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Abedini SS, Akhavantabasi S, Liang Y, Heng J, 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 2024:108509. [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 1,632 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
| | - 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, IR
| | - Yuheng Liang
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Julian 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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Cao T, Zhang S, Chen Q, Zeng C, Wang L, Jiao S, Chen H, Zhang B, Cai H. Long non-coding RNAs in schizophrenia: Genetic variations, treatment markers and potential targeted signaling pathways. Schizophr Res 2023; 260:12-22. [PMID: 37543007 DOI: 10.1016/j.schres.2023.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/19/2023] [Accepted: 07/23/2023] [Indexed: 08/07/2023]
Abstract
Schizophrenia (SZ), a complex and debilitating spectrum of psychiatric disorders, is now mainly attributed to multifactorial etiology that includes genetic and environmental factors. Long non-coding RNAs (lncRNAs) are gaining popularity as a way to better understand the comprehensive mechanisms beneath the clinical manifestation of SZ. Only in recent years has it been elucidated that mammalian genomes encode thousands of lncRNAs. Strikingly, roughly 30-40% of these lncRNAs are extensively expressed in different regions across the brain, which may be closely associated with SZ. The therapeutic and adverse effects of atypical antipsychotic drugs (AAPDs) are partially reflected by their role in the regulation of lncRNAs. This begs the question directly, do any lncRNAs exist as biomarkers for AAPDs treatment? Furthermore, we comprehend a range of mechanistic investigations that have revealed the regulatory roles for lncRNAs both involved in the brain and the periphery of SZ. More crucially, we also combine insights from a variety of signaling pathways to argue that lncRNAs probably play critical roles in SZ via their interactive downstream factors. This review provides a thorough understanding regarding dysregulation of lncRNAs, corresponding genetic alternations, as well as their potential regulatory roles in the pathology of SZ, which might help reveal useful therapeutic targets in SZ.
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Affiliation(s)
- Ting Cao
- Department of Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Institute of Clinical Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan, China
| | - ShuangYang Zhang
- Department of Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Institute of Clinical Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Qian Chen
- Department of Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Institute of Clinical Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - CuiRong Zeng
- Department of Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Institute of Clinical Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - LiWei Wang
- Department of Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Institute of Clinical Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - ShiMeng Jiao
- Department of Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Institute of Clinical Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hui Chen
- Department of Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Institute of Clinical Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan, China
| | - BiKui Zhang
- Department of Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Institute of Clinical Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - HuaLin Cai
- Department of Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Institute of Clinical Pharmacy, Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
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3
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Xiong W, Zhang X, Peng B, Zhu H, Huang L, He S. Pan-glioma analyses reveal species- and tumor-specific regulation of neuron-glioma synapse genes by lncRNAs. Front Genet 2023; 14:1218408. [PMID: 37693314 PMCID: PMC10484416 DOI: 10.3389/fgene.2023.1218408] [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: 05/07/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023] Open
Abstract
Gliomas are highly heterogeneous and aggressive. Malignant cells in gliomas can contact normal neurons through a synapse-like structure (called neuron-to-glioma synapse, NGS) to promote their proliferation, but it is unclear whether NGS gene expression and regulation show species- and tumor-specificity. This question is important in that many anti-cancer drugs are developed upon mouse models. To address this question, we conducted a pan-glioma analysis using nine scRNA-seq datasets from humans and mice. We also experimentally validated the key element of our methods and verified a key result using TCGA datasets of the same glioma types. Our analyses revealed that NGS gene expression and regulation by lncRNAs are highly species- and tumor-specific. Importantly, simian-specific lncRNAs are more involved in NGS gene regulation than lncRNAs conserved in mammals, and transgenic mouse gliomas have little in common with PDX mouse models and human gliomas in terms of NGS gene regulation. The analyses suggest that simian-specific lncRNAs are a new and rich class of potential targets for tumor-specific glioma treatment, and provide pertinent data for further experimentally and clinically exmining the targets.
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Affiliation(s)
- Wei Xiong
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xuecong Zhang
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Bin Peng
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Hao Zhu
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Lijin Huang
- Neurosurgery Department, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Sha He
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
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4
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Lu Y, Zhou Y, Guo J, Qi M, Lin Y, Zhang X, Xiang Y, Fu Q, Wang B. Integrated analysis of copy number variation-associated lncRNAs identifies candidates contributing to the etiologies of congenital kidney anomalies. Commun Biol 2023; 6:735. [PMID: 37460814 DOI: 10.1038/s42003-023-05101-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/05/2023] [Indexed: 07/20/2023] Open
Abstract
Congenital anomalies of the kidney and urinary tract (CAKUT) are disorders resulting from defects in the development of the kidneys and their outflow tract. Copy number variations (CNVs) have been identified as important genetic variations leading to CAKUT, whereas most CAKUT-associated CNVs cannot be attributed to a specific pathogenic gene. Here we construct coexpression networks involving long noncoding RNAs (lncRNAs) within these CNVs (CNV-lncRNAs) using human kidney developmental transcriptomic data. The results show that CNV-lncRNAs encompassed in recurrent CAKUT associated CNVs have highly correlated expression with CAKUT genes in the developing kidneys. The regulatory effects of two hub CNV-lncRNAs (HSALNG0134318 in 22q11.2 and HSALNG0115943 in 17q12) in the module most significantly enriched in known CAKUT genes (CAKUT_sig1, P = 1.150 × 10-6) are validated experimentally. Our results indicate that the reduction of CNV-lncRNAs can downregulate CAKUT genes as predicted by our computational analyses. Furthermore, knockdown of HSALNG0134318 would downregulate HSALNG0115943 and affect kidney development related pathways. The results also indicate that the CAKUT_sig1 module has function significance involving multi-organ development. Overall, our findings suggest that CNV-lncRNAs play roles in regulating CAKUT genes, and the etiologies of CAKUT-associated CNVs should take account of effects on the noncoding genome.
