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Ravaei A, Emanuele M, Nazzaro G, Fadiga L, Rubini M. Placental DNA methylation profile as predicting marker for autism spectrum disorder (ASD). Mol Med 2023; 29:8. [PMID: 36647002 PMCID: PMC9843962 DOI: 10.1186/s10020-022-00593-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 12/15/2022] [Indexed: 01/18/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that impairs normal brain development and socio-cognitive abilities. The pathogenesis of this condition points out the involvement of genetic and environmental factors during in-utero life. Placenta, as an interface tissue between mother and fetus, provides developing fetus requirements and exposes it to maternal environment as well. Therefore, the alteration of DNA methylation as epigenetic consequence of gene-environmental interaction in the placenta could shed light on ASD pathogenesis. In this study, we reviewed the current findings on placental methylation status and its association with ASD. Differentially methylated regions (DMRs) in ASD-developing placenta were found to be mainly enriched in ASD gene loci affecting synaptogenesis, microtubule dynamics, neurogenesis and neuritogenesis. In addition, non-genic DMRs in ASD-placenta proposes an alternative contributing mechanism for ASD development. Our study highlights the importance of placental DNA methylation signature as a biomarker for ASD prediction.
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Affiliation(s)
- Amin Ravaei
- grid.8484.00000 0004 1757 2064Medical Genetics Laboratory, Department of Neuroscience and Rehabilitation, University of Ferrara, Via Fossato di Mortara 74, 44121 Ferrara, Italy
| | - Marco Emanuele
- grid.8484.00000 0004 1757 2064Section of Physiology, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy ,grid.25786.3e0000 0004 1764 2907IIT@UniFe Center for Translational Neurophysiology of Speech and Communication (CTNSC), Istituto Italiano di Tecnologia, Ferrara, Italy
| | - Giovanni Nazzaro
- grid.8484.00000 0004 1757 2064Section of Physiology, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy ,grid.25786.3e0000 0004 1764 2907IIT@UniFe Center for Translational Neurophysiology of Speech and Communication (CTNSC), Istituto Italiano di Tecnologia, Ferrara, Italy
| | - Luciano Fadiga
- grid.8484.00000 0004 1757 2064Section of Physiology, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy ,grid.25786.3e0000 0004 1764 2907IIT@UniFe Center for Translational Neurophysiology of Speech and Communication (CTNSC), Istituto Italiano di Tecnologia, Ferrara, Italy
| | - Michele Rubini
- grid.8484.00000 0004 1757 2064Medical Genetics Laboratory, Department of Neuroscience and Rehabilitation, University of Ferrara, Via Fossato di Mortara 74, 44121 Ferrara, Italy
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Chang-Gonzalez AC, Gibbs HC, Lekven AC, Yeh AT, Hwang W. Building a three-dimensional model of early-stage zebrafish embryo brain. ACTA ACUST UNITED AC 2021; 1. [PMID: 34693392 PMCID: PMC8535780 DOI: 10.1016/j.bpr.2021.100003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
We introduce a computational approach to build three-dimensional (3D) surface mesh models of the early-stage zebrafish brain primordia from time-series microscopy images. The complexity of the early-stage brain primordia and lack of recognizable landmarks pose a distinct challenge for feature segmentation and 3D modeling. Additional difficulty arises because of noise and variations in pixel intensity. We overcome these by using a hierarchical approach in which simple geometric elements, such as "beads" and "bonds," are assigned to represent local features and their connectivity is used to smoothen the surface while retaining high-curvature regions. We apply our method to build models of two zebrafish embryo phenotypes at discrete time points between 19 and 28 h post-fertilization and collect measurements to quantify development. Our approach is fast and applicable to building models of other biological systems, as demonstrated by models from magnetic resonance images of the human fetal brain. The source code, input scripts, sample image files, and generated outputs are publicly available on GitHub.
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Affiliation(s)
- Ana C Chang-Gonzalez
- Department of Biomedical Engineering, Texas A&M University, College Station, Texas
| | - Holly C Gibbs
- Department of Biomedical Engineering, Texas A&M University, College Station, Texas.,Microscopy and Imaging Center, Texas A&M University, College Station, Texas
| | - Arne C Lekven
- Department of Biology and Biochemistry, University of Houston, Houston, Texas
| | - Alvin T Yeh
- Department of Biomedical Engineering, Texas A&M University, College Station, Texas
| | - Wonmuk Hwang
- Department of Biomedical Engineering, Texas A&M University, College Station, Texas.,Department of Materials Science & Engineering, Texas A&M University, College Station, Texas.,Department of Physics & Astronomy, Texas A&M University, College Station, Texas.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea
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Kern C, Wang Y, Xu X, Pan Z, Halstead M, Chanthavixay G, Saelao P, Waters S, Xiang R, Chamberlain A, Korf I, Delany ME, Cheng HH, Medrano JF, Van Eenennaam AL, Tuggle CK, Ernst C, Flicek P, Quon G, Ross P, Zhou H. Functional annotations of three domestic animal genomes provide vital resources for comparative and agricultural research. Nat Commun 2021; 12:1821. [PMID: 33758196 PMCID: PMC7988148 DOI: 10.1038/s41467-021-22100-8] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 03/01/2021] [Indexed: 01/31/2023] Open
Abstract
Gene regulatory elements are central drivers of phenotypic variation and thus of critical importance towards understanding the genetics of complex traits. The Functional Annotation of Animal Genomes consortium was formed to collaboratively annotate the functional elements in animal genomes, starting with domesticated animals. Here we present an expansive collection of datasets from eight diverse tissues in three important agricultural species: chicken (Gallus gallus), pig (Sus scrofa), and cattle (Bos taurus). Comparative analysis of these datasets and those from the human and mouse Encyclopedia of DNA Elements projects reveal that a core set of regulatory elements are functionally conserved independent of divergence between species, and that tissue-specific transcription factor occupancy at regulatory elements and their predicted target genes are also conserved. These datasets represent a unique opportunity for the emerging field of comparative epigenomics, as well as the agricultural research community, including species that are globally important food resources.
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Affiliation(s)
- Colin Kern
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Ying Wang
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Xiaoqin Xu
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Zhangyuan Pan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Michelle Halstead
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Ganrea Chanthavixay
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Perot Saelao
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Susan Waters
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Ruidong Xiang
- Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Melbourne, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Amanda Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Ian Korf
- Genome Center, University of California, Davis, Davis, CA, USA
| | - Mary E Delany
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Hans H Cheng
- USDA-ARS, Avian Disease and Oncology Laboratory, East Lansing, MI, USA
| | - Juan F Medrano
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | | | - Chris K Tuggle
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Catherine Ernst
- Department of Animal Science, Michigan State University, East Lansing, MI, USA
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Gerald Quon
- Department of Molecular and Cellular Biology, University of California, David, Davis, CA, USA
| | - Pablo Ross
- Department of Animal Science, University of California, Davis, Davis, CA, USA.
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, Davis, CA, USA.
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