Ting-He Zhang, Sumin Jo, Michelle Zhang, Kai Wang, Shou-Jiang Gao, Yufei Huang
{"title":"了解 m6A-BERT-Deg 介导的 YTHDF2- mRNA 降解","authors":"Ting-He Zhang, Sumin Jo, Michelle Zhang, Kai Wang, Shou-Jiang Gao, Yufei Huang","doi":"arxiv-2401.08004","DOIUrl":null,"url":null,"abstract":"N6-methyladenosine (m6A) is the most abundant mRNA modification within\nmammalian cells, holding pivotal significance in the regulation of mRNA\nstability, translation, and splicing. Furthermore, it plays a critical role in\nthe regulation of RNA degradation by primarily recruiting the YTHDF2 reader\nprotein. However, the selective regulation of mRNA decay of the m6A-methylated\nmRNA through YTHDF2 binding is poorly understood. To improve our understanding,\nwe developed m6A-BERT-Deg, a BERT model adapted for predicting YTHDF2-mediated\ndegradation of m6A-methylated mRNAs. We meticulously assembled a high-quality\ntraining dataset by integrating multiple data sources for the HeLa cell line.\nTo overcome the limitation of small training samples, we employed a\npre-training-fine-tuning strategy by first performing a self-supervised\npre-training of the model on 427,760 unlabeled m6A site sequences. The test\nresults demonstrated the importance of this pre-training strategy in enabling\nm6A-BERT-Deg to outperform other benchmark models. We further conducted a\ncomprehensive model interpretation and revealed a surprising finding that the\npresence of co-factors in proximity to m6A sites may disrupt YTHDF2-mediated\nmRNA degradation, subsequently enhancing mRNA stability. We also extended our\nanalyses to the HEK293 cell line, shedding light on the context-dependent\nYTHDF2-mediated mRNA degradation.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Understanding YTHDF2-mediated mRNA Degradation By m6A-BERT-Deg\",\"authors\":\"Ting-He Zhang, Sumin Jo, Michelle Zhang, Kai Wang, Shou-Jiang Gao, Yufei Huang\",\"doi\":\"arxiv-2401.08004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"N6-methyladenosine (m6A) is the most abundant mRNA modification within\\nmammalian cells, holding pivotal significance in the regulation of mRNA\\nstability, translation, and splicing. Furthermore, it plays a critical role in\\nthe regulation of RNA degradation by primarily recruiting the YTHDF2 reader\\nprotein. However, the selective regulation of mRNA decay of the m6A-methylated\\nmRNA through YTHDF2 binding is poorly understood. To improve our understanding,\\nwe developed m6A-BERT-Deg, a BERT model adapted for predicting YTHDF2-mediated\\ndegradation of m6A-methylated mRNAs. We meticulously assembled a high-quality\\ntraining dataset by integrating multiple data sources for the HeLa cell line.\\nTo overcome the limitation of small training samples, we employed a\\npre-training-fine-tuning strategy by first performing a self-supervised\\npre-training of the model on 427,760 unlabeled m6A site sequences. The test\\nresults demonstrated the importance of this pre-training strategy in enabling\\nm6A-BERT-Deg to outperform other benchmark models. We further conducted a\\ncomprehensive model interpretation and revealed a surprising finding that the\\npresence of co-factors in proximity to m6A sites may disrupt YTHDF2-mediated\\nmRNA degradation, subsequently enhancing mRNA stability. We also extended our\\nanalyses to the HEK293 cell line, shedding light on the context-dependent\\nYTHDF2-mediated mRNA degradation.\",\"PeriodicalId\":501325,\"journal\":{\"name\":\"arXiv - QuanBio - Molecular Networks\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuanBio - Molecular Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2401.08004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Molecular Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2401.08004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Understanding YTHDF2-mediated mRNA Degradation By m6A-BERT-Deg
N6-methyladenosine (m6A) is the most abundant mRNA modification within
mammalian cells, holding pivotal significance in the regulation of mRNA
stability, translation, and splicing. Furthermore, it plays a critical role in
the regulation of RNA degradation by primarily recruiting the YTHDF2 reader
protein. However, the selective regulation of mRNA decay of the m6A-methylated
mRNA through YTHDF2 binding is poorly understood. To improve our understanding,
we developed m6A-BERT-Deg, a BERT model adapted for predicting YTHDF2-mediated
degradation of m6A-methylated mRNAs. We meticulously assembled a high-quality
training dataset by integrating multiple data sources for the HeLa cell line.
To overcome the limitation of small training samples, we employed a
pre-training-fine-tuning strategy by first performing a self-supervised
pre-training of the model on 427,760 unlabeled m6A site sequences. The test
results demonstrated the importance of this pre-training strategy in enabling
m6A-BERT-Deg to outperform other benchmark models. We further conducted a
comprehensive model interpretation and revealed a surprising finding that the
presence of co-factors in proximity to m6A sites may disrupt YTHDF2-mediated
mRNA degradation, subsequently enhancing mRNA stability. We also extended our
analyses to the HEK293 cell line, shedding light on the context-dependent
YTHDF2-mediated mRNA degradation.