Once thought to be transcriptional noise, large non-coding RNAs （IncRNAs） have recently been demonstrated to be functional molecules. The cell-type-specific expression patterns of lncRNAs suggest that their transcri...Once thought to be transcriptional noise, large non-coding RNAs （IncRNAs） have recently been demonstrated to be functional molecules. The cell-type-specific expression patterns of lncRNAs suggest that their transcription may be regulated epigenetically. Using a custom-designed microarray, here we examine the expression profile of IncRNAs in embryonic stem （ES） cells, lineage-restricted neuronal progenitor cells, and terminally differentiated fibroblasts. In addition, we also analyze the relationship between their expression and their promoter H3K4 and H3K27 methyla- tion patterns. We find that numerous lncRNAs in these cell types undergo changes in the levels of expression and promoter H3K4me3 and H3K27me3. Interestingly, lncRNAs that are expressed at lower levels in ES cells exhibit higher levels of H3K27me3 at their promoters. Consistent with this result, knockdown of the H3K27me3 methyltransferase Ezh2 results in derepression of these IncRNAs in ES cells. Thus, our results establish a role for Ezh2-mediated H3K27 methylation in lncRNA silencing in ES cells and reveal that lncRNAs are subject to epigenetic regulation in a similar manner to that of the protein-coding genes.展开更多
Advances in functional genomics have led to discovery of a large group of previous uncharacterized long non-coding RNAs （IncRNAs）. Emerging evidence indicates that IncRNAs may serve as master gene regulators through...Advances in functional genomics have led to discovery of a large group of previous uncharacterized long non-coding RNAs （IncRNAs）. Emerging evidence indicates that IncRNAs may serve as master gene regulators through various mechanisms. Dysregulation of IncRNAs is often associated with a variety of human diseases including cancer. Of significant interest, recent studies suggest that IncRNAs participate in the p53 tumor suppressor regulatory network. In this review, we discuss how IncRNAs serve as p53 regulators or p53 effectors. Further characterization of these p53-associated IncRNAs in cancer will provide a better understanding of lncRNA- mediated gene regulation in the p53 pathway. As a result, IncRNAs may prove to be valuable biomarkers for cancer diagnosis or poten- tial targets for cancer therapy.展开更多
In vitro, mouse embryonic stem （ES） cells can differentiate into many somatic cell types, including neurons and glial cells. When cultured in serum-free medium, ES cells convert spontaneously and efficiently to a ne...In vitro, mouse embryonic stem （ES） cells can differentiate into many somatic cell types, including neurons and glial cells. When cultured in serum-free medium, ES cells convert spontaneously and efficiently to a neural fate. Previous studies have shown that the neural conversion of mouse ES cells includes both the participation of neural-specific transcription factors and the regulation of epigenetic modifications. However, the intracellular mechanism underlying this intrinsic transition still re- mains to be further elucidated. Herein, we describe a long intergenic non-coding RNA, LincRNA1230, which participates in the regulation of the neural lineage specification of mouse ES cells. The ectopic forced expression of LincRNAI230 dramatically inhibited mouse ES cells from adopting a neural cell fate, while LincRNA1230 knockdown promoted the conversion of mouse ES cells towards neural progenitors. Mechanistic studies have shown that LincRNA1230 inhibits the activation of early neural genes, such as Pax6 and Soxl, through the modulation of bivalent modifications （tri-methylation of histone3 lysine4 and his- tone3 lysine27） at the promoters of these genes. The interaction of LincRNA1230 with Wdr5 blocked the localization of Wdr5 at the promoters of early neural genes, thereby inhibiting the enrichment of H3K4me3 modifications at these loci. Collectively, these findings revealed a crucial role for LincRNA1230 in the regulation of the neural differentiation of mouse ES cells.展开更多