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Affiliation(s)
- Yibo Lu
- Pediatric Translational Medicine Institute, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yiyang Zhou
- Pediatric Translational Medicine Institute, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Jing Guo
- Pediatric Translational Medicine Institute, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Ming Qi
- Pediatric Translational Medicine Institute, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Yuwan Lin
- Pediatric Translational Medicine Institute, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Xingyu Zhang
- Pediatric Translational Medicine Institute, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Ying Xiang
- Pediatric Translational Medicine Institute, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
- Shanghai Key Laboratory of Clinical Molecular Diagnostics for Pediatrics, Shanghai, 200127, China.
| | - Qihua Fu
- Pediatric Translational Medicine Institute, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
- Shanghai Key Laboratory of Clinical Molecular Diagnostics for Pediatrics, Shanghai, 200127, China.
| | - Bo Wang
- Pediatric Translational Medicine Institute, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
- Shanghai Key Laboratory of Clinical Molecular Diagnostics for Pediatrics, Shanghai, 200127, China.
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5
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Rahaie Z, Rabiee HR, Alinejad-Rokny H. DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants. PLoS Comput Biol 2023; 19:e1011249. [PMID: 37486921 PMCID: PMC10399873 DOI: 10.1371/journal.pcbi.1011249] [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: 12/07/2022] [Revised: 08/03/2023] [Accepted: 06/06/2023] [Indexed: 07/26/2023] Open
Abstract
The genetic etiology of brain disorders is highly heterogeneous, characterized by abnormalities in the development of the central nervous system that lead to diminished physical or intellectual capabilities. The process of determining which gene drives disease, known as "gene prioritization," is not entirely understood. Genome-wide searches for gene-disease associations are still underdeveloped due to reliance on previous discoveries and evidence sources with false positive or negative relations. This paper introduces DeepGenePrior, a model based on deep neural networks that prioritizes candidate genes in genetic diseases. Using the well-studied Variational AutoEncoder (VAE), we developed a score to measure the impact of genes on target diseases. Unlike other methods that use prior data to select candidate genes, based on the "guilt by association" principle and auxiliary data sources like protein networks, our study exclusively employs copy number variants (CNVs) for gene prioritization. By analyzing CNVs from 74,811 individuals with autism, schizophrenia, and developmental delay, we identified genes that best distinguish cases from controls. Our findings indicate a 12% increase in fold enrichment in brain-expressed genes compared to previous studies and a 15% increase in genes associated with mouse nervous system phenotypes. Furthermore, we identified common deletions in ZDHHC8, DGCR5, and CATG00000022283 among the top genes related to all three disorders, suggesting a common etiology among these clinically distinct conditions. DeepGenePrior is publicly available online at http://git.dml.ir/z_rahaie/DGP to address obstacles in existing gene prioritization studies identifying candidate genes.
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Affiliation(s)
- Zahra Rahaie
- BCB Group, DML, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Hamid R. Rabiee
- BCB Group, DML, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Hamid Alinejad-Rokny
- UNSW Biomedical Machine Learning Lab (BML), the Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, Australia
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6
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Mollon J, Almasy L, Jacquemont S, Glahn DC. The contribution of copy number variants to psychiatric symptoms and cognitive ability. Mol Psychiatry 2023; 28:1480-1493. [PMID: 36737482 PMCID: PMC10213133 DOI: 10.1038/s41380-023-01978-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 02/05/2023]
Abstract
Copy number variants (CNVs) are deletions and duplications of DNA sequence. The most frequently studied CNVs, which are described in this review, are recurrent CNVs that occur in the same locations on the genome. These CNVs have been strongly implicated in neurodevelopmental disorders, namely autism spectrum disorder (ASD), intellectual disability (ID), and developmental delay (DD), but also in schizophrenia. More recent work has also shown that CNVs increase risk for other psychiatric disorders, namely, depression, bipolar disorder, and post-traumatic stress disorder. Many of the same CNVs are implicated across all of these disorders, and these neuropsychiatric CNVs are also associated with cognitive ability in the general population, as well as with structural and functional brain alterations. Neuropsychiatric CNVs also show incomplete penetrance, such that carriers do not always develop any psychiatric disorder, and may show only mild symptoms, if any. Variable expressivity, whereby the same CNVs are associated with many different phenotypes of varied severity, also points to highly complex mechanisms underlying disease risk in CNV carriers. Comprehensive and longitudinal phenotyping studies of individual CNVs have provided initial insights into these mechanisms. However, more work is needed to estimate and predict the effect of non-recurrent, ultra-rare CNVs, which also contribute to psychiatric and cognitive outcomes. Moreover, delineating the broader phenotypic landscape of neuropsychiatric CNVs in both clinical and general population cohorts may also offer important mechanistic insights.
