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Zhang J, Hu G, Lu Y, Ren H, Huang Y, Wen Y, Ji B, Wang D, Wang H, Liu H, Ma N, Zhang L, Pan G, Qu Y, Wang H, Zhang W, Miao Z, Yao H. CTCF mutation at R567 causes developmental disorders via 3D genome rearrangement and abnormal neurodevelopment. Nat Commun 2024; 15:5524. [PMID: 38951485 PMCID: PMC11217373 DOI: 10.1038/s41467-024-49684-1] [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: 05/12/2023] [Accepted: 06/14/2024] [Indexed: 07/03/2024] Open
Abstract
The three-dimensional genome structure organized by CTCF is required for development. Clinically identified mutations in CTCF have been linked to adverse developmental outcomes. Nevertheless, the underlying mechanism remains elusive. In this investigation, we explore the regulatory roles of a clinically relevant R567W point mutation, located within the 11th zinc finger of CTCF, by introducing this mutation into both murine models and human embryonic stem cell-derived cortical organoid models. Mice with homozygous CTCFR567W mutation exhibit growth impediments, resulting in postnatal mortality, and deviations in brain, heart, and lung development at the pathological and single-cell transcriptome levels. This mutation induces premature stem-like cell exhaustion, accelerates the maturation of GABAergic neurons, and disrupts neurodevelopmental and synaptic pathways. Additionally, it specifically hinders CTCF binding to peripheral motifs upstream to the core consensus site, causing alterations in local chromatin structure and gene expression, particularly at the clustered protocadherin locus. Comparative analysis using human cortical organoids mirrors the consequences induced by this mutation. In summary, this study elucidates the influence of the CTCFR567W mutation on human neurodevelopmental disorders, paving the way for potential therapeutic interventions.
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Affiliation(s)
- Jie Zhang
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Gongcheng Hu
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China
| | - Yuli Lu
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Huawei Ren
- College of Veterinary Medicine, Shanxi Agricultural University, Jinzhong, China
| | - Yin Huang
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China
| | - Yulin Wen
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Binrui Ji
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Diyang Wang
- Key Laboratory of CNS Regeneration (Ministry of Education), Guangdong-Hong Kong-Macau Institute of CNS Regeneration, Jinan University, Guangzhou, China
| | - Haidong Wang
- College of Veterinary Medicine, Shanxi Agricultural University, Jinzhong, China
| | - Huisheng Liu
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China
| | - Ning Ma
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China
| | - Lingling Zhang
- Institute of Clinical Pharmacology, Key Laboratory of Anti-Inflammatory and Immune Medicine (Ministry of Education), Anhui Medical University, Hefei, China
| | - Guangjin Pan
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yibo Qu
- Key Laboratory of CNS Regeneration (Ministry of Education), Guangdong-Hong Kong-Macau Institute of CNS Regeneration, Jinan University, Guangzhou, China
| | - Hua Wang
- Institute of Clinical Pharmacology, Key Laboratory of Anti-Inflammatory and Immune Medicine (Ministry of Education), Anhui Medical University, Hefei, China
| | - Wei Zhang
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China
| | - Zhichao Miao
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China
| | - Hongjie Yao
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China.
- University of Chinese Academy of Sciences, Beijing, China.
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Zuo Z. The successive emergence of ERVL-MaLRs in primates. Virus Evol 2023; 9:vead072. [PMID: 38131004 PMCID: PMC10735291 DOI: 10.1093/ve/vead072] [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: 09/06/2023] [Revised: 11/01/2023] [Accepted: 12/01/2023] [Indexed: 12/23/2023] Open
Abstract
Although the ERVL-mammalian-apparent LTR retrotransposons (MaLRs) are the fourth largest family of transposable elements in the human genome, their evolutionary history and relationship have not been thoroughly studied. In this study, through RepeatMasker annotations of some representative species and construction of phylogenetic tree by sequence similarity, all primate-specific MaLR members are found to descend from MLT1A1 retrotransposon. Comparative genomic analysis, transposition-in-transposition inference, and sequence feature comparisons consistently show that each MaLR member evolved from its predecessor successively and had a limited activity period during primate evolution. Accordingly, a novel MaLR member was discovered as successor of MSTB1 in Tarsiiformes. At last, the identification of candidate precursor and intermediate THE1A elements provides further evidence for the previously proposed arms race model between ZNF430/ZNF100 and THE1B/THE1A. Taken together, this study sheds light on the evolutionary history of MaLRs and can serve as a foundation for future research on their interactions with zinc finger genes, gene regulation, and human health implications.