目的:探讨长链非编码RNA TFAP2A-AS1对子宫内膜癌细胞增殖、侵袭和迁移的影响及机制。方法:选择50例子宫内膜癌组织和对应的癌旁组织。选取子宫内膜癌细胞株(RL95-2、HEC-1A、HHUA、HEC-1B及Ishikawa),子宫内膜上皮细胞hEEC。子宫内膜...目的:探讨长链非编码RNA TFAP2A-AS1对子宫内膜癌细胞增殖、侵袭和迁移的影响及机制。方法:选择50例子宫内膜癌组织和对应的癌旁组织。选取子宫内膜癌细胞株(RL95-2、HEC-1A、HHUA、HEC-1B及Ishikawa),子宫内膜上皮细胞hEEC。子宫内膜癌细胞株Ishikawa转染si-TFAP2A-AS1(si-TFAP2A-AS1组)、si-NC(si-NC组)、miR-9-5p mimics(miR-9-5p组)及miR-NC(miR-NC组);RL95-2细胞转染OE-TFAP2A-AS1(TFAP2A-AS1组)、Vector(Vector组)、sh-miR-9-5p(sh-miR-9-5p组)及sh-NC(sh-NC组)。CCK-8检测细胞增殖能力,Transwell检测细胞侵袭、迁移能力,双荧光素酶实验、pull down实验分析TFAP2A-AS1与miR-9-5p的靶向关系;miR-9-5p与ERK的靶向关系,实时荧光定量PCR(RT-qPCR)检测子宫内膜癌组织、癌旁组织、子宫内膜癌细胞及hEEC细胞TFAP2AAS1、miR-9-5p表达水平,Western blot检测细胞外调节蛋白激酶(ERK)、磷酸化ERK(p-ERK)水平。结果:子宫内膜癌组织中TFAP2A-AS1表达水平低于癌旁组织,miR-9-5p表达水平高于癌旁组织(P<0.05)。子宫内膜癌组织TFAP2A-AS1、miR-9-5p表达水平与分化程度、肿瘤直径、TNM分期及淋巴结转移有关。子宫内膜癌组织中TFAP2A-AS1、miR-9-5p表达水平呈负相关(r=-0.782,P=0.002)。与hEEC细胞比较,RL95-2、HEC-1A、HHUA、HEC-1B及Ishikawa细胞TFAP2A-AS1表达水平降低,miR-9-5p表达水平升高(P<0.05)。与si-NC组比较,si-TFAP2A-AS1组细胞48 h、72 h OD值,侵袭、迁移细胞数均升高(P<0.05);与Vector组比较,TFAP2A-AS1组细胞48 h、72 h OD值,侵袭、迁移细胞数均降低(P<0.05)。与miR-NC组比较,miR-9-5p组细胞48 h、72 h OD值,侵袭、迁移细胞数均升高(P<0.05);与sh-NC组比较,sh-miR-9-5p组细胞48 h、72 h OD值,侵袭、迁移细胞数均降低(P<0.05)。TFAP2A-AS1负性调控miR-9-5p、p-ERK表达水平,miR-9-5p可靶向调控ERK蛋白。结论:过表达TFAP2A-AS1通过靶向下调miR-9-5p,抑制ERK通路,最终抑制子宫内膜癌细胞恶性生物学行为。展开更多
Understanding the subcellular localization of long non-coding RNAs(IncRNAs)is crucial for unraveling their functional mechanisms.While previous computational methods have made progress in predicting IncRNA subcellular...Understanding the subcellular localization of long non-coding RNAs(IncRNAs)is crucial for unraveling their functional mechanisms.While previous computational methods have made progress in predicting IncRNA subcellular localization,most of them ignore the sequence order information by relying on k-mer frequency features to encode IncRNA sequences.In the study,we develope SGCL-LncLoc,a novel interpretable deep learning model based on supervised graph contrastive learning.SGCL-LncLoc transforms IncRNA sequences into de Bruijn graphs and uses the Word2Vec technique to learn the node representation of the graph.Then,SGCL-LncLoc applies graph convolutional networks to learn the comprehensive graph representation.Additionally,we propose a computational method to map the attention weights of the graph nodes to the weights of nucleotides in the IncRNA sequence,allowing SGCL-LncLoc to serve as an interpretable deep learning model.Furthermore,SGCL-LncLoc employs a supervised contrastive learning strategy,which leverages the relationships between different samples and label information,guiding the model to enhance representation learning for IncRNAs.Extensive experimental results demonstrate that SGCL-LncLoc outperforms both deep learning baseline models and existing predictors,showing its capability for accurate IncRNA subcellular localization prediction.Furthermore,we conduct a motif analysis,revealing that SGCL-LncLoc successfully captures known motifs associated with IncRNA subcellular localization.The SGCL-LncLoc web server is available at http://csuligroup.com:8000/SGCL-LncLoc.The source code can be obtained from https://github.com/CSUBioGroup/SGCL-LncLoc.展开更多
