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Abedini SS, Akhavantabasi S, Liang Y, Heng JIT, Alizadehsani R, Dehzangi I, Bauer DC, Alinejad-Rokny H. A critical review of the impact of candidate copy number variants on autism spectrum disorder. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2024; 794:108509. [PMID: 38977176 DOI: 10.1016/j.mrrev.2024.108509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 04/14/2024] [Accepted: 07/02/2024] [Indexed: 07/10/2024]
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder (NDD) influenced by genetic, epigenetic, and environmental factors. Recent advancements in genomic analysis have shed light on numerous genes associated with ASD, highlighting the significant role of both common and rare genetic mutations, as well as copy number variations (CNVs), single nucleotide polymorphisms (SNPs) and unique de novo variants. These genetic variations disrupt neurodevelopmental pathways, contributing to the disorder's complexity. Notably, CNVs are present in 10 %-20 % of individuals with autism, with 3 %-7 % detectable through cytogenetic methods. While the role of submicroscopic CNVs in ASD has been recently studied, their association with genomic loci and genes has not been thoroughly explored. In this review, we focus on 47 CNV regions linked to ASD, encompassing 1632 genes, including protein-coding genes and long non-coding RNAs (lncRNAs), of which 659 show significant brain expression. Using a list of ASD-associated genes from SFARI, we detect 17 regions harboring at least one known ASD-related protein-coding gene. Of the remaining 30 regions, we identify 24 regions containing at least one protein-coding gene with brain-enriched expression and a nervous system phenotype in mouse mutants, and one lncRNA with both brain-enriched expression and upregulation in iPSC to neuron differentiation. This review not only expands our understanding of the genetic diversity associated with ASD but also underscores the potential of lncRNAs in contributing to its etiology. Additionally, the discovered CNVs will be a valuable resource for future diagnostic, therapeutic, and research endeavors aimed at prioritizing genetic variations in ASD.
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Affiliation(s)
- Seyedeh Sedigheh Abedini
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia; School of Biotechnology & Biomolecular Sciences, UNSW Sydney, Sydney, NSW 2052, Australia
| | - Shiva Akhavantabasi
- Department of Molecular Biology and Genetics, Yeni Yuzyil University, Istanbul, Turkey; Ghiaseddin Jamshid Kashani University, Andisheh University Town, Danesh Blvd, 3441356611, Abyek, Qazvin, Iran
| | - Yuheng Liang
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
| | - Julian Ik-Tsen Heng
- Curtin Health Innovation Research Institute, Curtin University, Bentley 6845, Australia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
| | - Iman Dehzangi
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA; Department of Computer Science, Rutgers University, Camden, NJ 08102, USA
| | - Denis C Bauer
- Transformational Bioinformatics, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, Australia; Applied BioSciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, Australia
| | - Hamid Alinejad-Rokny
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia; Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, Sydney, NSW 2052, Australia.
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2
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Mylarshchikov D, Nikolskaya A, Bogomaz O, Zharikova A, Mironov A. BaRDIC: robust peak calling for RNA-DNA interaction data. NAR Genom Bioinform 2024; 6:lqae054. [PMID: 38774512 PMCID: PMC11106031 DOI: 10.1093/nargab/lqae054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 04/29/2024] [Accepted: 05/09/2024] [Indexed: 05/24/2024] Open
Abstract
Chromatin-associated non-coding RNAs play important roles in various cellular processes by targeting genomic loci. Two types of genome-wide NGS experiments exist to detect such targets: 'one-to-al', which focuses on targets of a single RNA, and 'all-to-al', which captures targets of all RNAs in a sample. As with many NGS experiments, they are prone to biases and noise, so it becomes essential to detect 'peaks'-specific interactions of an RNA with genomic targets. Here, we present BaRDIC-Binomial RNA-DNA Interaction Caller-a tailored method to detect peaks in both types of RNA-DNA interaction data. BaRDIC is the first tool to simultaneously take into account the two most prominent biases in the data: chromatin heterogeneity and distance-dependent decay of interaction frequency. Since RNAs differ in their interaction preferences, BaRDIC adapts peak sizes according to the abundances and contact patterns of individual RNAs. These features enable BaRDIC to make more robust predictions than currently applied peak-calling algorithms and better handle the characteristic sparsity of all-to-all data. The BaRDIC package is freely available at https://github.com/dmitrymyl/BaRDIC.
