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Chen F, He X, Xu W, Zhou L, Liu Q, Chen W, Zhu WG, Zhang J. Chromatin lysine acylation: On the path to chromatin homeostasis and genome integrity. Cancer Sci 2024. [PMID: 39155589 DOI: 10.1111/cas.16321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 07/25/2024] [Accepted: 08/06/2024] [Indexed: 08/20/2024] Open
Abstract
The fundamental role of cells in safeguarding the genome's integrity against DNA double-strand breaks (DSBs) is crucial for maintaining chromatin homeostasis and the overall genomic stability. Aberrant responses to DNA damage, known as DNA damage responses (DDRs), can result in genomic instability and contribute significantly to tumorigenesis. Unraveling the intricate mechanisms underlying DDRs following severe damage holds the key to identify therapeutic targets for cancer. Chromatin lysine acylation, encompassing diverse modifications such as acetylation, lactylation, crotonylation, succinylation, malonylation, glutarylation, propionylation, and butyrylation, has been extensively studied in the context of DDRs and chromatin homeostasis. Here, we delve into the modifying enzymes and the pivotal roles of lysine acylation and their crosstalk in maintaining chromatin homeostasis and genome integrity in response to DDRs. Moreover, we offer a comprehensive perspective and overview of the latest insights, driven primarily by chromatin acylation modification and associated regulators.
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Affiliation(s)
- Feng Chen
- International Cancer Center, Guangdong Key Laboratory of Genome Instability and Human Disease Prevention, Marshall Laboratory of Biomedical Engineering, Department of Biochemistry and Molecular Biology, Shenzhen University Medical School, Shenzhen, China
| | - Xingkai He
- International Cancer Center, Guangdong Key Laboratory of Genome Instability and Human Disease Prevention, Marshall Laboratory of Biomedical Engineering, Department of Biochemistry and Molecular Biology, Shenzhen University Medical School, Shenzhen, China
| | - Wenchao Xu
- International Cancer Center, Guangdong Key Laboratory of Genome Instability and Human Disease Prevention, Marshall Laboratory of Biomedical Engineering, Department of Biochemistry and Molecular Biology, Shenzhen University Medical School, Shenzhen, China
| | - Linmin Zhou
- International Cancer Center, Guangdong Key Laboratory of Genome Instability and Human Disease Prevention, Marshall Laboratory of Biomedical Engineering, Department of Biochemistry and Molecular Biology, Shenzhen University Medical School, Shenzhen, China
| | - Qi Liu
- International Cancer Center, Guangdong Key Laboratory of Genome Instability and Human Disease Prevention, Marshall Laboratory of Biomedical Engineering, Department of Biochemistry and Molecular Biology, Shenzhen University Medical School, Shenzhen, China
- Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Weicheng Chen
- International Cancer Center, Guangdong Key Laboratory of Genome Instability and Human Disease Prevention, Marshall Laboratory of Biomedical Engineering, Department of Biochemistry and Molecular Biology, Shenzhen University Medical School, Shenzhen, China
| | - Wei-Guo Zhu
- International Cancer Center, Guangdong Key Laboratory of Genome Instability and Human Disease Prevention, Marshall Laboratory of Biomedical Engineering, Department of Biochemistry and Molecular Biology, Shenzhen University Medical School, Shenzhen, China
| | - Jun Zhang
- International Cancer Center, Guangdong Key Laboratory of Genome Instability and Human Disease Prevention, Marshall Laboratory of Biomedical Engineering, Department of Biochemistry and Molecular Biology, Shenzhen University Medical School, Shenzhen, China
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Song M, Zhao J, Zhang C, Jia C, Yang J, Zhao H, Zhai J, Lei B, Tao S, Chen S, Su R, Ma C. PEA-m6A: an ensemble learning framework for accurately predicting N6-methyladenosine modifications in plants. PLANT PHYSIOLOGY 2024; 195:1200-1213. [PMID: 38428981 DOI: 10.1093/plphys/kiae120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 01/11/2024] [Accepted: 02/01/2024] [Indexed: 03/03/2024]
Abstract
N 6-methyladenosine (m6A), which is the mostly prevalent modification in eukaryotic mRNAs, is involved in gene expression regulation and many RNA metabolism processes. Accurate prediction of m6A modification is important for understanding its molecular mechanisms in different biological contexts. However, most existing models have limited range of application and are species-centric. Here we present PEA-m6A, a unified, modularized and parameterized framework that can streamline m6A-Seq data analysis for predicting m6A-modified regions in plant genomes. The PEA-m6A framework builds ensemble learning-based m6A prediction models with statistic-based and deep learning-driven features, achieving superior performance with an improvement of 6.7% to 23.3% in the area under precision-recall curve compared with state-of-the-art regional-scale m6A predictor WeakRM in 12 plant species. Especially, PEA-m6A is capable of leveraging knowledge from pretrained models via transfer learning, representing an innovation in that it can improve prediction accuracy of m6A modifications under small-sample training tasks. PEA-m6A also has a strong capability for generalization, making it suitable for application in within- and cross-species m6A prediction. Overall, this study presents a promising m6A prediction tool, PEA-m6A, with outstanding performance in terms of its accuracy, flexibility, transferability, and generalization ability. PEA-m6A has been packaged using Galaxy and Docker technologies for ease of use and is publicly available at https://github.com/cma2015/PEA-m6A.
