1
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Yang J, He Y, Kang Y, Shen L, Zhang W, Yan Y, Li X, Huang W, Xu X. Virtual Screening and Molecular Docking: Discovering Novel METTL3 Inhibitors. ACS Med Chem Lett 2024; 15:1491-1499. [PMID: 39291017 PMCID: PMC11403746 DOI: 10.1021/acsmedchemlett.4c00216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 07/29/2024] [Accepted: 08/01/2024] [Indexed: 09/19/2024] Open
Abstract
Methyltransferase-like 3 (METTL3) is an RNA methyltransferase that catalyzes the N6 -methyladenosine (m6A) modification of mRNA in eukaryotic cells. Past studies have shown that METTL3 is highly expressed in various cancers and is closely related to tumor development. Therefore, METTL3 inhibitors have received widespread attention as effective treatments for different types of tumors. This study proposes a hybrid high-throughput virtual screening (HTVS) protocol that combines structure-based methods with geometric deep learning-based DeepDock algorithms. We identified unique skeleton inhibitors of METTL3 from our self-built internal database. Among them, compound C3 showed significant inhibitory activity on METTL3, and further molecular dynamics simulations were performed to provide more details about the binding conformation. Overall, our research demonstrates the effectiveness of hybrid virtual algorithms, which is of great significance for understanding the biological functions of METTL3 and developing treatment methods for related diseases.
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Affiliation(s)
- Junyi Yang
- Affiliated Yongkang First People's Hospital and School of Pharmaceutical Sciences, Hangzhou Medical College, Hangzhou, Zhejiang 311399, China
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, School of Pharmacy, Hangzhou Medical College, Hangzhou, Zhejiang 310013, China
| | - Yanwen He
- Affiliated Yongkang First People's Hospital and School of Pharmaceutical Sciences, Hangzhou Medical College, Hangzhou, Zhejiang 311399, China
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, School of Pharmacy, Hangzhou Medical College, Hangzhou, Zhejiang 310013, China
| | - Youkun Kang
- Affiliated Yongkang First People's Hospital and School of Pharmaceutical Sciences, Hangzhou Medical College, Hangzhou, Zhejiang 311399, China
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, School of Pharmacy, Hangzhou Medical College, Hangzhou, Zhejiang 310013, China
| | - Liteng Shen
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Wen Zhang
- Affiliated Yongkang First People's Hospital and School of Pharmaceutical Sciences, Hangzhou Medical College, Hangzhou, Zhejiang 311399, China
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, School of Pharmacy, Hangzhou Medical College, Hangzhou, Zhejiang 310013, China
| | - Yumeng Yan
- Affiliated Yongkang First People's Hospital and School of Pharmaceutical Sciences, Hangzhou Medical College, Hangzhou, Zhejiang 311399, China
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, School of Pharmacy, Hangzhou Medical College, Hangzhou, Zhejiang 310013, China
| | - Xinyi Li
- Affiliated Yongkang First People's Hospital and School of Pharmaceutical Sciences, Hangzhou Medical College, Hangzhou, Zhejiang 311399, China
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, School of Pharmacy, Hangzhou Medical College, Hangzhou, Zhejiang 310013, China
| | - Wenhai Huang
- Affiliated Yongkang First People's Hospital and School of Pharmaceutical Sciences, Hangzhou Medical College, Hangzhou, Zhejiang 311399, China
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, School of Pharmacy, Hangzhou Medical College, Hangzhou, Zhejiang 310013, China
| | - Xiangwei Xu
- Affiliated Yongkang First People's Hospital and School of Pharmaceutical Sciences, Hangzhou Medical College, Hangzhou, Zhejiang 311399, China
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, School of Pharmacy, Hangzhou Medical College, Hangzhou, Zhejiang 310013, China
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2
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Abbas Z, Rehman MU, Tayara H, Lee SW, Chong KT. m5C-Seq: Machine learning-enhanced profiling of RNA 5-methylcytosine modifications. Comput Biol Med 2024; 182:109087. [PMID: 39232403 DOI: 10.1016/j.compbiomed.2024.109087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 08/13/2024] [Accepted: 08/29/2024] [Indexed: 09/06/2024]
Abstract
Epigenetic modifications, particularly RNA methylation and histone alterations, play a crucial role in heredity, development, and disease. Among these, RNA 5-methylcytosine (m5C) is the most prevalent RNA modification in mammalian cells, essential for processes such as ribosome synthesis, translational fidelity, mRNA nuclear export, turnover, and translation. The increasing volume of nucleotide sequences has led to the development of machine learning-based predictors for m5C site prediction. However, these predictors often face challenges related to training data limitations and overfitting due to insufficient external validation. This study introduces m5C-Seq, an ensemble learning approach for RNA modification profiling, designed to address these issues. m5C-Seq employs a meta-classifier that integrates 15 probabilities generated from a novel, large dataset using systematic encoding methods to make final predictions. Demonstrating superior performance compared to existing predictors, m5C-Seq represents a significant advancement in accurate RNA modification profiling. The code and the newly established datasets are made available through GitHub at https://github.com/Z-Abbas/m5C-Seq.
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Affiliation(s)
- Zeeshan Abbas
- Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, South Korea
| | - Mobeen Ur Rehman
- Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, United Arab Emirates
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, South Korea
| | - Seung Won Lee
- Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea; Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea.
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3
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Chen M, Zou Q, Qi R, Ding Y. PseU-KeMRF: A Novel Method for Identifying RNA Pseudouridine Sites. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:1423-1435. [PMID: 38625768 DOI: 10.1109/tcbb.2024.3389094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
Pseudouridine is a type of abundant RNA modification that is seen in many different animals and is crucial for a variety of biological functions. Accurately identifying pseudouridine sites within the RNA sequence is vital for the subsequent study of various biological mechanisms of pseudouridine. However, the use of traditional experimental methods faces certain challenges. The development of fast and convenient computational methods is necessary to accurately identify pseudouridine sites from RNA sequence information. To address this, we introduce a novel pseudouridine site prediction model called PseU-KeMRF, which can identify pseudouridine sites in three species, H. sapiens, S. cerevisiae, and M. musculus. Through comprehensive analysis, we selected four RNA coding schemes, including binary feature, position-specific trinucleotide propensity based on single strand (PSTNPss), nucleotide chemical property (NCP) and pseudo k-tuple composition (PseKNC). Then the support vector machine-recursive feature elimination (SVM-RFE) method was used for feature selection and the feature subset was optimized. Finally, the best feature subsets are input into the kernel based on multinomial random forests (KeMRF) classifier for cross-validation and independent testing. As a new classification method, compared with the traditional random forest, KeMRF not only improves the node splitting process of decision tree construction based on multinomial distribution, but also combines the easy to interpret kernel method for prediction, which makes the classification performance better. Our results indicate superior predictive performance of PseU-KeMRF over other existing models, which can prove that PseU-KeMRF is a highly competitive predictive model that can successfully identify pseudouridine sites in RNA sequences.
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4
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Adjibade P, Di-Marco S, Gallouzi IE, Mazroui R. The RNA Demethylases ALKBH5 and FTO Regulate the Translation of ATF4 mRNA in Sorafenib-Treated Hepatocarcinoma Cells. Biomolecules 2024; 14:932. [PMID: 39199320 PMCID: PMC11352178 DOI: 10.3390/biom14080932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 07/29/2024] [Accepted: 07/30/2024] [Indexed: 09/01/2024] Open
Abstract
Translation is one of the main gene expression steps targeted by cellular stress, commonly referred to as translational stress, which includes treatment with anticancer drugs. While translational stress blocks the translation initiation of bulk mRNAs, it nonetheless activates the translation of specific mRNAs known as short upstream open reading frames (uORFs)-mRNAs. Among these, the ATF4 mRNA encodes a transcription factor that reprograms gene expression in cells responding to various stresses. Although the stress-induced translation of the ATF4 mRNA relies on the presence of uORFs (upstream to the main ATF4 ORF), the mechanisms mediating this effect, particularly during chemoresistance, remain elusive. Here, we report that ALKBH5 (AlkB Homolog 5) and FTO (FTO: Fat mass and obesity-associated protein), the two RNA demethylating enzymes, promote the translation of ATF4 mRNA in a transformed liver cell line (Hep3B) treated with the chemotherapeutic drug sorafenib. Using the in vitro luciferase reporter translational assay, we found that depletion of both enzymes reduced the translation of the reporter ATF4 mRNA upon drug treatment. Consistently, depletion of either protein abrogates the loading of the ATF3 mRNA into translating ribosomes as assessed by polyribosome assays coupled to RT-qPCR. Collectively, these results indicate that the ALKBH5 and FTO-mediated translation of the ATF4 mRNA is regulated at its initiation step. Using in vitro methylation assays, we found that ALKBH5 is required for the inhibition of the methylation of a reporter ATF4 mRNA at a conserved adenosine (A235) site located at its uORF2, suggesting that ALKBH5-mediated translation of ATF4 mRNA involves demethylation of its A235. Preventing methylation of A235 by introducing an A/G mutation into an ATF4 mRNA reporter renders its translation insensitive to ALKBH5 depletion, supporting the role of ALKBH5 demethylation activity in translation. Finally, targeting either ALKBH5 or FTO sensitizes Hep3B to sorafenib-induced cell death, contributing to their resistance. In summary, our data show that ALKBH5 and FTO are novel factors that promote resistance to sorafenib treatment, in part by mediating the translation of ATF4 mRNA.
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Affiliation(s)
- Pauline Adjibade
- Centre de Recherche du CHU de Québec-Université Laval, Axe Oncologie, Département de Biologie Moléculaire, Biochimie Médicale et Pathologie, Faculté de Médecine, Université Laval, Québec, QC G1V 0A6, Canada;
| | - Sergio Di-Marco
- KAUST Smart-Health Initiative (KSHI) and Biological and Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Jeddah 21589, Saudi Arabia; (S.D.-M.); (I.-E.G.)
- Department of Biochemistry, McGill University, Montreal, QC H3G 1Y6, Canada
- Rosalind & Morris Goodman Cancer Institute, McGill University, Montreal, QC H3A 1A3, Canada
| | - Imed-Eddine Gallouzi
- KAUST Smart-Health Initiative (KSHI) and Biological and Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Jeddah 21589, Saudi Arabia; (S.D.-M.); (I.-E.G.)
| | - Rachid Mazroui
- Centre de Recherche du CHU de Québec-Université Laval, Axe Oncologie, Département de Biologie Moléculaire, Biochimie Médicale et Pathologie, Faculté de Médecine, Université Laval, Québec, QC G1V 0A6, Canada;
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Song R, J Sutton G, Li F, Liu Q, Wong JJL. Variable calling of m6A and associated features in databases: a guide for end-users. Brief Bioinform 2024; 25:bbae434. [PMID: 39258883 PMCID: PMC11388104 DOI: 10.1093/bib/bbae434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 07/01/2024] [Accepted: 08/19/2024] [Indexed: 09/12/2024] Open
Abstract
N6-methyladenosine (m$^{6}$A) is a widely-studied methylation to messenger RNAs, which has been linked to diverse cellular processes and human diseases. Numerous databases that collate m$^{6}$A profiles of distinct cell types have been created to facilitate quick and easy mining of m$^{6}$A signatures associated with cell-specific phenotypes. However, these databases contain inherent complexities that have not been explicitly reported, which may lead to inaccurate identification and interpretation of m$^{6}$A-associated biology by end-users who are unaware of them. Here, we review various m$^{6}$A-related databases, and highlight several critical matters. In particular, differences in peak-calling pipelines across databases drive substantial variability in both peak number and coordinates with only moderate reproducibility, and the inclusion of peak calls from early m$^{6}$A sequencing protocols may lead to the reporting of false positives or negatives. The awareness of these matters will help end-users avoid the inclusion of potentially unreliable data in their studies and better utilize m$^{6}$A databases to derive biologically meaningful results.
