| Literature DB >> 30906419 |
Si-Ming Ma1, Jing-Wen Yang1, Jian-Feng Tu2, Na-Na Yang1, Yu-Zheng Du3, Xue-Rui Wang2, Lu Wang1, Jin Huang2, Cun-Zhi Liu1.
Abstract
Hypertension is a global health problem. It has been reported that acupuncture at Taichong acupoints (LR3) decreases high blood pressure in spontaneously hypertensive rats. A transcriptome analysis can profile gene expression and its relationship with acupuncture. In this study, rats were treated with 2 weeks of acupuncture followed by regular recording of blood pressure (BP). The mRNA changes in the rostral ventrolateral medulla (RVLM) were evaluated to uncover the genetic mechanisms of acupuncture by using a whole transcript array (Affymetrix Rat Gene 1.0 ST array). BP measurements showed that acupuncture significantly decreased systolic blood pressure (SBP), mean arterial pressure (MAP), and heart rate (HR). In the bioinformatics results, 2371 differentially expressed genes (DEGs) were identified, where 83 DEGs were overlapped among Wistar-Kyoto rats (WKYs), spontaneously hypertensive rats (SHRs), and SHRs + acupuncture rats (SHRs+Acu). Gene ontology (GO) and pathway analysis revealed that 279 GO terms and 20 pathways with significant differences were related to oxidative stress, inflammation, and vascular endothelial function. In addition, coexpressed DEGs networks indicated that Cd4 and Il-33 might mediate the cascade of inflammation and oxidative stress responses, which could serve as a potential target of acupuncture treatment. In conclusion, our study demonstrated that acupuncture is a promising therapy for treating hypertension and could regulate multiple biological processes mainly involving oxidative stress, inflammation, and vascular endothelial function.Entities:
Year: 2019 PMID: 30906419 PMCID: PMC6398018 DOI: 10.1155/2019/9541079
Source DB: PubMed Journal: Evid Based Complement Alternat Med ISSN: 1741-427X Impact factor: 2.629
Figure 1The antihypertensive effects of acupuncture on spontaneously hypertensive rats (SHRs). (a) Systolic blood pressure; (b) mean arterial pressure; (c) heart rate. N=10, data are represented as mean ± SEM, ∗p < 0.05 SHRs vs WKYs, #p < 0.05 SHRs+Acu vs SHRs (Scheffe multiple-range analysis), respectively.
The differentially expressed genes among the 3 groups.
| Gene Symbol | Gene Description | p-value | FDR | WKYs | SHRs | SHRs+Acu |
|---|---|---|---|---|---|---|
| Haghl | hydroxyacylglutathione hydrolase-like | 1.00E-07 | 4.08E-05 | 35.99 | 22.81 | 23.69 |
| Fmo2 | flavin containing monooxygenase 2 | 1.00E-07 | 4.08E-05 | 9.86 | 7.32 | 6.74 |
| Mgst3 | microsomal glutathione S-transferase 3 | 1.00E-07 | 4.08E-05 | 377.27 | 527.33 | 567.29 |
| Dusp12 | dual specificity phosphatase 12 | 1.00E-07 | 4.08E-05 | 76.85 | 104.65 | 114.5 |
| Cdkn1a | cyclin-dependent kinase inhibitor 1A | 1.00E-07 | 4.08E-05 | 21.74 | 33.7 | 31.53 |
| Glo1 | glyoxalase 1 | 1.00E-07 | 4.08E-05 | 229.94 | 165.71 | 177.26 |
| Crot | carnitine O-octanoyltransferase | 1.00E-07 | 4.08E-05 | 58.96 | 34.31 | 38.22 |
| Nmnat1 | nicotinamide nucleotide adenylyltransferase 1 | 1.00E-07 | 4.08E-05 | 103.73 | 171.11 | 173.61 |
| Eapp | E2F-associated phosphoprotein | 1.00E-07 | 4.08E-05 | 292.06 | 199.04 | 208.1 |
| Mettl7a | methyltransferase like 7A | 1.00E-07 | 4.08E-05 | 52.61 | 79.12 | 75.12 |
Figure 2The genes expression that might be regulated by acupuncture in 3 conditions was profiled. (a) 83 DEGs among 3 groups (ANOVA analysis, fold change < 1.2, p < 0.05); (b) the number of overlapped DEGs between each pair of groups; (c) up/downregulated DEGs in each pair of compared groups.
Figure 3Significant profiles and genes that could be regulated by acupuncture. (a) The ascending trend of profile 2 is mirrored by the descending trend of profile 7; (b) 226 genes were classified into profile 2, and 197 genes were classified into profile 7; (c) validation of 4 representative genes by qRT-PCR, including Cox5b and Sirt6 in profile 2, Nf1 and Gabbr1 in profile 7.
Figure 4Bioinformatics analysis of gene ontology (GO) analysis and pathway analysis with KEGG. (a) GO analysis reflects functional characteristics of 20 representative overrepresented genes that might be regulated by acupuncture; (b) pathway analysis demonstrates 20 significantly signaling pathways that acupuncture might be involved in.
Partial genes and pathways screened by GO and pathway analysis.
| Gene name | GO Term | Pathway Name |
|---|---|---|
| Fgfr2 | epithelial cell differentiation | MAPK signaling pathway |
| positive regulation of epithelial cell proliferation | ||
| response to stress | ||
| protein phosphorylation | ||
| JNK cascade | ||
| positive regulation of ERK1 and ERK2 cascade | ||
| negative regulation of MAPK cascade | ||
| regulation of Ras GTPase activity | ||
| positive regulation of stress-activated MAPK cascade | ||
| Vegfa | positive regulation of I-kappaB kinase/NF-kappaB cascade | NF-kappaB signaling pathway |
| regulation of apoptotic process | ||
| immunoglobulin mediated immune response | ||
| positive regulation of T cell activation | ||
| protein autophosphorylation | ||
| apoptotic process | ||
| apoptotic process | ||
| Nf1 | glutathione metabolic process | Glutathione metabolism |
| negative regulation of neuron apoptotic process | ||
| response to oxidative stress | ||
| glutathione biosynthetic process | ||
| regulation of blood vessel size | ||
| positive regulation of glutamate-cysteine ligase activity | ||
| Ptgsl | positive regulation of transcription from RNA polymerase II promoter | Neurotrophin signaling pathway |
| positive regulation of smooth muscle cell proliferation | ||
| Ripk2 | response to interleukin-1 | Neurotrophin signaling pathway |
| toll-like receptor 2 signaling pathway | ||
| positive regulation of NF-kappaB transcription factor activity | ||
| toll-like receptor 4 signaling pathway | ||
| positive regulation of stress-activated MAPK cascade | ||
| Gabbr1 | positive regulation of glial cell proliferation | Insulin signaling pathway |
| response to interleukin-1 | ||
| Gclm | angiogenesis | Insulin signaling pathway |
| negative regulation of neuron apoptotic process | ||
| positive regulation of NF-kappaB transcription factor activity | ||
| negative regulation of apoptotic process | ||
| protein phosphorylation | ||
| Grm1 | JNK cascade | Insulin signaling pathway |
| protein phosphorylation | ||
| negative regulation of angiogenesis |
Figure 5(a) The coexpressing gene network of 347 DEGs in hypertension; (b) the subnetwork of coexpressing DEGs that are at the top of list. Circles represent genes (pink circles: DEGs, yellow circles: DEGs on the top of list), the edges between nodes represent gene-gene interaction, and the diameter of circles represents the degree of gene-gene interaction.