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Affiliation(s)
- Josephine Mollon
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Laura Almasy
- Department of Genetics, Perelman School of Medicine, Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - Sebastien Jacquemont
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Center Hospitalier Universitaire Sainte-Justine Research Center, Montreal, QC, Canada
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
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7
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Copy number variation-associated lncRNAs may contribute to the etiologies of congenital heart disease. Commun Biol 2023; 6:189. [PMID: 36806749 PMCID: PMC9938258 DOI: 10.1038/s42003-023-04565-z] [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: 06/23/2022] [Accepted: 02/08/2023] [Indexed: 02/19/2023] Open
Abstract
Copy number variations (CNVs) have long been recognized as pathogenic factors for congenital heart disease (CHD). Few CHD associated CNVs could be interpreted as dosage effect due to disruption of coding sequences. Emerging evidences have highlighted the regulatory roles of long noncoding RNAs (lncRNAs) in cardiac development. Whereas it remains unexplored whether lncRNAs within CNVs (CNV-lncRNAs) could contribute to the etiology of CHD associated CNVs. Here we constructed coexpression networks involving CNV-lncRNAs within CHD associated CNVs and protein coding genes using the human organ developmental transcriptomic data, and showed that CNV-lncRNAs within 10 of the non-syndromic CHD associated CNVs clustered in the most significant heart correlated module, and had highly correlated coexpression with multiple key CHD genes. HSALNG0104472 within 15q11.2 region was identified as a hub CNV-lncRNA with heart-biased expression and validated experimentally. Our results indicated that HSALNG0104472 should be a main effector responsible for cardiac defects of 15q11.2 deletion through regulating cardiomyocytes differentiation. Our findings suggested that CNV-lncRNAs could potentially contribute to the pathologies of a maximum proportion of 68.4% (13/19) of non-syndromic CHD associated CNVs. These results indicated that explaining the pathogenesis of CHD associated CNVs should take account of the noncoding regions.
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Labani M, Beheshti A, Argha A, Alinejad-Rokny H. A Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants. Int J Mol Sci 2023; 24:ijms24032472. [PMID: 36768794 PMCID: PMC9916892 DOI: 10.3390/ijms24032472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/18/2023] [Accepted: 01/23/2023] [Indexed: 01/31/2023] Open
Abstract
Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies have shown that genomic alterations represent the most common mechanism for molecular alterations responsible for the development and progression of PC. This highlights the importance of identifying functional genomic variants for early detection in high-risk PC individuals. Great efforts have been made to identify common protein-coding genetic variations; however, the impact of non-coding variations, including regulatory genetic variants, is not well understood. Identification of these variants and the underlying target genes will be a key step in improving the detection and treatment of PC. To gain an understanding of the functional impact of genetic variants, and in particular, regulatory variants in PC, we developed an integrative pipeline (AGV) that uses whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants could affect 131 coding and non-coding genes. Interestingly, our study identified the 131 protein-coding genes that are involved in disease-related pathways, including Reactome and MSigDB, for most of which targeted treatment options are currently available. Notably, our analysis revealed several non-coding RNAs, including RP11-136K7.2 and RAMP2-AS1, as potential enhancer elements of the protein-coding genes CDH12 and EZH1, respectively. Our results provide a comprehensive map of genomic variants in PC and reveal their potential contribution to prostate cancer progression and development.
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Affiliation(s)
- Mahdieh Labani
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
- Data Analytic Lab, Department of Computing, Macquarie University, Sydney, NSW 2109, Australia
| | - Amin Beheshti
- Data Analytic Lab, Department of Computing, Macquarie University, Sydney, NSW 2109, Australia
| | - Ahmadreza Argha
- The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
- UNSW Data Science Hub, The University of New South Wales, Sydney, NSW 2052, Australia
- Health Data Analytics Program, Centre for Applied AI, Macquarie University, Sydney, NSW 2109, Australia
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Belyaeva EO, Lebedev IN. Interloci CNV Interactions in Variability of the Phenotypes of Neurodevelopmental Disorders. RUSS J GENET+ 2022. [DOI: 10.1134/s1022795422100027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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10
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Labani M, Afrasiabi A, Beheshti A, Lovell NH, Alinejad-Rokny H. PeakCNV: A multi-feature ranking algorithm-based tool for genome-wide copy number variation-association study. Comput Struct Biotechnol J 2022; 20:4975-4983. [PMID: 36147666 PMCID: PMC9478359 DOI: 10.1016/j.csbj.2022.09.001] [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: 06/15/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 11/25/2022] Open
Abstract
Copy Number Variation (CNV) refers to a type of structural genomic alteration in which a segment of chromosome is duplicated or deleted. To date, many CNVs have been identified as causative genetic elements for several diseases and phenotypes. However, performing a CNV-based genome-wide association study is challenging due to inconsistency in length and occurrence of CNVs across different individuals under investigation. One of the most efficient strategies to address this issue is building CNV regions (genomic regions in which CNVs are overlapping - CNVRs). However, this approach is susceptible to a high false positive rate due to overlapping and co-occurring of confounding CNVRs with true positive CNVRs. Here, we develop PeakCNV that differentiates false-positive CNVRs from true positives by calculating a new metric, independence ranking score, (IR-score) via a feature ranking approach. We compared the performance of PeakCNV with other current existing tools by carrying out two case studies one using the CNV genotype data for individuals with prostate cancer (194 cases and 2,392 healthy individuals) and the second one for individuals with neurodevelopmental disorders (19,642 cases and 6,451 healthy individuals). Crucially, our benchmarking analyses on prostate cancer cohort indicated that PeakCNV identifies a fewer risk candidate CNVRs with shorter lengths compared to other tools. Importantly, these CNVRs cover a greater proportion of case over healthy individuals compared to other tools. The accuracy of PeakCNV in identifying relevant candidate CNVRs was reproducible in the case study on neurodevelopmental disorders. Using data from the FANTOM5 expression atlas and the Clinical Genomic Database, we show that the candidate CNVRs identified by PeakCNV for neurodevelopmental disorders overlap with a greater number of genes with the brain-enriched expression, and a greater number of genes that are associated with neurological conditions compared to candidate CNVRs identified by other tools. Taken together, PeakCNV outperformed current existing CNV association study tools by identifying more biologically meaningful CNVRs relevant to the phenotype of interest. PeakCNV is publicly available for the analysis of CNV-associated diseases and is accessible from https://rdrr.io/github/mahdieh1/PeakCNV.