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Affiliation(s)
- Renhua Song
- Epigenetics and RNA Biology Laboratory, School of Medical Sciences, The University of Sydney, Camperdown, NSW 2050, Australia
- Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2050, Australia
| | - Gavin J Sutton
- Epigenetics and RNA Biology Laboratory, School of Medical Sciences, The University of Sydney, Camperdown, NSW 2050, Australia
- Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2050, Australia
| | - Fuyi Li
- College of Information Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China
- South Australian immunoGENomics Cancer Institute (SAiGENCI), The University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Qian Liu
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, Maryland Pkwy, NV 89154, United States
- School of Life Sciences, College of Sciences, University of Nevada, Las Vegas, Maryland Pkwy, NV 89154, United States
| | - Justin J-L Wong
- Epigenetics and RNA Biology Laboratory, School of Medical Sciences, The University of Sydney, Camperdown, NSW 2050, Australia
- Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2050, Australia
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6
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Zhang Z, Wu H, Gong X, Yan Y, Li X, Yang R, Wu M, Xu M. A comprehensive epigenetic network can influence the occurrence of thyroid-associated ophthalmopathy by affecting immune and inflammatory response. Sci Rep 2024; 14:13545. [PMID: 38867076 PMCID: PMC11169257 DOI: 10.1038/s41598-024-64415-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 06/08/2024] [Indexed: 06/14/2024] Open
Abstract
The primary objective of this study is to understand the regulatory role of epigenetics in thyroid-associated ophthalmopathy (TAO) using multi-omics sequencing data. We utilized tRFs sequencing data, DNA methylation sequencing data, and lncRNA/circRNA/mRNA sequencing data, as well as several RNA methylation target prediction websites, to analyze the regulatory effect of DNA methylation, non-coding RNA, and RNA methylation on TAO-associated genes. Through differential expression analysis, we identified 1019 differentially expressed genes, 985 differentially methylated genes, and 2601 non-coding RNA. Functional analysis showed that differentially expressed genes were mostly associated with the PI3K signaling pathway and the IL17 signaling pathway. Genes regulated by DNA epigenetic regulatory networks were mainly related to the Cytokine-cytokine receptor interaction pathway, whereas genes regulated by RNA epigenetic regulatory networks were primarily related to the T cell receptor signaling pathway. Finally, our integrated regulatory network analysis revealed that epigenetics mainly impacts the occurrence of TAO through its effects on key pathways such as cell killing, cytokine production, and immune response. In summary, this study is the first to reveal a new mechanism underlying the development of TAO and provides new directions for future TAO research.
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Affiliation(s)
- Zhuo Zhang
- Department of Endocrinology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hongshi Wu
- Department of Endocrinology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xun Gong
- Department of Endocrinology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yuerong Yan
- Department of Endocrinology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaohui Li
- Department of Endocrinology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Rongxue Yang
- Department of Endocrinology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Muchao Wu
- Department of Endocrinology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Mingtong Xu
- Department of Endocrinology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
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7
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Wang X, Gan M, Wang Y, Wang S, Lei Y, Wang K, Zhang X, Chen L, Zhao Y, Niu L, Zhang S, Zhu L, Shen L. Comprehensive review on lipid metabolism and RNA methylation: Biological mechanisms, perspectives and challenges. Int J Biol Macromol 2024; 270:132057. [PMID: 38710243 DOI: 10.1016/j.ijbiomac.2024.132057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 04/26/2024] [Accepted: 05/01/2024] [Indexed: 05/08/2024]
Abstract
Adipose tissue plays a crucial role in maintaining energy balance, regulating hormones, and promoting metabolic health. To address disorders related to obesity and develop effective therapies, it is essential to have a deep understanding of adipose tissue biology. In recent years, RNA methylation has emerged as a significant epigenetic modification involved in various cellular functions and metabolic pathways. Particularly in the realm of adipogenesis and lipid metabolism, extensive research is ongoing to uncover the mechanisms and functional importance of RNA methylation. Increasing evidence suggests that RNA methylation plays a regulatory role in adipocyte development, metabolism, and lipid utilization across different organs. This comprehensive review aims to provide an overview of common RNA methylation modifications, their occurrences, and regulatory mechanisms, focusing specifically on their intricate connections to fat metabolism. Additionally, we discuss the research methodologies used in studying RNA methylation and highlight relevant databases that can aid researchers in this rapidly advancing field.
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Affiliation(s)
- Xingyu Wang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Mailin Gan
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Yan Wang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Saihao Wang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Yuhang Lei
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Kai Wang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Xin Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Lei Chen
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Ye Zhao
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Lili Niu
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Shunhua Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Li Zhu
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China.
| | - Linyuan Shen
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China.
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8
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Wang X, Li P, Wang R, Gao X. PseUpred-ELPSO Is an Ensemble Learning Predictor with Particle Swarm Optimizer for Improving the Prediction of RNA Pseudouridine Sites. BIOLOGY 2024; 13:248. [PMID: 38666860 PMCID: PMC11048358 DOI: 10.3390/biology13040248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 03/27/2024] [Accepted: 04/01/2024] [Indexed: 04/28/2024]
Abstract
RNA pseudouridine modification exists in different RNA types of many species, and it has a significant role in regulating the expression of biological processes. To understand the functional mechanisms for RNA pseudouridine sites, the accurate identification of pseudouridine sites in RNA sequences is essential. Although several fast and inexpensive computational methods have been proposed, the challenge of improving recognition accuracy and generalization still exists. This study proposed a novel ensemble predictor called PseUpred-ELPSO for improved RNA pseudouridine site prediction. After analyzing the nucleotide composition preferences between RNA pseudouridine site sequences, two feature representations were determined and fed into the stacking ensemble framework. Then, using five tree-based machine learning classifiers as base classifiers, 30-dimensional RNA profiles are constructed to represent RNA sequences, and using the PSO algorithm, the weights of the RNA profiles were searched to further enhance the representation. A logistic regression classifier was used as a meta-classifier to complete the final predictions. Compared to the most advanced predictors, the performance of PseUpred-ELPSO is superior in both cross-validation and the independent test. Based on the PseUpred-ELPSO predictor, a free and easy-to-operate web server has been established, which will be a powerful tool for pseudouridine site identification.
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Affiliation(s)
- Xiao Wang
- School of Computer Science and Technology, Zhengzhou University of Light Industry, No. 136, Science Avenue, Zhengzhou 450002, China; (X.W.); (P.L.)
- Henan Provincial Key Laboratory of Data Intelligence for Food Safety, Zhengzhou University of Light Industry, No. 136, Science Avenue, Zhengzhou 450002, China
| | - Pengfei Li
- School of Computer Science and Technology, Zhengzhou University of Light Industry, No. 136, Science Avenue, Zhengzhou 450002, China; (X.W.); (P.L.)
| | - Rong Wang
- School of Electronic Information, Zhengzhou University of Light Industry, No. 136, Science Avenue, Zhengzhou 450002, China;
| | - Xu Gao
- National Supercomputing Center in Zhengzhou, School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
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Chen M, Sun M, Su X, Tiwari P, Ding Y. Fuzzy kernel evidence Random Forest for identifying pseudouridine sites. Brief Bioinform 2024; 25:bbae169. [PMID: 38622357 PMCID: PMC11018548 DOI: 10.1093/bib/bbae169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/27/2024] [Accepted: 03/31/2024] [Indexed: 04/17/2024] Open
Abstract
Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, there is a growing need for computational techniques that can reliably and quickly identify pseudouridine sites from vast amounts of RNA sequencing data. In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation tests and independent tests on benchmark datasets have shown that PseU-FKeERF has better predictive performance than several state-of-the-art methods. This new method not only improves the accuracy of pseudouridine site identification, but also provides a certain reference for disease control and related drug development in the future.
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Affiliation(s)
- Mingshuai Chen
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China
| | - Mingai Sun
- Beidahuang Industry Group General Hospital, Harbin 150001, China
| | - Xi Su
- Foshan Women and Children Hospital, Foshan 528000, China
| | - Prayag Tiwari
- School of Information Technology, Halmstad University, Sweden
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China
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10
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Harun-Or-Roshid M, Maeda K, Phan LT, Manavalan B, Kurata H. Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach. Comput Biol Med 2024; 169:107848. [PMID: 38145601 DOI: 10.1016/j.compbiomed.2023.107848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 11/14/2023] [Accepted: 12/11/2023] [Indexed: 12/27/2023]
Abstract
Dihydrouridine (DHU, D) is one of the most abundant post-transcriptional uridine modifications found in tRNA, mRNA, and snoRNA, closely associated with disease pathogenesis and various biological processes in eukaryotes. Identifying D sites is important for understanding the modification mechanisms and/or epigenetic regulation. However, biological experiments for detecting D sites are time-consuming and expensive. Given these challenges, computational methods have been developed for accurately identifying the D sites in genome-wide datasets. However, existing methods have some limitations, and their prediction performance needs to be improved. In this work, we have developed a new computational predictor for accurately identifying D sites called Stack-DHUpred. Briefly, we trained 66 baseline models or single-feature models by connecting six machine learning classifiers with eleven different feature encoding methods and stacked different baseline models to build stacked ensemble learning models. Subsequently, the optimal combination of the baseline models was identified for the construction of the final stacked model. Remarkably, the Stack-DHUpred outperformed the existing predictors on our new independent dataset, indicating that the stacking approach significantly improved the prediction performance. We have made Stack-DHUpred available to the public through a web server (http://kurata35.bio.kyutech.ac.jp/Stack-DHUpred) and a standalone program (https://github.com/kuratahiroyuki/Stack-DHUpred). We believe that Stack-DHUpred will be a valuable tool for accelerating the discovery of D modifications and understanding their role in post-transcriptional regulation.
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Affiliation(s)
- Md Harun-Or-Roshid
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Kazuhiro Maeda
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Le Thi Phan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
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11
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Wang L, Zhou Y. MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features. RNA Biol 2024; 21:1-10. [PMID: 38357904 PMCID: PMC10877979 DOI: 10.1080/15476286.2024.2315384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/02/2024] [Indexed: 02/16/2024] Open
Abstract
RNA modifications play crucial roles in various biological processes and diseases. Accurate prediction of RNA modification sites is essential for understanding their functions. In this study, we propose a hybrid approach that fuses a pre-trained sequence representation with various sequence features to predict multiple types of RNA modifications in one combined prediction framework. We developed MRM-BERT, a deep learning method that combined the pre-trained DNABERT deep sequence representation module and the convolutional neural network (CNN) exploiting four traditional sequence feature encodings to improve the prediction performance. MRM-BERT was evaluated on multiple datasets of 12 commonly occurring RNA modifications, including m6A, m5C, m1A and so on. The results demonstrate that our hybrid model outperforms other models in terms of area under receiver operating characteristic curve (AUC) for all 12 types of RNA modifications. MRM-BERT is available as an online tool (http://117.122.208.21:8501) or source code (https://github.com/abhhba999/MRM-BERT), which allows users to predict RNA modification sites and visualize the results. Overall, our study provides an effective and efficient approach to predict multiple RNA modifications, contributing to the understanding of RNA biology and the development of therapeutic strategies.
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Affiliation(s)
- Linshu Wang
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Yuan Zhou
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, China
- Department of Biomedical Informatics, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
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12
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Liang X, Wang X. LncRNAs: Current understanding, future directions, and challenges. Animal Model Exp Med 2023; 6:505-507. [PMID: 38146076 PMCID: PMC10757209 DOI: 10.1002/ame2.12371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 12/03/2023] [Indexed: 12/27/2023] Open
Affiliation(s)
- Xiaolin Liang
- Department of Geriatrics, Gerontology Institute of Anhui Province, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital, Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina
- Anhui Province Key Laboratory of Geriatric Immunology and Nutrition TherapyHefeiAnhuiChina
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Anhui Provincial Engineering Research Center for Elderly Care ProductsUniversity of Science and Technology of ChinaHefeiAnhuiChina
| | - Xiangting Wang
- Department of Geriatrics, Gerontology Institute of Anhui Province, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital, Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina
- Anhui Province Key Laboratory of Geriatric Immunology and Nutrition TherapyHefeiAnhuiChina
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Anhui Provincial Engineering Research Center for Elderly Care ProductsUniversity of Science and Technology of ChinaHefeiAnhuiChina
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13
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Yang Y, Liu Z, Lu J, Sun Y, Fu Y, Pan M, Xie X, Ge Q. Analysis approaches for the identification and prediction of N6-methyladenosine sites. Epigenetics 2023; 18:2158284. [PMID: 36562485 PMCID: PMC9980620 DOI: 10.1080/15592294.2022.2158284] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The global dynamics in a variety of biological processes can be revealed by mapping transcriptional m6A sites, in particular full-transcriptome m6A. And individual m6A sites have contributed to biological function, which can be evaluated by stoichiometric information obtained from the single nucleotide resolution. Currently, the identification of m6A sites is mainly carried out by experiment and prediction methods, based on high-throughput sequencing and machine learning model respectively. This review summarizes the recent topics and progress made in bioinformatics methods of deciphering the m6A methylation, including the experimental detection of m6A methylation sites, techniques of data analysis, the way of predicting m6A methylation sites, m6A methylation databases, and detection of m6A modification in circRNA. At the end, the essay makes a brief discussion for the development perspective in this area.