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Affiliation(s)
- Minggui Song
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
- Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Jiawen Zhao
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
- Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Chujun Zhang
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
- Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Chengchao Jia
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Jing Yang
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
- Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Haonan Zhao
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Jingjing Zhai
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Beilei Lei
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
- Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Shiheng Tao
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Siqi Chen
- School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
| | - Ran Su
- School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
| | - Chuang Ma
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
- Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, Shaanxi 712100, China
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Tan Q, Xiao J, Chen J, Wang Y, Zhang Z, Zhao T, Li Y. ifDEEPre: large protein language-based deep learning enables interpretable and fast predictions of enzyme commission numbers. Brief Bioinform 2024; 25:bbae225. [PMID: 38942594 PMCID: PMC11213619 DOI: 10.1093/bib/bbae225] [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: 12/11/2023] [Revised: 03/26/2024] [Accepted: 04/22/2024] [Indexed: 06/30/2024] Open
Abstract
Accurate understanding of the biological functions of enzymes is vital for various tasks in both pathologies and industrial biotechnology. However, the existing methods are usually not fast enough and lack explanations on the prediction results, which severely limits their real-world applications. Following our previous work, DEEPre, we propose a new interpretable and fast version (ifDEEPre) by designing novel self-guided attention and incorporating biological knowledge learned via large protein language models to accurately predict the commission numbers of enzymes and confirm their functions. Novel self-guided attention is designed to optimize the unique contributions of representations, automatically detecting key protein motifs to provide meaningful interpretations. Representations learned from raw protein sequences are strictly screened to improve the running speed of the framework, 50 times faster than DEEPre while requiring 12.89 times smaller storage space. Large language modules are incorporated to learn physical properties from hundreds of millions of proteins, extending biological knowledge of the whole network. Extensive experiments indicate that ifDEEPre outperforms all the current methods, achieving more than 14.22% larger F1-score on the NEW dataset. Furthermore, the trained ifDEEPre models accurately capture multi-level protein biological patterns and infer evolutionary trends of enzymes by taking only raw sequences without label information. Meanwhile, ifDEEPre predicts the evolutionary relationships between different yeast sub-species, which are highly consistent with the ground truth. Case studies indicate that ifDEEPre can detect key amino acid motifs, which have important implications for designing novel enzymes. A web server running ifDEEPre is available at https://proj.cse.cuhk.edu.hk/aihlab/ifdeepre/ to provide convenient services to the public. Meanwhile, ifDEEPre is freely available on GitHub at https://github.com/ml4bio/ifDEEPre/.