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Affiliation(s)
- Mahdieh Labani
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia.,Data Analytics Lab, School of Computing, Macquarie University, Sydney, NSW 2109, Australia
| | - Ali Afrasiabi
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
| | - Amin Beheshti
- Data Analytics Lab, School of Computing, Macquarie University, Sydney, NSW 2109, Australia
| | - Nigel H Lovell
- The Graduate School of Biomedical Engineering (GSBmE), UNSW Sydney, Sydney, NSW, 2052, Australia.,Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, Sydney, NSW 2052, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia.,UNSW Data Science Hub, The University of New South Wales, Sydney, NSW 2052, Australia.,Health Data Analytics Program, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, Australia
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11
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Rezaie N, Bayati M, Hamidi M, Tahaei MS, Khorasani S, Lovell NH, Breen J, Rabiee HR, Alinejad-Rokny H. Somatic point mutations are enriched in non-coding RNAs with possible regulatory function in breast cancer. Commun Biol 2022; 5:556. [PMID: 35672401 PMCID: PMC9174258 DOI: 10.1038/s42003-022-03528-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 05/24/2022] [Indexed: 11/09/2022] Open
Abstract
Non-coding RNAs (ncRNAs) form a large portion of the mammalian genome. However, their biological functions are poorly characterized in cancers. In this study, using a newly developed tool, SomaGene, we analyze de novo somatic point mutations from the International Cancer Genome Consortium (ICGC) whole-genome sequencing data of 1,855 breast cancer samples. We identify 1030 candidates of ncRNAs that are significantly and explicitly mutated in breast cancer samples. By integrating data from the ENCODE regulatory features and FANTOM5 expression atlas, we show that the candidate ncRNAs significantly enrich active chromatin histone marks (1.9 times), CTCF binding sites (2.45 times), DNase accessibility (1.76 times), HMM predicted enhancers (2.26 times) and eQTL polymorphisms (1.77 times). Importantly, we show that the 1030 ncRNAs contain a much higher level (3.64 times) of breast cancer-associated genome-wide association (GWAS) single nucleotide polymorphisms (SNPs) than genome-wide expectation. Such enrichment has not been seen with GWAS SNPs from other cancers. Using breast cell line related Hi-C data, we then show that 82% of our candidate ncRNAs (1.9 times) significantly interact with the promoter of protein-coding genes, including previously known cancer-associated genes, suggesting the critical role of candidate ncRNA genes in the activation of essential regulators of development and differentiation in breast cancer. We provide an extensive web-based resource (https://www.ihealthe.unsw.edu.au/research) to communicate our results with the research community. Our list of breast cancer-specific ncRNA genes has the potential to provide a better understanding of the underlying genetic causes of breast cancer. Lastly, the tool developed in this study can be used to analyze somatic mutations in all cancers. The SomaGene tool is developed to identify non-coding RNAs (ncRNAs) mutated in breast cancer but can be used for other cancers. Candidate ncRNAs are shown to be enriched for regulatory features and to contain specific trait loci polymorphisms.
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Affiliation(s)
- Narges Rezaie
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, 92697, USA
| | - Masroor Bayati
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, Tehran, 11365, Iran
| | - Mehrab Hamidi
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, Tehran, 11365, Iran
| | - Maedeh Sadat Tahaei
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, Tehran, 11365, Iran
| | - Sadegh Khorasani
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, Tehran, 11365, Iran
| | - Nigel H Lovell
- Tyree Institute of Health Engineering and The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - James Breen
- South Australian Health & Medical Research Institute, Adelaide, SA, 5000, Australia.,Robinson Research Institute, University of Adelaide, Adelaide, SA, 5006, Australia.,Bioinformatics Hub, University of Adelaide, Adelaide, SA, 5006, Australia
| | - Hamid R Rabiee
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, Tehran, 11365, Iran.
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia. .,UNSW Data Science Hub, The University of New South Wales (UNSW Sydney), Sydney, NSW, 2052, Australia. .,Health Data Analytics Program, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney, NSW, 2109, Australia.
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12
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The Emerging Roles of Long Non-Coding RNAs in Intellectual Disability and Related Neurodevelopmental Disorders. Int J Mol Sci 2022; 23:ijms23116118. [PMID: 35682796 PMCID: PMC9181295 DOI: 10.3390/ijms23116118] [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: 04/30/2022] [Revised: 05/23/2022] [Accepted: 05/27/2022] [Indexed: 02/05/2023] Open
Abstract
In the human brain, long non-coding RNAs (lncRNAs) are widely expressed in an exquisitely temporally and spatially regulated manner, thus suggesting their contribution to normal brain development and their probable involvement in the molecular pathology of neurodevelopmental disorders (NDD). Bypassing the classic protein-centric conception of disease mechanisms, some studies have been conducted to identify and characterize the putative roles of non-coding sequences in the genetic pathogenesis and diagnosis of complex diseases. However, their involvement in NDD, and more specifically in intellectual disability (ID), is still poorly documented and only a few genomic alterations affecting the lncRNAs function and/or expression have been causally linked to the disease endophenotype. Considering that a significant fraction of patients still lacks a genetic or molecular explanation, we expect that a deeper investigation of the non-coding genome will unravel novel pathogenic mechanisms, opening new translational opportunities. Here, we present evidence of the possible involvement of many lncRNAs in the etiology of different forms of ID and NDD, grouping the candidate disease-genes in the most frequently affected cellular processes in which ID-risk genes were previously collected. We also illustrate new approaches for the identification and prioritization of NDD-risk lncRNAs, together with the current strategies to exploit them in diagnosis.