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Affiliation(s)
- Yuwei Yang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, People's Republic of China
| | - Zhiyu Liu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, People's Republic of China
| | - Junru Lu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, People's Republic of China
| | - Yuqing Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, People's Republic of China
| | - Yue Fu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, People's Republic of China
| | - Min Pan
- Department of Pathology and Pathophysiology School of Medicine, Southeast University, Nanjing, China
| | - Xueying Xie
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, People's Republic of China
| | - Qinyu Ge
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, People's Republic of China
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14
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Pham NT, Rakkiyapan R, Park J, Malik A, Manavalan B. H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA. Brief Bioinform 2023; 25:bbad476. [PMID: 38180830 PMCID: PMC10768780 DOI: 10.1093/bib/bbad476] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 01/07/2024] Open
Abstract
2'-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high-throughput detection, the chemical stability of 2OM makes it difficult to detect and map in messenger RNA. Therefore, bioinformatics tools have been developed using machine learning (ML) algorithms to identify 2OM sites. These tools have made significant progress, but their performances remain unsatisfactory and need further improvement. In this study, we introduced H2Opred, a novel hybrid deep learning (HDL) model for accurately identifying 2OM sites in human RNA. Notably, this is the first application of HDL in developing four nucleotide-specific models [adenine (A2OM), cytosine (C2OM), guanine (G2OM) and uracil (U2OM)] as well as a generic model (N2OM). H2Opred incorporated both stacked 1D convolutional neural network (1D-CNN) blocks and stacked attention-based bidirectional gated recurrent unit (Bi-GRU-Att) blocks. 1D-CNN blocks learned effective feature representations from 14 conventional descriptors, while Bi-GRU-Att blocks learned feature representations from five natural language processing-based embeddings extracted from RNA sequences. H2Opred integrated these feature representations to make the final prediction. Rigorous cross-validation analysis demonstrated that H2Opred consistently outperforms conventional ML-based single-feature models on five different datasets. Moreover, the generic model of H2Opred demonstrated a remarkable performance on both training and testing datasets, significantly outperforming the existing predictor and other four nucleotide-specific H2Opred models. To enhance accessibility and usability, we have deployed a user-friendly web server for H2Opred, accessible at https://balalab-skku.org/H2Opred/. This platform will serve as an invaluable tool for accurately predicting 2OM sites within human RNA, thereby facilitating broader applications in relevant research endeavors.
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Affiliation(s)
- Nhat Truong Pham
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Rajan Rakkiyapan
- Department of Mathematics, Bharathiar University, Coimbatore - 641046, Tamil Nadu, India
| | - Jongsun Park
- InfoBoss inc. and InfoBoss Research Center, Gangnam-gu, Seoul 06278, Republic of Korea
| | - Adeel Malik
- Institute of Intelligence Informatics Technology, Sangmyung University, Seoul, 03016, Republic of Korea
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
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15
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Jin Z, Sheng J, Hu Y, Zhang Y, Wang X, Huang Y. Shining a spotlight on m6A and the vital role of RNA modification in endometrial cancer: a review. Front Genet 2023; 14:1247309. [PMID: 37886684 PMCID: PMC10598767 DOI: 10.3389/fgene.2023.1247309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 09/19/2023] [Indexed: 10/28/2023] Open
Abstract
RNA modifications are mostly dynamically reversible post-transcriptional modifications, of which m6A is the most prevalent in eukaryotic mRNAs. A growing number of studies indicate that RNA modification can finely tune gene expression and modulate RNA metabolic homeostasis, which in turn affects the self-renewal, proliferation, apoptosis, migration, and invasion of tumor cells. Endometrial carcinoma (EC) is the most common gynecologic tumor in developed countries. Although it can be diagnosed early in the onset and have a preferable prognosis, some cases might develop and become metastatic or recurrent, with a worse prognosis. Fortunately, immunotherapy and targeted therapy are promising methods of treating endometrial cancer patients. Gene modifications may also contribute to these treatments, as is especially the case with recent developments of new targeted therapeutic genes and diagnostic biomarkers for EC, even though current findings on the relationship between RNA modification and EC are still very limited, especially m6A. For example, what is the elaborate mechanism by which RNA modification affects EC progression? Taking m6A modification as an example, what is the conversion mode of methylation and demethylation for RNAs, and how to achieve selective recognition of specific RNA? Understanding how they cope with various stimuli as part of in vivo and in vitro biological development, disease or tumor occurrence and development, and other processes is valuable and RNA modifications provide a distinctive insight into genetic information. The roles of these processes in coping with various stimuli, biological development, disease, or tumor development in vivo and in vitro are self-evident and may become a new direction for cancer in the future. In this review, we summarize the category, characteristics, and therapeutic precis of RNA modification, m6A in particular, with the purpose of seeking the systematic regulation axis related to RNA modification to provide a better solution for the treatment of EC.
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Affiliation(s)
- Zujian Jin
- Department of Gynecology and Obstetrics, The Fourth Affiliated Hospital, Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Jingjing Sheng
- Department of Gynecology and Obstetrics, The Fourth Affiliated Hospital, Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Yingying Hu
- Department of Gynecology and Obstetrics, The Fourth Affiliated Hospital, Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Yu Zhang
- Department of Gynecology and Obstetrics, The Fourth Affiliated Hospital, Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Xiaoxia Wang
- Reproductive Medicine Center, School of Medicine, The Fourth Affiliated Hospital, Zhejiang University, Yiwu, Zhejiang, China
| | - Yiping Huang
- Department of Gynecology and Obstetrics, The Fourth Affiliated Hospital, Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
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16
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Krejčí J, Arcidiacono OA, Čegan R, Radaszkiewicz K, Pacherník J, Pirk J, Pešl M, Fila P, Bártová E. Cell Differentiation and Aging Lead To Up-Regulation of FTO, While the ALKBH5 Protein Level Was Stable During Aging but Up-Regulated During in vitro-Induced Cardiomyogenesis. Physiol Res 2023; 72:425-444. [PMID: 37795886 PMCID: PMC10634569 DOI: 10.33549/physiolres.935078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/25/2023] [Indexed: 01/05/2024] Open
Abstract
FTO and ALKBH5 proteins are essential erasers of N6-adenosine methylation in RNA. We studied how levels of FTO and ALKBH5 proteins changed during mouse embryonic development, aging, cardiomyogenesis, and neuroectodermal differentiation. We observed that aging in male and female mice was associated with FTO up-regulation in mouse hearts, brains, lungs, and kidneys, while the ALKBH5 level remained stable. FTO and ALKBH5 proteins were up-regulated during experimentally induced cardiomyogenesis, but the level of ALKBH5 protein was not changed when neuroectodermal differentiation was induced. HDAC1 depletion in mouse ES cells caused FTO down-regulation. In these cells, mRNA, carrying information from genes that regulate histone signature, RNA processing, and cell differentiation, was characterized by a reduced level of N6-adenosine methylation in specific gene loci, primarily regulating cell differentiation into neuroectoderm. Together, when we compared both RNA demethylating proteins, the FTO protein level undergoes the most significant changes during cell differentiation and aging. Thus, we conclude that during aging and neuronal differentiation, m6A RNA demethylation is likely regulated by the FTO protein but not via the function of ALKBH5.
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Affiliation(s)
- J Krejčí
- Department of Cell Biology and Epigenetics, Institute of Biophysics, Academy of Sciences of the Czech Republic, Brno, Czech Republic.
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17
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Song B, Huang D, Zhang Y, Wei Z, Su J, Pedro de Magalhães J, Rigden DJ, Meng J, Chen K. m6A-TSHub: Unveiling the Context-specific m 6A Methylation and m 6A-affecting Mutations in 23 Human Tissues. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:678-694. [PMID: 36096444 PMCID: PMC10787194 DOI: 10.1016/j.gpb.2022.09.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 08/19/2022] [Accepted: 09/02/2022] [Indexed: 06/15/2023]
Abstract
As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs (lncRNAs), N6-methyladenosine (m6A) RNA methylation has been shown to participate in essential biological processes. Recent studies have revealed the distinct patterns of m6A methylome across human tissues, and a major challenge remains in elucidating the tissue-specific presence and circuitry of m6A methylation. We present here a comprehensive online platform, m6A-TSHub, for unveiling the context-specific m6A methylation and genetic mutations that potentially regulate m6A epigenetic mark. m6A-TSHub consists of four core components, including (1) m6A-TSDB, a comprehensive database of 184,554 functionally annotated m6A sites derived from 23 human tissues and 499,369 m6A sites from 25 tumor conditions, respectively; (2) m6A-TSFinder, a web server for high-accuracy prediction of m6A methylation sites within a specific tissue from RNA sequences, which was constructed using multi-instance deep neural networks with gated attention; (3) m6A-TSVar, a web server for assessing the impact of genetic variants on tissue-specific m6A RNA modifications; and (4) m6A-CAVar, a database of 587,983 The Cancer Genome Atlas (TCGA) cancer mutations (derived from 27 cancer types) that were predicted to affect m6A modifications in the primary tissue of cancers. The database should make a useful resource for studying the m6A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue (or cancer type). m6A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m6ats.
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Affiliation(s)
- Bowen Song
- Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, China; Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Daiyun Huang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; Department of Computer Science, University of Liverpool, Liverpool L69 7ZB, United Kingdom.
| | - Yuxin Zhang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
| | - Zhen Wei
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; Institute of Ageing & Chronic Disease, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Jionglong Su
- School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
| | - João Pedro de Magalhães
- Institute of Ageing & Chronic Disease, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Jia Meng
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom; Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
| | - Kunqi Chen
- Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, China.
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18
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Liu P, Chen Y, Zhang Z, Yuan Z, Sun JG, Xia S, Cao X, Chen J, Zhang CJ, Chen Y, Zhan H, Jin Y, Bao X, Gu Y, Zhang M, Xu Y. Noncanonical contribution of microglial transcription factor NR4A1 to post-stroke recovery through TNF mRNA destabilization. PLoS Biol 2023; 21:e3002199. [PMID: 37486903 PMCID: PMC10365314 DOI: 10.1371/journal.pbio.3002199] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 06/14/2023] [Indexed: 07/26/2023] Open
Abstract
Microglia-mediated neuroinflammation is involved in various neurological diseases, including ischemic stroke, but the endogenous mechanisms preventing unstrained inflammation is still unclear. The anti-inflammatory role of transcription factor nuclear receptor subfamily 4 group A member 1 (NR4A1) in macrophages and microglia has previously been identified. However, the endogenous mechanisms that how NR4A1 restricts unstrained inflammation remain elusive. Here, we observed that NR4A1 is up-regulated in the cytoplasm of activated microglia and localizes to processing bodies (P-bodies). In addition, we found that cytoplasmic NR4A1 functions as an RNA-binding protein (RBP) that directly binds and destabilizes Tnf mRNA in an N6-methyladenosine (m6A)-dependent manner. Remarkably, conditional microglial deletion of Nr4a1 elevates Tnf expression and worsens outcomes in a mouse model of ischemic stroke, in which case NR4A1 expression is significantly induced in the cytoplasm of microglia. Thus, our study illustrates a novel mechanism that NR4A1 posttranscriptionally regulates Tnf expression in microglia and determines stroke outcomes.
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Affiliation(s)
- Pinyi Liu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Yan Chen
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Zhi Zhang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Zengqiang Yuan
- The Brain Science Center, Beijing Institute of Basic Medical Sciences, Beijing, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
| | - Jian-Guang Sun
- The Brain Science Center, Beijing Institute of Basic Medical Sciences, Beijing, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
| | - Shengnan Xia
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Xiang Cao
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Jian Chen
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Cun-Jin Zhang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Yanting Chen
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Hui Zhan
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Yuexinzi Jin
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Xinyu Bao
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Yue Gu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Meijuan Zhang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neurology Clinic Medical Center, Nanjing, China
- Institute of Brain Sciences, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China
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19
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Yoodee S, Thongboonkerd V. Epigenetic regulation of epithelial-mesenchymal transition during cancer development. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2023; 380:1-61. [PMID: 37657856 DOI: 10.1016/bs.ircmb.2023.05.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Abstract
Epithelial-mesenchymal transition (EMT) plays essential roles in promoting malignant transformation of epithelial cells, leading to cancer progression and metastasis. During EMT-induced cancer development, a wide variety of genes are dramatically modified, especially down-regulation of epithelial-related genes and up-regulation of mesenchymal-related genes. Expression of other EMT-related genes is also modified during the carcinogenic process. Especially, epigenetic modifications are observed in the EMT-related genes, indicating their involvement in cancer development. Mechanically, epigenetic modifications of histone, DNA, mRNA and non-coding RNA stably change the EMT-related gene expression at transcription and translation levels. Herein, we summarize current knowledge on epigenetic regulatory mechanisms observed in EMT process relate to cancer development in humans. The better understanding of epigenetic regulation of EMT during cancer development may lead to improvement of drug design and preventive strategies in cancer therapy.