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Affiliation(s)
- Dmitry E Mylarshchikov
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory, Moscow 119234, Russia
| | - Arina I Nikolskaya
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory, Moscow 119234, Russia
| | - Olesja D Bogomaz
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory, Moscow 119234, Russia
| | - Anastasia A Zharikova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory, Moscow 119234, Russia
- Kharkevich Institute for Information Transmission Problems RAS, Bolshoy Karetny per., Moscow 127051, Russia
| | - Andrey A Mironov
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory, Moscow 119234, Russia
- Kharkevich Institute for Information Transmission Problems RAS, Bolshoy Karetny per., Moscow 127051, Russia
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3
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Li NN, Lun DX, Gong N, Meng G, Du XY, Wang H, Bao X, Li XY, Song JW, Hu K, Li L, Li SY, Liu W, Zhu W, Zhang Y, Li J, Yao T, Mou L, Han X, Hao F, Hu Y, Liu L, Zhu H, Wu Y, Liu B. Targeting the chromatin structural changes of antitumor immunity. J Pharm Anal 2024; 14:100905. [PMID: 38665224 PMCID: PMC11043877 DOI: 10.1016/j.jpha.2023.11.012] [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: 06/16/2023] [Revised: 09/28/2023] [Accepted: 11/21/2023] [Indexed: 04/28/2024] Open
Abstract
Epigenomic imbalance drives abnormal transcriptional processes, promoting the onset and progression of cancer. Although defective gene regulation generally affects carcinogenesis and tumor suppression networks, tumor immunogenicity and immune cells involved in antitumor responses may also be affected by epigenomic changes, which may have significant implications for the development and application of epigenetic therapy, cancer immunotherapy, and their combinations. Herein, we focus on the impact of epigenetic regulation on tumor immune cell function and the role of key abnormal epigenetic processes, DNA methylation, histone post-translational modification, and chromatin structure in tumor immunogenicity, and introduce these epigenetic research methods. We emphasize the value of small-molecule inhibitors of epigenetic modulators in enhancing antitumor immune responses and discuss the challenges of developing treatment plans that combine epigenetic therapy and immunotherapy through the complex interaction between cancer epigenetics and cancer immunology.
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Affiliation(s)
- Nian-nian Li
- Weifang People's Hospital, Weifang, Shandong, 261000, China
- School of Life Sciences, Nankai University, Tianjin, 300071, China
| | - Deng-xing Lun
- Weifang People's Hospital, Weifang, Shandong, 261000, China
| | - Ningning Gong
- Weifang Traditional Chinese Medicine Hospital, Weifang, Shandong, 261000, China
| | - Gang Meng
- Shaanxi Key Laboratory of Sericulture, Ankang University, Ankang, Shaanxi, 725000, China
| | - Xin-ying Du
- Weifang People's Hospital, Weifang, Shandong, 261000, China
| | - He Wang
- Weifang People's Hospital, Weifang, Shandong, 261000, China
| | - Xiangxiang Bao
- Weifang People's Hospital, Weifang, Shandong, 261000, China
| | - Xin-yang Li
- Guizhou Education University, Guiyang, 550018, China
| | - Ji-wu Song
- Weifang People's Hospital, Weifang, Shandong, 261000, China
| | - Kewei Hu
- Weifang Traditional Chinese Medicine Hospital, Weifang, Shandong, 261000, China
| | - Lala Li
- Guizhou Normal University, Guiyang, 550025, China
| | - Si-ying Li
- Weifang People's Hospital, Weifang, Shandong, 261000, China
| | - Wenbo Liu
- Weifang People's Hospital, Weifang, Shandong, 261000, China
| | - Wanping Zhu
- Weifang People's Hospital, Weifang, Shandong, 261000, China
