Emerging evidence demonstrates that cryptic translation from RNAs previously annotated as noncoding might generate microproteins with oncogenic functions.However,the importance and underlying mechanisms of these micro...Emerging evidence demonstrates that cryptic translation from RNAs previously annotated as noncoding might generate microproteins with oncogenic functions.However,the importance and underlying mechanisms of these microproteins in alternative splicing-driven tumor progression have rarely been studied.Here,we show that the novel protein TPM3P9,encoded by the lncRNA tropomyosin 3 pseudogene 9,exhibits oncogenic activity in clear cell renal cell carcinoma(ccRCC)by enhancing oncogenic RNA splicing.Overexpression of TPM3P9 promotes cell proliferation and tumor growth.Mechanistically,TPM3P9 binds to the RRM1 domain of the splicing factor RBM4 to inhibit RBM4-mediated exon skipping in the transcription factor TCF7L2.This results in increased expression of the oncogenic splice variant TCF7L2-L,which activates NF-κB signaling via its interaction with SAM68 to transcriptionally induce RELB expression.From a clinical perspective,TPM3P9 expression is upregulated in cancer tissues and is significantly correlated with the expression of TCF7L2-L and RELB.High TPM3P9 expression or low RBM4 expression is associated with poor survival in patients with ccRCC.Collectively,our findings functionally and clinically characterize the“noncoding RNA”-derived microprotein TPM3P9 and thus identify potential prognostic and therapeutic factors in renal cancer.展开更多
随着高通量测序技术的发展,海量的基因组序列数据为了解基因组的结构提供了数据基础。剪接位点识别是基因组学研究的重要环节,在基因发现和确定基因结构方面发挥着重要作用,且有利于理解基因性状的表达。针对现有模型对脱氧核糖核酸(DNA...随着高通量测序技术的发展,海量的基因组序列数据为了解基因组的结构提供了数据基础。剪接位点识别是基因组学研究的重要环节,在基因发现和确定基因结构方面发挥着重要作用,且有利于理解基因性状的表达。针对现有模型对脱氧核糖核酸(DNA)序列高维特征提取能力不足的问题,构建了由BERT(Bidirectional Encoder Representations from Transformer)和平行的卷积神经网络(CNN)组合而成的剪接位点预测模型——BERT-splice。首先,采用BERT预训练方法训练DNA语言模型,从而提取DNA序列的上下文动态关联特征,并且使用高维矩阵映射DNA序列特征;其次,采用人类参考基因组序列hg19数据,使用DNA语言模型将该数据映射为高维矩阵后作为平行CNN分类器的输入进行再训练;最后,在上述基础上构建了剪接位点预测模型。实验结果表明,BERT-splice模型在DNA剪接位点供体集上的预测准确率为96.55%,在受体集上的准确率为95.80%,相较于BERT与循环卷积神经网络(RCNN)构建的预测模型BERT-RCNN分别提高了1.55%和1.72%;同时,在5条完整的人类基因序列上测试得到的所提模型的供体/受体剪接位点平均假阳性率(FPR)为4.74%。以上验证了BERT-splice模型用于基因剪接位点预测的有效性。展开更多
RNA-binding proteins (RBPs) play an important role in post-transcriptional gene regulation. However, the functions of RBPs in plants remain poorly understood. Maize kernel mutant dek42 has small defective kernels and ...RNA-binding proteins (RBPs) play an important role in post-transcriptional gene regulation. However, the functions of RBPs in plants remain poorly understood. Maize kernel mutant dek42 has small defective kernels and lethal seedlings. Dek42 was cloned by Mutator tag isolation and further confirmed by an independent mutant allele and clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 materials. Dek42 encodes an RRM_RBM48 type RNA-binding protein that localizes to the nucleus. Dek42 is constitutively expressed in various maize tissues. The dek42 mutation caused a significant reduction in the accumulation of DEK42 protein in mutant kernels. RNA-seq analysis showed that the dek42 mutation significantly disturbed the expression of thousands of genes during maize kernel development. Sequence analysis also showed that the dek42 mutation significantly changed alternative splicing in expressed genes, which were especially enriched for the U12-type intron-retained type. Yeast two-hybrid screening identified SF3a1 as a DEK42-interacting protein. DEK42 also interacts with the spliceosome component U1-70K. These results suggested that DEK42 participates in the regulation of pre-messenger RNA splicing through its interaction with other spliceosome components. This study showed the function of a newly identified RBP and provided insights into alternative splicing regulation during maize kernel development.展开更多
基金supported by the National Key Research and Development Program of China(2022YFA1304604,2020YFE0202200)the Fundamental Research Funds for the Central Universities(21624109)+3 种基金the Young Scientists Fund of the National Natural Science Foundation of China(82303050)the Guangdong Natural Science Foundation(2022A1515012388)the Natural Science Foundation of Guangdong Province(2023A1515011901)the Guangdong Basic and Applied Basic Research Foundation(2022A1515111106).