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Affiliation(s)
- Sunisa Yoodee
- Medical Proteomics Unit, Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Visith Thongboonkerd
- Medical Proteomics Unit, Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
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20
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Yu L, Zhang Y, Xue L, Liu F, Jing R, Luo J. Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy. Front Microbiol 2023; 14:1175925. [PMID: 37275146 PMCID: PMC10232852 DOI: 10.3389/fmicb.2023.1175925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/27/2023] [Indexed: 06/07/2023] Open
Abstract
Post-transcriptionally RNA modifications, also known as the epitranscriptome, play crucial roles in the regulation of gene expression during development. Recently, deep learning (DL) has been employed for RNA modification site prediction and has shown promising results. However, due to the lack of relevant studies, it is unclear which DL architecture is best suited for some pyrimidine modifications, such as 5-methyluridine (m5U). To fill this knowledge gap, we first performed a comparative evaluation of various commonly used DL models for epigenetic studies with the help of autoBioSeqpy. We identified optimal architectural variations for m5U site classification, optimizing the layer depth and neuron width. Second, we used this knowledge to develop Deepm5U, an improved convolutional-recurrent neural network that accurately predicts m5U sites from RNA sequences. We successfully applied Deepm5U to transcriptomewide m5U profiling data across different sequencing technologies and cell types. Third, we showed that the techniques for interpreting deep neural networks, including LayerUMAP and DeepSHAP, can provide important insights into the internal operation and behavior of models. Overall, we offered practical guidance for the development, benchmark, and analysis of deep learning models when designing new algorithms for RNA modifications.
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Affiliation(s)
- Lezheng Yu
- School of Chemistry and Materials Science, Guizhou Education University, Guiyang, China
| | - Yonglin Zhang
- Department of Pharmacy, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Li Xue
- School of Public Health, Southwest Medical University, Luzhou, China
| | - Fengjuan Liu
- School of Geography and Resources, Guizhou Education University, Guiyang, China
| | - Runyu Jing
- School of Cyber Science and Engineering, Sichuan University, Chengdu, China
| | - Jiesi Luo
- Basic Medical College, Southwest Medical University, Luzhou, China
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability Evaluation, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, Southwest Medical University, Luzhou, China
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21
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Zhu J, Tong H, Sun Y, Li T, Yang G, He W. YTHDF1 Promotes Bladder Cancer Cell Proliferation via the METTL3/YTHDF1-RPN2-PI3K/AKT/mTOR Axis. Int J Mol Sci 2023; 24:ijms24086905. [PMID: 37108067 PMCID: PMC10139185 DOI: 10.3390/ijms24086905] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/23/2023] [Accepted: 04/04/2023] [Indexed: 04/29/2023] Open
Abstract
N6-methyladenosine (m6A) is the most common mRNA modification and it plays a critical role in tumor progression, prognoses and therapeutic response. In recent years, more and more studies have shown that m6A modifications play an important role in bladder carcinogenesis and development. However, the regulatory mechanisms of m6A modifications are complex. Whether the m6A reading protein YTHDF1 is involved in the development of bladder cancer remains to be elucidated. The aims of this study were to determine the association between METTL3/YTHDF1 and bladder cancer cell proliferation and cisplatin resistance to explore the downstream target genes of METTL3/YTHDF1 and to explore the therapeutic implications for bladder cancer patients. The results showed that the reduced expression of METTL3/YTHDF1 could lead to decreased bladder cancer cell proliferation and cisplatin sensitivity. Meanwhile, overexpression of the downstream target gene, RPN2, could rescue the effect of reduced METTL3/YTHDF1 expression on bladder cancer cells. In conclusion, this study proposes a novel METTL3/YTHDF1-RPN2-PI3K/AKT/mTOR regulatory axis that affects bladder cancer cell proliferation and cisplatin sensitivity.
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Affiliation(s)
- Junlong Zhu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Hang Tong
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yan Sun
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Tinghao Li
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Guang Yang
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Weiyang He
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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22
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Yang YH, Ma CY, Gao D, Liu XW, Yuan SS, Ding H. i2OM: Toward a better prediction of 2'-O-methylation in human RNA. Int J Biol Macromol 2023; 239:124247. [PMID: 37003392 DOI: 10.1016/j.ijbiomac.2023.124247] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/06/2023] [Accepted: 03/22/2023] [Indexed: 04/03/2023]
Abstract
2'-O-methylation (2OM) is an omnipresent post-transcriptional modification in RNAs. It is important for the regulation of RNA stability, mRNA splicing and translation, as well as innate immunity. With the increase in publicly available 2OM data, several computational tools have been developed for the identification of 2OM sites in human RNA. Unfortunately, these tools suffer from the low discriminative power of redundant features, unreasonable dataset construction or overfitting. To address those issues, based on four types of 2OM (2OM-adenine (A), cytosine (C), guanine (G), and uracil (U)) data, we developed a two-step feature selection model to identify 2OM. For each type, the one-way analysis of variance (ANOVA) combined with mutual information (MI) was proposed to rank sequence features for obtaining the optimal feature subset. Subsequently, four predictors based on eXtreme Gradient Boosting (XGBoost) or support vector machine (SVM) were presented to identify the four types of 2OM sites. Finally, the proposed model could produce an overall accuracy of 84.3 % on the independent set. To provide a convenience for users, an online tool called i2OM was constructed and can be freely access at i2om.lin-group.cn. The predictor may provide a reference for the study of the 2OM.
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Affiliation(s)
- Yu-He Yang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Cai-Yi Ma
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Dong Gao
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiao-Wei Liu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Shi-Shi Yuan
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hui Ding
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
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23
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Hao L, Zhang J, Liu Z, Lin X, Guo J. Epitranscriptomics in the development, functions, and disorders of cancer stem cells. Front Oncol 2023; 13:1145766. [PMID: 37007137 PMCID: PMC10063963 DOI: 10.3389/fonc.2023.1145766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 02/10/2023] [Indexed: 03/19/2023] Open
Abstract
Biomolecular modifications play an important role in the development of life, and previous studies have investigated the role of DNA and proteins. In the last decade, with the development of sequencing technology, the veil of epitranscriptomics has been gradually lifted. Transcriptomics focuses on RNA modifications that affect gene expression at the transcriptional level. With further research, scientists have found that changes in RNA modification proteins are closely linked to cancer tumorigenesis, progression, metastasis, and drug resistance. Cancer stem cells (CSCs) are considered powerful drivers of tumorigenesis and key factors for therapeutic resistance. In this article, we focus on describing RNA modifications associated with CSCs and summarize the associated research progress. The aim of this review is to identify new directions for cancer diagnosis and targeted therapy.
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Affiliation(s)
- Linlin Hao
- Department of Tumor Radiotherapy, The Second Hospital of Jilin University, Changchun, China
| | - Jian Zhang
- School of Life Sciences, Department of Biology, Southern University of Science and Technology, Shenzhen, China
| | - Zhongshan Liu
- Department of Tumor Radiotherapy, The Second Hospital of Jilin University, Changchun, China
| | - Xia Lin
- Department of Tumor Radiotherapy, The Second Hospital of Jilin University, Changchun, China
| | - Jie Guo
- Department of Tumor Radiotherapy, The Second Hospital of Jilin University, Changchun, China
- *Correspondence: Jie Guo,
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24
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Rehman MU, Tayara H, Chong KT. DL-m6A: Identification of N6-Methyladenosine Sites in Mammals Using Deep Learning Based on Different Encoding Schemes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:904-911. [PMID: 35857733 DOI: 10.1109/tcbb.2022.3192572] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
N6-methyladenosine (m6A) is a common post-transcriptional alteration that plays a critical function in a variety of biological processes. Although experimental approaches for identifying m6A sites have been developed and deployed, they are currently expensive for transcriptome-wide m6A identification. Some computational strategies for identifying m6A sites have been presented as an effective complement to the experimental procedure. However, their performance still requires improvement. In this study, we have proposed a novel tool called DL-m6A for the identification of m6A sites in mammals using deep learning based on different encoding schemes. The proposed tool uses three encoding schemes which give the required contextual feature representation to the input RNA sequence. Later these contextual feature vectors individually go through several neural network layers for shallow feature extraction after which they are concatenated to a single feature vector. The concatenated feature map is then used by several other layers to extract the deep features so that the insight features of the sequence can be used for the prediction of m6A sites. The proposed tool is firstly evaluated on the tissue-specific dataset and later on a full transcript dataset. To ensure the generalizability of the tool we assessed the proposed model by training it on a full transcript dataset and test on the tissue-specific dataset. The achieved results by the proposed model have outperformed the existing tools. The results demonstrate that the proposed tool can be of great use for the biology experts and therefore a freely accessible web-server is created which can be accessed at: http://nsclbio.jbnu.ac.kr/tools/DL-m6A/.
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25
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Zhang X, Wang S, Xie L, Zhu Y. PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites. Front Genet 2023; 14:1121694. [PMID: 36741328 PMCID: PMC9892456 DOI: 10.3389/fgene.2023.1121694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/09/2023] [Indexed: 01/20/2023] Open
Abstract
Background: Pseudouridine (Ψ) is one of the most abundant RNA modifications found in a variety of RNA types, and it plays a significant role in many biological processes. The key to studying the various biochemical functions and mechanisms of Ψ is to identify the Ψ sites. However, identifying Ψ sites using experimental methods is time-consuming and expensive. Therefore, it is necessary to develop computational methods that can accurately predict Ψ sites based on RNA sequence information. Methods: In this study, we proposed a new model called PseU-ST to identify Ψ sites in Homo sapiens (H. sapiens), Saccharomyces cerevisiae (S. cerevisiae), and Mus musculus (M. musculus). We selected the best six encoding schemes and four machine learning algorithms based on a comprehensive test of almost all of the RNA sequence encoding schemes available in the iLearnPlus software package, and selected the optimal features for each encoding scheme using chi-square and incremental feature selection algorithms. Then, we selected the optimal feature combination and the best base-classifier combination for each species through an extensive performance comparison and employed a stacking strategy to build the predictive model. Results: The results demonstrated that PseU-ST achieved better prediction performance compared with other existing models. The PseU-ST accuracy scores were 93.64%, 87.74%, and 89.64% on H_990, S_628, and M_944, respectively, representing increments of 13.94%, 6.05%, and 0.26%, respectively, higher than the best existing methods on the same benchmark training datasets. Conclusion: The data indicate that PseU-ST is a very competitive prediction model for identifying RNA Ψ sites in H. sapiens, M. musculus, and S. cerevisiae. In addition, we found that the Position-specific trinucleotide propensity based on single strand (PSTNPss) and Position-specific of three nucleotides (PS3) features play an important role in Ψ site identification. The source code for PseU-ST and the data are obtainable in our GitHub repository (https://github.com/jluzhangxinrubio/PseU-ST).
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26
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Guo X, Li F, Song J. Predicting Pseudouridine Sites with Porpoise. Methods Mol Biol 2023; 2624:139-151. [PMID: 36723814 DOI: 10.1007/978-1-0716-2962-8_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Pseudouridine is a ubiquitous RNA modification and plays a crucial role in many biological processes. However, it remains a challenging task to identify pseudouridine sites using expensive and time-consuming experimental research. To this end, we present Porpoise, a computational approach to identify pseudouridine sites from RNA sequence data. Porpoise builds on a stacking ensemble learning framework with several informative features and achieves competitive performance compared with state-of-the-art approaches. This protocol elaborates on step-by-step use and execution of the local stand-alone version and the webserver of Porpoise. In addition, we also provide a general machine learning framework that can help identify the optimal stacking ensemble learning model using different combinations of feature-based features. This general machine learning framework can facilitate users to build their pseudouridine predictors using their in-house datasets.
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Affiliation(s)
- Xudong Guo
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Fuyi Li
- College of Information Engineering, Northwest A&F University, Yangling, China.
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia.
| | - Jiangning Song
- Biomedicine Discovery Institute, Monash University, Melbourne, VIC, Australia.
- Monash Data Futures Institute, Monash University, Melbourne, VIC, Australia.
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27
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Abstract
The field of epitranscriptomics has expanded dramatically in recent years, both in the number of identified RNA modifications and the number of researchers studying them. As knowledge of post-transcriptional modifications continues to expand, numerous new methods have been developed to detect these modifications. Additionally, modifications are being extended to therapeutic settings, such as with recent mRNA vaccines. With this increase in knowledge and use, the community is recognizing the necessity for user-friendly databases to (i) store information from both high- and low-throughput studies and (ii) provide prediction software on how RNA modifications contribute to RNA function and disease. This mini-review highlights select RNA modification databases and their key attributes with the aim of providing a resource to researchers in the field of epitranscriptomics.
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Affiliation(s)
- Jillian Ramos
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, Colorado 80045, USA
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28
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Peng J, Ghosh D, Zhang F, Yang L, Wu J, Pang J, Zhang L, Yin S, Jiang Y. Advancement of epigenetics in stroke. Front Neurosci 2022; 16:981726. [PMID: 36312038 PMCID: PMC9610114 DOI: 10.3389/fnins.2022.981726] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 09/27/2022] [Indexed: 10/14/2023] Open
Abstract
A wide plethora of intervention procedures, tissue plasminogen activators, mechanical thrombectomy, and several neuroprotective drugs were reported in stroke research over the last decennium. However, against this vivid background of newly emerging pieces of evidence, there is little to no advancement in the overall functional outcomes. With the advancement of epigenetic tools and technologies associated with intervention medicine, stroke research has entered a new fertile. The stroke involves an overabundance of inflammatory responses arising in part due to the body's immune response to brain injury. Neuroinflammation contributes to significant neuronal cell death and the development of functional impairment and even death in stroke patients. Recent studies have demonstrated that epigenetics plays a key role in post-stroke conditions, leading to inflammatory responses and alteration of the microenvironment within the injured tissue. In this review, we summarize the progress of epigenetics which provides an overview of recent advancements on the emerging key role of secondary brain injury in stroke. We also discuss potential epigenetic therapies related to clinical practice.
