationship, with the aforementioned 8 genes being highlighted. Additionally, their mRNA and protein levels were validated by RT-qPCR and WB.

Conclusion: Our results demonstrated that 1mA and 3mA intensities induce distinct gene expression patterns at the transcriptional level, associated with shared and 1mA vs 3mA-specific enriched pathways. Genes Mrc1, Gpnmb, Trem1, and Aqp9 emerge as promising targets, and further studies are needed to elucidate their functional consequences in COPD.

Keywords: chronic obstructive pulmonary disease; electroacupuncture; intensity; transcriptome profiling; weighted gene co-expression network analysis.

Figure 1

The study timeline and effects of EA on lung function and morphology. (A) Schematic showing establishment of a COPD rat model and EA treatment schedule. (B) Effects of EA on lung function assessed by FEV_0.1/FVC and FEV_0.3/FVC ratios, A/S ratio, and quantitative analysis of inflammatory cells in NC, COPD, EA-1 and EA-3 groups (n=6 per group). (C) Representative images of HE stained lung sections from each group (n=6 per group). Scale bar: 50μm; *p<0.05, **p<0.01.

Figure 2

Effects of EA on cytokines and oxidative stress markers. (A) Effects of EA on serum cytokines (IL-6, IL-1β, IL-10, and TNF-α) measured by ELISA assay (n=6 per group). (B) Effects of EA on oxidative stress markers (GSH-PX, SOD and MDA) examined in serum and (C) in lung tissues (n=6 per group). *p<0.05, **p<0.01.

Figure 3

Results of transcriptome profiling. (A) Hierarchical cluster heatmap showing the expression patterns of differentially expressed transcripts across groups. (B) Venn diagram analysis depicting shared and unique genes among group comparison: downregulated DEGs in COPD vs NC group, upregulated DEGs in EA-1 vs COPD group, and upregulated DEGs in EA-3 vs COPD group. Likewise, shared and unique genes in (C) upregulated DEGs in COPD vs NC, downregulated DEGs in EA-1 group vs COPD group, and downregulated DEGs in EA-3 group vs COPD group. Arrows indicate selected target genes. (D) KEGG pathway enrichment analysis between COPD and NC groups, (E) between EA-1 and COPD groups, and (F) between EA-3 and COPD groups. Red color marks the shared pathways among the three comparisons. (G) The selected GO terms enriched between COPD and NC groups, (H) between EA-1 and COPD groups, and (I) between EA-3 and COPD groups; Biological process (red), cellular component (blue), and molecular function (green).

Figure 4

WGCNA analysis, RT-qPCR and WB results. (A) Clustering dendrogram of DEGs revealing 5 module eigengenes (MEs), including MEturquoise, MEbrown, MEblue, MEyellow and MEgrey. (B) Heatmap plot of gene network displaying topological overlap, with light colors denoting low overlap and progressively darker colors higher overlap. Blocks of darker colors along the diagonal are the identified modules. (C) Module-trait relationship by Pearson correlation analysis. Each row corresponds to a ME and each column to a trait. Correlation coefficients (r value, scaled by colors) and corresponding P values are displayed in each rectangle. Positive correlation (Red); Negative correlation (Green); No correlation (White). (D) The qPCR results of Mrc1, Gpnmb, Aqp9, Trem1, Msr1, Slc26a4, Pde3a and Bmp6. Abbreviations: RNE, RNE = 2^−∆∆Ct. (E) The WB results of Mrc1, Gpnmb, Aqp9, Trem1, Msr1, Slc26a4, Pde3a and Bmp6 (n=4). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.