Department of Urology, Gyeongsang National University Changwon Hospital, Gyeongsang National University School of Medicine, Changwon, Korea.
Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, UT, USA.
Investig Clin Urol. 2016 Jul;57(4):286-97. doi: 10.4111/icu.2016.57.4.286. Epub 2016 Jul 12.
To investigate the effects of cavernous nerve injury (CNI) on gene expression profiles in the cavernosal tissue of a CNI-induced erectile dysfunction (ED) model and to provide a basis for future investigations to discover potential target genes for ED treatment.
Young adult rats were divided randomly into 2 groups: sham operation and bilateral CN resection. At 12 weeks after CNI we measured erectile responses and performed microarray experiments and gene set enrichment analysis to reveal gene signatures that were enriched in the CNI-induced ED model. Alterations in gene signatures were compared with those in the diabetes-induced ED model. The diabetic-induced ED data is taken from GSE2457.
The mean ratio of intracavernosal pressure/blood pressure for the CNI group (0.54±0.4 cmH2O) was significantly lower than that in the sham operation group (0.73±0.8 cmH2O, p<0.05). Supervised and unsupervised clustering analysis showed that the diabetes- and CNI-induced ED cavernous tissues had different gene expression profiles from normal cavernous tissues. We identified 46 genes that were upregulated and 77 genes that were downregulated in both the CNI- and diabetes-induced ED models.
Our genome-wide and computational studies provide the groundwork for understanding complex mechanisms and molecular signature changes in ED.
研究海绵体神经损伤(CNI)对 CNI 诱导的勃起功能障碍(ED)模型海绵体组织中基因表达谱的影响,为未来发现 ED 治疗潜在靶基因的研究提供依据。
年轻成年大鼠随机分为 2 组:假手术组和双侧 CN 切除组。在 CNI 后 12 周,我们测量了勃起反应,并进行了微阵列实验和基因集富集分析,以揭示在 CNI 诱导的 ED 模型中富集的基因特征。将基因特征的变化与糖尿病诱导的 ED 模型进行比较。取自 GSE2457 的糖尿病诱导的 ED 数据。
CNI 组(0.54±0.4 cmH2O)的海绵体内压/血压平均比值明显低于假手术组(0.73±0.8 cmH2O,p<0.05)。监督和非监督聚类分析表明,糖尿病和 CNI 诱导的 ED 海绵体组织与正常海绵体组织具有不同的基因表达谱。我们在 CNI 和糖尿病诱导的 ED 模型中均鉴定出 46 个上调基因和 77 个下调基因。
我们的全基因组和计算研究为理解 ED 中的复杂机制和分子特征变化提供了基础。