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Affiliation(s)
- Jianhua Peng
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Institute of Epigenetics and Brain Science, Southwest Medical University, Luzhou, China
- Academician (Expert) Workstation of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Dipritu Ghosh
- Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Fan Zhang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Lei Yang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jinpeng Wu
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jinwei Pang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Lifang Zhang
- Sichuan Clinical Research Center for Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Shigang Yin
- Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Institute of Epigenetics and Brain Science, Southwest Medical University, Luzhou, China
| | - Yong Jiang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Institute of Epigenetics and Brain Science, Southwest Medical University, Luzhou, China
- Sichuan Clinical Research Center for Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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29
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Wang H, Zhao S, Cheng Y, Bi S, Zhu X. MTDeepM6A-2S: A two-stage multi-task deep learning method for predicting RNA N6-methyladenosine sites of Saccharomyces cerevisiae. Front Microbiol 2022; 13:999506. [PMID: 36274691 PMCID: PMC9579691 DOI: 10.3389/fmicb.2022.999506] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/16/2022] [Indexed: 11/13/2022] Open
Abstract
N6-methyladenosine (m6A) is one of the most important RNA modifications, which is involved in many biological activities. Computational methods have been developed to detect m6A sites due to their high efficiency and low costs. As one of the most widely utilized model organisms, many methods have been developed for predicting m6A sites of Saccharomyces cerevisiae. However, the generalization of these methods was hampered by the limited size of the benchmark datasets. On the other hand, over 60,000 low resolution m6A sites and more than 10,000 base resolution m6A sites of Saccharomyces cerevisiae are recorded in RMBase and m6A-Atlas, respectively. The base resolution m6A sites are often obtained from low resolution results by post calibration. In view of these, we proposed a two-stage deep learning method, named MTDeepM6A-2S, to predict RNA m6A sites of Saccharomyces cerevisiae based on RNA sequence information. In the first stage, a multi-task model with convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) deep framework was built to not only detect the low resolution m6A sites but also assign a reasonable probability for the predicted site. In the second stage, a transfer-learning strategy was used to build the model to predict the base resolution m6A sites from those low resolution m6A sites. The effectiveness of our model was validated on both training and independent test sets. The results show that our model outperforms other state-of-the-art models on the independent test set, which indicates that our model holds high potential to become a useful tool for epitranscriptomics analysis.
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30
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RNA modifications in aging-associated cardiovascular diseases. Aging (Albany NY) 2022; 14:8110-8136. [PMID: 36178367 PMCID: PMC9596201 DOI: 10.18632/aging.204311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 09/17/2022] [Indexed: 11/25/2022]
Abstract
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide that bears an enormous healthcare burden and aging is a major contributing factor to CVDs. Functional gene expression network during aging is regulated by mRNAs transcriptionally and by non-coding RNAs epi-transcriptionally. RNA modifications alter the stability and function of both mRNAs and non-coding RNAs and are involved in differentiation, development, and diseases. Here we review major chemical RNA modifications on mRNAs and non-coding RNAs, including N6-adenosine methylation, N1-adenosine methylation, 5-methylcytidine, pseudouridylation, 2′ -O-ribose-methylation, and N7-methylguanosine, in the aging process with an emphasis on cardiovascular aging. We also summarize the currently available methods to detect RNA modifications and the bioinformatic tools to study RNA modifications. More importantly, we discussed the specific implication of the RNA modifications on mRNAs and non-coding RNAs in the pathogenesis of aging-associated CVDs, including atherosclerosis, hypertension, coronary heart diseases, congestive heart failure, atrial fibrillation, peripheral artery disease, venous insufficiency, and stroke.
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31
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Yang H, Messina-Pacheco J, Corredor ALG, Gregorieff A, Liu JL, Nehme A, Najafabadi HS, Riazalhosseini Y, Gao B, Gao ZH. An integrated model of acinar to ductal metaplasia-related N7-methyladenosine regulators predicts prognosis and immunotherapy in pancreatic carcinoma based on digital spatial profiling. Front Immunol 2022; 13:961457. [PMID: 35979350 PMCID: PMC9377277 DOI: 10.3389/fimmu.2022.961457] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 06/24/2022] [Indexed: 12/14/2022] Open
Abstract
Acinar-to-ductal metaplasia (ADM) is a recently recognized, yet less well-studied, precursor lesion of pancreatic ductal adenocarcinoma (PDAC) developed in the setting of chronic pancreatitis. Through digital spatial mRNA profiling, we compared ADM and adjacent PDAC tissues from patient samples to unveil the bridging genes during the malignant transformation of pancreatitis. By comparing the bridging genes with the 7-methylguanosine (m7G)-seq dataset, we screened 19 m7G methylation genes for a subsequent large sample analysis. We constructed the “m7G score” model based on the RNA-seq data for pancreatic cancer in The Cancer Genome Atlas (TCGA) database and The Gene Expression Omnibus (GEO) database. Tumors with a high m7G score were characterized by increased immune cell infiltration, increased genomic instability, higher response rate to combined immune checkpoint inhibitors (ICIs), and overall poor survival. These findings indicate that the m7G score is associated with tumor invasiveness, immune cell infiltration, ICI treatment response, and overall patients’ survival. We also identified FN1 and ITGB1 as core genes in the m7Gscore model, which affect immune cell infiltration and genomic instability not only in pancreatic cancer but also in pan-cancer. FN1 and ITGB1 can inhibit immune T cell activition by upregulation of macrophages and neutrophils, thereby leading to immune escape of pancreatic cancer cells and reducing the response rate of ICI treatment.
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Affiliation(s)
- Hao Yang
- Department of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Julia Messina-Pacheco
- Department of Pathology, McGill University and the Research Institute of McGill University Health Centre, Montreal, QC, Canada
| | - Andrea Liliam Gomez Corredor
- Department of Pathology, McGill University and the Research Institute of McGill University Health Centre, Montreal, QC, Canada
| | - Alex Gregorieff
- Department of Pathology, McGill University and the Research Institute of McGill University Health Centre, Montreal, QC, Canada
| | - Jun-li Liu
- MeDic Program, The Research Institute of McGill University Health Centre, & Division of Endocrinology and Metabolism, Department of Medicine, McGill University, Montreal, QC, Canada
| | - Ali Nehme
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- McGill University Genome Centre, Montreal, QC, Canada
| | - Hamed S. Najafabadi
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- McGill University Genome Centre, Montreal, QC, Canada
| | - Yasser Riazalhosseini
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- McGill University Genome Centre, Montreal, QC, Canada
| | - Bo Gao
- Department of General Surgery, Peking University People’s Hospital, Beijing, China
- *Correspondence: Zu-hua Gao, ; Bo Gao,
| | - Zu-hua Gao
- Department of Pathology and Laboratory Medicine, British Columbia (BC) Cancer Research Center, University of British Columbia, Vancouver, BC, Canada
- *Correspondence: Zu-hua Gao, ; Bo Gao,
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32
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Yang X, Patil S, Joshi S, Jamla M, Kumar V. Exploring epitranscriptomics for crop improvement and environmental stress tolerance. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2022; 183:56-71. [PMID: 35567875 DOI: 10.1016/j.plaphy.2022.04.031] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/27/2022] [Accepted: 04/30/2022] [Indexed: 06/15/2023]
Abstract
Climate change and stressful environmental conditions severely hamper crop growth, development and yield. Plants respond to environmental perturbations, through their plasticity provided by key-genes, governed at post-/transcriptional levels. Gene-regulation in plants is a multilevel process controlled by diverse cellular entities that includes transcription factors (TF), epigenetic regulators and non-coding RNAs beside others. There are successful studies confirming the role of epigenetic modifications (DNA-methylation/histone-modifications) in gene expression. Recent years have witnessed emergence of a highly specialized field the "Epitranscriptomics". Epitranscriptomics deals with investigating post-transcriptional RNA chemical-modifications present across the life forms that change structural, functional and biological characters of RNA. However, deeper insights on of epitranscriptomic modifications, with >140 types known so far, are to be understood fully. Researchers have identified epitranscriptome marks (writers, erasers and readers) and mapped the site-specific RNA modifications (m6A, m5C, 3' uridylation, etc.) responsible for fine-tuning gene expression in plants. Simultaneous advancement in sequencing platforms, upgraded bioinformatic tools and pipelines along with conventional labelled techniques have further given a statistical picture of these epitranscriptomic modifications leading to their potential applicability in crop improvement and developing climate-smart crops. We present herein the insights on epitranscriptomic machinery in plants and how epitranscriptome and epitranscriptomic modifications underlying plant growth, development and environmental stress responses/adaptations. Third-generation sequencing technology, advanced bioinformatics tools and databases being used in plant epitranscriptomics are also discussed. Emphasis is given on potential exploration of epitranscriptome engineering for crop-improvement and developing environmental stress tolerant plants covering current status, challenges and future directions.
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Affiliation(s)
- Xiangbo Yang
- College of Agriculture, Jilin Agricultural Science and Technology University, Jilin, 132101, PR China.
| | - Suraj Patil
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Ganeshkhind, Pune, 411016, India
| | - Shrushti Joshi
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Ganeshkhind, Pune, 411016, India
| | - Monica Jamla
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Ganeshkhind, Pune, 411016, India
| | - Vinay Kumar
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Ganeshkhind, Pune, 411016, India.
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Shi H, Xiang T, Feng J, Yang X, Li Y, Fang Y, Xu L, Qi Q, Shen J, Tang L, Shen Q, Wang X, Xu H, Rao J. N6-Methyladenosine Methylomic Landscape of Ureteral Deficiency in Reflux Uropathy and Obstructive Uropathy. Front Med (Lausanne) 2022; 9:924579. [PMID: 35795641 PMCID: PMC9251069 DOI: 10.3389/fmed.2022.924579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
Background Congenital anomalies of the kidneys and urinary tracts (CAKUT) represent the most prevalent cause for renal failure in children. The RNA epigenetic modification N6-methyladenosine (m6A) methylation modulates gene expression and function post-transcriptionally, which has recently been revealed to be critical in organ development. However, it is uncertain whether m6A methylation plays a role in the pathogenesis of CAKUT. Thus, we aimed to explore the pattern of m6A methylation in CAKUT. Methods Using m6A-mRNA epitranscriptomic microarray, we investigated the m6A methylomic landscape in the ureter tissue of children with obstructive megaureter (M group) and primary vesicoureteral reflux (V group). Results A total of 228 mRNAs engaged in multiple function-relevant signaling pathways were substantially differential methylated between the “V” and “M” groups. Additionally, 215 RNA-binding proteins that recognize differentially methylated regions were predicted based on public databases. The M group showed significantly higher mRNA levels of m6A readers/writers (YTHDF1, YTHDF2, YTHDC1, YTHDC2 and WTAP) and significantly lower mRNA levels of m6A eraser (FTO) according to real-time PCR. To further investigate the differentially methylated genes, m6A methylome and transcriptome data were integrated to identified 298 hypermethylated mRNAs with differential expressions (265 upregulation and 33 downregulation) and 489 hypomethylated mRNAs with differential expressions (431 upregulation and 58 downregulation) in the M/V comparison. Conclusion The current results highlight the pathogenesis of m6A methylation in obstructive and reflux uropathy.