| | - Yunlong Zhang
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, 261053, China
| | - Jikai Li
- Department of Bone and Soft Tissue Oncology, Tianjin Hospital, Tianjin, 300299, China
| | - Ting Yao
- School of Life Sciences, Nankai University, Tianjin, 300071, China
- Teda Institute of Biological Sciences & Biotechnology, Nankai University, Tianjin, 300457, China
| | - Leming Mou
- Weifang People's Hospital, Weifang, Shandong, 261000, China
| | - Xiaoqing Han
- Weifang People's Hospital, Weifang, Shandong, 261000, China
| | - Furong Hao
- Weifang People's Hospital, Weifang, Shandong, 261000, China
| | - Yongcheng Hu
- Weifang People's Hospital, Weifang, Shandong, 261000, China
| | - Lin Liu
- School of Life Sciences, Nankai University, Tianjin, 300071, China
| | - Hongguang Zhu
- Weifang People's Hospital, Weifang, Shandong, 261000, China
| | - Yuyun Wu
- Xinqiao Hospital of Army Military Medical University, Chongqing, 400038, China
| | - Bin Liu
- Weifang People's Hospital, Weifang, Shandong, 261000, China
- School of Life Sciences, Nankai University, Tianjin, 300071, China
- Teda Institute of Biological Sciences & Biotechnology, Nankai University, Tianjin, 300457, China
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Subramanian S, Thoms JAI, Huang Y, Cornejo-Páramo P, Koch FC, Jacquelin S, Shen S, Song E, Joshi S, Brownlee C, Woll PS, Chacon-Fajardo D, Beck D, Curtis DJ, Yehson K, Antonenas V, O'Brien T, Trickett A, Powell JA, Lewis ID, Pitson SM, Gandhi MK, Lane SW, Vafaee F, Wong ES, Göttgens B, Alinejad-Rokny H, Wong JWH, Pimanda JE. Genome-wide transcription factor-binding maps reveal cell-specific changes in the regulatory architecture of human HSPCs. Blood 2023; 142:1448-1462. [PMID: 37595278 PMCID: PMC10651876 DOI: 10.1182/blood.2023021120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/06/2023] [Accepted: 07/25/2023] [Indexed: 08/20/2023] Open
Abstract
Hematopoietic stem and progenitor cells (HSPCs) rely on a complex interplay among transcription factors (TFs) to regulate differentiation into mature blood cells. A heptad of TFs (FLI1, ERG, GATA2, RUNX1, TAL1, LYL1, LMO2) bind regulatory elements in bulk CD34+ HSPCs. However, whether specific heptad-TF combinations have distinct roles in regulating hematopoietic differentiation remains unknown. We mapped genome-wide chromatin contacts (HiC, H3K27ac, HiChIP), chromatin modifications (H3K4me3, H3K27ac, H3K27me3) and 10 TF binding profiles (heptad, PU.1, CTCF, STAG2) in HSPC subsets (stem/multipotent progenitors plus common myeloid, granulocyte macrophage, and megakaryocyte erythrocyte progenitors) and found TF occupancy and enhancer-promoter interactions varied significantly across cell types and were associated with cell-type-specific gene expression. Distinct regulatory elements were enriched with specific heptad-TF combinations, including stem-cell-specific elements with ERG, and myeloid- and erythroid-specific elements with combinations of FLI1, RUNX1, GATA2, TAL1, LYL1, and LMO2. Furthermore, heptad-occupied regions in HSPCs were subsequently bound by lineage-defining TFs, including PU.1 and GATA1, suggesting that heptad factors may prime regulatory elements for use in mature cell types. We also found that enhancers with cell-type-specific heptad occupancy shared a common grammar with respect to TF binding motifs, suggesting that combinatorial binding of TF complexes was at least partially regulated by features encoded in DNA sequence motifs. Taken together, this study comprehensively characterizes the gene regulatory landscape in rare subpopulations of human HSPCs. The accompanying data sets should serve as a valuable resource for understanding adult hematopoiesis and a framework for analyzing aberrant regulatory networks in leukemic cells.