文摘Once thought to be transcriptional noise, large non-coding RNAs （IncRNAs） have recently been demonstrated to be functional molecules. The cell-type-specific expression patterns of lncRNAs suggest that their transcription may be regulated epigenetically. Using a custom-designed microarray, here we examine the expression profile of IncRNAs in embryonic stem （ES） cells, lineage-restricted neuronal progenitor cells, and terminally differentiated fibroblasts. In addition, we also analyze the relationship between their expression and their promoter H3K4 and H3K27 methyla- tion patterns. We find that numerous lncRNAs in these cell types undergo changes in the levels of expression and promoter H3K4me3 and H3K27me3. Interestingly, lncRNAs that are expressed at lower levels in ES cells exhibit higher levels of H3K27me3 at their promoters. Consistent with this result, knockdown of the H3K27me3 methyltransferase Ezh2 results in derepression of these IncRNAs in ES cells. Thus, our results establish a role for Ezh2-mediated H3K27 methylation in lncRNA silencing in ES cells and reveal that lncRNAs are subject to epigenetic regulation in a similar manner to that of the protein-coding genes.
文摘Advances in functional genomics have led to discovery of a large group of previous uncharacterized long non-coding RNAs （IncRNAs）. Emerging evidence indicates that IncRNAs may serve as master gene regulators through various mechanisms. Dysregulation of IncRNAs is often associated with a variety of human diseases including cancer. Of significant interest, recent studies suggest that IncRNAs participate in the p53 tumor suppressor regulatory network. In this review, we discuss how IncRNAs serve as p53 regulators or p53 effectors. Further characterization of these p53-associated IncRNAs in cancer will provide a better understanding of lncRNA- mediated gene regulation in the p53 pathway. As a result, IncRNAs may prove to be valuable biomarkers for cancer diagnosis or poten- tial targets for cancer therapy.
基金supported by National Natural Science Foundation of China (81530042, 31571529, 31210103905, 31571519, 31571390, 31371510, 31301208, 31471250, 31401257)the Ministry of Science and Technology (2012CB966603, 2013CB967600, 2013CB967401)+2 种基金Science and Technology Commission of Shanghai Municipality (15JC1403200, 15JC1403201)Shanghai Rising-Star Program (14QA1403900)the Fundamental Research Funds for the Central Universities (2000219099)
文摘In vitro, mouse embryonic stem （ES） cells can differentiate into many somatic cell types, including neurons and glial cells. When cultured in serum-free medium, ES cells convert spontaneously and efficiently to a neural fate. Previous studies have shown that the neural conversion of mouse ES cells includes both the participation of neural-specific transcription factors and the regulation of epigenetic modifications. However, the intracellular mechanism underlying this intrinsic transition still re- mains to be further elucidated. Herein, we describe a long intergenic non-coding RNA, LincRNA1230, which participates in the regulation of the neural lineage specification of mouse ES cells. The ectopic forced expression of LincRNAI230 dramatically inhibited mouse ES cells from adopting a neural cell fate, while LincRNA1230 knockdown promoted the conversion of mouse ES cells towards neural progenitors. Mechanistic studies have shown that LincRNA1230 inhibits the activation of early neural genes, such as Pax6 and Soxl, through the modulation of bivalent modifications （tri-methylation of histone3 lysine4 and his- tone3 lysine27） at the promoters of these genes. The interaction of LincRNA1230 with Wdr5 blocked the localization of Wdr5 at the promoters of early neural genes, thereby inhibiting the enrichment of H3K4me3 modifications at these loci. Collectively, these findings revealed a crucial role for LincRNA1230 in the regulation of the neural differentiation of mouse ES cells.