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Affiliation(s)
- Hua Shi
- Department of Nephrology, Children's Hospital of Fudan University, Shanghai, China
- Shanghai Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
- Shanghai Key Lab of Birth Defect, Children's Hospital of Fudan University, Shanghai, China
| | - Tianchao Xiang
- Department of Nephrology, Children's Hospital of Fudan University, Shanghai, China
- Shanghai Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
- Shanghai Key Lab of Birth Defect, Children's Hospital of Fudan University, Shanghai, China
| | - Jiayan Feng
- Department of Pathology, Children's Hospital of Fudan University, Shanghai, China
| | - Xue Yang
- Department of Nephrology, Children's Hospital of Fudan University, Shanghai, China
- Shanghai Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
- Shanghai Key Lab of Birth Defect, Children's Hospital of Fudan University, Shanghai, China
| | - Yaqi Li
- Department of Nephrology, Children's Hospital of Fudan University, Shanghai, China
- Shanghai Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
- Shanghai Key Lab of Birth Defect, Children's Hospital of Fudan University, Shanghai, China
| | - Ye Fang
- Department of Nephrology, Children's Hospital of Fudan University, Shanghai, China
- Shanghai Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
- Shanghai Key Lab of Birth Defect, Children's Hospital of Fudan University, Shanghai, China
| | - Linan Xu
- Department of Nephrology, Children's Hospital of Fudan University, Shanghai, China
- Shanghai Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
- Shanghai Key Lab of Birth Defect, Children's Hospital of Fudan University, Shanghai, China
| | - Qi Qi
- Department of Nephrology, Children's Hospital of Fudan University, Shanghai, China
- Shanghai Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
- Shanghai Key Lab of Birth Defect, Children's Hospital of Fudan University, Shanghai, China
| | - Jian Shen
- Department of Urology, Children's Hospital of Fudan University, Shanghai, China
| | - Liangfeng Tang
- Department of Urology, Children's Hospital of Fudan University, Shanghai, China
| | - Qian Shen
- Department of Nephrology, Children's Hospital of Fudan University, Shanghai, China
- Shanghai Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
- Shanghai Key Lab of Birth Defect, Children's Hospital of Fudan University, Shanghai, China
| | - Xiang Wang
- Department of Urology, Children's Hospital of Fudan University, Shanghai, China
- *Correspondence: Xiang Wang
| | - Hong Xu
- Department of Nephrology, Children's Hospital of Fudan University, Shanghai, China
- Shanghai Kidney Development and Pediatric Kidney Disease Research Center, Shanghai, China
- Shanghai Key Lab of Birth Defect, Children's Hospital of Fudan University, Shanghai, China
- Hong Xu
| | - Jia Rao
- Department of Nephrology, Children's Hospital of Fudan University, Shanghai, China
- Shanghai Key Lab of Birth Defect, Children's Hospital of Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science and School of Basic Medical Science, Fudan University, Shanghai, China
- Jia Rao
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EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction. BMC Bioinformatics 2022; 23:221. [PMID: 35676633 PMCID: PMC9178860 DOI: 10.1186/s12859-022-04756-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 05/27/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Recent research recommends that epi-transcriptome regulation through post-transcriptional RNA modifications is essential for all sorts of RNA. Exact identification of RNA modification is vital for understanding their purposes and regulatory mechanisms. However, traditional experimental methods of identifying RNA modification sites are relatively complicated, time-consuming, and laborious. Machine learning approaches have been applied in the procedures of RNA sequence features extraction and classification in a computational way, which may supplement experimental approaches more efficiently. Recently, convolutional neural network (CNN) and long short-term memory (LSTM) have been demonstrated achievements in modification site prediction on account of their powerful functions in representation learning. However, CNN can learn the local response from the spatial data but cannot learn sequential correlations. And LSTM is specialized for sequential modeling and can access both the contextual representation but lacks spatial data extraction compared with CNN. There is strong motivation to construct a prediction framework using natural language processing (NLP), deep learning (DL) for these reasons. RESULTS This study presents an ensemble multiscale deep learning predictor (EMDLP) to identify RNA methylation sites in an NLP and DL way. It organically combines the dilated convolution and Bidirectional LSTM (BiLSTM), which helps to take better advantage of the local and global information for site prediction. The first step of EMDLP is to represent the RNA sequences in an NLP way. Thus, three encodings, e.g., RNA word embedding, One-hot encoding, and RGloVe, which is an improved learning method of word vector representation based on GloVe, are adopted to decipher sites from the viewpoints of the local and global information. Then, a dilated convolutional Bidirectional LSTM network (DCB) model is constructed with the dilated convolutional neural network (DCNN) followed by BiLSTM to extract potential contributing features for methylation site prediction. Finally, these three encoding methods are integrated by a soft vote to obtain better predictive performance. Experiment results on m1A and m6A reveal that the area under the receiver operating characteristic(AUROC) of EMDLP obtains respectively 95.56%, 85.24%, and outperforms the state-of-the-art models. To maximize user convenience, a user-friendly webserver for EMDLP was publicly available at http://www.labiip.net/EMDLP/index.php ( http://47.104.130.81/EMDLP/index.php ). CONCLUSIONS We developed a predictor for m1A and m6A methylation sites.
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Comprehensive Analysis of Long Noncoding RNA Modified by m 6A Methylation in Oxidative and Glycolytic Skeletal Muscles. Int J Mol Sci 2022; 23:ijms23094600. [PMID: 35562992 PMCID: PMC9105514 DOI: 10.3390/ijms23094600] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/18/2022] [Accepted: 04/18/2022] [Indexed: 12/23/2022] Open
Abstract
N6-methyladenosine (m6A) is the most common modification in eukaryotic RNAs. Accumulating evidence shows m6A methylation plays vital roles in various biological processes, including muscle and fat differentiation. However, there is a lack of research on lncRNAs’ m6A modification in regulating pig muscle-fiber-type conversion. In this study, we identified novel and differentially expressed lncRNAs in oxidative and glycolytic skeletal muscles through RNA-seq, and further reported the m6A-methylation patterns of lncRNAs via MeRIP-seq. We found that most lncRNAs have one m6A peak, and the m6A peaks were preferentially enriched in the last exon of the lncRNAs. Interestingly, we found that lncRNAs’ m6A levels were positively correlated with their expression homeostasis and levels. Furthermore, we performed conjoint analysis of MeRIP-seq and RNA-seq data and obtained 305 differentially expressed and differentially m6A-modified lncRNAs (dme-lncRNAs). Through QTL enrichment analysis of dme-lncRNAs and PPI analysis for their cis-genes, we finally identified seven key m6A-modified lncRNAs that may play a potential role in muscle-fiber-type conversion. Notably, inhibition of one of the key lncRNAs, MSTRG.14200.1, delayed satellite cell differentiation and stimulated fast-to-slow muscle-fiber conversion. Our study comprehensively analyzed m6A modifications on lncRNAs in oxidative and glycolytic skeletal muscles and provided new targets for the study of pig muscle-fiber-type conversion.
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Wang H, Wang S, Zhang Y, Bi S, Zhu X. A brief review of machine learning methods for RNA methylation sites prediction. Methods 2022; 203:399-421. [DOI: 10.1016/j.ymeth.2022.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/15/2022] [Accepted: 03/01/2022] [Indexed: 02/07/2023] Open
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Liu M, Xu K, Saaoud F, Shao Y, Zhang R, Lu Y, Sun Y, Drummer C, Li L, Wu S, Kunapuli SP, Criner GJ, Sun J, Shan H, Jiang X, Wang H, Yang X. 29 m 6A-RNA Methylation (Epitranscriptomic) Regulators Are Regulated in 41 Diseases including Atherosclerosis and Tumors Potentially via ROS Regulation - 102 Transcriptomic Dataset Analyses. J Immunol Res 2022; 2022:1433323. [PMID: 35211628 PMCID: PMC8863469 DOI: 10.1155/2022/1433323] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/31/2021] [Indexed: 12/12/2022] Open
Abstract
We performed a database mining on 102 transcriptomic datasets for the expressions of 29 m6A-RNA methylation (epitranscriptomic) regulators (m6A-RMRs) in 41 diseases and cancers and made significant findings: (1) a few m6A-RMRs were upregulated; and most m6A-RMRs were downregulated in sepsis, acute respiratory distress syndrome, shock, and trauma; (2) half of 29 m6A-RMRs were downregulated in atherosclerosis; (3) inflammatory bowel disease and rheumatoid arthritis modulated m6A-RMRs more than lupus and psoriasis; (4) some organ failures shared eight upregulated m6A-RMRs; end-stage renal failure (ESRF) downregulated 85% of m6A-RMRs; (5) Middle-East respiratory syndrome coronavirus infections modulated m6A-RMRs the most among viral infections; (6) proinflammatory oxPAPC modulated m6A-RMRs more than DAMP stimulation including LPS and oxLDL; (7) upregulated m6A-RMRs were more than downregulated m6A-RMRs in cancer types; five types of cancers upregulated ≥10 m6A-RMRs; (8) proinflammatory M1 macrophages upregulated seven m6A-RMRs; (9) 86% of m6A-RMRs were differentially expressed in the six clusters of CD4+Foxp3+ immunosuppressive Treg, and 8 out of 12 Treg signatures regulated m6A-RMRs; (10) immune checkpoint receptors TIM3, TIGIT, PD-L2, and CTLA4 modulated m6A-RMRs, and inhibition of CD40 upregulated m6A-RMRs; (11) cytokines and interferons modulated m6A-RMRs; (12) NF-κB and JAK/STAT pathways upregulated more than downregulated m6A-RMRs whereas TP53, PTEN, and APC did the opposite; (13) methionine-homocysteine-methyl cycle enzyme Mthfd1 downregulated more than upregulated m6A-RMRs; (14) m6A writer RBM15 and one m6A eraser FTO, H3K4 methyltransferase MLL1, and DNA methyltransferase, DNMT1, regulated m6A-RMRs; and (15) 40 out of 165 ROS regulators were modulated by m6A eraser FTO and two m6A writers METTL3 and WTAP. Our findings shed new light on the functions of upregulated m6A-RMRs in 41 diseases and cancers, nine cellular and molecular mechanisms, novel therapeutic targets for inflammatory disorders, metabolic cardiovascular diseases, autoimmune diseases, organ failures, and cancers.
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Affiliation(s)
- Ming Liu
- Cardiovascular Research Center, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
- Department of Cell Biology and Genetics, School of Basic Medical Science, Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Keman Xu
- Cardiovascular Research Center, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Fatma Saaoud
- Cardiovascular Research Center, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Ying Shao
- Cardiovascular Research Center, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Ruijing Zhang
- Cardiovascular Research Center, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
- Department of Nephrology, The Affiliated People's Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Yifan Lu
- Cardiovascular Research Center, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Yu Sun
- Cardiovascular Research Center, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Charles Drummer
- Cardiovascular Research Center, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Li Li
- Department of Cell Biology and Genetics, School of Basic Medical Science, Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Sheng Wu
- Metabolic Disease Research; Inflammation, Translational & Clinical Lung Research, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Satya P. Kunapuli
- Thrombosis Research, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Gerard J. Criner
- Thoracic Medicine and Surgery, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Jianxin Sun
- Center for Translational Medicine, Department of Medicine, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Huimin Shan
- Metabolic Disease Research; Inflammation, Translational & Clinical Lung Research, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Xiaohua Jiang
- Cardiovascular Research Center, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
- Metabolic Disease Research; Inflammation, Translational & Clinical Lung Research, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Hong Wang
- Metabolic Disease Research; Inflammation, Translational & Clinical Lung Research, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
| | - Xiaofeng Yang
- Cardiovascular Research Center, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
- Metabolic Disease Research; Inflammation, Translational & Clinical Lung Research, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
- Thrombosis Research, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140, USA
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Cai T, Atteh LL, Zhang X, Huang C, Bai M, Ma H, Zhang C, Fu W, Gao L, Lin Y, Meng W. The N6-Methyladenosine Modification and Its Role in mRNA Metabolism and Gastrointestinal Tract Disease. Front Surg 2022; 9:819335. [PMID: 35155557 PMCID: PMC8831730 DOI: 10.3389/fsurg.2022.819335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
The N6-methyladenosine (m6A) modification is the most abundant internal modification of messenger RNA (mRNA) in higher eukaryotes. Under the actions of methyltransferase, demethylase and methyl-binding protein, m6A resulting from RNA methylation becomes dynamic and reversible, similar to that from DNA methylation, and this effect allows the generated mRNA to participate in metabolism processes, such as splicing, transport, translation, and degradation. The most common tumors are those found in the gastrointestinal tract, and research on these tumors has flourished since the discovery of m6A. Overall, further analysis of the mechanism of m6A and its role in tumors may contribute to new ideas for the treatment of tumors. m6A also plays an important role in non-tumor diseases of the gastrointestinal tract. This manuscript reviews the current knowledge of m6A-related proteins, mRNA metabolism and their application in gastrointestinal tract disease.