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Affiliation(s)
- Shruthi Subramanian
- School of Clinical Medicine, University of New South Wales, Sydney, Australia
| | - Julie A. I. Thoms
- School of Biomedical Sciences, University of New South Wales, Sydney, Australia
| | - Yizhou Huang
- Centre for Health Technologies and the School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | | | - Forrest C. Koch
- School of Biotechnology and Biomolecular Sciences, Faculty of Science, University of New South Wales, Sydney, Australia
| | | | - Sylvie Shen
- Bone Marrow Transplant Laboratory, NSW Health Pathology, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Emma Song
- Bone Marrow Transplant Laboratory, NSW Health Pathology, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Swapna Joshi
- School of Clinical Medicine, University of New South Wales, Sydney, Australia
| | - Chris Brownlee
- Mark Wainwright Analytical Centre, University of New South Wales, Sydney, Australia
| | - Petter S. Woll
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Diego Chacon-Fajardo
- Centre for Health Technologies and the School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Dominik Beck
- Centre for Health Technologies and the School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - David J. Curtis
- Australian Centre for Blood Diseases, Monash University, Melbourne, VIC, Australia
| | - Kenneth Yehson
- Blood Transplant and Cell Therapies Laboratory, NSW Health Pathology, Westmead, NSW, Australia
| | - Vicki Antonenas
- Blood Transplant and Cell Therapies Laboratory, NSW Health Pathology, Westmead, NSW, Australia
| | | | - Annette Trickett
- Bone Marrow Transplant Laboratory, NSW Health Pathology, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Jason A. Powell
- Centre for Cancer Biology, SA Pathology, University of South Australia, Adelaide, Australia
- Adelaide Medical School, The University of Adelaide, Adelaide, Australia
| | - Ian D. Lewis
- Centre for Cancer Biology, SA Pathology, University of South Australia, Adelaide, Australia
| | - Stuart M. Pitson
- Centre for Cancer Biology, SA Pathology, University of South Australia, Adelaide, Australia
| | - Maher K. Gandhi
- Blood Cancer Research Group, Mater Research, The University of Queensland, Brisbane, QLD, Australia
| | - Steven W. Lane
- Cancer Program, QIMR Berghofer Medical Research, Brisbane, Australia
| | - Fatemeh Vafaee
- School of Biotechnology and Biomolecular Sciences, Faculty of Science, University of New South Wales, Sydney, Australia
- UNSW Data Science Hub, University of New South Wales, Sydney, Australia
| | - Emily S. Wong
- Victor Chang Cardiac Research Institute, Sydney, Australia
- School of Biotechnology and Biomolecular Sciences, Faculty of Science, University of New South Wales, Sydney, Australia
| | - Berthold Göttgens
- Wellcome-MRC Cambridge Stem Cell Institute, Cambridge, United Kingdom
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
| | - Jason W. H. Wong
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - John E. Pimanda
- School of Clinical Medicine, University of New South Wales, Sydney, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, Australia
- Haematology Department, Prince of Wales Hospital, Sydney, Australia
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MethEvo: an accurate evolutionary information-based methylation site predictor. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07738-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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6
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Alinejad-Rokny H, Ghavami Modegh R, Rabiee HR, Ramezani Sarbandi E, Rezaie N, Tam KT, Forrest ARR. Correction: MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments. PLoS Comput Biol 2022; 18:e1010515. [PMID: 36083878 PMCID: PMC9462553 DOI: 10.1371/journal.pcbi.1010515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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