目的 研究糖尿病、阳痿、慢性肾炎患者中筛选出的典型肾阳虚证病例的转录组学特征，揭示出肾阳虚证“同证异治”的生物学基础。 方法 分别对9例肾阳虚证患者进行Agilent人444k表达谱芯片实验，对差异表达基因进行基因本休论（GO）、Pathway分析。 结果 找出332条共同差异表达基因，其中有注释的基因为181条。通过GO分析发现肾阳虚证在免疫系统、氨基酸分解和合成、脂类代谢、生殖、能量代谢及肿瘤相关的基因有密切联系，通过Pathway分析发现与肾阳虚证相关的信号通路有39个。 结论 肾阳虚证可能导致免疫系统低下，氨基酸分解和合成、脂类代谢、生殖、能量代谢功能降低，以及与肿瘤形成相关。
ObjectiveTo study the abnormal biological pathways of intrahepatic cholangiocarcinoma (ICC) from the transcriptomics level and identify genes associated with the prognosis of ICC.MethodsThe differentially expressed genes were screened by t test and fold change method, then KEGG functional enrichment analysis was performed on related genes. The STRING database was applied to construct protein interaction network and find the hub nodes of the network by calculating the degree, betweenness, and closeness of each node. Kaplan-Meier survival analysis was performed using log-rank test to identify prognostic genes related to ICC.ResultsAll of 1 134 differentially expressed genes were overlapped in 3 datasets, which were mainly involved in 15 pathways, including DNA replication, cell cycle, drug metabolism, RNA transport, etc. signaling pathways and amino acid synthesis. According to protein interaction network analysis, TAF1, GRB2, E2F4, HNF4A, MYC, and TP53 genes were hub nodes. As GRB2 and TP53 genes were also the death related genes of ICC, it was found that patients with lower GRB2 gene expression had a better overall survival than those with higher GRB2 gene expression (P=0.040 9), while patients with lower TP53 had a worse overall survival than those with higher TP53 gene expression (P=0.027 3), which were also verified in the TCGA database.ConclusionsThe abnormal cell metabolism is notably related to the tumorigenesis of ICC. TAF1, GRB2, E2F4, HNF4A, MYC, and TP53 are the key genes in the carcinogenesis and progression of ICC. Expressions of GRB2 and TP53 genes are associated with the prognosis of ICC.
Objective To study the differential expression profiling of the transcripts modified by m5C methylation in a rat model of N-methyl-D-aspartate (NMDA)-induced retinal excitotoxicity. MethodsA total of 65 Sprague Dawley male rats aged 7-8 weeks were randomly divided into two groups: normal control group and NMDA group. The right eye (model eye) of rats in the NMDA group were injected with 50.0 mmol/L of NMDA 3 μl in the vitreous cavity, while in the normal control group, equal volume of normal saline was injected into the vitreous cavity. After 1 week of the injection, the optic nerve conduction function of rats was detected by visual evoked potential. The whole structure of rat retina was observed by hematoxylin-eosin staining, and the thickness of each retinal layer and the number of retinal ganglion cell layer were detected. The number of β3 tubulin immunofluorescence positive cells was detected by immunofluorescence staining on retinal stretched preparation. Total RNA was extracted from the retinas of normal control group and NMDA group, and high-throughput m5C modified RNA was sequenced, and bioinformatics analysis was performed. The relative expression levels of SLFN3, PLXNB3, CD36 and HIC2 mRNA in retina were detected by real-time quantitative polymerase chain reaction. The comparison between the two groups was performed using an unpaired t test. ResultsThe P1 latency of control group and NMDA group were (117.86±6.48) and (148.46±3.78) ms, and the amplitudes were (42.57±2.41) and (8.68±0.63) μV, respectively. Compared with the normal control group, the latency period was prolonged and the amplitude was significantly decreased in the NMDA group, with statistical significance (P＜0.001). In normal control group, retinal ganglion cells (RGC) were uniformly arranged with large round nuclei. In NMDA group, the volume of retinal RGC was atrophied and the number of RGC was reduced. The total retinal thickness in the control group and NMDA group was (207.51±12.76) μm and (187.51±12.54) μm, respectively. The number of β3 tubulin positive cells was 79.86±6.56 and 29.36±2.16, respectively. Compared with normal control group, the total retinal thickness and the number of β3 tubulin positive cells in NMDA group were decreased, with statistical significance (P＜0.001). Compared with the control group, 576 differentially expressed m5C mRNA were screened in the NMDA group, among which 230 up-regulated and 346 down-regulated genes were detected, respectively. The results of biological information analysis showed that compared with the control group, the upregulated m5C mRNA in the NMDA group was mainly involved in biological processes such as perception and cell-cell adhesion, and was mainly concentrated in the cytokine-cytokine receptor interaction and neural active ligand-receptor interaction pathway. The biological processes in which down-regulated m5C mRNA was mainly involved in biological processes such as G-protein-coupled receptor signaling pathway and cell communication, which were mainly concentrated in primary immune deficiency pathway and neural active ligand-receptor interaction pathway. Real-time quantitative polymerase chain reaction detection results showed that compared with the normal control group, the relative expression levels of SLFN3 and PLXNB3 mRNA in the retina of rats in NMDA group were significantly increased, while the relative expression levels of CD36 and HIC2 mRNA were significantly decreased, with statistical significance (P＜0.05). ConclusionIn NMDA induced retinal excitatory toxicity rat models, m5C modified retinal transcriptome showed abnormal expression.
Due to the high dimensionality and complexity of the data, the analysis of spatial transcriptome data has been a challenging problem. Meanwhile, cluster analysis is the core issue of the analysis of spatial transcriptome data. In this article, a deep learning approach is proposed based on graph attention networks for clustering analysis of spatial transcriptome data. Our method first enhances the spatial transcriptome data, then uses graph attention networks to extract features from nodes, and finally uses the Leiden algorithm for clustering analysis. Compared with the traditional non-spatial and spatial clustering methods, our method has better performance in data analysis through the clustering evaluation index. The experimental results show that the proposed method can effectively cluster spatial transcriptome data and identify different spatial domains, which provides a new tool for studying spatial transcriptome data.