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13
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Bertini V, Milone R, Cristofani P, Cambi F, Bosetti C, Barbieri F, Bertelloni S, Cioni G, Valetto A, Battini R. Enhancing DLG2 Implications in Neuropsychiatric Disorders: Analysis of a Cohort of Eight Patients with 11q14.1 Imbalances. Genes (Basel) 2022; 13:genes13050859. [PMID: 35627244 PMCID: PMC9140951 DOI: 10.3390/genes13050859] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/10/2022] [Accepted: 05/10/2022] [Indexed: 11/20/2022] Open
Abstract
Neurodevelopmental disorders (NDDs) are considered synaptopathies, as they are due to anomalies in neuronal connectivity during development. DLG2 is a gene involved insynaptic function; the phenotypic effect of itsalterations in NDDs has been underestimated since few cases have been thoroughly described.We report on eight patients with 11q14.1 imbalances involving DLG2, underlining its potential effects on clinical presentation and its contribution to NDD comorbidity by accurate neuropsychiatric data collection. DLG2 is a very large gene in 11q14.1, extending over 2.172 Mb, with alternative splicing that gives rise to numerous isoforms differentially expressed in brain tissues. A thorough bioinformatic analysis of the altered transcripts was conducted for each patient. The different expression profiles of the isoforms of this gene and their influence on the excitatory–inhibitory balance in crucial brain structures could contribute to the phenotypic variability related to DLG2 alterations. Further studies on patients would be helpful to enrich clinical and neurodevelopmental findings and elucidate the molecular mechanisms subtended to NDDs.
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Affiliation(s)
- Veronica Bertini
- Cytogenetic Unit, Department of Laboratory Medicine, Azienda Ospedaliero-Univeristaria Pisana, Via Roma 57, 56100 Pisa, Italy; (V.B.); (F.C.)
| | - Roberta Milone
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, 56125 Pisa, Italy; (R.M.); (P.C.); (C.B.); (G.C.); (R.B.)
| | - Paola Cristofani
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, 56125 Pisa, Italy; (R.M.); (P.C.); (C.B.); (G.C.); (R.B.)
| | - Francesca Cambi
- Cytogenetic Unit, Department of Laboratory Medicine, Azienda Ospedaliero-Univeristaria Pisana, Via Roma 57, 56100 Pisa, Italy; (V.B.); (F.C.)
| | - Chiara Bosetti
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, 56125 Pisa, Italy; (R.M.); (P.C.); (C.B.); (G.C.); (R.B.)
| | - Filippo Barbieri
- Mental Health Department, ASL Toscana Nordovest, 56100 Pisa, Italy;
| | - Silvano Bertelloni
- Pediatric Endocrinology, Department of Obstetrics, Gynecology and Pediatrics, Azienda Ospedaliero-Universitaria Pisana, Via Roma 57, 56100 Pisa, Italy;
| | - Giovanni Cioni
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, 56125 Pisa, Italy; (R.M.); (P.C.); (C.B.); (G.C.); (R.B.)
- Department of Clinical and Experimental Medicine, University of Pisa, 56100 Pisa, Italy
| | - Angelo Valetto
- Cytogenetic Unit, Department of Laboratory Medicine, Azienda Ospedaliero-Univeristaria Pisana, Via Roma 57, 56100 Pisa, Italy; (V.B.); (F.C.)
- Correspondence:
| | - Roberta Battini
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, 56125 Pisa, Italy; (R.M.); (P.C.); (C.B.); (G.C.); (R.B.)
- Department of Clinical and Experimental Medicine, University of Pisa, 56100 Pisa, Italy
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14
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Dashti H, Dehzangi I, Bayati M, Breen J, Beheshti A, Lovell N, Rabiee HR, Alinejad-Rokny H. Integrative analysis of mutated genes and mutational processes reveals novel mutational biomarkers in colorectal cancer. BMC Bioinformatics 2022; 23:138. [PMID: 35439935 PMCID: PMC9017053 DOI: 10.1186/s12859-022-04652-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 03/24/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Recent studies have observed causative mutations in susceptible genes related to colorectal cancer in 10 to 15% of the patients. This highlights the importance of identifying mutations for early detection of this cancer for more effective treatments among high risk individuals. Mutation is considered as the key point in cancer research. Many studies have performed cancer subtyping based on the type of frequently mutated genes, or the proportion of mutational processes. However, to the best of our knowledge, combination of these features has never been used together for this task. This highlights the potential to introduce better and more inclusive subtype classification approaches using wider range of related features to enable biomarker discovery and thus inform drug development for CRC. RESULTS In this study, we develop a new pipeline based on a novel concept called 'gene-motif', which merges mutated gene information with tri-nucleotide motif of mutated sites, for colorectal cancer subtype identification. We apply our pipeline to the International Cancer Genome Consortium (ICGC) CRC samples and identify, for the first time, 3131 gene-motif combinations that are significantly mutated in 536 ICGC colorectal cancer samples. Using these features, we identify seven CRC subtypes with distinguishable phenotypes and biomarkers, including unique cancer related signaling pathways, in which for most of them targeted treatment options are currently available. Interestingly, we also identify several genes that are mutated in multiple subtypes but with unique sequence contexts. CONCLUSION Our results highlight the importance of considering both the mutation type and mutated genes in identification of cancer subtypes and cancer biomarkers. The new CRC subtypes presented in this study demonstrates distinguished phenotypic properties which can be effectively used to develop new treatments. By knowing the genes and phenotypes associated with the subtypes, a personalized treatment plan can be developed that considers the specific phenotypes associated with their genomic lesion.