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Affiliation(s)
- Teng Cai
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
- Gansu Province Key Laboratory Biotherapy and Regenerative Medicine, Lanzhou, China
| | | | - Xianzhuo Zhang
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Chongfei Huang
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Mingzhen Bai
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Haidong Ma
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Chao Zhang
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Wenkang Fu
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Long Gao
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Yanyan Lin
- The Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China
- Gansu Province Institute of Hepatopancreatobiliary Surgery, Lanzhou, China
- Yanyan Lin
| | - Wenbo Meng
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
- The Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China
- Gansu Province Institute of Hepatopancreatobiliary Surgery, Lanzhou, China
- *Correspondence: Wenbo Meng
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Kanoria S, Rennie WA, Carmack CS, Lu J, Ding Y. N 6-methyladenosine enhances post-transcriptional gene regulation by microRNAs. BIOINFORMATICS ADVANCES 2022; 2:vbab046. [PMID: 35098135 PMCID: PMC8792947 DOI: 10.1093/bioadv/vbab046] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 11/09/2021] [Indexed: 01/27/2023]
Abstract
MOTIVATION N 6-methyladenosine (m6A) is the most prevalent modification in eukaryotic messenger RNAs. MicroRNAs (miRNAs) are abundant post-transcriptional regulators of gene expression. Correlation between m6A and miRNA-targeting sites has been reported to suggest possible involvement of m6A in miRNA-mediated gene regulation. However, it is unknown what the regulatory effects might be. In this study, we performed comprehensive analyses of high-throughput data on m6A and miRNA target binding and regulation. RESULTS We found that the level of miRNA-mediated target suppression is significantly enhanced when m6A is present on target mRNAs. The evolutionary conservation for miRNA-binding sites with m6A modification is significantly higher than that for miRNA-binding sites without modification. These findings suggest functional significance of m6A modification in post-transcriptional gene regulation by miRNAs. We also found that methylated targets have more stable structure than non-methylated targets, as indicated by significantly higher GC content. Furthermore, miRNA-binding sites that can be potentially methylated are significantly less accessible without methylation than those that do not possess potential methylation sites. Since either RNA-binding proteins or m6A modification by itself can destabilize RNA structure, we propose a model in which m6A alters local target secondary structure to increase accessibility for efficient binding by Argonaute proteins, leading to enhanced miRNA-mediated regulation. AVAILABILITY AND IMPLEMENTATION N/A.
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Affiliation(s)
- Shaveta Kanoria
- Wadsworth Center, New York State Department of Health, Center for Medical Science, Albany, NY 12208, USA
| | - William A Rennie
- Wadsworth Center, New York State Department of Health, Center for Medical Science, Albany, NY 12208, USA
| | - Charles Steven Carmack
- Wadsworth Center, New York State Department of Health, Center for Medical Science, Albany, NY 12208, USA
| | - Jun Lu
- Department of Genetics and Yale Stem Cell Center, Yale University, New Haven, CT 06520, USA,To whom correspondence should be addressed. or
| | - Ye Ding
- Wadsworth Center, New York State Department of Health, Center for Medical Science, Albany, NY 12208, USA,To whom correspondence should be addressed. or
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Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4035462. [PMID: 35071593 PMCID: PMC8776474 DOI: 10.1155/2022/4035462] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/07/2021] [Accepted: 12/22/2021] [Indexed: 12/15/2022]
Abstract
Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m5C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m5C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from
-mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated.
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He S, Gu C, Su T, Qiu Q. Research Progress of circRNAs in Inflammatory Mechanisms of Diabetic Retinopathy: An Emerging Star with Potential Therapeutic Targets. Curr Eye Res 2021; 47:165-178. [PMID: 34963381 DOI: 10.1080/02713683.2021.1995002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
PURPOSE We summarized the existing studies to elaborate the biogenesis and function of circRNAs, the effect of aberrant circRNAs expression in the mechanism of inflammation and diabetic retinopathy (DR) respectively and further explored the vital roles of circRNAs in inflammation involved in DR. Methods: We conducted a systematical literature search of abundant electronic databases (PubMed, GeneMedical and MEDLINE) up to August 2021. Results: In this review, we exhibited the biogenesis and function of circRNAs and highlighted the components of inflammatory mediators implicated in DR. Numerous circRNAs, such as circHIPK3, circZNF609, circRNA_0084043, circ_0002570, circ_0041795, circEhmt1 and circ-ITCH were discovered to play vital roles in inflammation involved in DR, which provided new ideas for diagnosis and treatment of DR. Moreover, we proposed not only the epigenetic functions of circRNAs but also novel forms of the inflammatory response, including pyroptosis, to inspire further exploration and creative research in this field. Conclusion: CircRNAs were implicated in the progression and development of inflammation in DR via aberrant expression and modulation of gene expression, serving as an emerging star with potential therapeutic targets.
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Affiliation(s)
- Shuai He
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.,Department of Ophthalmology, National Clinical Research Center for Eye Diseases; Shanghai Key Laboratory of Ocular Fundus Diseases; Shanghai Engineering Center for Visual Science and Photomedicine; Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Department of Ophthalmology, Shanghai, PR China
| | - Chufeng Gu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.,Department of Ophthalmology, National Clinical Research Center for Eye Diseases; Shanghai Key Laboratory of Ocular Fundus Diseases; Shanghai Engineering Center for Visual Science and Photomedicine; Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Department of Ophthalmology, Shanghai, PR China
| | - Tong Su
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.,Department of Ophthalmology, National Clinical Research Center for Eye Diseases; Shanghai Key Laboratory of Ocular Fundus Diseases; Shanghai Engineering Center for Visual Science and Photomedicine; Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Department of Ophthalmology, Shanghai, PR China
| | - Qinghua Qiu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.,Department of Ophthalmology, National Clinical Research Center for Eye Diseases; Shanghai Key Laboratory of Ocular Fundus Diseases; Shanghai Engineering Center for Visual Science and Photomedicine; Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Department of Ophthalmology, Shanghai, PR China.,Department of Ophthalmology, Shigatse People's Hospital, Shigatse, Xizang, PR China
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Dou L, Zhou W, Zhang L, Xu L, Han K. Accurate identification of RNA D modification using multiple features. RNA Biol 2021; 18:2236-2246. [PMID: 33729104 PMCID: PMC8632091 DOI: 10.1080/15476286.2021.1898160] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 02/13/2021] [Accepted: 02/23/2021] [Indexed: 10/21/2022] Open
Abstract
As one of the common post-transcriptional modifications in tRNAs, dihydrouridine (D) has prominent effects on regulating the flexibility of tRNA as well as cancerous diseases. Facing with the expensive and time-consuming sequencing techniques to detect D modification, precise computational tools can largely promote the progress of molecular mechanisms and medical developments. We proposed a novel predictor, called iRNAD_XGBoost, to identify potential D sites using multiple RNA sequence representations. In this method, by considering the imbalance problem using hybrid sampling method SMOTEEEN, the XGBoost-selected top 30 features are applied to construct model. The optimized model showed high Sn and Sp values of 97.13% and 97.38% over jackknife test, respectively. For the independent experiment, these two metrics separately achieved 91.67% and 94.74%. Compared with iRNAD method, this model illustrated high generalizability and consistent prediction efficiencies for positive and negative samples, which yielded satisfactory MCC scores of 0.94 and 0.86, respectively. It is inferred that the chemical property and nucleotide density features (CPND), electron-ion interaction pseudopotential (EIIP and PseEIIP) as well as dinucleotide composition (DNC) are crucial to the recognition of D modification. The proposed predictor is a promising tool to help experimental biologists investigate molecular functions.
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Affiliation(s)
- Lijun Dou
- School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, GuangdongChina
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, SichuanChina
| | - Wenyang Zhou
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, HeilongjiangChina
| | - Lichao Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, Guangdong, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, GuangdongChina
| | - Ke Han
- School of Computer and Information Engineering, Harbin University of Commerce, Harbin, HeilongjiangChina
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Issah MA, Wu D, Zhang F, Zheng W, Liu Y, Fu H, Zhou H, Chen R, Shen J. Epigenetic modifications in acute myeloid leukemia: The emerging role of circular RNAs (Review). Int J Oncol 2021; 59:107. [PMID: 34792180 PMCID: PMC8651224 DOI: 10.3892/ijo.2021.5287] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/13/2021] [Indexed: 11/06/2022] Open
Abstract
Canonical epigenetic modifications, which include histone modification, chromatin remodeling and DNA methylation, play key roles in numerous cellular processes. Epigenetics underlies how cells that posses DNA with similar sequences develop into different cell types with different functions in an organism. Earlier epigenetic research has primarily been focused at the chromatin level. However, the number of studies on epigenetic modifications of RNA, such as N1‑methyladenosine, 2'‑O‑ribosemethylation, inosine, 5‑methylcytidine, N6‑methyladenosine (m6A) and pseudouridine, has seen an increase. Circular RNAs (circRNAs), a type of RNA species that lacks a 5' cap or 3' poly(A) tail, are abundantly expressed in acute myeloid leukemia (AML) and may regulate disease progression. circRNAs possess various functions, including microRNA sponging, gene transcription regulation and RNA‑binding protein interaction. Furthermore, circRNAs are m6A methylated in other types of cancer, such as colorectal and hypopharyngeal squamous cell cancers. Therefore, the critical roles of circRNA epigenetic modifications, particularly m6A, and their possible involvement in AML are discussed in the present review. Epigenetic modification of circRNAs may become a diagnostic and therapeutic target for AML in the future.
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Affiliation(s)
- Mohammed Awal Issah
- Fujian Institute of Hematology, Fujian Medical Center of Hematology, Clinical Research Center for Hematological Malignancies of Fujian Province, Fuzhou, Fujian 350001, P.R. China
- Fujian Provincial Key Laboratory on Hematology, Fujian Medical University Union Hospital, Fuzhou, Fujian 350001, P.R. China
| | - Dansen Wu
- Medical Intensive Care Unit, Fujian Provincial Hospital, Fuzhou, Fujian 350001, P.R. China
| | - Feng Zhang
- Fujian Institute of Hematology, Fujian Medical Center of Hematology, Clinical Research Center for Hematological Malignancies of Fujian Province, Fuzhou, Fujian 350001, P.R. China
- Fujian Provincial Key Laboratory on Hematology, Fujian Medical University Union Hospital, Fuzhou, Fujian 350001, P.R. China
| | - Weili Zheng
- Fujian Institute of Hematology, Fujian Medical Center of Hematology, Clinical Research Center for Hematological Malignancies of Fujian Province, Fuzhou, Fujian 350001, P.R. China
- Fujian Provincial Key Laboratory on Hematology, Fujian Medical University Union Hospital, Fuzhou, Fujian 350001, P.R. China
| | - Yanquan Liu
- Fujian Institute of Hematology, Fujian Medical Center of Hematology, Clinical Research Center for Hematological Malignancies of Fujian Province, Fuzhou, Fujian 350001, P.R. China
- Fujian Provincial Key Laboratory on Hematology, Fujian Medical University Union Hospital, Fuzhou, Fujian 350001, P.R. China
| | - Haiying Fu
- Fujian Institute of Hematology, Fujian Medical Center of Hematology, Clinical Research Center for Hematological Malignancies of Fujian Province, Fuzhou, Fujian 350001, P.R. China
- Fujian Provincial Key Laboratory on Hematology, Fujian Medical University Union Hospital, Fuzhou, Fujian 350001, P.R. China
| | - Huarong Zhou
- Fujian Institute of Hematology, Fujian Medical Center of Hematology, Clinical Research Center for Hematological Malignancies of Fujian Province, Fuzhou, Fujian 350001, P.R. China
- Fujian Provincial Key Laboratory on Hematology, Fujian Medical University Union Hospital, Fuzhou, Fujian 350001, P.R. China
| | - Rong Chen
- Fujian Institute of Hematology, Fujian Medical Center of Hematology, Clinical Research Center for Hematological Malignancies of Fujian Province, Fuzhou, Fujian 350001, P.R. China
- Fujian Provincial Key Laboratory on Hematology, Fujian Medical University Union Hospital, Fuzhou, Fujian 350001, P.R. China
| | - Jianzhen Shen
- Fujian Institute of Hematology, Fujian Medical Center of Hematology, Clinical Research Center for Hematological Malignancies of Fujian Province, Fuzhou, Fujian 350001, P.R. China
- Fujian Provincial Key Laboratory on Hematology, Fujian Medical University Union Hospital, Fuzhou, Fujian 350001, P.R. China
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Cai L, Xuan J, Lin Q, Wang J, Liu S, Xie F, Zheng L, Li B, Qu L, Yang J. Pol3Base: a resource for decoding the interactome, expression, evolution, epitranscriptome and disease variations of Pol III-transcribed ncRNAs. Nucleic Acids Res 2021; 50:D279-D286. [PMID: 34747466 PMCID: PMC8728242 DOI: 10.1093/nar/gkab1033] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/30/2021] [Accepted: 10/15/2021] [Indexed: 12/20/2022] Open
Abstract
RNA polymerase III (Pol III) transcribes hundreds of non-coding RNA genes (ncRNAs), which involve in a variety of cellular processes. However, the expression, functions, regulatory networks and evolution of these Pol III-transcribed ncRNAs are still largely unknown. In this study, we developed a novel resource, Pol3Base (http://rna.sysu.edu.cn/pol3base/), to decode the interactome, expression, evolution, epitranscriptome and disease variations of Pol III-transcribed ncRNAs. The current release of Pol3Base includes thousands of regulatory relationships between ∼79 000 ncRNAs and transcription factors by mining 56 ChIP-seq datasets. By integrating CLIP-seq datasets, we deciphered the interactions of these ncRNAs with >240 RNA binding proteins. Moreover, Pol3Base contains ∼9700 RNA modifications located within thousands of Pol III-transcribed ncRNAs. Importantly, we characterized expression profiles of ncRNAs in >70 tissues and 28 different tumor types. In addition, by comparing these ncRNAs from human and mouse, we revealed about 4000 evolutionary conserved ncRNAs. We also identified ∼11 403 tRNA-derived small RNAs (tsRNAs) in 32 different tumor types. Finally, by analyzing somatic mutation data, we investigated the mutation map of these ncRNAs to help uncover their potential roles in diverse diseases. This resource will help expand our understanding of potential functions and regulatory networks of Pol III-transcribed ncRNAs.