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Affiliation(s)
- Zheng Zuo
- School of Life Science and Technology, Southeast University, Nanjing 210096, China
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Zuo Z. Quantifying the arms race between LINE-1 and KRAB-zinc finger genes through TECookbook. NAR Genom Bioinform 2023; 5:lqad078. [PMID: 37680368 PMCID: PMC10480687 DOI: 10.1093/nargab/lqad078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 07/13/2023] [Accepted: 08/22/2023] [Indexed: 09/09/2023] Open
Abstract
To defend against the invasion of transposons, hundreds of KRAB-zinc finger genes (ZNFs) evolved to recognize and silence various repeat families specifically. However, most repeat elements reside in the human genome with high copy numbers, making the ChIP-seq reads of ZNFs targeting these repeats predominantly multi-mapping reads. This complicates downstream data analysis and signal quantification. To better visualize and quantify the arms race between transposons and ZNFs, the R package TECookbook has been developed to lift ChIP-seq data into reference repeat coordinates with proper normalization and extract all putative ZNF binding sites from defined loci of reference repeats for downstream analysis. In conjunction with specificity profiles derived from in vitro Spec-seq data, human ZNF10 has been found to bind to a conserved ORF2 locus of selected LINE-1 subfamilies. This provides insight into how LINE-1 evaded capture at least twice and was subsequently recaptured by ZNF10 during evolutionary history. Through similar analyses, ZNF382 and ZNF248 were shown to be broad-spectrum LINE-1 binders. Overall, this work establishes a general analysis workflow to decipher the arms race between ZNFs and transposons through nucleotide substitutions rather than structural variations, particularly in the protein-coding region of transposons.
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Affiliation(s)
- Zheng Zuo
- Shenzhen University, Shenzhen, China
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Chu SK, Stormo GD. Finding motifs using DNA images derived from sparse representations. Bioinformatics 2023; 39:btad378. [PMID: 37294804 PMCID: PMC10290554 DOI: 10.1093/bioinformatics/btad378] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 05/10/2023] [Accepted: 06/08/2023] [Indexed: 06/11/2023] Open
Abstract
MOTIVATION Motifs play a crucial role in computational biology, as they provide valuable information about the binding specificity of proteins. However, conventional motif discovery methods typically rely on simple combinatoric or probabilistic approaches, which can be biased by heuristics such as substring-masking for multiple motif discovery. In recent years, deep neural networks have become increasingly popular for motif discovery, as they are capable of capturing complex patterns in data. Nonetheless, inferring motifs from neural networks remains a challenging problem, both from a modeling and computational standpoint, despite the success of these networks in supervised learning tasks. RESULTS We present a principled representation learning approach based on a hierarchical sparse representation for motif discovery. Our method effectively discovers gapped, long, or overlapping motifs that we show to commonly exist in next-generation sequencing datasets, in addition to the short and enriched primary binding sites. Our model is fully interpretable, fast, and capable of capturing motifs in a large number of DNA strings. A key concept emerged from our approach-enumerating at the image level-effectively overcomes the k-mers paradigm, enabling modest computational resources for capturing the long and varied but conserved patterns, in addition to capturing the primary binding sites. AVAILABILITY AND IMPLEMENTATION Our method is available as a Julia package under the MIT license at https://github.com/kchu25/MOTIFs.jl, and the results on experimental data can be found at https://zenodo.org/record/7783033.
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Affiliation(s)
- Shane K Chu
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Gary D Stormo
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, United States
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