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Affiliation(s)
- Hamed Dashti
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, 11365, Tehran, Iran
| | - Iman Dehzangi
- Center for Computational and Integrative Biology (CCIB), Rutgers University, Camden, NJ, 08102, USA
| | - Masroor Bayati
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, 11365, Tehran, Iran
| | - James Breen
- South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia.,Robinson Research Institute, University of Adelaide, Adelaide, SA, 5006, Australia.,Bioinformatics Hub, University of Adelaide, Adelaide, SA, 5006, Australia
| | - Amin Beheshti
- Department of Computing, Macquarie University, Sydney, NSW, 2109, Australia
| | - Nigel Lovell
- Tyree Institute of Health Engineering and The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Hamid R Rabiee
- Bioinformatics and Computational Biology Lab, Department of Computer Engineering, Sharif University of Technology, 11365, Tehran, Iran.
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia. .,UNSW Data Science Hub, The University of New South Wales, Sydney, NSW, 2052, Australia. .,Health Data Analytics Program, AI-Enabled Processes (AIP) Research Centre, Macquarie University, Sydney, 2109, Australia.
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15
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Kyzar EJ, Bohnsack JP, Pandey SC. Current and Future Perspectives of Noncoding RNAs in Brain Function and Neuropsychiatric Disease. Biol Psychiatry 2022; 91:183-193. [PMID: 34742545 PMCID: PMC8959010 DOI: 10.1016/j.biopsych.2021.08.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 08/05/2021] [Accepted: 08/12/2021] [Indexed: 02/07/2023]
Abstract
Noncoding RNAs (ncRNAs) represent the majority of the transcriptome and play important roles in regulating neuronal functions. ncRNAs are exceptionally diverse in both structure and function and include enhancer RNAs, long ncRNAs, and microRNAs, all of which demonstrate specific temporal and regional expression in the brain. Here, we review recent studies demonstrating that ncRNAs modulate chromatin structure, act as chaperone molecules, and contribute to synaptic remodeling and behavior. In addition, we discuss ncRNA function within the context of neuropsychiatric diseases, particularly focusing on addiction and schizophrenia, and the recent methodological developments that allow for better understanding of ncRNA function in the brain. Overall, ncRNAs represent an underrecognized molecular contributor to complex neuronal processes underlying neuropsychiatric disorders.
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Affiliation(s)
- Evan J Kyzar
- Center for Alcohol Research in Epigenetics, Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois; Department of Psychiatry, Columbia University Irving Medical Center, New York State Psychiatric Institute, New York, New York
| | - John Peyton Bohnsack
- Center for Alcohol Research in Epigenetics, Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois
| | - Subhash C Pandey
- Center for Alcohol Research in Epigenetics, Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois; Jesse Brown Veterans Affairs Medical Center, University of Illinois at Chicago, Chicago, Illinois; Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, Illinois.
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16
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Milone R, Tancredi R, Cosenza A, Ferrari AR, Scalise R, Cioni G, Battini R. 17q12 Recurrent Deletions and Duplications: Description of a Case Series with Neuropsychiatric Phenotype. Genes (Basel) 2021; 12:genes12111660. [PMID: 34828266 PMCID: PMC8620923 DOI: 10.3390/genes12111660] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/15/2021] [Accepted: 10/19/2021] [Indexed: 12/22/2022] Open
Abstract
Syndromic neurodevelopmental disorders are usually investigated through genetics technologies, within which array comparative genomic hybridization (Array-CGH) is still considered the first-tier clinical diagnostic test. Among recurrent syndromic imbalances, 17q12 deletions and duplications are characterized by neurodevelopmental disorders associated with visceral developmental disorders, although expressive variability is common. Here we describe a case series of 12 patients with 17q12 chromosomal imbalances, in order to expand the phenotypic characterization of these recurrent syndromes whose diagnosis is often underestimated, especially if only mild traits are present. Gene content and genotype-phenotype correlations have been discussed, with special regard to neuropsychiatric features, whose impact often requires etiologic analysis.
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Affiliation(s)
- Roberta Milone
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, 56128 Pisa, Italy; (R.M.); (R.T.); (A.C.); (A.R.F.); (R.S.); (G.C.)
| | - Raffaella Tancredi
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, 56128 Pisa, Italy; (R.M.); (R.T.); (A.C.); (A.R.F.); (R.S.); (G.C.)
| | - Angela Cosenza
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, 56128 Pisa, Italy; (R.M.); (R.T.); (A.C.); (A.R.F.); (R.S.); (G.C.)
| | - Anna Rita Ferrari
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, 56128 Pisa, Italy; (R.M.); (R.T.); (A.C.); (A.R.F.); (R.S.); (G.C.)
| | - Roberta Scalise
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, 56128 Pisa, Italy; (R.M.); (R.T.); (A.C.); (A.R.F.); (R.S.); (G.C.)
- Tuscan PhD Program of Neuroscience, University of Florence, Pisa and Siena, 50139 Florence, Italy
| | - Giovanni Cioni
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, 56128 Pisa, Italy; (R.M.); (R.T.); (A.C.); (A.R.F.); (R.S.); (G.C.)
- Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy
| | - Roberta Battini
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Calambrone, 56128 Pisa, Italy; (R.M.); (R.T.); (A.C.); (A.R.F.); (R.S.); (G.C.)
- Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy
- Correspondence:
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17
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Servetti M, Pisciotta L, Tassano E, Cerminara M, Nobili L, Boeri S, Rosti G, Lerone M, Divizia MT, Ronchetto P, Puliti A. Neurodevelopmental Disorders in Patients With Complex Phenotypes and Potential Complex Genetic Basis Involving Non-Coding Genes, and Double CNVs. Front Genet 2021; 12:732002. [PMID: 34621295 PMCID: PMC8490884 DOI: 10.3389/fgene.2021.732002] [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: 06/28/2021] [Accepted: 09/03/2021] [Indexed: 12/15/2022] Open
Abstract
Neurodevelopmental disorders (NDDs) are a heterogeneous class of brain diseases, with a complex genetic basis estimated to account for up to 50% of cases. Nevertheless, genetic diagnostic yield is about 20%. Array-comparative genomic hybridization (array-CGH) is an established first-level diagnostic test able to detect pathogenic copy number variants (CNVs), however, most identified variants remain of uncertain significance (VUS). Failure of interpretation of VUSs may depend on various factors, including complexity of clinical phenotypes and inconsistency of genotype-phenotype correlations. Indeed, although most NDD-associated CNVs are de novo, transmission from unaffected parents to affected children of CNVs with high risk for NDDs has been observed. Moreover, variability of genetic components overlapped by CNVs, such as long non-coding genes, genomic regions with long-range effects, and additive effects of multiple CNVs can make CNV interpretation challenging. We report on 12 patients with complex phenotypes possibly explained by complex genetic mechanisms, including involvement of antisense genes and boundaries of topologically associating domains. Eight among the 12 patients carried two CNVs, either de novo or inherited, respectively, by each of their healthy parents, that could additively contribute to the patients’ phenotype. CNVs overlapped either known NDD-associated or novel candidate genes (PTPRD, BUD13, GLRA3, MIR4465, ABHD4, and WSCD2). Bioinformatic enrichment analyses showed that genes overlapped by the co-occurring CNVs have synergistic roles in biological processes fundamental in neurodevelopment. Double CNVs could concur in producing deleterious effects, according to a two-hit model, thus explaining the patients’ phenotypes and the incomplete penetrance, and variable expressivity, associated with the single variants. Overall, our findings could contribute to the knowledge on clinical and genetic diagnosis of complex forms of NDD.
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Affiliation(s)
- Martina Servetti
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy.,Medical Genetics Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Livia Pisciotta
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy.,Child Neuropsychiatry Unit, ASST Fatebenefratelli Sacco, Milano, Italy
| | - Elisa Tassano
- Human Genetics Laboratory, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Maria Cerminara
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy
| | - Lino Nobili
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy.,Child Neuropsychiatry Unit, Istituto Giannina Gaslini, Genoa, Italy
| | - Silvia Boeri
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy.,Child Neuropsychiatry Unit, Istituto Giannina Gaslini, Genoa, Italy
| | - Giulia Rosti
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy
| | - Margherita Lerone
- Medical Genetics Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | | | - Patrizia Ronchetto
- Human Genetics Laboratory, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Aldamaria Puliti
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy.,Medical Genetics Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
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18
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Quantitative neurogenetics: applications in understanding disease. Biochem Soc Trans 2021; 49:1621-1631. [PMID: 34282824 DOI: 10.1042/bst20200732] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/11/2021] [Accepted: 06/21/2021] [Indexed: 12/31/2022]
Abstract
Neurodevelopmental and neurodegenerative disorders (NNDs) are a group of conditions with a broad range of core and co-morbidities, associated with dysfunction of the central nervous system. Improvements in high throughput sequencing have led to the detection of putative risk genetic loci for NNDs, however, quantitative neurogenetic approaches need to be further developed in order to establish causality and underlying molecular genetic mechanisms of pathogenesis. Here, we discuss an approach for prioritizing the contribution of genetic risk loci to complex-NND pathogenesis by estimating the possible impacts of these loci on gene regulation. Furthermore, we highlight the use of a tissue-specificity gene expression index and the application of artificial intelligence (AI) to improve the interpretation of the role of genetic risk elements in NND pathogenesis. Given that NND symptoms are associated with brain dysfunction, risk loci with direct, causative actions would comprise genes with essential functions in neural cells that are highly expressed in the brain. Indeed, NND risk genes implicated in brain dysfunction are disproportionately enriched in the brain compared with other tissues, which we refer to as brain-specific expressed genes. In addition, the tissue-specificity gene expression index can be used as a handle to identify non-brain contexts that are involved in NND pathogenesis. Lastly, we discuss how using an AI approach provides the opportunity to integrate the biological impacts of risk loci to identify those putative combinations of causative relationships through which genetic factors contribute to NND pathogenesis.
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19
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Barros II, Leão V, Santis JO, Rosa RCA, Brotto DB, Storti CB, Siena ÁDD, Molfetta GA, Silva WA. Non-Syndromic Intellectual Disability and Its Pathways: A Long Noncoding RNA Perspective. Noncoding RNA 2021; 7:ncrna7010022. [PMID: 33799572 PMCID: PMC8005948 DOI: 10.3390/ncrna7010022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/05/2020] [Accepted: 12/07/2020] [Indexed: 02/06/2023] Open
Abstract
Non-syndromic intellectual disability (NS-ID or idiopathic) is a complex neurodevelopmental disorder that represents a global health issue. Although many efforts have been made to characterize it and distinguish it from syndromic intellectual disability (S-ID), the highly heterogeneous aspect of this disorder makes it difficult to understand its etiology. Long noncoding RNAs (lncRNAs) comprise a large group of transcripts that can act through various mechanisms and be involved in important neurodevelopmental processes. In this sense, comprehending the roles they play in this intricate context is a valuable way of getting new insights about how NS-ID can arise and develop. In this review, we attempt to bring together knowledge available in the literature about lncRNAs involved with molecular and cellular pathways already described in intellectual disability and neural function, to better understand their relevance in NS-ID and the regulatory complexity of this disorder.
