Genomewide analysis of rat periaqueductal gray-dorsal horn reveals time-, region- and frequency-specific mRNA expression changes in response to electroacupuncture stimulation

Abstract

Electroacupuncture (EA) has been widely applied for illness prevention, treatment or rehabilitation in the clinic, especially for pain management. However, the molecular events that induce these changes remain largely uncharacterized. The periaqueductal gray (PAG) and the spinal dorsal horn (DH) have been verified as two critical regions in the response to EA stimulation in EA analgesia. In this study, a genetic screen was conducted to delineate the gene expression profile in the PAG-DH regions of rats to explore the molecular events of the analgesic effect induced by low-frequency (2-Hz) and high-frequency (100-Hz) EAs. Microarray analysis at two different time points after EA stimulation revealed time-, region- and frequency-specific gene expression changes. These expression differences suggested that modulation of neural-immune interaction in the central nervous system played an important role during EA analgesia. Furthermore, low-frequency EA could regulate gene expression to a greater degree than high-frequency EA. Altogether, the present study offers, for the first time, a characterized transcriptional response pattern in the PAG-DH regions followed by EA stimulation and, thus, provides a solid experimental framework for future in-depth analysis of the mechanisms underlying EA-induced effects.

Figure 1. Analgesic effect induced by 2 Hz and 100 Hz EA in rats.

The analgesic effects of EA on acute thermal pain were quantified using the tail flick latency (TFL) test. Both 2 Hz and 100 Hz EA significantly increased the TFL at ten minutes after the end of EA stimulation. Data are represented as the mean ± SEM. **p < 0.01, ***p < 0.001 in comparison with restraint group. (two-way repeated measures ANOVA followed by Bonferroni's Test).

Figure 2. Global gene expression in the PAG-DH regions with either 2 Hz or 100 Hz EA at the 1-hr and 24-hr time points was shown by (a) hierarchical clustering and (b) principal component analysis (PCA).

Figure 3. Frequency distribution of expression log ratios after EA stimulation relative to control.

Histogram of differentially expressed genes (RankProd analysis, p value < 0.01 and false discovery rate ≤ 0.01) induced by EA stimulation at (a) 1-hr time point (n = 2756 genes) and (b) 24-hr time point (n = 2828 genes). The change in expression level for the most differentially expressed genes at both time points were at log ratios of −0.6 to 0.6 (fold change ≤ 1.5).

Figure 4. Enrichment of gene sets altered by EA.

Gene sets were classified into three functional categories based on the gene set name derived from the MGI database. The altered gene sets were enriched with PAGE analysis as described in the Methods. (a and b) Time-dependent altered gene sets. (c and d) Region-specific altered gene sets. (e and f) Different-frequency EAs altered gene sets (the differentially expressed genes, Group A, was significantly regulated not only in the 2 Hz vs. 100 Hz comparison but also in the 2 Hz and 100 Hz vs. restraint group comparison; Group B showed significantly different gene expression only when 2 Hz was compared with 100 Hz).