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Affiliation(s)
- Qingxiong Tan
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jin Xiao
- Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
| | - Jiayang Chen
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yixuan Wang
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Zeliang Zhang
- Department of Computer Science, University of Rochester, Rochester, New York State, USA
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | | | - Yu Li
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
- The CUHK Shenzhen Research Institute, Nanshan, Shenzhen, China
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Li CC, Liu KL, Lii CK, Yan WY, Lo CW, Chen CC, Yang YC, Chen HW. Benzyl isothiocyanate inhibits TNFα-driven lipolysis via suppression of the ERK/PKA/HSL signaling pathway in 3T3-L1 adipocytes. Nutr Res 2024; 121:95-107. [PMID: 38056034 DOI: 10.1016/j.nutres.2023.11.007] [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: 09/08/2023] [Revised: 11/11/2023] [Accepted: 11/11/2023] [Indexed: 12/08/2023]
Abstract
Tumor necrosis factor α (TNFα), an inflammatory cytokine, induces lipolysis and increases circulating concentrations of free fatty acids. In addition, TNFα is the first adipokine produced by adipose tissue in obesity, contributing to obesity-associated metabolic disease. Given that benzyl isothiocyanate (BITC) is a well-known anti-inflammatory agent, we hypothesized that BITC can ameliorate TNFα-induced lipolysis and investigated the working mechanisms involved. We first challenged 3T3-L1 adipocytes with TNFα to induce lipolysis, which was confirmed by increased glycerol release, decreased protein expression of peroxisome proliferator-activated receptor γ (PPARγ) and perilipin 1 (PLIN1), and increased phosphorylation of ERK, protein kinase A (PKA), and hormone-sensitive lipase (HSL). However, inhibition of ERK or PKA significantly attenuated the lipolytic activity of TNFα. Meanwhile, pretreatment with BITC significantly ameliorated the lipolytic activity of TNFα; the TNFα-induced phosphorylation of ERK, PKA, and HSL; the TNFα-induced ubiquitination of PPARγ; the TNFα-induced decrease in PPARγ nuclear protein binding to PPAR response element; and the TNFα-induced decrease in PLIN1 protein expression. Our results indicate that BITC ameliorates TNFα-induced lipolysis by inhibiting the ERK/PKA/HSL signaling pathway, preventing PPARγ proteasomal degradation, and maintaining PLIN1 protein expression.
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Affiliation(s)
- Chien-Chun Li
- Department of Nutrition, Chung Shan Medical University, Taichung, 402, Taiwan; Department of Nutrition, Chung Shan Medical University Hospital, Taichung, 402, Taiwan
| | - Kai-Li Liu
- Department of Nutrition, Chung Shan Medical University, Taichung, 402, Taiwan; Department of Nutrition, Chung Shan Medical University Hospital, Taichung, 402, Taiwan
| | - Chong-Kuei Lii
- Department of Nutrition, China Medical University, Taichung, 406, Taiwan; Department of Health and Nutrition Biotechnology, Asia University, Taichung, 413, Taiwan
| | - Wei-Ying Yan
- Department of Nutrition, China Medical University, Taichung, 406, Taiwan
| | - Chia-Wen Lo
- Department of Nutrition, China Medical University, Taichung, 406, Taiwan
| | - Chih-Chieh Chen
- Department of Sports Medicine, China Medical University, Taichung, 406, Taiwan
| | - Ya-Chen Yang
- Department of Health and Nutrition Biotechnology, Asia University, Taichung, 413, Taiwan
| | - Haw-Wen Chen
- Department of Nutrition, China Medical University, Taichung, 406, Taiwan.
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Zhang YJ, Luo Z, Sun Y, Liu J, Chen Z. From beasts to bytes: Revolutionizing zoological research with artificial intelligence. Zool Res 2023; 44:1115-1131. [PMID: 37933101 PMCID: PMC10802096 DOI: 10.24272/j.issn.2095-8137.2023.263] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/30/2023] [Indexed: 11/08/2023] Open
Abstract
Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision, natural language processing and speech recognition in revolutionizing zoological research. This review provides an overview of the primary tasks, core models, datasets, and applications of AI in zoological research, including animal classification, resource conservation, behavior, development, genetics and evolution, breeding and health, disease models, and paleontology. Additionally, we explore the challenges and future directions of integrating AI into this field. Based on numerous case studies, this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom. As we build a bridge between beast and byte realms, this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored.
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Affiliation(s)
- Yu-Juan Zhang
- Chongqing Key Laboratory of Vector Insects
- Chongqing Key Laboratory of Animal Biology
- College of Life Science, Chongqing Normal University, Chongqing 401331, China
| | - Zeyu Luo
- Chongqing Key Laboratory of Vector Insects
- Chongqing Key Laboratory of Animal Biology
- College of Life Science, Chongqing Normal University, Chongqing 401331, China
| | - Yawen Sun
- Chongqing Key Laboratory of Vector Insects
- Chongqing Key Laboratory of Animal Biology
- College of Life Science, Chongqing Normal University, Chongqing 401331, China
| | - Junhao Liu
- Chongqing Key Laboratory of Vector Insects
- Chongqing Key Laboratory of Animal Biology
- College of Life Science, Chongqing Normal University, Chongqing 401331, China
| | - Zongqing Chen
- School of Mathematical Sciences
- National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China. E-mail:
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