能量限制(Calorie Restriction, CR)是目前唯一的非药物作用的能够延长寿命的干预措施。竞争性内源RNA(Competing endogenous RNA,ceRNA)假说认为mRNA(messenger RNA,信使RNA)、lnCRNA(Long non-coding RNA,长链非编码RNA)等转录物可以通过miRNA应答元件( miRNA response element, MRE)竞争性结合microRNA,相互制约进而调控各自的表达水平,从而构成大规模的调控网络。其中 miRNA在调控网络中作为中介因子发挥重要作用,竞争性内源RNA特别是能量限制相关CeRNA的相互作用网络尚未完全阐明.因此,探索CR相关CeRNA的互作关系对于阐述CR的机制有重要意义。本研究首先系统分析了InCRNA、mRNA和miRNA三个层次的表达谱数据,找出在CR条件下显著差异表达的基因。然后,基于筛选得到的CR调控活性miRNA,利用高通量测序技术及相关数据库预测与miRNA相关的InCRNA,并通过CR miRNA-
mRNA关系对提取miRNA靶基因信息,以miRNA为中介因子,重新构建CeRNA相互作用调控网络。并对调控网络的性质评价和分析,构建加权调控网络.随后对调控网络进行拓扑网络分析,筛选出网络中的关键基因。对参与调控网络的靶基因在基因本体功能水平以及KEGG通路水平上进行功能富集分析。最后,采用实时荧光定量PCR对调控网络中的关键基因进行验证。最后以CR相关的关键基因作为映射,探索CR特异性的子网络,以挖掘调控网络中的关键因子。CR与AL(Ad Libitum,自由饮食)比较 ,有34个差异表达lnCRNA.其中，CR中有15个lnCRNA的表达量较AL上调 ,19个lnCRNA的表达量下调 。有110个差异表达 miRNA。其中,有33个miRNA的表达呈上调,有77个miRNA的表达量均下调 。有1179个差异表达mRNA。其中,CR中有594个mRNA的表达量较AL上调,有585个mRNA的表达量均下调。根据基因调控关系构建CeRNA网络并筛选出关键基因参与的核心网络筛选出拓扑网络分析分数最高三个用实时荧光定量PCR的加以验证结果显示一致。本研究整合组学的分析,筛选CR相关的功能性mRNA、InCRNA和 miRNA;并对数据进行深入挖掘,通过它们之间的调控关系进行预测分析和数据整合,构建CR相关CeRNA调控网络,从中寻找发挥关键作用的因子,为CR的机制研究提供新的思路。

Calorie restriction (CR) is Currently the only non-pharmacological intervention that Can Prolong life. Competing endogenous RNA (CeRNA) hypothesis holds the idea that transcripts such as mRNA and lnCRNA Cail Competitively bind to microRNA through miRNA response element (miRNA response element, MRE), interact with each other and regulate their respective expression levels, resulting in large-scale regulatory network.
Among them, miRNA Plays an important role as an intermediary factor in the network, and the interaction of Competitive endogenous RNA3 especially CeRNA related to energy limitation, has not been fully elucidated. Therefbre, exploring the interaction of CR-related CeRNA is Of great Significance to the mechanism of CR.
We integrated three levels of expression Profile data of lnCRNA, mRNA and miRNA, used topological network analysis algorithm to find active miRNAs with regulatory functions in CR, Based on the selected CR regulatory activity miRNAs, high-throughput Sequencing technology was used to Predict miRNA-related lncRNAs, and miRNA target gene information was extracted through the CR miRNA-mRNA sub-netWork) miRNA was used as an intermediary factor to reconstruct the CeRNA interaction network.We also evaluated and analyzed the nature of the network. Finally) We used CR-related key genes as a mapping to explore CR-specific sub-networks to discover key factors in the regulatory network. Then we used gene function enrichment analysis to screen the key genes related to CR among the target genes.
Compared with AL, there Were 34 differentially expressed lncRNAs. Among them, the expression of 15 lnCRNA in CR were up-regulated; the expression of 19 lnCRNA Were down-regulated. There were 110 differentially expressed miRNAs, of which 33 miRNAs were up-regulated. and 77 miRNAs were all down-regulated. There were 1179 differentially expressed mRNAs. Among them, the expression of 594 mRNAs in CR were up-regulated Compared to AL. The expression levels of 585 mRNAs Were all down- regulated. The endogenous Competitive regulatory networks were Constructed according to gene regulatory relationships, and the core networks with key genes were SCreened out by topological analysis. The top three topological networks with the highest analysis score were verified by reabtime fluorescence quantitative PCR.
This Study intends to screen for functional miRNAs related to gastric Cancer based On Systems biology and the Concept of integromics. We applied bioinfbrmatic methods to dig deeper into the data, then used the regulatory relationship between them to Perfbrm Predictive analysis and data integration to Construct a CR-related CeRNA regulatory network and lastly found the factors that Play a key role. our results Provide new ideas for CR mechanism.