文摘目的:探讨长链非编码RNA TFAP2A-AS1对子宫内膜癌细胞增殖、侵袭和迁移的影响及机制。方法:选择50例子宫内膜癌组织和对应的癌旁组织。选取子宫内膜癌细胞株(RL95-2、HEC-1A、HHUA、HEC-1B及Ishikawa),子宫内膜上皮细胞hEEC。子宫内膜癌细胞株Ishikawa转染si-TFAP2A-AS1(si-TFAP2A-AS1组)、si-NC(si-NC组)、miR-9-5p mimics(miR-9-5p组)及miR-NC(miR-NC组);RL95-2细胞转染OE-TFAP2A-AS1(TFAP2A-AS1组)、Vector(Vector组)、sh-miR-9-5p(sh-miR-9-5p组)及sh-NC(sh-NC组)。CCK-8检测细胞增殖能力,Transwell检测细胞侵袭、迁移能力,双荧光素酶实验、pull down实验分析TFAP2A-AS1与miR-9-5p的靶向关系;miR-9-5p与ERK的靶向关系,实时荧光定量PCR(RT-qPCR)检测子宫内膜癌组织、癌旁组织、子宫内膜癌细胞及hEEC细胞TFAP2AAS1、miR-9-5p表达水平,Western blot检测细胞外调节蛋白激酶(ERK)、磷酸化ERK(p-ERK)水平。结果:子宫内膜癌组织中TFAP2A-AS1表达水平低于癌旁组织,miR-9-5p表达水平高于癌旁组织(P<0.05)。子宫内膜癌组织TFAP2A-AS1、miR-9-5p表达水平与分化程度、肿瘤直径、TNM分期及淋巴结转移有关。子宫内膜癌组织中TFAP2A-AS1、miR-9-5p表达水平呈负相关(r=-0.782,P=0.002)。与hEEC细胞比较,RL95-2、HEC-1A、HHUA、HEC-1B及Ishikawa细胞TFAP2A-AS1表达水平降低,miR-9-5p表达水平升高(P<0.05)。与si-NC组比较,si-TFAP2A-AS1组细胞48 h、72 h OD值,侵袭、迁移细胞数均升高(P<0.05);与Vector组比较,TFAP2A-AS1组细胞48 h、72 h OD值,侵袭、迁移细胞数均降低(P<0.05)。与miR-NC组比较,miR-9-5p组细胞48 h、72 h OD值,侵袭、迁移细胞数均升高(P<0.05);与sh-NC组比较,sh-miR-9-5p组细胞48 h、72 h OD值,侵袭、迁移细胞数均降低(P<0.05)。TFAP2A-AS1负性调控miR-9-5p、p-ERK表达水平,miR-9-5p可靶向调控ERK蛋白。结论:过表达TFAP2A-AS1通过靶向下调miR-9-5p,抑制ERK通路,最终抑制子宫内膜癌细胞恶性生物学行为。
基金supported by the National Natural Science Foundation of China(No.62102457)the Hunan Provincial Natural Science Foundation of China(No.2023JJ40763)+1 种基金the Hunan Provincial Science and Technology Program(No.2021RC4008)the Fundamental Research Funds for the Central Universities of Central South University(No.CX20230271).
文摘Understanding the subcellular localization of long non-coding RNAs(IncRNAs)is crucial for unraveling their functional mechanisms.While previous computational methods have made progress in predicting IncRNA subcellular localization,most of them ignore the sequence order information by relying on k-mer frequency features to encode IncRNA sequences.In the study,we develope SGCL-LncLoc,a novel interpretable deep learning model based on supervised graph contrastive learning.SGCL-LncLoc transforms IncRNA sequences into de Bruijn graphs and uses the Word2Vec technique to learn the node representation of the graph.Then,SGCL-LncLoc applies graph convolutional networks to learn the comprehensive graph representation.Additionally,we propose a computational method to map the attention weights of the graph nodes to the weights of nucleotides in the IncRNA sequence,allowing SGCL-LncLoc to serve as an interpretable deep learning model.Furthermore,SGCL-LncLoc employs a supervised contrastive learning strategy,which leverages the relationships between different samples and label information,guiding the model to enhance representation learning for IncRNAs.Extensive experimental results demonstrate that SGCL-LncLoc outperforms both deep learning baseline models and existing predictors,showing its capability for accurate IncRNA subcellular localization prediction.Furthermore,we conduct a motif analysis,revealing that SGCL-LncLoc successfully captures known motifs associated with IncRNA subcellular localization.The SGCL-LncLoc web server is available at http://csuligroup.com:8000/SGCL-LncLoc.The source code can be obtained from https://github.com/CSUBioGroup/SGCL-LncLoc.