文摘Emerging evidence demonstrates that cryptic translation from RNAs previously annotated as noncoding might generate microproteins with oncogenic functions.However,the importance and underlying mechanisms of these microproteins in alternative splicing-driven tumor progression have rarely been studied.Here,we show that the novel protein TPM3P9,encoded by the lncRNA tropomyosin 3 pseudogene 9,exhibits oncogenic activity in clear cell renal cell carcinoma(ccRCC)by enhancing oncogenic RNA splicing.Overexpression of TPM3P9 promotes cell proliferation and tumor growth.Mechanistically,TPM3P9 binds to the RRM1 domain of the splicing factor RBM4 to inhibit RBM4-mediated exon skipping in the transcription factor TCF7L2.This results in increased expression of the oncogenic splice variant TCF7L2-L,which activates NF-κB signaling via its interaction with SAM68 to transcriptionally induce RELB expression.From a clinical perspective,TPM3P9 expression is upregulated in cancer tissues and is significantly correlated with the expression of TCF7L2-L and RELB.High TPM3P9 expression or low RBM4 expression is associated with poor survival in patients with ccRCC.Collectively,our findings functionally and clinically characterize the“noncoding RNA”-derived microprotein TPM3P9 and thus identify potential prognostic and therapeutic factors in renal cancer.
文摘随着高通量测序技术的发展,海量的基因组序列数据为了解基因组的结构提供了数据基础。剪接位点识别是基因组学研究的重要环节,在基因发现和确定基因结构方面发挥着重要作用,且有利于理解基因性状的表达。针对现有模型对脱氧核糖核酸(DNA)序列高维特征提取能力不足的问题,构建了由BERT(Bidirectional Encoder Representations from Transformer)和平行的卷积神经网络(CNN)组合而成的剪接位点预测模型——BERT-splice。首先,采用BERT预训练方法训练DNA语言模型,从而提取DNA序列的上下文动态关联特征,并且使用高维矩阵映射DNA序列特征;其次,采用人类参考基因组序列hg19数据,使用DNA语言模型将该数据映射为高维矩阵后作为平行CNN分类器的输入进行再训练;最后,在上述基础上构建了剪接位点预测模型。实验结果表明,BERT-splice模型在DNA剪接位点供体集上的预测准确率为96.55%,在受体集上的准确率为95.80%,相较于BERT与循环卷积神经网络(RCNN)构建的预测模型BERT-RCNN分别提高了1.55%和1.72%;同时,在5条完整的人类基因序列上测试得到的所提模型的供体/受体剪接位点平均假阳性率(FPR)为4.74%。以上验证了BERT-splice模型用于基因剪接位点预测的有效性。
基金supported by the National Key Research and Development Program of China (2016YFD0101003)the National Natural Science Foundation of China (91635303 and 31425019)
文摘RNA-binding proteins (RBPs) play an important role in post-transcriptional gene regulation. However, the functions of RBPs in plants remain poorly understood. Maize kernel mutant dek42 has small defective kernels and lethal seedlings. Dek42 was cloned by Mutator tag isolation and further confirmed by an independent mutant allele and clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 materials. Dek42 encodes an RRM_RBM48 type RNA-binding protein that localizes to the nucleus. Dek42 is constitutively expressed in various maize tissues. The dek42 mutation caused a significant reduction in the accumulation of DEK42 protein in mutant kernels. RNA-seq analysis showed that the dek42 mutation significantly disturbed the expression of thousands of genes during maize kernel development. Sequence analysis also showed that the dek42 mutation significantly changed alternative splicing in expressed genes, which were especially enriched for the U12-type intron-retained type. Yeast two-hybrid screening identified SF3a1 as a DEK42-interacting protein. DEK42 also interacts with the spliceosome component U1-70K. These results suggested that DEK42 participates in the regulation of pre-messenger RNA splicing through its interaction with other spliceosome components. This study showed the function of a newly identified RBP and provided insights into alternative splicing regulation during maize kernel development.