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Affiliation(s)
- Li Cai
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangdong, Guangzhou 510275, P.R. China
| | - Jiajia Xuan
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangdong, Guangzhou 510275, P.R. China
| | - Qiao Lin
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangdong, Guangzhou 510275, P.R. China
| | - Junhao Wang
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangdong, Guangzhou 510275, P.R. China
| | - Shurong Liu
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangdong, Guangzhou 510275, P.R. China
| | - Fangzhou Xie
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangdong, Guangzhou 510275, P.R. China
| | - Lingling Zheng
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangdong, Guangzhou 510275, P.R. China
| | - Bin Li
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangdong, Guangzhou 510275, P.R. China
| | - Lianghu Qu
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangdong, Guangzhou 510275, P.R. China
| | - Jianhua Yang
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangdong, Guangzhou 510275, P.R. China
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Yan Y, Peng J, Liang Q, Ren X, Cai Y, Peng B, Chen X, Wang X, Yi Q, Xu Z. Dynamic m6A-ncRNAs association and their impact on cancer pathogenesis, immune regulation and therapeutic response. Genes Dis 2021; 10:135-150. [PMID: 37013031 PMCID: PMC10066278 DOI: 10.1016/j.gendis.2021.10.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 10/11/2021] [Accepted: 10/25/2021] [Indexed: 02/08/2023] Open
Abstract
Several types of modifications have been proven to participate in the metabolism and processing of different RNA types, including non-coding RNAs (ncRNAs). N-6-methyladenosine (m6A) is a dynamic and reversible RNA modification that is closely involved in the ncRNA homeostasis, and serves as a crucial regulator for multiple cancer-associated signaling pathways. The ncRNAs usually regulate the epigenetic modification, mRNA transcription and other biological processes, displaying enormous roles in human cancers. In this review, we summarized the significant implications of m6A-ncRNA interaction in various types of cancers. In particular, the interplay between m6A and ncRNAs in cancer pathogenesis and therapeutic resistance are being widely recognized. We also discussed the relevance of m6A-ncRNA interaction in immune regulation, followed by the interference on cancer immunotherapeutic procedures. In addition, we briefly highlighted the computation tools that could identify the accurate features of m6A methylome among ncRNAs. In summary, this review would pave the way for a better understanding of the biological functions of m6A-ncRNA crosstalk in cancer research and treatment.
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46
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Guo Y, Dong X, Jin J, He Y. The Expression Patterns and Prognostic Value of the Proteasome Activator Subunit Gene Family in Gastric Cancer Based on Integrated Analysis. Front Cell Dev Biol 2021; 9:663001. [PMID: 34650966 PMCID: PMC8505534 DOI: 10.3389/fcell.2021.663001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 08/31/2021] [Indexed: 12/12/2022] Open
Abstract
Increasing evidence supports that proteasome activator subunit (PSME) genes play an indispensable role in multiple tumors. The diverse expression patterns, prognostic value, underlying mechanism, and the role in the immunotherapy of PSME genes in gastric cancer (GC) have yet to be fully elucidated. We systematically demonstrated the functions of these genes in GC using various large databases, unbiased in silico approaches, and experimental validation. We found that the median expression levels of all PSME genes were significantly higher in GC tissues than in normal tissues. Our findings showed that up-regulated PSME1 and PSME2 expression significantly correlated with favorable overall survival, post-progression survival, and first progression survival in GC patients. The expression of PSME1 and PSME2 was positively correlated with the infiltration of most immune cells and the activation of anti-cancer immunity cycle steps. Moreover, GC patients with high PSME1 and PSME2 expression have higher immunophenoscore and tumor mutational burden. In addition, a receiver operating characteristic analysis suggested that PSME3 and PSME4 had high diagnostic performance for distinguishing GC patients from healthy individuals. Moreover, our further analysis indicated that PSME genes exert an essential role in GC, and the present study indicated that PSME1 and PSME2 may be potential prognostic markers for enhancing survival and prognostic accuracy in GC patients and may even act as potential biomarkers for GC patients indicating a response to immunotherapy. PSME3 may serve as an oncogene in tumorigenesis and may be a promising therapeutic target for GC. PSME4 had excellent diagnostic performance and could serve as a good diagnostic indicator for GC.
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Affiliation(s)
- Yongdong Guo
- Cancer Institute, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xiaoping Dong
- Cancer Institute, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jing Jin
- Cancer Institute, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yutong He
- Cancer Institute, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
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47
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He X, Zhang S, Zhang Y, Lei Z, Jiang T, Zeng J. Characterizing RNA Pseudouridylation by Convolutional Neural Networks. GENOMICS, PROTEOMICS & BIOINFORMATICS 2021; 19:815-833. [PMID: 33631424 PMCID: PMC9170758 DOI: 10.1016/j.gpb.2019.11.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 09/15/2019] [Accepted: 11/13/2019] [Indexed: 12/12/2022]
Abstract
Pseudouridine (Ψ) is the most prevalent post-transcriptional RNA modification and is widespread in small cellular RNAs and mRNAs. However, the functions, mechanisms, and precise distribution of Ψs (especially in mRNAs) still remain largely unclear. The landscape of Ψs across the transcriptome has not yet been fully delineated. Here, we present a highly effective model based on a convolutional neural network (CNN), called PseudoUridyLation Site Estimator (PULSE), to analyze large-scale profiling data of Ψ sites and characterize the contextual sequence features of pseudouridylation. PULSE, consisting of two alternatively-stacked convolution and pooling layers followed by a fully-connected neural network, can automatically learn the hidden patterns of pseudouridylation from the local sequence information. Extensive validation tests demonstrated that PULSE can outperform other state-of-the-art prediction methods and achieve high prediction accuracy, thus enabling us to further characterize the transcriptome-wide landscape of Ψ sites. We further showed that the prediction results derived from PULSE can provide novel insights into understanding the functional roles of pseudouridylation, such as the regulations of RNA secondary structure, codon usage, translation, and RNA stability, and the connection to single nucleotide variants. The source code and final model for PULSE are available at https://github.com/mlcb-thu/PULSE.
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Affiliation(s)
- Xuan He
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Sai Zhang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Yanqing Zhang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Zhixin Lei
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Tao Jiang
- Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA; MOE Key Lab of Bioinformatics and Bioinformatics Division, BNRIST/Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; Institute of Integrative Genome Biology, University of California, Riverside, CA 92521, USA
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China; MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China.
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48
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Wei Q. Bioinformatical identification of key genes regulated by IGF2BP2-mediated RNA N6-methyladenosine and prediction of prognosis in hepatocellular carcinoma. J Gastrointest Oncol 2021; 12:1773-1785. [PMID: 34532127 DOI: 10.21037/jgo-21-306] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 08/17/2021] [Indexed: 12/22/2022] Open
Abstract
Background The treatment of hepatocellular carcinoma (HCC), a malignant cancer with global spread, remains unsatisfactory, and novel prognostic biomarkers need to be identified. N6-methyladenosine (m6A) has been found to regulate tumor initiation and progression through different mechanisms. As a dynamic and reversible messenger RNA (mRNA) modification, m6A can be read by insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2). IGF2BP2 targets thousands of mRNA transcripts, which may be involved in HCC progression. Methods In this study, we integrated 4 classes of datasets including The Cancer Genome Atlas (TCGA)-LICH, m6A-sequencing data of HepG2 cells, and RNA-sequencing data of IGF2BP2-knockdown HepG2 cells to explore the key genes regulated by IGF2BP2-mediated m6A in HCC. The expression and m6A modification of candidates were validation in independent microarray expression profile of HCC tissue and annotated m6A database RMBase. The relationship of immune cell infiltration and the genes expression was estimated by CIBERSORT and TIMER. Results A total of 89 candidate genes were filtered. Next, cluster analysis was performed base on functions and pathways to identify the enrichment pathways. By constructing a protein-protein interaction (PPI) network, we found 54 nodes. Ten significant genes were filtered from the PPI. These genes were validated in data of an independent microarray and an m6A database. We found that the upregulation of these 10 genes was associated with poor prognosis. In addition, we showed the expression of these 10 genes was associated with the infiltration of variety of immune cell and tumor purity. Conclusions These identified genes may provide novel insights and facilitate the development of potential biomarkers for HCC diagnosis, as well as provide clues for IGF2BP2 inhibition therapy in HCC.
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Affiliation(s)
- Qiang Wei
- Hepatological Surgery Department, Bethune International Peace Hospital of PLA, Shijiazhuang, China
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49
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Qin S, Mao Y, Chen X, Xiao J, Qin Y, Zhao L. The functional roles, cross-talk and clinical implications of m6A modification and circRNA in hepatocellular carcinoma. Int J Biol Sci 2021; 17:3059-3079. [PMID: 34421350 PMCID: PMC8375232 DOI: 10.7150/ijbs.62767] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 07/06/2021] [Indexed: 12/13/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. HCC has high rates of death and recurrence, as well as very low survival rates. N6-methyladenosine (m6A) is the most abundant modification in eukaryotic RNAs, and circRNAs are a class of circular noncoding RNAs that are generated by back-splicing and they modulate multiple functions in a variety of cellular processes. Although the carcinogenesis of HCC is complex, emerging evidence has indicated that m6A modification and circRNA play vital roles in HCC development and progression. However, the underlying mechanisms governing HCC, their cross-talk, and clinical implications have not been fully elucidated. Therefore, in this paper, we elucidated the biological functions and molecular mechanisms of m6A modification in the carcinogenesis of HCC by illustrating three different regulatory factors ("writer", "eraser", and "reader") of the m6A modification process. Additionally, we dissected the functional roles of circRNAs in various malignant behaviors of HCC, thereby contributing to HCC initiation, progression and relapse. Furthermore, we demonstrated the cross-talk and interplay between m6A modification and circRNA by revealing the effects of the collaboration of circRNA and m6A modification on HCC progression. Finally, we proposed the clinical potential and implications of m6A modifiers and circRNAs as diagnostic biomarkers and therapeutic targets for HCC diagnosis, treatment and prognosis evaluation.
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Affiliation(s)
- Sha Qin
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China; and Department of Pathology, School of Basic Medical Science, Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Yitao Mao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xue Chen
- Early Clinical Trial Center, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Juxiong Xiao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yan Qin
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Luqing Zhao
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China; and Department of Pathology, School of Basic Medical Science, Xiangya School of Medicine, Central South University, Changsha, Hunan, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
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50
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Cadoni E, De Paepe L, Manicardi A, Madder A. Beyond small molecules: targeting G-quadruplex structures with oligonucleotides and their analogues. Nucleic Acids Res 2021; 49:6638-6659. [PMID: 33978760 PMCID: PMC8266634 DOI: 10.1093/nar/gkab334] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/15/2021] [Accepted: 04/29/2021] [Indexed: 12/20/2022] Open
Abstract
G-Quadruplexes (G4s) are widely studied secondary DNA/RNA structures, naturally occurring when G-rich sequences are present. The strategic localization of G4s in genome areas of crucial importance, such as proto-oncogenes and telomeres, entails fundamental implications in terms of gene expression regulation and other important biological processes. Although thousands of small molecules capable to induce G4 stabilization have been reported over the past 20 years, approaches based on the hybridization of a synthetic probe, allowing sequence-specific G4-recognition and targeting are still rather limited. In this review, after introducing important general notions about G4s, we aim to list, explain and critically analyse in more detail the principal approaches available to target G4s by using oligonucleotides and synthetic analogues such as Locked Nucleic Acids (LNAs) and Peptide Nucleic Acids (PNAs), reporting on the most relevant examples described in literature to date.
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Affiliation(s)
- Enrico Cadoni
- Organic and Biomimetic Chemistry Research Group, Ghent University, Krijgslaan 281 S4, B-9000 Ghent, Belgium
| | - Lessandro De Paepe
- Organic and Biomimetic Chemistry Research Group, Ghent University, Krijgslaan 281 S4, B-9000 Ghent, Belgium
| | - Alex Manicardi
- Organic and Biomimetic Chemistry Research Group, Ghent University, Krijgslaan 281 S4, B-9000 Ghent, Belgium
| | - Annemieke Madder
- Organic and Biomimetic Chemistry Research Group, Ghent University, Krijgslaan 281 S4, B-9000 Ghent, Belgium
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