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Affiliation(s)
- Isabela I. Barros
- Department of Genetics at the Ribeirão Preto Medical School, University of São Paulo, Avenida Bandeirantes 3900, Monte Alegre, Ribeirão Preto, São Paulo 14049-900, Brazil; (I.I.B.); (V.L.); (J.O.S.); (R.C.A.R.); (D.B.B.); (C.B.S.); (Á.D.D.S.); (G.A.M.)
| | - Vitor Leão
- Department of Genetics at the Ribeirão Preto Medical School, University of São Paulo, Avenida Bandeirantes 3900, Monte Alegre, Ribeirão Preto, São Paulo 14049-900, Brazil; (I.I.B.); (V.L.); (J.O.S.); (R.C.A.R.); (D.B.B.); (C.B.S.); (Á.D.D.S.); (G.A.M.)
| | - Jessica O. Santis
- Department of Genetics at the Ribeirão Preto Medical School, University of São Paulo, Avenida Bandeirantes 3900, Monte Alegre, Ribeirão Preto, São Paulo 14049-900, Brazil; (I.I.B.); (V.L.); (J.O.S.); (R.C.A.R.); (D.B.B.); (C.B.S.); (Á.D.D.S.); (G.A.M.)
| | - Reginaldo C. A. Rosa
- Department of Genetics at the Ribeirão Preto Medical School, University of São Paulo, Avenida Bandeirantes 3900, Monte Alegre, Ribeirão Preto, São Paulo 14049-900, Brazil; (I.I.B.); (V.L.); (J.O.S.); (R.C.A.R.); (D.B.B.); (C.B.S.); (Á.D.D.S.); (G.A.M.)
| | - Danielle B. Brotto
- Department of Genetics at the Ribeirão Preto Medical School, University of São Paulo, Avenida Bandeirantes 3900, Monte Alegre, Ribeirão Preto, São Paulo 14049-900, Brazil; (I.I.B.); (V.L.); (J.O.S.); (R.C.A.R.); (D.B.B.); (C.B.S.); (Á.D.D.S.); (G.A.M.)
| | - Camila B. Storti
- Department of Genetics at the Ribeirão Preto Medical School, University of São Paulo, Avenida Bandeirantes 3900, Monte Alegre, Ribeirão Preto, São Paulo 14049-900, Brazil; (I.I.B.); (V.L.); (J.O.S.); (R.C.A.R.); (D.B.B.); (C.B.S.); (Á.D.D.S.); (G.A.M.)
| | - Ádamo D. D. Siena
- Department of Genetics at the Ribeirão Preto Medical School, University of São Paulo, Avenida Bandeirantes 3900, Monte Alegre, Ribeirão Preto, São Paulo 14049-900, Brazil; (I.I.B.); (V.L.); (J.O.S.); (R.C.A.R.); (D.B.B.); (C.B.S.); (Á.D.D.S.); (G.A.M.)
| | - Greice A. Molfetta
- Department of Genetics at the Ribeirão Preto Medical School, University of São Paulo, Avenida Bandeirantes 3900, Monte Alegre, Ribeirão Preto, São Paulo 14049-900, Brazil; (I.I.B.); (V.L.); (J.O.S.); (R.C.A.R.); (D.B.B.); (C.B.S.); (Á.D.D.S.); (G.A.M.)
| | - Wilson A. Silva
- Department of Genetics at the Ribeirão Preto Medical School, University of São Paulo, Avenida Bandeirantes 3900, Monte Alegre, Ribeirão Preto, São Paulo 14049-900, Brazil; (I.I.B.); (V.L.); (J.O.S.); (R.C.A.R.); (D.B.B.); (C.B.S.); (Á.D.D.S.); (G.A.M.)
- National Institute of Science and Technology in Stem Cell and Cell Therapy and Center for Cell Based Therapy, Ribeirão Preto Medical School, University of São Paulo, Rua Tenente Catão Roxo, 2501, Monte Alegre, Ribeirão Preto 14051-140, Brazil
- Center for Integrative Systems Biology-CISBi, NAP/USP, Ribeirão Preto Medical School, University of São Paulo, Rua Catão Roxo, 2501, Monte Alegre, Ribeirão Preto 14051-140, Brazil
- Department of Medicine at the Midwest State University of Paraná-UNICENTRO, and Guarapuava Institute for Cancer Research, Rua Fortim Atalaia, 1900, Cidade dos Lagos, Guarapuava 85100-000, Brazil
- Correspondence: ; Tel.: +55-16-3315-3293
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20
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Heidari R, Akbariqomi M, Asgari Y, Ebrahimi D, Alinejad-Rokny H. A systematic review of long non-coding RNAs with a potential role in breast cancer. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2021; 787:108375. [PMID: 34083033 DOI: 10.1016/j.mrrev.2021.108375] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 04/07/2021] [Accepted: 04/12/2021] [Indexed: 12/13/2022]
Abstract
The human transcriptome contains many non-coding RNAs (ncRNAs), which play important roles in gene regulation. Long noncoding RNAs (lncRNAs) are an important class of ncRNAs with lengths between 200 and 200,000 bases. Unlike mRNA, lncRNA lacks protein-coding features, specifically, open-reading frames, and start and stop codons. LncRNAs have been reported to play a role in the pathogenesis and progression of many cancers, including breast cancer (BC), acting as tumor suppressors or oncogenes. In this review, we systematically mined the literature to identify 65 BC-related lncRNAs. We then perform an integrative bioinformatics analysis to identify 14 lncRNAs with a potential regulatory role in BC. The biological function of these 14 lncRNAs, their regulatory mechanisms, and roles in the initiation and progression of BC are discussed in this review. Additionally, we elaborate on the current and future applications of lncRNAs as diagnostic and/or therapeutic biomarkers in BC.
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Affiliation(s)
- Reza Heidari
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran; Department of Molecular Medicine, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mostafa Akbariqomi
- Department of Molecular Medicine, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Yazdan Asgari
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Diako Ebrahimi
- Biomedical Informatics Lab, Texas Biomedical Research Institute, San Antonio, TX, 78227, United States
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia; Core Member of UNSW Data Science Hub, The University of New South Wales (UNSW Sydney), Sydney, NSW, 2052, Australia; Health Data Analytics Program Leader, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney, 2109, Australia.
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